Identification of necroptosis & mitophagy-related key genes and their prognostic value in colorectal cancer - Xiuling Zhang; Li Meng; Tingjian Zu; Qian Zhou (Discover Oncology)
Abstract Background Our study aimed to elucidate the potential necroptotic&mitophagy-related key genes in colorectal cancer (COAD) by bioinformatics analysis and identify their prognostic value in COAD. Methods Firstly, we integrated the cancer genome atlas (TCGA) and gene expression omnibus (GEO) datasets to identify necroptosis & mitophagy-related differentially expressed genes (N&MRDEGs) in COAD using “TCGAbiolinks” and “GEOquery” packages. Secondly, the obtained data were used for differential expression analysis using the “limma” package, and further functional enrichment analysis using the “clusterProfiler” package. Then, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were utilized to explore pathway associations of the N&MRDEGs. Thirdly, the predictive model was developed utilizing LASSO (Least absolute shrinkage and selection regression) regression implemented through the “glmnet” package and validated via Kaplan–Meier analysis. Finally, we validated the function of the key genes by receiver operating characteristic (ROC) curve analysis, multivariate cox proportional hazards model and COAD cell lines. Results There is a strong association between the 4 key genes (UCHL1, HSPA1A, MAPK8 , and PLEC) of COAD and the necroptotic&mitophagy, which were found to be lowly mRNA level in COAD cell lines. Among them, PLEC exhibited a pronounced contribution to the utility of the model in the TCGA database and UCHL1 has excellent diagnostic potential with an area under the curve (AUC) greater than 0.9. Conclusions The perspective of bioinformatics analysis provides robust evidence suggested that UCHL1, HSPA1A, MAPK8, and PLEC genes are the prognostic biomarkers of COAD, the predictive model established herein provides a novel tool for risk stratification in clinical practice and serves as a foundation for further investigation into its underlying molecular mechanisms.