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

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.