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...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。展开更多
胶质母细胞瘤(glioblastoma,GBM)是最常见的原发性颅内肿瘤,恶性程度极高,患者预后极差。为了识别GBM预后生物标记物,建立预后模型,本研究通过分析癌症基因组图谱计划(The Cancer Genome Atlas,TCGA)数据库中GBM的表达谱数据,筛选出不...胶质母细胞瘤(glioblastoma,GBM)是最常见的原发性颅内肿瘤,恶性程度极高,患者预后极差。为了识别GBM预后生物标记物,建立预后模型,本研究通过分析癌症基因组图谱计划(The Cancer Genome Atlas,TCGA)数据库中GBM的表达谱数据,筛选出不同生存期GBM患者差异基因。利用GISTIC软件和Kaplan-Meier(KM)生存分析方法分析TCGA数据库中的GBM拷贝数变异数据,识别影响生存的扩增基因(survival-associated amplified gene,SAG)。取短生存期组上调基因和SAG两者的交集基因,进行单因素Cox回归和迭代Lasso回归筛选重要候选基因并建立预后模型;计算预后评分,根据预后评分中位数将患者分为高风险组和低风险组。用ROC曲线判断模型的优良,KM生存分析高低风险组预后差异,并用GEO、CGGA和Rembrandt数据库3个外部数据集进行验证。多因素Cox回归分析判断预后评分的预后独立性。结果显示,GBM不同生存期差异分析得到上调基因426个,下调基因65个。短生存期组上调基因与SAG交集得到47个基因。经过筛选,最终确定六基因(EN2、PPBP、LRRC61、SEL1L3、CPA4、DDIT4L)预后模型。TCGA实验组和3个外部验证组模型的ROC曲线下面积均大于0.6,甚至达到0.912。KM分析显示高低风险组的预后都存在差异(P<0.05)。在多因素Cox回归分析中,六基因预后评分是GBM患者预后的独立影响因素(P<0.05)。通过一系列分析,本研究确立了六基因(EN2、PPBP、LRRC61、SEL1L3、CPA4、DDIT4L)的GBM预后模型,模型具有很好的预测能力,可作为预测GBM患者的独立预后标志物。展开更多
This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutti...This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutting-edge fields of genomics and bioinformatics, the application of AI is propelling a deeper understanding of complex genetic mechanisms and the development of innovative therapeutic approaches. The precision of AI in genomic sequence analysis, coupled with breakthroughs in precise gene editing, such as AI-designed gene editors, significantly enhances our comprehension of gene functions and disease associations . Moreover, AI’s capabilities in disease prediction, assessing individual disease risks through genomic data analysis, provide robust support for personalized medicine. AI applications extend beyond gene identification, gene expression pattern prediction, and genomic structural variant analysis, encompassing key areas such as epigenetics, multi-omics data integration, genetic disease diagnosis, evolutionary genomics, and non-coding RNA function prediction. Despite challenges including data privacy, algorithm transparency, and bioethical issues, the future of AI is expected to continue revolutionizing genomics and bioinformatics, ushering in a new era of personalized medicine and precision treatments.展开更多
文摘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。
文摘This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutting-edge fields of genomics and bioinformatics, the application of AI is propelling a deeper understanding of complex genetic mechanisms and the development of innovative therapeutic approaches. The precision of AI in genomic sequence analysis, coupled with breakthroughs in precise gene editing, such as AI-designed gene editors, significantly enhances our comprehension of gene functions and disease associations . Moreover, AI’s capabilities in disease prediction, assessing individual disease risks through genomic data analysis, provide robust support for personalized medicine. AI applications extend beyond gene identification, gene expression pattern prediction, and genomic structural variant analysis, encompassing key areas such as epigenetics, multi-omics data integration, genetic disease diagnosis, evolutionary genomics, and non-coding RNA function prediction. Despite challenges including data privacy, algorithm transparency, and bioethical issues, the future of AI is expected to continue revolutionizing genomics and bioinformatics, ushering in a new era of personalized medicine and precision treatments.
