Comprehensive Study of Tumor Immune Microenvironment and Relevant Genes in Hepatocellular Carcinoma Identifies Potential Prognostic Significance

Abstract

Background: The tumor immune microenvironment (TIME) is an external immune system that regulates tumorigenesis. However, cellular interactions involving the TIME in hepatocellular carcinoma (HCC) are poorly characterized. Methods: In this study, we used multidimensional bioinformatic methods to comprehensively analyze cellular TIME characteristics in 735 HCC patients. Additionally, we explored associations involving TIME molecular subtypes and gene types and clinicopathological features to construct a prognostic signature. Results: Based on their characteristics, we classified TIME and gene signatures into three phenotypes (TIME T1-3) and two gene clusters (Gene G1-2), respectively. Further analysis revealed that Gene G1 was associated with immune activation and surveillance and included CD8 + T cells, natural killer cell activation, and activated CD4 + memory T cells. In contrast, Gene G2 was characterized by increased M0 macrophage and regulatory T cell levels. After calculation of principal component algorithms, a TIME score (TS) model, including 78 differentially expressed genes, was constructed based on TIME phenotypes and gene clusters. Furthermore, we observed that the Gene G2 cluster was characterized by high TS, and Gene G1 was characterized by low TS, which correlated with poor and favorable prognosis of HCC, respectively. Correlation analysis showed that TS had a positive association with several clinicopathologic signatures [such as grade, stage, tumor (T), and node (N)] and known somatic gene mutations (such as TP53 and CTNNB1). The prognostic value of the TS model was verified using external data sets. Conclusion: We constructed a TS model based on differentially expressed genes and involving immune phenotypes and demonstrated that the TS model is an effective prognostic biomarker and predictor for HCC patients.

Prognostic Signature of Hepatocellular Carcinoma and Analysis of Immune Infiltration Based on m6A-Related lncRNAs

“…T cells follicular helper, T cells regulatory Tregs, and M0 macrophages were significantly increased in the high-risk group according to our results. Another study concluded that T cells follicular helper and M0 macrophages were unfavorable prognostic markers ( 30 ), which is corroborated herein. As is known, Tregs form an important part of the tumor immunosuppressive microenvironment.…”

Research on predicting the occurrence of hepatocellular carcinoma based on Notch signal-related genes using machine learning algorithms

“…The increase of Macrophages M0 is signi cantly correlated with OS and tumor stage of HCC [32]. Macrophages M0 can stimulate the production of TAM and Kupffer cells in the presence of carcinogenic factors, thereby inhibiting the progression of HCC caused by immunity [33]. This may be related to the malignant behavior of the highly expressed genes from our research and analysis.…”

Identification of prognostic alternative splicing events related to the immune microenvironment of hepatocellular carcinoma

“…However, current HCC immunotherapy still faces challenges, such as uncertainty in efficacy, numerous adverse events, and drug resistance even after initial patient benefits (Byun et al 2017 ; Sharma et al 2017 ). Previous studies have shown that the TME is closely related to the growth and development of HCC (Tahmasebi Birgani and Carloni 2017 ; Chen et al 2020 ). Thanks to the tremendous development of bioinformatics and high-throughput technology, cancer genomics research has been greatly facilitated.…”