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

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.

scholarly journals Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients

2020 ◽  
Vol 40 (6) ◽  
Author(s):  
Huamei Tang ◽  
Lijuan Kan ◽  
Tong Ou ◽  
Dayang Chen ◽  
Xiaowen Dou ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Overall Survival ◽  
Bladder Cancer ◽  
Cancer Patients ◽  
Cox Regression ◽  
Test Group ◽  
Training Group ◽  
The Cancer Genome Atlas ◽  
Methylation Site ◽  
Cox Regression Analysis

Abstract Background: Bladder cancer is one of the most common malignancies. So far, no effective biomarker for bladder cancer prognosis has been identified. Aberrant DNA methylation is frequently observed in the bladder cancer and holds considerable promise as a biomarker for predicting the overall survival (OS) of patients. Materials and methods: We downloaded the DNA methylation and transcriptome data for bladder cancer from The Cancer Genome Atlas (TCGA), a public database, screened hypo-methylated and up-regulated genes, similarly, hyper-methylation with low expression genes, then retrieved the relevant methylation sites. Cox regression analysis was used to identify a nine-methylation site signature of a training group. Predictive ability was validated in a test group by receiver operating characteristic (ROC) analysis. Results: We identified nine bladder cancer-specific methylation sites as potential prognostic biomarkers and established a risk score system based on the methylation site signature to evaluate the OS. The performance of the signature was accurate, with area under curve was 0.73 in the training group and 0.71 in the test group. Taking clinical features into consideration, we constructed a nomogram consisting of the nine-methylation site signature and patients’ clinical variables, and found that the signature was an independent risk factor. Conclusions: Overall, the significant nine methylation sites could be novel prediction biomarkers, which could aid in treatment and also predict the overall survival likelihoods of bladder cancer patients.

scholarly journals Identification of prognostic alternative splicing events in sarcoma

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.

scholarly journals Development and Validation of a Nine-Redox-Related Long Noncoding RNA Signature in Renal Clear Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-30
Author(s):  
Xia Qi-Dong ◽  
Xun Yang ◽  
Jun-Lin Lu ◽  
Chen-Qian Liu ◽  
Jian-Xuan Sun ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Clear Cell ◽  
Test Group ◽  
Training Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature

Background. Redox plays an essential role in the pathogeneses and progression of tumors, which could be regulated by long noncoding RNA (lncRNA). We aimed to develop and verify a novel redox-related lncRNA-based prognostic signature for clear cell renal cell carcinoma (ccRCC). Materials and Methods. A total of 530 ccRCC patients from The Cancer Genome Atlas (TCGA) were included in this study. All the samples were randomly split into training and test group at a 1 : 1 ratio. Then, we screened differentially expressed redox-related lncRNAs and constructed a novel prognostic signature from the training group using the least absolute shrinkage and selection operation (LASSO) and COX regression. Next, to verify the accuracy of the signature, we conducted risk and survival analysis, as well as the construction of ROC curve, nomogram, and calibration curves in the training group, test group, and all samples. Finally, the redox gene-redox-related lncRNA interaction network was constructed, and gene set enrichment analysis (GSEA) was performed to investigate the status of redox-related functions between high/low-risk groups. Results. A nine-redox-related lncRNA signature consisted of AC025580.3, COLCA1, AC027601.2, DLEU2, AC004918.3, AP006621.2, AL031670.1, SPINT1-AS1, and LAMA5-AS1 was significantly associated with overall survival in ccRCC patients. The signature proved efficient, and thus, a nomogram was successfully assembled. In addition, the GSEA results demonstrated that two major redox-related functions were enhanced in the high-risk group ccRCC patients. Conclusions. Our findings robustly demonstrate that the nine-redox-related lncRNA signature could serve as an efficient prognostic indicator for ccRCC.

scholarly journals Comprehensive Characterization of Alternative mRNA Splicing Events in Glioblastoma: Implications for Prognosis, Molecular Subtypes, and Immune Microenvironment Remodeling

