构建能有效预测人表皮生长因子受体（HER）2阳性型乳腺癌新辅助化疗联合靶向治疗响应情况的基因预测模型。

整合GEO数据库中进行新辅助化疗联合靶向治疗的HER2阳性型乳腺癌患者的数据集，通过差异基因分析、WGCNA分析及LASSO回归筛选关键基因并构建预测模型。利用基因集富集分析（GSEA）、免疫细胞浸润图谱、免疫评分、药物敏感性评估等分析方法探索潜在相关通路与机制。

筛选出9个关键基因（CD8A、CST7、CXCL13、IL2RB、LDHB、RTN3、SEMA4D、EPB41L1、MYO5C），构建的预测模型在训练集和验证集中均表现出良好的预测效能，曲线下面积分别为0.836、0.827。低风险组（敏感组）呈现免疫活化特征，高风险组（耐药组）相对处于免疫抑制状态。

本研究构建的基因模型可有效预测HER2阳性型乳腺癌新辅助治疗响应情况，免疫微环境活化状态与治疗敏感性密切相关。关键基因为个体化治疗提供潜在生物标志物及干预靶点。

To construct a gene prediction model that can effectively predict the response to neoadjuvant chemotherapy combined with targeted therapy in human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients.

The datasets from patients with HER2-positive breast cancer undergoing neoadjuvant chemotherapy combined with targeted therapy were integrated from the GEO database. Differential gene analysis, WGCNA analysis, and LASSO regression were used to screen key genes and construct a predictive model. Potential associated pathways and mechanisms were explored using GSEA, immune cell infiltration levels, immune scoring, and drug sensitivity assessments.

Nine key genes (CD8A, CST7, CXCL13, IL2RB, LDHB, RTN3, SEMA4D, EPB41L1, MYO5C) were identified, and the constructed predictive model demonstrated good predictive performance in both the training set (AUC=0.836) and the validation set (AUC=0.827). The low-risk group (sensitive to treatment) exhibited characteristics of immune activation, while the high-risk group (resistant to treatment) was relatively in an immunosuppressive state.

The gene model constructed in this study can effectively predict the response to neoadjuvant therapy in HER2-positive breast cancer, with the activation status of the immune microenvironment closely related to treatment sensitivity. The key genes provide potential biomarkers and intervention targets for personalized treatment.