The RNA binding protein TRA2A, a member of the transformer 2 homolog family, plays a crucial role in the alternative splicing of pre-mRNA. However, it remains unclear whether TRA2A is involved in non-coding RNA regulation and, if so, what are the functional consequences. By analyzing expression profiling data, we found that TRA2A is highly expressed in esophageal cancer and is associated with disease-free survival and overall survival time. Subsequent gain-and loss-of-function studies demonstrated that TRA2A promotes proliferation and migration of esophageal squamous cell carcinoma and adenocarcinoma cells. RNA immunoprecipitation and RNA pull-down assay indicated that TRA2A can directly bind specific sites on MALAT1 in cells. In addition, ectopic expression or depletion of TRA2A leads to MALAT expression changes accordingly, thus modulates EZH2/β-catenin pathway. Together, these findings elucidated that TRA2A triggers carcinogenesis via MALAT1 mediated EZH2/β-catenin axis in esophageal cancer cells.

Esophageal squamous cell carcinoma is a leading cause of cancer death. Mapping the transcriptional landscapes such as isoforms, fusion transcripts, as well as long noncoding RNAs have played a central role to understand the regulating mechanism during malignant processes. However, canonical methods such as short-read RNA-seq are difficult to define the entire polyadenylated RNA molecules. Here, we combined single-molecule real-time sequencing with RNA-seq to generate high-quality long reads and to survey the transcriptional program in esophageal squamous cells. Compared with the recent annotations of human transcriptome (Ensembl 38 release 91), single-molecule real-time data identified many unannotated transcripts, novel isoforms of known genes and an expanding repository of long intergenic noncoding RNAs (lincRNAs). By integrating with annotation of lincRNA catalog, 1,521 esophageal-cancer-specific lincRNAs were defined from single-molecule real-time reads. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these lincRNAs and their target genes are involved in a variety of cancer signaling pathways. Isoform usage analysis revealed the shifted alternative splicing patterns, which can be recaptured from clinical samples or supported by previous studies. Utilizing vigorous searching criteria, we also detected multiple transcript fusions, which are not documented in current gene fusion database or readily identified from RNA-seq reads. Two novel fusion transcripts were verified based on real-time PCR and Sanger sequencing. Overall, our long-read single-molecule sequencing largely expands current understanding of full-length transcriptome in esophageal cells and provides novel insights on the transcriptional diversity during oncogenic transformation.

Circular RNAs (circRNAs) play important roles in carcinogenesis. Here, we investigated the mechanisms and clinical significance of circ-NOL10 , a highly repressed circRNA in breast cancer. Subsequently, we also identified RNA-binding proteins (RBPs) that regulate circ-NOL10 . Bioinformatics analysis was utilized to predict regulatory RBPs as well as circ-NOL10 downstream microRNAs (miRNAs) and mRNA targets. RNA immunoprecipitation, luciferase assay, fluorescence in situ hybridization, cell proliferation, wound healing, Matrigel invasion, cell apoptosis assays, and a xenograft model were used to investigate the function and mechanisms of circ-NOL10 in vitro and in vivo . The clinical value of circ-NOL10 was evaluated in a large cohort of breast cancer by quantitative real-time PCR. Circ-NOL10 is downregulated in breast cancer and associated with aggressive characteristics and shorter survival time. Upregulation of circ-NOL10 promotes apoptosis, decreases proliferation, and inhibits invasion and migration. Furthermore, circ-NOL10 binds multiple miRNAs to alleviate carcinogenesis by regulating PDCD4. CASC3 and metadherin (MTDH) can bind directly to circ-NOL10 with characterized motifs. Accordingly, ectopic expression or depletion of CASC3 or MTDH leads to circ-NOL10 expression changes, suggesting that these two RBPs modulate circ-NOL10 in cancer cells. circ-NOL10 is a novel biomarker for diagnosis and prognosis in breast cancer. These results highlight the importance of therapeutic targeting of the RBP-noncoding RNA (ncRNA) regulation network.

Gene post-transcription regulation involves several critical regulators such as microRNAs (miRNAs) and RNA-binding proteins (RBPs). Accumulated experimental evidences have shown that miRNAs and RBPs can competitively regulate the shared targeting transcripts. Although this establishes a novel post-transcription regulation mechanism, there are currently no computational tools to scan for the possible competing miRNA and RBP pairs. Here, we developed a novel computational pipeline—RBPvsMIR—that enables us to statistically evaluate the competing relationship between miRNAs and RBPs. RBPvsMIR first combines with previously successful miRNAs and RBP motifs discovery applications to search for overlapping or adjacent binding sites along a given RNA sequence. Then a permutation test is performed to select the miRNA and RBP pairs with the significantly enriched binding sites. As an example, we used RBPvsMIR to identify 235 competing RBP-miRNA pairs for long non-coding RNA (lncRNA) MALAT1. Wet lab experiments verified that splicing factor SRSF2 competes with miR-383, miR-502 and miR-101 to regulate MALAT1 in esophageal squamous carcinoma cells. Our study also revealed the global mutual exclusive pattern for miRNAs and RBP to regulate human lncRNAs. In addition, we provided a convenient web server (), which should accelerate the exploration of competing miRNAs and RBP pairs regulating the shared targeting transcripts.

RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at .