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.

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.

Background: Research has shown that the progression of clear cell renal cell carcinoma (ccRCC) is modulated by long non-coding RNAs (lncRNAs). However, the roles of specific lncRNAs in the malignancy of ccRCC are still unknown.Methods: TCGA and GSE66272 datasets were used to predict differentially expressed genes (DEGs) in ccRCC. ENCORI database was employed to display BIRC5 miRNA network and potential lncRNA interactions for miRNAs. KM plotter and correlation analyses were performed to identify the overall survival (OS)-and BIRC5-related miRNAs. Quantitative real-time PCR (qRT-PCR) was used to verify the BIRC5 mRNA in the seventy paired clinical samples of ccRCC tissues. The ccRCC A498 and 786-O were individually transfected with lncRNA SNHG3 and LINC00997 and then western blotting was used to detect the BIRC5 protein expression. The Dual-luciferase reporter assay was used to examine the regulatory interaction between lncRNA SNHG3 and microRNA (miRNA/miR)-10b-5p.Results: BICR5 is associated with the progression of ccRCC. The two novel lncRNAs (LINC00997, SNHG3) were up-regulated in ccRCC tissues and positively with the BICR5 protein expression. However, Suppressing SNHG3 expression reduced BIRC5 protein expression compared with the LINC00997, most importantly, Suppressing SNHG3 expression suppressed tumor progression in vitro. In addition, SNHG3 promotes the expression of BIRC5 protein by sponging microRNA-10b-5p.Conclusions: Our findings suggest that SNHG3 plays a vital role in promoting ccRCC via the microRNA-10b-5p/BIRC5 axis and may serve as a novel therapeutic target for the treatment of patients with ccRCC.

During cancer development, host's tumorigenesis and immune signals are released to and informed by circulating molecules, like cell-free DNA (cfDNA) and RNA (cfRNA) in blood. However, these two kinds of molecules are still not systematically compared in gastrointestinal cancer. Here, we profiled 4 types of cell-free omics data from colorectal and stomach cancer patients, and assayed 15 types of genomic, epi-genomic, and transcriptomic variations. First, we demonstrated that the multi-omics data were more capable of detecting cancer genes than the single-omics data, where cfRNAs were more sensitive and informative than cfDNAs in terms of detection ratio, variation type, altered number, and enriched functional pathway. Moreover, we revealed several peripheral immune signatures that were suppressed in cancer patients and originated from specific circulating and tumor-microenvironment cells. Particularly, we defined a γδ-T-cell score and a cancer-associated-fibroblast (CAF) score using the cfRNA-seq data of 143 cancer patients. They were informative of clinical status like cancer stage, tumor size, and survival. In summary, our work reveals the cell-free multi-molecular landscape of colorectal and stomach cancer, and provides a potential monitoring utility in blood for the personalized cancer treatment.

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Multi-modal biological data integration can provide comprehensive views of gene regulation and cell development. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability. To address these challenges, we developed Pathformer, a biological pathway informed deep learning model based on Transformer with bias to integrate multi-modal data. Pathformer leverages criss-cross attention mechanism to capture crosstalk between different biological pathways and between different modalities (i.e., multi-omics). It also utilizes SHapley Additive Explanation method to reveal key pathways, genes, and regulatory mechanisms. Through benchmark studies on 28 TCGA datasets, we demonstrated the superior performance and interpretability of Pathformer on various cancer classification tasks, compared to other integration models. Furthermore, we applied Pathformer to liquid biopsy multi-modal data integration with high accuracy in cancer diagnosis. Meanwhile, Pathformer revealed interesting molecularly altered pathways in cancer patients’ body fluid, such as ligand binding of scavenger receptors, iron transport, and DAP12 signaling transmission, which are related to extracellular vesicle transport, platelet, and immune response.