Landscape of Prognostic m6A RNA Methylation Regulators in Hepatocellular Carcinoma to Aid Immunotherapy.

Qianhui Xu,Rongshan Deng,Zijie Wang,Yunuo Shen,Zhixuan Qi,Nanjun Li,Hao Xu,Jiaxin Zhao,Jingchao Wen,Ruiqi Mu,Di Weng,Wen Huang

doi:10.3389/fcell.2021.669145

Abstract

Background: Hepatocellular carcinoma (HCC) is the sixth most common malignancy with a high mortality worldwide. N6-methyladenosine (m6A) may participate extensively in tumor progression.Methods: To reveal the landscape of tumor immune microenvironment (TIME), ESTIMATE analysis, ssGSEA algorithm, and the CIBERSORT method were used. Taking advantage of consensus clustering, two different HCC categories were screened. We analyzed the correlation of clustering results with TIME and immunotherapy. Then, we yielded a risk signature by systematical bioinformatics analyses. Immunophenoscore (IPS) was implemented to estimate the immunotherapeutic significance of risk signature.Results: The m6A-based clusters were significantly correlated with overall survival (OS), immune score, immunological signature, immune infiltrating, and ICB-associated genes. Risk signature possessed robust prognostic validity and significantly correlated with TIME context. IPS was employed as a surrogate of immunotherapeutic outcome, and patients with low-risk scores showed significantly higher immunophenoscores.Conclusion: Collectively, m6A-based clustering subtype and signature was a robust prognostic indicator and correlated with TIME and immunotherapy, providing novel insight into antitumor management and prognostic prediction in HCC.

Highlights

Hepatocellular carcinoma (HCC) characterized by high mortality is one of the most common global malignancies (Bray et al, 2018; Forner et al, 2018; Yang et al, 2019) with an estimated 841,080 newly added tumor cases and an almost 781,631 HCC-related mortality documented in 2018 (Bray et al, 2018)
ribonucleic acid (RNA)-sequencing transcriptomic data in the fragments per kilobase per million (FPKM) format and the clinical information of HCC cases were obtained from The Cancer Genome Atlas (TCGA) portal1 for subsequent analysis
Because of genomic diversity and epigenetic complexity, HCC is characterized by high heterogeneity in the clinical and in the molecular level (Schulze et al, 2016; Cancer Genome Atlas Research Network, 2017; Woo and Kim, 2018)

Summary

Introduction

Hepatocellular carcinoma (HCC) characterized by high mortality is one of the most common global malignancies (Bray et al, 2018; Forner et al, 2018; Yang et al, 2019) with an estimated 841,080 newly added tumor cases and an almost 781,631 HCC-related mortality documented in 2018 (Bray et al, 2018). The high heterogeneity of HCC greatly weakened the therapeutic effects and makes prediction of clinical outcome considerably sophisticated (Forner et al, 2018; Zhang et al, 2019b). Immune checkpoint blockade (ICB) immunotherapy has yielded great therapeutic effects in a wide variety of malignancies due to its precision and fewer side effects. Preclinical trial results showed that about 20% of patients benefited from ICB immunotherapy, indicating that immune checkpoint inhibitors may be conducive to HCC clinical management (Cheng et al, 2019). The most effective tactic for the precise prognostic prediction of how a given malignancy will respond to immunotherapy or how clinical course will develop may be one derived from molecular risk distribution, identifying tumor patients based on particular biomarker signatures, generating an individualized program to improve efficacy .

Objectives

Methods

Results

Discussion

Conclusion