# Network Pharmacology and Molecular Docking Analysis on Molecular Targets and Mechanisms of Aidi Injection Treating of Nonsmall Cell Lung Cancer

Authors: Weizhou Zhang; Wenpan Peng; Yehui Li; Tingyu Pan; Fanchao Feng; Jie Xu; Xianmei Zhou
Journal: Evidence-Based Complementary and Alternative Medicine
 (2022)

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## Abstract

Background. Aidi injection (ADI) is a compound preparation injection of Chinese herbs used to treat patients of nonsmall cell lung cancer (NSCLC) in China. This study aimed to reveal the mechanism of ADI in the treatment of NSCLC by using network pharmacology and molecular docking. Methods. The related targets of ADI and NSCLC were obtained from multiple databases. The network diagram of disease-drug-components-targets (DDCT) and protein-protein interaction (PPI) was constructed to screen key targets. Then, the key targets and main signaling pathways were screened by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Next, in order to validate the results of network pharmacology, expression analysis and survival analysis of key genes were performed. Finally, we carried out the technology of molecular docking to further validate the accuracy of the above results. Results. A total of 207 targets of ADI and 5282 targets of NSCLC were obtained finally. Through the construction of DDCT and PPI network diagrams, 28 key targets were finally obtained. The results of the KEGG enrichment analysis indicated that multiple signaling pathways were associated with NSCLC, which included the MAPK signaling pathway, the IL-17 signaling pathway, and the PI3K/AKT signaling pathway. The key genes in the signaling pathway mainly include TP53, CASP3, MMP9, AKT1, PTGS2, and MAPK1. The results of differently expressed analysis of key genes showed that TP53, CASP3, MMP9, AKT1, PTGS2, and MAPK1 had statistical differences in lung squamous cell carcinoma (LUSC) compared with normal tissue p<0.001. In lung adenocarcinoma (LUAD), the expression of TP53, CASP3, MMP9, AKT1, and PTGS2 had statistical differences compared with normal tissue p<0.001, while the expression of MAPK1 had no statistical difference p>0.05. The results of survival analysis of key genes showed that AKT1, MAPK1, CASP3, MMP9, TP53, and PTGS2 had statistical differences in the OS or RFS of NSCLC patients p<0.05. In addition, the results of molecular docking indicated that the key genes and the main components have good docking activity. Conclusions. This study revealed the potential mechanism of ADI in the treatment of NSCLC with multipathways and multitargets and provided a scientific basis for the in-depth study of ADI in the treatment of NSCLC.


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## Body

## 1. Introduction

Lung cancer is the leading cause of cancer-related deaths worldwide, with the 5-year survival less than 21% [1]. Nonsmall cell lung cancer (NSCLC) accounts for 85% of all lung cancers. Lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the two main pathological types of NSCLC [2]. In the early stages of lung cancer, it can be cured by surgery, and the treatment methods in the middle and late stages mainly include chemotherapy, radiotherapy, targeted therapy, and immunotherapy. However, since NSCLC generally has no obvious clinical symptoms in the early stage, it is often discovered during the chest impact examination, leading most patients to seek medical attention. It was already in the middle and late stages, and the opportunity for surgery was lost. With the successive identification of oncogenic driver genes in lung cancer series, molecular targeted therapy plays an important role in the treatment of advanced NSCLC, which has greatly improved the prognosis of people with positive driver genes. In recent years, with the major breakthrough of immunotherapy in the field of lung cancer, the 5-year overall survival of some patients with negative driver genes has been significantly extended. However, due to the obvious adverse reactions of molecular targeted therapy and immunotherapy [3], easy drug resistance and high medical costs made it is still a refractory disease worldwide. At present, there are still many deficiencies in the conventional treatment of NSCLC. Therefore, it is urgent to study the molecular mechanisms of NSCLC and find efficient and safe therapeutic drugs. In recent years, there have been more and more clinical trials and basic researches on the treatment of NSCLC with traditional Chinese medicine (TCM), which providing new ideas and methods for NSCLC treatment.Aidi injection (ADI) is a compound preparation injection of Chinese herbs (Z52020236, CFDA), which is extracted from Mylabris (Banmao), Panax Ginseng (Renshen), Acanthopanax Senticosus (Ciwujia), and Astragali Radix (Huangqi), four traditional Chinese herbs [4]. In recent years, ADI combined with other antitumor drugs has been widely used in the treatment of NSCLC and has achieved good clinical effects. For example, ADI combined with chemotherapy (pemetrexed disodium and cisplatin or gemcitabine and cisplatin) had a positive effect in the treatment of NSCLC, which could effectively improve the Karnofsky performance status of patients, reduce the risk of adverse reactions, and improve the survival rate [5, 6]. In addition, studies showed that ADI combined with gefitinib had significant efficacy and high safety in the treatment of NSCLC [7, 8]. Although the clinical efficacy of ADI is remarkable, its mechanism is still unclear and needs to be further explored.Emphasizing the holistic concept when treating diseases with TCM, as a TCM injection preparation, the function of ADI naturally depends on the mutual coordination and complementation of each Chinese herb. In order to comprehensive inquiry the effect of ADI and reveal the mechanism of ADI in the treatment of NSCLC, we employed a network pharmacology strategy. Network pharmacology is a novel discipline based on the theory of systems biology, analyzing the network of biological systems and selecting specific signaling nodes for multitargets drug molecular design [9]. Its research concept coincides with the holism of TCM and is widely used for the discovery of drugs and active compounds in TCM and the interpretation of the all-sided mechanism, providing new scientific and technological support for clinical rational drug use and advancing drug research.


