Text Mining and Hub Gene Network Analysis of Endometriosis

Abstract

This study is aimed at systematically characterizing the endometriosis-associated genes based on text mining and at annotating the functions, pathways, and networks of endometriosis-associated hub genes. We extracted endometriosis-associated abstracts published between 1970 and 2020 from the PubMed database. A neural-named entity recognition and multitype normalization tool for biomedical text mining was used to recognize and normalize the genes and proteins embedded in the abstracts. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted to annotate the functions and pathways of recognized genes. Protein-protein interaction analysis was conducted on the genes significantly cooccurring with endometriosis to identify the endometriosis-associated hub genes. A total of 433 genes were recognized as endometriosis-associated genes ( $P<0.05$ ), and 154 pathways were significantly enriched ( $P<0.05$ ). A network of endometriosis-associated genes with 278 gene nodes and 987 interaction links was established. The 15 proteins that interacted with 20 or more other proteins were identified as the hub proteins of the endometriosis-associated protein network. This study provides novel insights into the hub genes that play key roles in the development of endometriosis and have implications for developing targeted interventions for endometriosis.