Cell cycle and aging, morphogenesis, and response to stimuli genes are individualized biomarkers of glioblastoma progression and survival.
Cell cycle and aging, morphogenesis, and response to stimuli genes are individualized biomarkers of glioblastoma progression and survival.
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DOI：
10.1186/1755-8794-4-49
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发表时间：
2011-06-07
影响因子：
2.7
通讯作者：
Rodriguez-Zas SL
中科院分区：
文献类型：
--
作者：
Serão NV;Delfino KR;Southey BR;Beever JE;Rodriguez-Zas SL
胶质母细胞瘤是一种复杂的多因素疾病，具有迅速和毁灭性的后果。很少有基因被一致认为是影响胶质母细胞瘤生存的预后生物标志物。这项研究的目的是确定一般的和临床相关的生物标记物基因和三个互补事件的生物学过程：生存期、总体和无进展的胶质母细胞瘤生存。一种新的分析策略被开发出来，以确定生物标记物和胶质母细胞瘤之间的一般关联，以及依赖于队列组的关联，如种族、性别和治疗。基因网络推断、交叉验证和功能分析进一步支持已确定的生物标志物。共有61、47和60个基因表达谱分别与生存期、总生存期和无进展生存期显著相关。这些基因中的绝大多数已被报道与胶质母细胞瘤相关(分别为35、24和35个基因)或与其他癌症相关(分别为10、19和15个基因)，其余(分别为16、4和10个基因)为新的相关基因。Pik3r1、E2f3、Akr1c3、CSF1、Jag2、PLCG1、Rpl37a、Sod2、Topors、HRAS、MDM2、Camk2g、FSTL1、IL13ra1、MTAP和TP53与多种生存事件相关。大多数基因(从90%到96%)以一般的或队列独立的方式与生存相关，因此在所研究的所有临床水平上都观察到了相同的趋势。观察到SYNE1、Pdcd4、Ighg1、Tgfa、Pla2g7和Paics的配置文件与存活率之间的最极端关联。有几个基因被发现与生存有队列依赖的关联，这些关联是个体化预后和基于基因的治疗的基础。C2、EGFR、Prkcb、Igf2bp3和Gdf10与性别相关；Sox10、Rps20、Rab31和Vav3与种族相关；CHI3L1、Prkcb、Polr2d和apool与治疗相关。与胶质母细胞瘤生存相关的生物学过程包括形态发生、细胞周期、衰老、对刺激的反应和细胞程序性死亡。已知的胶质母细胞瘤存活的生物标志物得到确认，新的一般和临床相关的基因图谱被发现。胶质母细胞瘤不同阶段的生物标记物的比较和功能分析提供了对基因作用的见解。这些发现支持开发更准确和个性化的预后工具和基于基因的治疗，以改善多形性胶质母细胞瘤患者的存活率和生活质量。
Glioblastoma is a complex multifactorial disorder that has swift and devastating consequences. Few genes have been consistently identified as prognostic biomarkers of glioblastoma survival. The goal of this study was to identify general and clinical-dependent biomarker genes and biological processes of three complementary events: lifetime, overall and progression-free glioblastoma survival. A novel analytical strategy was developed to identify general associations between the biomarkers and glioblastoma, and associations that depend on cohort groups, such as race, gender, and therapy. Gene network inference, cross-validation and functional analyses further supported the identified biomarkers. A total of 61, 47 and 60 gene expression profiles were significantly associated with lifetime, overall, and progression-free survival, respectively. The vast majority of these genes have been previously reported to be associated with glioblastoma (35, 24, and 35 genes, respectively) or with other cancers (10, 19, and 15 genes, respectively) and the rest (16, 4, and 10 genes, respectively) are novel associations. Pik3r1, E2f3, Akr1c3, Csf1, Jag2, Plcg1, Rpl37a, Sod2, Topors, Hras, Mdm2, Camk2g, Fstl1, Il13ra1, Mtap and Tp53 were associated with multiple survival events. Most genes (from 90 to 96%) were associated with survival in a general or cohort-independent manner and thus the same trend is observed across all clinical levels studied. The most extreme associations between profiles and survival were observed for Syne1, Pdcd4, Ighg1, Tgfa, Pla2g7, and Paics. Several genes were found to have a cohort-dependent association with survival and these associations are the basis for individualized prognostic and gene-based therapies. C2, Egfr, Prkcb, Igf2bp3, and Gdf10 had gender-dependent associations; Sox10, Rps20, Rab31, and Vav3 had race-dependent associations; Chi3l1, Prkcb, Polr2d, and Apool had therapy-dependent associations. Biological processes associated glioblastoma survival included morphogenesis, cell cycle, aging, response to stimuli, and programmed cell death. Known biomarkers of glioblastoma survival were confirmed, and new general and clinical-dependent gene profiles were uncovered. The comparison of biomarkers across glioblastoma phases and functional analyses offered insights into the role of genes. These findings support the development of more accurate and personalized prognostic tools and gene-based therapies that improve the survival and quality of life of individuals afflicted by glioblastoma multiforme.
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影响因子：
3.7
作者：
Brennan C;Momota H;Hambardzumyan D;Ozawa T;Tandon A;Pedraza A;Holland E
通讯作者：
Holland E
影响因子：
11.2
作者：
Christensen BC;Houseman EA;Poage GM;Godleski JJ;Bueno R;Sugarbaker DJ;Wiencke JK;Nelson HH;Marsit CJ;Kelsey KT
通讯作者：
Kelsey KT
影响因子：
3.3
作者：
Castells, Xavier;Jose Acebes, Juan;Arus, Carles
通讯作者：
Arus, Carles
影响因子：
15.8
作者：
Chan, Timothy A.;Glockner, Sabine;Yi, Joo Mi;Chen, Wei;Van Neste, Leander;Cope, Leslie;Herman, James G.;Velculescu, Victor;Schuebel, Kornel E.;Ahuja, Nita;Baylin, Stephen B.
通讯作者：
Baylin, Stephen B.
影响因子：
11.5
作者：
Blaveri, E;Brewer, JL;Waldman, FM
通讯作者：
Waldman, FM