Methylation analysis in spontaneous sputum for lung cancer diagnosis

Hübers, Annemarie; Drift, Miep A. van der; Prinsen, Clemens; Witte, Birgit I.; Wang, Yinghui; Shivapurkar, Narayan; Stastny, Victor; Bolijn, Anne S.; Hol, Bernard; Feng, Ziding; Dekhuijzen, P.N.R.; Gazdar, Adi F.; Thunnissen, Erik

doi:10.1016/j.lungcan.2014.01.019

Cited by 27 publications

(31 citation statements)

References 33 publications

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“…A lengthy interval is not desirable either as lung cancer diagnoses may be missed due to the absence of or a concentration below analytical detection limit of DNA hypermethylation in sputum, especially in preclinical phase. This is supported by the observation that the sensitivity of DNA hypermethylation is much lower than in our previous studies in symptomatic patients,11 12 in whom the content of tumour-related DNA in sputum was already close to the detection limit.…”

Section: Discussionsupporting

confidence: 81%

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DNA hypermethylation analysis in sputum of asymptomatic subjects at risk for lung cancer participating in the NELSON trial: argument for maximum screening interval of 2 years

et al. 2016

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Section: Discussionsupporting

confidence: 81%

“…Presence of RASSF1A hypermethylation within this time span showed high specificity (93%), although with low sensitivity (17%) in this population at risk for lung cancer. In sputum collected within 6 months in patients with clinically diagnosed lung cancer, the sensitivity is higher (40+%) 11 12…”

Section: Discussionmentioning

confidence: 99%

DNA hypermethylation analysis in sputum of asymptomatic subjects at risk for lung cancer participating in the NELSON trial: argument for maximum screening interval of 2 years

et al. 2016

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“…In the past few decades, molecular approaches to detect lung cancer, using sputum, have been reported (Table 2 and Table S2) [37,38,39,40,41,42,43,44,45,46,47,48,49,50]. Honorio et al [38] analyzed promoter hypermethylation of the RASSF1A gene in sputum in 2003.…”

Section: Use Of Bodily Fluids For Cancer Detectionmentioning

confidence: 99%

“…The sensitivity to detecting lung cancer in each gene was 31%, 38%, 23%, and 23%, respectively; however, the sensitivity increased to 54% and 62% with a specificity of 100% by the combination of 3-OST-2 and RASSF1A or all of four genes. In recent years, Hubers et al [47] have produced several reports regarding sputum hypermethylation for lung cancer diagnosis; their first report was published in 2014 [48]. In 2014, they analyzed the hypermethylation of three genes ( RASSF1A , APC , and cytoglobin ) in relatively large cohorts.…”

Section: Use Of Bodily Fluids For Cancer Detectionmentioning

confidence: 99%

Analysis of DNA Methylation Status in Bodily Fluids for Early Detection of Cancer

Yokoi

Yamashita

Watanabe

2017

IJMS

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Epigenetic alterations by promoter DNA hypermethylation and gene silencing in cancer have been reported over the past few decades. DNA hypermethylation has great potential to serve as a screening marker, a prognostic marker, and a therapeutic surveillance marker in cancer clinics. Some bodily fluids, such as stool or urine, were obtainable without any invasion to the body. Thus, such bodily fluids were suitable samples for high throughput cancer surveillance. Analyzing the methylation status of bodily fluids around the cancer tissue may, additionally, lead to the early detection of cancer, because several genes in cancer tissues are reported to be cancer-specifically hypermethylated. Recently, several studies that analyzed the methylation status of DNA in bodily fluids were conducted, and some of the results have potential for future development and further clinical use. In fact, a stool DNA test was approved by the U.S. Food and Drug Administration (FDA) for the screening of colorectal cancer. Another promising methylation marker has been identified in various bodily fluids for several cancers. We reviewed studies that analyzed DNA methylation in bodily fluids as a less-invasive cancer screening.

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“…The methylation levels of some of genes corresponding to the identified markers such as ERBB2, FAM19A4 and RASSF1 have been shown to be related to NSCLCs and may be utilized to distinguish NSCLCs and normal lung cells in previous studies [61][62][63] The methylation level of ST18 ranks the first in our optimal set of features, which is a repressor that binds to DNA sequences containing a bipartite element consisting of a direct repeat of the sequence 5'-AAAGTTT-3' separated by 2-9 nucleotides. Job et al 64 performed high-resolution array comparative genomic hybridization analysis of lung adenocarcinoma in sixty never smokers and identified fourteen new minimal common regions (MCR) of gain or loss, of which five contained a single gene (MOCS2, NSUN3, KHDRBS2, SNTG1 and ST18).…”

Section: Methylation Marker Genes Identified In This Studymentioning

confidence: 99%

Classification of lung cancer using ensemble-based feature selection and machine learning methods

et al. 2015

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Lung cancer is one of the leading causes of death worldwide. There are three major types of lung cancers, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and carcinoid. NSCLC is further classified into lung adenocarcinoma (LADC), squamous cell lung cancer (SQCLC) as well as large cell lung cancer. Many previous studies demonstrated that DNA methylation has emerged as potential lung cancer-specific biomarkers. However, whether there exists a set of DNA methylation markers simultaneously distinguishing such three types of lung cancers remains elusive. In the present study, ROC (Receiving Operating Curve), RFs (Random Forests) and mRMR (Maximum Relevancy and Minimum Redundancy) were proposed to capture the unbiased, informative as well as compact molecular signatures followed by machine learning methods to classify LADC, SQCLC and SCLC. As a result, a panel of 16 DNA methylation markers exhibits an ideal classification power with an accuracy of 86.54%, 84.6% and a recall 84.37%, 85.5% in the leave-one-out cross-validation (LOOCV) and independent data set test experiments, respectively. Besides, comparison results indicate that ensemble-based feature selection methods outperform individual ones when combined with the incremental feature selection (IFS) strategy in terms of the informative and compact property of features. Taken together, results obtained suggest the effectiveness of the ensemble-based feature selection approach and the possible existence of a common panel of DNA methylation markers among such three types of lung cancer tissue, which would facilitate clinical diagnosis and treatment.

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Methylation analysis in spontaneous sputum for lung cancer diagnosis

Cited by 27 publications

References 33 publications

DNA hypermethylation analysis in sputum of asymptomatic subjects at risk for lung cancer participating in the NELSON trial: argument for maximum screening interval of 2 years

DNA hypermethylation analysis in sputum of asymptomatic subjects at risk for lung cancer participating in the NELSON trial: argument for maximum screening interval of 2 years

Analysis of DNA Methylation Status in Bodily Fluids for Early Detection of Cancer

Classification of lung cancer using ensemble-based feature selection and machine learning methods

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