ArticleEuropean journal of medical research2024
Multi-omics cluster defines the subtypes of CRC with distinct prognosis and tumor microenvironment.
Article in European journal of medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 16 citations in OpenAlex.
- [Multi-omics integration deciphers immune-metabolic heterogeneity in colorectal cancer: a prognostic model and therapeutic strategies targeting ANGPTL4/FABP4/RBP7].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Integrative In Silico and FFPE Tissue Analyses Elucidate Upregulated Genes in Colorectal Cancer Enriched for Tie2-Expressing Macrophages/Monocytes.International journal of molecular sciences · 2026Article
- Machine Learning Reveals the Association Between Gene Expression and Immune Infiltration in Colorectal Cancer: A Comprehensive Study From Single-Cell to Survival Analysis.Journal of cellular and molecular medicine · 2026Article
- Article
- Molecular subtyping and prognostic modeling of colon adenocarcinoma based on programmed cell death features: a multi-omics and machine learning study.Frontiers in immunology · 2026Article
- Integrated multi-omics characterization of SPTBN2 overexpression reveals its pro-tumorigenic role and immune microenvironment remodeling in colorectal cancer.Frontiers in cell and developmental biology · 2026Article
- Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning.Scientific reports · 2025Article
- Multi-omics clustering analysis carries out the molecular-specific subtypes of thyroid carcinoma: implicating for the precise treatment strategies.Genes and immunity · 2025Article
- Integration of 101 machine learning algorithm combinations to unveil m6A/m1A/m5C/m7G-associated prognostic signature in colorectal cancer.Scientific reports · 2025Article
- Machine learning-based analysis identifies a 13-gene prognostic signature to improve the clinical outcomes of colorectal cancer.Journal of gastrointestinal oncology · 2024Article
- Exploring Predictive and Prognostic Biomarkers in Colorectal Cancer: A Comprehensive Review.Cancers · 2024Review
- An E3 ligase TRIM1 promotes colorectal cancer progression via K63-linked ubiquitination and activation of HIF1α.Oncogenesis · 2024Article
- Dynamics of the immune microenvironment and immune cell PANoptosis in colorectal cancer: recent advances and insights.Frontiers in immunology · 2024Review
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9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer (CRC) is a complex malignancy characterized by diverse molecular profiles, clinical outcomes, and limited precision in prognostic markers. Addressing these challenges, this study utilized multi-omics data to define consensus molecular subtypes in CRC and elucidate their association with clinical outcomes and underlying biological processes.
methodsConsensus molecular subtypes were obtained by applying ten integrated multi-omics clustering algorithms to analyze TCGA-CRC multi-omics data, including mRNA, lncRNA, miRNA, DNA methylation CpG sites, and somatic mutation data. The association of subtypes with prognoses, enrichment functions, immune status, and genomic alterations were further analyzed. Next, we conducted univariate Cox and Lasso regression analyses to investigate the potential prognostic application of biomarkers associated with multi-omics subtypes derived from weighted gene co-expression network analysis (WGCNA). The function of one of the biomarkers MID2 was validated in CRC cell lines.
resultsTwo CRC subtypes linked to distinct clinical outcomes were identified in TCGA-CRC cohort and validated with three external datasets. The CS1 subtype exhibited a poor prognosis and was characterized by higher tumor-related Hallmark pathway activity and lower metabolism pathway activity. In addition, the CS1 was predicted to have less immunotherapy responder and exhibited more genomic alteration compared to CS2. Then a prognostic model comprising five genes was established, with patients in the high-risk group showing substantial concordance with the CS1 subtype, and those in the low-risk group with the CS2 subtype. The gene MID2, included in the prognostic model, was found to be correlated with epithelial-mesenchymal transition (EMT) pathway and distinct DNA methylation patterns. Knockdown of MID2 in CRC cells resulted in reduced colony formation, migration, and invasion capacities.
conclusionThe integrative multi-omics subtypes proposed potential biomarkers for CRC and provided valuable knowledge for precision oncology.
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