## 2. Materials and Methods

### 2.1. Potential Targets of ADI

The active ingredients and related targets of Panax Ginseng, Astragali Radix, and Mylabris were obtained from the TCMSP (https://tcmspw.com/tcmsp.php) database. The active ingredients of senticosus were obtained from the TCMID (https://www.megabionet.org/tcmid/) database and then imported back into the TCMSP database one by one to obtain the related targets of senticosus. Screening conditions are oral bioavailability (OB) ≥30% and drug likeness (DL) ≥0.18.


### 2.2. NSCLC Related Targets

The NSCLC-related targets were obtained from the OMIM (https://omim.org/) and GeneCards Suite (https://www.genecards.org) databases, using nonsmall cell lung cancer or NSCLC as the search term. Merge all targets from the two databases and delete duplicate targets as the final target for NSCLC.


### 2.3. Intersecting Targets of ADI and NSCLC

In order to obtain the intersecting targets and make a Venn diagram of ADI and NSCLC, the related targets of ADI and NSCLC obtained above were inputted into the Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/index.html).


### 2.4. Construction of DDCT and PPI Network Diagrams

Running the prepared Cyto.pl script through the Perl program to obtain the linkage network file and classification file of nodes, the active ingredient file and the file of the intersecting targets of ADI and NSCLC, and he above files were imported into Cytoscape software (version 3.7.2) to build the DDCT network diagram. The PPI linkage file was obtained by using the intersecting targets of ADI and NSCLC in the STRING database (https://string-db.org). We imported the PPI linkage file into Cytoscape software to create a PPI network diagram and screened out targets with a degree and betweenness centrality (BC) greater than their mean values as the key targets for the study.


### 2.5. GO and KEGG Enrichment Analysis

The selected key targets were entered into the DAVID database (https://david.ncifcrf.gov/) for GO and KEGG enrichment analysis, and the species selected was “Homo sapiens.” The cluster profiler package and the ggplot2 data package [10] were used to perform statistical analysis and visualization for the key targets through R software (Version 3.6.3), and the main biological processes and signaling pathways involved in the treatment of NSCLC by ADI were screened out with p adjust <0.05. Genes in major signaling pathways were regarded as key genes for further study.


### 2.6. Expression Analysis of Key Genes

In order to explore the expression differences of the screened key genes in tumor tissue and normal tissue, we downloaded the Level 3 RNAseq data in HTSeq-FPKM format of key genes in LUSC and LUAD from the TCGA (https://portal.gdc.cancer.gov/) database. Then, the RNAseq data were converted in FPKM format to TPM format and log2 conversion. The R (3.6.3) software and ggplot2 package were used for statistical analysis and visualization [11].


