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基于骨肉瘤核心驱动基因筛选的生物信息学分析和患者生存期预测基因模型的构建
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作者 李苇航 丁子毅 +5 位作者 王栋 潘益凯 刘玉辉 张世磊 李靖 闫铭 《吉林大学学报(医学版)》 CAS CSCD 北大核心 2021年第6期1570-1580,共11页
目的:筛选骨肉瘤(OS)发生发展的核心驱动基因,从分子水平探讨OS的致病机制,并构建基因模型用于患者生存期的预测。方法:采用基因表达汇编(GEO)数据库下载OS芯片对应矩阵数据GSE12865、GSE14359和GSE36001。采用生物信息学方法筛选OS与... 目的:筛选骨肉瘤(OS)发生发展的核心驱动基因,从分子水平探讨OS的致病机制,并构建基因模型用于患者生存期的预测。方法:采用基因表达汇编(GEO)数据库下载OS芯片对应矩阵数据GSE12865、GSE14359和GSE36001。采用生物信息学方法筛选OS与正常组织的差异表达基因(DEGs)。通过基因本体论(GO)、京都基因和基因组百科全书(KEGG)分析全面了解DEGs富集的分子功能及通路,采用STRING数据库构建蛋白-蛋白相互作用(PPI)网络,采用Cytoscape软件对DEGs进行相关性分析,找出与OS进展最相关的基因集,明确OS核心致病基因。采用肿瘤基因组图谱(TCGA)数据库下载OS的379个样本相关的临床记录信息和转录组数据,进行Kaplan-Meier(K-M)生存分析以进一步明确和验证核心基因与OS患者预后之间的关系,并寻找性别和种族等与预后相关的因素。对6个基因特征集的表达量进行建模以预测OS患者的生存时间。结果:MCC算法获得的排名前十的DEGs为TYROBP、LAPTM5、FCER1G、CD74、HCLS1、ARHGDIB、HLADPA1、CD93、GIMAP4和LYZ,其表达水平在骨肉瘤患者与正常患者中比较差异有统计学意义(P<0.05)。GO和KEGG分析,DEGs在PI3K-AKT和Notch信号通路显著富集。K-M生存分析,6个基因(ARHGDIB、CD74、FCER1G、HCLS1、HLA-DPA1和TYROBP)表达量更低的OS患者较高表达患者的总生存时间更长(P<0.05)。由该6个基因组成的基因集在预测模型的构建中C指数为0.71。结论:筛选出的OS的核心驱动基因高表达与OS的发生发展相关。OS发生发展的异常信号通路为PI3K-AKT和Notch信号通路。6个核心驱动基因组成OS的特征基因集构建的预测模型有良好的预测能力。 展开更多
关键词 骨肉瘤 癌症基因组图谱数据库 分子机制 肿瘤标志物 肿瘤预后模型
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A Prognostic Model Based on Colony Stimulating Factors-related Genes in Triple-negative Breast Cancer
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作者 GUO Yu-Xuan WANG Zhi-Yu +7 位作者 XIAO Pei-Yao ZHENG Chan-Juan FU Shu-Jun HE Guang-Chun LONG Jun WANG Jie DENG Xi-Yun WANG Yi-An 《生物化学与生物物理进展》 SCIE CAS CSCD 北大核心 2024年第10期2741-2756,共16页
Objective Triple-negative breast cancer(TNBC)is the breast cancer subtype with the worst prognosis,and lacks effective therapeutic targets.Colony stimulating factors(CSFs)are cytokines that can regulate the production... Objective Triple-negative breast cancer(TNBC)is the breast cancer subtype with the worst prognosis,and lacks effective therapeutic targets.Colony stimulating factors(CSFs)are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells,playing an important role in the malignant progression of TNBC.This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes(CRGs),and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy.Methods We downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database.Through LASSO Cox regression analysis,we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score(CRRS).We further analyzed the correlation between CRRS and patient prognosis,clinical features,tumor microenvironment(TME)in both high-risk and low-risk groups,and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy.Results We identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model.Kaplan-Meier survival curves,time-dependent receiver operating characteristic curves,and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival,and the predictive ability of CRRS prognostic model was further validated using the GEO dataset.Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients.Moreover,patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil,ipatasertib,and paclitaxel.Conclusion We have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs,which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment.Moreover,the key genes within this model may represent potential molecular targets for future therapies of TNBC. 展开更多
关键词 triple-negative breast cancer colony stimulating factors prognostic model tumor microenvironment drug sensitivity
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