Alternative Splicing Events and Subtype Analysis of Esophageal Cancer

Abstract Aim: Alternative splicing (AS) has been widely demonstrated in the occurrence and progression of many cancers. Nevertheless, the involvement of cancer-associated splicing in the development of esophageal carcinoma (ESCA) is still ambiguous.Method: RNA-Seq data and the corresponding clinical information of the ESCA cohort was downloaded from The Cancer Genome Atlas database. The splicing percentage value was calculated using a Java application called SpliceSeq, and differently expressed AS (DEAS) events and their splicing network were further analyzed using bioinformatics methods. Kaplan–Meier, Cox regression, and unsupervised cluster analyses were used to assess the association between AS events and clinical characteristics of ESCA patients.Results: A total of 50,342 AS events were identified, of which 3,988 were DEAS events; 46 of these were associated with overall survival (OS) of ESCA patients, and the 5-year OS rate was 0.941. By constructing a network of AS events with survival-related splicing factors and variable-shear events associated with prognosis, the regulatory relationship was further predicted. Four clusters with different survival patterns were revealed using unsupervised cluster analysis.Conclusion: ESCA-associated AS events and splicing networks are of great value in deciphering the underlying mechanisms of AS in ESCA and providing clues for therapeutic goals for further validation.

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

BMC Cancer ◽

10.1186/s12885-020-07419-7 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Haitao Chen ◽

Jun Luo ◽

Jianchun Guo

Keyword(s):

Colon Cancer ◽

Alternative Splicing ◽

Cancer Survival ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Regulatory Relationship ◽

Predictive Values ◽

Entire Cohort ◽

Kaplan Meier ◽

Cox Analysis

Abstract 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.

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Prognostic Signatures of Alternative Splicing Events in Esophageal Carcinoma Based on TCGA Splice-Seq Data

Frontiers in Oncology ◽

10.3389/fonc.2021.658262 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ping Ye ◽

Yan Yang ◽

Liqiang Zhang ◽

Guixi Zheng

Keyword(s):

Alternative Splicing ◽

Esophageal Carcinoma ◽

Cox Regression ◽

Donor Site ◽

Area Under The Curve ◽

Clinical Information ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Acceptor Site ◽

Cox Regression Analysis

An alternative splicing (AS) event is a highly complex process that plays an essential role in post-transcriptional gene expression. Several studies have suggested that abnormal AS events were the primary element in the pathological process of cancer. However, few works are dedicated to the study of AS events in esophageal carcinoma (EC). In the present study, clinical information and RNA-seq data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The percent spliced in (PSI) values of AS events were acquired from the TCGA Splice-seq. A total of 183 EC patients were enrolled in this study, and 2,212 AS events were found significantly associated with the overall survival of these patients by univariate Cox regression analysis. The prognostic signatures based on AS events were built by multivariate Cox analysis. Receiver operating characteristic (ROC) curves displayed that the area under the curve (AUC) of the following prognostic signatures, including exon skip (ES), alternate terminator (AT), alternate acceptor site (AA), alternate promoter (AP), alternate donor site (AD), retained intron (RI), and total events, was greater than 0.8, suggesting that these seven signatures had valuable prognosis prediction capacity. Finally, the risk score of prognostic signatures was indicated as an independent risk factor of survival. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to explore the function of splicing factors (SFs) that were associated with AS events. Also, the interactive network between AS events and SFs identified several hub genes and AS events which need further study. This was a comprehensive study that explored prognosis-related AS events and established valuable prognosis signatures in EC patients. The network of interactions between AS events and SFs might offer novel insights into the fundamental mechanisms of tumorigenesis and progression of EC.

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

Bioscience Reports ◽

10.1042/bsr20202338 ◽

2020 ◽

Vol 40 (10) ◽

Author(s):

Yidi Wang ◽

Yaxuan Wang ◽

Kenan Li ◽

Yabing Du ◽

Kang Cui ◽

...

Keyword(s):

Alternative Splicing ◽

Lung Adenocarcinoma ◽

Prognostic Value ◽

Prognostic Index ◽

Clinical Information ◽

The Cancer Genome Atlas ◽

Splicing Factors ◽

Systematic Analysis ◽

Interactive Network ◽

Underlying Mechanisms

Abstract 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.

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Genome-Wide Analysis of Prognostic Alternative Splicing Signature and Splicing Factors in Lung Adenocarcinoma

Genes ◽

10.3390/genes11111300 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1300

Author(s):

Ya-Sian Chang ◽

Siang-Jyun Tu ◽

Hui-Shan Chiang ◽

Ju-Chen Yen ◽

Ya-Ting Lee ◽

...