### 2.7. Survival Analysis of Key Genes

To validate the results of network pharmacology, we used a survival analysis method to investigate whether the expression of key genes had an effect on survival rate with OS (overall survival) and RFS (relapse-free survival) as study indicators. The Kaplan–Meier plotter (https://kmplot.com/analysis/) was used for this study, which is a powerful online tool for assessing the correlation between the expression and survival of 30,000 genes in more than 25,000 samples from 21 tumor types [12]. We split patients by median and automatically selected the best cutoff. Survival analysis curves were plotted by a Kaplan–Meier plotter, and the log-rankp<0.05 was considered statistically significant.


### 2.8. Molecular Docking

To further verify the above results, we performed molecular docking on key genes. To further verify the above results, we selected the top 10 active compounds in the DDCT network and key genes in the main signaling pathway for molecular docking. The structures of the related active compounds and key genes were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov) and the RSCB PDB database (https://www.rcsb.org/), respectively. AutoDock Vina software (https://vina.scripps.edu/) was used to perform molecular docking of key proteins and active ingredients, and the affinity value represents the binding energy of both. It is generally believed that the lower the binding energy, the more stable the binding of active ingredients and proteins [13]. The best conformation of affinity was selected as the final docking conformation and finally plotted the best results obtained using PyMol software.


### 2.9. Statistical Methods

The cluster profiler and ggplot2 data packages of R software (Version 3.6.3) were used to perform statistical analysis on the results of enrichment analysis and gene expression differences. To compare survival curves, we used the log-rank test to calculate the HR and log-rankp value in the Kaplan-Meier plotter. All results were considered statistically significant when p<0.05, if not especially noted.


## 2.1. Potential Targets of ADI

The active ingredients and related targets of Panax Ginseng, Astragali Radix, and Mylabris were obtained from the TCMSP (https://tcmspw.com/tcmsp.php) database. The active ingredients of senticosus were obtained from the TCMID (https://www.megabionet.org/tcmid/) database and then imported back into the TCMSP database one by one to obtain the related targets of senticosus. Screening conditions are oral bioavailability (OB) ≥30% and drug likeness (DL) ≥0.18.


## 2.2. NSCLC Related Targets

The NSCLC-related targets were obtained from the OMIM (https://omim.org/) and GeneCards Suite (https://www.genecards.org) databases, using nonsmall cell lung cancer or NSCLC as the search term. Merge all targets from the two databases and delete duplicate targets as the final target for NSCLC.


## 2.3. Intersecting Targets of ADI and NSCLC

In order to obtain the intersecting targets and make a Venn diagram of ADI and NSCLC, the related targets of ADI and NSCLC obtained above were inputted into the Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/index.html).


## 2.4. Construction of DDCT and PPI Network Diagrams

Running the prepared Cyto.pl script through the Perl program to obtain the linkage network file and classification file of nodes, the active ingredient file and the file of the intersecting targets of ADI and NSCLC, and he above files were imported into Cytoscape software (version 3.7.2) to build the DDCT network diagram. The PPI linkage file was obtained by using the intersecting targets of ADI and NSCLC in the STRING database (https://string-db.org). We imported the PPI linkage file into Cytoscape software to create a PPI network diagram and screened out targets with a degree and betweenness centrality (BC) greater than their mean values as the key targets for the study.


## 2.5. GO and KEGG Enrichment Analysis

The selected key targets were entered into the DAVID database (https://david.ncifcrf.gov/) for GO and KEGG enrichment analysis, and the species selected was “Homo sapiens.” The cluster profiler package and the ggplot2 data package [10] were used to perform statistical analysis and visualization for the key targets through R software (Version 3.6.3), and the main biological processes and signaling pathways involved in the treatment of NSCLC by ADI were screened out with p adjust <0.05. Genes in major signaling pathways were regarded as key genes for further study.


## 2.6. Expression Analysis of Key Genes

In order to explore the expression differences of the screened key genes in tumor tissue and normal tissue, we downloaded the Level 3 RNAseq data in HTSeq-FPKM format of key genes in LUSC and LUAD from the TCGA (https://portal.gdc.cancer.gov/) database. Then, the RNAseq data were converted in FPKM format to TPM format and log2 conversion. The R (3.6.3) software and ggplot2 package were used for statistical analysis and visualization [11].


## 2.7. Survival Analysis of Key Genes

To validate the results of network pharmacology, we used a survival analysis method to investigate whether the expression of key genes had an effect on survival rate with OS (overall survival) and RFS (relapse-free survival) as study indicators. The Kaplan–Meier plotter (https://kmplot.com/analysis/) was used for this study, which is a powerful online tool for assessing the correlation between the expression and survival of 30,000 genes in more than 25,000 samples from 21 tumor types [12]. We split patients by median and automatically selected the best cutoff. Survival analysis curves were plotted by a Kaplan–Meier plotter, and the log-rankp<0.05 was considered statistically significant.


