MAGE Gene Expression in Bronchial Washing Fluid in Suspected Parenchymal Lung Cancer

Abstract Background DNA methylation is a key epigenetic modification that can directly affect gene regulation. DNA methylation is highly influenced by environmental factors such as cigarette smoking, which is causally related to chronic obstructive pulmonary disease (COPD) and lung cancer. To date, there have been few large-scale, combined analyses of DNA methylation and gene expression and their interrelations with lung diseases. Results We performed an epigenome-wide association study of whole blood gene expression in ~ 6000 individuals from four cohorts. We discovered and replicated numerous CpGs associated with the expression of cis genes within 500 kb of each CpG, with 148 to 1,741 cis CpG-transcript pairs identified across cohorts. We found that the closer a CpG resided to a transcription start site, the larger its effect size, and that 36% of cis CpG-transcript pairs share the same causal genetic variant. Mendelian randomization analyses revealed that hypomethylation and lower expression of CHRNA5, which encodes a smoking-related nicotinic receptor, are causally linked to increased risk of COPD and lung cancer. This putatively causal relationship was further validated in lung tissue data. Conclusions Our results provide a large and comprehensive association study of whole blood DNA methylation with gene expression. Expression platform differences rather than population differences are critical to the replication of cis CpG-transcript pairs. The low reproducibility of trans CpG-transcript pairs suggests that DNA methylation regulates nearby rather than remote gene expression. The putatively causal roles of methylation and expression of CHRNA5 in relation to COPD and lung cancer provide evidence for a mechanistic link between patterns of smoking-related epigenetic variation and lung diseases, and highlight potential therapeutic targets for lung diseases and smoking cessation.

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Biomarker using gene expression profile to detect lung cancer early

Oncology Times UK ◽

10.1097/01434893-200802000-00006 ◽

2008 ◽

Vol 5 (2) ◽

pp. 6

Author(s):

Robert H Carlson

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Gene Expression Profile ◽

Expression Profile

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Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep Learning

IEEE Access ◽

10.1109/access.2021.3071466 ◽

2021 ◽

pp. 1-1

Author(s):

Dong Sui ◽

Maozu Guo ◽

Xiaoxuan Ma ◽

Julian Baptiste ◽

Lei Zhang

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Deep Learning ◽

Gene Expression Data ◽

Imaging Biomarkers ◽

Expression Data ◽

Data Correlation

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Identification of key genes associated with lung adenocarcinoma by bioinformatics analysis

Science Progress ◽

10.1177/0036850421997276 ◽

2021 ◽

Vol 104 (1) ◽

pp. 003685042199727

Author(s):

Xinyu Wang ◽

Jiaojiao Yang ◽

Xueren Gao

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Enrichment Analysis ◽

Tumor Stage ◽

Gene Expression Omnibus ◽

Poor Overall Survival ◽

Key Genes ◽

Low Expression

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, comprising around 40% of all lung cancer. Until now, the pathogenesis of LUAD has not been fully elucidated. In the current study, we comprehensively analyzed the dysregulated genes in lung adenocarcinoma by mining public datasets. Two sets of gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. The dysregulated genes were identified by using the GEO2R online tool, and analyzed by R packages, Cytoscape software, STRING, and GPEIA online tools. A total of 275 common dysregulated genes were identified in two independent datasets, including 54 common up-regulated and 221 common down-regulated genes in LUAD. Gene Ontology (GO) enrichment analysis showed that these dysregulated genes were significantly enriched in 258 biological processes (BPs), 27 cellular components (CCs), and 21 molecular functions (MFs). Furthermore, protein-protein interaction (PPI) network analysis showed that PECAM1, ENG, KLF4, CDH5, and VWF were key genes. Survival analysis indicated that the low expression of ENG was associated with poor overall survival (OS) of LUAD patients. The low expression of PECAM1 was associated with poor OS and recurrence-free survival of LUAD patients. The cox regression model developed based on age, tumor stage, ENG, PECAM1 could effectively predict 5-year survival of LUAD patients. This study revealed some key genes, BPs, CCs, and MFs involved in LUAD, which would provide new insights into understanding the pathogenesis of LUAD. In addition, ENG and PECAM1 might serve as promising prognostic markers in LUAD.

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