ArticleCell genomics2025
Proteomic-based stemness score measures oncogenic dedifferentiation and enables the identification of druggable targets.
Article in Cell genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Nitrogen metabolism profiling reveals cell state-specific pyrimidine synthesis pathway choice.Nature metabolism · 2026Article
- Proteomic Analysis Uncovers Enhanced Inflammatory Phenotype and Distinct Metabolic Changes in IDH1 Mutant Glioma Cells.International journal of molecular sciences · 2025Article
- Improved quantitative accuracy in data-independent acquisition proteomics via retention time boundary imputation.bioRxiv : the preprint server for biology · 2025Article
- Cancer stem cells in hepatocellular carcinoma: therapy resistance and emerging treatments.Frontiers in immunology · 2025Review
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Authors and funding
28 authors.
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Abstract
Cancer progression and therapeutic resistance are closely linked to a stemness phenotype. Here, we introduce a protein-expression-based stemness index (PROTsi) to evaluate oncogenic dedifferentiation in relation to histopathology, molecular features, and clinical outcomes. Utilizing datasets from the Clinical Proteomic Tumor Analysis Consortium across 11 tumor types, we validate PROTsi's effectiveness in accurately quantifying stem-like features. Through integration of PROTsi with multi-omics, including protein post-translational modifications, we identify molecular features associated with stemness and proteins that act as active nodes within transcriptional networks, driving tumor aggressiveness. Proteins highly correlated with stemness were identified as potential drug targets, both shared and tumor specific. These stemness-associated proteins demonstrate predictive value for clinical outcomes, as confirmed by immunohistochemistry in multiple samples. The findings emphasize PROTsi's efficacy as a valuable tool for selecting predictive protein targets, a crucial step in customizing anti-cancer therapy and advancing the clinical development of cures for cancer patients.
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