Human mutation
Identification of Immuno-Diagnostic and Therapeutic Markers in Tumors and Inflammatory Diseases Based on Single-Cell and Machine Learning Methods
By Tang J, Yuan D, Wu Z and 2 others.
Abstract
Background
Chemotherapy is the mainstay of systemic treatment for colorectal cancer (CRC), but drug resistance and tumor heterogeneity markedly limit survival. Mitochondrial metabolism is increasingly recognized as a regulator of tumor progression, immune contexture, and therapeutic response. However, the prognostic relevance of mitochondrial metabolism-related transcriptional patterns and their relationships with chemotherapy-response signatures and immune features in CRC remain incompletely understood.
Methods
Clinical and transcriptomic data from The Cancer Genome Atlas colorectal cancer cohort (TCGA-CRC) and Gene Expression Omnibus cohorts GSE17537 and GSE104645 were analyzed. A curated set of mitochondrial metabolism-related genes (MMRGs) were used to calculate an MMRG score, whereas genes upregulated in chemotherapy-sensitive cases in GSE104645 were used to derive a drug-sensitivity gene-set score by single-sample gene set enrichment analysis (ssGSEA). Weighted gene coexpression network analysis (WGCNA), differential expression analysis, univariate Cox regression, least absolute shrinkage and selection operator regression, and multivariate Cox regression were used to construct a five-gene RiskScore. Prognostic associations were assessed using Kaplan-Meier curves, time-dependent receiver operating characteristic curves, external validation, and subgroup analyses. Immune-cell enrichment and pRRophetic-predicted half-maximal inhibitory concentration values were evaluated, and CD36 was examined using in vitro phenotypic assays.