Zhang et al.: Bardet-Biedl Syndrome 4 in Breast CancerBardet-Biedl syndrome 4 is the key protein to control cilia formation. In this study, bioinformatics method was used to screen the core genes related to the prognosis of breast cancer by analyzing the gene chip data of gene expression omnibus and the cancer genome atlas database, so as to provide a new candidate target for the treatment of breast cancer. Data were downloaded from the cancer genome atlas, gene expression omnibus to evaluate Bardet-Biedl syndrome 4 expression levels in breast cancer. Differentially expressed genes were screened by R package. Gene ontology and Kyoto encyclopedia of Genes and Genomes pathway enrichment analysis was used to explore the biological functions of differentially expressed genes. The correlation of differentially expressed genes used was, "corrplot" for visual analysis. The proteinprotein interaction relationship was constructed based on search tool for the retrieval of interacting genes/ proteins database and Cytoscape software and the key genes were obtained by module analysis with Cytoscape software molecular complex detection plugin and the prognostic value and survival of Bardet-Biedl syndrome 4 were evaluated by R package. Finally, the correlation between Bardet-Biedl syndrome 4 and clinicopathological parameters was also visualized by R package. These differentially expressed genes were mainly involved in response to peptide hormone, nuclear division, hormone secretion and transport and extracellular matrix. Genes were mainly involved in the Kyoto encyclopedia of genes and genomes pathway called Interleukin-17 signaling pathway. Bardet-Biedl syndrome 4 levels were found to be down regulated in breast cancer tissues compared with normal tissues. Survival analysis showed that low Bardet-Biedl syndrome 4 expression was associated with poor prognosis. These results were verified in clinical specimens, where in the Bardet-Biedl syndrome 4 protein levels were significantly down regulated in breast cancer tissues compared with non-breast cancer tissues. This study confirmed that Bardet-Biedl syndrome 4 can be used as an independent prognostic factor for the prognosis of breast cancer, which provides a basis for exploring a new target for the treatment of breast cancer.
Background Cardiotoxicity is a common complication following anthracycline chemotherapy and represents one of the serious adverse reactions affecting life, which severely limits the effective use of anthracyclines in cancer therapy. Although some genes have been investigated by individual studies, the comprehensive analysis of key genes and molecular regulatory network in anthracyclines-induced cardiotoxicity (AIC) is lacking but urgently needed. Methods The present study integrating several transcription profiling datasets aimed to identify key genes associated with AIC by weighted correlation network analysis (WGCNA) and differentially expressed analysis (DEA) and also constructed miRNA-transcription factor-gene regulatory network. A total of three transcription profiling datasets involving 47 samples comprising 41 rat heart tissues and 6 human induced pluripotent stem cell-derived cardiomyocytes (hiPSCMs) samples were enrolled. Results The WGCNA and DEA with E-MTAB-1168 identified 14 common genes affected by doxorubicin administrated by 4 weeks or 6 weeks. Functional and signal enrichment analyses revealed that these genes were mainly enriched in the regulation of heart contraction, muscle contraction, heart process, and oxytocin signaling pathway. Ten (Ryr2, Casq1, Fcgr2b, Postn, Tceal5, Ccn2, Tnfrsf12a, Mybpc2, Ankrd23, Scn3b) of the 14 genes were verified by another gene expression profile GSE154603. Importantly, three key genes (Ryr2, Tnfrsf12a, Scn3b) were further validated in a hiPSCMs-based in-vitro model. Additionally, the miRNA-transcription factor-gene regulatory revealed several top-ranked transcription factors including Tcf12, Ctcf, Spdef, Ebf1, Sp1, Rcor1 and miRNAs including miR-124-3p, miR-195-5p, miR-146a-5p, miR-17-5p, miR-15b-5p, miR-424-5p which may be involved in the regulation of genes associated with AIC. Conclusions Collectively, the current study suggested the important role of the key genes, oxytocin signaling pathway, and the miRNA-transcription factor-gene regulatory network in elucidating the molecular mechanism of AIC.
Objective. To investigate the mechanism of Folium Ginkgo (FG) against adriamycin-induced cardiotoxicity (AIC) through a network pharmacology approach. Methods. Active ingredients of FG were screened by TCMSP, and the targets of active ingredient were collected by Genclip3 and HERB databases. AIC-related target genes were predicted by Genecards, OMIM, and CTD databases. Protein-protein interaction (PPI) network was constructed by STRING platform and imported into Cytoscape software to construct the FG-active ingredients-targets-AIC network, and CytoNCA plug-in was used to analyze and identify the core target genes. The Metascape platform was used for transcription factor, GO and signaling pathway enrichment analysis. Results. 27 active ingredients of FG and 1846 potential targets were obtained and 358 AIC target genes were retrieved. The intersection of FG and AIC targets resulted in 218 target genes involved in FG action. The top 5 active ingredients with most targets were quercetin, luteolin, kaempferol, isorhamnetin, and sesamin. After constructing the FG-active ingredients-targets-AIC network, CytoNCA analysis yielded 51 core targets, of which the top ranked target was STAT3. Ninety important transcription factors were enriched by transcription factor enrichment analysis, including RELA, TP53, NFKB1, SP1, JUN, STAT3, etc. The results of GO enrichment analysis showed that the effective active ingredient targets of FG were involved in apoptotic signaling, response to growth factor, cellular response to chemical stress, reactive oxygen species metabolic process, etc. The signaling pathway enrichment analysis showed that there were many signaling pathways involved in AIC, mainly including pathways in cancer, FOXO signaling pathway, AGE-RAGE signaling pathway in diabetic complications, signaling by interleukins, and PI3K-AKT signaling pathway,. Conclusions. The study based on a network pharmacology approach demonstrates that the possible mechanisms of FG against AIC are the involvement of multicomponents, multitargets, and multipathways, and STAT3 may be a key target. Further experiments are needed to verify the results.
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