Integrative Analysis of Public Omics Data to Explore Potential Targets for TRT

Research context: At SCK CEN, research in nuclear medicine focuses on developing and optimizing Targeted Radionuclide Therapy (TRT). Understanding which molecular features in colorectal cancer (CRC) could be relevant for TRT requires integrating transcriptomic and proteomic data from multiple public sources.

Research questions: Can previously observed protein patterns in CRC be validated across independent omics datasets? Do these proteins show consistent expression trends, survival associations, and pathway enrichment that could guide future TRT research?

Datasets and methodology: The student will use open-access datasets (TCGA, GTEx, GEO, HPA) to perform differential expression, survival, and pathway analyses in R using tools such as limma, DESeq2, and clusterProfiler. Methodological concerns include potential incomplete data, batch effects, and variation in RNA–protein correspondence, which will be managed through standardized normalization and transparent reporting.

Expected contribution: The project will deliver a reproducible analysis pipeline and an integrated dataset validating molecular patterns in CRC. Key skills: R programming, biostatistical testing, and curiosity about gene-expression analysis and oncology data.