Evidence map›Paper›PMID 41053409›Full record

ReviewCancer metastasis reviews2025

Single-cell RNA-sequencing of circulating tumour cells: A practical guide to workflow and translational applications.

Francis Yew Fu Tieng, Learn-Han Lee, Nurul-Syakima Ab Mutalib

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Special Issue "Molecular Progression in Genome-Related Diseases".International journal of molecular sciences · 2026
    Article
  5. Article
  6. Targeting tumor transition windows.Exploration of targeted anti-tumor therapy · 2026
    Review
  7. Review
  8. Review
  9. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Francis Yew Fu TiengDepartment of Clinical Oncology, Faculty of Medicine, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia. francistieng@um.edu.my.ORCID 0000-0002-2504-3934
Learn-Han LeeMicrobiome Research Group, Research Center for Life Science and Healthcare, Nottingham, Ningbo, China.ORCID 0000-0002-8589-7456
Nurul-Syakima Ab MutalibUKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia (UKM), Cheras, 56000, Kuala Lumpur, Malaysia.ORCID 0000-0001-6914-2224

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Biomarkers, TumorNeoplasmsNeoplastic Cells, CirculatingSequence Analysis, RNASingle-Cell AnalysisHumansTranslational Research, BiomedicalWorkflowBiomarkers, TumorCancerCirculating tumour cellsHybrid cellsMachine learning integrationSingle-cell RNA sequencing

Identifiers

PMID41053409
PMCPMC12500777

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.