A single-cell analysis reveals tumor heterogeneity and immune environment of acral melanoma

Abstract

Acral melanoma is a dismal subtype of melanoma occurring in glabrous acral skin, and has a higher incidence in East Asians. We perform single-cell RNA sequencing for 63,394 cells obtained from 5 acral and 3 cutaneous melanoma samples to investigate tumor heterogeneity and immune environment. We define 5 orthogonal functional cell clusters that are involved in TGF-beta signaling, Type I interferon, Wnt signaling, Cell cycle, and Cholesterol efflux signaling. Signatures of enriched TGF-beta, Type I interferon, and cholesterol efflux signaling are significantly associated with good prognosis of melanoma. Compared with cutaneous melanoma, acral melanoma samples have significantly severe immunosuppressive state including depletion of cytotoxic CD8+ T cells, enrichment of Treg cells, and exhausted CD8+ T cells. PD1 and TIM-3 have higher expression in the exhaustive CD8+ T cells of acral melanoma. Key findings are verified in two independent validation sets. This study contributes to our better understanding of acral melanoma.

The combination of single-cell and bulk RNA sequencing analysis decodes the melanoma tumor microenvironment and identifies novel T cell-associated signature genes

“…Accordingly, developing a prognostic signature to predict the melanoma patient prognosis and immunotherapy effect is essential. This study collected scRNA-seq data from 63,394 cells from the GEO database of melanoma patients, which consisted of preand post-treatment samples obtained from a single patient who underwent immunotherapy [11], and identi ed 509 DEGs in the T cells. T cells in cancers have been identi ed to be related to better outcomes for patients in many human malignant tumors [21].For example, active CD8 + T cell in ltrations were recognized to be related to improved OS in melanoma patients [22].…”

“…Furthermore, Zhang et al have conducted single-cell RNA sequencing (scRNA-seq) analysis for 11 melanoma patients, revealing the gene expression levels in T cells, CAFs, and malignant cells. Surprisingly, these samples included pre-and postimmunotherapy tumor patients, who provided genetic changes in T cells before and after immunotherapy [11].…”

Analysing the causal relationship between potentially protective and risk factors and cutaneous melanoma: A Mendelian randomization study

“…Therefore, further identification of melanoma risk factors in observational studies and larger GWAS summary statistics are important for minimizing horizontal pleiotropy and validating MR results. Thirdly, cutaneous melanoma is now regarded as a heterogeneous disease with distinctive histological subtypes and different etiologic pathways, [41][42][43] but subtype-specific MR analysis is unrealizable due to the lack of such GWAS statistics available.…”

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single‐cell transcriptomics analysis via 101 machine learning algorithms

“…Sequencing data from the GEO database, identified as GSE215120, comprising 11 samples (seven acral melanoma and four cutaneous skin melanoma), was downloaded 15 . To handle the h5 file data type, the Read10X_h5 function was employed for reading.…”

“…Sequencing data from the GEO database, identified as GSE215120, comprising 11 samples (seven acral melanoma and four cutaneous skin melanoma), was downloaded. 15 To handle the h5 file data type, the Read10X_h5 function was employed for reading. In addition, bulk-transcriptome sequencing data sourced from GSE19234, GSE22153 and GSE65904 within the TCGA and GEO databases underwent batch effect removal and standardization using the 'sva' R package.…”