POSTAR3: an updated platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins.

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation. Accurate identification of RBP binding sites in multiple cell lines and tissue types from diverse species is a fundamental endeavor towards understanding the regulatory mechanisms of RBPs under both physiological and pathological conditions. Our POSTAR annotation processes make use of publicly available large-scale CLIP-seq datasets and external functional genomic annotations to generate a comprehensive map of RBP binding sites and their association with other regulatory events as well as functional variants. Here, we present POSTAR3, an updated database with improvements in data collection, annotation infrastructure, and analysis that support the annotation of post-transcriptional regulation in multiple species including: we made a comprehensive update on the CLIP-seq and Ribo-seq datasets which cover more biological conditions, technologies, and species; we added RNA secondary structure profiling for RBP binding sites; we provided miRNA-mediated degradation events validated by degradome-seq; we included RBP binding sites at circRNA junction regions; we expanded the annotation of RBP binding sites, particularly using updated genomic variants and mutations associated with diseases. POSTAR3 is freely available at http://postar.ncrnalab.org.

Noncoding RNAs Serve as Diagnosis and Prognosis Biomarkers for Hepatocellular Carcinoma.

BACKGROUND: Reliable noninvasive biomarkers for hepatocellular carcinoma (HCC) diagnosis and prognosis are urgently needed. We explored the potential of not only microRNAs (miRNAs) but other types of noncoding RNAs (ncRNAs) as HCC biomarkers. METHODS: Peripheral blood samples were collected from 77 individuals; among them, 57 plasma cell-free RNA transcriptomes and 20 exosomal RNA transcriptomes were profiled. Significantly upregulated ncRNAs and published potential HCC biomarkers were validated with reverse transcription (RT)-qPCR in an independent validation cohort (60-150 samples). We particularly investigated the diagnosis and prognosis performance and biological function for 1 ncRNA biomarker, RN7SL1, and its S fragment. RESULTS: We identified certain circulating ncRNAs escaping from RNase degradation, possibly through binding with RNA-binding proteins: 899 ncRNAs were highly upregulated in HCC patients. Among them, 337 genes were fragmented long noncoding RNAs, 252 genes were small nucleolar RNAs, and 134 genes were piwi-interacting RNAs. Forty-eight candidates were selected and validated with RT-qPCR, of which, 16 ncRNAs were verified to be significantly upregulated in HCC, including RN7SL1, SNHG1, ZFAS1, and LINC01359. Particularly, the abundance of RN7SL1 S fragment discriminated HCC samples from negative controls (area under the curve, 0.87; 95% CI, 0.817-0.920). HCC patients with higher concentrations of RN7SL1 S fragment had lower survival rates. Furthermore, RN7SL1 S fragment alone promoted cancer cell proliferation and clonogenic growth. CONCLUSIONS: Our results show that various ncRNA species, not only miRNAs, identified in the small RNA sequencing of plasma are also able to serve as noninvasive biomarkers. Particularly, we identified a domain of srpRNA RN7SL1 with reliable clinical performance for HCC diagnosis and prognosis.

Integrative analysis of long extracellular RNAs reveals a detection panel of noncoding RNAs for liver cancer.

Rationale: Long extracellular RNAs (exRNAs) in plasma can be profiled by new sequencing technologies, even with low abundance. However, cancer-related exRNAs and their variations remain understudied. Methods: We investigated different variations (i.e. differential expression, alternative splicing, alternative polyadenylation, and differential editing) in diverse long exRNA species (e.g. long noncoding RNAs and circular RNAs) using 79 plasma exosomal RNA-seq (exoRNA-seq) datasets of multiple cancer types. We then integrated 53 exoRNA-seq datasets and 65 self-profiled cell-free RNA-seq (cfRNA-seq) datasets to identify recurrent variations in liver cancer patients. We further combined TCGA tissue RNA-seq datasets and validated biomarker candidates by RT-qPCR in an individual cohort of more than 100 plasma samples. Finally, we used machine learning models to identify a signature of 3 noncoding RNAs for the detection of liver cancer. Results: We found that different types of RNA variations identified from exoRNA-seq data were enriched in pathways related to tumorigenesis and metastasis, immune, and metabolism, suggesting that cancer signals can be detected from long exRNAs. Subsequently, we identified more than 100 recurrent variations in plasma from liver cancer patients by integrating exoRNA-seq and cfRNA-seq datasets. From these datasets, 5 significantly up-regulated long exRNAs were confirmed by TCGA data and validated by RT-qPCR in an independent cohort. When using machine learning models to combine two of these validated circular and structured RNAs (SNORD3B-1, circ-0080695) with a miRNA (miR-122) as a panel to classify liver cancer patients from healthy donors, the average AUROC of the cross-validation was 89.4%. The selected 3-RNA panel successfully detected 79.2% AFP-negative samples and 77.1% early-stage liver cancer samples in the testing and validation sets. Conclusions: Our study revealed that different types of RNA variations related to cancer can be detected in plasma and identified a 3-RNA detection panel for liver cancer, especially for AFP-negative and early-stage patients.

RNA Biomarkers: Frontier of Precision Medicine for Cancer.

As an essential part of central dogma, RNA delivers genetic and regulatory information and reflects cellular states. Based on high-throughput sequencing technologies, cumulating data show that various RNA molecules are able to serve as biomarkers for the diagnosis and prognosis of various diseases, for instance, cancer. In particular, detectable in various bio-fluids, such as serum, saliva and urine, extracellular RNAs (exRNAs) are emerging as non-invasive biomarkers for earlier cancer diagnosis, tumor progression monitor, and prediction of therapy response. In this review, we summarize the latest studies on various types of RNA biomarkers, especially extracellular RNAs, in cancer diagnosis and prognosis, and illustrate several well-known RNA biomarkers of clinical utility. In addition, we describe and discuss general procedures and issues in investigating exRNA biomarkers, and perspectives on utility of exRNAs in precision medicine.