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El-Kamand S, Quinn JW, Sareen H, Becker T, Wong-Erasmus M, Cowley M. CRUX, a platform for visualising, exploring and analysing cancer genome cohort data. NAR Genom Bioinform 2024; 6:lqae003. [PMID: 38304083 PMCID: PMC10833466 DOI: 10.1093/nargab/lqae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 12/11/2023] [Accepted: 01/09/2024] [Indexed: 02/03/2024] Open
Abstract
To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and to create visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (i) it has an easy-to-use graphical interface; (ii) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (iii) it allows analyses to be run locally to address data privacy concerns (though an online version is also available) and (iv) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUX's capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).
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Affiliation(s)
- Sam El-Kamand
- Children's Cancer Institute, Randwick, NSW 2031, Australia
| | | | - Heena Sareen
- Centre for Circulating Tumour Cell Diagnostics and Research, Ingham Institute for Applied Medical Research, 1 Campbell St, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Therese M Becker
- Centre for Circulating Tumour Cell Diagnostics and Research, Ingham Institute for Applied Medical Research, 1 Campbell St, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
- School of Medicine, Western Sydney University, Campbelltown, NSW 2560, Australia
| | - Marie Wong-Erasmus
- Children's Cancer Institute, Randwick, NSW 2031, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia
| | - Mark J Cowley
- Children's Cancer Institute, Randwick, NSW 2031, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia
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Milner JJ, Zadinsky JK, Shiao SPK. Nursing Informatics and Epigenetics: Methodological Considerations for Big Data Analysis. Comput Inform Nurs 2023; 41:369-376. [PMID: 36728378 PMCID: PMC10241417 DOI: 10.1097/cin.0000000000000992] [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] [Indexed: 02/03/2023]
Abstract
Nursing informatics requires an understanding of patient-centered data and clinical workflow, and epigenetic research requires an understanding of data analysis. The purpose of this article is to document the methodology that nursing informatics specialists can use to conduct epigenetic research and subsequently strengthen patient-centered care. A pilot study of a secondary methylation data analysis using The Cancer Genome Atlas data from individuals with colon cancer is utilized to illustrate the methodology. The steps for conducting the study using public and free resources are discussed. These steps include finding a data source; downloading and analyzing differentially methylated regions; annotating differentially methylated region, gene ontology and function analysis; and reporting results. A model of epigenetic testing workflow is provided, as is a list of publicly available data and analysis sources that can be used to conduct epigenetic research.
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Obesity-Associated Differentially Methylated Regions in Colon Cancer. J Pers Med 2022; 12:jpm12050660. [PMID: 35629083 PMCID: PMC9142939 DOI: 10.3390/jpm12050660] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/11/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Obesity with adiposity is a common disorder in modern days, influenced by environmental factors such as eating and lifestyle habits and affecting the epigenetics of adipose-based gene regulations and metabolic pathways in colorectal cancer (CRC). We compared epigenetic changes of differentially methylated regions (DMR) of genes in colon tissues of 225 colon cancer cases (154 non-obese and 71 obese) and 15 healthy non-obese controls by accessing The Cancer Genome Atlas (TCGA) data. We applied machine-learning-based analytics including generalized regression (GR) as a confirmatory validation model to identify the factors that could contribute to DMRs impacting colon cancer to enhance prediction accuracy. We found that age was a significant predictor in obese cancer patients, both alone (p = 0.003) and interacting with hypomethylated DMRs of ZBTB46, a tumor suppressor gene (p = 0.008). DMRs of three additional genes: HIST1H3I (p = 0.001), an oncogene with a hypomethylated DMR in the promoter region; SRGAP2C (p = 0.006), a tumor suppressor gene with a hypermethylated DMR in the promoter region; and NFATC4 (p = 0.006), an adipocyte differentiating oncogene with a hypermethylated DMR in an intron region, are also significant predictors of cancer in obese patients, independent of age. The genes affected by these DMR could be potential novel biomarkers of colon cancer in obese patients for cancer prevention and progression.
