Targeting Splicing in Prostate Cancer

Comprehensive analysis of alternative splicing across multiple transcriptomic cohorts reveals prognostic signatures in prostate cancer

BACKGROUND

Alternative splicing (AS) plays a crucial role in transcriptomic diversity and is a hallmark of cancer that profoundly influences the development and progression of prostate cancer (PCa), a prevalent and potentially life-limiting cancer among men. Accumulating evidence has highlighted the association between AS dysregulation and the onset and progression of PCa. However, a comprehensive and integrative analysis of AS profiles at the event level, utilising data from multiple high-throughput cohorts and evaluating the prognosis of PCa progression, remains lacking and calls for thorough exploration.

RESULTS

We identified a differentially expressed retained intron event in ZWINT across three distinct cohorts, encompassing an original array-based dataset profiled by us previously and two RNA sequencing (RNA-seq) datasets. Subsequent in-depth analyses of these RNA-seq datasets revealed  141 altered events, of which 21 demonstrated a significant association with patients' biochemical recurrence-free survival (BCRFS). We formulated an AS event-based prognostic signature, capturing six pivotal events in genes CYP4F12, NFATC4, PIGO, CYP3A5, ALS2CL, and FXYD3. This signature effectively differentiated  high-risk patients diagnosed with PCa, who experienced shorter BCRFS, from their low-risk counterparts. Notably, the signature's predictive power surpassed traditional clinicopathological markers in forecasting 5-year BCRFS, demonstrating robust performance in both internal and external validation sets. Lastly, we constructed a novel nomogram that integrates patients' Gleason scores with pathological tumour stages, demonstrating improved prognostication of BCRFS.

CONCLUSIONS

Prediction of clinical progression remains elusive in PCa. This research uncovers novel splicing events associated with BCRFS, augmenting existing prognostic tools, thus potentially refining clinical decision-making.

Knowledge mapping of alternative splicing of cancer from 2012 to 2021: A bibliometric analysis

Background

As a processing method of RNA precursors, alternative splicing (AS) is critical to normal cellular activities. Aberrant AS events are associated with cancer development and can be promising targets to treat cancer. However, no detailed and unbiased study describes the current state of AS of cancer research. We aim to measure and recognize the current state and trends of AS cancer research in this study.

Methods

The Web of Science Core Collection was used to acquire the articles. Utilizing three bibliometric tools (CiteSpace, VOSviewer, R-bibliometrix), we were able to measure and recognize the influence and collaboration data of individual articles, journals, and co-citations. Analysis of co-occurrence and burst information helped us identify the trending research areas related to AS of cancer.

Results

From 2012 to 2021, the total number of papers on AS of cancer published in 766 academic journals was 3,507, authored by 20,406 researchers in 405 institutions from 80 countries/regions. Research involving AS of cancer genes was primarily conducted in the United States and China; simultaneously, the Chinese Academy of Sciences, Fudan University, and National Cancer Institute were the institutions with strong research capabilities. Scorilas Andreas is the scholar with the most publications, while the most co-citations were generated by Wang, Eric T. Plos One published the most papers on AS of cancer, while J Biol Chem was the most co-cited academic journal in this field. The results of keyword co-occurrence analysis can be divided into three types: molecular (P53, CD44, androgen receptor, srsf3, esrp1), pathological process (apoptosis, EMT, metastasis, angiogenesis, proliferation), and disease (breast cancer, colorectal cancer, prostate cancer, hepatocellular carcinoma, gastric cancer).

Conclusion

Research on AS of cancer has been increasing in intensity over the past decade. Current AS of cancer studies focused on the hallmarks of AS in cancer and AS signatures including diagnostic and therapeutic targets. Among them, the current trends are splicing factors regulating epithelial-mesenchymal transition and other hallmarks, aberrant splicing events in tumors, and further mechanisms. These might give researchers interested in this field a forward-looking perspective and inform further research.

