Fig 1.
scRNA-seq characteristics of metastatic melanoma by immunotherapy response.
(A) UMAP visualization of cell types in melanoma tissues. (B) UMAP plot illustrating the distribution of immunotherapy responders and non-responders. Responders (blue) and non-responders (gray) exhibit distinct clustering patterns. (C) Volcano plot showing differentially expressed genes (DEGs) between responders (n = 86) and non-responders (n = 124) identified across all cells regardless of cell type from scRNA-seq data (|avg log2FC| > 1). Top 10 genes based on log2FC in responders and non-responders are labeled. (D) Reactome pathway analysis of highly expressed genes identified from scRNA-seq data in responders (left) and non-responders (right). scRNA-seq, single cell RNA sequencing; FC, fold change.
Fig 2.
Gene-based classification model using scRNA-seq data for immunotherapy response.
(A) Schematic overview of classification model building using differentially expressed genes (DEGs). Dataset (n = 16,290; baseline and post-treatment) was split into 80% training and 20% test sets. Various models, including XGBoost, Random Forest, Logistic Regression, SVM, FNN, and 1D CNN, were trained using the training set. Hyperparameters were optimized through cross-validation and the best parameters were used to retrain the models. The retrained models were then evaluated on testing set to obtain final performance metrics. (B) ROC curves of classification models. XGBoost achieved the highest area under the curve (AUC = 0.87), followed by Random Forest (AUC = 0.86) and SVM (AUC = 0.86). (C) Scatter plot comparing feature importance rankings from XGBoost and Random Forest. A subset of 29 genes was consistently identified as important for classification. (D) To assess the reproducibility of the predictive model, we permuted the dataset 100 times and measured AUC of the model; the results were similar to the original test (Spearman correlation test). (E) Dot plot showing expression levels of 29 genes in responders and non-responders (related to Fig 2C). (F) Differences in CCR7 and MTRNR2L2 expression between responders and non-responders in baseline and post-treatment samples, respectively (Wilcoxon rank-sum test). %Exp indicates the percentage of cells expressing the gene.
Fig 3.
Expression patterns of CCR7 and MTRNR2L2 in scRNA-seq data.
(A) CCR7 is predominantly expressed in B cells and memory T cells within the responder group (Wilcoxon rank-sum test). (B) MTRNR2L2 was broadly expressed across multiple cell types; however, its expression was particularly elevated in exhausted CD8 ⁺ T cells within the non-responder group (Wilcoxon rank-sum test). (C) Pathway enrichment analysis of differentially expressed genes between CCR7-positive and CCR7-negative cells within the G1-B cell cluster revealed enrichment of B cell activation-related pathways, including NF-κB signaling, in CCR7-positive B cells. (D) Biomodal distribution pattern of MTRNR2L2 gene expression. (E) High MTRNR2L2 expression was associated with elevated immune exhaustion scores (Wilcoxon rank-sum test). (F) Proportion of exhausted CD8 ⁺ T cells increased in high-MTRNR2L2 expression group.
Fig 4.
Clinical significance of CCR7 and MTRNR2L2 across three independent melanoma cohorts with bulk RNA-seq data.
Box plots displaying the expression of genes (CCR7 and MTRNR2L2) in three independent bulk RNA-seq data from melanoma patients with CR/PR (complete response/partial response) and PD/SD (progressive disease/stable disease). Kaplan–Meier survival curves demonstrating associations of genes with overall survival (OS) and progression-free survival (PFS) (log-rank test). Hazard ratio (HR) and 95% confidence intervals from the univariate Cox regression analysis. High CCR7 expression is associated with improved survival, whereas high MTRNR2L2 expression is associated with worse prognosis.
Fig 5.
CCR7 and MTRNR2L2 expression according to the melanoma subtypes.
(A, B) CCR7 and MTRNR2L2 expression showed prognostic significance for overall survival, independent of the primary site (A) and histological subtype of melanoma (B). (C) Prognostic differences according to molecular subtypes, including microphthalmia-associated transcription factor (MITF)-low, immune, and keratin subtypes (log-rank test). (D) CCR7 expression patterns across molecular subtypes (Kruskal–Wallis test). (E) Multivariate Cox regression analysis of CCR7 expression in relation to molecular subtype. ALM, acral lentiginous melanoma; LMM, lentigo malignant melanoma; NM, nodular melanoma; SSM, superficial spreading melanoma. (F) Gene expression patterns across mutational subtypes (Kruskal–Wallis test).
Fig 6.
Pathway-based classification models based on the KEGG pathway.
(A) Single-cell gene expression mapped to KEGG pathways enabled predictive models showing relatively high performance across multiple AI algorithms. (B) Heatmap (left) showing differences in 2D-CNN feature-importance between responders and non-responders; numbers on x- and y-axes denote distinct genes and pathways, respectively. List of pathways and their constituent genes showing the largest differences between responders and non-responders (right panel). (C) Prognostic analysis across three bulk RNA-seq datasets using responder-specific signature scores derived from genes within the eight responder-associated pathways identified in the highest score spot in Fig 6B (log-rank test).
Fig 7.
AI-based identification of immunotherapy response markers from single-cell RNA-seq without cell type annotation, validated in independent bulk RNA-seq cohorts.