Dataset Information

Genetic variability of inflammatory genes in the Brazilian population.

ABSTRACT: Inflammatory gene variants have been associated with several diseases, including cancer, diabetes, vascular diseases, neurodegenerative diseases, arthritis, and others. Therefore, determining the population genetic composition of inflammation-related genes can be useful for the determination of general risk, prognostic and therapeutic strategies to prevent or cure specific diseases. We have aimed to identify polymorphism genotype frequencies in genes related to the inflammatory response in the Brazilian population, namely, IκBL -62AT, IκBL -262CT, tumor necrosis factors alpha (TNFa) -238GA, TNFa -308GA, lymphotoxin-alpha (LTa) +80AC, LTa +252AG, FAS -670AG, and FASL -844TC, considering the white, black, and Pardo ethnicities of the São Paulo State. Our results suggest that the Brazilian population is under a miscegenation process at the current time, since some genotypes are not in the Hardy-Weinberg equilibrium. In addition, we conclude that the Pardo ethnicity is derived from a complex mixture of ethnicities, including the native Indian population.

SUBMITTER: dos Santos M

PROVIDER: S-EPMC3816776 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Genetic variability of inflammatory genes in the Brazilian population.

dos Santos Marcelo M Stur Elaine E Maia Lucas Lima LL Agostini Lidiane Pignaton LP Peterle Gabriela Tonini GT Mendes Suzanny Oliveira SO Tajara Eloiza Helena EH de Carvalho Marcos Brasilino MB Louro Iúri Drumond ID Silva-Conforti Adriana Madeira Álvares AM

Genetic testing and molecular biomarkers 20130803 11

Inflammatory gene variants have been associated with several diseases, including cancer, diabetes, vascular diseases, neurodegenerative diseases, arthritis, and others. Therefore, determining the population genetic composition of inflammation-related genes can be useful for the determination of general risk, prognostic and therapeutic strategies to prevent or cure specific diseases. We have aimed to identify polymorphism genotype frequencies in genes related to the inflammatory response in the B ...[more]

PMID: 23909556

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Project description:Colorectal cancer (CRC) is the third most common type of cancer among men and the second among women in Brazil. Despite the high incidence and significance of CRC in Brazil, very little is known about its prevalence among the asymptomatic population. Recently, a CRC screening program was implemented at the Cancer Hospital of Barretos. Characterization of the clinical findings detected in the screening population and the prevalence of basal CRC might contribute to better organization of the program and define the best strategy for a future national screening program. We hypothesize that recruitment and the early outcomes of our screening program based on the fecal immunochemical test (FIT) will differ from the outcomes corresponding to other populations due to sociodemographic differences. Aims: i. To implement a data collection and storage system for follow-up of the screening program participants and to measure early outcomes (adenoma, advanced adenoma and cancer) and associate them with sociodemographic risk factors; ii. to quantify the risk of CRC in the Brazilian population and to develop algorithms for risk stratification of CRC screening; and iii. to compare the risk stratification to other countries with low, medium and high incomes. Methods: Individuals aged 50 to 65 years will be included in the HCB screening program from November 2017 to December 2018. The following data will be collected from all participants: sociodemographic and ethnic (skin color) characteristics; risk factors for CRC, such as smoking and drinking; comorbidities, including diabetes mellitus and arterial hypertension; and FIT, colonoscopy and histopathology examination results. Data collection will be performed using the REDCap data collection/database system. The risk score will be formulated using the Chi-square test (or Fisher’s exact test) and simple logistic regression, and the regression coefficients will be calculated. Then, the model identified for the training sample will be replicated with a validation sample. The resulting score will be used to calculate the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the receiver operating characteristic (ROC) curve and Kolmogorov D statistic.

Dataset Information

Genetic variability of inflammatory genes in the Brazilian population.

Publications

Genetic variability of inflammatory genes in the Brazilian population.

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