A Study on the Analysis of Important Gene Networks and Pathways Involved in Progression of Endometriosis to Ovarian Endometrioma Cyst

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This bioinformatics study identified five key genes (COMT, CYP19A1, GALT, LTA, and STAR) and related protein targets from 298 differentially expressed genes associated with endometriosis progression.

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The paper used bioinformatics analysis of microarray gene expression datasets retrieved from GEO, applying GEO Venn-logic plus DAVID to identify differentially expressed genes and associated pathways, and then mapping these to target proteins via STITCH. With a significant cutoff, it reported 298 unique DEGs, found that mRNA expression of all genes was upregulated in the PA1 cell line, and narrowed to five genes (COMT, CYP19A1, GALT, LTA, and STAR) alongside five protein targets linked with endometriosis. The explicit caveat is that the study is based on in silico reanalysis of existing expression profiles and does not provide experimental validation for the proposed gene-network connections. This paper is centrally about endometriosis — it specifically analyzes gene networks and pathways implicated in progression from endometriosis to ovarian endometrioma cyst.

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Abstract

Endometriosis (EM) is a gynecological condition known by the manifestation of endometrium alike soft tissue external to the usual place affecting up to 10% of all womenfolk in the reproductively active stage. However, the pathological process of endometriosis is not identified fully. The study aims to investigate the genes associated with the progression of endometriosis and its pathways using bioinformatics tools and techniques. The gene expression profile of three sets was retrieved, and bioinformatics data analysis was carried out for the microarray samples using GEO, DAVID, and STICH. Differently expressed genes (DEGs) refer to genes that exhibit significant changes in their expression levels between different conditions or groups, such as between different cell types, treatments, disease states, or developmental stages. DEG was determined based on a significant cutoff resulting in 298 unique elements based on the GEO Venn diagram map. DAVID (database for annotation, visualization, and integrated discovery) helps understand the biological significance of the data by identifying overrepresented biological terms, pathways, and functional annotations among a set of genes or proteins of interest. DAVID analysis revealed positively and negatively associated genes and followed by target proteins. DAVID is helpful for getting results of molecular mechanisms and pathways associated with DEGs. The gene expression studies showed that the m-RNA expression of all the genes was upregulated in the PA1 cell line. The present study identified five genes (COMT, CYP19A1, GALT, LTA, and STAR) from 298 unique DEGs using microarray data analysis, and 5 protein targets were also identified that were linked with EM. The study concludes that this information may provide a bridging gap in understanding the progression of endometriosis.
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Abstract

Endometriosis (EM) is a gynecological condition known by the manifestation of endometrium alike soft tissue external to the usual place affecting up to 10% of all womenfolk in the reproductively active stage. However, the pathological process of endometriosis is not identified fully. The study aims to investigate the genes associated with the progression of endometriosis and its pathways using bioinformatics tools and techniques. The gene expression profile of three sets was retrieved, and bioinformatics data analysis was carried out for the microarray samples using GEO, DAVID, and STICH. Differently expressed genes (DEGs) refer to genes that exhibit significant changes in their expression levels between different conditions or groups, such as between different cell types, treatments, disease states, or developmental stages. DEG was determined based on a significant cutoff resulting in 298 unique elements based on the GEO Venn diagram map. DAVID (database for annotation, visualization, and integrated discovery) helps understand the biological significance of the data by identifying overrepresented biological terms, pathways, and functional annotations among a set of genes or proteins of interest. DAVID analysis revealed positively and negatively associated genes and followed by target proteins. DAVID is helpful for getting results of molecular mechanisms and pathways associated with DEGs. The gene expression studies showed that the m-RNA expression of all the genes was upregulated in the PA1 cell line. The present study identified five genes (COMT, CYP19A1, GALT, LTA, and STAR) from 298 unique DEGs using microarray data analysis, and 5 protein targets were also identified that were linked with EM. The study concludes that this information may provide a bridging gap in understanding the progression of endometriosis. Similar content being viewed by others Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Abbreviations - COMT: - Catechol-O-methyltransferase - DAVID: - Database for annotation, visualization, and integrated discovery - DEGs: - Differently expressed genes - EM: - Endometriosis - EAOC: - Endometriosis-associated and non-endometriosis-associated (non-EAOC) - GEO: - Gene Expression Omnibus - GALT: - Gut-associated lymphoid tissue - KEGG: - Kyoto encyclopedia of genes and genomes - miRNAs: - Micro-RNAs - PMSF: - Phenylmethylsulfonyl fluoride - PPI: - Protein-protein interaction - qPCR: - Quantitative polymerase chain reaction - RIPA: - Radioimmunoprecipitation assay buffer - WGCNA: - Weighted gene co-expression network analysis - WHO: - World Health Organization

References

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All authors contributed to the study conception and design, material preparation, the experiments, data collection, and analysis. All authors wrote the first draft of the manuscript and commented on previous versions of the manuscript. All authors read and approved the final manuscript. Corresponding author Ethics declarations Ethics Approval Not applicable. Conflict of Interest The authors declare no competing interests. Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Zhang, Z., Singh, S.P. A Study on the Analysis of Important Gene Networks and Pathways Involved in Progression of Endometriosis to Ovarian Endometrioma Cyst. Appl Biochem Biotechnol 196, 4352–4365 (2024). https://doi.org/10.1007/s12010-023-04778-2 Accepted: Published: Version of record: Issue date: DOI: https://doi.org/10.1007/s12010-023-04778-2

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Condition tags

endometriosisendometrioma

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

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