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

陆眉; 杨晓娟; 邹洁雅; 郭瑢; 王鑫; 张倩; 邓学鹏; 陶建芬; 聂建云; 杨庄青

doi:10.3971/j.issn.1000-8578.2021.21.0414

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

Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes Expression

摘要

摘要:
目的筛选能预测乳腺癌患者新辅助化疗（NAC）疗效的基因，识别最适合NAC的乳腺癌人群。
方法前瞻性收集60例乳腺癌患者NAC前后标本，进行高通量RNA测序。选出与肿瘤化疗耐药相关候选基因AHNAK、CIDEA、ADIPOQ、AKAP12，采用Logistic回归分析以上基因与乳腺癌NAC疗效关系并构建疗效预测模型，采用列线图对预测模型进行展示。
结果 non-pCR组接受化疗后的患者残余癌组织中，四个基因表达较化疗前升高（P < 0.05）；与pCR组相比，non-pCR组的四个基因表达显著升高（P < 0.05），Logistic单因素及多因素分析显示，四个基因的高表达显著降低了乳腺癌NAC的pCR率（P < 0.05)。选取以上四个差异基因构建化疗疗效预测模型，H1拟合优度检验（χ²=6.3967, P=0.4945）及ROC曲线（AUC 0.8249, 95%CI: 0.7227~0.9271）结果显示模型的拟合效果好。
结论 AHNAK、CIDEA、ADIPOQ、AKAP12或可成为乳腺癌NAC疗效新标志基因。基于此构建的疗效预测模型有望成为挑选适合NAC的乳腺癌人群的新方法。

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.

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