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Qin C, Fan X, Sai X, Yin B, Zhou S, Addeo A, Bian T, Yu H. Development and validation of a DNA damage repair-related gene-based prediction model for the prognosis of lung adenocarcinoma. J Thorac Dis 2023; 15:6928-6945. [PMID: 38249902 PMCID: PMC10797339 DOI: 10.21037/jtd-23-1746] [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: 11/13/2023] [Accepted: 12/15/2023] [Indexed: 01/23/2024]
Abstract
Background Lung cancer is the leading cause of morbidity and mortality among all cancer types, with lung adenocarcinoma (LUAD) being the most prevalent subtype. DNA damage repair (DDR)-related genes are closely associated with cancer progression and treatment, with emerging evidence highlighting their correlation with tumor development. However, the relationship between LUAD prognosis and DDR-related genes remains unclear. Methods RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The GSE31210 dataset, utilized for external validation, was retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed DDR genes were identified, and a DDR-related prognostic model was established and validated using Kaplan-Meier (KM) survival analysis, time-dependent receiver operating characteristic (ROC) curves, gene set enrichment analysis (GSEA), tumor mutational burden (TMB) analysis, and immune cell infiltration. A P value of less than 0.05 was considered statistically significant. Results A total of 514 patients with LUAD from TCGA database were divided into distinct subtypes to characterize the diversity within the DDR pathway. DDR-activated and DDR-suppressed subgroups showed distinct clinical characteristics, molecular characteristics, and immune profiles. Nine genes were identified as hub DDR-related genes, including CASP14, DKK1, ECT2, FLNC, HMMR, IGFBP1, KRT6A, TYMS, and FCER2. By using the expression levels of these selected genes, the corresponding risk scores for each sample was predicted. In the training group, KM survival analysis revealed that the high-risk group exhibited significantly diminished overall survival (OS) [hazard ratio (HR) =3.341, P=1.38e-08]. The corresponding area under the curve (AUC) values for the 1-year follow-up periods was 0.767, respectively. Upon validation in the external cohort, patients with higher risk scores manifested significantly reduced OS (HR =2.372, P=1.87e-03). The AUC values of the ROC curves for the 1-year OS in the validation cohort was 0.87, respectively. Moreover, advanced DDR risk score was correlated with increased TMB scores, a heightened frequency of TP53 mutations, an increased abundance of cancer-testicular antigens (CTAs), and a lower tumor immune dysfunction and exclusion (TIDE) score in patients with LUAD (P<0.05). Conclusions A nine-gene risk signature associated with DDR in LUAD was effectively developed, demonstrating its potential as a robust and reliable classification tool for clinical practice. This model exhibited the capability to accurately predict the prognosis and survival outcomes of LUAD patients.
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Affiliation(s)
- Chu Qin
- Department of Respiratory Medicine, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China
| | - Xiaodong Fan
- Department of Respiratory Medicine, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China
| | - Xiaoyan Sai
- Department of Respiratory Medicine, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China
| | - Bo Yin
- Department of Respiratory Medicine, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China
| | - Shufang Zhou
- Department of Respiratory Medicine, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China
| | - Alfredo Addeo
- Oncology Department, Geneva University Hospital (CH), Geneva, Switzerland
| | - Tao Bian
- Department of Respiratory Medicine, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China
| | - Haoda Yu
- Department of Respiratory Medicine, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China
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2
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Kendall TJ, Jimenez-Ramos M, Turner F, Ramachandran P, Minnier J, McColgan MD, Alam M, Ellis H, Dunbar DR, Kohnen G, Konanahalli P, Oien KA, Bandiera L, Menolascina F, Juncker-Jensen A, Alexander D, Mayor C, Guha IN, Fallowfield JA. An integrated gene-to-outcome multimodal database for metabolic dysfunction-associated steatotic liver disease. Nat Med 2023; 29:2939-2953. [PMID: 37903863 PMCID: PMC10667096 DOI: 10.1038/s41591-023-02602-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 09/20/2023] [Indexed: 11/01/2023]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the commonest cause of chronic liver disease worldwide and represents an unmet precision medicine challenge. We established a retrospective national cohort of 940 histologically defined patients (55.4% men, 44.6% women; median body mass index 31.3; 32% with type 2 diabetes) covering the complete MASLD severity spectrum, and created a secure, searchable, open resource (SteatoSITE). In 668 cases and 39 controls, we generated hepatic bulk RNA sequencing data and performed differential gene expression and pathway analysis, including exploration of gender-specific differences. A web-based gene browser was also developed. We integrated histopathological assessments, transcriptomic data and 5.67 million days of time-stamped longitudinal electronic health record data to define disease-stage-specific gene expression signatures, pathogenic hepatic cell subpopulations and master regulator networks associated with adverse outcomes in MASLD. We constructed a 15-gene transcriptional risk score to predict future hepatic decompensation events (area under the receiver operating characteristic curve 0.86, 0.81 and 0.83 for 1-, 3- and 5-year risk, respectively). Additionally, thyroid hormone receptor beta regulon activity was identified as a critical suppressor of disease progression. SteatoSITE supports rational biomarker and drug development and facilitates precision medicine approaches for patients with MASLD.
