Work

A data-driven pan-cancer proteogenomic analysis reveals the characteristics of human cancer protein expression

Year: 2025

Type: article

Abstract: Genomics and epigenomics outline potential cellular changes, while proteomics reflects actual molecular events. To systematically bridge the molecular hierarchies and validate their functional interplay, we established the most comprehensive pan-cancer paired multi-omics resource to date, systematically integrating proteomic, transcriptomic, and genomic data from both tumor and adjacent normal tissues spanning 15 cancer types (2,555 tumor samples), thereby enabling a robust cross-omics exploration. Analysis revealed that tumor tissues exhibit higher correlation between transcriptomic and proteomic expression levels compared to normal tissues. Key tumor development pathways exhibited strong mRNA-protein correlations. Genes with high mRNA-protein correlation and high expression were associated with lower survival rates, highlighting potential therapeutic targets. We developed a comprehensive tool, the CPGTA R package, based on reintegrated datasets that facilitates multi-omics data integration and reanalysis. Our research enhances cancer molecular characterization while providing insights into mechanisms underlying cancer progression and treatment resistance.

Source: iScience

DOI: 10.1016/j.isci.2025.114500

Authors: Ying Li, Xin Liu, Zhen Wang, Qiong Wu, Chulin Sha, Xiaolin Li

Institutions: Chinese Academy of Sciences, University of Chinese Academy of Sciences, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Zhejiang Gongshang University

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,240