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An Integrated Approach for Identifying Molecular Subtypes in Human Colon Cancer Using Gene Expression Data.

ABSTRACT: Identifying molecular subtypes of colorectal cancer (CRC) may allow for more rational, patient-specific treatment. Various studies have identified molecular subtypes for CRC using gene expression data, but they are inconsistent and further research is necessary. From a methodological point of view, a progressive approach is needed to identify molecular subtypes in human colon cancer using gene expression data. We propose an approach to identify the molecular subtypes of colon cancer that integrates denoising by the Bayesian robust principal component analysis (BRPCA) algorithm, hierarchical clustering by the directed bubble hierarchical tree (DBHT) algorithm, and feature gene selection by an improved differential evolution based feature selection method (DEFS_W) algorithm. In this approach, the normal samples being completely and exclusively clustered into one class is considered to be the standard of reasonable clustering subtypes, and the feature selection pays attention to imbalances of samples among subtypes. With this approach, we identified the molecular subtypes of colon cancer on the mRNA gene expression dataset of 153 colon cancer samples and 19 normal control samples of the Cancer Genome Atlas (TCGA) project. The colon cancer was clustered into 7 subtypes with 44 feature genes. Our approach could identify finer subtypes of colon cancer with fewer feature genes than the other two recent studies and exhibits a generic methodology that might be applied to identify the subtypes of other cancers.

SUBMITTER: Wang WH

PROVIDER: S-EPMC6115727 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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An Integrated Approach for Identifying Molecular Subtypes in Human Colon Cancer Using Gene Expression Data.

Wang Wen-Hui WH Xie Ting-Yan TY Xie Guang-Lei GL Ren Zhong-Lu ZL Li Jin-Ming JM

Genes 20180802 8

Identifying molecular subtypes of colorectal cancer (CRC) may allow for more rational, patient-specific treatment. Various studies have identified molecular subtypes for CRC using gene expression data, but they are inconsistent and further research is necessary. From a methodological point of view, a progressive approach is needed to identify molecular subtypes in human colon cancer using gene expression data. We propose an approach to identify the molecular subtypes of colon cancer that integra ...[more]

PMID: 30072645

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Project description:BackgroundColon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes.MethodsIn this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR.ResultsThree clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, p<0.05). Integrative multi-omics analysis revealed biological processes contributing to colon cancer aggressiveness, recurrence, and progression. The developed MKPC score, based on gene pairs, was robust in predicting prognosis state (Log-Rank test, p<0.05), and risk-related genes were successfully verified by qPCR (t test, p<0.05). An easy-to-use web tool was created for risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.ConclusionIn conclusion, our study identified three clinically relevant subtypes of colon cancer and developed a robust prognostic model based on gene pairs. The developed web tool is a valuable resource for researchers and clinicians in risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.

Project description:BackgroundColon cancer (CC) pathological staging fails to accurately predict recurrence, and to date, no gene expression signature has proven reliable for prognosis stratification in clinical practice, perhaps because CC is a heterogeneous disease. The aim of this study was to establish a comprehensive molecular classification of CC based on mRNA expression profile analyses.Methods and findingsFresh-frozen primary tumor samples from a large multicenter cohort of 750 patients with stage I to IV CC who underwent surgery between 1987 and 2007 in seven centers were characterized for common DNA alterations, including BRAF, KRAS, and TP53 mutations, CpG island methylator phenotype, mismatch repair status, and chromosomal instability status, and were screened with whole genome and transcriptome arrays. 566 samples fulfilled RNA quality requirements. Unsupervised consensus hierarchical clustering applied to gene expression data from a discovery subset of 443 CC samples identified six molecular subtypes. These subtypes were associated with distinct clinicopathological characteristics, molecular alterations, specific enrichments of supervised gene expression signatures (stem cell phenotype-like, normal-like, serrated CC phenotype-like), and deregulated signaling pathways. Based on their main biological characteristics, we distinguished a deficient mismatch repair subtype, a KRAS mutant subtype, a cancer stem cell subtype, and three chromosomal instability subtypes, including one associated with down-regulated immune pathways, one with up-regulation of the Wnt pathway, and one displaying a normal-like gene expression profile. The classification was validated in the remaining 123 samples plus an independent set of 1,058 CC samples, including eight public datasets. Furthermore, prognosis was analyzed in the subset of stage II-III CC samples. The subtypes C4 and C6, but not the subtypes C1, C2, C3, and C5, were independently associated with shorter relapse-free survival, even after adjusting for age, sex, stage, and the emerging prognostic classifier Oncotype DX Colon Cancer Assay recurrence score (hazard ratio 1.5, 95% CI 1.1-2.1, p?=?0.0097). However, a limitation of this study is that information on tumor grade and number of nodes examined was not available.ConclusionsWe describe the first, to our knowledge, robust transcriptome-based classification of CC that improves the current disease stratification based on clinicopathological variables and common DNA markers. The biological relevance of these subtypes is illustrated by significant differences in prognosis. This analysis provides possibilities for improving prognostic models and therapeutic strategies. In conclusion, we report a new classification of CC into six molecular subtypes that arise through distinct biological pathways.

