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鉴定与细胞坏死性凋亡相关的基因特征，用于预测卵巢癌的预后。

Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer.

机构信息

Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixiange Road, Xicheng District, Beijing, 100053, China.

Shandong University of Traditional Chinese Medicine, Jinan, 250014, Shandong, China.

出版信息

Sci Rep. 2024 May 15;14(1):11133. doi: 10.1038/s41598-024-61849-y.

DOI:10.1038/s41598-024-61849-y
PMID:38750159
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11096311/
Abstract

Ovarian cancer (OC) is one of the most prevalent and fatal malignant tumors of the female reproductive system. Our research aimed to develop a prognostic model to assist inclinical treatment decision-making.Utilizing data from The Cancer Genome Atlas (TCGA) and copy number variation (CNV) data from the University of California Santa Cruz (UCSC) database, we conducted analyses of differentially expressed genes (DEGs), gene function, and tumor microenvironment (TME) scores in various clusters of OC samples.Next, we classified participants into low-risk and high-risk groups based on the median risk score, thereby dividing both the training group and the entire group accordingly. Overall survival (OS) was significantly reduced in the high-risk group, and two independent prognostic factors were identified: age and risk score. Additionally, three genes-C-X-C Motif Chemokine Ligand 10 (CXCL10), RELB, and Caspase-3 (CASP3)-emerged as potential candidates for an independent prognostic signature with acceptable prognostic value. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, pathways related to immune responses and inflammatory cell chemotaxis were identified. Cellular experiments further validated the reliability and precision of our findings. In conclusion, necroptosis-related genes play critical roles in tumor immunity, and our model introduces a novel strategy for predicting the prognosis of OC patients.

摘要

卵巢癌（OC）是女性生殖系统最常见和最致命的恶性肿瘤之一。我们的研究旨在开发一种预后模型，以协助临床治疗决策。利用来自癌症基因组图谱（TCGA）的数据和加利福尼亚大学圣克鲁兹分校（UCSC）数据库的拷贝数变异（CNV）数据，我们对 OC 样本的不同表达基因（DEGs）、基因功能和肿瘤微环境（TME）评分进行了分析。接下来，我们根据中位数风险评分将参与者分为低风险组和高风险组，从而将训练组和整个组进行相应的划分。高风险组的总体生存率（OS）显著降低，确定了两个独立的预后因素：年龄和风险评分。此外，三个基因-C-X-C 基序趋化因子配体 10（CXCL10）、RELB 和半胱天冬酶-3（CASP3）-被确定为具有可接受预后价值的独立预后特征的潜在候选基因。在基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析中，确定了与免疫反应和炎症细胞趋化相关的途径。细胞实验进一步验证了我们研究结果的可靠性和精确性。总之，坏死相关基因在肿瘤免疫中起着关键作用，我们的模型为预测 OC 患者的预后提供了一种新策略。

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