{"title":"预测慢性髓系白血病对伊马替尼治疗反应的免疫相关基因。","authors":"Pu Yang, Qian Yu","doi":"10.1093/carcin/bgae080","DOIUrl":null,"url":null,"abstract":"<p><p>Chronic myeloid leukemia (CML) is a malignant hyperplastic tumor that originates from pluripotent hematopoietic stem cells in the bone marrow. The introduction of tyrosine kinase inhibitors has significantly improved the survival rates of CML patients. This study aimed to identify immune-related genes associated with the response to imatinib (IM) therapy in CML. Gene expression profiles from IM-treated CML patients were obtained from the Gene Expression Omnibus database and categorized into high- and low-score groups based on immune scores calculated using the ESTIMATE algorithm. Subsequent bioinformatics analysis identified 428 differentially expressed immune-related genes in the CML context. Functional enrichment analysis revealed that these genes were involved in immune-related pathways, including T-cell receptor signaling and cytokine-cytokine receptor interaction. Finally, based on five modules in weighted gene co-expression network analysis and the top-ranked degree, 10 hub genes were identified. Receiver operating characteristic analysis in two Gene Expression Omnibus datasets identified IL10RA, SCN9A, and SLC26A11 as potential biomarkers for predicting IM response. We further validated these biomarkers in an independent clinical cohort of 60 CML patients treated with IM. Results from quantitative real-time polymerase chain reaction (qRT-PCR) revealed high expression of IL10RA and SLC26A11 in responders, while SCN9A showed low expression. All three genes had an area under the curve greater than 0.75, confirming their potential as predictive biomarkers. These findings deepen our understanding of functional characteristics and immune-related molecular mechanisms underlying IM response and offer promising predictive biomarkers.</p>","PeriodicalId":9446,"journal":{"name":"Carcinogenesis","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Immune-related genes for the prediction of response to imatinib therapy in chronic myeloid leukemia.\",\"authors\":\"Pu Yang, Qian Yu\",\"doi\":\"10.1093/carcin/bgae080\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Chronic myeloid leukemia (CML) is a malignant hyperplastic tumor that originates from pluripotent hematopoietic stem cells in the bone marrow. The introduction of tyrosine kinase inhibitors has significantly improved the survival rates of CML patients. This study aimed to identify immune-related genes associated with the response to imatinib (IM) therapy in CML. Gene expression profiles from IM-treated CML patients were obtained from the Gene Expression Omnibus database and categorized into high- and low-score groups based on immune scores calculated using the ESTIMATE algorithm. Subsequent bioinformatics analysis identified 428 differentially expressed immune-related genes in the CML context. Functional enrichment analysis revealed that these genes were involved in immune-related pathways, including T-cell receptor signaling and cytokine-cytokine receptor interaction. Finally, based on five modules in weighted gene co-expression network analysis and the top-ranked degree, 10 hub genes were identified. Receiver operating characteristic analysis in two Gene Expression Omnibus datasets identified IL10RA, SCN9A, and SLC26A11 as potential biomarkers for predicting IM response. We further validated these biomarkers in an independent clinical cohort of 60 CML patients treated with IM. Results from quantitative real-time polymerase chain reaction (qRT-PCR) revealed high expression of IL10RA and SLC26A11 in responders, while SCN9A showed low expression. All three genes had an area under the curve greater than 0.75, confirming their potential as predictive biomarkers. These findings deepen our understanding of functional characteristics and immune-related molecular mechanisms underlying IM response and offer promising predictive biomarkers.</p>\",\"PeriodicalId\":9446,\"journal\":{\"name\":\"Carcinogenesis\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-01-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Carcinogenesis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/carcin/bgae080\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Carcinogenesis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/carcin/bgae080","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
慢性髓性白血病(CML)是一种起源于骨髓中多能造血干细胞的恶性增生性肿瘤。酪氨酸激酶抑制剂(TKIs)的引入显著提高了CML患者的生存率。本研究旨在鉴定与CML患者对伊马替尼治疗反应相关的免疫相关基因(IRGs)。从Gene expression Omnibus (GEO)数据库中获得伊马替尼治疗的CML患者的基因表达谱,并根据使用ESTIMATE算法计算的免疫评分将其分为高分组和低分组。随后的生物信息学分析确定了CML背景下428个差异表达的IRGs。功能富集分析显示,这些基因参与免疫相关途径,包括T细胞受体信号传导和细胞因子-细胞因子受体相互作用。最后,基于加权基因共表达网络分析(WGCNA)的5个模块和排名最高的程度,鉴定出10个枢纽基因。两个GEO数据集的受试者工作特征(ROC)分析发现IL10RA、SCN9A和SLC26A11是预测伊马替尼反应的潜在生物标志物。我们在60名接受伊马替尼治疗的CML患者的独立临床队列中进一步验证了这些生物标志物。实时荧光定量PCR结果显示,应答者中IL10RA和SLC26A11高表达,SCN9A低表达。这三个基因的AUC都大于0.75,证实了它们作为预测性生物标志物的潜力。这些发现加深了我们对伊马替尼应答的功能特征和免疫相关分子机制的理解,并提供了有希望的预测性生物标志物。
Immune-related genes for the prediction of response to imatinib therapy in chronic myeloid leukemia.
Chronic myeloid leukemia (CML) is a malignant hyperplastic tumor that originates from pluripotent hematopoietic stem cells in the bone marrow. The introduction of tyrosine kinase inhibitors has significantly improved the survival rates of CML patients. This study aimed to identify immune-related genes associated with the response to imatinib (IM) therapy in CML. Gene expression profiles from IM-treated CML patients were obtained from the Gene Expression Omnibus database and categorized into high- and low-score groups based on immune scores calculated using the ESTIMATE algorithm. Subsequent bioinformatics analysis identified 428 differentially expressed immune-related genes in the CML context. Functional enrichment analysis revealed that these genes were involved in immune-related pathways, including T-cell receptor signaling and cytokine-cytokine receptor interaction. Finally, based on five modules in weighted gene co-expression network analysis and the top-ranked degree, 10 hub genes were identified. Receiver operating characteristic analysis in two Gene Expression Omnibus datasets identified IL10RA, SCN9A, and SLC26A11 as potential biomarkers for predicting IM response. We further validated these biomarkers in an independent clinical cohort of 60 CML patients treated with IM. Results from quantitative real-time polymerase chain reaction (qRT-PCR) revealed high expression of IL10RA and SLC26A11 in responders, while SCN9A showed low expression. All three genes had an area under the curve greater than 0.75, confirming their potential as predictive biomarkers. These findings deepen our understanding of functional characteristics and immune-related molecular mechanisms underlying IM response and offer promising predictive biomarkers.
期刊介绍:
Carcinogenesis: Integrative Cancer Research is a multi-disciplinary journal that brings together all the varied aspects of research that will ultimately lead to the prevention of cancer in man. The journal publishes papers that warrant prompt publication in the areas of Biology, Genetics and Epigenetics (including the processes of promotion, progression, signal transduction, apoptosis, genomic instability, growth factors, cell and molecular biology, mutation, DNA repair, genetics, etc.), Cancer Biomarkers and Molecular Epidemiology (including genetic predisposition to cancer, and epidemiology), Inflammation, Microenvironment and Prevention (including molecular dosimetry, chemoprevention, nutrition and cancer, etc.), and Carcinogenesis (including oncogenes and tumor suppressor genes in carcinogenesis, therapy resistance of solid tumors, cancer mouse models, apoptosis and senescence, novel therapeutic targets and cancer drugs).