Prognostic value and potential function of splicing events in prostate adenocarcinoma

Characterization of tumor suppressors and oncogenes evaluated from TCGA cancers

Mutations in oncogenes and tumor suppressor genes can significantly impact cellular function during cancer development. A comprehensive analysis of their mutation patterns and significant gene ontology terms can provide insights into cancer emergence and suggest potential targets for drug development. This study analyzes twelve cancer subtypes by focusing on significant genetic and molecular factors. Two common genetic mutations associated with cancer are single nucleotide variants (SNVs) and copy number alterations (CNAs). Oncogenes, derived from mutated proto-oncogenes, disrupt normal cell functions and promote cancer, while tumor suppressor genes, often inactivated by mutations, regulate cell processes like proliferation and DNA damage response. This study analyzed datasets from The Cancer Genome Atlas (TCGA), which provides extensive genomic data across various cancers. In our analysis results, many genes with significant p-values based on Kaplan Meier gene expression data were identified in eight cancers (BRCA, BLCA, HNSC, KIRC, LUAD, KIRP, LUSC, STAD). Moreover, STAD is the only cancer for genes with both significant p-values and functional terms reported. Interestingly, we found that LIHC was the cancer reported with only one CNA mutated gene and its survival plot p-value being significant. Additionally, KICH has no reported significant genes at all. Our study proposed the relationship between tumor suppressor and oncogenes and shed light on cancer tumorigenesis due to genetic mutations.

Alternative splicing in prostate cancer progression and therapeutic resistance

Prostate cancer (CaP) remains the second leading cause of cancer deaths in western men. CaP mortality results from diverse molecular mechanisms that mediate resistance to the standard of care treatments for metastatic disease. Recently, alternative splicing has been recognized as a hallmark of CaP aggressiveness. Alternative splicing events cause treatment resistance and aggressive CaP behavior and are determinants of the emergence of the two major types of late-stage treatment-resistant CaP, namely castration-resistant CaP (CRPC) and neuroendocrine CaP (NEPC). Here, we review recent multi-omics data that are uncovering the complicated landscape of alternative splicing events during CaP progression and the impact that different gene transcript isoforms can have on CaP cell biology and behavior. We discuss renewed insights in the molecular machinery by which alternative splicing occurs and contributes to the failure of systemic CaP therapies. The potential for alternative splicing events to serve as diagnostic markers and/or therapeutic targets is explored. We conclude by considering current challenges and promises associated with splicing-modulating therapies, and their potential for clinical translation into CaP patient care.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

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.

Temporal Modulation of Differential Alternative Splicing in HaCaT Human Keratinocyte Cell Line Chronically Exposed to Arsenic for up to 28 Wk

BACKGROUND

Chronic arsenic exposure via drinking water is associated with an increased risk of developing cancer and noncancer chronic diseases. Pre-mRNAs are often subject to alternative splicing, generating mRNA isoforms encoding functionally distinct protein isoforms. The resulting imbalance in isoform species can result in pathogenic changes in critical signaling pathways. Alternative splicing as a mechanism of arsenic-induced toxicity and carcinogenicity is understudied.

OBJECTIVE

This study aimed to accurately profile differential alternative splicing events in human keratinocytes induced by chronic arsenic exposure that might play a role in carcinogenesis.

METHODS

Independent quadruplicate cultures of immortalized human keratinocytes (HaCaT) were maintained continuously for 28 wk with 0 or 100  nM sodium arsenite. RNA-sequencing (RNA-Seq) was performed with poly(A) RNA isolated from cells harvested at 7, 19, and 28 wk with subsequent replicate multivariate analysis of transcript splicing (rMATS) analysis to detect and quantify differential alternative splicing events. Reverse transcriptase-polymerase chain reaction (RT-PCR) for selected alternative splicing events was performed to validate RNA-Seq predictions. Functional enrichment was performed by gene ontology (GO) analysis of the differential alternative splicing event data set at each time point.

