LINC00173 Interacts With DNMT1 to Regulate LINC00173 Expression via Promoter Methylation in Hydroquinone-Induced Malignantly Transformed TK6 Cells and Benzene-Exposed Workers.

Long-term exposure to benzene or its metabolite, hydroquinone (HQ), can causally contribute to acute myeloid leukemia. Long-noncoding RNAs are essential epigenetic regulators with critical roles in tumor initiation and malignant progression; however, the mechanism by which aberrantly expressed LINC00173 (long intergenic nonprotein coding RNA 173) regulates the pathogenesis of acute myeloid leukemia is not fully understood. Here, we found that the expression of LINC00173 decreased while the expression of DNA methyltransferase 1 (DNMT1) increased, and the methylation of LINC00173 promoter was negatively correlated with LINC00173 expression in GEPIA, CCLE databases, benzene-exposed workers, B-cell non-Hodgkin's lymphoma, K562, U937, or HQ-induced malignantly transformed TK6 (HQ-MT cells). Furthermore, in 5-aza-2'-deoxycytidine (DNA methyltransferase inhibitor) or trichostatin A (histone deacetylation inhibitor)-treated HQ-MT cells, the expression of LINC00173 was restored by reduced DNA promoter methylation levels. HQ-MT cells with DNMT1 knockout by CRISPR/Cas9 restored the expression of LINC00173 and inhibited the DNA methylation of its promoter as well as enrichment of DNMT1 to promoter. Overexpression of LINC00173 inhibited the expression of DNMT1, cell proliferation, tumor growth, enhanced chemosensitivity to cisplatin, and apoptosis in HQ-MT cells. LINC00173 interacts with DNMT1 to regulate the methylation of LINC00173 promoter. Overall, this study provides evidence that interaction between DNMT1 and LINC00173 regulates the expression of LINC00173 by regulating its promoter methylation level, thus regulating the function of HQ-MT cells in vitro and in vivo, providing a new therapeutic target for benzene-induced tumor.

Small extrachromosomal circular DNA (eccDNA): major functions in evolution and cancer.

Extrachromosomal circular DNA (eccDNA) refers to a type of circular DNA that originate from but are likely independent of chromosomes. Due to technological advancements, eccDNAs have recently emerged as multifunctional molecules with numerous characteristics. The unique topological structure and genetic characteristics of eccDNAs shed new light on the monitoring, early diagnosis, treatment, and prediction of cancer. EccDNAs are commonly observed in both normal and cancer cells and function via different mechanisms in the stress response to exogenous and endogenous stimuli, aging, and carcinogenesis and in drug resistance during cancer treatment. The structural diversity of eccDNAs contributes to the function and numerical diversity of eccDNAs and thereby endows eccDNAs with powerful roles in evolution and in cancer initiation and progression by driving genetic plasticity and heterogeneity from extrachromosomal sites, which has been an ignored function in evolution in recent decades. EccDNAs show great potential in cancer, and we summarize the features, biogenesis, evaluated functions, functional mechanisms, related methods, and clinical utility of eccDNAs with a focus on their role in evolution and cancer.

Stratified meta-analysis by ethnicity revealed that ADRB3 Trp64Arg polymorphism was associated with coronary artery disease in Asians, but not in Caucasians.

BACKGROUND: Previous studies demonstrated that ADRB3, beta-3 adrenergic receptor, participated in lipolysis and thermogenesis in adipose tissue. Consequently, this gene has attracted an increasing number of genetic studies examining its association with coronary artery disease (CAD) in different ethnicities in recent years, but no conclusion has been reached so far. The aim of this study was to explore whether the well-studied locus ADRB3 Trp64Arg in this gene confers a race-specific effect to CAD by conducting a stratified meta-analysis involving 15 independent studies and 11,802 subjects. METHODS: Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the strength of association. Publication bias was quantified and examined with Begg's funnel plot test and Egger's linear regression method. The overall meta-analysis or stratified meta-analysis by ethnicity was performed by using STATA 12.0 software. RESULTS: A total of 15 eligible studies involving 5779 CAD cases and 6023 health controls were included in this meta-analysis. The pooled results indicated that ADRB3 Trp64Arg polymorphism was significantly associated with an increased risk of CAD. Further stratified analysis by ethnicity revealed that ADRB3 Trp64Arg polymorphism was significantly associated with CAD in Asians (allelic: OR = 1.48, 95%CI 1.13-1.94, P = .005; homozygous: OR = 2.66, 95%CI 1.87-3.77, P < .001; recessive: OR = 2.46, 95%CI 1.74-3.47, P < .001), but not in Caucasians (allelic: OR = 1.09, 95%CI 0.93-1.27, P = .290; homozygous: OR = 1.31, 95%CI 0.61-2.86, P = .490; recessive: OR = 1.31, 95%CI 0.60-2.84, P = 2.494). CONCLUSIONS: This meta-analysis suggests that ADRB3 Trp64Arg polymorphism confers a race-specific effect to CAD.

