A Panel of MicroRNA Signature as a Tool for Predicting Survival of Patients with Urothelial Carcinoma of the Bladder.
A Panel of MicroRNA Signature as a Tool for Predicting Survival of Patients with Urothelial Carcinoma of the Bladder.
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DOI：
10.1155/2018/5468672
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Azuma H
中科院分区：
文献类型：
--
作者：
Inamoto T;Uehara H;Akao Y;Ibuki N;Komura K;Takahara K;Takai T;Uchimoto T;Saito K;Tanda N;Yoshikawa Y;Minami K;Hirano H;Nomi H;Kato R;Hayashi T;Azuma H
MicroRNA(MiRNA)在包括膀胱尿路上皮癌(UCB)在内的泌尿系统恶性肿瘤中的表达发生改变。已有研究表明，单个miRNAs可以调节多个信号通路，从而促进BC的发生。目的：利用miRNA表达水平确定一组能够预测脐带血侵袭性表型与正常非侵袭性表型的miRNA标志物，并评估该特异性miRNA标志物在脐带血患者中的预后价值。为了确定候选miRNAs作为区分侵袭性脐带脐带血侵袭性表型和非侵袭性表型的预后生物标志物，使用3D-gene miRNA标记试剂盒(日本东丽)分析了侵袭性表型和非侵袭性表型患者样本中miRNA的表达。为了创建预后指数模型，我们使用了基于癌症基因组图谱的9-miRNA签名的面板(TCGA数据门户(https://tcgadata.nci.nih.gov/tcga/tcgaHome2.jsp)).获得了84例脐带血患者的miRNA表达数据和相应的临床数据，包括预后和分期信息。两组的生存函数采用Kaplan-Meier和log-ranch检验进行量化。检测了9个miRNAs(hsa-miR-99a-5p、hsa-miR-100-5p、hsa-miR-125b-5p、hsa-miR-145-5p、hsa-miR-4324、hsa-miR-34b-5p、hsa-miR-29c-3p、hsa-miR-135a-3p和hsa-miR-33b-3p)在具有侵袭性表型的UCB患者中的表达。为了利用膀胱癌TCGA数据集验证9个标志性miRNAs的预后能力，根据预后评分值对所有84例TCGA UCB患者的生存状况和肿瘤miRNA表达进行了排序。在9个miRNA中，6个与高危相关(hsa-miR-99a-5p、hsa-miR-100-5p、hsa-miR-125b-5p、hsa-miR-4324、hsa-miR-34b-5p和hsa-miR-135a-3p)，3个被证明是保护性的(hsa-miR-145-5p、hsa-miR-29c-3p和hsa-miR-33b-3p)。具有高危miRNA特征的患者表现出比表达低风险miRNA特征的患者更差的OS(HR = 7.05，p<；0.001)。MiRNA阵列从临床样本中鉴定出9个表达异常的miRNAs。这组9个miRNA标记提供了对脐带血患者的预测和预后价值。
MicroRNA (miRNA) expression is altered in urologic malignancies, including urothelial carcinoma of the bladder (UCB). Individual miRNAs have been shown to modulate multiple signaling pathways that contribute to BC. To identify a panel of miRNA signature that can predict aggressive phenotype from normal nonaggressive counterpart using miRNA expression levels and to assess the prognostic value of this specific miRNA markers in patients with UCB. To determine candidate miRNAs as prognostic biomarkers for dividing aggressive type of UCB, miRNA expression was profiled in patients' samples with an aggressive phenotype or nonaggressive phenotype using 3D-Gene miRNA labeling kit (Toray, Japan). To create a prognostic index model, we used the panel of 9-miRNA signature based on Cancer Genome Atlas (TCGA) data portal (TCGA Data Portal (https://tcgadata.nci.nih.gov/tcga/tcgaHome2.jsp)). miRNA expression data and corresponding clinical data, including outcome and staging information of 84 UCB patients, were obtained. The Kaplan-Meier and log-rank test were performed to quantify the survival functions in two groups. Deregulation of nine miRNAs (hsa-miR-99a-5p, hsa-miR-100-5p, hsa-miR-125b-5p, hsa-miR-145-5p, hsa-miR-4324, hsa-miR-34b-5p, hsa-miR-29c-3p, hsa-miR-135a-3p, and hsa-miR-33b-3p) was determined in UCB patients with aggressive phenotype compared with nonaggressive subject. To validate the prognostic power of the nine-signature miRNAs using the TCGA dataset of bladder cancer, the survival status and tumor miRNA expression of all 84 TCGA UCB patients were ranked according to the prognostic score values. Of nine miRNAs, six were associated with high risk (hsa-miR-99a-5p, hsa-miR-100-5p, hsa-miR-125b-5p, hsa-miR-4324, hsa-miR-34b-5p, and hsa-miR-135a-3p) and three were shown to be protective (hsa-miR-145-5p, hsa-miR-29c-3p, and hsa-miR-33b-3p). Patients with the high-risk miRNA signature exhibited poorer OS than patients expressing the low-risk miRNA profile (HR = 7.05, p < 0.001). The miRNA array identified nine dysregulated miRNAs from clinical samples. This panel of nine-miRNA signature provides predictive and prognostic value of patients with UCB.
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影响因子：
5.6
作者：
Takai T;Yoshikawa Y;Inamoto T;Minami K;Taniguchi K;Sugito N;Kuranaga Y;Shinohara H;Kumazaki M;Tsujino T;Takahara K;Ito Y;Akao Y;Azuma H
通讯作者：
Azuma H
DOI：
10.1186/1756-9966-32-71
发表时间：
2013-09-28
期刊：
Journal of experimental & clinical cancer research : CR
影响因子：
--
作者：
Ma MZ;Kong X;Weng MZ;Cheng K;Gong W;Quan ZW;Peng CH
通讯作者：
Peng CH
DOI：
10.1083/jcb.201206033
发表时间：
2012-10-01
期刊：
The Journal of cell biology
影响因子：
--
作者：
Marzi MJ;Puggioni EM;Dall'Olio V;Bucci G;Bernard L;Bianchi F;Crescenzi M;Di Fiore PP;Nicassio F
通讯作者：
Nicassio F
影响因子：
7.7
作者：
Mattick, JS
通讯作者：
Mattick, JS
影响因子：
5.8
作者：
Darnet S;Moreira FC;Hamoy IG;Burbano R;Khayat A;Cruz A;Magalhães L;Silva A;Santos S;Demachki S;Assumpção M;Assumpção P;Ribeiro-Dos-Santos Â
通讯作者：
Ribeiro-Dos-Santos Â