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Valle F, Osella M, Caselle M. Multiomics Topic Modeling for Breast Cancer Classification. Cancers (Basel) 2022; 14:1150. [PMID: 35267458 PMCID: PMC8909787 DOI: 10.3390/cancers14051150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 02/18/2022] [Indexed: 12/04/2022] Open
Abstract
The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to accomplish this integration task. More specifically, we show how an algorithm based on a hierarchical version of stochastic block modeling can be naturally extended to integrate any combination of 'omics data. We test this approach on breast cancer samples from the TCGA database, integrating data on messenger RNA, microRNAs, and copy number variations. We show that the inclusion of the microRNA layer significantly improves the accuracy of subtype classification. Moreover, some of the hidden structures or "topics" that the algorithm extracts actually correspond to genes and microRNAs involved in breast cancer development and are associated to the survival probability.
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Affiliation(s)
- Filippo Valle
- Physics Department, University of Turin and INFN, via P. Giuria 1, 10125 Turin, Italy; (M.O.); (M.C.)
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Moiseev IS, Tcvetkov NY, Barkhatov IM, Barabanshikova MV, Bug DS, Petuhova NV, Tishkov AV, Bakin EA, Izmailova EA, Shakirova AI, Kulagin AD, Morozova EV. High mutation burden in the checkpoint and micro-RNA processing genes in myelodysplastic syndrome. PLoS One 2021; 16:e0248430. [PMID: 33730109 PMCID: PMC7968630 DOI: 10.1371/journal.pone.0248430] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 02/25/2021] [Indexed: 12/25/2022] Open
Abstract
A number of sequencing studies identified the prognostic impact of somatic mutations in myelodysplastic syndrome (MDS). However the majority of them focused on methylation regulation, apoptosis and proliferation genes. Despite the number of experimental studies published on the role of micro-RNA processing and checkpoint genes in the development of MDS, the clinical data about mutational landscape in these genes is limited. We performed a pilot study which evaluated mutational burden in these genes and their association with common MDS mutations. High prevalence of mutations was observed in the genes studied: 54% had mutations in DICER1, 46% had mutations in LAG3, 20% in CTLA4, 23% in B7-H3, 17% in DROSHA, 14% in PD-1 and 3% in PD-1L. Cluster analysis that included these mutations along with mutations in ASXL1, DNMT3A, EZH2, IDH1, RUNX1, SF3B1, SRSF2, TET2 and TP53 effectively predicted overall survival in the study group (HR 4.2, 95%CI 1.3-13.6, p = 0.016). The study results create the rational for incorporating micro-RNA processing and checkpoint genes in the sequencing panels for MDS and evaluate their role in the multicenter studies.
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Affiliation(s)
- Ivan Sergeevich Moiseev
- RM Gorbacheva Research Institute, Pavlov University, Saint-Petersburg, Russian Federation
- * E-mail:
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Valle F, Osella M, Caselle M. A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data. Cancers (Basel) 2020; 12:E3799. [PMID: 33339347 PMCID: PMC7766023 DOI: 10.3390/cancers12123799] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 12/07/2020] [Accepted: 12/11/2020] [Indexed: 01/18/2023] Open
Abstract
Topic modeling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modeling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The established cancer subtype organization is well reconstructed in the inferred latent topic structure. Moreover, we identify specific topics that are enriched in genes known to play a role in the corresponding disease and are strongly related to the survival probability of patients. Finally, we show that a simple neural network classifier operating in the low dimensional topic space is able to predict with high accuracy the cancer subtype of a test expression sample.
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Affiliation(s)
- Filippo Valle
- Physics Department, University of Turin and INFN, via P. Giuria 1, 10125 Turin, Italy; (M.O.); (M.C.)
