Genome-wide DNA methylation profiling reveals novel epigenetic signatures in squamous cell lung cancer

- Genome-wide DNA methylation profiling reveals novel epigenetic signatures in squamous cell lung cancer.
- The aim of this study was to identify epigenetic pattern in squamous cell lung cancer (LUSC) on a genome-wide scale..
- Results: Here we performed DNA methylation profiling on 24 LUSC and paired non-tumor lung (NTL) tissues by Illumina Human Methylation 450 K BeadArrays, and identified 5214 differentially methylated probes.
- By integrating DNA methylation and mRNA expression data, 449 aberrantly methylated genes accompanied with altered expression were identified.
- Then, we identified a panel of DNA methylation biomarkers (CLDN1, TP63, TBX5, TCF21, ADHFE1 and HNF1B) in LUSC.
- Moreover, hierarchical clustering analysis of the DNA methylation data identified two tumor subgroups, one of which showed increased DNA methylation..
- Conclusions: Collectively, these results suggest that DNA methylation plays critical roles in lung tumorigenesis and may potentially be proposed as a diagnostic biomarker..
- Early diagnosis of cancer is one of the most important factors contributing to the successful and effective treatment.
- Tumorigenesis involves a multi-step process, which is the result of the interactions of genetic, epigenetic and environmental factors.
- DNA methylation is a major epigenetic modification which leads to gene.
- Full list of author information is available at the end of the article.
- 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0.
- Thus, identification of DNA methyla- tion biomarkers has emerged as one of the most promis- ing approaches to improve cancer diagnosis, it presents several advantages compared with other markers [4, 5]..
- Additionally, the DNA methylation represents a very stable sign that can be de- tected in many different types of samples, including tumor tissues, cancer cells in body fluids [7, 8].
- Most im- portantly, DNA methylation can be detected by a wide range of sensitive and cost efficient techniques even in samples with low tumor purity..
- All the patients provided written informed consents in compliance with the code of ethics of the World Medical Association (Declaration of Helsinki) at the time of sur- gery for the donation of their tissue for this research..
- Genome-scale DNA methylation were analyzed by the Illumina Human Methylation 450 K BeadArrays according to manufacturer’s instructions in the laboratory of Ca- pitalBio Corporation (Beijing, China), which quantifies methylation levels (β-value) of 485,577 CpG-sites.
- one of the primers was biotinylated to enable capture by Streptavidin Sepharose (Additional file 2: Table S1)..
- DNA methylation datasets in LUSC were down- loaded from the Cancer Genome Atlas (TCGA) data portal (http://tcga-data.nci.nih.gov).
- We selected 343 tumor and 39 paired NTL samples, with both DNA methylation data and clinical features information available for performing the correlation analysis.
- Genome-wide DNA methylation patterns in LUSC.
- In brief, the DNA methylation levels gradually increased with the CpG sites far away from CGIs.
- A two-dimensional hierarchical clustering analysis of the 5214 probes revealed a clear sorting of tu- mors and NTLs, indicating a substantial difference in DNA methylation profiles between the tumor and non- tumor samples (Fig.
- The gene context regions of the hyper- or hypomethylated CpG sites were distributed similarly.
- How- ever, the CpG island-based regions of the significantly hyper- or hypomethylated CpG sites are distributed dif- ferently.
- 60% of the hypermethylated CpG sites are in CpG islands and that fewer are in the CpG shores (24%) and CpG shelves (4.
- In contrast, just 5% of the hypo- methylated CpG sites were in CpG islands, CpG shores.
- We also added a distribution analysis of 371,000 probes in Additional file 4: Figure S4, and we compared the distribution of the differentially methylated probes and the overall probes in the genomic context.
- Compared to the overall distribution of all probes, the differentially methylated probes are distrib- uted differently just in the CpG islands, 31% of the total probes located on the CpG island, 60% of the hyper- methylated probes were located on the CpG island, and just 5% of the hypomethylated probes were located on the CpG island..
- Identification of potentially functionally relevant DNA methylation changes in LUSC.
- We performed an exploratory two- dimensional hierarchical clustering of the differentially expressed probes, the mRNA expression profiles of tu- mors and NTL resulted in separate clusters (Fig.
- To further investigate the rela- tionships between DNA methylation and gene expres- sion, we selected ten genes for verification.
- To study biological functions of the 70 negatively corre- lated genes, Gene Ontology (GO) analysis was performed..
- In terms of the biological processes, most of the genes were related to development and adhesion.
- 7 of the top 10 categories of molecular function were related to protein binding, while cellular component mostly involved the plasma membrane and cell junction (Fig.
- Gene net- work analysis was further conducted using Ingenuity Path- ways Analysis (IPA), we found that top two gene networks might be affected by the aberrant DNA methylation of the 256 negative correlation genes (Fig.
- 4a, of the 256 genes with inverse correlations, a total of 229 genes were sig- nificantly differentially expressed in our study and TCGA dataset (LSCC), most of the genes were expressed at the same direction, while 2 up regulated genes were down regulated in the LSCC dataset, and 4 down regulated genes were up regulated in LSCC dataset.
- 1 Identification of DNA methylation differences between LUSC and NTL.
- b Two-dimensional hierarchical clustering was performed using the 5214 variable DNA methylation probes across all samples ( n = 48).
- Validation of the methylation biomarkers for LUSC diagnosis To confirm our previous results, we selected six genes (CLDN1, TP63, TBX5, TCF21, ADHFE1 and HNF1B).
