Construction of a model to predict the prognosis of patients with cholangiocarcinoma using alternative splicing events

Wu,Hua‑Yu; Wei,Yi; Liu,Li‑Min; Chen,Zhong‑Biao; Hu,Qi‑Ping; Pan,Shang‑Ling

doi:10.3892/ol.2019.10838

Construction of a model to predict the prognosis of patients with cholangiocarcinoma using alternative splicing events

Authors:
- Hua‑Yu Wu
- Yi Wei
- Li‑Min Liu
- Zhong‑Biao Chen
- Qi‑Ping Hu
- Shang‑Ling Pan
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Affiliations: Department of Pathophysiology, School of Pre‑Clinical Medicine, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China, Department of Toxicology, College of Pharmacy, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China, Department of General Surgery, The First People's Hospital of Yulin, Yulin, Guangxi 537000, P.R. China, Department of Cell Biology and Genetics, School of Pre‑Clinical Medicine, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
Published online on: September 10, 2019 https://doi.org/10.3892/ol.2019.10838
Pages: 4677-4690
Copyright: © Wu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

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.

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