In:
Frontiers in Immunology, Frontiers Media SA, Vol. 14 ( 2023-3-20)
Abstract:
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 & lt;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 & lt;0.05), and risk-related genes were successfully verified by qPCR (t test, p & lt;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.
Type of Medium:
Online Resource
ISSN:
1664-3224
DOI:
10.3389/fimmu.2023.1142609
DOI:
10.3389/fimmu.2023.1142609.s001
DOI:
10.3389/fimmu.2023.1142609.s002
DOI:
10.3389/fimmu.2023.1142609.s003
DOI:
10.3389/fimmu.2023.1142609.s004
DOI:
10.3389/fimmu.2023.1142609.s005
DOI:
10.3389/fimmu.2023.1142609.s006
DOI:
10.3389/fimmu.2023.1142609.s007
DOI:
10.3389/fimmu.2023.1142609.s008
DOI:
10.3389/fimmu.2023.1142609.s009
DOI:
10.3389/fimmu.2023.1142609.s010
DOI:
10.3389/fimmu.2023.1142609.s011
DOI:
10.3389/fimmu.2023.1142609.s012
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2023
detail.hit.zdb_id:
2606827-8
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