Construction and validation of a prognostic signature using CNV-driven genes for hepatocellular carcinoma

Abstract

Background: Hepatocellular carcinoma (HCC) is one of the major causes of cancer-related deaths worldwide. Copy number variations (CNVs) affect the expression of genes and play critical roles in carcinogenesis. We aimed to identify specific CNV-driven genes and establish a prognostic model for HCC. Methods: Integrative analysis of CNVs difference data and differentially expressed genes (DEGs) data from The Cancer Genome Atlas (TCGA) were conducted to identify critical CNV-driven genes for HCC. A risk model was constructed based on univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The associations between CNV-driven genes signature and infiltrating immune cells were explored. The International Cancer Genome Consortium (ICGC) dataset was utilized to validate this model. Results: After integrative analysis of CNVs and corresponding mRNA expression profiles, 568 CNVdriven genes were identified. Sixty-three CNV-driven genes were found to be markedly associated with overall survival (OS) after univariate Cox regression analysis. Finally, eight CNV-driven genes were screened to generate a prognostic risk model. Compared with low-risk group, the OS of patients in the high-risk group was significantly shorter in both the TCGA [hazard ratio (HR) =6.14, 95% confidence interval (CI): 2.72 & ndash;13.86, P<0.001] and ICGC (HR =3.23, 95% CI: 1.17 & ndash;8.92, P<0.001) datasets. Further analysis revealed the infiltrating neutrophils were positively correlated with risk score. Meanwhile, the high-risk group was associated with higher expression of immune checkpoint genes. Conclusions: A novel signature based on CNV-driven genes was built to predict the survival of HCC patients and showed good performance. The results of our study may improve understanding of the mechanism that drives HCC, and provide an immunological perspective for individualized therapies. FU - International Science and Technology Cooperation Projects [2016YFE0107100]; Capital Special Research Project for Health Development [2014-2-4012]; Beijing Natural Science FoundationBeijing Natural Science Foundation [L172055, 7192158]; National Ten-thousand Talent Program; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [3332018032]; CAMS Innovation Fund for Medical Science (CIFMS) [2017-I2M-4-003, 2018-I2M-3-001] FX - This work was supported by the International Science and Technology Cooperation Projects, No. 2016YFE0107100; Capital Special Research Project for Health Development, No. 2014-2-4012; Beijing Natural Science Foundation, No. L172055 and No. 7192158; National Ten-thousand Talent Program, the Fundamental Research Funds for the Central Universities, No. 3332018032; and CAMS Innovation Fund for Medical Science (CIFMS), No. 2017-I2M-4-003 and No. 2018-I2M-3-001. NR - 28 PU - AME PUBL CO PI - SHATIN PA - FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG 00000, PEOPLES R CHINA