Yin Zhenglang, Tao Jianfei, Liu Yanyan, Chen Haohao, Hu Kongwang, Wang Yao, Xiong Maoming
Department of General surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
Department of General surgery, The Third Affiliated Hospital of Anhui Medical University, Hefei, 230061, China.
J Cancer. 2024 Sep 30;15(18):6052-6072. doi: 10.7150/jca.101901. eCollection 2024.
The preoperative identification of neoadjuvant chemotherapy (NAC) treatment responsiveness in breast cancer (BC) patients is advantageous for tailoring treatment regimens. There is a relative scarcity in the current research exploring NAC treatment responsive biomarkers using bulk sequencing data obtained from fine-needle aspiration (FNA). Limma was employed for the selection of differentially expressed genes. Additionally, WGCNA, machine learning, and Genetic Perturbation Similarity Analysis (GPSA) were utilized to identify key genes associated with NAC treatment response. ConsensusClusterPlus was employed for unsupervised clustering. Rt-qPCR and WB were conducted to assess gene expression and protein levels in clinical tissues and cell lines. The Seahorse XF96 Extracellular Flux Analyzer was utilized to evaluate Extracellular Acidification Rate (ECAR) and Oxygen Consumption Rate (OCR). The "pRRophetic" package was used for drug sensitivity prediction, while CB-Dock2 was applied for molecular docking and optimal pose presentation. Spatial transcriptomic analysis was based on the CROST database. Eleven biomarkers were identified associated with NAC treatment response in BC patients, with FOXA1 identified as a pivotal hub gene among them. The expression levels of FOXA1 showed a significant positive correlation with genomic stability and a marked negative correlation with the homologous recombination deficiency (HRD) score. Downregulation of the FOXA1 gene resulted in reduced glycolysis in MCF-7 cells.Additionally, FOXA1 were found to serve as a biomarker for both NAC and PARP inhibitor treatment sensitivity in BC patients. Spatial transcriptomic analysis indicates significantly elevated infiltration of T follicular helper (T-FH) cells and mast cells surrounding tumors exhibiting high FOXA1 expression. In summary, our study involved the analysis of diverse sequencing datasets derived from various FNA samples to identify biomarkers sensitive to NAC, thereby offering novel insights into resources for future personalized clinical treatment strategies.
术前识别乳腺癌（BC）患者对新辅助化疗（NAC）的治疗反应性，有利于制定个性化的治疗方案。目前利用细针穿刺（FNA）获得的批量测序数据探索NAC治疗反应性生物标志物的研究相对较少。使用Limma软件筛选差异表达基因。此外，运用加权基因共表达网络分析（WGCNA）、机器学习和基因扰动相似性分析（GPSA）来识别与NAC治疗反应相关的关键基因。采用ConsensusClusterPlus进行无监督聚类。通过实时定量聚合酶链反应（Rt-qPCR）和蛋白质免疫印迹法（WB）检测临床组织和细胞系中的基因表达和蛋白质水平。利用海马XF96细胞外流量分析仪评估细胞外酸化率（ECAR）和耗氧率（OCR）。使用“pRRophetic”软件包进行药物敏感性预测，同时应用CB-Dock2进行分子对接和最佳构象展示。空间转录组分析基于CROST数据库。在BC患者中鉴定出11种与NAC治疗反应相关的生物标志物，其中叉头框蛋白A1（FOXA1）被确定为关键枢纽基因。FOXA1的表达水平与基因组稳定性呈显著正相关，与同源重组缺陷（HRD）评分呈显著负相关。FOXA1基因的下调导致MCF-7细胞的糖酵解减少。此外，FOXA1被发现可作为BC患者对NAC和聚（ADP-核糖）聚合酶（PARP）抑制剂治疗敏感性的生物标志物。空间转录组分析表明，在FOXA1高表达的肿瘤周围，T滤泡辅助（T-FH）细胞和肥大细胞的浸润显著增加。总之，我们的研究通过分析来自各种FNA样本的多样测序数据集，识别对NAC敏感的生物标志物，从而为未来个性化临床治疗策略的资源提供了新的见解。