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Affiliation(s)
- Timothy J Kendall
- Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
- Edinburgh Pathology, University of Edinburgh, Edinburgh, UK
| | - Maria Jimenez-Ramos
- Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
| | - Frances Turner
- Edinburgh Genomics (Bioinformatics), University of Edinburgh, Edinburgh, UK
| | | | - Jessica Minnier
- OHSU-PSU School of Public Health, Oregon Health & Sciences University, Portland, OR, USA
- Knight Cancer Institute Biostatistics Shared Resource, Oregon Health & Sciences University, Portland, OR, USA
| | - Michael D McColgan
- Precision Medicine Scotland-Innovation Centre (PMS-IC), University of Glasgow, Glasgow, UK
| | - Masood Alam
- Precision Medicine Scotland-Innovation Centre (PMS-IC), University of Glasgow, Glasgow, UK
| | - Harriet Ellis
- Precision Medicine Scotland-Innovation Centre (PMS-IC), University of Glasgow, Glasgow, UK
| | - Donald R Dunbar
- Edinburgh Genomics (Bioinformatics), University of Edinburgh, Edinburgh, UK
| | - Gabriele Kohnen
- Pathology Department, Queen Elizabeth University Hospital, Glasgow, UK
| | | | - Karin A Oien
- Pathology Department, Queen Elizabeth University Hospital, Glasgow, UK
| | - Lucia Bandiera
- School of Engineering, Institute of Bioengineering, University of Edinburgh, Edinburgh, UK
- Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
| | - Filippo Menolascina
- School of Engineering, Institute of Bioengineering, University of Edinburgh, Edinburgh, UK
- Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
| | | | | | - Charlie Mayor
- NHS Greater Glasgow and Clyde Safe Haven, Glasgow, UK
| | - Indra Neil Guha
- National Institute of Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, UK
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Rashid A, Brusletto BS, Al-Obeidat F, Toufiq M, Benakatti G, Brierley J, Malik ZA, Hussain Z, Alkhazaimi H, Sharief J, Kadwa R, Sarpal A, Chaussabel D, Malik RA, Quraishi N, Khilnani P, Zaki SA, Nadeem R, Shaikh G, Al-Dubai A, Hafez W, Hussain A. A TRANSCRIPTOMIC APPRECIATION OF CHILDHOOD MENINGOCOCCAL AND POLYMICROBIAL SEPSIS FROM A PRO-INFLAMMATORY AND TRAJECTORIAL PERSPECTIVE, A ROLE FOR VASCULAR ENDOTHELIAL GROWTH FACTOR A AND B MODULATION? Shock 2023; 60:503-516. [PMID: 37553892 PMCID: PMC10581425 DOI: 10.1097/shk.0000000000002192] [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: 04/20/2023] [Revised: 05/12/2023] [Accepted: 07/19/2023] [Indexed: 08/10/2023]
Abstract
ABSTRACT This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)-VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis.
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Affiliation(s)
- Asrar Rashid
- School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom
- NMC Royal Hospital, Abu Dhabi, United Arab Emirates
| | - Berit S. Brusletto
- The Blood Cell Research Group, Department of Medical Biochemistry, Oslo University Hospital, Ullevål, Norway
| | - Feras Al-Obeidat
- College of Technological Innovation at Zayed University, Abu Dhabi, United Arab Emirates
| | - Mohammed Toufiq
- The Jackson Laboratory for Genomic Medicine Farmington, Connecticut, USA
| | - Govind Benakatti
- Medanta Gururam, Delhi, India
- Yas Clinic, Abu Dhabi, United Arab Emirates
| | - Joe Brierley
- Great Ormond Street Children's Hospital, London, United Kingdom
| | - Zainab A. Malik
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Zain Hussain
- Edinburgh Medical School, University go Edinburgh, Edinburgh, United Kingdom
| | | | | | - Raziya Kadwa
- NMC Royal Hospital, Abu Dhabi, United Arab Emirates
| | - Amrita Sarpal
- Sidra Medicine, Doha, Qatar
- Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Damien Chaussabel
- The Jackson Laboratory for Genomic Medicine Farmington, Connecticut, USA
| | - Rayaz A. Malik
- Weill Cornell Medicine-Qatar, Doha, Qatar
- Institute of Cardiovascular Science, University of Manchester, Manchester, United Kingdom
| | - Nasir Quraishi
- Centre for Spinal Studies & Surgery, Queen's Medical Centre, The University of Nottingham, Nottingham, United Kingdom
| | | | - Syed A. Zaki
- All India Institute of Medical Sciences, Hyderabad, India
| | | | - Guftar Shaikh
- Endocrinology, Royal Hospital for Children, Glasgow, United Kingdom
| | - Ahmed Al-Dubai
- School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom
| | - Wael Hafez
- NMC Royal Hospital, Abu Dhabi, United Arab Emirates
- Medical Research Division, Department of Internal Medicine, The National Research Centre, Cairo, Egypt
| | - Amir Hussain
- School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom
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4
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Zhang C, Li X, Huang W, Wang L, Shi Q. Spatially aware self-representation learning for tissue structure characterization and spatial functional genes identification. Brief Bioinform 2023; 24:bbad197. [PMID: 37253698 DOI: 10.1093/bib/bbad197] [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: 10/18/2022] [Revised: 04/28/2023] [Accepted: 05/05/2023] [Indexed: 06/01/2023] Open
Abstract
Spatially resolved transcriptomics (SRT) enable the comprehensive characterization of transcriptomic profiles in the context of tissue microenvironments. Unveiling spatial transcriptional heterogeneity needs to effectively incorporate spatial information accounting for the substantial spatial correlation of expression measurements. Here, we develop a computational method, SpaSRL (spatially aware self-representation learning), which flexibly enhances and decodes spatial transcriptional signals to simultaneously achieve spatial domain detection and spatial functional genes identification. This novel tunable spatially aware strategy of SpaSRL not only balances spatial and transcriptional coherence for the two tasks, but also can transfer spatial correlation constraint between them based on a unified model. In addition, this joint analysis by SpaSRL deciphers accurate and fine-grained tissue structures and ensures the effective extraction of biologically informative genes underlying spatial architecture. We verified the superiority of SpaSRL on spatial domain detection, spatial functional genes identification and data denoising using multiple SRT datasets obtained by different platforms and tissue sections. Our results illustrate SpaSRL's utility in flexible integration of spatial information and novel discovery of biological insights from spatial transcriptomic datasets.
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Affiliation(s)
- Chuanchao Zhang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
| | - Xinxing Li
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Wendong Huang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Lequn Wang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qianqian Shi
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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Li W, Qin Y, Chen X, Wang X. Mining of clinical and prognosis related genes in the tumor microenvironment of endometrial cancer: A field synopsis of observational study. Medicine (Baltimore) 2023; 102:e34047. [PMID: 37352078 PMCID: PMC10289639 DOI: 10.1097/md.0000000000034047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/26/2023] [Accepted: 05/30/2023] [Indexed: 06/25/2023] Open
Abstract
Endometrial cancer (EC) is the sixth most common malignant tumor in women worldwide, and its morbidity and mortality are on the rise. The purpose of this study was to explore potential tumor microenvironment (TME)-related biomarkers associated with the clinical features and prognosis of EC. The Estimating Stromal and Immune Cells in Malignancy Using Expression Data (ESTIMATE) algorithm was used to calculate TME immune and stromal scores of EC samples and to analyze the relationship between immune/stromal scores, clinical features, and prognosis. Heat maps and Venn maps were used to screen for differentially expressed genes (DEGs). The ESTIMATE algorithm revealed immune score was significantly correlated with overall survival and tumor grade in patients with EC. A total of 1448 DEGs were screened, of which 387 were intersecting genes. Gene Ontology (GO) analysis revealed that the biological processes (BP) related to intersecting genes mainly included T cell activation and regulation of lymphocyte activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the intersecting genes were closely related to immune-related signaling pathways. Thirty core genes with more than 7 nodes were identified using protein-protein interaction (PPI) analysis. Six independent prognostic genes of EC were identified using Kaplan-Meier survival analysis and multivariate Cox analysis, namely CD5, BATF, CACNA2D2, LTA, CD52, and NOL4, which are all immune-infiltrating genes that are closely related to clinical features. The current study identified 6 key genes closely related to immune infiltration in the TME of EC that predict clinical outcomes, which may provide new insights into novel prognostic biomarkers and immunotherapy for patients with EC.
