The role of RNA alternative splicing in regulating cancer metabolism

Serine/Arginine-Rich Splicing Factor 7 Knockdown Inhibits Aerobic Glycolysis and Growth in HepG2 Cells by Regulating PKM2 Expression

Serine/arginine-rich splicing factors (SRSFs), part of the serine/arginine-rich (SR) protein family, play a crucial role in precursor RNA splicing. Abnormal expression of SRSFs in tumors can disrupt normal RNA splicing, contributing to tumor progression. Notably, SRSF7 has been found to be upregulated in hepatocellular carcinoma (HCC), yet its specific role and molecular mechanisms in HCC pathogenesis are not fully understood. We investigated the expression and prognostic significance of SRSF7 in HCC using bioinformatics database analysis. In HepG2 cells, the expressions of SRSF7 and glycolytic enzymes were analyzed using qRT-PCR, and Western blot. Glucose uptake and lactate production were quantified using relevant reagent kits. Additionally, cell proliferation, clonogenicity, invasion, and apoptosis were evaluated using MTS assay, clonal formation assay, Transwell assay, and mitochondrial membrane potential assay, respectively. This study demonstrated significant overexpression of SRSF7 in HCC tissue, correlating with poor prognosis. Knockdown of SRSF7 in HepG2 cells resulted in inhibited proliferation, clonogenicity, and invasion, while apoptosis was enhanced. This knockdown also decreased glucose uptake and lactate production, along with a reduction in the expression of glucose transporter 1 (GLUT1) and lactate dehydrogenase A (LDHA). Furthermore, SRSF7 downregulation increased the pyruvate kinase muscle 1 (PKM1)/PKM2 ratio. The glycolytic boost due to PKM2 overexpression partially counteracted the effects of SRSF7 silencing on HepG2 cell growth. The knockdown of SRSF7 impairs aerobic glycolysis and growth in HepG2 cells by downregulating PKM2 expression.

Transcriptome analysis of primary sporadic neuroendocrine tumours of the intestine identified three different molecular subgroups

BACKGROUND

Intestinal neuroendocrine tumours (I-NETs) represent a non-negligible entity among intestinal neoplasms, with metastatic spreading usually present at the time of diagnosis. In this context, effective molecular actionable targets are still lacking. Through transcriptome analysis, we aim at refining the molecular taxonomy of I-NETs, also providing insights towards the identification of new therapeutic vulnerabilities.

MATERIALS AND METHODS

A retrospective series of 38 primary sporadic, surgically-resected I-NETs were assessed for transcriptome profiling of 20,815 genes.

RESULTS

Transcriptome analysis detected 643 highly expressed genes. Unsupervised hierarchical clustering, differential expression analysis and gene set enriched analysis identified three different tumour clusters (CL): CL-A, CL-B, CL-C. CL-A showed the overexpression of ARGFX, BIRC8, NANOS2, and SSTR4 genes. Its most characterizing signatures were those related to cell-junctions, and activation of mTOR and WNT pathway. CL-A was also enriched in T CD8 + lymphocytes. CL-B showed the overexpression of PCSK1, QPCT, ST18, and TPH1 genes. Its most characterizing signatures were those related to adipogenesis, neuroendocrine metabolism, and splice site machinery-related processes. CL-B was also enriched in T CD4 + lymphocytes. CL-C showed the overexpression of ALB, ANG, ARG1, and HP genes. Its most characterizing signatures were complement/coagulation and xenobiotic metabolism. CL-C was also enriched in M1/2 macrophages. These CL-based differences may have therapeutic implications in refining the management of I-NET patients. At last, we described a specific gene-set for differentiating I-NET from pancreatic NET.

DISCUSSION

Our data represent an additional step for refining the molecular taxonomy of I-NET, identifying novel transcriptome subgroups with different biology and therapeutic opportunities.

Targeting splicing factors for cancer therapy

Alternative splicing (AS) of mRNAs is an essential regulatory mechanism in eukaryotic gene expression. AS misregulation, caused by either dysregulation or mutation of splicing factors, has been shown to be involved in cancer development and progression, making splicing factors suitable targets for cancer therapy. In recent years, various types of pharmacological modulators, such as small molecules and oligonucleotides, targeting distinct components of the splicing machinery, have been under development to treat multiple disorders. Although these approaches have promise, targeting the core spliceosome components disrupts the early stages of spliceosome assembly and can lead to nonspecific and toxic effects. New research directions have been focused on targeting specific splicing factors for a more precise effect. In this Perspective, we will highlight several approaches for targeting splicing factors and their functions and suggest ways to improve their specificity.

Genome-wide identification of alternative splicing associated with histone deacetylase inhibitor in cutaneous T-cell lymphomas

Cutaneous T-cell lymphomas (CTCLs) are a kind of non-Hodgkin lymphoma that originates from skin, which is difficult to treat with traditional drugs. Human histone deacetylase inhibitors (HDACi) targeted therapy has become a promising treatment strategy in recent years, but some patients can develop resistance to the drug, leading to treatment failure. There are no public reports on whether alternative splicing (AS) and RNA binding proteins (RBP) affect the efficacy of targeted therapy. Using data from the Gene Expression Omnibus (GEO) database, we established a co-change network of AS events and RBP in CTCLs for the first time, and analyzed the potential regulatory effects of RBP on HDACi-related AS events. The dataset GSE132053, which contained the RNA sequence data for 17 HDACi samples, was downloaded and clean reads were aligned to the human GRCh38 genome by hierarchical indexing for spliced alignment of the transcripts, allowing four mismatches. Gene expression levels were evaluated using exons per million fragments mapped for each gene. Student’s t-tests were performed to evaluate the significance of changes in ratios for AS events, and regulated alternative splicing events (RASEs) were defined as events with p values less than 0.05. To sort the differentially expressed genes functional categories, Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways were identified using the KOBAS 2.0 server. The regulatory mechanisms of the RASEs and RBPs were evaluated using Pearson’s correlation coefficient. Seven indirect events of HDACi resistance or sensitivity were identified: NIR_5151_RP11-977G19.10, NIR_4557_IRAG2, NIR_11870_SUMO1, NIR_5347_ING4, NIR_17935_DNAJC2, NIR_17974_CBLL1, and NIR_422_SLC50A1. The potential regulatory relationships between RBPs and HDACi-sensitive RASEs were also analyzed. LEPR and HNRNPAO significantly affected NIR_11870_SUMO1, suggesting a potential regulatory relationship. Additionally, CNN1 may regulate NIR_5347_ING4, CNOT3 may regulate NIR_17935_DNAJC2, and DQX1 and LENG9 may regulate NIR_422_SLC5A1. Overall, our findings establish a theoretical foundation for the precise targeted treatment of CTCLs with HDACi.

A novel 16-gene alternative mRNA splicing signature predicts tumor relapse and indicates immune activity in stage I–III hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a lethal disease with high relapse and dismal survival rates. Alternative splicing (AS) plays a crucial role in tumor progression. Herein, we aim to integratedly analyze the relapse-associated AS events and construct a signature predicting tumor relapse in stage I–III HCC.

Methods: AS events of stage I–III HCC with tumor relapse or long-term relapse-free survival were profiled to identify the relapse-associated AS events. A splicing network was set up to analyze the correlation between the relapse-associated AS events and splicing factors. Cox regression analysis and receiver operating characteristic curve were performed to develop and validate the relapse-predictive AS signature. Single-sample gene set enrichment analysis (ssGSEA) and the ESTIMATE algorithm were used to assess the immune infiltration status of the HCC microenvironment between different risk subgroups. Unsupervised cluster analysis was conducted to assess the relationship between molecular subtypes and local immune status and clinicopathological features.

