Label-Free Proteomics Revealed Oxidative Stress and Inflammation as Factors That Enhance Chemoresistance in Luminal Breast Cancer

Abstract

Breast cancer is the leading cause of cancer-associated death among women worldwide. Its high mortality rate is related to resistance towards chemotherapies, which is one of the major challenges of breast cancer research. In this study, we used label-free mass spectrometry- (MS-) based proteomics to investigate the differences between circulating proteins in the plasma of patients with chemoresponsive and chemoresistant luminal A breast cancer. MS analysis revealed 205 differentially expressed proteins. Furthermore, we used in silico tools to build protein-protein interaction networks. Most of the upregulated proteins in the chemoresistant group were closely related and tightly linked. The predominant networks were related to oxidative stress, the inflammatory response, and the complement cascade. Through this analysis, we identified inflammation and oxidative stress as central processes of breast cancer chemoresistance. Furthermore, we confirmed our hypothesis by evaluating oxidative stress and performing cytokine profiling in our cohort. The connections among oxidative stress, inflammation, and the complement system described in our study seem to indicate a pivotal axis in breast cancer chemoresistance. Hence, these findings will have significant clinical implications for improving therapies to bypass breast cancer chemoresistance in the future.

Figure 1

Schematic design of the study.

Figure 2

Network of interactions among the upregulated proteins in chemoresistant breast cancer identified by STRING software. (a) Proteins identified in the representative “response to oxidative stress” network are in blue. (b) Proteins identified in the representative “acute inflammatory response” network are in red. (c) Proteins identified in the representative “complement and coagulation cascades” network are in yellow. (d) Proteins identified in the representative “innate immune system” network are in green. The networks were generated with high interaction score > 0.9.

Figure 3

Network of interactions among the downregulated proteins in chemoresistant breast cancer identified by STRING software. Proteins were clustered according to the main representative networks identified. The networks were generated with high interaction score > 0.9.

Figure 4

Prooxidant parameters in plasma samples from responsive and chemoresistant patients. Carbonyl content (a), malondialdehyde levels (MDA, (b)) and nitric oxide content (NO, (c)) were measured to determine the prooxidant profile of both groups. ^∗ indicates a significant difference (p < 0.05). The line illustrates the mean levels of each parameter as determined in healthy controls.

Figure 5

Antioxidant profiling of plasma samples from responsive and chemoresistant patients. The total radical antioxidant parameter (TRAP, (a)) and reduced glutathione (GSH, (b)) levels were measured to determine the antioxidant profile of both groups. ^∗ indicates a significant difference (p < 0.05). The line illustrates the mean levels of each parameter as determined in healthy controls.

Figure 6

Cytokine profiling. The circulating levels of IL-10 (a), TGF-β1 (b), TNF-α (c), and IL-12 (d) were evaluated in both the responsive and resistant groups. ^∗ indicates statistical significance (p < 0.05). The line illustrates the mean levels of each parameter as determined in healthy controls.