## 2.8. Molecular Docking

To further verify the above results, we performed molecular docking on key genes. To further verify the above results, we selected the top 10 active compounds in the DDCT network and key genes in the main signaling pathway for molecular docking. The structures of the related active compounds and key genes were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov) and the RSCB PDB database (https://www.rcsb.org/), respectively. AutoDock Vina software (https://vina.scripps.edu/) was used to perform molecular docking of key proteins and active ingredients, and the affinity value represents the binding energy of both. It is generally believed that the lower the binding energy, the more stable the binding of active ingredients and proteins [13]. The best conformation of affinity was selected as the final docking conformation and finally plotted the best results obtained using PyMol software.


## 2.9. Statistical Methods

The cluster profiler and ggplot2 data packages of R software (Version 3.6.3) were used to perform statistical analysis on the results of enrichment analysis and gene expression differences. To compare survival curves, we used the log-rank test to calculate the HR and log-rankp value in the Kaplan-Meier plotter. All results were considered statistically significant when p<0.05, if not especially noted.


## 3. Results

In this study, network pharmacology was used to analyze the potential targets of ADI and NSCLC, and the Venn diagram was used to obtain intersecting targets for ADI and NSCLC at the same time. Next, to identify key targets, the intersecting targets of ADI and NSCLC were used to construct a network diagram of disease-drugcomponents-targets (DDCT) and protein-protein interactions (PPI). Then, we obtained the main biological functions and signaling pathways involved in the ADI treatment of NSCLC through key targets. In order to verify the targets and signaling pathways obtained in previous, we performed differential expression analysis and survival analysis of key genes in mainly signaling pathways. Finally, the molecular docking technique was used to further verify the above results. The workflow chart of this study is shown in Figure1.Figure 1
The workflow chart of this study. ADI, Aidi injection; NSCLC, nonsmall cell lung cancer; PPI, protein-protein interaction; DDCT, disease-drugcomponents-targets.


### 3.1. Active Compounds and Targets of ADI

Through screening, a total of 37 active compounds (Table1) and 207 targets of ADI (Supplementary Material 1) were obtained finally. Among the 37 active compounds, 15 came from Astragali Radix, 16 came from Panax Ginseng, 5 came from Acanthopanax Senticosus, and 1 came from Mylabris. The main active compounds were quercetin, formononetin, kaempferol, beta-sitosterol, stigmasterol, calycosin, isorhamnetin, methylnissolin, hederagenin, and 7-O-methylisomucronulatol. In these active compounds, some of them come from a variety of TCM.Table 1
Basic information of active ingredients of ADI.
Molecule IDMolecule nameOB (%)DLDrugMOL000098Quercetin46.430.28Astragali Radix, SenticosusMOL000422Kaempferol41.880.24Panax Ginseng, Astragali RadixMOL000392Formononetin69.670.21Astragali RadixMOL0003787-O-Methylisomucronulatol74.690.3Astragali RadixMOL0003799,10-Dimethoxypterocarpan-3-O-β-D-glucoside36.740.92Astragali RadixMOL000387Bifendate31.10.67Astragali RadixMOL001525Daucosterol36.910.75SenticosusMOL000433FA68.960.71Astragali RadixMOL000439Isomucronulatol-7,2′-di-O-glucoside49.280.62Astragali RadixMOL0004421,7-Dihydroxy-3,9-dimethoxy pterocarpene39.050.48Astragali RadixMOL000449Stigmasterol43.830.76Panax GinsengMOL0018513-Phenyl-4-azafluorene32.90.23MylabrisMOL000417Calycosin47.750.24Astragali RadixMOL009047Pinoresinol dimethyl ether33.290.62SenticosusMOL000354Isorhamnetin49.60.31Astragali RadixMOL000371Methylnissolin53.740.48Astragali RadixMOL000296Hederagenin36.910.75Astragali RadixMOL000358β-Sitosterol36.910.75Panax GinsengMOL005308Aposiopolamine66.650.22Panax GinsengMOL005317Deoxyharringtonine39.270.81Panax GinsengMOL005318Dianthramine40.450.2Panax GinsengMOL005320Arachidonate45.570.2Panax GinsengMOL005344Ginsenoside Rh236.320.56Panax GinsengMOL005348Ginsenoside Rh4_qt31.110.78Panax GinsengMOL005356Girinimbin61.220.31Panax GinsengMOL005384Suchilactone57.520.56Panax GinsengMOL005399Alexandrin_qt36.910.75Panax GinsengMOL001558Sesamin56.550.83SenticosusMOL000239Jaranol50.830.29Astragali RadixMOL000787Fumarine59.260.83Panax GinsengMOL003648Inermin65.830.54Panax GinsengMOL005321Frutinone A65.90.34Panax GinsengMOL005376Panaxadiol33.090.79Panax ginsengMOL002320γ-Sitosterol36.910.75SenticosusMOL002879Diop43.590.39Panax GinsengMOL000211Mairin55.380.78Astragali RadixMOL000380(6aR, 11aR)-9,10-Dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c] chromen-3-ol64.260.42Astragali RadixNote. ADI, Aidi injection; OB, oral bioavailability; DL, drug likeness.


