Detection of recurrent alternative splicing switches in tumor samples reveals novel signatures of cancer

Exploring the role of mitochondrial proteins SIRT5 and MRPL33 through Mendelian randomization in primary biliary cholangitis

BACKGROUND

Primary biliary cholangitis (PBC) is an autoimmune liver disease characterized by elevated serum antimitochondrial antibody levels in 90-95 % of cases. However, the exact causal relationship between mitochondrial proteins and PBC remains unclear. This study aims to investigate and clarify this relationship.

METHODS

Genome-wide association data for mitochondrial proteins and PBC were obtained from public databases. The assessment of causal relationships between exposures and outcomes employed the Inverse Variance Weighted (IVW) method, MR Egger regression, and Weighted Median. Sensitivity analyses were systematically carried out to appraise the robustness of the Mendelian Randomization (MR) findings.

RESULTS

The analysis revealed two mitochondrial proteins exhibiting a causal relationship with PBC. Elevated SIRT5 levels demonstrated a positive correlation with an augmented susceptibility to PBC in the IVW approach (odds ratio, OR: 1.2907, 95 % CI: 1.062-1.568, p = 0.0102). Conversely, increased MRPL33 levels were associated with a decreased risk of PBC (OR: 0.8957, 95 % CI: 0.807-0.993, p = 0.0376). Sensitivity analysis corroborated these findings consistently.

CONCLUSION

This investigation advances the notion of a potential causal association between elevated SIRT5 levels and an increased risk of PBC, alongside a decreased risk of PBC linked to elevated MRPL33 levels. The identified mitochondrial proteins may serve as viable biomarkers, offering pertinent insights for the understanding and addressing of PBC.

Pan-Cancer Single-Nucleus Total RNA Sequencing Using snHH-Seq

Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single-cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA-seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high-throughput and high-sensitivity method called snHH-seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full-length RNA-seq data is also established. snHH-seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan-cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full-length RNA at the single-nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.

Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions

RNA sequencing offers unique insights into transcriptome diversity, and a plethora of tools have been developed to analyze alternative splicing. One important task is to detect changes in the relative transcript abundance in differential transcript usage (DTU) analysis. The choice of the right analysis tool is non-trivial and depends on experimental factors such as the availability of single- or paired-end and bulk or single-cell data. To help users select the most promising tool for their task, we performed a comprehensive benchmark of DTU detection tools. We cover a wide array of experimental settings, using simulated bulk and single-cell RNA-seq data as well as real transcriptomics datasets, including time-series data. Our results suggest that DEXSeq, edgeR, and LimmaDS are better choices for paired-end data, while DSGseq and DEXSeq can be used for single-end data. In single-cell simulation settings, we showed that satuRn performs better than DTUrtle. In addition, we showed that Spycone is optimal for time series DTU/IS analysis based on the evidence provided using GO terms enrichment analysis.

Pan-cancer transcriptomic data of ABI1 transcript variants and molecular constitutive elements identifies novel cancer metastatic and prognostic biomarkers

BACKGROUND

Abelson interactor 1 (ABI1) is associated with the metastasis and prognosis of many malignancies. The association between ABI1 transcript spliced variants, their molecular constitutive exons and exon-exon junctions (EEJs) in 14 cancer types and clinical outcomes remains unsolved.

OBJECTIVE

To identify novel cancer metastatic and prognostic biomarkers from ABI1 total mRNA, TSVs, and molecular constitutive elements.

METHODS

Using data from TCGA and TSVdb database, the standard median of ABI1 total mRNA, TSV, exon, and EEJ expression was used as a cut-off value. Kaplan-Meier analysis, Chi-squared test (X2) and Kendall's tau statistic were used to identify novel metastatic and prognostic biomarkers, and Cox regression analysis was performed to screen and identify independent prognostic factors.

RESULTS

A total of 35 ABI1-related factors were found to be closely related to the prognosis of eight candidate cancer types. A total of 14 ABI1 TSVs and molecular constitutive elements were identified as novel metastatic and prognostic biomarkers in four cancer types. A total of 13 ABI1 molecular constitutive elements were identified as independent prognostic biomarkers in six cancer types.

CONCLUSIONS

In this study, we identified 14 ABI1-related novel metastatic and prognostic markers and 21 independent prognostic factors in total 8 candidate cancer types.

REVEILLE2 thermosensitive splicing: a molecular basis for the integration of nocturnal temperature information by the Arabidopsis circadian clock

Cold stress is one of the major environmental factors that limit growth and yield of plants. However, it is still not fully understood how plants account for daily temperature fluctuations, nor how these temperature changes are integrated with other regulatory systems such as the circadian clock. We demonstrate that REVEILLE2 undergoes alternative splicing after chilling that increases accumulation of a transcript isoform encoding a MYB-like transcription factor. We explore the biological function of REVEILLE2 in Arabidopsis thaliana using a combination of molecular genetics, transcriptomics, and physiology. Disruption of REVEILLE2 alternative splicing alters regulatory gene expression, impairs circadian timing, and improves photosynthetic capacity. Changes in nuclear gene expression are particularly apparent in the initial hours following chilling, with chloroplast gene expression subsequently upregulated. The response of REVEILLE2 to chilling extends our understanding of plants immediate response to cooling. We propose that the circadian component REVEILLE2 restricts plants responses to nocturnal reductions in temperature, thereby enabling appropriate responses to daily environmental changes.

RBFOX2 deregulation promotes pancreatic cancer progression and metastasis through alternative splicing

RNA splicing is an important biological process associated with cancer initiation and progression. However, the contribution of alternative splicing to pancreatic cancer (PDAC) development is not well understood. Here, we identify an enrichment of RNA binding proteins (RBPs) involved in splicing regulation linked to PDAC progression from a forward genetic screen using Sleeping Beauty insertional mutagenesis in a mouse model of pancreatic cancer. We demonstrate downregulation of RBFOX2, an RBP of the FOX family, promotes pancreatic cancer progression and liver metastasis. Specifically, we show RBFOX2 regulates exon splicing events in transcripts encoding proteins involved in cytoskeletal remodeling programs. These exons are differentially spliced in PDAC patients, with enhanced exon skipping in the classical subtype for several RBFOX2 targets. RBFOX2 mediated splicing of ABI1, encoding the Abelson-interactor 1 adapter protein, controls the abundance and localization of ABI1 protein isoforms in pancreatic cancer cells and promotes the relocalization of ABI1 from the cytoplasm to the periphery of migrating cells. Using splice-switching antisense oligonucleotides (AONs) we demonstrate the ABI1 ∆Ex9 isoform enhances cell migration. Together, our data identify a role for RBFOX2 in promoting PDAC progression through alternative splicing regulation.

RNA‑binding protein quaking 5 inhibits the progression of non‑small cell lung cancer by upregulating netrin‑4 expression

It was recently reported that netrin‑4 (Ntn‑4), a component of the extracellular matrix, when downregulated, is involved in the progression of several types of cancer, including breast cancer, colorectal tumours, neuroblastoma and gastric cancer. In the present study, the level of Ntn‑4 was examined in a public non‑small cell lung cancer (NSCLC) dataset from the Netherlands Cancer Institute. This analysis revealed that the mRNA expression level of Ntn‑4 was lower in the samples of patients with NSCLC compared with that in the control samples. Consistent with the mRNA level, the protein level of Ntn‑4 was also found to be decreased in NSCLC cells. However, both the function of Ntn‑4 and the underlying mechanisms of Ntn‑4 downregulation in NSCLC have yet to be fully elucidated. As was anticipated, the overexpression of Ntn‑4 led to a marked decrease in the proliferation, migration and invasion of NSCLC cells. Notably, RNA‑binding protein quaking 5 (Qki‑5) was found to exhibit antitumor activity in lung cancer, not only by enhancing the level of Ntn‑4 by binding to Ntn‑4 mRNA, but also by suppressing the proliferation, invasion and migration of NSCLC cells. However, Qki‑5 is known to be frequently downregulated in NSCLC. Moreover, the knockdown of Ntn‑4 was found to reverse the suppressive effects of Qki‑5 on NSCLC progression both in vitro and in vivo. Taken together, the findings of the present study demonstrate that Ntn‑4 is able to suppress the progression of NSCLC, and that the level of Ntn‑4 can be regulated by Qki‑5. Therefore, Ntn‑4 may be a novel diagnostic and therapeutic target for the treatment of NSCLC.

