Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes Expression
10.3971/j.issn.1000-8578.2021.21.0414
- VernacularTitle:运用乳腺癌新辅助化疗多基因表达差异构建化疗疗效预测模型
- Author:
Mei LU
1
;
Xiaojuan YANG
;
Jieya ZOU
;
Rong GUO
;
Xin WANG
;
Qian ZHANG
;
Xuepeng DENG
;
Jianfen TAO
;
Jianyun NIE
;
Zhuangqing YANG
Author Information
1. Department Ⅲ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, China
- Publication Type:Research Article
- Keywords:
Breast neoplasms;
Neoadjuvant chemotherapy;
Gene expression;
Efficacy prediction model
- From:
Cancer Research on Prevention and Treatment
2021;48(12):1071-1077
- CountryChina
- Language:Chinese
-
Abstract:
Objective To screen out significant differential genes for predicting the effect of neoadjuvant chemotherapy (NAC) and select the most suitable breast cancer patients for NAC. Methods A total of 60 breast cancer patients' samples before and after NAC were collected for high-throughput RNA-Seq. We selected AHNAK, CIDEA, ADIPOQ and AKAP12 as the candidate genes that related to tumor chemotherapeutic resistance. We analyzed the correlation of AHNAK, CIDEA, ADIPOQ, AKAP12 expression levels with the effect of NAC by logistic regression analysis, constructed a prediction model and demonstrated the model by the nomogram. Results AHNAK, CIDEA, ADIPOQ and AKAP12 expression were up-regulated in the residual tumor tissues of non-pCR group after NAC(P < 0.05). Compared with pCR group, non-pCR group presented higher expression levels of AHNAK, CIDEA, ADIPOQ and AKAP12 (P < 0.05). The high expression levels of AHNAK, CIDEA, ADIPOQ and AKAP12 significantly reduced the pCR rate of NAC for breast cancer (P < 0.05). Our prediction model which AHNAK, CIDEA, ADIPOQ and AKAP12 were involved in showed a good fitting effect with H1 test (χ2=6.3967, P=0.4945) and the ROC curve (AUC 0.8249, 95%CI: 0.722-0.9271). Conclusion AHNAK, CIDEA, ADIPOQ and AKAP12 may be novel marker genes for NAC effect on breast cancer. The efficacy prediction model based on this result is expected to be a new method to select the optimal patients of breast cancer for neoadjuvant chemotherapy.
