Identification of differentially expressed genes and signaling pathways involved in endometriosis by integrated bioinformatics analysis

Identification of potential diagnostic biomarkers and therapeutic targets for endometriosis based on bioinformatics and machine learning analysis

PURPOSE

Endometriosis (EMs) is a major gynecological condition in women. Due to the absence of definitive symptoms, its early detection is very challenging; thus, it is crucial to find biomarkers to ease its diagnosis and therapy. Here, we aimed to identify potential diagnostic and therapeutic targets for EMs by constructing a regulatory network and using machine learning approaches.

METHODS

Three Gene Expression Omnibus (GEO) datasets were merged, and differentially expressed genes (DEGS) were identified after preprocessing steps. Using the DEGs, a transcription factor (TF)-mRNA-miRNA regulatory network was constructed, and hub genes were detected based on four different algorithms in CytoHubba. The hub genes were used to build a GaussianNB diagnostic model and also in docking analysis that were performed using Discovery Studio and AutoDock Vina software.

RESULTS

A total of 119 DEGs were identified between EMs and non-EMs samples. A regulatory network consisting of 52 mRNAs, 249 miRNAs, and 37 TFs was then constructed. The diagnostic model was introduced using the hub genes selected from the network (GATA6, HMOX1, HS3ST1, NFASC, and PTGIS) that its area under the curve (AUC) was 0.98 and 0.92 in the training and validation cohorts, respectively. Based on docking analysis, two chemical compounds, rofecoxib and retinoic acid, had potential therapeutic effects on EMs.

CONCLUSION

In conclusion, this study identified potential diagnostic and therapeutic targets for EMs which demand more experimental confirmations.

Identification of NFASC and CHL1 as Two Novel Hub Genes in Endometriosis Using Integrated Bioinformatic Analysis and Experimental Verification

Background

Endometriosis (EMS) is a common and highly recurrent gynecological disease characterized by chronic pain and infertility. There are no definitive therapies for endometriosis since the pathogenesis remains undetermined. This study aimed to identify EMS-related functional modules and hub genes by integrated bioinformatics analysis.

Methods

Three endometriosis expression profiling series (GSE25628, GSE23339, and GSE7305) were obtained from Gene Expression Omnibus (GEO). The EMS-related module was constructed by weighted gene co-expression network analysis (WGCNA), followed by Gene Ontology (GO) enrichment analyses. Cytohubba and the MCODE plug-ins of Cytoscape were used to screen out the hub genes, which were verified via receiver operating characteristic (ROC) curves. Immunohistochemistry was performed to verify the protein expression of the hub genes in ectopic endometrial tissues. Moreover, CIBERSORT was used to analyze the relationship between the abundance of immune cells infiltration and the expression of hub genes.

Results

Among the 18 modules obtained, the darkmagenta module was identified as the EMS-related module, genes of which were significantly enriched to terms referring to cell migration and neurogenesis. NFASC and CHL1 were screened out and prioritized as hub genes through Cytoscape and confirmed to be differentially upregulated in ectopic endometrial samples. Finally, the expression of hub genes was related to the abundance of immune cells infiltration. The higher expression of NFASC or CHL1 correlated with increased M2 macrophages and decreased natural killer (NK) cells in ectopic lesions.

Conclusion

This study provided new insights into the molecular factors underlying the pathogenesis of endometriosis and provided a theoretical basis for the potential that the two hub genes, NFASC and CHL1, might be novel biomarkers and therapeutic targets in the future.

Bioinformatical analysis of the key differentially expressed genes and associations with immune cell infiltration in development of endometriosis

Background

This study explored the key genes related to immune cell infiltration in endometriosis.

Results

The Gene Expression Omnibus (GEO) datasets (GSE7305, GSE7307, and GSE11691), containing a total of 37 endometriosis and 42 normal tissues, were retrieved and analyzed to determine the differentially expressed genes (DEGs). Gene ontology (GO) annotations and Kyoto Encyclopedia of Genes (KEGG) analysis were performed to identify the pathways that were significantly enriched. The xCell software was used to analyze immune cell infiltration and correlation analyses were performed to uncover the relationship between key genes and immune cells. The analysis identified 1031 DEGs (581 upregulated and 450 downregulated DEGs), while GO analysis revealed altered extracellular matrix organization, collagen-containing extracellular matrix, and glycosaminoglycan binding and KEGG enrichment showed genes related to metabolic pathways, pathways in cancer, phosphatidylinositol 3-kinase-protein kinase B (PI3K-Akt) signaling, proteoglycans in cancer, and the mitogen-activated protein kinase (MAPK) signaling pathway. Furthermore, the protein–protein interaction network revealed 10 hub genes, i.e., IL6, FN1, CDH1, CXCL8, IGF1, CDK1, PTPRC, CCNB1, MKI67, and ESR1. The xCell analysis identified immune cells with significant changes in all three datasets, including CD4⁺ and CD8⁺ T cells, CD8⁺ Tem, eosinophils, monocytes, Th1 cells, memory B-cells, activated dendritic cells (aDCs), and plasmacytoid dendritic cells (pDCs). These 10 hub genes were significantly associated with at least three types of immune cells.

