Survival-associated Alternative Splicing Events Research Articles

Characterization of alternative splicing events and prognostic signatures in gastric cancer

BackgroundAccumulating evidences indicate that the specific alternative splicing (AS) events are linked to the occurrence and prognosis of gastric cancer (GC). Nevertheless, the impact of AS is still unclear and needed to further elucidation.MethodsThe expression profile of GC and normal samples were downloaded from TCGA. AS events were achieved from SpliceSeq database. Cox regression together with LASSO analysis were employed to identify survival-associated AS events (SASEs) and calculate risk scores. PPI and pathway enrichment analysis were implemented to determine the function and pathways of these genes. Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic Curves were used to evaluate the clinical significance of genes of SASEs. Q-PCR were applied to validate the hub genes on the survival prognosis in 47 GC samples. Drug sensitivity and immune cell infiltration analysis were conducted.ResultsIn total, 48 140 AS events in 10 610 genes from 361 GC and 31 normal samples were analyzed. Through univariate Cox regression, 855 SASEs in 763 genes were screened out. Further, these SASEs were analyzed by PPI and 17 hub genes were identified. Meanwhile, using Lasso and multivariate Cox regression analysis, 135 SASEs in 132 genes related to 7 AS forms were further screened and a GC prognostic model was constructed. K-M curves indicates that high-risk group has poorer prognosis. And the nomogram analysis on the basis of the multivariate Cox analysis was disclosed the interrelationships between 7 AS forms and clinical parameters in the model. Five key genes were then screened out by PPI analysis and Differential Expression Gene analysis based on TCGA and Combined-dataset, namely STAT3, RAD51B, SOCS2, POLE2 and TSR1. The expression levels of AS in STAT3, RAD51B, SOCS2, POLE2 and TSR1 were all significantly correlated with survival by qPCR verification. Nineteen drugs were sensitized to high-risk patients and eight immune cells showed significantly different infiltration between the STAD and normal groups.ConclusionsIn this research, the prognostic model constructed by SASEs can be applied to predict the prognosis of GC patients and the selected key genes are expected to become new biomarkers and therapeutical targets for GC treatment.

Construction and validation of a prognostic risk model for uterine corpus endometrial carcinoma based on alternative splicing events*

Abstract Objective To establish a prognostic risk model for uterine corpus endometrial carcinoma (UCEC) based on alternative splicing (AS) event data from The Cancer Genome Atlas (TCGA) and assess the accuracy of the model. Methods TCGA and SpliceSeq databases were used to acquire a summary of AS events and clinical data related to UCEC. Bioinformatic analysis was performed to identify differentially expressed AS events in UCEC. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were used for constructing a prognostic risk model. Next, using the receiver operating characteristic (ROC) curve, Kaplan-Meier survival analysis, and independent prognostic analysis, we assessed the accuracy of the model. In addition, a splicing network was established based on the association between potential splicing factors and AS events. Results We downloaded clinical data and AS events of 527 UCEC cases from TCGA and SpliceSeq databases, respectively. We obtained 18,779 survival-associated AS events in UCEC using univariate Cox regression analysis and 487 AS events using LASSO regression analysis. Multivariate Cox regression analysis established a prognostic risk model for UCEC based on the percentage splicing value of 13 AS events. Independent prognostic effect on UCEC risk was then assessed using multivariate and univariate Cox regression analyses (P < 0.001). The area under the curve was 0.827. The pathological stage and risk score were independent prognostic factors for UCEC. Herein, we established a regulatory network between alternative endometrial cancer-related splicing events and splicing factors. Conclusion We constructed a prognostic model of UCEC based on 13 AS events by analyzing datasets from TCGA and SpliceSeq databases with medium accuracy. The pathological stage and risk score were independent prognostic factors in the prognostic risk model.

Construction of the prognostic signature of alternative splicing revealed the prognostic predictor and immune microenvironment in head and neck squamous cell carcinoma.