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Affiliation(s)
- Wenxue Li
- Department of Obstetrics and Gynecology, The Affiliated Weihai Second Municipal Hospital of Qingdao University, Weihai, Shandong, China
| | - Yujing Qin
- Department of Obstetrics and Gynecology, The Affiliated Weihai Second Municipal Hospital of Qingdao University, Weihai, Shandong, China
| | - Xiujuan Chen
- Department of Obstetrics and Gynecology, The Affiliated Weihai Second Municipal Hospital of Qingdao University, Weihai, Shandong, China
| | - Xiaolei Wang
- Department of Obstetrics and Gynecology, The Affiliated Weihai Second Municipal Hospital of Qingdao University, Weihai, Shandong, China
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6
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Liu H, Yu Z, Liu Y, Li M, Chen C, Zhu Z, Liu F, Tan L. Investigation of Diagnostic and Prognostic Value of CLEC4M of Non-Small Cell Lung Carcinoma Associated with Immune Microenvironment. Int J Gen Med 2023; 16:1317-1332. [PMID: 37089135 PMCID: PMC10115202 DOI: 10.2147/ijgm.s397695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 03/30/2023] [Indexed: 04/25/2023] Open
Abstract
Purpose C-type lectin domain family 4 member M (CLEC4M) has been found to be involved in the occurrence and development of cancer, but its role in NSCLC remains to be fully explored. Our work aims to evaluate the diagnostic and prognostic value of CLEC4M in NSCLC and to investigate the underlying mechanisms of CLEC4M in the immune microenvironment of NSCLC. Methods Integrating publicly accessible data and clinical tissue samples to verify the expression of CLEC4M in NSCLC. The diagnostic value of CLEC4M was determined by receiver operating characteristic (ROC) curve. Kaplan-Meier survival analysis, nomogram plot, univariate and multivariate Cox regression models were performed to evaluate the prognostic impact of CLEC4M on NSCLC patients. The correlation between CLEC4M and tumor immune infiltration was estimated using TIMER and UALCAN databases. Functional assessments including GO, KEGG pathway and GSEA analyses were implemented to illustrate the potential mechanisms of CLEC4M in NSCLC. Results CLEC4M was significantly downregulated in NSCLC tissue, as confirmed by immunohistochemistry of clinical tissues. The high AUC value of ROC curves demonstrated the diagnostic accuracy of CLEC4M in NSCLC. Additionally, low CLEC4M expression was associated with poor survival in NSCLC patients. Furthermore, CLEC4M was found to be significantly associated with tumor immune infiltration, and CLEC4M may be involved in immune activation and proliferation inhibition through the functional assessment, suggesting that CLEC4M may be a therapeutic target for NSCLC patients. Conclusion Our findings reveal CLEC4M is significantly downregulated in NSCLC tissues, and illustrate the diagnostic and prognostic value of CLEC4M in NSCLC, as well as its potential serve as an immune-related therapeutic target.
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Affiliation(s)
- Huan Liu
- Department of Precision Medicine Center, The Second People’s Hospital of Huaihua, Huaihua, People’s Republic of China
- Key Laboratory of Cancer Prevention and Treatment of Huaihua, Huaihua, People’s Republic of China
| | - Zhiping Yu
- School of Pharmacy, Xuzhou Medical University, Xuzhou, People’s Republic of China
| | - Yueguang Liu
- Department of Clinicopathology Center, The Second People’s Hospital of Huaihua, Huaihua, People’s Republic of China
| | - Mingzhen Li
- Department of Precision Medicine Center, The Second People’s Hospital of Huaihua, Huaihua, People’s Republic of China
- Key Laboratory of Cancer Prevention and Treatment of Huaihua, Huaihua, People’s Republic of China
| | - Cheng Chen
- Department of Precision Medicine Center, The Second People’s Hospital of Huaihua, Huaihua, People’s Republic of China
- Key Laboratory of Cancer Prevention and Treatment of Huaihua, Huaihua, People’s Republic of China
| | - Zhiyu Zhu
- Department of Clinicopathology Center, The Second People’s Hospital of Huaihua, Huaihua, People’s Republic of China
| | - Fang Liu
- Department of Clinicopathology Center, The Second People’s Hospital of Huaihua, Huaihua, People’s Republic of China
| | - Liming Tan
- Key Laboratory of Cancer Prevention and Treatment of Huaihua, Huaihua, People’s Republic of China
- School of Pharmacy, Xuzhou Medical University, Xuzhou, People’s Republic of China
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7
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Warrick JI, Hu W, Yamashita H, Walter V, Shuman L, Craig JM, Gellert LL, Castro MAA, Robertson AG, Kuo F, Ostrovnaya I, Sarungbam J, Chen YB, Gopalan A, Sirintrapun SJ, Fine SW, Tickoo SK, Kim K, Thomas J, Karan N, Gao SP, Clinton TN, Lenis AT, Chan TA, Chen Z, Rao M, Hollman TJ, Li Y, Socci ND, Chavan S, Viale A, Mohibullah N, Bochner BH, Pietzak EJ, Teo MY, Iyer G, Rosenberg JE, Bajorin DF, Kaag M, Merrill SB, Joshi M, Adam R, Taylor JA, Clark PE, Raman JD, Reuter VE, Chen Y, Funt SA, Solit DB, DeGraff DJ, Al-Ahmadie HA. FOXA1 repression drives lineage plasticity and immune heterogeneity in bladder cancers with squamous differentiation. Nat Commun 2022; 13:6575. [PMID: 36323682 PMCID: PMC9630410 DOI: 10.1038/s41467-022-34251-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
Abstract
Cancers arising from the bladder urothelium often exhibit lineage plasticity with regions of urothelial carcinoma adjacent to or admixed with regions of divergent histomorphology, most commonly squamous differentiation. To define the biologic basis for and clinical significance of this morphologic heterogeneity, here we perform integrated genomic analyses of mixed histology bladder cancers with separable regions of urothelial and squamous differentiation. We find that squamous differentiation is a marker of intratumoral genomic and immunologic heterogeneity in patients with bladder cancer and a biomarker of intrinsic immunotherapy resistance. Phylogenetic analysis confirms that in all cases the urothelial and squamous regions are derived from a common shared precursor. Despite the presence of marked genomic heterogeneity between co-existent urothelial and squamous differentiated regions, no recurrent genomic alteration exclusive to the urothelial or squamous morphologies is identified. Rather, lineage plasticity in bladder cancers with squamous differentiation is associated with loss of expression of FOXA1, GATA3, and PPARG, transcription factors critical for maintenance of urothelial cell identity. Of clinical significance, lineage plasticity and PD-L1 expression is coordinately dysregulated via FOXA1, with patients exhibiting morphologic heterogeneity pre-treatment significantly less likely to respond to immune checkpoint inhibitors.