RESULTS

At least 600 differential alternative splicing events were detected at each time point tested, comprising all the five main types of alternative splicing and occurring in both open reading frames (ORFs) and untranslated regions (UTRs). Based on functional relevance ELK4, SHC1, and XRRA1 were selected for validation of predicted alternative splicing events at 7 wk by RT-PCR. Densitometric analysis of RT-PCR data corroborated the rMATS predicted alternative splicing for all three events. Protein expression validation of the selected alternative splicing events was challenging given that very few isoform-specific antibodies are available. GO analysis demonstrated that the enriched terms in differential alternatively spliced mRNAs changed dynamically with the time of exposure. Notably, RNA metabolism and splicing regulation pathways were enriched at the 7-wk time point, when the greatest number of differentially alternatively spliced mRNAs are detected. Our preliminary proteomic analysis demonstrated that the expression of the canonical isoforms of the splice regulators DDX42, RMB25, and SRRM2 were induced upon chronic arsenic exposure, corroborating the splicing predictions.

DISCUSSION

These results using cultures of HaCaT cells suggest that arsenic exposure disrupted an alternative splice factor network and induced time-dependent genome-wide differential alternative splicing that likely contributed to the changing proteomic landscape in arsenic-induced carcinogenesis. However, significant challenges remain in corroborating alternative splicing data at the proteomic level. https://doi.org/10.1289/EHP9676.

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 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.

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

Background

Alternative 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).

Methods

Univariate 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.

Results

A 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.

Conclusion

The 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.

An interactive network of alternative splicing events with prognostic value in geriatric lung adenocarcinoma via the regulation of splicing factors

Alternative splicing (AS), an essential process for the maturation of mRNAs, is involved in tumorigenesis and tumor progression, including angiogenesis, apoptosis, and metastasis. AS changes can be frequently observed in different tumors, especially in geriatric lung adenocarcinoma (GLAD). Previous studies have reported an association between AS events and tumorigenesis but have lacked a systematic analysis of its underlying mechanisms. In the present study, we obtained splicing event information from SpliceSeq and clinical information regarding GLAD from The Cancer Genome Atlas. Survival-associated AS events were selected to construct eight prognostic index (PI) models. We also constructed a correlation network between splicing factors (SFs) and survival-related AS events to identify a potential molecular mechanism involved in regulating AS-related events in GLAD. Our study findings confirm that AS has a strong prognostic value for GLAD and sheds light on the clinical significance of targeting SFs in the treatment of GLAD.

Identification of an alternative splicing signature as an independent factor in colon cancer

BACKGROUND

Colon cancer is a common malignant tumor with a poor prognosis. Abnormal alternative splicing (AS) events played a part in the occurrence and metastasis of the tumor. We aimed to develop a survival-associated AS signature in colon cancer.

METHODS

The Percent Spliced In values of AS events were available in The Cancer Genome Atlas (TCGA) SpliceSeq database. Univariate Cox analysis was carried out to detect the prognosis-related AS events. We created a predictive model on account of the survival-associated AS events, which was further validated with a training-testing group design. Kaplan-Meier analysis was applied to assess patient survival. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of this model. Meanwhile, the clinical relevance of the signature and its regulatory relationship with splicing factors (SFs) were also evaluated.

RESULTS

In total, 2132 survival-related AS events were identified from colon cancer samples. We developed an eleven-AS signature, in which the 5-year AUC value was 0.911. Meanwhile, the AUC values at five years were 0.782 and 0.855 in the testing and entire cohort, respectively. Multivariate Cox regression displayed that the T category and the risk score of the signature were independent risk factors of colon cancer survival. Also, we constructed an SFs-AS network based on 11 SFs and 48 AS events.

CONCLUSIONS

We identified an eleven-AS signature of colon cancer. This signature could be treated as an independent prognostic factor.

Comprehensive analysis of prognostic alternative splicing signature in cervical cancer

Background

Alternative splicing (AS) is a key factor in protein-coding gene diversity, and is associated with the development and progression of malignant tumours. However, the role of AS in cervical cancer is unclear.

Methods

The AS data for cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) were downloaded from The Cancer Genome Atlas (TCGA) SpliceSeq website. Few prognostic AS events were identified through univariate Cox analysis. We further identified the prognostic prediction models of the seven subtypes of AS events and assessed their predictive power. We constructed a clinical prediction model through global analysis of prognostic AS events and established a nomogram using the risk score calculated from the prognostic model and relevant clinical information. Unsupervised cluster analysis was used to explore the relationship between prognostic AS events in the model and clinical features.

