ArticleBioData mining2019
Integrative analysis of genetic and epigenetic profiling of lung squamous cell carcinoma (LSCC) patients to identify smoking level relevant biomarkers.
Article in BioData mining, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Single-cell RNA seq data analysis reveals molecular markers and possible treatment targets for laryngeal squamous cell carcinoma (LSCC): an in-silico approach.In silico pharmacology · 2025Article
- Air pollution-induced epigenetic changes: disease development and a possible link with hypersensitivity pneumonitis.Environmental science and pollution research international · 2021Review
- [Research Progress of Epigenetics in Pathogenesis and Treatment of Malignant Tumors].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIncidence 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.
resultsIn 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.
conclusionsGene 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.
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