Comprehensive multi-level expression profiling of key biomarkers in breast cancer patients

Objectives: In this comprehensive breast cancer (BC) study, we aimed to identify, validate, and characterize key biomarkers with significant implications in BC diagnosis, prognosis, and as therapeutic targets. Methods: Our research strategy involved a multi-level methodology, combining bioinformatic analysis with experimental validation. Results: Initially, we conducted an extensive literature search to identify BC biomarkers, selecting those with reported accuracies exceeding 20% in specificity and sensitivity. This yielded nine candidate biomarkers, which we subsequently analyzed using Cytoscape to identify a few key biomarkers. Based on the degree method, we denoted four key biomarkers, including progesterone receptor (PGR), epidermal growth factor receptor (EGFR), estrogen receptor 1 (ESR1), and Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2). Expression analysis using The Cancer Genome Atlas (TCGA) dataset revealed that PGR and EGFR exhibited significant (p-value < 0.05) down-regulation in BC samples when compared to controls, while ESR1 and ERBB2 showed up-regulation. To strengthen our findings, we collected clinical BC tissue samples from Pakistani patients and performed expression verification using real-time quantitative polymerase chain reaction (RT-qPCR). The results aligned with our initial TCGA dataset analysis, further validating the differential expression of these key biomarkers in BC. Furthermore, we utilized receiver operating characteristic (ROC) curves to demonstrate the diagnostic use of these biomarkers. Our analysis underscored their accuracy and sensitivity as diagnostic markers for BC. Survival analysis using the Kaplan-Meier Plotter tool revealed a prognostic significance of PGR, ESR1, EGFR, and ERBB2. Their expression levels were associated with poor overall survival (OS) of BC patients, shedding light on their roles as prognostic indicators in BC. Lastly, we explored DrugBank to identify drugs that may reverse the expression patterns, and estradiol, decitabine, and carbamazepine were singled out. Conclusion: Our study gives valuable insight into BC biomarkers, for diagnosis and prognosis. These findings have implications for BC management using personalized and targeted therapeutic approaches for BC patients.