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Identification of malignant cells in single-cell transcriptomics data

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Název: Identification of malignant cells in single-cell transcriptomics data
Autoři: Andreatta, Massimo, Garnica Caparros, Josep, Carmona, Santiago
Zdroj: Communications Biology. 8
Informace o vydavateli: Springer Science and Business Media LLC, 2025.
Rok vydání: 2025
Témata: Gene Expression Profiling / methods, Gene Expression Regulation, Neoplastic, Biomarkers, Tumor / genetics, Computational Biology / methods, Neoplasms / pathology, DNA Copy Number Variations, Single-Cell Analysis / methods, Humans, 616.07, Neoplasms / genetics, Transcriptome
Popis: 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.
Druh dokumentu: Article
Popis souboru: application/pdf
Jazyk: English
ISSN: 2399-3642
DOI: 10.1038/s42003-025-08695-4
Přístupová URL adresa: https://archive-ouverte.unige.ch/unige:187363
https://doi.org/10.1038/s42003-025-08695-4
Rights: CC BY
Přístupové číslo: edsair.doi.dedup.....97772894f6dc4773b16f1afa6504919c
Databáze: OpenAIRE
Popis
Abstrakt: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.
ISSN:23993642
DOI:10.1038/s42003-025-08695-4