Gene expression analysis reveals a 5-gene signature for progression-free survival in prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide, but effective biomarkers for the presence or progression risk of disease are currently elusive. In a series of nine matched histologically confirmed PCa and benign samples, we carried out an integrated transcriptome-wide gene expression analysis, including differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), which identified a set of potential gene markers highly associated with tumour status (malignant vs. benign). We then used these genes to establish a minimal progression-free survival (PFS)-associated gene signature (GS) (PCBP1, PABPN1, PTPRF, DANCR, and MYC) using least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) dataset. Our signature was able to predict PFS over 1, 3, and 5 years in TCGA-PRAD dataset, with area under the curve (AUC) of 0.64–0.78, and our signature remained as a prognostic factor independent of age, Gleason score, and pathological T and N stages. A nomogram combining the signature and Gleason score demonstrated improved predictive capability for PFS (AUC: 0.71–0.85) and was superior to the Cambridge Prognostic Group (CPG) model alone and some conventionally used clinicopathological factors in predicting PFS. In conclusion, we have identified and validated a novel five-gene signature and established a nomogram that effectively predicted PFS in patients with PCa. Findings may improve current prognosis tools for PFS and contribute to clinical decision-making in PCa treatment.

Targeting signaling pathways in prostate cancer: mechanisms and clinical trials

Prostate cancer (PCa) affects millions of men globally. Due to advances in understanding genomic landscapes and biological functions, the treatment of PCa continues to improve. Recently, various new classes of agents, which include next-generation androgen receptor (AR) signaling inhibitors (abiraterone, enzalutamide, apalutamide, and darolutamide), bone-targeting agents (radium-223 chloride, zoledronic acid), and poly(ADP-ribose) polymerase (PARP) inhibitors (olaparib, rucaparib, and talazoparib) have been developed to treat PCa. Agents targeting other signaling pathways, including cyclin-dependent kinase (CDK)4/6, Ak strain transforming (AKT), wingless-type protein (WNT), and epigenetic marks, have successively entered clinical trials. Furthermore, prostate-specific membrane antigen (PSMA) targeting agents such as ¹⁷⁷Lu-PSMA-617 are promising theranostics that could improve both diagnostic accuracy and therapeutic efficacy. Advanced clinical studies with immune checkpoint inhibitors (ICIs) have shown limited benefits in PCa, whereas subgroups of PCa with mismatch repair (MMR) or CDK12 inactivation may benefit from ICIs treatment. In this review, we summarized the targeted agents of PCa in clinical trials and their underlying mechanisms, and further discussed their limitations and future directions.

Role of alternative splicing events in endometrial cancer prognosis

OBJECTIVES

Alternative splicing (AS), as a potent and pervasive mechanism of transcriptional regulation, can expand the genome's coding capacity. Growing evidence suggests that the AS events may be associated with various types of cancer. This study aims to explore the prognostic value of AS in endometrial cancer (EC).

METHODS

Differently expressed AS (DEAS) events were screened by pairing the percent spliced in (PSI) value of tumor and paracancerous tissues in The Cancer Genome Atlas (TCGA) database, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on their parental gene analysis of organisms. Subsequently, univariate Cox analysis was used to identify the prognostic AS events and a stepwise multi-factor Cox regression analysis was performed to further construct prognostic models. Furthermore, the diagnostic value of the prognostic model was evaluated by receiver operating characteristic (ROC) curve and Kaplan-Meier analysis. Finally, the regulatory network of AS events and splicing factory in the model was also constructed.

RESULTS

A total of 28 281 AS events were detected in EC. Of them, 42 DEAS were identified, and their parental genes were involved in tumor-related processes such as meiotic nuclear division, alpha-amino acid biosynthetic process, nuclear division, and so on. Univariate Cox analysis identified 2 289 prognostic-related AS events and constructed Cox prognostic models based on 7 different types and all types of AS events, in which the area under the curve of ROC of all types was as high as 0.882 and was better than that of 7 different splicing types. Finally, 12 splicing factors and AS events showed an obvious regulatory relationship.

CONCLUSIONS

We use the whole genome analysis of AS events to establish a scientific prognostic prediction model for EC patients, which provides a reliable theoretical basis for the evaluation of EC clinical prognosis.

Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma

Glioblastoma (GBM) is the most aggressive malignant brain tumour, with high morbidity and mortality rates. Currently, there is a lack of systematic and comprehensive analysis on the prognostic significance of alternative splicing (AS) profiling for GBM. The GBM data, including RNA-sequencing, corresponding clinical information and the expression levels of splicing factor genes, were downloaded from The Cancer Genome Atlas and the SpliceAid2 database. The prognostic models were assessed by the least absolute shrinkage and selection operator Cox regression analysis. The correlation network between survival-associated AS events and splicing factors was plotted. Prognostic models were built for every AS event type and performed well for risk stratification in patients with GBM. The final prognostic signature served as an independent prognostic factor [hazard ratio (HR), 4.61; 95% confidence interval (CI), 2.97-7.16; P=9.66×10-12] for several clinical parameters, including age, sex, isocitrate dehydrogenase mutation, O6-methylguanine-DNA methyltransferase promoter methylation and risk score. The HR for risk score with GBM was 1.0063 (95% CI, 1.0024-1.0103). The splicing regulatory network indicated that heat shock protein b-1, protein arginine N-methyltransferase 5, protein FAM50B and endoplasmic reticulum chaperone BiP genes were independent prognostic factors for GBM. The results of the present study support the ongoing effort in developing novel genomic models and providing potentially more effective treatment options for patients with GBM.