The Role of NCS1 in Immunotherapy and Prognosis of Human Cancer

The Neural Calcium Sensor1 (NCS1) is a crucial protein that binds to Ca2+ and is believed to play a role in regulating tumor invasion and cell proliferation. However, the role of NCS1 in immune infiltration and cancer prognosis is still unknown. Our study aimed to explore the expression profile, immune infiltration pattern, prognostic value, biological function, and potential compounds targeting NCS1 using public databases. High expression of NCS1 was detected by immune histochemical staining in LIHC (Liver hepatocellular carcinoma), BRCA (Breast invasive carcinoma), KIRC (Kidney renal clear cell carcinoma), and SKCM (Skin Cutaneous Melanoma). The expression of NCS1 in cancer was determined by TCGA (The Cancer Genome Atlas Program), GTEx (The Genotype-Tissue Expression), the Kaplan-Meier plotter, GEO (Gene Expression Omnibus), GEPIA2.0 (Gene Expression Profiling Interactive Analysis 2.0), HPA (The Human Protein Atlas), UALCAN, TIMER2.0, TISIDB, Metascape, Drugbank, chEMBL, and ICSDB databases. NCS1 has genomic mutations as well as aberrant DNA methylation in multiple cancers compared to normal tissues. Also, NCS1 was significantly different in the immune microenvironment, tumor mutational burden (TMB), microsatellite instability (MSI), and immune infiltrate-associated cells in different cancers, which could be used for the typing of immune and molecular subtypes of cancer and the presence of immune checkpoint resistance in several cancers. Univariate regression analysis, multivariate regression analysis, and gene enrichment analysis to construct prognostic models revealed that NCS1 is involved in immune regulation and can be used as a prognostic biomarker for SKCM, LIHC, BRCA, COAD, and KIRC. These results provide clues from a bioinformatic perspective and highlight the importance of NCS1 in a variety of cancers.

Supervised Relevance-Redundancy assessments for feature selection in omics-based classification scenarios

BACKGROUND AND OBJECTIVE

Many classification tasks in translational bioinformatics and genomics are characterized by the high dimensionality of potential features and unbalanced sample distribution among classes. This can affect classifier robustness and increase the risk of overfitting, curse of dimensionality and generalization leaks; furthermore and most importantly, this can prevent obtaining adequate patient stratification required for precision medicine in facing complex diseases, like cancer. Setting up a feature selection strategy able to extract only proper predictive features by removing irrelevant, redundant, and noisy ones is crucial to achieving valuable results on the desired task.

METHODS

We propose a new feature selection approach, called ReRa, based on supervised Relevance-Redundancy assessments. ReRa consists of a customized step of relevance-based filtering, to identify a reduced subset of meaningful features, followed by a supervised similarity-based procedure to minimize redundancy. This latter step innovatively uses a combination of global and class-specific similarity assessments to remove redundant features while preserving those differentiated across classes, even when these classes are strongly unbalanced.

RESULTS

We compared ReRa with several existing feature selection methods to obtain feature spaces on which performing breast cancer patient subtyping using several classifiers: we considered two use cases based on gene or transcript isoform expression. In the vast majority of the assessed scenarios, when using ReRa-selected feature spaces, the performances were significantly increased compared to simple feature filtering, LASSO regularization, or even MRmr - another Relevance-Redundancy method. The two use cases represent an insightful example of translational application, taking advantage of ReRa capabilities to investigate and enhance a clinically-relevant patient stratification task, which could be easily applied also to other cancer types and diseases.

CONCLUSIONS

ReRa approach has the potential to improve the performance of machine learning models used in an unbalanced classification scenario. Compared to another Relevance-Redundancy approach like MRmr, ReRa does not require tuning the number of preserved features, ensures efficiency and scalability over huge initial dimensionalities and allows re-evaluation of all previously selected features at each iteration of the redundancy assessment, to ultimately preserve only the most relevant and class-differentiated features.

Quantitative Analysis of Isoform Switching in Cancer

Over the past 8 years, multiple studies examined the phenomenon of isoform switching in human cancers and discovered that isoform switching is widespread, with hundreds to thousands of such events per cancer type. Although all of these studies used slightly different definitions of isoform switching, which in part led to a rather poor overlap of their results, they all leveraged transcript usage, a proportion of the transcript's expression in the total expression level of the parent gene, to detect isoform switching. However, how changes in transcript usage correlate with changes in transcript expression is not sufficiently explored. In this article, we adopt the most common definition of isoform switching and use a state-of-the-art tool for the analysis of differential transcript usage, SatuRn, to detect isoform switching events in 12 cancer types. We analyze the detected events in terms of changes in transcript usage and the relationship between transcript usage and transcript expression on a global scale. The results of our analysis suggest that the relationship between changes in transcript usage and changes in transcript expression is far from straightforward, and that such quantitative information can be effectively used for prioritizing isoform switching events for downstream analyses.

Cell-type-specific alternative splicing in the Arabidopsis germline

During sexual reproduction in flowering plants, the two haploid sperm cells (SCs) embedded within the cytoplasm of a growing pollen tube are carried to the embryo sac for double fertilization. Pollen development in flowering plants is a dynamic process that encompasses changes at transcriptome and epigenome levels. While the transcriptome of pollen and SCs in Arabidopsis (Arabidopsis thaliana) is well documented, previous analyses have mostly been based on gene-level expression. In-depth transcriptome analysis, particularly the extent of alternative splicing (AS) at the resolution of SC and vegetative nucleus (VN), is still lacking. Therefore, we performed RNA-seq analysis to generate a spliceome map of Arabidopsis SCs and VN isolated from mature pollen grains. Based on our de novo transcriptome assembly, we identified 58,039 transcripts, including 9,681 novel transcripts, of which 2,091 were expressed in SCs and 3,600 in VN. Four hundred and sixty-eight genes were regulated both at gene and splicing levels, with many having functions in mRNA splicing, chromatin modification, and protein localization. Moreover, a comparison with egg cell RNA-seq data uncovered sex-specific regulation of transcription and splicing factors. Our study provides insights into a gamete-specific AS landscape at unprecedented resolution.

ESRP1-regulated isoform switching of LRRFIP2 determines metastasis of gastric cancer

Although accumulating evidence indicates that alternative splicing is aberrantly altered in many cancers, the functional mechanism remains to be elucidated. Here, we show that epithelial and mesenchymal isoform switches of leucine-rich repeat Fli-I-interacting protein 2 (LRRFIP2) regulated by epithelial splicing regulatory protein 1 (ESRP1) correlate with metastatic potential of gastric cancer cells. We found that expression of the splicing variants of LRRFIP2 was closely correlated with that of ESRP1. Surprisingly, ectopic expression of the mesenchymal isoform of LRRFIP2 (variant 3) dramatically increased liver metastasis of gastric cancer cells, whereas deletion of exon 7 of LRRFIP2 by the CRISPR/Cas9 system caused an isoform switch, leading to marked suppression of liver metastasis. Mechanistically, the epithelial LRRFIP2 isoform (variant 2) inhibited the oncogenic function of coactivator-associated arginine methyltransferase 1 (CARM1) through interaction. Taken together, our data reveals a mechanism of LRRFIP2 isoform switches in gastric cancer with important implication for cancer metastasis.

