Network Pharmacological Analysis and Experimental Validation of the Effects of Silybin on Proliferation, Migration, and Immunotherapy of Papillary Thyroid Cancer.

AIM: The study aimed to use network pharmacology research and in vitro experiments to investigate the material basis and molecular mechanisms of silybin in the treatment of papillary thyroid carcinoma. BACKGROUND: Papillary thyroid cancer (PTC) has a decent prognosis; however, recurrence and metastasis are the leading causes of death in patients with PTC. A key research focus in thyroid cancer treatment is the inhibition of PTC proliferation, invasion, and migration. Silybin, the major active element in the traditional Chinese herb silymarin, has been used to treat a range of diseases, including cancer, but no study has been undertaken to determine whether it can help prevent PTC. OBJECTIVE: In this study, we attempted to determine through network pharmacology and in vitro experiments if silybin inhibits the development of papillary thyroid cancer by inhibiting cell cycle and invasive migration. METHODS: To predict the probable targets and underlying mechanisms of silybin against PTC, a network pharmacology research was performed. In vitro experiments were conducted to further evaluate silybin's anti-cancer properties and priority targets against PTC. RESULTS: The datasets revealed a total of 489 silybin targets acting on PTC, and functional enrichment analysis suggested that the target genes were enriched in functions and pathways related to PTC development, invasion, migration, and immunotherapy. By constructing these target PPI networks, the seven hub genes, fibronectin 1 (FN1), tissue inhibitor of metalloproteinases 1 (TIMP1), N-cadherin (CDH2), collagen type III alpha 1 chain (COL3A1), cyclin D1 (CCND1), AP-1 transcription factor subunit (JUN), and hepatocyte growth factor receptor (MET) were found. These hub genes were determined to be highly linked to a worse clinicopathological form, a higher risk of metastatic recurrence, and a worse prognosis of PTC. The common immunological checkpoint gene expression levels were positively correlated with the expression levels of the hub genes. Silybin decreased the proliferative and metastatic capacity of PTC cells, according to in vitro investigations. When PTC was treated with silybin, the FN1/AKT signaling pathway was blocked, CCND1 expression was reduced, and CDH2, Vimentin, Snail, Slug and PD-L1 expressions were dramatically reduced, while E-cadherin expression was significantly elevated. CONCLUSION: These findings provide preliminary evidence that silybin inhibits PTC cell proliferation, metastasis, and invasion by altering the FN1/AKT signaling pathway and inhibiting the EMT process. Silybin can reverse immunosuppression in papillary thyroid cancer by affecting immunological checkpoint gene expression levels. These studies provide a theoretical and experimental scientific basis for the potential anticancer effects of silybin on PTC.

Identification of prognostic biomarkers of breast cancer based on the immune-related gene module.

Breast cancer (BC) is highly malignant and its mortality rate remains high. The development of immunotherapy has gradually improved the prognosis and survival rate of patients. Therefore, identifying molecular markers concerned with BC immunity is of great importance for the treatment of this disease. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) was utilized as the training set while the BC expression dataset from the gene expression omnibus database was taken as the validation set here. Weighted gene co-expression network analysis combined with Pearson analysis and Tumor immune estimation resource (TIMER) was used to obtain immune cell-related hub gene module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on this module. Then, receiver operating characteristic curves combining Kaplan-Meier was used to evaluate the effectiveness of the model. Feature genes were screened and the independence of risk score was evaluated by univariate and multivariate Cox analyses. Differences in immune characteristics were analyzed via single-sample gene set enrichment analysis and CIBERSORT, and differences in gene mutation frequency were assessed via GenVisR analysis. Finally, the expression levels of prognostic feature genes in BC cells were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). In this study, cell immune-related gene modules in TCGA-BRCA were successfully excavated, and a five-gene (TNFRSF14, NFKBIA, DLG3, IRF2, and CYP27A1) prognostic model was established. The prognostic model could effectively forecast the prognosis and survival rate of BC patients. The result showed that human leukocyte antigen-related proteins and macrophage M2 scores were remarkably highly expressed in the high-risk group, whereas CD8+ T cells, natural killer cells, M1, and other anti-tumor cells were lowly expressed. The model could be used as an independent prognostic factor to predict the prognosis of BC patients. The results of qRT-PCR validation were consistent with the results in the database, that is, except DLG3, the other four feature genes were lowly expressed in BC. The five-gene model established in this study can predict the prognostic and immune mode of BC patients effectively, which is anticipated to become a feasible molecular target for BC therapy.