Results: In total, 2441 ASs derived from 1634 mRNA were identified as relapse-associated AS events. By analyzing the proteins involved in the relapse-associated AS events, 1573 proteins with 11590 interactions were included in the protein–protein interaction (PPI) network. In total, 16 splicing factors and 61 relapse-associated AS events with 85 interactions were involved in the splicing network. The relevant genes involved in the PPI network and splicing network were also analyzed by Gene Ontology enrichment analysis. Finally, we established a robust 16-gene AS signature for predicting tumor relapse in stage I–III HCC with considerable AUC values in all of the training cohort, testing cohort, and entire cohort. The ssGSEA and ESTIMATE analyses showed that the AS signature was significantly associated with the immune status of the HCC microenvironment. Moreover, four molecular subgroups with distinguishing tumor relapse modes and local immune status were also revealed.

Conclusion: Our study built a novel 16-gene AS signature that robustly predicts tumor relapse and indicates immune activity in stage I–III HCC, which may facilitate the deep mining of the mechanisms associated with tumor relapse and tumor immunity and the development of novel individualized treatment targets for HCC.

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

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

Alternative RNA splicing in tumour heterogeneity, plasticity and therapy

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

Mitochondria and Their Relationship with Common Genetic Abnormalities in Hematologic Malignancies

Hematologic malignancies are known to be associated with numerous cytogenetic and molecular genetic changes. In addition to morphology, immunophenotype, cytochemistry and clinical characteristics, these genetic alterations are typically required to diagnose myeloid, lymphoid, and plasma cell neoplasms. According to the current World Health Organization (WHO) Classification of Tumors of Hematopoietic and Lymphoid Tissues, numerous genetic changes are highlighted, often defining a distinct subtype of a disease, or providing prognostic information. This review highlights how these molecular changes can alter mitochondrial bioenergetics, cell death pathways, mitochondrial dynamics and potentially be related to mitochondrial genetic changes. A better understanding of these processes emphasizes potential novel therapies.

Mucin Expression and Splicing Determine Novel Subtypes and Patient Mortality in Pancreatic Ductal Adenocarcinoma

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy demonstrating aberrant and progressive expression of mucins. The contribution of individual mucins has been extensively investigated in PDAC; however, comprehensive mucin profiling including splice variants in PDAC tumors has not been reported.

EXPERIMENTAL DESIGN

Using publicly available RNA sequencing (RNA-seq) datasets, we assess the expression of mucin family members and their splice variants (SV) in PDAC tumor samples for the first time. Mucin SVs that are correlated with PDAC patient survival are validated in a cohort of patient tumor samples. Further, we use computational methods to derive novel pancreatic tumor subtypes using mucin expression signatures and their associated activated pathways.

RESULTS

Principal component analysis identified four novel mucin-based PDAC subtypes. Pathway analysis implicated specific biological signatures for each subtype, labeled (i) immune activated, (ii) progressive, (iii) pancreatitis-initiated, and (iv) anti-inflammatory/PanIN-initiated. Assessing mucin SVs, significantly longer survival is observed with higher expression of 4 MUC1 and 1 MUC13 SVs, whereas patients expressing 2 MUC4 and 1 MUC16 SVs had shorter survival. Using a whole-transcriptome correlation, a three-gene panel, including ESRP2, PTK6, and MAGEH1, is designated to assess PDAC tumor sample cellularity by PCR. One MUC4 SV and one MUC13 SV are quantified in a separate PDAC patient cohort, and their effects on survival are experimentally validated.

CONCLUSIONS

Altogether, we demonstrate the unique expression pattern of mucins, four mucin-based PDAC subtypes, and the contribution of MUC1, MUC4, and MUC16 SVs in PDAC patient survival.

Non-Coding RNAs and Splicing Activity in Testicular Germ Cell Tumors

Testicular germ cell tumors (TGCTs) are the most common tumors in adolescent and young men. Recently, genome-wide studies have made it possible to progress in understanding the molecular mechanisms underlying the development of tumors. It is becoming increasingly clear that aberrant regulation of RNA metabolism can drive tumorigenesis and influence chemotherapeutic response. Notably, the expression of non-coding RNAs as well as specific splice variants is deeply deregulated in human cancers. Since these cancer-related RNA species are considered promising diagnostic, prognostic and therapeutic targets, understanding their function in cancer development is becoming a major challenge. Here, we summarize how the different expression of RNA species repertoire, including non-coding RNAs and protein-coding splicing variants, impacts on TGCTs’ onset and progression and sustains therapeutic resistance. Finally, the role of transcription-associated R-loop misregulation in the maintenance of genomic stability in TGCTs is also discussed.

Splice-switching as cancer therapy

In light of recent advances in RNA splicing modulation as therapy for specific genetic diseases, there is great optimism that this approach can be applied to treatment of cancer as well. Dysregulation of alternative RNA splicing is a common aberration detected in many cancers and thus, provides an attractive target for therapeutics. Here, we present and compare two promising approaches that are currently being investigated to manipulate alternative splicing and their potential use in therapy. The first strategy makes use of splice-switching oligonucleotides, whereas the second strategy uses CRISPR (clustered regularly interspaced short palindromic repeat Cas (CRISPR-associated) technology. We will discuss both the challenges and limitations of these technologies and progress being made to implement splice-switching as a potential cancer therapy.

The Physics-Biology continuum challenges darwinism: Evolution is directed by the homeostasis-dependent bidirectional relation between genome and phenotype

The physics-biology continuum relies on the fact that life emerged from prebiotic molecules. Here, I argue that life emerged from the coupling between nucleic acid and protein synthesis during which proteins (or proto-phenotypes) maintained the physicochemical parameter equilibria (or proto-homeostasis) in the proximity of their encoding nucleic acids (or proto-genomes). This protected the proto-genome physicochemical integrity (i.e., atomic composition) from environmental physicochemical constraints, and therefore increased the probability of reproducing the proto-genome without variation. From there, genomes evolved depending on the biological activities they generated in response to environmental fluctuations. Thus, a genome maintaining homeostasis (i.e., internal physicochemical parameter equilibria), despite and in response to environmental fluctuations, maintains its physicochemical integrity and has therefore a higher probability to be reproduced without variation. Consequently, descendants have a higher probability to share the same phenotype than their parents. Otherwise, the genome is modified during replication as a consequence of the imbalance of the internal physicochemical parameters it generates, until new mutation-deriving biological activities maintain homeostasis in offspring. In summary, evolution depends on feedforward and feedback loops between genome and phenotype, as the internal physicochemical conditions that a genome generates ─ through its derived phenotype in response to environmental fluctuations ─ in turn either guarantee its stability or direct its variation. Evolution may not be explained by the Darwinism-derived, unidirectional principle (random mutations-phenotypes-natural selection) but rather by the bidirectional relationship between genome and phenotype, in which the phenotype in interaction with the environment directs the evolution of the genome it derives from.

Characterization of alternative splicing events and prognostic signatures in breast cancer

Background

Breast cancer (BRCA) is one of the most common cancers worldwide. Abnormal alternative splicing (AS) frequently observed in cancers. This study aims to demonstrate AS events and signatures that might serve as prognostic indicators for BRCA.

Methods

Original data for all seven types of splice events were obtained from TCGA SpliceSeq database. RNA-seq and clinical data of BRCA cohorts were downloaded from TCGA database. Survival-associated AS events in BRCA were analyzed by univariate COX proportional hazards regression model. Prognostic signatures were constructed for prognosis prediction in patients with BRCA based on survival-associated AS events. Pearson correlation analysis was performed to measure the correlation between the expression of splicing factors (SFs) and the percent spliced in (PSI) values of AS events. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to demonstrate pathways in which survival-associated AS event is enriched.

