1
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Ran M, Sha O, Tam KY. Exploring casual effects and shared molecular mechanism between psoriasis and liver cancer through Mendelian randomization and comprehensive bioinformatic analyses. Comput Biol Chem 2024; 110:108089. [PMID: 38703750 DOI: 10.1016/j.compbiolchem.2024.108089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/10/2024] [Accepted: 04/24/2024] [Indexed: 05/06/2024]
Abstract
Psoriasis (Ps), a chronic inflammatory disease affecting approximately 2 % of the global population, has been associated with an increased risk of liver cancer in observational studies. However, their causal relationships as well as underlying shared molecular mechanisms between Ps and liver cancer remain unclear. Using bidirectional Mendelian randomization analysis, we revealed that a genetic predisposition to liver cancer increased the risk of Ps in European and East Asian populations but not the other way around. Moreover, we analyzed three transcriptomic datasets of patients with Ps and liver cancer from open-source databases. Differentially expressed genes (DEGs) and disease-specific gene co-expression module analyses revealed that cell-cycle dysregulation was the shared mechanism of Ps and liver cancer. Moreover, we identified a rank-conservative gene signature shared between these two diseases, which demonstrated significance in diagnostic and prognostic predictions. These findings provided valuable insights into the interconnections between Ps and liver cancer, which may be helpful to guide therapeutic management.
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Affiliation(s)
- Maoxin Ran
- Faculty of Health Sciences, University of Macau, Taipa, Macau
| | - Ou Sha
- School of Dentistry, Shenzhen University Medical School, Shenzhen, China.
| | - Kin Yip Tam
- Faculty of Health Sciences, University of Macau, Taipa, Macau.
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2
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Watanabe K, Wilmanski T, Baloni P, Robinson M, Garcia GG, Hoopmann MR, Midha MK, Baxter DH, Maes M, Morrone SR, Crebs KM, Kapil C, Kusebauch U, Wiedrick J, Lapidus J, Pflieger L, Lausted C, Roach JC, Glusman G, Cummings SR, Schork NJ, Price ND, Hood L, Miller RA, Moritz RL, Rappaport N. Lifespan-extending interventions induce consistent patterns of fatty acid oxidation in mouse livers. Commun Biol 2023; 6:768. [PMID: 37481675 PMCID: PMC10363145 DOI: 10.1038/s42003-023-05128-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 07/10/2023] [Indexed: 07/24/2023] Open
Abstract
Aging manifests as progressive deteriorations in homeostasis, requiring systems-level perspectives to investigate the gradual molecular dysregulation of underlying biological processes. Here, we report systemic changes in the molecular regulation of biological processes under multiple lifespan-extending interventions. Differential Rank Conservation (DIRAC) analyses of mouse liver proteomics and transcriptomics data show that mechanistically distinct lifespan-extending interventions (acarbose, 17α-estradiol, rapamycin, and calorie restriction) generally tighten the regulation of biological modules. These tightening patterns are similar across the interventions, particularly in processes such as fatty acid oxidation, immune response, and stress response. Differences in DIRAC patterns between proteins and transcripts highlight specific modules which may be tightened via augmented cap-independent translation. Moreover, the systemic shifts in fatty acid metabolism are supported through integrated analysis of liver transcriptomics data with a mouse genome-scale metabolic model. Our findings highlight the power of systems-level approaches for identifying and characterizing the biological processes involved in aging and longevity.
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Affiliation(s)
| | | | - Priyanka Baloni
- School of Health Sciences, Purdue University, West Lafayette, IN, USA
| | | | - Gonzalo G Garcia
- Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | | | | | | | - Michal Maes
- Institute for Systems Biology, Seattle, WA, USA
| | | | | | - Charu Kapil
- Institute for Systems Biology, Seattle, WA, USA
| | | | - Jack Wiedrick
- Oregon Health and Science University, Portland, OR, USA
| | - Jodi Lapidus
- Oregon Health and Science University, Portland, OR, USA
| | - Lance Pflieger
- Institute for Systems Biology, Seattle, WA, USA
- Phenome Health, Seattle, WA, USA
| | | | | | | | - Steven R Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Nicholas J Schork
- Department of Quantitative Medicine, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
- Department of Population Sciences and Molecular and Cell Biology, The City of Hope National Medical Center, Duarte, CA, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, USA.
- Phenome Health, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
- Department of Immunology, University of Washington, Seattle, WA, USA.
| | - Richard A Miller
- Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA
- University of Michigan Geriatrics Center, Ann Arbor, MI, USA
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Ribeiro HC, Zandonadi FDS, Sussulini A. An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value. Expert Rev Proteomics 2023; 20:267-280. [PMID: 37830362 DOI: 10.1080/14789450.2023.2267756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 09/12/2023] [Indexed: 10/14/2023]
Abstract
INTRODUCTION Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics. AREAS COVERED In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value. EXPERT OPINION A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.
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Affiliation(s)
- Henrique Caracho Ribeiro
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
| | - Flávia da Silva Zandonadi
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
| | - Alessandra Sussulini
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
- Instituto Nacional de Ciência e Tecnologia de Bioanalítica (INCTBio), Institute of Chemistry, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil
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4
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Roach JC, Hara J, Fridman D, Lovejoy JC, Jade K, Heim L, Romansik R, Swietlikowski A, Phillips S, Rapozo MK, Shay MA, Fischer D, Funk C, Dill L, Brant‐Zawadzki M, Hood L, Shankle WR. The Coaching for Cognition in Alzheimer's (COCOA) trial: Study design. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2022; 8:e12318. [PMID: 35910672 PMCID: PMC9322829 DOI: 10.1002/trc2.12318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 05/07/2022] [Accepted: 05/13/2022] [Indexed: 11/15/2022]
Abstract
Comprehensive treatment of Alzheimer's disease (AD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. We present the design and methodology for the Coaching for Cognition in Alzheimer's (COCOA) trial. AD and other dementias result from the interplay of multiple interacting dysfunctional biological systems. Monotherapies have had limited success. More interventional studies are needed to test the effectiveness of multimodal multi-domain therapies for dementia prevention and treatment. Multimodal therapies use multiple interventions to address multiple systemic causes and potentiators of cognitive decline and functional loss; they can be personalized, as different sets of etiologies and systems responsive to therapy may be present in different individuals. COCOA is designed to test the hypothesis that coached multimodal interventions beneficially alter the trajectory of cognitive decline for individuals on the spectrum of AD and related dementias (ADRD). COCOA is a two-arm prospective randomized controlled trial (RCT). COCOA collects psychometric, clinical, lifestyle, genomic, proteomic, metabolomic, and microbiome data at multiple timepoints across 2 years for each participant. These data enable systems biology analyses. One arm receives standard of care and generic healthy aging recommendations. The other arm receives standard of care and personalized data-driven remote coaching. The primary outcome measure is the Memory Performance Index (MPI), a measure of cognition. The MPI is a summary statistic of the MCI Screen (MCIS). Secondary outcome measures include the Functional Assessment Staging Test (FAST), a measure of function. COCOA began enrollment in January 2018. We hypothesize that multimodal interventions will ameliorate cognitive decline and that data-driven health coaching will increase compliance, assist in personalizing multimodal interventions, and improve outcomes for patients, particularly for those in the early stages of the AD spectrum. Highlights The Coaching for Cognition in Alzheimer's (COCOA) trial tests personalized multimodal lifestyle interventions for Alzheimer's disease and related dementias.Dense longitudinal molecular data will be useful for future studies.Increased use of Hill's criteria in analyses may advance knowledge generation.Remote coaching may be an effective intervention.Because lifestyle interventions are inexpensive, they may be particularly valuable in reducing global socioeconomic disparities in dementia care.
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Affiliation(s)
| | - Junko Hara
- Pickup Family Neurosciences InstituteHoag Memorial Hospital PresbyterianNewport BeachCaliforniaUSA
| | - Deborah Fridman
- Hoag Center for Research and EducationHoag Memorial Hospital PresbyterianNewport BeachCaliforniaUSA
| | | | | | - Laura Heim
- Hoag Center for Research and EducationHoag Memorial Hospital PresbyterianNewport BeachCaliforniaUSA
| | - Rachel Romansik
- Hoag Center for Research and EducationHoag Memorial Hospital PresbyterianNewport BeachCaliforniaUSA
| | - Adrienne Swietlikowski
- Hoag Center for Research and EducationHoag Memorial Hospital PresbyterianNewport BeachCaliforniaUSA
| | - Sheree Phillips
- Hoag Center for Research and EducationHoag Memorial Hospital PresbyterianNewport BeachCaliforniaUSA
| | | | | | - Dan Fischer
- Institute for Systems BiologySeattleWashingtonUSA
- Oregon Health & Science UniversityPortlandOregonUSA
| | - Cory Funk
- Institute for Systems BiologySeattleWashingtonUSA
| | - Lauren Dill
- Pickup Family Neurosciences InstituteHoag Memorial Hospital PresbyterianNewport BeachCaliforniaUSA
- VA Long Beach Healthcare SystemLong BeachCaliforniaUSA
| | - Michael Brant‐Zawadzki
- Pickup Family Neurosciences InstituteHoag Memorial Hospital PresbyterianNewport BeachCaliforniaUSA
| | - Leroy Hood
- Institute for Systems BiologySeattleWashingtonUSA
- Providence St. Joseph HealthRentonWashingtonUSA
| | - William R. Shankle
- Pickup Family Neurosciences InstituteHoag Memorial Hospital PresbyterianNewport BeachCaliforniaUSA
- Department of Cognitive SciencesUniversity of CaliforniaIrvineCaliforniaUSA
- Shankle ClinicNewport BeachCaliforniaUSA
- EMBIC CorporationNewport BeachCaliforniaUSA
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5
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IndGOterm: a qualitative method for the identification of individually dysregulated GO terms in cancer. Brief Bioinform 2022; 23:6526723. [DOI: 10.1093/bib/bbac012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 12/24/2021] [Accepted: 01/08/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Individual pathway analysis can dissect heterogeneities among different cancer patients and provide efficient guidelines for individualized therapy. However, the existence of the batch effect brings extensive limitations for the application of many individual methods for pathway analysis. Previously, researchers proposed that methods based on within-sample relative expression ordering (REO) of the genes are notably insensitive to ‘batch effects’. In this article, we focus on the Gene Ontology (GO) database and propose an individual qualitative GO term analysis method (IndGOterm) based on the REO of genes. Compared with some current widely used single-sample enrichment analysis methods, such as ssGSEA and GSVA, IndGOterm has a predominance of ignoring the batch effects caused by diverse technologies. Through the survival and drug responses analysis, we found IndGOterm could capture more terms connected to cancer than other single-sample enrichment analysis methods. Furthermore, through the application of IndGOterm, we found some terms that present different dysregulation models that manifest heterogenetic in homologous patients. Collectively, these results attested that IndGOterm could capture useful information from patients and be a useful tool to reveal the intrinsic characteristic of cancer. An open-source R statistical analysis package ‘IndGOterm’ is available at https://github.com/robert19960424/IndGOterm.
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6
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Su KM, Gao HW, Chang CM, Lu KH, Yu MH, Lin YH, Liu LC, Chang CC, Li YF, Chang CC. Synergistic AHR Binding Pathway with EMT Effects on Serous Ovarian Tumors Recognized by Multidisciplinary Integrated Analysis. Biomedicines 2021; 9:866. [PMID: 34440070 PMCID: PMC8389648 DOI: 10.3390/biomedicines9080866] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 07/12/2021] [Accepted: 07/20/2021] [Indexed: 12/11/2022] Open
Abstract
Epithelial ovarian cancers (EOCs) are fatal and obstinate among gynecological malignancies in advanced stage or relapsed status, with serous carcinomas accounting for the vast majority. Unlike EOCs, borderline ovarian tumors (BOTs), including serous BOTs, maintain a semimalignant appearance. Using gene ontology (GO)-based integrative analysis, we analyzed gene set databases of serous BOTs and serous ovarian carcinomas for dysregulated GO terms and pathways and identified multiple differentially expressed genes (DEGs) in various aspects. The SRC (SRC proto-oncogene, non-receptor tyrosine kinase) gene and dysfunctional aryl hydrocarbon receptor (AHR) binding pathway consistently influenced progression-free survival and overall survival, and immunohistochemical staining revealed elevated expression of related biomarkers (SRC, ARNT, and TBP) in serous BOT and ovarian carcinoma samples. Epithelial-mesenchymal transition (EMT) is important during tumorigenesis, and we confirmed the SNAI2 (Snail family transcriptional repressor 2, SLUG) gene showing significantly high performance by immunohistochemistry. During serous ovarian tumor formation, activated AHR in the cytoplasm could cooperate with SRC, enter cell nuclei, bind to AHR nuclear translocator (ARNT) together with TATA-Box Binding Protein (TBP), and act on DNA to initiate AHR-responsive genes to cause tumor or cancer initiation. Additionally, SNAI2 in the tumor microenvironment can facilitate EMT accompanied by tumorigenesis. Although it has not been possible to classify serous BOTs and serous ovarian carcinomas as the same EOC subtype, the key determinants of relevant DEGs (SRC, ARNT, TBP, and SNAI2) found here had a crucial role in the pathogenetic mechanism of both tumor types, implying gradual evolutionary tendencies from serous BOTs to ovarian carcinomas. In the future, targeted therapy could focus on these revealed targets together with precise detection to improve therapeutic effects and patient survival rates.
