Integrated network pharmacology and metabolomics to reveal the mechanism of Pinellia ternata inhibiting non-small cell lung cancer cells

Author:

Feng Fan,Hu Ping,Peng Lei,Xu Lisheng,Chen Jun,Chen Qiong,Zhang Xingtao,Tao Xingkui

Abstract

AbstractLung cancer is a malignant tumor with highly heterogeneous characteristics. A classic Chinese medicine, Pinellia ternata (PT), was shown to exert therapeutic effects on lung cancer cells. However, its chemical and pharmacological profiles are not yet understood. In the present study, we aimed to reveal the mechanism of PT in treating lung cancer cells through metabolomics and network pharmacology. Metabolomic analysis of two strains of lung cancer cells treated with Pinellia ternata extracts (PTE) was used to identify differentially abundant metabolites, and the metabolic pathways associated with the DEGs were identified by MetaboAnalyst. Then, network pharmacology was applied to identify potential targets against PTE-induced lung cancer cells. The integrated network of metabolomics and network pharmacology was constructed based on Cytoscape. PTE obviously inhibited the proliferation, migration and invasion of A549 and NCI-H460 cells. The results of the cellular metabolomics analysis showed that 30 metabolites were differentially expressed in the lung cancer cells of the experimental and control groups. Through pathway enrichment analysis, 5 metabolites were found to be involved in purine metabolism, riboflavin metabolism and the pentose phosphate pathway, including D-ribose 5-phosphate, xanthosine, 5-amino-4-imidazolecarboxyamide, flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD). Combined with network pharmacology, 11 bioactive compounds were found in PT, and networks of bioactive compound–target gene–metabolic enzyme–metabolite interactions were constructed. In conclusion, this study revealed the complicated mechanisms of PT against lung cancer. Our work provides a novel paradigm for identifying the potential mechanisms underlying the pharmacological effects of natural compounds.

Funder

the Suzhou University’s 2021 School-level Scientific Research Platform

the Key Natural Science Project of Anhui Provincial Education Department

the Suzhou University Scientific Research Development Fund Project

the Suzhou University Scientific Research Platform Open Project

Publisher

Springer Science and Business Media LLC

Reference62 articles.

1. Moris D, Ntanasis-Stathopoulos I, Tsilimigras DI, Adam MA, Yang C-FJ, Harpole D, Theocharis S. Insights into novel prognostic and possible predictive biomarkers of lung neuroendocrine tumors. Cancer Genomics Proteom. 2018;15(2):153–63.

2. He J, Li N, Chen WQ, Wu N, Shen HB, Jiang Y, Li J, Wang F, Tian JH. China guideline for the screening and early detection of lung cancer. Chin J Oncol. 2021;43(3):243–68.

3. Li Z, Feiyue Z, Gaofeng L. Traditional Chinese medicine and lung cancer–from theory to practice. Biomed Pharmacother. 2021;137:111381.

4. Jiang Y, Liu LS, Shen LP, Han ZF, Jian H, Liu JX, Xu L, Li HG, Tian JH, Mao ZJ. Traditional Chinese medicine treatment as maintenance therapy in advanced non-small-cell lung cancer: a randomized controlled trial. Complement Ther Med. 2016;24:55–62.

5. Ye L, Jia Y, Ji KE, Sanders AJ, Xue K, Ji J, Mason MD, Jiang WG. Traditional Chinese medicine in the prevention and treatment of cancer and cancer metastasis. Oncol Lett. 2015;10(3):1240–50.

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