Results

A total of 2860 prognostic AS events in cervical cancer were identified. The best predictive effect was shown by a single alternate acceptor subtype with an area under the curve of 0.96. Our clinical prognostic model included a nine-AS event signature, and the c-index of the predicted nomogram model was 0.764. SNRPA and CCDC12 were hub genes for prognosis-associated splicing factors. Unsupervised cluster analysis through the nine prognostic AS events revealed three clusters with different survival patterns.

Conclusions

AS events affect the prognosis and biological progression of cervical cancer. The identified prognostic AS events and splicing regulatory networks can increase our understanding of the underlying mechanisms of cervical cancer, providing new therapeutic strategies.

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.

A novel alternative splicing-based prediction model for uteri corpus endometrial carcinoma

Alternative splicing (AS) is crucial a mechanism by which the complexity of mammalian and viral proteom increased overwhelmingly. There lacks systematic and comprehensive analysis of the prognostic significance of AS profiling landscape for uteri corpus endometrial carcinoma (UCEC). In this study, univariate and multivariate Cox regression analyses were conducted to identify candidate survival-associated AS events curated from SpliceSeq for the construction of prognostic index (PI) models. A correlation network between splicing factor-related AS events and significant survival-associated AS events were constructed using Cytoscape 3.5. As consequence, 28281 AS events from 8137 genes were detected from 506 UCEC patients, including 2630 survival-associated AS events. Kaplan Meier survival analysis revealed that six of the seven PI models (AD, AP, AT, ME, RI and ALL) exhibited good performance in stratifying the prognosis of low risk and high risk group (P<0.05). Among the six PI models, PI-AT performed best with an area under curves (AUC) value of 0.758 from time-dependent receiver operating characteristic. Correlation network implicated potential regulatory mechanism of AS events in UCEC. PI models based on survival-associated AS events for UCEC in this study showed preferable prognosis-predicting ability and may be promising prognostic indicators for UCEC patients.

Summary: This is the first study to systematically investigate the prognostic value of AS in UCEC. Findings in the presents study supported the clinical potential of AS for UCEC and shed light on the potential AS-associated molecular basis of UCEC.

Prognostic Value and Potential Regulatory Mechanism of Alternative Splicing in Geriatric Breast Cancer

Breast cancer has the highest mortality and morbidity among women, especially in elderly women over 60 years old. Abnormal alternative splicing (AS) events are associated with the occurrence and development of geriatric breast cancer (GBC), yet strong evidence is lacking for the prognostic value of AS in GBC and the regulatory network of AS in GBC, which may highlight the mechanism through which AS contributes to GBC. In the present study, we obtained splicing event information (SpliceSeq) and clinical information for GBC from The Cancer Genome Atlas, and we constructed a GBC prognosis model based on AS events to predict the survival outcomes of GBC. Kaplan–Meier analysis was conducted to evaluate the predictive accuracy among different molecular subtypes of GBC. We conducted enrichment analysis and constructed a splicing network between AS and the splicing factor (SF) to examine the possible regulatory mechanisms of AS in GBC. We constructed eight prognostic signatures with very high statistical accuracy in predicting GBC survival outcomes from 45,421 AS events of 10,480 genes detected in 462 GBC patients; the prognostic model based on exon skip (ES) events had the highest accuracy, indicating its significant value in GBC prognosis. The constructed regulatory SF–AS network may explain the potential regulatory mechanism between SF and AS, which may be the mechanism through which AS events contribute to GBC survival outcomes. The findings confirm that AS events have a significant prognostic value in GBC, and we found a few effective prognostic signatures. We also hypothesized the mechanism underlying AS in GBC and discovered a potential regulatory mechanism between SF and AS.

Profiles of prognostic alternative splicing signature in hepatocellular carcinoma

Previous studies have demonstrated the role of abnormal alternative splicing (AS) in tumor progression. This study examines the prognostic index (PI) of alternative splices (ASs) in patients with hepatocellular carcinoma (HCC). The clinical features and splicing events of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed AS (DEAS) were compared between HCC and adjacent normal samples. Univariate Cox regression analysis was used to determine changes in DEAS associated with overall survival (OS). A PI was generated from OS‐associated DEASs using Kaplan‐Meier curves, receiver operating characteristic (ROC) curves, multivariate Cox regression, and cluster analysis. Then, the correlation between DEASs and splicing factors was assessed, followed by functional and pathway enrichment analysis. We identified 34 163 ASs of 8985 genes in HCC, and 153 OS‐ASs were identified using univariate Cox regression analysis. Low‐ and high‐PI groups were determined based on the median “PI‐ALL” value according to significantly different survival (P = 2.2e − 16). The ROC curve of all PI (PI‐ALL) had an area under the curve (AUC) of 0.993 for survival status in patients with HCC. A potential regulatory network associated with prognosis of patients with HCC was established. Enrichment analysis also resulted in the identification of several pathways potentially associated with carcinogenesis and progression of HCC. Four clusters were identified that were associated with clinical features and prognosis. Our study generated comprehensive profiles of ASs in HCC. The interaction network and functional connections were used to elucidate the underlying mechanisms of AS in HCC.

