Prognostic molecular subtyping of clear cell renal cell carcinoma based on multi-omics data
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摘要:
目的 探讨关联-信号-注释增强的相似网络融合(association-signal-annotation boosted similarity network fusion, ab-SNF)方法在透明细胞肾细胞癌(clear cell renal cell carcinoma, ccRCC)多组学数据整合分子分型中的应用，识别ccRCC不良预后患者，研究不同分型患者的潜在致病基因、通路活性及相关免疫浸润细胞。 方法 从癌症基因组图谱(the cancer genome atlas, TCGA)数据库中下载ccRCC的miRNA、mRNA表达数据及DNA甲基化数据。利用ab-SNF对ccRCC患者多组学数据进行整合分型；采用Cox回归分析模型研究不同分型患者的预后风险；针对不同分型，筛选差异表达mRNA(DEmRNAs)、miRNA(DEmiRNAs)及差异甲基化基因(differentially methylated genes, DMGs)，并对重合基因进行相关分析与基因本体(gene ontology, GO)富集分析；最后对不同分型患者进行免疫细胞浸润和通路活性分析。 结果 ab-SNF将ccRCC患者分为低危组和高危组，其中高危组患者的死亡风险是低危组的1.903倍；筛选出5 218个DEmRNAs，107个DEmiRNAs及2 625个DMGs。其中，20个差异表达基因受到DEmiRNA调控，567个基因差异表达的同时伴有异常甲基化；588个重合基因富集于有统计学意义的10个GO生物项。此外，筛选出有统计学意义的6种免疫浸润细胞和9条通路。 结论 ab-SNF能够有效地识别ccRCC亚型，筛选出的ccRCC潜在致病基因、重要通路及相关免疫浸润细胞，可为ccRCC靶向治疗提供新的参考。 Abstract:Objective This work is to explore the application of association-signal-annotation boosted similarity network fusion (ab-SNF) method in clear cell renal cell carcinoma (ccRCC) molecular subtyping based on multi-omics data integration, to identify patients with poor prognosis of ccRCC, and to detect potential pathogenic genes, pathways and related immune infiltrating cells of patients with different subtypes. Methods The miRNA/mRNA expression and DNA methylation data of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database. We applied ab-SNF to integrate multiple omics data of ccRCC to identify subtypes. Cox regression was conducted to evaluate the prognostic risk of different subtypes. Differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs) and differentially methylated genes (DMGs) were screened following subtyping. Correlation analysis and gene ontology (GO) enrichment analysis were performed for the overlapping genes. Finally, the immune cell infiltration analysis and pathway activity analysis of patients with different subtypes were carried out. Results ccRCC patients were divided into high-risk and low-risk groups. ccRCC patients in the high-risk group were 1.903 times more likely to die than the low-risk group. A total of 5 218 DEmRNAs, 107 DEmiRNAs and 2 625 DMGs were identified. Among them, 20 DEmRNAs were regulated by DEmiRNA, 567 DEmRNAs were accompanied by abnormal methylation, and 588 overlapping genes were enriched in 10 GO items. In addition, 6 immune filtrating cells and 9 pathways showed statistical significance. Conclusion ab-SNF can effectively identify the subtypes of ccRCC, and can identify potential pathogenic genes, important pathways and related immune infiltrating cells of ccRCC. The result of this study can provide new strategies for targeted therapy of ccRCC patients. -
Key words:
- Multi-omics data /
- ccRCC /
- ab-SNF /
- Molecular subtyping
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图 1 单组学与多组学整合数据相似矩阵热图
Figure 1. Similarity matrix heatmap of single-omics data and multi-omics data
图 2 单组学与多组学整合数据分型生存曲线
Figure 2. Kaplan-Meier survival curves of single-omics data and multi-omics data
图 4 不同分型中差异表达基因热图
Figure 4. Heat map of differentially expressed genes in different types
图 6 重合差异基因相关系数热图
Figure 6. Heatmap of correlation coefficient between overlap differential genes and DEmRNAs
图 8 免疫细胞浸润与通路活性在不同分型中的差异
Figure 8. The difference of immune cell infiltration and pathway activity in different types
表 1 ccRCC患者不同分型的基本资料[n (%)]
Table 1. Baseline characteristics on different subtypes of ccRCC patients [n (%)]
项目 低危组 高危组 例数 183(63.76) 104(36.24) 年龄(x±s, 岁) 59.96±10.83 60.37±10.38 性别 女 73(39.89) 27(25.96) 男 110(60.11) 77(74.04) 接受药物治疗 是 22(12.02) 23(22.12) 否 98(53.55) 34(32.69) 未知 63(34.43) 47(45.19) 接受放射治疗 是 16(8.74) 17(16.35) 否 106(57.92) 42(40.38) 未知 61(33.33) 45(43.27) 临床分期 Ⅰ期 112(61.20) 28(26.92) Ⅱ期 19(10.38) 10(9.62) Ⅲ期 28(15.30) 36(34.62) Ⅳ期 24(13.11) 30(28.85) 生存状态 存活 149(81.42) 56(53.85) 死亡 34(18.58) 48(46.15) 
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表 2 287例ccRCC患者Cox回归分析结果
Table 2. Cox regression analysis of 287 ccRCC patients
变量 β值 sx Z值 P值 HR(95% CI)值 分型 高危组a 0.644 0.239 2.698 0.007 1.903(1.193~3.038) 年龄 0.020 0.012 1.613 0.107 1.020(0.996~1.046) 性别 -0.167 0.245 -0.681 0.496 0.846(0.524~1.368) 病理分期 Ⅱ期 0.319 0.531 0.602 0.547 1.376(0.486~3.898) Ⅲ期a 1.156 0.360 3.214 0.001 3.178(1.570~6.431) Ⅳ期a 2.129 0.329 6.470 < 0.001 8.404(4.410~16.017) 注：a P < 0.05，差异有统计学意义。组间比较以低危组为参照。
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