Alternative Splicing: Expanding the Landscape of Cancer Biomarkers and Therapeutics

Alternative splicing (AS) is a critical post-transcriptional regulatory mechanism used by more than 95% of transcribed human genes and responsible for structural transcript variation and proteome diversity. In the past decade, genome-wide transcriptome sequencing has revealed that AS is tightly regulated in a tissue- and developmental stage-specific manner, and also frequently dysregulated in multiple human cancer types. It is currently recognized that splicing defects, including genetic alterations in the spliced gene, altered expression of both core components or regulators of the precursor messenger RNA (pre-mRNA) splicing machinery, or both, are major drivers of tumorigenesis. Hence, in this review we provide an overview of our current understanding of splicing alterations in cancer, and emphasize the need to further explore the cancer-specific splicing programs in order to obtain new insights in oncology. Furthermore, we also discuss the recent advances in the identification of dysregulated splicing signatures on a genome-wide scale and their potential use as biomarkers. Finally, we highlight the therapeutic opportunities arising from dysregulated splicing and summarize the current approaches to therapeutically target AS in cancer.

Role of global aberrant alternative splicing events in papillary thyroid cancer prognosis

BACKGROUND

Alternative splicing events have been increasingly reported for anomalous perturbations in various cancers, including papillary thyroid cancer (PTC).

METHODS

Integration analysis of RNA sequencing and clinical information were utilized to identify survival associated splicing events in PTC. Then, several prognosis-related splicing events were submitted to develop moderate predictors for survival monitoring by using least absolute shrinkage and selection operator model. In addition, several biomedical computational algorithms were conducted to identify pathways enriched by genes with prognostic splicing events and construct regulatory network dominated by splicing factors.

RESULTS

Survival analysis in 496 PTC patients indicated that TNM stage, tumor stage, distant metastasis and tumor status were significantly correlated with PTC patients' progression-free interval. 2799 splicing events were identified as prognostic molecular events. Functional enrichment analysis suggested that prognostic splicing events are associated with several energy metabolism-related processes. Based on these prognostic events, several prognostic signatures were developed. The final prognostic signature acted as an independent prognostic factor after adjusting for several clinical parameters. Interestingly, splicing regulatory network was constructed to display potential regulatory mechanisms of splicing events in PTC.

CONCLUSIONS

Our analysis provides the status of splicing events involved in the progression and may represent an underappreciated hallmark of PTC.

Pathway-guided analysis identifies Myc-dependent alternative pre-mRNA splicing in aggressive prostate cancers

We sought to define the landscape of alternative pre-mRNA splicing in prostate cancers and the relationship of exon choice to known cancer driver alterations. To do so, we compiled a metadataset composed of 876 RNA-sequencing (RNA-Seq) samples from five publicly available sources representing a range of prostate phenotypes from normal tissue to drug-resistant metastases. We subjected these samples to exon-level analysis with rMATS-turbo, purpose-built software designed for large-scale analyses of splicing, and identified 13,149 high-confidence cassette exon events with variable incorporation across samples. We then developed a computational framework, pathway enrichment-guided activity study of alternative splicing (PEGASAS), to correlate transcriptional signatures of 50 different cancer driver pathways with these alternative splicing events. We discovered that Myc signaling was correlated with incorporation of a set of 1,039 cassette exons enriched in genes encoding RNA binding proteins. Using a human prostate epithelial transformation assay, we confirmed the Myc regulation of 147 of these exons, many of which introduced frameshifts or encoded premature stop codons. Our results connect changes in alternative pre-mRNA splicing to oncogenic alterations common in prostate and many other cancers. We also establish a role for Myc in regulating RNA splicing by controlling the incorporation of nonsense-mediated decay-determinant exons in genes encoding RNA binding proteins.

