Alternative Splicing Signatures Research Articles

Ferroptosis-related alternative splicing signatures as potential biomarkers for predicting prognosis and therapy response in gastric cancer

Ferroptosis is linked to various tumor biological traits, and alternative splicing (AS), a crucial step in mRNA processing, plays a role in the post-transcriptional regulation of ferroptosis-related genes (FRGs). A least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis was utilized to build a prognostic signature based on 12 AS events (p < 0.05), which was validated in gastric cancer (GC) patients. The high-risk group (n = 203) showed enrichment in cancer and metastasis pathways (p < 0.05). Significant differences existed between the high- and low-risk groups in terms of tumor microenvironment (TME) cell infiltration and immune activities (p < 0.05). The low-risk group (n = 203) was characterized by immune activation and improved prognosis (p < 0.001). Additionally, targeted treatment and immunotherapy were more likely to benefit the low-risk group (p < 0.05). Correlation analysis was performed to detect related splicing factors (SF) (Cor>0.4, FDR<0.05). Furthermore, our functional assay results suggested that high SF3A2 expression might increase ferroptosis resistance and promote cell proliferation. In conclusion, the FRAs model we built has an advantage in predicting GC prognosis. The model's demonstration of variations in the immune microenvironment and drug response could potentially inform decisions regarding treatment strategies.

Identification of pancreatic adenocarcinoma immune subtype associated with tumor neoantigen from aberrant alternative splicing.

Pancreatic adenocarcinoma (PAAD) is referred to as an immunologically "cold" tumor that responds poorly to immunotherapy. A fundamental theory that explains the low immunogenicity of PAAD is the dramatically low tumor mutation burden (TMB) of PAAD tumors, which fails to induce sufficient immune response. Alternative splicing of pre-mRNA, which could alter the proteomic diversity of many cancers, has been reported to be involved in neoantigen production. Therefore, we aim to identify novel PAAD antigens and immune subtypes through systematic bioinformatics research. Data for splicing analysis were downloaded from The Cancer Genome Atlas (TCGA) SpliceSeq database. Among the available algorithms, we chose CIBERSORT to evaluate the immune cell distribution among PAADs. The TCGA-PAAD expression matrix was used to construct a co-expression network. Single-cell analysis was performed based on the Seurat workflow. Integrated analysis of aberrantly upregulated genes, alternatively spliced genes, genes associated with nonsense-mediated RNA decay (NMD) factors, antigen presentation and overall survival (OS) in TCGA-PAAD revealed that PLEC is a promising neoantigen for PAAD-targeted therapy. We identified a C2 TCGA-PAAD subtype that had better prognosis and more CD8+ T-cell infiltration. We propose a novel immune subtyping system for PAAD to indicate patient prognosis and opportunities for immunotherapy, such as immune checkpoint (ICP) inhibitors. In conclusion, the present study used a transcriptome-guided approach to screen neoantigen candidates based on alternative splicing, NMD factors, and antigen-presenting signatures for PAAD. A prognosis model with guidance of immunotherapy will aid in patient selection for appropriate treatment.

Landscape analysis of alternative splicing in kidney renal clear cell carcinoma and their clinical significance.

A growing number of studies reveal that alternative splicing (AS) is associated with tumorigenesis, progression, and metastasis. Systematic analysis of alternative splicing signatures in renal cancer is lacking. In our study, we investigated the AS landscape of kidney renal clear cell carcinoma (KIRC) and identified AS predictive model to improve the prognostic prediction of KIRC. We obtained clinical data and gene expression profiles of KIRC patients from the TCGA database to evaluate AS events. The calculation results for seven types of AS events indicated that 46276 AS events from 10577 genes were identified. Next, we applied Cox regression analysis to identify 5864 prognostic-associated AS events. We used the Metascape database to verify the potential pathways of prognostic-associated AS. Moreover, we constructed KIRC prediction systems with prognostic-associated AS events by the LASSO Cox regression model. AUCs demonstrated that these prediction systems had excellent prognostic accuracy simultaneously. We identified 34 prognostic associated splicing factors (SFs) and constructed homologous regulatory networks. Furthermore, in vitro experiments were performed to validate the favorable effect of SFs FMR1 in KIRC. In conclusion, we overviewed AS events in KIRC and identified AS-based prognostic models to assist the survival prediction of KIRC patients. Our study may provide a novel predictive signature to improve the prognostic prediction of KIRC, which might facilitate KIRC patient counseling and individualized management.

