Abstract
Alternative splicing (AS) is significantly related to the development of tumor and the clinical outcome of patients. In this study, our aim was to systematically analyze the survival-related AS signal in ovarian serous cystadenocarcinoma (OV) and estimate its prognostic validity in 48,049 AS events out of 21,854 genes. We studied 1,429 AS events out of 1,125 genes, which were significantly related to the overall survival (OS) in patients with OV. We established alternative splicing features on the basis of seven AS events and constructed a new comprehensive prognostic model. Kaplan-Meier curve analysis showed that seven AS characteristics and comprehensive prognostic models could strongly stratify patients with ovarian cancer and make them distinctive prognosis. ROC analysis from 0.781 to 0.888 showed that these models were highly efficient in distinguishing patient survival. We also verified the prognostic characteristics of these models in a testing cohort. In addition, uni-variate and multivariate Cox analysis showed that these models were superior independent risk factors for OS in patients with OV. Interestingly, AS events and splicing factor (SFs) networks revealed an important link between these prognostic alternative splicing genes and splicing factors. We also found that the comprehensive prognosis model signature had higher prediction ability than the mRNA signature. In summary, our study provided a possible prognostic prediction model for patients with OV and revealed the splicing network between AS and SFs, which could be used as a potential predictor and therapeutic target for patients with OV.
Highlights
Ovarian cancer is one of the most common malignant tumors in women and the fifth leading cause of death among women with serious gynecological problems
Alternative splicing (AS) can play an important role in maintaining the normal physiological process of human body, and a key mechanism leading to all kinds of pathology
In the field of AS research, recent studies have shown that several mutations in alternative splice and different splicing events in specific cancers may be used as indicators for the diagnosis, prediction, and prognosis of ovarian cancer
Summary
Ovarian cancer is one of the most common malignant tumors in women and the fifth leading cause of death among women with serious gynecological problems. Extensive studies have attempted to establish molecular characteristics based on gene expression data to predict the survival and prognosis of patients, including mRNA [9], microRNAs [3] and long non-coding RNA (LncRNA) based signatures [10]. Univariate Cox, LASSO, and multivariate analysis were performed to systematically develop AS events related to prognosis in OV, and to establish a predictive model based on AS to evaluate the prognostic ability of AS signatures in patients with OV and to improve the understanding of tumor biology and oncology applications. All SF genes were analyzed by uni-variate Cox analysis When their P < 0.05, these SF were considered to be survival-related splicing factors. All analyses were performed using R 3.5.3 software (https://www.rproject.org/, v3.5.3)
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