Changes in antioxidant status and DNA repair capacity are corroborated with molecular alterations in malignant thyroid tissue of patients with papillary thyroid cancer

BRAF and RET polymorphism association with thyroid cancer risk, a preliminary study from Khyber Pakhtunkhwa population

BACKGROUND

Thyroid cancer, originating in the neck's thyroid gland, encompasses various types. Genetic mutations, particularly in BRAF and RET genes are crucial in its development. This study investigates the association between BRAF (rs113488022) and RET (rs77709286) polymorphisms and thyroid cancer risk in the Khyber Pakhtunkhwa (KP) population.

METHODS

Blood samples from 100 thyroid cancer patients and 100 healthy controls were genotyped using ARMS-PCR followed by gel electrophoresis and statistical analysis.

RESULTS

Analysis revealed a significant association between the minor allele T of BRAF (rs113488022) and thyroid cancer risk (P = 0.0001). Both genotypes of BRAF (rs113488022) showed significant associations with thyroid cancer risk (AT; P = 0.0012 and TT; P = 0.045). Conversely, the minor allele G of RET (rs77709286) exhibited a non-significant association with thyroid cancer risk (P = 0.2614), and neither genotype showed significant associations (CG; P = 0.317, GG; P = 0.651). Demographic and clinical parameters analysis using SPSS showed a non-significant association between BRAF and RET variants and age group (P = 0.878 and P = 0.536), gender (P = 0.587 and P = 0.21), tumor size (P = 0.796 and P = 0.765), or tumor localization (P = 0.689 and P = 0.727).

CONCLUSION

In conclusion, this study emphasizes the significant association between BRAF polymorphism and thyroid cancer risk, while RET polymorphism showed a less pronounced impact. Further validation using larger and specific datasets is essential to establish conclusive results.

Risk of papillary thyroid carcinoma and nodular goiter associated with exposure to semi-volatile organic compounds: A multi-pollutant assessment based on machine learning algorithms

BACKGROUND

Exposure to semi-volatile organic compounds (SVOCs) may link to thyroid nodule risk, but studies of mixed-SVOCs exposure effects are lacking. Traditional analytical methods are inadequate for dealing with mixed exposures, while machine learning (ML) seems to be a good way to fill the gaps in the field of environmental epidemiology research.

OBJECTIVES

Different ML algorithms were used to explore the relationship between mixed-SVOCs exposure and thyroid nodule.

METHODS

A 1:1:1 age- and gender-matched case-control study was conducted in which 96 serum SVOCs were measured in 50 papillary thyroid carcinoma (PTC), 50 nodular goiters (NG), and 50 controls. Different ML techniques such as Random Forest, AdaBoost were selected based on their predictive power, and variables were selected based on their weights in the models. Weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were used to assess the mixed effects of the SVOCs exposure on thyroid nodule.

RESULTS

Forty-three of 96 SVOCs with detection rate >80 % were included in the analysis. ML algorithms showed a consistent selection of SVOCs associated with thyroid nodule. Fluazifop-butyl and fenpropathrin are positively associated with PTC and NG in single compound models (all P < 0.05). WQS model shows that exposure to mixed-SVOCs was associated with an increased risk of PTC and NG, with the mixture dominated by fenpropathrin, followed by fluazifop-butyl and propham. In the BKMR model, mixtures showed a significant positive association with thyroid nodule risk at high exposure levels, and fluazifop-butyl showed positive effects associated with PTC and NG.

CONCLUSION

This study confirms the feasibility of ML methods for variable selection in high-dimensional complex data and showed that mixed exposure to SVOCs was associated with increased risk of PTC and NG. The observed association was primarily driven by fluazifop-butyl and fenpropathrin. The findings warranted further investigation.