Abstract
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death. HBV infection is an important risk factor for the tumorigenesis of HCC, given that the inflammatory environment is closely related to morbidity and prognosis. Consequently, it is of urgent importance to explore the immunogenomic landscape to supplement the prognosis of HCC. The expression profiles of immune‐related genes (IRGs) were integrated with 377 HCC patients to generate differentially expressed IRGs based on the Cancer Genome Atlas (TCGA) dataset. These IRGs were evaluated and assessed in terms of their diagnostic and prognostic values. A total of 32 differentially expressed immune‐related genes resulted as significantly correlated with the overall survival of HCC patients. The Gene Ontology functional enrichment analysis revealed that these genes were actively involved in cytokine‐cytokine receptor interaction. A prognostic signature based on IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) stratified patients into high-risk versus low-risk groups in terms of overall survival and remained as an independent prognostic factor in multivariate analyses after adjusting for clinical and pathologic factors. Several IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) of clinical significance were screened in the present study, revealing that the proposed clinical-immune signature is a promising risk score for predicting the prognosis of HCC.
Highlights
Hepatocellular carcinoma (HCC) ranks seventh among malignant tumors in terms of incidence
Transcriptome Expression Data and Clinical Information Acquisition. e transcriptome expression profiles and corresponding clinical information of hepatocellular carcinoma were downloaded from the Genomic Data Commons Data Portal of the Cancer Genome Atlas (TCGA), which contained data from 374 hepatocellular carcinoma and 50 noncancerous liver tissues. e immune-related genes (IRGs) list was derived from the Immunology Database and Analysis Portal (ImmPort) database [25]
Expressed genes (DEGs) between HCC and nontumor samples were screened by the R software edgeR package to select differentially expressed genes (DEGs) related to hepatocarcinogenesis [26]. e raw data were normalized by the Trimmed mean of M values (TMM) implemented in the edgeR Bioconductor package
Summary
Hepatocellular carcinoma (HCC) ranks seventh among malignant tumors in terms of incidence. According to the latest Global Cancer Statistics, 841,080 new incidents of HCC and 781,631 deaths occurred during the year 2018 [1]. China is a country with a high incidence of HCC, which accounts for more than half of the world’s deaths [2]. With the development of modern medical science and technology, significant progress has been made in the treatment of HCC. As the clinical symptoms of early HCC are not typical, 70% to 80% of the patients have advanced disease at the time of diagnosis [3]. Existing treatment strategies are insufficient for patients with advanced HCC. Erefore, identifying novel and sensitive biomarkers is of critical importance for the early diagnosis of HCC Existing treatment strategies are insufficient for patients with advanced HCC. erefore, identifying novel and sensitive biomarkers is of critical importance for the early diagnosis of HCC
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