BACKGROUND: Endometrial cancer (EC) is a common malignancy of the female reproductive system worldwide. Increasing evidence has suggested that many transcription factors are aberrantly expressed in various cancers. This study aimed to develop a transcription factor-based prognostic signature for EC. METHODS: Gene expression data and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression and Multivariate Cox regression analysis was used to construct a prognostic signature. Then, the efficacy of the prognostic signature was validated in a training cohort, testing cohort and then the entire cohort. Correlations between clinical features and the model were also analyzed, and a nomogram based on the multivariate Cox analysis was developed. Furthermore, we verified the effect of a key transcription factor, E2F1, on biological functions of EC in vitro. RESULTS: We developed a nine-transcription factor (MSX1, HOXB9, E2F1, DLX4, BNC2, DLX2, PDX1, POU3F2, and FOXP3) prognostic signature. Compared with those in the low-risk group, patients in the high-risk group had worse clinical outcomes. The area under the curve (AUC) of this prognostic signature for 5-year survival was 0.806 in the training cohort, 0.710 in the testing cohort and 0.761 in the entire cohort. Gene set enrichment analysis (GSEA) revealed a correlation between the prognostic signature and various cancer signaling pathways, and a hub transcription factor regulatory network was constructed. The prognostic signature was confirmed to have independent predictive value. Finally, a nomogram based on the prognostic signature and clinical independent prognostic factors was also established and performed well according to the calibration curves. Further, knockdown of E2F1 inhibited invasion and metastasis of EC cells. CONCLUSION: Our study developed and validated a transcription factor-based prognostic signature that accurately predicts prognosis of EC patients. Moreover, E2F1 may represent a potential target for the treatment of EC.
BACKGROUND: Endometrial cancer (EC) is a common malignancy of the female reproductive system worldwide. Increasing evidence has suggested that many transcription factors are aberrantly expressed in various cancers. This study aimed to develop a transcription factor-based prognostic signature for EC. METHODS: Gene expression data and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression and Multivariate Cox regression analysis was used to construct a prognostic signature. Then, the efficacy of the prognostic signature was validated in a training cohort, testing cohort and then the entire cohort. Correlations between clinical features and the model were also analyzed, and a nomogram based on the multivariate Cox analysis was developed. Furthermore, we verified the effect of a key transcription factor, E2F1, on biological functions of EC in vitro. RESULTS: We developed a nine-transcription factor (MSX1, HOXB9, E2F1, DLX4, BNC2, DLX2, PDX1, POU3F2, and FOXP3) prognostic signature. Compared with those in the low-risk group, patients in the high-risk group had worse clinical outcomes. The area under the curve (AUC) of this prognostic signature for 5-year survival was 0.806 in the training cohort, 0.710 in the testing cohort and 0.761 in the entire cohort. Gene set enrichment analysis (GSEA) revealed a correlation between the prognostic signature and various cancer signaling pathways, and a hub transcription factor regulatory network was constructed. The prognostic signature was confirmed to have independent predictive value. Finally, a nomogram based on the prognostic signature and clinical independent prognostic factors was also established and performed well according to the calibration curves. Further, knockdown of E2F1 inhibited invasion and metastasis of EC cells. CONCLUSION: Our study developed and validated a transcription factor-based prognostic signature that accurately predicts prognosis of EC patients. Moreover, E2F1 may represent a potential target for the treatment of EC.
Authors: Paul Shannon; Andrew Markiel; Owen Ozier; Nitin S Baliga; Jonathan T Wang; Daniel Ramage; Nada Amin; Benno Schwikowski; Trey Ideker Journal: Genome Res Date: 2003-11 Impact factor: 9.043
Authors: Alvaro Tejerizo-García; Jesús S Jiménez-López; José L Muñoz-González; Sara Bartolomé-Sotillos; Laura Marqueta-Marqués; Gregorio López-González; José F Pérez-Regadera Gómez Journal: Onco Targets Ther Date: 2013-09-16 Impact factor: 4.147