Anti-GRM5 monoclonal antibody

Accumulating evidence suggests the roles of glutamate metabotropic receptors (GRMs) in cancer, in addition to synaptic signalling. The present study assessed the associations of genetic variants in eight GRM genes with regard to risk and overall survival (OS) in 780 renal cell carcinoma (RCC) patients and controls. After adjustment for known risk factors, GRM5 rs7102764 T was associated with an increased risk of RCC (P = 0.006). Additional analysis has provided evidence that rs7102764 T was correlated with a higher expression of GRM5, which is consistently found to be upregulated in tumours, compared to normal tissues. Furthermore, the GRM3 rs701332 C, GRM4 rs2499707 T, and GRM4 rs4713742 T alleles were significantly associated with a poorer OS (P <= 0.030). The three loci were also observed to have strong cumulative effects on OS. Additional analysis has revealed a significant genotype-expression correlation of rs2499707 T with increased GRM4 expression, which in turn leads to poorer OS in patients with RCC. GRMs might be involved in RCC development and progression, and genetic variants in GRMs might be promising biomarkers.

Autism spectrum disorder (ASD) depends on a clinical interview with no biomarkers to aid diagnosis. The current investigation interrogated single-nucleotide polymorphisms (SNPs) of individuals with ASD from the Autism Genetic Resource Exchange (AGRE) database. SNPs were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG)-derived pathways to identify affected cellular processes and develop a diagnostic test. This test was then applied to two independent samples from the Simons Foundation Autism Research Initiative (SFARI) and Wellcome Trust 1958 normal birth cohort (WTBC) for validation. Using AGRE SNP data from a Central European (CEU) cohort, we created a genetic diagnostic classifier consisting of 237 SNPs in 146 genes that correctly predicted ASD diagnosis in 85.6% of CEU cases. This classifier also predicted 84.3% of cases in an ethnically related Tuscan cohort; however, prediction was less accurate (56.4%) in a genetically dissimilar Han Chinese cohort (HAN). Eight SNPs in three genes (KCNMB4, GNAO1, GRM5) had the largest effect in the classifier with some acting as vulnerability SNPs, whereas others were protective. Prediction accuracy diminished as the number of SNPs analyzed in the model was decreased. Our diagnostic classifier correctly predicted ASD diagnosis with an accuracy of 71.7% in CEU individuals from the SFARI (ASD) and WTBC (controls) validation data sets. In conclusion, we have developed an accurate diagnostic test for a genetically homogeneous group to aid in early detection of ASD. While SNPs differ across ethnic groups, our pathway approach identified cellular processes common to ASD across ethnicities. Our results have wide implications for detection, intervention and prevention of ASD.