Conclusions

Aberrant gene expression was related to abnormal infiltration of different immune cells in endometriosis and was associated with endometriosis development by affecting the tissue microenvironment and growth of ectopic endometrial cells.

Identification of Candidate Gene Signatures and Regulatory Networks in Endometriosis and its Related Infertility by Integrated Analysis

Endometriosis is a common gynecological disease associated with infertility, and it represents an economic burden worldwide. However, the molecular mechanisms underlying endometriosis development have not yet been fully elucidated. Here, we aimed to identify reliable key genes and the related regulatory network that may be involved in endometriosis. Differentially expressed genes (DEGs) were identified through integrated analysis of four expression datasets of endometriosis from Gene Expression Omnibus. Gene functional analysis and protein-protein interaction network construction were performed to reveal the potential function of DEGs. Subsequently, candidate hub genes were defined and validated in GSE105764 dataset, and the associated regulatory networks were constructed. Additionally, GSE120103 dataset was applied to identify the differential expression between the infertile and fertile groups of patients with stage IV endometriosis. Finally, real-time quantitative polymerase chain reaction analysis was performed to identify the differential expression of hub genes in the collected clinical specimens. Robust rank aggregation integrated analysis determined 158 DEGs. Epithelial cell differentiation was the most significantly enriched biological process, and leukocyte transendothelial migration was the most significantly enriched pathway. Eight hub genes including CLDN3, CLDN5, CLDN7, CLDN11, HOXC8, HOXC6, HOXB6, and HOXB7 were identified, and most of these were validated as abnormally expressed genes in both the infertile group and patients with endometriosis. Transcriptional factors and microRNAs related to these genes were identified. Altogether, our integrated analysis identified critical gene signatures, involved pathways, and regulatory networks, which could provide clinically significant insights into the molecular mechanisms underlying endometriosis and its related infertility.

Five immune-related genes as diagnostic markers for endometriosis and their correlation with immune infiltration

Endometriosis (EMS) is a chronic disease that can cause dysmenorrhea, chronic pelvic pain, and infertility, among other symptoms. EMS diagnosis is often delayed compared to other chronic diseases, and there are currently no accurate, easily accessible, and non-invasive diagnostic tools. Therefore, it is important to elucidate the mechanism of EMS and explore potential biomarkers and diagnostic tools for its accurate diagnosis and treatment. In the present study, we comprehensively analyzed the differential expression, immune infiltration, and interactions of EMS-related genes in three Homo sapiens datasets. Our results identified 332 differentially expressed genes (DEGs) associated with EMS. Gene ontology analysis showed that these changes mainly focused on the positive regulation of endometrial cell proliferation, cell metabolism, and extracellular space, and EMS involved the integrin, complement activation, folic acid metabolism, interleukin, and lipid signaling pathways. The LASSO regression model was established using immune DEGs with an area under the curve of 0.783 for the internal dataset and 0.656 for the external dataset. Five genes with diagnostic value, ACKR1, LMNB1, MFAP4, NMU, and SEMA3C, were screened from M1 and M2 macrophages, activated mast cells, neutrophils, natural killer cells, follicular T helper cells, CD8⁺, and CD4⁺ cells. A protein-protein interaction network based on the immune DEGs was constructed, and ten hub genes with the highest scores were identified. Our results may provide a framework for the development of pathological molecular networks in EMS.

Identification and analysis of novel endometriosis biomarkers via integrative bioinformatics

Endometriosis is a gynecological disease prevalent in women of reproductive age, and it is characterized by the ectopic presence and growth of the eutopic endometrium. The pathophysiology and diagnostic biomarkers of endometriosis have not yet been comprehensively determined. To discover molecular markers and pathways underlying the pathogenesis of endometriosis, we identified differentially expressed genes (DEGs) in three Gene Expression Omnibus microarray datasets (GSE11691, GSE23339, and GSE7305) and performed gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analyses. We also validated the identified genes via immunohistochemical analysis of tissues obtained from patients with endometriosis or healthy volunteers. A total of 118 DEGs (79 upregulated and 39 downregulated) were detected in each dataset with a lower (fold change) FC cutoff (log2|FC| > 1), and 17 DEGs (11 upregulated and six downregulated) with a higher FC cutoff (log2|FC| > 2). KEGG and GO functional analyses revealed enrichment of signaling pathways associated with inflammation, complement activation, cell adhesion, and extracellular matrix in endometriotic tissues. Upregulation of seven genes (C7, CFH, FZD7, LY96, PDLIM3, PTGIS, and WISP2) out of 17 was validated via comparison with external gene sets, and protein expression of four genes (LY96, PDLIM3, PTGIS, and WISP2) was further analyzed by immunohistochemistry and western blot analysis. Based on these results, we suggest that TLR4/NF-κB and Wnt/frizzled signaling pathways, as well as estrogen receptors, regulate the progression of endometriosis. These pathways may be therapeutic and diagnostic targets for endometriosis.

