An Ensemble Method to Predict Target Genes and Pathways in Uveal Melanoma.
An Ensemble Method to Predict Target Genes and Pathways in Uveal Melanoma.
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DOI：
10.1515/biol-2018-0013
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发表时间：
2018-01
影响因子：
2.2
通讯作者：
Zhang H
中科院分区：
文献类型：
--
作者：
Wei C;Wang L;Zhang H
目的：基于系综方法和通路分析预测葡萄膜黑色素瘤（UM）的靶基因和通路。研究方法：集成方法集成了相关方法（Pearson相关系数，PCC），因果推理方法（IDA）和回归方法（Lasso），利用Borda计数选择方法。随后，为了验证PIL方法的性能，在确认的数据库和预测的miRNA靶标之间进行比较。最终，对前1000个miRNA-mRNA相互作用中的靶基因进行了途径富集分析，以确定UM患者的靶途径。结果如下：38个预测的相互作用与确认的相互作用相匹配，表明系综方法是预测miRNA靶点的合适且可行的方法。我们获得了UM患者的50个miRNA-mRNA相互作用的种子，并从这些相互作用中提取了靶基因，如ASPG，BSDC 1和C4 BP。前1,000个miRNA-mRNA相互作用中的601个靶基因分布在12个靶通路中，其中光转导是最重要的靶通路。结论：这些靶基因和途径可能为揭示UM的分子机制提供新的途径，并为该恶性肿瘤的靶向治疗和预防提供帮助。
Objective: This work proposes to predict target genes and pathways for uveal melanoma (UM) based on an ensemble method and pathway analyses. Methods: The ensemble method integrated a correlation method (Pearson correlation coefficient, PCC), a causal inference method (IDA) and a regression method (Lasso) utilizing the Borda count election method. Subsequently, to validate the performance of PIL method, comparisons between confirmed database and predicted miRNA targets were performed. Ultimately, pathway enrichment analysis was conducted on target genes in top 1000 miRNA-mRNA interactions to identify target pathways for UM patients. Results: Thirty eight of the predicted interactions were matched with the confirmed interactions, indicating that the ensemble method was a suitable and feasible approach to predict miRNA targets. We obtained 50 seed miRNA-mRNA interactions of UM patients and extracted target genes from these interactions, such as ASPG, BSDC1 and C4BP. The 601 target genes in top 1,000 miRNA-mRNA interactions were enriched in 12 target pathways, of which Phototransduction was the most significant one. Conclusion: The target genes and pathways might provide a new way to reveal the molecular mechanism of UM and give hand for target treatments and preventions of this malignant tumor.
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影响因子：
3
作者：
Le TD;Liu L;Liu B;Tsykin A;Goodall GJ;Satou K;Li J
通讯作者：
Li J
DOI：
10.1126/science.1194472
发表时间：
2010-12-03
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Harbour JW;Onken MD;Roberson ED;Duan S;Cao L;Worley LA;Council ML;Matatall KA;Helms C;Bowcock AM
通讯作者：
Bowcock AM
影响因子：
48
作者：
Marbach, Daniel;Costello, James C.;Kueffner, Robert;Vega, Nicole M.;Prill, Robert J.;Camacho, Diogo M.;Allison, Kyle R.;Kellis, Manolis;Collins, James J.;Stolovitzky, Gustavo
通讯作者：
Stolovitzky, Gustavo
影响因子：
14.9
作者：
Hsu SD;Tseng YT;Shrestha S;Lin YL;Khaleel A;Chou CH;Chu CF;Huang HY;Lin CM;Ho SY;Jian TY;Lin FM;Chang TH;Weng SL;Liao KW;Liao IE;Liu CC;Huang HD
通讯作者：
Huang HD
影响因子：
20.3
作者：
Buil, Alfonso;Tregouet, David-Alexandre;Morange, Pierre-Emmanuel
通讯作者：
Morange, Pierre-Emmanuel