Identification of a novel differentially methylated region adjacent to ATG16L2 in lung cancer cells using methyl-CpG binding domain protein-enriched genome sequencing

Genetic markers associated with resistance to radioiodine therapy in thyroid cancer patients: Prospective cohort study

Background. The indication for radiotherapy in oncological practice are metastases of differentiated thyroid cancer after thyroidectomy, the presence of distant metastases, or stage N1b, or negative dynamics of blood thyroglobulin levels after thyroidectomy for thyroid cancer. The mechanism of action of radiotherapy is based on provoking double-stranded DNA breaks. It is important to study the role of polymorphisms of NFKB1, ATM, ATG16L2 and ATG10 genes, products of which are involved in the processes of DNA damage response pathway and autophagy, in the formation of resistance to radioiodine therapy of thyroid cancer patients. Aim. To examine the association between NFKB1, ATM, ATG16L2 and ATG10 polymorphisms and resistance to radioiodine therapy in thyroid cancer patients. Materials and methods. The study included 181 patients (37 men, 144 women; mean age 53.515.7 years) with histologically confirmed thyroid cancer and a history of thyroidectomy who received radioiodine therapy. Carriage of single-nucleotide polymorphisms (rs230493) NFKB1, (rs11212570) ATM, (rs10898880) ATG16L2 and (rs10514231, rs1864183, rs4703533) ATG10 was determined by real-time PCR using TaqMan kits. Results. Among 181 patients, resistance to radioiodine therapy was observed in 11 (6.1%) cases. No significant associations between the individual polymorphisms and resistance to radioiodine therapy were obtained, p0.05. Haplotype analysis showed that carriage of the C-C ATG10 rs10514231-rs1864183 haplotype was associated with an increased risk of developing resistance to radioiodine therapy, p=0.04. Conclusion. Further studies on large samples of radioiodine therapy-resistant patients using whole-genome sequencing methods are required to specify the role of genetic factors in the response to 131I therapy.

The role of ATG16L2 in autophagy and disease

Macroautophagy/autophagy, a fundamental cell process for nutrient recycling and defense against pathogens (termed xenophagy), is crucial to human health. ATG16L2 (autophagy related 16 like 2) is an autophagic protein and a paralog of ATG16L1. Both proteins are implicated in similar diseases such as cancer and other chronic diseases; however, most autophagy studies to date have primarily focused on the function of ATG16L1, with ATG16L2 remaining uncharacterized and understudied. Overexpression of ATG16L2 has been reported in various cancers including colorectal, gastric, and prostate carcinomas, whereas altered methylation of ATG16L2 has been associated with lung cancer formation and poorer response to therapy in leukemia. In addition, ATG16L2 polymorphisms have been implicated in a range of other diseases including inflammatory bowel diseases and neurodegenerative disorders. Despite this likely role in human health, the function of this enigmatic protein in autophagy remains unknown. Here, we review current studies on ATG16L2 and collate evidence that suggests that this protein is a potential modulator of autophagy as well as the implications this has on pathogenesis.Abbreviations: ATG5: autophagy related 5; ATG12: autophagy related 12; ATG16L1: autophagy related 16 like 1; ATG16L2: autophagy related 16 like 2; CD: Crohn disease; IBD: inflammatory bowel diseases; IRGM: immunity related GTPase M; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; PE: phosphatidylethanolamine; RB1CC1: RB1 inducible coiled-coil 1; SLE: systemic lupus erythematosus; WIPI2B: WD repeat domain, phosphoinositide interacting 2B.

An 11-Gene Signature Risk-Prediction Model Based on Prognosis-Related miRNAs and Their Target Genes in Lung Adenocarcinoma

Aberrant expression of microRNAs may affect tumorigenesis and progression by regulating their target genes. This study aimed to construct a risk model for predicting the prognosis of patients with lung adenocarcinoma (LUAD) based on differentially expressed microRNA-regulated target genes. The miRNA sequencing data, RNA sequencing data, and patients’ LUAD clinical data were downloaded from the The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs and genes were screened out by combining differential analysis with LASSO regression analysis to further screen out miRNAs associated with patients’ prognosis, and target gene prediction was performed for these miRNAs using a target gene database. Overlapping gene screening was performed for target genes and differentially expressed genes. LASSO regression analysis and survival analysis were then used to identify key genes. Risk score equations for prognostic models were established using multifactorial COX regression analysis to construct survival prognostic models, and the accuracy of the models was evaluated using subject working characteristic curves. The groups were divided into high- and low-risk groups according to the median risk score, and the correlation with the clinicopathological characteristics of the patients was observed. A total of 123 up-regulated miRNAs and 22 down-regulated miRNAs were obtained in this study. Five prognosis-related miRNAs were screened using LASSO regression analysis and Kaplan-Meier method validation, and their target genes were screened with the overlap of differentially expressed genes before multifactorial COX analysis finally resulted in an 11-gene risk model for predicting patient prognosis. The area under the ROC curve proved that the model has high accuracy. The 11-gene risk-prediction model constructed in this study may be an effective predictor of prognosis.

High-Throughput Detection of Multiple miRNAs and Methylated DNA by Droplet Digital PCR

Altered miRNA expression and DNA methylation have highly active and diverse roles in carcinogenesis. Simultaneous detection of the molecular aberrations may have a synergistic effect on the diagnosis of malignancies. Herein, we develop a high-throughput assay for detecting multiple miRNAs and DNA methylation using droplet digital PCR (ddPCR) coupled with a 96-microwell plate. The microplate-based ddPCR could absolutely and reproducibly quantify 15 miRNAs and 14 DNA methylation sites with a high sensitivity (one copy/µL and 0.1%, respectively). Analyzing sputum and plasma of 40 lung cancer patients and 36 cancer-free smokers by this approach identified an integrated biomarker panel consisting of two sputum miRNAs (miRs-31-5p and 210-3p), one sputum DNA methylation (RASSF1A), and two plasma miRNAs (miR-21-5p and 126) for the diagnosis of lung cancer with higher sensitivity and specificity compared with a single type of biomarker. The diagnostic value of the integrated biomarker panel for the early detection of lung cancer was confirmed in a different cohort of 36 lung cancer patients and 39 cancer-free smokers. The high-throughput assay for quantification of multiple molecular aberrations across sputum and plasma could improve the early detection of lung cancer.