Protein‑protein interaction analysis to identify biomarker networks for endometriosis

Xiao H, Hong Xiao, Lihua Yang, Yang L, Jianjun Liu, Liu J, Jiao Yang, Yang Jiao, Jiao Y, Lin Lu, Lu L, Hongbo Zhao
Experimental and therapeutic medicine · 2017 · vol. 14(5) , pp. 4647–4654 · doi:10.3892/etm.2017.5185 · PMID:29201163 · PMC5704338 · W2758576304
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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 · read from full text ⓘ

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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Intro

Endometriosis is a benign gynecological disorder that occurs in 10% of women of reproductive age ( 1 ). The main symptoms include infertility and chronic pelvic pain ( 2 ). Although there are a number of studies on endometriosis, the majority of the mechanisms are not well understood ( 3 – 6 ). Identifying disease biomarkers and their interaction networks is important to improve the understanding of the causes of endometriosis, as well as to improve medical care. Several databases have been developed that store associations between genes and diseases, such as the Online Mendelian Inheritance in Man ( 7 ), the Human Gene Mutation Database ( 8 ) and the Genetic Association Database ( 9 ). Due to the nature of the database curation process, the data are incomplete. Some gene-disease databases that combine gene-associated diseases from several expert, public and curated data sources also exist ( 10 , 11 ). With the rapid accumulation of gene-disease data, increasing research has been utilizing the gene-disease database as a start-point to mine disease biomarkers ( 12 – 14 ). Protein-protein interaction (PPI) networks include information on the biological processes and molecular functions of cells and have been widely used to characterize the underlying mechanisms of genes associated with complex diseases ( 15 , 16 ). The majority of human diseases are caused by a group of correlated molecules or a network, rather than a single gene ( 17 ). Thus, identification and validation of biomarker networks is critical to disease diagnosis, prognosis and treatment. In the present study, a disease network of endometriosis that integrated human PPIs and known disease-causing genes was constructed. Endometriosis-causing genes were identified from gene-disease databases. Subsequently, bioinformatics approaches, including PPI network construction, module analysis, functional enrichment analysis and text mining, were utilized in the research. The results of the present study may provide new targets for endometriosis therapy and identify the potential mechanisms of the disease.

Results

A total of 271 and 229 genes were extracted from Genotator and DisGeNET, respectively. The common genes, of which there were 100, were used as seed genes to generate functional modules. Of 100 input genes, 96 were found in GenBank ( https://www.ncbi.nlm.nih.gov/genbank/ ), and network clustering identified six major sub network modules from the original PPI network ( Fig. 1 ). Hub genes in each module were identified ( Table I ). A total of 2,429 genes from the KEGG human database were added to the PPI network and genes in each module were selected for pathway enrichment analysis. The top 10 significantly enriched KEGG pathways for the six modules in endometriosis are demonstrated in Table II . Module A was a cancer cell proliferation module. The majority of the pathways in the first module were related to the proliferation of cancer cells and were associated with pathways in cancer, the cell cycle, oocyte meiosis, adherens junctions and the Wnt signaling pathway. The enriched pathways in module B were associated with the immune system and infectious diseases, including cytokine-cytokine receptor interaction, the mitogen-activated protein kinase signaling pathway, the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway, the intestinal immune network for immunoglobulin (Ig) A production and Toll-like receptor signaling pathways. Module C was associated with complement and coagulation cascades, extracellular matrix-receptor interaction, focal adhesion, and proteasome and hematopoietic cell lineages associated with immune and metastasis. The enriched pathways in module D were associated with inflammatory responses, including phagosome, cell adhesion molecules, antigen processing and presentation, natural killer cell mediated cytotoxicity, T cell receptor signaling pathways and the intestinal immune network for IgA production. The majority of the pathways in module E were related to processes of replication and repair, including DNA replication, base excision repair, nucleotide excision repair, mismatch repair and homologous recombination. A total of 15 genes, seven in the first module and eight in the second module, have previously been reported in literature to be candidate biomarkers for endometriosis ( Fig. 2 ). For example, women with endometriosis had significantly higher SOX2 expression levels compared to controls ( Fig. 2A ) ( 28 ). Also, various genes identified in the second module ( Fig. 2B ), including CASP3, S100A13 and IL1R2, have been reported to be associated with endometriosis ( 29 – 31 ). Details for the 15 literature-confirmed potential endometriosis biomarkers are listed in Table III .