### 3.2. NSCLC Related Targets

By searching each database, combining the results of each database and removing duplicates, a total of 5282 targets of NSCLC (Supplementary Material2) were obtained finally.


### 3.3. Intersecting Targets of ADI and NSCLC

Two hundred and seven drug targets and 5,282 disease targets were imported into Venny 2.1.0, and a total of 160 intersecting targets of ADI and NSCLC (Supplementary Material3) were obtained finally. The Venn diagram of ADI and NSCLC is shown in Figure 2.Figure 2
Venn diagram of components and disease related targets.


### 3.4. DDCT and PPI Network Diagrams

There are a total of 199 nodes and 650 interacting lines in the DDCT network diagram (Figure3, Supplementary Material 4). Among the 199 nodes, 37 were active ingredients, 160 were targeted proteins, one was a disease name node, and one was a drug name node. The top 10 compounds of degrees in the DDCT network diagram are shown in Table 2. The PPI network graph was obtained from the STRING database, which contained 158 nodes and 2903 interacting lines. We imported the PPI network graph file into Cytoscape software, screened out the nodes whose degree and BC were greater than their average, and finally obtained 28 key targets and 352 connecting lines (Table 3). The PPI diagram of key targets is shown in Figure 4.Figure 3
DDCT network diagram. The arrows represent active compounds; the circles represent potential targets; the diamonds represent disease; the triangles represent drug. DDCT, disease-drug-components-targets.Table 2
Basic information of the top 10 compounds of degree in the DDCT network diagram.
Molecule IDCompoundOB (%)DLBCDegreeMOL000098Quercetin46.430.280.23996567115MOL000422Kaempferol41.880.240.0328539581MOL000392Formononetin69.670.210.0105233224MOL0003787-O-Methylisomucronulatol74.690.30.0086774923MOL000358Beta-sitosterol36.910.750.010219821MOL000449Stigmasterol43.830.760.0088690719MOL000417Calycosin47.750.240.0039861817MOL000354Isorhamnetin49.60.310.0042817117MOL000371Methylnissolin53.740.480.0030240914MOL000296Hederagenin36.910.750.0038683913Note. DDCT, disease-drug-components-targets; OB, oral bioavailability; DL, drug likeness; BC, betweenness centrality.Table 3
Basic information of 28 key targets in the PPI network diagram.
SymbolTargetBCDegreeAKT1RAC-alpha serine/threonine-protein kinase0.0039251627TP53Cellular tumor antigen p530.0039251627CASP3Caspase-30.0039251627MAPK1Mitogen-activated protein kinase 140.0039251627PTGS2Prostaglandin G/H synthase 20.0039251627MMP9Matrix metalloproteinase-90.0039251627FOSProto-oncogenec-Fos0.0033617526JUNTranscription factor AP-10.0033617526VEGFAVascular endothelial growth factor A0.0033617526CXCL8Interleukin-80.0033617526MAPK8Mitogen-activated protein kinase 80.0033617526IL6Interleukin-60.0033617526TNFTumor necrosis factor0.0033617526EGFProepidermal growth factor0.0033617526MYCMyc proto-oncogene protein0.0023667626EGFREpidermal growth factor receptor0.0016279425CCND1G1/S-specific cyclin-D10.0019504925PPARGPeroxisome proliferator-activated receptor gamma0.0023778925IL4Interleukin-40.0023778925IL1BInterleukin-1 beta0.0019429124RELATranscription factor p650.0012135424STAT1Signal transducer and activator of transcription 1-alpha/beta0.0012260524ESR1Estrogen receptor0.0010840423SIRT1NAD-dependent deacetylase sirtuin-10.0014065923MPOMyeloperoxidase0.0010289722ERBB2Receptor tyrosine-protein kinase ERBB20.0006860022HMOX1Heme oxygenase 10.0010289722ARAndrogen receptor0.0003960021Note. PPI, protein-protein interaction; BC, betweenness centrality.Figure 4
PPI network diagram. PPI, protein-protein interaction; BC, betweenness centrality.