Results

A total of 45,421 AS events in 21,232 genes were identified. Among them, 1121 AS events in 931 genes significantly correlated with survival for BRCA. The established AS prognostic signatures of seven types could accurately predict BRCA prognosis. The comprehensive AS signature could serve as independent prognostic factor for BRCA. A SF-AS regulatory network was therefore established based on the correlation between the expression levels of SFs and PSI values of AS events.

Conclusions

This study revealed survival-associated AS events and signatures that may help predict the survival outcomes of patients with BRCA. Additionally, the constructed SF-AS networks in BRCA can reveal the underlying regulatory mechanisms in BRCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08305-6.

Alternative Splicing of the Inhibitory Immune Checkpoint Receptor SLAMF6 Generates a Dominant Positive Form, Boosting T-cell Effector Functions

SLAMF6 is a homotypic receptor of the Ig-superfamily associated with progenitor-exhausted T cells. Here we show that in humans, SLAMF6 has three splice isoforms involving its V-domain. Although the canonical receptor inhibited T-cell activation through SAP recruitment, the short isoform SLAMF6^Δ17-65 had a strong agonistic effect. The costimulatory action depended on protein phosphatase SHP1 and led to a cytotoxic molecular profile mediated by the expression of TBX21 and RUNX3. Patients treated with immune checkpoint blockade showed a shift toward SLAMF6^Δ17-65 in peripheral blood T cells. We developed splice-switching antisense oligonucleotides (ASO) designed to target the relevant SLAMF6 splice junction. Our ASOs enhanced SLAMF6^Δ17-65 expression in human tumor-infiltrating lymphocytes and improved their capacity to inhibit human melanoma in mice. The yin-yang relationship of SLAMF6 splice isoforms may represent a balancing mechanism that could be exploited to improve cancer immunotherapy.

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.

Alternative splicing events implicated in carcinogenesis and prognosis of thyroid gland cancer

Alternative splicing (AS), a critical post-transcriptional regulatory mechanism, expands gene expression patterns, thereby leading to increased protein diversity. Indeed, more than 95% of human genes undergo alternative splicing events (ASEs). In this study, we drew an all-around AS profile of thyroid cancer cells based on RNA-seq data. In total, there were 45,150 AS in 10,446 thyroid cancer cell genes derived from 506 patients, suggesting that ASEs is a common process in TC. Moreover, 1819 AS signatures were found to be significantly associated with the overall survival (OS) of TC patients. Kaplan–Meier survival analyses suggested that seven types of ASEs were associated with poor prognosis of TC (P < 0.05). Among them, exon skipping (ES) was the most common, with alternate promoter (AP) and alternate terminator (AT) coming second and third, respectively. Our results indicated that acceptor sites (AA) (AUC: 0.937), alternate donor sites (AD) (AUC: 0.965), AT (AUC: 0.964), ES (AUC: 0.999), mutually exclusive exons (ME) (AUC: 0.999), and retained intron (RI) (AUC: 0.837) exhibited an AUC greater than 0.6. In addition, age and risk score (All) were risk factors for TC patients. We also evaluated whether TC-ASEs are regulated by various splicing factors (SFs). We found that the expression of 90 SFs was associated with 469 ASEs and OS of TC patients. Our findings provide an insight into the role of spliceosomes in TC, which may offer novel perspectives in tumor research.

RUNX1/RUNX1T1 mediates alternative splicing and reorganises the transcriptional landscape in leukemia

The fusion oncogene RUNX1/RUNX1T1 encodes an aberrant transcription factor, which plays a key role in the initiation and maintenance of acute myeloid leukemia. Here we show that the RUNX1/RUNX1T1 oncogene is a regulator of alternative RNA splicing in leukemic cells. The comprehensive analysis of RUNX1/RUNX1T1-associated splicing events identifies two principal mechanisms that underlie the differential production of RNA isoforms: (i) RUNX1/RUNX1T1-mediated regulation of alternative transcription start site selection, and (ii) direct or indirect control of the expression of genes encoding splicing factors. The first mechanism leads to the expression of RNA isoforms with alternative structure of the 5'-UTR regions. The second mechanism generates alternative transcripts with new junctions between internal cassettes and constitutive exons. We also show that RUNX1/RUNX1T1-mediated differential splicing affects several functional groups of genes and produces proteins with unique conserved domain structures. In summary, this study reveals alternative splicing as an important component of transcriptome re-organization in leukemia by an aberrant transcriptional regulator.

TRIM58 is a prognostic biomarker remodeling the tumor microenvironment in KRAS-driven lung adenocarcinoma

Aim: To comprehensively analyze the expression profiles of ubiquitin-related genes (URGs) and determine potential biomarkers in KRAS-driven lung adenocarcinoma (LUAD). Materials & methods: Differential expression analyses were performed between KRAS-wild and KRAS-mutant LUAD samples from The Cancer Genome Atlas database, and 34 URGs were screened out. ESTIMATE and CIBERSORT methods were used to calculate the ratio of immune and stromal components. Results & conclusion: TRIM58 was positively correlated with abundances of M2 macrophages and resting mast cells and negatively correlated with follicular helper T-cell abundances in KRAS-driven LUAD. TRIM58 was a potential prognosis-associated indicator for tumor microenvironment modulation and played a key role in TME-specific AS landscapes alterations in KRAS-driven LUAD.

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

Background

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

Methods

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

Results

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

Conclusions

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

Splicing factors: Insights into their regulatory network in alternative splicing in cancer

More than 95% of all human genes are alternatively spliced after transcription, which enriches the diversity of proteins and regulates transcript and/or protein levels. The splicing isoforms produced from the same gene can manifest distinctly, even exerting opposite effects. Mounting evidence indicates that the alternative splicing (AS) mechanism is ubiquitous in various cancers and drives the generation and maintenance of various hallmarks of cancer, such as enhanced proliferation, inhibited apoptosis, invasion and metastasis, and angiogenesis. Splicing factors (SFs) play pivotal roles in the recognition of splice sites and the assembly of spliceosomes during AS. In this review, we mainly discuss the similarities and differences of SF domains, the details of SF function in AS, the effect of SF-driven pathological AS on different hallmarks of cancer, and the main drivers of SF expression level and subcellular localization. In addition, we briefly introduce the application prospects of targeted therapeutic strategies, including small-molecule inhibitors, siRNAs and splice-switching oligonucleotides (SSOs), from three perspectives (drivers, SFs and pathological AS). Finally, we share our insights into the potential direction of research on SF-centric AS-related regulatory networks.

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

Background

Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms.

Method

RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs).

Results

Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them.

Conclusion

In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

Splicing machinery dysregulation drives glioblastoma development/aggressiveness: oncogenic role of SRSF3

Glioblastomas remain the deadliest brain tumour, with a dismal ∼12–16-month survival from diagnosis. Therefore, identification of new diagnostic, prognostic and therapeutic tools to tackle glioblastomas is urgently needed. Emerging evidence indicates that the cellular machinery controlling the splicing process (spliceosome) is altered in tumours, leading to oncogenic splicing events associated with tumour progression and aggressiveness. Here, we identify for the first time a profound dysregulation in the expression of relevant spliceosome components and splicing factors (at mRNA and protein levels) in well characterized cohorts of human high-grade astrocytomas, mostly glioblastomas, compared to healthy brain control samples, being SRSF3, RBM22, PTBP1 and RBM3 able to perfectly discriminate between tumours and control samples, and between proneural-like or mesenchymal-like tumours versus control samples from different mouse models with gliomas. Results were confirmed in four additional and independent human cohorts. Silencing of SRSF3, RBM22, PTBP1 and RBM3 decreased aggressiveness parameters in vitro (e.g. proliferation, migration, tumorsphere-formation, etc.) and induced apoptosis, especially SRSF3. Remarkably, SRSF3 was correlated with patient survival and relevant tumour markers, and its silencing in vivo drastically decreased tumour development and progression, likely through a molecular/cellular mechanism involving PDGFRB and associated oncogenic signalling pathways (PI3K-AKT/ERK), which may also involve the distinct alteration of alternative splicing events of specific transcription factors controlling PDGFRB (i.e. TP73). Altogether, our results demonstrate a drastic splicing machinery-associated molecular dysregulation in glioblastomas, which could potentially be considered as a source of novel diagnostic and prognostic biomarkers as well as therapeutic targets for glioblastomas. Remarkably, SRSF3 is directly associated with glioblastoma development, progression, aggressiveness and patient survival and represents a novel potential therapeutic target to tackle this devastating pathology.

