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Vastrad, B.

Publications and source records attributed to Vastrad, B..

2 recordsLinked to original sources

Identification of differentially expressed genes and enriched pathways in inflammatory bowel disease using bioinformatics and next generation sequencing data analysis

Inflammatory bowel disease (IBD) is the most common chronic digestive disorders and inflammation in the gastrointestinal tract globally that is characterized by episodes of abdominal pain, diarrhea, bloody stools and weight loss. However, the pathophysiologic mechanisms of IBD have not been thoroughly investigated. To explore potential targets for treatment of IBD, we reorganized and analyzed next generation sequencing (NGS) dataset (GSE186507). The R package DESeq2 tool was used to screen for differentially expressed genes (DEGs) between IBD and normal control samples. We used the g:Profiler database to perform Gene Ontology (GO) enrichment analysis and the REACTOME for pathway enrichment analysis. Protein-protein interaction (PPI) network construction and module analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes. Then miRNA-hub gene regulatory network and TF-hub gene regulatory network of these hub genes were visualized by Cytoscape. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. A total of 957 DEGs (478 up regulated genes and 479 down regulated genes) were detected in NGS dataset. And they were mainly enriched in the terms of multicellular organismal process, response to stimulus, GPCR ligand binding and immune system. Based on the data of PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network the top hub genes were ranked, including IL7R, ERBB2, SMAD1, RPS26, TLE1, HNF4A, CDKN1A, SRPK1, H3C12 and SFN. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the development and progression of IDB, and certain novel genes might be used as candidate target molecules to diagnose, monitor and treat IDB.

bioinformatics↗

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Diabetic patients are prone to diabetic kidney disease (DKD), which may cause cardiovascular damage, hypertension and obesity, and reduce quality of life. As a result, the life quality of patients was seriously reduced. However, the pathogenesis of diabetic kidney disease (DKD) has not been fully elucidated, and current treatments remain inadequate. Therefore, it is essential to explore the molecular mechanism of DKD and its complications. Next Generation Sequancing (GSE217709) dataset was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were picked out by R software. Then Gene ontology (GO) and REACTOME pathway enrichment analysis were performed by g:Profiler database, protein-protein interaction (PPI) of DEGs was constructed by Human Integrated Protein-Protein Interaction rEference (HIPPIE) database. Module analysis was carried out by Cytoscape plug-in PEWCC. Subsequently, miRNA-hub gene regulatory network and TF-hub gene regulatory network were performed by miRNet database and NetworkAnalyst database. Finally, validation of hub genes was performed by receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. In total, 958 DEGs, including 479 up regulated and 479 down regulated genes, were identified. The GO and pathway enrichment changes of DEGs were mainly enriched in biological regulation, multicellular organismal process, signaling by GPCR and extracellular matrix organization. Ten hub genes (HSPA8, HSP90AA1, HSPA5, SDCBP, HSP90B1, VCAM1, MYH9, FLNA, MDFI and PML) associated with DKD and its complications were identified. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of DKD and its complications. The identified hub genes may participate in the onset and development of DKD and its complications and serve as therapeutic targets.

bioinformatics↗