Network Pharmacology Combined with GEO Analysis of the Mechanism of Qing-Jin-Hua-Tan Decoction in the Treatment of Non-small Cell Lung Cancer
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 2023 |
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| Erschienen: |
2024 |
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Zur Gesamtaufnahme - volume:20 |
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| Enthalten in: |
Current computer-aided drug design - 20(2023), 4 vom: 23., Seite 396-404 |
| Sprache: |
Englisch |
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CIBERSORT |
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Date Revised 13.10.2023 published: Print Citation Status Publisher |
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| doi: |
10.2174/1573409919666230523155830 |
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| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
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