Osteopontin-4 and Osteopontin-5 splice variants are expressed in several tumor cell lines

Among osteopontin splice variants (OPN-SV), the expression profile of osteopontin-4 (OPN4) and osteopontin-5 (OPN5) has not been addressed in distinct cancer types. We herein aimed to investigate their expression in several cancer cell lines, besides comparing it in relation to the three previously described OPN-SV: OPNa, OPNb and OPNc. Total RNA from cancer cell lines, including prostate (PC3 and DU145), ovarian (A2780), breast (MCF-7 and MDA-MB-231), colorectal (Caco-2, HT-29 and HCT-116), thyroid (TT, TPC1 and 8505c) and lung (A549 and NCI-H460) was extracted, followed by cDNA synthesis. OPN-SV transcript analysis by RT-PCR or RT-qPCR were performed using OPN-SV specific oligonucleotides and gapdh and actin transcripts were used as housekeeping controls. OPN4 and OPN5 transcripts displayed co-expression in most tested cell lines. OPN4 was found expressed in similar or higher levels in relation to OPN5. Moreover, in most tested cell lines, OPN4 is also expressed in similar levels to OPNa or OPNb. The expression of OPN5 is also generally variable in relation to the other OPN-SV, but expressed in similar or higher levels in relation to OPNc, depending on each tested cell line. OPN4 and OPN5 seem to be co-expressed in several tumor types and OPN4 is one of the most overexpressed OPN-SV in distinct tumor cell lines. Once both OPN4 and OPN5 are differentially expressed and also evidence tumor-specific expression patterns, we hypothesize that similarly to the other OPN-SV, they also possibly contribute to key aspects of tumor progression, what should be further functionally investigated in distinct tumor models.

Characterization and prognostic significance of alternative splicing events in lower-grade diffuse gliomas

Alternative splicing (AS) is assumed to play important roles in the progression and prognosis of cancer. Currently, the comprehensive analysis and clinical relevance of AS in lower-grade diffuse gliomas have not been systematically addressed. Here, we gathered alternative splicing data of lower-grade diffuse gliomas from SpliceSeq. Based on the Percent Spliced In (PSI) values of 515 lower-grade diffuse glioma patients from the Cancer Genome Atlas (TCGA), we performed subtype-differential AS analysis and consensus clustering to determine robust clusters of patients. A total of 48 050 AS events in 10 787 genes in lower-grade diffuse gliomas were profiled. Subtype-differential splicing analysis and functional annotation revealed that spliced genes were significantly enriched in numerous cancer-related biological phenotypes and signalling pathways. Consensus clustering using AS events identified three robust clusters of patients with distinguished pathological and prognostic features. Moreover, each cluster was also associated with distinct genomic alterations. Finally, we developed and validated an AS-related signature with Cox proportional hazards model. The signature, significantly associated with clinical and molecular features, could serve as an independent prognostic factor for lower-grade diffuse gliomas. Thus, our results indicated that AS events could discriminate molecular subtypes and have prognostic impact in lower-grade diffuse gliomas.

Identification of Survival-Associated Alternative Splicing Signatures in Lung Squamous Cell Carcinoma

Purpose: Alternative splicing (AS) is a post-transcriptional process that plays a significant role in enhancing the diversity of transcription and protein. Accumulating evidences have demonstrated that dysregulation of AS is associated with oncogenic processes. However, AS signature specifically in lung squamous cell carcinoma (LUSC) remains unknown. This study aimed to evaluate the prognostic values of AS events in LUSC patients.

Methods: The RNA-seq data, AS events data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was performed to identify survival-related AS events and survival-related parent genes were subjected to Gene Ontology enrichment analysis and gene network analysis. The least absolute shrinkage and selection operator (LASSO) method and multivariate Cox regression analysis were used to construct prognostic prediction models, and their predictive values were assessed by Kaplan-Meier analysis and receiver operating characteristic (ROC) curves. Then a nomogram was established to predict the survival of LUSC patients. And the interaction network of splicing factors (SFs) and survival-related AS events was constructed by Spearman correlation analysis and visualized by Cytoscape.

Results: Totally, 467 LUSC patients were included in this study and 1,991 survival-related AS events within 1,433 genes were identified. SMAD4, FOS, POLR2L, and RNPS1 were the hub genes in the gene interaction network. Eight prognostic prediction models (seven types of AS and all AS) were constructed and all exhibited high efficiency in distinguishing good or poor survival of LUSC patients. The final integrated prediction model including all types of AS events exhibited the best prognostic power with the maximum AUC values of 0.778, 0.816, 0.814 in 1, 3, 5 years ROC curves, respectively. Meanwhile, the nomogram performed well in predicting the 1-, 3-, and 5-year survival of LUSC patients. In addition, the SF-AS regulatory network uncovered a significant correlation between SFs and survival-related AS events.

Conclusion: This is the first comprehensive study to analyze the role of AS events in LUSC specifically, which improves our understanding of the prognostic value of survival-related AS events for LUSC. And these survival-related AS events might serve as novel prognostic biomarkers and drug therapeutic targets for LUSC.

Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer

Background: Pancreatic cancer (PC) is an aggressive cancer with worse survival in the world. Emerging evidence suggested that the imbalance of alternative splicing (AS) is a hallmark of cancer and indicated poor prognosis of patients. Genes-derived splicing events can produce neoepitopes for immunotherapy. However, the profound study of splicing profiling in PC is still elusive. We aimed to identification of novel prognostic signature across a comprehensive splicing landscape and reveal their relationship with tumor-infiltrating immune cells in pancreatic cancer microenvironment.

Methods: Based on integrated analysis of splicing profiling and clinical data, differentially splicing events were filtered out. Then, stepwise Cox regression analysis was applied to identify survival-related splicing events and construct prognostic signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognostic signature and infiltrating immune cells by CIBERSORT.

Results: According to systematic analyzing, a final six splicing events were identified and validated the good prognostic capability in entire TCGA dataset, validation set 1 and validation set 2 by Kaplan-Meier curves (P < 0.0001). The area under the curve (AUC) of ROC curves were also confirmed the high predictive efficiency of the prognostic signature in these three cohorts (AUC = 0.857, 0.895 and 0.788). In order to validate whether prognostic signature highlights a correlation between AS and immune contexture, CIBERSORT was performed to analyze the proportion of tumor-infiltrating immune cells in PC. Based on prognostic signature, we identified survival-related immune cells including CD8 T cells (P = 0.0111), activated CD4 memory T cells (P = 0.0329) and resting mast cells (P = 0.0352).

Conclusion: In conclusion, our study contribute to provide a promising prognostic signature based on six splicing events and revealed prognosis-related immune cells which indeed represented novel tumor drivers and provide potential targets for personalized therapeutic.