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Affiliation(s)
- Kuo-Min Su
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114, Taiwan; (K.-M.S.); (M.-H.Y.)
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (Y.-H.L.); (L.-C.L.); (C.-C.C.)
| | - Hong-Wei Gao
- Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
| | - Chia-Ming Chang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan;
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Kai-Hsi Lu
- Department of Medical Research and Education, Cheng-Hsin General Hospital, Taipei 112, Taiwan;
| | - Mu-Hsien Yu
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114, Taiwan; (K.-M.S.); (M.-H.Y.)
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (Y.-H.L.); (L.-C.L.); (C.-C.C.)
| | - Yi-Hsin Lin
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (Y.-H.L.); (L.-C.L.); (C.-C.C.)
| | - Li-Chun Liu
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (Y.-H.L.); (L.-C.L.); (C.-C.C.)
- Division of Obstetrics and Gynecology, Tri-Service General Hospital Songshan Branch, National Defense Medical Center, Taipei 105, Taiwan
| | - Chia-Ching Chang
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (Y.-H.L.); (L.-C.L.); (C.-C.C.)
| | - Yao-Feng Li
- Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
| | - Cheng-Chang Chang
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114, Taiwan; (K.-M.S.); (M.-H.Y.)
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (Y.-H.L.); (L.-C.L.); (C.-C.C.)
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7
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Nudelman I, Kudrin D, Nudelman G, Deshpande R, Hartmann BM, Kleinstein SH, Myers CL, Sealfon SC, Zaslavsky E. Comparing Host Module Activation Patterns and Temporal Dynamics in Infection by Influenza H1N1 Viruses. Front Immunol 2021; 12:691758. [PMID: 34335598 PMCID: PMC8317020 DOI: 10.3389/fimmu.2021.691758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/14/2021] [Indexed: 11/13/2022] Open
Abstract
Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.
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Affiliation(s)
- Irina Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Division of General Internal Medicine, New York University Langone Medical Centre, New York, NY, United States
| | - Daniil Kudrin
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - German Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Raamesh Deshpande
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, United States
| | - Boris M Hartmann
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Steven H Kleinstein
- Department of Pathology, Yale University School of Medicine, New Haven, CT, United States
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, United States.,Program in Biomedical Informatics and Computational Biology, University of Minnesota - Twin Cities, Minneapolis, MN, United States
| | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
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8
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Dysregulated Immunological Functionome and Dysfunctional Metabolic Pathway Recognized for the Pathogenesis of Borderline Ovarian Tumors by Integrative Polygenic Analytics. Int J Mol Sci 2021; 22:ijms22084105. [PMID: 33921111 PMCID: PMC8071470 DOI: 10.3390/ijms22084105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/09/2021] [Accepted: 04/13/2021] [Indexed: 12/20/2022] Open
Abstract
The pathogenesis and molecular mechanisms of ovarian low malignant potential (LMP) tumors or borderline ovarian tumors (BOTs) have not been fully elucidated to date. Surgery remains the cornerstone of treatment for this disease, and diagnosis is mainly made by histopathology to date. However, there is no integrated analysis investigating the tumorigenesis of BOTs with open experimental data. Therefore, we first utilized a functionome-based speculative model from the aggregated obtainable datasets to explore the expression profiling data among all BOTs and two major subtypes of BOTs, serous BOTs (SBOTs) and mucinous BOTs (MBOTs), by analyzing the functional regularity patterns and clustering the separate gene sets. We next prospected and assembled the association between these targeted biomolecular functions and their related genes. Our research found that BOTs can be accurately recognized by gene expression profiles by means of integrative polygenic analytics among all BOTs, SBOTs, and MBOTs; the results exhibited the top 41 common dysregulated biomolecular functions, which were sorted into four major categories: immune and inflammatory response-related functions, cell membrane- and transporter-related functions, cell cycle- and signaling-related functions, and cell metabolism-related functions, which were the key elements involved in its pathogenesis. In contrast to previous research, we identified 19 representative genes from the above classified categories (IL6, CCR2 for immune and inflammatory response-related functions; IFNG, ATP1B1, GAS6, and PSEN1 for cell membrane- and transporter-related functions; CTNNB1, GATA3, and IL1B for cell cycle- and signaling-related functions; and AKT1, SIRT1, IL4, PDGFB, MAPK3, SRC, TWIST1, TGFB1, ADIPOQ, and PPARGC1A for cell metabolism-related functions) that were relevant in the cause and development of BOTs. We also noticed that a dysfunctional pathway of galactose catabolism had taken place among all BOTs, SBOTs, and MBOTs from the analyzed gene set databases of canonical pathways. With the help of immunostaining, we verified significantly higher performance of interleukin 6 (IL6) and galactose-1-phosphate uridylyltransferase (GALT) among BOTs than the controls. In conclusion, a bioinformatic platform of gene-set integrative molecular functionomes and biophysiological pathways was constructed in this study to interpret the complicated pathogenic pathways of BOTs, and these important findings demonstrated the dysregulated immunological functionome and dysfunctional metabolic pathway as potential roles during the tumorigenesis of BOTs and may be helpful for the diagnosis and therapy of BOTs in the future.
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9
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Gong Y, Ji P, Yang YS, Xie S, Yu TJ, Xiao Y, Jin ML, Ma D, Guo LW, Pei YC, Chai WJ, Li DQ, Bai F, Bertucci F, Hu X, Jiang YZ, Shao ZM. Metabolic-Pathway-Based Subtyping of Triple-Negative Breast Cancer Reveals Potential Therapeutic Targets. Cell Metab 2021; 33:51-64.e9. [PMID: 33181091 DOI: 10.1016/j.cmet.2020.10.012] [Citation(s) in RCA: 219] [Impact Index Per Article: 73.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 08/16/2020] [Accepted: 10/13/2020] [Indexed: 02/06/2023]
Abstract
Triple-negative breast cancer (TNBC) remains an unmet medical challenge. We investigated metabolic dysregulation in TNBCs by using our multi-omics database (n = 465, the largest to date). TNBC samples were classified into three heterogeneous metabolic-pathway-based subtypes (MPSs) with distinct metabolic features: MPS1, the lipogenic subtype with upregulated lipid metabolism; MPS2, the glycolytic subtype with upregulated carbohydrate and nucleotide metabolism; and MPS3, the mixed subtype with partial pathway dysregulation. These subtypes were validated by metabolomic profiling of 72 samples. These three subtypes had distinct prognoses, molecular subtype distributions, and genomic alterations. Moreover, MPS1 TNBCs were more sensitive to metabolic inhibitors targeting fatty acid synthesis, whereas MPS2 TNBCs showed higher sensitivity to inhibitors targeting glycolysis. Importantly, inhibition of lactate dehydrogenase could enhance tumor response to anti-PD-1 immunotherapy in MPS2 TNBCs. Collectively, our analysis demonstrated the metabolic heterogeneity of TNBCs and enabled the development of personalized therapies targeting unique tumor metabolic profiles.
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Affiliation(s)
- Yue Gong
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China
| | - Peng Ji
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China
| | - Yun-Song Yang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China
| | - Shao Xie
- Institute of Pediatrics, Children's Hospital, Fudan University, Shanghai 201102, P.R. China
| | - Tian-Jian Yu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China
| | - Ming-Liang Jin
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China
| | - Ding Ma
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China
| | - Lin-Wei Guo
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China
| | - Yu-Chen Pei
- Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China
| | - Wen-Jun Chai
- Laboratory Animal Center, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China
| | - Da-Qiang Li
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China; Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China
| | - Fan Bai
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing 100871, P.R. China
| | - François Bertucci
- Predictive Oncology Laboratory and Department of Medical Oncology, CRCM, Institut Paoli-Calmettes, INSERM, CNRS, Aix-Marseille Université, Marseille, France
| | - Xin Hu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China; Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China.
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China; Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China; Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China.
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10
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Su KM, Lin TW, Liu LC, Yang YP, Wang ML, Tsai PH, Wang PH, Yu MH, Chang CM, Chang CC. The Potential Role of Complement System in the Progression of Ovarian Clear Cell Carcinoma Inferred from the Gene Ontology-Based Immunofunctionome Analysis. Int J Mol Sci 2020; 21:E2824. [PMID: 32316695 PMCID: PMC7216156 DOI: 10.3390/ijms21082824] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 03/27/2020] [Accepted: 04/15/2020] [Indexed: 02/07/2023] Open
Abstract
Ovarian clear cell carcinoma (OCCC) is the second most common epithelial ovarian carcinoma (EOC). It is refractory to chemotherapy with a worse prognosis after the preliminary optimal debulking operation, such that the treatment of OCCC remains a challenge. OCCC is believed to evolve from endometriosis, a chronic immune/inflammation-related disease, so that immunotherapy may be a potential alternative treatment. Here, gene set-based analysis was used to investigate the immunofunctionomes of OCCC in early and advanced stages. Quantified biological functions defined by 5917 Gene Ontology (GO) terms downloaded from the Gene Expression Omnibus (GEO) database were used. DNA microarray gene expression profiles were used to convert 85 OCCCs and 136 normal controls into to the functionome. Relevant offspring were as extracted and the immunofunctionomes were rebuilt at different stages by machine learning. Several dysregulated pathogenic functions were found to coexist in the immunopathogenesis of early and advanced OCCC, wherein the complement-activation-alternative-pathway may be the headmost dysfunctional immunological pathway in duality for carcinogenesis at all OCCC stages. Several immunological genes involved in the complement system had dual influences on patients' survival, and immunohistochemistrical analysis implied the higher expression of C3a receptor (C3aR) and C5a receptor (C5aR) levels in OCCC than in controls.
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Affiliation(s)
- Kuo-Min Su
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (K.-M.S.); (L.-C.L.); (M.-H.Y.)
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114, Taiwan
| | - Tzu-Wei Lin
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan; (T.-W.L.); (Y.-P.Y.); (M.-L.W.); (P.-H.T.)
| | - Li-Chun Liu
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (K.-M.S.); (L.-C.L.); (M.-H.Y.)
- Division of Obstetrics and Gynecology, Tri-Service General Hospital Songshan Branch, National Defense Medical Center, Taipei 105, Taiwan
| | - Yi-Pin Yang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan; (T.-W.L.); (Y.-P.Y.); (M.-L.W.); (P.-H.T.)
- School of Medicine, National Yang-Ming University, Taipei 112, Taiwan;
| | - Mong-Lien Wang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan; (T.-W.L.); (Y.-P.Y.); (M.-L.W.); (P.-H.T.)
- School of Medicine, National Yang-Ming University, Taipei 112, Taiwan;
| | - Ping-Hsing Tsai
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan; (T.-W.L.); (Y.-P.Y.); (M.-L.W.); (P.-H.T.)
- School of Medicine, National Yang-Ming University, Taipei 112, Taiwan;
| | - Peng-Hui Wang
- School of Medicine, National Yang-Ming University, Taipei 112, Taiwan;
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan
| | - Mu-Hsien Yu
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (K.-M.S.); (L.-C.L.); (M.-H.Y.)
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114, Taiwan
| | - Chia-Ming Chang
- School of Medicine, National Yang-Ming University, Taipei 112, Taiwan;
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Cheng-Chang Chang
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei 114, Taiwan; (K.-M.S.); (L.-C.L.); (M.-H.Y.)
