Network pharmacology pointed to FOS and IFNG as possible -related biomarkers for Shen Fukang treatment of .
Evidence
This computational biomarker study combined public gene databases, self-sequenced RIF data, homolog conversion, GeneMANIA, enrichment analysis, regulatory-network analysis, and molecular docking.
Caveat
The findings are prediction-based and do not show experimentally validated mechanisms or clinical treatment effects of Shen Fukang in renal interstitial fibrosis.
Simplified
BACKGROUND: Shen fukang (SFK), a Chinese herbal preparation, exhibits significant clinical efficacy in managing renal diseases. Studies have suggested that may promote the occurrence of (RIF), but the specific mechanisms are ambiguous. This study aims to elucidate the molecular regulatory mechanisms of autophagy during SFK treatment of RIF.
METHODS: In this study, autophagy-related genes were acquired from public databases and previous literature, SFK target genes were retrieved from public databases, and RIF data were acquired through self-sequencing. Initially, differentially expressed genes (DEGs1 and DEGs2) were identified separately between RIF and control, and SFK + RIF and RIF. In further analysis, we cross-compared genes with opposite expression change directions between DEGS1 and DEGS2.and then these intersected genes were merged and converted to human homologous genes to obtain the final DEGs. Biomarkers were obtained by intersecting final DEGs, autophagy-related genes, and SFK target genes. Following this, genes functionally related to biomarkers were analyzed by GeneMANIA.
RESULTS: In this study, 8244 DEGs1 and 915 DEGs2 were identified, with 195 genes showing opposite expression trends. Through homologous conversion, 179 final DEGs in humans were obtained, among which FOS and IFNG were biomarkers related to autophagy in the process of SFK treating RIF. GeneMANIA analysis predicted that JUN, IFNGR1, and IGFBP7 were functionally related to the biomarkers. Subsequent enrichment analysis revealed that propanoate metabolism and citrate cycle (TCA cycle) were pathways co-enriched in 2 biomarkers. Moreover, in the regulatory network, CREB1 and signal transduction and activator of transcription 3 were found to regulate both FOS and IFNG. Finally, molecular docking showed good docking effects of IFNG with quercetin and luteolin.
CONCLUSION: This study successfully identified FOS and IFNG as 2 key biomarkers related to autophagy in the treatment of RIF by SFK. Through a series of analyses, including GeneMANIA analysis, enrichment analysis, regulatory network construction, and molecular docking, the potential molecular mechanisms associated with these biomarkers were comprehensively explored.
