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Cancers Originate from Somatic Mutations in a Single Cell

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

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Updated: May 10, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Identification of malignant cells in single-cell transcriptomics data.

Massimo Andreatta^1,2,3, Josep Garnica^4,5,6, Santiago Javier Carmona^4,5,6

¹Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland. massimo.andreatta@unige.ch.

Communications Biology

|August 22, 2025

Summary

This summary is machine-generated.

Identifying cancer cells using single-cell transcriptomics requires analyzing RNA readouts. This review details molecular aberrations and computational methods to distinguish malignant cells from non-malignant ones.

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Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Single-cell transcriptomics reveals tumor cellular heterogeneity.
Distinguishing cancer cells from non-malignant cells is a key challenge.
Understanding RNA-level molecular aberrations is crucial for cancer cell identification.

Purpose of the Study:

To review molecular aberrations in cancer cells measurable by single-cell transcriptomics.
To explore features for distinguishing malignant from non-malignant cells.
To summarize computational approaches for single-cell tumor analysis.

Main Methods:

Focusing on RNA readouts of molecular aberrations.
Analyzing cell-of-origin markers, tumor heterogeneity, and copy-number alterations.
Considering single-nucleotide mutations, gene fusions, proliferation, and signaling pathways.

Main Results:

Cancer cell identification relies on combinations of features.
Specific cancer types may require additional classification markers.
Computational methods aid in analyzing tumoral single-cell data.

Conclusions:

Accurate cancer cell identification leverages diverse molecular features.
Exploring novel features can improve malignant cell detection.
Single-cell transcriptomics offers powerful tools for cancer research.