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.

Influence analysis and promotion countermeasures of green finance policy in China-Traceability based on grounded theory and validation using the csQCA method.

Green finance is closely related to sustainable energy development. Using the NVivo12plus software, a governance model of China's green finance policy was constructed using 22 green finance policy texts at the central level as research objects. Furthermore, based on the csQCA method, Tosmana software was used to develop and verify a theoretical model of 19 policy text cases. The research results demonstrate that policy belief, policy objectives, policy tools, policy feedback, and policy cycle are the main components of China's green finance policy governance. Furthermore, policy instruments are the fundamental factors affecting the governance effectiveness of China's green finance policy. Policy goals and policy feedback dominate the influence pattern of green finance policy in China. There are three modes driving the influence of green finance policies: regulation-oriented, collaborative-driven, and tool-guided. Finally, for the optimization and improvement of green finance policies, three forces must be improved: stimulus force, driving force, and promotion force.