Frontiers in immunology · 2025
Multi-omics analysis reveals ultraviolet response insights for immunotherapy and prognosis.
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Zhang DanHua, Dai Mei, Ying JiaFei, Huang YiFan, Liu ZiXuan, Wu ChenLu
Аннотация
BACKGROUND: Immune checkpoint inhibitors (ICIs) have revolutionized cancer immunotherapy, but many patients develop resistance. While the immunosuppressive effects of ultraviolet (UV) light are well-documented, its link to ICI resistance remains unclear.
METHODS: We analyzed publicly available single-cell RNA sequencing (scRNA-seq) datasets from ICI-treated patients to explore the relationship between UV response (UVR) and treatment outcomes. A novel UVR gene signature (UVR.Sig) was established using 34 scRNA-seq datasets and validated in The Cancer Genome Atlas (TCGA) pan-cancer cohorts and 10 ICI cohorts. Key genes (Hub-UVR.Sig) were identified via six machine learning algorithms, and breast cancer (BRCA) subtypes were classified through consensus clustering. Biological effects of Hub-UVR.Sig genes were confirmed in vitro.
RESULTS: UVR.Sig was associated with ICI resistance and correlated with inhibitory immune cell infiltration and pro-tumor pathways in pan-cancer data. The UVR.Sig-based model achieved good predictive performance for ICI outcomes (AUC = 0.727). In BRCA, Hub-UVR.Sig stratified patients into two subtypes, with high Hub-UVR.Sig expression linked to stronger immune evasion and lower immunogenicity. ENO2 and ATP6V1F were highly expressed in BRCA tissues, and ENO2 was correlated with worse prognosis in BRCA patients. Knockdown of ENO2 reduced cell proliferation and invasion.
CONCLUSION: We reveal for the first time that UVR is strongly associated with ICI resistance. The UVR.Sig feature offers the potential to identify patients who respond to immunotherapy and to tailor BRCA treatment strategies.
METHODS: We analyzed publicly available single-cell RNA sequencing (scRNA-seq) datasets from ICI-treated patients to explore the relationship between UV response (UVR) and treatment outcomes. A novel UVR gene signature (UVR.Sig) was established using 34 scRNA-seq datasets and validated in The Cancer Genome Atlas (TCGA) pan-cancer cohorts and 10 ICI cohorts. Key genes (Hub-UVR.Sig) were identified via six machine learning algorithms, and breast cancer (BRCA) subtypes were classified through consensus clustering. Biological effects of Hub-UVR.Sig genes were confirmed in vitro.
RESULTS: UVR.Sig was associated with ICI resistance and correlated with inhibitory immune cell infiltration and pro-tumor pathways in pan-cancer data. The UVR.Sig-based model achieved good predictive performance for ICI outcomes (AUC = 0.727). In BRCA, Hub-UVR.Sig stratified patients into two subtypes, with high Hub-UVR.Sig expression linked to stronger immune evasion and lower immunogenicity. ENO2 and ATP6V1F were highly expressed in BRCA tissues, and ENO2 was correlated with worse prognosis in BRCA patients. Knockdown of ENO2 reduced cell proliferation and invasion.
CONCLUSION: We reveal for the first time that UVR is strongly associated with ICI resistance. The UVR.Sig feature offers the potential to identify patients who respond to immunotherapy and to tailor BRCA treatment strategies.