An alternative splicing signature defines the basal-like phenotype and predicts worse clinical outcome in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is characterized by extremely poor prognosis. PDAC presents with molecularly distinct subtypes, with the basal-like one being associated with enhanced chemoresistance. Splicing dysregulation contributes to PDAC; however, its involvement in subtype specification remains elusive. Herein, we uncover a subtype-specific splicing signature associated with prognosis in PDAC and the splicing factor Quaking (QKI) as a determinant of the basal-like signature. Single-cell sequencing analyses highlight QKI as a marker of the basal-like phenotype. QKI represses splicing events associated with the classical subtype while promoting basal-like events associated with shorter survival. QKI favors a plastic, quasi-mesenchymal phenotype that supports migration and chemoresistance in PDAC organoids and cell lines, and its expression is elevated in high-grade primary tumors and metastatic lesions. These studies identify a splicing signature that defines PDAC subtypes and indicate that QKI promotes an undifferentiated, plastic phenotype, which renders PDAC cells chemoresistant and adaptable to environmental changes.

Clear cell renal cell carcinoma: immunological significance of alternative splicing signatures.

Renal cell carcinoma (RCC) accounts for 90% of renal cancers, of which clear cell carcinoma (ccRCC) is the most usual histological type. The process of alternative splicing (AS) contributes to protein diversity, and the dysregulation of protein diversity may have a great influence on tumorigenesis. We developed a prognostic signature and comprehensively analyzed the role of tumor immune microenvironment (TIME) and immune checkpoint blocking (ICB) treatment in ccRCC. To identify prognosis-related AS events, univariate Cox regression was used and functional annotation was performed using gene set enrichment analysis (GSEA). In this study, prognostic signatures were developed based on multivariate Cox, univariate Cox, and LASSO regression models. Moreover, to assess the prognostic value, the proportional hazards model, Kruskal-Wallis analysis, and ROC curves were used. To obtain a better understanding of TIME in ccRCC, the ESTIMATE R package, single sample gene set enrichment analysis (ssGSEA) algorithm, CIBERSORT method, and the tumor immune estimation resource (TIMER) were applied. The database was searched to verify the expression of C4OF19 in tumor and normal samples. Regulatory networks for AS-splicing factors (SFs) were visualized using Cytoscape 3.9.1. There were 9,347 AS cases associated with the survival of ccRCC patients screened. A total of eight AS prognostic signatures were developed with stable prognostic predictive accuracy based on splicing subtypes. In addition, a qualitative prognostic nomogram was developed, and the prognostic prediction showed high effectiveness. In addition, we found that the combined signature was significantly associated with the diversity of TIME and ICB treatment-related genes. C4ORF19 might become an important prognostic factor for ccRCC. Finally, the AS-SF regulatory network was established to clearly reveal the potential function of SFs. We found novel and robust indicators (i.e., risk signature, prognostic nomogram, etc.) for the prognostic prediction of ccRCC. A new and reliable prognostic nomogram was established to quantitatively predict the clinical outcome. The AS-SF networks could provide a new way for the study of potential regulatory mechanisms, and the important roles of AS events in the context of TIME and immunotherapy efficiency were exhibited. C4ORF19 was found to be a vital gene in TIME and ICB treatment.

Quaking isoforms cooperate to promote the mesenchymal phenotype.

