Investigating the causal relationship between human blood/urine metabolites and periodontal disease using two-sample Mendelian randomization.

Background and Aims: The aim is to investigate the cause-and-effect connection between metabolites found in blood/urine and the likelihood of developing periodontal disease (PD) through the utilization of a two-sample Mendelian randomization (MR) method. Methods: Using an inverse variance weighted (IVW) method and two additional two-sample MR models, we examined the relationship between blood/urine metabolites and PD by analyzing data from a comprehensive metabolome-based genome-wide association study and the Genome-Wide Association Studies (GWAS) of PD. To assess the consistency and dependability of the findings, diversity, cross-effects, and sensitivity analyses were conducted. Results: Out of the 35 metabolites found in blood and urine, a total of eight metabolites (C-reactive protein, Potassium in urine, Urea, Cystatin C, Non-albumin protein, Creatinine, estimated Glomerular Filtration Rate, and Phosphate) displayed a possible causal connection with the risk of dental caries/PD using the inverse variance weighted (IVW) method (p < 0.05). This includes five metabolites in the blood and three in the urine. No metabolites were statistically significant in IVW MR models (p < 3.68 × 10- 4). Even after conducting sensitivity analysis with the leave-one-out method and removing the confounding instrumental variables, the impact of these factors on dental caries/PD remained significant. Conclusion: Based on the available evidence, it is not possible to establish a significant causal link between the 35 blood metabolites and the likelihood of developing dental caries and PD.

Integrated analysis of NET-DNA receptor CCDC25 in malignant tumors: Pan-cancer analysis.

Background: Previously, it was reported that the coiled-coil domain containing 25 (CCDC25) plays a role in the formation of neutrophil extracellular traps (NETs). This study systematically analyzed the expression profiles of CCDC25 in 30 different types of cancer and one type of blood cancer, acute myeloid leukemia. Methods: The GTEx and CCLE databases were used to evaluate the distribution of CCDC25 expression in both normal tissue and cancer cell lines. A comparison was performed between normal tissue and tumor tissue to analyze the differential expression of CCDC25. We assessed the impact of CCDC25 on the clinical outlook in the TCGA pan-cancer data set by analyzing the Kaplan-Meier survival plot and conducting COX regression analysis. Moreover, the association between the expression levels of CCDC25 and the tumor microenvironment in multiple cancers was conducted. Additionally, the investigation also examined the link between CCDC25 and immune neoantigen, tumor mutational burden, microsatellite instability, mismatch repair genes (MMRs), HLA-related genes, and DNA methyltransferase (DNMT). Results: CCDC25 was expressed in nearly all of the 31 normal tissues while exhibiting a moderate to low level of expression in cancer cell lines. While abnormal expression was detected in the majority of malignancies, there was no link found between elevated CCDC25 levels and overall survival, disease-free survival, recurrence-free survival, and disease-free interval in the TCGA comprehensive cancer data set. Nevertheless, the expression of CCDC25 exhibited a notable link with the infiltration levels of activated CD4 memory T cells, quiescent mast cells, dendritic cells in an activated state, T cells that assist in follicle development, M2 macrophages, and neutrophils in various tumors. Conclusions: In most cancers, the results indicate that there is no link between CCDC25 and prognosis. However, CCDC25 can be targeted for therapeutic purposes concerning metastasis and immune infiltration.

Relationship between periodontitis and COVID-19: A bidirectional two-sample Mendelian randomization study.

Background and Aims: Coronavirus disease (COVID-19) is a major danger to world health and has been linked to periodontitis in a number of epidemiological observational studies. However, it is unclear whether COVID-19 causes periodontitis. COVID-19's causal influence on periodontitis was determined using bidirectional Mendelian randomization (MR). Methods: Large-scale COVID-19 and periodontitis genome wide association study data were analyzed. Inverse variance weighting, MR-Egger, weighted median, and MR-PRESSO were used to estimate causal effects. Sensitivity studies were conducted using the Cochran's Q test, the MR-Egger intercept test, the MR-PRESSO, and the leave-one-out (LOO) analysis. Further investigation of potential mediating factors was performed using risk factor analysis. Results: The MR presented no causal relationship between periodontitis and hospitalization for COVID-19 (odds ratio [OR] = 0.97, 95% confidence interval [CI] 0.78-1.20; p = 0.76), vulnerability to COVID-19 (OR = 1.04, 95% CI 0.88-1.21; p = 0.65), COVID-19 disease severity (OR = 1.01, 95% CI 0.92-1.11; p = 0.81). Meanwhile, a noncausal effect of genetic hospitalization for COVID-19, illness severity, and vulnerability to periodontitis was detected. Other MR methods yielded identical results to inverse variance weighting. According to sensitivity analysis, horizontal pleiotropy is unlikely to affect causal estimation. Conclusion: Periodontitis had no link to the risk of COVID-19 hospitalization, susceptibility, or severity. However, the substance in COVID-19 that is responsible for this effect must be studied further.

