Previous studies have suggested more than 20 genetic intervals that are associated with susceptibility to type 1 diabetes (T1D), but identification of specific genes has been challenging and largely limited to known candidate genes. Here, we report evidence for an association between T1D and multiple single-nucleotide polymorphisms in 197 kb of genomic DNA in the IDDM5 interval. We cloned a new gene (SUMO4), encoding small ubiquitin-like modifier 4 protein, in the interval. A substitution (M55V) at an evolutionarily conserved residue of the crucial CUE domain of SUMO4 was strongly associated with T1D (P = 1.9 x 10(-7)). SUMO4 conjugates to I kappa B alpha and negatively regulates NF kappa B transcriptional activity. The M55V substitution resulted in 5.5 times greater NF kappa B transcriptional activity and approximately 2 times greater expression of IL12B, an NF kappa B-dependent gene. These findings suggest a new pathway that may be implicated in the pathogenesis of T1D.

The lymphoid-specific phosphatase (LYP) encoded by PTPN22 is involved in preventing spontaneous T-cell activation by dephosphorylating and inactivating T-cell receptor-associated Csk kinase. We have genotyped 396 type 1 diabetic patients and 1,178 control subjects of Caucasian descent from north central Florida and report a strong association between type 1 diabetes and a polymorphism (R620W) in the PTPN22 gene. The homozygous genotype for the T allele encoding the 620W residue is associated with an increased risk for developing type 1 diabetes (odds ratio [OR] ‫؍‬ 3.4, P < 0.008), and the heterozygous genotype C/T had an OR of 1.7 (P ‫؍‬ 6 ؋ 10 ؊6 ). The C/C homozygous genotype is protective against type 1 diabetes (OR ‫؍‬ 0.5, P ‫؍‬ 6 ؋ 10 ؊6 ). Furthermore, transmission disequilibrium analysis of 410 affected sibpair and simplex families of Caucasian descent indicated that the type 1 diabetesassociated T allele is transmitted more often (57.2%) than randomly expected (P < 0.003). Together with previous reports of the association between PTPN22 and type 1 diabetes, as well as rheumatoid arthritis and systemic lupus erythematosus, these results provide compelling evidence that LYP is a critical player in multiple autoimmune disorders. Diabetes 54:906 -908, 2005

PurposeA prognostic nomogram was applied to predict survival in osteosarcoma patients.Patients and methodsData collected from 2,195 osteosarcoma patients in the Surveillance, Epidemiology, and End Results (SEER) database between 1983 and 2014 were analyzed. Independent prognostic factors were identified via univariate and multivariate Cox analyses. These were incorporated into a nomogram to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates. Internal and external data were used for validation. Concordance indices (C-indices) were used to estimate nomogram accuracy.ResultsPatients were randomly assigned into a training cohort (n=1,098) or validation cohort (n=1,097). Age at diagnosis, tumor site, histology, tumor size, tumor stage, use of surgery, and tumor grade were identified as independent prognostic factors via univariate and multivariate Cox analyses (all P<0.05) and then included in the prognostic nomogram. C-indices for OS and CSS prediction in the training cohort were 0.763 (95% CI 0.761–0.764) and 0.764 (95% CI 0.762–0.765), respectively. C-indices for OS and CSS prediction in the external validation cohort were 0.739 (95% CI 0.737–0.740) and 0.740 (95% CI, 0.738–0.741), respectively. Calibration plots revealed excellent consistency between actual survival and nomogram prediction.ConclusionNomograms were constructed to predict OS and CSS for osteosarcoma patients in the SEER database. They provide accurate and individualized survival prediction.

Our findings indicate loss of SLC5A8 expression as a result of aberrant promoter methylation in pancreatic adenocarcinoma. We suggest that SLC5A8 may function as a tumor suppressor gene whose silencing by epigenetic changes may contribute to carcinogenesis and progression of pancreatic cancer.

ShecDNA microarrays with >11,000 cDNA clones from an NOD spleen cDNA library were used to identify temporal gene expression changes in NOD mice (1-10 weeks), which spontaneously develop type 1 diabetes, and changes between NOD and NOD congenic mice (NO-D.Idd3/Idd10 and NOD.B10Sn-H2 b ), which have near zero incidence of insulitis and diabetes. The expression profiles identified two distinct groups of mice corresponding to an immature (1-4 weeks) and mature (6 -10 weeks) state. The rapid switch of gene expression occurring around 5 weeks of age defines a key immunological checkpoint. Sixty-two known genes are upregulated, and 18 are downregulated at this checkpoint in the NOD. The expression profiles are consistent with increased antibody production, antigen presentation, and cell proliferation associated with an active autoimmune response. Seven of these genes map to confirmed diabetes susceptibility regions. Of these seven, three are excellent candidate genes not previously implicated in type 1 diabetes. Ten genes are differentially expressed between the NOD and congenic NOD at the immature stage (Hspa8, Hif1a, and several involved in cellular functions), while the other 70 genes exhibit expression differences during the mature (6؊10 week) stage, suggesting that the expression differences of a small number of genes before onset of insulitis determine the disease progression. Diabetes 53: 366 -375, 2004 T ype 1 diabetes is a disease manifested when the insulin-producing pancreatic ␤-cells are destroyed by the immune system. Studies using the NOD mouse model, which spontaneously develops type 1 diabetes, have shown that both B-and T-cells are necessary for the development of the disease (1-5). Other immune cells, such as macrophages and dendritic cells, have also been implicated in the disease process (6). Despite extensive studies on this animal model, the underlying molecular mechanisms that lead to the development and progression of type 1 diabetes remain elusive.Previous studies primarily focused on one or a few molecules believed to be involved in the disease pathogenesis. The information obtained from these experiments is ideal for uncovering the role of specific genes. However, the single gene approach is not the ideal way to discover new genes or the molecular networks involved in the disease process. The conventional approaches of investigation have limited the rate of progress because of the complex nature of this disease. The recent development of microarray technology has provided a high-throughput approach to simultaneously analyze the expression of tens of thousands of genes. This genomic revolution has fundamentally changed how investigators can approach biomedical questions.Several studies have used the microarray approach to study gene expression in islet cells (7-9) and immune cells (10,11). Because of the preliminary nature of the published studies on the immune system, few definitive conclusions were reached. In this study, microarray technology was used to extensively profile splenic gene ...