Breast cancer, a major health concern worldwide, involves diverse molecular subtypes and complex gene expression patterns. This study conducted a comprehensive bioinformatics analysis of breast cancer, analyzing 10 gene expression datasets from the Gene Expression Omnibus archive to find common genes that exhibit differential expression (DEGs). Then, we conducted pan-cancer and functional enrichment analyses, including single-cell level investigations of DEGs. To identify potential hub genes, we built protein–protein interaction networks, which were further analyzed for methylation patterns and expression in various cancer stages. The study also explored interactions between these hub genes with microRNAs and transcription factors, along with their correlation with cytokine expression and infiltration of immune cells in the tumor microenvironment. The TCGA BRCA dataset was used for hub gene survival analysis. Key findings include the identification of 36 shared DEGs, with distinct expression patterns in principal component analysis, suggesting their potential as molecular signatures for diagnosis and prognosis. Investigation at the single-cell level revealed upregulated DEGs mainly expressed by tumor-associated fibroblast cells. Gene ontology unveiled upregulated genes mainly involved in the extracellular matrix, but most of the downregulated genes are tumor suppressors. The protein–protein interaction network analysis highlighted 7 hub genes: CAV1 , FN1 , BGN , CXCL12 , SPTBN1 , COL11A1 , and INHBA . Methylation analysis revealed intricate epigenetic regulation of these genes. MicroRNA and transcription factor interaction analysis underscored the complex regulatory networks influencing gene expression. Cytokine profile analysis in tumor microenvironment and its correlation with hub gene expression provided insights into the immunological landscape of breast cancer. Survival analysis indicated that both upregulated and downregulated hub genes are linked with patient overall survival outcomes, although it is not statistically significant. This study provides a detailed view of the underlying molecular mechanisms in breast cancer, suggesting potential biomarkers and therapeutic targets. Its findings can contribute significantly to understanding the complexity of the breast cancer microenvironment, leading to the development of more sophisticated and targeted treatment strategies.
Noman et al. (Fri,) studied this question.