Bridging the splicing gap in human genetics with long-read RNA sequencing: finding the protein isoform drivers of disease

Aberrant splicing underlies many human diseases, including cancer, cardiovascular diseases and neurological disorders. Genome-wide mapping of splicing quantitative trait loci (sQTLs) has shown that genetic regulation of alternative splicing is widespread. However, identification of the corresponding isoform or protein products associated with disease-associated sQTLs is challenging with short-read RNA-seq, which cannot precisely characterize full-length transcript isoforms. Furthermore, contemporary sQTL interpretation often relies on reference transcript annotations, which are incomplete. Solutions to these issues may be found through integration of newly emerging long-read sequencing technologies. Long-read sequencing offers the capability to sequence full-length mRNA transcripts and, in some cases, to link sQTLs to transcript isoforms containing disease-relevant protein alterations. Here, we provide an overview of sQTL mapping approaches, the use of long-read sequencing to characterize sQTL effects on isoforms, the linkage of RNA isoforms to protein-level functions and comment on future directions in the field. Based on recent progress, long-read RNA sequencing promises to be part of the human disease genetics toolkit to discover and treat protein isoforms causing rare and complex diseases.

Characterizing isoform switching events in esophageal adenocarcinoma

Isoform switching events with predicted functional consequences are common in many cancers, but characterization of switching events in esophageal adenocarcinoma (EAC) is lacking. Next-generation sequencing was used to detect levels of RNA transcripts and identify specific isoforms in treatment-naïve esophageal tissues ranging from premalignant Barrett’s esophagus (BE), BE with low- or high-grade dysplasia (BE.LGD, BE.HGD), and EAC. Samples were stratified by histopathology and TP53 mutation status, identifying significant isoform switching events with predicted functional consequences. Comparing BE.LGD with BE.HGD, a histopathology linked to cancer progression, isoform switching events were identified in 75 genes including KRAS, RNF128, and WRAP53. Stratification based on TP53 status increased the number of significant isoform switches to 135, suggesting switching events affect cellular functions based on TP53 mutation and tissue histopathology. Analysis of isoforms agnostic, exclusive, and shared with mutant TP53 revealed unique signatures including demethylation, lipid and retinoic acid metabolism, and glucuronidation, respectively. Nearly half of isoform switching events were identified without significant gene-level expression changes. Importantly, two TP53-interacting isoforms, RNF128 and WRAP53, were significantly linked to patient survival. Thus, analysis of isoform switching events may provide new insight for the identification of prognostic markers and inform new potential therapeutic targets for EAC.

Novel Method of Full-Length RNA-seq That Expands the Identification of Non-Polyadenylated RNAs Using Nanopore Sequencing

The occurrence of diseases displayed transcriptome alteration, including both coding and non-coding transcripts. The third-generation sequencing (TGS) technologies allow for intensive and comprehensive research of the transcriptome. However, the present standard TGS RNA sequencing method is unable to detect many of the non-polyadenylated [non-poly(A)] RNAs. To obtain more complete transcriptome information, we presented a new comprehensive sequencing approach by performing conventional poly(A) RNA-sequencing combined with the sequencing of non-poly(A) RNA fraction which was tailed by poly(U) on HepG2 and HL-7702 cell lines, enabling the detection of multiple categories of non-poly(A) RNAs excluded by the existing standard approach. Moreover, the length distribution of the full-splice match transcripts was longer than that assembled by short-reads, which contributed to characterizing alternative splicing events and provided a comprehensive portrait of transcriptional complexity. Besides the detection of genes with differential expression patterns in the poly(A) library between HepG2 and HL-7702, we also found a cancer-related non-coding gene in the poly(U) data, that is, growth arrest special 5 (GAS5). Collectively, our results suggested that the novel method effectively captured both poly(A) and non-poly(A) transcripts in the tested cell lines and allowed a deeper exploration of the transcriptome.

Functional Interaction Between the Oncogenic Kinase NEK2 and Sam68 Promotes a Splicing Program Involved in Migration and Invasion in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype. Poor prognosis in TNBC is partly due to lack of efficacious targeted therapy and high propensity to metastasize. Dysregulation of alternative splicing has recently emerged as a trait of TNBC, suggesting that unveiling the molecular mechanisms underlying its regulation could uncover new druggable cancer vulnerabilities. The oncogenic kinase NEK2 is significantly upregulated in TNBC and contributes to shaping their unique splicing profile. Herein, we found that NEK2 interacts with the RNA binding protein Sam68 in TNBC cells and that NEK2-mediated phosphorylation of Sam68 enhances its splicing activity. Genome-wide transcriptome analyses identified the splicing targets of Sam68 in TNBC cells and revealed a common set of exons that are co-regulated by NEK2. Functional annotation of splicing-regulated genes highlighted cell migration and spreading as biological processes regulated by Sam68. Accordingly, Sam68 depletion reduces TNBC cell migration and invasion, and these effects are potentiated by the concomitant inhibition of NEK2 activity. Our findings indicate that Sam68 and NEK2 functionally cooperate in the regulation of a splicing program that sustains the pro-metastatic features of TNBC cells.

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.

Histone marks regulate the epithelial-to-mesenchymal transition via alternative splicing

Histone modifications impact final splicing decisions. However, there is little evidence of the driving role of these marks in inducing cell-specific splicing changes. Using CRISPR epigenome editing tools, we show in an epithelial-to-mesenchymal cell reprogramming system (epithelial-to-mesenchymal transition [EMT]) that a single change in H3K27ac or H3K27me3 levels right at the alternatively spliced exon is necessary and sufficient to induce a splicing change capable of recapitulating important aspects of EMT, such as cell motility and invasiveness. This histone-mark-dependent splicing effect is highly dynamic and mediated by direct recruitment of the splicing regulator PTB to its RNA binding sites. These results support a role for H3K27 marks in inducing a change in the cell's phenotype via regulation of alternative splicing. We propose the dynamic nature of chromatin as a rapid and reversible mechanism to coordinate the splicing response to cell-extrinsic cues, such as induction of EMT.

A comprehensive long-read isoform analysis platform and sequencing resource for breast cancer

Tumors display widespread transcriptome alterations, but the full repertoire of isoform-level alternative splicing in cancer is unknown. We developed a long-read (LR) RNA sequencing and analytical platform that identifies and annotates full-length isoforms and infers tumor-specific splicing events. Application of this platform to breast cancer samples identifies thousands of previously unannotated isoforms; ~30% affect protein coding exons and are predicted to alter protein localization and function. We performed extensive cross-validation with -omics datasets to support transcription and translation of novel isoforms. We identified 3059 breast tumor–specific splicing events, including 35 that are significantly associated with patient survival. Of these, 21 are absent from GENCODE and 10 are enriched in specific breast cancer subtypes. Together, our results demonstrate the complexity, cancer subtype specificity, and clinical relevance of previously unidentified isoforms and splicing events in breast cancer that are only annotatable by LR-seq and provide a rich resource of immuno-oncology therapeutic targets.

