Rabbit Anti-Human ESR1 (Phospho-Ser104) polyclonal antibody

Canine mammary tumours (CMT) are among the most common canine cancer types in female dogs. Dogs provide an adaptable model system for human breast cancer studies. R is important to identify the underlying genetic basis to improve knowledge of pathways related to cancer pathogenesis in both species. In this study, we investigated CMT associated single nucleotide polymorphisms (SNP) in target regions of ESR1 and CDK5RAP2 genes. Partial sequencing of two genes in 25 cases with mammary tumours and 10 dogs with healthy mammary glands was performed. Two previously reported SNPs in ESR1 gene and one previously reported SNP and two novel SNPs were genotyped downstream CDK5RAP2 gene. According to the association analysis performed in cases and controls, no statistically significant association was found between these SNPs and CMTs. Comparison of the results from other studies revealed the genetic heterogeneity of ESR1 and CDK5RAP2 between different dog breeds. Larger datasets of different breeds should be analyzed in further studies to identify the possible effects of the two genes in mammary tumour development.

In this study, we split 2156 individuals from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) data into two groups, establishing a phenotype of exceptional longevity & normal cognition versus cognitive impairment. We conducted a genome-wide association study (GWAS) to identify significant genetic variants and biological pathways that are associated with cognitive impairment and used these results to construct polygenic risk scores. We elucidated the important and robust factors, both genetic and non-genetic, in predicting the phenotype, using several machine learning models. The GWAS identified 28 significant SNPs at p-value < 3 x 10(-5) significance level and we pinpointed four genes, ESR1, PHB, RYR3, GRIK2, that are associated with the phenotype though immunological systems, brain function, metabolic pathways, inflammation and diet in the CLHLS cohort. Using both genetic and non-genetic factors, four machine learning models have close prediction results for the phenotype measured in Area Under the Curve: random forest (0.782), XGBoost (0.781), support vector machine with linear kernel (0.780), and l(2) penalized logistic regression (0.780). The top four important and congruent features in predicting the phenotype identified by these four models are: polygenic risk score, sex, age, and education.