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Affiliation(s)
- Joshua I Warrick
- Department of Pathology and Laboratory Medicine, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Wenhuo Hu
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hironobu Yamashita
- Department of Pathology and Laboratory Medicine, Pennsylvania State University College of Medicine, Hershey, PA, USA
- Department of Urology, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Vonn Walter
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Lauren Shuman
- Department of Pathology and Laboratory Medicine, Pennsylvania State University College of Medicine, Hershey, PA, USA
- Department of Urology, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Jenna M Craig
- Department of Pathology and Laboratory Medicine, Pennsylvania State University College of Medicine, Hershey, PA, USA
- Department of Urology, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Lan L Gellert
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mauro A A Castro
- Bioinformatics and Systems Biology Laboratory, Federal University of Parana, Curitiba, Paraná, Brazil
| | - A Gordon Robertson
- BC Cancer, Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada
| | - Fengshen Kuo
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Irina Ostrovnaya
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Judy Sarungbam
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ying-Bei Chen
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anuradha Gopalan
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sahussapont J Sirintrapun
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samson W Fine
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Satish K Tickoo
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kwanghee Kim
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jasmine Thomas
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nagar Karan
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sizhi Paul Gao
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Timothy N Clinton
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew T Lenis
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Timothy A Chan
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ziyu Chen
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Manisha Rao
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Travis J Hollman
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yanyun Li
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicholas D Socci
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Shweta Chavan
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Agnes Viale
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Neeman Mohibullah
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bernard H Bochner
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Eugene J Pietzak
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Min Yuen Teo
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Gopa Iyer
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jonathan E Rosenberg
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dean F Bajorin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew Kaag
- Department of Urology, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Suzanne B Merrill
- Department of Urology, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Monika Joshi
- Department of Medicine, Division of Hematology-Oncology, Penn State Cancer Institute, Hershey, PA, USA
| | - Rosalyn Adam
- Department of Urology, Boston Children's Hospital, Boston, MA, USA
| | - John A Taylor
- Department of Urology, University of Kansas Medical Center, Kansas City, MO, USA
| | - Peter E Clark
- Levine Cancer Institute, Atrium Health, Charlotte, NC, USA
| | - Jay D Raman
- Department of Urology, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Victor E Reuter
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yu Chen
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samuel A Funt
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David B Solit
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David J DeGraff
- Department of Pathology and Laboratory Medicine, Pennsylvania State University College of Medicine, Hershey, PA, USA.
- Department of Urology, Pennsylvania State University College of Medicine, Hershey, PA, USA.
- Deparment of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, USA.
| | - Hikmat A Al-Ahmadie
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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8
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Muzzi JCD, Magno JM, Souza JS, Alvarenga LM, de Moura JF, Figueiredo BC, Castro MAA. Comprehensive Characterization of the Regulatory Landscape of Adrenocortical Carcinoma: Novel Transcription Factors and Targets Associated with Prognosis. Cancers (Basel) 2022; 14:5279. [PMID: 36358698 PMCID: PMC9657296 DOI: 10.3390/cancers14215279] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 10/24/2022] [Accepted: 10/25/2022] [Indexed: 08/31/2023] Open
Abstract
We reconstructed a transcriptional regulatory network for adrenocortical carcinoma (ACC) using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA)-ACC cohort. We investigated the association of transcriptional regulatory units (regulons) with overall survival, molecular phenotypes, and immune signatures. We annotated the ACC regulons with cancer hallmarks and assessed single sample regulon activities in the European Network for the Study of Adrenal Tumors (ENSAT) cohort. We found 369 regulons associated with overall survival and subdivided them into four clusters: RC1 and RC2, associated with good prognosis, and RC3 and RC4, associated with worse outcomes. The RC1 and RC3 regulons were highly correlated with the 'Steroid Phenotype,' while the RC2 and RC4 regulons were highly correlated with a molecular proliferation signature. We selected two regulons, NR5A1 (steroidogenic factor 1, SF-1) and CENPA (Centromeric Protein A), that were consistently associated with overall survival for further downstream analyses. The CENPA regulon was the primary regulator of MKI-67 (a marker of proliferation KI-67), while the NR5A1 regulon is a well-described transcription factor (TF) in ACC tumorigenesis. We also found that the ZBTB4 (Zinc finger and BTB domain-containing protein 4) regulon, which is negatively associated with CENPA in our transcriptional regulatory network, is also a druggable anti-tumorigenic TF. We anticipate that the ACC regulons may be used as a reference for further investigations concerning the complex molecular interactions in ACC tumors.