The RNA-binding protein Quaking (QKI) has widespread effects on mRNA regulation including alternative splicing, stability, translation, and localization of target mRNAs. Recently, QKI was found to be induced during epithelial-mesenchymal transition (EMT), where it promotes a mesenchymal alternative splicing signature that contributes to the mesenchymal phenotype. QKI is itself alternatively spliced to produce three major isoforms, QKI-5, QKI-6, and QKI-7. While QKI-5 is primarily localized to the nucleus where it controls mesenchymal splicing during EMT, the functions of the two predominantly cytoplasmic isoforms, QKI-6 and QKI-7, in this context remain uncharacterized. Here we used CRISPR-mediated depletion of QKI in a human mammary epithelial cell model of EMT and studied the effects of expressing the QKI isoforms in isolation and in combination. QKI-5 was required to induce mesenchymal morphology, while combined expression of QKI-5 with either QKI-6 or QKI-7 further enhanced mesenchymal morphology and cell migration. In addition, we found that QKI-6 and QKI-7 can partially localize to the nucleus and contribute to alternative splicing of QKI target genes. These findings indicate that the QKI isoforms function in a dynamic and cooperative manner to promote the mesenchymal phenotype.

Identification of Bone Metastatic and Prognostic Alternative Splicing Signatures in Prostate Adenocarcinoma

As the most common nonepithelial malignancy, prostate adenocarcinoma (PRAD) is the fifth chief cause of cancer mortality in men. Distant metastasis often occurs in advanced PRAD and most patients are dying from it. However, the mechanism of PRAD progression and metastasis is still unclear. It’s widely reported that more than 94% of genes are selectively splicing in humans and many isoforms are particularly related with cancer progression and metastasis. Spliceosome mutations occur in a mutually exclusive manner in breast cancer, and different components of spliceosomes are targets of somatic mutations in different types of breast cancer. Existing evidence strongly supports the key role of alternative splicing in breast cancer biology, and innovative tools are being developed to use splicing events for diagnostic and therapeutic purposes. In order to identify if the PRAD metastasis is associated with alternative splicing events (ASEs), the RNA sequencing data and ASEs data of 500 PRAD patients were retrieved from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases. By Lasso regression, five genes were screened to construct the prediction model, with a good reliability by ROC curve. Additionally, results in both univariate and multivariate Cox regression analysis confirmed the well prognosis efficacy of the prediction model (both P < 0.001). Moreover, a potential splicing regulatory network was established and after multiple-database validation, we supposed that the signaling axis of HSPB1 up-regulating the PIP5K1C − 46,721 − AT (P < 0.001) might mediate the tumorigenesis, progression and metastasis of PRAD via the key members of Alzheimer’s disease pathway (SRC, EGFR, MAPT, APP and PRKCA) (P < 0.001).

Novel, alternative splicing signature to detect lymph node metastasis in prostate adenocarcinoma with machine learning.

The presence of lymph node metastasis leads to a poor prognosis for prostate cancer (Pca). Recently, many studies have indicated that gene signatures may be able to predict the status of lymph nodes. The purpose of this study is to probe and validate a new tool to predict lymph node metastasis (LNM) based on alternative splicing (AS). Gene expression profiles and clinical information of prostate adenocarcinoma cohort were retrieved from The Cancer Genome Atlas (TCGA) database, and the corresponding RNA-seq splicing events profiles were obtained from the TCGA SpliceSeq. Limma package was used to identify the differentially expressed alternative splicing (DEAS) events between LNM and non-LNM groups. Eight machine learning classifiers were built to train with stratified five-fold cross-validation. SHAP values was used to explain the model. 333 differentially expressed alternative splicing (DEAS) events were identified. Using correlation filter and the least absolute shrinkage and selection operator (LASSO) method, a 96 AS signature was identified that had favorable discrimination in the training set and validated in the validation set. The linear discriminant analysis (LDA) was the best classifier after 100 iterations of training. The LDA classifier was able to distinguish between LNM and non-LNM with an area under the receiver operating curve of 0.962 ± 0.026 in the training set (D1 = 351) and 0.953 in the validation set (D2 = 62). The decision curve analysis plot proved the clinical application of the AS-based model. Machine learning combined with AS data could robustly distinguish between LNM and non-LNM in Pca.

