SFPQ promotes an oncogenic transcriptomic state in melanoma

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Abstract

The multifunctional protein, splicing factor, proline- and glutamine-rich (SFPQ) has been implicated in numerous cancers often due to interaction with coding and non-coding RNAs, however, its role in melanoma remains unclear. We report that knockdown of SFPQ expression in melanoma cells decelerates several cancer-associated cell phenotypes, including cell growth, migration, epithelial to mesenchymal transition, apoptosis, and glycolysis. RIP-seq analysis revealed that the SFPQ-RNA interactome is reprogrammed in melanoma cells and specifically enriched with key melanoma-associated coding and long non-coding transcripts, including SOX10, AMIGO2 and LINC00511 and in most cases SFPQ is required for the efficient expression of these genes. Functional analysis of two SFPQ-enriched lncRNA, LINC00511 and LINC01234, demonstrated that these genes independently contribute to the melanoma phenotype and a more detailed analysis of LINC00511 indicated that this occurs in part via modulation of the miR-625-5p/ PKM2 axis. Importantly, analysis of a large clinical cohort revealed that elevated expression of SFPQ in primary melanoma tumours may have utility as a prognostic biomarker. Together, these data suggest that SFPQ is an important driver of melanoma, likely due to SFPQ–RNA interactions promoting the expression of numerous oncogenic transcripts.

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starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data

Jun-Hao Li, Shun Liu, Hui Zhou … (2013)

Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 (http://starbase.sysu.edu.cn/) to systematically identify the RNA–RNA and protein–RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285 000 protein–RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10 000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.

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GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis

Zefang Tang, Boxi Kang, Chenwei Li … (2019)

Abstract Introduced in 2017, the GEPIA (Gene Expression Profiling Interactive Analysis) web server has been a valuable and highly cited resource for gene expression analysis based on tumor and normal samples from the TCGA and the GTEx databases. Here, we present GEPIA2, an updated and enhanced version to provide insights with higher resolution and more functionalities. Featuring 198 619 isoforms and 84 cancer subtypes, GEPIA2 has extended gene expression quantification from the gene level to the transcript level, and supports analysis of a specific cancer subtype, and comparison between subtypes. In addition, GEPIA2 has adopted new analysis techniques of gene signature quantification inspired by single-cell sequencing studies, and provides customized analysis where users can upload their own RNA-seq data and compare them with TCGA and GTEx samples. We also offer an API for batch process and easy retrieval of the analysis results. The updated web server is publicly accessible at http://gepia2.cancer-pku.cn/.

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Defining a Cancer Dependency Map

Aviad Tsherniak, Francisca Vázquez, Phil Montgomery … (2017)

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on-from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six standard deviations from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression-based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.