Construction and evaluation of a new risk model of basement membrane-related genes for predict the prognosis of breast cancer patients
10.3760/cma.j.cn115396-20221223-00435
- VernacularTitle:一种新的预测乳腺癌患者预后的基底膜相关基因风险模型的构建和评估
- Author:
Jian LI
1
;
Xia YAN
;
Tongxing LI
;
Yan WANG
Author Information
1. 青岛大学附属泰安市中心医院乳腺疾病诊疗中心，泰安 271000
- Keywords:
Breast neoplasms;
Basement membrane;
Nomograms;
Predictive model;
TCGA
- From:
International Journal of Surgery
2023;50(10):686-691
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a novel prognostic risk model using basement membrane-related genes (BMRG) to explore the relationship between breast cancer and basement membrane.Methods:Transcriptome and clinical data were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, the TCGA data was used as the training set and the GEO database as the validation set. Then univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to build a BMRG prognostic model. The risk model was further validated and evaluated by Kaplan-Meier method and receiver operating characteristic (ROC) curve. The risk model and clinical characteristics were then combined to construct a nomogram to predict the overall survival of breast cancer. The biological pathways that may be involved were investigated by gene set enrichment analysis (GSEA). In addition, the differences in drug sensitivity between high-risk and low-risk groups of patients by the Wilcoxon rank sum test.Results:A total of 193 differentially expressed genes were identified, and risk models based on eight BMRG was constructed, including COL6A2, CTSA, EVA1B, ITGAX, MMP-1, ROBO3, SDC1, and UNC5A. Kaplan-Meier and ROC analyses showed that the model could well predict the prognosis of breast cancer, with an area under the curve of 0.779, indicating a high degree of accuracy as well. In addition, the nomogram showed good predictive consistency and net clinical benefit. Univariate and multivariate Cox regression analyses validated the BMRG model as an independent risk factor for breast cancer. GSEA analysis showed that the high-risk group was predominantly enriched in the extracellular matrix receptor interaction pathway. In addition, high-risk patients were more sensitive to taxanes chemotherapeutic agents and targeted therapeutic agents, while low-risk patients were more sensitive to gemcitabine and rapamycin. Conclusion:The risk model constructed based on eight BMRG can be used as a valid prognostic indicator for breast cancer and can improve the prediction of patient response to treatment.