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Affiliation(s)
- João C. D. Muzzi
- Laboratório de Imunoquímica (LIMQ), Pós-Graduação em Microbiologia, Parasitologia e Patologia, Departamento de Patologia Básica, Universidade Federal do Paraná (UFPR), Curitiba 81530-990, Brazil
- Laboratório de Bioinformática e Biologia de Sistemas, Pós-Graduação em Bioinformática, Universidade Federal do Paraná (UFPR), Curitiba 81520-260, Brazil
- Oncology Division, Instituto de Pesquisa Pelé Pequeno Príncipe, Curitiba 80250-060, Brazil
| | - Jéssica M. Magno
- Laboratório de Bioinformática e Biologia de Sistemas, Pós-Graduação em Bioinformática, Universidade Federal do Paraná (UFPR), Curitiba 81520-260, Brazil
- Oncology Division, Instituto de Pesquisa Pelé Pequeno Príncipe, Curitiba 80250-060, Brazil
| | - Jean S. Souza
- Oncology Division, Instituto de Pesquisa Pelé Pequeno Príncipe, Curitiba 80250-060, Brazil
| | - Larissa M. Alvarenga
- Laboratório de Imunoquímica (LIMQ), Pós-Graduação em Microbiologia, Parasitologia e Patologia, Departamento de Patologia Básica, Universidade Federal do Paraná (UFPR), Curitiba 81530-990, Brazil
| | - Juliana F. de Moura
- Laboratório de Imunoquímica (LIMQ), Pós-Graduação em Microbiologia, Parasitologia e Patologia, Departamento de Patologia Básica, Universidade Federal do Paraná (UFPR), Curitiba 81530-990, Brazil
| | - Bonald C. Figueiredo
- Oncology Division, Instituto de Pesquisa Pelé Pequeno Príncipe, Curitiba 80250-060, Brazil
- Molecular Oncology Laboratory, Centro de Genética Molecular e Pesquisa do Câncer em Crianças (CEGEMPAC), Curitiba 80030-110, Brazil
| | - Mauro A. A. Castro
- Laboratório de Bioinformática e Biologia de Sistemas, Pós-Graduação em Bioinformática, Universidade Federal do Paraná (UFPR), Curitiba 81520-260, Brazil
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9
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Warrick JI, Knowles MA, Hurst CD, Shuman L, Raman JD, Walter V, Putt J, Dyrskjøt L, Groeneveld C, Castro MAA, Robertson AG, DeGraff DJ. A transcriptional network of cell cycle dysregulation in noninvasive papillary urothelial carcinoma. Sci Rep 2022; 12:16538. [PMID: 36192513 PMCID: PMC9529892 DOI: 10.1038/s41598-022-20927-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 09/21/2022] [Indexed: 11/09/2022] Open
Abstract
Human cancers display a restricted set of expression profiles, despite diverse mutational drivers. This has led to the hypothesis that select sets of transcription factors act on similar target genes as an integrated network, buffering a tumor’s transcriptional state. Noninvasive papillary urothelial carcinoma (NIPUC) with higher cell cycle activity has higher risk of recurrence and progression. In this paper, we describe a transcriptional network of cell cycle dysregulation in NIPUC, which was delineated using the ARACNe algorithm applied to expression data from a new cohort (n = 81, RNA sequencing), and two previously published cohorts. The transcriptional network comprised 121 transcription factors, including the pluripotency factors SOX2 and SALL4, the sex hormone binding receptors ESR1 and PGR, and multiple homeobox factors. Of these 121 transcription factors, 65 and 56 were more active in tumors with greater and less cell cycle activity, respectively. When clustered by activity of these transcription factors, tumors divided into High Cell Cycle versus Low Cell Cycle groups. Tumors in the High Cell Cycle group demonstrated greater mutational burden and copy number instability. A putative mutational driver of cell cycle dysregulation, such as homozygous loss of CDKN2A, was found in only 50% of High Cell Cycle NIPUC, suggesting a prominent role of transcription factor activity in driving cell cycle dysregulation. Activity of the 121 transcription factors strongly associated with expression of EZH2 and other members of the PRC2 complex, suggesting regulation by this complex influences expression of the transcription factors in this network. Activity of transcription factors in this network also associated with signatures of pluripotency and epithelial-to-mesenchymal transition (EMT), suggesting they play a role in driving evolution to invasive carcinoma. Consistent with this, these transcription factors differed in activity between NIPUC and invasive urothelial carcinoma.
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Affiliation(s)
- Joshua I Warrick
- Department of Pathology and Laboratory Medicine, Pennsylvania State University College of Medicine, 500 University Drive, Hershey, PA, 17033, USA. .,Department of Urology, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
| | - Margaret A Knowles
- Divison of Molecular Medicine, Leeds Institute of Molecular Research at St James's, St James's University Hospital, Beckett Street, Leeds, LS9 7TF, UK
| | - Carolyn D Hurst
- Divison of Molecular Medicine, Leeds Institute of Molecular Research at St James's, St James's University Hospital, Beckett Street, Leeds, LS9 7TF, UK
| | - Lauren Shuman
- Department of Urology, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Jay D Raman
- Department of Urology, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Vonn Walter
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.,Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Jeffrey Putt
- Department of Pathology and Laboratory Medicine, Pennsylvania State University College of Medicine, 500 University Drive, Hershey, PA, 17033, USA
| | - Lars Dyrskjøt
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Clarice Groeneveld
- Cartes d'Identité des Tumeurs (CIT) Program, Ligue Nationale Contre le Cancer, Équipe Oncologie Moleculaire, Institut Curie, Paris, France
| | - Mauro A A Castro
- Bioinformatics and Systems Biology Laboratory, Federal University of Paraná, Curitiba, PR, 81520-260, Brazil
| | | | - David J DeGraff
- Department of Pathology and Laboratory Medicine, Pennsylvania State University College of Medicine, 500 University Drive, Hershey, PA, 17033, USA. .,Department of Urology, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA. .,Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
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10
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Wen J, Wan L, Dong X. The prognostic value of autophagy related genes with potential protective function in Ewing sarcoma. BMC Bioinformatics 2022; 23:306. [PMID: 35902797 PMCID: PMC9335970 DOI: 10.1186/s12859-022-04849-x] [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: 04/24/2022] [Accepted: 07/19/2022] [Indexed: 11/19/2022] Open
Abstract
Background Ewing sarcoma (ES) is the second most common primary malignant bone tumor mainly occurring in children, adolescents and young adults with high metastasis and mortality. Autophagy has been reported to be involved in the survival of ES, but the role remains unclear. Therefore, it’s necessary to investigate the prognostic value of autophagy related genes using bioinformatics methods. Results ATG2B, ATG10 and DAPK1 were final screened genes for a prognostic model. KM and risk score plots showed patients in high score group had better prognoses both in training and validation sets. C-indexes of the model for training and validation sets were 0.68 and 0.71, respectively. Calibration analyses indicated the model had high prediction accuracy in training and validation sets. The AUC values of ROC for 1-, 3-, 5-year prediction were 0.65, 0.73 and 0.84 in training set, 0.88, 0.73 and 0.79 in validation set, which suggested high prediction accuracy of the model. Decision curve analyses showed that patients could benefit much from the model. Differential and functional analyses suggested that autophagy and apoptosis were upregulated in high risk score group. Conclusions ATG2B, ATG10 and DAPK1 were autophagy related genes with potential protective function in ES. The prognostic model established by them exhibited excellent prediction accuracy and discriminatory capacities. They might be used as potential prognostic biomarkers and therapeutic targets in ES. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04849-x.