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Silva TC, Coetzee SG, Gull N, Yao L, Hazelett DJ, Noushmehr H, Lin DC, Berman BP. ELMER v.2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles. Bioinformatics 2020; 35:1974-1977. [PMID: 30364927 PMCID: PMC6546131 DOI: 10.1093/bioinformatics/bty902] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 10/01/2018] [Accepted: 10/25/2018] [Indexed: 12/18/2022] Open
Abstract
Motivation DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set. Results We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings. We show that Supervised mode significantly increases statistical power and identifies additional GRNs and associated Master Regulators, such as SOX11 and KLF5 in Basal-like breast cancer. Availability and implementation ELMER v.2 is available as an R/Bioconductor package at http://bioconductor.org/packages/ELMER/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tiago C Silva
- Department of Biomedical Sciences, Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA.,Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Simon G Coetzee
- Department of Biomedical Sciences, Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nicole Gull
- Department of Biomedical Sciences, Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Lijing Yao
- Bioinformatics Research & Early Development, Roche Sequencing Solutions, Belmont, CA, USA
| | - Dennis J Hazelett
- Department of Biomedical Sciences, Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Houtan Noushmehr
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.,Department of Neurosurgery, Henry Ford Hospital, Detroit, MI, USA
| | - De-Chen Lin
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Benjamin P Berman
- Department of Biomedical Sciences, Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA.,Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Silva VM, Gomes JA, Tenório LPG, de Omena Neta GC, da Costa Paixão K, Duarte AKF, da Silva GCB, Ferreira RJS, Koike BDV, de Sales Marques C, da Silva Miguel RD, de Queiroz AC, Pereira LX, de Carvalho Fraga CA. Schwann cell reprogramming and lung cancer progression: a meta-analysis of transcriptome data. Oncotarget 2019; 10:7288-7307. [PMID: 31921388 PMCID: PMC6944448 DOI: 10.18632/oncotarget.27204] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 07/29/2019] [Indexed: 11/25/2022] Open
Abstract
Schwann cells were identified in the tumor surrounding area prior to initiate the invasion process underlying connective tissue. These cells promote cancer invasion through direct contact, while paracrine signaling and matrix remodeling are not sufficient to proceed. Considering the intertwined structure of signaling, regulatory, and metabolic processes within a cell, we employed a genome-scale biomolecular network. Accordingly, a meta-analysis of Schwann cells associated transcriptomic datasets was performed, and the core information on differentially expressed genes (DEGs) was obtained by statistical analyses. Gene set over-representation analyses was performed on core DEGs to identify significantly functional and pathway enrichment analysis between Schwann cells and, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). DEGs were further integrated with genome-scale human biomolecular networks. miRNAs were proposed by the reconstruction of a transcriptional and post-transcriptional regulatory network. Moreover, microarray-based transcriptome profiling was performed, and the prognostic power of selected dedifferentiated Schwann cell biomolecules was predicted. We observed that pathways associated with Schwann cells dedifferentiation was overexpressed in lung cancer samples. However, genes associated with Schwann cells migration inhibition system were downregulated. Besides, miRNA targeting those pathways were also deregulated. In this study, we report valuable data for further experimental and clinical analysis, because the proposed biomolecules have significant potential as systems biomarkers for screening or for therapeutic purposes in perineural invasion of lung cancer.
