INTRODUCTION: Our understanding of the pathophysiology of psychiatric disorders, including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD), lags behind other fields of medicine. The diagnosis and study of these disorders currently depend on behavioral, symptomatic characterization. Defining genetic contributions to disease risk allows for biological, mechanistic understanding but is challenged by genetic complexity, polygenicity, and the lack of a cohesive neurobiological model to interpret findings. RATIONALE: The transcriptome represents a quantitative phenotype that provides biological context for understanding the molecular pathways disrupted in major psychiatric disorders. RNA sequencing (RNA-seq) in a large cohort of cases and controls can advance our knowledge of the biology disrupted in each disorder and provide a foundational resource for integration with genomic and genetic data. RESULTS: Analysis across multiple levels of transcriptomic organization—gene expression, local splicing, transcript isoform expression, and coexpression networks for both protein-coding and noncoding genes—provides an in-depth view of ASD, SCZ, and BD molecular pathology. More than 25% of the transcriptome exhibits differential splicing or expression in at least one disorder, including hundreds of noncoding RNAs (ncRNAs), most of which have unexplored functions but collectively exhibit patterns of selective constraint. Changes at the isoform level, as opposed to the gene level, show the largest effect sizes and genetic enrichment and the greatest disease specificity. We identified coexpression modules associated with each disorder, many with enrichment for cell type–specific markers, and several modules significantly dysregulated across all three disorders. These enabled parsing of down-regulated neuronal and synaptic components into a variety of cell type– and disease-specific signals, including multiple excitatory neuron and distinct interneuron modules with differential patterns of disease association, as well as common and rare genetic risk variant enrichment. The glial-immune signal demonstrates shared disruption of the blood-brain barrier and up-regulation of NFkB-associated genes, as well as disease-specific alterations in microglial-, astrocyte-, and interferon-response modules. A coexpression module associated with psychiatric medication exposure in SCZ and BD was enriched for activity-dependent immediate early gene pathways. To identify causal drivers, we integrated polygenic risk scores and performed a transcriptome-wide association study and summary-data–based Mendelian randomization. Candidate risk genes—5 in ASD, 11 in BD, and 64 in SCZ, including shared genes between SCZ and BD—are supported by multiple methods. These analyses begin to define a mechanistic basis for the composite activity of genetic risk variants. CONCLUSION: Integration of RNA-seq and genetic data from ASD, SCZ, and BD provides a quantitative, genome-wide resource for mechanistic insight and therapeutic development at Resource.PsychENCODE.org. These data inform the molecular pathways and cell types involved, emphasizing the importance of splicing and isoform-level gene regulatory mechanisms in defining cell type and disease specificity, and, when integrated with genome-wide association studies, permit the discovery of candidate risk genes. The PsychENCODE cross-disorder transcriptomic resource.: Human brain RNA-seq was integrated with genotypes across individuals with ASD, SCZ, BD, and controls, identifying pervasive dysregulation, including protein-coding, noncoding, splicing, and isoform-level changes. Systems-level and integrative genomic analyses prioritize previously unknown neurogenetic mechanisms and provide insight into the molecular neuropathology of these disorders.

Currently, nonalcoholic fatty liver disease (NAFLD) is the main cause of chronic liver disease and liver-related mortality worldwide. Hepatocellular carcinoma (HCC) is a fatal complication in patients with NAFLD. However, whether statins can reduce the risk of HCC in patients with NAFLD remains controversial. We aimed to determine the relationship between statin use and HCC occurrence among patients with NAFLD. We independently retrieved related studies from PubMed, EMBASE, Cochrane Library, Web of Science, and ClinicalTrial.gov (from 1 January 2000 to 27 February 2022). The main outcome was the development of HCC. A fixed-effects model was used to merge odds ratio (OR) in the meta-analysis. Five studies involving 684 363 patients were included. The results of the meta-analysis suggested a significantly lower risk of HCC among statin users with NAFLD [OR = 0.59; 95% confidence interval (CI), 0.39–0.89; I ² = 87.90%]. Additionally, a lower risk of HCC was observed among patients with NAFLD aged less than 65 years (OR = 0.59; 95% CI, 0.46–0.77; I ² = 20.50%). Statins can reduce the risk of HCC in patients aged less than 65 years with NAFLD.

