Identification of Prognostic Alternative Splicing Signature in Breast Carcinoma

Identification of disease-related aberrantly spliced transcripts in myeloma and strategies to target these alterations by RNA-based therapeutics

Novel drug discoveries have shifted the treatment paradigms of most hematological malignancies, including multiple myeloma (MM). However, this plasma cell malignancy remains incurable, and novel therapies are therefore urgently needed. Whole-genome transcriptome analyses in a large cohort of MM patients demonstrated that alterations in pre-mRNA splicing (AS) are frequent in MM. This manuscript describes approaches to identify disease-specific alterations in MM and proposes RNA-based therapeutic strategies to eradicate such alterations. As a "proof of concept", we examined the causes of aberrant HMMR (Hyaluronan-mediated motility receptor) splicing in MM. We identified clusters of single nucleotide variations (SNVs) in the HMMR transcript where the altered splicing took place. Using bioinformatics tools, we predicted SNVs and splicing factors that potentially contribute to aberrant HMMR splicing. Based on bioinformatic analyses and validation studies, we provided the rationale for RNA-based therapeutic strategies to selectively inhibit altered HMMR splicing in MM. Since splicing is a hallmark of many cancers, strategies described herein for target identification and the design of RNA-based therapeutics that inhibit gene splicing can be applied not only to other genes in MM but also more broadly to other hematological malignancies and solid tumors as well.

Short-term hypoxia triggers ROS and SAFB mediated nuclear matrix and mRNA splicing remodeling

The cellular response to hypoxia, in addition to HIF-dependent transcriptional reprogramming, also involves less characterized transcription-independent processes, such as alternative splicing of the VEGFA transcript leading to the production of the proangiogenic VEGF form. We now show that this event depends on reorganization of the splicing machinery, triggered after short-term hypoxia by ROS production and intranuclear redistribution of the nucleoskeletal proteins SAFB1/2. Exposure to low oxygen causes fast dissociation of SAFB1/2 from the nuclear matrix, which is reversible, inhibited by antioxidant treatment, and also observed under normoxia when the mitochondrial electron transport chain is blocked. This is accompanied by altered interactions between SAFB1/2 and the splicing machinery, translocation of kinase SRPK1 to the cytoplasm, and dephosphorylation of RS-splicing factors. Depletion of SAFB1/2 under normoxia phenocopies the hypoxic and ROS-mediated switch in VEGF mRNA splicing. These data suggest that ROS-dependent remodeling of the nuclear architecture can promote production of splicing variants that facilitate adaptation to hypoxia.

MiR-95-3p acts as a prognostic marker and promotes cervical cancer progression by targeting VCAM1

BACKGROUND

Cervical cancer patients have a high risk of metastasis and a poor prognosis with shorter disease-free survival. Thus, novel biomarkers and feasible therapies urgently need to be discovered. Previous studies have shown that miR-95-3p plays crucial roles in several cancer types. However, the roles of miR-95-3p in cervical cancer remain unknown.

METHODS

The micro ribonucleic acid (miRNA) expression data and clinical characteristics of cervical cancer samples were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were conducted to identify the prognostic-related miRNAs. The potential target genes of miR-95-3p were predicted by the TargetScan database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to explore the target gene of miR-95-3p. The effects of miR-95-3p inhibition and overexpression on cell proliferation were inspected by cell counting kit-8 (CCK-8) assays and cell colony formation assays. Wound-healing assays and transwell assays were also used to examine cell migration ability in HeLa and SiHa cells.

RESULTS

MiR-95-3p was the only miRNA significantly associated with the poor prognosis of cervical squamous cell carcinoma. A further analysis suggested that vascular cell adhesion molecule 1 (VCAM1) is a target gene of miR-95-3p in cervical cancer, and miR-95-3p promotes the malignant behavior of cervical cancer cells by inhibiting the expression of VCAM1. The CCK-8 and cell colony assays showed that miR-95-3p downregulation significantly suppressed cell proliferation in the HeLa and SiHa cells. The transwell and wound-healing assays showed that miR-95-3p inhibition suppressed cell migration in the HeLa and SiHa cells. Further the Western blot analysis and the quantitative real-time-polymerase chain reaction (qRT-PCR) showed that the knockdown of miR-95-3p in HeLa cells resulted in increased VCAM1 expression. And VCAM1 was highly expressed in the paired adjacent normal cervical epithelium tissue samples, but lowly expressed in the cervical tumor tissue samples.

