Integrating single-cell sequencing and machine learning to uncover the role of mitophagy in subtyping and prognosis of esophageal cancer

Abstract

Globally, esophageal cancer stands as a prominent contributor to cancer-related fatalities, distinguished by its poor prognosis. Mitophagy has a significant impact on the process of cancer progression. This study investigated the prognostic significance of mitophagy-related genes (MRGs) in esophageal carcinoma (ESCA) to elucidate molecular subtypes. By analyzing RNA-seq data from The Cancer Genome Atlas (TCGA), 6451 differentially expressed genes (DEGs) were identified. Cox regression analysis narrowed this list to 14 MRGs with potential prognostic implications. ESCA patients were classified into two distinct subtypes (C1 and C2) based on these genes. Furthermore, leveraging the differentially expressed genes between Cluster 1 and Cluster 2, ESCA patients were classified into two novel subtypes (CA and CB). Importantly, patients in C2 and CA subtypes exhibited inferior prognosis compared to those in C1 and CB (p < 0.05). Functional enrichments and immune microenvironments varied significantly among these subtypes, with C1 and CB demonstrating higher immune checkpoint expression levels. Employing machine learning algorithms like LASSO regression, Random Forest and XGBoost, alongside multivariate COX regression analysis, two core genes: HSPD1 and MAP1LC3B were identified. A prognostic model based on these genes was developed and validated in two external cohorts. Additionally, single-cell sequencing analysis provided novel insights into esophageal cancer microenvironment heterogeneity. Through Coremine database screening, Icaritin emerged as a potential therapeutic candidate to potentially improve esophageal cancer prognosis. Molecular docking results indicated favorable binding efficacies of Icaritin with HSPD1 and MAP1LC3B, contributing to the understanding of the underlying molecular mechanisms of esophageal cancer and offering therapeutic avenues.