Uncovering the impacts of alternative splicing on the proteome with current omics techniques

The high‐throughput sequencing of cellular RNAs has underscored a broad effect of isoform diversification through alternative splicing on the transcriptome. Moreover, the differential production of transcript isoforms from gene loci has been recognized as a critical mechanism in cell differentiation, organismal development, and disease. Yet, the extent of the impact of alternative splicing on protein production and cellular function remains a matter of debate. Multiple experimental and computational approaches have been developed in recent years to address this question. These studies have unveiled how molecular changes at different steps in the RNA processing pathway can lead to differences in protein production and have functional effects. New and emerging experimental technologies open exciting new opportunities to develop new methods to fully establish the connection between messenger RNA expression and protein production and to further investigate how RNA variation impacts the proteome and cell function.

This article is categorized under:

RNA Processing > Splicing Regulation/Alternative Splicing

Translation > Regulation

RNA Evolution and Genomics > Computational Analyses of RNA

Identification of an mRNA isoform switch for HNRNPA1 in breast cancers

Roles of HNRNPA1 are beginning to emerge in cancers; however, mechanisms causing deregulation of HNRNPA1 function remain elusive. Here, we describe an isoform switch between the 3'-UTR isoforms of HNRNPA1 in breast cancers. We show that the dominantly expressed isoform in mammary tissue has a short half-life. In breast cancers, this isoform is downregulated in favor of a stable isoform. The stable isoform is expressed more in breast cancers, and more HNRNPA1 protein is synthesized from this isoform. High HNRNPA1 protein levels correlate with poor survival in patients. In support of this, silencing of HNRNPA1 causes a reversal in neoplastic phenotypes, including proliferation, clonogenic potential, migration, and invasion. In addition, silencing of HNRNPA1 results in the downregulation of microRNAs that map to intragenic regions. Among these miRNAs, miR-21 is known for its transcriptional upregulation in breast and numerous other cancers. Altogether, the cancer-specific isoform switch we describe here for HNRNPA1 emphasizes the need to study gene expression at the isoform level in cancers to identify novel cases of oncogene activation.

Impact of Alternative Splicing Variants on Liver Cancer Biology

Simple Summary

Among the top ten deadly solid tumors are the two most frequent liver cancers, hepatocellular carcinoma, and intrahepatic cholangiocarcinoma, whose development and malignancy are favored by multifactorial conditions, which include aberrant maturation of pre-mRNA due to abnormalities in either the machinery involved in the splicing, i.e., the spliceosome and associated factors, or the nucleotide sequences of essential sites for the exon recognition process. As a consequence of cancer-associated aberrant splicing in hepatocytes- and cholangiocytes-derived cancer cells, abnormal proteins are synthesized. They contribute to the dysregulated proliferation and eventually transformation of these cells to phenotypes with enhanced invasiveness, migration, and multidrug resistance, which contributes to the poor prognosis that characterizes these liver cancers.

Abstract

The two most frequent primary cancers affecting the liver, whose incidence is growing worldwide, are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA), which are among the five most lethal solid tumors with meager 5-year survival rates. The common difficulty in most cases to reach an early diagnosis, the aggressive invasiveness of both tumors, and the lack of favorable response to pharmacotherapy, either classical chemotherapy or modern targeted therapy, account for the poor outcome of these patients. Alternative splicing (AS) during pre-mRNA maturation results in changes that might affect proteins involved in different aspects of cancer biology, such as cell cycle dysregulation, cytoskeleton disorganization, migration, and adhesion, which favors carcinogenesis, tumor promotion, and progression, allowing cancer cells to escape from pharmacological treatments. Reasons accounting for cancer-associated aberrant splicing include mutations that create or disrupt splicing sites or splicing enhancers or silencers, abnormal expression of splicing factors, and impaired signaling pathways affecting the activity of the splicing machinery. Here we have reviewed the available information regarding the impact of AS on liver carcinogenesis and the development of malignant characteristics of HCC and iCCA, whose understanding is required to develop novel therapeutical approaches aimed at manipulating the phenotype of cancer cells.

The oncogenic kinase NEK2 regulates an RBFOX2-dependent pro-mesenchymal splicing program in triple-negative breast cancer cells

BACKGROUND

Triple-negative breast cancer (TNBC) is the most heterogeneous and malignant subtype of breast cancer (BC). TNBC is defined by the absence of expression of estrogen, progesterone and HER2 receptors and lacks efficacious targeted therapies. NEK2 is an oncogenic kinase that is significantly upregulated in TNBC, thereby representing a promising therapeutic target. NEK2 localizes in the nucleus and promotes oncogenic splice variants in different cancer cells. Notably, alternative splicing (AS) dysregulation has recently emerged as a featuring trait of TNBC that contributes to its aggressive phenotype.

METHODS

To investigate whether NEK2 modulates TNBC transcriptome we performed RNA-sequencing analyses in a representative TNBC cell line (MDA-MB-231) and results were validated in multiple TNBC cell lines. Bioinformatics and functional analyses were carried out to elucidate the mechanism of splicing regulation by NEK2. Data from The Cancer Genome Atlas were mined to evaluate the potential of NEK2-sensitive exons as markers to identify the TNBC subtype and to assess their prognostic value.

RESULTS

Transcriptome analysis revealed a widespread impact of NEK2 on the transcriptome of TNBC cells, with 1830 AS events that are susceptible to its expression. NEK2 regulates the inclusion of cassette exons in splice variants that discriminate TNBC from other BC and that correlate with poor prognosis, suggesting that this kinase contributes to the TNBC-specific splicing program. NEK2 elicits its effects by modulating the expression of the splicing factor RBFOX2, a well-known regulator of epithelial to mesenchymal transition (EMT). Accordingly, NEK2 splicing-regulated genes are enriched in functional terms related to cell adhesion and contractile cytoskeleton and NEK2 depletion in mesenchymal TNBC cells induces phenotypic and molecular traits typical of epithelial cells. Remarkably, depletion of select NEK2-sensitive splice-variants that are prognostic in TNBC patients is sufficient to interfere with TNBC cell morphology and motility, suggesting that NEK2 orchestrates a pro-mesenchymal splicing program that modulates migratory and invasive properties of TNBC cells.

CONCLUSIONS

Our study uncovers an extensive splicing program modulated by NEK2 involving splice variants that confer an invasive phenotype to TNBCs and that might represent, together with NEK2 itself, valuable therapeutic targets for this disease.

3D RNA-seq: a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists

RNA-sequencing (RNA-seq) analysis of gene expression and alternative splicing should be routine and robust but is often a bottleneck for biologists because of different and complex analysis programs and reliance on specialized bioinformatics skills. We have developed the '3D RNA-seq' App, an R shiny App and web-based pipeline for the comprehensive analysis of RNA-seq data from any organism. It represents an easy-to-use, flexible and powerful tool for analysis of both gene and transcript-level gene expression to identify differential gene/transcript expression, differential alternative splicing and differential transcript usage (3D) as well as isoform switching from RNA-seq data. 3D RNA-seq integrates state-of-the-art differential expression analysis tools and adopts best practice for RNA-seq analysis. The program is designed to be run by biologists with minimal bioinformatics experience (or by bioinformaticians) allowing lab scientists to analyse their RNA-seq data. It achieves this by operating through a user-friendly graphical interface which automates the data flow through the programs in the pipeline. The comprehensive analysis performed by 3D RNA-seq is extremely rapid and accurate, can handle complex experimental designs, allows user setting of statistical parameters, visualizes the results through graphics and tables, and generates publication quality figures such as heat-maps, expression profiles and GO enrichment plots. The utility of 3D RNA-seq is illustrated by analysis of data from a time-series of cold-treated Arabidopsis plants and from dexamethasone-treated male and female mouse cortex and hypothalamus data identifying dexamethasone-induced sex- and brain region-specific differential gene expression and alternative splicing.