CONCLUSIONS

Our study was the first to show that miR-95-3p could serve as a prognostic biomarker of cervical cancer. Mechanistically, we discovered that miR-95-3p inhibited the expression of the cell adhesion molecule VCAM1 and thus promoted further tumor progression.

Immunological significance of alternative splicing prognostic signatures for bladder cancer

Background

Bladder cancer (BLCA) is the most common malignant tumor in the genitourinary system, and the complex tumor microenvironment (TME) of BLCA is the main factor in its difficult treatment. Accumulated evidence supports that alternative splicing (AS) events frequently occur in cancer and are closely related to the TME. Therefore, there is an urgent need to comprehensively analyze the prognostic value of AS events in BLCA.

Method

The clinical, transcriptome and AS data of BLCA were downloaded from the Cancer Genome Atlas database, and a Cox proportional hazard regression model and LASSO regression were used to establish a prognostic signature. Then, the prognostic value of the signature was verified by clinical survival status, clinicopathologic features, tumor immune microenvironment (TIME), and immune checkpoint. Next, we screened the AS-related genes with the largest expression differences between tumor and normal samples by gene differential expression analysis. Finally, the regulatory network of AS-splicing factors (SFs) was established to unravel the potential regulatory mechanism of AS events in BLCA.

Results

A BLCA prognostic signature related to seven AS events was constructed, and the prognostic value of the signature was also verified from multiple perspectives. Moreover, there was significant abnormal expression of PTGER3, a gene implicated in AS events, the expression of which was associated with the survival, clinicopathological features, TIME, and immunotherapy of BLCA, suggesting that it has potential clinical application value. Furthermore, the AS-SF regulatory network indicated that splicing factors (PRPF39, LUC7L, HSPA8 and DDX21) might be potential biomarkers of BLCA.

Conclusions

Our study revealed the potential role of AS events in the prognosis, TIME and immunotherapy of BLCA and yielded new insights into the molecular mechanisms of and personalized immunotherapy for BLCA.

Alternative Splicing-Based Differences Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: Genes, Immune Microenvironment, and Survival Prognosis

Alternative splicing (AS) event is a novel biomarker of tumor tumorigenesis and progression. However, the comprehensive analysis of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) is lacking. Differentially expressed analysis was used to identify the differentially expressed alternative splicing (DEAS) events between HCC or ICC tissues and their normal tissues. The correlation between DEAS events and functional analyses or immune features was evaluated. The cluster analysis based on DEAS can accurately reflect the differences in the immune microenvironment between HCC and ICC. Forty-five immune checkpoints and 23 immune features were considered statistically significant in HCC, while only seven immune checkpoints and one immune feature in ICC. Then, the prognostic value of DEAS events was studied, and two transcripts with different basic cell functions (proliferation, cell cycle, invasion, and migration) were produced by ADHFE1 through alternative splicing. Moreover, four nomograms were established in conjunction with relevant clinicopathological factors. Finally, we found two most significant splicing factors and further showed their protein crystal structure. The joint analysis of the AS events in HCC and ICC revealed novel insights into immune features and clinical prognosis, which might provide positive implications in HCC and ICC treatment.

