Abstract 1082: A comprehensive platform for stage-specific analysis of colon cancer: integrating transcriptomic and clinical data for biomarker discovery

Introduction: Understanding the molecular mechanisms underlying stage-specific colon cancer progression is essential for identifying biomarkers and developing effective therapies. Aim: To address this need, we extended our previously established comprehensive database with stage-specific clinical and transcriptomic data. The new updated database was employed to mine stage-specific patterns and explore functional trends across stage-stratified colon cancer cohorts. Methods: We used data from GEO (gene arrays), TCGA, and GTEx (RNA-seq) databases. A total of 1,926 colon cancer samples from gene chip studies were manually curated from GEO, and RNA-seq data comprising 469 samples were included from TCGA. To identify differentially expressed genes across stages, we first performed ANOVA tests, adjusting p-values using the Benjamini-Hochberg method with a cutoff of 0.01. Effect size was then estimated using the eta squared approach, applying a moderate effect size threshold of 0.06. Results: Stage-specific data were incorporated into our previously established colon cancer dataset, including 37 (array) and 75 (RNAseq) Stage I samples, 291 (array) and 176 (RNAseq) Stage II samples, 197 (array) and 128 (RNAseq) Stage III samples, and 123 (array) and 64 (RNAseq) Stage IV samples. The expanded dataset facilitates a detailed investigation of gene expression changes across colon cancer stages, uncovering stage-dependent molecular signatures. We utilized the integrated database to uncover genes with consistent expression change across tumor progression. We identified 94 stage-specific genes with significant differential expression and at least a moderate effect size. These genes were primarily associated with transcription and phosphorylation. Among them, COG5 (pAdj = 8.17E-09), AMMECR1 (pAdj = 9.76E-10), and SOWAHC (pAdj = 8.51E-10) showed the most consistent differences across stages. The new integrated platform with all transcriptomic and clinical data is accessible at www.tnmplot.com Conclusion: Our enriched platform provides a powerful resource for the comprehensive exploration of stage-stratified colon cancer data, offering valuable insights into cancer biology and supporting biomarker discovery. Citation Format: Aron Baratha, Balazs Gyorffy. A comprehensive platform for stage-specific analysis of colon cancer: integrating transcriptomic and clinical data for biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1082.