GCAS: An Integrated R Package and Shiny App for Comprehensive Cancer Data Analysis

Cancer research is pivotal for understanding cancer biology, discovering new therapeutic targets, and advancing precision medicine. However, it faces challenges such as data complexity, dispersed analytical tools, and the lack of a unified platform. To address these issues, we developed the GEO Cancer Analysis Suite (GCAS), an R package and visualization interface via shinyApp. GCAS includes four main modules: differential gene expression analysis, correlation studies, pan-cancer analysis, and immune infiltration and drug sensitivity analysis. These modules facilitate the identification of potential cancer biomarkers, elucidation of gene regulatory networks, comprehensive multi-cancer analysis, and assessment of gene expression in relation to immune cell infiltration and drug sensitivity. Using GCAS, GAPDH was found to be upregulated in multiple lung cancer and breast datasets and positively correlated with the m⁶A regulatory gene IGF2BP3. Further in vitro assays suggested that IGF2BP3 regulates GAPDH mRNA stability. Immune infiltration analysis indicated a negative correlation between GAPDH expression and CD4 T cell infiltration scores. Drug sensitivity analysis revealed a significant negative correlation between GAPDH expression and sensitivity to EGFR-targeting drugs, particularly Erlotinib. GCAS is a crucial tool in cancer research, simplifying data analysis and enhancing the discovery of novel biomarkers, immune landscape profiles, and drug sensitivity predictions, significantly contributing to cancer research and precision medicine.