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Validation of SCT Methylation as a Hallmark Biomarker for Lung Cancers.

ABSTRACT: The human secretin gene (SCT) encodes secretin, a hormone with limited tissue distribution. Analysis of the 450k methylation array data in The Cancer Genome Atlas (TCGA) indicated that the SCT promoter region is differentially hypermethylated in lung cancer. Our purpose was to validate SCT methylation as a potential biomarker for lung cancer.We analyzed data from TCGA and developed and applied SCT-specific bisulfite DNA sequencing and quantitative methylation-specific polymerase chain reaction assays.The analyses of TCGA 450K data for 801 samples showed that SCT hypermethylation has an area under the curve (AUC) value greater than 0.98 that can be used to distinguish lung adenocarcinomas or squamous cell carcinomas from nonmalignant lung tissue. Bisulfite sequencing of lung cancer cell lines and normal blood cells allowed us to confirm that SCT methylation is highly discriminative. By applying a quantitative methylation-specific polymerase chain reaction assay, we found that SCT hypermethylation is frequently detected in all major subtypes of malignant non-small cell lung cancer (AUC = 0.92, n = 108) and small cell lung cancer (AUC = 0.93, n = 40) but is less frequent in lung carcinoids (AUC = 0.54, n = 20). SCT hypermethylation appeared in samples of lung carcinoma in situ during multistage pathogenesis and increased in invasive samples. Further analyses of TCGA 450k data showed that SCT hypermethylation is highly discriminative in most other types of malignant tumors but less frequent in low-grade malignant tumors. The only normal tissue with a high level of methylation was the placenta.Our findings demonstrated that SCT methylation is a highly discriminative biomarker for lung and other malignant tumors, is less frequent in low-grade malignant tumors (including lung carcinoids), and appears at the carcinoma in situ stage.

SUBMITTER: Zhang YA

PROVIDER: S-EPMC4809190 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Validation of SCT Methylation as a Hallmark Biomarker for Lung Cancers.

Zhang Yu-An YA Ma Xiaotu X Sathe Adwait A Fujimoto Junya J Wistuba Ignacio I Lam Stephen S Yatabe Yasushi Y Wang Yi-Wei YW Stastny Victor V Gao Boning B Larsen Jill E JE Girard Luc L Liu Xiaoyun X Song Kai K Behrens Carmen C Kalhor Neda N Xie Yang Y Zhang Michael Q MQ Minna John D JD Gazdar Adi F AF

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 20151225 3

<h4>Introduction</h4>The human secretin gene (SCT) encodes secretin, a hormone with limited tissue distribution. Analysis of the 450k methylation array data in The Cancer Genome Atlas (TCGA) indicated that the SCT promoter region is differentially hypermethylated in lung cancer. Our purpose was to validate SCT methylation as a potential biomarker for lung cancer.<h4>Methods</h4>We analyzed data from TCGA and developed and applied SCT-specific bisulfite DNA sequencing and quantitative methylation ...[more]

PMID: 26725182

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Project description:BackgroundIt is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods.Methodology/principal findingsUsing melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer.Conclusions/significanceWe thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases.

Dataset Information

Validation of SCT Methylation as a Hallmark Biomarker for Lung Cancers.

Publications

Validation of SCT Methylation as a Hallmark Biomarker for Lung Cancers.

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