Comprehensive analysis of RNA-binding protein SRSF2-dependent alternative splicing signature in malignant proliferation of colorectal carcinoma

Aberrant expression of serine/arginine-rich splicing factor 2 (SRSF2) can lead to tumorigenesis, but its molecular mechanism in colorectal cancer is currently unknown. Herein, we found SRSF2 to be highly expressed in human colorectal cancer (CRC) samples compared with normal tissues. Both invitro and invivo, SRSF2 significantly accelerated the proliferation of colon cancer cells. Using RNA-seq, we screened and identified 33 alternative splicing events regulated by SRSF2. Knockdown of SLMAP-L or CETN3-S splice isoform could suppress the growth of colon cancer cells, predicting their role in malignant proliferation of colon cancer cells. Mechanistically, the invivo crosslinking immunoprecipitation assay demonstrated the direct binding of the RNA recognition motif of SRSF2 protein to SLMAP and CETN3 pre-mRNAs. SRSF2 activated the inclusion of SLMAP alternative exon 24 by binding to constitutive exon 25, while SRSF2 facilitated the exclusion of CETN3 alternative exon 5 by binding to neighboring exon 6. Knockdown of SRSF2, its splicing targets SLMAP-L, or CETN3-S caused colon cancer cells to arrest in G1 phase of the cell cycle. Rescue of SLMAP-L or CETN3-S splice isoform in SRSF2 knockdown colon cancer cells could effectively reverse the inhibition of cell proliferation by SRSF2 knockdown through mediating cell cycle progression. Importantly, the percentage of SLMAP exon 24 inclusion increased and CETN3 exon 5 inclusion decreased in CRC samples compared to paired normal samples. Collectively, our findings identify that SRSF2 dysregulates colorectal carcinoma proliferation at the molecular level of splicing regulation and reveal potential splicing targets in CRC patients.

Identification and validation of RNA methylation-related alternative splicing gene signature for low-grade glioma to predict survival and immune landscapes.

Low-grade glioma (LGG) is a crucial pathological type of glioma. The present study aimed to explore multiple RNA methylation regulator-related AS events and investigate their prognostic values in LGG. The prognostic model for low-grade glioma was established using the LASSO regression analysis. To validate prognostic value, we performed Kaplan-Maier survival analysis, ROC curves and nomograms. The ESTIMATE algorithm, the CIBERSORT algorithm and the ssGSEA algorithm were utilized to explore the role of the immune microenvironment in LGG. Subsequently, we then used GO, KEGG and GSEA enrichment analysis to explore the functional roles of these genes. In addition, we employed the GDSC database to screen potential chemotherapeutic agents. Eight RNA methylation related AS events were involved in construct a survival and prognosis model, which had good ability of independent prediction for patients with LGG. Patients in the high-risk group had shorter life expectancy and higher mortality, while patients in the low-risk group had a better prognosis. We constructed a nomogram which showed an excellent predictive performance for individual OS. The risk score exhibited a close correlation with some immune cells and expression of immune checkpoints. Patients in high-risk group were characterized by immunosuppressive microenvironment and poor response to immunotherapy, and were sensitive to more chemotherapeutic drugs. Pathway and functional enrichment analyses further confirmed that significant differences existed in immune landscape between the two subgroups. The prognostic RNA methylation-related alternative splicing signature constructed could constitute a promising prognostic biomarker, which could serve to optimize treatment regimens.

Comprehensive analysis of alternative splicing signatures in pancreatic head cancer.

The correlation between dysregulation of splicing and cancers has been increasingly recognised and confirmed. The identification of valuable alternative splicing (AS) in pancreatic head cancer (PHC) has a great significance. AS profiles in PHC were generated using the data from The Cancer Genome Atlas and TCGASpliceSeq. Then, the NMF clustering method was performed to identify overall survival-associated AS (OS-AS) subtypes in PHC patients. Subsequently, we used least absolute shrinkage and selection operator Cox regression analysis to construct an AS-related risk model. The splicing regulatory network was uncovered by Cytoscape 3.7. A total of 1694 OS-AS events were obtained. The PHC patients were divided into clusters 1 and 2. Cluster 1 had poorer prognosis and lower infiltration of immune cells. Subsequently, a prognostic signature was established that showed good performance in predicting OS and progression-free survival. The risk score of this signature was associated with the unique tumour immunity. Moreover, a nomogram incorporating the risk score and clinicopathological parameters was established. Finally, a splicing factor-AS regulatory network was developed. A comprehensive analysis of the AS events in PHC associated with prognosis and tumour immunity may help provide reliable information to guide individual treatment strategies.

Alternative splicing signature of alveolar type II epithelial cells of Tibetan pigs under hypoxia-induced.

