Integrated bioinformatics analysis of prognostic significance and molecular regulation of mitochondrial metabolism-related genes in colorectal cancer.

Authors

Xinyu Li, Kai Zhang, Taotao Liu, Lina Liu, Huan He, Zeyu Xu, Xiaohui Xi, Ke Wang, Xiaoliang Li

Published in

Translational gastroenterology and hepatology. Volume 11. Pages 97. Epub Aug 31, 2026.

Abstract

The development and progression of colorectal cancer (CRC) are closely associated with metabolic reprogramming, in which mitochondrial metabolism is critically associated with tumor cell survival and proliferation. This study aims to provide novel insights for mitochondrial metabolism-related genes (MMRGs)-based prognosis prediction, biomarker discovery, and the development of targeted therapeutic strategies in CRC.
Based on multi-omics data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) (including single-cell RNA-seq), we systematically analyzed MMRGs through differential expression screening, functional enrichment, least absolute shrinkage and selection operator (LASSO) regression modeling, and independent validation, supplemented by cell-cell communication, immune infiltration, drug sensitivity, and regulatory network analyses.
We identified 7 major cell types and uncovered the immune microenvironment characteristics in CRC patients. We constructed a novel MMRGs-centric prognostic signature (CD36/ACOX1) validated in TCGA and GEO cohorts, effectively stratifying patients into distinct risk groups with significant survival differences. The risk model demonstrated independent prognostic value, correlated with distinct immune landscapes and genomic features. Regulatory network analysis revealed miRNA/TF interactions targeting CD36/ACOX1, while drug sensitivity screening identified 81 therapeutic agents with enhanced efficacy in high-risk patients. Pseudotime trajectory analysis further uncovered novel B-cell differentiation dynamics associated with prognostic gene expression.
The mitochondrial metabolic signature CD36/ACOX1 enables robust survival stratification in CRC. This clinically applicable model informs tumor microenvironment dynamics and precision oncology frameworks for CRC.

PMID:
42866842
Bibliographic data and abstract were imported from PubMed on 14 Oct 2026.

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