OBJECTIVES: Immunogenic cell death (ICD) has been demonstrated to play a critical role in the development and progression of malignant tumors by modulating the anti-tumor immune response. However, its function in cervical cancer (CC) remains largely unexplored. In this study, we aimed to construct an ICD-related gene signature to predict patient prognosis and immune cell infiltration in CC. METHODS: The gene expression profiles and clinical data of CC were downloaded from The Cancer Genome Alas (TCGA) and Gene Expression Omnibus (GEO) datasets, serving as the training and testing groups, respectively. An ICD-related gene signature was developed using the LASSO-Cox model. The expression levels of the associated ICD-related genes were evaluated using single-cell data, CC cell lines, and clinical samples in vitro. RESULTS: Two ICD-associated subtypes (cluster 1 and cluster 2) were identified through consensus clustering. Patients classified into cluster 2 demonstrated higher levels of immune cell infiltration and exhibited a more favorable prognosis. Subsequently, an ICD-related gene signature comprising 3 genes (IL1B, IFNG, and FOXP3) was established for CC. Based on the median risk score, patients in both training and testing cohorts were segregated into high-risk and low-risk groups. Further analyses indicated that the estimated risk score functioned as an independent prognostic factor for CC and influenced immune cell abundance within the tumor microenvironment. The up-regulation of the identified ICD-related genes was further validated in CC cell lines and collected clinical samples. CONCLUSION: In summary, the stratification based on ICD-related genes demonstrated strong efficacy in predicting patient prognosis and immune cell infiltration, which also provides valuable new perspectives for the diagnosis and prognosis of CC.
An Immunogenic Cell Death-Related Gene Signature Predicts the Prognosis and Immune Infiltration of Cervical Cancer.
免疫原性细胞死亡相关基因特征可预测宫颈癌的预后和免疫浸润
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作者:Sun Fangfang, Sun Yuanyuan, Tian Hui
| 期刊: | Cancer Informatics | 影响因子: | 2.500 |
| 时间: | 2025 | 起止号: | 2025 Feb 24; 24:11769351251323239 |
| doi: | 10.1177/11769351251323239 | 研究方向: | 细胞生物学 |
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