Background: The relationship between single nucleotide polymorphisms (SNPs) and ovarian cancer (OC) risk remains controversial. This systematic review and network meta‐analysis was aimed to determine the association between SNPs and OC risk.Methods: Several databases (PubMed, EMBASE, China National Knowledge Infrastructure, Wanfang databases, China Science and Technology Journal Database, and China Biology Medicine disc) were searched to summarize the association between SNPs and OC published throughout April 2021. Direct meta‐analysis was used to identify SNPs that could predict the incidence of OC. Ranking probability resulting from network meta‐analysis and the Thakkinstian’s algorithm was used to select the most appropriate gene model. The false positive report probability (FPRP) and Venice criteria were further tested for credible relationships. Subgroup analysis was also carried out to explore whether there are racial differences.Results: A total of 63 genes and 92 SNPs were included in our study after careful consideration. Fok1 rs2228570 is likely a dominant risk factor for the development of OC compared to other selected genes. The dominant gene model of Fok1 rs2228570 (pooled OR = 1.158, 95% CI: 1.068–1.256) was determined to be the most suitable model with a FPRP <0.2 and moderate credibility.Conclusions: Fok1 rs2228570 is closely linked to OC risk, and the dominant gene model is likely the most appropriate model for estimating OC susceptibility.

Background. Peutz‐Jeghers syndrome (PJS) is a rare autosomal dominant inherited disease caused by a germline mutation in the STK11 gene. It is characterized by mucocutaneous pigmentation, gastrointestinal hamartomatous polyps, and cancer predisposition. Aims. We aimed to summarize the main clinical and genetic features of Chinese PJS patients and assessed the genotype‐phenotype correlations. Methods. Thirty‐eight patients clinically diagnosed with Peutz‐Jeghers syndrome were included in this study from 2016 to 2019. Combined direct sequencing and multiplex ligation‐dependent probe amplification tests were used to detect germline heterogeneous STK11 mutations. RNA sequencing was performed in polyps of PJS patients and control groups to evaluate the difference in expression of STK11. The genotype‐phenotype correlations were calculated by Kaplan‐Meier analyses. Results. All 26 probands and 12 affected relatives had germline heterogeneous STK11 mutations among which 8 variants were novel. Individuals with missense mutations had their first surgery and other symptoms significantly later than individuals with null mutations. Conclusion. This study expanded the spectrum of STK11 gene mutations and further elucidated individuals with null mutations of STK11 typically had an earlier onset of PJS symptoms and needed earlier management.

Background: The national Comprehensive Cancer Network has suggested pembrolizumab as a second-line therapy for esophageal squamous cell carcinoma (ESCC) patients with a programmed death ligand-1 (PD-L1) combined positive score (CPS) ≥ 10. However, despite the increased survival rate associated with pembrolizumab in these patient population, the high cost of pembrolizumab may influence its antitumor effect. This study aimed to evaluate the cost-effectiveness of pembrolizumab compared to chemotherapy as second-line treatments for esophageal carcinoma (EC) based on KEYNOTE-181 trial.Methods: A Markov model was constructed using TreeAge 2021 based on three different groups: all intent-to-treat patients (ITT population), patients with ESCC (ESCC population), and patients with a PD-L1 CPS ≥10 (CPS ≥10 population). Incremental cost, Incremental effect, Life-years, quality-adjusted life-years (QALYs) and incremental cost–effectiveness ratio (ICER) were calculated. Analyses were conducted on the setting of a willingness-to-pay threshold of $150,000 from the US perspective.Results: The ICERs for pembrolizumab were $157,589.545 per QALY, $60,238.823 per QALY, and $100,114.929 per QALY compared with chemotherapy in the ITT, ESCC, and CPS≥10 populations, respectively. The ICER of the ITT population was higher than $150,000, suggesting that pembrolizumab was not a cost-effective treatment scheme in patients with a PD-L1 CPS ≤ 10 or esophageal adenocarcinoma. The ICER was < $150,000 in the ESCC and CPS≥10 populations, indicating that pembrolizumab was cost-effective in these two subgroups.Conclusion: The determining of pembrolizumab as a cost-effective second-line therapy for EC in the United States depends on the histologic type and PD-L1 expression.