ABSTRACT
Kaixinsan powder (KXS), a classic prescription of traditional Chinese Medicine (TCM), is widely used in the treatment of depression, but its mechanism remains unclear. The network pharmacology method was used to constructe the "herb-component-target" network, and elucidated KXS potential mechanisms of action in the treatment of depression. Moreover, molecular docking was applied to valid the important interactions between the ingredients and the target protein. The "herb-component-target" network indicated that the ingredients of Girinimbin, Gomisin B and Asarone, and the protein targets of ESR, AR and NR3C1 mostly contribute to the antidepressant effect of KXS. KEGG pathway analysis highlighted the most significant pathways associated with depression treatment, including neuroactive ligand-receptor interaction pathway, serotonergic synapse pathway, PI3K-Akt signaling pathway and MAPK signaling pathway. Go enrichment analysis indicated that the mechanism of KXS in treating depression was involved in the biological process of GPCR signal transduction, hormone metabolism and nerve cell apoptosis. Moreover, molecular docking results showed that Polygalaxanthone III, Girinimbine and Pachymic acid performed greater binding ability with key antidepressant target 5-HTR. In conclusion, this study preliminarily revealed key active components in KXS, including Gomisin B, Asarone, Ginsenoside Rg1, Polygalaxanthone III and Pachymic acid, could interact with multiple targets (5-HTR, DR, ADRA, AR, ESR, NR3C1) and modulate the activation of multiple pathways (Neuroactive ligand -receptor interaction pathway, serotonergic synapse pathway, PI3K-Akt signaling pathway and MAPK signaling pathway).
Subject(s)
Depression , Phosphatidylinositol 3-Kinases , Powders , Molecular Docking Simulation , Depression/drug therapy , Ligands , Proto-Oncogene Proteins c-akt , Antidepressive Agents/pharmacology , Antidepressive Agents/therapeutic useABSTRACT
BACKGROUND Thyroid cancer is a type of endocrine cancers with rapidly increased incidence. Recent studies have indicated long non-coding RNAs (lncRNAs) played crucial roles in thyroid cancer tumorigenesis and progression. However, the roles of most lncRNAs in thyroid cancer were still unclear. MATERIAL AND METHODS We used TCGA (The Cancer Genome Atlas), GSE50901, GSE29265, and GSE33630 datasets to analyze the expression pattern of ZFAS1 (ZNFX1 antisense RNA 1). The correlation between ZFAS1 and clinicopathological features in thyroid cancer was analyzed. Cell proliferation and cell cycle assays were used to validate the roles of ZFAS1 in thyroid cancer cell lines. DAVID (the database for annotation, visualization and integrated discover) system was used to perform GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis. The starBase datasets and Cytoscape was used to perform ceRNA (competitive endogenous RNA) network. RESULTS We demonstrated ZFAS1 was highly expressed in thyroid cancer compared to normal samples. Moreover, upregulation of ZFAS1 was positively correlated with clinicopathological features and poor prognosis in thyroid cancer. Functional validation showed knockdown of ZFAS1 suppressed cell proliferation and cell cycle in thyroid cancer cells. Bioinformatics analysis showed ZFAS1 was associated with translation, rRNA processing, intra-Golgi vesicle-mediated transport, ribosome, and ubiquitin-mediated proteolysis. CONCLUSIONS Our study suggested ZFAS1 could serve as a biomarker for thyroid cancer.
Subject(s)
RNA, Long Noncoding/genetics , Thyroid Neoplasms/genetics , Biomarkers, Tumor/genetics , Biomarkers, Tumor/metabolism , Carcinogenesis , Cell Cycle/genetics , Cell Line, Tumor , Cell Movement/genetics , Cell Proliferation/genetics , Databases, Genetic , Disease Progression , Epithelial-Mesenchymal Transition , Gene Ontology , Humans , Neoplasm Invasiveness , Prognosis , RNA, Long Noncoding/metabolism , Thyroid Neoplasms/metabolism , Thyroid Neoplasms/pathologyABSTRACT
Thyroid cancer (TC) is the one of the most common endocrine malignancy. However, currently there are no specific and sensitive biomarkers for predicting the prognosis for TC. In this study, we for the first time showed MIR22HG was down-regulated in thyroid cancer by analyzing public datasets, including TCGA, GSE29265, GSE33630, and GSE55091. Furthermore, we observed the lower expression levels of MIR22HG were significantly related to higher age, lymph node metastasis status, residual tumor status, N stage, Grade, and T stage in TC. We also observed higher MIR22HG expression was associated with longer overall and disease-free survival time in TC. In order to explore the potential mechanisms of MIR22HG regulating TC progression, 4 hub gene networks regulated by MIR22HG were constructed in the present study. Bioinformatics analysis showed MIR22HG was associated with apoptotic process, regulation of transcription, mRNA splicing, regulation of cell cycle, and Hippo signaling pathway in TC. These results suggested MIR22HG could serve as a novel biomarker for thyroid cancer.