评估蛋白质组学引导的蛋白质特征，用于乳腺组织中的乳腺癌检测
Evaluation of a Proteomics-Guided Protein Signature for Breast Cancer Detection in Breast Tissue
影响因子:3.60000
分区:生物学2区 / 生化研究方法2区
发表日期:2024 Nov 01
作者:
Aldo Moreno-Ulloa, Vareska L Zárate-Córdova, Israel Ramírez-Sánchez, Juan Carlos Cruz-López, Andric Perez-Ortiz, Cynthia Villarreal-Garza, José Díaz-Chávez, Benito Estrada-Mena, Bani Antonio-Aguirre, Perla Ximena López-Almanza, Esmeralda Lira-Romero, Fco Javier Estrada-Mena
摘要
在临床环境中，非癌性和癌性乳腺组织之间的区别是具有挑战性的，并且发现新的基于蛋白质组学的生物标志物仍然没有被忽视。通过一项初步蛋白质组学研究（Discovery Cohort），我们旨在确定使用六个已发表的蛋白质组学/转录组学数据集（验证群体）的乳腺癌的蛋白质特征，以随后验证乳腺癌。基于理论（SWATH）的所有基于理论（Swath）的顺序获取，揭示了非癌组织和乳腺癌之间的370个差异丰富的蛋白质。蛋白质 - 蛋白质相互作用的网络和富集分析显示，与细胞外基质组织，血小板脱粒，先天免疫系统和RNA代谢相关的途径失调。通过多元无监督分析，我们确定了能够区分乳腺癌的四蛋白特征（OGN，Lum，DCN和COL14A1）。在各种蛋白质组学和转录组学数据集中，始终验证这种失调模式。与Luminal A.与腔内的A.诊断（接收器的操作特征（ROC）曲线）相比，在不良预感乳腺癌亚型（如基础）亚型（例如基础）亚型（如基础）亚型中的失调幅度明显更高，该标志是区分乳腺癌与曲线下的非癌性组织（AUC）下的乳腺癌（AUC）（AUC）下的诊断特征（AUC），范围为0.87至0.9至0.9至0.9至80％至82％的预测精度。分层后，仅包括基础样/三阴性亚型，RocAUC增加到0.922-0.959，预测精度为84.2％-89％。这些发现表明，已确定的签名在区分癌症和非癌性乳腺组织中的潜在作用，从而提供了提高诊断准确性的见解。
Abstract
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.