Spatial Proteomics as a Potential Decision-Support Layer for Early Melanoma: A Narrative Review

Distinguishing early melanoma from borderline, atypical, or biologically intermediate melanocytic lesions remains one of the most consequential and urgent diagnostic challenges in dermato-oncology, since melanoma causes nearly 95% of skin cancer deaths. This narrative review establishes the biological relevance and evaluates the clinical maturity of mass spectrometry-based proteomics, with particular emphasis on spatially resolved approaches, as a powerful strategy to address this critical diagnostic gap. We position spatial proteomics within the broader landscape of DNA-, RNA-, and protein-based molecular diagnostics for functional manifestation, highlighting that genomic and transcriptomic methods offer standardized, higher-throughput workflows but limited insight into the tumor microenvironment and functional protein-level biology. Laser capture microdissection-mass spectrometry, imaging mass spectrometry, and Deep Visual Proteomics each preserve spatial and single-cell resolution. However, protein expression is inherently plastic, shaped by microenvironment, tissue handling, and technical artifact, and no existing study has been powered specifically for the diagnostically indeterminate categories where clinical need is greatest. We discuss how compartment-resolved "mitochondrial-high" profiles and "immune-low" microenvironment states, evidence largely extrapolated from progression and metastatic biology, might inform future risk stratification. Spatial proteomics represents a biologically promising, but not yet clinically validated, extension of morphology-led melanoma diagnosis, with the potential to resolve functional states within annotated tumor compartments. Integrated with AI-guided tissue annotation, compartment-resolved mass spectrometry and spatial proteomic profiling could, following prospective validation, help improve the distinction between borderline melanocytic lesions and melanoma while refining patient risk stratification.