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114, Taiwan
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11
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Ozcan M, Altay O, Lam S, Turkez H, Aksoy Y, Nielsen J, Uhlen M, Boren J, Mardinoglu A. Improvement in the Current Therapies for Hepatocellular Carcinoma Using a Systems Medicine Approach. ACTA ACUST UNITED AC 2020; 4:e2000030. [PMID: 32529800 DOI: 10.1002/adbi.202000030] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 03/02/2020] [Accepted: 03/09/2020] [Indexed: 12/24/2022]
Abstract
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death primarily due to the lack of effective targeted therapies. Despite the distinct morphological and phenotypic patterns of HCC, treatment strategies are restricted to relatively homogeneous therapies, including multitargeted tyrosine kinase inhibitors and immune checkpoint inhibitors. Therefore, more effective therapy options are needed to target dysregulated metabolic and molecular pathways in HCC. Integrative genomic profiling of HCC patients provides insight into the most frequently mutated genes and molecular targets, including telomerase reverse transcriptase, the TP53 gene, and the Wnt/β-catenin signaling pathway oncogene (CTNNB1). Moreover, emerging techniques, such as genome-scale metabolic models may elucidate the underlying cancer-specific metabolism, which allows for the discovery of potential drug targets and identification of biomarkers. De novo lipogenesis has been revealed as consistently upregulated since it is required for cell proliferation in all HCC patients. The metabolic network-driven stratification of HCC patients in terms of redox responses, utilization of metabolites, and subtype-specific pathways may have clinical implications to drive the development of personalized medicine. In this review, the current and emerging therapeutic targets in light of molecular approaches and metabolic network-based strategies are summarized, prompting effective treatment of HCC patients.
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Affiliation(s)
- Mehmet Ozcan
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE 17121, Sweden.,Department of Medical Biochemistry, Faculty of Medicine, Hacettepe University, Ankara, 06100, Turkey
| | - Ozlem Altay
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE 17121, Sweden
| | - Simon Lam
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, 25240, Turkey
| | - Yasemin Aksoy
- Department of Medical Biochemistry, Faculty of Medicine, Hacettepe University, Ankara, 06100, Turkey
| | - Jens Nielsen
- Prof. J. Nielsen, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-41296, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE 17121, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, The Wallenberg Laboratory, Sahlgrenska University Hospital, Gothenburg, SE-413 45, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE 17121, Sweden.,Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
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12
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Su KM, Wang PH, Yu MH, Chang CM, Chang CC. The recent progress and therapy in endometriosis-associated ovarian cancer. J Chin Med Assoc 2020; 83:227-232. [PMID: 31985569 DOI: 10.1097/jcma.0000000000000262] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Endometriosis-associated ovarian cancers (EAOCs) including endometrioid and clear cell ovarian carcinoma are subgroups of epithelial ovarian carcinomas (EOCs), which is generally acknowledged as the most lethal gynecological malignancy. Endometriosis (ES), a common clinical disease among women, presents with clinical symptoms of pelvic pain, infertility, or adnexal masses with the formation of endometrioma. It has long been considered to be a potential risk factor for developing EOCs, mainly of endometrioid and clear cell subtypes. Here, we compiled data from previous researches on deregulated molecular functions among ES and EOCs using gene set-based integrative analysis to decipher molecular and genetic relationships between ovarian ES and EOCs, especially EAOCs. We conclude that epidermal growth factor receptor (ERBB) and Phosphoinositide 3-kinases (PI3K)-related pathways are important in the carcinogenesis of type I EOCs, including clear cell, endometrioid, and mucinous ovarian carcinoma. Dysfunctional molecular pathways, such as deregulated oxidoreductase activity, metabolism, hormone activity, inflammatory response, innate immune response, and cell-cell signaling, played key roles in the malignant transformation of EAOCs. Nine genes related to inflammasome complex and inflammasome-related pathway were identified, indicating the importance of inflammation/immunity in EAOC transformation. We also collect progressive treatments of EAOC focused on targeted therapies and immunotherapy so far. This summarized information can contribute toward effective detection and treatment of EAOCs in the future.
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Affiliation(s)
- Kuo-Min Su
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Peng-Hui Wang
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- School of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC
| | - Mu-Hsien Yu
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Chia-Ming Chang
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- School of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC
| | - Cheng-Chang Chang
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
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13
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Davis-Marcisak EF, Sherman TD, Orugunta P, Stein-O'Brien GL, Puram SV, Roussos Torres ET, Hopkins AC, Jaffee EM, Favorov AV, Afsari B, Goff LA, Fertig EJ. Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data. Cancer Res 2019; 79:5102-5112. [PMID: 31337651 PMCID: PMC6844448 DOI: 10.1158/0008-5472.can-18-3882] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 05/13/2019] [Accepted: 07/19/2019] [Indexed: 12/20/2022]
Abstract
Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies have revealed the prevalence of intratumor and intertumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intratumor and intertumor molecular heterogeneity. In this study, we adapted our algorithm for pathway dysregulation, Expression Variation Analysis (EVA), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. EVA has high sensitivity and specificity to detect pathways with true differential heterogeneity in simulated data. EVA was applied to several public domain scRNA-seq tumor datasets to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics such as immunogenicity, metastasis, and cancer subtypes. Immune pathway heterogeneity of hematopoietic cell populations in breast tumors corresponded to the amount of diversity present in the T-cell repertoire of each individual. Cells from head and neck squamous cell carcinoma (HNSCC) primary tumors had significantly more heterogeneity across pathways than cells from metastases, consistent with a model of clonal outgrowth. Moreover, there were dramatic differences in pathway dysregulation across HNSCC basal primary tumors. Within the basal primary tumors, there was increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. These results demonstrate the broad utility of EVA to quantify intertumor and intratumor heterogeneity from scRNA-seq data without reliance on low-dimensional visualization. SIGNIFICANCE: This study presents a robust statistical algorithm for evaluating gene expression heterogeneity within pathways or gene sets in single-cell RNA-seq data.
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Affiliation(s)
- Emily F Davis-Marcisak
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Thomas D Sherman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Pranay Orugunta
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Genevieve L Stein-O'Brien
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Sidharth V Puram
- Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri
| | - Evanthia T Roussos Torres
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Alexander C Hopkins
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan
| | - Elizabeth M Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Alexander V Favorov
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
- Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Bahman Afsari
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Loyal A Goff
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Elana J Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland.
- Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland
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14
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Ho CH, Chang CM, Li HY, Shen HY, Lieu FK, Wang PSG. Dysregulated immunological and metabolic functions discovered by a polygenic integrative analysis for PCOS. Reprod Biomed Online 2019; 40:160-167. [PMID: 31780352 DOI: 10.1016/j.rbmo.2019.09.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 08/22/2019] [Accepted: 09/23/2019] [Indexed: 12/28/2022]
Abstract
RESEARCH QUESTION Polycystic ovary syndrome (PCOS) is a complex disease and its pathophysiology is still unclear. This polygenic study may provide some clues. DESIGN A polygenic, functionome-based study with the ovarian gene expression profiles downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, including 48 PCOS and 181 normal control samples. These profiles were converted to the gene set regularity (GSR) indices, which were computed by the modified differential rank conversion algorithm and were defined by the gene ontology terms. RESULTS Machine learning could accurately recognize the patterns of functional regularities between PCOS and normal controls. The significantly aberrant functions in PCOS included transporter activity, catalytic activity, the receptor signalling pathway via signal transducer and activator of transcription (STAT), the cellular metabolic process, and immune response. CONCLUSION This study provided a comprehensive view of the dysregulated functions and information for further studies on the management of PCOS.
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Affiliation(s)
- Chi-Hong Ho
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei Taiwan, Republic of China; Institute of Physiology, School of Medicine, National Yang-Ming University, Taipei Taiwan, Republic of China
| | - Chia-Ming Chang
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei Taiwan, Republic of China; Department of Obstetrics and Gynecology, School of Medicine, National Yang-Ming University, Taipei Taiwan, Republic of China
| | - Hsin-Yang Li
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei Taiwan, Republic of China
| | - Heng-Yi Shen
- Department of Rehabilitation, Cheng Hsin General Hospital, Taipei Taiwan, Republic of China; Department of Physical Medicine and Rehabilitation, National Defense Medical Center, Taipei Taiwan, Republic of China
| | - Fu-Kong Lieu
- Department of Rehabilitation, Cheng Hsin General Hospital, Taipei Taiwan, Republic of China; Department of Physical Medicine and Rehabilitation, National Defense Medical Center, Taipei Taiwan, Republic of China
| | - Paulus Shyi-Gang Wang
- Institute of Physiology, School of Medicine, National Yang-Ming University, Taipei Taiwan, Republic of China; Medical Center of Ageing Research, China Medical University Hospital, Taichung Taiwan, Republic of China; Department of Biotechnology, College of Health Science, Asia University, Taichung Taiwan, Republic of China; Department of Medical Research, Taipei Veterans General Hospital, Taipei Taiwan, Republic of China.
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15
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Afsari B, Guo T, Considine M, Florea L, Kagohara LT, Stein-O'Brien GL, Kelley D, Flam E, Zambo KD, Ha PK, Geman D, Ochs MF, Califano JA, Gaykalova DA, Favorov AV, Fertig EJ. Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer. Bioinformatics 2019; 34:1859-1867. [PMID: 29342249 DOI: 10.1093/bioinformatics/bty004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 01/10/2018] [Indexed: 12/22/2022] Open
Abstract
Motivation Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches. Results We introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g. tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVA's performance against EBSeq, DiffSplice and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVA in identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery. These results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data. Availability and implementation SEVA is implemented in the R/Bioconductor package GSReg. Contact bahman@jhu.edu or favorov@sensi.org or ejfertig@jhmi.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bahman Afsari
- Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center
| | - Theresa Guo
- Department of Otolaryngology-Head and Neck Surgery
| | - Michael Considine
- Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center
| | - Liliana Florea
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Luciane T Kagohara
- Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center
| | - Genevieve L Stein-O'Brien
- Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center
| | - Dylan Kelley
- Department of Otolaryngology-Head and Neck Surgery
| | - Emily Flam
- Department of Otolaryngology-Head and Neck Surgery
| | | | - Patrick K Ha
- Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, CA 94158, USA
| | - Donald Geman
- Department of Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Michael F Ochs
- Department of Mathematics & Statistics, The College of New Jersey, Ewing, NJ 08628, USA
| | - Joseph A Califano
- Division of Otolaryngology, Department of Surgery, University of California, San Diego, CA 92093, USA
| | | | - Alexander V Favorov
- Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center.,Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, RAS, Moscow 119333, Russia
| | - Elana J Fertig
- Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center
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16
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Paquette AG, Brockway HM, Price ND, Muglia LJ. Comparative transcriptomic analysis of human placentae at term and preterm delivery. Biol Reprod 2019; 98:89-101. [PMID: 29228154 PMCID: PMC5803773 DOI: 10.1093/biolre/iox163] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Accepted: 11/30/2017] [Indexed: 12/11/2022] Open
Abstract
Preterm birth affects 1 out of every 10 infants in the United States, resulting in substantial neonatal morbidity and mortality. Currently, there are few predictive markers and few treatment options to prevent preterm birth. A healthy, functioning placenta is essential to positive pregnancy outcomes. Previous studies have suggested that placental pathology may play a role in preterm birth etiology. Therefore, we tested the hypothesis that preterm placentae may exhibit unique transcriptomic signatures compared to term samples reflective of their abnormal biology leading to this adverse outcome. We aggregated publicly available placental villous microarray data to generate a preterm and term sample dataset (n = 133, 55 preterm placentae and 78 normal term placentae). We identified differentially expressed genes using the linear regression for microarray (LIMMA) package and identified perturbations in known biological networks using Differential Rank Conservation (DIRAC). We identified 129 significantly differentially expressed genes between term and preterm placenta with 96 genes upregulated and 33 genes downregulated (P-value <0.05). Significant changes in gene expression in molecular networks related to Tumor Protein 53 and phosphatidylinositol signaling were identified using DIRAC. We have aggregated a uniformly normalized transcriptomic dataset and have identified novel and established genes and pathways associated with developmental regulation of the placenta and potential preterm birth pathology. These analyses provide a community resource to integrate with other high-dimensional datasets for additional insights in normal placental development and its disruption.