Comprehensive Analysis of Splicing Factor and Alternative Splicing Event to Construct Subtype-Specific Prognosis-Predicting Models for Breast Cancer

Recent evidence suggests that splicing factors (SFs) and alternative splicing (AS) play important roles in cancer progression. We constructed four SF-risk-models using 12 survival-related SFs. In Luminal-A, Luminal-B, Her-2, and Basal-Like BRCA, SF-risk-models for three genes (PAXBP1, NKAP, and NCBP2), four genes (RBM15B, PNN, ACIN1, and SRSF8), three genes (LSM3, SNRNP200, and SNU13), and three genes (SRPK3, PUF60, and PNN) were constructed. These models have a promising prognosis-predicting power. The co-expression and protein-protein interaction analysis suggest that the 12 SFs are highly functional-connected. Pathway analysis and gene set enrichment analysis suggests that the functional role of the selected 12 SFs is highly context-dependent among different BRCA subtypes. We further constructed four AS-risk-models with good prognosis predicting ability in four BRCA subtypes by integrating the four SF-risk-models and 21 survival-related AS-events. This study proposed that SFs and ASs were potential multidimensional biomarkers for the diagnosis, prognosis, and treatment of BRCA.

Role of alternative splicing events in endometrial cancer prognosis

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Correlations Between the Characteristics of Alternative Splicing Events, Prognosis, and the Immune Microenvironment in Breast Cancer

Objective

Alternative splicing (AS) is the mechanism by which a few genes encode numerous proteins, and it redefines the concept of gene expression regulation. Recent studies showed that dysregulation of AS was an important cause of tumorigenesis and microenvironment formation. Therefore, we performed a systematic analysis to examine the role of AS in breast cancer (Breast Cancer, BrCa) progression.

Methods

The present study included 993 BrCa patients from The Cancer Genome Atlas (TCGA) database in the genome-wide analysis of AS events. We used differential and prognostic analyses and found differentially expressed alternative splicing (DEAS) events and independent prognostic factors related to patients' overall survival (OS) and disease-free survival (DFS). We divided the patients into two groups based on these AS events and analyzed their clinical features, molecular subtyping and immune characteristics. We also constructed a splicing factor (SF) regulation network for key AS events and verified the existence of AS events in tissue samples using real-time quantitative PCR.

Results

A total of 678 AS events were identified as differentially expressed, of which 13 and 10 AS events were independent prognostic factors of patients' OS and DFS, respectively. Unsupervised clustering analysis based on these prognostic factors indicated that the Cluster 1 group had a better prognosis and more immune cell infiltration. SFs were significantly related to the expression of AS events, and AA-RPS21 was significantly upregulated in tumors.

Conclusion

Alternative splicing expands the mechanism of breast cancer progression from a new perspective. Notably, alternative splicing may affect the patient's prognosis by affecting the infiltration of immune cells. Our research provides important guidance for subsequent studies of AS in breast cancer.

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.

6-lncRNA Assessment Model for Monitoring and Prognosis of HER2-Positive Breast Cancer: Based on Transcriptome Data

Background: In view of the high malignancy and poor prognosis of human epidermal growth factor receptor 2 (HER2)-positive breast cancer, we analyzed the RNA expression profiles of HER2-positive breast cancer samples to identify the new prognostic biomarkers.

Methods: The linear fitting method was used to identify the differentially expressed RNAs from the HER2-positive breast cancer RNA expression profiles in the Cancer Genome Atlas (TCGA). Then, a series of methods including univariate Cox, Kaplan-Meier, and random forests, were used to identify the core long non-coding RNAs (lncRNAs) with stable prognostic value for HER2-positive breast cancer. A clinical feature analysis was performed, and a competing endogenous RNA network was constructed to explore the role of these core lncRNAs in HER2-positive breast cancer. In addition, a functional analysis of differentially expressed messenger RNAs in HER-2 positive breast cancer also provided us with some enlightening insights.

Results: The high expression of four core lncRNAs (AC010595.1, AC046168.1, AC069277.1, and AP000904.1) was associated with worse overall survival, while the low expression of LINC00528 and MIR762HG was associated with worse overall survival. The 6-lncRNA model has an especially good predictive power for overall survival (p < 0.0001) and 3-year survival (the area under the curve = 0.980) in HER2-positive breast cancer patients.

Conclusion: This study provides a new efficient prognostic model and biomarkers of HER2-positive breast cancer. Meanwhile, it also provides a new perspective for elucidating the molecular mechanisms underlying HER2-positive breast cancer.

