10-Gene Prognostic Signature and Subtypes of Colon Adenocarcinoma via Multi-algorithm Consensus Learning

Yiming Wang

doi:10.63313/hmt.9023

Authors

Yiming Wang Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, China Author

DOI:

https://doi.org/10.63313/hmt.9023

Keywords:

Colon Adenocarcinoma, Multi-Omics, Molecular Subtyping

Abstract

Colon adenocarcinoma (COAD) exhibits profound molecular heterogeneity and limited targeted therapy options, necessitating precision medicine strategies based on tumor molecular profiles to improve advanced disease outcomes. Leveraging multi-omics data from TCGA-COAD, we integrated ten clustering algorithms with ten machine learning methods to develop a CMLS prognostic model. Multi-omics clustering stratified COAD into four subtypes, with CS2 demonstrating optimal survival. The ten-gene CMLS signature (GDI1, CLK1, SEMA4C, RBP7, HSPA1A, CALB2, DCBLD2, ITLN1, ATOH1, UQCRC1) enabled risk stratification by median scores, revealing superior survival. This study provides a valuable supplementary approach for the precise management of COAD, which is expected to enrich current clinical decision-making strategies.

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