在临床环境中，非癌性和癌性乳腺组织之间的区别具有挑战性，并且发现新的基于蛋白质组学的生物标志物仍未得到充分探索。通过一项试点蛋白质组学研究（发现队列），我们的目的是确定表明乳腺癌的蛋白质特征，以便使用六个已发表的蛋白质组学/转录组数据集（验证队列）进行后续验证。所有基于理论 (SWATH) 的质谱的连续窗口采集揭示了非癌组织和乳腺癌之间存在 370 种差异丰富的蛋白质。基于蛋白质-蛋白质相互作用的网络和富集分析揭示了乳腺癌中与细胞外基质组织、血小板脱颗粒、先天免疫系统和 RNA 代谢相关的通路的失调。通过多变量无监督分析，我们确定了能够区分乳腺癌的四种蛋白质特征（OGN、LUM、DCN 和 COL14A1）。这种失调模式在不同的蛋白质组学和转录组学数据集中得到了一致的验证。与 Luminal A 相比，Basal-Like 和 HER2 等预后不良的乳腺癌亚型的失调程度明显更高。区分乳腺癌和非癌组织的特征的诊断评估（受试者工作特征 (ROC) 曲线）揭示了曲线下面积（ AUC）范围为 0.87 至 0.9，预测准确度为 80% 至 82%。分层后，仅包括基础样/三阴性亚型，ROC AUC 增加至 0.922-0.959，预测准确度为 84.2%-89%。这些发现表明，所识别的特征在区分癌性乳腺组织和非癌性乳腺组织方面具有潜在作用，为提高诊断准确性提供了见解。

The distinction between noncancerous and cancerous breast tissues is challenging in clinical settings, and discovering new proteomics-based biomarkers remains underexplored. Through a pilot proteomic study (discovery cohort), we aimed to identify a protein signature indicative of breast cancer for subsequent validation using six published proteomics/transcriptomics data sets (validation cohorts). Sequential window acquisition of all theoretical (SWATH)-based mass spectrometry revealed 370 differentially abundant proteins between noncancerous tissue and breast cancer. Protein-protein interaction-based networks and enrichment analyses revealed dysregulation in pathways associated with extracellular matrix organization, platelet degranulation, the innate immune system, and RNA metabolism in breast cancer. Through multivariate unsupervised analysis, we identified a four-protein signature (OGN, LUM, DCN, and COL14A1) capable of distinguishing breast cancer. This dysregulation pattern was consistently verified across diverse proteomics and transcriptomics data sets. Dysregulation magnitude was notably higher in poor-prognosis breast cancer subtypes like Basal-Like and HER2 compared to Luminal A. Diagnostic evaluation (receiver operating characteristic (ROC) curves) of the signature in distinguishing breast cancer from noncancerous tissue revealed area under the curve (AUC) ranging from 0.87 to 0.9 with predictive accuracy of 80% to 82%. Upon stratifying, to solely include the Basal-Like/Triple-Negative subtype, the ROC AUC increased to 0.922-0.959 with predictive accuracy of 84.2%-89%. These findings suggest a potential role for the identified signature in distinguishing cancerous from noncancerous breast tissue, offering insights into enhancing diagnostic accuracy.