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

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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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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Features of alternative splicing in stomach adenocarcinoma and their clinical implication: A research based on massive sequencing data

10.21203/rs.2.20011/v3 ◽

2020 ◽

Author(s):

Yuanyuan Zhang ◽

Qian Niu ◽

Yun Han ◽

Xingyu Liu ◽

Jie Jiang ◽

...

Keyword(s):

Alternative Splicing ◽

High Efficiency ◽

Cox Regression ◽

Area Under The Curve ◽

The Cancer Genome Atlas ◽

Regulation Mechanism ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Molecular Events ◽

Stomach Adenocarcinoma

Abstract Background: Alternative splicing (AS) offers a main mechanism to form protein polymorphism. A growing body of evidence indicates the correlation between splicing disorders and carcinoma. Nevertheless, an overall analysis of AS signatures in stomach adenocarcinoma (STAD) is absent and urgently needed.Methods: Within this work, genetic expression and clinical data of STAD were queried from The Cancer Genome Atlas (TCGA), and profiles of AS events were searched from the SpliceSeq database. Cox regression analysis found survival associated AS events. Finally, the splicing network was constructed to reflect the correlation between survival associated AS events and splicing factors (SF).Results: 2042 splicing events were confirmed as prognostic molecular events. Furthermore, the final prognostic signature constructed by 10 AS events gave good result with an area under the curve (AUC) of receiver operating characteristic (ROC) curve up to 0.902 for 5 years, showing high potency in predicting patient outcome. We built the splicing regulatory network to show the internal regulation mechanism of splicing events in STAD. QKI may play a significant part in the prognosis induced by splicing events.Conclusions: In our study, a high-efficiency prognostic prediction model was built for STAD patients, and the results showed that AS events could become potential prognostic biomarkers for STAD. Meanwhile, QKI may become an important target for drug design in the future.

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Features of alternative splicing in stomach adenocarcinoma and their clinical implication: A research based on massive sequencing data

10.21203/rs.2.20011/v2 ◽

2020 ◽

Author(s):

Yuanyuan Zhang ◽

Qian Niu ◽

Yun Han ◽

Xingyu Liu ◽

Jie Jiang ◽

...

Keyword(s):

Alternative Splicing ◽

High Efficiency ◽

Cox Regression ◽

Area Under The Curve ◽

The Cancer Genome Atlas ◽

Regulation Mechanism ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Molecular Events ◽

Stomach Adenocarcinoma

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Identification of a m6A RNA methylation regulators-based signature for predicting the prognosis of esophageal adenocarcinoma

10.21203/rs.3.rs-50655/v1 ◽

2020 ◽

Author(s):

Yue Zhou ◽

Shuyan Li ◽

Liqing Zou ◽

Tiantian Guo ◽

Xi Yang ◽

...

Keyword(s):

Esophageal Adenocarcinoma ◽

Clustering Analysis ◽

Cox Regression ◽

Area Under The Curve ◽

Clinical Information ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Cox Regression Analysis ◽

Rna Methylation ◽

M6a Rna Methylation

Abstract BackgroundN6-methyladenosine (m6A) is an abundant modification in RNAs that affects RNA metabolism, and it is reported to be closely related to cancer occurrence and metastasis. The aim of this study was to identify novel prognostic biomarkers by using m6A RNA methylation regulators capable of improving the risk-stratification criteria of survival for esophageal adenocarcinoma patients.MethodsThe gene expression data of 16 m6A methylation regulators and its relevant clinical information were extracted from The Cancer Genome Atlas (TCGA) database. The expression pattern of these m6A methylation regulators was evaluated. Consensus clustering analysis was conducted to identify clusters of esophageal adenocarcinoma patients with different prognosis. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were performed to construct multiple-gene risk signature. A survival analysis was carried out to determine the prognosis significance.ResultsTen m6A methylation regulators (HNRNPA2B1, HNRNPC, YTHDF1, METTL3, YTHDF2, RBM15, YTHDC1, WTAP, KIAA1429 and YTHDF3) showed significant up-regulation in tumor tissue. Consensus clustering analysis identified three clusters of esophageal adenocarcinoma patients with different overall survival. A five-gene signature, HNRNPA2B1, KIAA1429, WTAP, METTL16 and ALKBH5, was constructed to serve as a prognostic indicator for distinguish esophageal adenocarcinoma patients with different prognosis. The receiver operator characteristic (ROC) curve which indicated the area under the curve (AUC) were 0.803, demonstrated that the prognostic signature had preferable prediction efficiency.Conclusionsm6A methylation regulators exert as potential biomarkers for prognostic stratification of esophageal adenocarcinoma patients and might help clinicians make individualized therapy for this patient population.

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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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Features of alternative splicing in stomach adenocarcinoma and their clinical implication: A research based on massive sequencing data

10.21203/rs.2.20011/v1 ◽

2020 ◽

Author(s):

Yuanyuan Zhang ◽

Qian Niu ◽

Yun Han ◽

Xingyu Liu ◽

Jie Jiang ◽

...

