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Updated: Jul 10, 2026

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing
Published on: September 25, 2011
An Integrative Genomics Approach for the Discovery of Potential Clinically Actionable Diagnostic and Prognostic
Mark Fertel1, Duaa Mohammad Alawad1, Chindo Hicks1
1Department of Genetics and the Bioinformatics and Computational Medicine Program, School of Medicine, Louisiana State University Health Sciences Center, 533 Bolivar, New Orleans, LA 70112, USA.
Abstract:
Background: Despite remarkable progress in clinical management of patients and intensified screening, colorectal cancer remains the second most common cause of cancer-related death in the United States. The recent surge of next generation sequencing has enabled genomic analysis of colorectal cancer genomes. However, to date, there is little information about leveraging gene expression data and integrating it with somatic mutation information to discover potential biomarkers and therapeutic targets. Here, we integrated gene expression data with somatic mutation information to discover potential diagnostic and prognostic biomarkers and molecular drivers of colorectal cancer. Methods: We used publicly available gene expression and somatic mutation data generated on the same patient populations from The Cancer Genome Atlas. We compared gene expression data between tumors and controls and between dead and alive. Significantly differentially expressed genes were evaluated for the presence of somatic mutations and subjected to functional enrichment analysis to discover molecular networks and signaling pathways enriched for somatic mutations. Results: The investigation revealed a signature of somatic mutated genes transcriptionally associated with colorectal cancer and a signature of significantly differentially expressed somatic mutated genes distinguishing dead from alive. Enrichment analysis revealed molecular networks and signaling pathways enriched for somatic mutations. Conclusions: Integrative bioinformatics analysis combining gene expression with somatic mutation data is a powerful approach for the discovery of potential diagnostic and prognostic biomarkers and potential drivers of colorectal cancer.

