基于MOVICS集成聚类的低级别胶质瘤多组学数据整合分子分型  

Multi-omics data integration molecular subtyping of lower-grade gliomas based on MOVICS clustering ensemble

作　　者：赵鑫[1] 魏亿芳 李灵梅 师国京 房瑞玲 曹红艳[1,2] ZHAO Xin;WEI Yi-fang;LI Ling-mei;SHI Guo-jing;FANG Rui-ling;CAO Hong-yan(Department of Health Statistics,Shanxi Medical University,Taiyuan 030001,China;Department of Math,Shanxi Medical University,Taiyuan 030001,China)

机构地区：[1]山西医科大学卫生统计教研室,太原030001 [2]山西医科大学数学教研室,太原030001

出　　处：《中华疾病控制杂志》2023年第2期216-223,共8页Chinese Journal of Disease Control & Prevention

基　　金：国家自然科学基金资助项目(71403156);山西省应用基础研究计划项目(201901D111204);山西医科大学校级博士启动基金项目(BS201722)。

摘　　要：目的探讨癌症分型中的多组学整合与可视化(multi-omics integration and visualization in cancer subtyping,MOVICS)集成聚类方法在低级别胶质瘤(lower-grade gliomas,LGG)多组学数据整合分型中的应用,识别LGG高危患者,筛选出潜在的生物标志物和重要通路。方法采用MOVICS方法集成LGG多组学数据的10种整合方法的分型结果,得到LGG的稳健分子分型,进一步采用Cox回归研究不同分型患者的死亡风险;针对不同分型,筛选差异表达的mRNA(DEmRNAs),miRNA(DEmiRNAs)以及差异甲基化基因(differential methylation genes,DMGs),对三者进行联合分析得到重合基因,利用GO和KEGG分析得到重合基因富集通路,进一步分析核心基因的表达水平对生存率的影响,最后对不同分型患者进行通路活性分析。结果LGG患者分为三型,其中,分型3患者的死亡风险是分型1的2.794倍;筛选出1569个DEmRNAs,140个DEmiRNAs以及337个DMGs,119个重合基因富集到有统计学差异的26条GO生物功能项和7条KEGG通路;生存分析表明DNAJB14和MTUS1可能与患者生存结局相关。通路活性分析结果显示Androgen、EGFR、Trail和VEGF通路的活性在不同分型间差异有统计学意义。结论MOVICS聚类集成方法能够有效地对LGG患者进行分型,识别预后高风险患者,筛选出潜在生物标志物以及重要通路,为LGG患者个体化治疗策略的制定提供理论依据。Objective To investigate the application of multi-omics integration and visualization in cancer subtyping(MOVICS)clustering ensemble method in multi-omics data integration for lower-grade gliomas(LGG)subtyping,and high risk LGG group identification,and further screen potential biomarkers and important pathways.Methods The MOVICS method was used to integrate the subtyping results of 10 integration methods based on the LGG multi-omics data,to obtain a robust molecular subtyping of LGG patients.Cox regression analysis was carried out to evaluate the mortality risk of different patients.Differentially expressed mRNA(DEmRNAs),miRNA(DEmiRNAs)and differential methylation genes(DMGs)analyses were conducted between different subtypes.Overlapping genes among the three omics data types were used for GO term and KEGG pathway enrichment analysis.Additional analysis was conducted to identify hub genes and further evaluate their influence on patients survival outcome.Finally,pathway activity analysis between different subtypes was performed.Results LGG patients were divided into three subtypes.Patients in subtype 3 were 2.794 times more likely to die than patients in subtype 1.A total of 1569 DEmRNAs,140 DEmiRNAs and 337 DMGs were screened,the combined analysis genes yielded 119 genes which are regulated by mRNA,miRNA and DNA methylation and enriched 26 GO items and 7 KEGG pathways with statistical differences.Survival analysis showed that DNAJB14 and MTUS1 were significantly associated with survival outcome.Pathway activity analysis indicated that activities of Androgen,EGFR,Trail and VEGF showed significant difference between subtypes.Conclusions MOVICS classified LGG patients into three subtypes with distinct survival outcomes.Potential biomarkers,hub genes and important pathways were identified,which provided novel insights into the underlying differences between subtypes in molecular levels.These molecular signatures could offer new opportunities for individualized treatment and prevention of LGG patients.

关 键 词：聚类集成 多组学数据 分子亚型 低级别胶质瘤 

分 类 号：R181.2[医药卫生—流行病学] R739.41[医药卫生—公共卫生与预防医学]