Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer.
Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer.
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基于免疫相关基因的新亚型建立预测前列腺癌风险的模型
DOI：
10.3389/fgene.2020.595657
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发表时间：
2020
影响因子：
3.7
通讯作者：
Song Y
中科院分区：
文献类型：
--
作者：
Zhang E;He J;Zhang H;Shan L;Wu H;Zhang M;Song Y
背景前列腺癌（PCa）具有明显的异质性，但免疫状态可反映其预后。本研究旨在探索基于免疫相关基因的新型亚型，并利用它们建立预测PCa风险的模型。方法从TCGA数据库下载487例PCa患者的基因组数据。我们使用免疫相关基因作为共识聚类的输入，并应用生存分析和主成分分析来确定亚型的属性。我们还探讨了亚型之间的体细胞变异、拷贝数变异、TMPRSS 2-ERG融合和雄激素受体（AR）评分的差异。然后，我们检查了不同免疫细胞在每个亚型中向肿瘤微环境的浸润。我们接下来进行基因集富集分析（GSEA）以说明亚型的特征。最后，基于亚型，我们构建了一个风险预测模型，并在TCGA，基因表达Omnibus（GEO），cBioPortal和国际癌症基因组联盟（ICGC）数据库中进行了验证。结果PCa有C1、C2、C3、C4四种亚型。C3亚型患者的预后最差，而其他三组在预后方面没有显著差异。主成分分析明确区分了高风险（C3）和低风险（C1 + 2 + 4）患者。与低危亚型相比，SPOP在高危亚型中的突变频率更高，转录水平更低。在C3中，也有更高频率的拷贝数改变（CNA），CLU（CLU）和较低的CLU表达。此外，C3具有更高的TMPRSS 2-ERG融合频率和更高的AR评分。M2巨噬细胞在高风险亚型中也显示出显著更高的浸润，而CD 8 + T细胞和树突状细胞在低风险亚型中具有显著更高的浸润。GSEA显示，与低危亚型相比，高危亚型中MYC、雄激素和KRAS相对活化，p53相对抑制。最后，我们训练了一个六基因签名风险预测模型，该模型在TCGA、GEO、cBioPortal和ICGC数据库中表现良好。结论根据免疫相关基因将PCa分为4种亚型，其中C3亚型预后较差。基于这些亚型，开发了风险预测模型，该模型可以指示患者预后。
Background There is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk of PCa. Methods We downloaded the data of 487 PCa patients from The Cancer Genome Atlas (TCGA) database. We used immunologically relevant genes as input for consensus clustering and applied survival analysis and principal component analysis to determine the properties of the subtypes. We also explored differences of somatic variations, copy number variations, TMPRSS2-ERG fusion, and androgen receptor (AR) scores among the subtypes. Then, we examined the infiltration of different immune cells into the tumor microenvironment in each subtype. We next performed Gene Set Enrichment Analysis (GSEA) to illustrate the characteristics of the subtypes. Finally, based on the subtypes, we constructed a risk predictive model and verified it in TCGA, Gene Expression Omnibus (GEO), cBioPortal, and International Cancer Genome Consortium (ICGC) databases. Results Four PCa subtypes (C1, C2, C3, and C4) were identified on immune status. Patients with the C3 subtype had the worst prognosis, while the other three groups did not differ significantly from each other in terms of their prognosis. Principal component analysis clearly distinguished high-risk (C3) and low-risk (C1 + 2 + 4) patients. Compared with the case in the low-risk subtype, the Speckle-type POZ Protein (SPOP) had a higher mutation frequency and lower transcriptional level in the high-risk subtype. In C3, there was also a higher frequency of copy number alterations (CNA) of Clusterin (CLU) and lower CLU expression. In addition, C3 had a higher frequency of TMPRSS2-ERG fusion and higher AR scores. M2 macrophages also showed significantly higher infiltration in the high-risk subtype, while CD8+ T cells and dendritic cells had significantly higher infiltration in the low-risk subtype. GSEA revealed that MYC, androgen, and KRAS were relatively activated and p53 was relatively suppressed in high-risk subtype, compared with the levels in the low-risk subtype. Finally, we trained a six-gene signature risk predictive model, which performed well in TCGA, GEO, cBioPortal, and ICGC databases. Conclusion PCa can be divided into four subtypes based on immune-related genes, among which the C3 subtype is associated with a poor prognosis. Based on these subtypes, a risk predictive model was developed, which could indicate patient prognosis.
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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：
64.5
作者：
Cancer Genome Atlas Research Network
通讯作者：
Cancer Genome Atlas Research Network
影响因子：
9.8
作者：
Bhattacharya S;Dunn P;Thomas CG;Smith B;Schaefer H;Chen J;Hu Z;Zalocusky KA;Shankar RD;Shen-Orr SS;Thomson E;Wiser J;Butte AJ
通讯作者：
Butte AJ
影响因子：
2.8
作者：
Erlandsson, Ann;Carlsson, Jessica;Davidsson, Sabina
通讯作者：
Davidsson, Sabina
影响因子：
3.8
作者：
Eminaga, Okyaz;Fries, Jochen;Warnecke-Eberz, Ute
通讯作者：
Warnecke-Eberz, Ute