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Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer.
Wang, Zi-Hao; Zhang, Yun-Zheng; Wang, Yu-Shan; Ma, Xiao-Xin.
Afiliación
  • Wang ZH; Department of Obstetrics and Gynecology, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shengjing Hospital of China Medical University, 39 Huaxiang Road, Shenyang, 110021 People's Republic of China.
  • Zhang YZ; Department of Obstetrics and Gynecology, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shengjing Hospital of China Medical University, 39 Huaxiang Road, Shenyang, 110021 People's Republic of China.
  • Wang YS; Department of Obstetrics and Gynecology, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shengjing Hospital of China Medical University, 39 Huaxiang Road, Shenyang, 110021 People's Republic of China.
  • Ma XX; Department of Obstetrics and Gynecology, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shengjing Hospital of China Medical University, 39 Huaxiang Road, Shenyang, 110021 People's Republic of China.
Cancer Cell Int ; 19: 296, 2019.
Article en En | MEDLINE | ID: mdl-31807118
ABSTRACT

BACKGROUND:

Endometrial cancer (EC) is one of the three major gynecological malignancies. Numerous biomarkers that may be associated with survival and prognosis have been identified through database mining in previous studies. However, the predictive ability of single-gene biomarkers is not sufficiently specific. Genetic signatures may be an improved option for prediction. This study aimed to explore data from The Cancer Genome Atlas (TCGA) to identify a new genetic signature for predicting the prognosis of EC.

METHODS:

mRNA expression profiling was performed in a group of patients with EC (n = 548) from TCGA. Gene set enrichment analysis was performed to identify gene sets that were significantly different between EC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Quantitative real-time-PCR was used to verify the reliability of the expression of selected mRNAs. Subsequent multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan-Meier survival estimates and the log-rank test were used to validate the significance of risk parameters for prognosis prediction.

RESULT:

Nine genes associated with glycolysis (CLDN9, B4GALT1, GMPPB, B4GALT4, AK4, CHST6, PC, GPC1, and SRD5A3) were found to be significantly related to overall survival. The results of mRNA expression analysis by PCR were consistent with those of bioinformatics analysis. Based on the nine-gene signature, the 548 patients with EC were divided into high/low-risk subgroups. The prognostic ability of the nine-gene signature was not affected by other factors.

CONCLUSION:

A nine-gene signature associated with cellular glycolysis for predicting the survival of patients with EC was developed. The findings provide insight into the mechanisms of cellular glycolysis and identification of patients with poor prognosis in EC.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cancer Cell Int Año: 2019 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cancer Cell Int Año: 2019 Tipo del documento: Article