Immunogenic cell death-related gene landscape predicts the overall survival and immune infiltration status of ovarian cancer - Zhang, Wenwen; Liu, Tianbo; Jiang, Liangliang; Chen, Jiarong; Li, Qiuli; Wang, Jing (Frontiers in Genetics, 2022)
Background:Ovarian Cancer (OC) is the most troubling malignant tumor of the female reproductive system. It has a low early diagnosis rate and a high tumor recurrence rate. Immunogenic cell death (ICD) is a unique form of regulated cell death (RCD), which can activate the adaptive immune system through the release of DAMPs and cytokines in immunocompromised hosts and establish long-term immunologic memory. Therefore, the present study aims to explore the prognostic value and underlying mechanisms of ICD-related genes in OC. Methods：the gene expression profiles and clinical information of OC were downloaded from TCGA and GEO. And ICD-related genes were collected from the Genecards. ICD-related prognostic genes were obtained by intersecting. We further performed functional enrichment, genetic mutation and immune infiltration analysis. Subsequently, we developed a TCGA cohort-based prognostic risk model by Cox regression and LASSO analysis, while external validation was performed in two GEO cohorts. A nomogram was established to predict the survival probability. Finally, we performed functional enrichment and immune infiltration analysis. Results：Here, utilizing the 9 genes (ERBB2, RB1, CCR7, CD38, IFNB1, ANXA2, CXCL9, SLC9A1 and SLAMF7), we constructed an ICD-related prognostic signature. Subsequently, patients were subdivided into high- and low-risk subgroups according to the median value of the risk score. In multivariate Cox regression analyses, risk score was an independent prognostic factor (hazard ratio [HR] = 2.783; P< 0.01). In the TCGA training cohort and the two GEO validation cohorts, patients with high-risk score had a worse prognosis than patients with low-risk score (P<0.05). The time-dependent receiver operating characteristic (ROC) curve further validated the prognostic power of the signature. Finally, The Gene Set Enrichment Analysis (GSEA) indicated that multiple oncological pathways were significantly enriched in the high-risk subgroup. Whereas the low-risk subgroup was strongly related to the immune-related signaling pathways. The immune infiltration analysis further revealed that most immune cells showed higher levels of infiltration in the low-risk subgroup relative to the high-risk subgroup. Conclusion: we constructed a novel ICD-related gene model for predicting prognosis and immune infiltration of OC. In the future, new ICD-related genes may provide new potential targets for therapeutic intervention of OC.