Proteomics Analysis of Formalin Fixed Paraffin Embedded Tissues in the Investigation of Prostate Cancer

Prostate cancer (PCa) is one of the leading causes of death in men worldwide. The molecular features, associated with the onset and progression of the disease, are under vigorous investigation. Formalin-fixed paraffin-embedded (FFPE) tissues are valuable resources for large-scale studies; however, their application in proteomics is limited due to protein cross-linking. In this study, the adjustment of a protocol for the proteomic analysis of FFPE tissues was performed which was followed by a pilot application on FFPE PCa clinical samples to investigate whether the optimized protocol can provide biologically relevant data for the investigation of PCa. For the optimization, FFPE mouse tissues were processed using seven protein extraction protocols including combinations of homogenization methods (beads, sonication, boiling) and buffers (SDS based and urea-thiourea based). The proteome extraction efficacy was then evaluated based on protein identifications and reproducibility using SDS electrophoresis and high resolution LC-MS/MS analysis. Comparison between the FFPE and matched fresh frozen (FF) tissues, using an optimized protocol involving protein extraction with an SDS-based buffer following beads homogenization and boiling, showed a substantial overlap in protein identifications with a strong correlation in relative abundances (r_s = 0.819, p < 0.001). Next, FFPE tissues (3 sections, 15 μm each per sample) from 10 patients with PCa corresponding to tumor (GS = 6 or GS ≥ 8) and adjacent benign regions were processed with the optimized protocol. Extracted proteins were analyzed by GeLC-MS/MS followed by statistical and bioinformatics analysis. Proteins significantly deregulated between PCa GS ≥ 8 and PCa GS = 6 represented extracellular matrix organization, gluconeogenesis, and phosphorylation pathways. Proteins deregulated between cancerous and adjacent benign tissues, reflected increased translation, peptide synthesis, and protein metabolism in the former, which is consistent with the literature. In conclusion, the results support the relevance of the proteomic findings in the context of PCa and the reliability of the optimized protocol for proteomics analysis of FFPE material.

Comprehensive investigation of alternative splicing and development of a prognostic risk score for prostate cancer based on six-gene signatures

Purpose: To systematically document alternative splicing profiles of prostate cancer in relatively large populations in order to construct a prognostic predictors model for prostate cancer.

Methods: Splicing data and clinical information of 495 prostate cancer patients were obtained from The Cancer Genome Atlas (TCGA). The SpliceSeq database was used to extract information regarding splicing events. Multiple bioinformatic tools were used for functional and pathway enrichment analysis as well as for construction of gene interaction networks. Candidate gene expression profiles were verified with clinical samples using QRT-PCR.

Results: We detected a total of 44070 alternative splicing events of 10381 genes in prostate cancer. 7 and 14 KEGG pathways were enriched and were associated with overall and recurrence-free survival, respectively. The expression of 396 genes among the 1526 overall survival genes associated alternative splicing events were associated with overall survival. The expression of 483 genes among the 1916 recurrence-free survival genes associated alternative splicing events were associated with recurrence-free survival. Lastly, we constructed the prognosis risk score system based on the expression profiles of six-gene signatures which in combination had an AUC of 0.941 for overall survival associated alternative splicing events, followed by overall survival associated gene expressions with an AUC of 0.794, a recurrence-free survival associated gene expression with an AUC of 0.752 and recurrence-free survival associated alternative splicing events with an AUC of 0.735, indicating its strong ability to predict patient outcome. The expression profile of the six genes was also confirmed in different prostate cell lines and clinic samples.

Conclusion: Our comprehensive investigation of alternative splicing not only provided insight into the biological pathways of alternative splicing involved in the development of prostate cancer but also revealed new potential biomarkers for prognosticating as well as novel therapeutic targets for development of prostate cancer treatment.

Splice-switching small molecules: A new therapeutic approach to modulate gene expression

Manipulating alternative RNA splicing events with small molecules is emerging as a viable mechanism for the development of therapeutics. A salient challenge in the field is understanding the molecular determinants defining the selectivity of splice-switching events and their mechanisms of action. In this review, the current state-of-the-art in splice-switching small molecules is described. Three examples of splice-switching small molecules are presented, and the differences in their modes of action compared.

