Department of Obstetrics, First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
Department of Obstetrics and Gynaecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, China.
J Gene Med. 2024 Jan;26(1):e3568. doi: 10.1002/jgm.3568. Epub 2023 Jul 16.
The present study aims to develop a metabolic gene signature to evaluate the survival rate of ovarian cancer (OC) patients and analyze the potential mechanisms of metabolic genes in OC because the difficulty in early detection of OC often leads to poor treatment outcomes.
A non-negative matrix factorization algorithm was applied to determine molecular subtypes according to metabolism genes. To build a risk prognosis model, least absolute shrinkage and selection operator multivariate Cox analysis was carried out with weighted correlation network analysis (WCGNA). Glycolytic flux and mitochondrial function were evaluated by conducting seahorse analysis.
On the basis of metabolism-related genes, the two subtypes of OC samples present in The Cancer Genome Atlas database were distinguished. An analysis of WGCNA identified 1056 genes. Lastly, a 10-gene signature (CMAS, ADH1B, PLA2G2D, BHMT, CACNA1C, AADAC, ALOX12, CYP2R1, SCN1B and ME1) was constructed that demonstrated promising performance in predicting outcome in patients with OC. The RiskScore of the gene signature was linked to microenvironment cell infiltration and immune checkpoint. Higher RiskScores were associated with poorer results for OC patients. Seahorse analysis shows the influence of CMAS in cell energy metabolism.
In the present study, a novel marker for evaluating the survival of OC patients was developed through the creation of a gene signature incorporating metabolism-related genes. Our knowledge of immunotherapy and microenvironment cell infiltration may be enriched by evaluating metabolism-related gene modification patterns.
本研究旨在开发一种代谢基因特征，以评估卵巢癌（OC）患者的生存率，并分析代谢基因在 OC 中的潜在机制，因为 OC 的早期检测困难往往导致治疗效果不佳。
应用非负矩阵分解算法根据代谢基因确定分子亚型。通过加权相关网络分析（WCGNA）进行最小绝对收缩和选择算子多变量 Cox 分析，构建风险预后模型。通过 Seahorse 分析评估糖酵解通量和线粒体功能。
基于代谢相关基因，区分了 TCGA 数据库中存在的两种 OC 样本亚型。WGCNA 分析鉴定出 1056 个基因。最后，构建了一个由 10 个基因组成的特征（CMAS、ADH1B、PLA2G2D、BHMT、CACNA1C、AADAC、ALOX12、CYP2R1、SCN1B 和 ME1），该特征在预测 OC 患者预后方面表现出良好的性能。基因特征的 RiskScore 与微环境细胞浸润和免疫检查点相关。较高的 RiskScore 与 OC 患者的不良预后相关。 Seahorse 分析显示了 CMAS 在细胞能量代谢中的影响。
本研究通过创建包含代谢相关基因的基因特征，开发了一种评估 OC 患者生存的新标志物。通过评估代谢相关基因修饰模式，我们对免疫治疗和微环境细胞浸润的认识可能会得到丰富。
