Dataset Information

Identification of modules and functional analysis in CRC subtypes by integrated bioinformatics analysis.

ABSTRACT: Colorectal cancer is one of the top three causes of cancer-related mortality globally, but no predictive molecular biomarkers are currently available for identifying the disease stage of colorectal cancer patients. Common molecular patterns in the disease, beyond superficial manifestations, can be significant in determining treatment choices. In this study, we used microarray data from colorectal cancer and adjacent normal tissue from the GEO database. These data were categorized into four consensus molecular subtypes based on distinct gene expression signatures. Weighted gene-based protein-protein interaction network analysis was performed for each subtype. NUSAP1, CD44, and COL4A1 modules were found to be statistically significant and present among all the subtypes and displayed though similar but not identical functional enrichment results. Reference of the characteristics of the subtypes to functional modules is necessary since the latter can stay resistant to platform changes and technique noise when compared with other analyses. The CMS4-mesenchymal group, which currently has a poor prognosis, was examined in the study. It is composed mainly of genes involved in immune and stromal expression, with modules focused on ECM dysregulation and chemokine biological processes. Hub genes detection and its' mapping into the protein-protein interaction network can be indicative of possible targets against specific modules. This approach identified subtypes using enrichment-oriented analysis in functional modules. Proper annotation of functional analysis of modules from different subtypes of CRC might be directive for finding extra options for treatment targets and guiding clinical routines.

SUBMITTER: Chen R

PROVIDER: S-EPMC6716647 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of modules and functional analysis in CRC subtypes by integrated bioinformatics analysis.

Chen Ru R Sugiyama Aiko A Seno Hiroshi H Sugimoto Masahiro M

PloS one 20190830 8

Colorectal cancer is one of the top three causes of cancer-related mortality globally, but no predictive molecular biomarkers are currently available for identifying the disease stage of colorectal cancer patients. Common molecular patterns in the disease, beyond superficial manifestations, can be significant in determining treatment choices. In this study, we used microarray data from colorectal cancer and adjacent normal tissue from the GEO database. These data were categorized into four conse ...[more]

PMID: 31469863

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Project description:Background: Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease that may lead to end-stage renal disease. However, few specifific biomarkers are available for AAV-related renal injury. The aim of this study was to identify important biomarkers and explore new immune subtypes of AAV-related renal injury. Methods: In this study, messenger RNA expression profiles for antibody-associated vasculitis and AAV-associated kidney injury were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes to AAV. Key module genes from WGCNA were then intersected with AAV- and nephropathy-related genes from the Genecards database to identify key genes for AAV-associated kidney injury. Subsequently, the expression of key genes was validated in independent datasets and the correlation of genes with clinical traits of kidney injury was verified by the Nephroseq database. Finally, non-negative matrix factorization (NMF) clustering was performed to identify the immune subtypes associated with the key genes. Results: Eight co-key genes (AGTR2, ANPTL2, BDKRB1, CSF2, FGA, IL1RAPL2, PCDH11Y, and PGR) were identifified, and validated the expression levels independent datasets. Receiver operating characteristic curve analysis revealed that these eight genes have major diagnostic value as potential biomarkers of AAV-related renal injury. Through our comprehensive gene enrichment analyses, we found that they are associated with immune-related pathways. NMF clustering of key genes identified two and three immune-related molecular subtypes in the glomerular and tubular data, respectively. A correlation analysis with prognostic data from the Nephroseq database indicated that the expression of co-key genes was positively co-related with the glomerular filtration rate. Discussion: Altogether, we identifified 8 valuable biomarkers that firmly correlate with the diagnosis and prognosis of AAV-related renal injury. These markers may help identify new immune subtypes for AAV-related renal injury.

Project description:Background and objectiveMorphologic variations of disease are often linked to underlying molecular events and patient outcome, suggesting that quantitative morphometric analysis may provide further insight into disease mechanisms. In this paper a methodology for the subclassification of disease is developed using image analysis techniques. Morphologic signatures that represent patient-specific tumor morphology are derived from the analysis of hundreds of millions of cells in digitized whole slide images. Clustering these signatures aggregates tumors into groups with cohesive morphologic characteristics. This methodology is demonstrated with an analysis of glioblastoma, using data from The Cancer Genome Atlas to identify a prognostically significant morphology-driven subclassification, in which clusters are correlated with transcriptional, genetic, and epigenetic events.Materials and methodsMethodology was applied to 162 glioblastomas from The Cancer Genome Atlas to identify morphology-driven clusters and their clinical and molecular correlates. Signatures of patient-specific tumor morphology were generated from analysis of 200 million cells in 462 whole slide images. Morphology-driven clusters were interrogated for associations with patient outcome, response to therapy, molecular classifications, and genetic alterations. An additional layer of deep, genome-wide analysis identified characteristic transcriptional, epigenetic, and copy number variation events.Results and discussionAnalysis of glioblastoma identified three prognostically significant patient clusters (median survival 15.3, 10.7, and 13.0 months, log rank p=1.4e-3). Clustering results were validated in a separate dataset. Clusters were characterized by molecular events in nuclear compartment signaling including developmental and cell cycle checkpoint pathways. This analysis demonstrates the potential of high-throughput morphometrics for the subclassification of disease, establishing an approach that complements genomics.

Dataset Information

Identification of modules and functional analysis in CRC subtypes by integrated bioinformatics analysis.

Publications

Identification of modules and functional analysis in CRC subtypes by integrated bioinformatics analysis.

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