Analysis of Melanoma Gene Expression Signatures at the Single-Cell Level Uncovers 45-Gene Signature Related to Prognosis

• Published in: Biomedicines Vol. 10; no. 7; p. 1478
• Main Authors: Bakr, Mohamed Nabil, Takahashi, Haruko, Kikuchi, Yutaka
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 22.06.2022; MDPI
• Subjects: Apoptosis; bulk RNA-sequencing; Cell cycle; Cell interactions; Drug resistance; Gene expression; gene expression signatures; Genomes; Keratin; Medical prognosis; Melanoma; Mesenchyme; Metastasis; Patients; Prognosis; prognostic signature; single-cell RNA-sequencing; Skin cancer; Survival analysis; The Cancer Genome Atlas
• ISSN: 2227-9059; 2227-9059
• DOI: 10.3390/biomedicines10071478

Since the current melanoma clinicopathological staging system remains restricted to predicting survival outcomes, establishing precise prognostic targets is needed. Here, we used gene expression signature (GES) classification and Cox regression analyses to biologically characterize melanoma cells at the single-cell level and construct a prognosis-related gene signature for melanoma. By analyzing publicly available scRNA-seq data, we identified six distinct GESs (named: “Anti-apoptosis”, “Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, “Extracellular structure organization”, and “Epithelial-Mesenchymal Transition (EMT)”). We verified these GESs in the bulk RNA-seq data of patients with skin cutaneous melanoma (SKCM) from The Cancer Genome Atlas (TCGA). Four GESs (“Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, and “Extracellular structure organization”) were significantly correlated with prognosis (p = 1.08 × 10−5, p = 0.042, p = 0.001, and p = 0.031, respectively). We identified a prognostic signature of melanoma composed of 45 genes (MPS_45). MPS_45 was validated in TCGA-SKCM (HR = 1.82, p = 9.08 × 10−6) and three other melanoma datasets (GSE65904: HR = 1.73, p = 0.006; GSE19234: HR = 3.83, p = 0.002; and GSE53118: HR = 1.85, p = 0.037). MPS_45 was independently associated with survival (p = 0.002) and was proved to have a high potential for predicting prognosis in melanoma patients.