Integrated bioinformatics analysis uncovers characteristic genes and molecular subtyping system for endometriosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36059959", "kind": "science-record", "revision": 4, "content_hash": "sha256:ff2fb360b23d8ee7f561b3b51b237db9f8ed111bed17effbf210b8ebf8a9a7a8", "created_at": "2026-11-01T18:15:15Z", "updated_at": "2026-11-01T22:07:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated bioinformatics analysis uncovers characteristic genes and molecular subtyping system for endometriosis.", "doi": "10.3389/fphar.2022.932526", "pmid": "36059959", "authors": ["Zhaowei Wang", "Jia Liu", "Miaoli Li", "Lishan Lian", "Xiaojie Cui", "Tai-Wei Ng", "Maoshu Zhu"], "journal": "Frontiers in pharmacology", "year": 2022, "abstract": "Objective: Endometriosis is a chronic inflammatory estrogen-dependent disease with the growth of endometrial tissues outside the uterine cavity. Nevertheless, the etiology of endometriosis is still unclear. Integrated bioinformatics analysis was implemented to reveal the molecular mechanisms underlying this disease. Methods: A total of four gene expression datasets (GSE7305, GSE11691, GSE23339, and GSE25628) were retrieved from the GEO, which were merged into a meta-dataset, followed by the removal of batch effects via the sva package. Weighted gene co-expression network analysis (WGCNA) was implemented, and endometriosis-related genes were screened under normal and endometriosis conditions. Thereafter, characteristic genes were determined via Lasso analysis. The diagnostic performance was estimated via receiver operating characteristic curves, and epigenetic and post-transcriptional modifications were analyzed. Small molecular compounds were predicted. Unsupervised clustering analysis was conducted via non-negative matrix factorization algorithm. The enriched pathways were analyzed via gene set enrichment analysis or GSVA. Immune features were evaluated according to immune-checkpoints, HLA, receptors, chemokines, and immune cells. Results: In total, four characteristic genes (BGN, AQP1, ELMO1, and DDR2) were determined for endometriosis, all of which exhibited the favorable efficacy in diagnosing endometriosis. Their aberrant levels were modulated by epigenetic and post-transcriptional modifications. In total, 51 potential drugs were predicted against endometriosis. The characteristic genes exhibited remarkable associations with immunological function. Three subtypes were classified across endometriosis, with different mechanisms and immune features. Conclusion: Our study reveals the characteristic genes and novel molecular subtyping of endometriosis, contributing to the early diagnosis and intervention in endometriosis.", "claims": [{"id": "claim-method", "statement": "Through Lasso Cox regression algorithm, over-fitting risk was minimized with the glmnet package. The alteration trajectory of each variable was assessed and 10-fold cross-validated.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36059959", "locator": "sections[6] \"Screening characteristic genes\" paragraph 2", "quote": "Through Lasso Cox regression algorithm, over-fitting risk was minimized with the glmnet package. The alteration trajectory of each variable was assessed and 10-fold cross-validated."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36059959", "locator": "sections[29] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The AUCs (95%CI) of AQP1, BGN, DDR2, and ELMO1 were 0.96 (1.00–0.89), 0.98 (1.00–0.95), 1.00 (1.00–0.99), and 0.99 (1.00–0.98), respectively, demonstrating that each characteristic gene enabled to diagnose endometriosis accurately and sensitively (Figures 4D–G).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The AUCs (95%CI) of AQP1, BGN, DDR2, and ELMO1 were 0.96 (1.00–0.89), 0.98 (1.00–0.95), 1.00 (1.00–0.99), and 0.99 (1.00–0.98), respectively, demonstrating that each characteristic gene enabled to diagnose endometriosis accurately and sensitively (Figures 4D–G)."}, "evidence": [{"asset": "source:pmid:36059959", "locator": "sections[17] \"Identification of four characteristic genes in endometriosis\" paragraph 2", "quote": "Through Lasso algorithm, four characteristic genes were determined among the shared endometriosis-related genes, containing BGN, AQP1, ELMO1, and DDR2 (Figures 4A,B)."}, {"asset": "source:pmid:36059959", "locator": "sections[17] \"Identification of four characteristic genes in endometriosis\" paragraph 2", "quote": "The AUCs (95%CI) of AQP1, BGN, DDR2, and ELMO1 were 0.96 (1.00–0.89), 0.98 (1.00–0.95), 1.00 (1.00–0.99), and 0.99 (1.00–0.98), respectively, demonstrating that each characteristic gene enabled to diagnose endometriosis accurately and sensitively (Figures 4D–G)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The AUCs (95%CI) of AQP1, BGN, DDR2, and ELMO1 were 0.96 (1.00–0.89), 0.98 (1.00–0.95), 1.00 (1.00–0.99), and 0.99 (1.00–0.98), respectively, demonstrating that each characteristic gene enabled to diagnose endometriosis accurately and sensitively (Figures 4D–G).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36059959"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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