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Affiliation(s)
- Jian Wen
- Medical College of Nanchang University, Nanchang, 330006, Jiangxi, China.,Department of Orthopedics, Jiangxi Provincial People's Hospital, 152 Aiguo Road, Nanchang, 330006, Jiangxi, China.,JXHC Key Laboratory of Digital Orthopedics (Jiangxi Provincial People's Hospital), 152 Aiguo Road, Nanchang, 330006, Jiangxi, China
| | - Lijia Wan
- Department of Pediatrics, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China
| | - Xieping Dong
- Medical College of Nanchang University, Nanchang, 330006, Jiangxi, China. .,Department of Orthopedics, Jiangxi Provincial People's Hospital, 152 Aiguo Road, Nanchang, 330006, Jiangxi, China. .,JXHC Key Laboratory of Digital Orthopedics (Jiangxi Provincial People's Hospital), 152 Aiguo Road, Nanchang, 330006, Jiangxi, China.
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11
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Qi X, Che X, Li Q, Wang Q, Wu G. Potential Application of Pyroptosis in Kidney Renal Clear Cell Carcinoma Immunotherapy and Targeted Therapy. Front Pharmacol 2022; 13:918647. [PMID: 35795559 PMCID: PMC9252305 DOI: 10.3389/fphar.2022.918647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 05/30/2022] [Indexed: 12/21/2022] Open
Abstract
Renal cell carcinoma (RCC) is a type of cancer with an increasing rate of morbidity and mortality and is a serious threat to human health. The treatment of RCC, especially kidney renal clear cell carcinoma (KIRC), has always been the focus of clinical treatment. Using The Cancer Genome Atlas (TCGA) database as a starting point, we explored the feasibility of applying the pyroptosis mechanism to KIRC treatment by searching for cancer markers associated with pyroptosis and cancer treatment signatures. The obtained samples were clustered using unsupervised clustering analysis to define the different KIRC subtypes with different pyroptosis expression levels. Based on this, a gene expression analysis was performed to explore the carcinogenic mechanism that is markedly related to pyroptosis. The Genomics of Drug Sensitivity in Cancer database and single sample gene set enrichment analysis (ssGSEA) algorithm were used to analyze the different treatment methods of the current prominent KIRC to determine whether pyroptosis plays a role. Finally, LASSO regression was used to screen for related genes and construct a model to predict patient prognosis. The expression levels of GSDME, CASP3, CASP4, CASP5, CHMP3, and CHMP4C were incorporated into the model construction. After verification, the prediction accuracy of the 3-, 5-, 7- and 10 years survival rates of our prognostic model were 0.66, 0.701, 0.719, and 0.728, respectively. Through the above analysis, we demonstrated the feasibility of pyroptosis in the clinical treatment of KIRC and provided novel ideas and suggestions for the clinical treatment of KIRC.
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Affiliation(s)
| | | | | | - Qifei Wang
- *Correspondence: Guangzhen Wu, ; Qifei Wang,
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12
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Liu T, Chen L, Gao G, Liang X, Peng J, Zheng M, Li J, Ye Y, Shao C. Development of a Gene Risk Signature for Patients of Pancreatic Cancer. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4136825. [PMID: 35035831 PMCID: PMC8759853 DOI: 10.1155/2022/4136825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 10/25/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Pancreatic cancer is a highly malignant solid tumor with a high lethality rate, but there is a lack of clinical biomarkers that can assess patient prognosis to optimize treatment. METHODS Gene-expression datasets of pancreatic cancer tissues and normal pancreatic tissues were obtained from the GEO database, and differentially expressed genes analysis and WGCNA analysis were performed after merging and normalizing the datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were used to screen the prognosis-related genes in the modules with the strongest association with pancreatic cancer and construct risk signatures. The performance of the risk signature was subsequently validated by Kaplan-Meier curves, receiver operating characteristic (ROC), and univariate and multivariate Cox analyses. RESULT A three-gene risk signature containing CDKN2A, BRCA1, and UBL3 was established. Based on KM curves, ROC curves, and univariate and multivariate Cox regression analyses in the TRAIN cohort and TEST cohort, it was suggested that the three-gene risk signature had better performance in predicting overall survival. CONCLUSION This study identifies a three-gene risk signature, constructs a nomogram that can be used to predict pancreatic cancer prognosis, and identifies pathways that may be associated with pancreatic cancer prognosis.
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Affiliation(s)
- Tao Liu
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
- Department of Hepatobiliary Surgery, Heze Municipal Hospital, No. 2888, Caozhou Road, Mudan District, Heze 274000, Shandong, China
| | - Long Chen
- Department of Gastrointestinal Surgery, Heze Municipal Hospital, No. 2888, Caozhou Road, Mudan District, Heze 274000, Shandong, China
| | - Guili Gao
- Department of Cardiology, Heze Municipal Hospital, No. 2888, Caozhou Road, Mudan District, Heze 274000, Shandong, China
| | - Xing Liang
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Junfeng Peng
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Minghui Zheng
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Judong Li
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Yongqiang Ye
- Department of Hepatobiliary Surgery, Heze Municipal Hospital, No. 2888, Caozhou Road, Mudan District, Heze 274000, Shandong, China
| | - Chenghao Shao
- Department of Pancreatic-biliary Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
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13
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Hurst CD, Cheng G, Platt FM, Castro MA, Marzouka NADS, Eriksson P, Black EV, Alder O, Lawson AR, Lindskrog SV, Burns JE, Jain S, Roulson JA, Brown JC, Koster J, Robertson AG, Martincorena I, Dyrskjøt L, Höglund M, Knowles MA. Stage-stratified molecular profiling of non-muscle-invasive bladder cancer enhances biological, clinical, and therapeutic insight. Cell Rep Med 2021; 2:100472. [PMID: 35028613 PMCID: PMC8714941 DOI: 10.1016/j.xcrm.2021.100472] [Citation(s) in RCA: 10] [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: 02/09/2021] [Revised: 08/09/2021] [Accepted: 11/18/2021] [Indexed: 12/26/2022]
Abstract
Understanding the molecular determinants that underpin the clinical heterogeneity of non-muscle-invasive bladder cancer (NMIBC) is essential for prognostication and therapy development. Stage T1 disease in particular presents a high risk of progression and requires improved understanding. We present a detailed multi-omics study containing gene expression, copy number, and mutational profiles that show relationships to immune infiltration, disease recurrence, and progression to muscle invasion. We compare expression and genomic subtypes derived from all NMIBCs with those derived from the individual disease stages Ta and T1. We show that sufficient molecular heterogeneity exists within the separate stages to allow subclassification and that this is more clinically meaningful for stage T1 disease than that derived from all NMIBCs. This provides improved biological understanding and identifies subtypes of T1 tumors that may benefit from chemo- or immunotherapy.
