Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Endoplasmic Reticulum Stress-Related Gene Analysis.

Peng Liu,Jianjun Qian,Heng Duan,Jinhong Wei,Jie Yao,Xiaodong Wang,Zhennan Li,Zechang Xin,Yan Du,Feiyu Mao

doi:10.3389/fonc.2021.641487

Abstract

Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide and its incidence continues to increase year by year. Endoplasmic reticulum stress (ERS) caused by protein misfolding within the secretory pathway in cells and has an extensive and deep impact on cancer cell progression and survival. Growing evidence suggests that the genes related to ERS are closely associated with the occurrence and progression of HCC. This study aimed to identify an ERS-related signature for the prospective evaluation of prognosis in HCC patients. RNA sequencing data and clinical data of patients from HCC patients were obtained from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC). Using data from TCGA as a training cohort (n=424) and data from ICGC as an independent external testing cohort (n=243), ERS-related genes were extracted to identify three common pathways IRE1, PEKR, and ATF6 using the GSEA database. Through univariate and multivariate Cox regression analysis, 5 gene signals in the training cohort were found to be related to ERS and closely correlated with the prognosis in patients of HCC. A novel 5-gene signature (including HDGF, EIF2S1, SRPRB, PPP2R5B and DDX11) was created and had power as a prognostic biomarker. The prognosis of patients with high-risk HCC was worse than that of patients with low-risk HCC. Multivariate Cox regression analysis confirmed that the signature was an independent prognostic biomarker for HCC. The results were further validated in an independent external testing cohort (ICGC). Also, GSEA indicated a series of significantly enriched oncological signatures and different metabolic processes that may enable a better understanding of the potential molecular mechanism mediating the progression of HCC. The 5-gene biomarker has a high potential for clinical applications in the risk stratification and overall survival prediction of HCC patients. In addition, the abnormal expression of these genes may be affected by copy number variation, methylation variation, and post-transcriptional regulation. Together, this study indicated that the genes may have potential as prognostic biomarkers in HCC and may provide new evidence supporting targeted therapies in HCC.

Highlights

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related mortality worldwide [1]
Studies have revealed that ERS is positively associated with the occurrence and development of various human diseases including cancer [7], Studies have revealed that Pekinenin E can inhibit the growth of HCC by promoting ERS-mediated cell death and cell cycle arrest [8], and Endoplasmic reticulum stress can promote HCC immune escape by transferring specific miRNAs to macrophages infiltrating the tumor microenvironment [9] yet the role of ERSrelated prediction model in HCC remains to be determined
Based on comprehensive genomic data analysis, we aimed to demonstrate the value of an ERS-related signature to improve prognosis in HCC

Summary

Introduction

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related mortality worldwide [1]. There is an urgent need to establish efficient and accurate risk assessment models to assess the prognosis in HCC patients to improve diagnosis and treatment. Specific tumor biomarkers including a-fetoprotein, carcinoembryonic antigen, cytokines, nucleic acids and microRNAs have been used for early diagnosis and prognosis of HCC [5, 6]. Studies have revealed that ERS is positively associated with the occurrence and development of various human diseases including cancer [7], Studies have revealed that Pekinenin E can inhibit the growth of HCC by promoting ERS-mediated cell death and cell cycle arrest [8], and Endoplasmic reticulum stress can promote HCC immune escape by transferring specific miRNAs to macrophages infiltrating the tumor microenvironment [9] yet the role of ERSrelated prediction model in HCC remains to be determined

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