基因表达谱技术彻底改变了细胞生物学,使研究人员能够识别与黑色素瘤的各种生物学属性相关的基因特征,如色素沉着状态,分化状态,增殖能力与侵袭能力,和疾病进展。尽管基因特征的发现显著增强了我们对黑素细胞表型的理解,协调独立研究和不同分析平台报告的众多签名仍然是一个挑战。目前用于分类黑素细胞基因特征的方法取决于确切的基因重叠和与未标准化的基线转录组的比较。在这项研究中,我们的目的是根据临床皮肤黑素瘤标本中相似的表达模式,将已发表的基因特征分类成簇.我们分析了来自6个基因表达库的近800个黑色素瘤样本,并开发了一个基因标签分类框架,该框架可抵抗跨分析平台的基因识别偏差和基线标准的不一致。使用39个经常引用的已发表基因签名,我们的分析揭示了与先前鉴定的表型相关的七类主要基因特征:分化,有丝分裂/MYC,AXL,失色症,神经,低代谢,和入侵。每个类别都与组成基因签名所代表的表型一致,并且我们的分类方法不依赖于签名之间的重叠基因。为了促进更广泛的应用,我们创建了WIMMS(什么是我的黑素细胞签名,可在https://wimms。tanlab.org/),一个用户友好的Web应用程序。WIMMS允许用户对任何基因签名进行分类,确定其与主要引用的签名的关系及其在七个主要类别中的代表性。
Gene expression profiling technologies have revolutionized cell biology, enabling researchers to identify gene signatures linked to various biological attributes of melanomas, such as pigmentation status, differentiation state, proliferative versus invasive capacity, and disease progression. Although the discovery of gene signatures has significantly enhanced our understanding of melanocytic phenotypes, reconciling the numerous signatures reported across independent studies and different profiling platforms remains a challenge. Current methods for classifying melanocytic gene signatures depend on exact gene overlap and comparison with unstandardized baseline transcriptomes. In this study, we aimed to categorize published gene signatures into clusters based on their similar patterns of expression across clinical cutaneous melanoma specimens. We analyzed nearly 800 melanoma samples from six gene expression repositories and developed a classification framework for gene signatures that is resilient against biases in gene identification across profiling platforms and inconsistencies in baseline standards. Using 39 frequently cited published gene signatures, our analysis revealed seven principal classes of gene signatures that correlate with previously identified phenotypes: Differentiated, Mitotic/MYC, AXL, Amelanotic, Neuro, Hypometabolic, and Invasive. Each class is consistent with the phenotypes that the constituent gene signatures represent, and our classification method does not rely on overlapping genes between signatures. To facilitate broader application, we created WIMMS (what is my melanocytic signature, available at https://wimms.tanlab.org/), a user-friendly web application. WIMMS allows users to categorize any gene signature, determining its relationship to predominantly cited signatures and its representation within the seven principal classes.