The Bioinformatical Identification of Potential Biomarkers in Heart Failure Diagnosis and Treatment

Background Heart failure (HF) is defined as the inability of the heart's systolic and diastolic function to properly discharge blood flow from the veins to the heart. The goal of our research is to look into the possible mechanism that causes HF. Methods The GSE5406 database was used for screening the differentially expressed genes (DEGs). Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI) network were applied to analyze DEGs. Besides, cell counting Kit-8 (CCK-8) was conducted to observe the knockdown effect of hub genes on cell proliferation. Results Finally, 377 upregulated and 461 downregulated DEGs came out, enriched in the extracellular matrix organization and gap junction. According to GSEA results, Hoft cd4 positive alpha beta memory t cell bcg vaccine age 18–45 yo id 7 dy top 100 deg ex vivo up, Sobolev t cell pandemrix age 18–64 yo 7 dy dn, and so on were significantly related to gene set GSE5406. 7 hub genes, such as COL1A1, UBB, COL3A1, HSP90AA1, MYC, STAT3 and MAPK1, were selected from PPI networks. CCK-8 indicated silencing of STAT3 promoted the proliferation of H9C2 cells and silencing of UBB inhibited the proliferation of H9C2 cells. Conclusion Our analysis reveals that COL1A1, UBB, COL3A1, HSP90AA1, MYC, STAT3, and MAPK1 might promote the progression of HF and become the biomarkers for diagnosis and treatment of HF.