Computational Stemness and Cancer Stem Cell Markers in Oral Squamous Cell Carcinoma: A Systematic Review, Dual Meta-Analysis, and Functional Meta-Synthesis

<b>Background/Objectives:</b> Stemness has been proposed as a unifying driver of invasion, treatment resistance, and relapse in oral squamous cell carcinoma (OSCC). We synthesized two complementary evidence streams to determine whether higher stemness predicts poorer survival in OSCC: (i) computational stemness signatures derived from transcriptomic/epigenetic data and (ii) tissue cancer stem cell (CSC) immunophenotypes by immunohistochemistry (IHC). <b>Methods:</b> Following PRISMA 2020, we searched PubMed/MEDLINE, Embase, Scopus, and SciELO. Adults with histologically confirmed OSCC were eligible. Primary outcome was overall survival (OS); disease-specific survival (DSS) and recurrence-free survival (RFS) were secondary. Two parallel meta-analyses pooled effects within domains; random-effects restricted maximum likelihood (REML) models were applied. <b>Results:</b> Of 785 records, 11 studies met criteria. For computational signatures (k = 6), higher stemness was associated with poorer OS (pooled HR 2.24, 95% CI 1.61-3.12; I² ≈ 49%). Sensitivity excluding the single unadjusted Kaplan-Meier (KM)-derived estimate yielded a similar effect (HR 2.13, 95% CI 1.56-2.89). For CSC-IHC (main analysis, k = 2), CSC-positive profiles predicted worse OS (pooled HR 2.01, 95% CI 1.42-2.84; I² ≈ 0%); results were robust to excluding an internally inconsistent study (single-study HR 2.078). An exploratory sensitivity analysis, including a 1-year HR (different time horizon), increased heterogeneity and was not considered definitive. A functional meta-synthesis converged on epithelial-mesenchymal transition/extracellular matrix remodeling, hypoxia/glycolysis, redox/ferroptosis resistance, and ribosome/rRNA biogenesis, supporting biological plausibility across modalities. <b>Conclusions:</b> Across computational and IHC evidence, stemness consistently portends inferior OS in OSCC, offering a biologically anchored framework for risk stratification and testable therapeutic hypotheses.