Discussion

The cause of endometriosis is not entirely understood. No single theory is able to explain all cases of endometriosis. The present study implemented PPI for endometriosis biomarker network analysis and identified biologically relevant functional modules. A number of genes and pathways identified in the modules have already been reported to participate in the pathogenesis of endometriosis ( 32 – 36 ). Although endometriosis is a benign disorder, several common characteristics of this disease are shared with invasive cancer ( 37 ). Previous epidemiologic studies have demonstrated that women with endometriosis have an increased risk of ovarian and breast cancer ( 38 , 39 ). Coincidentally, the three chromosomal regions (9p, 11q and 22q) that have demonstrated loss of heterozygosity in ovarian endometriosis were the same that were observed in ovarian tumors ( 40 ). These studies have demonstrated that the inactivation of tumor suppressor genes has an important role in the development of endometriosis. The results of the present study demonstrated that expression of cancer-related pathways are significantly imbalanced in endometriosis in module A. The hub genes identified were AHR, AR, ARNT, ESR1, NRIP1, ESR2, TP53, NR5A1, NR3C1 and PGR. The enriched pathways in module B were associated with the immune system and infectious diseases. The presence of proinflammatory cytokines in the peritoneal fluid of patients with endometriosis has been reported in previous studies ( 41 – 43 ). Cytokines may regulate the actions of leukocytes in the peritoneal fluid or may act directly on the ectopic endometrium ( 44 ). Dysregulation of the JAK-STAT pathway is associated with various immune disorders ( 45 ), which was also demonstrated in the results of the present study. IL10RA, IL15, IL10 and JAK3 from the Toll-like receptor signaling pathway and CASP1, IL18, IL1B and TRAF6 from the NOD-like receptor signaling pathway, which are important for generating mature proinflammatory cytokines, were also identified in this module and are confirmed by previous studies ( 35 , 46 ). Module B also included the osteoclastogenesis pathway, which is predominantly regulated by signaling pathways activated by immune receptors ( 47 ). Matrix metalloproteinases (MMPs) are a family of proteolytic enzymes that share a conserved domain structure. MMPs are capable of degrading various types of extracellular matrix (ECM) and serve an important function in tissue remodeling associated with various physiological and pathological processes ( 48 ). The expression of several MMPs is maximal during the menstrual phase in the human endometrium ( 49 ). MMPs also have a vital role in the pathogenesis of endometriosis and cancer, particularly in the processes of metastasis and invasion ( 33 , 50 ). MMP1, MMP7, MMP12, MMP13, IGF1, IGFBP1, PAPPA and TIMP2 were identified as the hub genes in module C. ECM-receptor interaction, focal adhesion and proteasomes were also identified in this module, as in previous studies ( 32 , 51 , 52 ). The immune response is one of the major factors influencing pathogenesis of endometriosis. Numerous genes in the fourth module are involved in the function of the immune system. Hub genes in this module are members of the HLA gene family, including HLA-A, -B, -C, -DPB1, -DQA1, -DQB1 and -DRB1, which have key roles in the immune response, and it appears that endometriosis shares many similarities with autoimmune diseases ( 34 , 53 ). It has been demonstrated that patients with endometriosis display a significantly higher expression of HLA I and II molecules compared with individuals without endometriosis ( 54 ). Oxidative stress has been proposed as a potential factor involved in the pathophysiology of endometriosis ( 55 ). Accumulation of reactive oxygen species may induce cellular injury, such as DNA damage. The present study demonstrated that the majority of the pathways in module E were related to replication and repair. APEX1, OGG1, XRCC1, ERCC2 and ERCC5 were the seed genes identified in this module. APEX1 and XRCC1 are key genes involved in the base excision repair pathway, which removes DNA adducts induced predominantly by oxidation and alkylation ( 56 ). APEX1 is an essential enzyme and has a central role in the DNA repair system; however, a study by Hsu et al ( 57 ) demonstrated that APEX1 Asp148Glu was not associated with endometriosis in patients in Taiwan. Future studies may confirm the association between APEX1 and the risk of endometriosis. XRCC1 has been demonstrated to physically interact with several enzymes known to be involved in the repair of single-strand breaks in DNA ( 58 ). A study by Hsieh et al ( 36 ) indicated that XRCC1 Arg399Gln polymorphism is correlated with a higher susceptibility to endometriosis. In conclusion, the pathogenesis of endometriosis is likely multifactorial. The present study constructed a disease network of endometriosis that integrated human protein-protein interactions and known disease-causing genes. The present study has identified a number of biological mechanisms that may be associated with endometriosis. Further studies on the specific function and interactions of the genes in related modules are required to improve the understanding of endometriosis.

Materials|Methods

Endometriosis-related genes were obtained from Genotator ( http://genotator.hms.harvard.edu/ ) ( 10 ) and DisGeNET ( http://www.disgenet.org ) ( 11 ). For each tool, gene lists were extracted using the query term, endometriosis. Genotator provides high quality gene-disease associations based upon data from 11 trustworthy resources. DisGeNET is a discovery platform that integrates information on gene-disease associations from several public data sources and literature ( 11 ). Thus, a list of genes that had been experimentally validated to be associated with endometriosis were obtained. Endometriosis-associated genes were submitted to atBioNet ( https://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm ) and PPIs were obtained. atBioNet is a network analysis tool that provides a systematic insight into gene interactions by examining significant functional modules ( 18 ). The default option is ‘Human Database’ that combines data from a variety of public PPI sources, including BioGRID ( 19 ), the Database of Interacting Proteins ( 20 ), the Human Protein Reference Database ( 21 ), IntAct ( 22 ), the Molecular INTeraction database ( 23 ), REACTOME ( 24 ) and the Signaling Pathways Integrated Knowledge Engine ( 25 ). The protein interaction network included 12,043 human proteins and 132,605 interactions. SCAN algorithm was used to identify functional modules and perform assessment of generated gene networks for biomarker discovery ( 26 ). To identify potential roles of genes in endometriosis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) ( 27 ) pathway analysis component in atBioNet was used. Overrepresented KEGG pathways for each module were ranked according to the P-value obtained from Fisher's exact tests. To identify the genes associated with endometriosis, mining from the PubMed database ( https://www.ncbi.nlm.nih.gov/pubmed ) with keywords ‘gene symbol’ and ‘endometriosis’ was conducted. Subsequently, the articles associated with endometriosis were screened manually. A high number of papers indicated that the relationship between potential biomarker genes and endometriosis is well studied and documented.

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