Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.
BackgroundThe dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RBPs in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. The aims of this study were to explore the associated molecular mechanisms and to develop an RNA binding protein-associated prognostic model for UCEC.MethodsBased on The Cancer Genome Atlas (TCGA) database, differentially expressed RBPs were identified between UCEC tumor tissues and normal tissues. Hub RBPs were then found by univariate and multivariate Cox regression analysis. The cBioPortal platform, R packages (DESeq2, edgeR and ggplot2) and The Human Protein Atlas (HPA) online database were used to explore the molecular mechanisms of UCEC. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.ResultsIn total, 128 differentially expressed RBPs between UCEC tumor tissues and normal tissues were identified. Seven RBP genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model, which are particularly important to prospectively predict patient prognosis and help them get treatment accordingly. Further analysis indicated that the patients in the high-risk subgroup had poor overall survival (OS) compared to those in low-risk subgroup based on the model. We also established a nomogram based on seven RBPs, which potentially improved individualized diagnostic and therapeutic strategies for UCEC.ConclusionOur work focused on the systematic analysis of a large cohort of patients in the TCGA database, which allowed us to construct a robust prognostic model based on seven RBPs, that may be of great value in clinical applications.
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