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

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Cancer research faces challenges with complex data and fragmented tools.
• A unified platform is needed for efficient cancer data analysis and biomarker discovery.
• Advancing precision medicine requires integrated tools for multi-omics cancer studies.

Purpose of the Study:

• To develop the GEO Cancer Analysis Suite (GCAS), an R package and Shiny interface.
• To provide modules for differential gene expression, correlation, pan-cancer, and immune/drug sensitivity analysis.
• To facilitate biomarker identification, network elucidation, and drug sensitivity prediction in cancer.

Main Methods:

• Development of GCAS, an R package with a Shiny visualization interface.
• Implementation of four analysis modules: differential gene expression, correlation, pan-cancer, and immune/drug sensitivity.
• Application of GCAS to analyze lung and breast cancer datasets, focusing on GAPDH and IGF2BP3.

Main Results:

• GCAS identified GAPDH upregulation in lung and breast cancers, correlating with IGF2BP3.
• IGF2BP3 was shown to regulate GAPDH mRNA stability.
• GAPDH expression negatively correlated with CD4 T cell infiltration and sensitivity to EGFR inhibitors like Erlotinib.

Conclusions:

• GCAS is a valuable tool for simplifying complex cancer data analysis.
• The suite enhances the discovery of novel cancer biomarkers, immune profiles, and drug sensitivities.
• GCAS contributes significantly to advancing cancer research and precision medicine.