Background: Head and neck squamous cell carcinoma (HNSC) is a prevalent and heterogeneous malignancy with poor prognosis and high mortality rates. There is significant evidence of alternative splicing (AS) contributing to tumor development, suggesting its potential in predicting prognosis and therapeutic efficacy. This study aims to establish an AS-based prognostic signature in HNSC patients. Methods: The expression profiles and clinical information of 486 HNSC patients were downloaded from the TCGA database, and the AS data were downloaded from the TCGA SpliceSeq database. The survival-associated AS events were identified by conducting a Cox regression analysis and utilized to develop a prognostic signature by fitting into a LASSO-regularized Cox regression model. Survival analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve analysis were performed to evaluate the signature and an independent cohort was used for validation. The immune cell function and infiltration were analyzed by CIBERSORT and the ssGSEA algorithm. Results: Univariate Cox regression analysis identified 2726 survival-associated AS events from 1714 genes. The correlation network reported DDX39B, PRPF39, and ARGLU1 as key splicing factors (SF) regulating these AS events. Eight survival-associated AS events were selected and validated by LASSO regression to develop a prognostic signature. It was confirmed that this signature could predict HNSC outcomes independent of other variables via multivariate Cox regression analysis. The risk score AUC was more than 0.75 for 3years, highlighting the signature's prediction capability. Immune infiltration analysis reported different immune cell distributions between the two risk groups. The immune cell content was higher in the high-risk group than in the low-risk group. The correlation analysis revealed a significant correlation between risk score, immune cell subsets, and immune checkpoint expression. Conclusion: The prognostic signature developed from survival-associated AS events could predict the prognosis of HNSC patients and their clinical response to immunotherapy. However, this signature requires further research and validation in larger cohort studies.

The link between aberrant splicing and B cell infiltration in breast cancer hormone therapy

Increasing evidence has suggested alternative splicing (AS) is an essential mechanism for tumor initiation and progression. However, the functions of aberrant splicing have not been well understood in hormone therapy-treated breast cancer (HTBC) patients yet. By mining the SpliceSeq data of TCGA, we characterized 1388 survival-associated AS events (SASEs) that are markedly related to the overall survival (OS) of HTBC patients. Subsequently, a SASE prognostic signature was deduced via the least absolute shrinkage and selection operator (LASSO) and multivariate Cox analysis, and the HTBC patients were stratified into high- and low-risk groups based on the signature. The differentially expressed genes between the high- and low-risk patients were enriched with immune response genes. Furthermore, we revealed that B cell infiltration was significantly low in the high-risk patients with single-sample gene set enrichment analysis (ssGSEA), suggesting a link between AS abnormality and B cell infiltration in tumor immune microenvironment (TIME). Next, a similar prognostic signature based on splicing factor expression was deduced using Molecular Taxonomy of Breast Cancer (METABRIC) HTBC patients. When this prognostic signature was applied to stratify HTBC patients in the METABRIC and TCGA cohorts, the high-risk subgroups both had significantly low B cell infiltration in TIME. CIBERSORT analysis further demonstrated plasma cells infiltrated less in TIME. Also, both the AS (P < 0.0001) and SF (P = 0.009) prognostic signatures performed well in predicting OS of HTBC patients. In summary, our study has demonstrated that AS abnormality is a potential cause of B cell infiltration alteration, which is a critical factor for cancer death risks in HTBC patients and might be exploited therapeutically in the future.

Identification of survival-related alternative splicing signatures in acute myeloid leukemia.

Aberrant RNA alternative splicing (AS) variants play critical roles in tumorigenesis and prognosis in human cancers. Here, we conducted a comprehensive profiling of aberrant AS events in acute myeloid leukemia (AML). RNA AS profile, including seven AS types, and the percent spliced in (PSI) value for each patient were generated by SpliceSeq using RNA-seq data from TCGA. Univariate followed by multivariate Cox regression analysis were used to identify survival-related AS events and develop the AS signatures. A nomogram was developed, and its predictive efficacy was assessed. About 27,892 AS events and 3,178 events were associated with overall survival (OS) after strict filtering. Parent genes of survival-associated AS events were mainly enriched in leukemia-associated processes including chromatin modification, autophagy, and T-cell receptor signaling pathway. The 10 AS signature based on seven types of AS events showed better efficacy in predicting OS of patients than those built on a single AS event type. The area under curve (AUC) value of the 10 AS signature for 3-year OS was 0.91. Gene set enrichment analysis (GSEA) confirmed that these survival-related AS events contribute to AML progression. Moreover, the nomogram showed good predictive performance for patient's prognosis. Finally, the correlation network of AS variants with splicing factor genes found potential important regulatory genes in AML. The present study presented a systematic analysis of survival-related AS events and developed AS signatures for predicting the patient’s survival. Further studies are needed to validate the signatures in independent AML cohorts and might provide a promising perspective for developing therapeutic targets.

Survival-associated alternative splicing events interact with the immune microenvironment in stomach adenocarcinoma.

