A combinatorially regulated RNA splicing signature predicts breast cancer EMT states and patient survival

RPS24 alternative splicing is a marker of cancer progression and epithelial-mesenchymal transition

Although alternative splicing (AS) is a major mechanism that adds diversity to gene expression patterns, its precise role in generating variability in ribosomal proteins, known as ribosomal heterogeneity, remains unclear. The ribosomal protein S24 (RPS24) gene, encoding a ribosomal component, undergoes AS; however, in-depth studies have been challenging because of three microexons between exons 4 and 6. We conducted a detailed analysis of RPS24 AS isoforms using a direct approach to investigate the splicing junctions related to these microexons, focusing on four AS isoforms. Each of these isoforms showed tissue specificity and relative differences in expression among cancer types. Significant differences in the proportions of these RPS24 AS isoforms between cancerous and normal tissues across diverse cancer types were also observed. Our study highlighted a significant correlation between the expression levels of a specific RPS24 AS isoform and the epithelial-mesenchymal transition process in lung and breast cancers. Our research contributes to a better understanding of the intricate regulatory mechanisms governing AS of ribosomal protein genes and highlights the biological implications of RPS24 AS isoforms in tissue development and tumorigenesis.

Alternative splicing in EMT and TGF-β signaling during cancer progression

Epithelial to mesenchymal transition (EMT) is a physiological process during development where epithelial cells transform to acquire mesenchymal characteristics, which allows them to migrate and colonize secondary tissues. Many cellular signaling pathways and master transcriptional factors exert a myriad of controls to fine tune this vital process to meet various developmental and physiological needs. Adding to the complexity of this network are post-transcriptional and post-translational regulations. Among them, alternative splicing has been shown to play important roles to drive EMT-associated phenotypic changes, including actin cytoskeleton remodeling, cell-cell junction changes, cell motility and invasiveness. In advanced cancers, transforming growth factor-β (TGF-β) is a major inducer of EMT and is associated with tumor cell metastasis, cancer stem cell self-renewal, and drug resistance. This review aims to provide an overview of recent discoveries regarding alternative splicing events and the involvement of splicing factors in the EMT and TGF-β signaling. It will emphasize the importance of various splicing factors involved in EMT and explore their regulatory mechanisms.

Satellite double-stranded RNA induces mesenchymal transition in pancreatic cancer by regulating alternative splicing

Human satellite II (HSATII), composed of tandem repeats in pericentromeric regions, is aberrantly transcribed in epithelial cancers, particularly pancreatic cancer. Dysregulation of repetitive elements in cancer tissues can facilitate incidental dsRNA formation; however, it remains controversial whether dsRNAs play tumor-promoting or tumor-suppressing roles during cancer progression. Therefore, we focused on the double-stranded formation of HSATII RNA and explored its molecular function. The overexpression of double-stranded HSATII (dsHSATII) RNA promoted mesenchymal-like morphological changes and enhanced the invasiveness of pancreatic cancer cells. We identified an RNA-binding protein, spermatid perinuclear RNA-binding protein (STRBP), which preferentially binds to dsHSATII RNA rather than single-stranded HSATII RNA. The mesenchymal transition of dsHSATII-expressing cells was rescued by STRBP overexpression. Mechanistically, STRBP is involved in the alternative splicing of genes associated with epithelial-mesenchymal transition (EMT). We also confirmed that isoform switching of CLSTN1, driven by dsHSATII overexpression or STRBP depletion, induced EMT-like morphological changes. These findings reveal a novel tumor-promoting function of dsHSATII RNA, inducing EMT-like changes and cell invasiveness, thus enhancing our understanding of the biological significance of aberrant expression of satellite arrays in malignant tumors.

