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Bioinformatics analysis based on pelvic organ prolapse related aging genes of GEO Database and LASSO regression algorithm
Minqi NING,Yong HE,Bingshu LI,Guotao HUANG,Xiaohu ZUO,Zhihan ZHAO,Wuyue HAN,Li HONG
PDF(2432 KB)
PDF(2432 KB)
Bioinformatics analysis based on pelvic organ prolapse related aging genes of GEO Database and LASSO regression algorithm
)Objective To screen the aging genes closely associated with pelvic organ prolapse （POP） by bioinformatics techniques， and to clarify the potential clinical significance and value of key genes. Methods Gene Expression Omnibus （GEO） Database was used to download the datasets GSE53868 and GSE151188 for POP-related genes with the keyword “pelvic organ prolapse”. The aging-related genes were obtained from Aging Atlas， CellAge， and the Human Ageing Genomic Resources （HAGR） Databases；the intersection of genes related with POP in two groups provided a list of differentially expressed genes （DEGs） associated with aging in POP； gene Set Enrichment Analysis （GSEA） was conducted with R software version 4.2.1； Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） signaling pathway enrichment analysis of DEGs were conducted by the Database for Annotation， Visualization and Integrated Discovery （DAVID）； the protein-protein interaction （PPI） network was constructed with Cytoscape 3.9.1 software；the top 10 Hub genes were selected by cytoHubba plugin； the infiltration of 22 types of immune cells in the patients in POP group and control group was analyzed by CIBERSORT deconvolution method using R software；the key genes were further screened by LASSO regression algorithm； the correlation and diagnostic efficacy between key genes and immune cell infiltration were analyzed. Results From the Aging Atlas， CellAge， and HAGR Databases， 724 aging-related genes were identified. Intersection with the POP expression profile yielded an aging gene expression matrix related to POP containing 624 genes， and 29 POP-related DEGs were identified after differential analysis， including 2 upregulated genes and 27 downregulated genes. The GSEA results showed that the upregulated pathways were mainly related to diabetes and cellular senescence， whereas the downregulated pathways included Alzheimer’s disease and hypoxia-inducible factor-1 （HIF-1） signaling pathways.The GO functional enrichment analysis mainly enriched in the biological processes such as the response of the cells to lipopolysaccharide， inflammatory response， and negative regulation of cell proliferation. The KEGG signaling pathway enrichment analysis mainly enriched in interleukin-17 （IL-17）， tumor necrosis factor （TNF）， and nuclear factor-kappa B （NF-κB） signaling pathways. The PPI network analysis got 10 Hub genes including interleukin-6 （IL-6）， interleukin-1B （IL-1B）， prostaglandin-endoperoxide synthase 2 （PTGS2）， and NF-kappa-B inhibitor alpha （NFKBIA）. The CIBERSORT deconvolution method results showed a relatively higher infiltration proportion of neutrophils and activated mast cells in the patients in POP group， the activated mast cells had a positive correlation with most of the DEGs （r>0.5） and the macrophages had a significant positive correlation with IL-1B （r>0.6）. The key genes Jun D proto-oncogene （JUND）， Snail homolog 1 （SNAI1）， amphiregulin （AREG）， Lamin A/C （LMNA）， and superoxide dismutase 2 （SOD2） selected by LASSO regression analysis had high diagnostic efficacies， and the area under receiver operating characteristic curve （ROC） （AUC） were all greater than 0.75. Conclusion During the aging process，the genes such as JUND，SNAI1，AREG，LMNA，and SOD2 may participate in the pathophysiology of POP through various pathways，including inflammation-related pathways，transcription regulation，and affecting collagen secretion and metabolism，thereby influence the connective tissue support function and promote the occurrence and development of POP.
Pelvic organ prolapse / Bioinformatics / Differential genes / Enrichment analysis
R711.23
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