Integrative analysis of genetic and epigenetic profiling of lung squamous cell carcinoma (LSCC) patients to identify smoking level relevant biomarkers

BioData Mining

Jiaohong Wu¹  Jizhou Zhang²  Bin Zhou²  Zhiyou Huang²  Bidong Ma²  Qian Wang³ 

[1] Department of Gynecology and Oncology, Wen Zhou Medical University affiliated People’s Hospital;Department of Medical Oncology, Zhe Jiang Chinese Medicine University affiliated Chinese Medicine Hospital;Tianjia Genomes Tech CO., LTD.;

关键词: Lung squamous cell carcinoma;    Data mining;    RNA-seq;    Methylation;    The Cancer genome atlas;    Smoking intensity;   

DOI  :  10.1186/s13040-019-0207-y

来源: DOAJ

【 摘 要 】

Abstract Background Incidence and mortality of lung cancer have dramatically decreased during the last decades, yet still approximately 160,000 deaths per year occurred in United States. Smoking intensity, duration, starting age, as well as environmental cofactors including air-pollution, showed strong association with major types of lung cancer. Lung squamous cell carcinoma is a subtype of non-small cell lung cancer, which represents 25% of the cases. Thus, exploring the molecular pathogenic mechanisms of lung squamous cell carcinoma plays crucial roles in lung cancer clinical diagnosis and therapy. Results In this study, we performed integrative analyses on 299 comparative datasets of RNA-seq and methylation data, collected from 513 lung squamous cell carcinoma cases in The Cancer Genome Atlas. The data were divided into high and low smoking groups based on smoking intensity (Numbers of packs per year). We identified 1002 significantly up-regulated genes and 534 significantly down-regulated genes, and explored their cellular functions and signaling pathways by bioconductor packages GOseq and KEGG. Global methylation status was analyzed and visualized in circular plot by CIRCOS. RNA-and methylation data were correlatively analyzed, and 24 unique genes were identified, for further investigation of regional CpG sites’ interactive patterns by bioconductor package coMET. AIRE, PENK, and SLC6A3 were the top 3 genes in the high and low smoking groups with significant differences. Conclusions Gene functions and DNA methylation patterns of these 24 genes are important and useful in disclosing the differences of gene expression and methylation profiling caused by different smoking levels.

【 授权许可】

Unknown