Identification of Inflammation-related Molecular Targets and Construction of Prognostic Models for Pien Tze Huang in the Treatment of Hepatocellular Carcinoma Based on Biometric Analysis and Network Pharmacology
DOI:10.13748/j.cnki.issn1007-7693.20214309
- VernacularTitle:基于生信分析和网络药理学的片仔癀治疗肝细胞癌的炎症相关分子靶点的鉴定和预后模型构建
- Author:
ZHANG Zexin
1
;
CHEN Xujing
1
;
WU Wenfeng
1
;
GAO Chaoxin
2
;
WANG Yongchen
1
;
ZHONG Chong
3
;
LI Jing
4
Author Information
1. Guangzhou University of Chinese Medicine, The Second Clinical College, Guangzhou 510405, China
2. Guangzhou University of Chinese Medicine, The Fourth Clinical College, Guangzhou 510405, China
3. The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510405, China
4. The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha 410000, China
- Publication Type:Journal Article
- Keywords:
Pien Tze Huang / hepatocellular carcinoma / inflammation / prognostic model / biometric analysis
- From:
Chinese Journal of Modern Applied Pharmacy
2023;40(21):2052-2963
- CountryChina
- Language:Chinese
-
Abstract:
Abstract:OBJECTIVE To analyze the inflammation-related molecular targets of Pien Tze Huang in the treatment of hepatocellular carcinoma and to preliminary explore its mechanism. METHODS Obtain the ingredients and targets of Pien Tze Huang through TCMSP and BATMAN databases. Obtain the disease targets of hepatocellular carcinoma through Genecards, OMIM and TCGA databases. Take the intersection of compound targets and disease targets to get Pien Tze Huang’s target for the treatment of hepatocellular carcinoma. Obtain the related genes of inflammation pathway from the GSEA database, and then analyze the correlation between Pien Tze Huang’s therapeutic targets for hepatocellular carcinoma and inflammation-related genes to screen out inflammation-related targets, and explore the mechanism through GO and KEGG enrichment analysis. Then, single-factor cox analysis and LASSO regression were performed to construct related prognostic models. The 10 core targets were screened out through the protein-protein interaction(PPI) network. The model gene and the core target were intersected. The core compounds were screened out through the drug-compound-target network. Perform molecular docking verification between the core compound and the target. Construct a nomogram to assess the prognosis of patients. RESULTS Obtained 162 Pien Tze Huang targets, 522 hepatocellular carcinoma targets, 20 Pien Tze Huang therapeutic targets for hepatocellular carcinoma, and 16 inflammation-related targets. The enrichment analysis of GO and KEGG showed that their effects were mainly through biological functions such as monooxygenase activity, oxidoreductase activity, and chemical carcinogenesis-receptor activation. The ROC curve of the prognosis model calculated AUC as 0.780 in 1 year, 0.688 in 3 years, and 0.642 in 5 years, indicating that the model was reliable. The prognostic model intersects with the core target of PPI to get 5 targets: PON1, IGF2, NQO1, CCNB1 and IGFBP3. The nomogram was constructed using CCNB1, NQO1, and T staging, and its c-index was 0.726, indicating the reliability of the model. The drug-compound-target network suggested that quercetin was the core compound and targets the above two genes. CONCLUSION Pien Tze Huang’s treatment of hepatocellular carcinoma mainly uses quercetin to target CCNB1 and NQO1 to exert anti-inflammatory effects, and its prognostic model can be used to predict the survival of patients.