Discovery of Functional Alternatively Spliced PKM Transcripts in Human Cancers

Simple Summary

Pyruvate kinase muscle type (PKM) is a key enzyme in glycolysis and is a mediator of the Warburg effect in tumors. The association of PKM with survival of cancer patients is controversial. In this study, we investigated the associations of the alternatively spliced transcripts of PKM with cancer patients’ survival outcomes and explained the conflicts in previous studies. We discovered three poorly studied alternatively spliced PKM transcripts that exhibited opposite prognostic indications in different human cancers based on integrative systems analysis. We also detected their protein products and explored their potential biological functions based on in-vitro experiments. Our analysis demonstrated that alternatively spliced transcripts of not only PKM but also other genes should be considered in cancer studies, since it may enable the discovery and targeting of the right protein product for development of the efficient treatment strategies.

Abstract

Pyruvate kinase muscle type (PKM) is a key enzyme in glycolysis and plays an important oncological role in cancer. However, the association of PKM expression and the survival outcome of patients with different cancers is controversial. We employed systems biology methods to reveal prognostic value and potential biological functions of PKM transcripts in different human cancers. Protein products of transcripts were shown and detected by western blot and mass spectrometry analysis. We focused on different transcripts of PKM and investigated the associations between their mRNA expression and the clinical survival of the patients in 25 different cancers. We find that the transcripts encoding PKM2 and three previously unstudied transcripts, namely ENST00000389093, ENST00000568883, and ENST00000561609, exhibited opposite prognostic indications in different cancers. Moreover, we validated the prognostic effect of these transcripts in an independent kidney cancer cohort. Finally, we revealed that ENST00000389093 and ENST00000568883 possess pyruvate kinase enzymatic activity and may have functional roles in metabolism, cell invasion, and hypoxia response in cancer cells. Our study provided a potential explanation to the controversial prognostic indication of PKM, and could invoke future studies focusing on revealing the biological and oncological roles of these alternative spliced variants of PKM.

Integrative Expression and Prognosis Analysis of DHX37 in Human Cancers by Data Mining

DHEA-Box Helicase 37 (DHX37) is a putative RNA helicase. It is involved in various RNA secondary structure alteration processes, including translation, nuclear splicing, and ribosome assembly. It is reported to be associated with the neurodevelopmental disorder with brain anomalies, and a recent study suggests that DHX37 is a functional regulator of CD8 T cells. Dysregulation of the CD8 T cell function is closely related to defective antitumor immune responses. In the present study, we investigated the expression, mutation, and prognostic role of DHX37 in human cancers, mainly by mining publicly available datasets. Our results suggested that DHX37 was significantly upregulated in 17 kinds of tumors. Mutations including deletions, insertions, and substitutions of DHX37 were widely detected. Besides, the expression of DHX37 was negatively correlated with immune-related genes PD-L1, RGS16, and TOX, and it was positively associated with TIM3, LAG3, and NCOR2. Through biofunctional analysis, we observed that DHX37 was significantly enriched in cancer-related pathways such as cell cycle, DNA replication, mismatch repair, RNA degradation, and RNA polymerase. In conclusion, the study explored the significance of DHX37 in human cancers. DHX37 may serve as a potential target for cancer immunotherapy.

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

Background

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

Methods

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

Results

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

Conclusions

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

Alternative splicing: Human disease and quantitative analysis from high-throughput sequencing

Alternative splicing contributes to the majority of protein diversity in higher eukaryotes by allowing one gene to generate multiple distinct protein isoforms. It adds another regulation layer of gene expression. Up to 95% of human multi-exon genes undergo alternative splicing to encode proteins with different functions. Moreover, around 15% of human hereditary diseases and cancers are associated with alternative splicing. Regulation of alternative splicing is attributed to a set of delicate machineries interacting with each other in aid of important biological processes such as cell development and differentiation. Given the importance of alternative splicing events, their accurate mapping and quantification are paramount for downstream analysis, especially for associating disease with alternative splicing. However, deriving accurate isoform expression from high-throughput RNA-seq data remains a challenging task. In this mini-review, we aim to illustrate I) mechanisms and regulation of alternative splicing, II) alternative splicing associated human disease, III) computational tools for the quantification of isoforms and alternative splicing from RNA-seq.