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Affiliation(s)
- Carolyn D. Hurst
- Division of Molecular Medicine, Leeds Institute of Medical Research at St James’s, St James’s University Hospital, Beckett Street, Leeds LS9 7TF, UK
| | - Guo Cheng
- Division of Molecular Medicine, Leeds Institute of Medical Research at St James’s, St James’s University Hospital, Beckett Street, Leeds LS9 7TF, UK
| | - Fiona M. Platt
- Division of Molecular Medicine, Leeds Institute of Medical Research at St James’s, St James’s University Hospital, Beckett Street, Leeds LS9 7TF, UK
| | - Mauro A.A. Castro
- Bioinformatics and Systems Biology Laboratory, Federal University of Paraná, Curitiba, Brazil
| | | | - Pontus Eriksson
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Emma V.I. Black
- Division of Molecular Medicine, Leeds Institute of Medical Research at St James’s, St James’s University Hospital, Beckett Street, Leeds LS9 7TF, UK
| | - Olivia Alder
- Division of Molecular Medicine, Leeds Institute of Medical Research at St James’s, St James’s University Hospital, Beckett Street, Leeds LS9 7TF, UK
| | - Andrew R.J. Lawson
- Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - Sia V. Lindskrog
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Julie E. Burns
- Division of Molecular Medicine, Leeds Institute of Medical Research at St James’s, St James’s University Hospital, Beckett Street, Leeds LS9 7TF, UK
| | - Sunjay Jain
- Pyrah Department of Urology, St James’s University Hospital, Beckett Street, Leeds LS9 7TF, UK
| | - Jo-An Roulson
- Department of Histopathology, St James’s University Hospital, Beckett Street, Leeds LS9 7TF, UK
| | - Joanne C. Brown
- Division of Molecular Medicine, Leeds Institute of Medical Research at St James’s, St James’s University Hospital, Beckett Street, Leeds LS9 7TF, UK
| | - Jan Koster
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Cancer Center Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - A. Gordon Robertson
- Canada’s Michael Smith Genome Sciences Center, BC Cancer, Vancouver, BC V5Z 4S6, Canada
| | - Inigo Martincorena
- Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - Lars Dyrskjøt
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Mattias Höglund
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Margaret A. Knowles
- Division of Molecular Medicine, Leeds Institute of Medical Research at St James’s, St James’s University Hospital, Beckett Street, Leeds LS9 7TF, UK
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14
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Feng T, Zhao J, Wei D, Guo P, Yang X, Li Q, Fang Z, Wei Z, Li M, Jiang Y, Luo Y. Immunogenomic Analyses of the Prognostic Predictive Model for Patients With Renal Cancer. Front Immunol 2021; 12:762120. [PMID: 34712244 PMCID: PMC8546215 DOI: 10.3389/fimmu.2021.762120] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 09/27/2021] [Indexed: 01/03/2023] Open
Abstract
Background Renal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients. Here, we aim to construct an immune prognostic predictive model to assist clinician to predict RCC prognosis. Methods Herein, an immune prognostic signature was developed, and its predictive ability was confirmed in the kidney renal clear cell carcinoma (KIRC) cohorts based on The Cancer Genome Atlas (TCGA) dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy. Results The immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response. Conclusions The immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.
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Affiliation(s)
- Tao Feng
- Department of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Jiahui Zhao
- Department of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Dechao Wei
- Department of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Pengju Guo
- Department of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Xiaobing Yang
- Department of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Qiankun Li
- Department of Urology, Beijing Huairou Hospital, Beijing, China
| | - Zhou Fang
- Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Ziheng Wei
- Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Mingchuan Li
- Department of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Yongguang Jiang
- Department of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Yong Luo
- Department of Urology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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15
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Pan Y, Wu L, He S, Wu J, Wang T, Zang H. Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis. Bioengineered 2021; 12:2928-2940. [PMID: 34167437 PMCID: PMC8806580 DOI: 10.1080/21655979.2021.1940615] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The aim of this study was to identify hub genes closely related to the pathogenesis and prognosis of thyroid carcinoma (THCA) by integrated bioinformatics analysis. In this study, through differential gene expression analysis, 1916 and 665 differentially expressed genes (DEGs) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, and 7 and 11 co-expressed modules were identified from the TCGA-THCA and GSE153659 datasets, respectively, by weighted gene co-expression network analysis. We identified 162 overlapping genes between the DEGs and co-expression module genes as candidate hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the 162 overlapping DEGs identified significant functions and pathways of THCA, such as thyroid hormone generation and metabolic process. A protein-protein interaction (PPI) analysis detected the top 10 hub genes (QSOX1, WFS1, EVA1A, FSTL3, CHRDL1, FABP4, PRDM16, PPARGC1A, PPARG, COL23A1). Finally, survival analysis, clinical correlation analysis, and protein abundance validation confirmed that 3 of the 10 hub genes were associated with survival prognosis of patients with THCA, and 8 of them were associated with the clinical stages of THCA. In summary, we identified hub genes and key modules that were closely related to THCA, and validated these genes by survival analysis, clinical correlation analysis, and Human Protein Atlas image analysis. Our results provide important information that will help to elucidate the pathogenesis of THCA and identify novel candidate prognostic biomarkers and potential therapeutic targets.
