An estrogen receptor (ER)-related signature in predicting prognosis of ER-positive breast cancer following endocrine treatment

Abstract

Quite a few estrogen receptor (ER)-positive breast cancer patients receiving endocrine therapy are at risk of disease recurrence and death. ER-related genes are involved in the progression and chemoresistance of breast cancer. In this study, we identified an ER-related gene signature that can predict the prognosis of ER-positive breast cancer patient receiving endocrine therapy. We collected RNA expression profiling from Gene Expression Omnibus database. An ER-related signature was developed to separate patients into high-risk and low-risk groups. Patients in the low-risk group had significantly better survival than those in the high-risk group. ROC analysis indicated that this signature exhibited good diagnostic efficiency for the 1-, 3- and 5-year disease-relapse events. Moreover, multivariate Cox regression analysis demonstrated that the ER-related signature was an independent risk factor when adjusting for several clinical signatures. The prognostic value of this signature was validated in the validation sets. In addition, a nomogram was built and the calibration plots analysis indicated the good performance of this nomogram. In conclusion, combining with ER status, our results demonstrated that the ER-related prognostic signature is a promising method for predicting the prognosis of ER-positive breast cancer patients receiving endocrine therapy.

Keywords: breast cancer; estrogen receptor; nomogram; prognosis.

Figure 1

Flow chart and 10‐fold cross‐validation for tuning parameter selection. A, Flow chart indicating the process used to select target genes included in the analysis. B, 10‐fold cross‐validation for tuning parameter selection in the Lasso model. C, LASSO coefficient profiles of the 28 prognostic genes. A vertical line is drawn at the value chosen by 10‐fold cross‐validation

Figure 2

Univariate Cox regression analysis of the ten prognostic genes in the signature. A, CCNE1. B, CITED2. C, DDX54. D, EGFR. E, MDM2. F, MED1. G, SFRP1. H, CASP9. I, FOXH1. J, UBA5

Figure 3

Validation of prognostic risk score model in training set. A, Time‐dependent ROC curves of the ER‐related signature. B, Kaplan‐Meier survival analysis of the ER‐related signature

Figure 4

Kaplan‐Meier survival analysis for patients according to the ER‐related‐based signature stratified by clinicopathological risk factors. (A,B). Age. (C,D). Tumour size. (E,F). Lymph node status. (G,H). Tumour grade

Figure 5

Validation of ER‐related signature in validation sets. A, Time‐dependent ROC curves of the ER‐related signature in validation set I. B, Kaplan‐Meier survival analysis of the ER‐related signature in validation set I. C, Time‐dependent ROC curves of the ER‐related signature in validation set II. D, Kaplan‐Meier survival analysis of the ER‐related signature in validation set II. E, Time‐dependent ROC curves of the ER‐related signature in validation set III. F, Kaplan‐Meier survival analysis of the ER‐related signature in validation set III

Figure 6

Nomogram to predict risk of cancer recurrence. A, Nomograms to predict risk of cancer recurrence. B, 3‐year nomogram calibration curves of training set. C, 5‐year nomogram calibration curves of training set. D, 3‐year nomogram calibration curves of validation set I. E, 5‐year nomogram calibration curves of validation set I. F, 3‐year nomogram calibration curves of validation set II. G, 5‐year nomogram calibration curves of validation set II