Establishment of Novel Prostate Cancer Risk Subtypes and A Twelve-Gene Prognostic Model.
Establishment of Novel Prostate Cancer Risk Subtypes and A Twelve-Gene Prognostic Model.
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新型前列腺癌风险亚型和十二基因预后模型的建立
DOI：
10.3389/fmolb.2021.676138
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发表时间：
2021
影响因子：
5
通讯作者：
Shan L
中科院分区：
文献类型：
--
作者：
Zhang E;Shiori F;Zhang M;Wang P;He J;Ge Y;Song Y;Shan L
Prostate cancer (PCa) is the most common malignancy among men worldwide. However, its complex heterogeneity makes treatment challenging. In this study, we aimed to identify PCa subtypes and a gene signature associated with PCa prognosis. In particular, nine PCa-related pathways were evaluated in patients with PCa by a single-sample gene set enrichment analysis (ssGSEA) and an unsupervised clustering analysis (i.e., consensus clustering). We identified three subtypes with differences in prognosis (Risk_H, Risk_M, and Risk_L). Differences in the proliferation status, frequencies of known subtypes, tumor purity, immune cell composition, and genomic and transcriptomic profiles among the three subtypes were explored based on The Cancer Genome Atlas database. Our results clearly revealed that the Risk_H subtype was associated with the worst prognosis. By a weighted correlation network analysis of genes related to the Risk_H subtype and least absolute shrinkage and selection operator, we developed a 12-gene risk-predicting model. We further validated its accuracy using three public datasets. Effective drugs for high-risk patients identified using the model were predicted. The novel PCa subtypes and prognostic model developed in this study may improve clinical decision-making.
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影响因子：
15.9
作者：
Jayaprakash, Priyamvada;Ai, Midan;Curran, Michael A.
通讯作者：
Curran, Michael A.
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：
21.3
作者：
Liu H;Tang X;Srivastava A;Pécot T;Daniel P;Hemmelgarn B;Reyes S;Fackler N;Bajwa A;Kladney R;Koivisto C;Chen Z;Wang Q;Huang K;Machiraju R;Sáenz-Robles MT;Cantalupo P;Pipas JM;Leone G
通讯作者：
Leone G
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：
64.5
作者：
Cancer Genome Atlas Research Network
通讯作者：
Cancer Genome Atlas Research Network