Generic placeholder image

Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Research Article

Network Pharmacology Combined with GEO Analysis of the Mechanism of Qing-Jin-Hua-Tan Decoction in the Treatment of Non-small Cell Lung Cancer

Author(s): Yi Wei and Chao Liu*

Volume 20, Issue 4, 2024

Published on: 05 June, 2023

Page: [396 - 404] Pages: 9

DOI: 10.2174/1573409919666230523155830

Price: $65

Abstract

Background: Non-small-cell lung cancer (NSCLC) is one of the most prevalent malignancies and poses a significant threat to human health. Qing-Jin-Hua-Tan (QJHT) decoction is a classical herbal remedy that has demonstrated therapeutic effects in various diseases, including NSCLC, and can improve the quality of life of patients with respiratory conditions. However, the mechanism underlying the effect of the QJHT decoction on NSCLC remains unclear and requires further investigation.

Methods: We collected NSCLC-related gene datasets from the GEO database and performed differential gene analysis, followed by using WGCNA to identify the core set of genes associated with NSCLC development. The TCMSP and HERB databases were searched to identify the active ingredients and drug targets, and the core gene target datasets related to NSCLC were merged to identify the intersecting targets of drugs and diseases for GO and KEGG pathway enrichment analysis. We then constructed a protein-protein interaction (PPI) network map of drug diseases using the MCODE algorithm and identified key genes using topology analysis. The disease-gene matrix underwent immunoinfiltration analysis, and we analyzed the association between intersecting targets and immunoinfiltration.

Results: We obtained the GSE33532 dataset that met the screening criteria, and a total of 2211 differential genes were identified using differential gene analysis. We performed GSEA analysis and WGCNA analysis for a crossover with differential genes, resulting in 891 key targets for NSCLC. The drug database was screened to obtain 217 active ingredients and 339 drug targets of QJHT. By constructing a PPI network, the active ingredients of QJHT decoction were intersected with the targets of NSCLC, resulting in 31 intersected genes. Enrichment analysis of the intersection targets showed that 1112 biological processes, 18 molecular functions, and 77 cellular compositions were enriched in GO functions, and 36 signaling pathways were enriched in KEGG pathways. Based on immune-infiltrating cell analysis, we found that the intersection targets were significantly associated with multiple infiltrating immune cells.

Conclusion: Our analysis using network pharmacology and mining of the GEO database revealed that QJHT decoction can potentially treat NSCLC through multi-target and multi-signaling pathways, while also regulating multiple immune cells.

