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

Original Article Page 1 of 10 Construction and validation of a prognostic signature using CNV-driven genes for hepatocellular carcinoma Jin Bian1, Junyu Long1, Xu Yang1, Xiaobo Yang1, Yiyao Xu1, Xin Lu1, Mei Guan2, Xinting Sang1, Haitao Zhao1^ 1 Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China; 2Department of Medical Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China Contributions: (I) Conception and design: J Bian, J Long, M Guan, X Sang, H Zhao; (II) Administrative support: X Lu, X Sang, H Zhao; (III) Provision of study materials or patients: J Bian, X Yang, X Yang, X Lu; (IV) Collection and assembly of data: J Bian, X Yang, Y Xu; (V) Data analysis and interpretation: J Bian, J Long, M Guan; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. Correspondence to: Mei Guan, MD. Department of Medical Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), 1 Shuaifuyuan, Wangfujing, Beijing 100730, China. Email: ; Xinting Sang, MD. Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), 1 Shuaifuyuan, Wangfujing, Beijing 100730, China. Email: ; Haitao Zhao, MD. Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), 1 Shuaifuyuan, Wangfujing, Beijing 100730, China. Email: . 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): 2.72–13.86, P<0.001] and ICGC (HR =3.23, 95% CI: 1.17–8.92, 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. Keywords: Copy number variation-driven genes (CNV-driven genes); hepatocellular carcinoma (HCC); prognosis; immune microenvironment Submitted Oct 25, 2020. Accepted for publication Mar 10, 2021. doi: 10.21037/atm-20-7101 View this article at: http://dx.doi.org/10.21037/atm-20-7101 ^ ORCID: 0000-0002-3444-8044. © Annals of Translational Medicine. All rights reserved. Ann Transl Med 2021;9(9):765 | http://dx.doi.org/10.21037/atm-20-7101 Page 2 of 10 Bian et al. Prediction model based on CNV-driven genes for HCC Introduction Methods Hepatocellular carcinoma (HCC) is a lethal malignancy and accounts for approximately 85% to 90% of primary liver cancers (1,2). Although targeted therapy and immunotherapy have emerged as potential therapies, curative therapies for HCC remain limited (3). Moreover, high post-operative recurrence rates and rare complete cures make it difficult for achieving long term survival. A study on natural history of HCC indicated that patients with advanced stage (Barcelona Clinic Liver Cancer Stage C) had a survival of only 3.4 months if untreated (4). HCC develops following a step-wise manner with abundant genetic and epigenetic molecular alterations (5). Therefore, it is crucial to achieve a better understanding of the underlying molecular mechanism that drives HCC occurrence and development. Exploring prediction model based on the factors that drive HCC can be useful for individualized therapy option and prognosis prediction for HCC patients. As critical subclasses of somatic mutations, copy number variations (CNVs) refer to duplications or deletions of DNA segments, which are greater than 1 kb compared to a reference genome (6). CNVs account for the accumulation of genomic DNA aberrations, and play important role in cancer pathogenesis. Notably, CNVs can result in activation of oncogenes or inactivation of tumor suppressor genes, which drives cancer development (7,8). Multiple CNVs have been reported to be implicated in the pathogenesis and prognosis of cancers including HCC (9-12). Frequent CNVs of subpopulations of cancer cells were reported to contribute to HCC heterogeneity, indicating a critical role of CNVs in HCC development and progression (13). However, most previous studies focused on CNVs or transcriptome alterations separately, and a comprehensive study of how CNVs drives HCC is still lacking. Combining analysis of CNVs and corresponding gene expression will promote more accurate identification of the specific cancer signatures for HCC. In this study, we used transcriptomic and CNVs profiles to identify CNV-driven genes and aimed to construct a prognostic model for HCC. Our research may contribute to better understanding of the underlying mechanisms, and provide novel therapeutic targets for HCC treatment. We present the following article in accordance with the TRIPOD reporting checklist (available at http://dx.doi. org/10.21037/atm-20-7101). Data collection © Annals of Translational Medicine. All rights reserved. Gene expression profiles (374 tumor samples and 50 normal samples) and DNA CNVs data (379 HCC samples and 389 nontumor samples) of HCC patients were obtained from The Cancer Genome Atlas (TCGA) (https://portal.gdc. cancer.gov/, up to November 1, 2019). The corresponding cl (...truncated)