- DNA methylation was detected by using pyrosequencing, mRNA expression was identified by using realtime PCR..
- 2 Identification of genes showing coordinately changed DNA methylation and gene expression.
- a Volcano plot and two-dimensional hierarchical clustering of the differential mRNA expression analysis.
- b Starburst plot integrating differential DNA methylation and gene expression analyses.
- Three-dimensional starburst plot of 123 genes, integrating significant changes in DNA methylation (x-axis) and gene expression (y-axis), with a mean twofold or greater change in gene expression (z-axis).
- d Correlation plots of DNA methylation versus gene expression in tumors and normal tissues for selected genes.
- x-axis: DNA methylation level ( β value), y-axis: mRNA expression level, r: correlation coefficient.
- The methylation levels of the six selected genes were similar to those of our clinical validation cohort with significant differences between tumor and NTL (Additional file 5: Figure S3A), suggesting that the methyla- tion statuses of the six selected biomarkers are a common feature for LUSC.
- Details of the CGs dinucleotides for these six genes are listed in Additional file 2: Table S5..
- Patients were divided into two groups according to each of the following five factors: age (<60 or ≥60 years old), smoking status (smokers or non-smokers), differenti- ation (poorly or moderately), complication (with or with- out) and TNM stages (I/II or III).
- 3 GO and pathway analysis of significant DNA methylation changes associated with significant inverse gene expression changes.
- b Gene networks identified through integrative pathways analysis of the negatively correlated genes.
- a Clinical validation of DNA methylation levels of selected genes in paired LUSC and adjacent NTL tissue by using pyrosequencing.
- Then, to identify squamous cell lung cancer DNA methylation-based sub- classes, we used the 5214 differentially methylated probes to perform an unsupervised hierarchical clustering.
- In the present study, we investigated the genome-wide DNA methylation patterns in 24 paired LUSC and adja- cent NTL tissues by microarray, and identified 5214.
- 6 Subgroup analysis of DNA methylation between LUSC and NTL.
- a Subgroup analysis of DNA methylation between LUSC and NTL according to smoking status, differentiation, TNM stage, age and complications.
- DNA methylation levels of Cluster 1, Custer 2 and NTL are shown using M-values.
- c Two-dimensional hierarchical clustering of the 2470 differentially methylated probes (on the CpG island) was performed, two distinct clusters were identified.
- By integrating DNA methylation and mRNA expression data, 449 aberrantly methylated genes accompanied with altered expression were identi- fied.
- Then, we identified a panel of DNA methylation bio- markers in LUSC.
- Finally, hierarch- ical clustering analysis of the DNA methylation data identified two tumor subgroups, one of which showed increased DNA methylation..
- The pattern of DNA methylation in some certain types of cancers has been investigated including NSCLC [12]..
- DNA methylation analysis of cell-free blood samples has a substantial potential to serve as a minimally invasive tool for early diagnosis and clinical monitoring of can- cer.
- showed that patterns of DNA methylation can divide NSCLCs into these two phenotypically dis- tinct subtypes of tumors and provide proof of principle that differences in DNA methylation can be used as a platform for predictive biomarker discovery and devel- opment [20].
- Their study provides an insightful perspective on smoking-associated DNA methylation and its role in tumorigenesis of the lung [21].
- One of the previously reported methylated genes was SOX17 , a canonical WNT antagonist previously shown functionally hypermethylated in breast, colorectal and lung cancers [22–24].
- How- ever, most of the differentially methylated genes identified in this study were novel.
- ADHFE1, a member of the group III metal dependent alcohol de- hydrogenase family.
- To further validate our findings, we asked if the findings of the three most highly correlated investigations (LSCC,.
- The high reliability and reproducibility of the microarray technology in identifying the six genes are essential for its application in discovering the clinical biomarkers..
- We just examined the methylation of the target genes in tissue samples.
- The current study demonstrated that differences in genome- wide DNA methylation and gene expression patterns exist between LUSC and NTL.
- Our results suggested that DNA methylation plays critical roles in lung tumorigenesis and may potentially be proposed as a diagnostic biomarker..
- Sketch and pipeline of the study design..
- Validation of the methylation biomarkers using 343 LUSC and 39 NTL tissue from TCGA database.
- LUSC: Squamous cell lung cancer.
- NSCLC: Non-small cell lung cancer.
- All the patients provided written informed consents in compliance with the code of ethics of the World Medical Association (Declaration of Helsinki) at the time of surgery for the donation of their tissue for this research.
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- A novel epigenetic signature for early diagnosis in lung cancer.
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- Genome-scale long noncoding RNA expression pattern in squamous cell lung cancer.
- Diagnostic performance of plasma DNA Methylation profiles in lung cancer, pulmonary fibrosis and COPD.
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- Epigenomic analysis of lung adenocarcinoma reveals novel DNA methylation patterns associated with smoking.
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- Methylation of the claudin 1 promoter is associated with loss of expression in estrogen receptor positive breast cancer.
- Ogoshi K, Hashimoto S-i, Nakatani Y, Qu W, Oshima K, Tokunaga K, Sugano S, Hattori M, Morishita S, Matsushima K: Genome-wide profiling of DNA methylation in human cancer cells.
- p40: a p63 isoform useful for lung cancer diagnosis - a review of the physiological and pathological role of p63.
- Methylation of the candidate biomarker TCF21 is very frequent across a spectrum of early-stage nonsmall cell lung cancers.
- Epigenetic regulation of the tumor suppressor gene TCF21 on 6q23-q24 in lung and head and neck cancer

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