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Affiliation(s)
| | - Heather M Brockway
- Division of Human Genetics, Center for Prevention of Preterm Birth, Cincinnati Children's, Hospital Medical Center, Cincinnati, Ohio, USA
| | | | - Louis J Muglia
- Division of Human Genetics, Center for Prevention of Preterm Birth, Cincinnati Children's, Hospital Medical Center, Cincinnati, Ohio, USA
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17
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Turanli B, Zhang C, Kim W, Benfeitas R, Uhlen M, Arga KY, Mardinoglu A. Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning. EBioMedicine 2019; 42:386-396. [PMID: 30905848 PMCID: PMC6491384 DOI: 10.1016/j.ebiom.2019.03.009] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 02/28/2019] [Accepted: 03/04/2019] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Genome-scale metabolic models (GEMs) offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a time- and cost-effective method of drug discovery that can be applied together with GEMs for effective cancer treatment. METHODS In this study, we reconstruct a prostate cancer (PRAD)-specific GEM for exploring prostate cancer metabolism and also repurposing new therapeutic agents that can be used in development of effective cancer treatment. We integrate global gene expression profiling of cell lines with >1000 different drugs through the use of prostate cancer GEM and predict possible drug-gene interactions. FINDINGS We identify the key reactions with altered fluxes based on the gene expression changes and predict the potential drug effect in prostate cancer treatment. We find that sulfamethoxypyridazine, azlocillin, hydroflumethiazide, and ifenprodil can be repurposed for the treatment of prostate cancer based on an in silico cell viability assay. Finally, we validate the effect of ifenprodil using an in vitro cell assay and show its inhibitory effect on a prostate cancer cell line. INTERPRETATION Our approach demonstate how GEMs can be used to predict therapeutic agents for cancer treatment based on drug repositioning. Besides, it paved a way and shed a light on the applicability of computational models to real-world biomedical or pharmaceutical problems.
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Affiliation(s)
- Beste Turanli
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden; Department of Bioengineering, Marmara University, Istanbul, Turkey; Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | | | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-41296, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom.
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18
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Precision Medicine in Pancreatic Disease-Knowledge Gaps and Research Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop. Pancreas 2019; 48:1250-1258. [PMID: 31688587 PMCID: PMC7282491 DOI: 10.1097/mpa.0000000000001412] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
A workshop on research gaps and opportunities for Precision Medicine in Pancreatic Disease was sponsored by the National Institute of Diabetes and Digestive Kidney Diseases on July 24, 2019, in Pittsburgh. The workshop included an overview lecture on precision medicine in cancer and 4 sessions: (1) general considerations for the application of bioinformatics and artificial intelligence; (2) omics, the combination of risk factors and biomarkers; (3) precision imaging; and (4) gaps, barriers, and needs to move from precision to personalized medicine for pancreatic disease. Current precision medicine approaches and tools were reviewed, and participants identified knowledge gaps and research needs that hinder bringing precision medicine to pancreatic diseases. Most critical were (a) multicenter efforts to collect large-scale patient data sets from multiple data streams in the context of environmental and social factors; (b) new information systems that can collect, annotate, and quantify data to inform disease mechanisms; (c) novel prospective clinical trial designs to test and improve therapies; and (d) a framework for measuring and assessing the value of proposed approaches to the health care system. With these advances, precision medicine can identify patients early in the course of their pancreatic disease and prevent progression to chronic or fatal illness.
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19
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Chang CC, Su KM, Lu KH, Lin CK, Wang PH, Li HY, Wang ML, Lin CK, Yu MH, Chang CM. Key Immunological Functions Involved in the Progression of Epithelial Ovarian Serous Carcinoma Discovered by the Gene Ontology-Based Immunofunctionome Analysis. Int J Mol Sci 2018; 19:ijms19113311. [PMID: 30356023 PMCID: PMC6274992 DOI: 10.3390/ijms19113311] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 10/19/2018] [Accepted: 10/23/2018] [Indexed: 11/16/2022] Open
Abstract
Serous carcinoma (SC) is the most common and lethal subtype of epithelial ovarian carcinoma; immunotherapy is a potential treatment for SC, however, the global immunological functions of SC as well as their change during the progression of SC have not been investigated in detail till now. We conducted a genome-wide integrative analysis to investigate the immunofunctionomes of SC at four tumor stages by quantifying the immunological functions defined by the Gene Ontology gene sets. DNA microarray gene expression profiles of 1100 SCs and 136 normal ovarian tissue controls were downloaded from the Gene Expression Omnibus database and converted to the functionome. Then the immunofunctionomes were reconstructed by extracting the offspring from the functionome for the four SC staging groups. The key immunological functions extracted from immunofunctionomes with a series of filters revealed that the immunopathy of SC consisted of a group of deregulated functions with the core members including B cell activation and differentiation, regulation of leukocyte chemotaxis/cellular extravasation, antigen receptor mediated signaling pathway, T helper mediated immunity and macrophage activation; and the auxiliary elements included leukocyte mediated immunity, regulation of inflammatory response, T cell differentiation, mononuclear cell migration, megakaryocyte differentiation, complement activation and cytokine production. These deregulated immunological functions reveal the candidates to target in the immunotherapy.
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Affiliation(s)
- Cheng-Chang Chang
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei 114, Taiwan.
| | - Kuo-Min Su
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei 114, Taiwan.
| | - Kai-Hsi Lu
- Department of Medical Research and Education, Cheng-Hsin Hospital, Taipei 112, Taiwan.
| | - Chi-Kang Lin
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei 114, Taiwan.
| | - Peng-Hui Wang
- School of Medicine, National Yang-Ming University, Taipei 112, Taiwan.
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 112, Taiwan.
- Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan.
| | - Hsin-Yang Li
- School of Medicine, National Yang-Ming University, Taipei 112, Taiwan.
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 112, Taiwan.
| | - Mong-Lien Wang
- Department of Medical Research and Education, Cheng-Hsin Hospital, Taipei 112, Taiwan.
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan.
| | - Cheng-Kuo Lin
- Department of Obstetrics and Gynecology, Taoyuan Armed Forces General Hospital, Taoyuan 325, Taiwan.
| | - Mu-Hsien Yu
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei 114, Taiwan.
| | - Chia-Ming Chang
- School of Medicine, National Yang-Ming University, Taipei 112, Taiwan.
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 112, Taiwan.
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20
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Chakrabarty P, Li A, Ladd TB, Strickland MR, Koller EJ, Burgess JD, Funk CC, Cruz PE, Allen M, Yaroshenko M, Wang X, Younkin C, Reddy J, Lohrer B, Mehrke L, Moore BD, Liu X, Ceballos-Diaz C, Rosario AM, Medway C, Janus C, Li HD, Dickson DW, Giasson BI, Price ND, Younkin SG, Ertekin-Taner N, Golde TE. TLR5 decoy receptor as a novel anti-amyloid therapeutic for Alzheimer's disease. J Exp Med 2018; 215:2247-2264. [PMID: 30158114 PMCID: PMC6122970 DOI: 10.1084/jem.20180484] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/09/2018] [Accepted: 05/09/2018] [Indexed: 12/22/2022] Open
Abstract
There is considerable interest in harnessing innate immunity to treat Alzheimer's disease (AD). Here, we explore whether a decoy receptor strategy using the ectodomain of select TLRs has therapeutic potential in AD. AAV-mediated expression of human TLR5 ectodomain (sTLR5) alone or fused to human IgG4 Fc (sTLR5Fc) results in robust attenuation of amyloid β (Aβ) accumulation in a mouse model of Alzheimer-type Aβ pathology. sTLR5Fc binds to oligomeric and fibrillar Aβ with high affinity, forms complexes with Aβ, and blocks Aβ toxicity. Oligomeric and fibrillar Aβ modulates flagellin-mediated activation of human TLR5 but does not, by itself, activate TLR5 signaling. Genetic analysis shows that rare protein coding variants in human TLR5 may be associated with a reduced risk of AD. Further, transcriptome analysis shows altered TLR gene expression in human AD. Collectively, our data suggest that TLR5 decoy receptor-based biologics represent a novel and safe Aβ-selective class of biotherapy in AD.
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Affiliation(s)
- Paramita Chakrabarty
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
- McKnight Brain Institute, University of Florida, Gainesville, FL
| | - Andrew Li
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
| | - Thomas B Ladd
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
| | - Michael R Strickland
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
| | - Emily J Koller
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
| | | | | | - Pedro E Cruz
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL
| | - Mariya Yaroshenko
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
| | - Xue Wang
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL
| | - Curtis Younkin
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL
| | - Joseph Reddy
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL
| | | | - Leonie Mehrke
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL
| | - Brenda D Moore
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
| | - Xuefei Liu
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
| | - Carolina Ceballos-Diaz
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
| | - Awilda M Rosario
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
| | | | - Christopher Janus
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
| | | | | | - Benoit I Giasson
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
- McKnight Brain Institute, University of Florida, Gainesville, FL
| | | | | | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL
- Department of Neurology, Mayo Clinic, Jacksonville, FL
| | - Todd E Golde
- Center for Translational Research in Neurodegenerative Disease, Department of Neuroscience, University of Florida, Gainesville, FL
- McKnight Brain Institute, University of Florida, Gainesville, FL
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21
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Abstract
Technological advances enable increasingly comprehensive profiling of the molecular landscapes of cells, and these data can inform the personalized treatment of complex diseases. Two major obstacles are the complexity of these data and the high degree of person-to-person heterogeneity. We develop a highly simplified, personalized data representation by comparing the profile of an individual to the range of landscapes in a baseline population, thereby mimicking basic clinical diagnostic testing for departures of selected variables from normal levels. Moreover, our method can be applied to any data modality and at any level of granularity, from single features to any subset of features treated as a single entity, for example the gene expression levels in a pathway. Experiments involve both healthy human tissues and various cancer subtypes. Data collected from omics technologies have revealed pervasive heterogeneity and stochasticity of molecular states within and between phenotypes. A prominent example of such heterogeneity occurs between genome-wide mRNA, microRNA, and methylation profiles from one individual tumor to another, even within a cancer subtype. However, current methods in bioinformatics, such as detecting differentially expressed genes or CpG sites, are population-based and therefore do not effectively model intersample diversity. Here we introduce a unified theory to quantify sample-level heterogeneity that is applicable to a single omics profile. Specifically, we simplify an omics profile to a digital representation based on the omics profiles from a set of samples from a reference or baseline population (e.g., normal tissues). The state of any subprofile (e.g., expression vector for a subset of genes) is said to be “divergent” if it lies outside the estimated support of the baseline distribution and is consequently interpreted as “dysregulated” relative to that baseline. We focus on two cases: single features (e.g., individual genes) and distinguished subsets (e.g., regulatory pathways). Notably, since the divergence analysis is at the individual sample level, dysregulation can be analyzed probabilistically; for example, one can estimate the probability that a gene or pathway is divergent in some population. Finally, the reduction in complexity facilitates a more “personalized” and biologically interpretable analysis of variation, as illustrated by experiments involving tissue characterization, disease detection and progression, and disease–pathway associations.
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Paquette AG, Shynlova O, Kibschull M, Price ND, Lye SJ. Comparative analysis of gene expression in maternal peripheral blood and monocytes during spontaneous preterm labor. Am J Obstet Gynecol 2018; 218:345.e1-345.e30. [PMID: 29305255 DOI: 10.1016/j.ajog.2017.12.234] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 12/07/2017] [Accepted: 12/27/2017] [Indexed: 10/18/2022]
Abstract
BACKGROUND Preterm birth is the leading cause of newborn death worldwide, and is associated with significant cognitive and physiological challenges in later life. There is a pressing need to define the mechanisms that initiate spontaneous preterm labor, and for development of novel clinical biomarkers to identify high-risk pregnancies. Most preterm birth studies utilize fetal tissues, and there is limited understanding of the transcriptional changes that occur in mothers undergoing spontaneous preterm labor. Earlier work revealed that a specific population of maternal peripheral leukocytes (macrophages/monocytes) play an active role in the initiation of labor. Thus, we hypothesized that there are dynamic gene expression changes in maternal blood leukocytes during preterm labor. OBJECTIVE Using next-generation sequencing we aim to characterize the transcriptome in whole blood leukocytes and peripheral monocytes of women undergoing spontaneous preterm labor compared to healthy pregnant women who subsequently delivered at full term. STUDY DESIGN RNA sequencing was performed in both whole blood and peripheral monocytes from women who underwent preterm labor (24-34 weeks of gestation, N = 20) matched for gestational age to healthy pregnant controls (N = 30). All participants were a part of the Ontario Birth Study cohort (Toronto, Ontario, Canada). RESULTS We identified significant differences in expression of 262 genes in peripheral monocytes and 184 genes in whole blood of women who were in active spontaneous preterm labor compared to pregnant women of the same gestational age not undergoing labor, with 43 of these genes differentially expressed in both whole blood and peripheral monocytes. ADAMTS2 expression was significantly increased in women actively undergoing spontaneous preterm labor, which we validated through digital droplet reverse transcriptase polymerase chain reaction. Intriguingly, we have also identified a number of gene sets including signaling by stem cell factor-KIT, nucleotide metabolism, and trans-Golgi network vesicle budding, which exhibited changes in relative gene expression that was predictive of preterm labor status in both maternal whole blood and peripheral monocytes. CONCLUSION This study is the first to investigate changes in both whole blood leukocytes and peripheral monocytes of women actively undergoing spontaneous preterm labor through robust transcript measurements from RNA sequencing. Our unique study design overcame confounding based on gestational age by collecting blood samples from women matched by gestational age, allowing us to study transcriptomic changes directly related to the active preterm parturition. We performed RNA profiling using whole genome sequencing, which is highly sensitive and allowed us to identify subtle changes in specific genes. ADAMTS2 expression emerged as a marker of prematurity within peripheral blood leukocytes, an accessible tissue that plays a functional role in signaling during the onset of labor. We identified changes in relative gene expression in a number of gene sets related to signaling in monocytes and whole blood of women undergoing spontaneous preterm labor compared to controls. These genes and pathways may help identify potential targets for the development of novel drugs for preterm birth prevention.
