蛋白质组
计算生物学
蛋白质组学
地图集(解剖学)
生物
质谱法
癌症
生物标志物发现
癌症研究
生物信息学
化学
遗传学
基因
色谱法
古生物学
作者
Jaco C. Knol,Mengge Lyu,Franziska Böttger,Madalena Monteiro,Thang V. Pham,Frank Rolfs,Andrea Vallés‐Martí,Tim Schelfhorst,Richard R. de Goeij‐de Haas,Irene V. Bijnsdorp,Shuaiyao Wang,Fangfei Zhang,Jun Arita,Bart A. Westerman,Barbara Sitek,Janne Lehtiö,Jan Köster,Jan N.M. IJzermans,Hanneke W. M. van Laarhoven,Maarten F. Bijlsma
出处
期刊:Cancer Cell
[Cell Press]
日期:2025-05-29
卷期号:43 (7): 1328-1346.e8
被引量:10
标识
DOI:10.1016/j.ccell.2025.05.003
摘要
Most cancer proteomics studies to date have focused on a single cancer type. We report The Pan-Cancer Proteome Atlas (TPCPA) based on data-independent acquisition mass spectrometry, to better understand cancer biology and identify therapeutic targets and biomarkers. TPCPA includes 9,670 proteins derived from 999 primary tumors representing 22 cancer types. We describe pan-cancer and cancer type-enriched proteins with extensive external annotation, prioritizing candidate drug targets and biomarkers. Relevant for proteolysis-targeting chimeras, we identify E3-ubiquitin ligases highly expressed in specific tumor types, including HERC5 (esophageal cancer) and RNF5 (liver cancer). Co-expression analysis reveals 13 modules, including unexpected hub proteins as potential drug targets (e.g., GFPT1, LRPPRC, PINK1, DOCK2, and PTPN6). Analysis of 195 colorectal cancers identifies protein markers for RNA-based consensus molecular subtypes (CMSs) and two immune subtypes with prognostic value. We report a cancer type classifier for identification of cancers of unknown primary origin. All TPCPA data can be queried in a dedicated web resource.
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