Alternative Splicing Events and Splicing Factors Are Prognostic in Adrenocortical Carcinoma

Alternative splicing is involved in the pathogenesis of human diseases, including cancer. Here, we investigated the potential application of alternative splicing events (ASEs) and splicing factors (SFs) in the prognosis of adrenocortical carcinoma (ACC). Transcriptome data from 79 ACC cases were downloaded from The Cancer Genome Atlas database, and percent spliced-in values of seven splicing types were downloaded from The Cancer Genome Atlas SpliceSeq database. By the univariate Cox regression analysis, 1,839 survival-related ASEs were identified. Prognostic indices based on seven types of survival-related ASEs were calculated by multivariate Cox regression analysis. Survival curves and receiver operating characteristic curves were used to assess the diagnostic value of the prognostic model. Independent prognosis analysis identified several ASEs (e.g., THNSL2| 54469| ME) that could be used as biomarkers to predict the prognosis of patients with ACC accurately. By analyzing the co-expression correlation between SFs and ASEs, 188 highly correlated interactions were established. From the protein interaction network, we finally screened six hub SFs, including YBX1, SART1, PRCC, SNRPG, SNRPE, and SF3B4, whose expression levels were significantly related to the overall survival and prognosis of ACC. Our findings provide a reliable model for predicting the prognosis of ACC patients based on aberrant alternative splicing patterns.

Comprehensive Analysis of Prognostic Alternative Splicing Signatures in Oral Squamous Cell Carcinoma

Background

Alternative splicing (AS) plays an essential role in tumorigenesis and progression. This study aimed to develop a novel prognostic model based on the AS events to obtain more accurate survival prediction and search for potential therapeutic targets in oral squamous cell carcinoma (OSCC).

Methods

Seven types of AS events in 326 OSCC patients with RNA-seq were obtained from the TCGA SpliceSeq tool and the TCGA database. Cox analysis, the least absolute shrinkage and selection operator Cox regression and random forest were employed to establish prognostic models. Genomics of Drug Sensitivity in Cancer (GDSC) was adopted to estimate the possible drug sensiticity. Prognostic splicing factor (SF)-AS network was constructed by Cytoscape.

Results

The final model included 12 AS events, showing satisfactory performance. The area under the curve for 3- and 5-year survival in the training cohort was 0.83 and 0.82, respectively while that in internal validation was 0.83 and 0.82 accordingly. The calibration curve also indicated a satisfactory agreement between the observation and the predictive values. Low-risk patients stratified by the final model presented higher sensitivity to three chemo drugs. Besides, the prognostic SF-AS regulatory network contained five key SFs and 62 AS events.

Conclusions

We developed a powerful prognostic AS signature for OSCC and deepened the understanding of SF-AS network regulatory mechanisms. Low-risk patients tended to be more sensitive to the three chemo drugs while five key SFs including CELF2, TIA1, HNRNPC, HNRNPK, and SRSF9 were identified as potential prognostic biomarkers, which may offer new prospects for effective therapies of OSCC.

Systematic Analysis of Alternative Splicing Landscape in Pancreatic Adenocarcinoma Reveals Regulatory Network Associated with Tumorigenesis and Immune Response

BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive gastrointestinal tumors and has an extremely high mortality rate. Recent studies indicate that alternative splicing (AS), a common post-transcriptional process, has important roles in tumor biological behaviors and may provide novel immunotherapeutic targets. This study systematically analyzes AS profiles in PDAC and reveals their potential regulatory effects on cancer immune response. MATERIAL AND METHODS AS event, RNA sequencing, and splicing factor (SF) data were extracted from SpliceSeq, The Cancer Genome Atlas, and SpliceAid2, respectively. Overall survival (OS)-associated AS events and SFs were identified with univariate analysis. The LASSO method and multivariate Cox regression analysis were used to construct predictive signatures for the prediction of patient prognosis. The proportions of immune cells within PDAC samples were evaluated using the CIBERSORT algorithm. The correlations among AS events, SFs, and immune cell proportions were calculated using Spearman correlation analysis. Consensus clustering and immune classification were performed on the PDAC cohort. RESULTS A total of 4812 OS-related AS events from 3341 parent genes were identified, and 8 AS-based predictive models were constructed for PDAC. An OS-related SF-AS regulatory network was constructed. The AS events regulated by ELAVL4 exhibited strong correlations with CD8 T cells and regulatory T cells. In addition, AS-based clusters demonstrated distinct OS outcomes and immune features. CONCLUSIONS AS-based predictive models with high accuracy were constructed to facilitate prognosis prediction and treatment of PDAC. An SF-AS regulatory network was constructed, revealing the potential relationships among SF, AS, and immune response.

Analysis of relapse-associated alternative mRNA splicing and construction of a prognostic signature predicting relapse in I-III colon cancer

The literature comprehensively analyzed alternative splicing (AS) events in colon cancer is little and corresponding prognostic signature is still a lack. Based on data of TCGA, the relapse-associated ASs were comprehensively analyzed and a signature was further constructed to predict the relapse in I-III colon cancer. In total 1912 ASs of 1384 mRNA were identified as relapse-associated ASs, protein-protein interactions (PPI) and ASs-splicing factors (SF) interactions network were identified. We finally built a robust signature to predict the relapse of I-III colon cancer with a considerable AUC value in both the training group and the test group. The AUC in the entire set at 1, 3 and 5 year was 0.85, 0.83 and 0.836. Our study provided a profile of relapse-associated ASs in I-III colon cancer and built a robust signature to predict the relapse of I-III colon cancer.

A signature predicting relapse based on integrated analysis on relapse-associated alternative mRNA splicing in I-III rectal cancer

Researches focusing on the effects of alternative splicing (AS) on relapse of rectal cancer is little and signature based on the AS is blank. In this study, bioinformatic analysis was performed to identify and analyze the relapse-associated ASs, a signature was also constructed. In conclusion, 829 relapse-associated ASs of 676 mRNA were identified. 603 proteins with 2119 interactions were involved in the PPI (protein-protein interactions) network. 43 relapse-associated ASs and 64 SFs (splicing factors) with 160 interactions were indicated. Finally, we built a robust signature to predict the relapse of I-III rectal cancer with a high AUC (0.98) of ROC at 1 year. Based on the ASs involved in the signature, 4 molecular subgroups that could distinguish the relapse rate in diverse groups were identified. Our research provided an overview of relapse-associated ASs in I-III rectal cancer.

TNNT2 as a potential biomarker for the progression and prognosis of colorectal cancer

Colorectal cancer (CRC) is the third most common cancer worldwide. At present, there are limited effective biomarkers of CRC. The present study aimed to identify potential signatures associated with the tumorigenesis and prognosis of CRC using publicly available databases, and further validate the identified biomarkers in CRC cell lines. Identification of differentially expressed mRNAs between CRC and paracancerous samples was conducted based on data from The Cancer Genome Atlas (TCGA; 471 tumor samples and 41 normal samples). Survival analysis was performed to explore the prognostic value of troponin 2 (TNNT2) in the TCGA training set, which was further validated in an external dataset, GSE17531. Functional enrichment analysis was conducted to determine the possible biological functions using GSEA 3.0. Reverse transcription-quantitative PCR (RT-qPCR) and western blotting were utilized to detect the mRNA and protein expression levels of TNNT2 between CRC and normal colorectal cells. Immunohistochemistry was performed to detect the protein expression of TNNT2 in CRC and normal tissues. TNNT2 was significantly upregulated in CRC samples compared with adjacent normal samples in the TCGA dataset. Increased expression of TNNT2 was associated with inferior prognosis in the TCGA training dataset and GSE17531 validation dataset. Functional enrichment analysis revealed that the ErbB signaling pathway and glycerophospholipid metabolism pathway were significantly activated in the TNNT2 high expression group. Overexpression of TNNT2 mRNA and TNNT2 protein in CRC tumor cells was confirmed by RT-qPCR and western blotting, respectively. Immunohistochemistry indicated increased protein expression levels of TNNT2 in CRC tissues in comparison with normal tissues. TNNT2 was associated with the tumorigenesis and prognosis of CRC, which may be useful for novel biomarker identification and targeted therapeutic strategy development.

