Glutamine metabolism-related genes predict the prognostic risk of acute myeloid leukemia and stratify patients by subtype analysis

IF 2.7 3区生物学

Hereditas Pub Date : 2024-09-19 DOI:10.1186/s41065-024-00338-8

Jie Zhou, Na Zhang, Yan Zuo, Feng Xu, Lihua Cheng, Yuanyuan Fu, Fudong Yang, Min Shu, Mi Zhou, Wenting Zou, Shengming Zhang

{"title":"Glutamine metabolism-related genes predict the prognostic risk of acute myeloid leukemia and stratify patients by subtype analysis","authors":"Jie Zhou, Na Zhang, Yan Zuo, Feng Xu, Lihua Cheng, Yuanyuan Fu, Fudong Yang, Min Shu, Mi Zhou, Wenting Zou, Shengming Zhang","doi":"10.1186/s41065-024-00338-8","DOIUrl":null,"url":null,"abstract":"Acute myeloid leukemia (AML) is a genetically heterogeneous disease in which glutamine (Gln) contributes to AML progression. Therefore, this study aimed to identify potential prognostic biomarkers for AML based on Gln metabolism-related genes. Gln-related genes that were differentially expressed between Cancer Genome Atlas-based AML and normal samples were analyzed using the limma package. Univariate, least absolute shrinkage, selection operators, and stepwise Cox regression analyses were used to identify prognostic signatures. Risk score-based prognostic and nomogram models were constructed to predict the prognostic risk of AML. Subsequently, consistent cluster analysis was performed to stratify patients into different subtypes, and subtype-related module genes were screened using weighted gene co-expression network analysis. Through a series of regression analyses, HGF, ANGPTL3, MB, F2, CALR, EIF4EBP1, EPHX1, and PDHA1 were identified as potential prognostic biomarkers of AML. Prognostic and nomogram models constructed based on these genes could significantly differentiate between high- and low-risk AML with high predictive accuracy. The eight-signature also stratified patients with AML into two subtypes, among which Cluster 2 was prone to a high risk of AML prognosis. These two clusters exhibited different immune profiles. Of the subtype-related module genes, the HOXA and HOXB family genes may be genetic features of AML subtypes. Eight Gln metabolism-related genes were identified as potential biomarkers of AML to predict prognostic risk. The molecular subtypes clustered by these genes enabled prognostic risk stratification. ","PeriodicalId":12862,"journal":{"name":"Hereditas","volume":"36 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hereditas","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s41065-024-00338-8","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

Acute myeloid leukemia (AML) is a genetically heterogeneous disease in which glutamine (Gln) contributes to AML progression. Therefore, this study aimed to identify potential prognostic biomarkers for AML based on Gln metabolism-related genes. Gln-related genes that were differentially expressed between Cancer Genome Atlas-based AML and normal samples were analyzed using the limma package. Univariate, least absolute shrinkage, selection operators, and stepwise Cox regression analyses were used to identify prognostic signatures. Risk score-based prognostic and nomogram models were constructed to predict the prognostic risk of AML. Subsequently, consistent cluster analysis was performed to stratify patients into different subtypes, and subtype-related module genes were screened using weighted gene co-expression network analysis. Through a series of regression analyses, HGF, ANGPTL3, MB, F2, CALR, EIF4EBP1, EPHX1, and PDHA1 were identified as potential prognostic biomarkers of AML. Prognostic and nomogram models constructed based on these genes could significantly differentiate between high- and low-risk AML with high predictive accuracy. The eight-signature also stratified patients with AML into two subtypes, among which Cluster 2 was prone to a high risk of AML prognosis. These two clusters exhibited different immune profiles. Of the subtype-related module genes, the HOXA and HOXB family genes may be genetic features of AML subtypes. Eight Gln metabolism-related genes were identified as potential biomarkers of AML to predict prognostic risk. The molecular subtypes clustered by these genes enabled prognostic risk stratification.

查看原文本刊更多论文

谷氨酰胺代谢相关基因可预测急性髓性白血病的预后风险，并通过亚型分析对患者进行分层

急性髓性白血病（AML）是一种基因异质性疾病，其中谷氨酰胺（Gln）是导致AML进展的因素之一。因此，本研究旨在根据 Gln 代谢相关基因确定急性髓性白血病的潜在预后生物标志物。研究人员使用 limma 软件包分析了基于癌症基因组图谱的急性髓细胞性白血病样本和正常样本中差异表达的 Gln 相关基因。使用单变量、最小绝对缩减、选择算子和逐步考克斯回归分析来确定预后特征。构建了基于风险评分的预后模型和提名图模型，以预测急性髓细胞性白血病的预后风险。随后，通过一致聚类分析将患者分为不同亚型，并利用加权基因共表达网络分析筛选出与亚型相关的模块基因。通过一系列回归分析，HGF、ANGPTL3、MB、F2、CALR、EIF4EBP1、EPHX1和PDHA1被确定为AML潜在的预后生物标志物。根据这些基因构建的预后模型和提名图模型可显著区分高危和低危急性髓细胞性白血病，且预测准确性高。这八个特征还将急性髓细胞性白血病患者分为两个亚型，其中第2群组的急性髓细胞性白血病预后风险较高。这两个群组表现出不同的免疫特征。在与亚型相关的模块基因中，HOXA 和 HOXB 家族基因可能是急性髓细胞性白血病亚型的遗传特征。8个与Gln代谢相关的基因被鉴定为AML的潜在生物标志物，可预测预后风险。根据这些基因聚类的分子亚型可对预后风险进行分层。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Hereditas Biochemistry, Genetics and Molecular Biology-Genetics

CiteScore

3.80

自引率

3.70%

发文量

期刊介绍： For almost a century, Hereditas has published original cutting-edge research and reviews. As the Official journal of the Mendelian Society of Lund, the journal welcomes research from across all areas of genetics and genomics. Topics of interest include human and medical genetics, animal and plant genetics, microbial genetics, agriculture and bioinformatics.