Unknown

Dataset Information

0

A 4-Gene Signature Associated With Recurrence in Low- and Intermediate-Risk Endometrial Cancer.


ABSTRACT:

Background

The molecular profile of endometrial cancer has become an important tool in determining patient prognosis and their optimal adjuvant treatment. In addition to The Cancer Genome Atlas (TCGA), simpler tools have been developed, such as the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE). We attempted to determine a genetic signature to build a recurrence risk score in patients diagnosed with low- and intermediate-risk endometrial cancer.

Methods

A case-control study was conducted. The eligible patients were women diagnosed with recurrence low- and intermediate-risk endometrial cancer between January 2009 and December 2014 at a single institution; the recurrence patients were matched to two nonrecurrence patients with the same diagnosis by age and surgical staging. Following RNA isolation of 51 cases, 17 recurrence and 34 nonrecurrence patients, the expression profile was determined using the nCounter® PanCancer Pathways Panel, which contains 770 genes.

Results

The expression profile was successfully characterized in 49/51 (96.1%) cases. We identified 12 genes differentially expressed between the recurrence and nonrecurrence groups. The ROC curve for each gene was generated, and all had AUCs higher than 0.7. After backward stepwise logistic regression, four genes were highlighted: FN1, DUSP4, LEF1, and SMAD9. The recurrence risk score was calculated, leading to a ROC curve of the 4-gene model with an AUC of 0.93, sensitivity of 100%, and specificity of 72.7%.

Conclusion

We identified a four-gene signature that may be associated with recurrence in patients with low- and intermediate-risk endometrial cancer. This finding suggests a new prognostic factor in this poorly explored group of patients with endometrial cancer.

SUBMITTER: de Andrade DAP 

PROVIDER: S-EPMC8416164 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

2021-07-27 | GSE178671 | GEO
| PRJNA739987 | ENA
| S-EPMC5664005 | biostudies-literature
| S-EPMC6786591 | biostudies-literature
2013-06-23 | E-GEOD-47109 | biostudies-arrayexpress
2013-06-23 | GSE47109 | GEO
2020-12-22 | GSE139308 | GEO
2017-12-31 | GSE100078 | GEO