The Construction of Bone Metastasis-Specific Prognostic Model and Co-expressed Network of Alternative Splicing in Breast Cancer

Background

Breast cancer (BRCA) ranks among the top most common female malignancies and was regarded as incurable when combined with bone and distant metastasis. Alternative splicing events (ASEs) together with splicing factors (SFs) were considered responsible for the development and progression of tumors.

Methods

Datasets including RNA sequencing and ASEs of BRCA samples were achieved from TCGA and TCGASpliceSeq databases. Then, a survival model was built including 15 overall-survival-associated splicing events (OS-SEs) by Cox regression and Lasso regression. The co-expressed SFs of each bone-and-distant-metastasis-related OS-SE were discovered by Pearson correlation analysis. Additionally, Gene Set Variation Analysis (GSVA) was performed to identify the downstream mechanisms of the key OS-SEs. Finally, the results were validated in different online platforms.

Results

A reliable survival model was established (the area under ROC = 0.856), and CIRBP was found co-expressed with FAM110B (R = 0.320, P < 0.001) associated with the fatty acid metabolism pathway.

Conclusion

Aberrant SF, CIRBP, regulated a specific ASE, exon skip (ES) of FAM110B, during which the fatty acid metabolism pathway played an essential part in tumorigenesis and prognosis of BRCA.

Effects of Alternative Splicing Events on Acute Myeloid Leukemia

As suggested by an increasing amount of evidence, there is alternative splicing (AS) modification within malignancy, which is related to cancer occurrence and development. AS within acute myeloid leukemia (AML) has not yet been systematically analyzed yet. This study analyzed the transcriptomic profiling and corresponding clinical data from AML cases based on The Cancer Genome Atlas (TCGA). In addition, the prediction model, along with the splicing network, was used to analyze the prognosis for AML patients according to the seven different AS event types. Among the 34,984 AS events across the 8830 genes, 2896 AS events were detected among 1905 genes, showing marked correlation with the overall survival of patients. The risk scoring model based on all AS event types was the most efficient in identifying the prognosis for AML patients. Meanwhile, the area under the curve at 1-, 3-, 5-year were 0.852, 0.935, 0.955, respectively. At the same time, the splicing regulating network, which was constituted by 21 splicing factor genes as well as 32 AS events related to survival, was characterized. In conclusion, our predictive model constructed based on the AS events accurately predicts the survival for AML patients. In addition, the network between AS events and splicing factor is established, which may serve as a potential mechanism.

Systematic Profiling of Alternative Splicing for Sarcoma Patients Reveals Novel Prognostic Biomarkers Associated with Tumor Microenvironment and Immune Cells

Background

Alternative splicing (AS) events is a novel biomarker of tumor prognosis, but the role of AS events in sarcoma patients remains unclear.

Material/Methods

RNA-seq and clinicopathologic data of the sarcoma cohort were extracted from the TCGA database and data on AS events were downloaded from the TCGASpliceSeq database. Univariate Cox analysis, LASSO regression analysis, and multivariate Cox analysis were performed to determine the overall survival (OS)- and disease-free survival (DFS)-related AS events. Two nomograms were developed based on the independent variables, and subgroup analysis was performed. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomograms. Then, we used the CIBERSORT and ESTIMATE package to determine the immune cell proportion and tumor microenvironment (TME) score, respectively. The associations between AS events-based clusters and TME and immune cells were studied.

Results

We identified 1945 and 1831 AS events as OS- and DFS-related AS events, respectively. Two nomograms based on the AS events and clinical data were established and the AUCs of nomograms ranged from 0.807 to 0.894. The calibration curve and DCA showed excellent performance of nomograms. In addition, the results indicated the distinct relationships between AS events-based clusters and OS, DFS, immune score, stromal score, and 10 immune cells.

