Large-scale multi-omics datasets, most prominently from the TCGA consortium, have been made available to the public for systematic characterization of human cancers. However, to date, there is a lack of corresponding online resources to utilize these valuable data to study gene expression dysregulation and viral infection, two major causes for cancer development and progression. To address these unmet needs, we established OncoDB, an online database resource to explore abnormal patterns in gene expression as well as viral infection that are correlated to clinical features in cancer. Specifically, OncoDB integrated RNA-seq, DNA methylation, and related clinical data from over 10 000 cancer patients in the TCGA study as well as from normal tissues in the GTEx study. Another unique aspect of OncoDB is its focus on oncoviruses. By mining TCGA RNA-seq data, we have identified six major oncoviruses across cancer types and further correlated viral infection to changes in host gene expression and clinical outcomes. All the analysis results are integratively presented in OncoDB with a flexible web interface to search for data related to RNA expression, DNA methylation, viral infection, and clinical features of the cancer patients. OncoDB is freely accessible at http://oncodb.org.
Background The coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has killed millions of people worldwide. The current crisis has created an unprecedented demand for rapid test of SARS-CoV-2 infection. Methods Reverse transcription loop-mediated isothermal amplification (RT-LAMP) is a fast and convenient method to amplify and identify the transcripts of a targeted pathogen. However, the sensitivity and specificity of RT-LAMP were generally regarded as inferior when compared with the gold standard RT-qPCR. To address this issue, we combined bioinformatic and experimental analyses to improve the assay performance for COVID-19 diagnosis. Findings First, by experimental screening as well as high-throughput sequencing studies, we discovered new primer features that impacted LAMP sensitivity and specificity. These features were then used to build an improved bioinformatics algorithm to design LAMP primers targeting SARS-CoV-2. We further rigorously validated these new assays for their efficacy and specificity. We demonstrated that multiplexed RT-LAMP assay could directly detect as low as 1.5 copies/µL of SARS-CoV-2 particles in saliva, without the need of RNA isolation. We further tested this ultra-sensitive and specific RT-LAMP assay using saliva samples from COVID-19 patients. Clinical validation results indicated that the new RT-LAMP assay was comparable to standard RT-qPCR in overall assay sensitivity and specificity. Interpretation In summary, our new LAMP primer design algorithm along with the validated assays provide a fast and reliable method for the diagnosis of COVID-19 cases. Funding National Institutes of Health.
Extracellular RNAs (exRNAs) have attracted great attention due to their essential role in cell-to-cell communication as well as their potential as non-invasive disease biomarkers. However, at present, there is no consensus on the best method to profile exRNA expression, which leads to significant variability across studies. To address this issue, we established an experimental pipeline for comprehensive profiling of small exRNAs isolated from cell culture. By evaluating six RNA extraction protocols, we developed an improved method for robust recovery of vesicle-bound exRNAs. With this method, we performed small RNA sequencing of exosomes (EXOs), microvesicles (MVs) and source cells from 14 cancer cell lines. Compared to cells, EXOs and MVs were similarly enriched in tRNAs and rRNAs, but depleted in snoRNAs. By miRNA profiling analysis, we identified a subset of miRNAs, most noticeably miR-122-5p, that were significantly over-represented in EXOs and MVs across all 14 cell lines. In addition, we also identified a subset of EXO miRNAs associated with cancer type or human papillomavirus (HPV) status, suggesting their potential roles in HPV-induced cancers. In summary, our work has laid a solid foundation for further standardization on exRNA analysis across various cellular systems.
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