Identification of the therapeutic mechanism of the saffron crocus on glioma through network pharmacology and bioinformatics analysis.
Xiaobing YangDulegeqi ManPeng ZhaoXingang LiPublished in: Medical oncology (Northwood, London, England) (2023)
Saffron crocus is a herbal medicine of traditional Tibetan medicine (TTM). Saffron extract has been indicated to inhibit tumor cell growth and promote tumor cell apoptosis in a variety of cancers, including glioma, but the specific mechanism is not clear. To study the possible mechanism of saffron action on glioma, network pharmacology and bioinformatics analysis methods were used in this study. We used the online database to obtain the active ingredients of saffron and their targets. Glioma-related targets were also acquired from online database. We intersected drug targets with glioma-related targets and conducted PPI network analysis to obtain network core genes. Then, we obtained RNA-seq data from The Cancer Genome Atlas (TCGA) database for glioma patients. Through different expression analysis and lasso regression, further screening of core genes in the network was conducted, and a prognostic model was established. The sample was divided into two groups with high and low risk using this model. The RNA-seq data from the Chinese Glioma Genome Atlas (CGGA) database were used to further validate our prediction model. Then, we explored the difference in pathways enrichment between high-risk patients and low-risk patients and calculated the difference in immune microenvironment between the two groups. Finally, we used scRNA-seq data in the CGGA database to analyze the cell types in which the model gene is mainly enriched and predicted the cell types which saffron effected on.
Keyphrases
- single cell
- rna seq
- bioinformatics analysis
- end stage renal disease
- ejection fraction
- newly diagnosed
- genome wide
- network analysis
- chronic kidney disease
- stem cells
- prognostic factors
- healthcare
- cell proliferation
- oxidative stress
- health information
- small molecule
- big data
- machine learning
- cell therapy
- patient reported outcomes
- mesenchymal stem cells
- deep learning
- bone marrow
- genome wide identification