Role of alternative splicing signatures in the prognosis of glioblastoma

BACKGROUND

Increasing evidence has validated the crucial role of alternative splicing (AS) in tumors. However, comprehensive investigations on the entirety of AS and their clinical value in glioblastoma (GBM) are lacking.

METHODS

The AS profiles and clinical survival data related to GBM were obtained from The Cancer Genome Atlas database. Univariate and multivariate Cox regression analyses were performed to identify survival-associated AS events. A risk score was calculated, and prognostic signatures were constructed using seven different types of independent prognostic AS events, respectively. The Kaplan-Meier estimator was used to display the survival of GBM patients. The receiver operating characteristic curve was applied to compare the predictive efficacy of each prognostic signature. Enrichment analysis and protein interactive networks were conducted using the gene symbols of the AS events to investigate important processes in GBM. A splicing network between splicing factors and AS events was constructed to display the potential regulatory mechanism in GBM.

RESULTS

A total of 2355 survival-associated AS events were identified. The splicing prognostic model revealed that patients in the high-risk group have worse survival rates than those in the low-risk group. The predictive efficacy of each prognostic model showed satisfactory performance; among these, the Alternate Terminator (AT) model showed the best performance at an area under the curve (AUC) of 0.906. Enrichment analysis uncovered that autophagy was the most enriched process of prognostic AS gene symbols in GBM. The protein network revealed that UBC, VHL, KCTD7, FBXL19, RNF7, and UBE2N were the core genes in GBM. The splicing network showed complex regulatory correlations, among which ELAVL2 and SYNE1_AT_78181 were the most correlated (r = -.506).

CONCLUSIONS

Applying the prognostic signatures constructed by independent AS events shows promise for predicting the survival of GBM patients. A splicing regulatory network might be the potential splicing mechanism in GBM.

Construction of a model to predict the prognosis of patients with cholangiocarcinoma using alternative splicing events

Cholangiocarcinoma (CCA) is a type of malignant tumor that originates in the mucosal epithelial cells of the biliary system. It is a highly aggressive cancer that progresses rapidly, has low surgical resection rates and a high recurrence. At present, no prognostic molecular biomarker for CCA has been identified. However, CCA progression is affected by mRNA precursors that modify gene expression levels and protein structures through alternative splicing (AS) events, which create molecular indicators that may potentially be used to predict CCA outcomes. The present study aimed to construct a model to predict CCA prognosis based on AS events. Using prognostic data available from The Cancer Genome Atlas, including the percent spliced index of AS events obtained from TCGASpliceSeq in 32 CCA cases, univariate and multivariate Cox regression analyses were performed to assess the associations between AS events and the overall survival (OS) rates of patients with CCA. Additional multivariate Cox regression analyses were used to identify AS events that were significantly associated with prognosis, which were used to construct a prediction model with a prognostic index (PI). A receiver operating characteristic (ROC) curve was used to determine the predictive value of the PI, and Pearson's correlation analysis was used to determine the association between OS-related AS events and splicing factors. A total of 38,804 AS events were identified in 9,673 CCA genes, among which univariate Cox regression analysis identified 1,639 AS events associated with OS (P<0.05); multivariate Cox regression analysis narrowed this list to 23 CCA AS events (P<0.001). The final PI model was constructed to predict the survival of patients with CCA; the ROC curve demonstrated that it had a high predictive power for CCA prognosis, with a highest area under the curve of 0.986. Correlations between 23 OS-related AS events and splicing factors were also noted, and may thus, these AS events may be used to improve predictions of OS. In conclusion, AS events exhibited potential for predicting the prognosis of patients with CCA, and thus, the effects of AS events in CCA required further examination.

