Artificial Intelligence-driven spatial transcriptomics in OSCC: Mapping the tumor microenvironment and personalizing therapy.

Spatial transcriptomics (ST) represents a transformative approach in cancer research, offering high-resolution insights into the spatial organization of gene expression within tissues, particularly relevant for the complex tumor microenvironment (TME) of oral squamous cell carcinoma (OSCC). Unlike conventional bulk RNA sequencing, which masks spatial heterogeneity, ST retains the architectural context of tumors, enabling the mapping of molecular gradients, tumor-stroma interactions, and immune cell localization. Various ST platforms-such as 10x Genomics Visium, Slide-seqV2, MERFISH, NanoString GeoMx DSP, CosMx SMI, and BGI. Stereo-seq-each offers unique advantages in resolution, sample compatibility, and transcriptome depth. Their application in OSCC has led to the identification of spatially distinct gene signatures, aiding in the stratification of tumor subtypes and uncovering novel prognostic markers. Furthermore, the integration of ST with artificial intelligence (AI) and machine learning has enhanced its analytical capabilities, enabling automated feature extraction, spatial clustering, and predictive modeling of disease progression. Despite these advancements, limitations such as high computational demands, limited access to fresh-frozen tissues, and platform-specific biases persist. Nonetheless, the synergy between ST and AI heralds a new era in precision pathology, with the potential to revolutionize diagnosis, risk assessment, and personalized therapeutic strategies for OSCC.