Conclusions

Our study indicated that AS events are novel prognostic biomarkers for sarcoma patients that may be associated with the TME and immune cells.

Novel Insights Into Triple-Negative Breast Cancer Prognosis by Comprehensive Characterization of Aberrant Alternative Splicing

Background

Alternative splicing (AS) is important in the regulation of gene expression and aberrant AS is emerging as a major factor in the pathogenesis of human conditions, including cancer. Triple-negative breast cancer (TNBC) is the most challenging subtype of breast cancer with strong invasion, high rate of metastasis, and poor prognosis. Here we report a systematic profiling of aberrant AS in TNBC.

Methods

The percent spliced in (PSI) values for AS events in 151 TNBC patients were obtained from The Cancer Genome Atlas (TCGA) SpliceSeq database. Univariate Cox and stepwise Multivariate Cox regression analyses were conducted to find the best prognostic AS model. Splicing regulatory networks were constructed by prognosis-related spliceosome and aberrant AS events. Additionally, pathway enrichment and gene set enrichment analysis (GSEA) were further employed to reveal the significant pathways for prognosis-related AS genes. Finally, splicing regulatory networks were constructed via Spearman's rank correlation coefficients between prognosis-related AS events and splicing factor expressions.

Results

A total of 1,397 prognosis-associated AS events were identified in TNBC. The majority of the parent genes of prognostic AS events exhibited direct interactions to each other in the STRING gene network. Pathways of focal adhesion (p < 0.001), RNA splicing (p = 0.007), homologous recombination (p = 0.042) and ECM-receptor interaction (p = 0.046) were found to be significantly enriched for prognosis-related AS. Additionally, the area under curve (AUC) of the best AS prognostic predictor model reached 0.949, showing a powerful capability to predict outcomes. The Exon Skip (ES) type of AS events displayed more robust and efficient capacity in predicting performance than any other specific AS events type in terms of prognosis. The ES AS signature might confer a strong oncogenic phenotype in the high-risk group with elevated activities in cell cycle and SUMOylating pathways of tumorigenesis, while programmed cell death and metabolism pathways were found to be enriched in the low-risk group of TNBC. The splicing correlation network also revealed a regulatory mode of prognostic splicing factors (SFs) in TNBC.

Conclusion

Our analysis of AS events in TNBC could not only contribute to elucidating the tumorigenesis mechanism of AS but also provide clues to uncovering underlying prognostic biomarkers and therapeutic targets for further study.

Prognostic Alternative mRNA Splicing Signature and a Novel Biomarker in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a high-risk subtype of breast cancer defined by negative expression of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Accumulating evidence indicates that alternative splicing (AS) events are correlated with the prognosis of cancer. RNA sequencing data and AS event data were manually curated from The Cancer Genome Atlas (TCGA) dataset and TCGA Splice Seq, respectively. Univariate and multivariate Cox regression analyses were applied to screen AS events associated with TNBC survival and to establish a prognostic model. A receiver operating characteristic (ROC) curve was used to evaluate the performance of the prognostic model. Differentially expressed gene analysis and functional enrichment analysis were harnessed to reveal the functional role of gene sets and to screen novel biomarkers. By integrated bioinformatics analysis of AS events and gene expression in TNBC, our study is the first to generate specific AS event profiles, prognostic AS event interaction networks, and splice factor-AS interaction networks for TNBC. Surprisingly, we found that the performance of the AS-based prognostic model was encouraging with a mean area under the ROC curve of 0.957 at 2-10 years. We also found that chemokine (C-C motif) ligand 16 (CCL16) expression was correlated with TNBC grade and could be a potential novel biomarker. In conclusion, this study provided a systematic analysis of prognostic AS event profiles and gene expression in TNBC. A novel prognostic model based on AS events may establish a foundation for future research investigating the diagnosis and treatment of TNBC.

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.