Alternative splicing in lung cancer

Lung cancer has the highest mortality rate of all cancers worldwide. Lung cancer is a very heterogeneous disease that is often diagnosed at later stages which have a poor prognosis. Aberrant alternative splicing patterns found in lung cancer contribute to important cell functions. These include changes in splicing for the BCL2L1, MDM2, MDM4, NUMB and MET genes during lung tumourigenesis, to affect pathways involved in apoptosis, cell proliferation and cellular cohesion. Global analyses of RNASeq datasets suggest there may be many more potentially influential aberrant splicing events that need to be investigated in lung cancer. Changes in expression of the splicing factors that regulate alternative splicing events have also been identified in lung cancer. Of these, changes in expression of QKI, RBM4, RBM5, RBM6, RBM10 and SRSF1 proteins regulate many of the most frequently referenced aberrant splicing events in lung cancer. The expanding list of genes known to be aberrantly spliced in lung cancer along with the altered expression of splicing factors that regulate them are providing new clues as to how lung cancer develops, and how these events can be exploited for better treatment. This article is part of a Special Issue entitled: RNA structure and splicing regulation edited by Francisco Baralle, Ravindra Singh and Stefan Stamm.

Alternative Acts: Oncogenic Splicing of Steroidogenic Enzymes in Prostate Cancer

Castration-resistant prostate cancer is characterized by loss of the androgen inactivation enzyme HSD17B2, emphasizing the importance of intratumoral androgens in tumor progression. Inactive isoforms generated by alternative splicing destabilize the wild-type enzyme, adding steroidogenesis to other prostate cancer drivers that undergo oncogenic splicing, highlighting aberrant splicing as a therapeutic target.See related article by Gao et al., p. 1291.

Role of Alternative Splicing in Prostate Cancer Aggressiveness and Drug Resistance in African Americans

Alternative splicing, the process of removing introns and joining exons of pre-mRNA, is critical for growth, development, tissue homeostasis, and species diversity. Dysregulation of alternative splicing can initiate and drive disease. Aberrant alternative splicing has been shown to promote the "hallmarks of cancer" in both hematological and solid cancers. Of interest, recent work has focused on the role of alternative splicing in prostate cancer and prostate cancer health disparities. We will provide a review of prostate cancer health disparities involving the African American population, alternative RNA splicing, and alternative splicing in prostate cancer. Lastly, we will summarize our work on differential alternative splicing in prostate cancer disparities and its implications for disparate health outcomes and therapeutic targets.

Canonical and Noncanonical Androgen Metabolism and Activity

Androgens are critical drivers of prostate cancer. In this chapter we first discuss the canonical pathways of androgen metabolism and their alterations in prostate cancer progression, including the classical, backdoor and 5α-dione pathways, the role of pre-receptor DHT metabolism, and recent findings on oncogenic splicing of steroidogenic enzymes. Next, we discuss the activity and metabolism of non-canonical 11-oxygenated androgens that can activate wild-type AR and are less susceptible to glucuronidation and inactivation than the canonical androgens, thereby serving as an under-recognized reservoir of active ligands. We then discuss an emerging literature on the potential non-canonical role of androgen metabolizing enzymes in driving prostate cancer. We conclude by discussing the potential implications of these findings for prostate cancer progression, particularly in context of new agents such as abiraterone and enzalutamide, which target the AR-axis for prostate cancer therapy, including mechanisms of response and resistance and implications of these findings for future therapy.

Aberrant RNA Splicing in Cancer and Drug Resistance

More than 95% of the 20,000 to 25,000 transcribed human genes undergo alternative RNA splicing, which increases the diversity of the proteome. Isoforms derived from the same gene can have distinct and, in some cases, opposing functions. Accumulating evidence suggests that aberrant RNA splicing is a common and driving event in cancer development and progression. Moreover, aberrant splicing events conferring drug/therapy resistance in cancer is far more common than previously envisioned. In this review, aberrant splicing events in cancer-associated genes, namely BCL2L1, FAS, HRAS, CD44, Cyclin D1, CASP2, TMPRSS2-ERG, FGFR2, VEGF, AR and KLF6, will be discussed. Also highlighted are the functional consequences of aberrant splice variants (BCR-Abl35INS, BIM-γ, IK6, p61 BRAF V600E, CD19-∆2, AR-V7 and PIK3CD-S) in promoting resistance to cancer targeted therapy or immunotherapy. To overcome drug resistance, we discuss opportunities for developing novel strategies to specifically target the aberrant splice variants or splicing machinery that generates the splice variants. Therapeutic approaches include the development of splice variant-specific siRNAs, splice switching antisense oligonucleotides, and small molecule inhibitors targeting splicing factors, splicing factor kinases or the aberrant oncogenic protein isoforms.