In vivo and in vitro studies of Alloimperatorin induced autophagy in cervical cancer cells via reactive oxygen species pathway

Study on the mechanism of action of Saposhnikovia divaricata and its key phytochemical on rheumatoid arthritis based on network pharmacology and bioinformatics

ETHNOPHARMACOLOGICAL RELEVANCE

Saposhnikovia divaricata (Turcz.) Schischk (SD; called "fangfeng" in China) has been widely used in the clinical treatment of rheumatoid arthritis (RA) and has shown well therapeutic effects, but the specific mechanisms of action of its bioactive phytochemicals remain unclear.

AIM OF THE STUDY

This study aimed to investigate the molecular biological mechanism of SD in treating RA through a pharmacology-based strategy. The SD-specific core ingredient Prangenidin was screened for further in-depth study.

MATERIALS AND METHODS

The bioactive phytochemicals of SD and potential targets for the treatment of RA were screened by network pharmacology, and phytochemicals-related parameters such as pharmacology, and toxicology were evaluated. The protein interaction network was established to screen the core targets, and the correlation between the core targets and RA was further validated by bioinformatics strategy. Finally, molecular docking of core components and corresponding targets was performed. The in vitro experiments were performed to elucidate the regulation of Prangenidin on MH7A cells and on the PI3K/AKT pathway, and the in vivo therapeutic effect of Prangenidin was validated in collagen-induced arthritis (CIA) mice.

RESULTS

A total of 18 bioactive phytochemicals and 66 potential target genes intersecting with the screened RA disease target genes were identified from SD. Finally, core ingredients such as wogonin, beta-sitosterol, 5-O-Methylvisamminol, and prangenidin and core targets such as PTGS2, RELA, and AKT1 were obtained. The underlying mechanism of SD in treating RA might be achieved by regulating pathways such as PI3K/AKT, IL-17 pathway, apoptosis, and multiple biological processes to exert anti-inflammatory and immunomodulatory effects. Molecular docking confirmed that all core ingredients and key targets had great docking activity. Prangenidin inhibited viability, migration, and invasion, and induced apoptosis in MH7A cells. Prangenidin also reduced the production of IL-1β, IL-6, IL-8, MMP-1, and MMP-3. Molecular analysis showed that Prangenidin exerts its regulatory effect on MH7A cells by inhibiting PI3K/AKT pathway. Treatment with Prangenidin ameliorated synovial inflammation in the joints of mice with CIA.

CONCLUSION

Our findings provide insights into the therapeutic effects of SD on RA, successfully predicting the effective ingredients and potential targets, which could suggest a novel theoretical basis for further exploration of its molecular mechanisms. It also revealed that Prangenidin inhibited viability, migration, invasion, cytokine, and MMPs expression, and induced apoptosis in RA FLSs via the PI3K/AKT pathway.

Comprehensive analysis of autophagy associated genes and immune infiltrates in cervical cancer

Objectives

Cervical cancer (CC) is the most common gynecological malignant tumor and the fourth leading cause of cancer-related death in women. The progression of CC is significantly affected by autophagy. Our objective was to use bioinformatics analysis to explore the expression, prognostic significance, and immune infiltration of autophagy-related genes in CC.

Materials and Methods

We identified a set of autophagy-related differentially expressed genes (ARDEGs) from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ARDEGs were further validated by The Human Protein Atlas (HPA), GSE52903, and GSE39001 dataset. Hub genes were found by the STRING network and Cytoscape. We performed Gene Set Enrichment Analysis (GSEA), Gene ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and immune infiltration analysis to further understand the functions of the hub genes. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) were used to check the hub genes.

Results

A total of 10 up-regulated (CXCR4, BAX, SPHK1, EIF2AK2, TBK1, TNFSF10, ITGB4, CDKN2A, IL24, and BIRC5) and 19 down-regulated (PINK1, ATG16L2, ATG4D, IKBKE, MLST8, MAPK3, ERBB2, ULK3, TP53INP2, MTMR14, BNIP3, FOS, CCL2, FAS, CAPNS1, HSPB8, PTK6, FKBP1B , and DNAJB1) ARDEGs were identified. The ARDEGs were enriched in cell growth, apoptosis, human papillomavirus infection, and cytokine-mediated. Then, we found that low expression of MAPK3 was associated with poor prognosis in CC patients and was significantly enriched in immune pathways. In addition, the expression of MAPK3 was significantly positively correlated with the infiltration levels of macrophages, B cells, mast cell activation, and cancer-associated fibroblasts. Furthermore, MAPK3 was positively correlated with LGALS9, and negatively correlated with CTLA4 and CD40.

Conclusion

Our results show that MAPK3 can be used as a new prognostic biomarker to predict the prognosis of patients with CC.