Identification and Analysis of Immune-Related Gene Signature in Hepatocellular Carcinoma

Abstract

Background: Hepatocellular carcinoma (HCC) originates from the hepatocytes and accounts for 90% of liver cancer. The study intends to identify novel prognostic biomarkers for predicting the prognosis of HCC patients based on TCGA and GSE14520 cohorts. Methods: Differential analysis was employed to obtain the DEGs (Differentially Expressed Genes) of the TCGA-LIHC-TPM cohort. The lasso regression analysis was applied to build the prognosis model through using the TCGA cohort as the training group and the GSE14520 cohort as the testing group. Next, based on the prognosis model, we performed the following analyses: the survival analysis, the independent prognosis analysis, the clinical feature analysis, the mutation analysis, the immune cell infiltration analysis, the tumor microenvironment analysis, and the drug sensitivity analysis. Finally, the survival time of HCC patients was predicted by constructing nomograms. Results: Through the lasso regression analysis, we obtained a prognosis model of ten genes including BIRC5 (baculoviral IAP repeat containing 5), CDK4 (cyclin-dependent kinase 4), DCK (deoxycytidine kinase), HSPA4 (heat shock protein family A member 4), HSP90AA1 (heat shock protein 90 α family class A member 1), PSMD2 (Proteasome 26S Subunit Ubiquitin Receptor, Non-ATPase 2), IL1RN (interleukin 1 receptor antagonist), PGF (placental growth factor), SPP1 (secreted phosphoprotein 1), and STC2 (stanniocalcin 2). First, we found that the risk score is an independent prognosis factor and is related to the clinical features of HCC patients, covering AFP (α-fetoprotein) and stage. Second, we observed that the p53 mutation was the most obvious mutation between the high-risk and low-risk groups. Third, we also discovered that the risk score is related to some immune cells, covering B cells, T cells, dendritic, macrophages, neutrophils, etc. Fourth, the high-risk group possesses a lower TIDE score, a higher expression of immune checkpoints, and higher ESTIMATE score. Finally, nomograms include the clinical features and risk signatures, displaying the clinical utility of the signature in the survival prediction of HCC patients. Conclusions: Through the comprehensive analysis, we constructed an immune-related prognosis model to predict the survival of HCC patients. In addition to predicting the survival time of HCC patients, this model significantly correlates with the tumor microenvironment. Furthermore, we concluded that these ten immune-related genes (BIRC5, CDK4, DCK, HSPA4, HSP90AA1, PSMD2, IL1RN, PGF, SPP1, and STC2) serve as novel targets for antitumor immunity. Therefore, this study plays a significant role in exploring the clinical application of immune-related genes.

Identification and Validation of Mitochondrial Oxidative Stress- Related Prognostic Signature with Clinical Characteristics and Immune Filtration in Liver Hepatocellular Carcinoma

“…BIRC5 appears to be an independent, unfavorable prognostic biomarker in multiple types of tumors and has potential as a clinical target for future cancer therapies 30 . Moreover, BIRC5 has been shown to play a role in the construction of prognostic models for various types of LIHC [31][32][33] , suggesting its potential as a valuable biomarker in the clinical management of cancer. Studies indicated that MMPs are directly or indirectly involved in the regulation of mitochondrial oxidative stress.…”

Identification of oncogenes and tumor-suppressor genes with hepatocellular carcinoma: A comprehensive analysis based on TCGA and GEO datasets

“…There is an urgent need to find new targets for the diagnosis and treatment of liver cancer in the direction of a breakthrough in the current treatment limitations. Some previous studies used TCGA and GEO databases to screen oncogenes and identify prognostic or immune-related genes ( Chen et al, 2020 ; Gao et al, 2021 ; Yan et al, 2021 ; Shen et al, 2022a ; Shen et al, 2022b ; Ding et al, 2022 ; Gao et al, 2022 ; Long et al, 2022 ; Yang et al, 2022 ). However, in this study, four GSE datasets, GSE36376, GSE102079, GSE54236, and GSE45267, were summarized for analysis, hoping to find the mechanism of cancer-promoting and tumor-suppressor factors in liver cancer from the perspective of improving the accuracy of the research results.…”

“…Currently, public data and bioinformatic analysis methods have provided us with invaluable resources to find HCC-related oncogenes and tumor-suppressor genes. Some studies searched cancer-related target genes through public databases, whether prognostic oncogene identification ( Gao et al, 2021 ; Yan et al, 2021 ; Shen et al, 2022a ; Gao et al, 2022 ), single-gene research studies ( Ding et al, 2022 ; Yang et al, 2022 ), or immune-related oncogenes ( Chen et al, 2020 ; Shen et al, 2022b ; Long et al, 2022 ), all of which provided a strong basis for targeted therapy and precise treatment of liver cancer. However, many studies only consider the differential expression of oncogenes in tumors, ignoring the important role and potential association of tumor-suppressor genes.…”

An Inflammation-Associated Prognosis Model for Hepatocellular Carcinoma Based on Adenylate Uridylate- (AU-) Rich Element Genes

“…Meanwhile, amino acid metabolism therapy was also proposed [ 47 ]. Shen et al constructed a ten-immune-related gene risk model to predict the survival of patients with HCC in terms of immune regulation, providing a novel target for the treatment of patients with HCC [ 48 ]. In comparison, the five DE-AREG prognostic models in this study present more carcinogenic factors.…”