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Purpose@#Breast cancer is known to be influenced by genetic and environmental factors, and several susceptibility genes have been discovered. Still, the majority of genetic contributors remain unknown. We aimed to analyze the plasma proteome of breast cancer patients in comparison to healthy individuals to identify differences in protein expression profiles and discover novel biomarkers. @*Methods@#This pilot study was conducted using bioresources from Seoul National University Bundang Hospital’s Human Bioresource Center. Serum samples from 10 breast cancer patients and 10 healthy controls were obtained. Liquid chromatography-mass spectrometry analysis was performed to identify differentially expressed proteins. @*Results@#We identified 891 proteins; 805 were expressed in the breast cancer group and 882 in the control group. Gene set enrichment and differential expression analysis identified 30 upregulated and 100 downregulated proteins in breast cancer. Among these, 10 proteins were selected as potential biomarkers. Three proteins were upregulated in breast cancer patients, including cluster of differentiation 44, eukaryotic translation initiation factor 2-α kinase 3, and fibronectin 1. Seven proteins downregulated in breast cancer patients were also selected: glyceraldehyde-3-phosphate dehydrogenase, α-enolase, heat shock protein member 8, integrin‑linked kinase, tissue inhibitor of metalloproteinases-1, vasodilatorstimulated phosphoprotein, and 14-3-3 protein gamma. All proteins had been previously reported to be related to tumor development and progression. @*Conclusion@#The findings suggest that plasma proteome profiling can reveal potential diagnostic biomarkers for breast cancer and may contribute to early detection and personalized treatment strategies. A further validation study with a larger sample cohort of breast cancer patients is planned.
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In the article, “A Validation Study of a Multiple Reaction Monitoring-Based Proteomic Assay to Diagnose Breast Cancer†in Volume 22(4), page 579-586 was error in the table. In Table 1, the value of pN0 was incorrectly listed as 29 (56.9) in ‘diagnosed as normal by biomarker’ and corrected to 39 (76.5). The authors apologize for any inconvenience that this may have caused.
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Asunto(s)
Humanos , Anestesia , Biomarcadores , Proteínas Sanguíneas , Neoplasias de la Mama , Mama , Anhidrasas Carbónicas , Estudios de Cohortes , Neoplasias del Colon , Diagnóstico , Pulmón , Mamografía , Tamizaje Masivo , Espectrometría de Masas , Moléculas de Adhesión de Célula Nerviosa , Péptidos , Plasma , Proteómica , Curva ROC , Sensibilidad y Especificidad , Glándula TiroidesRESUMEN
Breast cancer is the most common cancer in women worldwide. It is necessary to identify biomarkers for early detection, to make accurate prognoses, and to monitor for any recurrence of the cancer. In order to identify potential breast cancer biomarkers, we analyzed the plasma samples of women diagnosed with breast cancer and age-matched normal healthy women by mTRAQ-based stable isotope-labeling mass spectrometry. We identified and quantified 204 proteins including thrombospondin-1 (THBS1) and bromodomain and WD repeat-containing protein 3 (BRWD3) which were increased by more than 5-fold in breast cancer plasma. The plasma levels of the two proteins were evaluated by Western blot assay to confirm for their diagnostic value as serum markers. A 1.8-fold increase in BRWD3 was observed while comparing the plasma levels of breast cancer patients (n = 54) with age-matched normal healthy controls (n = 30), and the area under the receiver operating characteristic curve (AUC) was 0.917. THBS1 was detected in pooled breast cancer plasma at the ratio similar to mTRAQ ratio (> 5-fold). The AUC value for THBS1 was 0.875. The increase of THBS1 was more prominent in estrogen receptor negative and progesterone receptor negative patients than receptor-positive patients. Our results are evidence of the diagnostic value of THBS1 in detecting breast cancer. Based on our findings, we suggest a proteomic method for protein identification and quantification lead to effective biomarker discovery.