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

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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.

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Data availability

The datasets involved in this study can all be found in online databases, which are all freely available to the public. There are six databases involved in this study, including the TCGA database (https://portal.gdc.cancer.gov/), the ICGC database (https://dcc.icgc.org/releases/current/Projects/LIRI-JP), the GSE14520 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520), the HPA database (https://www.proteinatlas.org/), the CPTAC database (https://proteomics.cancer.gov/programs/cptac), and the TISCH database (http://tisch1.comp-genomics.org/home/).

Abbreviations

HCC:

hepatocellular carcinoma

COVID-19:

corona virus disease 2019

SARS-CoV-2:

severe acute respiratory syndrome coronavirus type 2

TCGA:

The Cancer Genome Atlas

ICGC:

International Cancer Genome Consortium

GEO:

Gene Expression Omnibus

cNMF:

consensus nonnegative matrix factorization

ROC:

receiver operating characteristic

GSEA:

gene set enrichment analysis

OS:

overall survival

TCGA-LIHC:

The Cancer Genome Atlas Liver Hepatocellular Carcinoma

HPA:

Human Protein Atlas

PRCG:

prognosis-related COVID-19 genes

KM:

Kaplan–Meier

GSVA:

gene set variation analysis

HLA:

human leukocyte antigens

LASSO:

least absolute shrinkage and selection operator

DCA:

decision curve analysis

AUC:

area under curve

KEGG:

Kyoto Encyclopedia of Genes and Genomes

GO:

gene ontology

CPTAC:

Clinical Proteomic Tumor Analysis Consortium

TISCH:

tumor immune single-cell hub

LSECs:

liver sinusoidal endothelial cells

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Acknowledgements

We express our appreciation for all the open public databases mentioned in this article.

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Authors and Affiliations

  1. Division of Life Sciences and Medicine, Ward 4 of the Department of Oncology, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China

    Shuang Gao

  2. Department of Oncology Surgery, The Second Affiliated Hospital of Bengbu Medical University, Bengbu, 233080, Anhui, China

    Lei Zhang

  3. Division of Life Sciences and Medicine, Department of Laboratory Diagnostics, the First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China

    Huiyan Wang

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SG was responsible for the design of the study, collecting and processing data, and generating images, as well as the writing and submission of the paper. LZ contributed technical assistance. Corresponding author HW supervised this study throughout and reviewed the article.

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Correspondence to Huiyan Wang.

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Supplementary Figure S1 The work-flow of the study. Schematic diagram of this study is shown in the work-flow. (JPG 2562 kb)

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Gao, S., Zhang, L. & Wang, H. Characterizing the key genes of COVID-19 that regulate tumor immune microenvironment and prognosis in hepatocellular carcinoma. Funct Integr Genomics 23, 262 (2023). https://doi.org/10.1007/s10142-023-01184-z

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