BACKGROUNDAlternative splicing (AS) increases the diversity of mRNA during transcription; it might play a role in alteration of the immune microenvironment, which could influence the development of immunotherapeutic strategies against cancer.AIMTo obtain the transcriptomic and clinical features and AS events in stomach adenocarcinoma (STAD) from the database. The overall survival data associated with AS events were used to construct a signature prognostic model for STAD. METHODSDifferentially expressed immune-related genes were identified between subtypes on the basis of the prognostic model. In STAD, 2042 overall-survival-related AS events were significantly enriched in various pathways and influenced several cellular functions. Furthermore, the network of splicing factors and overall-survival-associated AS events indicated potential regulatory mechanisms underlying the AS events in STAD.RESULTSAn eleven-AS-signature prognostic model (CD44|14986|ES, PPHLN1|21214|AT, RASSF4|11351|ES, KIAA1147|82046|AP, PPP2R5D|76200|ES, LOH12CR1|20507|ES, CDKN3|27569|AP, UBA52|48486|AD, CADPS|65499|AT, SRSF7| 53276|RI, and WEE1|14328|AP) was constructed and significantly related to STAD overall survival, immune cells, and cancer-related pathways. The differentially expressed immune-related genes between the high- and low-risk score groups were significantly enriched in cancer-related pathways. CONCLUSIONThis study provided an AS-related prognostic model, potential mechanisms for AS, and alterations in the immune microenvironment (immune cells, genes, and pathways) for future research in STAD.

Systematic Identification of Survival-Associated Alternative Splicing Events in Kidney Renal Clear Cell Carcinoma.

There is growing evidence that aberrant alternative splicing (AS) is highly correlated with driving tumorigenesis, but its function in kidney renal clear cell carcinoma (KIRC) remains to be discovered. In this study, we obtained the level-3 RNA sequencing and clinical data of KIRC from The Cancer Genome Atlas (TGCA). Combining with the splicing event detail information from TGCA SpliceSeq database, we established the independent prognosis signatures for KIRC with the univariate and multivariate Cox regression analyses. Then, we used the Kaplan-Meier analysis and receiver operating characteristic curves (ROCs) to assess the accuracy of prognosis signatures. We also constructed the regulatory network of splicing factors (SFs) and AS events. Our results showed that a total of 12029 survival-associated AS events of 5761 genes were found in 524 KIRC patients. All types of prognosis signatures displayed a satisfactory ability to reliably predict, especially in exon skip model which the area under curve of ROC was 0.802. Moreover, 18 splicing factors (SFs) highly correlated to AS events were identified. With the construction of the SF-AS interactive network, we found that SF powerfully promotes the occurrence of abnormal AS and may have a profound role in KIRC. Collectively, we screened survival-associated AS events and established prognosis signatures for KIRC, coupling with the SF-AS interactive network, which might provide a key perspective to clarify the potential mechanism of AS in KIRC.

Prognostic Alternative mRNA Splicing in Adrenocortical Carcinoma.

BackgroundThis paper aims to identify alternative RNA splicing landscape and its prognostic value in adrenocortical carcinoma.MethodsThe alternative splicing events data with corresponding clinical information data of 79 ACC patients were obtained from the Cancer Genome Atlas and SpliceSeq package. Prognosis-associated AS events by using univariate Cox regression analysis were selected. Gene functional enrichment analysis demonstrated the potential pathways enriched by survival-associated AS. Prognosis-related splicing events were submitted to develop moderate predictors using Lasso regression model.ResultsOne thousand five survival-associated alternative splicing events were identified. The prognostic genes included ATXN2L, MEIS1, IKBKB, COX4I1. Functional enrichment analysis suggested that prognostic splicing events are associated with Wnt signaling pathway. A prediction model including 12 alternative splicing events was constructed by Lasso regression using train set. ROC analysis showed good performance of the prediction model in test set. Then, a nomogram integrating the clinical-pathological factors and riskscore was constructed for predicting 1‐ and 3‐year survival rate.ConclusionOur data provide a comprehensive bioinformatics analysis of AS events in ACC, providing biomarkers for disease progression and a potentially rich source of novel therapeutic targets.

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

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

Prognostic significance of survival-associated alternative splicing events in gastric cancer.

