Frontiers in oncology · 2025
Ferroptosis-related gene signature-based subtype identification of triple-negative breast cancer to prioritize treatment strategies.
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Chen Yongzhen, Huang Xiaoying, Wang Junqi, Hu Chao, Zheng Yanan, Wang Yumeng, Zheng Shuqian, Ji Guozhong, You Qiang
Аннотация
PURPOSE: Ferroptosis, an iron-dependent form of regulated cell death (RCD), has been proven to affect the response to antineoplastic therapies. However, little is known about the role of ferroptosis in chemotherapy and immune checkpoint inhibitor (ICI) therapy responses, as well as the molecular subtype identification of triple-negative breast cancer (TNBC).
METHODS: We performed unsupervised clustering to stratify patients with TNBC in the Fudan University Shanghai Cancer Center (FUSCC) TNBC cohort into distinct ferroptosis-related subtypes according to the expression of eight ferroptosis-related genes (FRGs): EMC2, FTH1, HMOX1, LPCAT3, NOX4, SOCS1, BAP1, and ISCU. We conducted Gene Ontology (GO) analysis and gene set variation analysis (GSVA) to characterize the immune phenotype and enriched pathways of the distinct subtypes of TNBC. We constructed the FerrScore model to identify the most promising candidate compounds and predict ICI therapy benefits for patients with TNBC.
RESULTS: We identified two distinct ferroptosis-related subtypes with different overall survival (OS). Patients in cluster 1 exhibited better OS, which had a phenotype of a "hot" tumor with abundant immune cell infiltration and higher expression of immune checkpoints compared to cluster 2. We screened everolimus as the most promising candidate drug for patients with high FerrScore referring to comprehensive factors including CMap score, experimental evidence, and clinical trial status. Further, we confirmed that FerrScore was a potentially powerful metric to predict anti-PD-L1, anti-PD-1, and anti-PD-1 + CTLA-4 ICI therapy benefits.
CONCLUSIONS: Ferroptosis reprogrammed the tumor microenvironment (TME) and classified patients into distinct subgroups with significantly different OS. FerrScore was a potentially powerful metric to screen candidate compounds and predict ICI therapy benefits for patients with TNBC, which prioritized clinical treatment strategies.
METHODS: We performed unsupervised clustering to stratify patients with TNBC in the Fudan University Shanghai Cancer Center (FUSCC) TNBC cohort into distinct ferroptosis-related subtypes according to the expression of eight ferroptosis-related genes (FRGs): EMC2, FTH1, HMOX1, LPCAT3, NOX4, SOCS1, BAP1, and ISCU. We conducted Gene Ontology (GO) analysis and gene set variation analysis (GSVA) to characterize the immune phenotype and enriched pathways of the distinct subtypes of TNBC. We constructed the FerrScore model to identify the most promising candidate compounds and predict ICI therapy benefits for patients with TNBC.
RESULTS: We identified two distinct ferroptosis-related subtypes with different overall survival (OS). Patients in cluster 1 exhibited better OS, which had a phenotype of a "hot" tumor with abundant immune cell infiltration and higher expression of immune checkpoints compared to cluster 2. We screened everolimus as the most promising candidate drug for patients with high FerrScore referring to comprehensive factors including CMap score, experimental evidence, and clinical trial status. Further, we confirmed that FerrScore was a potentially powerful metric to predict anti-PD-L1, anti-PD-1, and anti-PD-1 + CTLA-4 ICI therapy benefits.
CONCLUSIONS: Ferroptosis reprogrammed the tumor microenvironment (TME) and classified patients into distinct subgroups with significantly different OS. FerrScore was a potentially powerful metric to screen candidate compounds and predict ICI therapy benefits for patients with TNBC, which prioritized clinical treatment strategies.