Mitochondrial-Localized Protein Transcript Abundance can Predict the Prognosis of Endometrial Carcinoma: A Retrospective Analysis

Abstract

Background: Uterine corpus endometrial carcinoma (UCEC) is among the most common malignant tumors affecting women’s reproductive systems. Patients’ postoperative survival results differ greatly because of the significant heterogeneity of UCEC. The activity of mitochondria in UCEC and normal endometrium was shown to be substantially different. The objective of this research was the creation of better tools for predicting UCEC patient survival to provide more accurate and effective treatment strategies. Methods: The UCEC RNA sequencing data was accessed at the Cancer Genome Atlas project, containing 539 UCEC samples and 35 tumor-adjacent tissue. The differentially expressed genes (DEGs) were identified through the R package ‘limma’. The mitochondrial protein genes were subjected to a Cox regression analysis using the absolute shrinkage and selection operator (LASSO). The differences (variations) in the biological processes between the patient groups were examined through gene set variation analysis (GSVA). Results: Results of gene set enrichment analysis (GSEA) analysis revealed that mitochondria-related pathways were more active in endometrial cancer than in tumor-adjacent tissue. Through the screening of LASSO-cox and multi-cox analysis, we obtained 14 mitochondrial protein genes (CKMT1B, CYP27A1, GPX1, GPX4, GRPEL2, HPDL, MALSU1, MRPS5, NDUFC1, OPA3, OXSM, POLRMT, SAMM50, TOMM40L) related to patient prognosis. Based on the expression levels of these 14 genes in each patient, we developed a new scoring algorithm. Compared with the traditional TNM classification system, the algorithm has better accuracy in predicting patient prognosis. Moreover, a nomogram was constructed through the combination of the scoring algorithm and the patient’s clinical features. Conclusions: The scoring algorithm based on mitochondrial gene expression can assist clinicians in predicting the postoperative survival rate of patients, allowing them to devise more precise treatment programs.