3D RNA-seq: a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists

RNA-sequencing (RNA-seq) analysis of gene expression and alternative splicing should be routine and robust but is often a bottleneck for biologists because of different and complex analysis programs and reliance on specialized bioinformatics skills. We have developed the '3D RNA-seq' App, an R shiny App and web-based pipeline for the comprehensive analysis of RNA-seq data from any organism. It represents an easy-to-use, flexible and powerful tool for analysis of both gene and transcript-level gene expression to identify differential gene/transcript expression, differential alternative splicing and differential transcript usage (3D) as well as isoform switching from RNA-seq data. 3D RNA-seq integrates state-of-the-art differential expression analysis tools and adopts best practice for RNA-seq analysis. The program is designed to be run by biologists with minimal bioinformatics experience (or by bioinformaticians) allowing lab scientists to analyse their RNA-seq data. It achieves this by operating through a user-friendly graphical interface which automates the data flow through the programs in the pipeline. The comprehensive analysis performed by 3D RNA-seq is extremely rapid and accurate, can handle complex experimental designs, allows user setting of statistical parameters, visualizes the results through graphics and tables, and generates publication quality figures such as heat-maps, expression profiles and GO enrichment plots. The utility of 3D RNA-seq is illustrated by analysis of data from a time-series of cold-treated Arabidopsis plants and from dexamethasone-treated male and female mouse cortex and hypothalamus data identifying dexamethasone-induced sex- and brain region-specific differential gene expression and alternative splicing.

Impact of alternative splicing on mechanisms of resistance to anticancer drugs

A shared characteristic of many tumors is the lack of response to anticancer drugs. Multiple mechanisms of pharmacoresistance (MPRs) are involved in permitting cancer cells to overcome the effect of these agents. Pharmacoresistance can be primary (intrinsic) or secondary (acquired), i.e., triggered or enhanced in response to the treatment. Moreover, MPRs usually result in the lack of sensitivity to several agents, which accounts for diverse multidrug-resistant (MDR) phenotypes. MPRs are based on the dynamic expression of more than one hundred genes, constituting the so-called resistome. Alternative splicing (AS) during pre-mRNA maturation results in changes affecting proteins involved in the resistome. The resulting splicing variants (SVs) reduce the efficacy of anticancer drugs by lowering the intracellular levels of active agents, altering molecular targets, enhancing both DNA repair ability and defensive mechanism of tumors, inducing changes in the balance between pro-survival and pro-apoptosis signals, modifying interactions with the tumor microenvironment, and favoring malignant phenotypic transitions. Reasons accounting for cancer-associated aberrant splicing include mutations that create or disrupt splicing sites or splicing enhancers or silencers, abnormal expression of splicing factors, and impaired signaling pathways affecting the activity of the splicing machinery. Here we have reviewed the impact of AS on MPR in cancer cells.

Diverse Neoantigens and the Development of Cancer Therapies

Cancer is the manifestation of uncontrolled cellular growth and immune escape mechanisms. Unrestrained tumor growth can be associated with incidental errors in the genome during replication and genotoxic agents can alter the structure and sequence of our DNA. Among all genetic aberrations in cancer, only limited number of mutations can produce immunogenic antigens which have the potential to bind human leukocyte antigen class I or human leukocyte antigen class II, and help activate the adaptive immune system. These neoantigens can be recognized by CD8⁺ and CD4⁺ neoantigen-specific T lymphocytes. Recently, several immune checkpoint targeting drugs have been approved for clinical use. Primarily, these drugs expand and facilitate the cytotoxic activity of neoantigen-specific T cells to eradicate tumors. Differential drug response across cancers could be attributed, at least in part, to differences in the 'tumor antigen landscape' and 'antigen presentation pathway' in patients. Although tumor mutational burden correlates with response to immune checkpoint inhibitors in many cancer types and has evolved as a broad biomarker, a comprehensive understanding of the neoantigen landscape and the function of cognate T cell responses is lacking and is needed for improved patient selection criteria and neoantigen vaccine design. Here, we review cancer neoantigens, their implications for antitumor responses, the dynamics of neoantigen-specific T cells, and the advancement of neoantigen-based therapy in proposed clinical trials.

A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy

Genetic splice variants have become of central interest in recent years, as they play an important role in different cancers. Little is known about splice variants in melanoma. Here, we analyzed a genome-wide transcriptomic dataset of benign melanocytic nevi and primary melanomas (n = 80) for the expression of specific splice variants. Using kallisto, a map for differentially expressed splice variants in melanoma vs. benign melanocytic nevi was generated. Among the top genes with differentially expressed splice variants were Ras-related in brain 6B (RAB6B), a member of the RAS family of GTPases, Macrophage Scavenger Receptor 1 (MSR1), Collagen Type XI Alpha 2 Chain (COLL11A2), and LY6/PLAUR Domain Containing 1 (LYPD1). The Gene Ontology terms of differentially expressed splice variants showed no enrichment for functional gene sets of melanoma vs. nevus lesions, but between type 1 (pigmentation type) and type 2 (immune response type) melanocytic lesions. A number of genes such as Checkpoint Kinase 1 (CHEK1) showed an association of mutational patterns and occurrence of splice variants in melanoma. Moreover, mutations in genes of the splicing machinery were common in both benign nevi and melanomas, suggesting a common mechanism starting early in melanoma development. Mutations in some of these genes of the splicing machinery, such as Serine and Arginine Rich Splicing Factor A3 and B3 (SF3A3, SF3B3), were significantly enriched in melanomas as compared to benign nevi. Taken together, a map of splice variants in melanoma is presented that shows a multitude of differentially expressed splice genes between benign nevi and primary melanomas. The underlying mechanisms may involve mutations in genes of the splicing machinery.

SUVA: splicing site usage variation analysis from RNA-seq data reveals highly conserved complex splicing biomarkers in liver cancer

Most of the current alternative splicing (AS) analysis tools are powerless to analyse complex splicing. To address this, we developed SUVA (Splice sites Usage Variation Analysis) that decomposes complex splicing events into five types of splice junction pairs. By analysing real and simulated data, SUVA showed higher sensitivity and accuracy in detecting AS events than the compared methods. Notably, SUVA detected extensive complex AS events and screened out 69 highly conserved and dominant AS events associated with cancer. The cancer-associated complex AS events in FN1 and the co-regulated RNA-binding proteins were significantly correlated with patient survival.

HNRNPM controls circRNA biogenesis and splicing fidelity to sustain cancer cell fitness

High spliceosome activity is a dependency for cancer cells, making them more vulnerable to perturbation of the splicing machinery compared to normal cells. To identify splicing factors important for prostate cancer (PCa) fitness, we performed pooled shRNA screens in vitro and in vivo. Our screens identified heterogeneous nuclear ribonucleoprotein M (HNRNPM) as a regulator of PCa cell growth. RNA- and eCLIP-sequencing identified HNRNPM binding to transcripts of key homeostatic genes. HNRNPM binding to its targets prevents aberrant exon inclusion and backsplicing events. In both linear and circular mis-spliced transcripts, HNRNPM preferentially binds to GU-rich elements in long flanking proximal introns. Mimicry of HNRNPM-dependent linear-splicing events using splice-switching-antisense-oligonucleotides was sufficient to inhibit PCa cell growth. This suggests that PCa dependence on HNRNPM is likely a result of mis-splicing of key homeostatic coding and non-coding genes. Our results have further been confirmed in other solid tumors. Taken together, our data reveal a role for HNRNPM in supporting cancer cell fitness. Inhibition of HNRNPM activity is therefore a potential therapeutic strategy in suppressing growth of PCa and other solid tumors.

