{"title":"Identification and Validation of Key Genes Involved in the Coupling of Mitochondria-Associated Endoplasmic Reticulum Membrane in Hemorrhoidal Disease.","authors":"Lihua Mao, Zhiying Rao, Yanru Wang, Jun Yang, Junmei He, Zhi Zheng, Lanyu Chen","doi":"10.2147/IJGM.S511281","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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, <i>MUC16</i> and <i>DEFA5</i>. 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 <i>DEFA5</i>. Additionally, <i>MUC16</i> was significantly overexpressed in patients with HD, while <i>DEFA5</i> exhibited down-regulation compared to controls.</p><p><strong>Conclusion: </strong>This study identifies <i>MUC16</i> and <i>DEFA5</i> 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.</p>","PeriodicalId":14131,"journal":{"name":"International Journal of General Medicine","volume":"18 ","pages":"2781-2798"},"PeriodicalIF":2.0000,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12136070/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of General Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/IJGM.S511281","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
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, MUC16 and DEFA5. 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 DEFA5. Additionally, MUC16 was significantly overexpressed in patients with HD, while DEFA5 exhibited down-regulation compared to controls.
Conclusion: This study identifies MUC16 and DEFA5 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.
期刊介绍:
The International Journal of General Medicine is an international, peer-reviewed, open access journal that focuses on general and internal medicine, pathogenesis, epidemiology, diagnosis, monitoring and treatment protocols. The journal is characterized by the rapid reporting of reviews, original research and clinical studies across all disease areas.
A key focus of the journal is the elucidation of disease processes and management protocols resulting in improved outcomes for the patient. Patient perspectives such as satisfaction, quality of life, health literacy and communication and their role in developing new healthcare programs and optimizing clinical outcomes are major areas of interest for the journal.
As of 1st April 2019, the International Journal of General Medicine will no longer consider meta-analyses for publication.