Keyword(s):

Alternative Splicing ◽

High Efficiency ◽

Cox Regression ◽

Area Under The Curve ◽

The Cancer Genome Atlas ◽

Regulation Mechanism ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Molecular Events ◽

Stomach Adenocarcinoma

Abstract Background: Alternative splicing (AS) offers a main mechanism to form protein polymorphism. A growing body of evidence indicates the correlation between splicing disorders and carcinoma. Nevertheless, an overall analysis of AS signatures in stomach adenocarcinoma (STAD) is absent and urgently needed. Methods: Within this work, genetic expression and clinical data of STAD were queried from The Cancer Genome Atlas (TCGA), and profiles of AS events were searched from the SpliceSeq database. Cox regression analysis found survival associated AS events. Finally, the splicing network was constructed to reflect the correlation between survival associated AS events and splicing factors (SF). Results: 2042 splicing events were confirmed as prognostic molecular events. Furthermore, the final prognostic signature constructed by 10 AS events gave good result with an area under the curve (AUC) of receiver operating characteristic (ROC) curve up to 0.902 for 5 years, showing high potency in predicting patient outcome. We built the splicing regulatory network to show the internal regulation mechanism of splicing events in STAD. QKI may play a significant part in the prognosis induced by splicing events. Conclusions: In our study, a high-efficiency prognostic prediction model was built for STAD patients, and the results showed that AS events could become potential prognostic biomarkers for STAD. Meanwhile, QKI may become an important target for drug design in the future.

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PDK2: a novel diagnostic and prognostic biomarker for liver cancer

10.21203/rs.2.24570/v1 ◽

2020 ◽

Author(s):

Zhi-Cheng Liu ◽

Yan-Qing Li ◽

Yan Jiao ◽

Yue-Chen Zhao

Keyword(s):

Liver Cancer ◽

Pyruvate Dehydrogenase ◽

Cox Regression ◽

Area Under The Curve ◽

Normal Liver ◽

The Cancer Genome Atlas ◽

High Morbidity ◽

Cox Regression Analysis ◽

Diagnosis And Prognosis ◽

Potential Biomarker

Abstract Background: Liver cancer (LC) is a common malignancy with very high morbidity. Pyruvate dehydrogenase kinases (PDKs) are regulators of mitochondrial pyruvate dehydrogenase complexes (PDCs) and play an important role in regulating cellular energy metabolism. In this study, The Cancer Genome Atlas (TCGA) database was used to analyze the expression of PDK2 mRNA in LC, and to explore the value of PDK2 in the diagnosis and prognosis of LC.Methods: The TCGA database, containing the clinical data of 373 LC patients, includes information on PDK2 expression values. The receiver operating characteristic (ROC) curve of PDK2 was drawn to evaluate its diagnostic ability. Patients were divided into PDK2 high- and low-expressing groups by threshold levels. The Chi-square test was used to evaluate the correlation between PDK2 levels and clinicopathological characteristics. The Kaplan-Meier estimator and Cox regression analysis were performed to assess the effect of PDK2 levels on survival outcomes.Results: PDK2 expression in LC tissue was lower than that in normal liver tissues. According to the area under the curve (AUC) value calculated by ROC, PDK2 has a considerable diagnostic value for LC prognosis. The decreased expression of PDK2 is associated with clinical parameters, such as histologic grade ( P =0.0001), radiation therapy ( P =0.0490), vital status ( P =0.0240), and overall survival (OS) ( P =0.0222). Multivariate analysis shows that decreased PDK2 level is an independent risk factor for predicting poor prognosis in LC.Conclusions: PDK2 has a significant impact on the prognosis of LC and is a potential biomarker for the diagnosis and prognosis of LC.

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Analysis of relapse-associated alternative mRNA splicing and construction of a prognostic signature predicting relapse in I–III colon cancer

10.21203/rs.3.rs-17501/v1 ◽

2020 ◽

Author(s):

Zhiyuan Zhang ◽

Qingyang Feng ◽

Peng Zheng ◽

Yang Lv ◽

Yihao Mao ◽

...

Keyword(s):

Colon Cancer ◽

Alternative Splicing ◽

Protein Interactions ◽

Cox Regression ◽

Molecular Subtypes ◽

Test Group ◽

Training Group ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Alternative Mrna Splicing

Abstract Background : The literature depicting the effects of alternative splicing (AS) events on relapse of colon cancer is little and there is no signature based on the alternative splicing. Methods : The bioinformatic analysis was performed based on data of The Cancer Genome Atlas (TCGA) to identify the relapse-associated ASs, the potential interactions were further analyzed and a robust signature was built after univariate Cox regression, LASSO Cox regression, and multivariate Cox regression analysis to predict the relapse in I–III colon cancer. Molecular subtypes was identified based on the signature. Results : We identified 1912 ASs of 1384 mRNA, based on the relapse-associated ASs, we constructed the network of protein-protein interactions (PPI) and ASs-splicing factors (SF) interactions. 1294 of proteins with 7396 interactions were included in the PPI network. 14 SFs combined with 78 relapse-associated ASs were included in the AS-SF network. We finally built a robust signature to predict the relapse of I–III colon cancer with a considerable AUC value in both the training group and the test group (0.857,0.839). Based on the ASs involved in the signature, samples were classified into 4 molecular subgroups distinguishing the relapse rate in diverse groups. Conclusion : Our study provides a profile of relapse-associated ASs in I–III colon cancer and build a robust signature to predict the relapse of I–III colon cancer patients and further classify the patients into 4 molecular subtypes.