Prognostic role of alternative splicing events in head and neck squamous cell carcinoma

Background

Aberrant alternative splicing (AS) is implicated in biological processes of cancer. This study aims to reveal prognostic AS events and signatures that may serve as prognostic predictors for head and neck squamous cell carcinoma (HNSCC).

Methods

Prognostic AS events in HNSCC were identified by univariate COX analysis. Prognostic signatures comprising prognostic AS events were constructed for prognosis prediction in patients with HNSCC. The correlation between the percent spliced in (PSI) values of AS events and the expression of splicing factors (SFs) was analyzed by Pearson correlation analysis. Gene functional annotation analysis was performed to reveal pathways in which prognostic AS is enriched.

Results

A total of 27,611 AS events in 15,873 genes were observed, and there were 3433 AS events in 2624 genes significantly associated with overall survival (OS) for HNSCC. Moreover, we found that AS prognostic signatures could accurately predict HNSCC prognosis. SF-AS regulatory networks were constructed according to the correlation between PSI values of AS events and the expression levels of SFs.

Conclusions

Our study identified prognostic AS events and signatures. Furthermore, it established SF-AS networks in HNSCC that were valuable in predicting the prognosis of patients with HNSCC and elucidating the regulatory mechanisms underlying AS in HNSCC.

Identification and validation of an alternative splicing-based prognostic signature for head and neck squamous cell carcinoma

Increasing evidence has demonstrated that changes in alternative splicing (AS) events are closely associated with the initiation and progression of cancer. However, the concrete role of AS in tumorigenesis of head and neck squamous cell carcinoma (HNSCC) is poorly known. In this study, we aimed to investigate the AS profile in HNSCC, and build up a robust AS-based prognostic signature for HNSCC. Our results revealed a total of 4068 overall survival (OS) associated AS events in the TCGA HNSCC cohort. The whole TCGA HNSCC cohort was randomly divided into discovery cohort and validation cohort. A prognostic signature including five AS events was developed with the discovery cohort based on the most significant OS-associated AS events. Then it was further successfully validated in the validation cohort. The AS-based risk signature was an independent prognostic indicator in both discovery cohort and validation cohort. This prognostic signature-based nomogram model showed excellent performance for predicting the OS of HNSCC. Splicing network analysis have identified the most correlated splicing factor-AS network in HNSCC. Collectively, we have constructed a robust AS-based prognostic signature which might contribute to improve the clinical outcome of HNSCC.

Roles and mechanisms of alternative splicing in cancer - implications for care

Removal of introns from messenger RNA precursors (pre-mRNA splicing) is an essential step for the expression of most eukaryotic genes. Alternative splicing enables the regulated generation of multiple mRNA and protein products from a single gene. Cancer cells have general as well as cancer type-specific and subtype-specific alterations in the splicing process that can have prognostic value and contribute to every hallmark of cancer progression, including cancer immune responses. These splicing alterations are often linked to the occurrence of cancer driver mutations in genes encoding either core components or regulators of the splicing machinery. Of therapeutic relevance, the transcriptomic landscape of cancer cells makes them particularly vulnerable to pharmacological inhibition of splicing. Small-molecule splicing modulators are currently in clinical trials and, in addition to splice site-switching antisense oligonucleotides, offer the promise of novel and personalized approaches to cancer treatment.

Chemical Modulation of Pre-mRNA Splicing in Mammalian Systems

RNA splicing is a key component of gene expression and proteomic diversity in humans. The spliceosome assembles on and processes individual nascent pre-mRNA transcripts into distinct mature mRNAs that can code for different proteins. Splicing programs can be affected by somatic mutations and changes in response to exogenous stimuli. Importantly, alterations in splicing can be direct drivers of diseases including cancers. This Review describes recent advances and the potential for targeting and controlling pre-mRNA splicing in humans with small molecules, ranging from targeting spliceosomal proteins to direct targeting of individual RNA transcripts.

Survival-associated alternative splicing signatures in non-small cell lung cancer

Alternative splicing (AS) is fundamental to transcriptome and proteome richness, and data from recent studies suggested a critical association between AS and oncogenic processes. To date, no systematic analysis has been conducted on AS from the perspective of different sexes and subtypes in non-small-cell lung cancer (NSCLC). Thus, we integrated the information of NSCLC patients from The Cancer Genome Atlas (TCGA) and evaluated AS profiles from the perspectives of sex and subtype. Eventually, a total of 813 and 1020 AS events were found to be significantly related to the overall survival (OS) of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. Four prognostic prediction models performed well at 1, 3, and 5 years, with an area under the receiver operating characteristic (ROC) curve (AUC) greater than 0.75. Notably, we explored the upstream splicing factors (SFs) and downstream regulatory mechanisms of the OS-associated AS events and verified four differentially expressed alternative splicing (DEAS) events via qPCR. These findings can provide important guidance for subsequent studies. In addition, we also constructed nomograms to facilitate early screening by clinicians and to determine patient outcomes in NSCLC.

An alternatively spliced, non-signaling insulin receptor modulates insulin sensitivity via insulin peptide sequestration in C. elegans

In the nematode C. elegans, insulin signaling regulates development and aging in response to the secretion of numerous insulin peptides. Here, we describe a novel, non-signaling isoform of the nematode insulin receptor (IR), DAF-2B, that modulates insulin signaling by sequestration of insulin peptides. DAF-2B arises via alternative splicing and retains the extracellular ligand binding domain but lacks the intracellular signaling domain. A daf-2b splicing reporter revealed active regulation of this transcript through development, particularly in the dauer larva, a diapause stage associated with longevity. CRISPR knock-in of mScarlet into the daf-2b genomic locus confirmed that DAF-2B is expressed in vivo and is likely secreted. Genetic studies indicate that DAF-2B influences dauer entry, dauer recovery and adult lifespan by altering insulin sensitivity according to the prevailing insulin milieu. Thus, in C. elegans alternative splicing at the daf-2 locus generates a truncated IR that fine-tunes insulin signaling in response to the environment.

Germ Cell-Specific Gene 1-Like Protein Regulated by Splicing Factor CUGBP Elav-Like Family Member 5 and Primary Bile Acid Biosynthesis are Prognostic in Glioblastoma Multiforme

Background

Alternative splicing (AS) modifies 92-94% human genes, abnormal splicing events might relate to tumor development and invasion. Glioblastoma Multiforme (GBM) is a fatal, invasive, and malignant tumor in nervous system. The recurrence and development leads to poor prognosis. However, few studies have focused on AS in GBM.

Methods

RNA-seq and Alternative splicing events (ASEs) data of GBM samples were downloaded from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases, respectively. Firstly, the Cox regression analysis was utilized to identify the overall survival splicing events (OS-SEs). Secondly, a multivariable model was applied to access the prognostic value of risk score. Then, we constructed a co-expressed network between splicing factors (SFs) and overall survival alternative splicing events (OS-SEs). Additionally, to explore the relationship between the potential prognostic signaling pathways and OS-SEs, we constructed a network between these pathways and OS-SEs. Ultimately, to better explain the results, validations from multi-dimension platforms were applied.