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Chang CM, Wang ML, Lu KH, Yang YP, Juang CM, Wang PH, Hsu RJ, Yu MH, Chang CC. Integrating the dysregulated inflammasome-based molecular functionome in the malignant transformation of endometriosis-associated ovarian carcinoma. Oncotarget 2017; 9:3704-3726. [PMID: 29423077 PMCID: PMC5790494 DOI: 10.18632/oncotarget.23364] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 10/29/2017] [Indexed: 11/30/2022] Open
Abstract
The coexistence of endometriosis (ES) with ovarian clear cell carcinoma (CCC) or endometrioid carcinoma (EC) suggested that malignant transformation of ES leads to endometriosis associated ovarian carcinoma (EAOC). However, there is still lack of an integrating data analysis of the accumulated experimental data to provide the evidence supporting the hypothesis of EAOC transformation. Herein we used a function-based analytic model with the publicly available microarray datasets to investigate the expression profiling between ES, CCC, and EC. We analyzed the functional regularity pattern of the three type of samples and hierarchically clustered the gene sets to identify key mechanisms regulating the malignant transformation of EAOC. We identified a list of 18 genes (NLRP3, AIM2, PYCARD, NAIP, Caspase-4, Caspase-7, Caspase-8, TLR1, TLR7, TOLLIP, NFKBIA, TNF, TNFAIP3, INFGR2, P2RX7, IL-1B, IL1RL1, IL-18) closely related to inflammasome complex, indicating an important role of inflammation/immunity in EAOC transformation. We next explore the association between these target genes and patient survival using Gene Expression Omnibus (GEO), and found significant correlation between the expression levels of the target genes and the progression-free survival. Interestingly, high expression levels of AIM2 and NLRP3, initiating proteins of inflammasomes, were significantly correlated with poor progression-free survival. Immunohistochemistry staining confirmed a correlation between high AIM2 and high Ki-67 in clinical EAOC samples, supporting its role in disease progression. Collectively, we established a bioinformatic platform of gene-set integrative molecular functionome to dissect the pathogenic pathways of EAOC, and demonstrated a key role of dysregulated inflammasome in modulating the malignant transformation of EAOC.
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Affiliation(s)
- Chia-Ming Chang
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Mong-Lien Wang
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Kai-Hsi Lu
- Department of Medical Research and Education, Cheng-Hsin Hospital, Taipei, Taiwan
| | - Yi-Ping Yang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chi-Mou Juang
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Peng-Hui Wang
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan.,Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Ren-Jun Hsu
- Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan.,Biobank Management Center of Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Mu-Hsien Yu
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Cheng-Chang Chang
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
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24
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Chang CM, Wang PH, Horng HC. Gene set-based analysis of mucinous ovarian carcinoma. Taiwan J Obstet Gynecol 2017; 56:210-216. [PMID: 28420510 DOI: 10.1016/j.tjog.2016.12.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/27/2016] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVE Mucinous ovarian carcinoma (MOC) is an uncommon subtype of epithelial ovarian cancers, and the pathogenesis is still poorly understood because of its rarity. We conducted a gene set-based analysis to investigate the pathogenesis of MOC by integrating microarray gene expression datasets based on the regularity of functions defined by gene ontology or canonical pathway databases. MATERIALS AND METHODS Forty-five pairs of MOC and normal ovarian tissue sample gene expression profiles were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus database. The gene expression profiles were converted to the gene set regularity indexes by measuring the change of gene expression ordering in a gene set. Then the pathogenesis of MOC was investigated with the differences of function regularity with the gene set regularity indexes between the MOC and normal control samples. RESULTS The informativeness of the gene set regularity indexes was sufficient for machine learning to accurately recognize and classify the functional regulation patterns with an accuracy of 99.44%. The statistical analysis revealed that the GTPase regulators and receptor tyrosine kinase erbB-2 (ERBB2) were the most important aberrations; the exploratory factor analysis revealed phosphoinositide 3-kinase-activating kinase, G-protein coupled receptor pathway, oxidoreductase activity, immune response, peptidase activity, regulation of translation, and transport and channel activity were also involved in the pathogenesis of MOC. CONCLUSION Investigating the pathogenesis of MOC with the functionome provided a comprehensive view of the deregulated functions of this disease. In addition to GTPase regulators and ERBB2, a plenty of deregulated functions such as phosphoinositide 3-kinase, G-protein coupled receptor pathway, and immune response also participated in the interaction network of MOC pathogenesis.
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Affiliation(s)
- Chia-Ming Chang
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Oral Biology, National Yang-Ming University, Taipei, Taiwan; Department of Obstetrics and Gynecology, National Yang-Ming University, Taipei, Taiwan
| | - Peng-Hui Wang
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Obstetrics and Gynecology, National Yang-Ming University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan; Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Huann-Cheng Horng
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Obstetrics and Gynecology, National Yang-Ming University, Taipei, Taiwan.
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25
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Discovering the Deregulated Molecular Functions Involved in Malignant Transformation of Endometriosis to Endometriosis-Associated Ovarian Carcinoma Using a Data-Driven, Function-Based Analysis. Int J Mol Sci 2017; 18:ijms18112345. [PMID: 29113136 PMCID: PMC5713314 DOI: 10.3390/ijms18112345] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 11/03/2017] [Accepted: 11/04/2017] [Indexed: 12/12/2022] Open
Abstract
The clinical characteristics of clear cell carcinoma (CCC) and endometrioid carcinoma EC) are concomitant with endometriosis (ES), which leads to the postulation of malignant transformation of ES to endometriosis-associated ovarian carcinoma (EAOC). Different deregulated functional areas were proposed accounting for the pathogenesis of EAOC transformation, and there is still a lack of a data-driven analysis with the accumulated experimental data in publicly-available databases to incorporate the deregulated functions involved in the malignant transformation of EOAC. We used the microarray gene expression datasets of ES, CCC and EC downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) database. Then, we investigated the pathogenesis of EAOC by a data-driven, function-based analytic model with the quantified molecular functions defined by 1454 Gene Ontology (GO) term gene sets. This model converts the gene expression profiles to the functionome consisting of 1454 quantified GO functions, and then, the key functions involving the malignant transformation of EOAC can be extracted by a series of filters. Our results demonstrate that the deregulated oxidoreductase activity, metabolism, hormone activity, inflammatory response, innate immune response and cell-cell signaling play the key roles in the malignant transformation of EAOC. These results provide the evidence supporting the specific molecular pathways involved in the malignant transformation of EAOC.
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Klein MI, Stern DF, Zhao H. GRAPE: a pathway template method to characterize tissue-specific functionality from gene expression profiles. BMC Bioinformatics 2017. [PMID: 28651562 PMCID: PMC5485588 DOI: 10.1186/s12859-017-1711-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Personalizing treatment regimes based on gene expression profiles of individual tumors will facilitate management of cancer. Although many methods have been developed to identify pathways perturbed in tumors, the results are often not generalizable across independent datasets due to the presence of platform/batch effects. There is a need to develop methods that are robust to platform/batch effects and able to identify perturbed pathways in individual samples. RESULTS We present Gene-Ranking Analysis of Pathway Expression (GRAPE) as a novel method to identify abnormal pathways in individual samples that is robust to platform/batch effects in gene expression profiles generated by multiple platforms. GRAPE first defines a template consisting of an ordered set of pathway genes to characterize the normative state of a pathway based on the relative rankings of gene expression levels across a set of reference samples. This template can be used to assess whether a sample conforms to or deviates from the typical behavior of the reference samples for this pathway. We demonstrate that GRAPE performs well versus existing methods in classifying tissue types within a single dataset, and that GRAPE achieves superior robustness and generalizability across different datasets. A powerful feature of GRAPE is the ability to represent individual gene expression profiles as a vector of pathways scores. We present applications to the analyses of breast cancer subtypes and different colonic diseases. We perform survival analysis of several TCGA subtypes and find that GRAPE pathway scores perform well in comparison to other methods. CONCLUSIONS GRAPE templates offer a novel approach for summarizing the behavior of gene-sets across a collection of gene expression profiles. These templates offer superior robustness across distinct experimental batches compared to existing methods. GRAPE pathway scores enable identification of abnormal gene-set behavior in individual samples using a non-competitive approach that is fundamentally distinct from popular enrichment-based methods. GRAPE may be an appropriate tool for researchers seeking to identify individual samples displaying abnormal gene-set behavior as well as to explore differences in the consensus gene-set behavior of groups of samples. GRAPE is available in R for download at https://CRAN.R-project.org/package=GRAPE .
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Affiliation(s)
- Michael I Klein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - David F Stern
- Department of Pathology, Yale University, New Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, 60 College Street, P.O. Box 208034, New Haven, 06520-8034, CT, USA.
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27
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Hong G, Li H, Zhang J, Guan Q, Chen R, Guo Z. Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings. Sci Rep 2017; 7:1348. [PMID: 28465555 PMCID: PMC5431047 DOI: 10.1038/s41598-017-01536-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 03/30/2017] [Indexed: 12/31/2022] Open
Abstract
Due to the invasiveness nature of tissue biopsy, it is common that investigators cannot collect sufficient normal controls for comparison with diseased samples. We developed a pathway enrichment tool, DRFunc, to detect significantly disease-disrupted pathways by incorporating normal controls from other experiments. The method was validated using both microarray and RNA-seq expression data for different cancers. The high concordant differentially ranked (DR) gene pairs were identified between cases and controls from different independent datasets. The DR gene pairs were used in the DRFunc algorithm to detect significantly disrupted pathways in one-phenotype expression data by combing controls from other studies. The DRFunc algorithm was exemplified by the detection of significant pathways in glioblastoma samples. The algorithm can also be used to detect altered pathways in the datasets with weak expression signals, as shown by the analysis on the expression data of chemotherapy-treated breast cancer samples.
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Affiliation(s)
- Guini Hong
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China.
| | - Hongdong Li
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China
| | - Jiahui Zhang
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China
| | - Qingzhou Guan
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China
| | - Rou Chen
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China
| | - Zheng Guo
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China.
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, 350108, China.
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28
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Buzdin AA, Prassolov V, Zhavoronkov AA, Borisov NM. Bioinformatics Meets Biomedicine: OncoFinder, a Quantitative Approach for Interrogating Molecular Pathways Using Gene Expression Data. Methods Mol Biol 2017; 1613:53-83. [PMID: 28849558 DOI: 10.1007/978-1-4939-7027-8_4] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
We propose a biomathematical approach termed OncoFinder (OF) that enables performing both quantitative and qualitative analyses of the intracellular molecular pathway activation. OF utilizes an algorithm that distinguishes the activator/repressor role of every gene product in a pathway. This method is applicable for the analysis of any physiological, stress, malignancy, and other conditions at the molecular level. OF showed a strong potential to neutralize background-caused differences between experimental gene expression data obtained using NGS, microarray and modern proteomics techniques. Importantly, in most cases, pathway activation signatures were better markers of cancer progression compared to the individual gene products. OF also enables correlating pathway activation with the success of anticancer therapy for individual patients. We further expanded this approach to analyze impact of micro RNAs (miRs) on the regulation of cellular interactome. Many alternative sources provide information about miRs and their targets. However, instruments elucidating higher level impact of the established total miR profiles are still largely missing. A variant of OncoFinder termed MiRImpact enables linking miR expression data with its estimated outcome on the regulation of molecular processes, such as signaling, metabolic, cytoskeleton, and DNA repair pathways. MiRImpact was used to establish cancer-specific and cytomegaloviral infection-linked interactomic signatures for hundreds of molecular pathways. Interestingly, the impact of miRs appeared orthogonal to pathway regulation at the mRNA level, which stresses the importance of combining all available levels of gene regulation to build a more objective molecular model of cell.
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Affiliation(s)
- Anton A Buzdin
- Pathway Pharmaceuticals, Wan Chai, Hong Kong SAR.
- Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre "Kurchatov Institute", Bldg 140, Suite 415, 1, Akademika Kurchatova sq., Moscow, 123182, Russia.
- Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- Laboratory of Bioinformatics, D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.
| | - Vladimir Prassolov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilova street 32, Mosow, 119991, Russia
| | - Alex A Zhavoronkov
- Pathway Pharmaceuticals, Wan Chai, Hong Kong SAR
- Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Nikolay M Borisov
- Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre "Kurchatov Institute", Bldg 140, Suite 415, 1, Akademika Kurchatova sq., Moscow, 123182, Russia
- Department of Personalized Medicine, First Oncology Research and Advisory Center, Moscow, Russia
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29
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Artcibasova AV, Korzinkin MB, Sorokin MI, Shegay PV, Zhavoronkov AA, Gaifullin N, Alekseev BY, Vorobyev NV, Kuzmin DV, Kaprin АD, Borisov NM, Buzdin AA. MiRImpact, a new bioinformatic method using complete microRNA expression profiles to assess their overall influence on the activity of intracellular molecular pathways. Cell Cycle 2016; 15:689-98. [PMID: 27027999 PMCID: PMC4845938 DOI: 10.1080/15384101.2016.1147633] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
MicroRNAs (miRs) are short noncoding RNA molecules that regulate expression of target mRNAs. Many published sources provide information about miRs and their targets. However, bioinformatic tools elucidating higher level impact of the established total miR profiles, are still largely missing. Recently, we developed a method termed OncoFinder enabling quantification of the activities of intracellular molecular pathways basing on gene expression data. Here we propose a new technique, MiRImpact, which enables to link miR expression data with its estimated outcome on the regulation of molecular pathways, like signaling, metabolic, cytoskeleton rearrangement, and DNA repair pathways. MiRImpact uses OncoFinder rationale for pathway activity calculations, with the major distinctions that (i) it deals with the concentrations of miRs - known regulators of gene products participating in molecular pathways, and (ii) miRs are considered as negative regulators of target molecules, if other is not specified. MiRImpact operates with 2 types of databases: for molecular targets of miRs and for gene products participating in molecular pathways. We applied MiRImpact to compare regulation of human bladder cancer-specific signaling pathways at the levels of mRNA and miR expression. We took 2 most complete alternative databases of experimentally validated miR targets – miRTarBase and DianaTarBase, and an OncoFinder database featuring 2725 gene products and 271 signaling pathways. We showed that the impact of miRs is orthogonal to pathway regulation at the mRNA level, which stresses the importance of studying posttranscriptional regulation of gene expression. We also report characteristic set of miR and mRNA regulation features linked with bladder cancer.
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Affiliation(s)
- Alina V Artcibasova
- a Pathway Pharmaceuticals , Wan Chai , Hong Kong, Hong Kong SAR.,b Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology , Moscow , Russia
| | | | - Maksim I Sorokin
- a Pathway Pharmaceuticals , Wan Chai , Hong Kong, Hong Kong SAR.,c First Oncology Research and Advisory Center , Moscow , Russia
| | - Peter V Shegay
- d P.A. Herzen Moscow Oncological Research Institute , Moscow , Russia
| | - Alex A Zhavoronkov
- b Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology , Moscow , Russia
| | - Nurshat Gaifullin
- e Moscow State University, Faculty of Fundamental Medicine , Moscow , Russia
| | - Boris Y Alekseev
- d P.A. Herzen Moscow Oncological Research Institute , Moscow , Russia
| | | | - Denis V Kuzmin
- f Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Moscow , Russia
| | - Аndrey D Kaprin
- d P.A. Herzen Moscow Oncological Research Institute , Moscow , Russia
| | - Nikolay M Borisov
- g National Research Centre "Kurchatov Institute," Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies , Moscow , Russia
| | - Anton A Buzdin
- b Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology , Moscow , Russia.,f Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Moscow , Russia.,g National Research Centre "Kurchatov Institute," Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies , Moscow , Russia
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30
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Shepelin D, Korzinkin M, Vanyushina A, Aliper A, Borisov N, Vasilov R, Zhukov N, Sokov D, Prassolov V, Gaifullin N, Zhavoronkov A, Bhullar B, Buzdin A. Molecular pathway activation features linked with transition from normal skin to primary and metastatic melanomas in human. Oncotarget 2016; 7:656-70. [PMID: 26624979 PMCID: PMC4808024 DOI: 10.18632/oncotarget.6394] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2015] [Accepted: 11/11/2015] [Indexed: 12/14/2022] Open
Abstract
Melanoma is the most aggressive and dangerous type of skin cancer, but its molecular mechanisms remain largely unclear. For transcriptomic data of 478 primary and metastatic melanoma, nevi and normal skin samples, we performed high-throughput analysis of intracellular molecular networks including 592 signaling and metabolic pathways. We showed that at the molecular pathway level, the formation of nevi largely resembles transition from normal skin to primary melanoma. Using a combination of bioinformatic machine learning algorithms, we identified 44 characteristic signaling and metabolic pathways connected with the formation of nevi, development of primary melanoma, and its metastases. We created a model describing formation and progression of melanoma at the level of molecular pathway activation. We discovered six novel associations between activation of metabolic molecular pathways and progression of melanoma: for allopregnanolone biosynthesis, L-carnitine biosynthesis, zymosterol biosynthesis (inhibited in melanoma), fructose 2, 6-bisphosphate synthesis and dephosphorylation, resolvin D biosynthesis (activated in melanoma), D-myo-inositol hexakisphosphate biosynthesis (activated in primary, inhibited in metastatic melanoma). Finally, we discovered fourteen tightly coordinated functional clusters of molecular pathways. This study helps to decode molecular mechanisms underlying the development of melanoma.
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Affiliation(s)
- Denis Shepelin
- Pathway Pharmaceuticals, Wan Chai, Hong Kong, Hong Kong SAR.,Group for Genomic Analysis of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Mikhail Korzinkin
- Pathway Pharmaceuticals, Wan Chai, Hong Kong, Hong Kong SAR.,First Oncology Research and Advisory Center, Moscow, Russia
| | - Anna Vanyushina
- Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Alexander Aliper
- Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Nicolas Borisov
- First Oncology Research and Advisory Center, Moscow, Russia.,National Research Centre "Kurchatov Institute", Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow, Russia
| | - Raif Vasilov
- National Research Centre "Kurchatov Institute", Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow, Russia
| | - Nikolay Zhukov
- First Oncology Research and Advisory Center, Moscow, Russia.,Pirogov Russian National Research Medical University, Department of Oncology, Hematology and Radiotherapy, Moscow, Russia
| | | | - Vladimir Prassolov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Mosow, Russia
| | - Nurshat Gaifullin
- Moscow State University, Faculty of Fundamental Medicine, Moscow, Russia
| | - Alex Zhavoronkov
- Insilico Medicine, Inc, ETC, Johns Hopkins University, Baltimore, MD, USA
| | | | - Anton Buzdin
- Pathway Pharmaceuticals, Wan Chai, Hong Kong, Hong Kong SAR.,Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.,National Research Centre "Kurchatov Institute", Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow, Russia
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31
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Nilsson A, Nielsen J. Genome scale metabolic modeling of cancer. Metab Eng 2016; 43:103-112. [PMID: 27825806 DOI: 10.1016/j.ymben.2016.10.022] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 10/19/2016] [Accepted: 10/31/2016] [Indexed: 10/25/2022]
Abstract
Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome. Cancer specific models of metabolism have also been generated by reducing the number of reactions in the generic model based on high throughput expression data, e.g. transcriptomics and proteomics. Targets for drugs and bio markers for diagnostics have been identified using these models. They have also been used as scaffolds for analysis of high throughput data to allow mechanistic interpretation of changes in expression. Finally, GEMs allow quantitative flux predictions using flux balance analysis (FBA). Here we critically review the requirements for successful FBA simulations of cancer cells and discuss the symmetry between the methods used for modeling of microbial and cancer metabolism. GEMs have great potential for translational research on cancer and will therefore become of increasing importance in the future.
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Affiliation(s)
- Avlant Nilsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2970 Hørsholm, Denmark.
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32
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Artemov A, Aliper A, Korzinkin M, Lezhnina K, Jellen L, Zhukov N, Roumiantsev S, Gaifullin N, Zhavoronkov A, Borisov N, Buzdin A. A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation. Oncotarget 2016; 6:29347-56. [PMID: 26320181 PMCID: PMC4745731 DOI: 10.18632/oncotarget.5119] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2015] [Accepted: 07/24/2015] [Indexed: 02/07/2023] Open
Abstract
A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.
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Affiliation(s)
- Artem Artemov
- Pathway Pharmaceuticals, Wan Chai, Hong Kong, Hong Kong SAR.,D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Alexander Aliper
- D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.,First Oncology Research and Advisory Center, Moscow, Russia
| | | | | | - Leslie Jellen
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Nikolay Zhukov
- D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.,First Oncology Research and Advisory Center, Moscow, Russia.,Pirogov Russian National Research Medical University, Department of Oncology, Hematology and Radiotherapy, Moscow, Russia
| | - Sergey Roumiantsev
- D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.,Pirogov Russian National Research Medical University, Department of Oncology, Hematology and Radiotherapy, Moscow, Russia
| | - Nurshat Gaifullin
- Moscow State University, Faculty of Fundamental Medicine, Moscow, Russia
| | - Alex Zhavoronkov
- Insilico Medicine, Inc., ETC, Johns Hopkins University, Baltimore, MD, USA
| | | | - Anton Buzdin
- Pathway Pharmaceuticals, Wan Chai, Hong Kong, Hong Kong SAR.,D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.,Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
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33
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Gene Set-Based Integrative Analysis Revealing Two Distinct Functional Regulation Patterns in Four Common Subtypes of Epithelial Ovarian Cancer. Int J Mol Sci 2016; 17:ijms17081272. [PMID: 27527159 PMCID: PMC5000670 DOI: 10.3390/ijms17081272] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Revised: 07/22/2016] [Accepted: 07/27/2016] [Indexed: 01/04/2023] Open
Abstract
Clear cell (CCC), endometrioid (EC), mucinous (MC) and high-grade serous carcinoma (SC) are the four most common subtypes of epithelial ovarian carcinoma (EOC). The widely accepted dualistic model of ovarian carcinogenesis divided EOCs into type I and II categories based on the molecular features. However, this hypothesis has not been experimentally demonstrated. We carried out a gene set-based analysis by integrating the microarray gene expression profiles downloaded from the publicly available databases. These quantified biological functions of EOCs were defined by 1454 Gene Ontology (GO) term and 674 Reactome pathway gene sets. The pathogenesis of the four EOC subtypes was investigated by hierarchical clustering and exploratory factor analysis. The patterns of functional regulation among the four subtypes containing 1316 cases could be accurately classified by machine learning. The results revealed that the ERBB and PI3K-related pathways played important roles in the carcinogenesis of CCC, EC and MC; while deregulation of cell cycle was more predominant in SC. The study revealed that two different functional regulation patterns exist among the four EOC subtypes, which were compatible with the type I and II classifications proposed by the dualistic model of ovarian carcinogenesis.
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34
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Gene set-based integrative analysis of ovarian clear cell carcinoma. Taiwan J Obstet Gynecol 2016; 55:552-7. [DOI: 10.1016/j.tjog.2016.06.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2016] [Indexed: 12/21/2022] Open
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35
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Gene Set-Based Functionome Analysis of Pathogenesis in Epithelial Ovarian Serous Carcinoma and the Molecular Features in Different FIGO Stages. Int J Mol Sci 2016; 17:ijms17060886. [PMID: 27275818 PMCID: PMC4926420 DOI: 10.3390/ijms17060886] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 05/07/2016] [Accepted: 05/16/2016] [Indexed: 12/27/2022] Open
Abstract
Serous carcinoma (SC) is the most common subtype of epithelial ovarian carcinoma and is divided into four stages by the Federation of Gynecologists and Obstetrics (FIGO) staging system. Currently, the molecular functions and biological processes of SC at different FIGO stages have not been quantified. Here, we conducted a whole-genome integrative analysis to investigate the functions of SC at different stages. The function, as defined by the GO term or canonical pathway gene set, was quantified by measuring the changes in the gene expressional order between cancerous and normal control states. The quantified function, i.e., the gene set regularity (GSR) index, was utilized to investigate the pathogenesis and functional regulation of SC at different FIGO stages. We showed that the informativeness of the GSR indices was sufficient for accurate pattern recognition and classification for machine learning. The function regularity presented by the GSR indices showed stepwise deterioration during SC progression from FIGO stage I to stage IV. The pathogenesis of SC was centered on cell cycle deregulation and accompanied with multiple functional aberrations as well as their interactions.
