Characterization of key genes and immune cell infiltration associated with endometriosis through integrating bioinformatics and experimental analyses
Ying Peng, Xiangdong She, Ying Peng

TL;DR
This study identifies three key genes and explores immune cell involvement in endometriosis, offering potential new targets for diagnosis and treatment.
Contribution
The study combines bioinformatics and experimental methods to discover novel therapeutic targets for endometriosis.
Findings
Three key genes (APLNR, HLA-DPA1, and AP1S2) were identified as potentially important in endometriosis.
EM immune cell infiltration is closely linked to these three genes.
APLNR was shown to reduce cell viability and increase apoptosis in endometriosis cells in vitro.
Abstract
Endometriosis (EM) is the most common gynecological disease in women of childbearing age. This study aims to identify key genes and screen drugs that may contribute to EM treatment. The differentially expressed genes (DEGs) were identified using limma analysis in the GSE11691 dataset. The protein–protein network (PPI) was constructed. Four machine learning methods, including LASSO, SVM-RFE, random forest, and Boruta, were applied to identify the key genes associated with EM. Flow cytometry, wound healing, and migration assays were applied to assess the cell functions of APLNR on hEM15A. The immune cell infiltration of each sample in EM was calculated using a single-sample gene set enrichment analysis (ssGSEA) algorithm. The potential drugs were screened using the Connectivity Map (CMAP) database, based on the DEGs. Finally, the expression levels of the three genes were further…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsEndometriosis Research and Treatment · Reproductive System and Pregnancy · Endometrial and Cervical Cancer Treatments
