ArticleTumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine2015
RNA-seq analysis of lung adenocarcinomas reveals different gene expression profiles between smoking and nonsmoking patients.
Article in Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Systematic comparison of ranking aggregation methods for gene lists in experimental results.Bioinformatics (Oxford, England) · 2022Pooled it
- Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers.Oncology reports · 2018Pooled it
- Investigating the role of oncogenic FAM83A as a prognostic biomarker in lung adenocarcinoma: Insights from smoker and non-smoker cohorts.Journal, genetic engineering & biotechnology · 2025Article
- Alternative polyadenylation shapes the molecular and clinical features of lung adenocarcinoma.Human molecular genetics · 2025Article
- Expression and Purification of Recombinant ADAMTS8.Methods in molecular biology (Clifton, N.J.) · 2024Article
- A comprehensive assessment of cell type-specific differential expression methods in bulk data.Briefings in bioinformatics · 2023Article
- Molecular Pathology of Lung Cancer.Cold Spring Harbor perspectives in medicine · 2022Review
- Integrated Analysis of Transcriptomic and Genomic Data Reveals Blood Biomarkers With Diagnostic and Prognostic Potential in Non-small Cell Lung Cancer.Frontiers in molecular biosciences · 2022Article
- Involvement of MicroRNA-1-FAM83A Axis Dysfunction in the Growth and Motility of Lung Cancer Cells.International journal of molecular sciences · 2020Article
- FAM83A exerts tumor‑suppressive roles in cervical cancer by regulating integrins.International journal of oncology · 2020Article
- Bioinformatics Analysis to Reveal Potential Differentially Expressed Long Non-Coding RNAs and Genes Associated with Tumour Metastasis in Lung Adenocarcinoma.OncoTargets and therapy · 2020Article
- CA-125, CA-153, and CYFRA21-1 as clinical indicators in male lung cancer with ocular metastasis.Journal of Cancer · 2020Article
- Effect of tobacco smoking on the epigenetic age of human respiratory organs.Clinical epigenetics · 2019Article
- Survival-related risk score of lung adenocarcinoma identified by weight gene co-expression network analysis.Oncology letters · 2019Article
- Overexpression of Family with Sequence Similarity 83, Member A (FAM83A) Predicts Poor Clinical Outcomes in Lung Adenocarcinoma.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
- A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications.Briefings in bioinformatics · 2019Article
- A Bayesian latent variable approach to aggregation of partial and top-ranked lists in genomic studies.Statistics in medicine · 2018Article
- Article
- Environmental perturbations lead to extensive directional shifts in RNA processing.PLoS genetics · 2017Article
- Impaired response of the bronchial epithelium to inflammation characterizes severe equine asthma.BMC genomics · 2017Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Lung adenocarcinoma is caused by the combination of genetic and environmental effects, and smoking plays an important role in the disease development. Exploring the gene expression profile and identifying genes that are shared or vary between smokers and nonsmokers with lung adenocarcinoma will provide insights into the etiology of this complex cancer. We obtained RNA-seq data from paired normal and tumor tissues from 34 nonsmoking and 34 smoking patients with lung adenocarcinoma (GEO: GSE40419). R Bioconductor, edgeR, was adopted to conduct differential gene expression analysis between paired normal and tumor tissues. A generalized linear model was applied to identify genes that were differentially expressed in nonsmoker and smoker patients as well as genes that varied between these two groups. We identified 2273 genes that showed differential expression with FDR < 0.05 and |logFC| >1 in nonsmoker tumor versus normal tissues; 3030 genes in the smoking group; and 1967 genes were common to both groups. Sixty-eight and 70% of the identified genes were downregulated in nonsmoking and smoking groups, respectively. The 20 genes such as SPP1, SPINK1, and FAM83A with largest fold changes in smokers also showed similar large and highly significant fold changes in nonsmokers and vice versa, showing commonalities in expression changes for adenocarcinomas in both smokers and nonsmokers for these genes. We also identified 175 genes that were significantly differently expressed between tumor samples from nonsmoker and smoker patients. Gene expression profile varied substantially between smoker and nonsmoker patients with lung adenocarcinoma. Smoking patients overall showed far more complicated disease mechanism and have more dysregulation in their gene expression profiles. Our study reveals pathogenetic differences in smoking and nonsmoking patients with lung adenocarcinoma from transcriptome analysis. We provided a list of candidate genes for further study for disease detection and treatment in both smoking and nonsmoking patients with lung adenocarcinoma.
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