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pmid:37533633

To explore the effect of kaempferol on non-small cell lung cancer based on network pharmacology and molecular docking.

Junli Zhang · Xiangqi Liu · Guoying Zhang · Junling Wu · Zhongfang Liu · Chuanguo Liu · Hui Wang · Shuxin Miao · Lei Deng · Kuan Cao · Miwei Shang · Qingjun Zhu · Peng Sun

Frontiers in pharmacology · 2023 doi:10.3389/fphar.2023.1148171

Paper

complete

Experiment

partial

Data

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Analysis

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Execution

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title
To explore the effect of kaempferol on non-small cell lung cancer based on network pharmacology and molecular docking.
doi
10.3389/fphar.2023.1148171
pmid
37533633
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[0]
Junli Zhang
[1]
Xiangqi Liu
[2]
Guoying Zhang
[3]
Junling Wu
[4]
Zhongfang Liu
[5]
Chuanguo Liu
[6]
Hui Wang
[7]
Shuxin Miao
[8]
Lei Deng
[9]
Kuan Cao
[10]
Miwei Shang
[11]
Qingjun Zhu
[12]
Peng Sun
journal
Frontiers in pharmacology
year
2023
abstract
Non-small cell lung cancer (NSCLC) is a common pathological type of lung cancer, which has a serious impact on human life, health, psychology and life. At present, chemotherapy, targeted therapy and other methods commonly used in clinic are prone to drug resistance and toxic side effects. Natural extracts of traditional Chinese medicine (TCM) have attracted wide attention in cancer treatment because of their small toxic and side effects. Kaempferol is a flavonoid from natural plants, which has been proved to have anticancer properties in many cancers such as lung cancer, but the exact molecular mechanism is still unclear. Therefore, on the basis of in vitro experiments, we used network pharmacology and molecular docking methods to study the potential mechanism of kaempferol in the treatment of non-small cell lung cancer. The target of kaempferol was obtained from the public database (PharmMapper, Swiss target prediction), and the target of non-small cell lung cancer was obtained from the disease database (Genecards and TTD). At the same time, we collected gene chips GSE32863 and GSE75037 in conjunction with GEO database to obtain differential genes. By drawing Venn diagram, we get the intersection target of kaempferol and NSCLC. Through enrichment analysis, PI3K/AKT is identified as the possible key signal pathway. PIK3R1, AKT1, EGFR and IGF1R were selected as key targets by topological analysis and molecular docking, and the four key genes were further verified by analyzing the gene and protein expression of key targets. These findings provide a direction for further research of kaempferol in the treatment of NSCLC.
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Kaplan-Meier analysis of four key genes, PIK3R1, AKT, EGFR and IGF1R, in lung cancer patients.
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Publicly available datasets were analyzed in this study. This data can be found here: Pubchem (https://pubchem.ncbi.nlm.nih.gov/) Swiss Target Prediction (https://www.swisstargetprediction.ch/) Pharm Mapper (https://www.lilab-ecust.cn/pharmmapper/). UniprotKB (https://www.uniprot.gov/) Gene Cards database and TTD database, GEO database, GEPIA (https://gepia.cancer-pku.cn/), HPA (https://www.proteinatlas.org/), Kaplan-Meier Plotter Database (https://kmplot.com/analysis/).
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sections[26] "Data availability statement" paragraph 2
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Publicly available datasets were analyzed in this study. This data can be found here: Pubchem (https://pubchem.ncbi.nlm.nih.gov/) Swiss Target Prediction (https://www.swisstargetprediction.ch/) Pharm Mapper (https://www.lilab-ecust.cn/pharmmapper/). UniprotKB (https://www.uniprot.gov/) Gene Cards database and TTD database, GEO database, GEPIA (https://gepia.cancer-pku.cn/), HPA (https://www.proteinatlas.org/), Kaplan-Meier Plotter Database (https://kmplot.com/analysis/).
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The expression of PIK3R1, AKT, EGFR, and IGF1R was significantly correlated with the prognosis of NSCLC patients.
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Survival analysis of four key genes, PIK3R1, AKT, EGFR and IGF1R (Figure 9), showed that the expression of these four key genes was significantly correlated with the prognosis of patients (p < 0.05).
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sections[23] "3.9 Survival analysis" paragraph 2
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Survival analysis of four key genes, PIK3R1, AKT, EGFR and IGF1R (Figure 9), showed that the expression of these four key genes was significantly correlated with the prognosis of patients (p < 0.05).
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Publicly available datasets were analyzed in this study. This data can be found here: Pubchem (https://pubchem.ncbi.nlm.nih.gov/) Swiss Target Prediction (https://www.swisstargetprediction.ch/) Pharm Mapper (https://www.lilab-ecust.cn/pharmmapper/). UniprotKB (https://www.uniprot.gov/) Gene Cards database and TTD database, GEO database, GEPIA (https://gepia.cancer-pku.cn/), HPA (https://www.proteinatlas.org/), Kaplan-Meier Plotter Database (https://kmplot.com/analysis/).
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ignaling and other processes related to cell apoptosis. The molecular functions were related to cel
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lture and cell viability detection At first, CCK-8 kit was used to evaluate the effect of kaemp
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