The Level of Vitamin D in Children and Adolescents with Nonalcoholic Fatty Liver Disease: A Meta-Analysis.

BACKGROUND: The relationship between vitamin D level and NAFLD has not been investigated in children and adolescents. We performed a meta-analysis of published observational studies to assess this association between vitamin D levels (measured as serum 25-hydroxy vitamin D [25(OH)D]) and NAFLD in this age group. METHODS: Relevant studies conducted before May 20, 2018, were identified from the following electronic databases: PubMed, the Cochrane Library, Embase, and the Chinese CNKI databases. The quality of the included studies was evaluated using the Newcastle Ottawa Scale, and associations between vitamin D levels and NAFLD were estimated using standardised mean differences (SMD) and 95% confidence interval (CI). Subgroup and sensitivity analysis were used to identify sources of heterogeneity, and publication bias was evaluated using funnel plots. RESULTS: Eight articles were included in this meta-analysis. A significant difference was observed between low 25(OH)D levels and NAFLD in children and adolescents (SMD = -0.59, 95%CI = -0.98, -0.20, P <  0.01). Subgroup analysis revealed no differences in the study type, geographic location, BMI, and age subgroups. CONCLUSIONS: Low vitamin D levels were associated with NAFLD in children and adolescents.

Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data.

Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium (WTCCC) SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene-gene interactions involved in these susceptible pathways with their protein-protein interaction (PPI) knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer's disease, non-alcoholic fatty liver disease, and Huntington's disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer's disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer's disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.

Association between apolipoprotein B EcoRI polymorphisms and coronary heart disease : A meta-analysis.

OBJECTIVE: The study was carried out to examine the association between apolipoprotein B (ApoB) EcoRI polymorphism (E- vs. E+) (rs1042031) and coronary heart disease (CHD) risk by systematically analyzing multiple independent studies. METHODS: The Hardy-Weinberg equilibrium (HWE) test was applied to assess genotype frequency distribution in healthy controls. The quality of the studies was assessed using the Newcastle-Ottawa scale (NOS). Power analysis was performed with Power and Precision V4 software. A fixed effect model was used because no deviation from homogeneity was found. Publication bias was quantified and examined with Begg's funnel plot test and Egger's linear regression method. The meta-analysis was performed by Stata 12.0 software. RESULTS: A total of 21 eligible association studies were merged in this meta-analysis and the pooled sample consisted of 2994 CHD patients and 3258 healthy controls. No significant publication bias and heterogeneity were observed in these studies. The pooled odds ratio (OR) and 95% confidence interval (CI) of E- vs. E+ were 1.18 (1.06-1.32). The pooled OR (95% CI) of E+ E- + E- E- vs. E+ E+ was 1.18 (1.04-1.34). CONCLUSIONS: This meta-analysis indicated that ApoB EcoRI confers a moderate risk for CHD and the E- allele at this locus might be a susceptibility allele for the development of CHD.

Association of DRD3, COMT, and SLC6A4 Gene Polymorphisms with Type 2 Diabetes in Southern Chinese: A Hospital-Based Case-Control Study.

AIM: The aim of this study was to assess the associations of six single nucleotide polymorphisms (SNPs) of three genes (DRD3, COMT, and SCL6A4) with type 2 diabetes mellitus (T2DM) in Southern Chinese. SUBJECTS AND METHODS: Five hundred ninety-five cases with T2DM and 725 healthy controls of Han origin were recruited from six hospitals in Guangdong Province, Southern China. Fasting serum concentrations of markers of interest (total cholesterol, triglyceride, plasma glucose, etc.) were measured in hospitals. SNP genotyping was performed using a custom-by-design 2-×48-Plex SNPscan™ kit (Genesky Biotechnologies Inc., Shanghai, China). Single-point SNP analysis, haplotype analysis, and SNP-SNP interactions were carried out. RESULTS: SNP rs4646312 in COMT achieved statistical significance in both allelic association and genotypic association and even after adjusting covariates (odds ratio [OR]=1.26; 95% confidence interval [CI], 1.04-1.53; P=0.021). Two haplotypes consisting of rs4646312 and rs4680 were also significantly associated with T2DM, of which C-G was a protective haplotype for T2DM (OR=0.83; 95% CI, 0.70-0.98; P=0.029), whereas T-A was a risk one (OR=1.23, 95% CI, 1.03-1.46; P=0.022). Interaction analysis identified a significant epistatic effect between rs4680 in COMT and rs2066713 in SCL6A4 after adjusting for covariates (OR=3.59, 95% CI, 1.72-7.48; P=0.001 for dominant-dominant model). However, only the interaction between rs4680 and rs2066713 was significant, and haplotype T-A showed a marginally increased risk after Bonferroni correction. CONCLUSIONS: The genetic polymorphisms in COMT and SCL6A4 confer significant effects in joint actions to T2DM in Southern Chinese.

