Network-Driven Methods Using Gene Expression Signatures to Find Therapeutic Targets in Breast Cancer Validated via Molecular Dynamics Studies

Abstract

Breast cancer (BC) is the second most common cause of cancer in women and the most common kind of cancer diagnosed with a high mortality rate. This heterogeneous disease is classified into multiple subtypes based on the expression of key biomarkers, including human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR). These biomarkers have significantly transformed breast cancer treatment and played a crucial role in improving the patient prognosis. Given the complexity of BC, there is a pressing need to develop additional therapeutic agents and pharmacological targets. To address this, network-based gene expression profiling has emerged as a valuable method for identifying potential therapeutic targets, as it considers various factors such as disease conditions, gene expression levels, and protein–protein interactions We began our analysis by employing statistical methods, including p-values and false discovery rates (FDR), to identify differentially expressed genes (DEGs) as potential biomarkers in breast cancer (BC). A total of 123 DEGs were identified, with 101 genes showing downregulation and 11 genes exhibiting upregulation. Survival and expression analyses indicated that each hub gene plays a crucial role in the initiation and progression of BC. An enrichment analysis revealed that most of these genes are integral components of various signaling networks. Additionally, we identified key kinases and transcription factors that regulate the proteins involved in protein–protein interactions (PPIs) associated with the DEGs. From this analysis, we also deduced potential pharmaceuticals that could interact with these hub genes. Notably, HMOX1 (Heme Oxygenase 1) emerged as a particularly promising hub gene based on our computational analysis. Promising novel compounds were investigated, resulting in high potency of binding affinities through docking and simulation investigation. The molecular dynamics simulation demonstrated significant stability of the anticipated compounds, especially the top2 complex system at the docked site. The significant binding affinity between the chemical and the binding pockets of HMOX1 complexes was confirmed by the calculation of binding free energies using MMPBSA and MMGBSA followed by hydrogen bond analysis. Hence, these findings significantly enhance our understanding of critical biomarkers in breast cancer.