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Affiliation(s)
- Yangwang Pan
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Bejing, People's Republic of China
| | - Linjing Wu
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Bejing, People's Republic of China
| | - Shuai He
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Bejing, People's Republic of China
| | - Jun Wu
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Bejing, People's Republic of China
| | - Tong Wang
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Bejing, People's Republic of China
| | - Hongrui Zang
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Bejing, People's Republic of China
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16
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Qin F, Xu H, Wei G, Ji Y, Yu J, Hu C, Yuan C, Ma Y, Qian J, Li L, Huo J. A Prognostic Model Based on the Immune-Related lncRNAs in Colorectal Cancer. Front Genet 2021; 12:658736. [PMID: 33959151 PMCID: PMC8093825 DOI: 10.3389/fgene.2021.658736] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 03/22/2021] [Indexed: 12/12/2022] Open
Abstract
Background Colorectal cancer (CRC) is one of the most common malignant tumors with a poor prognosis. At present, the pathogenesis is not completely clear. Therefore, finding reliable prognostic indicators for CRC is of important clinical significance. In this study, bioinformatics methods were used to screen the prognostic immune-related lncRNAs of CRC, and a prognostic risk scoring model based on immune-related lncRNAs signatures were constructed to provide a basis for prognostic evaluation and immunotherapy of CRC patients. Methods The clinical information and RNA-seq data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database. The information of immune-related lncRNA was downloaded from the immunology database and analysis portal. The differentially expressed immune-related lncRNAs (IRLs) were screened by the edgeR package of R software. The prognostic value of IRLs was studied. Based on Cox regression analysis, a prognostic index (IRLPI) based on IRLs was established, and the relationship between the risk score and the clinicopathological characteristics of CRC was analyzed to determine the effectiveness of the risk score model as an independent prognostic factor. Results A total of 240 differentially expressed IRLs were identified between normal colorectal cancer tissues and normal colorectal cancer tissues, in which 8 were significantly associated with the survival of CRC patients (P < 0.05), including LINC00461, LINC01055, ELFN1-AS1, LMO7-AS1, CYP4A22-AS1, AC079612.1, LINC01351, and MIR31HG. And most of the lncRNAs related to survival were risk factors for the prognosis of CRC. The index established based on the 7 survival-related IRLs found to be highly accurate in monitoring CRC prognosis. Besides, IRLPI was significantly correlated with a variety of pathological factors and immune cell infiltration. Conclusion Eight immune-related lncRNAs closely related to the prognosis of CRC patients were identified from the TCGA database. At the same time, an independent IRLPI was constructed, which may be helpful for clinicians to assess the prognosis of patients with CRC and to formulate individualized treatment plans.
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Affiliation(s)
- Fengxia Qin
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Houxi Xu
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, Nanjing, China
| | - Guoli Wei
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Yi Ji
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Jialin Yu
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Canhong Hu
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Chunyi Yuan
- Department of Oncology, Ganyu District Hospital of Traditional Chinese Medicine, Lianyungang, China
| | - Yuzhu Ma
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Jun Qian
- School of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Lingchang Li
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Jiege Huo
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
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Mathias C, Groeneveld CS, Trefflich S, Zambalde EP, Lima RS, Urban CA, Prado KB, Ribeiro EMSF, Castro MAA, Gradia DF, de Oliveira JC. Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells. Int J Mol Sci 2021; 22:ijms22052420. [PMID: 33670895 PMCID: PMC7957645 DOI: 10.3390/ijms22052420] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 01/24/2021] [Accepted: 01/25/2021] [Indexed: 12/18/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.
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Affiliation(s)
- Carolina Mathias
- Post-Graduation Program in Genetics, Department of Genetics, Federal University of Parana, Curitiba 81530-900, PR, Brazil; (C.M.); (E.P.Z.); (K.B.P.); (E.M.S.F.R.); (D.F.G.)
| | - Clarice S. Groeneveld
- Cartes d’Identité des Tumeurs Program, Ligue Nationale Contre le Cancer, 75013 Paris, France;
- Oncologie Moleculaire, Institut Curie, CNRS, UMR144, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France
| | - Sheyla Trefflich
- Bioinformatics and Systems Biology Laboratory, Polytechnic Center, Federal University of Parana (UFPR), Curitiba 81520-260, PR, Brazil; (S.T.); (M.A.A.C.)
| | - Erika P. Zambalde
- Post-Graduation Program in Genetics, Department of Genetics, Federal University of Parana, Curitiba 81530-900, PR, Brazil; (C.M.); (E.P.Z.); (K.B.P.); (E.M.S.F.R.); (D.F.G.)
| | - Rubens S. Lima
- Breast Disease Center, Hospital Nossa Senhora das Graças, Curitiba 80810040, PR, Brazil; (R.S.L.); (C.A.U.)
| | - Cícero A. Urban
- Breast Disease Center, Hospital Nossa Senhora das Graças, Curitiba 80810040, PR, Brazil; (R.S.L.); (C.A.U.)
| | - Karin B. Prado
- Post-Graduation Program in Genetics, Department of Genetics, Federal University of Parana, Curitiba 81530-900, PR, Brazil; (C.M.); (E.P.Z.); (K.B.P.); (E.M.S.F.R.); (D.F.G.)
| | - Enilze M. S. F. Ribeiro
- Post-Graduation Program in Genetics, Department of Genetics, Federal University of Parana, Curitiba 81530-900, PR, Brazil; (C.M.); (E.P.Z.); (K.B.P.); (E.M.S.F.R.); (D.F.G.)
| | - Mauro A. A. Castro
- Bioinformatics and Systems Biology Laboratory, Polytechnic Center, Federal University of Parana (UFPR), Curitiba 81520-260, PR, Brazil; (S.T.); (M.A.A.C.)
| | - Daniela F. Gradia
- Post-Graduation Program in Genetics, Department of Genetics, Federal University of Parana, Curitiba 81530-900, PR, Brazil; (C.M.); (E.P.Z.); (K.B.P.); (E.M.S.F.R.); (D.F.G.)
| | - Jaqueline C. de Oliveira
- Post-Graduation Program in Genetics, Department of Genetics, Federal University of Parana, Curitiba 81530-900, PR, Brazil; (C.M.); (E.P.Z.); (K.B.P.); (E.M.S.F.R.); (D.F.G.)
- Correspondence:
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18
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Mercatelli D, Scalambra L, Triboli L, Ray F, Giorgi FM. Gene regulatory network inference resources: A practical overview. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2019; 1863:194430. [PMID: 31678629 DOI: 10.1016/j.bbagrm.2019.194430] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 09/06/2019] [Accepted: 09/09/2019] [Indexed: 02/08/2023]
Abstract
Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
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Affiliation(s)
- Daniele Mercatelli
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Laura Scalambra
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Luca Triboli
- Centre for Integrative Biology (CIBIO), University of Trento, Italy
| | - Forest Ray
- Department of Systems Biology, Columbia University Medical Center, New York, NY, United States
| | - Federico M Giorgi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
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