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.

Background: Despite remarkable progress in clinical management of patients and intensified screening, colorectal cancer remains the second most common cause of cancer-related death in the United States. The recent surge of next generation sequencing has enabled genomic analysis of colorectal cancer genomes. However, to date, there is little information about leveraging gene expression data and integrating it with somatic mutation information to discover potential biomarkers and therapeutic targets. Here, we integrated gene expression data with somatic mutation information to discover potential diagnostic and prognostic biomarkers and molecular drivers of colorectal cancer. Methods: We used publicly available gene expression and somatic mutation data generated on the same patient populations from The Cancer Genome Atlas. We compared gene expression data between tumors and controls and between dead and alive. Significantly differentially expressed genes were evaluated for the presence of somatic mutations and subjected to functional enrichment analysis to discover molecular networks and signaling pathways enriched for somatic mutations. Results: The investigation revealed a signature of somatic mutated genes transcriptionally associated with colorectal cancer and a signature of significantly differentially expressed somatic mutated genes distinguishing dead from alive. Enrichment analysis revealed molecular networks and signaling pathways enriched for somatic mutations. Conclusions: Integrative bioinformatics analysis combining gene expression with somatic mutation data is a powerful approach for the discovery of potential diagnostic and prognostic biomarkers and potential drivers of colorectal cancer.