Alternative splicing (AS) allows the generation of multiple transcript variants from a single gene and affects biological processes by generating protein diversity in organisms. In total, 41,642 AS events corresponding to 9,924 genes were identified, and SE is the most abundant alternatively spliced type. The analysis of functional categories demonstrates that alternatively spliced differentially expressed genes (DEGs) were enriched in the MAPK signaling pathway and hypoxia-inducible factor 1 (HIF-1) signaling pathway. Proteoglycans in cancer between the normoxic (21% O2, TN and LN) and hypoxic (2% O2, TL and LL) groups, such as SLC2A1, HK1, HK2, ENO3, and PFKFB3, have the potential to rapidly proliferate alveolar type II epithelial (ATII) cells by increasing the intracellular levels of glucose and quickly divert to anabolic pathways by glycolysis intermediates under hypoxia. ACADL, EHHADH, and CPT1A undergo one or two AS types with different frequencies in ATII cells between TN and TL groups (excluding alternatively spliced DEGs shared between normoxic and hypoxic groups), and a constant supply of lipids might be obtained either from the circulation or de novo synthesis for better growth of ATII cells under hypoxia condition. MCM7 and MCM3 undergo different AS types between LN and LL groups (excluding alternatively spliced DEGs shared between normoxic and hypoxic groups), which may bind to the amino-terminal PER-SIM-ARNT domain and the carboxyl terminus of HIF-1α to maintain their stability. Overall, AS and expression levels of candidate mRNAs between Tibetan pigs and Landrace pigs revealed by RNA-seq suggest their potential involvement in the ATII cells grown under hypoxia conditions.

Identification of RNA Modification-Associated Alternative Splicing Signature as an Independent Factor in Head and Neck Squamous Cell Carcinoma

Objective Head and neck squamous cell carcinoma (HNSCC) is a highly heterotopic malignant tumor. Alternative splicing (AS) and RNA modification have been reported to be involved in tumorigenesis. Therefore, we constructed RNA modification-associated AS (RMA-AS) signature model to predict the prognosis of HNSCC. Methods AS events and RNA-modified gene expression information were downloaded from TCGA-HNSCC database. Univariate Cox regression analysis was employed for analyzing prognosis-related AS events. RMA-AS events were obtained by constructing a coexpression network between RNA modification-associated genes and AS events using WGCNA package. The prognostic signatures were analyzed by LASSO, univariate Cox, and multivariate Cox regression. Kaplan-Meier survival analysis, proportional hazard model, and ROC curve were performed to verify the prognostic value. “ESTIMATE” R package, ssGSEA algorithm, and CIBERSORT method were used to detect immune microenvironment in HNSCC. Cytoscape was utilized to build a regulatory network of splicing factor-regulated RMA-AS. Results There were 16,574 prognostic AS events and 4 differentially expressed RNA modification-associated genes in HNSCC. Based on RMA-AS events, we obtained a risk model consisting of 14 AS events, named RMA-AS_Score. The samples were divided into RMA-AS_Score high- and RMA-AS_Score low-risk groups, according to the risk score. The RMA-AS_Score high group was related to poor prognosis. Moreover, the RMA-AS_Score signature was an independent prognostic predictor and was related to tumor grade. Meanwhile, the AUC value of RMA-AS_Score was 0.652, which is better than other clinical characteristics. Besides, a nomogram prediction model of quantitative prognosis has also been developed, which has robust effectiveness in predicting prognosis. In addition, the prognostic signature was observably related to immune microenvironment and immune checkpoint. Finally, 14 splicing factors were identified and constructed into a network of splicing factor-regulated RMA-AS. Conclusion We identified the RMA-AS signature of HNSCC. This signature could be treated as an independent prognostic predictor.

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.

Alternative splicing signatures of congenital heart disease and induced pluripotent stem cell-derived cardiomyocytes from congenital heart disease patients.