Keyword(s):

Alternative Splicing ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Area Under The Curve ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Rank Test ◽

Splicing Factors ◽

Cox Regression Analysis ◽

Underlying Mechanisms

Analysis of The Cancer Genome Atlas data revealed that alternative splicing (AS) events could serve as prognostic biomarkers in various cancer types. This study examined lung adenocarcinoma (LUAD) tissues for AS and assessed AS events as potential indicators of prognosis in our cohort. RNA sequencing and bioinformatics analysis were performed. We used SUPPA2 to analyze the AS profiles. Using univariate Cox regression analysis, overall survival (OS)-related AS events were identified. Genes relating to the OS-related AS events were imported into Cytoscape, and the CytoHubba application was run. OS-related splicing factors (SFs) were explored using the log-rank test. The relationship between the percent spliced-in value of the OS-related AS events and SF expression was identified by Spearman correlation analysis. We found 1957 OS-related AS events in 1151 genes, and most were protective factors. Alternative first exon splicing was the most frequent type of splicing event. The hub genes in the gene network of the OS-related AS events were FBXW11, FBXL5, KCTD7, UBB and CDC27. The area under the curve of the MIX prediction model was 0.847 for 5-year survival based on seven OS-related AS events. Overexpression of SFs CELF2 and SRSF5 was associated with better OS. We constructed a correlation network between SFs and OS-related AS events. In conclusion, we identified prognostic predictors using AS events that stratified LUAD patients into high- and low-risk groups. The discovery of the splicing networks in this study provides an insight into the underlying mechanisms.

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The Functional Impact of Alternative Splicing on the Survival Prognosis of Triple-Negative Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2020.604262 ◽

2021 ◽

Vol 11 ◽

Author(s):

Sijia Wu ◽

Jiachen Wang ◽

Xinchao Zhu ◽

Jacqueline Chyr ◽

Xiaobo Zhou ◽

...

Keyword(s):

Breast Cancer ◽

Risk Factor ◽

Alternative Splicing ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Survival Prognosis ◽

Kaplan Meier

PurposeTriple-negative breast cancer (TNBC) is a type of breast cancer (BC) showing a high recurrence ratio and a low survival probability, which requires novel actionable molecular targets. The involvement of alternative splicing (AS) in TNBC promoted us to study the potential roles of AS events in the survival prognosis of TNBC patients.MethodsA total of 150 TNBC patients from The Cancer Genome Atlas (TCGA) were involved in this work. To study the effects of AS in the recurrence-free survival (RFS) prognosis of TNBC, we performed the analyses as follows. First, univariate Cox regression model was applied to identify RFS-related AS events. Their host genes were analyzed by Metascape to discover the potential functions and involved pathways. Next, least absolute shrinkage and selection operator (LASSO) method was used to select the most informative RFS-related AS events to constitute an AS risk factor for RFS prognosis, which was evaluated by Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves in all the data and also in different clinical subgroups. Furthermore, we analyzed the relationships between splicing factors (SFs) and these RFS-related AS events to seek the possibility that SFs regulated AS events to influence RFS. Then, we evaluated the potential of these RFS-related AS events in the overall survival (OS) prognosis from all the above aspects.ResultsWe identified a total of 546 RFS-related AS events, which were enriched in some splicing and TNBC-associated pathways. Among them, seven RFS-related events were integrated into a risk factor, exhibiting satisfactory RFS prognosis alone and even better performance when combined with clinical tumor–node–metastasis stages. Furthermore, the correlation analysis between SFs and the seven AS events revealed the hypotheses that SRPK3 might upregulate PCYT2_44231_AA to have an effect on RFS prognosis and that three other SFs may work together to downregulate FLAD1_7874_RI to influence RFS prognosis. In addition, the seven RFS-related AS events were validated to be promising in the OS prognosis of TNBC as well.ConclusionThe abnormal AS events regulated by SFs may act as a kind of biomarker for the survival prognosis of TNBC.

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Identification of lncRNA prognostic signature and analysis of their functions for HCV-related hepatocellular carcinoma

10.21203/rs.3.rs-50007/v1 ◽

2020 ◽

Author(s):

Kun Wang ◽

Wenxin Li ◽

Yefu Liu ◽

Zhiqiang Hao ◽

Xiangdong Hua ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Cox Regression ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Clinical Variables ◽

