@ARTICLE{10.3389/fonc.2021.690878, AUTHOR={Zhu, Li-Min and Shi, Hai-Xia and Sugimoto, Masahiro and Bandow, Kenjiro and Sakagami, Hiroshi and Amano, Shigeru and Deng, Hai-Bin and Ye, Qing-Yu and Gai, Yun and Xin, Xiao-Li and Xu, Zhen-Ye}, TITLE={Feiyanning Formula Induces Apoptosis of Lung Adenocarcinoma Cells by Activating the Mitochondrial Pathway}, JOURNAL={Frontiers in Oncology}, VOLUME={11}, YEAR={2021}, URL={https://www.frontiersin.org/articles/10.3389/fonc.2021.690878}, DOI={10.3389/fonc.2021.690878}, ISSN={2234-943X}, ABSTRACT={Feiyanning formula (FYN) is a traditional Chinese medicine (TCM) prescription used for more than 20 years in the treatment of lung cancer. FYN is composed of Astragalus membranaceus, Polygonatum sibiricum, Atractylodes macrocephala, Cornus officinalis, Paris polyphylla, and Polistes olivaceous, etc. All of them have been proved to have anti-tumor effect. In this study, we used the TCM network pharmacological analysis to perform the collection of compound and disease target, the prediction of compound target and biological signal and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. It was found that the activation of mitochondrial pathway might be the molecular mechanism of the anti-lung cancer effect of FYN. The experimental results showed that FYN had an inhibitory effect on the growth of lung cancer cells in a dose-dependent and time-dependent manner. Moreover, FYN induced G2/M cell cycle arrest and apoptotic cell death as early as 6 h after treatment. In addition, FYN significantly induced mitochondrial membrane depolarization and increased calreticulin expression. Metabolomics analysis showed the increase of ATP utilization (assessed by a significant increase of the AMP/ATP and ADP/ATP ratio, necessary for apoptosis induction) and decrease of polyamines (that reflects growth potential). Taken together, our study suggested that FYN induced apoptosis of lung adenocarcinoma cells by promoting metabolism and changing the mitochondrial membrane potential, further supporting the validity of network pharmacological prediction.} }