Characterizing the key genes of COVID-19 that regulate tumor immune microenvironment and prognosis in hepatocellular carcinoma.

Abstract

Hepatocellular carcinoma (HCC), a highly heterogeneous malignant tumor associated with a poor prognosis, is a common cause of cancer-related deaths worldwide, with a limited survival benefit for patients despite ongoing therapeutic breakthroughs. Coronavirus disease 2019 (COVID-19), a severe infectious disease caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), is a global pandemic and a serious threat to human health. The increased susceptibility to SARS-CoV-2 infection and a poor prognosis in patients with cancer necessitate the exploration of the potential link between the two. No studies have investigated the relationship of COVID-19 genes with the prognosis and tumor development in patients with HCC. We screened prognosis-related COVID-19 genes in HCC, performed molecular typing, developed a stable and reliable COVID-19 genes signature for predicting survival, characterized the immune microenvironment in HCC patients, and explored new molecular therapeutic targets. Datasets of HCC patients, including RNA sequencing data and clinical information, were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Prognosis-related COVID-19 genes were identified by univariate Cox analysis. Molecular typing of HCC was performed using the consensus non-negative matrix factorization method (cNMF), followed by the analysis of survival, tumor microenvironment, and pathway enrichment for each subtype. Prognostic signatures were constructed using LASSO-Cox regression models, and receiver operating characteristic (ROC) curves were used to validate the predictive performance of the signature. The same approach was used for the test and external validation sets. Seven software packages were applied to determine the abundance of immune infiltration in HCC patients and investigate its relationship with the risk scores. Gene set enrichment analysis (GSEA) was used to explore the potential mechanisms by which the COVID-19 genes affect hepatocarcinogenesis and prognosis. Three types of machine learning methods were combined to identify the most critical genes in the signature and localize their expression at the single cell level. We identified 53 prognosis-related COVID-19 genes and classified HCC into two molecular subtypes (C1, C2) by using the NMF method. The prognosis of C2 was significantly better than that of C1, and the two subtypes differed remarkably in terms of the tumor immune microenvironment and biological functions. The 17 COVID-19 genes were screened using the LASSO regression method to develop a 17 COVID-19 genes signature, which demonstrated a good predictive performance for 1-, 2- and 3-year OS of patients with HCC. The risk score as an independent prognostic factor for HCC has better predictive accuracy than traditional clinical variables. Patients in the TCGA cohort were categorized by risk score into the high- and low-risk groups, with the high-risk group mainly enriched in the immune modulation-related pathways and the low-risk group mainly enriched in the metabolism-related pathways, suggesting that the COVID-19 genes may affect disease progression and prognosis by regulating the tumor immune microenvironment and metabolism in HCC. NOL10 was identified as the most critical gene in the signature and hypothesized to be a potential therapeutic target for HCC. Objectively, the COVID-19 genes signature developed in this study, as an independent prognostic factor in HCC patients, is closely associated with the prognosis and tumor immune microenvironment of HCC patients and indicates that they may regulate the development of HCC in multiple ways, providing us with new perspectives for understanding the molecular mechanisms of HCC and finding effective therapeutic targets.