Pan Wenjing, Jia Zhaoyang, Zhao Xibo, Chang Kexin, Liu Wei, Tan Wenhua
Department of Gynecology, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Department of Gynecological Oncology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Sun Yat-Sen University of Medical Sciences, Guangzhou, China.
PeerJ. 2024 Dec 13;12:e18690. doi: 10.7717/peerj.18690. eCollection 2024.
Immunogenic cell death (ICD) has been associated with enhanced anti-tumor immunotherapy by stimulating adaptive immune responses and remodeling the immune microenvironment in tumors. Nevertheless, the role of ICD-related genes in ovarian cancer (OC) and tumor microenvironment remains unexplored.
In this study, high-throughput transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases as training and validation sets separately were obtained and proceeded to explore ICD-related clusters, and an ICD-related risk signature was conducted based on the least absolute shrinkage and selection operator (LASSO) Cox regression model by iteration. Multiple tools including CIBERSORT, ESTIMATE, GSEA, TIDE, and immunohistochemistry were further applied to illustrate the biological roles of ICD-related genes as well as the prognostic capacity of ICD risk signature in OC.
Two ICD-related subtypes were identified, with the ICD-high subtype showing more intense immune cell infiltration and higher activities of immune response signaling, along with a favorable prognosis. Additionally, four candidate ICD genes (IFNG, NLRP3, FOXP3, and IL1B) were determined to potentially impact OC prognosis, with an upregulated expression of NLRP3 in OC and metastatic omental tissues. A prognostic model based on these genes was established, which could predict overall survival (OS) and response to immunotherapy for OC patients, with lower-risk patients benefiting more from immunotherapy.
Our research conducted a prognostic and prediction of immunotherapy response model based on ICD genes, which could be instrumental in assessing prognosis and assigning immunotherapeutic strategies for OC patients. NLRP3 is a promising target for prognosis in OC.
免疫原性细胞死亡（ICD）通过刺激适应性免疫反应和重塑肿瘤免疫微环境，与增强抗肿瘤免疫治疗相关。然而，ICD相关基因在卵巢癌（OC）和肿瘤微环境中的作用仍未得到探索。
在本研究中，分别从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）获取高通量转录组数据作为训练集和验证集，进而探索ICD相关聚类，并通过迭代基于最小绝对收缩和选择算子（LASSO）Cox回归模型构建ICD相关风险特征。进一步应用包括CIBERSORT、ESTIMATE、GSEA、TIDE和免疫组织化学在内的多种工具，以阐明ICD相关基因的生物学作用以及ICD风险特征在OC中的预后能力。
鉴定出两种ICD相关亚型，ICD高亚型显示出更强的免疫细胞浸润和更高的免疫反应信号活性，以及良好的预后。此外，确定了四个候选ICD基因（IFNG、NLRP3、FOXP3和IL1B）可能影响OC预后，其中NLRP3在OC和转移性网膜组织中表达上调。基于这些基因建立了一个预后模型，该模型可以预测OC患者的总生存期（OS）和对免疫治疗的反应，低风险患者从免疫治疗中获益更多。
我们的研究基于ICD基因构建了一个预后和免疫治疗反应预测模型，这有助于评估OC患者的预后并制定免疫治疗策略。NLRP3是OC预后的一个有前景的靶点。
