Deciphering the Tumor Microenvironment: An Integrated Single-Cell RNA-Seq and AI Framework for Novel Biomarker and Therapeutic Target Discovery in Melanoma

Background: Melanoma represents a highly immunogenic and therapeutically challenging malignancy. The complex cellular ecosystem of the tumor microenvironment (TME) acts as a critical driver of immune evasion, patient prognosis, and treatment resistance. Traditional bulk sequencing often fails to resolve these high-resolution intercellular dynamics. This investigation presents an end-to-end explainable machine learning and network biology framework designed to deconstruct TME single-cell heterogeneity, discover novel candidate biomarkers, and map actionable cell-cell communication networks. Methodology: High-quality single-cell RNA sequencing (scRNA-seq) expression data from melanoma lesions (GSE115978) were processed using a multi-phase computational workflow. Following cellular filtration, library size normalisation, and highly variable gene (HVG) selection, cells were partitioned using unsupervised Leiden clustering and annotated via signature gene scoring matrix methods (Wolf et al., 2018; Traag et al., 2019). An optimal gradient-boosted tree ensemble classifier (XGBoost) was constructed for cross-compartment biomarker screening (Chen & Guestrin, 2016), interpreted using SHAP (SHapley Additive exPlanations) values (Lundberg & Lee, 2017), and augmented with a PyTorch deep autoencoder. Downstream systems analyses included biological pathway enrichment via gseapy (Kuleshov et al., 2016), ligand-receptor communication mapping via LIANA (Efremova et al., 2020; Dimitrov et al., 2022), and Pearson co-expression network profiling. Cross-cohort validation was conducted on an independent melanoma cohort (GSE72056) (Tirosh et al., 2016b), and prognostic utility was clinically validated using empirical patient survival data from the TCGA-SKCM cohort (TCGA Research Network, 2015; Davidson-Pilon, 2019). Results: Unsupervised Leiden clustering partitioned the cellular atlas into seven major structural and immunological compartments: Melanoma/Tumor, T-Cells, B-Cells, Macrophages, NK Cells, Endothelial cells, and Cancer-Associated Fibroblasts (CAFs). The trained XGBoost model prioritized CD79A, MLANA, LYZ, MFAP4, and CDH5 as top candidate biomarkers across distinct microenvironmental niches. SHAP explainability analysis confirmed MLANA and S100B as key positive predictors of tumor cell identity, while highlighting a bidirectional distribution for B2M linked to antigen presentation downregulation. Functional enrichment mapped Coagulation and Epithelial-Mesenchymal Transition (EMT) as highly dysregulated pathways driving the malignant state. Cell-cell communication profiling inferred highly significant SERPINE1 signalling axes to LRP1 and PLAUR receptors. SPARC was identified as the central co-expression hub regulator. Cross-cohort screening in GSE72056 confirmed biomarker stability across independent patient profiles. Empirical clinical survival validation within the TCGA-SKCM cohort (n=314) demonstrated that heightened expression of CD79A correlates with a statistically significant survival advantage (Log-Rank p = 4.4202 * 10-3), extending median overall survival from 26.7 months to 44.8 months. Conclusion: This computational pipeline systematically resolves TME heterogeneity, revealing a robust biomarker signature centred on CD79A and MLANA, alongside SERPINE1-driven immune-stromal crosstalk. The discovery of the protective prognostic role of CD79A links single-cell immune networks directly to clinical patient outcomes, providing a reproducible roadmap for anti-tumour immune engagement and immunotherapeutic stratification. Keywords: Melanoma, Tumor Microenvironment, Single-Cell RNA-Seq, scRNA-seq, XGBoost, SHAP, LIANA, TCGA-SKCM, CD79A, SPARC, SERPINE1, Biomarker Discovery, Leiden Clustering