Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature.
Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature.
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基于 DNA 甲基化的新型前列腺癌亚型和风险预测八基因特征的建立
DOI：
10.3389/fcell.2021.639615
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发表时间：
2021
影响因子：
5.5
通讯作者：
Song Y
中科院分区：
文献类型：
--
作者：
Zhang E;Shiori F;Mu OY;He J;Ge Y;Wu H;Zhang M;Song Y
前列腺癌（PCa）是世界范围内影响男性的最常见的恶性肿瘤。PCa的实质异质性对分子分析、患者分层和治疗提出了重大挑战。使用最小绝对收缩和选择算子来选择8个风险CpG位点。使用无监督聚类分析，称为共识聚类，我们发现PCa患者可以根据这些CpG位点的DNA甲基化状态分为两种亚型（Methylation_H和Methylation_L）。使用癌症基因组图谱数据库探索鉴定的亚型之间的表观基因组、基因组、转录组、疾病状态、免疫细胞组成和功能的差异。该分析清楚地揭示了Methylation_H亚型的风险特征。利用加权相关网络分析筛选风险相关基因，并采用最小绝对收缩和选择算子，构建了基于亚型分类的预后预测特征。我们使用四个公共数据集进一步验证了其有效性。本研究中开发的两种新的PCa亚型和风险预测特征可能是预测预后的有效指标。
Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites. Using an unsupervised clustering analysis, called consensus clustering, we found that patients with PCa could be divided into two subtypes (Methylation_H and Methylation_L) based on the DNA methylation status at these CpG sites. Differences in the epigenome, genome, transcriptome, disease status, immune cell composition, and function between the identified subtypes were explored using The Cancer Genome Atlas database. This analysis clearly revealed the risk characteristics of the Methylation_H subtype. Using a weighted correlation network analysis to select risk-related genes and least absolute shrinkage and selection operator, we constructed a prediction signature for prognosis based on the subtype classification. We further validated its effectiveness using four public datasets. The two novel PCa subtypes and risk predictive signature developed in this study may be effective indicators of prognosis.
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影响因子：
14.9
作者：
Howe KL;Achuthan P;Allen J;Allen J;Alvarez-Jarreta J;Amode MR;Armean IM;Azov AG;Bennett R;Bhai J;Billis K;Boddu S;Charkhchi M;Cummins C;Da Rin Fioretto L;Davidson C;Dodiya K;El Houdaigui B;Fatima R;Gall A;Garcia Giron C;Grego T;Guijarro-Clarke C;Haggerty L;Hemrom A;Hourlier T;Izuogu OG;Juettemann T;Kaikala V;Kay M;Lavidas I;Le T;Lemos D;Gonzalez Martinez J;Marugán JC;Maurel T;McMahon AC;Mohanan S;Moore B;Muffato M;Oheh DN;Paraschas D;Parker A;Parton A;Prosovetskaia I;Sakthivel MP;Salam AIA;Schmitt BM;Schuilenburg H;Sheppard D;Steed E;Szpak M;Szuba M;Taylor K;Thormann A;Threadgold G;Walts B;Winterbottom A;Chakiachvili M;Chaubal A;De Silva N;Flint B;Frankish A;Hunt SE;IIsley GR;Langridge N;Loveland JE;Martin FJ;Mudge JM;Morales J;Perry E;Ruffier M;Tate J;Thybert D;Trevanion SJ;Cunningham F;Yates AD;Zerbino DR;Flicek P
通讯作者：
Flicek P
影响因子：
7.3
作者：
Gao J;Aksoy BA;Dogrusoz U;Dresdner G;Gross B;Sumer SO;Sun Y;Jacobsen A;Sinha R;Larsson E;Cerami E;Sander C;Schultz N
通讯作者：
Schultz N
影响因子：
5.8
作者：
Liberzon, Arthur;Subramanian, Aravind;Mesirov, Jill P.
通讯作者：
Mesirov, Jill P.
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
3.6
作者：
Darnel, Andrew D.;LaFargue, Christopher J.;Bismar, Tarek A.
通讯作者：
Bismar, Tarek A.