Congenital heart disease (CHD) is the most serious congenital defect in newborns with higher mortality. Alternative splicing (AS) plays an essential role in numerous heart diseases. However, our understanding of the link between mRNA splicing and CHD in humans is limited. Here, we try to investigate the genome-wide AS events in CHD using bioinformatics methods. We collected available RNA-seq datasets of CHD-induced pluripotent stem cell-cardiomyocytes (iPSC-CMs) (including single ventricle disease [SVD] and tetralogy of Fallot [TOF]) and non-CHD from the Gene Expression Omnibus database. Then, we unprecedentedly performed AS profiles in CHD-iPSC-CMs and non-CHD-iPSC-CMs. The rMAPS was used to generate RNA-maps for the analysis of RNA-binding proteins’ (RBPs) binding sites. We used StringTie to identify and quantify the transcripts from aligned RNA-Seq reads. A quantification matrix was generated with respect to different groups by extracting the transcripts per million values from StringTie outputs. Then, this matrix was used for correlation analysis between the expression level of RBP and AS level. Finally, we validated our AS results using RNA-seq data from CHD and non-CHD patient tissue samples. We identified CHD-related AS events using CHD-iPSC-CMs and CHD samples from patients. The results showed that functional enrichment of abnormal AS in SVD and TOF was transcription factor-related. Using rMAPS, RNA-binding proteins which regulated these AS were also determined, and RBP-AS regulatory network was constructed. Overall, we identified abnormal AS in CHD-iPSC-CMs and CHD samples from patients. We predicted AS regulators in SVD and TOF, respectively. At last, we concluded that AS played a key role in the pathogenesis of CHD.

Development of a Prognostic Alternative Splicing Signature Associated With Tumor Microenvironment Immune Profiles in Lung Adenocarcinoma

BackgroundAlternative splicing (AS), a pivotal post-transcriptional process across more than 95% of human transcripts, is involved in transcript structural variations and protein complexity. Clinical implications of AS events and their interaction with tumor immunity were systematically analyzed in lung adenocarcinoma (LUAD).MethodsTranscriptome profiling as well as AS data of LUAD were retrospectively curated. Then, the network of the overall survival (OS)-relevant AS events with splicing factors was established. After screening OS-relevant AS events, a LASSO prognostic model was conducted and evaluated with ROC curves. A nomogram that integrated independent prognostic indicators was created. Immune response and immune cell infiltration were estimated with ESTIMATE, CIBERSORT, and ssGSEA algorithms. Drug sensitivity was inferred with pRRophetic package.ResultsIn total, 2415 OS-relevant AS events were identified across LUAD patients. The interaction network of splicing factors with OS-relevant AS events uncovered the underlying regulatory mechanisms of AS events in LUAD. Thereafter, a prognostic model containing 12 AS events was developed, which acted as a reliable and independent prognostic indicator following verification. A nomogram that constituted stage and risk score displayed great effectiveness in evaluating the survival likelihood. Moreover, the AS-based prognostic model was in relation to immune response and immune cell infiltration. Patients with a high-risk score displayed therapeutic superiority to cisplatin, erlotinib, gefitinib, and gemcitabine. Finally, three AS-relevant genes (CDKN2A, TTC39C, and PKIB) were identified as prognostic markers.ConclusionCollectively, our findings developed an AS event signature with powerful prognostic predictive efficacy in LUAD.

Identification and Validation of Novel Immune-Related Alternative Splicing Signatures as a Prognostic Model for Colon Cancer.

BackgroundIndividual immune-related alternative splicing (AS) events have been found to be significant in immune regulation and cancer prognosis. However, a comprehensive analysis of AS events in cancer cells based on immune-related genes (IRGs) has not been performed, and its clinical value is unknown.MethodsColon cancer cases with AS data were obtained from TCGA, and then, we identified overall survival-related AS events (OS-ASEs) based on IRGs by univariate analyses. Using Lasso regression, multivariate Cox regression, Kaplan–Meier analysis and nomograms, we constructed an AS risk model based on the calculated risk score. Furthermore, associations of the risk score with clinical and immune features were confirmed through the Wilcoxon rank sum test, association analysis, etc. Finally, by qRT–PCR, cell coculture and CCK-8 analyses, we validated the significance of OS-ASEs in colon cancer cell lines and clinical samples.ResultsA total of 3,119 immune-related AS events and 183 OS-ASEs were identified, and 9 OS-ASEs were ultimately used to construct a comprehensive risk model for colon cancer patients. Low-risk patients had better OS and DFS rates than high risk patients. Furthermore, a high risk score corresponded to high numbers of multiple tumour-infiltrating immune cells and high expression of HLA-D region genes and immune checkpoint genes. Notably, we identified for the first time that anti-PD-L1 or anti-CTLA-4 antibodies may decrease the OS of specific colon cancer patients in the low-risk group. Additionally, the in vitro experiment validated that CD46-9652-ES and PSMC5-43011-ES are positively correlated with the infiltration of immune cells and promote the growth of colon cancer cells. CD46-9652-ES can contribute to T cell-mediated tumour cell killing. PSMC5-43011-ES was observed to induce M2 polarization of macrophages.ConclusionsThis study identified and validated immune-related prognostic AS signatures that can be used as a novel AS prognostic model and provide a novel understanding of the relationship between the immune microenvironment and clinical outcomes.

