A recent review shows that proteomics-driven multi-omics integration reveals tumor heterogeneity and functional regulatory networks beyond genomic information, facilitating the discovery of clinically relevant biomarkers and therapeutic targets.
ELSPApr 28 2026 A recent review shows that proteomics-driven multi-omics integration reveals tumor heterogeneity and functional regulatory networks beyond genomic information, facilitating the discovery of clinically relevant biomarkers and therapeutic targets.
Emerging single-cell and spatial proteomics technologies, together with AI-driven analysis, offer new opportunities to advance precision oncology. Cancer is a highly complex and dynamic biological system in which molecular regulation extends beyond static genomic alterations to encompass multiple functional layers. In a recent review published in Advanced Cancer Research, the authors provide an overview of advances in proteomics-driven precision oncology, highlighting its pivotal role in biomarker discovery and mechanistic insights.
By profiling protein abundance, post-translational modifications, and signaling pathway activities, proteomics helps bridge the critical gap between genotype and phenotype that cannot be fully captured by genomic data alone. With continuous improvements in mass spectrometry, proteomics now enables high-resolution, large-scale analysis spanning bulk tissues to single-cell levels. This work systematically summarizes proteomics data across diverse cancer types, uncovering clinically relevant biomarkers and potential therapeutic targets.
Key highlights include: Technological advancement: Advances in mass spectrometry enable scalable and high-resolution proteomics across bulk, single-cell, and spatial contexts. Biomarker discovery: Mass spectrometry-based proteomics facilitates the discovery of cancer biomarkers. Related StoriesAccelerated translation: Artificial intelligence-assisted multi-omics integration accelerates clinical translation in precision oncology. Emerging single-cell and spatial proteomics technologies are shifting research from population averages to precise, cell- and context-specific insights.
The review further highlights that integrating proteomics with artificial intelligence and clinical data will enable predictive and clinically interpretable models, accelerating the translation of biomarkers into practice. This work outlines the functional landscape of precision oncology, highlighting the proteome as both an indicator and a driver of cancer progression and treatment response. Source:ELSP Journal reference:Shi Y, Yang S, Chen Y, Chen J, Hao B. Proteomics-driven precision oncology: from molecular profiling to biomarker discovery. Advanced Cancer Research. DOI: 10.55092/acr20260002. https://www.elspub.com/papers/j/2013510201467346944.html
Oncology Proteomics Artificial Intelligence Cancer Cell Genomic Mass Spectrometry Research Spectrometry Translation Tumor
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