医学分子生物学杂志 ›› 2025, Vol. 22 ›› Issue (6): 586-593.doi: 10.3870/j.issn.1672-8009.2025.06.008

• 论著 • 上一篇    下一篇

前列腺癌中迁移体相关基因的鉴定及免疫学特征

陈莎莎1,2,3, 蔡维望2, 骆佳梅4, 张勇4   

  1. 1锦州医科大学研究生学院 辽宁省锦州市,121001
    2黄石市第二医院检验科 湖北省黄石市,435000
    3黄石医养医学检验有限公司 湖北省黄石市,435000
    4黄石市第五医院检验科 湖北省黄石市,435000
  • 收稿日期:2025-02-06 出版日期:2025-11-30 发布日期:2025-12-25
  • 通讯作者: 张勇(E-mail:3760543054@qq.com)

Identification and Immunological Characteristics of Migrasome-related Genes in Prostate Cancer

CHEN Shasha1,2,3, CAI Weiwang2, LUO Jiamei4, ZHANG Yong4   

  1. 1Graduate School of Jinzhou Medical University,Jinzhou,Liaoning,121001,China
    2Department of Laboratory,Huangshi Second People’s Hospital,Huangshi,Hubei,435000,China
    3Huangshi Medical Maintenance Medical Inspection Co.,LTD,Huangshi,Hubei,435000,China
    4Department of Laboratory,Huangshi Fifth People’s Hospital,Huangshi,Hubei,435000,China
  • Received:2025-02-06 Online:2025-11-30 Published:2025-12-25
  • Contact: ZHANG Yong(E-mail:3760543054@qq.com)

摘要: 目的 应用生物信息学探索迁移体相关基因在前列腺癌中表达模式,并阐明其潜在机制。方法 从TCGA数据库中获取498例前列腺癌转录组数据。使用“limma”包识别前列腺癌组织中差异表达基因,并且在GSE70770和GSE88808数据集中进行验证。通过蛋白注释数据库筛选迁移体相关差异表达基因(migrasome-related differentially expressed genes,MRDEGs)。应用基因本体论(GO)研究MRDEGs功能富集。使用LASSO回归和SVM-RFE模型筛选诊断前列腺癌关键MRDEGs,构建预测前列腺癌风险的列线图。分析22种浸润性免疫细胞与MRDEGs之间相关性。结果 在前列腺癌组织中鉴定出6个MRDEGs。GSE70770和GSE88808中验证PKD1、ITGB1及ITGA5在前列腺癌组织中稳定低表达。GO分析显示MRDEGs主要富集于细胞周期蛋白依赖性丝氨酸/苏氨酸蛋白激酶活性调节、钙通道活性及跨膜转运蛋白结合等分子功能。机器学习筛选出ITGB1和ITGA5作为诊断前列腺癌的关键MRDEGs。其构建的预测前列腺癌列线图在训练集TCGA数据库中AUC值为0.833,在外部验证集GSE70770中AUC值为0.868。发现静息状态树突细胞、中性粒细胞与ITGB1和ITGA5的表达水平呈正相关;而调节性T细胞(Tregs)、M0型巨噬细胞则与ITGB1和ITGA5的表达水平呈负相关。结论 我们的研究采用生物信息学方法系统地阐明了迁移体相关基因ITGB1和ITGA5在前列腺癌发病机制发挥重要作用,可能为治疗前列腺癌的分子靶点提供新的见解。

关键词: 前列腺癌, 迁移体, 诺莫图, 生物信息学分析

Abstract: Objective To explore the expression patterns of migrasome-related genes(MRGs)in prostate cancer and elucidate their underlying mechanisms using bioinformatics tools. Methods Transcriptomic data of 498 cases of prostate cancer were obtained from The Cancer Genome Atlas(TCGA)database.The limma package was used to identify differentially expressed genes in prostate cancer tissues,and the results were validated using GSE70770 and GSE88808 datasets.Migrasome-related differentially expressed genes(MRDEGs)were screened using protein annotation databases.Gene Ontology(GO)was applied to investigate the functional enrichment of MRDEGs.The LASSO regression and SVM-RFE model were used to screen key diagnostic MRDEGs for prostate cancer,and a predictive Nomogram was constructed.The correlation between 22 types of infiltrating immune cells and MRDEGs was analyzed. Results Six MRDEGs were identified in prostate cancer tissues.PKD1,ITGB1 and ITGA5 were significantly expressed in the GSE70770 and GSE88808 datasets.GO enrichment analysis showed that these genes were primarily enriched in biological processes or molecular functions such as the positive regulation of cyclin-dependent serine/threonine protein kinase activity,calcium channel activity,and transmembrane transporter binding.Machine learning identified ITGB1 and ITGA5 as the key MRDEGs for diagnosing prostate cancer.The prediction model constructed based on the key MRDEGs for prostate cancer in the training set TCGA database had an AUC value of 0.833,and the AUC value in the external validation set GSE70770 was 0.868.Resting dendritic cells and neutrophils showed a positive correlation with the expression levels of ITGB1 and ITGA5,while regulatory T cells(Tregs)and M0 macrophages showed a negative correlation. Conclusion Our study employed bioinformatics methods to systematically elucidate the significant role of the migration-related genes ITGB1 and ITGA5 in the pathogenesis of prostate cancer,potentially providing new insights into molecular targets for the treatment of prostate cancer.

Key words: prostate cancer, migrasome, nomogram, bioinformatics analysis

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