Gene Expression Analysis in Advanced Gastric Cancer
Overview #
Statistical and computational analysis of transcriptomic data from a multicenter gastric cancer cohort to identify gene expression signatures associated with venous thromboembolism (VTE) risk and clinical subtypes.
Problem #
High-dimensional gene expression datasets in clinical cohorts present challenges such as multiple hypothesis testing, batch effects and subtype heterogeneity. Identifying robust disease-associated gene signatures requires rigorous statistical frameworks that control for false discovery rates while preserving biological signal across stratified patient subgroups.
Solution #
Performed differential gene expression and association analyses using R and specialized genomic statistical frameworks. Applied robust normalization (SST-RMA), empirical Bayes methods (eBayes) and stratified statistical testing to identify VTE-associated genes. Integrated multivarate logistic regression models and pathway enrichment analysis to evaluate biological relevance across clinical subtypes.
Stack #
R · SST-RMA · eBayes · Logistic regression · Differential expression analysis · Multiple testing (FDR) · Linux
Impact #
- Identified gene expression signatures associated with thrombosis risk in advanced gastric cancer patients.
- Integrated multi-cohort clinical and transcriptomic datasets for subtype-stratified analysis.
- Applied robust statistical frameworks for high-dimensional biological data analysis.
- Contributed to understanding of molecular mechanisms linking hemostasis and cancer progression.
Related Publications #
Zaragoza-Huesca, D., Garrido-Rodríguez, P., Jiménez-Fonseca, P., Martínez de Castro, E., Sánchez-Cánovas, M., Visa, L., Custodio, A., Fernández-Montes, A., Peñas-Martínez, J., Morales del Burgo, P., Gallego, J., Luengo-Gil, G., Vicente, V., Martínez-Martínez, I., & Carmona-Bayonas, A. (2022). Identification of Thrombosis-Related Genes in Patients with Advanced Gastric Cancer: Data from AGAMENON-SEOM Registry. Biomedicines, 10(1), 148.