ReviewCancer metastasis reviews2025
Single-cell RNA-sequencing of circulating tumour cells: A practical guide to workflow and translational applications.
Review in Cancer metastasis reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- Adaptive epithelial-mesenchymal bi-directional transition: A key invasive plasticity for tumor metastasis.iScience · 2026Review
- Circulating Tumor Cells: Isolation, Preclinical Models, and Clinical Applications for Personalized Cancer Therapy.Biomolecules · 2026Review
- Direct detection of rare circulating tumor cells in peripheral blood mononuclear cells by scRNA seq: Spike-in strategy based feasibility study.The journal of liquid biopsy · 2026Article
- Special Issue "Molecular Progression in Genome-Related Diseases".International journal of molecular sciences · 2026Article
- Cancer-related microangiopathic hemolytic anemia (CR-MAHA) in a metastatic breast cancer patient with a germ-line ATM single nucleotide variant and an ESR1 fusion variant: insights from a case report on early diagnosis and improved outcomes.Frontiers in oncology · 2026Article
- Targeting tumor transition windows.Exploration of targeted anti-tumor therapy · 2026Review
- Breast cancer pathogenesis, diagnosis and treatment: a comprehensive review.Frontiers in oncology · 2026Review
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- Advances in the identification of novel cell signatures in benign prostatic hyperplasia and prostate cancer using single-cell RNA sequencing.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The global burden of cancer is rising, with treatment failures often due to the metastatic nature of late-stage malignancies. Circulating tumour cells (CTCs) are metastatic precursors shed from primary tumours, which survive in circulation, extravasate and colonise distant organs. The advent of high-throughput single-cell RNA sequencing (scRNA-seq) has revolutionised the investigation of transcriptomic landscape at single-cell resolution, enabling deep transcriptomic profiling, re-stratifying CTC subtypes and improving the detection of rare new subpopulations. Applications extend to understanding tumour microenvironments, characterising cellular heterogeneity, uncovering metastasis molecular mechanisms and improving prognosis and diagnostic strategies. A timeline of key milestones in CTC scRNA-seq research is also provided. Nevertheless, a knowledge gap remains due to unstandardised protocols and fragmented resources in CTC scRNA-seq research. We address this gap by proposing a 12-step CTC-specific scRNA-seq workflow to overcome methodological inconsistencies. This workflow spans the entire process from enrichment, single-cell sorting and sequencing to data pre-processing and downstream analyses, with a detailed compilation of data analysis tools. An in-depth discussion of the pros and cons of commonly used scRNA-seq tools is also included, specifically evaluating their suitability for CTC research. Additionally, emerging research frontiers, including the discovery of hybrid cells-fusion products of tumour and normal cells-and the integration of machine learning (ML) into scRNA-seq workflows, are explored. Future research should prioritise CTC scRNA-seq workflow standardisation, integrate ML-driven analysis and investigate rare and hybrid populations to advance metastasis research. This review supports these goals by guiding methods, informing tool selection and promoting data sharing for reproducibility.
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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.