Reveal the mechanisms of prescriptions for liver cancer' treatment based on two illustrious senior TCM physicians.

OBJECTIVE: To explore the core herbs of two illustrious senior Traditional Chinese Medicine (TCM) Physicians for the treatment of liver cancer, and to further clarify the gene targets and pathway mechanisms of liver cancer that the core prescription (CP) may regulate. METHODS: We used the patient information of two illustrious senior TCM physicians from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine and the Affiliated Hospital of Capital Medical University as the database. The CP was analyzed using the community network algorithm. The pathway mechanism was analyzed using network pharmacology method. And the prognostic survival genes were identified using Single factor cox regression analysis. Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine (TCMIP), Herb, Online Mendelian Inheritance in Man (OMIM), Genecards, Kyoto Encyclopedia of Genes and Genomes (KEGG), Genomic Data Commons (GDC), The Cancer Genome Atlas (TCGA), Gephi and R were used to mine CP, building a pathway network diagram. All the analyses were visualized. RESULTS: We found that the CP consistes of Huangqi (), Danshen (), Shuihonghuazi (), Baihuasheshecao (), Banzhilian (), and Ezhu (), which were attributed to the two physicians respectively. The CP played an anti-cancer role through such pathways as signal transduction, energy metabolism, immune system and other pathways, covering a total of 112 pathways and 176 herb-disease-related genes. Fourteen genes have a significant impact on the prognosis and survival of liver cancer. CONCLUSION: Based on the liver cancer cases of two illustrious senior TCM physicians, we obtained the CP through data mining. The CP may mainly exert anti-cancer effects by inhibiting inflammatory response, angiogenesis, and enhancing the body's immune response. We screened out 14 genes in the CP that may be related to the prognosis of liver cancer, and these genes may play an important regulatory role in the prognosis of liver cancer.

A predictive model and survival analysis for local recurrence in differentiated thyroid carcinoma.

BACKGROUND: Local recurrence (LR) is associated with poor outcome in patients with differentiated thyroid carcinoma (DTC). The aim of this study was to explore potential risk factors for LR and build a predictive model. METHODS: The medical data of patients who were diagnosed with DTC after initial surgery in three medical centers (2000-2018) were reviewed. Detailed clinicopathologic characteristics of all cases were identified. RESULTS: Multiple factors, including extrathyroidal extension (ETE), histology, symptoms, multifocality, and tumor diameter, were significantly different between the LR and no evidence of disease groups in univariate and multivariate analysis (P˂0.05). Tumor diameter, symptoms, and ETE made the greatest contributions to prognosis according to decision tree analysis and random forest algorithm. The predictive model constructed from these data achieved 98.7% accuracy of classification. A five-fold cross-validation confirmed that the model has 84.7-89.7% accuracy of classification. Additionally, symptoms and ETE were independent predictors on survival analysis (P˂0.05). CONCLUSIONS: This study optimized the weight of risk factors, including tumor diameter, symptoms, ETE, and multifocality, in predicting LR in patients with DTC. Our predictive model provides a strong tool to distinguish between high-risk and low-risk DTC.