UMAP-Based Transcriptional Profiling and Differential Expression Analysis of Breast Cancer Datasets GSE65194 and GSE42568
Abstract
Background: Breast cancer remains a major global health alarm. By analyzing transcriptomic datasets GSE65194 & GSE42568, we identified quite a lot of consistently dysregulated genes that may impact cancer progression & serve as potential therapeutic targets.
Methods: With the GEO2R for a differential gene expression study, we identified many weighty genes (p < 0.05, absolute log2 fold change > 1), as well as both favorably upregulated & downregulated applicants. Fundamental visualizations contained within volcano plots, MA plots, UMAP for dimensionality reduction, & box plots. With the help of Venn diagram to validate overlapping differentially expressed genes transversely datasets, confirming sturdy shared expression patterns.
Results: Breast cancer gene expression datasets GSE65194 & GSE42568, which both equivalence cancerous breast tissue to normal controls, identified 5554 & 2957 differentially expressed genes, correspondingly. Definitely, in GSE65194, genes were upregulated (4968), & were downregulated (586), whereas in GSE42568, genes were upregulated (1512) & were downregulated (1445). In particular, among the genes in GSE65194, COL11A1 showed the greatest expression change, with a log2 fold change of 7.69. UMAP study of both datasets evidently separated cancerous & normal samples, prominence different gene expression profiles.
Conclusion: More than a few genes are dependably upregulated across breast cancer datasets, portentous shared disease-related pathways & latent value as biomarkers or therapeutic targets, though clinical authentication is still needed.
Keywords:
Gene Expression Omnibus, Breast Cancer, Differential Expression Analysis, Transcriptomics, UMAP, BiomarkersFull Text:
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DOI: https://doi.org/10.62940/als.v13i1.3885
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