Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine.

BACKGROUND: With the development of cancer precision medicine, a huge amount of high-dimensional cancer information has rapidly accumulated regarding gene alterations, diseases, therapeutic interventions and various annotations. The information is highly fragmented across multiple different sources, making it highly challenging to effectively utilize and exchange the information. Therefore, it is essential to create a resource platform containing well-aggregated, carefully mined, and easily accessible data for effective knowledge sharing. METHODS: In this study, we have developed "Consensus Cancer Core" (Tri©DB), a new integrative cancer precision medicine knowledgebase and reporting system by mining and harmonizing multifaceted cancer data sources, and presenting them in a centralized platform with enhanced functionalities for accessibility, annotation and analysis. RESULTS: The knowledgebase provides the currently most comprehensive information on cancer precision medicine covering more than 40 annotation entities, many of which are novel and have never been explored previously. Tri©DB offers several unique features: (i) harmonizing the cancer-related information from more than 30 data sources into one integrative platform for easy access; (ii) utilizing a variety of data analysis and graphical tools for enhanced user interaction with the high-dimensional data; (iii) containing a newly developed reporting system for automated annotation and therapy matching for external patient genomic data. Benchmark test indicated that Tri©DB is able to annotate 46% more treatments than two officially recognized resources, oncoKB and MCG. Tri©DB was further shown to have achieved 94.9% concordance with administered treatments in a real clinical trial. CONCLUSIONS: The novel features and rich functionalities of the new platform will facilitate full access to cancer precision medicine data in one single platform and accommodate the needs of a broad range of researchers not only in translational medicine, but also in basic biomedical research. We believe that it will help to promote knowledge sharing in cancer precision medicine. Tri©DB is freely available at www.biomeddb.org , and is hosted on a cutting-edge technology architecture supporting all major browsers and mobile handsets.

An Integrative Analysis Framework for Identifying the Prognostic Markers from Multidimensional RNA Data of Clear Cell Renal Cell Carcinoma.

The altered regulatory status of long noncoding RNA (lncRNA), miRNA, and mRNA and their interactions play critical roles in tumor proliferation, metastasis, and progression, which ultimately influence cancer prognosis. However, there are limited studies of comprehensive identification of prognostic biomarkers from combined data sets of the three RNA types in the highly metastatic clear cell renal cell carcinoma (ccRCC). The current study employed an integrative analysis framework of functional genomics approaches and machine learning methods to the lncRNA, miRNA, and mRNA data and identified 16 RNAs (3 lncRNAs, 6 miRNAs, and 7 mRNAs) of prognostic value, with 9 of them novel. A 16 RNA-based score was established for prognosis prediction of ccRCC with significance (P < 0.0001). The area under the curve for the score model was 0.868 to 0.870 in the training cohort and 0.714 to 0.778 in the validation cohort. Construction of the lncRNA-miRNA-mRNA interaction network showed that the downstream mRNAs and upstream lncRNAs in the network initiated from the miRNA or lncRNA markers exhibit significant enrichment in functional classifications associated with cancer metastasis, proliferation, progression, or prognosis. The functional analysis provided clear support for the role of the RNA biomarkers in predicting cancer prognosis. This study provides promising biomarkers for predicting prognosis of ccRCC using multidimensional RNA data, and these findings are expected to facilitate potential clinical applications of the biomarkers.

Intermittent energy restriction vs. continuous energy restriction on cardiometabolic risk factors in patients with metabolic syndrome: a meta-analysis and systematic review.

Background: This is a systematic review and meta-analysis to compare the efficacy of intermittent energy restriction (IER) vs. continuous energy restriction (CER) on weight loss, body composition, blood pressure, and other cardiometabolic risk factors in patients with metabolic syndrome (MetS) risk factors. Methods: We searched and screened PubMed, Embase, Cochrane Library, and Web of Science from inception to May 8, 2022 for randomized controlled trials. Two review authors independently selected studies, extracted data, assessed quality and risk of bias and cross-checked extracts to resolve discrepancies when required. We expressed effect size as mean difference (MD) and 95% confidence interval (CI). The major outcome was the improvement of MetS risk factors, including changes in waist circumference (WC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), blood pressure (BP), and fasting plasma glucose (FPG) levels. The secondary outcomes were body weight (BW), body mass index (BMI), body fat (BF), fat free mass (FFM), hip circumference (HC), fasting insulin (FINs), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-c). Results: The meta-analysis included 16 articles (20 trials) with a total of 1,511 participants. All studies had a low risk of bias for random sequence generation. The IER and CER intervention equally improved MetS risk factors WC (MD = -0.47, 95% CI [-1.19, 0.25]), TG (MD = -0.02 mmol/L, 95% CI [-0.11, 0.07]), FPG (MD = -0.02 mmol/L, 95% CI [-0.10, 0.05]) and BP (systolic blood pressure: MD = 0.93 mmHg, 95% CI [-2.74, 4.61]; diastolic blood pressure: MD =1.15 mmHg, 95% CI [-0.24, 2.55]), but HDL-c (MD = 0.03 mmol/L, 95% CI [0.01, 0.05]) was significant improved in IER when compared with CER. For second outcomes, BW (MD = -0.8 kg, 95% CI [-1.26, -0.33]), BF (MD = -0.75 kg, 95% CI [-1.73, -0.13]) and FFM (MD = -0.49 kg, 95% CI [-0.92, -0.05]) were also significant improved in IER, and not for other outcomes. Conclusion: Both IER and CER could improve MetS biomarkers, but IER was more effective than CER in the improvement of HDL-c only. For secondary outcomes, IER was also more effective for BW, BF and FFM, but there were no differences in effects for other outcomes.

Genotyping and Molecular Characterization of Classical Swine Fever Virus Isolated in China during 2016-2018.

Classical swine fever (CSF) is a highly contagious disease of swine caused by classical swine fever virus (CSFV). For decades the disease has been controlled in China by a modified live vaccine (C-strain) of genotype 1. The emergent genotype 2 strains have become predominant in China in the past years that are genetically distant from the vaccine strain. Here, we aimed to evaluate the current infectious status of CSF, and for this purpose 24 isolates of CSFV were identified from different areas of China during 2016-2018. Phylogenetic analysis of NS5B, E2 and full genome revealed that the new isolates were clustered into subgenotype 2.1d and 2.1b, while subgenotype 2.1d was predominant. Moreover, E2 and Erns displayed multiple variations in neutralizing epitope regions. Furthermore, the new isolates exhibited capacity to escape C-strain-derived antibody neutralization compared with the Shimen strain (genotype 1). Potential positive selection sites were identified in antigenic regions of E2 and Erns, which are related with antibody binding affinity. Recombination events were predicted in the new isolates with vaccine strains in the E2 gene region. In conclusion, the new isolates showed molecular variations and antigenic alterations, which provide evidence for the emergence of vaccine-escaping mutants and emphasize the need of updated strategies for CSF control.