### 3.5. GO and KEGG Enrichment Analysis

GO enrichment analysis mainly describes genes from three aspects, namely, biological process (BP), cellular component (CC), and molecular function (MF). A total of 1850 entries withp<0.05 of GO enrichment analysis were obtained finally, which included 1752 BP entries, 21 CC entries, and 77 MF entries (Supplementary Material 5). The 15 entries closely related to the disease were selected for visualization, respectively. As is shown in Figure 5(a), the results of BP mainly involved epithelial cell proliferation and regulation, regulation of angiogenesis, regulation of the DNA metabolic process, and regulation of macro-autophagy. CC mainly included nuclear chromatin, membrane microdomain, membrane region, and vesicle lumen (Figure 5(b)). MF mainly contained phosphatase binding, cytokine activity, growth factor receptor binding, and growth factor activity (Figure 5(c)). A total of 145 entries with p<0.05 of KEGG enrichment analysis were screened (Supplementary Material 6). The results of KEGG enrichment analysis indicated that multiple signaling pathways were associated with NSCLC, which included the MAPK signaling pathway, the IL-17 signaling pathway, and PI3K/AKT signaling pathway, etc. The difference in the MAPK signaling pathway is the most obvious. The 15 entries that were closely related to NSCLC were selected for visualization (Figure 6). The key genes in these signaling pathways mainly included TP53, AKT1, CASP3, MMP9, MAPK1, and PTGS2. The results of the above enrichment analysis suggested that ADI plays an anti-NSCLC role by affecting cell apoptosis, proliferation, migration, and up-regulating or down-regulating the expression of key genes in these signaling pathways.Figure 5
The results of GO enrichment analysis. GO, gene ontology; BP, biological process; CC, cellular component; MF, molecular function.
(a)(b)(c)Figure 6
The results of KEGG enrichment analysis. KEGG, Kyoto Encyclopedia of Genes and Genomes.


### 3.6. Differently Expressed Analysis of Key Genes

The results of the differently expressed analysis of key genes showed that TP53, CASP3, MMP9, AKT1, PTGS2, and MAPK1 had statistical differences in LUSC compared with normal tissuep<0.001 (Figures 7(a)–7(f)). In LUAD, the expression of TP53, CASP3, MMP9, AKT1, and PTGS2 had statistical differences compared with normal tissue p<0.001 (Figures 7(g)–7(k)), while, the expression of MAPK1 had no statistical difference p>0.05 (Figure 7(l)). Compared with normal tissues, TP53, CASP3, and MMP9 were up-regulated in LUSC and LUAD (Figures 7(a)–7(c) and 7(g)–7(i)). Similarly, MAPK1 was up-regulated in LUSC compared with normal tissues (Figure 7(f)). Conversely, AKT1 and PTGS2 were down-regulated in LUSC and LUAD compared with normal tissues (Figures 7(d)–7(e) and 7(j)–7(k)). These results indicated that ADI exerted its therapeutic effect by affecting the expression of these key genes in tumor tissue.Figure 7
The results of key gene expression analysis. LUSC, lung squamous cell carcinoma; LUAD, lung adenocarcinoma.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)


