Identification and validation of novel DNA methylation markers for early diagnosis of lung adenocarcinoma

Li, Miao; Zhang, Chen; Zhou, Lijun; Li, Siyu; Cao, Yuan; Wang, Longlong; Xiang, Rong; Shi, Yi; Piao, Yongjun

doi:10.1002/1878-0261.12767

Cited by 19 publications

(15 citation statements)

References 54 publications

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“…Beyond DNA methylation-based estimators of chronological age and TL, studies utilise DNA-methylation data for purposes such as classification of cancer and other diseases [36, 37, 63, 70, 71]. Some studies utilise a single feature-selection technique, while others combine several approaches.…”

Section: Resultsmentioning

confidence: 99%

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

Doherty

Dempster

Hannon

et al. 2022

Preprint

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The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine-guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, we test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. In contrast, the baseline model of elastic net regression with no prior feature reduction stage performed worst - suggesting a prior feature-selection stage may have important utility. The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

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Section: Resultsmentioning

confidence: 99%

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

Doherty

Dempster

Hannon

et al. 2022

Preprint

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“…Therefore, the substring [9,4,2] was selected and removed from the first parent. The remaining items [1,10,3,5,7,8,6] were removed from the second parent. Then, the remaining items were consecutively inserted into the first parent.…”

Section: Fig 2 Illustration Of Ward's Methodsmentioning

confidence: 99%

“…DNA methylation is an epigenetic process that regulates gene expression without altering the DNA sequence. It is widely believed to be a key to better cancer diagnosis [ 5 , 6 ]. It can be used to study different gene functions that are otherwise incomprehensible using traditional alterations in DNA sequences.…”

Section: Introductionmentioning

confidence: 99%

A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation

2022

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Background DNA Methylation is one of the most important epigenetic processes that are crucial to regulating the functioning of the human genome without altering the DNA sequence. DNA Methylation data for cancer patients are becoming more accessible than ever, which is attributed to newer DNA sequencing technologies, notably, the relatively low-cost DNA microarray technology by Illumina Infinium. This technology makes it possible to study DNA methylation at hundreds of thousands of different loci. Currently, most of the research found in the literature focuses on the discovery of DNA methylation markers for specific cancer types. A relatively small number of studies have attempted to find unified DNA methylation biomarkers that can diagnose different types of cancer (pan-cancer classification). Results In this study, the aim is to conduct a pan-classification of cancer disease. We retrieved individual data for different types of cancer patients from The Cancer Genome Atlas (TCGA) portal. We selected data for many cancer types: Breast Cancer (BRCA), Ovary Cancer (OV), Stomach Cancer (STOMACH), Colon Cancer (COAD), Kidney Cancer (KIRC), Liver Cancer (LIHC), Lung Cancer (LUSC), Prostate Cancer (PRAD) and Thyroid cancer (THCA). The data was pre-processed and later used to build the required dataset. The system that we developed consists of two main stages. The purpose of the first stage is to perform feature selection and, therefore, decrease the dimensionality of the DNA methylation loci (features). This is accomplished using an unsupervised metaheuristic technique. As for the second stage, we used supervised machine learning and developed deep neural network (DNN) models to help classify the samples’ malignancy status and cancer type. Experimental results showed that compared to recently published methods, our proposed system achieved better classification results in terms of recall, and similar and higher results in terms of precision and accuracy. The proposed system also achieved an excellent receiver operating characteristic area under the curve (ROC AUC) values varying from 0.85 to 0.89. Conclusions This research presented an effective new approach to classify different cancer types based on DNA methylation data retrieved from TCGA. The performance of the proposed system was compared to recently published works, using different performance metrics. It provided better results, confirming the effectiveness of the proposed method for classifying different cancer types based on DNA methylation data.

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“…Most patients are diagnosed at an advanced stage, which is one of the primary causes of poor survival. Early diagnosis is one of the best ways to increase a lung cancer patient's chances of survival and prognosis (5)(6)(7). Thus, to develop novel treatment drugs to improve patient survival, it is crucial to comprehend the molecular mechanisms underlying the occurrence and development of lung adenocarcinoma and establish biomarkers that may be detected at the early stages of the disease.…”

Section: Introductionmentioning

confidence: 99%

In lung adenocarcinoma, low expression of the cell surface extracellular nucleotidase CD39 is related to immune infiltration and a poor prognosis

Song¹,

Wang²,

Yu³

et al. 2022

J Thorac Dis

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Background: Extracellular nucleotidase on the cell surface CD39 plays a crucial role in the tumor microenvironment in the immunosuppressive adenosine pathway. However, the association between CD39 and lung adenocarcinoma has rarely been recorded. This study aimed to explore the involvement of CD39 in the biological processes of lung cancer.Methods: First, a prediction model was established by analyzing the expression of CD39 in lung adenocarcinoma and its relationships with clinical evidence of lung adenocarcinoma using The Cancer Genome Atlas (TCGA) and Tumor IMmune Estimation Resource (TIMER) databases. In the TCGA and TIMER databases, the relationship between CD39 and immune cells and the relationship with immunerelated expressed genes were studied. Subsequently, using gene set enrichment analysis (GSEA), the potential mechanism of action was investigated.Results: Lung adenocarcinoma patients with elevated CD39 expression had improved overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). CD39 expression was reduced in lung adenocarcinoma tumor tissue in the TCGA and TIMER databases. The nomogram's C-index was 0.688 (0.665-0.712), indicating some consistency in the prediction model. According to the TIMER and TCGA databases, CD39 expression was strongly connected with several immune cells invading and with immune checkpoint-related markers such as PDCD1, CD274, CTLA-4, and several functional T cells. GSEA revealed that CD39 influences the extracellular matrix, immunological microenvironment, programmed death 1 (PD-1) expression, glucose metabolism, PTEN stability, inflammatory response, and angiogenesis in lung cancer. Conclusions:The current study's findings demonstrated that CD39 can be employed as a possible predictive biomarker for lung adenocarcinoma and may enhance the patients' poor prognosis by preventing the immunological escape of tumor cells from the lung adenocarcinoma tumor microenvironment.

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Identification and validation of novel DNA methylation markers for early diagnosis of lung adenocarcinoma

Cited by 19 publications

References 54 publications

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation

In lung adenocarcinoma, low expression of the cell surface extracellular nucleotidase CD39 is related to immune infiltration and a poor prognosis

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