Construction of a Novel Mitochondria-Associated Gene Model for Assessing ESCC Immune Microenvironment and Predicting Survival

Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent malignant tumors in the digestive system, ranking as the sixth leading cause of cancer-related deaths globally. To investigate gene associations with ESCC, the dataset GSE20347 was downloaded from the Gene Expression Omnibus (GEO) database, and weighted gene co-expression network analysis (WGCNA) was applied to identify genes significantly linked to ESCC. Additionally, 91 transcriptomic expression profiles and corresponding clinical data were acquired from The Cancer Genome Atlas (TCGA) database. A mitochondria-associated risk (MAR) model was then developed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, with validation performed using the GSE161533 dataset. The MAR model was further used to examine the tumor microenvironment and drug sensitivity in ESCC.

To verify the model’s predictive capacity, we built a prognostic model and evaluated its accuracy. The MAR model revealed significant differences in immune cell infiltration and tumor microenvironment characteristics Wnt-C59 between high- and low-risk groups. It also demonstrated a strong correlation between risk scores and common immunological checkpoints. In terms of drug efficacy, AZD1332 and AZD7762 were more beneficial for low-risk patients, while Entinostat, Nilotinib, Ruxolitinib, and Wnt.c59 were more effective for high-risk patients. Furthermore, knockdown of the TYMS gene significantly suppressed the proliferation and invasion of ESCC cells in vitro. Overall, the MAR model offers reliable prognostic insights and may serve as a valuable biomarker for personalized treatment strategies in ESCC patients.