Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China.
Key Laboratory of Tumor Biotherapy of Heilongjiang Province, Harbin Medical University Cancer Hospital, Harbin, China.
Cancer Control. 2024 Jan-Dec;31:10732748241288118. doi: 10.1177/10732748241288118.
Breast cancer is one of the most prevalent types of cancer and a leading cause of cancer-related death among females worldwide. Anoikis, a specific type of apoptosis that is triggered by the loss of anchoring between cells and the native extracellular matrix, plays a vital role in cancer invasion and metastasis. However, studies that focus on the prognostic values of anoikis-related genes (ARGs) in breast cancer are scarce.
Gene expression data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Five anoikis-related signatures (ARS) were selected from ARGs through univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. Subsequently, an ARGs risk score model was established, and breast cancer patients were divided into high and low risk groups. The correlation between risk groups and overall survival (OS), tumor mutation burden (TMB), tumor microenvironment (TME), stemness, and drug sensitivity were analyzed. Moreover, RT-qPCR was performed to verify the gene expression levels of the five ARS in breast cancer tissues. Furthermore, a nomogram model was constructed based on ARGs risk score and clinicopathological factors.
A novel ARGs risk score model was constructed based on five ARS (CEMIP, LAMB3, CD24, PTK6, and PLK1), and breast cancer patients were divided into high and low risk groups. Correlation analysis showed that the high and low risk groups had different OS, TMB, TME, stemness, and drug sensitivity. Both the ARGs risk score model and the nomogram showed promising prognosis predictive value in breast cancer.
ARS could be used as promising biomarkers for breast cancer prognosis predication and treatment options selection.
乳腺癌是最常见的癌症类型之一，也是全球女性癌症相关死亡的主要原因。失巢凋亡是一种特定类型的细胞凋亡，由细胞与天然细胞外基质之间的锚定丧失引发，在癌症侵袭和转移中起着至关重要的作用。然而，关于乳腺癌中与失巢凋亡相关基因（ARGs）的预后价值的研究很少。
从癌症基因组图谱（TCGA）、基因表达综合数据库（GEO）和乳腺癌国际分子分类联盟（METABRIC）数据库中获取基因表达数据。通过单变量 Cox 回归分析、LASSO 回归分析和多变量 Cox 回归分析，从 ARGs 中选择了五个失巢凋亡相关签名（ARS）。随后，建立了 ARGs 风险评分模型，并将乳腺癌患者分为高风险组和低风险组。分析了风险组与总生存期（OS）、肿瘤突变负担（TMB）、肿瘤微环境（TME）、干性和药物敏感性的相关性。此外，通过 RT-qPCR 验证了乳腺癌组织中五个 ARS 的基因表达水平。进一步基于 ARGs 风险评分和临床病理因素构建了列线图模型。
构建了一个基于五个 ARS（CEMIP、LAMB3、CD24、PTK6 和 PLK1）的新的 ARGs 风险评分模型，并将乳腺癌患者分为高风险组和低风险组。相关性分析表明，高风险组和低风险组的 OS、TMB、TME、干性和药物敏感性不同。ARGs 风险评分模型和列线图均显示出在乳腺癌中具有良好的预后预测价值。
ARS 可以作为乳腺癌预后预测和治疗选择的有前途的生物标志物。
