Poly C Binding Protein 1 (PCBP1) is an RNA-binding protein that binds and regulates translational activity of subsets of cellular mRNAs. Depletion of PCBP1 is implicated in various carcinomas, but the underlying mechanism in tumorigenesis remains elusive. We performed a transcriptome-wide screen to identify novel bounding mRNA of PCBP1. The bind regions between PCBP1 with target mRNA were investigated by using point mutation and luciferase assay. Cell proliferation, cell cycle, tumorigenesis and cell apoptosis were also evaluated in ovary and colon cancer cell lines. The mechanism that PCBP1 affects p27 was analyzed by mRNA stability and ribosome profiling assays. We analyzed PCBP1 and p27 expression in ovary, colon and renal tumor samples and adjacent non-tumor tissues using RT-PCR, Western Blotting and immunohistochemistry. The prognostic significance of PCBP1 and p27 also analyzed using online databases. We identified cell cycle inhibitor p27Kip1 (p27) as a novel PCBP1-bound transcript. We then demonstrated that binding of PCBP1 to p27 3’UTR via its KH1 domain mainly stabilizes p27 mRNA, while enhances its translation to fuel p27 expression, prior to p27 protein degradation. The upregulated p27 consequently inhibits cell proliferation, cell cycle progression and tumorigenesis, whereas promotes cell apoptosis under paclitaxel treatment. Conversely, knockdown of PCBP1 in turn compromises p27 mRNA stability, leading to lower p27 level and tumorigenesis in vivo. Moreover, forced depletion of p27 counteracts the tumor suppressive ability of PCBP1 in the same PCBP1 over-expressing cells. Physiologically, we showed that decreases of both p27 mRNA and its protein expressions are well correlated to PCBP1 depletion in ovary, colon and renal tumor samples, independent of the p27 ubiquitin ligase Skp2 level. Correlation of PCBP1 with p27 is also found in the tamoxifen, doxorubincin and lapatinib resistant breast cancer cells of GEO database. Our results thereby indicate that loss of PCBP1 expression firstly attenuates p27 expression at post-transcriptional level, and subsequently promotes carcinogenesis. PCBP1 could be used as a diagnostic marker to cancer patients.

Microbes play important roles in human health and disease. The interaction between microbes and hosts is a reciprocal relationship, which remains largely under-explored. Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes, microbial core genes, and disease phenotypes. We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data. MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites. MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes. Disease phenotypes are classified and described using the Experimental Factor Ontology (EFO). A refined score model was developed to prioritize the associations based on evidential metrics. The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly. MicroPhenoDB offers data browsing, searching, and visualization through user-friendly web interfaces and web service application programming interfaces. MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes, core genes, and disease phenotypes. It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases. MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2.sysu.edu.cn/microphenodb.

Many circRNA transcriptome data were deposited in public resources, but these data show great heterogeneity. Researchers without bioinformatics skills have difficulty in investigating these invaluable data or their own data. Here, we specifically designed circMine (http://hpcc.siat.ac.cn/circmine and http://www.biomedical-web.com/circmine/) that provides 1 821 448 entries formed by 136 871 circRNAs, 87 diseases and 120 circRNA transcriptome datasets of 1107 samples across 31 human body sites. circMine further provides 13 online analytical functions to comprehensively investigate these datasets to evaluate the clinical and biological significance of circRNA. To improve the data applicability, each dataset was standardized and annotated with relevant clinical information. All of the 13 analytic functions allow users to group samples based on their clinical data and assign different parameters for different analyses, and enable them to perform these analyses using their own circRNA transcriptomes. Moreover, three additional tools were developed in circMine to systematically discover the circRNA–miRNA interaction and circRNA translatability. For example, we systematically discovered five potential translatable circRNAs associated with prostate cancer progression using circMine. In summary, circMine provides user-friendly web interfaces to browse, search, analyze and download data freely, and submit new data for further integration, and it can be an important resource to discover significant circRNA in different diseases.

Many open access transcriptomic data of coronavirus disease 2019 (COVID-19) were generated, they have great heterogeneity and are difficult to analyze. To utilize these invaluable data for better understanding of COVID-19, additional software should be developed. Especially for researchers without bioinformatic skills, a user-friendly platform is mandatory. We developed the COVID19db platform (http://hpcc.siat.ac.cn/covid19db & http://www.biomedical-web.com/covid19db) that provides 39 930 drug–target–pathway interactions and 95 COVID-19 related datasets, which include transcriptomes of 4127 human samples across 13 body sites associated with the exposure of 33 microbes and 33 drugs/agents. To facilitate data application, each dataset was standardized and annotated with rich clinical information. The platform further provides 14 different analytical applications to analyze various mechanisms underlying COVID-19. Moreover, the 14 applications enable researchers to customize grouping and setting for different analyses and allow them to perform analyses using their own data. Furthermore, a Drug Discovery tool is designed to identify potential drugs and targets at whole transcriptomic scale. For proof of concept, we used COVID19db and identified multiple potential drugs and targets for COVID-19. In summary, COVID19db provides user-friendly web interfaces to freely analyze, download data, and submit new data for further integration, it can accelerate the identification of effective strategies against COVID-19.

Noncoding RNAs (ncRNAs) play critical roles in many critical biological processes and have become a novel class of potential targets and bio-markers for disease diagnosis, therapy, and prognosis. Annotating and analyzing ncRNA-disease association data are essential but challenging. Current computational resources lack comprehensive database platforms to consistently interpret and prioritize ncRNA-disease association data for biomedical investigation and application. Here, we present the ncRPheno database platform (http://lilab2.sysu.edu.cn/ncrpheno), which comprehensively integrates and annotates ncRNA-disease association data and provides novel searches, visualizations, and utilities for association identification and validation. ncRPheno contains 482,751 non-redundant associations between 14,494 ncRNAs and 3,210 disease phenotypes across 11 species with supporting evidence in the literature. A scoring model was refined to prioritize the associations based on evidential metrics. Moreover, ncRPheno provides user-friendly web interfaces, novel visualizations, and programmatic access to enable easy exploration, analysis, and utilization of the association data. A case study through ncRPheno demonstrated a comprehensive landscape of ncRNAs dysregulation associated with 22 cancers and uncovered 821 cancer-associated common ncRNAs. As a unique database platform, ncRPheno outperforms the existing similar databases in terms of data coverage and utilities, and it will assist studies in encoding ncRNAs associated with phenotypes ranging from genetic disorders to complex diseases.