Exploring potential therapeutic strategy for hepatocellular carcinoma and COVID-19 using bioinformatics analysis

Abstract

Abstract Background Hepatocellular carcinoma (HCC) constitutes an important contributor to fatalities. Coronavirus disease 2019 (COVID-19) frequently presents with complications such as respiratory distress, systemic inflammatory responses, and damage to various organs. Several studies have investigated the relationship between COVID-19 and mortality in patients with liver cancer, but there are few research on the relationship between them. This study is to explore the correlation between the two diseases and drugs treating them. Methods The Gene Expression Omnibus (GEO) database provides gene datasets of COVID-19 patients and HCC patients. Through differential gene analysis and weighted gene co-expression network analysis, we determined 223 genes represented in HCC and COVID-19. We then used functional annotation, protein–protein interaction network construction, predictive model development and verification, prognostic value analysis, and miRNA–gene network construction. Besides, we created a drug–hub–gene network by predicting possible medications that interact with hub genes using the Drug–Gene Interaction Database (DGIdb). Ultimately, we applied immunohistochemistry to ascertain the hub genes expression. Results This study revealed that eight core genes (RRM2, TPX2, DTL, CDT1, TYMS, CDCA5, CDC25C, and HJURP) co-existed in both HCC and COVID-19 and were differentially expressed in both HCC and normal tissues.CDC25C, RRM2, CDCA5, and HJURP had diagnostic value (AUC > 0.8) and prognostic value (adjusted P-value < 0.05). Genome enrichment analysis indicated that eight genes may function in liver cancer through engagement in the cell cycle, DNA replication, etc. In liver cancer samples, these genes were significantly and adversely associated with plasma cells while RRM2 was positively associated with neutrophil and NK cell activation and with dendritic cell resting. Using the miRNAnet database and DGIdb, 9 transcription factors, 7 miRNAs, and 51 drugs or molecular compounds were predicted to interact with the hub genes. Finally, RRM2 expression showed significant variation in clinical specimens, and analysis of the association of RRM2 with immunomodulators indicated that RRM2 was closely connected to MICB and CD276. Conclusions Our study revealed several metabolic genes related to HCC and COVID-19. Moreover, potential drugs related to central genes were predicted. These findings may provide new ideas for treating COVID-19 and HCC.