Multi-Dataset Identification and Validation of New Gene Expression Signatures: Insights into Matrix Remodeling Pathways in NSCLC - Alghamdi, Rashed Mohammed (International Journal of Pharmaceutical Investigation, 2025)
Background Non-Small Cell Lung Cancer (NSCLC) epitomizes 85% of lung cancer cases with poor survival rates. Understanding molecular mechanisms through gene expression analysis is important for developing real treatments. This study intended to identify consistent molecular signatures in NSCLC using publicly available transcriptome data. Materials and Methods We analyzed gene expression profiles from two independent NSCLC datasets (GSE33532 and GSE19188) from the Gene Expression Omnibus database. Differential gene expression examination was performed to identify consistently dysregulated genes across both datasets. Statistical validation included Pearson correlation analysis and significance testing to ensure result reliability. Results Analysis revealed 53 consistently altered genes across datasets, comprising 28 upregulated (42.9%) and 25 downregulated (60%) genes, with exceptional correlation (r=0.9927). Upregulated genes included matrix remodeling factors (COL11A1, MMP12, MMP1) and cell proliferation markers (TOP2A), while downregulated genes included tissue-specific factors (CLDN18, AGER, SFTPC, SCGB1A1). These alterations indicate significant changes in extracellular matrix organization, cell proliferation and lung tissue homeostasis. Dataset-specific expressions (28.6% upregulated, 20% downregulated) reflected NSCLC's molecular heterogeneity. Conclusion Our analysis identified reproducible gene expression signatures in NSCLC, providing insights into disease mechanisms and potential therapeutic targets. The strong correlation between datasets validates these molecular signatures' biological significance. These findings suggest multiple therapeutic approaches, including matrix remodeling inhibition and restoration of tissue-specific gene expression. While these results offer promising directions for NSCLC treatment, further functional validation studies will inherently add more to its clinical utility.
Keywords: Affymetrix; Correlation Coefficient; Gene Expression Omnibus; GEO2R; NSCLC Cancer; Venn Diagram; Volcano Plot