Exploring the hub genes and mechanisms of Daphne altaica treating esophageal squamous cell carcinoma based on network pharmacology and bioinformatics analysis

Abstract

Esophageal squamous cell carcinoma (ESCC), is a frequent digestive tract malignant carcinoma with a high fatality rate. Daphne altaica. (D. altaica), a medicinal plant that is frequently employed in Kazakh traditional medicine, and which has traditionally been used to cure cancer and respiratory conditions, but research on the mechanism is lacking. Therefore, we examined and veri ed the hub genes and mechanism of D. altaica treating ESCC. MethodsActive compounds and targets of D. altaica were screened by databases such as TCMSP, and ESCC targets were screened by databases such as GeneCards and constructed the compound-target network and PPI network. Meantime, datasets between tissues and adjacent non-cancerous tissues from GEO database (GSE100942, GPL570) were analyzed to obtain DEGs using the limma package in R. Hub genes were validated using data from the Kaplan-Meier plotter database, TIMER2.0 and GEPIA2 databases.Finally, AutoDock software was used to predict the binding sites through molecular docking. ResultsIn total, 830 compound targets were obtained from TCMSP and other databases. And 17710 disease targets were acquired based on GeneCards and other databases. And we constructed the compoundtarget network and PPI network. Then, 127 DEGs were observed (82 up-regulated and 45 down-regulated genes). Hub genes were screened including TOP2A, NUF2, CDKN2A, BCHE, and NEK2, and had been validated with the help of several publicly available databases. Finally, molecular docking results showed more stable binding between ve hub genes and active compounds . ConclusionsIn the present study, ve hub genes were screened and validated, and potential mechanisms of action were predicted, which could provide a theoretical understanding of the treatment of ESCC with D. altaica.2. In the above screening outcomes, we got the two most active compounds of the drug (Apigenin and Luteolin). Using the TCMSP and PubChem databases, we obtained the protein structures of the compounds (Kim et al. 2019). In the same process as in the previous step, the protein was modi ed in AutoDock4.2.6 to remove the water and add hydrogen, and the ligand les were saved in PDBQT format.3. To perform molecular docking, we imported the above two structure les into AutoDock4.2.6. The minimum binding energy was calculated using the PDBQT format. The PDBQT format was then converted to the PDB format using the OpenBabel software (O'Boyle et al. 2011). 4. Finally, PyMOL2.5 software was used to visualize the molecular docking maps.