摘要
出身背景
癌症是全世界女性人口中最常见的恶性肿瘤。失巢是恶性细胞发生和转移过程中的一个关键进展。很少有研究调查乳腺癌症患者失巢凋亡与预后之间的关系。
方法
Anoikis相关基因（ARGs）是从GeneCards和Harmonicome门户数据库中获得的。根据预后ARG的表达模式，将患者分为两种亚型，并构建ARG风险特征。根据该公式，计算每个个体的风险评分。然后用ROC曲线和诺模图检验预后预测能力。最后，我们分析了TME、信号通路富集和不同风险组之间的治疗反应之间的相关性。
后果
根据ARG的表达将患者分为两组。集群B的特点是操作系统更长。根据聚类之间预后ARGs的表达谱，我们构建了基于五个基因的风险评分特征。根据评分将患者再次分为高风险组和低风险组。高危组的特征是诊断较差，活化的免疫细胞浸润较少，对免疫检查点抑制剂的治疗反应较差。最后，药物敏感性分析揭示了该模型在支持临床决策方面的潜在优势。
结论
我们成功建立了一个ARG风险评分系统，将ARG的表达谱与临床病理特征联系起来，使乳腺癌症管理更加个性化和合理化。
Abstract
Background
Breast cancer is the most prevalent malignant among female population worldwide. Anoikis is a key progress during genesis and metastasis of malignant cells. Few studies investigate connections between anoikis and prognosis in breast cancer patients.

Methods
Anoikis-related genes (ARGs) were achieved from GeneCards and Harmonizome portals database. Based on expression patterns of prognostic ARGs, patients were classified as two subtypes and an ARG risk signature was constructed. Based on the formulation, risk score of every individual was calculated. Then, the ability of prognosis prediction was examined by ROC curve and Nomogram. Finally, we analyzed the correlation between TME, signal pathways enriched and treatment response between different risk groups.

Results
Patients were classified into two clusters based on ARG expression. Cluster B was featured by a longer OS. According to the expression profile of prognostic ARGs between clusters, we constructed a risk scoring signature based on five genes. Patients were again divided into the high- and low-risk group according to the score. The high-risk group was characterized by poorer diagnosis, fewer activated immune cells infiltration and worse treatment response to immune checkpoint inhibitors. Finally, the drug sensitivity analysis revealed the potential benefit of the model in supporting clinical decision.

Conclusion
We successfully established an ARG risk scoring system associating expression profile of ARGs with clinicopathological features to make breast cancer management more individualized and rationalized.

https://www.researchsquare.com/article/rs-2775172/v1