目的：失巢凋亡（anoikis）是一种特殊类型的细胞凋亡,主要发生在细胞与基质联系丧失（失去锚定）时,对维护体内环境稳定起着重要的作用。本研究旨在构建一个在三阴性乳腺癌（triple-negative breast cancer,TNBC）中与失巢凋亡相关的基因集以及预后模型,同时可以用于预测患者有效治疗药物。方法：对TCGA-BRCA数据集中的TNBC患者进行单因素和多因素Cox分析,以得出失巢凋亡相关基因集（anoikis-related gene signature,ARGS）。采用Gene Expression Omnibus（GEO）数据集进行独立验证。将ARGS与临床病理参数结合生成一个诺莫图模型（Nomogram Model）。采用各类统计学方法进行预测能力评价。根据ARGS所计算的中位数进行分层的高低风险TNBC患者组中进行治疗药物敏感性的分析。CCK-8细胞活力实验与流式细胞术将用来分析TNBC细胞对治疗药物的敏感性。结果：我们构建了一个由7个基因组成的ARGS,并用于计算风险分数,将TNBC患者分为高风险和低风险组,高风险组患者比低风险组患者的总生存期（overall survival,OS）明显更差。随后生成一个具有优异性能的能对患者生存情况分层的诺莫图模型,预测出了高风险的TNBC患者对替尼类药物（酪氨酸激酶抑制剂）的敏感性更强。在体外实验中,我们发现细胞系风险分数越高,则其对替尼类药物的敏感性更强。结论：在TNBC中构建了可以预测患者生存以及药物敏感性的失巢凋亡相关基因集,同时构建了结合患者临床病理信息可以预测患者生存情况的诺莫图模型。
Abstract
Objective Anoikis is a distinct form of apoptosis that primarily takes place when cells become detached from the extracellular matrix, resulting in the loss of anchoring. It plays a crucial role in preserving the homeostasis of the in vivo environment. This study is aimed at developing a gene set and prognostic model associated with anoikis in triple-negative breast cancer (TNBC), which could also be utilized to predict effective therapeutic interventions for patients. Methods Univariate and multivariate Cox analyses were conducted on TNBC patients within the TCGA-BRCA dataset to identify the anoikis-related gene signature (ARGS). The Gene Expression Omnibus (GEO) dataset was employed for independent validation. Subsequently, a nomogram model was constructed by integrating ARGS with clinicopathological parameters. Multiple statistical methods were employed to assess the predictive capability of the model. The sensitivity to therapeutic agents was analyzed in stratified TNBC patients based on ARGS. To investigate the predicted drug susceptibility, CCK-8 cell viability assay and flow cytometry were performed. Results We constructed a seven-gene ARGS and calculated the risk score, stratifying TNBC patients into high-risk and low-risk groups. Significantly worse overall survival (OS) was observed in the high-risk group compared with the low-risk group. Subsequently, we generated a nomogram model exhibiting excellent performance and stratification of patient survival, predicting heightened sensitivity to tyrosine kinase inhibitors (TKIs) in high-risk TNBC patients. In addition, we found that the higher the risk score of the TNBC cell lines, the greater the sensitivity to TKIs. Conclusions An ARGS was established in TNBC to predict patient survival and drug sensitivity. Additionally, a nomogram model was constructed, incorporating clinicopathological parameters to further enhance the prediction ability of patient survival.
关键词
三阴性乳腺癌 /
失巢凋亡 /
基因集 /
药物敏感性
Key words
triple-negative breast cancer /
anoikis /
gene signature /
tyrosine kinase inhibitors
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基金
国家自然科学基金（82173374,82103342）
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