Ovarian cancer (OV) is one of the leading causes of cancer deaths in women worldwide. Late diagnosis and heterogeneous treatment result to poor survival outcomes for patients with OV. Therefore, we aimed to develop novel biomarkers for prognosis prediction from the potential molecular mechanism of tumorigenesis. Eight eligible data sets related to OV in GEO database were integrated to identify differential expression genes (DEGs) between tumour tissues and normal. Enrichment analyses discovered DEGs were most significantly enriched in G2/M checkpoint signalling pathway. Subsequently, we constructed a multi‐gene signature based on the LASSO Cox regression model in the TCGA database and time‐dependent ROC curves showed good predictive accuracy for 1‐, 3‐ and 5‐year overall survival. Utility in various types of OV was validated through subgroup survival analysis. Risk scores formulated by the multi‐gene signature stratified patients into high‐risk and low‐risk, and the former inclined worse overall survival than the latter. By incorporating this signature with age and pathological tumour stage, a visual predictive nomogram was established, which was useful for clinicians to predict survival outcome of patients. Furthermore, SNRPD1 and EFNA5 were selected from the multi‐gene signature as simplified prognostic indicators. Higher EFNA5 expression or lower SNRPD1 indicated poorer outcome. The correlation between signature gene expression and clinical characteristics was observed through WGCNA. Drug‐gene interaction was used to identify 16 potentially targeted drugs for OV treatment. In conclusion, we established novel gene signatures as independent prognostic factors to stratify the risk of OV patients and facilitate the implementation of personalized therapies.

Background: This study aimed to investigate the prevalence and risk factors for hypertension, diabetes, and dyslipidemia, and to evaluate their additive effects on myocardial infarction (MI) and stroke in Nanjing in East China.Methods: A multistage, stratified random cluster sampling method was used to select representative participants. All eligible participants completed questionnaires, physical measurements, and blood tests.Multivariable and univariable logistic regression analyses were used to identify associated risk factors and evaluate additive effects on cardiovascular events, respectively.Results: Hypertension was the most prevalent chronic disease among 11,036 participants enrolled (18.5%), followed by dyslipidemia (8.3%) and diabetes (6.0%). The prevalence of hypertension was higher in men than in women while no sex-related difference was observed in the prevalence of diabetes and dyslipidemia.Older age and higher body mass index were risk factors for all three diseases. Sex, central obesity, smoking, number of family members, salt intake, and family history of hypertension were associated with hypertension; central obesity, smoking, alcohol assumption, and family history of diabetes correlated with diabetes; and female sex, higher education, and alcohol assumption were risk factors for dyslipidemia. Hypertension complicated with dyslipidemia conferred more risk of MI and stroke than independent effects. Diabetes also contributed to risk based on hypertension or dyslipidemia. Conclusions:The burden of hypertension and diabetes has stopped increasing. However, total cholesterol (TC) concentration in the population has not been well controlled. A more comprehensive approach to managing dyslipidemia, hypertension, and diabetes needs to be developed, especially for individuals with multiple cardiovascular risk factors.

Introduction: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide due to poor survival outcome. Thus, there is an urgent need to identify effective biomarkers for early diagnosis and prognosis prediction. Methods: A total of 389 differentially expressed genes (DEGs) between HCC samples and normal were selected based on the Robust Rank Aggregation (RRA) method. We combined DEGs expression and clinical traits to construct a gene co-expression network through WGCNA. Forty hub genes were selected from the key module. Among them, YWHAB, PPAT, NOL10 were eventually identified as prognostic biomarkers using multivariate Cox regression model. Biomarkers expression pattern was investigated by informatic analysis and verified by RNA-seq of 32 patients with HCC. DiseaseMeth 2.0, MEXPRESS, and Tumor Immune Estimation Resource (TIMER) were used to assess the methylation and immune status of biomarkers. GSVA, CCK8, colony formation assay, Edu imaging kit, wound-healing assay, and xenograft tumor model were utilized to investigate the effects of biomarkers on proliferation, metastasis of HCC cells in vitro, and in vivo. The Kaplan-Meier (KM) plotter and ROC curves were used to validate the prognostic and diagnostic value of biomarker expression. Results: All the selected biomarkers were upregulated in HCC samples and higher expression levels were associated with advanced tumor stages and T grades. The regulation of YWHAB, PPAT, NOL10 promoter methylation varied in tumors, and precancerous normal tissues. Immune infiltration analysis suggested that the abnormal regulations of these biomarkers were likely attributed to B cells and dendritic cells. GSVA for these biomarkers showed their great contributions to proliferation of HCC. Specific Hu et al. Novel Biomarkers for Prognosis of Hepatocellular Carcinoma inhibition of their expression had strong effects on tumorigenesis in vitro and in vivo. ROC and KM curves confirmed their usefulness of diagnosis and prognosis of HCC. Conclusions: These findings identified YWHAB, PPAT, and NOL10 as novel biomarkers and validated their diagnostic and prognostic value for HCC.

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Backgrounds: Hypertension is one of the most prevalent non-communicable diseases (NCDs).However, unbalanced regional development and different research designs lead to greater heterogeneity of hypertension data in China, and lack of a summary of long-term variation trends. The aim was to estimate the pooled prevalence of hypertension and to describe the secular trend in hypertension.Methods: Literatures, related to the prevalence of hypertension among Chinese adults, were searched through both English and Chinese databases. The pooled prevalence was estimated with random effects. Subgroup analysis and meta-regression was conducted to address heterogeneity. Continuous fractional polynomial regression model and compound model were used to estimate the trend of hypertension prevalence with time.Results: A total of 18 studies were included and the whole population was 9, 191, 121. The pooled prevalence of hypertension among Chinese adults was 24.3% (95% CI: 18.8-29.8%), increasing from the west to the east. Hypertension was more common in male than in female (27.8% vs. 25.1%) and in urban population than in rural population (27.0% vs. 26.0%). The annual increase of prevalence was about 0.29% nonlinearly before 2004 and maintained approximately 2.45% per year between 2004 and 2010. After a significant decline in 2011, there was a slight incline. Conclusions:The prevalence of hypertension in Chinese adults has been increasing, indicating that more efforts should be strengthened for hypertension management in China.