Graphical Abstract

[1]
Siegel, R.L.; Miller, K.D.; Wagle, N.S.; Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin., 2023, 73(1), 17-48.
[http://dx.doi.org/10.3322/caac.21763] [PMID: 36633525]
[2]
Nasim, F.; Sabath, B.F.; Eapen, G.A. Lung Cancer. Med. Clin. North Am., 2019, 103(3), 463-473.
[http://dx.doi.org/10.1016/j.mcna.2018.12.006] [PMID: 30955514]
[3]
Duma, N.; Santana-Davila, R.; Molina, J.R. Non–Small Cell Lung Cancer: Epidemiology, Screening, Diagnosis, and Treatment. Mayo Clin. Proc., 2019, 94(8), 1623-1640.
[http://dx.doi.org/10.1016/j.mayocp.2019.01.013] [PMID: 31378236]
[4]
Herbst, R.S.; Morgensztern, D.; Boshoff, C. The Biology and Management of Non-small Cell Lung Cancer. Nature, 2018, 553(7689), 446-454.
[http://dx.doi.org/10.1038/nature25183] [PMID: 29364287]
[5]
VanderLaan, P.A.; Roy-Chowdhuri, S. Current and Future Trends in Non-small Cell Lung Cancer Biomarker Testing: The American Experience. Cancer Cytopathol., 2020, 128(9), 629-636.
[http://dx.doi.org/10.1002/cncy.22313] [PMID: 32885913]
[6]
Wu, F.; Wang, L.; Zhou, C. Lung Cancer in China: Current and Prospect. Curr. Opin. Oncol., 2021, 33(1), 40-46.
[http://dx.doi.org/10.1097/CCO.0000000000000703] [PMID: 33165004]
[7]
Luo, T.; Lu, Y.; Yan, S.; Xiao, X.; Rong, X.; Guo, J. Network Pharmacology in Research of Chinese Medicine Formula: Methodology, Application and Prospective. Chin. J. Integr. Med., 2020, 26(1), 72-80.
[http://dx.doi.org/10.1007/s11655-019-3064-0] [PMID: 30941682]
[8]
Nogales, C.; Mamdouh, Z.M.; List, M.; Kiel, C.; Casas, A.I.; Schmidt, H.H.H.W. Network Pharmacology: Curing Causal Mechanisms Instead of Treating Symptoms. Trends Pharmacol. Sci., 2022, 43(2), 136-150.
[http://dx.doi.org/10.1016/j.tips.2021.11.004] [PMID: 34895945]
[9]
Zhang, R.; Zhu, X.; Bai, H.; Ning, K. Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment. Front. Pharmacol., 2019, 10, 123.
[http://dx.doi.org/10.3389/fphar.2019.00123] [PMID: 30846939]
[10]
Barrett, T.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.; Sherman, P.M.; Holko, M.; Yefanov, A.; Lee, H.; Zhang, N.; Robertson, C.L.; Serova, N.; Davis, S.; Soboleva, A. NCBI GEO: Archive for Functional Genomics Data Sets-Update. Nucleic Acids Res., 2013, 41(Database issue), D991-D995.
[PMID: 23193258]
[11]
Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. Limma Powers Differential Expression Analyses for RNA-sequencing and Microarray Studies. Nucleic Acids Res., 2015, 43(7), e47.
[http://dx.doi.org/10.1093/nar/gkv007] [PMID: 25605792]
[12]
Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y.; Xu, X.; Li, Y.; Wang, Y.; Yang, L. TCMSP: A Database of Systems Pharmacology for Drug Discovery from Herbal Medicines. J. Cheminform., 2014, 6(1), 13.
[http://dx.doi.org/10.1186/1758-2946-6-13] [PMID: 24735618]
[13]
Fang, S.; Dong, L.; Liu, L.; Guo, J.; Zhao, L.; Zhang, J.; Bu, D.; Liu, X.; Huo, P.; Cao, W.; Dong, Q.; Wu, J.; Zeng, X.; Wu, Y.; Zhao, Y. HERB: A High-throughput Experiment and Reference-Guided Database of Traditional Chinese Medicine. Nucleic Acids Res., 2021, 49(D1), D1197-D1206.
[http://dx.doi.org/10.1093/nar/gkaa1063] [PMID: 33264402]
[14]
Langfelder, P.; Horvath, S. WGCNA: An R Package for Weighted Correlation Network Analysis. BMC Bioinformatics, 2008, 9(1), 559.
[http://dx.doi.org/10.1186/1471-2105-9-559] [PMID: 19114008]
[15]
Bateman, A.; Martin, M-J.; Orchard, S.; Magrane, M.; Agivetova, R.; Ahmad, S.; Alpi, E.; Bowler-Barnett, E.H.; Britto, R.; Bursteinas, B.; Bye-A-Jee, H.; Coetzee, R.; Cukura, A.; Da Silva, A.; Denny, P.; Dogan, T.; Ebenezer, T.G.; Fan, J.; Castro, L.G.; Garmiri, P.; Georghiou, G.; Gonzales, L.; Hatton-Ellis, E.; Hussein, A.; Ignatchenko, A.; Insana, G.; Ishtiaq, R.; Jokinen, P.; Joshi, V.; Jyothi, D.; Lock, A.; Lopez, R.; Luciani, A.; Luo, J.; Lussi, Y.; MacDougall, A.; Madeira, F.; Mahmoudy, M.; Menchi, M.; Mishra, A.; Moulang, K.; Nightingale, A.; Oliveira, C.S.; Pundir, S.; Qi, G.; Raj, S.; Rice, D.; Lopez, M.R.; Saidi, R.; Sampson, J.; Sawford, T.; Speretta, E.; Turner, E.; Tyagi, N.; Vasudev, P.; Volynkin, V.; Warner, K.; Watkins, X.; Zaru, R.; Zellner, H.; Bridge, A.; Poux, S.; Redaschi, N.; Aimo, L.; Argoud-Puy, G.; Auchincloss, A.; Axelsen, K.; Bansal, P.; Baratin, D.; Blatter, M-C.; Bolleman, J.; Boutet, E.; Breuza, L.; Casals-Casas, C.; de