The impact of circulating protein levels identified by affinity proteomics on short-term, overall breast cancer risk
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Updated: Jul 7, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Felix Grassmann1,2, Anders Mälarstig3,4, Leo Dahl5
1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. felix.grassmann@ki.se.
Objective:
Current breast cancer risk prediction scores and algorithms can potentially be further improved by including molecular markers. To this end, we studied the association of circulating plasma proteins using Proximity Extension Assay (PEA) with incident breast cancer risk.
Subjects:
In this study, we included 1577 women participating in the prospective KARMA mammographic screening cohort.
Results:
In a targeted panel of 164 proteins, we found 8 candidates nominally significantly associated with short-term breast cancer risk (P < 0.05). Similarly, in an exploratory panel consisting of 2204 proteins, 115 were found nominally significantly associated (P < 0.05). However, none of the identified protein levels remained significant after adjustment for multiple testing. This lack of statistically significant findings was not due to limited power, but attributable to the small effect sizes observed even for nominally significant proteins. Similarly, adding plasma protein levels to established risk factors did not improve breast cancer risk prediction accuracy.
Conclusions:
Our results indicate that the levels of the studied plasma proteins captured by the PEA method are unlikely to offer additional benefits for risk prediction of short-term overall breast cancer risk but could provide interesting insights into the biological basis of breast cancer in the future.
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