Integrated omics analysis identifies mitochondria and hormone metabolism linked biomarkers governing esophageal squamous cell carcinoma progression and microenvironment crosstalk

Esophageal squamous cell carcinoma (ESCC) is potentially linked to sex hormone-mediated mitochondrial metabolic dysregulation. This study conducted a comprehensive investigation of the genes related to mitochondria and hormone metabolism during ESCC pathogenesis. We integrated bulk transcriptomics and machine learning to identify the candidate genes. SVM-RFE and LASSO regression were employed to determine the key biomarkers, and a predictive nomogram was established. Immune infiltration was assessed utilizing CIBERSORT. Biomarker expression was validated by RT-qPCR and in the transcriptomic cohort. scRNA-seq was utilized to illustrate the role of biomarkers in ESCC progression. Integrated transcriptomic analysis revealed 11 dysregulated mitochondria and hormone metabolism-related genes in ESCC, from which five core biomarkers (CYP11A1, ALDH9A1, CYP27B1, RDH13 and CYP11B1) were determined by machine learning algorithms. These genes demonstrated significant predictive value, and a combined nomogram achieved superior predictive performance (AUC = 0.996). Multi-platform validation in tumors consistently showed downregulation of ALDH9A1, CYP11A1, and RDH13, with upregulation of CYP27B1 and CYP11B1. Functional enrichment analysis linked these expression changes to the dysregulation of key oncogenic pathways and altered immune microenvironment profiles. Five mitochondria-hormone axis biomarkers (CYP11A1, ALDH9A1, CYP27B1, RDH13 and CYP11B1) were identified as key drivers of ESCC progression through metabolic dysregulation, transcriptional networks and microenvironment crosstalk.