Predicting recurrence and metastasis risk of endometrial carcinoma via prognostic signatures identified from multi-omics data - Li, Ling; Qiu, Wenjing; Lin, Liang; Liu, Jinyang; Shi, Xiaoli; Shi, Yi (Frontiers in Oncology, 2022)
Objectives: Endometrial carcinoma is one of the three major gynecological malignancies, in which 15% - 20% patients will have recurrence and metastasis. Though there are many studies on the prognosis on this cancer, the performances of existing models evaluating the risk of its recurrence and metastasis are yet to be improved. In addition, a comprehensive multi-omics analyses on the prognostic signatures of endometrial carcinoma are on demand. In this study, we aimed to construct a relatively stable and reliable model for predicting recurrence and metastasis of endometrial carcinoma. This will help determine the risk level of patients and choose appropriate adjuvant therapy, thereby avoiding improper treatment, and improving the prognosis of patients. Methods: The mRNA, miRNA, lncRNA, CNV data and clinical information of patients with endometrial carcinoma were downloaded from The Cancer Genome Atlas (TCGA). Differential expression analyses were performed between the recurrence or metastasis group and the non-recurrence/metastasis group. Then, we screened potential prognostic markers from the four kinds of omics data respectively and established prediction models using three classifiers. Results: We achieved differential expressed mRNAs, lncRNAs, miRNAs and CNVs between the two groups. According to feature selection scores by the random forest algorithm, 275 CNV features, 50 lncRNA features, 150 miRNA features and 150 mRNA features were selected, respectively. And the prediction model constructed by the features of lncRNA data using random forest method showed the best performance, with an area under the curve of 0.763, and an accuracy of 0.819 under 10-fold cross-validation. Conclusion: We developed a computational model integrating multi-omics prognostic signatures, which is able to predicting recurrence and metastasis risk of endometrial carcinoma accurately.