Evidence map›Paper›PMID 37193189›Full record

ArticleAmerican journal of translational research2023

Multicellular network analysis of melanoma heterogeneity by scRNA-seq.

Shun Li, Long Chen

Abstract read
In one paragraph

Article in American journal of translational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Shun LiDepartment of Immunology, School of Basic Medical Sciences, Chengdu Medical College Chengdu 610500, Sichuan, China.
Long ChenDepartment of Immunology, School of Basic Medical Sciences, Chengdu Medical College Chengdu 610500, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveMelanoma neoplasia is a complicated process. Not only melanocytes are involved, stromal cells and immune cells would also regulate cancer development. However, the cell type composition and tumor immune microenvironment of melanoma are poorly understood.

methodHere we report a map of the cellular landscape of human melanoma via analyzing published single-cell RNA sequencing (scRNA-seq) dataset. Transcriptional profiles of 4645 cells obtained from 19 melanoma tissues were dissected.

resultUsing gene expression patterns and flow cytometry, 8 discrete cell populations were identified including endothelial cells (ECs), cancer related fibroblasts (CAFs), macrophages, B cells, T cells (NK cells), memory T cells (MTCs), melanocytes and podocytes. By constructing the cell-specific network (CSN) for each cell population, scRNA-seq data can be used for clustering and pseudo-trajectory analyzing from network perspective. In addition, the differentially expressed genes (DEGs) between malignant and non-malignant melanocytes were identified and analyzed together with clinical data from The Cancer Genome Atlas (TCGA).

conclusionThis study shows a comprehensive view of melanoma at the single cell resolution which outlines the characteristics of resident cells in tumor. Particular, it provides an immune microenvironment map of melanoma.

Indexed as

heterogeneityimmune cellMelanomanetworkscRNA-seq

Identifiers

PMID37193189
PMCPMC10182521

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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.