Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis.
Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis.
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DOI：
10.1089/cmb.2022.0002
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发表时间：
2022-08
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--
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--
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利用多组学数据开发癌症预后模型是精确肿瘤学的主要目标。DNA甲基化提供了有希望的预后生物标志物，已被用于预测实体瘤或血浆样本的生存和治疗反应。这篇综述文章概述了最近发表的DNA甲基化对癌症预后的计算分析。为了解决使用高维甲基化数据进行生存分析的挑战，各种特征选择方法已被应用于筛选信息标记的子集。使用与生存相关的候选标记物，预后模型要么预测风险评分，要么将患者分层为亚型。该模型的判别能力可以通过多个评价指标来评估。最后，我们讨论了现有研究的局限性，并提出了应用机器学习算法来充分利用DNA甲基化的预测价值的前景。
Developing cancer prognostic models using multiomics data is a major goal of precision oncology. DNA methylation provides promising prognostic biomarkers, which have been used to predict survival and treatment response in solid tumor or plasma samples. This review article presents an overview of recently published computational analyses on DNA methylation for cancer prognosis. To address the challenges of survival analysis with high-dimensional methylation data, various feature selection methods have been applied to screen a subset of informative markers. Using candidate markers associated with survival, prognostic models either predict risk scores or stratify patients into subtypes. The model's discriminatory power can be assessed by multiple evaluation metrics. Finally, we discuss the limitations of existing studies and present the prospects of applying machine learning algorithms to fully exploit the prognostic value of DNA methylation.
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影响因子：
4.4
作者：
Bibikova, Marina;Barnes, Bret;Shen, Richard
通讯作者：
Shen, Richard
影响因子：
64.8
作者：
Capper D;Jones DTW;Sill M;Hovestadt V;Schrimpf D;Sturm D;Koelsche C;Sahm F;Chavez L;Reuss DE;Kratz A;Wefers AK;Huang K;Pajtler KW;Schweizer L;Stichel D;Olar A;Engel NW;Lindenberg K;Harter PN;Braczynski AK;Plate KH;Dohmen H;Garvalov BK;Coras R;Hölsken A;Hewer E;Bewerunge-Hudler M;Schick M;Fischer R;Beschorner R;Schittenhelm J;Staszewski O;Wani K;Varlet P;Pages M;Temming P;Lohmann D;Selt F;Witt H;Milde T;Witt O;Aronica E;Giangaspero F;Rushing E;Scheurlen W;Geisenberger C;Rodriguez FJ;Becker A;Preusser M;Haberler C;Bjerkvig R;Cryan J;Farrell M;Deckert M;Hench J;Frank S;Serrano J;Kannan K;Tsirigos A;Brück W;Hofer S;Brehmer S;Seiz-Rosenhagen M;Hänggi D;Hans V;Rozsnoki S;Hansford JR;Kohlhof P;Kristensen BW;Lechner M;Lopes B;Mawrin C;Ketter R;Kulozik A;Khatib Z;Heppner F;Koch A;Jouvet A;Keohane C;Mühleisen H;Mueller W;Pohl U;Prinz M;Benner A;Zapatka M;Gottardo NG;Driever PH;Kramm CM;Müller HL;Rutkowski S;von Hoff K;Frühwald MC;Gnekow A;Fleischhack G;Tippelt S;Calaminus G;Monoranu CM;Perry A;Jones C;Jacques TS;Radlwimmer B;Gessi M;Pietsch T;Schramm J;Schackert G;Westphal M;Reifenberger G;Wesseling P;Weller M;Collins VP;Blümcke I;Bendszus M;Debus J;Huang A;Jabado N;Northcott PA;Paulus W;Gajjar A;Robinson GW;Taylor MD;Jaunmuktane Z;Ryzhova M;Platten M;Unterberg A;Wick W;Karajannis MA;Mittelbronn M;Acker T;Hartmann C;Aldape K;Schüller U;Buslei R;Lichter P;Kool M;Herold-Mende C;Ellison DW;Hasselblatt M;Snuderl M;Brandner S;Korshunov A;von Deimling A;Pfister SM
通讯作者：
Pfister SM
影响因子：
5.5
作者：
Guo Y;Yin J;Dai Y;Guan Y;Chen P;Chen Y;Huang C;Lu YJ;Zhang L;Song D
通讯作者：
Song D
影响因子：
3.7
作者：
Doherty R;Couldrey C
通讯作者：
Couldrey C
影响因子：
82.9
作者：
Beer, DG;Kardia, SLR;Hanash, S
通讯作者：
Hanash, S