Leveraging Multi-Model Machine Learning Algorithms for Tumor–Normal Classification and Discovery of Biomarkers in Colorectal Cancer Using Multi-Omics Data.

Document Type

Article

Publication Date

5-1-2026

Publication Title

Cancers

Abstract

Simple Summary: Colorectal cancer is the second leading cause of cancer-related deaths worldwide. A critical unmet medical need in clinical management of colorectal cancer centers around the discovery of biomarkers, therapeutic targets, predictors of survival outcome, and the development of more accurate algorithms to identify individuals at high risk of developing aggressive disease, who could be prioritized for treatment. With the availability of multi-omics data, we are now well-positioned to address this critical unmet medical need. Here, we leveraged multi-model integrative Machine Learning algorithms using RNA-Seq and somatic mutation data for tumor–normal classification and the identification of clinically relevant diagnostic biomarkers and molecular targets. Machine Learning algorithms accurately classified tumor–normal samples and identified a signature for potential clinically actionable diagnostic biomarkers, therapeutic targets, and predictors of survival outcome. Background: Despite remarkable progress in clinical management and screening, colorectal cancer (CRC) remains a major cause of cancer-related deaths worldwide. Sadly, both the number of CRC incidences and the mortality rate are trending upwards, particularly in younger individuals. There is an urgent need for the identification of reliable diagnostic biomarkers and therapeutic targets, and the development of accurate algorithms to guide therapeutic decision-making at the point of care. Here, we leverage multi-model integrative Machine Learning (ML) algorithms using RNA-Seq and somatic mutation data for the classification of tumor–normal samples and the discovery of potential biomarkers and therapeutic targets. Methods: We used RNA sequencing (RNA-Seq) and somatic mutation data from The Cancer Genome Atlas (TCGA) for the development of classification models and the discovery of biomarkers and therapeutic targets. The models were validated using two independent datasets. Results: ML algorithms accurately classified tumor samples and identified a signature for 58 genes, which could serve as potential diagnostic biomarkers. Functional analysis revealed the Wnt and GPCR signaling pathways enriched for somatic mutations. Conclusions: Multi-model integrative ML algorithms integrating gene expression with somatic mutation data represent a powerful approach to the classification of tumor samples and the discovery of biomarkers.

First Page

1503

Volume

18

Issue

10

Publisher

MDPI

Rights

© 2026 by the authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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