The roles and prognostic significance of ABI1-TSV-11 expression in patients with left-sided colorectal cancer

Abnormally expressed and/or phosphorylated Abelson interactor 1 (ABI1) participates in the metastasis and progression of colorectal cancer (CRC). ABI1 presents as at least 12 transcript variants (TSVs) by mRNA alternative splicing, but it is unknown which of them is involved in CRC metastasis and prognosis. Here, we firstly identified ABI1-TSV-11 as a key TSV affecting the metastasis and prognosis of left-sided colorectal cancer (LsCC) and its elevated expression is related to lymph node metastasis and shorter overall survival (OS) in LsCC by analyzing data from The Cancer Genome Atlas and TSVdb. Secondly, ABI1-TSV-11 overexpression promoted LoVo and SW480 cells adhesion and migration in vitro, and accelerated LoVo and SW480 cells lung metastasis in vivo. Finally, mechanism investigations revealed that ABI1-isoform-11 interacted with epidermal growth factor receptor pathway substrate 8 (ESP8) and regulated actin dynamics to affect LoVo and SW480 cells biological behaviors. Taken together, our data demonstrated that ABI1-TSV-11 plays an oncogenic role in LsCC, it is an independent risk factor of prognosis and may be a potential molecular marker and therapeutic target in LsCC.

Abelson interactor 1 splice isoform-L plays an anti-oncogenic role in colorectal carcinoma through interactions with WAVE2 and full-length Abelson interactor 1

BACKGROUND

Expression of the full-length isoform of Abelson interactor 1 (ABI1), ABI1-p65, is increased in colorectal carcinoma (CRC) and is thought to be involved in one or more steps leading to tumor progression or metastasis. The ABI1 splice isoform-L (ABI1-SiL) has conserved WAVE2-binding and SH3 domains, lacks the homeo-domain homologous region, and is missing the majority of PxxP- and Pro-rich domains found in full-length ABI1-p65. Thus, ABI1-SiL domain structure suggests that the protein may regulate CRC cell morphology, adhesion, migration, and metastasis via interactions with the WAVE2 complex pathway.

AIM

To investigate the potential role and underlying mechanisms associated with ABI1-SiL-mediated regulation of CRC.

METHODS

ABI1-SiL mRNA expression in CC tissue and cell lines was measured using both qualitative reverse transcriptase-polymerase chain reaction (RT-PCR) and real-time quantitative RT-PCR. A stably ABI1-SiL overexpressing SW480 cell model was constructed using Lipofectamine 2000, and cells selected with G418. Image J software, CCK8, and transwell assays were used to investigate SW480 cell surface area, proliferation, migration, and invasion. Immunoprecipitation, Western blot, and co-localization assays were performed to explore intermolecular interactions between ABI1-SiL, WAVE2, and ABI1-p65 proteins.

RESULTS

ABI1-SiL was expressed in normal colon tissue and was significantly decreased in CRC cell lines and tissues. Overexpression of ABI1-SiL in SW480 cells significantly increased the cell surface area and inhibited the adhesive and migration properties of the cells, but did not alter their invasive capacity. Similar to ABI1-p65, ABI1-SiL still binds WAVE2, and the ABI1-p65 isoform in SW480 cells. Furthermore, co-localization assays confirmed these intermolecular interactions.

CONCLUSION

These results support a model in which ABI1-SiL plays an anti-oncogenic role by competitively binding to WAVE2 and directly interacting with phosphorylated and non-phosphorylated ABI1-p65, functioning as a dominant-negative form of ABI1-p65.

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.

Alternative splicing perturbation landscape identifies RNA binding proteins as potential therapeutic targets in cancer

Alternative splicing (AS) plays an important role in gene regulation, and AS perturbations are frequently observed in cancer. RNA binding protein (RBP) is one of the molecular determinants of AS, and perturbations in RBP-gene network activity are causally associated with cancer development. Here, we performed a systematic analysis to characterize the perturbations in AS events across 18 cancer types. We showed that AS alterations were prevalent in cancer and involved in cancer-related pathways. Given that the extent of AS perturbation was associated with disease severity, we proposed a computational pipeline to identify RBP regulators. Pan-cancer analysis identified a number of conserved RBP regulators, which play important roles in regulating AS of genes involved in cancer hallmark pathways. Our application analysis revealed that the expression of 68 RBP regulators helped in cancer subtyping. Specifically, we identified four subtypes of kidney cancer with differences in cancer hallmark pathway activities and prognosis. Finally, we identified the small molecules that can potentially target the RBP genes and suggested potential candidates for cancer therapy. In summary, our comprehensive AS perturbation landscape analysis identified RBPs as potential therapeutic targets in cancer and provided novel insights into the regulatory functions of RBPs in cancer.

Novel insights into clear cell renal cell carcinoma prognosis by comprehensive characterization of aberrant alternative splicing signature: a study based on large-scale sequencing data

Clear cell renal cell carcinoma (ccRCC) is the most common type with poor prognosis in kidney tumor. Growing evidence has indicated that aberrant alternative splicing (AS) events are efficacious signatures for tumor prognosis prediction and therapeutic targets. However, the detailed roles of AS events in ccRCC are largely unknown. In our study, level 3 RNA-seq data was acquired from The Cancer Genome Atlas dataset and corresponding AS profiles were detected with the assistance of SpliceSeq software. A total of 2100 aberrant survival-associated AS events were identified via differential expression and univariate cox regression analysis. The final prognostic panel formed by 17 specific events was developed by stepwise least absolute shrinkage and selection operator (LASSO) penalty, with the area under curve (AUC) values of receiver operator characteristic (ROC) curves keeping above 0.7 spanning 1 year to 5 years. And the results from functional enrichment analyses are unanimous that autophagy could be a potential mechanism of splicing regulation in ccRCC. Furthermore, splicing regulatory network was constructed via Spearman correlation between splicing factors and AS events. Finally, unsupervised clustering analysis revealed three clusters with distinct survival patterns, and associated with specific clinicopathological phenotypes. In overall, we developed a robust and individualized predictive model based on large-scale sequencing data. The identified AS events and splicing network may be valuable in deciphering the crucial posttranscriptional mechanisms on tumorigenesis of ccRCC.

Differential alternative RNA splicing and transcription events between tumors from African American and White patients in The Cancer Genome Atlas

Individuals of African ancestry suffer disproportionally from higher incidence, aggressiveness, and mortality for particular cancers. This disparity likely results from an interplay among differences in multiple determinants of health, including differences in tumor biology. We used The Cancer Genome Atlas (TCGA) SpliceSeq and TCGA aggregate expression datasets and identified differential alternative RNA splicing and transcription events (ARS/T) in cancers between self-identified African American (AA) and White (W) patients. We found that retained intron events were enriched among race-related ARS/T. In addition, on average, 12% of the most highly ranked race-related ARS/T overlapped between any two analyzed cancers. Moreover, the genes undergoing race-related ARS/T functioned in cancer-promoting pathways, and a number of race-related ARS/T were associated with patient survival. We built a web-application, CanSplice, to mine genomic datasets by self-identified race. The race-related targets have the potential to aid in the development of new biomarkers and therapeutics to mitigate cancer disparity.