Project description:BackgroundBiliary atresia (BA) is the most common form of severe neonatal obstructive jaundice. The etiology and pathogenesis of BA are multifactorial, and different factors may interact to produce heterogeneous pathological features and clinical outcomes. Despite different pathological features, all patients received the same treatment strategy. This study performed integrative clustering analysis based on multiple high-throughput datasets to identify the molecular subtypes of BA and provide a new treatment strategy for personalized treatment of the different subtypes of BA.MethodsThe RNA sequence dataset GSE122340 in the Gene Expression Omnibus (GEO) database was downloaded; 31 BA and 20 control normal liver tissues were collected at our center for transcriptome sequencing, and clinical and follow-up data of BA patients were available. Molecular subtypes were identified using integrated unsupervised cluster analysis involving gene expression, biliary fibrosis, and immune enrichment scores based on the transcriptome dataset, and the results were validated using independent datasets.ResultsBased on the results of the integrated unsupervised clustering analysis, four molecular subtypes were identified: autoimmune, inflammatory, virus infection-related, and oxidative stress. The autoimmune subtype with a moderate prognosis was dominated by autoimmune responses and morphogenesis, such as the Fc-gamma receptor and Wnt signaling pathway. The biological process of the inflammatory subtype was mainly the inflammatory response, with the best prognosis, youngest age at surgery, and lowest liver stiffness. The virus infection-related subtype had the worst prognosis and was enriched for a variety of biological processes such as viral infection, immunity, anatomical morphogenesis, and epithelial mesenchymal transition. The oxidative stress subtype was characterized by the activation of oxidative stress and various metabolic pathways and had a poor prognosis. The above results were verified independently in the validation sets.ConclusionsThis study identified four molecular subtypes of BA with distinct prognosis and biological processes. According to the pathological characteristics of the different subtypes, individualized perioperative and preoperative treatment may be a new strategy to improve the prognosis of BA.

Project description:The Colorectal Cancer Subtyping Consortium established four Consensus Molecular Subtypes (CMS) in colorectal cancer: CMS1 (microsatellite-instability [MSI], Immune), CMS2 (Canonical, epithelial), CMS3 (Metabolic), and CMS4 (Mesenchymal). However, in the clinical practice only patients with MSI tumors have experienced a change in the management of their disease. Proteomics has experimented significant technical advances with the implementation of data-independent acquisition (DIA) as the gold-standard for the analysis of large cohort of patients, providing a dynamic framework of the disease and direct information about the effectors of the biological processes. The aim of this study is the characterization of the proteome of colon cancer CMS by DIA–based proteomics and expand the utility of CMS in the clinical practice. CMS were assigned and differences in the protein profiles between CMS were defined and evaluated using a Systems Biology approach. One hundred fifty-eight paraffin samples from colon cancer patients with stage II-III disease treated with adjuvant chemotherapy were analyzed. After applying quality criteria, 1,940 identified and quantified proteins were used for the analyses. CMS1 had an overexpression of proteins related to immune system, specifically neutrophils, phagocytosis and antimicrobial response, and a glycolytic profile. CMS3 had an overexpression of proteins related to metabolic pathways, while CMS2 showed an intermediate protein profile between CMS1-3, but with a characteristic profile regarding translation, adhesion, and mitochondria and metabolism biological processes. Two proteomics subtypes within CMS2 with different protein characteristics and prognosis were identified. CMS4 was the most different group with an overexpression of proteins related to angiogenesis, extracellular matrix, focal adhesion, and complement activation. In conclusion, DIA LC-MS/MS proteomics characterization of CRC CMS established new features for each CMS, such as a higher mitochondrial activity in CMS2 or an overexpression of proteins related to phagocytosis in CMS1, but also confirmed previous findings established using transcriptomics such as an overexpression of proteins related to angiogenesis in CMS4. In this CMS4 subtype, several adhesion-related proteins suggested a high metastatic profile and a possible chemoresistance that may explain the worse prognosis of this subtype. CMS1 was characterized as immune and glycolitic-active subtype, being the combination of immunotherapy and glycolytic inhibitors a possible therapeutic strategy. Additionally, two different CMS2 proteomics-based subtypes with different prognosis and possible therapeutic targets were identified. These proccesses could be used as therapeutic targets in the future.

Dataset Information

An Integrated Approach for Identifying Molecular Subtypes in Human Colon Cancer Using Gene Expression Data.

Publications

An Integrated Approach for Identifying Molecular Subtypes in Human Colon Cancer Using Gene Expression Data.

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