An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics’ sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the ‘Cancer’ and ‘Gene’ sections. The ‘Survival’ panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, ‘Survival Analysis’ in ‘Customized-analysis,’ allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics’ sophisticated information, and also constructed a Summary panel in the ‘Cancer’ and ‘Gene’ sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.

Although rapid progress has been made in computational approaches for prioritizing cancer driver genes, research is far from achieving the ultimate goal of discovering a complete catalog of genes truly associated with cancer. Driver gene lists predicted from these computational tools lack consistency and are prone to false positives. Here, we developed an approach (DriverML) integrating Rao’s score test and supervised machine learning to identify cancer driver genes. The weight parameters in the score statistics quantified the functional impacts of mutations on the protein. To obtain optimized weight parameters, the score statistics of prior driver genes were maximized on pan-cancer training data. We conducted rigorous and unbiased benchmark analysis and comparisons of DriverML with 20 other existing tools in 31 independent datasets from The Cancer Genome Atlas (TCGA). Our comprehensive evaluations demonstrated that DriverML was robust and powerful among various datasets and outperformed the other tools with a better balance of precision and sensitivity. In vitro cell-based assays further proved the validity of the DriverML prediction of novel driver genes. In summary, DriverML uses an innovative, machine learning-based approach to prioritize cancer driver genes and provides dramatic improvements over currently existing methods. Its source code is available at https://github.com/HelloYiHan/DriverML .

We previously presented the YM500 database, which contains >8000 small RNA sequencing (smRNA-seq) data sets and integrated analysis results for various cancer miRNome studies. In the updated YM500v3 database (http://ngs.ym.edu.tw/ym500/) presented herein, we not only focus on miRNAs but also on other functional small non-coding RNAs (sncRNAs), such as PIWI-interacting RNAs (piRNAs), tRNA-derived fragments (tRFs), small nuclear RNAs (snRNAs) and small nucleolar RNAs (snoRNAs). There is growing knowledge of the role of sncRNAs in gene regulation and tumorigenesis. We have also incorporated >10 000 cancer-related RNA-seq and >3000 more smRNA-seq data sets into the YM500v3 database. Furthermore, there are two main new sections, ‘Survival' and ‘Cancer', in this updated version. The ‘Survival’ section provides the survival analysis results in all cancer types or in a user-defined group of samples for a specific sncRNA. The ‘Cancer’ section provides the results of differential expression analyses, miRNA–gene interactions and cancer miRNA-related pathways. In the ‘Expression’ section, sncRNA expression profiles across cancer and sample types are newly provided. Cancer-related sncRNAs hold potential for both biotech applications and basic research.

BackgroundToll-like receptors (TLRs) are involved in the initiation of Schwann cell activation and subsequent recruitment of macrophages for clearance of degenerated myelin and neuronal debris after nerve injury. The present study was designed to investigate the regenerative outcome and expression of myelination-related factors in Tlr-knockout mice following a sciatic nerve crush injury.Materials and methodsA standard sciatic nerve crush injury, induced by applying constant pressure to the nerve with a No. 5 jeweler's forceps for 30 s, was performed in C57BL/6, Tlr2−/−, Tlr3−/−, Tlr4−/−, Tlr5−/−, and Tlr7−/− mice. Quantitative histomorphometric analysis of toluidine blue-stained nerve specimens and walking track analysis were performed to evaluate nerve regeneration outcomes. PCR Arrays were used to detect the expression of neurogenesis-related genes of dorsal root ganglia as well as of myelination-related genes of the distal nerve segments.ResultsWorse nerve regeneration after nerve crush injury was found in all Tlr-knockout mice than in C57BL/6 mice. Delayed expression of myelin genes and a different expression pattern of myelination-related neurotrophin genes and transcription factors were found in Tlr-knockout mice in comparison to C57BL/6 mice. In these TLR-mediated pathways, insulin-like growth factor 2 and brain-derived neurotrophic factor, as well as early growth response 2 and N-myc downstream-regulated gene 1, were significantly decreased in the early and late stages, respectively, of nerve regeneration after a crush injury.ConclusionsKnockout of Tlr genes decreases the expression of myelination-related factors and impairs nerve regeneration after a sciatic nerve crush injury.

Background: The Consolidated Standards of Reporting Trials (CONSORT) Statement recommends that studies report results beyond p values and include treatment effect(s) and measures of precision (e.g., confidence intervals [CIs]) to facilitate the interpretation of results. The objective of this systematic review was to assess the reporting and interpretation of patient-reported outcome measure (PROM) results in clinical studies from high-impact orthopaedic journals, to determine the proportion of studies that (1) only reported a p value; (2) reported a treatment effect, CI, or minimal clinically important difference (MCID); and (3) offered an interpretation of the results beyond interpreting a p value. Methods: We included studies from 5 high-impact-factor orthopaedic journals published in 2017 and 2019 that compared at least 2 intervention groups using PROMs. Results: A total of 228 studies were analyzed, including 126 randomized controlled trials, 35 prospective cohort studies, 61 retrospective cohort studies, 1 mixed cohort study, and 5 case-control studies. Seventy-six percent of studies (174) reported p values exclusively to express and interpret between-group differences, and only 22.4% (51) reported a treatment effect (mean difference, mean change, or odds ratio) with 95% CI. Of the 54 studies reporting a treatment effect, 31 interpreted the results using an important threshold (MCID, margin, or Cohen d), but only 3 interpreted the CIs. We found an absolute improvement of 35.5% (95% CI, 20.8% to 48.4%) in the reporting of the MCID between 2017 and 2019. Conclusions: The majority of interventional studies reporting PROMs do not report CIs around between-group differences in outcome and do not define a clinically meaningful difference. A p value cannot effectively communicate the readiness for implementation in a clinical setting and may be misleading. Thus, reporting requirements should be expanded to require authors to define and provide a rationale for between-group clinically important difference thresholds, and study findings should be communicated by comparing CIs with these thresholds.