Development and validation of prognostic prediction model for breast cancer based on metabolism-related gene
10.3760/cma.j.cn115396-20220418-00126
- VernacularTitle:基于代谢相关基因的乳腺癌预后预测模型的建立和验证
- Author:
Jian LI
1
;
Yan WANG
;
Fei LIU
;
Zongwang ZHANG
Author Information
1. 青岛大学附属泰安市中心医院乳腺疾病诊疗部，泰安 271000
- Keywords:
Breast neoplasms;
Metabolism;
Nomograms;
Predictive model;
Tumor microenvironment
- From:
International Journal of Surgery
2022;49(10):684-689,C3
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct and validate prognostic model for breast cancer based on metabolic pathway-related genes.Methods:Gene expression data and clinical information of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) website. Then all metabolic pathway-related genes were extracted from the Gene Set Enrichment Analysis (GSEA) website for differential analysis to obtain differentially expressed genes between tumor and normal tissues, and then differential metabolic genes associated with prognosis for constructing a prognostic risk score were screened by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Patients were divided into high-risk group and low-risk group based on the median risk scores, and the efficacy of the prognostic model was evaluated using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis. The nomogram was constructed by combining this model with other clinical factors to predict the survival rate of breast cancer patients. Finally, the model was validated using the Gene Expression Omnibus (GEO) database.Results:A total of six metabolism-related genes ( NT5 E, PAICS, PFKL, PLA2 G2 D, QPRT and SHMT2) were finally screened by univariate Cox and LASSO regression for prognosis model. The prognostic risk score was an independent risk factor for breast cancer in both the training set and validating set, and the results of the Kaplan-Meier survival analysis suggested that the overall survival of patients in the high-risk group was significantly lower than that in the low-risk group, the difference was statistically significant ( P<0.001). The results of the ROC curve indicated that the nomogram model had higher predictive accuracy than other clinicopathological features, with an area under the curve value of 0.794 for both. Calibration curve showed good agreement between predicted and actual values. Based on GSEA, it was determined that the model could reveal metabolic features while monitoring the status of the tumor microenvironment (TME). Conclusion:The metabolism-related gene prognostic model constructed in this study may serve as a promising independent prognostic marker for breast cancer patients and may indicate the status of TME.
