Grützmann Konrad, Kraft Theresa, Meinhardt Matthias, Meier Friedegund, Westphal Dana, Seifert Michael
Institute for Medical Informatics and Biometry, Faculty of Medicine, TU Dresden, 01307 Dresden, Germany.
Department of Pathology, University Hospital Carl Gustav Carus Dresden, TU Dresden, 01307 Dresden, Germany.
Comput Struct Biotechnol J. 2024 Feb 17;23:1036-1050. doi: 10.1016/j.csbj.2024.02.013. eCollection 2024 Dec.
Melanoma, the deadliest form of skin cancer, can metastasize to different organs. Molecular differences between brain and extracranial melanoma metastases are poorly understood. Here, promoter methylation and gene expression of 11 heterogeneous patient-matched pairs of brain and extracranial metastases were analyzed using melanoma-specific gene regulatory networks learned from public transcriptome and methylome data followed by network-based impact propagation of patient-specific alterations. This innovative data analysis strategy allowed to predict potential impacts of patient-specific driver candidate genes on other genes and pathways. The patient-matched metastasis pairs clustered into three robust subgroups with specific downstream targets with known roles in cancer, including melanoma (SG1: , , SG2: , SG3: , ). Patient subgroups and ranking of target gene candidates were confirmed in a validation cohort. Summarizing, computational network-based impact analyses of heterogeneous metastasis pairs predicted individual regulatory differences in melanoma brain metastases, cumulating into three consistent subgroups with specific downstream target genes.
黑色素瘤是最致命的皮肤癌形式,可转移至不同器官。脑转移黑色素瘤与颅外黑色素瘤转移灶之间的分子差异尚不清楚。在这里,我们使用从公开的转录组和甲基化组数据中学习到的黑色素瘤特异性基因调控网络,分析了11对异质性患者匹配的脑转移和颅外转移灶的启动子甲基化和基因表达,随后对患者特异性改变进行基于网络的影响传播分析。这种创新的数据分析策略能够预测患者特异性驱动候选基因对其他基因和通路的潜在影响。患者匹配的转移灶对聚为三个稳定的亚组,这些亚组具有在癌症(包括黑色素瘤)中起已知作用的特定下游靶点(SG1: , ,SG2: ,SG3: , )。在验证队列中证实了患者亚组和候选靶基因的排名。总之,基于计算网络的异质性转移灶对影响分析预测了黑色素瘤脑转移中的个体调控差异,这些差异累积形成了三个具有特定下游靶基因的一致亚组。