Castro, E.; Echioukh, K.C.; Coudert, E.; Cuche, B.; Doche, M.; Dornevil, D.; Estreicher, A.; Famiglietti, M.L.; Feuermann, M.; Gasteiger, E.; Gehant, S.; Gerritsen, V.; Gos, A.; Gruaz-Gumowski, N.; Hinz, U.; Hulo, C.; Hyka-Nouspikel, N.; Jungo, F.; Keller, G.; Kerhornou, A.; Lara, V.; Le Mercier, P.; Lieberherr, D.; Lombardot, T.; Martin, X.; Masson, P.; Morgat, A.; Neto, T.B.; Paesano, S.; Pedruzzi, I.; Pilbout, S.; Pourcel, L.; Pozzato, M.; Pruess, M.; Rivoire, C.; Sigrist, C.; Sonesson, K.; Stutz, A.; Sundaram, S.; Tognolli, M.; Verbregue, L.; Wu, C.H.; Arighi, C.N.; Arminski, L.; Chen, C.; Chen, Y.; Garavelli, J.S.; Huang, H.; Laiho, K.; McGarvey, P.; Natale, D.A.; Ross, K.; Vinayaka, C.R.; Wang, Q.; Wang, Y.; Yeh, L-S.; Zhang, J.; Ruch, P.; Teodoro, D. UniProt: The Universal Protein Knowledgebase in 2021. Nucleic Acids Res., 2021, 49(D1), D480-D489.
[http://dx.doi.org/10.1093/nar/gkaa1100] [PMID: 33237286]
[16]
Ke, W.; Zhang, L.; Dai, Y. The Role of IL‐6 in Immunotherapy of Non‐small Cell Lung Cancer (NSCLC) with Immune‐related Adverse Events (irAEs). Thorac. Cancer, 2020, 11(4), 835-839.
[http://dx.doi.org/10.1111/1759-7714.13341] [PMID: 32043828]
[17]
Scott, L.J. Tocilizumab: A Review in Rheumatoid Arthritis. Drugs, 2017, 77(17), 1865-1879.
[http://dx.doi.org/10.1007/s40265-017-0829-7] [PMID: 29094311]
[18]
Li, Q.; Han, Y.; Fei, G.; Guo, Z.; Ren, T.; Liu, Z. IL-17 Promoted Metastasis of Non-small-cell Lung Cancer Cells. Immunol. Lett., 2012, 148(2), 144-150.
[http://dx.doi.org/10.1016/j.imlet.2012.10.011] [PMID: 23089548]
[19]
Wagner, N.; Wagner, K.D. PPAR Beta/Delta and the Hallmarks of Cancer. Cells, 2020, 9(5), 1133.
[http://dx.doi.org/10.3390/cells9051133] [PMID: 32375405]
[20]
Yang, J. [PPAR-γ Silencing Inhibits the Apoptosis of A549 Cells by Upregulating Bcl-2] Zhongguo Fei Ai Za Zhi, 2013, 16(3), 125-130.
[PMID: 23514940]
[21]
Chuang, C.H.; Yeh, C.L.; Yeh, S.L.; Lin, E.S.; Wang, L.Y.; Wang, Y.H. Quercetin Metabolites Inhibit MMP-2 Expression in A549 Lung Cancer Cells by PPAR-γ Associated Mechanisms. J. Nutr. Biochem., 2016, 33, 45-53.
[http://dx.doi.org/10.1016/j.jnutbio.2016.03.011] [PMID: 27260467]
[22]
Lim, K.H.; Staudt, L.M. Toll-like Receptor Signaling. Cold Spring Harb. Perspect. Biol., 2013, 5(1), a011247.
[http://dx.doi.org/10.1101/cshperspect.a011247] [PMID: 23284045]
[23]
Pahlavanneshan, S.; Sayadmanesh, A.; Ebrahimiyan, H.; Basiri, M. Toll-Like Receptor-Based Strategies for Cancer Immunotherapy. J. Immunol. Res., 2021, 2021, 1-14.
[http://dx.doi.org/10.1155/2021/9912188] [PMID: 34124272]
[24]
Tran, T.H.; Tran, T.T.P.; Truong, D.H.; Nguyen, H.T.; Pham, T.T.; Yong, C.S.; Kim, J.O. Toll-like Receptor-targeted Particles: A Paradigm to Manipulate the Tumor Microenvironment for Cancer Immunotherapy. Acta Biomater., 2019, 94, 82-96.
[http://dx.doi.org/10.1016/j.actbio.2019.05.043] [PMID: 31129358]
[25]
Wang, K.; Wang, J.; Wei, F.; Zhao, N.; Yang, F.; Ren, X. Expression of TLR4 in Non-Small Cell Lung Cancer Is Associated with PD-L1 and Poor Prognosis in Patients Receiving Pulmonectomy. Front. Immunol., 2017, 8, 456.
[http://dx.doi.org/10.3389/fimmu.2017.00456] [PMID: 28484456]
[26]
Tavora, B.; Mederer, T.; Wessel, K.J.; Ruffing, S.; Sadjadi, M.; Missmahl, M.; Ostendorf, B.N.; Liu, X.; Kim, J.Y.; Olsen, O.; Welm, A.L.; Goodarzi, H.; Tavazoie, S.F. Tumoural Activation of TLR3–SLIT2 Axis in Endothelium Drives Metastasis. Nature, 2020, 586(7828), 299-304.
[http://dx.doi.org/10.1038/s41586-020-2774-y] [PMID: 32999457]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy