ReviewBriefings in bioinformatics2025
Single-cell multi-omics and machine learning for dissecting stemness in cancer.
Review in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Unique CD8bioRxiv : the preprint server for biology · 2026Article
- Cancer Heterogeneity and Cancer Cell Plasticity: Molecular Mechanisms and Precision Therapy.MedComm · 2026Review
- Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Tumor suppressor network dysregulation in neuroblastoma: molecular mechanisms and precision therapeutic opportunities.Frontiers in cell and developmental biology · 2026Review
- Integrating single-cell and spatial multi-omics for precision oncology: from tumor ecosystems to clinical decision-making.Frontiers in oncology · 2026Review
- GPCR signaling systems facilitate precision interventions for multi-oncology therapy.Frontiers in systems biology · 2026Review
- Long noncoding RNAs in tumor stemness: emerging mechanisms and therapeutic opportunities.Frontiers in genetics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Cancer stem cells (CSCs) are a subpopulation of tumor cells with self-renewal capacity and the ability to drive tumor growth, metastasis, and relapse. They are widely recognized as major contributors to therapeutic resistance. Despite extensive efforts to characterize and target CSCs, their elusive nature continues to drive therapeutic resistance and relapse in epithelial malignancies. Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of tumor biology. It enables high-resolution profiling of rare subpopulations (<5%) and reveals the functional heterogeneity that contributes to treatment failure. In this review, we discuss evolving evidence for a paradigm shift, enabled by rapidly advancing single-cell technologies, from a static, marker-based definition of CSCs to a dynamic and functional perspective. We explore how trajectory inference and spatial transcriptomics redefine stemness by context-dependent dynamic-state modelling. We also highlight emerging platforms, including artificial intelligence-driven predictive modelling, multi-omics integration, and functional CRISPR screens. These approaches have the potential to uncover new vulnerabilities in CSC populations. Together, these advances should lead to new precision medicine strategies for disrupting CSC plasticity, niche adaptation, and immune evasion.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.