Comprehensive databases of microRNA–disease associations are continuously demanded in biomedical researches. The recently launched version 3.0 of Human MicroRNA Disease Database (HMDD v3.0) manually collects a significant number of miRNA–disease association entries from literature. Comparing to HMDD v2.0, this new version contains 2-fold more entries. Besides, the associations have been more accurately classified based on literature-derived evidence code, which results in six generalized categories (genetics, epigenetics, target, circulation, tissue and other) covering 20 types of detailed evidence code. Furthermore, we added new functionalities like network visualization on the web interface. To exemplify the utility of the database, we compared the disease spectrum width of miRNAs (DSW) and the miRNA spectrum width of human diseases (MSW) between version 3.0 and 2.0 of HMDD. HMDD is freely accessible at http://www.cuilab.cn/hmdd. With accumulating evidence of miRNA–disease associations, HMDD database will keep on growing in the future.

Mounting evidence suggested that dysfunction of long non-coding RNAs (lncRNAs) is involved in a wide variety of diseases. A knowledgebase with systematic collection and curation of lncRNA-disease associations is critically important for further examining their underlying molecular mechanisms. In 2013, we presented the first release of LncRNADisease, representing a database for collection of experimental supported lncRNA-disease associations. Here, we describe an update of the database. The new developments in LncRNADisease 2.0 include (i) an over 40-fold lncRNA-disease association enhancement compared with the previous version; (ii) providing the transcriptional regulatory relationships among lncRNA, mRNA and miRNA; (iii) providing a confidence score for each lncRNA-disease association; (iv) integrating experimentally supported circular RNA disease associations. LncRNADisease 2.0 documents more than 200 000 lncRNA-disease associations. We expect that this database will continue to serve as a valuable source for potential clinical application related to lncRNAs. LncRNADisease 2.0 is freely available at http://www.rnanut.net/lncrnadisease/.

The Chinese visceral adiposity index (CVAI) is a recently developed indicator of visceral adiposity. We investigated the predictive value of the CVAI for the development of dysglycemia (pre-diabetes and type 2 diabetes) and compared its predictive power with that of the Visceral adiposity index (VAI) and various anthropometric indices. This community-based study included 2,383 participants. We assessed the predictive power of adiposity indices by performing univariate and multivariate binary logistic regression analysis and calculating the area under the receiver-operating characteristic (ROC) curve according to their quartiles. Logistic regression analysis showed that individuals in higher CVAI quartiles at baseline were more likely to develop dysglycemia than those in lower CVAI quartiles. The area under the ROC curve for CVAI was significantly higher than that of other adiposity indices. In addition, among the various adiposity indices tested, the CVAI had the greatest Youden index for identifying dysglycemia in both genders. Our data demonstrate that the CVAI is a better predictor of type 2 diabetes and pre-diabetes than the VAI, BMI, waist circumference, waist-to-hip ratio and waist-to-height ratio in Chinese adults.

The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.

PurposeWe aimed to investigate the relationship between pretreatment neutrophil-to-lymphocyte ratio (NLR)/platelet-to-lymphocyte ratio (PLR) and the estimation of hormone-receptor-negative (HR−) breast cancer patients’ survival in a Chinese cohort.Patients and methodsOf 434 consecutive HR− nonmetastatic breast cancer patients treated between 2004 and 2010 in the Affiliated Hospital of Academy of Military Medical Sciences, 318 eligible cases with complete data were included in the present study. Kaplan–Meier analysis was performed to determine the overall survival (OS) and disease-free survival (DFS). Univariate and multivariate Cox proportional hazards models were used to test the usefulness of NLR and PLR.ResultsUnivariate analysis indicated that both elevated NLR and PLR (both P<0.001) were associated with poor OS. The utility of NLR remained in the multivariate analysis (P<0.001), but not PLR (P=0.104). The analysis results for DFS were almost the same as OS. Subgroup analysis revealed a significant association between increased NLR and PLR (P<0.001 and P=0.011) and poor survival in triple-negative breast cancer. However, for human epidermal growth factor receptor 2-positive breast cancer, only NLR was significantly associated with OS in the multivariate analysis (P=0.001).ConclusionThe present study indicates that both increased NLR and PLR are associated with poor survival in HR−breast cancer patients. Meanwhile, NLR is independently correlated with OS and DFS, but PLR is not.