Construction of a nine DNA repair-related gene prognostic classifier to predict prognosis in patients with endometrial carcinoma

Abstract

Background Endometrial cancer (EC) is one of the most common gynecological malignancies worldwide. However, the molecular mechanisms and the prognostic prediction for EC patients remain unclear. Methods In the current study, we performed an in-depth analysis of over 500 patients which were obtained from the Cancer Genome Atlas (TCGA) database. The bioinformatics analysis included gene set enrichment analysis (GSEA) and Cox and lasso regression analyses to ensure overall survival (OS)-related genes, moreover, to construct a prognostic model and a nomogram for EC patients. Results GSEA identified 4 gene sets significantly associated with EC, which are DNA repair, unfolded protein response, reactive oxygen species pathway and UV response up. Twenty-five OS-related DNA repair genes were screened out, after that, a 9-mRNA signature was constructed to measure the risk scores of patients with different outcomes. In addition, a nomogram contained the 9-mRNA model and clinical parameters was also presented to assess the prognosis. Patients with low risk were more likely to have sensitivity to paclitaxel, vinblastine, rapamycin, metformin, imatinib, Akt inhibitor and lapatinib. Conclusions The identified highly enriched gene sets may offer a novel insight into the tumorigenesis and treatment of EC. Additionally, the constructed 9-mRNA model and the nomogram have prominent clinical implications for prognosis evaluation and specific therapy guidance for EC patients.

Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

“…Previous studies have established a prognostic model for UCEC, but they have not specifically targeted grade III high-risk patients with a poor prognosis. 18 , 21 , 22 , 23 , 24 , 47 , 48 , 49 Among the four prognostic risk models selected for comparison, our model had the highest C-index, indicating that its overall performance was superior. Taken together, our model was a better predictor of patients’ outcome.…”

An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients

“…We compared the prediction performance of the novel immune-related pseudogene signature with four recently published signatures: nine-lncRNA signature (LINC02387, FUT8-AS1, UBXN10-AS1, LINC00473, AL353194.1, FAM222A-AS1, AP002761.3, AL731566.2, and AP001021.2) derived from Xu’s research [ 9 ], nine-mRNA signature (CYP4F3, LYPLA2, CEL, PHGDH, GPAT3, HNMT, UCK2, CKM, and ACACB) derived from Jiang’s study [ 32 ], nine-mRNA signature (TP53, RAE1, RFC2, TAF10, DDB2, UMPS, TAF12, ERCC2, SEC61A1) derived from Liu’s research [ 33 ], and five-lncRNA signature (AL121906.2, BOLA3-AS1, LINC01833, AC016405.3, and RAB11B-AS1) from Jiang’s study [ 34 ] using the same TCGA EC patient cohort. As illustrated in Fig.…”

Construction and validation of an IHC-based risk model predicting postoperative disease- free survival and overall survival in Chinese endometrial cancer patients

“…The clinical heterogeneity of patients with EC probably re ects variation at the molecular level 21 ; thus, the prognostic mechanisms of EC likely vary as well. One study constructed a nine DNA repair-related gene prognostic classi er to predict prognosis in patients with EC, and patients with low risk were more likely to have sensitivity to paclitaxel, vinblastine, rapamycin, metformin, imatinib, Akt inhibitor and lapatinib 22 .…”