Identification and validation of mitophagy-related signatures as a novel prognostic model for colorectal cancer

Abstract

Background Colorectal cancer (CRC) is one of the most commonly diagnosed cancers in the world. Mitophagy is associated with tumorigenesis and development of malignancy. However, the specific role of mitophagy has yet not been systematically explored in CRC. Methods The RNA-sequencing dataset of CRC from The Cancer Genome Atlas (TCGA) and microarray data of gene expression profiles of CRC from Gene Expression Omnibus (GEO) were downloaded. Mitophagy-related gene sets were obtained from the Pathway Unification database. The package “limma” was used for differential gene expression analysis. Kaplan-Meier (KM) survival analyses were utilized to evaluate the prognostic value of the mitophagy regulators. Single-sample gene set enrichment analysis (ssGSEA) was used to estimate the infiltrating immune cells and the activity of immune response. The ConsensusClusterPlus algorithm was used to determine mitophagy-related subtypes. Principal component analysis (PCA) was used to create composite measurement of mitophagy scores. The R packages “survminer” and “ReGlot” were used to plot the nomogram and calibration curves. Results Integrated analysis of the GEO and TCGA databases revealed some common differentially expressed genes (DEGs) in CRC. MFN2 , UBB , PINK1 , and PRKN were significantly downregulated in CRC samples as compared to normal samples, and other genes were significantly upregulated in CRC samples. KM survival analyses showed that high expression of ATG12 and MAP1LC3B predicted a poor prognosis, whereas high expression of TOMM22 and TOMM40 predicted a better prognosis. Mitophagy showed significant correlation with immune-related pathways in CRC samples. We identified 2 distinct CRC subtypes with different mitophagy accumulation, of which subtype B had better prognosis and immune activity. The mitophagy score may be employed as a new and efficient clinical predictor in conjunction with other clinical indicators to predict the prognosis of CRC patients. Conclusions We systematically investigated the CRC heterogeneity with reference to mitophagy based on bioinformatics analyses, and the findings of this study might provide some guidance for future research into potential biomarkers for diagnosis and prognosis prediction of CRC patients.