Single-cell sequencing analysis related to sphingolipid metabolism guides immunotherapy and prognosis of skin cutaneous melanoma

Yantao Ding; Zhijie Zhao; Huabao Cai; Yi Zhou; He Chen; Yun Bai; Zhenran Liu; Shengxiu Liu; Wenming Zhou

doi:10.3389/fimmu.2023.1304466

Single-cell sequencing analysis related to sphingolipid metabolism guides immunotherapy and prognosis of skin cutaneous melanoma

Front Immunol. 2023 Nov 23:14:1304466. doi: 10.3389/fimmu.2023.1304466. eCollection 2023.

Authors

Yantao Ding^{1

2

3}, Zhijie Zhao⁴, Huabao Cai⁵, Yi Zhou^{1

2

3}, He Chen⁶, Yun Bai⁷, Zhenran Liu⁸, Shengxiu Liu^{1

2

3}, Wenming Zhou^{1

2

3}

Affiliations

¹ Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
² Key Laboratory of Dermatology (Anhui Medical University), Ministry of Education, Hefei, Anhui, China.
³ Inflammation and Immune-Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China.
⁴ Department of Plastic Surgery, The Ninth Affiliated Hospital of Shanghai Jiaotong University, Shanghai, China.
⁵ Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
⁶ Department of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
⁷ Department of Plastic Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
⁸ Department of Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.

Abstract

Background: We explore sphingolipid-related genes (SRGs) in skin melanoma (SKCM) to develop a prognostic indicator for patient outcomes. Dysregulated lipid metabolism is linked to aggressive behavior in various cancers, including SKCM. However, the exact role and mechanism of sphingolipid metabolism in melanoma remain partially understood.

Methods: We integrated scRNA-seq data from melanoma patients sourced from the GEO database. Through the utilization of the Seurat R package, we successfully identified distinct gene clusters associated with patient survival in the scRNA-seq data. Key prognostic genes were identified through single-factor Cox analysis and used to develop a prognostic model using LASSO and stepwise regression algorithms. Additionally, we evaluated the predictive potential of these genes within the immune microenvironment and their relevance to immunotherapy. Finally, we validated the functional significance of the high-risk gene IRX3 through in vitro experiments.

Results: Analysis of scRNA-seq data identified distinct expression patterns of 4 specific genes (SRGs) in diverse cell subpopulations. Re-clustering cells based on increased SRG expression revealed 7 subgroups with significant prognostic implications. Using marker genes, lasso, and Cox regression, we selected 11 genes to construct a risk signature. This signature demonstrated a strong correlation with immune cell infiltration and stromal scores, highlighting its relevance in the tumor microenvironment. Functional studies involving IRX3 knockdown in A375 and WM-115 cells showed significant reductions in cell viability, proliferation, and invasiveness.

Conclusion: SRG-based risk signature holds promise for precise melanoma prognosis. An in-depth exploration of SRG characteristics offers insights into immunotherapy response. Therapeutic targeting of the IRX3 gene may benefit melanoma patients.

Keywords: IRX3; immunotherapy; melanoma; single-cell sequencing; sphingolipid.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Immunotherapy
Lipid Metabolism
Melanoma* / genetics
Melanoma* / therapy
Prognosis
Skin Neoplasms* / genetics
Skin Neoplasms* / therapy
Tumor Microenvironment / genetics

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The present study was financially supported by the Scientific research project of universities in Anhui Province (grant nos. 2022AH040163), and the Postgraduate Innovation Research and Practice Program of Anhui Medical University (grant nos. YJS20230068 and YJS20230074).