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36
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Björnson E, Mukhopadhyay B, Asplund A, Pristovsek N, Cinar R, Romeo S, Uhlen M, Kunos G, Nielsen J, Mardinoglu A. Stratification of Hepatocellular Carcinoma Patients Based on Acetate Utilization. Cell Rep 2015; 13:2014-26. [PMID: 26655911 DOI: 10.1016/j.celrep.2015.10.045] [Citation(s) in RCA: 104] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 09/18/2015] [Accepted: 10/14/2015] [Indexed: 02/07/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a deadly form of liver cancer that is increasingly prevalent. We analyzed global gene expression profiling of 361 HCC tumors and 49 adjacent noncancerous liver samples by means of combinatorial network-based analysis. We investigated the correlation between transcriptome and proteome of HCC and reconstructed a functional genome-scale metabolic model (GEM) for HCC. We identified fundamental metabolic processes required for cell proliferation using the network centric view provided by the GEM. Our analysis revealed tight regulation of fatty acid biosynthesis (FAB) and highly significant deregulation of fatty acid oxidation in HCC. We predicted mitochondrial acetate as an emerging substrate for FAB through upregulation of mitochondrial acetyl-CoA synthetase (ACSS1) in HCC. We analyzed heterogeneous expression of ACSS1 and ACSS2 between HCC patients stratified by high and low ACSS1 and ACSS2 expression and revealed that ACSS1 is associated with tumor growth and malignancy under hypoxic conditions in human HCC.
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Affiliation(s)
- Elias Björnson
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
| | - Bani Mukhopadhyay
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892, USA
| | - Anna Asplund
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 751 85 Uppsala, Sweden
| | - Nusa Pristovsek
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 751 85 Uppsala, Sweden
| | - Resat Cinar
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stefano Romeo
- Department of Molecular and Clinical Medicine, the Sahlgrenska Center for Cardiovascular and Metabolic Research/Wallenberg Laboratory, University of Gothenburg, 413 45 Gothenburg, Sweden; Cardiology Department, Sahlgrenska University Hospital, 416 50 Gothenburg, Sweden; Clinical Nutrition Unit, Department of Medical and Surgical Sciences, University Magna Graecia, 88100 Catanzaro, Italy
| | - Mathias Uhlen
- Department of Proteomics, KTH-Royal Institute of Technology, 106 91 Stockholm, Sweden; Science for Life Laboratory, KTH-Royal Institute of Technology, 171 21 Stockholm, Sweden
| | - George Kunos
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden; Science for Life Laboratory, KTH-Royal Institute of Technology, 171 21 Stockholm, Sweden
| | - Adil Mardinoglu
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden; Science for Life Laboratory, KTH-Royal Institute of Technology, 171 21 Stockholm, Sweden.
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37
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Obulkasim A, Fornerod M, Zwaan MC, Reinhardt D, van den Heuvel-Eibrink MM. Subtype prediction in pediatric acute myeloid leukemia: classification using differential network rank conservation revisited. BMC Bioinformatics 2015; 16:305. [PMID: 26399969 PMCID: PMC4580220 DOI: 10.1186/s12859-015-0737-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2014] [Accepted: 09/11/2015] [Indexed: 11/10/2022] Open
Abstract
Background One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make over-fitting commonplace. Promising classification results often fail to generalize across datasets with different sources, platforms, or preprocessing. Recently a novel differential network rank conservation (DIRAC) algorithm to characterize cancer phenotypes using transcriptomic data. DIRAC is a member of a family of algorithms that have shown useful for disease classification based on the relative expression of genes. Combining the robustness of this family’s simple decision rules with known biological relationships, this systems approach identifies interpretable, yet highly discriminate networks. While DIRAC has been briefly employed for several classification problems in the original paper, the potentials of DIRAC in cancer phenotype classification, and especially robustness against artifacts in transcriptomic data have not been fully characterized yet. Results In this study we thoroughly investigate the potentials of DIRAC by applying it to multiple datasets, and examine the variations in classification performances when datasets are (i) treated and untreated for batch effect; (ii) preprocessed with different techniques. We also propose the first DIRAC-based classifier to integrate multiple networks. We show that the DIRAC-based classifier is very robust in the examined scenarios. To our surprise, the trained DIRAC-based classifier even translated well to a dataset with different biological characteristics in the presence of substantial batch effects that, as shown here, plagued the standard expression value based classifier. In addition, the DIRAC-based classifier, because of the integrated biological information, also suggests pathways to target in specific subtypes, which may enhance the establishment of personalized therapy in diseases such as pediatric AML. In order to better comprehend the prediction power of the DIRAC-based classifier in general, we also performed classifications using publicly available datasets from breast and lung cancer. Furthermore, multiple well-known classification algorithms were utilized to create an ideal test bed for comparing the DIRAC-based classifier with the standard gene expression value based classifier. We observed that the DIRAC-based classifier greatly outperforms its rival. Conclusions Based on our experiments with multiple datasets, we propose that DIRAC is a promising solution to the lack of generalizability in classification efforts that uses transcriptomic data. We believe that superior performances presented in this study may motivate other to initiate a new aline of research to explore the untapped power of DIRAC in a broad range of cancer types. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0737-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Askar Obulkasim
- Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands.
| | - Maarten Fornerod
- Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands.
| | - Michel C Zwaan
- Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands.,Dutch Children's Oncology Group, Erasmus-MC Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Dirk Reinhardt
- AML-BFM Study Group, Pediatric Hematology/Oncology, Essen, Germany
| | - Marry M van den Heuvel-Eibrink
- Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands.,Dutch Children's Oncology Group, Erasmus-MC Sophia Children's Hospital, Rotterdam, The Netherlands.,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
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Saha A, Jeon M, Tan AC, Kang J. iCOSSY: An Online Tool for Context-Specific Subnetwork Discovery from Gene Expression Data. PLoS One 2015; 10:e0131656. [PMID: 26147457 PMCID: PMC4492968 DOI: 10.1371/journal.pone.0131656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Accepted: 06/04/2015] [Indexed: 12/22/2022] Open
Abstract
Pathway analyses help reveal underlying molecular mechanisms of complex biological phenotypes. Biologists tend to perform multiple pathway analyses on the same dataset, as there is no single answer. It is often inefficient for them to implement and/or install all the algorithms by themselves. Online tools can help the community in this regard. Here we present an online gene expression analytical tool called iCOSSY which implements a novel pathway-based COntext-specific Subnetwork discoverY (COSSY) algorithm. iCOSSY also includes a few modifications of COSSY to increase its reliability and interpretability. Users can upload their gene expression datasets, and discover important subnetworks of closely interacting molecules to differentiate between two phenotypes (context). They can also interactively visualize the resulting subnetworks. iCOSSY is a web server that finds subnetworks that are differentially expressed in two phenotypes. Users can visualize the subnetworks to understand the biology of the difference.
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Affiliation(s)
- Ashis Saha
- Department of Computer Science and Engineering, Korea University, Seoul, Korea
| | - Minji Jeon
- Department of Computer Science and Engineering, Korea University, Seoul, Korea
| | - Aik Choon Tan
- Department of Medicine/Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul, Korea
- Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Korea
- * E-mail:
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Geman D, Ochs M, Price ND, Tomasetti C, Younes L. An argument for mechanism-based statistical inference in cancer. Hum Genet 2015; 134:479-95. [PMID: 25381197 PMCID: PMC4612627 DOI: 10.1007/s00439-014-1501-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2014] [Accepted: 10/14/2014] [Indexed: 01/07/2023]
Abstract
Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.
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Affiliation(s)
- Donald Geman
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21210, USA,
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40
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Handelman SK, Aaronson JM, Seweryn M, Voronkin I, Kwiek JJ, Sadee W, Verducci JS, Janies DA. Cladograms with Path to Event (ClaPTE): a novel algorithm to detect associations between genotypes or phenotypes using phylogenies. Comput Biol Med 2015; 58:1-13. [PMID: 25577610 PMCID: PMC4331246 DOI: 10.1016/j.compbiomed.2014.12.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2014] [Revised: 12/09/2014] [Accepted: 12/15/2014] [Indexed: 12/20/2022]
Abstract
BACKGROUND Associations between genotype and phenotype provide insight into the evolution of pathogenesis, drug resistance, and the spread of pathogens between hosts. However, common ancestry can lead to apparent associations between biologically unrelated features. The novel method Cladograms with Path to Event (ClaPTE) detects associations between character-pairs (either a pair of mutations or a mutation paired with a phenotype) while adjusting for common ancestry, using phylogenetic trees. METHODS ClaPTE tests for character-pairs changing close together on the phylogenetic tree, consistent with an associated character-pair. ClaPTE is compared to three existing methods (independent contrasts, mixed model, and likelihood ratio) to detect character-pair associations adjusted for common ancestry. Comparisons utilize simulations on gene trees for: HIV Env, HIV promoter, and bacterial DnaJ and GuaB; and case studies for Oseltamavir resistance in Influenza, and for DnaJ and GuaB. Simulated data include both true-positive/associated character-pairs, and true-negative/not-associated character-pairs, used to assess type I (frequency of p-values in true-negatives) and type II (sensitivity to true-positives) error control. RESULTS AND CONCLUSIONS ClaPTE has competitive sensitivity and better type I error control than existing methods. In the Influenza/Oseltamavir case study, ClaPTE reports no new permissive mutations but detects associations between adjacent (in primary sequence) amino acid positions which other methods miss. In the DnaJ and GuaB case study, ClaPTE reports more frequent associations between positions both from the same protein family than between positions from different families, in contrast to other methods. In both case studies, the results from ClaPTE are biologically plausible.
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Affiliation(s)
- Samuel K Handelman
- Department of Pharmacology, Ohio State University College of Medicine, 5072 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States; Mathematical Biosciences Institute, The Ohio State University, Jennings Hall 3rd Floor, 1735 Neil Avenue, Columbus, OH 43210, United States.
| | - Jacob M Aaronson
- Department of Biomedical Informatics, Ohio State University College of Medicine, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States
| | - Michal Seweryn
- Mathematical Biosciences Institute, The Ohio State University, Jennings Hall 3rd Floor, 1735 Neil Avenue, Columbus, OH 43210, United States
| | - Igor Voronkin
- Department of Biomedical Informatics, Ohio State University College of Medicine, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States
| | - Jesse J Kwiek
- Department of Microbial Infection & Immunity and Department of Microbiology, The Ohio State University, 788 Biomedical Research Tower, 460 West 12th Avenue, Columbus, OH 43210, United States
| | - Wolfgang Sadee
- Department of Pharmacology, Ohio State University College of Medicine, 5072 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, United States
| | - Joseph S Verducci
- Department of Statistics, The Ohio State University, 404 Cockins Hall, 1958 Neil Avenue, Columbus, OH 43210-1247, United States
| | - Daniel A Janies
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223-0001, United States
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Chakrabarty P, Li A, Ceballos-Diaz C, Eddy JA, Funk CC, Moore B, DiNunno N, Rosario AM, Cruz PE, Verbeeck C, Sacino A, Nix S, Janus C, Price ND, Das P, Golde TE. IL-10 alters immunoproteostasis in APP mice, increasing plaque burden and worsening cognitive behavior. Neuron 2015; 85:519-33. [PMID: 25619653 DOI: 10.1016/j.neuron.2014.11.020] [Citation(s) in RCA: 268] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2013] [Revised: 10/24/2014] [Accepted: 11/20/2014] [Indexed: 01/27/2023]
Abstract
Anti-inflammatory strategies are proposed to have beneficial effects in Alzheimer's disease. To explore how anti-inflammatory cytokine signaling affects Aβ pathology, we investigated the effects of adeno-associated virus (AAV2/1)-mediated expression of Interleukin (IL)-10 in the brains of APP transgenic mouse models. IL-10 expression resulted in increased Aβ accumulation and impaired memory in APP mice. A focused transcriptome analysis revealed changes consistent with enhanced IL-10 signaling and increased ApoE expression in IL-10-expressing APP mice. ApoE protein was selectively increased in the plaque-associated insoluble cellular fraction, likely because of direct interaction with aggregated Aβ in the IL-10-expressing APP mice. Ex vivo studies also show that IL-10 and ApoE can individually impair glial Aβ phagocytosis. Our observations that IL-10 has an unexpected negative effect on Aβ proteostasis and cognition in APP mouse models demonstrate the complex interplay between innate immunity and proteostasis in neurodegenerative diseases, an interaction we call immunoproteostasis.
