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, Hua Li, Li Chen
In: Clinical and Experimental Obstetrics & Gynecology · 2022 · vol. 49(8) · doi:10.31083/j.ceog4908168 · W4286472374
article OA: gold CC0 ⤵ 1 in-corpus citation
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⚙ AI-generated summary by claude@2026-06, 2026-06-06 ⓘ

This study utilized text mining and bioinformatics databases to identify 49 existing drugs targeting 39 genes that could potentially treat endometriosis-induced infertility.

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

The paper used biomedical text mining of published literature to identify differentially expressed genes related to endometriosis and to infertility, then intersected these gene sets (550 shared genes). The authors applied GeneCodis for GO and KEGG enrichment (with a stringent p-value cutoff), STRING plus Cytoscape/MCODE for protein-protein interaction network/module prioritization, and DGIdb to map enriched pathway genes to known drug–gene interactions, yielding 39 potentially targetable genes linked to 49 candidate drugs. A major limitation is that all results are derived from online bioinformatics databases and therefore require validation experiments, and some highlighted drugs may have toxicity concerns with safety data needed before any human work. This paper is centrally about endometriosis — computational drug discovery for endometriosis-induced infertility using text mining and pathway/drug-target databases.

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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. However, all the results were based on online bioinformatics databases, and as such require validation experiments. And some of the drugs highlighted as possibly relevant may be toxic and as such safely data is required before any experiments are undertaken in humans.

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endometriosisinfertility

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