Kaplan Meier ◽

Underlying Mechanisms

Abstract Background Hepatitis C virus (HCV) infection is a main contribution to the increase in hepatocellular carcinoma (HCC) incidence and patients’ death recently, but prognostic biomarkers for HCV-related HCC remain rarely reported. This study was to identify an lncRNA prognostic signature for HCV-HCC patients and explore their underlying function mechanisms. Methods In total, 102 HCV-HCC samples and 50 normal control samples were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analysis were conducted to screen an lncRNA signature that could predict overall survival (OS) and then, the risk score was calculated using this signature. The prognostic potential of this risk score was evaluated by drawing Kaplan-Meier, receiver operating characteristic (ROC) curves and performing multivariate Cox regression analyses with clinical variables. Furthermore, a co-expression and competing endogenous RNA (ceRNA) networks were constructed to explore the functional mechanisms of lncRNAs. Results Multivariate Cox regression showed six lncRNAs (SLC16A1-AS1, ZFPM2-AS1, JARID2-AS1, LINC01426, USP3-AS1 and LYPLAL1-AS1) were significantly associated with OS of HCV-HCC patients. These six lncRNAs were used to establish a risk score model, which displayed a higher prognosis prediction accuracy [area under the ROC curve (AUC) = 0.95 for training set; AUC = 0.885 for testing; AUC = 0.907 for entire set]. Also, this was independent of various clinical variables. The crucial co-expression (LINC01426/SLC16A1-AS1-AURKA/SFN/CCNB1, ZFPM2-AS1/LYPLAL1-AS1/JARID2-AS1-TSSK6) or ceRNA (USP3-AS1-hsa-miR-383-SFN) interaction axes were identified. Conclusion Our study identified a novel six-lncRNA prognosis signature for HCV-HCC patients and indicated their underlying mechanisms for HCC progression.

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Identification of prognostic alternative splicing events in sarcoma

Scientific Reports ◽

10.1038/s41598-021-94485-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hongshuai Li ◽

Jie Yang ◽

Guohui Yang ◽

Jia Ren ◽

Yu Meng ◽

...

Keyword(s):

Alternative Splicing ◽

Cox Regression ◽

Univariate Analysis ◽

Area Under The Curve ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Functional Roles ◽

Cancer Pathogenesis ◽

Rare Malignancy

AbstractSarcoma is a rare malignancy with unfavorable prognoses. Accumulating evidence indicates that aberrant alternative splicing (AS) events are generally involved in cancer pathogenesis. The aim of this study was to identify the prognostic value of AS-related survival genes as potential biomarkers, and highlight the functional roles of AS events in sarcoma. RNA-sequencing and AS-event datasets were downloaded from The Cancer Genome Atlas (TCGA) sarcoma cohort and TCGA SpliceSeq, respectively. Survival-related AS events were further assessed using a univariate analysis. A multivariate Cox regression analysis was also performed to establish a survival-gene signature to predict patient survival, and the area-under-the-curve method was used to evaluate prognostic reliability. KOBAS 3.0 and Cytoscape were used to functionally annotate AS-related genes and to assess their network interactions. We detected 9674 AS events in 40,184 genes from 236 sarcoma samples, and the 15 most significant genes were then used to construct a survival regression model. We further validated the involvement of ten potential survival-related genes (TUBB3, TRIM69, ZNFX1, VAV1, KCNN2, VGLL3, AK7, ARMC4, LRRC1, and CRIP1) in the occurrence and development of sarcoma. Multivariate survival model analyses were also performed, and validated that a model using these ten genes provided good classifications for predicting patient outcomes. The present study has increased our understanding of AS events in sarcoma, and the gene-based model using AS-related events may serve as a potential predictor to determine the survival of sarcoma patients.

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Co‑expression Network Analysis by WGCNA and Identify Potential Prognostic Markers Associated with Lung Metastasis in Breast Cancer

10.21203/rs.3.rs-182567/v1 ◽

2021 ◽

Author(s):

xixun zhang

Keyword(s):

Breast Cancer ◽

Network Analysis ◽

Lung Metastasis ◽

Prognostic Markers ◽

Clinical Information ◽

Preventive Treatment ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Breast Cancer Patients ◽

Kaplan Meier

Abstract Backgroud: Breast cancer (BC) is an aggressive cancer with a high percentage recurrence and metastasis. As one of the most common distant metastasis organ in breast cancer, lung metastasis has a worse prognosis than that of liver and bone. Therefore, it’s important to explore some potential prognostic markers associated with the lung metastasis in breast cancer for preventive treatment. Methods: In our study, transcriptomic data and clinical information of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Co-expression modules was built by Weighted gene co-expression network analysis (WGCNA) to find out the royalbule modules which is significantly associated with lung metastasis in breast cancer. Then, co-expression genes were analyzed for functional enrichment. Furthermore, the prognostic value of these genes was assessed by GEPIA Database and Kaplan-Meier Plotter. Results: Results showed that the hub genes, LMNB and CDC20, were up-regulated in breast cancer and indicated worse survival. Therefore, we speculate that these two genes play crucial roles in the process of lung metastasis in breast cancer, and can be used as potential prognostic markers in lung metastasis of breast cancer. Conclusion: Collectively, our study identified two potential key genes in the lung metastasis of breast cancer, which might be applied as the prognostic markers of the precise treatment in breast cancer with lung metastasis.

