ArticleFrontiers in immunology2025
Multi-omics analysis reveals ultraviolet response insights for immunotherapy and prognosis.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.