Identification of Key Genes in Purine Metabolism as Prognostic Biomarker for Hepatocellular Carcinoma

Wen-Jing Su; Pei-Zhi Lu; Yong Wu; Kumari Kalpana; Cheng-Kun Yang; Guo-Dong Lu

doi:10.3389/fonc.2020.583053

Identification of Key Genes in Purine Metabolism as Prognostic Biomarker for Hepatocellular Carcinoma

Front Oncol. 2021 Jan 14:10:583053. doi: 10.3389/fonc.2020.583053. eCollection 2020.

Authors

Wen-Jing Su¹, Pei-Zhi Lu¹, Yong Wu¹, Kumari Kalpana², Cheng-Kun Yang³, Guo-Dong Lu^{1

2

4}

Affiliations

¹ Department of Toxicology, School of Public Health, Guangxi Medical University, Nanning, China.
² Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
³ Department of Hepatobiliary Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, China.
⁴ Key Laboratory of High-incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education of China, Nanning, China.

Abstract

Background: Deregulated purine metabolism is critical for fast-growing tumor cells by providing nucleotide building blocks and cofactors. Importantly, purine antimetabolites belong to the earliest developed anticancer drugs and are still prescribed in clinics today. However, these antimetabolites can inhibit non-tumor cells and cause undesired side effects. As liver has the highest concentration of purines, it makes liver cancer a good model to study important nodes of dysregulated purine metabolism for better patient selection and precisive cancer treatment.

Methods: By using a training dataset from TCGA, we investigated the differentially expressed genes (DEG) of purine metabolism pathway (hsa00230) in hepatocellular carcinoma (HCC) and determined their clinical correlations to patient survival. A prognosis model was established by Lasso-penalized Cox regression analysis, and then validated through multiple examinations including Cox regression analysis, stratified analysis, and nomogram using another ICGC test dataset. We next treated HCC cells using chemical drugs of the key enzymes in vitro to determine targetable candidates in HCC.

Results: The DEG analysis found 43 up-regulated and 2 down-regulated genes in the purine metabolism pathway. Among them, 10 were markedly associated with HCC patient survival. A prognostic correlation model including five genes (PPAT, DCK, ATIC, IMPDH1, RRM2) was established and then validated using the ICGC test dataset. Multivariate Cox regression analysis found that both prognostic risk model (HR = 4.703 or 3.977) and TNM stage (HR = 2.303 or 2.957) independently predicted HCC patient survival in the two datasets respectively. The up-regulations of the five genes were further validated by comparing between 10 pairs of HCC tissues and neighboring non-tumor tissues. In vitro cellular experiments further confirmed that inhibition of IMPDH1 significantly repressed HCC cell proliferation.

Conclusion: In summary, this study suggests that purine metabolism is deregulated in HCC. The prognostic gene correlation model based on the five purine metabolic genes may be useful in predicting HCC prognosis and patient selection. Moreover, the deregulated genes are targetable by specific inhibitors.

Keywords: bioinformatics; biomarker; hepatocellular carcinoma; prognosis risk model; purine metabolism.