Identification of possible molecular markers and functional gene modules associated with lung adenocarcinoma based on weighted co-expression network analysis method
10.7501/j.issn.0253-2670.2019.24.023
- Author:
Yan-Ling LIU
1
Author Information
1. Jinhua Hospital of TCM
- Publication Type:Journal Article
- Keywords:
GEO database;
Lung adenocarcinoma;
Mechanisms;
Solanum nigrum L.;
Weighted co-expression network analysis
- From:
Chinese Traditional and Herbal Drugs
2019;50(24):6073-6083
- CountryChina
- Language:Chinese
-
Abstract:
Objective: The potential biological targets for anti-lung adenocarcinoma of Solanum nigrum were scored using the weighted co-expression network analysis (WGCNA) method. Methods: A database of chemical components of S. nigrum was established through oral bioavailability (OB), drug-likeness (DL) based on Traditional Chinese Medicine Systems Pharmacology (TCMSP) and literature retrieval. The targets of active ingredients of S. nigrum were predicted based on reverse docking with DRAR-CPI server, and combined with WGCNA to mine GSE10072 dataset in Gene Expression Omnibus (GEO) database to obtain coexpression gene module. Furthermore, the potential anti-lung adenocarcinoma targets of S. nigrum were confirmed under intersected with predicted targets and coexpression genes. The GO terms of biological processes and KEGG pathway enrichment analysis of predicted targets and anti-lung adenocarcinoma targets were performed by Metascape database, respectively. Using the targets-pathways networks to study the mechanisms of S. nigrum in the fight against cancer. The transcriptional level expression of key String database combined with Cytoscape software to draw the proteins-proteins interactions (PPI), and active ingredients-targets-pathways networks to study the mechanisms of S. nigrum in the fight against cancer. The transcriptional level expression of key genes in lung adenocarcinoma cancer tissues and normal lung tissues was assessed based on UALCAN dataset. And the correlation between key genes and prognosis of lung cancer patients was calculated by KM plotter analysis. Results: This study collected nine active components of S. nigrum, including medioresinol, sitosterol, diosgenin, solanocapsine, quercetin, α-chaconine, solasonin, solamargine, and solasodine. Totally 271 targets were predicted, and 41 potential anticancer targets were confirmed. The potential regulatory pathways included pathway in cancer, PI3K-Akt signaling pathway, chemical carcinogenesis, central carbon metabolism in cancer and so on. From the PPI network, we found that hub genes EGFR, CASP8, HPGDS, FYN, and high expression of EGFR and CASP8 were related to the poor overall survival in patient with lung adenocarcinoma. Oncontrary, lower expression of HPGDS and FYN were also associated with poor overall survival. Conclusion: This study reflects the multi-component, multi-target and multi-pathway features of S. nigrum, and provides a scientific basis for anticancer substance and elucidating the mechanisms of action of S. nigrum, as well as a reference for the study of mechanisms.
