Identification and Validation of Key Genes Involved in the Coupling of Mitochondria-Associated Endoplasmic Reticulum Membrane in Hemorrhoidal Disease.

Background: Hemorrhoidal disease (HD) is the most prevalent rectal disorder, with various cellular processes influenced by the mitochondria-associated endoplasmic reticulum membrane (MAM). Potential therapeutic mechanisms for HD may be associated with MAM. This study aims to identify key genes linked to MAM in HD and to provide novel therapeutic targets.

Methods: Transcriptome data and MAM-related genes (MAM-RGs) were obtained from the Gene Expression Omnibus (GEO) database and relevant literature. Differential expression analysis and single-sample Gene Set Enrichment Analysis (ssGSEA) scores were initially employed to identify candidate genes. Key genes were further refined using Least Absolute Shrinkage and Selection Operator (LASSO) and Protein-Protein Interaction (PPI) networks. A nomogram based on these key genes was developed and assessed. Additionally, CIBERSORT algorithms were utilized to evaluate immune cell infiltration abundance, differences, and correlations in the samples. Finally, the expression of key genes was validated via reverse transcription-quantitative PCR (RT-qPCR).

Results: Differential expression analysis identified 956 differentially expressed genes (DEGs), and ssGSEA identified 143 differentially expressed MAM-RGs. A total of 50 candidate genes were selected through their intersection. Machine learning identified two key genes, and . A nomogram with strong predictive capability was constructed. Immune cell analysis revealed two types of differential immune cells-activated dendritic cells and plasma cells-where activated dendritic cells were more highly expressed in the case group, and plasma cells showed a strong positive correlation with . Additionally, was significantly overexpressed in patients with HD, while exhibited down-regulation compared to controls.

Conclusion: This study identifies and as key genes associated with HD and MAM and presents a predictive nomogram with high accuracy. These findings provide novel insights into the mechanisms and potential treatment targets for HD.