| Yi Cheng,Long Li,Chen Gong,Kai Qin. Construction and validation of a prognostic risk model for uterine corpus endometrial carcinoma based on alternative splicing events. Oncol Transl Med, 2022, 8: 276-284. |
| Construction and validation of a prognostic risk model for uterine corpus endometrial carcinoma based on alternative splicing events |
| Received:August 07, 2022 Revised:November 28, 2022 |
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| KeyWord:TCGA; SpliceSeq; uterine corpus endometrial carcinoma; alternative splicing event; prognostic model |
| Author Name | Affiliation | E-mail | | Yi Cheng | Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan | yi_chengtj@163.com | | Long Li | Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan | | | Chen Gong | Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan | | | Kai Qin | Department of Oncology,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan | qinkaitj@126.com |
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| Abstract: |
| Objective To establish a prognostic risk model for uterine corpus endometrial carcinoma (UCEC) based
on alternative splicing (AS) event data from The Cancer Genome Atlas (TCGA) and assess the accuracy
of the model.
Methods TCGA and SpliceSeq databases were used to acquire a summary of AS events and clinical
data related to UCEC. Bioinformatic analysis was performed to identify differentially expressed AS
events in UCEC. Least absolute shrinkage and selection operator (LASSO) regression and multivariate
Cox regression analyses were used for constructing a prognostic risk model. Next, using the receiver
operating characteristic (ROC) curve, Kaplan-Meier survival analysis, and independent prognostic analysis,
we assessed the accuracy of the model. In addition, a splicing network was established based on the
association between potential splicing factors and AS events.
Results We downloaded clinical data and AS events of 527 UCEC cases from TCGA and SpliceSeq
databases, respectively. We obtained 18,779 survival-associated AS events in UCEC using univariate Cox
regression analysis and 487 AS events using LASSO regression analysis. Multivariate Cox regression
analysis established a prognostic risk model for UCEC based on the percentage splicing value of 13 AS
events. Independent prognostic effect on UCEC risk was then assessed using multivariate and univariate
Cox regression analyses (P < 0.001). The area under the curve was 0.827. The pathological stage and risk
score were independent prognostic factors for UCEC. Herein, we established a regulatory network between
alternative endometrial cancer-related splicing events and splicing factors.
Conclusion We constructed a prognostic model of UCEC based on 13 AS events by analyzing datasets
from TCGA and SpliceSeq databases with medium accuracy. The pathological stage and risk score were
independent prognostic factors in the prognostic risk model. |
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