Methylated +322-327 CpG site decreases hOGG1 mRNA expression in non-small cell lung cancer

A Systematic Review of Progress toward Unlocking the Power of Epigenetics in NSCLC: Latest Updates and Perspectives

Epigenetic research has the potential to improve our understanding of the pathogenesis of cancer, specifically non-small-cell lung cancer, and support our efforts to personalize the management of the disease. Epigenetic alterations are expected to have relevance for early detection, diagnosis, outcome prediction, and tumor response to therapy. Additionally, epi-drugs as therapeutic modalities may lead to the recovery of genes delaying tumor growth, thus increasing survival rates, and may be effective against tumors without druggable mutations. Epigenetic changes involve DNA methylation, histone modifications, and the activity of non-coding RNAs, causing gene expression changes and their mutual interactions. This systematic review, based on 110 studies, gives a comprehensive overview of new perspectives on diagnostic (28 studies) and prognostic (25 studies) epigenetic biomarkers, as well as epigenetic treatment options (57 studies) for non-small-cell lung cancer. This paper outlines the crosstalk between epigenetic and genetic factors as well as elucidates clinical contexts including epigenetic treatments, such as dietary supplements and food additives, which serve as anti-carcinogenic compounds and regulators of cellular epigenetics and which are used to reduce toxicity. Furthermore, a future-oriented exploration of epigenetic studies in NSCLC is presented. The findings suggest that additional studies are necessary to comprehend the mechanisms of epigenetic changes and investigate biomarkers, response rates, and tailored combinations of treatments. In the future, epigenetics could have the potential to become an integral part of diagnostics, prognostics, and personalized treatment in NSCLC.

Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers

Applying the knowledge that methyltransferases and demethylases can modify adjacent cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand, we found that combining multiple CpGs into a single block may improve cancer diagnosis. However, survival prediction remains a challenge. In this study, we developed a pipeline named "stacked ensemble of machine learning models for methylation-correlated blocks" (EnMCB) that combined Cox regression, support vector regression (SVR), and elastic-net models to construct signatures based on DNA methylation-correlated blocks for lung adenocarcinoma (LUAD) survival prediction. We used methylation profiles from the Cancer Genome Atlas (TCGA) as the training set, and profiles from the Gene Expression Omnibus (GEO) as validation and testing sets. First, we partitioned the genome into blocks of tightly co-methylated CpG sites, which we termed methylation-correlated blocks (MCBs). After partitioning and feature selection, we observed different diagnostic capacities for predicting patient survival across the models. We combined the multiple models into a single stacking ensemble model. The stacking ensemble model based on the top-ranked block had the area under the receiver operating characteristic curve of 0.622 in the TCGA training set, 0.773 in the validation set, and 0.698 in the testing set. When stratified by clinicopathological risk factors, the risk score predicted by the top-ranked MCB was an independent prognostic factor. Our results showed that our pipeline was a reliable tool that may facilitate MCB selection and survival prediction.

hOGG1 promoter methylation, hOGG1 genetic variants and their interactions for risk of coal-borne arsenicosis: A case-control study

To identify the effect of hOGG1 methylation, Ser326Cys polymorphism and their interactions on the risk of coal-borne arsenicosis, 113 coal-borne arsenicosis subjects and 55 reference subjects were recruited. Urinary arsenic contents were analyzed with ICP-MS. hOGG1 methylation and Ser326Cys polymorphism was measured by mehtylation-specific PCR and restriction fragment length polymorphism PCR in PBLCs, respectively. The results showed that the prevalence of methylated hOGG1 and variation genotype (326 ^Ser/Cys & 326 ^Cys/Cys) were increased with raised levels of urinary arsenic in arsenicosis subjects. Increased prevalence of methylated hOGG1 and variation genotype were associated with raised risk of arsenicosis. Moreover, the results revealed that variant genotype might increase the susceptibility to hOGG1 methylation. The interactions of methylated hOGG1 and variation genotype were also found to contribute to increased risk of arsenicosis. Taken together, hOGG1 hypermethylation, hOGG1 variants and their interactions might be potential biomarkers for evaluating risk of coal-borne arsenicosis.