Frontiers in Cell and Developmental Biology (Feb 2022)
The Iron-Inflammation Axis in Early-Stage Triple-Negative Breast Cancer
Abstract
The iron-related homeostasis and inflammatory biomarker have been identified as prognostic factors for cancers. We aimed to explore the prognostic value of a novel comprehensive biomarker, the iron-monocyte-to-lymphocyte ratio (IronMLR) score, in patients with early-stage triple-negative breast cancer (TNBC) in this study. We retrospectively analysed a total of 257 early-stage TNBC patients treated at Sun Yat-sen University Cancer Center (SYSUCC) between March 2006 and October 2016. Their clinicopathological information and haematological data tested within 1 week of the diagnosis were collected. According to the IronMLR score cutoff value of 6.07 μmol/L determined by maximally selected rank statistics, patients were stratified into the low- and high-IronMLR groups, after a median follow-up of 92.3 months (95% confidence interval [CI] 76.0–119.3 months), significant differences in 5-years disease-free survival (DFS) rate (81.2%, 95% CI 76.2%–86.5% vs. 65.5%, 95% CI 50.3%–85.3%, p = 0.012) and 5-years overall survival (OS) rate (86.0%, 95% CI 81.6%–90.7% vs. 65.5%, 95% CI 50.3%–85.3%, p = 0.011) were seen between two groups. Further multivariate Cox regression analysis revealed the IronMLR score as an independent predictor for DFS and OS, respectively, we then established a prognostic nomogram integrating the IronMLR score, T stage and N stage for individualized survival predictions. The prognostic model showed good predictive performance with a C-index of DFS 0.725 (95% CI 0.662–0.788) and OS 0.758 (95% CI 0.689–0.826), respectively. Besides, calibration curves for 1-, 3-, 5-DFS, and OS represented satisfactory consistency between actual and nomogram predicted survival. In conclusion, the Iron-inflammation axis might be a potential prognostic biomarker of survival outcomes for patients with early-stage TNBC, prognostic nomograms based on it with good predictive performance might improve individualized survival predictions.
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