Screening differentially expressed genes between endometriosis and ovarian cancer to find new biomarkers for endometriosis

AIM

Endometriosis is one of the most common reproductive system diseases, but the mechanisms of disease progression are still unclear. Due to its high recurrence rate, searching for potential therapeutic biomarkers involved in the pathogenesis of endometriosis is an urgent issue.

METHODS

Due to the similarities between endometriosis and ovarian cancer, four endometriosis datasets and one ovarian cancer dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) analyses. Then, we validated gene expression and performed survival analysis with ovarian serous cystadenocarcinoma (OV) datasets in TCGA/GTEx database, and searched for potential drugs in the Drug-Gene Interaction Database. Finally, we explored the miRNAs of key genes to find biomarkers associated with the recurrence of endometriosis.

RESULTS

In total, 104 DEGs were identified in the endometriosis datasets, and the main enriched GO functions included cell adhesion, extracellular exosome and actin binding. Fifty DEGs were identified between endometriosis and ovarian cancer datasets including 11 consistently regulated genes, and nine DEGs with significant expression in TCGA/GTEx. Only IGHM had both significant expression and an association with survival, three module DEGs and two significantly expressed DEGs had drug associations, and 10 DEGs had druggability.

CONCLUSIONS

ITGA7, ITGBL1 and SORBS1 may help us understand the invasive nature of endometriosis, and IGHM might be related to recurrence; moreover, these genes all may be potential therapeutic targets.KEY MESSAGEThis manuscript used a bioinformatics approach to find target genes for the treatment of endometriosis.This manuscript used a new approach to find target genes by drawing on common characteristics between ovarian cancer and endometriosis.We screened relevant therapeutic agents for target genes in the drug database, and performed histological validation of target genes with both expression and survival analysis difference in cancer databases.

Bioinformatics Analysis Identifies Molecular Markers Regulating Development and Progression of Endometriosis and Potential Therapeutic Drugs

Endometriosis, a common disease that presents as polymorphism, invasiveness, and extensiveness, with clinical manifestations including dysmenorrhea, infertility, and menstrual abnormalities, seriously affects quality of life in women. To date, its underlying etiological mechanism of action and the associated regulatory genes remain unclear. This study aimed to identify molecular markers and elucidate mechanisms underlying the development and progression of endometriosis. Specifically, we downloaded five microarray expression datasets, namely, GSE11691, GSE23339, GSE25628, GSE7305, and GSE105764, from the Gene Expression Omnibus (GEO) database. These datasets, obtained from endometriosis tissues, alongside normal controls, were subjected to in-depth bioinformatics analysis for identification of differentially expressed genes (DEGs), followed by analysis of their function and pathways via gene ontology (GO) and KEGG pathway enrichment analyses. Moreover, we constructed a protein–protein interaction (PPI) network to explore the hub genes and modules, and then applied machine learning algorithms support vector machine-recursive feature elimination and least absolute shrinkage and selection operator (LASSO) analysis to identify key genes. Furthermore, we adopted the CIBERSORTx algorithm to estimate levels of immune cell infiltration while the connective map (CMAP) database was used to identify potential therapeutic drugs in endometriosis. As a result, a total of 423 DEGs, namely, 233 and 190 upregulated and downregulated, were identified. On the other hand, a total of 1,733 PPIs were obtained from the PPI network. The DEGs were mainly enriched in immune-related mechanisms. Furthermore, machine learning and LASSO algorithms identified three key genes, namely, apelin receptor (APLNR), C–C motif chemokine ligand 21 (CCL21), and Fc fragment of IgG receptor IIa (FCGR2A). Furthermore, 16 small molecular compounds associated with endometriosis treatment were identified, and their mechanism of action was also revealed. Taken together, the findings of this study provide new insights into the molecular factors regulating occurrence and progression of endometriosis and its underlying mechanism of action. The identified therapeutic drugs and molecular markers may have clinical significance in early diagnosis of endometriosis.