Abstract
It is increasingly appreciated that long non-coding RNAs (lncRNAs) associated with alternative splicing (AS) could be involved in aggressive hepatocellular carcinoma. Although many recent studies show the alteration of RNA alternative splicing by deregulated lncRNAs in cancer, the extent to which and how lncRNAs impact alternative splicing at the genome scale remains largely elusive. We analyzed RNA-seq data obtained from 369 hepatocellular carcinomas (HCCs) and 160 normal liver tissues, quantified 198,619 isoform transcripts, and identified a total of 1,375 significant AS events in liver cancer. In order to predict novel AS-associated lncRNAs, we performed an integration of co-expression, protein-protein interaction (PPI) and epigenetic interaction networks that links lncRNA modulators (such as splicing factors, transcript factors, and miRNAs) along with their targeted AS genes in HCC. We developed a random walk-based multi-graphic (RWMG) model algorithm that prioritizes functional lncRNAs with their associated AS targets to computationally model the heterogeneous networks in HCC. RWMG shows a good performance evaluated by the ROC curve based on cross-validation and bootstrapping strategies. As a conclusion, our robust network-based framework has derived 31 AS-related lncRNAs that not only validates known cancer-associated cases MALAT1 and HOXA11-AS, but also reveals new players such as DNM1P35 and DLX6-AS1with potential functional implications. Survival analysis further provides insights into the clinical significance of identified lncRNAs.
Highlights
Alternative splicing (AS) events are frequently observed in tumorigenesis and serve as cancerdriving genes
hepatocellular carcinomas (HCCs)-specific miRNA-target networks have been described in our previous published results (Wang et al, 2018); transcriptional factors (TFs)-target predicted interaction networks were manually curated from the following databases and publications: Chiu et al (2018) (Supplementary Table S5), HTRIdb (Bovolenta et al, 2012), Whitfield (Whitfield et al, 2012), and TRANSFAC (Matys et al, 2006) that were based on combined evidence from ENCODE ChIP-Seq assays and positioned weighted matrix (PWM) for TF motif analysis
We identified 369 differentially expressed (DE) long non-coding RNAs (lncRNAs) genes and 171 DE pseudogenes from tumor and normal samples (T/N) comparison (Supplementary Table S1)
Summary
Alternative splicing (AS) events are frequently observed in tumorigenesis and serve as cancerdriving genes. LncRNAs may interact with specific alternative splicing factors (ASF) or with other intermediate molecules that affect chromatin remodeling to fine tune the splicing of target genes (Romero-Barrios et al, 2018). Proteins that have multiple splicing regulators and that promote the transformation of target genes generally get triggered by transcriptional factors (TFs). The transcription regulator MYC, induces upregulation of hnRNP A1/2, that, in turn, regulates alternative splicing events in expressing the cancer-associated pyruvate kinase M2 (PKM2) isoform (David et al, 2010; Koh et al, 2015). The RWMG model simultaneously integrates sophisticated biological connections among lncRNA targets [such as transcription factors (TF), alternative splice factors (ASF), and microRNAs] based on both biophysical interaction networks and their co-expression profiles within a single analytical framework. To increase the performance of such analysis, we combined healthy liver tissue samples that were downloaded from GTEx along with expression data from TCGA
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