基于蛋白质组学的蛋白签名在乳腺组织乳腺癌检测中的评估

Evaluation of a Proteomics-Guided Protein Signature for Breast Cancer Detection in Breast Tissue

JOURNAL OF PROTEOME RESEARCH

影响因子:3.6

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

DOI: 10.1021/acs.jproteome.4c00295

摘要

在临床实践中，非癌性与癌性乳腺组织的区分具有挑战性，且基于蛋白组学的生物标志物的研究仍较为不足。本研究采用一项初步蛋白质组学研究（发现队列），旨在识别乳腺癌的蛋白签名，用于后续在六个已发表的蛋白组学/转录组学数据集（验证队列）中验证。通过全理论窗口采集（SWATH）质谱技术，发现非癌组织与乳腺癌之间存在370个差异表达的蛋白。蛋白-蛋白相互作用网络和富集分析显示，乳腺癌中细胞外基质组织、血小板脱颗粒、先天免疫系统及RNA代谢相关通路异常。通过多变量无监督分析，筛选出一个由OGN、LUM、DCN和COL14A1组成的四蛋白签名，能够区分乳腺癌。这一异常表达模式在不同的蛋白组学和转录组学数据集中得到一致验证。在预后较差的乳腺癌亚型如Basal-Like和HER2中，异常程度明显高于Luminal A。在诊断评估（受试者工作特征曲线，ROC）中，该签名区分乳腺癌与非癌组织的AUC值范围为0.87至0.9，预测准确率为80%至82%。当仅考虑Basal-Like/三阴性亚型时，ROC AUC升至0.922-0.959，预测准确率达84.2%-89%。这些结果提示，该蛋白签名在区分癌性与非癌性乳腺组织方面具有潜在应用价值，可提升诊断的准确性。

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.