Wang Yali, Zhao Peng, Sun Xude, Batalini Felipe, Levin Gabriel, Soleymani Majd Hooman, Chen Hao, Gao Tingting
Department of Obstetrics and Gynecology, Maternal and Child Health Center in Fuping County, Fuping, China.
Oncology Department, Xi'an Daxing Hospital, Xi'an, China.
Transl Cancer Res. 2025 Feb 28;14(2):1359-1374. doi: 10.21037/tcr-2025-118. Epub 2025 Feb 26.
Ovarian cancer (OC) is one of the most lethal malignancies in women, primarily due to the absence of reliable predictive biomarkers and effective therapies. The complex role of immunogenic cell death (ICD) in OC remains poorly understood, despite its critical implications for enhancing immune responses against tumors. We are committed to developing and validating a novel ICD-related gene signature and producing certain guiding value for the clinical treatment of OC.
We employed single-sample gene set enrichment analysis (ssGSEA) and weighted gene coexpression network analysis (WGCNA) on The Cancer Genome Atlas (TCGA)-ovarian carcinoma dataset to identify ICD-associated genes. A combination of 10 different machine learning approaches was used to construct an ICD-related signature (ICDRS), which was then validated across multiple datasets. The model's predictive power was integrated into a clinical nomogram to predict patient outcomes. Ultimately, we assessed the reaction of various risk subgroups to screen pharmaceuticals designed to address specific risk factors in the context of personalized medicine.
We identified 72 prognostic genes related to ICD. An unanimous ICDRS was developed using a 101-combination machine learning computational structure, demonstrating outstanding predictive accuracy for prognosis and clinical use. Patients categorized as low ICDRS varied from those of high ICDRS in terms of biological processes, mutation profiles, and immune cell penetration in the tumor microenvironment. In addition, potential medications that target specific subgroups at risk were identified.
The ICDRS presents a significant advancement for prognostication of patients with OC, facilitating refined predictions and the exploration of personalized treatment pathways. Prospective clinical trials are necessary to validate its clinical utility and expand the application of this model to other cancer types.
卵巢癌（OC）是女性中最致命的恶性肿瘤之一，主要原因是缺乏可靠的预测生物标志物和有效的治疗方法。尽管免疫原性细胞死亡（ICD）在增强抗肿瘤免疫反应方面具有关键意义，但其在OC中的复杂作用仍知之甚少。我们致力于开发和验证一种新型的ICD相关基因特征，并为OC的临床治疗提供一定的指导价值。
我们对癌症基因组图谱（TCGA）-卵巢癌数据集采用单样本基因集富集分析（ssGSEA）和加权基因共表达网络分析（WGCNA）来识别与ICD相关的基因。使用10种不同的机器学习方法构建了一个ICD相关特征（ICDRS），然后在多个数据集中进行验证。该模型的预测能力被整合到临床列线图中以预测患者预后。最终，我们评估了不同风险亚组对旨在解决个性化医疗背景下特定风险因素的筛选药物的反应。
我们鉴定出72个与ICD相关的预后基因。使用101组合的机器学习计算结构开发了一个一致的ICDRS，显示出对预后和临床应用的出色预测准确性。在生物学过程、突变谱和肿瘤微环境中的免疫细胞浸润方面，低ICDRS分类的患者与高ICDRS分类的患者不同。此外，还确定了针对特定风险亚组的潜在药物。
ICDRS在OC患者的预后预测方面取得了重大进展，有助于进行精确预测和探索个性化治疗途径。有必要进行前瞻性临床试验以验证其临床效用，并将该模型的应用扩展到其他癌症类型。
