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.
Keywords: Endometriosis; Gene expression; Gene networks; Microarray; Protein network.
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.