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Background: Heart failure (HF) is a complex clinical syndrome characterized by the inability to match cardiac output with metabolic needs. Research on regulatory mechanism of fibrosis-related genes in patients with HF is very limited. In order to understand the mechanism of fibrosis in the development and progression of HF, fibrosis -related hub genes in HF are screened and verified.Methods: RNA sequencing data was obtained from the Gene Expression Omnibus (GEO) cohorts to identify differentially expressed genes (DEGs). Thereafter, fibrosis-related genes were obtained from the GSEA database and that associated with HF were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis was carried out to analyze the biological function of fibrosis-related DEGs. The protein-protein interaction (PPI) network of hub genes was constructed via the STRING database. Moreover, the diagnostic value of hub genes for HF was confirmed using ROC curves and expression analysis. Finally, quantitative real time PCR was used to detect the expression levels of mRNAs.Results: A total of 3, 469 DEGs were identified closely related to HF, and 1, 187 fibrosis-related DEGs were obtained and analyzed for GO and KEGG enrichment. The enrichment results of fibrosis-related DEGs were consistent with that of DEGs. A total of 10 hub genes (PPARG, KRAS, JUN, IL10, TLR4, STAT3, CXCL8, CCL2, IL6, IL1β) were selected via the PPI network. Receiver operating characteristic curve analysis was estimated in the test cohort, and 6 genes (PPARG, KRAS, JUN, IL10, TLR4, STAT3) with AUC more than 0.7 were identified as diagnosis genes. Moreover, miRNA-mRNA and TF-mRNA regulatory networks were constructed. Finally, quantitative real time PCR revealed these 6 genes may be used as the potential diagnostic biomarkers of HF.Conclusion: In this study, 10 fibrosis-related hub genes in the HF were identified and 6 of them were demonstrated as potential diagnostic biomarkers for HF.

Background: Breast cancer (BC) is a major leading cause of woman deaths worldwide. Increasing evidence has revealed that stemness features are related to the prognosis and progression of tumors. Nevertheless, the roles of stemness-index-related long noncoding RNAs (lncRNAs) in BC remain unclear.Methods: Differentially expressed stemness-index-related lncRNAs between BC and normal samples in The Cancer Genome Atlas database were screened based on weighted gene co-expression network analysis and differential analysis. Univariate Cox and least absolute shrinkage and selection operator regression analyses were performed to identify prognostic lncRNAs and construct a stemness-index-related lncRNA signature. Time-dependent receiver operating characteristic curves were plotted to evaluate the predictive capability of the stemness-index-related lncRNA signature. Moreover, correlation analysis and functional enrichment analyses were conducted to investigate the stemness-index-related lncRNA signature-related biological function. Finally, a quantitative real-time polymerase chain reaction was used to detect the expression levels of lncRNAs.Results: A total of 73 differentially expressed stemness-index-related lncRNAs were identified. Next, FAM83H-AS1, HID1-AS1, HOXB-AS1, RP11-1070N10.3, RP11-1100L3.8, and RP11-696F12.1 were used to construct a stemness-index-related lncRNA signature, and receiver operating characteristic curves indicated that stemness-index-related lncRNA signature could predict the prognosis of BC well. Moreover, functional enrichment analysis suggested that differentially expressed genes between the high-risk group and low-risk group were mainly involved in immune-related biological processes and pathways. Furthermore, functional enrichment analysis of lncRNA-related protein-coding genes revealed that FAM83H-AS1, HID1-AS1, HOXB-AS1, RP11-1070N10.3, RP11-1100L3.8, and RP11-696F12.1 were associated with neuroactive ligand–receptor interaction, AMPK signaling pathway, PPAR signaling pathway, and cGMP-PKG signaling pathway. Finally, quantitative real-time polymerase chain reaction revealed that FAM83H-AS1, HID1-AS1, RP11-1100L3.8, and RP11-696F12.1 might be used as the potential diagnostic biomarkers of BC.Conclusion: The stemness-index-related lncRNA signature based on FAM83H-AS1, HID1-AS1, HOXB-AS1, RP11-1070N10.3, RP11-1100L3.8, and RP11-696F12.1 could be used as an independent predictor for the survival of BC, and FAM83H-AS1, HID1-AS1, RP11-1100L3.8, and RP11-696F12.1 might be used as the diagnostic markers of BC.