Identification of Potential Prognostic Biomarker for Predicting Survival in Multiple Myeloma Using Bioinformatics Analysis and Experiments

To investigate the function of age-related genes in different subtypes of asthma based on bioinformatics analysis

Asthma is a heterogeneous airway inflammatory disease that can be classified according to the inflammatory phenotype. The pathogenesis, clinical features, response to hormone therapy, and prognosis of different inflammatory phenotypes differ significantly. This condition also refers to age-related chronic ailments. Here, we intend to identify the function of aging-related genes in different inflammatory phenotypes of asthma using bioinformatic analyses. Initially, the research adopted the GSEA analysis to understand the fundamental mechanisms that govern different inflammatory phenotypes of asthma pathogenesis and use the CIBERSORT algorithm to assess the immune cell composition. The differentially expressed genes (DEGs) of eosinophilic asthma (EA), neutrophilic asthma (NA), and paucigranulocytic asthma (PGA) were identified through the limma R package. Aging-related genes, screened from multiple databases, were intersected with DEGs of asthma to obtain the asthma-aging-related DEGs. Then, the GO and KEGG pathway enrichment analyses showed that the NA- and EA-aging-related DEGs are involved in the various cytokine-mediated signaling pathways. PPI network and correlation analysis were performed to identify and evaluate the correlation of the hub genes. Further, the clinical characteristics of asthma-aging-related DEGs were explored through ROC analysis. 3 and 12 aging-related DEGs in EA and NA patients show high diagnostic accuracy, respectively (AUC >0.7). This study provided valuable insights into aging-related gene therapy for phenotype-specific asthma. Moreover, the study suggests that effective interventions against asthma may operate by disrupting the detrimental cycle of "aging induces metabolic diseases, which exacerbate aging".

Screening of traditional Chinese medicine monomers as ribonucleotide reductase M2 inhibitors for tumor treatment

BACKGROUND

Ribonucleotide reductase (RR) is a key enzyme in tumor proliferation, especially its subunit-RRM2. Although there are multiple therapeutics for tumors, they all have certain limitations. Given their advantages, traditional Chinese medicine (TCM) monomers have become an important source of anti-tumor drugs. Therefore, screening and analysis of TCM monomers with RRM2 inhibition can provide a reference for further anti-tumor drug development.

AIM

To screen and analyze potential anti-tumor TCM monomers with a good binding capacity to RRM2.

METHODS

The Gene Expression Profiling Interactive Analysis database was used to analyze the level of RRM2 gene expression in normal and tumor tissues as well as RRM2's effect on the overall survival rate of tumor patients. TCM monomers that potentially act on RRM2 were screened via literature mining. Using AutoDock software, the screened monomers were docked with the RRM2 protein.

RESULTS

The expression of RRM2 mRNA in multiple tumor tissues was significantly higher than that in normal tissues, and it was negatively correlated with the overall survival rate of patients with the majority of tumor types. Through literature mining, we discovered that berberine, ursolic acid, gambogic acid, cinobufagin, quercetin, daphnetin, and osalmide have inhibitory effects on RRM2. The results of molecular docking identified that the above TCM monomers have a strong binding capacity with RRM2 protein, which mainly interacted through hydrogen bonds and hydrophobic force. The main binding sites were Arg330, Tyr323, Ser263, and Met350.

CONCLUSION

RRM2 is an important tumor therapeutic target. The TCM monomers screened have a good binding capacity with the RRM2 protein.