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A New Stemness-Related Prognostic Model for Predicting the Prognosis in Pancreatic Ductal Adenocarcinoma

BioMed Research International ◽

10.1155/2021/6669570 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Xiao-Yan Huang ◽

Wen-Tao Qin ◽

Qi-Sheng Su ◽

Cheng-Cheng Qiu ◽

Ruo-Chuan Liu ◽

...

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Ductal Adenocarcinoma ◽

Cox Regression Analysis ◽

Calibration Curves ◽

Key Genes

Objective. This study is aimed at identifying stemness-related genes in pancreatic ductal adenocarcinoma (PDAC). Methods. The RNA-seq data of PADC patients were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The mRNA expression-based stemness index (mRNAsi) and epigenetically regulated mRNAsi (EREG-mRNAsi) of PADC patients were evaluated. The mRNAsi-related gene sets in PADC were identified by weighted gene coexpression network analysis (WGCNA). The key genes were further analyzed using functional enrichment analysis. The Kaplan-Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of the key genes. Prognostic hub genes were used to establish nomograms. The receiver operating characteristic (ROC) curves, concordance index ( C -index), and calibration curves were used to assess the discrimination and accuracy of the nomogram. Finally, these results were validated in the Gene Expression Omnibus (GEO) database. Results. A total of 36 key genes related to mRNAsi were identified by WGCNA. A prognostic gene signature compromising seven genes (TPX2, ZWINT, UBE2C, CCNB2, CDK1, BUB1, and BIRC5) was established to predict the overall survival (OS) of PADC patients. The Cox regression analysis revealed that the risk score was an independent prognostic factor for PADC. Patients were then divided into the high-risk and low-risk groups. The ROC curves, C -index, and calibration curves indicated good performance of the prognostic signature in the TCGA and GEO datasets. Moreover, the nomogram incorporating clinical parameters showed better sensitivity and specificity for predicting the OS of PADC patients. Conclusion. The stemness-related prognostic model successfully predicted the OS of PADC patients and could be used for the treatment of PADC.

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An Integrated Nomogram Combining Clinical Factors and Microtubule-Associated Protein 1 Light Chain 3B Expression to Predict Postoperative Prognosis in Patients with Intrahepatic Cholangiocarcinoma

Cancer Research and Treatment ◽

10.4143/crt.2019.423 ◽

2020 ◽

Vol 52 (2) ◽

pp. 469-480 ◽

Cited By ~ 1

Author(s):

Liang Chen ◽

Hongyuan Fu ◽

Tongyu Lu ◽

Jianye Cai ◽

Wei Liu ◽

...

Keyword(s):

Light Chain ◽

Intrahepatic Cholangiocarcinoma ◽

Cox Regression ◽

Area Under The Curve ◽

Clinical Information ◽

Cancer Antigen 125 ◽

Curative Intent ◽

Cox Regression Analysis ◽

Neutrophil Lymphocyte Ratio ◽

Effective Models

PurposeMicrotubule-associated protein 1 light chain 3B (LC3B) serves as a key component of autophagy, which is associated with the progression of carcinoma. Yet, it is still unclear whether LC3B is also an independent risk factor for intrahepatic cholangiocarcinoma (ICC). We aim to explore the predictive value of LC3B on prognosis of ICC, and to establish a novel and available nomogram to predict relapse-free survival (RFS) and overall survival (OS) for these patients after curative-intent hepatectomy.Materials and MethodsFrom August 2004 to March 2017, 105 ICC patients were eligibly enrolled in the Third Affiliated Hospital of Sun Yat-sen University. Preoperative clinical information of enrolled patients was collected. Expression LC3B in the ICC specimen was detected by immunohistochemistry.ResultsThe 5-year RFS and OS in this cohort were 15.7% and 29.6%, respectively. On multivariate Cox regression analysis, independent risk factors for 5-year OS were cancer antigen 125, microvascular invasion, LC3B expression and lymph node metastasis. Except for the above 4 factors, neutrophil/lymphocyte ratio and tumor differentiation were independent factors for 5-year RFS. The area under the curve of nomograms for OS and RFS were 0.820 and 0.747, respectively.ConclusionThe nomograms based on LC3B can be considered as effective models to predict postoperative survival for ICC patients.

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