Analysis of molecular networks and targets mining of Chinese herbal medicines on anti-aging.

BACKGROUND: Many kidney-tonifying Chinese herbal medicines exert effects on anti-aging by comprehensive interactions of multiple targets. However, the interactions of multi-targets targeted by effective ingredients of kidney-tonifying Chinese herbal medicines are unknown. In this study, to explore the systems pharmacology mechanisms of kidney-tonifying Chinese medicines on anti-aging, we establish the molecular networks with the interactions of multi-targets, analyze bio-functions and pathways with IPA, and calculated the mutual interaction pairs of targets (target pairs) with data mining, respectively. METHODS: Kidney-tonifying Chinese medicines with anti-aging effects were screened from the Chinese Pharmacopoeia and the literatures. Target proteins of these herbal medicines were obtained from bioinformatics databases. Comparisons of molecular networks, bio-functions and pathways given by Ingenuity Pathway Analysis system showed the similarities and the differences between kidney Yin-tonifying herbal medicines and kidney Yang-tonifying herbal medicines. Target pairs with high correlation related to anti-aging were also discovered by data mining algorithm. And regulatory networks of targets were built based on the target pairs. RESULTS: Twenty-eight kidney-tonifying herbal medicines with anti-aging effects and 717 related target proteins were collected. The main bio-functions that all targets enriched in were "Cell Death and Survival", "Free Radical Scavenging" and "Cellular Movement", etc. The results of comparison analysis showed that kidney Yin-tonifying herbal medicines focused more on "Cancer related signaling", "Apoptosis related signaling" and "Cardiovascular related signaling". And kidney Yang-tonifying herbal medicines focused more on "Cellular stress and injury related signaling" and "Cellular growth, proliferation and development related signaling". Moreover, the results of regulatory network showed that the anti-aging related target pairs with high correlated degrees of Kidney Yin-tonifying herbal medicines included TNF-PTGS2, TNF-CASP3, PTGS2-CASP3, CASP3-NOS2 and TNF-NOS2, and that of kidney Yang-tonifying herbal medicines included REAL-TNF, REAL-NFKBIA, REAL-JUN, PTGS2-SOD1 and TNF-IL6. CONCLUSIONS: In this study, we achieved some important targets, target pairs and regulatory networks with bioinformatics and data mining, to discuss the systems pharmacology mechanisms of kidney-tonifying herbal medicines acting on anti-aging. Mutual target pairs related to anti-aging found in this study included TNF-PTGS2, TNF-CASP3, PTGS2-CASP3, CASP3-NOS2, TNF-NOS2, REAL-TNF, REAL-NFKBIA, REAL-JUN, PTGS2-SOD1 and TNF-IL6. Target pairs and regulatory networks of targets could reflect more potential interactions between targets and comprehensive effects on anti-aging. Compared with the existing researches, it was found that the kidney-tonifying herbal medicines may exert anti-aging effects in multiple pathways in this study.

Identification of potential crucial genes associated with the pathogenesis and prognosis of liver hepatocellular carcinoma.

AIMS: Liver hepatocellular carcinoma (LIHC) is the main manifestation of primary liver cancer, with low survival rate and poor prognosis. Medical decision-making process of LIHC is so complex that new biomarkers for diagnosis and prognosis have yet to be explored, this study aimed to identify the genes involved in the pathophysiology of LIHC and biomarkers that can be used to predict the prognosis of LIHC. METHODS: Six Gene Expression Omnibus (GEO) datasets selected from GEO were screened and integrated to find out the differential expression genes (DEGs) obtained from LIHC and normal hepatic tissues. The Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis of DEGs was implemented by DAVID. The Protein-protein interaction network was performed via STRING. In addition, Cox regression model was used to construct a gene prognostic signature. RESULTS: We ascertained 10 hub genes, nine of them (CDK1, CDC20, CCNB1, Thymidylate synthetase, Nuclear division cycle80, NUF2, MAD2L1, CCNA2 and BIRC5) as biomarkers of progression in LIHC patients. We also build a six gene prognosis signature (SOCS2, GAS2L3, NLRP5, TAF3, UTP11 and GAGE2A), which can be implemented to predict over survival effectively. CONCLUSIONS: We revealed promising genes that may participate in the pathophysiology of LIHC, and found available biomarkers for LIHC prognosis prediction, which were significant for researchers to further understand the molecular basis of LIHC and direct the synthesis medicine of LIHC.