ABSTRACT
Introduction: Coronavirus disease 2019 (COVID-19) has become a global pandemic and poses a serious threat to human health. Many studies have shown that pre-existing nonalcoholic steatohepatitis (NASH) can worsen the clinical symptoms in patients suffering from COVID-19. However, the potential molecular mechanisms between NASH and COVID-19 remain unclear. To this end, key molecules and pathways between COVID-19 and NASH were herein explored by bioinformatic analysis. Methods: The common differentially expressed genes (DEGs) between NASH and COVID-19 were obtained by differential gene analysis. Enrichment analysis and protein-protein interaction (PPI) network analysis were carried out using the obtained common DEGs. The key modules and hub genes in PPI network were obtained by using the plug-in of Cytoscape software. Subsequently, the hub genes were verified using datasets of NASH (GSE180882) and COVID-19 (GSE150316), and further evaluated by principal component analysis (PCA) and receiver operating characteristic (ROC). Finally, the verified hub genes were analyzed by single-sample gene set enrichment analysis (ssGSEA) and NetworkAnalyst was used for the analysis of transcription factor (TF)-gene interactions, TF-microRNAs (miRNA) coregulatory network, and Protein-chemical Interactions. Results: A total of 120 DEGs between NASH and COVID-19 datasets were obtained, and the PPI network was constructed. Two key modules were obtained via the PPI network, and enrichment analysis of the key modules revealed the common association between NASH and COVID-19. In total, 16 hub genes were obtained by five algorithms, and six of them, namely, Kruppel-like factor 6 (KLF6), early growth response 1 (EGR1), growth arrest and DNA-damage-inducible 45 beta (GADD45B), JUNB, FOS, and FOS-like antigen 1 (FOSL1) were confirmed to be closely related to NASH and COVID-19. Finally, the relationship between hub genes and related pathways was analyzed, and the interaction network of six hub genes was constructed with TFs, miRNAs, and compounds. Conclusion: This study identified six hub genes related to COVID-19 and NASH, providing a new perspective for disease diagnosis and drug development.
ABSTRACT
Background: Hepatocellular carcinoma (HCC) represents a global health challenge. Effective biomarkers are required for an early diagnosis to improve the survival rates of HCC patients. Exonuclease 1 (EXO1) plays a significant role in the DNA repair and recombination mechanisms. This study aimed to investigate the diagnostic and prognostic roles of EXO1 in HCC. Methods: We analyzed the EXO1 expression levels in various cancers including HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. RNA sequencing data were analyzed using the R packages to determine differentially expressed genes (DEGs) between high- and low-EXO1 expressing HCC tissues from the TCGA-LIHC database. A Spearman's correlation analysis was performed to determine the association between EXO1 expression and immune cell infiltration, and immune checkpoint genes and TP53. MethSurv and CBioPortal databases were used to evaluate the DNA methylation changes and genetic alterations in the EXO1 gene. A logistic regression analysis was performed to determine the association between EXO1 expression and the clinicopathological characteristics of the HCC patients. The diagnostic and prognostic predictive values of EXO1 were evaluated using the Kaplan-Meier (K-M) survival curves, diagnostic receiver operating characteristic (ROC) curves, nomogram model, and Cox regression analysis. Results: EXO1 expression levels were significantly higher in the tumor tissues and serums of HCC patients compared to the corresponding controls. The DEGs associated with EXO1 were significantly enriched in the cell proliferation pathways. EXO1 expression levels significantly correlated with immune cell infiltration, immune checkpoint genes, and TP53 in the HCC tissues. The DNA methylation status in five CpG islands of the EXO1 gene was associated with the prognosis of HCC. EXO1 expression levels in the HCC tissues were associated with the tumor grades, alpha-fetoprotein (AFP) levels, and the tumor stages. Cox regression analysis showed that EXO1 was a potential independent risk factor for the overall survival (OS) and disease-specific survival (DSS) of HCC patients. ROC curve analysis showed that EXO1 expression levels accurately distinguished HCC tissues from the adjacent normal liver tissues. Conclusion: Our study demonstrated that EXO1 was a potential diagnostic and prognostic biomarker, and a promising therapeutic target in HCC.
