ReviewCommunications biology2025
Identification of malignant cells in single-cell transcriptomics data.
Review in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Reprogramming Autophagy to Strengthen Antitumour Immunity: Advances in Immunotherapeutic Strategies.Immunology · 2026Review
- A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma.Functional & integrative genomics · 2026Article
- Quantitative coupling of clonal CNV evolution and spatially restricted malignant states identifies MFGE8 as a candidate late-state-associated target in breast cancer.Journal of translational medicine · 2026Article
- Advancing prussian blue nanoparticle-mediated photothermal therapy through machine learning and multiomics integration.Nanomedicine (London, England) · 2026Review
- Multi-omics identification and functional validation of signal regulatory protein gamma as a prognostic biomarker and immune regulator in head and neck squamous cell carcinoma.Frontiers in immunology · 2026Article
- Artificial Intelligence-Enabled Multi-Omics for Predicting Immune Checkpoint Inhibitor Response and Resistance.Journal of multidisciplinary healthcare · 2026Review
- Beyond counting: how single-cell long-read sequencing turns transcriptome complexity into precision targets.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
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
Single-cell transcriptomics has significantly advanced our ability to uncover the cellular heterogeneity of tumors. A key challenge in single-cell transcriptomics is identifying cancer cells and, in particular, distinguishing them from non-malignant cells of the same cell lineage. Focusing on features that can be measured by single-cell transcriptomics, this review explores the molecular aberrations of cancer cells and their observable readouts at the RNA level. Identification of bona fide cancer cells typically relies on three main features, alone or in combination: i) expression of cell-of-origin marker genes; ii) inter-patient tumor heterogeneity; iii) inferred copy-number alterations. Depending on the cancer type, however, alternative or additional features may be necessary for accurate classification, such as single-nucleotide mutations, gene fusions, increased cell proliferation, and altered activation of signaling pathways. We summarize computational approaches commonly applied in single-cell analysis of tumoral samples, as well as less explored features that may aid the identification of malignant cells.
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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.