Mendelian Randomisation Study on Association of Gut Microbiota and Periodontitis.

OBJECTIVE: Several studies have demonstrated the possible association between gut microbiota and periodontitis. The mechanism by which gut microbiota contribute to periodontitis remains unknown. METHODS: A 2-sample Mendelian randomisation (MR) study was conducted using publicly available Genome-Wide Association Studies (GWAS) data of European ancestry. The relationships between gut microbiota and tooth loss and periodontitis were assessed using summary-level data. Moreover, inverse variance weighted (IVW), MR-Egger, weighted median, and simple Mendelian were used. The results were further validated using sensitivity analyses. RESULTS: A total of 211 gut microbiota were studied, including 9 phyla, 16 classes, 20 orders, 35 families, and 131 genera. The IVW method identified 16 bacterial genera related to the risk of periodontitis and tooth loss. Lactobacillaceae was associated with an increased risk of periodontitis (odds ratio [OR], 1.40, 95% confidence interval [CI], 1.03-1.91, P<.001) and tooth loss (OR, 1.12; 95% CIs, 1.02-1.24, P = .002), whereas Lachnospiraceae UCG008 was linked to a lower risk of tooth loss (P = .041). There was no heterogeneity and horizontal pleiotropy in the sensitivity analysis. CONCLUSIONS: Several microorganisms were identified to be linked to the risk of periodontitis. Furthermore, the findings improved our understanding of gut microbiota and periodontitis pathology.

The causal links between gut microbiota and COVID-19: A Mendelian randomization study.

Several studies have shown a possible correlation between gut microbiota and COVID-19. However, the cause-and-effect relationship between the two has not been investigated. We conducted a two-sample Mendelian randomization study (MR) study using publicly available GWAS data. Inverse variance weighted (IVW) analysis was the main MR analysis technique and was supplemented with other sensitivity analyses. Forty-two bacterial genera were associated with COVID-19 susceptibility, hospitalization, and severity in the IVW method. Among these gut microbiota, five gut microbiota (genus unknowngenus [id.1000005472], family unknownfamily [id.1000005471], genus Tyzzerella3, order MollicutesRF9.id.11579, and phylum Actinobacteria) were significantly associated with COVID-19 hospitalization and severity. Three gut microbiota (class Negativicutes, order Selenomonadales, and class Actinobacteria) were significantly associated with COVID-19 hospitalization and susceptibility, while two microbiota (class Negativicutes and order Selenomonadales) were significantly associated with COVID-19 hospitalization and severity, and susceptibility. Sensitivity analysis did not detect any heterogeneity and horizontal pleiotropy. Our findings demonstrated that several microorganisms were causally linked to COVID-19, and improved our understanding of the relationship between gut microbiota and COVID-19 pathology.

Overexpression of YTHDC2 contributes to the progression of prostate cancer and predicts poor outcomes in patients with prostate cancer.

YTH domain-containing protein 2 (YTHDC2), a member of N6-methyladenosine (m6A) readers, has been reported to be closely associated with multiple cancer types. However, very little is known about the YTHDC2 gene and its involvement in prostate cancer. YTHDC2 protein expression level was analyzed and correlated to clinical outcomes in prostate cancer patients who underwent prostatectomy in Guizhou Provincial People's Hospital. The YTHDC2 expression level was also detected in prostate cancer cell lines and an immortalized prostate epithelial cell line BPH-1 and RWPE1 by quantitative real-time reverse transcription polymerase chain reaction. Furthermore, we established stable cell lines (DU145 and PC-3) transfected with either empty vector or the full-length YTHDC2 gene and conducted cell function assays in vitro. Fisher's exact test and Pearson χ2 test were employed, Kaplan-Meier method was used for the survival analysis. Of 32 patient samples who enrolled in this study, YTHDC2 was significantly upregulated in prostate cancer (PCa) patients with higher Gleason scores and serum prostate-specific antigen levels. YTHDC2 expression was significantly elevated in all PCa cell lines compared to BPH-1 and RWPE1 (all p < 0.05). Functionally, the enforced expression of YTHDC2 markedly promoted cell growth, migration, and invasion efficacies in prostate cancer cells. Our data indicate that YTHDC2 upregulation may be potentially associated with the prognosis of prostate cancer patients.

Systematic analysis of the function and prognostic value of RNA binding protein in head and neck squamous cell carcinoma.

OBJECTIVE: Dysregulation of RNA binding proteins (RBPs) plays an important role in controlling processes in cancer development. However, the function of RBPs has not been thoroughly and systematically documented in head and neck cancer. We aim to explore the role of RPB in the pathogenesis of HNSC. METHODS: We obtained HNSC gene expression data and corresponding clinical information from The Cancer Genome Atlas (TCGA) and the GEO databases, and identified aberrantly expressed RBPs between tumors and normal tissues. Meanwhile, we performed a series of bioinformatics to explore the function and prognostic value of these RBPs. RESULTS: A total of 249 abnormally expressed RBPs were identified, including 101 downregulated RBPs and 148 upregulated RBPs. Using least absolute shrinkage and selection operator (LASSO) and univariate Cox regression analysis, the 15 RPBs were identified as hub genes. With the 15 RPBS, the prognostic prediction model was constructed. Further analysis showed that the high-risk score of the patients expressed a better survival outcome. The prediction model was validated in another HNSC dataset, and similar findings were observed. CONCLUSIONS: Our results provide novel insights into the pathogenesis of HNSC. The fifteen RBP gene signature exhibited the predictive value of moderate HNSC prognosis, and have potential application value in clinical decision-making and individualized treatment.

miRNA-Based Feature Classifier Is Associated with Tumor Mutational Burden in Head and Neck Squamous Cell Carcinoma.

Tumor mutation burden (TMB) is considered to be an independent genetic biomarker that can predict the tumor patient's response to immune checkpoint inhibitors (ICIs). Meanwhile, microRNA (miRNA) plays a key role in regulating the anticancer immune response. However, the correlation between miRNA expression patterns and TMB is not elucidated in HNSCC. In the HNSCC cohort of the TCGA dataset, miRNAs that were differentially expressed in high TMB and low TMB samples were screened. The least absolute contraction and selection operator (LASSO) method is used to construct a miRNA-based feature classifier to predict the TMB level in the training set. The test set is used to verify the classifier. The correlation between the miRNA-based classifier index and the expression of three immune checkpoints (PD1, PDL1, and CTLA4) was explored. We further perform functional enrichment analysis on the miRNA contained in the miRNA-based feature classifier. Twenty-five differentially expressed miRNAs are used to build miRNA-based feature classifiers to predict TMB levels. The accuracy of the 25-miRNA-based signature classifier is 0.822 in the training set, 0.702 in the test set, and 0.774 in the total set. The miRNA-based feature classifier index showed a low correlation with PD1 and PDL1, but no correlation with CTLA4. The enrichment analysis of these 25 miRNAs shows that they are involved in many immune-related biological processes and cancer-related pathways. The miRNA expression patterns are related to tumor mutation burden, and miRNA-based feature classifiers can be used as biomarkers to predict TMB levels in HNSCC.

Co-expression of key gene modules and pathways of human breast cancer cell lines.

Breast cancer (BC) is the most common leading cause of cancer-related death in women worldwide. Gene expression profiling analysis for human BCs has been studied previously. However, co-expression analysis for BC cell lines is still devoid to date. The aim of the study was to identify key pathways and hub genes that may serve as a biomarker for BC and uncover potential molecular mechanism using weighted correlation network analysis. We analyzed microarray data of BC cell lines (GSE 48213) listed in the Gene Expression Omnibus database. Gene co-expression networks were used to construct and explore the biological function in hub modules using the weighted correlation network analysis algorithm method. Meanwhile, Gene ontology and KEGG pathway analysis were performed using Cytoscape plug-in ClueGo. The network of the key module was also constructed using Cytoscape. A total of 5000 genes were selected, 28 modules of co-expressed genes were identified from the gene co-expression network, one of which was found to be significantly associated with a subtype of BC lines. Functional enrichment analysis revealed that the brown module was mainly involved in the pathway of the autophagy, spliceosome, and mitophagy, the black module was mainly enriched in the pathway of colorectal cancer and pancreatic cancer, and genes in midnightblue module played critical roles in ribosome and regulation of lipolysis in adipocytes pathway. Three hub genes CBR3, SF3B6, and RHPN1 may play an important role in the development and malignancy of the disease. The findings of the present study could improve our understanding of the molecular pathogenesis of breast cancer.

Systematic analysis of differentially methylated expressed genes and site-specific methylation as potential prognostic markers in head and neck cancer.

Head and neck cancer (HNC) remains one of the most malignant tumors with a significantly high mortality. DNA methylation exerts a vital role in the prognosis of HNC. In this study, we try to screen abnormal differential methylation genes (DMGs) and pathways in Head-Neck Squamous Cell Carcinoma via integral bioinformatics analysis. Data of gene expression microarrays and gene methylation microarrays were obtained from the Cancer Genome Atlas database. Aberrant DMGs were identified by the R Limma package. We conducted the Cox regression analysis to select the prognostic aberrant DMGs and site-specific methylation. Five aberrant DMGs were recognized that significantly correlated with overall survival. The prognostic model was constructed based on five DMGs (PAX9, STK33, GPR150, INSM1, and EPHX3). The five DMG models acted as prognostic biomarkers for HNC. The area under the curve based on the five DMGs predicting 5-year survival is 0.665. Moreover, the correlation between the DMGs/site-specific methylation and gene expression was also explored. The findings demonstrated that the five DMGs can be used as independent prognostic biomarkers for predicting the prognosis of patients with HNC. Our study might lay the groundwork for further mechanism exploration in HNC and may help identify diagnostic biomarkers for early stage HNC.

Systematic analysis of survival-associated alternative splicing signatures uncovers prognostic predictors for head and neck cancer.

BACKGROUND: Previous studies have shown that alternative splicing (AS) plays a key role in carcinogenesis and prognosis of cancer. However, systematic profiles of AS signatures in head and neck cancer (HNC) have not yet been reported. METHODS: In this study, AS data, RNA-Seq data, and corresponding clinicopathological information of 489 HNC patients were downloaded from The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses were performed to screen for survival-associated AS events. Functional and pathway enrichment analysis was also performed. The prognostic models and splicing networks were constructed using integrated bioinformatics analysis tools. RESULTS: Among the 42,849 alternating splicing events identified in 10,121 genes, 5,165 survival-associated AS events in 2,419 genes were observed in univariate Cox regression analysis. Among the seven types, alternate terminator events were the most powerful prognostic factors. Multivariate Cox analysis was then used to screen for the AS genes with prognostic value. Four candidate genes (TPM1, CLASRP, PRRC1, and DNASE1L1) were found to be independent prognostic factors for HNC patients. A prognostic prediction model was built based on the four genes. The area under the receiver operating characteristic risk score curve for predicting the survival status of HNC patients was 0.704. In addition, splicing interaction network indicated that the splicing factors have significant functions in HNC. CONCLUSION: A comprehensive analysis of AS events in HNC was performed. A powerful prognostic predictor for HNC patients was established based on AS events could.

Effects of CPAP on the transcriptional signatures in patients with obstructive sleep apnea via coexpression network analysis.

A growing number of studies provide epidemiological evidence linking obstructive sleep apnea (OSA) with a number of chronic disorders. Transcriptional analyses have been conducted to analyze the gene expression data. However, the weighted gene coexpression network analysis (WGCNA) method has not been applied to determine the transcriptional consequence of continuous positive airway pressure (CPAP) therapy in patients with severe OSA. The aim of this study was to identify key pathways and genes in patients with OSA that are influenced by CPAP treatment and uncover/unveil potential molecular mechanisms using WGCNA. We analyzed the microarray data of OSA (GSE 49800) listed in the Gene Expression Omnibus database. Coexpression modules were constructed using WGCNA. In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were also conducted. After the initial data processing, 5101 expressed gene profiles were identified. Next, a weighted gene coexpression network was established and 16 modules of coexpressed genes were identified. The interaction analysis demonstrated a relative independence of gene expression in these modules. The black module, tan module, midnightblue module, pink module, and greenyellow module were significantly associated with the alterations in circulating leukocyte gene expression at baseline and after exposure to CPAP. The five hub genes were considered to be candidate OSA-related genes after CPAP treatment. Functional enrichment analysis revealed that steroid biosynthesis, amino sugar and nucleotide sugar metabolism, protein processing in the endoplasmic reticulum, and the insulin signaling pathway play critical roles in the development of OSA in circulating leukocyte gene expression at baseline and after exposure to CPAP. Using this new systems biology approach, we identified several genes and pathways that appear to be critical to OSA after CPAP treatment, and these findings provide a better understanding of OSA pathogenesis.

The relationship between tooth loss and mortality from all causes, cardiovascular diseases, and coronary heart disease in the general population: systematic review and dose-response meta-analysis of prospective cohort studies.

Background: The association of tooth loss with mortality from all causes, cardiovascular diseases (CVD), and coronary heart disease (CHD) has been studied for many years; however, the results are inconsistent.Method: PubMed, Embase, Web of Knowledge, and Cochrane Oral Health Group's Trials Register databases were searched for papers published from 1966 to August 2018. We conducted dose-response meta-analysis to quantitatively evaluate the relation between tooth loss and risk of mortality from all causes, CVD, and CHD.Results: In the present study, 18 prospective studies conducted until August 2018 were considered eligible for analysis. In the analysis of linear association, the summarized relative risk (RR) values for each 10-, 20-, and 32-tooth loss for all-cause mortality were 1.15 (1.11-1.19), 1.33 (1.23-1.29), and 1.57 (1.39-1.51), respectively. Subgroup and sensitivity analyses showed consistent results. A linear relationship was found among all-cause mortality, with Pnonlinearity = 0.306. The susceptibility to all-cause mortality increased by almost 1.48 times at very high tooth loss (28-32), and slight flattening of the curve was noted. However, the summarized RR values for increment for 10-, 20-, and 32-tooth loss were not or were marginally related to increased risk of mortality from CVD/CHD. Subgroup and sensitivity analyses revealed inconsistent results. Tooth loss showed linear association with CHD mortality but not with CVD mortality. The susceptibility to all-cause mortality increased by almost 1.48 and 1.70 times for CVD and CHD, respectively, at very high tooth loss (28-32). The curve exhibited slight flattening; however, no statistical significance was detected.Conclusion: In the meta-analysis, our findings confirmed the positive relationship between tooth loss and susceptibility to all-cause mortality, but not for circulatory mortality. However, the finding that tooth loss might play a harmful role in the development of all-cause mortality remains inconclusive. Tooth loss may be a potential risk marker for all-cause mortality: however, their association must be further validated through large prospective studies.

Combined Analysis of the Aberrant Epigenetic Alteration of Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most fatal malignancies due to its high morbidity and mortality. DNA methylation exerts a vital part in the development of PDAC. However, a mechanistic role of mutual interactions between DNA methylation and mRNA as epigenetic regulators on transcriptomic alterations and its correlation with clinical outcomes such as survival have remained largely uncovered in cancer. Therefore, elucidation of aberrant epigenetic alteration in the development of PDAC is an urgent problem to be solved. In this work, we conduct an integrative epigenetic analysis of PDAC to identify aberrant DNA methylation-driven cancer genes during the occurrence of cancer. METHODS: DNA methylation matrix and mRNA profile were obtained from the TCGA database. The integration of methylation and gene expression datasets was analyzed using an R package MethylMix. The genes with hypomethylation/hypermethylation were further validated in the Kaplan-Meier analysis. The correlation analysis of gene expression and aberrant DNA methylation was also conducted. We performed a pathway analysis on aberrant DNG methylation genes identified by MethylMix criteria using ConsensusPathDB. RESULTS: 188 patients with both methylation data and mRNA data were considered eligible. A mixture model was constructed, and differential methylation genes in normal and tumor groups using the Wilcoxon rank test was performed. With the inclusion criteria, 95 differential methylation genes were detected. Among these genes, 74 hypermethylation and 21 hypomethylation genes were found. The pathway analysis revealed an increase in hypermethylation of genes involved in ATP-sensitive potassium channels, Robo4, and VEGF signaling pathways crosstalk, and generic transcription pathway. CONCLUSION: Integrated analysis of the aberrant epigenetic alteration in pancreatic ductal adenocarcinoma indicated that differentially methylated genes could play a vital role in the occurrence of PDAC by bioinformatics analysis. The present work can help clinicians to elaborate on the function of differentially methylated expressed genes and pathways in PDAC. CDO1, GJD2, ID4, NOL4, PAX6, TRIM58, and ZNF382 might act as aberrantly DNA-methylated biomarkers for early screening and therapy of PDAC in the future.

Reconstruction and analysis of the aberrant lncRNA-miRNA-mRNA network based on competitive endogenous RNA in CESC.

A growing body of studies has demonstrated that long non-coding RNA (lncRNA) are regarded as the primary section of the ceRNA network. This is thought to be the case owing to its regulation of protein-coding gene expression by functioning as miRNA sponges. However, functional roles and regulatory mechanisms of lncRNA-mediated ceRNA in cervical squamous cell carcinoma (CESC), as well as their use for potential prediction of CESC prognosis, remains unknown. The aberrant expression profiles of mRNA, lncRNA, and miRNA of 306 cervical squamous cancer tissues and three adjacent cervical tissues were obtained from the TCGA database. A lncRNA-mRNA-miRNA ceRNA network in CESC was constructed. Meanwhile, Gene Ontology (GO) and KEGG pathway analysis were performed using Cytoscape plug-in BinGo and DAVID database. We identified a total of 493 lncRNA, 70 miRNA, and 1921 mRNA as differentially expressed profiles. An aberrant lncRNA-mRNA-miRNA ceRNA network was constructed in CESC, it was composed of 50 DElncRNA, 18 DEmiRNA, and 81 DEmRNA. According to the overall survival analysis, 3 out of 50 lncRNA, 10 out of 81 mRNA, and 1 out of 18 miRNA functioned as prognostic biomarkers for patients with CESC (P value < 0.05). We extracted the sub-network in the ceRNA network and found that two novel lncRNA were recognized as key genes. These included lncRNA MEG3 and lncRNA ADAMTS9-AS2. The present study provides a new insight into a better understanding of the lncRNA-related ceRNA network in CESC, and the novel recognized ceRNA network will help us to improve our understanding of lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of CESC.

Five key lncRNAs considered as prognostic targets for predicting pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and the 5-year survival rate was only 7.7%. To improve prognosis, a screening biomarker for early diagnosis of pancreatic cancer is in urgent need. Long non-coding RNA (lncRNA) expression profiles as potential cancer prognostic biomarkers play critical roles in development of tumorigenesis and metastasis of cancer. However, lncRNA signatures in predicting the survival of a patient with PDAC remain unknown. In the current study, we try to identify potential lncRNA biomarkers and their prognostic values in PDAC. LncRNAs expression profiles and corresponding clinical information for 182 cases with PDAC were acquired from The Cancer Genome Atlas (TCGA). A total of 14 470 lncRNA were identified in the cohort, and 175 PDAC patients had clinical variables. We obtained 108 differential expressed lncRNA via R packages. Univariate and multivariate Cox proportional hazards regression, lasso regression was performed to screen the potential prognostic lncRNA. Five lncRNAs have been recognized to significantly correlate with OS. We established a linear prognostic model of five lncRNA (C9orf139, MIR600HG, RP5-965G21.4, RP11-436K8.1, and CTC-327F10.4) and divided patients into high- and low-risk group according to the prognostic index. The five lncRNAs played independent prognostic biomarkers of OS of PDAC patients and the AUC of the ROC curve for the five lncRNAs signatures prediction 5-year survival was 0.742. In addition, targeted genes of MIR600HG, C9orf139, and CTC-327F10.4 were explored and functional enrichment was also conducted. These results suggested that this five-lncRNAs signature could act as potential prognostic biomarkers in the prediction of PDAC patient's survival.

Significant association between lncRNA H19 polymorphisms and cancer susceptibility: a meta-analysis.

Previous epidemiological research suggests polymorphisms in long non-coding RNA (lncRNA) H19 are associated with an increased risk of cancer, but the results are inconsistent. We therefore conducted a meta-analysis to more accurately determine the association between lncRNA H19 polymorphisms and cancer risk. The PubMed, Embase, and Science Citation Index online databases were searched and 11 relevant studies involving a total of 33,209 participants were identified. Odds ratios (ORs) and corresponding 95% confidence interval (CIs) from these studies were used to detect associations between H19 polymorphisms and cancer risk using five genetic models. The pooled result suggested that the rs2839698 G>A polymorphism was associated with digestive cancer risk in all five models. Moreover, a protective effect against cancer development was observed for the T allele variant of the rs2107425 C>T polymorphism, especially in Caucasian patient populations. No significant associations were found between lncRNA H19 rs217727 G>A polymorphism and cancer risk. In summary, the rs2839698 G>A and rs2107425 C>T polymorphisms in lncRNA H19 may therefore play opposing roles during cancer development, and their effects may vary depending on cancer type and patient ethnicity.

Association between cadmium exposure and diabetes mellitus risk: a prisma-compliant systematic review and meta-analysis.

Cadmium (Cd) is a pollutant with multiple adverse health effects: cancer, renal dysfunction, osteoporosis and fracture, and cardiovascular disease. Several population-based studies found an association between Cd and diabetes mellitus (DM), but this association is inconsistent with other research. We conducted meta-analysis to examine relationship between urinary/blood Cd exposure and DM risk. Pertinent studies were identified by searching PubMed and Embase databases, and combined odds ratio (OR) and corresponding 95% confidence interval (CI) were applied to evaluate said association. Meta-analysis showed that high U-Cd exposure is not correlated with DM risk (OR = 1.19; 95% CI = 0.83-1.71), and high B-Cd exposure is also not associated with increased risk of DM (OR = 1.16; 95% CI = 0.84-1.62) in the general population. Subgroup and sensitivity analysis proved similar results, with little evidence of publication bias. This meta-analysis suggests that high U-Cd/B-Cd exposure may not be risk factor for DM in general populations. However, large prospective studies are needed to confirm this finding.

Association between tooth loss and risk of oesophageal cancer: a dose-response meta-analysis.

Many epidemiological studies have found that tooth loss is associated with susceptibility to oesophageal cancer. However, a definitive answer is yet to be discovered, and the findings are inconclusive. We performed a meta-analysis to assess the relationship between tooth loss and oesophageal cancer risk. We searched PubMed and Embase databases to screen eligible studies up to June 2015. Nine observational studies (eight articles) involving 2604 patients and 113,995 participants were included in the meta-analysis. The combined odds ratio for tooth loss and oesophageal cancer was 1.53 (95 % CI 1.02-2.29) for the high versus lowest teeth loss categories. However, inconsistent results were detected in the stratified and sensitivity analysis. In dose-response analysis, the summary odds ratio for each one tooth loss increment was 1.01 (95 % CI 1.00-1.02). The current evidence, based solely on six case-control studies and three cohort studies, suggests that tooth loss is a potential marker of oesophageal cancer. However, no firm conclusion can be drawn at this time that tooth loss may play a causal role in development of oesophageal cancer. Additional large-scale and high-quality prospective studies are required to evaluate the association between tooth loss and risk of oesophageal cancer.

Association between Tooth Loss and Gastric Cancer: A Meta-Analysis of Observational Studies.

Observational studies showed that tooth loss is associated with gastric cancer, but the findings are inconsistent. In this study, a meta-analysis was conducted to evaluate the relationship between tooth loss and gastric cancer. Relevant studies were screened in PubMed and Embase databases, and nine observational studies were considered eligible for the analysis. The combined relative risks for the highest versus the lowest categories of tooth loss were 1.86 (95% CI: 1.08-3.21) and 1.31 (95% CI: 1.12-1.53) in case control and cohort studies, respectively. However, unstable results were observed in the stratified and sensitivity analysis. The current evidence, based solely on four case-control studies and five cohort studies, suggested that tooth loss is a potential marker of gastric cancer. However, we can not concluded at this time that tooth loss may be a risk factor for gastric cancer due to significant heterogeneity among studies and mixed results between case-control studies and cohort studies. Additional large-scale and high-quality prospective studies are required to evaluate the association between tooth loss and risk of gastric cancer.