A systematic review and network meta-analysis of single nucleotide polymorphisms associated with pancreatic cancer risk.

In this meta-analysis, we systematically investigated the correlation between single nucleotide polymorphisms (SNPs) and pancreatic cancer (PC) risk. We searched PubMed, Network Science, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Science and Technology Periodical Database (VIP), and Wanfang databases up to January 2020 for studies on PC risk-associated SNPs. We identified 45 case-control studies (36,360 PC patients and 54,752 non-cancer individuals) relating to investigations of 27 genes and 54 SNPs for this meta-analysis. Direct meta-analysis followed by network meta-analysis and Thakkinstian algorithm analysis showed that homozygous genetic models for CTLA-4 rs231775 (OR =0.326; 95% CI: 0.218-0.488) and VDR rs2228570 (OR = 1.976; 95% CI: 1.496-2.611) and additive gene model for TP53 rs9895829 (OR = 1.231; 95% CI: 1.143-1.326) were significantly associated with PC risk. TP53 rs9895829 was the most optimal SNP for diagnosing PC susceptibility with a false positive report probability < 0.2 at a stringent prior probability value of 0.00001. This systematic review and meta-analysis suggest that TP53 rs9895829, VDR rs2228570, and CTLA-4 rs231775 are significantly associated with PC risk. We also demonstrate that TP53 rs9895829 is a potential diagnostic biomarker for estimating PC risk.

Traditional Chinese medicine for the treatment of pulmonary fibrosis: A protocol for systematic review and meta-analysis of overview.

BACKGROUND: Since December 2019, there have been many cases of viral pneumonia of unknown causes in Wuhan City, Hubei Province. During the period of novel coronavirus, according to the observation of limited autopsy and biopsy pathological results, pulmonary interstitial fibrosis appeared in some pathological changes of lung. Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial pneumonia with unknown etiology and pathological changes limited to the lung. At present, there is still a lack of reevaluation of systematic evaluation of traditional Chinese medicine treatment IPF. Therefore, a systematic re-evaluation of the systematic evaluation of traditional Chinese medicine in the treatment of pulmonary fibrosis may help to understand the effective treatment scheme of traditional Chinese medicine in the treatment of pulmonary fibrosis and provide more reliable evidence for the first-line clinicians to treat novel coronavirus. METHODS: We will search 3 foreign electronic databases (Cochrane Library, Embase, PubMed) and 4 Chinese electronic databases (China National Knowledge Infrastructure [CNKI], WangFang Database, Chinese Biomedical Literature Database [CBM], and Chinese Scientific Journal Database [VIP]) to collect potential systematic reviews from their inceptions to February 2020. The language of publication is limited to Chinese or English. We will consider SRs and meta-analysis of Traditional Chinese Medicine for the Treatment of pulmonary fibrosis. Two reviewers will identify relevant studies, and then assess the methodological quality by assessment of multiple systematic reviews-2 tool. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) report checklist to assess the quality of reports included in the study. In order to better evaluate the systematic evaluation included in this research, risk of bias in systematic review tool is included in this research to evaluate the methodological quality. The quality of evidence of the included systematic reviews was assessed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. The Primary outcomes include: Clinical total effective rate, curative effect of TCM symptoms, pulmonary function and blood gas analysis. RESULTS: The results of this study will be published in a peer-reviewed journal. CONCLUSIONS: We expect to obtain reliable evidence from systematic analysis of traditional Chinese medicine treatment of pulmonary fibrosis in an available and useful document. REGISTRATION NUMBER: INPLASY202060029.

A comprehensive evaluation of single nucleotide polymorphisms associated with atrophic gastritis risk: A protocol for systematic review and network meta-analysis.

BACKGROUND: Single nucleotide polymorphisms (SNPs) have been inconsistently associated with atrophic gastritis (AG) risk. This meta-analysis aimed to synthesize relevant data on SNPs associated with AG. METHODS: To identify all associated studies of SNPs and AG published, databases had been searched through January 2020 from the databases of PubMed, China National Knowledge Infrastructure (CNKI), Web of Science, Embase, the Chinese Science and Technology Periodical Database (VIP), Cochrane Library, and Wanfang databases. With the help of network meta-analysis and Thakkinstian algorithm, the best genetic model with the strongest correlation with AG was selected, the final result - matching to the noteworthy correlation - was obtained by referring to the false positive reporting rate (false positive report probability, FPRP). Based on STREGA's stated criteria, the methodological quality of the data we collected was valued. Both Stata 14.0 and GeMTC will be used for a comprehensive review of the system and will be used in our meta-analysis. RESULTS: This study will provide a high-quality evidence to find the SNP most associated with AG susceptibility and the best genetic model. CONCLUSIONS: This study will explore which SNP is most associated with AG susceptibility. REGISTRATION: INPLASY202050016.

A comprehensive evaluation of single nucleotide polymorphisms associated with gastric cancer risk: A protocol for systematic review and network meta-analysis.

BACKGROUND: Single nucleotide polymorphisms (SNPs) have been inconsistently associated with gastric cancer (GC) risk. This meta-analysis aimed to synthesize relevant data on SNPs associated with GC. METHODS: Databases were searched to identify association studies of SNPs and GC published through January 2020 from the databases of PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, the Chinese Science and Technology Periodical Database, and Wan fang databases. Network meta-analysis and Thakkinstian algorithm were used to select the most appropriate genetic model, along with false positive report probability for noteworthy associations. The methodological quality of data was assessed based on the STrengthening the REporting of Genetic Association Studies statement Stata 14.0 will be used for systematic review and meta-analysis. RESULTS: This study will provide a high-quality evidence to find the SNP most associated with GC susceptibility and the best genetic model. CONCLUSIONS: This study will explore which SNP is most associated with GC susceptibility. REGISTRATION: INPLASY202040132.

