Abstract
Background: Increasing evidence showed that the clinical significance of the interaction between hypoxia and immune status in tumor microenvironment. However, reliable biomarkers based on the hypoxia and immune status in triple-negative breast cancer (TNBC) have not been well established. This study aimed to explore a gene signature based on the hypoxia and immune status for predicting prognosis, risk stratification, and individual treatment in TNBC.Methods: Hypoxia-related genes (HRGs) and Immune-related genes (IRGs) were identified using the weighted gene co-expression network analysis (WGCNA) method and the single-sample gene set enrichment analysis (ssGSEA Z-score) with the transcriptomic profiles from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Then, prognostic hypoxia and immune based genes were identified in TNBC patients from the METABRIC (N = 221), The Cancer Genome Atlas (TCGA) (N = 142), and GSE58812 (N = 107) using univariate cox regression model. A robust hypoxia-immune based gene signature for prognosis was constructed using the least absolute shrinkage and selection operator (LASSO) method. Based on the cross-cohort prognostic hypoxia–immune related gene signature, a comprehensive index of hypoxia and immune was developed and two risk groups with distinct hypoxia–immune status were identified. The prognosis value, hypoxia and immune status, and therapeutic response in different risk groups were analyzed. Furthermore, a nomogram was constructed to predict the prognosis for individual patients, and an independent cohort from the gene expression omnibus (GEO) database was used for external validation.Results: Six cross-cohort prognostic hypoxia–immune related genes were identified to establish the comprehensive index of hypoxia and immune. Then, patients were clustered into high- and low-risk groups based on the hypoxia–immune status. Patients in the high-risk group showed poorer prognoses to their low-risk counterparts, and the nomogram we constructed yielded favorable performance to predict survival and risk stratification. Besides, the high-risk group had a higher expression of hypoxia-related genes and correlated with hypoxia status in tumor microenvironment. The high-risk group had lower fractions of activated immune cells, and exhibited lower expression of immune checkpoint markers. Furthermore, the ratio of complete response (CR) was greatly declined, and the ratio of breast cancer related events were significantly elevated in the high-risk group.Conclusion: The hypoxia–immune based gene signature we constructed for predicting prognosis was developed and validated, which may contribute to the optimization of risk stratification for prognosis and personalized treatment in TNBC patients.
Highlights
Triple-negative breast cancer (TNBC) is a special subtype of breast cancer that lacks the expression of ER, PR, and HER2
With transcriptomic profiles and hypoxia ssGSEA Z-scores in the METABRIC dataset, weighted gene co-expression network analysis (WGCNA) was applied to screen for hypoxia related candidates (Figure 2B). the optimal soft threshold was determined with a power of β = 4 (Figure 2C), 47 modules were established (Figure 2D), and the pink module showed the highest correlated with hypoxia (Figure 2E), 840 promising candidates were identified from the pink model
Based on the ssGSEA scores that specified the abundance and efficacy of immune cell fractions, triple-negative breast cancer (TNBC) samples in the METABRIC cohort were hierarchically assembled in immune-high, -median and -low groups, which displayed distinct abundance and efficacy of immunocytes (Figure 3A)
Summary
Triple-negative breast cancer (TNBC) is a special subtype of breast cancer that lacks the expression of ER (estrogen receptor), PR (progesterone receptor), and HER2 (human epidermal growth factor receptor 2). Bareche et al observed a higher expression level of immune signatures and checkpoint inhibitor genes in the IM subtype, which implied a better prognosis [5] These efforts indicated that the heterogeneous immune profile in tumor microenvironment, and immunotherapies might be practical in some specific subtypes of TNBC. This study aimed to explore a gene signature based on the hypoxia and immune status for predicting prognosis, risk stratification, and individual treatment in TNBC. Based on the crosscohort prognostic hypoxia–immune related gene signature, a comprehensive index of hypoxia and immune was developed and two risk groups with distinct hypoxia–immune status were identified. Conclusion: The hypoxia–immune based gene signature we constructed for predicting prognosis was developed and validated, which may contribute to the optimization of risk stratification for prognosis and personalized treatment in TNBC patients
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