[A novel biological pathway expansion method based on the knowledge of protein-protein interactions].

Biological pathways have been widely used in gene function studies; however, the current knowledge for biological pathways is per se incomplete and has to be further expanded. Bioinformatics prediction provides us a cheap but effective way for pathway expansion. Here, we proposed a novel method for biological pathway prediction, by intergrating prior knowledge of protein?protein interactions and Gene Ontology (GO) database. First, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to which the interacting neighbors of a targe gene (at the level of protein?protein interaction) belong were chosen as the candidate pathways. Then, the pathways to which the target gene belong were determined by testing whether the genes in the candidate pathways were enriched in the GO terms to which the target gene were annotated. The protein?protein interaction data obtained from the Human Protein Reference Database (HPRD) and Biological General Repository for Interaction Datasets (BioGRID) were respectively used to predict the pathway attribution(s) of the target gene. The results demanstrated that both the average accuracy (the ratio of the correctly predicted pathways to the totally pathways to which all the target genes were annotated) and the relative accuracy (of the genes with at least one annotated pathway being successful predicted, the percentage of the genes with all the annotated pathways being correctly predicted) for pathway predictions were increased with the number of the interacting neighbours. When the number of interacting neighbours reached 22, the average accuracy was 96.2% (HPRD) and 96.3% (BioGRID), respectively, and the relative accuracy was 93.3% (HPRD) and 84.1% (BioGRID), respectively. Further validation analysis of 89 genes whose pathway knowledge was updated in a new database release indicated that 50 genes were correctly predicted for at least one updated pathway, and 43 genes were accurately predicted for all the updated pathways, giving an estimate of the relative accuracy of 86.0%. These results demonstrated that the proposed approach was a reliable and effective method for pathway expansion.

[Study on the association between catechol-O-methyltransferase gene polymorphisms and type 2 diabetes mellitus].

OBJECTIVE: To evaluate the association between the two single nucleotide polymorphisms located in catechol-O-methyltransferase (COMT) gene and type 2 diabetes mellitus (T2DM)in Han population in Guangdong province. METHODS: Two tagSNPs (rs4646312 and rs4680) were picked out from COMT gene. Using the SNPscan(TM) Kit, SNP genotyping was then performed, in two cohorts, including 595 cases and 725 controls. Finally, Chi-square test, logistic regression model and other methods were employed for statistical analysis. RESULTS: The frequencies of TT, CT and CC of rs4646312 appeared to be 304(51.1%), 234(39.3%)and 57 (8.6%) in cases, 323 (44.6%), 319 (44.0%) and 83(11.4%)in controls, respectively. The frequencies of GG,GA and AA of rs4680 were 311(52.4%), 236 (39.8%) and 46(7.8%)in cases, 417(57.7%), 265 (36.6%) and 41 (5.7%) in controls, respectively. RESULTS: showed that SNP rs4646312 was significantly associated with T2DM both in allelic association analysis (P = 0.020,OR = 1.26, 95%CI:1.04-1.53)and in recessive model (P = 0.022, OR = 1.35, 95% CI:1.05-1.74)after adjustment for sex,BMI and TG. The association between rs4680 and T2DM was not significant, but BMI was remarkably different among the three genotypes of rs4680 after controlling for other factors. CONCLUSION: SNP rs4646312 of COMT gene was associated with the increased risk of T2DM in Han population in Guangdong province. However, rs4680 was not significantly associated with T2DM.

Pathway-based analysis of the hidden genetic heterogeneities in cancers.

Many cancers apparently showing similar phenotypes are actually distinct at the molecular level, leading to very different responses to the same treatment. It has been recently demonstrated that pathway-based approaches are robust and reliable for genetic analysis of cancers. Nevertheless, it remains unclear whether such function-based approaches are useful in deciphering molecular heterogeneities in cancers. Therefore, we aimed to test this possibility in the present study. First, we used a NCI60 dataset to validate the ability of pathways to correctly partition samples. Next, we applied the proposed method to identify the hidden subtypes in diffuse large B-cell lymphoma (DLBCL). Finally, the clinical significance of the identified subtypes was verified using survival analysis. For the NCI60 dataset, we achieved highly accurate partitions that best fit the clinical cancer phenotypes. Subsequently, for a DLBCL dataset, we identified three hidden subtypes that showed very different 10-year overall survival rates (90%, 46% and 20%) and were highly significantly (P=0.008) correlated with the clinical survival rate. This study demonstrated that the pathway-based approach is promising for unveiling genetic heterogeneities in complex human diseases.

To control false positives in gene-gene interaction analysis: two novel conditional entropy-based approaches.

Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects.