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

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          Abstract

          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.

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          Most cited references51

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)
            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              WGCNA: an R package for weighted correlation network analysis

              Peter Langfelder, Steve Horvath (2008)
              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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                Author and article information

                Journal
                Journal ID (nlm-ta): Pharmgenomics Pers Med
                Journal ID (iso-abbrev): Pharmgenomics Pers Med
                Journal ID (publisher-id): pgpm
                Title: Pharmacogenomics and Personalized Medicine
                Publisher: Dove
                ISSN (Electronic): 1178-7066
                Publication date (Electronic): 22 April 2022
                Publication date Collection: 2022
                Volume: 15
                Pages: 377-392
                Affiliations
                [1 ]Department of Obstetrics and Gynecology, Women’s Hospital, Zhejiang University School of Medicine , Hangzhou, People’s Republic of China
                Author notes
                Correspondence: Ruijin Wu, Department of Obstetrics and Gynecology, Women’s Hospital, Zhejiang University School of Medicine , Hangzhou, 310006, People’s Republic of China, Tel +86 571-8706223, Email wurj@zju.edu.cn
                Article
                Publisher ID: 354957
                DOI: 10.2147/PGPM.S354957
                PMC ID: 9041605
                SO-VID: 3f1036a9-df96-4dad-91f9-35a6d8feb9f0
                Copyright © © 2022 Chen et al.
                License:

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                Date received : 22 December 2021
                Date accepted : 11 April 2022
                Page count
                Figures: 7, References: 54, Pages: 16
                Funding
                Funded by: Ministerial and Provincial Joint Construction Major Projects of Zhejiang province;
                Funded by: Key Research and Development Program of Zhejiang Province;
                This work was supported by Ministerial and Provincial Joint Construction Major Projects of Zhejiang province (No. WKJ-ZJ-1907) and Key Research and Development Program of Zhejiang Province (No. 2021C03095).
                Categories
                Subject: Original Research

                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
                Keywords: endometriosis,bioinformatics analysis,wgcna,immune cell infiltration
                Data availability:
                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
                Keywords: endometriosis, bioinformatics analysis, wgcna, immune cell infiltration

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