Identification of an Endoplasmic Reticulum Stress-Related Gene Signature to Evaluate the Immune Status and Predict the Prognosis of Hepatocellular Carcinoma

Liver cancer is the sixth most frequently diagnosed primary malignancy and ranks as the third leading cause of cancer-related death worldwide in 2020. ER stress also plays a vital role in the pathogenesis of malignancies. In the current study, we aimed to construct an endoplasmic reticulum stress-related genes (ERGs) signature to predict the overall survival (OS) of patients with HCC. Differentially expressed ERGs (DE-ERGs) were analyzed using The Cancer Genome Atlas (TCGA-LIHC cohort) and International Cancer Genome Consortium (ICGC-LIRI-JP cohort) databases. The prognostic gene signature was identified by the univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression analysis. The predictive ability of the model was evaluated by utilizing Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves. Gene set variant analysis (GSVA) was performed to explore the underlying biological processes and signaling pathways. CIBERPORT and single-sample Gene Set Enrichment Analysis (ssGSEA) were implemented to estimate the immune status between the different risk groups. A total of 113 DE-ERGs were identified between 50 normal samples and 365 HCC samples in the TCGA-LIHC cohort, and 48 DE-ERGs were associated with OS through the univariate Cox regression. A six DE-ERGs (PPARGC1A, SQSTM1, SGK1, PON1, CDK1, and G6PD) signature was constructed and classified patients into high-risk and low-risk groups. The risk score was an independent prognostic indicator for OS (HR > 1, p < 0.001). The function enrichment analysis indicated that cell cycle, RNA degradation, protein localization, and cell division were the main biological processes. The high-risk group had higher immune cell infiltration levels than those of the low-risk group. We predicted the response to targeted therapy in high- and low-risk patients with HCC and found that the high-risk patients were more sensitive to pazopanib. At last, we verified the expression of the six gene patterns in HCC tissues by qRT-PCR and immunohistochemistry. This signature may be a potential tool to provide a choice for prognosis prediction and personal management of patients with HCC.