Immune system‑associated genes increase malignant progression and can be used to predict clinical outcome in patients with hepatocellular carcinoma

Abstract

Hepatocellular carcinoma (HCC) is one of the most malignant types of cancer, and is associated with high recurrence rates and a poor response to chemotherapy. Immune signatures in the microenvironment of HCC have not been well explored systematically. The aim of the present study was to identify prognostic immune signatures and build a nomogram for use in clinical evaluation. Using bioinformatics analysis, RNA-seq data and overall survival (OS) information on 370 HCC cases from TCGA and 232 HCC cases from ICGC were analyzed. The differential expression of select immune genes, based on previously published studies, between HCC and adjacent tissue were analyzed using the limma package in R. Enrichment of pathways and gene ontology analysis was performed using clusterProfiler. Subsequently, univariate Cox regression analysis, Lasso penalty linear regression and multivariate Cox regression models were used to construct a model for immune risk score (IRS). The R packages, survival and survivalROC, were used to plot survival and the associated receiver operating characteristic curves. Infiltration of immune cells was calculated using Tumor IMmune Estimation Resource, with significance examined using a Pearson's correlation test. P<0.05 was considered significant. Based on the analysis, expression of 200 immune genes were upregulated and 47 immune genes were downregulated immune genes. In the multivariate Cox model, 5 genes (enhancer of zest homology 2, ferritin light chain, complement factor H related 3, isthmin 2, cyclin dependent kinase 5) were used to generate the IRS. By stratifying according to the median IRS, it was shown that patients with a high IRS had poor OS rates after 1, 2, 3 and 5 years, and this result was consistent across the testing, training and independent validation cohorts. Additionally, the IRS was correlated with the abundance of infiltrating immune cells. The nomogram built using IRS and clinical characteristics, was able to predict 1, 3 and 5 year OS with area under the curve values of >0.8. These results suggest that the model developed to calculate the IRS may be used to monitor the effectiveness of treatment strategies and for prognostic prediction.

Prognostic Value and Regulatory Network of Immune-Related Genes in Hepatocellular Carcinoma

“…Then, the KEGG pathway enrichment analysis of these IRDEGs indicated that the primary pathway of IRDEGs is cytokine-cytokine receptor interaction and MAPK signaling pathway (Fig. 1E), and the results are similar to a previous study (17) . The GO analysis showed that the IRDEGs were primarily associated with signaling receptor activator and ligand activity, immune cells migration, chemotaxis, and cytokines secretion, and the response of immune cells to cytokines (Fig.…”

Identification of Hub Genes Associated With Immune Infiltration and Predict Prognosis in Hepatocellular Carcinoma via Bioinformatics Approaches

“…Numerous studies have demonstrated that the infiltration of tumor-associated immune cells and immune-related genes is correlated with the development and prognosis of HCC ( Duan et al, 2019 ; Huang et al, 2020 ; Tang et al, 2020 ). Remarkably, targeting on these immune cells and/or genes has been a prospective workable approach in HCC therapy ( Jayant et al, 2020 ).…”

Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

“…The data of the training set were used to generate the prognostic signature through forward conditional stepwise regression with multivariable Cox analysis using the OS-related AR-lncRNAs (Yang et al, 2018 ). A prognostic multi-lncRNA signature was conducted in which the risk score was calculated as follows: ( Coef i was the estimated regression coefficient derived from multivariate Cox regression analysis using the R/survival package (Huang et al, 2020 ), and x i was the expression value of each selected AR-lncRNA). The median risk score in the training set was used as the cutoff point that divided HCC patients into a high-risk group and a low-risk group.…”

“…Log-rank testing method was used to compare the differences of OS outcomes between the high- and low-risk groups via Kaplan–Meier survival analysis (Yang et al, 2018 ). The receiver operating characteristic (ROC) curve analysis in the “survivalROC” package (Heagerty and Zheng, 2005 ; Huang et al, 2017 , 2020 ) was applied to examine the accuracy of the identified AR-lncRNA signature. Area under the curve (AUC) of 3-year OS outcomes based on the time-dependent ROC curves was used to compare the prediction accuracy of our newly identified AR-lncRNA signature with other three recently published signatures.…”