Background: Endometrial cancer (EnCa) ranks fourth in menace within women's malignant tumors. Large numbers of studies have proven that functional genes can change the process of tumors by regulating the cell cycle, thereby achieving the goal of targeted therapy.
Methods: The transcriptional data of EnCa samples obtained from the TCGA database was analyzed. A battery of bioinformatics strategies, which included GSEA, Cox and LASSO regression analysis, establishment of a prognostic signature and a nomogram for overall survival (OS) assessment. The GEPIA and CPTAC analysis were applied to validate the dysregulation of hub genes. For mutation analysis, the "maftools" package was used.
Results: GSEA identified that cell cycle was the most associated pathway to EnCa. Five cell cycle-related genes including HMGB3, EZH2, NOTCH2, UCK2 and ODF2 were identified as prognosis-related genes to build a prognostic signature. Based on this model, the EnCa patients could be divided into low- and high-risk groups, and patients with high-risk score exhibited poorer OS. Time-dependent ROC and Cox regression analyses revealed that the 5-gene signature could predict EnCa prognosis exactly and independently. GEPIA and CPTAC validation exhibited that these genes were notably dysregulated between EnCa and normal tissues. Lower mutation rates of PTEN, TTN, ARID1A, and etc. were found in samples with high-risk score compared with that with low-risk score. GSEA analysis suggested that the samples of the low- and high-risk groups were concentrated on various pathways, which accounted for the different oncogenic mechanisms in patients in two groups.
Conclusion: The current research construct a 5-gene signature to evaluate prognosis of EnCa patients, which may innovative clinical application of prognostic assessment.
Keywords: Cell cycle; Endometrial cancer; GSEA; Prognostic model; TCGA.
© The Author(s) 2020.