| Autor: |
Moreno-Ulloa A; Laboratorio MS2, Departamento de Innovación Biomédica, CICESE, Ensenada 22860, Baja California, México., Zárate-Córdova VL; Laboratorio MS2, Departamento de Innovación Biomédica, CICESE, Ensenada 22860, Baja California, México.; Posgrado en Ciencias de la Vida, CICESE, Ensenada 22860, Baja California, México., Ramírez-Sánchez I; Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, IPN, Ciudad de México 11340, México., Cruz-López JC; Hospital Puebla, Puebla 72197, Pue., México.; Hospital General Zona Norte SSEP, Puebla 72200, Pue., México., Perez-Ortiz A; Escuela de Medicina, Universidad Panamericana, Ciudad de México 03920, México.; Departamento de Cirugía, Centro Médico ABC, Ciudad de México 05348, México., Villarreal-Garza C; Breast Cancer Center, Hospital Zambrano Hellion TecSalud, Tecnologico de Monterrey, Monterrey 66260, Nuevo León, México., Díaz-Chávez J; Unidad de Investigación Biomédica en Cáncer, Instituto de Investigaciones Biomédicas, UNAM/Instituto Nacional de Cancerología, Ciudad de México 14080, México., Estrada-Mena B; Escuela de Enfermería, Universidad Panamericana, Ciudad de México 03920, México.; Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México 04510, México., Antonio-Aguirre B; Escuela de Medicina, Universidad Panamericana, Ciudad de México 03920, México., López-Almanza PX; Escuela de Medicina, Universidad Panamericana, Ciudad de México 03920, México., Lira-Romero E; Escuela de Medicina, Universidad Panamericana, Ciudad de México 03920, México., Estrada-Mena FJ; Escuela de Medicina, Universidad Panamericana, Ciudad de México 03920, México. |
| Abstrakt: |
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. |