OScc: an online survival analysis web server to evaluate the prognostic value of biomarkers in cervical cancer

Aim: To establish a web server that can mutually validate prognostic biomarkers of cervical cancer. Methods: Four datasets including expression profiling and relative clinical follow-up data were collected from Gene Expression Omnibus and The Cancer Genome Atlas. The web server was developed by R software. Results: The web server was named OScc including 690 patients and can be accessed at http://bioinfo.henu.edu.cn/CESC/CESCList.jsp . The Kaplan–Meier survival curves with log-rank p-value and hazard ratio will be generated of interested gene in OScc. Compared with previous predictive tools, OScc had the advantages of registration-free, larger sample size and subgroup analysis. Conclusion: The OScc is highly valuable to perform the preliminary assessment and validation of new or interested prognostic biomarkers for cervical cancer.

Exploring specific prognostic biomarkers in triple-negative breast cancer

Lacking of both prognostic biomarkers and therapeutic targets, triple-negative breast cancer (TNBC) underscores pivotal needs to uncover novel biomarkers and viable therapies. MicroRNAs have broad biological functions in cancers and may serve as ideal biomarkers. In this study, by data mining of the Cancer Genome Atlas database, we screened out 4 differentially-expressed microRNAs (DEmiRNAs) between TNBC and normal samples: miR-135b-5p, miR-9-3p, miR-135b-3p and miR-455-5p. They were specially correlated with the prognosis of TNBC but not non-TNBC. The weighted correlation network analysis (WGCNA) for potential target genes of 3 good prognosis-related DEmiRNAs (miR-135b-5p, miR-9-3p, miR-135b-3p) identified 4 hub genes with highly positive correlation with TNBC subtype: FOXC1, BCL11A, FAM171A1 and RGMA. The targeting relationships between miR-9-3p and FOXC1/FAM171A1, miR-135b-3p and RGMA were validated by dual-luciferase reporter assays. Importantly, the regulatory functions of 4 DEmiRNAs and 3 verified target genes on cell proliferation and migration were explored in TNBC cell lines. In conclusion, we shed lights on these 4 DEmiRNAs (miR-135b-5p, miR-9-3p, miR-135b-3p, miR-455-5p) and 3 hub genes (FOXC1, FAM171A1, RGMA) as specific prognostic biomarkers and promising therapeutic targets for TNBC.

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.

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.

Determining the prognostic significance of alternative splicing events in soft tissue sarcoma using data from The Cancer Genome Atlas

BACKGROUND

Surgery, adjuvant chemotherapy, and radiotherapy are the primary treatment options for soft tissue sarcomas (STSs). However, identifying ways to improve the prognosis of patients with STS remains a considerable challenge. Evidence shows that the dysregulation of alternative splicing (AS) events is involved in tumor pathogenesis and progression. The present study objective was to identify survival-associated AS events that could serve as prognostic biomarkers and potentially serve as tumor-selective STS drug targets.

METHODS

STS-specific 'percent spliced in' (PSI) values for splicing events in 206 STS samples were downloaded from The Cancer Genome Atlas SpliceSeq^® database. Prognostic analyses were performed on seven types of AS events to determine their prognostic value in STS patients, for which prediction models were constructed with the risk score formula [Formula: see text]. Prediction models were also constructed to determine the prognostic value of AS events, and Spearman's rank correlation coefficients were calculated to determine the degree of correlation between splicing factor expression and the PSI values.

RESULTS

A total 10,439 events were found to significantly correlate with patient survival rates. The area under the time-dependent receiver operating characteristic curve for the prognostic predictor of STS overall survival was 0.826. Notably, the splicing events of certain STS key genes were significantly associated with STS 2-year overall survival in the present study, including exon skip (ES) events in MDM2 and EWSR1, alternate terminator events in CDKN2A and HMGA2 for dedifferentiated liposarcoma, ES in MDM2 and alternate promoter events in CDKN2A for leiomyosarcoma, and ES in EWSR1 for undifferentiated pleomorphic sarcoma. Moreover, splicing correlation networks between AS events and splicing factors revealed that almost all of the AS events showed negatively correlations with the expression of splicing factors.

CONCLUSION

An in-depth analysis of alternative RNA splicing could provide new insights into the mechanisms of STS oncogenesis and the potential for novel approaches to this type of cancer therapy.