Comprehensive Analysis Based on the TCGA Database Identified SCIN as a Key DNA Methylation-Driver Gene in Epstein-Barr Virus-Associated Gastric Cancer

Zhiyuan Gong; Chunxia Bi; Wen Liu; Bing Luo

doi:10.1007/s10528-024-10702-y

Comprehensive Analysis Based on the TCGA Database Identified SCIN as a Key DNA Methylation-Driver Gene in Epstein-Barr Virus-Associated Gastric Cancer

Biochem Genet. 2024 Feb 27. doi: 10.1007/s10528-024-10702-y. Online ahead of print.

Authors

Zhiyuan Gong¹, Chunxia Bi², Wen Liu³, Bing Luo⁴

Affiliations

¹ Department of Pathogenic Biology, School of Basic Medicine, Qingdao University, Qingdao, 266071, China.
² Department of Clinical Laboratory, Qingdao Municipal Hospital, Qingdao, Shandong, China.
³ Department of Pathogenic Biology, School of Basic Medicine, Qingdao University, Qingdao, 266071, China. liu.wen@hotmail.com.
⁴ Department of Pathogenic Biology, School of Basic Medicine, Qingdao University, Qingdao, 266071, China. qdluobing@163.com.

PMID: 38411940
DOI: 10.1007/s10528-024-10702-y

Abstract

An important feature of EBV-associated gastric cancer (EBVaGC) is extensive methylation of viral and host genomes. This study aims to analyze DNA methylation-driven genes (DMDG) in EBVaGC through bioinformatics methods, providing an important bioinformatics basis for the differential diagnosis and treatment of potential methylation biomarkers in EBVaGC. We downloaded the mRNA expression profiles and methylation datasets of EBVaGC and EBV-negative gastric cancer (EBVnGC) through the TCGA database to screen methylated-differentially expressed genes (MDEGs). DNA methylation-driver genes were identified based on MethylMix algorithm and key genes were further identified by LASSO regression and Random Forest algorithm. Then, we performed gene enrichment analysis for key genes and validated them by GEO database. Gene expression differences in EBVaGC and EBVnGC cell lines was determined by quantitative real-time PCR (qRT-PCR) and western blotting and in GT38 cell and SNU719 cell which all treated by 5-Aza-CdR. Finally, the effect of key gene on the migration and proliferation capacity of EBVaGC cells was determined by Transwells assay and Cell counting Kit-8 (CCK-8) assay. We obtained a total of 687 hypermethylation-low expression genes (Hyper-LGs) and further obtained 53 DNA methylation-driver genes based on the MethylMix algorithm. A total of six key genes (SCIN, ETNK2, PCDH20, PPP1R3C, MATN2, and HOXA5) were identified by LASSO regression and Random Forest algorithm. Among them, SCIN expression was significantly lower in EBVaGC cell lines than in EBVnGC cell lines, and its expression was significantly recovered in EBVaGC cell lines treated with 5-Aza-CdR. Overexpression of SCIN can promote the proliferation and migration capacity of EBVaGC cells. Our study will provide some bioinformatics basis for the study of EBVaGC-related methylation. SCIN may be used as potential methylation biomarkers for the diagnosis and treatment of EBVaGC.

Keywords: Bioinformatics; DNA methylation; Epstein-Barr virus; Gastric cancer; Scinderin.

Abstract

Grants and funding