RESUMO
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.
Assuntos
Antígeno CTLA-4/genética , Predisposição Genética para Doença/genética , Neoplasias Pancreáticas/genética , Receptores de Calcitriol/genética , Proteína Supressora de Tumor p53/genética , Humanos , Metanálise em Rede , Polimorfismo de Nucleotídeo Único/genéticaRESUMO
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.
Assuntos
Neoplasias Ósseas/genética , Osteossarcoma/genética , Polimorfismo de Nucleotídeo Único , Predisposição Genética para Doença , Humanos , Metanálise em Rede , Projetos de Pesquisa , Medição de Risco , Revisões Sistemáticas como AssuntoRESUMO
It has been hypothesized that a principal route of human exposure to polybrominated diphenyl ethers (PBDEs), used as flame retardants, is through fish consumption. Between 1999 and 2005 PBDE-47, -99, -100, -153, and -154 were analyzed in 3797 biological samples of 33 species of the Hudson River, New York. Approximately 98.4% of the samples contained PBDEs between 0.5 and 37 169 ng g(-1) lipid, with a median concentration of 772 ng g(-1) lipid. Yearly median sigmaPBDE concentrations fluctuated. Samples from river miles 112 and 153 contained higher sigmaPBDEs than those from other locations of the river. The 7-year median sigmaPBDE concentrations were the highest in large carnivorous fishes and the lowest in insects. The median abundance of congener PBDE-47 decreased from 80% to 63% with decreasing levels of sigmaPBDEs in the samples, while an increase from 2% to 23% was observed for PBDE-99. The median abundance of other congeners did not change with concentrations of sigmaPBDEs. Positive-, negative-, and no-correlation between sigmaPBDE concentrations and fish weight were observed for different species and for the same species from different locations of the river. The sources of PBDE contamination, diet, metabolic activity, and sediment chemistry might affect the levels of PBDEs in a fish.
