Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: UNIPROT:P10636 (tau protein)
 5,110 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Rare mutations in PARK loci genes cause Parkinson's disease (PD) in some families and isolated populations. We investigated the association of common variants in PARK loci and related genes with PD susceptibility and age at onset in an outbred population. A total of 1,103 PD cases from the upper Midwest, USA, were individually matched to unaffected siblings (n = 654) or unrelated controls (n = 449) from the same region. Using a sequencing approach in 25 cases and 25 controls, single nucleotide polymorphisms (SNPs) in species-conserved regions of PARK loci and related genes were detected. We selected additional tag SNPs from the HapMap. We genotyped a total of 235 SNPs and two variable number tandem repeats in the ATP13A2, DJ1, LRRK1, LRRK2, MAPT, Omi/HtrA2, PARK2, PINK1, SNCA, SNCB, SNCG, SPR, and UCHL1 genes in all 2,206 subjects. Case-control analyses were performed to study association with PD susceptibility, while cases-only analyses were used to study association with age at onset. Only MAPT SNP rs2435200 was associated with PD susceptibility after correction for multiple testing (OR = 0.74, 95% CI = 0.64-0.86, uncorrected P < 0.0001, log additive model); however, 16 additional MAPT variants, seven SNCA variants, and one LRRK2, PARK2, and UCHL1 variants each had significant uncorrected P-values. There were no significant associations for age at onset after correction for multiple testing. Our results confirm the association of MAPT and SNCA genes with PD susceptibility but show limited association of other PARK loci and related genes with PD.
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PMID:Common variants in PARK loci and related genes and Parkinson's disease. 2141 35

Transcriptomic analysis of global gene expression in ovarian carcinoma can identify dysregulated genes capable to serve as molecular markers for histology subtypes and survival. The aim of our study was to validate previous candidate signatures in an independent setting and to identify single genes capable to serve as biomarkers for ovarian cancer progression. As several datasets are available in the GEO today, we were able to perform a true meta-analysis. First, 829 samples (11 datasets) were downloaded, and the predictive power of 16 previously published gene sets was assessed. Of these, eight were capable to discriminate histology subtypes, and none was capable to predict survival. To overcome the differences in previous studies, we used the 829 samples to identify new predictors. Then, we collected 64 ovarian cancer samples (median relapse-free survival 24.5 months) and performed TaqMan Real Time Polimerase Chain Reaction (RT-PCR) analysis for the best 40 genes associated with histology subtypes and survival. Over 90% of subtype-associated genes were confirmed. Overall survival was effectively predicted by hormone receptors (PGR and ESR2) and by TSPAN8. Relapse-free survival was predicted by MAPT and SNCG. In summary, we successfully validated several gene sets in a meta-analysis in large datasets of ovarian samples. Additionally, several individual genes identified were validated in a clinical cohort.
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PMID:Meta-analysis of gene expression profiles associated with histological classification and survival in 829 ovarian cancer samples. 2185 9