PCB: A pseudotemporal causality-based Bayesian approach to identify EMT-associated regulatory relationships of AS events and RBPs during breast cancer progression

During breast cancer metastasis, the developmental process epithelial-mesenchymal (EM) transition is abnormally activated. Transcriptional regulatory networks controlling EM transition are well-studied; however, alternative RNA splicing also plays a critical regulatory role during this process. Alternative splicing was proved to control the EM transition process, and RNA-binding proteins were determined to regulate alternative splicing. A comprehensive understanding of alternative splicing and the RNA-binding proteins that regulate it during EM transition and their dynamic impact on breast cancer remains largely unknown. To accurately study the dynamic regulatory relationships, time-series data of the EM transition process are essential. However, only cross-sectional data of epithelial and mesenchymal specimens are available. Therefore, we developed a pseudotemporal causality-based Bayesian (PCB) approach to infer the dynamic regulatory relationships between alternative splicing events and RNA-binding proteins. Our study sheds light on facilitating the regulatory network-based approach to identify key RNA-binding proteins or target alternative splicing events for the diagnosis or treatment of cancers. The data and code for PCB are available at: http://hkumath.hku.hk/~wkc/PCB(data+code).zip.

AS-CMC: a pan-cancer database of alternative splicing for molecular classification of cancer

Alternative splicing (AS) is a post-transcriptional regulation that leads to the complexity of the transcriptome. Despite the growing importance of AS in cancer research, the role of AS has not been systematically studied, especially in understanding cancer molecular classification. Herein, we analyzed the molecular subtype-specific regulation of AS using The Cancer Genome Atlas data and constructed a web-based database, named Alternative Splicing for Cancer Molecular Classification (AS-CMC). Our system harbors three analysis modules for exploring subtype-specific AS events, evaluating their phenotype association, and performing pan-cancer comparison. The number of subtype-specific AS events was found to be diverse across cancer types, and some differentially regulated AS events were recurrently found in multiple cancer types. We analyzed a subtype-specific AS in exon 11 of mitogen-activated protein kinase kinase 7 (MAP3K7) as an example of a pan-cancer AS biomarker. This AS marker showed significant association with the survival of patients with stomach adenocarcinoma. Our analysis revealed AS as an important determinant for cancer molecular classification. AS-CMC is the first web-based resource that provides a comprehensive tool to explore the biological implications of AS events, facilitating the discovery of novel AS biomarkers.

Dysregulation of splicing variants and spliceosome components in breast cancer

The dysregulation of the splicing process has emerged as a novel hallmark of metabolic and tumor pathologies. In breast cancer (BCa), which represents the most diagnosed cancer type among women worldwide, the generation and/or dysregulation of several oncogenic splicing variants have been described. This is the case of the splicing variants of HER2, ER, BRCA1, or the recently identified by our group, In1-ghrelin and SST5TMD4, which exhibit oncogenic roles, increasing the malignancy, poor prognosis, and resistance to treatment of BCa. This altered expression of oncogenic splicing variants has been closely linked with the dysregulation of the elements belonging to the macromolecular machinery that controls the splicing process (spliceosome components and the associated splicing factors). In this review, we compile the current knowledge demonstrating the altered expression of splicing variants and spliceosomal components in BCa, showing the existence of a growing body of evidence supporting the close implication of the alteration in the splicing process in mammary tumorigenesis.

The Role of Alternative Splicing in Cancer: Regulatory Mechanism, Therapeutic Strategy, and Bioinformatics Application

[Formula: see text] Alternative splicing (AS) can generate distinct transcripts and subsequent isoforms that play differential functions from the same pre-mRNA. Recently, increasing numbers of studies have emerged, unmasking the association between AS and cancer. In this review, we arranged AS events that are closely related to cancer progression and presented promising treatments based on AS for cancer therapy. Obtaining proliferative capacity, acquiring invasive properties, gaining angiogenic features, shifting metabolic ability, and getting immune escape inclination are all splicing events involved in biological processes. Spliceosome-targeted and antisense oligonucleotide technologies are two novel strategies that are hopeful in tumor therapy. In addition, bioinformatics applications based on AS were summarized for better prediction and elucidation of regulatory routines mingled in. Together, we aimed to provide a better understanding of complicated AS events associated with cancer biology and reveal AS a promising target of cancer treatment in the future.

