Zheng Mingjun, Kessler Mirjana, Jeschke Udo, Reichenbach Juliane, Czogalla Bastian, Keckstein Simon, Schroeder Lennard, Burges Alexander, Mahner Sven, Trillsch Fabian, Kaltofen Till
Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Marchioninistrasse 15, 81377 Munich, Germany.
Department of Gynaecology and Obstetrics, Shengjing Hospital, China Medical University, Sanhao Street 36, Shenyang 110055, China.
Cancers (Basel). 2025 Jan 15;17(2):271. doi: 10.3390/cancers17020271.
This study aimed to construct a risk score (RS) based on necroptosis-associated genes to predict the prognosis of patients with advanced epithelial ovarian cancer (EOC). EOC data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) series 140082 (GSE140082) were used. Based on known necroptosis-associated genes, clustering was performed to identify molecular subtypes of EOC. A least absolute shrinkage and selection operator (LASSO)-Cox regression analysis identified key genes related to prognosis. The expression of one of them, , was analyzed via immunohistochemistry in an EOC cohort. An RS made from ten genes (, , , , , , , , and ) was developed. Tumor samples were divided into a high-risk group (HRG) and low-risk group (LRG) using the RS. The model is able to predict the overall survival (OS) of EOC and distinguish the prognosis of different clinical subgroups. Immunohistochemical verification of the receptor-interacting serine/threonine-protein kinase (RIPK) 3 confirmed that high nuclear expression is correlated with a longer OS. In addition, the score can predict the response to a programmed death ligand 1 (PD-L1) blockade treatment in selected solid malignancies. Patients from the LRG seem to benefit more from it than patients from the HRG. Our RS based on necroptosis-associated genes might help to predict the prognosis of patients with advanced EOC and gives an idea on how the use of immunotherapy can potentially be guided.
本研究旨在构建基于坏死性凋亡相关基因的风险评分（RS），以预测晚期上皮性卵巢癌（EOC）患者的预后。使用了来自癌症基因组图谱（TCGA）的EOC数据以及基因表达综合数据库（GEO）系列140082（GSE140082）。基于已知的坏死性凋亡相关基因进行聚类，以识别EOC的分子亚型。采用最小绝对收缩和选择算子（LASSO）-Cox回归分析确定与预后相关的关键基因。通过免疫组织化学在一个EOC队列中分析了其中一个基因（此处原文缺失该基因名称）的表达。构建了一个由十个基因（此处原文缺失十个基因的具体名称）组成的RS。使用该RS将肿瘤样本分为高风险组（HRG）和低风险组（LRG）。该模型能够预测EOC的总生存期（OS），并区分不同临床亚组的预后。对受体相互作用丝氨酸/苏氨酸蛋白激酶（RIPK）3的免疫组织化学验证证实，高核表达与较长的OS相关。此外，该评分可以预测在选定的实体恶性肿瘤中对程序性死亡配体1（PD-L1）阻断治疗的反应。LRG组的患者似乎比HRG组的患者从该治疗中获益更多。我们基于坏死性凋亡相关基因的RS可能有助于预测晚期EOC患者的预后，并为如何潜在地指导免疫治疗的使用提供思路。
