Objectives: The signal transducer and activator of transcription 4 (STAT4) gene encodes an important transcription factor that transmits signals induced by several cytokines associated with autoimmune diseases and has been identified as a susceptibility gene for numerous autoimmune disorders. The association between STAT4 rs7574865 polymorphism and the susceptibility of autoimmune thyroid disease (AITD) has been investigated in previous case-control studies. However, the investigation results were inconsistent. Hence, a meta-analysis was performed to draw a more reliable conclusion about it. Methods: All relevant studies were searched in Embase, PubMed, Web of Science, and China National Knowledge Infrastructure, till August 20, 2018. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate the strength of the association. Results: A total of five independent case-control studies with 1707 AITD patients and 2316 controls were included in the present meta-analysis. The overall pooled analysis indicated that STAT4 rs7574865 polymorphism was significantly associated with AITD susceptibility [TT vs. GG: OR = 1.63, 95%CI = 1.24-2.15, P Z = 0.0005; TT vs. (TG+GG): OR = 1.55, 95%CI = 1.26-1.91, P Z < 0.0001]. However, the subgroup analysis showed a significant association of STAT4 rs7574865 polymorphism with AITD susceptibility in Asian population, but not in African population. STAT4 rs7574865 polymorphism was significantly associated both with Graves' disease (GD) and Hashimoto's thyroiditis (HT) susceptibility. Conclusion: This meta-analysis showed a significant association between STAT4 rs7574865 polymorphism and AITD susceptibility. However, further studies with larger sample sizes and other ethnicities are still required to confirm the findings.

Liver cancer is the main reason of cancer deaths globally, with an unfavorable prognosis. DNA methylation is one of the epigenetic modifications and maintains the right adjustment of gene expression and steady gene silencing. We aim to explore the novel signatures for prognosis by using DNA methylation-driven genes. To acquire the DNA methylation-driven genes, we perform the difference analysis from the gene expression data and DNA methylation data in TCGA or GEO databases. And we obtain the 31 DNA methylation-driven genes. Subsequently, consensus clustering analysis was utilized to identify the molecular subtypes based on the 31 DNA methylation-driven genes. So, two molecular subtypes were identified to perform those analyses: Survival, immune cell infiltration, and tumor mutation. Results showed that two subtypes were clustered with distinct prognoses, tumor-infiltrating immune cell and tumor mutation burden. Furthermore, the 31 DNA methylation-driven genes were applied to perform the survival analysis to select the 14 survival-related genes. Immediately, a five methylation-driven genes risk model was built, and the patients were divided into high and low-risk groups. The model was established with TCGA as the training cohort and GSE14520 as the validation cohort. According to the risk model, we perform the systematical analysis, including survival, clinical feature, immune cell infiltration, somatic mutation status, underlying mechanisms, and drug sensitivity. Results showed that the high and low groups possessed statistical significance. In addition, the ROC curve was utilized to measure the accuracy of the risk model. AUCs at 1-year, 3-years, and 5-years were respectively 0.770, 0.698, 0.676 in training cohort and 0.717, 0.649, 0.621 in validation cohort. Nomogram was used to provide a better prediction for patients' survival. Risk score increase the accuracy of survival prediction in HCC patients. In conclusion, this study developed a novel risk model of five methylation-driven genes based on the comprehensive bioinformatics analysis, which accurately predicts the survival of HCC patients and reflects the immune and mutation features of HCC. This study provides novel insights for immunotherapy of HCC patients and promotes medical progress.

Alternative splicing (AS) is the mechanism by which a few genes encode numerous proteins, and it redefines the concept of gene expression regulation. Recent studies showed that dysregulation of AS was an important cause of tumorigenesis and microenvironment formation. Therefore, we performed a systematic analysis to examine the role of AS in breast cancer (Breast Cancer, BrCa) progression.

Autism spectrum disorder (ASD) is a group of clinically and genetically heterogeneous neurodevelopmental disorders. Recent tremendous advances in the whole exome sequencing (WES) enable rapid identification of variants associated with ASD including single nucleotide variations (SNVs) and indels. To further explore genetic etiology of ASD in Chinese children with negative findings of copy number variants (CNVs), we applied WES in 80 simplex families with a single affected offspring with ASD or suspected ASD, and validated variations predicted to be damaging by Sanger sequencing. The results showed that an overall diagnostic yield of 8.8% (9.2% in the group of ASD and 6.7% in the group of suspected ASD) was observed in our cohort. Among patients with diagnosed ASD, developmental delay or intellectual disability (DD/ID) was the most common comorbidity with a diagnostic yield of 13.3%, followed by seizures (50.0%) and craniofacial anomalies (40.0%). All of identified de novo SNVs and indels among patients with ASD were loss of function (LOF) variations and were slightly more frequent among female (male vs. female: 7.3% vs. 8.5%). A total of seven presumed causative genes (CHD8, AFF2, ADNP, POGZ, SHANK3, IL1RAPL1, and PTEN) were identified in this study. In conclusion, WES is an efficient diagnostic tool for diagnosed ASD especially those with negative findings of CNVs and other neurological disorders in clinical practice, enabling early identification of disease related genes and contributing to precision and personalized medicine.