Identification of prognostic signature in cancer based on DNA methylation interaction network.
Identification of prognostic signature in cancer based on DNA methylation interaction network.
复制标题
基于 DNA 甲基化相互作用网络的癌症预后特征识别
DOI：
10.1186/s12920-017-0307-9
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发表时间：
2017-12-21
影响因子：
2.7
通讯作者：
Zhou XH
中科院分区：
文献类型：
--
作者：
Hu WL;Zhou XH
背景识别癌症患者的预后生物标志物对于癌症研究至关重要。近年来，DNA甲基化已被证明与癌症预后相关。然而，基于DNA甲基化数据系统地识别肿瘤预后标志物的方法并不多见，特别是考虑到DNA甲基化位点之间的相互作用，本文首先评估了microRNA、mRNA和DNA甲基化数据在肿瘤预后中的稳定性。在此基础上，采用基于秩的方法构建了DNA甲基化相互作用网络。在这个网络中，度最大的节点（所有节点的10%）被选为枢纽。应用考克斯回归选择枢纽作为预后标志。在这种预后特征中，每个DNA甲基化位点的DNA甲基化水平与癌症患者的结果相关。在获得这些预后基因后，我们在训练组和测试组中进行了生存分析，以验证这些基因的可靠性。（卵巢癌、乳腺癌和多形性胶质母细胞瘤）。在这三种癌症中，DNA甲基化数据中从不同样本中选出的预后基因有更多共同点，与基因表达数据和miRNA表达数据相比，这表明DNA甲基化数据在癌症预后中可能更稳定。幂律分布拟合表明，DNA甲基化相互作用网络是无标度的。从这三个网络中选出的枢纽都被癌症相关的通路所丰富。生存分析表明，它们在训练数据集和测试数据集中均能区分肿瘤患者的预后，优于对照组。结论提出了一种构建DNA甲基化相互作用网络的计算方法，该网络可用于癌症预后特征的筛选。
BackgroundThe identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites.MethodsIn this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes.ResultsWe applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme).In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures.ConclusionsA computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.
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影响因子：
3.7
作者：
Bartlett TE;Olhede SC;Zaikin A
通讯作者：
Zaikin A
蛋白质相互作用网络的结构及其对药物设计的影响。
影响因子：
4.3
作者：
Hase T;Tanaka H;Suzuki Y;Nakagawa S;Kitano H
通讯作者：
Kitano H
DOI：
10.1016/s1470-2045(14)70119-6
发表时间：
2014-09
期刊：
The Lancet. Oncology
影响因子：
--
作者：
Ahern TP;Lash TL;Damkier P;Christiansen PM;Cronin-Fenton DP
通讯作者：
Cronin-Fenton DP
影响因子：
16.6
作者：
Li, Jie;Lenferink, Anne E. G.;Deng, Yinghai;Collins, Catherine;Cui, Qinghua;Purisima, Enrico O.;O'Connor-McCourt, Maureen D.;Wang, Edwin
通讯作者：
Wang, Edwin
影响因子：
64.8
作者：
Bell, D.;Berchuck, A.;Birrer, M.;Chien, J.;Cramer, D. W.;Dao, F.;Dhir, R.;DiSaia, P.;Gabra, H.;Glenn, P.;Godwin, A. K.;Gross, J.;Hartmann, L.;Huang, M.;Huntsman, D. G.;Iacocca, M.;Imielinski, M.;Kalloger, S.;Karlan, B. Y.;Levine, D. A.;Mills, G. B.;Morrison, C.;Mutch, D.;Olvera, N.;Orsulic, S.;Park, K.;Petrelli, N.;Rabeno, B.;Rader, J. S.;Sikic, B. I.;Smith-McCune, K.;Sood, A. K.;Bowtell, D.;Penny, R.;Testa, J. R.;Chang, K.;Dinh, H. H.;Drummond, J. A.;Fowler, G.;Gunaratne, P.;Hawes, A. C.;Kovar, C. L.;Lewis, L. R.;Morgan, M. B.;Newsham, I. F.;Santibanez, J.;Reid, J. G.;Trevino, L. R.;Wu, Y. -Q.;Wang, M.;Muzny, D. M.;Wheeler, D. A.;Gibbs, R. A.;Getz, G.;Lawrence, M. S.;Cibulskis, K.;Sivachenko, A. Y.;Sougnez, C.;Voet, D.;Wilkinson, J.;Bloom, T.;Ardlie, K.;Fennell, T.;Baldwin, J.;Gabriel, S.;Lander, E. S.;Ding, L.;Fulton, R. S.;Koboldt, D. C.;McLellan, M. D.;Wylie, T.;Walker, J.;O'Laughlin, M.;Dooling, D. J.;Fulton, L.;Abbott, R.;Dees, N. D.;Zhang, Q.;Kandoth, C.;Wendl, M.;Schierding, W.;Shen, D.;Harris, C. C.;Schmidt, H.;Kalicki, J.;Delehaunty, K. D.;Fronick, C. C.;Demeter, R.;Cook, L.;Wallis, J. W.;Lin, L.;Magrini, V. J.;Hodges, J. S.;Eldred, J. M.;Smith, S. M.;Pohl, C. S.;Vandin, F.;Raphael, B. J.;Weinstock, G. M.;Mardis, R.;Wilson, R. K.;Meyerson, M.;Winckler, W.;Getz, G.;Verhaak, R. G. W.;Carter, S. L.;Mermel, C. H.;Saksena, G.;Nguyen, H.;Onofrio, R. C.;Lawrence, M. S.;Hubbard, D.;Gupta, S.;Crenshaw, A.;Ramos, A. H.;Ardlie, K.;Chin, L.;Protopopov, A.;Zhang, Juinhua;Kim, T. M.;Perna, I.;Xiao, Y.;Zhang, H.;Ren, G.;Sathiamoorthy, N.;Park, R. W.;Lee, E.;Park, P. J.;Kucherlapati, R.;Absher, D. M.;Waite, L.;Sherlock, G.;Brooks, J. D.;Li, J. Z.;Xu, J.;Myers, R. M.;Laird, P. W.;Cope, L.;Herman, J. G.;Shen, H.;Weisenberger, D. J.;Noushmehr, H.;Pan, F.;Triche, T., Jr.;Berman, B. P.;Van den Berg, D. J.;Buckley, J.;Baylin, S. B.;Spellman, P. T.;Purdom, E.;Neuvial, P.;Bengtsson, H.;Jakkula, L. R.;Durinck, S.;Han, J.;Dorton, S.;Marr, H.;Choi, Y. G.;Wang, V.;Wang, N. J.;Ngai, J.;Conboy, J. G.;Parvin, B.;Feiler, H. S.;Speed, T. P.;Gray, J. W.;Levine, D. A.;Socci, N. D.;Liang, Y.;Taylor, B. S.;Schultz, N.;Borsu, L.;Lash, A. E.;Brennan, C.;Viale, A.;Sander, C.;Ladanyi, M.;Hoadley, K. A.;Meng, S.;Du, Y.;Shi, Y.;Li, L.;Turman, Y. J.;Zang, D.;Helms, E. B.;Balu, S.;Zhou, X.;Wu, J.;Topal, M. D.;Hayes, D. N.;Perou, C. M.;Getz, G.;Voet, D.;Saksena, G.;Zhang, Junihua;Zhang, H.;Wu, C. J.;Shukla, S.;Cibulskis, K.;Lawrence, M. S.;Sivachenko, A.;Jing, R.;Park, R. W.;Liu, Y.;Park, P. J.;Noble, M.;Chin, L.;Carter, H.;Kim, D.;Karchin, R.;Spellman, P. T.;Purdom, E.;Neuvial, P.;Bengtsson, H.;Durinck, S.;Han, J.;Korkola, J. E.;Heiser, L. M.;Cho, R. J.;Hu, Z.;Parvin, B.;Speed, T. P.;Gray, J. W.;Schultz, N.;Cerami, E.;Taylor, B. S.;Olshen, A.;Reva, B.;Antipin, Y.;Shen, R.;Mankoo, P.;Sheridan, R.;Ciriello, G.;Chang, W. K.;Bernanke, J. A.;Borsu, L.;Levine, D. A.;Ladanyi, M.;Sander, C.;Haussler, D.;Benz, C. C.;Stuart, J. M.;Benz, S. C.;Sanborn, J. Z.;Vaske, C. J.;Zhu, J.;Szeto, C.;Scott, G. K.;Yau, C.;Hoadley, K. A.;Du, Y.;Balu, S.;Hayes, D. N.;Perou, C. M.;Wilkerson, M. D.;Zhang, N.;Akbani, R.;Baggerly, K. A.;Yung, W. K.;Mills, G. B.;Weinstein, J. N.;Penny, R.;Shelton, T.;Grimm, D.;Hatfield, M.;Morris, S.;Yena, P.;Rhodes, P.;Sherman, M.;Paulauskis, J.;Millis, S.;Kahn, A.;Greene, J. M.;Sfeir, R.;Jensen, M. A.;Chen, J.;Whitmore, J.;Alonso, S.;Jordan, J.;Chu, A.;Zhang, Jinghui;Barker, A.;Compton, C.;Eley, G.;Ferguson, M.;Fielding, P.;Gerhard, D. S.;Myles, R.;Schaefer, C.;Shaw, K. R. Mills;Vaught, J.;Vockley, J. B.;Good, P. J.;Guyer, M. S.;Ozenberger, B.;Peterson, J.;Thomson, E.
通讯作者：
Thomson, E.