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Affiliation(s)
| | - Jessica Alves Gomes
- Department of Medicine, Federal University of Alagoas, Campus Arapiraca, Brazil
| | | | | | | | | | | | | | - Bruna Del Vechio Koike
- Department of Medicine, Federal University of the São Francisco Valley, Petrolina, Brazil
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Pereira LX, Alves da Silva LC, de Oliveira Feitosa A, Santos Ferreira RJ, Fernandes Duarte AK, da Conceição V, de Sales Marques C, Barros Ferreira Rodrigues AK, Del Vechio Koike B, Cavalcante de Queiroz A, Guimaraes TA, Freire de Souza CD, Alberto de Carvalho Fraga C. Correlation between renin-angiotensin system (RAS) related genes, type 2 diabetes, and cancer: Insights from metanalysis of transcriptomics data. Mol Cell Endocrinol 2019; 493:110455. [PMID: 31145933 DOI: 10.1016/j.mce.2019.110455] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 05/21/2019] [Accepted: 05/22/2019] [Indexed: 12/26/2022]
Abstract
Although studies have provided significant evidence about the role of RAS in mediating cancer risk in type 2 diabetes mellitus (DM), conclusions about the central molecular mechanisms underlying this disease remain to be reached, because this type of information requires an integrative multi-omics approach. In the current study, meta-analysis was performed on type 2 diabetes and breast, bladder, liver, pancreas, colon and rectum cancer-associated transcriptome data, and reporter biomolecules were identified at RNA, protein, and metabolite levels using the integration of gene expression profiles with genome-scale biomolecular networks in diabetes samples. This approach revealed that RAS biomarkers could be associated with cancer initiation and progression, which include metabolites (particularly, aminoacyl-tRNA biosynthesis and ABC transporters) as novel biomarker candidates and potential therapeutic targets. We detected downregulation and upregulation of differentially expressed genes (DEGs) in blood, pancreatic islets, liver and skeletal muscle from normal and diabetic patients. DEGs were combined with 211 renin-angiotensin-system related genes. Upregulated genes were enriched using Pathway analysis of cancer in pancreatic islets, blood and skeletal muscle samples. It seems that the changes in mRNA are contributing to the phenotypic changes in carcinogenesis, or that they are as a result of the phenotypic changes associated with the malignant transformation. Our analyses showed that Ctsg and Ednrb are downregulated in cancer samples. However, by immunohistochemistry experiments we observed that EDNRB protein showed increased expression in tumor samples. It is true that alterations in mRNA expression do not always reflect alterations in protein expression, since post-translational changes can occur in proteins. In this study, we report valuable data for further experimental and clinical analysis, because the proposed biomolecules have significant potential as systems biomarkers for screening or for therapeutic purposes in type 2 diabetes and cancer-associated pathways.
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Affiliation(s)
- Luciana Xavier Pereira
- Federal University of Alagoas, Campus Arapiraca. Av. Manoel Severino Barbosa, Bom Sucesso, Arapiraca, AL, 57309-005, Brazil
| | | | - Alexya de Oliveira Feitosa
- Federal University of Alagoas, Campus Arapiraca. Av. Manoel Severino Barbosa, Bom Sucesso, Arapiraca, AL, 57309-005, Brazil
| | - Ricardo Jansen Santos Ferreira
- Federal University of Alagoas, Campus Arapiraca. Av. Manoel Severino Barbosa, Bom Sucesso, Arapiraca, AL, 57309-005, Brazil
| | - Ana Kelly Fernandes Duarte
- Federal University of Alagoas, Campus Arapiraca. Av. Manoel Severino Barbosa, Bom Sucesso, Arapiraca, AL, 57309-005, Brazil
| | - Valdemir da Conceição
- Federal University of Alagoas, Campus Arapiraca. Av. Manoel Severino Barbosa, Bom Sucesso, Arapiraca, AL, 57309-005, Brazil
| | - Carolinne de Sales Marques
- Federal University of Alagoas, Campus Arapiraca. Av. Manoel Severino Barbosa, Bom Sucesso, Arapiraca, AL, 57309-005, Brazil
| | | | - Bruna Del Vechio Koike
- Federal University of Alagoas, Campus Arapiraca. Av. Manoel Severino Barbosa, Bom Sucesso, Arapiraca, AL, 57309-005, Brazil
| | - Aline Cavalcante de Queiroz
- Federal University of Alagoas, Campus Arapiraca. Av. Manoel Severino Barbosa, Bom Sucesso, Arapiraca, AL, 57309-005, Brazil
| | - Talita Antunes Guimaraes
- Federal University of Alagoas, Campus Arapiraca. Av. Manoel Severino Barbosa, Bom Sucesso, Arapiraca, AL, 57309-005, Brazil
| | - Carlos Dornels Freire de Souza
- Federal University of Alagoas, Campus Arapiraca. Av. Manoel Severino Barbosa, Bom Sucesso, Arapiraca, AL, 57309-005, Brazil
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