2021 ◽  
Vol 10 ◽  
Author(s):  
Liang Zhao ◽  
Jiayue Zhang ◽  
Zhiyuan Liu ◽  
Yu Wang ◽  
Shurui Xuan ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Cox Model ◽  
Molecular Classification ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Alternative Mrna Splicing

Alternative splicing (AS) of pre-mRNA has been widely reported to be associated with the progression of malignant tumors. However, a systematic investigation into the prognostic value of AS events in glioblastoma (GBM) is urgently required. The gene expression profile and matched AS events data of GBM patients were obtained from The Cancer Genome Atlas Project (TCGA) and TCGA SpliceSeq database, respectively. 775 AS events were identified as prognostic factors using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) cox model was performed to narrow down candidate AS events, and a risk score model based on several AS events were developed subsequently. The risk score-based signature was proved as an efficient predictor of overall survival and was closely related to the tumor purity and immunosuppression in GBM. Combined similarity network fusion and consensus clustering (SNF-CC) analysis revealed two distinct GBM subtypes based on the prognostic AS events, and the associations between this novel molecular classification and clinicopathological factors, immune cell infiltration, as well as immunogenic features were further explored. We also constructed a regulatory network to depict the potential mechanisms that how prognostic splicing factors (SFs) regulate splicing patterns in GBM. Finally, a nomogram incorporating AS events signature and other clinical-relevant covariates was built for clinical application. This comprehensive analysis highlights the potential implications for predicting prognosis and clinical management in GBM.

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

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.

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

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.

scholarly journals Biological Functions and Prognostic Value of Ferroptosis-Related Genes in Bladder Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Kezhen Yi ◽  
JingChong Liu ◽  
Yuan Rong ◽  
Cheng Wang ◽  
Xuan Tang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Protein Interactions ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Prognostic Information ◽  
Cox Regression Analysis

Background: Every year, nearly 170,000 people die from bladder cancer worldwide. A major problem after transurethral resection of bladder tumor is that 40–80% of the tumors recur. Ferroptosis is a type of regulatory necrosis mediated by iron-catalyzed, excessive oxidation of polyunsaturated fatty acids. Increasing the sensitivity of tumor cells to ferroptosis is a potential treatment option for cancer. Establishing a diagnostic and prognostic model based on ferroptosis-related genes may provide guidance for the precise treatment of bladder cancer.Methods: We downloaded mRNA data in Bladder Cancer from The Cancer Genome Atlas and analyzed differentially expressed genes based on and extract ferroptosis-related genes. We identified relevant pathways and annotate the functions of ferroptosis-related DEGs using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Ontology functions. On the website of Search Tool for Retrieving Interacting Genes database (STRING), we downloaded the protein-protein interactions of DEGs, which were drawn by the Cytoscape software. Then the Cox regression analysis were performed so that the prognostic value of ferroptosis-related genes and survival time are combined to identify survival- and ferroptosis-related genes and establish a prognostic formula. Survival analysis and receiver operating characteristic curvevalidation were then performed. Risk curves and nomograms were generated for both groups to predict survival. Finally, RT-qPCR was applied to analyze gene expression.Results: Eight ferroptosis-related genes with prognostic value (ISCU, NFE2L2, MAFG, ZEB1, VDAC2, TXNIP, SCD, and JDP2) were identified. With clinical data, we established a prognostic model to provide promising diagnostic and prognostic information of bladder cancer based on the eight ferroptosis-related genes. RT-qPCR revealed the genes that were differentially expressed between normal and cancer tissues.Conclusion: This study found that the ferroptosis-related genes is associated with bladder cancer, which may serve as new target for the treatment of bladder cancer.

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

BMC Cancer ◽  
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.

scholarly journals A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Aisha Al-Dherasi ◽  
Qi-Tian Huang ◽  
Yuwei Liao ◽  
Sultan Al-Mosaib ◽  
Rulin Hua ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Training Group ◽  
The Cancer Genome Atlas ◽  
Individual Treatment ◽  
Significant Implication ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients. Methods Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature. Results A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis. Conclusion Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

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

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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