Identification of prognostic alternative splicing signature in gastric cancer

BackgroundAberrant alternative splicing (AS) events could be viewed as prognostic indicators in a large number of malignancies. This study aims to identify prognostic AS events, illuminate the function of the splicing variants biomarkers and provide reliable evidence for formulating public health strategies for gastric cancer (GC) surveillance.MethodsRNA-Seq data, clinical information and percent spliced in (PSI) values were available in The cancer genome atlas (TCGA) and TCGA SpliceSeq data portal. A three-step regression method was conducted to identify prognostic AS events and construct multi-AS-based signatures. The associations between prognostic AS events and splicing factors were also investigated.ResultsWe identified a total of 1,318 survival-related AS events in GC, parent genes of which were implicated in numerous oncogenic pathways. The final prognostic signatures stratified by seven types of AS events or not stratified performed well in risk prediction for GC patients. Moreover, five signatures based on AA, AD, AT, ES and RI events function as independent prognostic indicators after multivariate adjustment of other clinical variables. Splicing network also showed marked correlation between the expression of splicing factors and PSI value of AS events in GC patients.ConclusionOur findings provide a landscape of AS events and regulatory network in GC, indicating that AS events might serve as prognostic biomarkers and therapeutic targets for GC.

DJExpress: An Integrated Application for Differential Splicing Analysis and Visualization.

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

Immunotherapeutic Significance of a Prognostic Alternative Splicing Signature in Bladder Cancer.

Objectives: Bladder cancer is the fourth most common malignancy in men in the United States. Aberrant alternative splicing (AS) events are involved in the carcinogenesis, but the association between AS and bladder cancer remains unclear. This study aimed to construct an AS-based prognostic signature and elucidate the role of the tumor immune microenvironment (TIME) and the response to immunotherapy and chemotherapy in bladder cancer. Methods: Univariate Cox regression analysis was performed to detect prognosis-related AS events. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were employed to build prognostic signatures. Kaplan–Meier survival analysis, multivariate Cox regression analysis, and receiver operating characteristic (ROC) curves were conducted to validate the prognostic signatures. Then, the Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and tumor immune estimation resource (TIMER) databases were searched and the single-sample gene set enrichment analysis (ssGSEA) algorithm and CIBERSORT method were performed to uncover the context of TIME in bladder cancer. The Tumor Immune Dysfunction and Exclusion (TIDE) web tool and pRRophetic algorithm were used to predict the response to immunotherapy and chemotherapy. Finally, we constructed a correlation network between splicing factors (SFs) and survival-related AS events. Results: A total of 4684 AS events were significantly associated with overall survival in patients with bladder cancer. Eight prognostic signatures of bladder cancer were established, and a clinical survival prediction model was built. In addition, the consolidated prognostic signature was closely related to immune infiltration and the response to immunotherapy and chemotherapy. Furthermore, the correlation identified EIF3A, DDX21, SDE2, TNPO1, and RNF40 as hub SFs, and function analysis found ubiquitin-mediated proteolysis is correlated most significantly with survival-associated AS events. Conclusion: Our findings highlight the prognostic value of AS for patients with bladder cancer and reveal pivotal players of AS events in the context of TIME and the response to immunotherapy and chemotherapy, which may be important for patient management and treatment.