A comprehensive assessment of single nucleotide polymorphisms associated with pancreatic cancer risk: A protocol for systematic review and network meta-analysis.

BACKGROUND: Single nucleotide polymorphisms (SNPs) have been inconsistently associated with pancreatic cancer (PC) risk. This meta-analysis aimed to synthesize relevant data on SNPs associated with PC. METHODS: Databases were searched to identify association studies of SNPs and PC published through January 2020 from the databases of PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, the Chinese Science and Technology Periodical Database (VIP) and Wanfang databases. Network meta-analysis and Thakkinstian algorithm were used to select the most appropriate genetic model, along with false positive report probability (FPRP) for noteworthy associations. The methodological quality of data was assessed based on the STREGA statement Stata 14.0 will be used for systematic review and meta-analysis. RESULTS: This study will provide a high-quality evidence to find the SNP most associated with pancreatic cancer susceptibility and the best genetic model. CONCLUSIONS: This study will explore which SNP is most associated with pancreatic cancer susceptibility.Registration: INPLASY202040023.

A comprehensive evaluation of single nucleotide polymorphisms associated with osteosarcoma risk: A protocol for systematic review and network meta-analysis.

BACKGROUND: Single nucleotide polymorphisms (SNPs) have been inconsistently associated with osteosarcoma (OS) risk. This meta-analysis aimed to synthesize relevant data on SNPs associated with OS. METHODS: Databases were searched to identify association studies of SNPs and OS published through January 2020 from the databases of PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, the Chinese Science and Technology Periodical Database, and Wan fang databases. Network meta-analysis and Thakkinstian algorithm were used to select the most appropriate genetic model, along with false positive report probability for noteworthy associations. The methodological quality of data was assessed based on the STrengthening the REporting of Genetic Association Studies statement Stata 14.0 will be used for systematic review and meta-analysis. RESULTS: This study will provide a high-quality evidence to find the SNP most associated with OS susceptibility and the best genetic model. CONCLUSIONS: This study will explore which SNP is most associated with OS susceptibility. REGISTRATION: INPLASY202040023.

Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of osteosarcoma.

This work aimed to investigate tumor-infiltrating immune cells (TIICs) and immune-associated genes in the tumor microenvironment of osteosarcoma. An algorithm known as ESTIMATE was applied for immune score assessment, and osteosarcoma cases were assigned to the high and low immune score groups. Immune-associated genes between these groups were compared, and an optimal immune-related risk model was built by Cox regression analyses. The deconvolution algorithm (referred to as CIBERSORT) was applied to assess 22 TIICs for their amounts in the osteosarcoma microenvironment. Osteosarcoma cases with high immune score had significantly improved outcome (P<0.01). The proportions of naive B cells and M0 macrophages were significantly lower in high immune score tissues compared with the low immune score group (P<0.05), while the amounts of M1 macrophages, M2 macrophages, and resting dendritic cells were significantly higher (P<0.05). Important immune-associated genes were determined to generate a prognostic model by Cox regression analysis. Interestingly, cases with high risk score had poor outcome (P<0.01). The areas under the curve (AUC) for the risk model in predicting 1, 3 and 5-year survival were 0.634, 0.781, and 0.809, respectively. Gene set enrichment analysis suggested immunosuppression in high-risk osteosarcoma patients, in association with poor outcome.

Lack of association between ALOX5AP genetic polymorphisms and risk of ischemic stroke: evidence from meta-analyses.

BACKGROUND: In recent years, there has been substantial research evaluating the relationship between arachidonate 5-lipoxygenase-activating protein (ALOX5AP) polymorphisms and ischemic stroke (IS). The objective of this study was to systematically review and analyze the existing evidence. METHODS: A comprehensive search of major electronic databases for studies published between 1990 and 2018 was carried out. Data were synthesized as OR and 95% CI using fixed-effects and random-effects models. RESULTS: A total of 30 studies were available for analysis. The aggregate sample size across all studies was 32,782 (16,294 cases and 16,488 controls). We found no association of the ALOX5AP rs10507391 (OR=1.03 for A allele vs T allele; 95% CI: 0.93-1.14; P=0.557), rs4769874 (OR=1.13 for A allele vs G allele; 95% CI: 1.00-1.28; P=0.050), rs9551963 (OR=1.03 for A allele vs C allele; 95% CI: 0.96-1.11; P=0.372), rs17222814 (OR=1.09 for A allele vs G allele; 95% CI: 0.96-1.24; P=0.195), rs17222919 (OR=0.89 for G allele vs T allele; 95% CI: 0.75-1.06; P=0.175), and rs4073259 (OR=1.20 for A allele vs G allele; 95% CI: 1.00-1.45; P=0.056) polymorphisms with IS risk. Haplotype analysis also did not yield significant findings for the HapA (rs17222814G-rs10507391T-rs4769874G-rs9551963A; OR=1.20; 95% CI: 0.91-1.56; P=0.192) and HapB (rs17216473A-rs10507391A-rs9315050A-rs17222842G; OR=1.11; 95% CI: 0.90-1.38; P=0.339) haplotypes. CONCLUSION: Current evidence does not support an association of rs10507391, rs4769874, rs9551963, rs17222814, rs17222919, rs4073259, and HapA and HapB with IS risk.