Computational Drug Discovery in Patients with Endometriosis-Induced Infertility via Text Mining and Biomedical Databases

Yingying Lin, Zhiwei Chen, Jianping Huang, Jiaoning Fang, Yuanjie Qi, Li Xie, Li Chen, Hua Li
In: Research Square · 2021 · doi:10.21203/rs.3.rs-744890/v1 · W3191764733
preprint OA: green CC0
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This study used text mining and bioinformatic tools to identify 49 existing drugs targeting 39 genes that may treat endometriosis-induced infertility.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-09 ⓘ

This preprint used text mining of published biomedical literature to derive differentially expressed genes associated with endometriosis and infertility, then intersected these sets to focus on EMT-induced infertility targets. Using GeneCodis for GO and KEGG pathway enrichment, STRING and Cytoscape/MCODE to identify a significant protein–protein interaction network module (with medium confidence), and DGIdb to map pathway-relevant genes to existing drug–gene interactions, the authors reported 550 common EMT/infertility genes and 39 potentially targetable genes linked to 49 drugs not previously used for EMT-induced infertility. A stated limitation is that the work is an in silico computational analysis based on publicly available databases and preprint-level methods, without experimental validation in patients or models. This paper is centrally about endometriosis—computational drug discovery for endometriosis-induced infertility using text mining and biomedical pathway/database analyses.

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Abstract

Abstract Background: Endometriosis (EMT) is the most common benign gynecological disease among women of reproductive age, causing infertility and seriously affects women's physical and mental health. However, the current treatment was not always effective. This study was designed to use publicly available data to identify drugs targeting the relevant gene with EMT-induced-infertility using computational tools. Methods: EMT and infertility genes were determined by text mining, and the GeneCodis program was used to analyzed gene ontology of the intersection of the two gene sets. A string database was used to analyze the protein-protein interaction network. The Drug-Gene Interaction database is queried for the rich gene set belonging to the identified pathways to find drug candidates that can be used in EMT-induced infertility. Results: Our analysis identified 550 genes common to both the EMT and infertility by text mining. Gene enrichment analysis and protein-protein interaction analysis found 39 genes potentially targetable by a total of 49 drugs that could be formulated for application, which have not been used in EMT-induced infertility. Conclusions: The findings from the present analysis can facilitate the Identification of existing drugs that have the potential of topical administration to improve EMT-induced infertility and present tremendous opportunities to study novel targets pharmacology using in silico text mining and pathway analysis tools.

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Condition tags

endometriosisinfertility

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (22)

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