ArticleInternational journal of general medicine2025
Identification and Validation of Key Genes Involved in the Coupling of Mitochondria-Associated Endoplasmic Reticulum Membrane in Hemorrhoidal Disease.
Article in International journal of general medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A Synthetic Lethality-Informed Multi-Omic Framework for Identifying a Five-Gene Diagnostic Signature in Chronic Obstructive Pulmonary Disease.Current issues in molecular biology · 2026Article
- Structural and Functional Alterations of MAMs and Their Immunomodulatory Roles in Sepsis-Induced Lung Injury.Journal of immunology research · 2026Review
- Endoplasmic reticulum stress in skeletal muscle dysfunction of type 2 diabetes: mechanisms and therapeutic implications.Frontiers in endocrinology · 2026Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, Conclusion: This study identifies
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