<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Wang L</submitter><funding>Shanghai Hospital Development Center</funding><pagination>1352</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9407481</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>13(8)</volume><pubmed_abstract>Objective: Endometriosis is a benign gynecological disease characterized by distant metastasis. Previous studies have discovered abnormal numbers and function of immune cells in endometriotic lesions. We aimed to find potential biomarkers of endometriosis and to explore the relationship between ASPN and the immune microenvironment of endometriosis. Methods: We obtained the GSE141549 and GSE7305 datasets containing endometriosis and normal endometrial samples from the Gene Expression Omnibus database (GEO). In the GSE141549 dataset, differentially expressed genes (DEGs) were found. The Least Absolute Shrinkage and Selection Operator (Lasso) regression and generalized linear models (GLMs) were used to screen new biomarkers. The expression levels and diagnostic utility of biomarkers were asse</pubmed_abstract><journal>Genes</journal><pubmed_title>ASPN Is a Potential Biomarker and Associated with Immune Infiltration in Endometriosis.</pubmed_title><pmcid>PMC9407481</pmcid><funding_grant_id>SHDC12019113</funding_grant_id><pubmed_authors>Sun J</pubmed_authors><pubmed_authors>Wang L</pubmed_authors></additional><is_claimable>false</is_claimable><name>ASPN Is a Potential Biomarker and Associated with Immune Infiltration in Endometriosis.</name><description>Objective: Endometriosis is a benign gynecological disease characterized by distant metastasis. Previous studies have discovered abnormal numbers and function of immune cells in endometriotic lesions. We aimed to find potential biomarkers of endometriosis and to explore the relationship between ASPN and the immune microenvironment of endometriosis. Methods: We obtained the GSE141549 and GSE7305 datasets containing endometriosis and normal endometrial samples from the Gene Expression Omnibus database (GEO). In the GSE141549 dataset, differentially expressed genes (DEGs) were found. The Least Absolute Shrinkage and Selection Operator (Lasso) regression and generalized linear models (GLMs) were used to screen new biomarkers. The expression levels and diagnostic utility of biomarkers were asse</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Jul</publication><modification>2025-04-27T02:28:33.787Z</modification><creation>2025-02-19T04:37:49.378Z</creation></dates><accession>S-EPMC9407481</accession><cross_references><pubmed>36011263</pubmed><doi>10.3390/genes13081352</doi></cross_references></HashMap>