Protein‑protein interaction analysis to identify biomarker networks for endometriosis

Hong Xiao, Lihua Yang, Jianjun Liu, Jiao Yang, Yang Jiao, Lin Lu, Hongbo Zhao
Experimental and therapeutic medicine · 2017 · vol. 14(5) , pp. 4647–4654 · doi:10.3892/etm.2017.5185 · PMID:29201163 · PMC5704338 · W2758576304
article OA: gold CC0 ⤵ 4 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This study integrated human protein-protein interactions and disease-causing genes to construct biomarker networks for endometriosis, identifying modules enriched in pathways related to cell proliferation, immune response, inflammation, and DNA repair.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-08 ⓘ

The study constructed an endometriosis biomarker network by integrating experimentally associated endometriosis genes mined from Genotator and DisGeNet (seed set of 100 common genes) with human protein–protein interaction data using atBioNet, followed by functional module detection and KEGG pathway enrichment. From the input, 96 genes mapped to six functional modules, with module-level pathway themes including cancer-cell proliferation, immune/infectious signaling (e.g., JAK-STAT, Toll-like and NOD-like receptor pathways), inflammation, and replication/repair processes; 15 genes in modules A and B were reported as candidate biomarkers in prior literature. A key limitation is that the work is computational and depends on the completeness/accuracy of the input gene–disease associations and the PPI database, rather than presenting direct experimental validation. This paper is centrally about endometriosis — it identifies protein-protein interaction–based biomarker networks and enriched functional modules for endometriosis.

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Abstract

The identification of biomarkers and their interaction network involved in the processes of endometriosis is a critical step in understanding the underlying mechanisms of the disease. The aim of the present study was to construct biomarker networks of endometriosis that integrated human protein-protein interactions and known disease-causing genes. Endometriosis-associated genes were extracted from Genotator and DisGeNet and biomarker network and pathway analyses were constructed using atBioNet. Of 100 input genes, 96 were strongly mapped to six major modules. The majority of the pathways in the first module were associated with the proliferation of cancer cells, the enriched pathways in module B were associated with the immune system and infectious diseases, module C included pathways related to immune and metastasis, the enriched pathways in module D were associated with inflammatory processes, and the majority of the pathways in module E were related to replication and repair. The present approach identified known and potential biomarkers in endometriosis. The identified biomarker networks are highly enriched in biological pathways associated with endometriosis, which may provide further insight into the molecular mechanisms underlying endometriosis.

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endometriosis

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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.

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