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Ferroptosis-Related Long Non-Coding RNAs and the Roles of LASTR in Stomach Adenocarcinoma

10.21203/rs.3.rs-714235/v1 ◽

2021 ◽

Author(s):

Gongjun Wang ◽

Libin Sun ◽

Shasha Wang ◽

Jing Guo ◽

Hui Li ◽

...

Keyword(s):

Cox Regression ◽

Comprehensive Evaluation ◽

Progression Free Survival ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Sequencing Data ◽

Regulatory Relationship ◽

Data Resource ◽

Stomach Adenocarcinoma

Abstract Background: Ferroptosis is a form of cell death involved in diverse physiological context. Increasing evidence suggests that there is a closely regulatory relationship between ferroptosis and long noncoding RNAs (lncRNAs).Method: RNA-sequencing data from The Cancer Genome Atlas (TCGA) data resource and ferroptosis-related genes from FerrDb (http://www.zhounan.org/ferrdb/) data resource were employed to select differentially expressed lncRNAs. We performed Univariate Cox regression and multivariate Cox analyses analysis on these differentially expressed lncRNAs to screen independent predictive factors. Subsequently, we established two signatures for predicting overall survival (OS) and progression-free survival (PFS). Finally, experiments were conducted to verify the roles of LASTR in gastric cancer (GC).Results: We identified 12 differentially expressed lncRNAs linked with OS and 13 associated with PFS. Kaplan-Meier(K-M) analyses exhibited that the high-risk group was related to a poor prognosis of stomach adenocarcinoma (STAD). The AUCs of the OS, as well as PFS signatures of lncRNAs were 0.734 and 0.771, respectively, indicating their excellent efficacy in predicting STAD prognosis. Our experimental results illustrated that the inhibition of LASTR inhibited tumor proliferation and migration in GC.Conclusion: This comprehensive evaluation of the ferroptosis-related lncRNA landscape in STAD unearthed novel lncRNAs related to carcinogenesis. In addition, we also experimentally confirmed the effects of LASTR on proliferation, migration and ferroptosis. These results provide potential novel targets for tumor treatment and promote personalized medicine.

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Co-Expression of Coxsackievirus/Adenovirus Receptors and Desmoglein 2 in Lung Adenocarcinoma: A Comprehensive Analysis of Bioinformatics and Tissue Microarrays

Journal of Clinical Medicine ◽

10.3390/jcm9113693 ◽

2020 ◽

Vol 9 (11) ◽

pp. 3693

Author(s):

Ching-Fu Weng ◽

Chi-Jung Huang ◽

Mei-Hsuan Wu ◽

Henry Hsin-Chung Lee ◽

Thai-Yen Ling

Keyword(s):

Overall Survival ◽

Survival Analysis ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Early Stage ◽

The Cancer Genome Atlas ◽

Cancer Tissue ◽

Mesenchymal Transition ◽

Kaplan Meier

Introduction: Coxsackievirus/adenovirus receptors (CARs) and desmoglein-2 (DSG2) are similar molecules to adenovirus-based vectors in the cell membrane. They have been found to be associated with lung epithelial cell tumorigenesis and can be useful markers in predicting survival outcome in lung adenocarcinoma (LUAD). Methods: A gene ontology enrichment analysis disclosed that DSG2 was highly correlated with CAR. Survival analysis was then performed on 262 samples from the Cancer Genome Atlas, forming “Stage 1A” or “Stage 1B”. We therefore analyzed a tissue microarray (TMA) comprised of 108 lung samples and an immunohistochemical assay. Computer counting software was used to calculate the H-score of the immune intensity. Cox regression and Kaplan–Meier analyses were used to determine the prognostic value. Results: CAR and DSG2 genes are highly co-expressed in early stage LUAD and associated with significantly poorer survival (p = 0.0046). TMA also showed that CAR/DSG2 expressions were altered in lung cancer tissue. CAR in the TMA was correlated with proliferation, apoptosis, and epithelial–mesenchymal transition (EMT), while DSG2 was associated with proliferation only. The Kaplan–Meier survival analysis revealed that CAR, DSG2, or a co-expression of CAR/DSG2 was associated with poorer overall survival. Conclusions: The co-expression of CAR/DSG2 predicted a worse overall survival in LUAD. CAR combined with DSG2 expression can predict prognosis.

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