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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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Related Experiment Video

Updated: Oct 13, 2025

Patient Derived Cell Culture and Isolation of CD133+ Putative Cancer Stem Cells from Melanoma

Patient Derived Cell Culture and Isolation of CD133+ Putative Cancer Stem Cells from Melanoma

Published on: March 13, 2013

Cell type identification from single-cell transcriptomes in melanoma.

Qiuyan Huo¹, Yu Yin¹, Fangfang Liu¹

¹School of Computer Science and Technology, Xidian University, Xi'an, 710071, China.

BMC Medical Genomics

|November 17, 2021

Summary

This summary is machine-generated.

This study introduces a computational framework to identify cell types using single-cell sequencing data, considering cell dropout. The method identified eight cell types in melanoma, revealing potential therapeutic targets.

Keywords:

Cell marker Cell type Melanoma Single-cell sequencing lncRNA

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Last Updated: Oct 13, 2025

Patient Derived Cell Culture and Isolation of CD133+ Putative Cancer Stem Cells from Melanoma

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Published on: March 13, 2013

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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

Area of Science:

Genomics
Computational Biology
Cancer Research

Background:

Single-cell sequencing enables gene expression analysis at the individual cell level.
This approach presents both opportunities and challenges for studying complex diseases like cancer.

Purpose of the Study:

To develop a computational framework for cell type identification using single-cell gene and lncRNA expression.
To address challenges posed by cell dropout characteristics in single-cell data.

Main Methods:

Defined cell dropout features and identified dropout clusters.
Constructed differential co-expression networks and identified differential modules.
Utilized differential modules for cell type identification.

Main Results:

Applied the framework to single-cell melanoma data, identifying eight distinct cell types.
Enrichment analysis linked two key cell types to major histocompatibility complex (MHC) activities.
One cell type was associated with mitosis, the other with pathways related to ten diseases.

Conclusions:

Explored melanoma's molecular mechanisms through key cell type identification and analysis.
Provided insights into melanoma research and identified potential therapeutic targets.