Bioinformatics-driven discovery and experimental validation of differentially expressed genes in colorectal adenomas.
Summary
This study employed a bioinformatics approach to identify differentially expressed genes (DEGs) in various types of colorectal adenomas (tubular, tubulovillous, villous) compared to normal tissues. Gene expression data analysis revealed 1,024 common DEGs, with COL1A2 and CXCL8 identified as hub genes. Specific genes for each adenoma type were also discovered. RT-qPCR validation and ROC analysis confirmed the discriminative potential of CXCL8 and COL1A2 in distinguishing adenomatous/cancerous tissues from normal mucosa. These findings offer valuable insights into adenoma biology and suggest potential targets for colorectal cancer prevention and treatment.
Analysis
CLINIQUE: CXCL8 and COL1A2 are highlighted as potential biomarkers for distinguishing colorectal adenomas/cancers from normal tissues, with ROC analysis supporting their discriminative potential. While promising, these findings are preliminary and would require further clinical studies to validate their diagnostic and prognostic utility. The direct clinical impact is long-term (5+ years), warranting trials to assess their role in screening or monitoring patients at risk for colorectal cancer. BIOMOL: The study identified differentially expressed genes, including hub genes like COL1A2 and CXCL8, as well as genes specific to each adenoma subtype (NTRK2, JUN, LAT, DRD2 for tubular adenomas; H2AFZ, NME1, MRTO4, SSRP1 for tubulovillous adenomas; WDR43, POLR1B, NHP2 for villous adenomas). These discoveries, based on gene expression data analysis and validated by RT-qPCR, shed light on the molecular mechanisms of adenoma progression. They could lead to the identification of new therapeutic targets or biomarkers for future clinical tests, potentially via NGS panels or ddPCR tests for early detection. BIOINFO: The bioinformatics approach was central, utilizing public datasets (GSE117606 and GSE117607) for DEG identification, functional enrichment, and protein-protein interaction network analysis. This methodology enabled the discovery of molecular signatures specific to different adenoma types. Clinical deployment of these findings could involve integrating these gene signatures into bioinformatics analysis pipelines for biopsy classification or risk stratification, potentially improving diagnostic accuracy and personalized treatment.