Necroptosis-Related Gene Signature Predicts Prognosis in Patients with Advanced Ovarian Cancer.

IF 4.4 2区医学 Q1 ONCOLOGY Cancers Pub Date : 2025-01-15 DOI:10.3390/cancers17020271

Mingjun Zheng, Mirjana Kessler, Udo Jeschke, Juliane Reichenbach, Bastian Czogalla, Simon Keckstein, Lennard Schroeder, Alexander Burges, Sven Mahner, Fabian Trillsch, Till Kaltofen

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Abstract

Background/Objectives: 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). Methods: 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, RIPK3, was analyzed via immunohistochemistry in an EOC cohort. Results: An RS made from ten genes (IDH2, RIPK3, FASLG, BRAF, ITPK1, TNFSF10, ID1, PLK1, MLKL and HSPA4) 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. Conclusions: 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.

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与坏死相关的基因特征可预测晚期卵巢癌患者的预后

背景/目的：本研究旨在构建基于坏死相关基因的风险评分（RS）来预测晚期上皮性卵巢癌（EOC）患者的预后。方法：使用癌症基因组图谱（TCGA）和基因表达图谱（GEO） 140082系列（GSE140082）的EOC数据。基于已知的坏死相关基因，进行聚类以确定EOC的分子亚型。最小绝对收缩和选择算子(LASSO)-Cox回归分析确定了与预后相关的关键基因。通过免疫组织化学分析了其中一种RIPK3在EOC队列中的表达。结果：由10个基因（IDH2、RIPK3、FASLG、BRAF、ITPK1、TNFSF10、ID1、PLK1、MLKL和HSPA4）组成了RS。采用RS将肿瘤样本分为高危组（HRG）和低危组（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。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，该评分可以预测选定实体恶性肿瘤对程序性死亡配体1 （PD-L1）阻断治疗的反应。LRG的患者似乎比HRG的患者受益更多。结论：我们基于坏死相关基因的RS可能有助于预测晚期EOC患者的预后，并为如何指导免疫治疗的使用提供了思路。

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来源期刊

Cancers Medicine-Oncology

CiteScore

8.00

自引率

9.60%

发文量

5371

审稿时长

18.07 days

期刊介绍： Cancers (ISSN 2072-6694) is an international, peer-reviewed open access journal on oncology. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.