2. Academic Validation
  3. Identification of key genes of diabetic cardiomyopathy in hiPSCs-CMs based on bioinformatics analysis

Identification of key genes of diabetic cardiomyopathy in hiPSCs-CMs based on bioinformatics analysis

  • Mol Cell Biochem. 2024 Feb 21. doi: 10.1007/s11010-023-04915-9.
Shuo An # 1 2 3,  Hongchen Bi # 1,  Xiaoli Luo 1,  Caiying Zhu 4,  Min Wang 1,  Aiming Pang 5,  Yujie Cui 6
Affiliations

Affiliations

  • 1 School of Medical Laboratory, Tianjin Medical University, Tianjin, 300203, China.
  • 2 Department of Clinical Laboratory, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China.
  • 3 Tianjin Key Laboratory of Digestive Cancer, Tianjin, 300060, China.
  • 4 State Key Laboratory of Experimental Hematology, Hematopoietic Stem Cell Transplantation Center, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
  • 5 State Key Laboratory of Experimental Hematology, Hematopoietic Stem Cell Transplantation Center, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China. pangaiming@ihcams.ac.cn.
  • 6 School of Medical Laboratory, Tianjin Medical University, Tianjin, 300203, China. yujiecui1@126.com.
  • # Contributed equally.
PMID: 38381273 DOI: 10.1007/s11010-023-04915-9
Abstract

Diabetic cardiomyopathy (DbCM) is one of the most common vascular complications of diabetes, and can cause heart failure and threaten the life of patients. The pathogenesis is complex, and key genes have not fully identified. In this study, bioinformatics analysis was used to predict DbCM-related gene targets. Published datasets from the NCBI Gene Expression Omnibus with accession numbers GSE62203 and GSE197850 were selected for analysis. Differentially expressed genes (DEGs) were identified by the online tool GEO2R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID online database. Protein-protein interaction network construction and hub gene identification were performed using STRING and Cytoscape. We used 30 mM and 1 μM hydrocortisone-stimulated AC16 cells as an in vitro model of diabetic cardiomyopathy. Quantitative Real-Time PCR (qRT-PCR) was performed to validate the expression levels of hub genes. A total of 73 common DEGs were identified in both datasets, including 47 upregulated and 26 downregulated genes. GO and KEGG pathway enrichment analyses revealed that the DEGs were significantly enriched in metabolism, hypoxia response, Apoptosis, cell proliferation regulation, and cytoplasmic and HIF signalling pathways. The top 10 hub genes were LDHA, PGK1, SLC2A1, ENO1, PFKFB3, EGLN1, MYC, PDK1, EGLN3 and BNIP3. In our in vitro study, we found that PGK1, SLC2A1, PFKFB3, EGLN1, MYC, EGLN3 and BNIP3 were upregulated, ENO1 was downregulated, and LDHA was unchanged. Except for PGK1 and ENO1, these hub genes have been previously reported to be involved in DbCM. In summary, we identified DEGs and hub genes and first reported PGK1 and ENO1 in DbCM, which may serve as potential candidate genes for DbCM targeted therapy.

Keywords

Bioinformatics; Diabetic cardiomyopathy; Differentially expressed genes; Transcriptomics; hiPSCs-CMs.

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