### 3.7. Survival Analysis of Key Genes

In our study, the results of the survival analysis of key genes showed that AKT1, MAPK1, CASP3, MMP9, TP53, and PTGS2 had statistical differences in the OS or RFS of NSCLC patientsp<0.05 (Figure 8). Patients with higher expression of AKT1 had longer OS in LUAD (Figure 8(a)), and MAPK1 had longer OS in LUSC (Figure 8(b)). Patients with lower expression of CASP3 and MMP9 had longer RFS in LUAD (Figures 8(c) and 8(d)), and similarly, in LUSC, patients with lower expression of TP53 had longer RFS (Figure 8(e)). Conversely, patients with higher expression of PTGS2 had longer RFS in LUSC (Figure 8(f)). The results of the above survival analysis were consistent with the results of the differential analysis of key gene expression, which further illustrated the accuracy of the results of this study.Figure 8
The results of key genes survival analysis. LUSC, lung squamous cell carcinoma; LUAD, lung adenocarcinoma; OS, overall survival; RFS, relapse-free survival.
(a)(b)(c)(d)(e)(f)


### 3.8. Molecular Docking

To further verify the accuracy of the results, we performed molecular docking technique on the top 10 compounds in terms of degree in the DDCT network diagram and the top 5 key genes (AKT1, CASP3, MAPK1, PTGS2, and TP53). As shown in Table4, the results of molecular docking indicated that the lowest binding energy was stigmasterol and AKT1 with a binding energy was −10 kcal/mol. The most stable binding to CASP3 was kaempferol, and the binding energy was −8.3 kcal/mol. MAPK1 and stigmasterol had the lowest binding energy, which was stigmasterol. The most stable compounds that bind to PTGS2 and TP53 were quercetin and isorhamnetin, with binding energies of −7.5 kcal/mol and −5.8 kcal/mol, respectively. In addition, we also found that the top 6 binding energies from low to high were AKT1 and stigmasterol, quercetin, kaempferol, hederagenin, calycosin, and beta-sitosterol, respectively, which demonstrated that AKT1 may be a very critical target for ADI in the treatment of NSCLC. These results showed that the core compounds and key genes had good binding activity, which further verified that ADI exerted its therapeutic effect through these key genes. The top 6 molecular docking patterns in terms of binding energies were shown in Figure 9.Table 4
Molecular docking results of the top 10 components and the top 5 genes.
Molecule IDCompoundBinding energy (kcal/mol)AKT1CASP3MAPK1PTGS2TP53MOL000098Quercetin−9.7−8.0−7.7−7.5−5.6MOL000392Formononetin−9.0−7.4−7.8−6.6−5.3MOL000422Kaempferol−9.6−8.3−7.8−7.2−5.7MOL000358Beta-sitosterol−9.1−6.9−8.3−6.4−5.0MOL000449Stigmasterol−10−7.3−8.8−7.1−4.8MOL000417Calycosin−9.2−7.9−7.7−6.7−5.5MOL000354Isorhamnetin9.6−8.2−7.9−7.4−5.8MOL000371Methylnissolin−8.7−6.9−7.6−7.0−5.0MOL000296Hederagenin−9.4−7.7−8.0−6.3−5.3MOL0003787-O-Methylisomucronulatol−8.2−6.6−7.4−6.2−4.6Figure 9
Molecular docking pattern diagram of the top 6 binding energies. (a) Quercetin and AKT1, (b) beta-sitosterol and AKT1, (c) kaempferol and AKT1, (d) hederagenin and AKT1, (e) calycosin and AKT1, and (f) stigmasterol and AKT1.
(a)(b)(c)(d)(e)(f)


