ArticleFrontiers in immunology2023
Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Exploring the mystery of colon cancer from the perspective of molecular subtypes and treatment.Scientific reports · 2024Article
- Immunotherapy and Cancer: The Multi-Omics Perspective.International journal of molecular sciences · 2024Review
- Application progress of artificial intelligence in tumor diagnosis and treatment.Frontiers in artificial intelligence · 2024Review
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
- Genetic Variants in the Mitochondrial Thymidylate Biosynthesis Pathway Increase Colorectal Cancer Risk.Current oncology (Toronto, Ont.) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Colon 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. Methods: In 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. Results: Three 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. Conclusion: In 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.
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