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Updated: Oct 27, 2025

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

Published on: March 8, 2022

Single-Cell Transcriptome Analysis in Melanoma Using Network Embedding.

Liming Wang¹, Fangfang Liu¹, Longting Du¹

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

Frontiers in Genetics

|July 22, 2021

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This study introduces a computational framework using graph embedding to analyze cell interactions in melanoma, identifying key genes like ETS1 and TP53 associated with the disease.

Area of Science:

Computational biology
Genomics
Cancer research

Background:

Single-cell sequencing (scRNA-seq) offers insights into complex disease pathology.
Understanding melanoma's molecular mechanisms requires advanced analytical tools.

Purpose of the Study:

To develop a novel computational framework for exploring melanoma's molecular mechanisms.
To identify key genes and pathways involved in melanoma progression.

Main Methods:

Constructed a disease-specific cell-cell interaction network.
Employed node2vec graph embedding for feature learning.
Utilized consensus clustering to identify cell clusters.
Integrated gene regulation pairs for marker and gene analysis.

Keywords:

cell type gene regulatory network melanoma network embedding single cell

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DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

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DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

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Published on: June 6, 2025

Main Results:

Consensus clustering based on network embedding (CCNE) effectively distinguished cell clusters.
KEGG pathway analysis revealed strong associations with microRNAs in cancer and HTLV-I infection.
Identified hub genes (ETS1, TP53, E2F1, GATA3) highly associated with melanoma.

Conclusions:

The proposed CCNE framework provides a robust method for analyzing scRNA-seq data in cancer.
The identified genes and pathways offer potential targets for melanoma research.
The framework is adaptable for analyzing other scRNA-seq datasets.