Journal Article Epublish
PubMed: 40188118 DOI: 10.1186/s12905-025-03697-0
Identifying key palmitoylation-associated genes in endometriosis through genomic data analysis.
BMC Womens Health 25(1):161
Authors: Jinyan Kai, Jiaqi Su, Yinping You, Xiaoliang Liang, Haitao Huang, Jie Fang, Qiong Chen
Affiliations
- Department of Clinical Medical Laboratory, The Affiliated Second Hospital of Xiamen Medical College, Xiamen, Fujian, China.
- Department of Pathology, The Affiliated Second Hospital of Xiamen Medical College, Xiamen, Fujian, China.
- Department of Microbiology, Guilin Medical University, Guilin, Guangxi, China.
- Department of Laboratory Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Traditional Chinese Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine (Originally Named "Shanghai First People's Hospital"), Shanghai, China.
Abstract
BACKGROUND
Palmitoylation, a post-translational lipid modification, has garnered increasing attention for its role in inflammatory processes and tumorigenesis. Emerging evidence suggests a potential association between palmitoylation and inflammatory responses in the pathogenesis of endometriosis. However, the precise mechanistic interplay remains elusive, necessitating further investigation.
METHODS
This study integrated transcriptomic analysis and Mendelian randomization (MR) to identify a causal gene set implicated in endometriosis. Differentially expressed genes (DEGs) were first identified in the training dataset using the limma package in R. Weighted gene co-expression network analysis (WGCNA) was subsequently performed, leveraging Single Sample Gene Set Enrichment Analysis (ssGSEA)-derived scores of palmitoylation-related genes (PRGs) as phenotypic traits to identify key modular genes. The intersection of these key modular genes with DEGs yielded a refined gene set. Machine learning algorithms were then applied to further optimize gene selection, followed by external validation, immune infiltration analysis, RNA network construction, and exploration of potential targeted drug candidates.
RESULTS
Through a rigorous screening process, VRK1, GALNT12, and RMI1 emerged as key genes associated with palmitoylation, exhibiting significant downregulation in endometriosis samples (P < 0.05), indicative of a potential protective role. Immune infiltration analysis further revealed strong correlations between these genes and M2 macrophages as well as resting Natural Killer (NK) cells. Additionally, investigations into the targeted RNA network and drug association profiling provided novel insights, laying the groundwork for future high-quality validation studies.
CONCLUSIONS
This study employed a comprehensive analytical framework to identify palmitoylation-associated key genes in endometriosis. The integration of immunoinfiltration analysis, RNA network construction, and drug association profiling offers valuable insights for advancing clinical diagnostics, disease monitoring, and therapeutic development in endometriosis.
MeSH
Endometriosis, Genes, Pharmaceutical Preparations, Data Analysis