The pattern of expression and prognostic value of key regulators for m7G RNA methylation in hepatocellular carcinoma

Jianxing Chen; Shibin Yao; Zhijuan Sun; Yanjun Wang; Jili Yue; Yongkang Cui; Chengping Yu; Haozhi Xu; Linqiang Li

doi:10.3389/fgene.2022.894325

The pattern of expression and prognostic value of key regulators for m⁷G RNA methylation in hepatocellular carcinoma

Front Genet. 2022 Sep 2:13:894325. doi: 10.3389/fgene.2022.894325. eCollection 2022.

Authors

Jianxing Chen^{1

2}, Shibin Yao³, Zhijuan Sun⁴, Yanjun Wang⁵, Jili Yue⁶, Yongkang Cui², Chengping Yu², Haozhi Xu², Linqiang Li^{2

7}

Affiliations

¹ College of Chemistry and Life Science, Chifeng University, Chifeng, China.
² Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
³ Department of Emergency, Affiliated Hospital of Chifeng University, Chifeng, China.
⁴ International Education School, Chifeng University, Chifeng, China.
⁵ Department of Pediatrics, Affiliated Hospital of Chifeng University, Chifeng, China.
⁶ Department of General Surgery, Affiliated Hospital of Chifeng University, Chifeng, China.
⁷ Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin Medical University, Harbin, China.

Abstract

N7-methylguanosine (m⁷G) modification on internal RNA positions plays a vital role in several biological processes. Recent research shows m⁷G modification is associated with multiple cancers. However, in hepatocellular carcinoma (HCC), its implications remain to be determined. In this place, we need to interrogate the mRNA patterns for 29 key regulators of m⁷G RNA modification and assess their prognostic value in HCC. Initial, the details from The Cancer Genome Atlas (TCGA) database concerning transcribed gene data and clinical information of HCC patients were inspected systematically. Second, according to the mRNA profiles of 29 m⁷G RNA methylation regulators, two clusters (named 1 and 2, respectively) were identified by consensus clustering. Furthermore, robust risk signature for seven m⁷G RNA modification regulators was constructed. Last, we used the Gene Expression Omnibus (GEO) dataset to validate the prognostic associations of the seven-gene risk signature. We figured out that 24/29 key regulators of m⁷G RNA modification varied remarkably in their grades of expression between the HCC and the adjacent tumor control tissues. Cluster one compared with cluster two had a substandard prognosis and was also positively correlated with T classification (T), pathological stage, and vital status (fustat) significantly. Consensus clustering results suggested the expression pattern of m⁷G RNA modification regulators was correlated with the malignancy of HCC strongly. In addition, cluster one was extensively enriched in metabolic-related pathways. Seven optimal genes (METTL1, WDR4, NSUN2, EIF4E, EIF4E2, NCBP1, and NCBP2) were selected to establish the risk model for HCC. Indicating by further analyses and validation, the prognostic model has fine anticipating command and this probability signature might be a self supporting presage factor for HCC. Finally, a new prognostic nomogram based on age, gender, pathological stage, histological grade, and prospects were established to forecast the prognosis of HCC patients accurately. In essence, we detected association of HCC severity and expression levels of m⁷G RNA modification regulators, and developed a risk score model for predicting prognosis of HCC patients' progression.

Keywords: bioinformatics; hepatocellular carcinoma; m7G; prognosis; risk signature.