Journal of Developmental Medicine(Electronic Version)  2025, Vol. 13 Issue (3): 167-174 DOI: 10.3969/j.issn.2095-5340.2025.03.002 |
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| Bioinformatics analysis of the pathogenic mechanisms of premature ovarian insufficiency |
| Bai Zheqiao, Jiang Honghong, Zhang Wendan, et al
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(1. MedicalSchool of Chinese PLA Medical School, Beijing 100853, China; 2. Department of Pediatrics, the Seventh Medical Center of Chinese PLA General Hospital, Beijing 100700, China; 3. Department of Obstetrics andGynecology, the Seventh Medical Center of Chinese PLA General Hospital, Beijing 100700, China)
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Abstract 【Abstract】 Objective The in-depth analysis of transcriptome microarray data from patients with premature ovarian insufficiency (POI) using bioinformatics methods aims to identify the characteristic genes and key pathways of the disease, with a view to providing a more precise understanding of the molecular genetic mechanism ofPOI, and thus providing new ideas for its therapeutic means. Method In this study, the GSE201276 dataset was selected from the Gene Expression Omnibus (GEO) database, and the resulting expression profiles weredifferentially analyzed using high-throughput sequencing to screen for differentially expressed gene (DEG), and subjected to Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathwayanalysis. Next, weighted gene co-expression network analysis (WGCNA) was constructed for the expression profiling dataset, and then modular genes highly correlated with specific traits were selected from the WGCNAnetwork and intersected with DEG to identify key POI-related genes, and construct the protein-protein interaction(PPI) network, and then obtain the key genes related to POI. Result A total of 352 DEG (59 up-expressed genesand 293 down-expressed genes) and 18 key genes (CDK1, TOP2A, CCNB2, CENPA, BIRC5, CCNB1, KIF2C,DLGAP5, MKI67, KIF4A, CDCA8, NUSAP1, CENPF, UBE2C, PBK, HJURP, SPAG5, AURKB). According tothe results of KEGG pathway analysis, these DEG were significantly enriched mainly in 13 relevant pathways,including cell cycle, oocyte meiosis, cytokine-cytokine receptor interactions, p53 signaling pathway, and Wntsignaling pathway. Conclusion In this study, a bioinformatics approach was used to successfully reveal the key
genes of POI. These identified key genes also provide new potential targets for the treatment of POI
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Received: 26 January 2025
Published: 31 May 2025
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