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Transcriptome profiling of colorectal cancer and paired normal tissues using Affymetrix HGU133-X3P arrays

ABSTRACT: Background: Colorectal cancer (CRC) is a major global health concern, responsible for a significant number of cancer-related deaths. In this context, the development of accurate gene signatures is of paramount importance for predicting prognosis and guiding therapeutic decisions. This study was undertaken with the specific aim of creating an expression-based risk assessment model to predict overall survival (OS) in CRC patients. Methods: We analysed 49 paired FFPE tumours and normal tissue samples from sporadic CRC cases using Affymetrix HGU133-X3P arrays. We identified candidate prognostic genes by combining transcriptome profiling with public mRNA and miRNA microarray datasets. Univariate Cox regression analysis determined essential genes predicting OS. A prognostic risk score was developed using the expression of selected genes and patient stage, validated with two independent GEO datasets (GSE17536 and GSE40967). Results: We found 845 differentially expressed genes (FDR≤0.001, fold change ≥2) mainly enriched in the ECM-receptor interaction pathway. Four overexpressed genes in this pathway were identified as critical regulators of CRC. The high expression of these genes correlated with poor prognosis. Our four-gene prognostic signature, validated with independent datasets, stratified CRC patients into low- and high-risk groups with significant OS differences. Conclusion: Our risk-score model, developed and validated in this study, holds significant potential as a prognostic tool for predicting survival and clinicopathological features in CRC patients. Its successful application could enhance the precision of therapeutic decisions in clinical practice.

ORGANISM(S): Homo sapiens

PROVIDER: GSE271719 | GEO | 2025/07/21

REPOSITORIES: GEO

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Transcriptome profiling of colorectal cancer and paired normal tissues using Affymetrix HGU133-X3P arrays

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