Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
ISSN: 0974-276X
Yang Wang, Ding-Yuan Wang, Ke-Na Bu, Ji-Dong Gao, Bai-Lin Zhang*
Background: As the malignant tumor with the highest incidence in the world, Breast Cancer (BC) is the number one killer of women's healthy life. Recently, tumor metabolic reprogramming has attracted extensive attention, and mitochondria play a vital role in this process as a metabolic hub. In this study, we developed a risk signature of Mitochondrial-Associated Genes (MRGs) and evaluated its ability to predict prognosis and risk stratification in BC patients.
Methods: The transcriptomic data and clinical features of BC samples were extracted from TCGA and METABRIC. We constructed an 8 MRG signature by LASSO combined with Cox regression and assessed the performance by ROC curve. Subsequently, we combined the risk scores based on the signature with clinical features to construct a nomogram model and evaluated its accuracy by clinical calibration curve and decision curve analysis. Functional enrichments and immune-related analyses were performed to compare the different status between high and low-risk groups. Finally, we compared the mutation landscape and drug sensitivity to explore the treatment response.
Results: A total of 8 MRGs (ACSL1, ALDH2, MTHFD2, MRPL13, TP53AIP1, SLC1A1, ME3 and BCL2A1) was constructed. This signature was an independent risk predictor for the survival of BC patients with a high hazard ratio (HR=3.028 95% CI 2.038-4.499). The low-risk group has a better prognosis, enhanced immune infiltration, significantly different mutation landscapes, and a more sensitive response to antitumor drugs, while the high-risk showed the opposite trend.
Conclusion: The MRG signature is a novel prognostic risk signature that can be used as a predictor for patient stratification in BC.
Published Date: 2024-03-10; Received Date: 2023-10-05