Democratizing Risk Stratification: In-Silico Validation and Pharmacoeconomic Assessment of SLC2A1 as a Low-Cost Prognostic Biomarker for Cervical Cancer
Fernando Augusto Siuta dos Santos, Pedro Dytz Marin, Giulia Scain, Isabela Alicia Fink, Julia Loureiro Chaves Cé, Stéphanie Klein Brum, Mariana Weremeier Weber, Martina de Sá, Rafaela Sumienski Girardi, Solana de MeloPURPOSE
High-cost prognostic genomic panels exacerbate health disparities, particularly for cervical cancer in low- and middle-income countries (LMICs). This study validates the transcriptomic prognostic power of the hypoxia biomarker SLC2A1 (GLUT1), evaluating the clinical and economic viability of utilizing highly affordable immunohistochemistry (IHC) to democratize precision risk stratification in resource-constrained public health systems.
METHODS
A retrospective in-silico survival and pharmacoeconomic analysis was performed using the Kaplan-Meier Plotter RNA-seq database for cervical squamous cell carcinoma (CESC). Patients were stratified by SLC2A1 mRNA expression. Prognostic impact was quantified via overall survival (OS), log-rank testing, and hazard ratios (HRs). An economic impact model compared the utility of a proposed sub-$30 US dollars (USD) IHC proxy test against standard $3,000 USD comprehensive genomic profiling.
RESULTS
Analysis of the CESC cohort (N = 304) established high SLC2A1 expression as a critical, independent predictor of mortality. Stratification yielded n = 80 high-expression and n = 224 low-expression patients. The high-expression cohort experienced a drastically accelerated mortality rate, characterized by an HR of 1.87 (95% CI, 1.16 to 3.04). This translates to an 87% increased risk of death (
CONCLUSION
SLC2A1 overexpression is a robust, quantifiable predictor of severe mortality in cervical cancer. Translating this transcriptomic data into a low-cost IHC clinical assay offers a 99% cost-saving alternative to broad genomic panels. Prioritizing single-biomarker validation is a high-yield pharmacoeconomic strategy to bridge the precision oncology gap and optimize resource allocation in LMICs.