The potential mechanism of antifluorescent lung cancer by Chinese medicine Huang Qin: Based on bioinformatics molecular, network pharmacology and imaging histology analysis

Abstract

This study provides a deep analysis of the potential mechanisms and effects of the traditional Chinese medicine Scutellaria baicalensis in the treatment of non-small cell lung cancer (NSCLC). By integrating public databases and clinical resources, we adopted a comprehensive strategy combining bioinformatics, network pharmacology, and machine learning techniques to screen out tumor biomarkers closely related to the prognosis of NSCLC, and constructed an accurate predictive model to comprehensively elucidate the complex interactions between the active components of Scutellaria baicalensis and the prognosis of NSCLC. The incorporation of radiomics technology enabled us to extract high-throughput radiological features from medical images, achieving non-invasive prediction of tumor biomarker expression status, further enriching our research methods. We constructed a Scutellaria baicalensis-NSCLC interaction network, accurately calculating the intersection of drug-specific targets and disease-related targets, and utilized protein-protein interaction (PPI) networks and functional enrichment analyses to deeply explore the potential mechanisms of action between Scutellaria baicalensis components and NSCLC. With the help of machine learning tools, we successfully identified key hub genes and verified their importance in lung cancer treatment protocols through immune infiltration analysis and molecular docking studies. The study results showed that 45 active components were screened out, with 628 active component-related target sites, 3076 differentially expressed genes, and 5628 co-expressed genes related to the disease Module genes, intersecting targets 98; GO functional enrichment analysis mainly enriched to BP entries 581, cell composition CC entries 23, molecular function MF entries 30 (p.adj <0.01); KEGG pathway enrichment analysis screened out 111 significant signaling pathways (P < 0.05), mainly involving IL-17 Signaling Pathway, TNF Pathway, AGE-RAGE Signaling Pathway in diabaetic,P53 Signaling Pathway, Toll-like rector et al.; molecular docking showed that compounds have good affinity with the screened core targets GAPDHIL6, TNF, JUN, MMP9, CDH1. Machine learning predicted the intersection of core target genes, among which 5 (FABP4, XDH, GPBAR1, CA4, CDH1) were identified as key target genes for drug therapy. This study not only revealed the significant potential and mechanism of action of Scutellaria baicalensis in anti-non-small cell lung cancer but also provided new perspectives and insights for the development of multi-target drug therapy strategies. By integrating various advanced technologies and methods, we have provided a solid theoretical basis and practical guidance for precision treatment and personalized medication for NSCLC, offering new hope for lung cancer patients.