Different Expression of Mitochondrial and Endoplasmic Reticulum Stress Genes in Epicardial Adipose Tissue Depends on Coronary Atherosclerosis

Examining the effects of coronary artery disease- and mitochondrial biogenesis-related genes' and microRNAs' expression levels on metabolic disorders in epicardial adipose tissue

BACKGROUND AND AIMS

Epicardial adipose tissue (EAT) surrounds the heart and coronary arteries and is important for comprehending the pathogenesis of coronary artery disease (CAD). We aimed to evaluate the expressions of mitochondrial biogenesis- and CAD-related genes and miRNAs in EAT by comparing them to visceral adipose tissue (VAT) in CAD, diabetes, and obesity subgroups.

METHODS

In this study, a total of 93 individuals were recruited, and EAT samples (63 CAD; 30 non-CAD) and VAT samples from 65 individuals (46 CAD; 19 non-CAD) were collected. For further analysis, the study population was divided according to obesity and diabetes status. PRKAA1, PPARGC1A, SIRT1, RELA, TNFA, and miR-155-5p, let-7g-5p, miR-1247-5p, miR-326 expression levels were examined.

RESULTS

PRKAA1 and let-7g-5p were differentially expressed in EAT compared to VAT. TNFA expression was upregulated significantly in both tissues of CAD patients. In EAT, PRKAA1, PPARGC1A, and SIRT1 were downregulated with diabetes. Moreover, PPARGC1A expression is decreased under the condition of obesity in both tissues. EAT expressions of miR-1247-5p and miR-326 were downregulated with obesity, while miR-155-5p is decreased only in the VAT of obese. Also, miRNAs and genes were correlated with biochemical parameters and each other in EAT and VAT (p < 0.050).

CONCLUSIONS

The findings demonstrating distinct let-7g-5p and AMPKα1 mRNA expression between EAT and VAT underscores the importance of tissue-specific regulation in different clinical outcomes. In addition, the differential expressions of investigated genes and miRNAs highlight their responsiveness to obesity, DM, and CAD in adipose tissues.

Identification of key genes and mechanisms of epicardial adipose tissue in patients with diabetes through bioinformatic analysis

Background

In recent years, peri-organ fat has emerged as a diagnostic and therapeutic target in metabolic diseases, including diabetes mellitus. Here, we performed a comprehensive analysis of epicardial adipose tissue (EAT) transcriptome expression differences between diabetic and non-diabetic participants and explored the possible mechanisms using various bioinformatic tools.

Methods

RNA-seq datasets GSE108971 and GSE179455 for EAT between diabetic and non-diabetic patients were obtained from the public functional genomics database Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were identified using the R package DESeq2, then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed. Next, a PPI (protein–protein interaction) network was constructed, and hub genes were mined using STRING and Cytoscape. Additionally, CIBERSORT was used to analyze the immune cell infiltration, and key transcription factors were predicted based on ChEA3.

Results

By comparing EAT samples between diabetic and non-diabetic patients, a total of 238 DEGs were identified, including 161 upregulated genes and 77 downregulated genes. A total of 10 genes (IL-1β, CD274, PDCD1, ITGAX, PRDM1, LAG3, TNFRSF18, CCL20, IL1RN, and SPP1) were selected as hub genes. GO and KEGG analysis showed that DEGs were mainly enriched in the inflammatory response and cytokine activity. Immune cell infiltration analysis indicated that macrophage M2 and T cells CD4 memory resting accounted for the largest proportion of these immune cells. CSRNP1, RELB, NFKB2, SNAI1, and FOSB were detected as potential transcription factors.

Conclusion

Comprehensive bioinformatic analysis was used to compare the difference in EAT between diabetic and non-diabetic patients. Several hub genes, transcription factors, and immune cell infiltration were identified. Diabetic EAT is significantly different in the inflammatory response and cytokine activity. These findings may provide new targets for the diagnosis and treatment of diabetes, as well as reduce potential cardiovascular complications in diabetic patients through EAT modification.

Endoplasmic Reticulum Stress-Related Signature for Predicting Prognosis and Immune Features in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) with cancer cells under endoplasmic reticulum (ER) stress has a poor prognosis. This study is aimed at discovering credible biomarkers for predicting the prognosis of HCC based on ER stress-related genes (ERSRGs). We constructed a novel four-ERSRG prognostic risk model, including PON1, AGR2, SSR2, and TMCC1, through a series of bioinformatic approaches, which can accurately predict survival outcomes in HCC patients. Higher risk scores were linked to later grade, recurrence, advanced TNM stage, later T stage, and HBV infection. In addition, 20 fresh frozen tumors and normal tissues from HCC patients were collected and used to validate the genes expressed in the signature by qRT-PCR and immunohistochemical (IHC) assays. Moreover, we found the ER stress-related signature could reflect the infiltration levels of different immune cells in the tumor microenvironment (TME) and forecast the efficacy of immune checkpoint inhibitor (ICI) treatment. Finally, we created a nomogram incorporating this ER stress-related signature. In conclusion, our constructed four-gene risk model associated with ER stress can accurately predict survival outcomes in HCC patients, and the model's risk score is associated with the poor clinical classification.