MDICC: novel method for multi-omics data integration and cancer subtype identification

Each type of cancer usually has several subtypes with distinct clinical implications, and therefore the discovery of cancer subtypes is an important and urgent task in disease diagnosis and therapy. Using single-omics data to predict cancer subtypes is difficult because genomes are dysregulated and complicated by multiple molecular mechanisms, and therefore linking cancer genomes to cancer phenotypes is not an easy task. Using multi-omics data to effectively predict cancer subtypes is an area of much interest; however, integrating multi-omics data is challenging. Here, we propose a novel method of multi-omics data integration for clustering to identify cancer subtypes (MDICC) that integrates new affinity matrix and network fusion methods. Our experimental results show the effectiveness and generalization of the proposed MDICC model in identifying cancer subtypes, and its performance was better than those of currently available state-of-the-art clustering methods. Furthermore, the survival analysis demonstrates that MDICC delivered comparable or even better results than many typical integrative methods.

A Quality Control Mechanism of Splice Site Selection Abrogated under Stress and in Cancer

Simple Summary

Splicing and alternative splicing play a major role in regulating gene expression, and mis-regulation of splicing can lead to several diseases, including cancer. The aim of this review is to summarize the current knowledge of a quality control mechanism of splice site selection termed Suppression of Splicing (SOS), proposed to protect cells from splicing at the numerous intronic unused 5′ splice sites, and emphasize its relevance to cancer. This relevance stems from the finding that SOS is abrogated under stress and in cancer resulting in the expression of thousands of aberrant nonsense mRNAs that may be toxic to cells. These findings highlight the unexplored potential of such aberrant isoforms as novel targets for cancer diagnosis and therapies.

Abstract

Latent 5’ splice sites, highly abundant in human introns, are not normally used. This led to the proposal of a quality control mechanism, Suppression of Splicing (SOS), which protects cells from splicing at the numerous intronic latent sites, and whose activation can generate nonsense mRNAs. SOS was shown to be independent of Nonsense-Mediated mRNA Decay (NMD). Efforts to decipher the SOS mechanism revealed a pivotal role for initiator-tRNA, independent of protein translation. Recently, nucleolin (a multifunctional protein) was found to directly and specifically bind the initiator-tRNA in the nucleus and was shown to be a protein component of SOS, enabling an updated model of the SOS mechanism. Importantly, SOS is abrogated under stress and in cancer (e.g., in breast cancer cells and gliomas), generating thousands of nonsense mRNAs due to activation of latent splicing. The resulting affected human genes cover a variety of functional groups, including genes involved in cell proliferation and differentiation. Furthermore, in oligodendroglioma, the extent of activation of latent splicing increases with the severity of the cancer. Interesting examples are genes expressing aberrant nonsense mRNAs in both breast cancer and glioma, due to latent splicing activation. These findings highlight the unexplored potential of such aberrant isoforms as novel targets for cancer diagnosis and therapies.

DJExpress: An Integrated Application for Differential Splicing Analysis and Visualization

RNA-seq analysis of alternative pre-mRNA splicing has facilitated an unprecedented understanding of transcriptome complexity in health and disease. However, despite the availability of countless bioinformatic pipelines for transcriptome-wide splicing analysis, the use of these tools is often limited to expert bioinformaticians. The need for high computational power, combined with computational outputs that are complicated to visualize and interpret present obstacles to the broader research community. Here we introduce DJExpress, an R package for differential expression analysis of transcriptomic features and expression-trait associations. To determine gene-level differential junction usage as well as associations between junction expression and molecular/clinical features, DJExpress uses raw splice junction counts as input data. Importantly, DJExpress runs on an average laptop computer and provides a set of interactive and intuitive visualization formats. In contrast to most existing pipelines, DJExpress can handle both annotated and de novo identified splice junctions, thereby allowing the quantification of novel splice events. Moreover, DJExpress offers a web-compatible graphical interface allowing the analysis of user-provided data as well as the visualization of splice events within our custom database of differential junction expression in cancer (DJEC DB). DJEC DB includes not only healthy and tumor tissue junction expression data from TCGA and GTEx repositories but also cancer cell line data from the DepMap project. The integration of DepMap functional genomics data sets allows association of junction expression with molecular features such as gene dependencies and drug response profiles. This facilitates identification of cancer cell models for specific splicing alterations that can then be used for functional characterization in the lab. Thus, DJExpress represents a powerful and user-friendly tool for exploration of alternative splicing alterations in RNA-seq data, including multi-level data integration of alternative splicing signatures in healthy tissue, tumors and cancer cell lines.

