Systematic Study to Unveil Novel Biomarkers, Regulatory Pathways and Exploring Therapeutic Targets for sepsis and associated complications Using Next Generation Sequencing Data Analysis and In Silico Drug Design

Sepsis is the leading systemic inflammatory response syndrome in worldwide, yet relatively little is known about the genes and signaling pathways involved in sepsis progression. The current investigation aimed to elucidate potential key candidate genes and pathways in sepsis and its associated complications. Next generation sequencing (NGS) dataset (GSE185263) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 348 sepsis samples and 44 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the DESeq2 R package. Next, we made use of the g:Profiler to analyze gene ontology (GO) and REACTOME pathway. Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Furthermore, we constructed miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network among hub genes utilizing miRNet and NetworkAnalyst online databases tool and Cytoscape software. We performed receiver operating characteristic (ROC) curve analysis to determine diagnostic ability of hub genes. Finally, we conducted QSAR, molecular docking and ADMET studies In total, 958 DEGs were identified, of which 479 were up-regulated genes and 479 were down-regulated genes. GO and REACTOME results showed that DEGs mainly enriched in regulation of cellular process, response to stimulus, extracellular matrix organization and immune system. The hub genes of PRKN, KIT, FGFR2, GATA3, ERBB3, CDK1, PPARG, H2BC5, H4C4 and CDC20 might be associated with sepsis and its associated complications. Predicted miRNAs (e.g., hsa-mir-548ad-5p and hsa-mir-2113), TFs (e.g., YAP1 and TBX5) and drug molecules (Gemigliptin and Methotrexate) were found to be significantly correlated with sepsis and its associated complications. QSAR, molecular docking, and ADMET studies of the 1H-pyrazolo[3,4-d]pyrimidin-4-amine derivatives give a consistent mechanistic explanation for their inhibitory potential against JAK2. In conclusion, Bioinformatics methods might be useful method to explore the mechanisms of TS. In addition, MKI67, CCNB1, and CCNB2 might be the most significant genes of sepsis and its associated complications.