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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Mining Chemotherapy Resistance Related Genes in Breast Cancer to Construct a New Prognosis Prediction Model-Based on
Zhaozhen Qiu1, Xiaodong Dai2, Jianfeng Zeng1
1Department of Breast Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China, fjmu.edu.cn.
Objective:
The chemotherapy resistance genes in breast cancer are closely related to prognosis. This study is aimed at exploring the key genes that may be involved in chemotherapy resistance of breast cancer and establishing a prognostic model.
Methods:
Using data from the GEO database, differentially expressed genes (DEGs) related to chemotherapy resistance in breast cancer were identified. Univariate and multivariate Cox regression were used to identify the association between DEGs and prognosis. Subsequently, functional analysis was conducted to characterize the functions of DEGs. In addition, immune-related analysis was performed to study the functions of these hub genes. LASSO-Cox regression analysis narrowed the range of hub genes. A DRFS prognostic nomogram model was constructed using the hub genes. A total of 60 breast cancer patients from the Second Affiliated Hospital of Fujian Medical University were selected as the external validation set.
Results:
By comparing the gene expression profiles of the Rx_Insensitive group and the Rx_Sensitive group, 162 DEGs were screened out, among which 53 DEGs were upregulated and 109 DEGs were downregulated. Univariate Cox regression analysis of the 162 DEGs with survival showed that GREB1, DACH1, STAP1, TDRD12, and SCGB1D2 were significantly associated with prognosis (all p < 0.05). Further multivariate Cox regression analysis revealed that GREB1 (HR = 0.653), DACH1 (HR = 1.217), STAP1 (HR = 1.140), and SCGB1D2 (HR = 1.074) were independent risk factors for prognosis (all p < 0.05). Moreover, the expression levels of GREB1, DACH1, STAP1, and SCGB1D2 were significantly correlated with the infiltration levels of various immune cells (p < 0.05). Based on these five breast cancer chemotherapy resistance-related genes, a new prognostic model for breast cancer was constructed. The 1-year AUC of this model was 0.748, 3-year AUC was 0.735, and 5-year AUC was 0.679. In the validation set, the 1-year AUC was 0.744, 3-year AUC was 0.696, and 5-year AUC was 0.650. The calibration curve showed that the predicted probabilities of the model were close to the true values. The model's prediction accuracy on the external validation set for 1 year was 0.823.
Conclusions:
The prognostic model developed based on the five breast cancer chemotherapy resistance-related genes (GREB1, DACH1, STAP1, TDRD12, and SCGB1D2) has good predictive performance for BRCA patients.
