Diagnosis and prognosis models for hepatocellular carcinoma patient's management based on tumor mutation burden

Bufu Tang; Jinyu Zhu; Zhongwei Zhao; Chenying Lu; Siyu Liu; Shiji Fang; Liyun Zheng; Nannan Zhang; Minjiang Chen; Min Xu; Risheng Yu; Jiansong Ji

doi:10.1016/j.jare.2021.01.018

Diagnosis and prognosis models for hepatocellular carcinoma patient's management based on tumor mutation burden

J Adv Res. 2021 Feb 9:33:153-165. doi: 10.1016/j.jare.2021.01.018. eCollection 2021 Nov.

Authors

Bufu Tang^{1

2}, Jinyu Zhu^{1

2}, Zhongwei Zhao^{1

3}, Chenying Lu^{1

3}, Siyu Liu⁴, Shiji Fang³, Liyun Zheng³, Nannan Zhang^{1

2}, Minjiang Chen^{1

3}, Min Xu^{1

3}, Risheng Yu², Jiansong Ji^{1

3}

Affiliations

¹ Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital, School of Medicine, Zhejiang University, Lishui 323000, China.
² Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
³ Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
⁴ Department of Laboratory, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.

Abstract

Introduction: The development and prognosis of HCC involve complex molecular mechanisms, which affect the effectiveness of its treatment strategies. Tumor mutational burden (TMB) is related to the efficacy of immunotherapy, but the prognostic role of TMB-related genes in HCC has not yet been determined clearly.

Objectives: In this study, we identified TMB-specific genes with good prognostic value to build diagnostic and prognostic models and provide guidance for the treatment of HCC patients.

Methods: Weighted gene co-expression network analysis (WGCNA) was applied to identify the TMB-specific genes. And LASSO method and Cox regression were used in establishing the prognostic model.

Results: The prognostic model based on SMG5 and MRPL9 showed patients with higher prognostic risk had a remarkedly poorer survival probability than their counterparts with lower prognostic risk in both a TCGA cohort (P < 0.001, HR = 1.93) and an ICGC cohort (P < 0.001, HR = 3.58). In addition, higher infiltrating fractions of memory B cells, M0 macrophages, neutrophils, activated memory CD4 + T cells, follicular helper T cells and regulatory T cells and higher expression of B7H3, CTLA4, PD1, and TIM3 were present in the high-risk group than in the low-risk group (P < 0.05). Patients with high prognostic risk had higher resistance to some chemotherapy and targeted drugs, such as methotrexate, vinblastine and erlotinib, than those with low prognostic risk (P < 0.05). And a diagnostic model considering two genes was able to accurately distinguish patients with HCC from normal samples and those with dysplastic nodules. In addition, knockdown of SMG5 and MRPL9 was determined to significantly inhibit cell proliferation and migration in HCC.

Conclusion: Our study helps to select patients suitable for chemotherapy, targeted drugs and immunotherapy and provide new ideas for developing treatment strategies to improve disease outcomes in HCC patients.

Keywords: Diagnosis; Hepatocellular carcinoma (HCC); Immune checkpoint; Prognosis; TMB.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Carcinoma, Hepatocellular* / diagnosis
Carcinoma, Hepatocellular* / genetics
Carcinoma, Hepatocellular* / therapy
Gene Expression Regulation, Neoplastic
Humans
Liver Neoplasms* / diagnosis
Liver Neoplasms* / genetics
Liver Neoplasms* / therapy
Memory B Cells
Mutation
Prognosis