Background: Aberrant or loss of cell adhesion drives invasion and metastasis, key hallmarks of cancer progression. In this work, we hypothesized that a gene signature related to cell adhesion could predict breast cancer prognosis. Methods: Highly variant genes were tested for association with overall survival using Cox regression. Adhesion-related genes were identified through gene ontology analysis and multivariate Cox regression, with AIC selection, defined the prognostic signature. TheAdhesionScorewas then calculated as a weighted sum of gene expression, with risk stratification assessed by Kaplan–Meier and log-rank tests. Results: We found that theAdhesionScorewas a significant independent predictor of poor survival in three large independent datasets, as it provided a robust stratification of patient prognosis in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (HR: 2.65; 95% CI: 2.33–3.0,p= 2.34 × 10−51), The Cancer Genome Atlas (TCGA) (HR: 3.46; 95% CI: 2.35–5.09,p= 3.50 × 10−10), and the GSE96058 (HR: 2.83; 95% CI: 2.20–3.65,p= 6.29 × 10−16) datasets. The 5-year risk of death in the high-risk group was 32.41% for METABRIC, 27.8% for TCGA, and 17.54% for GSE96058 datasets. Consistently, HER2-enriched and triple-negative breast carcinomas (TNBC) cases showed higherAdhesionScoresthan luminal subtypes, indicating an association with aggressive tumor biology. Conclusions: We have developed, for the first time, a molecular signature based on cell adhesion, as well as an associatedAdhesionScorethat can predict patient prognosis in invasive breast cancer, with potential clinical application. We developed a novel adhesion-based molecular signature, theAdhesionScore, that robustly predicts prognosis in breast cancer across independent cohorts, highlighting its potential clinical utility for patient risk stratification.
背景：细胞黏附的异常或缺失是驱动肿瘤侵袭和转移的关键因素，也是癌症进展的重要标志。本研究假设与细胞黏附相关的基因特征能够预测乳腺癌的预后。方法：通过Cox回归分析高变异基因与总生存期的关联性。通过基因本体分析和多变量Cox回归（采用AIC准则筛选）鉴定黏附相关基因，并构建预后特征模型。随后通过基因表达量的加权总和计算黏附评分，并采用Kaplan-Meier法和时序检验进行风险分层评估。结果：在三个大型独立数据集中，黏附评分均被证实是生存不良的显著独立预测因子：乳腺癌国际分子分类联盟（METABRIC）数据集（HR: 2.65; 95% CI: 2.33–3.0, p=2.34×10⁻⁵¹）、癌症基因组图谱（TCGA）数据集（HR: 3.46; 95% CI: 2.35–5.09, p=3.50×10⁻¹⁰）和GSE96058数据集（HR: 2.83; 95% CI: 2.20–3.65, p=6.29×10⁻¹⁶）均显示该评分能有效分层患者预后。高风险组的5年死亡风险在METABRIC数据集中为32.41%，TCGA数据集中为27.8%，GSE96058数据集中为17.54%。值得注意的是，HER2富集型和三阴性乳腺癌病例的黏附评分持续高于管腔亚型，表明该评分与侵袭性肿瘤生物学特征相关。结论：本研究首次开发了基于细胞黏附的分子特征及其对应的黏附评分系统，能够预测浸润性乳腺癌患者的预后，具有潜在的临床应用价值。我们构建的新型黏附相关分子特征——黏附评分，在多个独立队列中均能稳健预测乳腺癌预后，凸显了其在患者风险分层中的临床转化潜力。
肿瘤(癌症)患者之家