AUTHOR=Yi Tianfei , Yang Zhenglun , Shen Peng , Huang Yan 

TITLE=Immunogenic cell death-related genes as prognostic biomarkers and therapeutic insights in uterine corpus endometrial carcinoma: an integrative bioinformatics analysis

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1588703

DOI=10.3389/fonc.2025.1588703

ISSN=2234-943X

ABSTRACT=IntroductionImmunogenic cell death (ICD) is the phenomenon in which tumor cells undergo the transition from a non-immunogenic state to an immunogenic state upon their demise as a result of external stimuli. While ICD systems have been widely adopted in oncological research, their specific utilization for Uterine Corpus Endometrial Carcinoma (UCEC) investigations has received comparatively little attention.MethodsThe ICD score was assessed using single-sample gene set enrichment analysis (ssGSEA). Differentially expressed genes (DEGs) were identified from transcriptomic data processed with the "DESeq2" R package. A prognostic model was then developed by integrating these DEGs with clinical variables. The immune landscape was characterized through multiple bioinformatics approaches, and immunotherapy response was predicted using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Additionally, drug sensitivity analysis was performed based on the Genomics of Drug Sensitivity in Cancer (GDSC) database.ResultsIn this study, we calculated ICD scores based on 74 ICD-related genes to explore the role of ICD in UCEC progression. We observed that patients with higher ICD scores exhibited a more favorable prognosis, and the score showed a positive correlation with mutation burden (r=0.16, P<0.001). Then we identified 587 upregulated DEGs and 153 downregulated DEGs in high-ICD group compared to low-ICD group. The former was predominantly associated with immune pathways, which was validated in GEO dataset. Using the 64 common DEGs obtained from both TCGA and GEO datasets, we developed a prognostic model specifically tailored for UCEC patients, incorporating five optimal prognostic genes (CD52, SLC30A3, ST8SIA5, STAT1 and TRBC1). Furthermore, the inclusion of clinical factors (stage and ICD score) significantly enhanced the model's predictive ability. The ICD score exhibited positive correlations with immune cell infiltration, as verified by ESTIMATE, xCell, TIMER, MCPcounter, EPIC, and IPS algorithms. Finally, we found that hyper-immunogenicity may be sensitive to immunotherapy and certain drugs (AZD5991, Ibrutinib, Osimertinib, AGI-5198, Savolitinib, Sapitinib, AZ960, AZD3759 and Ruxolitinib), while PCI-34051 and Vorinostat showed sensitivity in patients with hypo-immunogenicity.DiscussionOur results demonstrate that ICD plays an important role in UCEC progression, suggesting that ICD-related markers could serve as potential targets for prognosis and treatment.