Alternative splicing events are a major source of transcript and protein diversity in eukaryotes. Aberrant alternative splicing events have been increasingly reported in various cancers, including gastric cancer. To further explore the prognostic significance of alternative splicing events in gastric cancer patients, a comprehensive and systematic investigation was conducted by integrating alternative splicing event data and clinical information. Univariate Cox regression analysis identified 1383 alternative splicing events to be significantly associated with the overall survival of gastric cancer patients. Then, least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were performed for the development of prognostic signatures. The final prognostic signature based on all seven types of alternative splicing events can act as an independent prognostic indicator after multivariate adjustment of several clinical parameters. Furthermore, the correlation and function analysis identified CELF2, BAG2, RBFOX2, PTBP2 and QKI as hub splicing factors, and the focal adhesion signaling pathway was most significantly correlated with survival-associated alternative splicing events. The results of this study may establish a foundation for further research investigating the underlying mechanism of alternative splicing events in the progression of gastric cancer.

Survival-Associated Alternative Splicing Events and Prognostic Signatures in Pancreatic Cancer.

BackgroundAlternative splicing (AS) is reported to be related to the biological process of multiple malignancies. This study is conducted to identify survival-associated AS events and prognostic signatures that may serve as prognostic indicators for pancreatic cancer (PC).MethodsUnivariate Cox analysis was used to determine the survival-associated AS events in PC. Prognostic signatures were constructed by LASSO Cox analysis based on seven types of survival-associated AS events. The correlation between the expression of splicing factors (SFs) and the percent spliced in values of AS events was analyzed by Pearson correlation analysis. Risk scores were calculated to determine high- or low-risk patients with different types of AS events. Gene functional annotation analysis was performed to reveal pathways in which prognostic AS is enriched.ResultsA total of 45,313 AS events in 10,624 genes were observed, and there were 1,565 AS events in 1,223 genes significantly correlated with overall survival for PC. Kaplan–Meier analysis, receiver-operator characteristic curve, univariate and multivariate Cox analyses showed that AS prognostic signatures could effectively predict prognosis of patients with PC. Splicing factors–AS regulatory networks were established to demonstrate the interaction between AS events and SFs.ConclusionThe survival-associated AS events and prognostic signatures identified in this study can serve as useful tool for predicting prognosis of patients with PC. Moreover, the SF–AS regulatory networks may provide clues for the mechanisms underlying AS in PC.

Prognostic alternative splicing signature in cervical squamous cell carcinoma.

Basing on alternative splicing events (ASEs) databases, the authors herein aim to explore potential prognostic biomarkers for cervical squamous cell carcinoma (CESC). mRNA expression profiles and relevant clinical data of 223 patients with CESC were obtained from The Cancer Genome Atlas (TCGA). Correlated genes, ASEs and percent-splice-in (PSI) were downloaded from SpliceSeq, respectively. The PSI values of survival-associated alternative splicing events (SASEs) were used to construct the basis of a prognostic index (PI). A protein-protein interaction (PPI) network of genes related to SASEs was generated by STRING and analysed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Consequently, 41,776 ASEs were discovered in 19,724 genes, 2596 of which linked with 3669 SASEs. The PPI network of SASEs related genes revealed that TP53 and UBA52 were core genes. The low-risk group had a longer survival period than high-risk counterparts, both groups being defined according to PI constructed upon the top 20 splicing events or PI on the overall splicing events. The AUC value of ROC reached up to 0.88, demonstrating the prognostic potential of PI in CESC. These findings suggested that ASEs involve in the pathogenesis of CESC and may serve as promising prognostic biomarkers for this female malignancy.

Construction of prognostic predictor by comprehensive analyzing alternative splicing events for colon adenocarcinoma

BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors, with high incidence and mortality rates worldwide. Reliable prognostic biomarkers are needed to guide clinical practice.MethodsComprehensive gene expression with alternative splicing (AS) profiles for each patient was downloaded using the SpliceSeq database from The Cancer Genome Atlas. Cox regression analysis was conducted to screen for prognostic AS events. The R package limma was used to screen differentially expressed genes (DEGs) between normal and tumor samples in the COAD cohort. A Venn plot analysis was performed between DEGs and prognostic AS events, and the DEGs that co-occurred with prognostic AS events (DEGAS) were identified. The top 30 most-connected DEGAS in protein–protein interaction analysis were identified through Cox proportional hazards regression to establish prognostic models.ResultsIn total, 350 patients were included in the study. A total of 22,451 AS events were detected, of which 2004 from 1439 genes were significantly associated with survival time. By overlapping these 1439 genes with 6455 DEGs, 211 DEGs with AS events were identified. After the construction of the protein–protein interaction network, the top 30 hub genes were included in a multivariate analysis. Finally, a risk score based on 12 genes associated with overall survival was established (P < 0.05). The area under the curve was 0.782. The risk score was an independent predictor (P < 0.001).ConclusionsBy exploring survival-associated AS events, a powerful prognostic predictor consisting of 12 DEGAS was built. This study aims to propose a novel method to provide treatment targets for COAD and guide clinical practice in the future.

