鉴定卵巢癌患者的糖基转移酶相关基因特征并进行综合分析。

IF 1.4 Q4 IMMUNOLOGY
American journal of clinical and experimental immunology Pub Date : 2024-02-25 eCollection Date: 2024-01-01
Yanqiu Zhang, Tong Zhou, Qingqin Tang, Bin Feng, Yuting Liang
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引用次数: 0

摘要

背景:糖基转移酶(GT)在糖基化反应中起着至关重要的作用,糖基转移酶相关基因(GTs)的异常表达会导致糖基化异常,而糖基化异常与肿瘤进展有关。然而,GTs异常表达在卵巢癌(OC)中的预后价值以及GTs与肿瘤微环境(TME)之间的相关性仍然未知:方法:利用TCGA和GSE53963数据库获取OC患者样本数据。方法:利用 TCGA 和 GSE53963 数据库获取 OC 患者样本数据,分析 GTs 与 OC 的关联。通过共识无监督聚类确定分子亚型,然后进行免疫浸润和功能富集分析。利用卡普兰-梅耶曲线和对数秩检验进行了生存分析。利用最小绝对收缩和选择操作器(LASSO)和多因素cox回归筛选与OC相关的特征基因,并用于建立预后模型:结果:采用共识无监督聚类分析将OC患者分为5个GTs群。聚类D和E在生存、信号通路和免疫浸润方面存在显著差异。然后,根据这12个特征基因建立了一个风险模型,该模型能更准确地评估OC患者的预后。我们根据风险评分将患者分为高风险组和低风险组,发现高风险组患者的生存率明显低于低风险组。此外,风险评分与肿瘤微环境、免疫浸润和化疗敏感性有明显相关性:总之,我们对OC患者的GT进行了全面分析,并建立了OC的风险模型。总之,我们对 OC 患者的 GTs 进行了全面分析,并建立了 OC 的风险模型,我们的研究结果将为 OC 的预后和治疗提供新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of glycosyltransferase-related genes signature and integrative analyses in patients with ovarian cancer.

Background: Glycosyltransferases (GT) play a crucial role in glycosylation reactions, and aberrant expression of glycosyltransferase-related genes (GTs) leads to abnormal glycosylation, which is associated with tumor progression. However, the prognostic value of aberrant expression of GTs in ovarian cancer (OC) and the correlation between GTs and tumor microenvironment (TME) remain unknown.

Methods: TCGA and GSE53963 databases were used to obtain data on OC patient samples. The association of GTs with OC was analyzed. Molecular subtypes were identified by consensus unsupervised clustering, followed by immune infiltration and functional enrichment analyses. Survival analysis was performed using Kaplan-Meier curves and log-rank tests. Least Absolute Shrinkage and Selection Operator (LASSO) and multifactorial cox regression were used to screen for signature genes associated with OC and used to establish prognostic models.

Result: OC patients were categorized into 5 GTs clusters using consensus unsupervised cluster analysis. Clusters D and E showed significant differences between survival, signaling pathways and immune infiltration. Then, a risk model was developed based on the 12 signature genes, which provides a more accurate evaluation of the prognosis of OC patients. We categorized patients into high-risk and low-risk groups based on the risk score and found that the survival of patients in the high-risk group was significantly lower than that in the low-risk group. Moreover, the risk score was significantly correlated with tumor microenvironment, immune infiltration, and chemotherapy sensitivity.

Conclusion: Overall, we performed a comprehensive analysis of GTs in OC patients and developed a risk model for OC. Our findings will provide a new insight to OC prognosis and treatment.

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