HU Yunshuang,ZHANG Ying,ZENG Haiping.. Bioinformatics-based prognostic lncRNAs signature study in endometrial cancer[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2021, 51(5): 381-388.
Abstract：Objective: To find prognostic lncRNA signature of endometrial cancer, and to provide useful
guidance for predicting the prognosis and individualized therapy of patients with endometrial cancer. Methods: A
total of 523 endometrial cancer tissues were downloaded from the TCGA database and then randomly randomly
assigned to a training set (n=262) and a testing set (n=261). Univariate Cox regression and LASSO regression
analysis were employed to screen prognosis-related lncRNA signature in the training set, and lncRNA risk score
models were constructed to predict the prognosis of endometrial cancer, which was validated in the testing set
(n=261). Finally, gene set enrichment analysis (GSEA) was used to discover the differences in biological pathways between the high and low risk groups predicted by the lncRNA signature model. Results: Based on LASSO Cox regression analysis, 13 differential lncRNAs (P<0.001) were significantly associated with the prognosis of endometrial cancer, and lncRNA risk score models were constructed to divide patients with endometrial cancer as high risk group and low risk group; survival curve analysis showed that the overall survival of patients in the low risk group was significantly better than that in the high risk group in the training set (P<0.001) and the testing set (P<0.001), respectively. Multivariate Cox regression analysis showed that the 13 lncRNAs in both the training set (HR: 1.08, 95%CI: 1.06-1.10, P<0.001) and the testing set (HR: 1.54, 95%CI: 1.34-1.78, P<0.001) were independent risk factors affecting the prognosis of endometrial cancer. Moreover, a combined model of lncRNAs signatureand clinical features was constructed. ROC curve demonstrated that the combinedmodel could improve the prediction efficiency. GSEA enrichment analysis revealed that cell cycle regulation-related genes were significantly enriched in the high-risk group, while immune and metabolic-related pathways were enriched in the low-risk group. Conclusion: This study has identified prognostic lncRNAs signature of endometrial cancer. A risk assessment model based on 13 lncRNAs could be viewed as a potential biomarker for predicting the prognosis of endometrial cancer.