Genome-wide profiling of prognosis-related alternative splicing signatures in sarcoma

BACKGROUND

Sarcomas (SARCs) are rare malignant tumors with poor prognosis. Increasing evidence has suggested that aberrant alternative splicing (AS) is strongly associated with tumor initiation and progression. We considered whether survival-related AS events might serve as prognosis predictors and underlying targeted molecules in SARC treatment.

METHODS

RNA-Seq data of the SARC cohort were downloaded from The Cancer Genome Atlas (TCGA) database. Survival-related AS events were selected by univariate and multivariate Cox regression analyses. Metascape was used for constructing a gene interaction network and performing functional enrichment analysis. Then, prognosis predictors were established based on statistically significant survival-related AS events and evaluated by receiver operator characteristic (ROC) curve analysis. Finally, the potential regulatory network was analyzed via Pearson's correlation between survival-related AS events and splicing factors (SFs).

RESULTS

A total of 3,610 AS events and 2,291 genes were found to be prognosis-related in 261 SARC samples. The focal adhesion pathway was identified as the most critical molecular mechanism corresponding to poor prognosis. Notably, several prognosis predictors based on survival-related AS events showed excellent performance in prognosis prediction. The area under the curve of the ROC of the risk score was 0.85 in the integrated predictor. The splicing network proved complicated regulation between prognosis-related SFs and AS events. Also, driver gene mutations were significantly associated with AS in SARC patients.

CONCLUSIONS

Survival-related AS events may become ideal indictors for the prognosis prediction of SARCs. Corresponding splicing regulatory mechanisms are worth further exploration.

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.

Prognostic alternative splicing signatures and underlying regulatory network in esophageal carcinoma

Alternative splicing (AS) has been widely reported to play an important role in cancers, including esophageal carcinoma (ESCA). However, no study has comprehensively investigated the clinical use of combination of prognostic AS events and clinicopathological parameters. Therefore, we collected 165 ESCA patients including 83 esophageal adenocarcinoma (EAC) and 82 esophageal squamous cell carcinoma (ESCC) patients from The Cancer Genome Atlas to explore the survival rate associated with seven types of AS events. Prognostic predictors for the clinical outcomes of ESCA patients were built. Predictive prognosis models of the alternative acceptor site in ESCA (area under the curve [AUC] = 0.83), alternative donor site in EAC (AUC = 0.99), and alternative terminator site in ESCC (AUC = 0.974) showed the best predictive efficacy. A novel combined prognostic model of AS events and clinicopathological parameters in ESCA was also constructed. Combined prognostic models of ESCA all showed better predictive efficacy than independent AS models or clinicopathological parameters model. Through constructing splicing regulatory network, the expression of AS factor was found to be negatively correlated with the most favorable AS events. Moreover, gene amplification, mutation, and copy number variation of AS genes were commonly observed, which may indicate the molecular mechanism of how the AS events influence survival. Conclusively, the constructed prognostic models based on AS events, especially the combined prognostic models of AS signatures and clinicopathological parameters could be used to predict the outcome of ESCA patients. Moreover, the splicing regulatory network and genomic alteration in ESCA could be used for illuminating the potential molecular mechanism.

Alternative splicing events are prognostic in hepatocellular carcinoma

Alternative splicing events (ASEs) play a role in cancer development and progression. We investigated whether ASEs are prognostic for overall survival (OS) in hepatocellular carcinoma (HCC). RNA sequencing data was obtained for 343 patients included in The Cancer Genome Atlas. Matched splicing event data for these patients was then obtained from the TCGASpliceSeq database, which includes data for seven types of ASEs. Univariate and multivariate Cox regression analysis demonstrated that 3,814 OS-associated splicing events (OS-SEs) were correlated with OS. Prognostic indices were developed based on the most significant OS-SEs. The prognostic index based on all seven types of ASEs (PI-ALL) demonstrated superior efficacy in predicting OS of HCC patients at 2,000 days compared to those based on single ASE types. Patients were stratified into two risk groups (high and low) based on the median prognostic index. Kaplan-Meier survival analysis demonstrated that PI-ALL had the greatest capacity to distinguish between patients with favorable vs. poor outcomes. Finally, univariate Cox regression analysis demonstrated that the expression of 23 splicing factors was correlated with OS-SEs in the HCC cohort. Our data indicate that a prognostic index based on ASEs is prognostic for OS in HCC.