Assessing the Impact of Patient Selection and Analytical Methods on mRNA Biomarker Identification in Ovarian Cancer...

Area of Science:

• Genomics
• Bioinformatics
• Oncology

Background:

• The Cancer Genome Atlas (TCGA) is a valuable resource for identifying differentially expressed genes (DEGs) in high-grade serous ovarian cancer (HGSOC).
• Current platinum-sensitivity biomarkers derived from TCGA data have not achieved clinical application.
• Variability in patient selection, experimental platforms, and analysis pipelines impacts DEG discovery in HGSOC.

Purpose of the Study:

• To propose a robust analytical framework for TCGA platinum sensitivity studies in HGSOC.
• To systematically evaluate how patient characteristics influence DEG analyses between platinum-sensitive and resistant groups.
• To assess the impact of experimental platforms and analysis pipelines on DEG outcomes.

Main Methods:

• Retrospective analysis of publicly available TCGA microarray and RNA-seq gene expression data.
• Assembly of three TCGA-based cohorts with varying patient numbers and stage distributions.
• Differential gene expression analysis using DESeq2, edgeR, limma-voom, and Wilcoxon rank-sum test.

Main Results:

• Partial overlap of DEGs was observed across different platforms and analysis pipelines.
• Both patient selection and analytical workflow significantly influenced the identification of DEGs.
• A robust analytical framework was established, including patient barcodes and openly shared workflows for replication.

Conclusions:

• Reproducibility of TCGA studies is limited by inconsistencies in patient selection, platinum sensitivity definitions, data sources, and analysis pipelines.
• The developed framework provides a template for standardized data analysis and transparent reporting in HGSOC platinum sensitivity biomarker studies.
• This approach aims to enhance the identification and validation of predictive signatures for platinum sensitivity in HGSOC patients.