Human Anti-Mouse TNFRSF11A monoclonal Antibody, clone SFB072 [Biotin]

IMPORTANCE Myasthenia gravis is a chronic, autoimmune, neuromuscular disease characterized by fluctuating weakness of voluntary muscle groups. Although genetic factors are known to play a role in this neuroimmunological condition, the genetic etiology underlying myasthenia gravis is not well understood. OBJECTIVE To identify genetic variants that alter susceptibility to myasthenia gravis, we performed a genome-wide association study. DESIGN, SETTING, AND PARTICIPANTS DNA was obtained from 1032 white individuals from North America diagnosed as having acetylcholine receptor antibody-positive myasthenia gravis and 1998 race/ethnicity-matched control individuals from January 2010 to January 2011. These samples were genotyped on Illumina OmniExpress single-nucleotide polymorphism arrays. An independent cohort of 423 Italian cases and 467 Italian control individuals were used for replication. MAIN OUTCOMES AND MEASURES We calculated P values for association between 8 114 394 genotyped and imputed variants across the genome and risk for developing myasthenia gravis using logistic regression modeling. A threshold P value of 5.0 x 10(-8) was set for genome-wide significance after Bonferroni correction for multiple testing. RESULTS In the overall case-control cohort, we identified association signals at CTLA4 (rs231770; P = 3.98 x 10(-8); odds ratio, 1.37; 95% CI, 1.25-1.49), HLA-DQA1 (rs9271871; P = 1.08 x 10(-8); odds ratio, 2.31; 95% CI, 2.02 - 2.60), and TNFRSF11A (rs4263037; P = 1.60 x 10(-9); odds ratio, 1.41; 95% CI, 1.29-1.53). These findings replicated for CTLA4 and HLA-DQA1 in an independent cohort of Italian cases and control individuals. Further analysis revealed distinct, but overlapping, disease-associated loci for early- and late-onset forms of myasthenia gravis. In the late-onset cases, we identified 2 association peaks: one was located in TNFRSF11A (rs4263037; P = 1.32 x 10(-12); odds ratio, 1.56; 95% CI, 1.44-1.68) and the other was detected in the major histocompatibility complex on chromosome 6p21 (HLA-DQA1; rs9271871; P = 7.02 x 10(-18); odds ratio, 4.27; 95% CI, 3.92-4.62). Association within the major histocompatibility complex region was also observed in early-onset cases (HLA-DQA1; rs601006; P = 2.52 x 10(-11); odds ratio, 4.0; 95% CI, 3.57-4.43), although the set of single-nucleotide polymorphisms was different from that implicated among late-onset cases. CONCLUSIONS AND RELEVANCE Our genetic data provide insights into aberrant cellular mechanisms responsible for this prototypical autoimmune disorder. They also suggest that clinical trials of immunomodulatory drugs related to CTLA4 and that are already Food and Drug Administration approved as therapies for other autoimmune diseases could be considered for patients with refractory disease.

Gastric cancer (GC) is a major tumor throughout the world with remaining high morbidity and mortality. The aim is to generate a gene model to assess the prognoses risk of patients with GC. Gene expression profiling of gastric cancer patients, GSE62254 (300 samples) and GSE26253 (432 samples), was downloaded from Gene Expression Omnibus (GEO) database. Univariate survival analysis and LASSO (Least Absolute Shrinkage and Selectionator operator) (1000 iterations) of differentially expressed genes in GSE62254 was assessed using survival and glmnet in R package, respectively. Kaplan-Meier analysis on the clustering algorithm from each regression model was performed to calculate the influence to the prognosis. Random samples in GSE26253 were analyzed in multivariate and univariate survival analysis for one thousand times to calculate statistical stability of each regression model. A total of 854 Genes were identified differentially expressed in GSE62254, among which 367 Genes were found influencing the prognoses. Six gene clusters were selected with good stability. Hereinto, five or more genes in 11-Gene model, TRPC1, SGCE, TNFRSF11A, LRRN1, HLF, CYS1, PPP1R14A, NOV, NBEA, CES1 and RGN, was available to evaluate the prognostic risk of GC patients in GSE26253 (P = 0.00445). The validity and reliability was validated. In conclusion, we successfully generated a stable 5-Gene model, which could be utilized to predict prognosis of GC patients and would contribute to postoperational treatment and follow-up strategies.