Alternative RNA splicing in tumour heterogeneity, plasticity and therapy

Alternative splicing is a process by which a single gene is able to encode multiple different protein isoforms. It is regulated by the inclusion or exclusion of introns and exons that are joined in different patterns prior to protein translation, thus enabling transcriptomic and proteomic diversity. It is now widely accepted that alternative splicing is dysregulated across nearly all cancer types. This widespread dysregulation means that nearly all cellular processes are affected - these include processes synonymous with the hallmarks of cancer - evasion of apoptosis, tissue invasion and metastasis, altered cellular metabolism, genome instability and drug resistance. Emerging evidence indicates that the dysregulation of alternative splicing also promotes a permissive environment for increased tumour heterogeneity and cellular plasticity. These are fundamental regulators of a patient's response to therapy. In this Review, we introduce the mechanisms of alternative splicing and the role of aberrant splicing in cancer, with particular focus on newfound evidence of alternative splicing promoting tumour heterogeneity, cellular plasticity and altered metabolism. We discuss recent in vivo models generated to study alternative splicing and the importance of these for understanding complex tumourigenic processes. Finally, we review the effects of alternative splicing on immune evasion, cell death and genome instability, and how targeting these might enhance therapeutic efficacy.

Epithelial-to-Mesenchymal Transition Is Not a Major Modulating Factor in the Cytotoxic Response to Natural Products in Cancer Cell Lines

Numerous natural products exhibit antiproliferative activity against cancer cells by modulating various biological pathways. In this study, we investigated the potential use of eight natural compounds (apigenin, curcumin, epigallocatechin gallate, fisetin, forskolin, procyanidin B2, resveratrol, urolithin A) and two repurposed agents (fulvestrant and metformin) as chemotherapy enhancers and mesenchymal-to-epithelial (MET) inducers of cancer cells. Screening of these compounds in various colon, breast, and pancreatic cancer cell lines revealed anti-cancer activity for all compounds, with curcumin being the most effective among these in all cell lines. Although some of the natural products were able to induce MET in some cancer cell lines, the MET induction was not related to increased synergy with either 5-FU, irinotecan, gemcitabine, or gefitinib. When synergy was observed, for example with curcumin and irinotecan, this was unrelated to MET induction, as assessed by changes in E-cadherin and vimentin expression. Our results show that MET induction is compound and cell line specific, and that MET is not necessarily related to enhanced chemosensitivity.

A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

BACKGROUND

Breast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers.

RESULTS

Transcriptomics analysis of alternative splicing events between luminal, basal A and basal B breast cancer cell lines identified a unique splicing signature for a subtype of tumours, the basal B, whose classification is not in use in the clinic yet. Basal B cell lines, in contrast with luminal and basal A, are highly metastatic and express epithelial-to-mesenchymal (EMT) markers, which are hallmarks of cell invasion and resistance to drugs. By developing a semi-supervised machine learning approach, we transferred the molecular knowledge gained from these cell lines into patients to subclassify basal-like triple negative tumours into basal A- and basal B-like categories. Changes in splicing of 25 alternative exons, intimately related to EMT and cell invasion such as ENAH, CD44 and CTNND1, were sufficient to identify the basal-like patients with the worst prognosis. Moreover, patients expressing this basal B-specific splicing signature also expressed newly identified biomarkers of metastasis-initiating cells, like CD36, supporting a more invasive phenotype for this basal B-like breast cancer subtype.

