转录组
计算生物学
癌症
生物标志物
生物标志物发现
乳腺癌
交互网络
表型
生物
基因
计算机科学
生物信息学
基因表达
蛋白质组学
遗传学
作者
Daniele Mercatelli,Chiara Cabrelle,Pierangelo Veltri,Federico M. Giorgi,Pietro Hiram Guzzi
摘要
Cell surface proteins have been used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. Many of these proteins lie at the top of signaling cascades regulating cell responses and gene expression, therefore acting as 'signaling hubs'. It has been previously demonstrated that the integrated network analysis on transcriptomic data is able to infer cell surface protein activity in breast cancer. Such an approach has been implemented in a publicly available method called 'SURFACER'. SURFACER implements a network-based analysis of transcriptomic data focusing on the overall activity of curated surface proteins, with the final aim to identify those proteins driving major phenotypic changes at a network level, named surface signaling hubs. Here, we show the ability of SURFACER to discover relevant knowledge within and across cancer datasets. We also show how different cancers can be stratified in surface-activity-specific groups. Our strategy may identify cancer-wide markers to design targeted therapies and biomarker-based diagnostic approaches.
科研通智能强力驱动
Strongly Powered by AbleSci AI