Abstract
Melanoma, which originates from pigment-producing melanocytes, is an aggressive and deadly skin cancer. Despite extensive research, its mechanisms of progression and metastasis remain unclear. This study uses quantitative phase imaging via digital holographic microscopy, Principal Component Analysis (PCA), and t-distributed Stochastic Neighbor Embedding (t-SNE) to identify the morphological, optical, and behavioral differences between normal melanocytes and SK-MEL-28 melanoma cells. Our findings reveal significant differences in cell shape, size, and internal organization, with SK-MEL-28 cells displaying greater size variability, more polygonal shapes, and higher optical thickness. Phase shift parameters and surface roughness analyses underscore melanoma cells' uniform and predictable textures. Violin plots highlight the dynamic and varied migration of SK-MEL-28 cells, contrasting with the localized movement of melanocytes. Hierarchical clustering of correlation matrices provides further insights into complex morphological and optical relationships. Integrating label-free imaging with robust analytical methods enhances understanding of melanoma's aggressive behavior, supporting targeted therapies and highlighting potential biomarkers for precise melanoma diagnostics and treatment.