CONCLUSIONS

Using a novel machine learning approach, we have identified an EMT-related splicing signature capable of subclassifying the most aggressive type of breast cancer, which are basal-like triple negative tumours. This proof-of-concept demonstrates that the biological knowledge acquired from cell lines can be transferred to patients data for further clinical investigation. More studies, particularly in 3D culture and organoids, will increase the accuracy of this transfer of knowledge, which will open new perspectives into the development of novel therapeutic strategies and the further identification of specific biomarkers for drug resistance and cancer relapse.

Prediction of RNA-binding protein and alternative splicing event associations during epithelial-mesenchymal transition based on inductive matrix completion

MOTIVATION

The developmental process of epithelial-mesenchymal transition (EMT) is abnormally activated during breast cancer metastasis. Transcriptional regulatory networks that control EMT have been well studied; however, alternative RNA splicing plays a vital regulatory role during this process and the regulating mechanism needs further exploration. Because of the huge cost and complexity of biological experiments, the underlying mechanisms of alternative splicing (AS) and associated RNA-binding proteins (RBPs) that regulate the EMT process remain largely unknown. Thus, there is an urgent need to develop computational methods for predicting potential RBP-AS event associations during EMT.

RESULTS

We developed a novel model for RBP-AS target prediction during EMT that is based on inductive matrix completion (RAIMC). Integrated RBP similarities were calculated based on RBP regulating similarity, and RBP Gaussian interaction profile (GIP) kernel similarity, while integrated AS event similarities were computed based on AS event module similarity and AS event GIP kernel similarity. Our primary objective was to complete missing or unknown RBP-AS event associations based on known associations and on integrated RBP and AS event similarities. In this paper, we identify significant RBPs for AS events during EMT and discuss potential regulating mechanisms. Our computational results confirm the effectiveness and superiority of our model over other state-of-the-art methods. Our RAIMC model achieved AUC values of 0.9587 and 0.9765 based on leave-one-out cross-validation (CV) and 5-fold CV, respectively, which are larger than the AUC values from the previous models. RAIMC is a general matrix completion framework that can be adopted to predict associations between other biological entities. We further validated the prediction performance of RAIMC on the genes CD44 and MAP3K7. RAIMC can identify the related regulating RBPs for isoforms of these two genes.

AVAILABILITY AND IMPLEMENTATION

The source code for RAIMC is available at https://github.com/yushanqiu/RAIMC.

CONTACT

zouquan@nclab.net online.

Profiles of alternative splicing landscape in breast cancer and their clinical significance: an integrative analysis based on large-sequencing data

Background

Alternative splicing (AS) is closely correlated with the initiation and progression of carcinoma. The systematic analysis of its biological and clinical significance in breast cancer (BRCA) is, however, lacking.

Methods

Clinical data and RNA-seq were obtained from the TCGA dataset and differentially expressed AS (DEAS) events between tumor and paired normal BRCA tissues were identified. Enrichment analysis was then used to reveal the potential biological functions of DEAS events. We performed protein-protein interaction (PPI) analysis of DEAS events by using STRING and the correlation network between splicing factors (SFs) and AS events was constructed. The LASSO Cox model, Kaplan-Meier and log-rank tests were used to construct and evaluate DEAS-related risk signature, and the association between DEAS events and clinicopathological features were then analyzed.

Results

After strict filtering, 35,367 AS events and 973 DEAS events were detected. DEAS corresponding genes were significantly enriched in pivotal pathways including cell adhesion, cytoskeleton organization, and extracellular matrix organization. A total of 103 DEAS events were correlated with disease free survival. The DEAS-related risk signature stratified BRCA patients into two groups and the area under curve (AUC) was 0.754. Moreover, patients in the high-risk group had enriched basel-like subtype, advanced clinical stages, proliferation, and metastasis potency.

Conclusions

Collectively, the profile of DEAS landscape in BRCA revealed the potential biological function and prognostic value of DEAS events.