Comprehensive Characterization of the Prognosis Value of Alternative Splicing Events in Acute Myeloid Leukemia.

Increasing evidence have demonstrated that dysregulated alternative splicing (AS) events promoted tumor development and was correlated with worse prognosis in the context of certain malignancies. Nevertheless, a comprehensive examination of the prognosis role of AS events in acute myeloid leukemia (AML) has not yet been illuminated. In this study, univariate and multivariate Cox regression analysis were used to identify survival-related AS events and independent prognostic predictors. The interaction between splicing factors (SFs) and AS events was visualized by Cytoscape. A total of 3013 survival-associated AS events in 1977 genes were screened in 151 AML patients. Interestingly, the majority (2031 events) were revealed to be protective factors. Furthermore, the prediction models were constructed for each type of AS and all of them displayed good performance in predicting prognosis, considering their area under curve values of the receiver operating characteristic were all above 0.7. Notably, the splicing regulatory network displayed the underlying interaction networks between SFs and AS events. Taken together, our study demonstrated the survival-related AS events in AML and uncovered the possible association between SFs and prognostic AS events, which provide new prognostic biomarkers and aid to develop novel targets for AML therapy.

Identification of survival-associated alternative splicing events and signatures in adrenocortical carcinoma based on TCGA SpliceSeq data.

Objective: To explore the correlations among alternative splicing (AS), splicing factors (SF) and survival outcome in adrenocortical carcinoma (ACC) patients.Results: A total of 92 ACC patients were included. Univariate analysis identified 3919 AS events significantly associated with overall survival. Lasso method followed by multivariate analysis revealed that the prognostic capacity of these survival-related AS events is satisfactory. Interestingly, we found that the area under the curve (AUC) of AA, AD, AP and RI were more than 0.9, indicating that these four types of AS were of great significance. Independent prognostic analysis showed that only the risk score was the independent risk factor of ACC survival. Finally, we constructed an interesting interaction network between AS and SF.Conclusions: This is the first and most comprehensive study to explore the aberrant AS variants in ACC, which might provide novel insights into molecular mechanism of ACC.Methods: The transcriptome data, clinical information and Percent Spliced In (PSI) values of the ACC were obtained from TCGA database and TCGA SpliceSeq data portal. Lasso method and uni/multivariate Cox regression analysis were used to identify survival-related AS events and develop multi-AS-based signatures. The relationship between AS events and SFs was also investigated.

A Novel Prognostic Index Based on Alternative Splicing in Papillary Renal Cell Carcinoma.

BackgroundPapillary renal cell carcinoma (pRCC) is a heterogeneous multifocal or isolated tumor with an invasive phenotype. Previous studies presented that alternative splicing, as a crucial posttranscriptional regulator in gene expression, is associated with tumorigenesis. However, the association between alternative splicing and pRCC has not been clarifiedMethodsThe RNA sequencing data and clinical information were downloaded from The Cancer Genome Atlas database and mRNA splicing profiles from TCGASpliceSeq. The percent spliced in data of alternative splicing merged with survival information was firstly calculated by univariate Cox regression analysis to screen for survival‐associated alternative splicing events, and survival‐associated alternative splicing events were then analyzed by Gene Ontology categories using Kyoto Encyclopedia of Genes and Genomes. Meanwhile, the least absolute shrinkage and selection operator Cox analysis and multivariate Cox analysis were performed to calculate the prognostic index for each alternative splicing type. In addition, clinical factors were introduced to assess the performance of prognostic index.ResultsA total of 4,084 candidate survival-associated alternative splicing events in 2,558 genes were screened out. Patients were divided into the low-risk group and the high-risk group based on the median prognostic index value. The Kaplan-Meier survival analysis (p < 0.05) and receiver operating characteristics curves (AUC>0.9) indicated that prognostic index was effective and stable for predicting the prognosis of pRCC patients. Furthermore, a regulatory network was constructed incorporating alternative splicing events and survival-associated splicing factors.ConclusionOur study provides new insights into the mechanism of alternative splicing events in tumorigenesis and their clinical potential for pRCC.

