Integrative Transcriptomic Analysis of GSE65194 and GSE45827 Datasets Identifying Consistent Gene Expression Signatures and New putative Target in Breast Cancer

Misbahuddin M Rafeeq, Ziaullah M Sain, Dalia M Alammari, Hanadi M Baeissa, Hind M Naffadi, Afnan Alkathiri, Abdulrahman Almutairi, Rashed Ahmed Alniwaider

Abstract


Background: Breast cancer represents a complex molecular disease with high heterogeneity &mortality rates globally. Despite advances in treatment strategies, understanding the underlying transcriptional alterations remains critical for developing effective therapies. This study conducted an integrative analysis of transcriptomic datasets GSE65194 and GSE45827 to identify consistent gene expression signatures and potential therapeutic targets in breast cancer.

Methods: Differential gene expression analysis was performed using GEO2R. The datasets were analyzed for upregulated and downregulated genes using stringent criteria (adjusted p-value < 0.05, |log2 fold change| > 1). Statistical validation included volcano plots, MA plots, UMAP visualization, and Pearson correlation analysis. Gene overlaps were assessed through Venn diagram analysis.

Results: Analysis revealed 5,554 differentially expressed genes in GSE65194 (4,968 upregulated, 586 downregulated) and 4,757 in GSE45827 (4,683 upregulated, 74 downregulated). The datasets showed remarkable correlation (r = 0.9992) and 82.8% overlap in upregulated genes. Key genes including COL11A1 (log2FC = 7.69), COL10A1 (log2FC = 7.33), and CXCL10 showed consistent upregulation across datasets. UMAP analysis demonstrated clear separation between cancer and normal samples, validating the distinct transcriptional profiles.

Conclusion: The strong correlation between datasets and consistent gene expression patterns identify reliable molecular signatures in breast cancer. The identified genes, particularly those involved in extracellular matrix remodeling and immune response, represent potential therapeutic targets and diagnostic biomarkers. These findings provide a robust foundation for developing targeted therapeutic strategies, though further functional validation is essential for clinical translation.

Keywords: Gene Expression Omnibus; Microarray; Affymetrix; Breast Cancer; Venn Diagram; Pearson Coefficient


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DOI: http://dx.doi.org/10.62940/als.v12i4.3736

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