Results

In the first step, 1,062 OS-SEs were selected by Cox regression. Then, 11 OS-SEs were integrated in a multivariate model by Lasso regression. The area under the curve (AUC) of receiver operator characteristic (ROC) curve was 0.861. In addition, the risk score generated from the multivariate model was confirmed to be an independent prognostic factor (P < 0.001). What's more, in the network of SFs and ASEs, CELF5 significantly regulated GSG1L|35696|AP and GSG1L|35698|AP (P < 0.001, R = 0.511 and = -0.492). Additionally, GSG1L|35696|AP (P = 0.006) and GSG1L|35698|AP (P = 0.007) showed a significant relationship with cancer status. Eventually, KEGG pathways related to prognosis of GBM were selected by GSVA. The primary bile acid synthesis (P < 0.001, R = 0.420) was the significant pathway co-expressed with Germ Cell-Specific Gene 1-Like Protein (GSG1L).

Conclusions

Based on the comprehensive bioinformatics analysis, we proposed that aberrant splicing factor CUGBP Elav-like family member 5 (CELF5) significantly, positively and negatively, regulated ASE of GSG1L, and the primary bile acid synthesis pathway might play an important role in tumorigenesis and prognosis of GBM.

miR-335 modulates Numb alternative splicing via targeting RBM10 in endometrial cancer

Numb is a conserved protein plays important roles in the development of cancer. Two Numb isoforms have been found produced by alternative splicing and play contrast roles in regulating cellular functions. It is reported that the expression of Numb long isoform (Numb-L) was increased in various kinds of cancers, but in endometrial cancer, the condition is still unknown. The level of two Numb transcripts and protein isoforms were detected by semiquantitative polymerase chain reaction and immunoblotting in 47 paired endometrial tumor and adjacent non-tumor control tissues. The level of three alternative splicing related proteins: RBM5, RBM6, and RBM10 was determined by immunoblotting. MiRNAs targeting RBM10 were predicted by bioinformatics tools and their interaction with RBM10 was confirmed by luciferase assay and immunoblotting. The function of miR-335 in endometrial cancer was examined in xenograft mouse model. Numb-L level was increased in tumors and negatively correlated with RBM10 protein level. RBM10 mRNA level was not significantly altered in endometrial tumors suggesting its expression may regulated by post transcriptional regulators such as miRNAs. We identified miR-133a, miR-133b, and miR-335 directly target RBM10, but only miR-335 level increased in tumors and negatively correlated with RBM10 protein level. miR-335 overexpression promoted tumor growth by downregulating RBM10 and upregulating Numb-L level in xenograft mouse model. miR-335 overexpression promoted Numb-L expression via targeting RBM10 in endometrial cancer, which may provide new biomarkers for EC diagnosis.

An Intricate Connection between Alternative Splicing and Phenotypic Plasticity in Development and Cancer

During tumor progression, hypoxia, nutrient deprivation or changes in the extracellular environment (i.e., induced by anti-cancer drugs) elicit adaptive responses in cancer cells. Cellular plasticity increases the chance that tumor cells may survive in a challenging microenvironment, acquire new mechanisms of resistance to conventional drugs, and spread to distant sites. Re-activation of stem pathways appears as a significant cause of cellular plasticity because it promotes the acquisition of stem-like properties through a profound phenotypic reprogramming of cancer cells. In addition, it is a major contributor to tumor heterogeneity, depending on the coexistence of phenotypically distinct subpopulations in the same tumor bulk. Several cellular mechanisms may drive this fundamental change, in particular, high-throughput sequencing technologies revealed a key role for alternative splicing (AS). Effectively, AS is one of the most important pre-mRNA processes that increases the diversity of transcriptome and proteome in a tissue- and development-dependent manner. Moreover, defective AS has been associated with several human diseases. However, its role in cancer cell plasticity and tumor heterogeneity remains unclear. Therefore, unravelling the intricate relationship between AS and the maintenance of a stem-like phenotype may explain molecular mechanisms underlying cancer cell plasticity and improve cancer diagnosis and treatment.

Alternative splicing of VEGFA is regulated by RBM10 in endometrial cancer

Vascular endothelial growth factor A (VEGFA) gene has three alternative exons which results in multiple isoforms. VEGFA has been found overexpressed in patients with endometrial cancer, but the VEGFA expression pattern and how it is regulated are still unknown. The level of VEGFA transcripts and protein isoforms were detected by semi-quantitative Polymerase chain reaction (PCR) and immunoblotting in 29 paired endometrial tumor and adjacent nontumor control tissues. The level of three alternative splicing related proteins: RBM5, RBM6, and RBM10 was determined by immunoblotting. The H3K27Ac level in RBM10 promoter region was detected by ChIP-PCR. The RBM10 promoter region methylation level were quantified by methylation-sensitive high resolution melting. VEGFA165a was overexpressed and VEGFA165b level was reduced in tumors. RBM10 level was reduced in tumors. RBM10 level was negatively correlated with VEGFA165a level and positively correlated with VEGFA165b level in tumors. Using HEC-1-A and RL95-2 cells, we confirmed that VEGFA165a/b expressed pattern was controlled by RBM10. MALAT1 level was increased in tumors but not involved in VEGFA alternative splicing. Reduced H3K27Ac level and increased DNA methylation in the promoter region controlled RBM10 expression in tumors. VEGFA alternative splicing in endometrial cancer was regulated by RBM10, the expression of which was controlled by histone acetylation and DNA methylation.

Identification of prognosis-related alternative splicing events in kidney renal clear cell carcinoma

Alternative splicing (AS) contributes to protein diversity by modifying most gene transcriptions. Cancer generation and progression are associated with specific splicing events. However, AS signature in kidney renal clear cell carcinoma (KIRC) remains unknown. In this study, genome‐wide AS profiles were generated in 537 patients with KIRC in the cancer genome atlas. With a total of 42 522 mRNA AS events in 10 600 genes acquired, 8164 AS events were significantly associated with the survival of patients with KIRC. Logistic regression analysis of the least absolute shrinkage and selection operator was conducted to identify an optimized multivariate prognostic predicting mode containing four predictors. In this model, the receptor‐operator characteristic curves of the training set were built, and the areas under the curves (AUCs) at different times were >0.88, thus indicating a stable and powerful ability in distinguishing patients' outcome. Similarly, the AUCs of the test set at different times were >0.73, verifying the results of the training set. Correlation and gene ontology analyses revealed some potential functions of prognostic AS events. This study provided an optimized survival‐predicting model and promising data resources for future in‐depth studies on AS mechanisms in KIRC.

Survival-Associated Alternative Messenger RNA Splicing Signatures in Pancreatic Ductal Adenocarcinoma: A Study Based on RNA-Sequencing Data

Multiple studies have shown that cancer-specific alternative splicing (AS) alterations are associated with clinical outcome. In this study, we aimed to profile prognostic AS signatures for pancreatic ductal adenocarcinoma (PDAC). We integrated the percent-spliced-in (PSI) data of AS in 140 PDAC patients based on the Cancer Genome Atlas (TCGA) dataset. We identified overall survival (OS)-associated AS events using univariate Cox regression analysis. Then, prognostic AS signatures were constructed for OS and chemoresistance prediction using the least absolute shrinkage and selection operator (LASSO) method. We also analyzed splicing factors (SFs) regulatory networks by Pearson's correlation. We detected 677 OS-related AS events in 485 genes by profiling 10,354 AS events obtained from 140 PDAC patients. Gene functional enrichment analysis demonstrated the pathways enriched by survival-associated AS. The AS signatures constructed with significant survival-associated AS events revealed high performance in predicting PDAC survival and gemcitabine chemoresistance. The area under the receiver operator characteristic curve was 0.937 in training cohort and 0.748 in validation cohort at 2000 days of OS. Furthermore, we identified prognostic SFs (e.g., ESRP1 and HNRNPC) to build the AS regulatory network. We constructed AS signatures for OS and gemcitabine chemoresistance in PDAC patients, which may provide clues for further experiment-based mechanism study.