Silencing of SRRM4 suppresses microexon inclusion and promotes tumor growth across cancers

RNA splicing is widely dysregulated in cancer, frequently due to altered expression or activity of splicing factors (SFs). Microexons are extremely small exons (3–27 nucleotides long) that are highly evolutionarily conserved and play critical roles in promoting neuronal differentiation and development. Inclusion of microexons in mRNA transcripts is mediated by the SF Serine/Arginine Repetitive Matrix 4 (SRRM4), whose expression is largely restricted to neural tissues. However, microexons have been largely overlooked in prior analyses of splicing in cancer, as their small size necessitates specialized computational approaches for their detection. Here, we demonstrate that despite having low expression in normal nonneural tissues, SRRM4 is further silenced in tumors, resulting in the suppression of normal microexon inclusion. Remarkably, SRRM4 is the most consistently silenced SF across all tumor types analyzed, implying a general advantage of microexon down-regulation in cancer independent of its tissue of origin. We show that this silencing is favorable for tumor growth, as decreased SRRM4 expression in tumors is correlated with an increase in mitotic gene expression, and up-regulation of SRRM4 in cancer cell lines dose-dependently inhibits proliferation in vitro and in a mouse xenograft model. Further, this proliferation inhibition is accompanied by induction of neural-like expression and splicing patterns in cancer cells, suggesting that SRRM4 expression shifts the cell state away from proliferation and toward differentiation. We therefore conclude that SRRM4 acts as a proliferation brake, and tumors gain a selective advantage by cutting off this brake.

Using data from The Cancer Genome Atlas, this study shows that the splicing factor SRRM4 and its program of differentiation-promoting microexons are downregulated across tumor types with remarkable consistency, providing tumors with a proliferative advantage.

Identification of Mutator-Derived Alternative Splicing Signatures of Genomic Instability for Improving the Clinical Outcome of Cholangiocarcinoma

BACKGROUND

Cholangiocarcinoma is an aggressive carcinoma with increasing incidence and poor outcomes worldwide. Genomic instability and alternative splicing (AS) events are hallmarks of carcinoma development and progression. The relationship between genomic instability, AS events, and tumor immune microenvironment remain unclear.

METHODS

The splicing profiles of patients with cholangiocarcinoma were obtained from The Cancer Genome Atlas (TCGA) spliceSeq database. The transcriptomics, simple nucleotide variation (SNP) and clinical data of patients with cholangiocarcinoma were obtained from TCGA database. Patients were divided into genomic unstable (GU-like) and genomic stable (GS-like) groups according to their somatic mutations. Survival-related differential AS events were identified through integrated analysis of splicing profiling and clinical data. Kyoto Encyclopedia of Genes and Genomes enrichment analysis was used to identify AS events occurring in genes enriched in cancer pathways. Pearson correlation was applied to analyze the splicing factors regulating AS events. CIBERSORT was used identify differentially infiltrating immune cells.

RESULTS

A prognostic signature was constructed with six AS events. Using this signature, the hazard ratio of risk score for overall survival is 2.362. For TCGA patients with cholangiocarcinoma, the area under the receiver operating characteristic curve is 0.981. CDK11A is a negative regulator of survival associated AS events. Additionally, the CD8+ T cell proportion and PD-L1 expression are upregulated in patients with cholangiocarcinoma and high splicing signatures.

CONCLUSION

We provide a prognostic signature for cholangiocarcinoma overall survival. The CDK11A splicing factor and SLC46A1-39899-ES and IARS-86836-ES AS events may be potential targets for cholangiocarcinoma therapy. Patients with high AS risk score may be more sensitive to anti-PD-L1/PD1 immunotherapy.

Clusterization in acute myeloid leukemia based on prognostic alternative splicing signature to reveal the clinical characteristics in the bone marrow microenvironment

Background

Alternative splicing (AS), a crucial post-transcriptional regulatory mechanism in expanding the coding capacities of genomes and increasing the diversity of proteins, still faces various challenges in the splicing regulation mechanism of acute myeloid leukemia (AML) and microenvironmental changes.

Results

A total of 27,833 AS events were detected in 8337 genes in 178 AML patients, with exon skip being the predominant type. Approximately 11% of the AS events were significantly related to prognosis, and the prediction models based on various events demonstrated high classification efficiencies. Splicing factors correlation networks further altered the diversity of AS events through epigenetic regulation and clarified the potential mechanism of the splicing pathway. Unsupervised cluster analysis revealed significant correlations between AS and immune features, molecular mutations, immune checkpoints and clinical outcome. The results suggested that AS clusters could be used to identify patient subgroups with different survival outcomes in AML, among which C1 was both associated with good outcome in overall survival. Interestingly, C1 was associated with lower immune scores compared with C2 and C3, and favorable-risk cytogenetics was rarely distributed in C2, but much more common in C1.

Conclusions

This study revealed a comprehensive landscape of AS events, and provides new insight into molecular targeted therapy and immunotherapy strategy for AML.

Two-step mixed model approach to analyzing differential alternative RNA splicing

Changes in gene expression can correlate with poor disease outcomes in two ways: through changes in relative transcript levels or through alternative RNA splicing leading to changes in relative abundance of individual transcript isoforms. The objective of this research is to develop new statistical methods in detecting and analyzing both differentially expressed and spliced isoforms, which appropriately account for the dependence between isoforms and multiple testing corrections for the multi-dimensional structure of at both the gene- and isoform- level. We developed a linear mixed effects model-based approach for analyzing the complex alternative RNA splicing regulation patterns detected by whole-transcriptome RNA-sequencing technologies. This approach thoroughly characterizes and differentiates three types of genes related to alternative RNA splicing events with distinct differential expression/splicing patterns. We applied the concept of appropriately controlling for the gene-level overall false discovery rate (OFDR) in this multi-dimensional alternative RNA splicing analysis utilizing a two-step hierarchical hypothesis testing framework. In the initial screening test we identify genes that have differentially expressed or spliced isoforms; in the subsequent confirmatory testing stage we examine only the isoforms for genes that have passed the screening tests. Comparisons with other methods through application to a whole transcriptome RNA-Seq study of adenoid cystic carcinoma and extensive simulation studies have demonstrated the advantages and improved performances of our method. Our proposed method appropriately controls the gene-level OFDR, maintains statistical power, and is flexible to incorporate advanced experimental designs.

Pathogenic impact of transcript isoform switching in 1,209 cancer samples covering 27 cancer types using an isoform-specific interaction network

Under normal conditions, cells of almost all tissue types express the same predominant canonical transcript isoform at each gene locus. In cancer, however, splicing regulation is often disturbed, leading to cancer-specific switches in the most dominant transcripts (MDT). To address the pathogenic impact of these switches, we have analyzed isoform-specific protein-protein interaction disruptions in 1,209 cancer samples covering 27 different cancer types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project of the International Cancer Genomics Consortium (ICGC). Our study revealed large variations in the number of cancer-specific MDT (cMDT) with the highest frequency in cancers of female reproductive organs. Interestingly, in contrast to the mutational load, cancers arising from the same primary tissue had a similar number of cMDT. Some cMDT were found in 100% of all samples in a cancer type, making them candidates for diagnostic biomarkers. cMDT tend to be located at densely populated network regions where they disrupted protein interactions in the proximity of pathogenic cancer genes. A gene ontology enrichment analysis showed that these disruptions occurred mostly in protein translation and RNA splicing pathways. Interestingly, samples with mutations in the spliceosomal complex tend to have higher number of cMDT, while other transcript expressions correlated with mutations in non-coding splice-site and promoter regions of their genes. This work demonstrates for the first time the large extent of cancer-specific alterations in alternative splicing for 27 different cancer types. It highlights distinct and common patterns of cMDT and suggests novel pathogenic transcripts and markers that induce large network disruptions in cancers.

A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis

Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining such splicing components, however, is not a straightforward task mainly due to the heterogeneity of tumors, the variability across samples, and the fat-short characteristic of genomic datasets. In this work, a supervised machine learning-based methodology is proposed, allowing the determination of subsets of relevant splicing components that best discriminate samples. The methodology comprises three main phases: first, a ranking of features is determined by means of applying feature weighting algorithms that compute the importance of each splicing component; second, the best subset of features that allows the induction of an accurate classifier is determined by means of conducting an effective heuristic search; then the confidence over the induced classifier is assessed by means of explaining the individual predictions and its global behavior. At the end, an extensive experimental study was conducted on a large collection of transcript-based datasets, illustrating the utility and benefit of the proposed methodology for analyzing dysregulation in splicing machinery.

