Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis DOI Creative Commons
Basavaraj Vastrad, Chanabasayya Vastrad

Egyptian Journal of Medical Human Genetics, Journal Year: 2024, Volume and Issue: 25(1)

Published: Oct. 12, 2024

Abstract Background Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain intercourse and infertility. However, early diagnosis endometriosis still restricted. The purpose this investigation to identify validate key biomarkers endometriosis. Methods Next-generation sequencing dataset GSE243039 was obtained from Gene Expression Omnibus database, differentially expressed genes (DEGs) between normal control samples were identified. After screening DEGs, gene ontology (GO) REACTOME pathway enrichment analyses performed. Furthermore, protein–protein interaction (PPI) network constructed modules analyzed using Human Integrated Protein–Protein Interaction rEference database Cytoscape software, hub Subsequently, miRNAs genes, TFs miRNet NetworkAnalyst tool, possible predicted. Finally, receiver operating characteristic curve analysis used genes. Results A total 958 including 479 upregulated downregulated screened samples. GO DEGs showed that they mainly involved in multicellular organismal process, developmental signaling by GPCR muscle contraction. Further PPI identified 10 vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 actc1. Possible target miRNAs, hsa-mir-3143 hsa-mir-2110, TFs, tcf3 (transcription factor 3) clock (clock circadian regulator), predicted constructing miRNA-hub regulatory TF-hub network. Conclusions This bioinformatics techniques explore potential novel biomarkers. These might provide new ideas methods for diagnosis, treatment monitoring

Language: Английский

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis DOI Open Access
Basavaraj Vastrad, Chanabasayya Vastrad

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 8, 2024

Abstract Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain intercourse and infertility. However, early diagnosis endometriosis still restricted. The purpose this investigation to identify validate key biomarkers endometriosis. Next generation sequencing (NGS) dataset GSE243039 was obtained from Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) between normal control samples were identified. After screening DEGs, gene ontology (GO) REACTOME pathway enrichment analyses performed. Furthermore, protein-protein interaction (PPI) network constructed modules analysed using Human Integrated Protein-Protein Interaction rEference (HIPIE) database Cytoscape software, hub Subsequantely, miRNAs genes, TFss miRNet NetworkAnalyst tool, possible TFs predicted. Finally, receiver operating characteristic curve (ROC) analysis used genes. A total 958 including 479 up regulated down screened samples. GO DEGs showed that they mainly involved in multicellular organismal process, developmental signaling by GPCR muscle contraction. Further PPI identified 10 VCAM1, SNCA, PRKCB, ADRB2, FOXQ1, MDFI, ACTBL2, PRKD1, DAPK1 ACTC1. Possible target miRNAs, hsa-mir-3143 hsa-mir-2110, TFs, TCF3 CLOCK, predicted constructing miRNA-hub regulatory TF-hub network. This bioinformatics techniques explore potential novel biomarkers. These might provide new ideas methods for diagnosis, treatment, monitoring

Language: Английский

Citations

1
Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis DOI Creative Commons
Basavaraj Vastrad, Chanabasayya Vastrad

Egyptian Journal of Medical Human Genetics, Journal Year: 2024, Volume and Issue: 25(1)

Published: Oct. 12, 2024

Abstract Background Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain intercourse and infertility. However, early diagnosis endometriosis still restricted. The purpose this investigation to identify validate key biomarkers endometriosis. Methods Next-generation sequencing dataset GSE243039 was obtained from Gene Expression Omnibus database, differentially expressed genes (DEGs) between normal control samples were identified. After screening DEGs, gene ontology (GO) REACTOME pathway enrichment analyses performed. Furthermore, protein–protein interaction (PPI) network constructed modules analyzed using Human Integrated Protein–Protein Interaction rEference database Cytoscape software, hub Subsequently, miRNAs genes, TFs miRNet NetworkAnalyst tool, possible predicted. Finally, receiver operating characteristic curve analysis used genes. Results A total 958 including 479 upregulated downregulated screened samples. GO DEGs showed that they mainly involved in multicellular organismal process, developmental signaling by GPCR muscle contraction. Further PPI identified 10 vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 actc1. Possible target miRNAs, hsa-mir-3143 hsa-mir-2110, TFs, tcf3 (transcription factor 3) clock (clock circadian regulator), predicted constructing miRNA-hub regulatory TF-hub network. Conclusions This bioinformatics techniques explore potential novel biomarkers. These might provide new ideas methods for diagnosis, treatment monitoring

Language: Английский

Citations

0