Prognostic alternative splicing regulatory network of splicing events in acute myeloid leukemia patients based on SpliceSeq data from 136 cases.

This study aimed to create prognostic signatures to predict AML patients' survival using alternative splicing (AS) events. The AS data, RNA sequencing data, and the survival statistics of 136 AML patients were obtained from The Cancer Genome Atlas (TCGA) and TCGA SpliceSeq databases. Total 34,984 AS events generated from 8,656 genes, 2,583 of which were survival-associated AS events, were identified using univariate Cox regression. The prognostic models constructed using independent survival-associated AS events revealed that low-risk splicing better predicted patients' survival. ROC analysis indicated that the predictive efficacy of the alternate terminator model was best in the area under the curve at 0.781. Enrichment analysis revealed several important genes (TP53, BCL2, AURKB, PPP2R1B, FOS, and BIRC5) and pathways, such as the protein processing pathway in the endoplasmic reticulum, RNA transport pathway, and HTLV-I infection pathway. The splicing network of splicing events and factors revealed interesting interactions, such as the positive correlation between HNRNPH3 and CALHM2-13010-AT, which may indicate the potential splicing regulatory mechanism. Taken together, survival-associated splicing events and the prognostic signatures for predicting survival can help provide an overview of splicing in AML patients and facilitate clinical practice. The splicing regulatory network may improve the understanding of spliceosomes in AML.

Survival-Associated Alternative Splicing Events in Pan-Renal Cell Carcinoma.

Alternative splicing is an important modification process for the genome to generate mature mRNA by transcription, which has been found associated with survival in some tumors. However, systematic analysis of AS events in pan-renal cell carcinoma at the genome-wide level has been seldom conducted yet. In the current study, Upset plot and Venn plot were utilized to present the distribution characteristics of AS events. Those SREs were screened out with multivariate COX regression analyses, and functional enrichment analysis was performed to figure out potential pathways. ROC model was conducted to compare the efficiency of those potential SREs. A total of 2,169, 1,671, and 1,414 SREs were found in renal clear cell carcinoma (KIRC), renal chromophobe cell carcinoma (KICH), and renal papillary cell carcinoma (KIRP), respectively. Functional enrichment analysis results suggested possible mechanism such as changes in the branched-chain amino acid catabolic process due to SREs might play a key role in KIRC. The binary logistic regression equation based on the SREs had a good performance in each model compared to the single factor. The 5 year survival model presented that the AUC of the predicted probabilities in KIRC, KICH, and KIRP were 0.754, 1 and 0.841, and in the diagnostic model were 0.988, 0.970, and 0.999, respectively. Some AS types that were significantly different in pan-RCC and paracancerous tissues have also been discovered to play a role in carcinoma screening. To sum up, alternative splicing events significantly interfere with the prognosis of patients with pan-RCC and are capable as biomarkers for prognosis.

Identification of a prognostic alternative splicing signature in oral squamous cell carcinoma.

Alternative splicing (AS) is critically associated with tumorigenesis and patient's prognosis. Here, we systematically analyzed survival-associated AS signatures in oral squamous cell carcinoma (OSCC) and evaluated their prognostic predictive values. Survival-related AS events were identified by univariate and multivariate Cox regression analyses using OSCC data from the TCGA head neck squamous cell carcinoma data set. ThePercent Spliced In calculated by SpliceSeq from 0 to 1 was used to quantify seven types of AS events. A predictive model based on AS events was constructed by least absolute shrinkage and selection operator Cox regression assay and further validated using a training-testing cohort design. Patient survival was estimated using the Kaplan-Meier method and compared with Log-rank test. The receiver operating characteristics curve area under the curves was used to evaluate the predictive abilities of these predictive models. Furthermore, gene-gene interaction networks and the splicing factors (SFs)-AS regulatory network was generated by Cytoscape. A total of 825 survival-related AS events within 719 genes were identified in OSCC samples. The integrative predictive model was betteratpredicting outcomes of patients as compared to those models built with the individual AS event. The predictive model based on three AS-related genes also effectively predicted patients' survival. Moreover, seven survival-related SFs were detected in OSCC including RBM4, HNRNPD, and HNRNPC, which have been linked to tumorigenesis. The SF-AS network revealed a significant correlation between survival-related AS genes and these SFs. Our findings revealed a systemic portrait of survival-associated AS events and the splicing network in OSCC, suggesting that AS events might serve as novel prognostic biomarkers and therapeutic targets for OSCC.

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.