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Breast neoplasms, Vascular endothelial growth factor A, Gene regulatory networks, What&,rsquo s Known The literature highlights the central role of angiogenesis in tumor progression, metastasis, and therapeutic resistance. What&,rsquo s New Interaction network analysis identified MMP9, VEGFA, HIF1A, APOE, TNF, IL1A, MMP14, and TNF as central hub genes with extensive interactions across co-expression, physical interactions, and shared pathways. The results predict that the hub genes correlated with angiogenesis may serve as potential therapeutic targets or could be biomarkers for breast cancer. IntroductionBreast cancer is a global health concern with a significant effect on public health. 1, There were about 2.26 million new cases of breast cancer in 2020, accounting for about 11.6% of all new cancer incidents and surpassing lung cancer as the most common cancer globally. 2, Among individuals diagnosed with early-stage breast cancer, approximately 30% of node-negative breast cancers and up to 70% of node-positive cancers can progress to metastatic disease, depending on various risk factors. 3, Understanding the genetic background of this malignancy is important for early detection, accurate diagnosis, and the development of specific treatments. 4, The formation of new blood vessels from preexisting ones in postnatal life, known as angiogenesis, plays a vital role in both normal bodily functions and pathological conditions. 5, The angiogenesis process is tightly regulated and involves the interaction of various pro- and anti-angiogenic factors. 6, When the balance between these factors is disrupted, it can result in either excessive or inhibited angiogenesis, which plays an important role in the development of different diseases. 7, Tumors cannot expand more than a certain volume unless they establish a blood supply to provide them with the necessary nutrients and oxygen for their growth. 8, Angiogenic growth factors, including epidermal growth factor (EGF), vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF), and transforming growth factor (TGF), are crucial in facilitating angiogenesis and tumor development. 9, Among these factors, VEGF is pivotal in controlling the growth of endothelial cells, which are essential for the formation of new blood vessels. 10, VEGF is expressed in response to some soluble mediators, including growth factors and cytokines. By enhancing endothelial permeability, VEGF stimulates endothelial cell proliferation, survival, migration, cell-cell contact, and lumen formation. VEGF carries out its functions by binding to cell surface receptors, which are a group of membrane tyrosine kinase receptors. 11, By interacting with cell surface receptors, especially at the site of inflammation, it initiates intracellular signaling pathways and activates multiple genes that regulate essential cellular processes involved in angiogenesis. 12, Because tumor angiogenesis is crucial for tumor growth, studying the pathways and hub genes involved in it is seen as a promising approach to developing therapies that can hinder tumor growth and development. 11, Gene network formalism is the primary method used to uncover the role of genes that are overexpressed in cancer samples compared to normal ones. Innovations in in-silico techniques have streamlined research into gene function, disease, and precision medicine at the molecular level by employing an integrated approach to identify a group of genes linked to angiogenesis, migration, inflammation, and apoptosis that have a significant effect on the survival of breast cancer patients. 13, The objective of this study was to conduct a deep bioinformatics analysis of publicly available datasets to pinpoint hub genes and key pathways involved in angiogenesis and migration, particularly in their correlation with the inflammation pathway in breast cancer. Materials and MethodsThis study was conducted at the National Institute of Genetic Engineering and Biotechnology (NIGEB) in collaboration with Tarbiat Modares University of Iran, between 2023 and 2025. Data Collection and Processing Statistical analysis of differential gene expression was performed using the Limma-Voom pipeline. The empirical Bayes method implemented in the Limma package was used to moderate standard errors across genes. P values were adjusted for multiple testing using the Benjamini&,ndash Hochberg procedure. Genes with an adjusted P&,lt 0.05 and absolute log2 fold change greater than 1 were considered significantly differentially expressed.This dataset initially contained 114 normal samples however, one sample was excluded due to incomplete metadata, resulting in 113 normal samples and 1109 tumor samples used in the analysis. WebGestalt (WEB-based GEne SeT AnaLysis Toolkit) The bioinformatics resource called WebGestalt (https,//www.webgestalt.org/,) was employed to determine gene ontology (GO) functional annotation (GO terms, including Biological Process and Molecular Function). Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathways enrichment analysis were performed. An adjusted P&,lt &,#8197 0.05 was set to show a statistically significant difference. GeneMANIA GeneMANIA (https,//genemania.org/,) is a server for evaluating protein and genetic interactions, co-localization, pathways, co-expression, and domain-protein similarity of target genes. In this research, we assessed the relationship between matrix metalloproteinase-9 (MMP9), matrix metallopeptidase 14 (MMP14), Endogenous Vascular Endothelial Growth Factor-A (VEGFA), Apolipoprotein E (APOE), Hypoxia-Inducible Factor-1 Alpha (HIF1A), Tumor necrosis factor (TNF), Interleukin-1 alpha precursor (IL1A), and their interactive genes via this web tool. Verification of Expression The GEO database (https,//www.ncbi.nlm.nih.gov/geo/,) was used to determine the mRNA expression levels in BRCA (BReast CAncer gene) subjects. Two datasets of microarray, namely GSE37751 and GSE42568, were downloaded. R packages (affy and Limma) were used to screen differentially expressed genes for both datasets. P value less than 0.05 and logFC higher and lower than 0 were the filter conditions to explore DEGs between BRCA and normal specimens. On the other hand, the open cancer sources GENT2 (http,//gent2.appex.kr/gent2/,), TNMplot (https,//tnmplot.com/,), UCSCXena (https,//xenabrowser.net/,), ENCORI platform (https,//rnasysu.com/encori/index.php,), BioXpress (https,//hivelab.biochemistry.gwu.edu/bioxpress,), OncoDB (https,//oncodb.org/,), OncoMX (https,//www.oncomx.org/,), and GEPIA2 (http,//gepia2.cancer-pku.cn/#index,) were employed to assay differential expression of mRNAs in patients with BRCA.ResultsWe performed a comprehensive analysis of angiogenic hub genes (MMP9, MMP14, VEGFA, APOE, HIF1A, TNF, IL1A) to investigate their expression patterns and biological relevance in breast cancer, focusing on their roles in inflammation and cell migration Differential Gene Expression Using RNA-seq data from TCGA and subsequent normalization through Limma and Voom packages, we identified significant changes in the expression of angiogenic hub genes. Among the analyzed genes, MMP9 exhibited the highest positive fold change with a logFC of 2.75 (figure 1A, and table 1,). Similarly, MMP14 showed substantial upregulation with a logFC of 1.07 (figure 1B, and table 1,). Other notable upregulated genes included VEGFA with a logFC of 0.55 (figure 1C, and table 1,), and APOE with a logFC of 0.66 (figure 1D, and table 1,).Figure 1. The expression of MMP9 in BRCA and normal tissue is shown in panel A. The expression of MMP14 in BRCA and normal tissues is shown in panel B. The expression of VEGFA in BRCA and normal tissues is shown in panel C. The expression of APOE in BRCA and normal tissues is shown in panel D. The expression of APOE in BRCA and normal tissues is shown in panel E. The expression of HIF1A in BRCA and normal tissue is shown in panel F. RCA, Breast invasive carcinoma MMP9, Matrix metalloproteinase 9 MMP14, Matrix metalloproteinase 14 VEGFA, Vascular endothelial growth factor A APOE, Apolipoprotein E TCGA, The cancer genome atlas HIF1A, Hypoxia-inducible factor 1 Alpha TNF, Tumor necrosis factor BRCA, Breast invasive carcinoma. RCA, Breast invasive carcinoma MMP9, Matrix metalloproteinase 9 MMP14, Matrix metalloproteinase 14 VEGFA, Vascular endothelial growth factor A APOE, Apolipoprotein E TCGA, The cancer genome atlas HIF1A, Hypoxia-inducible factor 1 Alpha TNF, Tumor necrosis factor BRCA, Breast invasive carcinomaGenesLogFCP valueAdjusted P valueMMP92.74954.84E-411.02E-39VEGFA0.55498.57E-103.30E-09APOE0.65762.04E-065.87E-06HIF1A0.08060.42440.4992TNF0.052700.75050.8001MMP141.071343.51E-233.11E-22IL1A0.40970.10630.1501The data includes statistical measures to assess the significance of these changes. MMP9, Matrix metalloproteinase 9 VEGFA, Vascular endothelial growth factor A APOE, Apolipoprotein E HIF1A, Hypoxia-inducible factor 1 Alpha TNF, Tumor necrosis factor MMP14, Matrix metalloproteinase 14 IL1A, Interleukin 1 Alpha LOGFC, Log fold change |