Splicing Regulator p54nrb /Non-POU Domain-Containing Octamer-Binding Protein Enhances Carcinogenesis Through Oncogenic Isoform Switch of MYC Box-Dependent Interacting Protein 1 in Hepatocellular Carcinoma

BACKGROUND AND AIMS

Alternative splicing (AS) is a key step that increases the diversity and complexity of the cancer transcriptome. Recent evidence has highlighted that AS has an increasingly crucial role in cancer. Nonetheless, the mechanisms underlying AS and its dysregulation in hepatocellular carcinoma (HCC) remain elusive. Here, we report that the expression of RNA-binding protein p54^nrb /non-POU domain-containing octamer-binding protein (NONO) is frequently increased in patients with HCC and is associated with poor outcomes.

APPROACH AND RESULTS

Knockdown of NONO significantly abolished liver cancer cell proliferation, migration, and tumor formation. RNA-sequencing revealed that NONO regulates MYC box-dependent interacting protein 1 (or bridging integrator 1 [BIN1]; also known as amphiphysin 2 3P9) exon 12a splicing. In the normal liver, BIN1 generates a short isoform (BIN1-S) that acts as a tumor suppressor by inhibiting the binding of c-Myc to target gene promoters. In HCC, NONO is highly up-regulated and produces a long isoform (BIN1-L, which contains exon 12a) instead of BIN1-S. High levels of BIN1-L promote carcinogenesis by binding with the protein polo-like kinase 1 to enhance its stability through the prevention of ubiquitin/proteasome-dependent cullin 3 degradation. Further analysis revealed that NONO promotes BIN1 exon 12a inclusion through interaction with DExH-box helicase 9 (DHX9) and splicing factor proline and glutamine-rich (SFPQ). Notably, frequent coexpression of DHX9-NONO-SFPQ is observed in patients with HCC.

CONCLUSIONS

Taken together, our findings identify the DHX9-NONO-SFPQ complex as a key regulator manipulating the oncogenic splicing switch of BIN1 and as a candidate therapeutic target in liver cancer.

Exploring transcriptional switches from pairwise, temporal and population RNA-Seq data using deepTS

Transcriptional switch (TS) is a widely observed phenomenon caused by changes in the relative expression of transcripts from the same gene, in spatial, temporal or other dimensions. TS has been associated with human diseases, plant development and stress responses. Its investigation is often hampered by a lack of suitable tools allowing comprehensive and flexible TS analysis for high-throughput RNA sequencing (RNA-Seq) data. Here, we present deepTS, a user-friendly web-based implementation that enables a fully interactive, multifunctional identification, visualization and analysis of TS events for large-scale RNA-Seq datasets from pairwise, temporal and population experiments. deepTS offers rich functionality to streamline RNA-Seq-based TS analysis for both model and non-model organisms and for those with or without reference transcriptome. The presented case studies highlight the capabilities of deepTS and demonstrate its potential for the transcriptome-wide TS analysis of pairwise, temporal and population RNA-Seq data. We believe deepTS will help research groups, regardless of their informatics expertise, perform accessible, reproducible and collaborative TS analyses of large-scale RNA-Seq data.

HBA-DEALS: accurate and simultaneous identification of differential expression and splicing using hierarchical Bayesian analysis

We present Hierarchical Bayesian Analysis of Differential Expression and ALternative Splicing (HBA-DEALS), which simultaneously characterizes differential expression and splicing in cohorts. HBA-DEALS attains state of the art or better performance for both expression and splicing and allows genes to be characterized as having differential gene expression, differential alternative splicing, both, or neither. HBA-DEALS analysis of GTEx data demonstrated sets of genes that show predominant DGE or DAST across multiple tissue types. These sets have pervasive differences with respect to gene structure, function, membership in protein complexes, and promoter architecture.

Reference genome and annotation updates lead to contradictory prognostic predictions in gene expression signatures: a case study of resected stage I lung adenocarcinoma

RNA-sequencing enables accurate and low-cost transcriptome-wide detection. However, expression estimates vary as reference genomes and gene annotations are updated, confounding existing expression-based prognostic signatures. Herein, prognostic 9-gene pair signature (GPS) was applied to 197 patients with stage I lung adenocarcinoma derived from previous and latest data from The Cancer Genome Atlas (TCGA) processed with different reference genomes and annotations. For 9-GPS, 6.6% of patients exhibited discordant risk classifications between the two TCGA versions. Similar results were observed for other prognostic signatures, including IRGPI, 15-gene and ORACLE. We found that conflicting annotations for gene length and overlap were the major cause of their discordant risk classification. Therefore, we constructed a prognostic 40-GPS based on stable genes across GENCODE v20-v30 and validated it using public data of 471 stage I samples (log-rank P < 0.0010). Risk classification was still stable in RNA-sequencing data processed with the newest GENCODE v32 versus GENCODE v20-v30. Specifically, 40-GPS could predict survival for 30 stage I samples with formalin-fixed paraffin-embedded tissues (log-rank P = 0.0177). In conclusion, this method overcomes the vulnerability of existing prognostic signatures due to reference genome and annotation updates. 40-GPS may offer individualized clinical applications due to its prognostic accuracy and classification stability.

Ribosome profiling at isoform level reveals evolutionary conserved impacts of differential splicing on the proteome

The differential production of transcript isoforms from gene loci is a key cellular mechanism. Yet, its impact in protein production remains an open question. Here, we describe ORQAS (ORF quantification pipeline for alternative splicing), a pipeline for the translation quantification of individual transcript isoforms using ribosome-protected mRNA fragments (ribosome profiling). We find evidence of translation for 40-50% of the expressed isoforms in human and mouse, with 53% of the expressed genes having more than one translated isoform in human, and 33% in mouse. Differential splicing analysis revealed that about 40% of the splicing changes at RNA level are concordant with changes in translation. Furthermore, orthologous cassette exons between human and mouse preserve the directionality of the change, and are enriched in microexons in a comparison between glia and glioma. ORQAS leverages ribosome profiling to uncover a widespread and evolutionarily conserved impact of differential splicing on translation, particularly of microexon-containing isoforms.

An atlas of transposable element-derived alternative splicing in cancer

Transposable element (TE)-derived sequences comprise more than half of the human genome, and their presence has been documented to alter gene expression in a number of different ways, including the generation of alternatively spliced transcript isoforms. Alternative splicing has been associated with tumorigenesis for a number of different cancers. The objective of this study was to broadly characterize the role of human TEs in generating alternatively spliced transcript isoforms in cancer. To do so, we screened for the presence of TE-derived sequences co-located with alternative splice sites that are differentially used in normal versus cancer tissues. We analysed a comprehensive set of alternative splice variants characterized for 614 matched normal-tumour tissue pairs across 13 cancer types, resulting in the discovery of 4820 TE-generated alternative splice events distributed among 723 cancer-associated genes. Short interspersed nuclear elements (Alu) and long interspersed nuclear elements (L1) were found to contribute the majority of TE-generated alternative splice sites in cancer genes. A number of cancer-associated genes, including MYH11, WHSC1 and CANT1, were shown to have overexpressed TE-derived isoforms across a range of cancer types. TE-derived isoforms were also linked to cancer-specific fusion transcripts, suggesting a novel mechanism for the generation of transcriptome diversity via trans-splicing mediated by dispersed TE repeats. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.