No abstract

Hepatocellular carcinoma (HCC) cells often invade the portal venous system and subsequently develop into portal vein tumour thrombosis (PVTT). Long noncoding RNAs (lncRNAs) have been associated with HCC, but a comprehensive analysis of their specific association with HCC metastasis has not been conducted. Here, by analysing 60 clinical samples' RNA-seq data from 20 HCC patients, we have identified and characterized 8,603 candidate lncRNAs. The expression patterns of 917 recurrently deregulated lncRNAs are correlated with clinical data in a TCGA cohort and published liver cancer data. Matched array data from the 60 samples show that copy number variations (CNVs) and alterations in DNA methylation contribute to the observed recurrent deregulation of 235 lncRNAs. Many recurrently deregulated lncRNAs are enriched in co-expressed clusters of genes related to cell adhesion, immune response and metabolic processes. Candidate lncRNAs related to metastasis, such as HAND2-AS1, were further validated using RNAi-based loss-of-function assays. Thus, we provide a valuable resource of functional lncRNAs and biomarkers associated with HCC tumorigenesis and metastasis.

These authors contributed equally to this work. SUMMARYRecently, in addition to poly(A)+ long non-coding RNAs (lncRNAs), many lncRNAs without poly(A) tails, have been characterized in mammals. However, the non-polyA lncRNAs and their conserved motifs, especially those associated with environmental stresses, have not been fully investigated in plant genomes. We performed poly(A)À RNA-seq for seedlings of Arabidopsis thaliana under four stress conditions, and predicted lncRNA transcripts. We classified the lncRNAs into three confidence levels according to their expression patterns, epigenetic signatures and RNA secondary structures. Then, we further classified the lncRNAs to poly(A)+ and poly(A)À transcripts. Compared with poly(A)+ lncRNAs and coding genes, we found that poly(A)À lncRNAs tend to have shorter transcripts and lower expression levels, and they show significant expression specificity in response to stresses. In addition, their differential expression is significantly enriched in drought condition and depleted in heat condition. Overall, we identified 245 poly(A)+ and 58 poly(A)À lncRNAs that are differentially expressed under various stress stimuli. The differential expression was validated by qRT-PCR, and the signaling pathways involved were supported by specific binding of transcription factors (TFs), phytochrome-interacting factor 4 (PIF4) and PIF5. Moreover, we found many conserved sequence and structural motifs of lncRNAs from different functional groups (e.g. a UUC motif responding to salt and a AU-rich stem-loop responding to cold), indicated that the conserved elements might be responsible for the stress-responsive functions of lncRNAs.

Recent genomic studies suggest that novel long non-coding RNAs (lncRNAs) are specifically expressed and far outnumber annotated lncRNA sequences. To identify and characterize novel lncRNAs in RNA sequencing data from new samples, we have developed COME, a coding potential calculation tool based on multiple features. It integrates multiple sequence-derived and experiment-based features using a decompose–compose method, which makes it more accurate and robust than other well-known tools. We also showed that COME was able to substantially improve the consistency of predication results from other coding potential calculators. Moreover, COME annotates and characterizes each predicted lncRNA transcript with multiple lines of supporting evidence, which are not provided by other tools. Remarkably, we found that one subgroup of lncRNAs classified by such supporting features (i.e. conserved local RNA secondary structure) was highly enriched in a well-validated database (lncRNAdb). We further found that the conserved structural domains on lncRNAs had better chance than other RNA regions to interact with RNA binding proteins, based on the recent eCLIP-seq data in human, indicating their potential regulatory roles. Overall, we present COME as an accurate, robust and multiple-feature supported method for the identification and characterization of novel lncRNAs. The software implementation is available at https://github.com/lulab/COME.