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Affiliation(s)
- Paramita Chakrabarty
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA.
| | - Andrew Li
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Carolina Ceballos-Diaz
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - James A Eddy
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, WA 98109, USA
| | - Cory C Funk
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, WA 98109, USA
| | - Brenda Moore
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Nadia DiNunno
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Awilda M Rosario
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Pedro E Cruz
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Christophe Verbeeck
- Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, FL 32224, USA
| | - Amanda Sacino
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Sarah Nix
- Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, FL 32224, USA
| | - Christopher Janus
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Nathan D Price
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, WA 98109, USA
| | - Pritam Das
- Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, FL 32224, USA
| | - Todd E Golde
- Department of Neuroscience, Center for Translational Research in Neurodegenerative Disease, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA.
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Vo NS, Phan V. Exploiting dependencies of pairwise comparison outcomes to predict patterns of gene response. BMC Bioinformatics 2014; 15 Suppl 11:S2. [PMID: 25350806 PMCID: PMC4251046 DOI: 10.1186/1471-2105-15-s11-s2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The analysis of gene expression has played an important role in medical and bioinformatics research. Although it is known that a large number of samples is needed to determine the patterns of gene expression accurately, practical designs of gene expression studies occasionally have insufficient numbers of samples, making it difficult to ascertain true response patterns of variantly expressed genes. RESULTS We describe an approach to cope with the challenge of predicting true orders of gene response to treatments. We show that true patterns of gene response must be orderable sets. In experiments with few samples, we modify the conventional pairwise comparison tests and increase the significance level α intelligently to deduce orderable patterns, which are most likely true orders of gene response. Additionally, motivated by the fact that a gene can be involved in multiple biological functions, our method further resamples experimental replicates and predicts multiple response patterns for each gene. CONCLUSIONS This method can be useful in designing cost-effective experiments with small sample sizes. Patterns of highly-variantly expressed genes can be predicted by varying α intelligently. Furthermore, clusters are labeled meaningfully with patterns that describe precisely how genes in such clusters respond to treatments.
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Afsari B, Geman D, Fertig EJ. Learning dysregulated pathways in cancers from differential variability analysis. Cancer Inform 2014; 13:61-7. [PMID: 25392694 PMCID: PMC4218688 DOI: 10.4137/cin.s14066] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Revised: 08/13/2014] [Accepted: 08/14/2014] [Indexed: 12/16/2022] Open
Abstract
Analysis of gene sets can implicate activity in signaling pathways that is responsible for cancer initiation and progression, but is not discernible from the analysis of individual genes. Multiple methods and software packages have been developed to infer pathway activity from expression measurements for set of genes targeted by that pathway. Broadly, three major methodologies have been proposed: over-representation, enrichment, and differential variability. Both over-representation and enrichment analyses are effective techniques to infer differentially regulated pathways from gene sets with relatively consistent differentially expressed (DE) genes. Specifically, these algorithms aggregate statistics from each gene in the pathway. However, they overlook multivariate patterns related to gene interactions and variations in expression. Therefore, the analysis of differential variability of multigene expression patterns can be essential to pathway inference in cancers. The corresponding methodologies and software packages for such multivariate variability analysis of pathways are reviewed here. We also introduce a new, computationally efficient algorithm, expression variation analysis (EVA), which has been implemented along with a previously proposed algorithm, Differential Rank Conservation (DIRAC), in an open source R package, gene set regulation (GSReg). EVA inferred similar pathways as DIRAC at reduced computational costs. Moreover, EVA also inferred different dysregulated pathways than those identified by enrichment analysis.
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Affiliation(s)
- Bahman Afsari
- Postdoctoral Fellow, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
| | - Donald Geman
- Professor, Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Elana J Fertig
- Assistant Professor, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
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Zeng T, Zhang W, Yu X, Liu X, Li M, Liu R, Chen L. Edge biomarkers for classification and prediction of phenotypes. SCIENCE CHINA-LIFE SCIENCES 2014; 57:1103-14. [PMID: 25326072 DOI: 10.1007/s11427-014-4757-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Accepted: 08/07/2014] [Indexed: 12/19/2022]
Abstract
In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.
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Affiliation(s)
- Tao Zeng
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
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45
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Identification of phenotype deterministic genes using systemic analysis of transcriptional response. Sci Rep 2014; 4:4413. [PMID: 24642983 PMCID: PMC3958917 DOI: 10.1038/srep04413] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2013] [Accepted: 03/03/2014] [Indexed: 11/09/2022] Open
Abstract
Systemic identification of deterministic genes for different phenotypes is a primary application of high-throughput expression profiles. However, gene expression differences cannot be used when the differences between groups are not significant. Therefore, novel methods incorporating features other than expression differences are required. We developed a promising method using transcriptional response as an operational feature, which is quantified as the correlation between expression levels of pathway genes and target genes of the pathway. We applied this method to identify causative genes associated with chemo-sensitivity to tamoxifen and epirubicin. Genes whose transcriptional response was dysregulated only in the drug-resistant patient group were chosen for in vitro validation in human breast cancer cells. Finally, we discovered two genes responsible for tamoxifen sensitivity and three genes associated with epirubicin sensitivity. The method we propose here can be widely applied to identify deterministic genes for different phenotypes with only minor differences in gene expression levels.
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46
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Saha A, Tan AC, Kang J. Automatic context-specific subnetwork discovery from large interaction networks. PLoS One 2014; 9:e84227. [PMID: 24392115 PMCID: PMC3877685 DOI: 10.1371/journal.pone.0084227] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2013] [Accepted: 11/21/2013] [Indexed: 01/18/2023] Open
Abstract
Genes act in concert via specific networks to drive various biological processes, including progression of diseases such as cancer. Under different phenotypes, different subsets of the gene members of a network participate in a biological process. Single gene analyses are less effective in identifying such core gene members (subnetworks) within a gene set/network, as compared to gene set/network-based analyses. Hence, it is useful to identify a discriminative classifier by focusing on the subnetworks that correspond to different phenotypes. Here we present a novel algorithm to automatically discover the important subnetworks of closely interacting molecules to differentiate between two phenotypes (context) using gene expression profiles. We name it COSSY (COntext-Specific Subnetwork discoverY). It is a non-greedy algorithm and thus unlikely to have local optima problems. COSSY works for any interaction network regardless of the network topology. One added benefit of COSSY is that it can also be used as a highly accurate classification platform which can produce a set of interpretable features.
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Affiliation(s)
- Ashis Saha
- Department of Computer Science and Engineering, Korea University, Seoul, Korea
| | - Aik Choon Tan
- Department of Medicine/Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul, Korea
- Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Korea
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47
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Simcha DM, Younes L, Aryee MJ, Geman D. Identification of direction in gene networks from expression and methylation. BMC SYSTEMS BIOLOGY 2013; 7:118. [PMID: 24182195 PMCID: PMC4228359 DOI: 10.1186/1752-0509-7-118] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2012] [Accepted: 10/17/2013] [Indexed: 01/27/2023]
Abstract
BACKGROUND Reverse-engineering gene regulatory networks from expression data is difficult, especially without temporal measurements or interventional experiments. In particular, the causal direction of an edge is generally not statistically identifiable, i.e., cannot be inferred as a statistical parameter, even from an unlimited amount of non-time series observational mRNA expression data. Some additional evidence is required and high-throughput methylation data can viewed as a natural multifactorial gene perturbation experiment. RESULTS We introduce IDEM (Identifying Direction from Expression and Methylation), a method for identifying the causal direction of edges by combining DNA methylation and mRNA transcription data. We describe the circumstances under which edge directions become identifiable and experiments with both real and synthetic data demonstrate that the accuracy of IDEM for inferring both edge placement and edge direction in gene regulatory networks is significantly improved relative to other methods. CONCLUSION Reverse-engineering directed gene regulatory networks from static observational data becomes feasible by exploiting the context provided by high-throughput DNA methylation data.An implementation of the algorithm described is available at http://code.google.com/p/idem/.
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Affiliation(s)
- David M Simcha
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
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48
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Wang C, Funk CC, Eddy JA, Price ND. Transcriptional analysis of aggressiveness and heterogeneity across grades of astrocytomas. PLoS One 2013; 8:e76694. [PMID: 24146911 PMCID: PMC3795736 DOI: 10.1371/journal.pone.0076694] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2013] [Accepted: 08/27/2013] [Indexed: 11/19/2022] Open
Abstract
Astrocytoma is the most common glioma, accounting for half of all primary brain and spinal cord tumors. Late detection and the aggressive nature of high-grade astrocytomas contribute to high mortality rates. Though many studies identify candidate biomarkers using high-throughput transcriptomic profiling to stratify grades and subtypes, few have resulted in clinically actionable results. This shortcoming can be attributed, in part, to pronounced lab effects that reduce signature robustness and varied individual gene expression among patients with the same tumor. We addressed these issues by uniformly preprocessing publicly available transcriptomic data, comprising 306 tumor samples from three astrocytoma grades (Grade 2, 3, and 4) and 30 non-tumor samples (normal brain as control tissues). Utilizing Differential Rank Conservation (DIRAC), a network-based classification approach, we examined the global and individual patterns of network regulation across tumor grades. Additionally, we applied gene-based approaches to identify genes whose expression changed consistently with increasing tumor grade and evaluated their robustness across multiple studies using statistical sampling. Applying DIRAC, we observed a global trend of greater network dysregulation with increasing tumor aggressiveness. Individual networks displaying greater differences in regulation between adjacent grades play well-known roles in calcium/PKC, EGF, and transcription signaling. Interestingly, many of the 90 individual genes found to monotonically increase or decrease with astrocytoma grade are implicated in cancer-affected processes such as calcium signaling, mitochondrial metabolism, and apoptosis. The fact that specific genes monotonically increase or decrease with increasing astrocytoma grade may reflect shared oncogenic mechanisms among phenotypically similar tumors. This work presents statistically significant results that enable better characterization of different human astrocytoma grades and hopefully can contribute towards improvements in diagnosis and therapy choices. Our results also identify a number of testable hypotheses relating to astrocytoma etiology that may prove helpful in developing much-needed biomarkers for earlier disease detection.
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Affiliation(s)
- Chunjing Wang
- Institute for Systems Biology, Seattle, Washington, United States of America
- Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, Illinois, United States of America
| | - Cory C. Funk
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - James A. Eddy
- Institute for Systems Biology, Seattle, Washington, United States of America
- Department of Bioengineering, University of Illinois, Urbana, Illinois, United States of America
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, Washington, United States of America
- Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, Illinois, United States of America
- Department of Bioengineering, University of Illinois, Urbana, Illinois, United States of America
- * E-mail:
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Abstract
High throughput technologies have been applied to investigate the underlying mechanisms of complex diseases, identify disease-associations and help to improve treatment. However it is challenging to derive biological insight from conventional single gene based analysis of "omics" data from high throughput experiments due to sample and patient heterogeneity. To address these challenges, many novel pathway and network based approaches were developed to integrate various "omics" data, such as gene expression, copy number alteration, Genome Wide Association Studies, and interaction data. This review will cover recent methodological developments in pathway analysis for the detection of dysregulated interactions and disease-associated subnetworks, prioritization of candidate disease genes, and disease classifications. For each application, we will also discuss the associated challenges and potential future directions.
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50
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Kessler T, Hache H, Wierling C. Integrative analysis of cancer-related signaling pathways. Front Physiol 2013; 4:124. [PMID: 23760067 PMCID: PMC3671203 DOI: 10.3389/fphys.2013.00124] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2012] [Accepted: 05/12/2013] [Indexed: 12/11/2022] Open
Abstract
Identification and classification of cancer types and subtypes is a major issue in current cancer research. Whole genome expression profiling of cancer tissues is often the basis for such subtype classifications of tumors and different signatures for individual cancer types have been described. However, the search for best performing discriminatory gene-expression signatures covering more than one cancer type remains a relevant topic in cancer research as such a signature would help understanding the common changes in signaling networks in these disease types. In this work, we explore the idea of a top down approach for sample stratification based on a module-based network of cancer relevant signaling pathways. For assembly of this network, we consider several of the most established cancer pathways. We evaluate our sample stratification approach using expression data of human breast and ovarian cancer signatures. We show that our approach performs equally well to previously reported methods besides providing the advantage to classify different cancer types. Furthermore, it allows to identify common changes in network module activity of those cancer samples.
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Affiliation(s)
- Thomas Kessler
- Systems Biology Group, Department Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Hendrik Hache
- Systems Biology Group, Department Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Christoph Wierling
- Systems Biology Group, Department Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
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