Systematic analysis of survival-associated alternative splicing signatures uncovers prognostic predictors for head and neck cancer

BACKGROUND

Previous studies have shown that alternative splicing (AS) plays a key role in carcinogenesis and prognosis of cancer. However, systematic profiles of AS signatures in head and neck cancer (HNC) have not yet been reported.

METHODS

In this study, AS data, RNA-Seq data, and corresponding clinicopathological information of 489 HNC patients were downloaded from The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses were performed to screen for survival-associated AS events. Functional and pathway enrichment analysis was also performed. The prognostic models and splicing networks were constructed using integrated bioinformatics analysis tools.

RESULTS

Among the 42,849 alternating splicing events identified in 10,121 genes, 5,165 survival-associated AS events in 2,419 genes were observed in univariate Cox regression analysis. Among the seven types, alternate terminator events were the most powerful prognostic factors. Multivariate Cox analysis was then used to screen for the AS genes with prognostic value. Four candidate genes (TPM1, CLASRP, PRRC1, and DNASE1L1) were found to be independent prognostic factors for HNC patients. A prognostic prediction model was built based on the four genes. The area under the receiver operating characteristic risk score curve for predicting the survival status of HNC patients was 0.704. In addition, splicing interaction network indicated that the splicing factors have significant functions in HNC.

CONCLUSION

A comprehensive analysis of AS events in HNC was performed. A powerful prognostic predictor for HNC patients was established based on AS events could.

Identification of neoantigens derived from alternative splicing and RNA modification

The acquisition of somatic mutations is the most common event in cancer. Neoantigens expressed from genes with mutations acquired during carcinogenesis can be tumor-specific. Since the immune system recognizes tumor-specific peptides, they are potential targets for personalized neoantigen-based immunotherapy. However, the discovery of druggable neoantigens remains challenging, suggesting that a deeper understanding of the mechanism of neoantigen generation and better strategies to identify them will be required to realize the promise of neoantigen-based immunotherapy. Alternative splicing and RNA editing events are emerging mechanisms leading to neoantigen production. In this review, we outline recent work involving the large-scale screening of neoantigens produced by alternative splicing and RNA editing. We also describe strategies to predict and validate neoantigens from RNA sequencing data.

Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma

There is growing evidence that alternative splicing (AS) plays an important role in cancer development. However, a comprehensive analysis of AS signatures in kidney renal clear cell carcinoma (KIRC) is lacking and urgently needed. It remains unclear whether AS acts as diagnostic biomarkers in predicting the prognosis of KIRC patients. In the work, gene expression and clinical data of KIRC were obtained from The Cancer Genome Atlas (TCGA), and profiles of AS events were downloaded from the SpliceSeq database. The RNA sequence/AS data and clinical information were integrated, and we conducted the Cox regression analysis to screen survival‐related AS events and messenger RNAs (mRNAs). Correlation between prognostic AS events and gene expression were analyzed using the Pearson correlation coefficient. Protein‐protein interaction analysis was conducted for the prognostic AS‐related genes, and a potential regulatory network was built using Cytoscape (version 3.6.1). Meanwhile, functional enrichment analysis was conducted. A prognostic risk score model is then established based on seven hub genes (KRT222, LENG8, APOB, SLC3A1, SCD5, AQP1, and ADRA1A) that have high performance in the risk classification of KIRC patients. A total 46,415 AS events including 10,601 genes in 537 patients with KIRC were identified. In univariate Cox regression analysis, 13,362 survival associated AS events and 8,694 survival‐specific mRNAs were detected. Common 3,105 genes were screen by overlapping 13,362 survival associated AS events and 8,694 survival‐specific mRNAs. The Pearson correlation analysis suggested that 13 genes were significantly correlated with AS events (Pearson correlation coefficient >0.8 or <−0.8). Then, We conducted multivariate Cox regression analyses to select the potential prognostic AS genes. Seven genes were identified to be significantly related to OS. A prognostic model based on seven genes was constructed. The area under the ROC curve was 0.767. In the current study, a robust prognostic prediction model was constructed for KIRC patients, and the findings revealed that the AS events could act as potential prognostic biomarkers for KIRC.

Alteration of splicing factors' expression during liver disease progression: impact on hepatocellular carcinoma outcome

PURPOSE

Trans-acting splicing factors (SF) shape the eukaryotic transcriptome by regulating alternative splicing (AS). This process is recurrently modulated in liver cancer suggesting its direct contribution to the course of liver disease. The aim of our study was to investigate the relationship between the regulation of SFs expression and liver damage.

METHODS

The expression profile of 10 liver-specific SF and the AS events of 7 genes associated with liver disorders was assessed by western-blotting in 6 murine models representing different stages of liver damage, from inflammation to hepatocellular carcinoma (HCC). Relevant SFs (PSF, SRSF3, and SRSF6) and target genes (INSR, SRSF3, and SLK) modulated in mice were investigated in a cohort of 179 HCC patients.

RESULTS

Each murine model of liver disease was characterized by a unique SF expression profile. Changes in the SF profile did not affect AS events of the selected genes despite the presence of corresponding splicing sites. In human HCC expression of SFs, including the tumor-suppressor SRSF3, and AS regulation of genes studied were frequently upregulated in tumor versus non-tumor tissues. Risk of tumor recurrence positively correlated with AS isoform of the INSR gene. In contrast, increased levels of SFs expression correlated with an extended overall survival of patients.

CONCLUSIONS

Dysregulation of SF expression is an early event occurring during liver injury and not just at the stage of HCC. Besides impacting on AS regulation, overexpression of SF may contribute to preserving hepatocyte homeostasis during liver pathogenesis.

Prognostic alternative mRNA splicing signature in hepatocellular carcinoma: a study based on large-scale sequencing data

Most genes are alternatively spliced and increasing number of evidences show that alternative splicing (AS) is modified and related to tumor progression. Systematic profiles of AS signature in hepatocellular carcinoma (HCC) is absent and urgently needed. Here, differentially spliced AS transcripts between HCC and non-HCC tissues were compared, prognosis-associated AS events by using univariate Cox regression analysis were selected. Our gene functional enrichment analysis demonstrated the potential pathways enriched by survival-associated AS. Prognostic AS signatures were then constructed for HCC prognosis prediction by Lasso regression model. We also analyzed splicing factors (SFs) regulating underlying mechanisms by Pearson correlation and then built corresponding regulatory networks. In addition, we explored the performance of AS signature in the mutated HCC samples. Genome-wide AS events in 377 HCC patients from TCGA were profiled. Among 34 163 AS events in 8985 genes, 3950 AS events in 2403 genes associated with overall survival (OS) significantly for HCC were detected. In addition, computational algorithm results showed that metabolic and ribosome pathways may be the potential molecular mechanisms regulating the poor prognosis. More importantly, survival-associated AS signatures revealed high performance in predicting HCC prognosis. The area under curve for AS signature was 0.806 in all HCC and 0.944 in TP53 mutated HCC samples at 2000 days of OS. We submitted prognostic SFs to build the AS regulatory network, from which we found prognostic AS events were significantly enriched in metabolism-related pathways. A robust AS signature for HCC patients and revealed the regulatory splicing networks contributing to the potential significantly enriched metabolism-related pathways.