Though widely used in the treatment of coronary heart disease (CHD), the mechanism of traditional Chinese medicine (TCM) is still unclear because of its complex prescription rules. This study prospectively collected 715 prescriptions of TCM for the treatment of CHD. The characteristics of TCM in prescriptions were described and analyzed, and the rules of prescriptions were analyzed by using association rules. Frequency statistics showed that the high-frequency herbs with a frequency of more than 60% were Gan-cao, Huang-qi, Dang-gui, Chuan-xiong, Yan-hu-suo, and San-qi. The high-frequency herb combinations were summarized by using association rules. By using the method of the “Top N groups” to excavate the empirical prescriptions, the basic prescriptions for treating CHD were summarized. We named the intersection herbs of the basic prescriptions and the high frequency herbs as the core herbal prescription. To explore the possible mechanisms underlying the anti-CHD effect of the core herbal prescription, the bioactive components of core herbal prescription and their targets were screened out by using network pharmacology. Molecular docking was performed between the bioactive components and core targets. A total of 28 potential active ingredients and 5 core targets were identified for the treatment of CHD with core herbal prescription. The enrichment analysis results indicated that the mechanism of action mainly involved neuroactive ligand-receptor interaction and calcium signaling pathway. The commonly used herbal pairs for CHD with qi deficiency and blood stasis syndrome were Huang-qi and Dang-gui. The mechanism of action of common herbal pairs was also studied by network pharmacology. This study summarized the prescription rule of TCM in the treatment of CHD and may provide a new idea for the treatment of CHD.

BackgroundAt present, there is a lack of studies focusing on the survival prediction of patients with non-small cell lung cancer (NSCLC) receiving atezolizumab in light of gene mutation characteristic.MethodsPatients with NSCLC receiving atezolizumab from the OAK study were defined as the training group. LASSO Cox regressions were applied to establish the gene mutation signature model to predict the overall survival (OS) rate of the training group. NSCLC patients receiving atezolizumab from the POPLAR study were defined as the testing group to validate the gene mutation signature model. In addition, we compared the OS rate between patients receiving atezolizumab and docetaxel classified according to their risk score based on our gene mutation signature model.ResultsWe successfully established a 5-genomic mutation signature that included CREBBP, KEAP1, RAF1, STK11 and TP53 mutations. We found it was superior to the blood tumor mutation burden (bTMB) score and programmed death ligand 1 (PDL1) expression in the prediction of the OS rate for patients receiving atezolizumab. High-risk patients receiving atezolizumab had a worse OS rate compared with low-risk patients in the training (P = 0.0004) and testing (P = 0.0001) groups. In addition, low-risk patients using atezolizumab had a better OS rate compared with those in use of docetaxel for the training (P <0.0001) and testing groups (P = 0.0095). High-risk patients of the training group (P = 0.0265) using atezolizumab had a better OS rate compared with those using docetaxel. However, the OS difference between atezolizumab and docetaxel was not found in high-risk patients from the testing group (P = 0.6403). Multivariate Cox regression analysis showed that the risk model in light of 5-genomic mutation signature was an independent prognostic factor on OS for patients receiving atezolizumab (P <0.0001). In addition, significant OS benefit could only be found in low-risk patients receiving atezolizumab compared with docetaxel (P <0.0001).ConclusionsThe 5-genomic mutation signature could predict OS benefit for patients with NSCLC receiving atezolizumab. Therefore, the establishment of the 5-genomic mutation panel will guide clinicians to identify optimal patients who could benefit from atezolizumab treatment.

Background: This study uses the tandem mass tag (TMT)-labeled quantitative proteomic analysis to identify potential therapeutic protein targets of a Chinese prescription called Tang-Yi-Ping (TYP) for the treatment of impaired glucose tolerance (IGT) in rats.Methods: A total of 31 specific-pathogen free (SPF) male Wistar rats were used in our study. Ten were randomly selected as a control group, while 21 received a high-sugar and high-fat diet combined with an intraperitoneal injection of streptozotocin to establish IGT subjects. After eliminating 2 rats without successful modeling, 19 were randomly divided into a TYP group (n=9) and IGT model group (n=10).The TYP group was given a TYP decoction of 6.36 mg/kg−1/d−1. After 8 weeks of intervention, blood glucose-related indicators were measured, and cell morphology was observed by hematoxylin and eosin (HE) staining. TMT-labeled proteomic analysis was applied to detect the differentially expressed proteins (DEPs) in the pancreases of the three groups. The intersection of the DEPs in both the TYP group and IGT model group underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to identify the related biological functions and signal transduction pathways. Finally, western blot (WB) was used to verify the TMT proteomics results.Results: TYP can effectively reduce blood glucose and improve islet morphology in IGT rats. We identified a total of 16 potential therapeutic protein targets of TYP, 4 of which were upregulated, while 12 were downregulated, including Rbp4, Fam3b, Flot2, etc. [fold change (FC) >1.1, P<0.05]. The significant signal transduction pathways included arginine and proline metabolism, glyceride metabolism, glycerophospholipid metabolism, mTOR, Wnt, and insulin signaling pathways.Conclusions: For anti-IGT therapy, we found TYP regulates 16 protein targets, multiple biological functions, and multiple signal transduction pathways. This study thus makes a significant contribution to identifying new potential therapeutic targets for treating IGT.