Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals.
Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals.
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DOI：
10.1002/1878-0261.13499
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发表时间：
2023-11
影响因子：
6.6
通讯作者：
Ping Y
中科院分区：
文献类型：
--
作者：
Xu J;Pang B;Lan Y;Dou R;Wang S;Kang S;Zhang W;Liu Y;Zhang Y;Ping Y
癌症群体基因组和表型的高度异质性使得基于群体的共同驱动基因难以应用于癌症个体的诊断和治疗。多形性胶质母细胞瘤（GBM）个体的个性化驱动机制的表征和识别是实现精准医疗的关键。我们提出了一种综合方法，通过整合癌症个体的基因表达和遗传改变来识别个性化的驱动基因集。该方法将遗传算法与随机行走相结合，最大限度地识别出能够解释转录组表型异常的最优基因集。采用本方法鉴定了99例GBM个体的个性化驱动基因集。我们发现，1到7个驱动基因的基因组改变可以最大限度地解释GBM个体的癌症特征功能障碍。即使在转录组表型显著相似的GBM个体中，驱动基因组也是不同的。我们的方法鉴定出MCM4具有罕见的遗传改变，是以前未知的致癌基因，其高表达与GBM不良预后显著相关。功能实验证实，敲低MCM4可显著抑制GBM细胞系U251和U118MG的增殖、侵袭、迁移和克隆形成，过表达MCM4可显著促进GBM细胞系U87MG的增殖、侵袭、迁移和克隆形成。我们的方法可以剖析个性化的驱动基因改变集，这对于开发靶向治疗策略和精准医学至关重要。我们的方法可以扩展到从其他层面识别关键驱动因素，并可以应用于更多的癌症类型。作者将遗传算法与随机行走相结合，确定最大程度上解释癌症个体转录组表型异常的最优个性化驱动基因集。遗传算法从具有遗传改变的基因中随机搜索候选子集，并随机行走评估每个候选子集对共表达蛋白相互作用网络中基因的驱动效应。
High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population‐based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of MCM4 could significantly inhibit proliferation, invasion, migration, and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration, and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels and could be applied to more cancer types. The authors coupled genetic algorithm and random walk to identify the optimal personalized driver gene sets that could explain abnormality of transcriptome phenotype of cancer individuals to the maximum extent. The genetic algorithm randomly searches candidate subsets from genes with genetic alterations, and random walk evaluates the driver effect of each candidate subset on genes in co‐expression protein interaction networks.
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影响因子：
14.9
作者：
An O;Dall'Olio GM;Mourikis TP;Ciccarelli FD
通讯作者：
Ciccarelli FD
影响因子：
12.3
作者：
Bashashati A;Haffari G;Ding J;Ha G;Lui K;Rosner J;Huntsman DG;Caldas C;Aparicio SA;Shah SP
通讯作者：
Shah SP
DOI：
10.1007/s00432-005-0011-6
发表时间：
2005-10-01
影响因子：
3.6
作者：
Huang, XP;Rong, TH;Fang, Y
通讯作者：
Fang, Y
影响因子：
9.7
作者：
Ghorai, Atanu;Mahaddalkar, Tejashree;Dutt, Shilpee
通讯作者：
Dutt, Shilpee
影响因子：
4.3
作者：
Ma JC;Sun XW;Su H;Chen Q;Guo TK;Li Y;Chen XC;Guo J;Gong ZQ;Zhao XD;Qi JB
通讯作者：
Qi JB