Single-cell sequencing reveals the functional heterogeneity of melanoma cells and their crosstalk with the tumor microenvironment

Background: Cutaneous melanoma is a highly aggressive malignancy characterized by significant heterogeneity, rapid progression, and variable treatment responses. Understanding the functional diversity of melanoma cells and their interactions with the tumor microenvironment (TME) is crucial for developing effective therapeutic strategies and identifying prognostic biomarkers. Methods: We performed comprehensive single-cell RNA sequencing (scRNA-seq) analysis of 70,760 cells from 11 melanoma samples. Data processing was conducted using Seurat v4.3.0 with Harmony integration. Cell-cell communication was inferred using CellChat, and pseudotime trajectory analysis was performed using Monocle 2. A prognostic model was constructed by integrating 10 machine learning algorithms within a leave-one-out cross-validation (LOOCV) framework using the TCGA-SKCM cohort. Experimental validation was performed using immunofluorescence analysis on clinical specimens from seven melanoma patients. Results: T cells being the predominant signal recipients. Pseudotime trajectory analysis identified critical genes (CYR61, JUN, RHOC) involved in melanoma cell state transitions. Using an integrative machine learning approach, we developed a melanoma cell-associated signature (MRS) comprising 15 genes that achieved a mean C-index of 0.675 across validation cohorts. Furthermore, High EIF5A expression was significantly associated with poor patient outcomes (p < 0.001), Immunofluorescence analysis showing significantly elevated EIF5A expression in melanoma tissues compared to controls (p < 0.01). Conclusion: This study reveals the functional heterogeneity of melanoma cells and their interactions with the immune microenvironment, identifies key subpopulations, prognostic signatures, and EIF5A as a plausible prognostic biomarker candidate and potential therapeutic target that warrants mechanistic validation in melanoma.

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Publication Details

Journal
Frontiers in Genetics
Published
2026-04-01
DOI
https://doi.org/10.3389/fgene.2026.1779213
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

Single-cell sequencing reveals the functional heterogeneity of melanoma cells and their crosstalk with the tumor microenvironment

Jianxiong Qiao, Xiaxia Li, Hanghang Zhou, Peigang Zhao et al.
Frontiers in Genetics
Single-cell and spatial transcriptomics
article

Single-cell sequencing reveals the functional heterogeneity of melanoma cells and their crosstalk with the tumor microenvironment

Jianxiong Qiao, Xiaxia Li, Hanghang Zhou, Peigang Zhao, Zhen Qin, Yue Yuan, Lin Zhong, Lihong Qiu
article en

Abstract

Background: Cutaneous melanoma is a highly aggressive malignancy characterized by significant heterogeneity, rapid progression, and variable treatment responses. Understanding the functional diversity of melanoma cells and their interactions with the tumor microenvironment (TME) is crucial for developing effective therapeutic strategies and identifying prognostic biomarkers. Methods: We performed comprehensive single-cell RNA sequencing (scRNA-seq) analysis of 70,760 cells from 11 melanoma samples. Data processing was conducted using Seurat v4.3.0 with Harmony integration. Cell-cell communication was inferred using CellChat, and pseudotime trajectory analysis was performed using Monocle 2. A prognostic model was constructed by integrating 10 machine learning algorithms within a leave-one-out cross-validation (LOOCV) framework using the TCGA-SKCM cohort. Experimental validation was performed using immunofluorescence analysis on clinical specimens from seven melanoma patients. Results: T cells being the predominant signal recipients. Pseudotime trajectory analysis identified critical genes (CYR61, JUN, RHOC) involved in melanoma cell state transitions. Using an integrative machine learning approach, we developed a melanoma cell-associated signature (MRS) comprising 15 genes that achieved a mean C-index of 0.675 across validation cohorts. Furthermore, High EIF5A expression was significantly associated with poor patient outcomes (p < 0.001), Immunofluorescence analysis showing significantly elevated EIF5A expression in melanoma tissues compared to controls (p < 0.01). Conclusion: This study reveals the functional heterogeneity of melanoma cells and their interactions with the immune microenvironment, identifies key subpopulations, prognostic signatures, and EIF5A as a plausible prognostic biomarker candidate and potential therapeutic target that warrants mechanistic validation in melanoma.

Frontiers in GeneticsVol. 17
Augusta University (US), Lanzhou University Second Hospital (CN), Tianjin Stomatological Hospital (CN), First Hospital of Lanzhou University (CN), Lanzhou University (CN)
Natural Science Foundation of Gansu Province, First Clinical Medical School, Lanzhou University
Openalex Percentile: Top 36%
Single-cell and spatial transcriptomics
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