## 3.1. Active Compounds and Targets of ADI

Through screening, a total of 37 active compounds (Table1) and 207 targets of ADI (Supplementary Material 1) were obtained finally. Among the 37 active compounds, 15 came from Astragali Radix, 16 came from Panax Ginseng, 5 came from Acanthopanax Senticosus, and 1 came from Mylabris. The main active compounds were quercetin, formononetin, kaempferol, beta-sitosterol, stigmasterol, calycosin, isorhamnetin, methylnissolin, hederagenin, and 7-O-methylisomucronulatol. In these active compounds, some of them come from a variety of TCM.Table 1
Basic information of active ingredients of ADI.
Molecule IDMolecule nameOB (%)DLDrugMOL000098Quercetin46.430.28Astragali Radix, SenticosusMOL000422Kaempferol41.880.24Panax Ginseng, Astragali RadixMOL000392Formononetin69.670.21Astragali RadixMOL0003787-O-Methylisomucronulatol74.690.3Astragali RadixMOL0003799,10-Dimethoxypterocarpan-3-O-β-D-glucoside36.740.92Astragali RadixMOL000387Bifendate31.10.67Astragali RadixMOL001525Daucosterol36.910.75SenticosusMOL000433FA68.960.71Astragali RadixMOL000439Isomucronulatol-7,2′-di-O-glucoside49.280.62Astragali RadixMOL0004421,7-Dihydroxy-3,9-dimethoxy pterocarpene39.050.48Astragali RadixMOL000449Stigmasterol43.830.76Panax GinsengMOL0018513-Phenyl-4-azafluorene32.90.23MylabrisMOL000417Calycosin47.750.24Astragali RadixMOL009047Pinoresinol dimethyl ether33.290.62SenticosusMOL000354Isorhamnetin49.60.31Astragali RadixMOL000371Methylnissolin53.740.48Astragali RadixMOL000296Hederagenin36.910.75Astragali RadixMOL000358β-Sitosterol36.910.75Panax GinsengMOL005308Aposiopolamine66.650.22Panax GinsengMOL005317Deoxyharringtonine39.270.81Panax GinsengMOL005318Dianthramine40.450.2Panax GinsengMOL005320Arachidonate45.570.2Panax GinsengMOL005344Ginsenoside Rh236.320.56Panax GinsengMOL005348Ginsenoside Rh4_qt31.110.78Panax GinsengMOL005356Girinimbin61.220.31Panax GinsengMOL005384Suchilactone57.520.56Panax GinsengMOL005399Alexandrin_qt36.910.75Panax GinsengMOL001558Sesamin56.550.83SenticosusMOL000239Jaranol50.830.29Astragali RadixMOL000787Fumarine59.260.83Panax GinsengMOL003648Inermin65.830.54Panax GinsengMOL005321Frutinone A65.90.34Panax GinsengMOL005376Panaxadiol33.090.79Panax ginsengMOL002320γ-Sitosterol36.910.75SenticosusMOL002879Diop43.590.39Panax GinsengMOL000211Mairin55.380.78Astragali RadixMOL000380(6aR, 11aR)-9,10-Dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c] chromen-3-ol64.260.42Astragali RadixNote. ADI, Aidi injection; OB, oral bioavailability; DL, drug likeness.


## 3.2. NSCLC Related Targets

By searching each database, combining the results of each database and removing duplicates, a total of 5282 targets of NSCLC (Supplementary Material2) were obtained finally.


## 3.3. Intersecting Targets of ADI and NSCLC

Two hundred and seven drug targets and 5,282 disease targets were imported into Venny 2.1.0, and a total of 160 intersecting targets of ADI and NSCLC (Supplementary Material3) were obtained finally. The Venn diagram of ADI and NSCLC is shown in Figure 2.Figure 2
Venn diagram of components and disease related targets.


## 3.4. DDCT and PPI Network Diagrams

There are a total of 199 nodes and 650 interacting lines in the DDCT network diagram (Figure3, Supplementary Material 4). Among the 199 nodes, 37 were active ingredients, 160 were targeted proteins, one was a disease name node, and one was a drug name node. The top 10 compounds of degrees in the DDCT network diagram are shown in Table 2. The PPI network graph was obtained from the STRING database, which contained 158 nodes and 2903 interacting lines. We imported the PPI network graph file into Cytoscape software, screened out the nodes whose degree and BC were greater than their average, and finally obtained 28 key targets and 352 connecting lines (Table 3). The PPI diagram of key targets is shown in Figure 4.Figure 3
DDCT network diagram. The arrows represent active compounds; the circles represent potential targets; the diamonds represent disease; the triangles represent drug. DDCT, disease-drug-components-targets.Table 2
Basic information of the top 10 compounds of degree in the DDCT network diagram.
Molecule IDCompoundOB (%)DLBCDegreeMOL000098Quercetin46.430.280.23996567115MOL000422Kaempferol41.880.240.0328539581MOL000392Formononetin69.670.210.0105233224MOL0003787-O-Methylisomucronulatol74.690.30.0086774923MOL000358Beta-sitosterol36.910.750.010219821MOL000449Stigmasterol43.830.760.0088690719MOL000417Calycosin47.750.240.0039861817MOL000354Isorhamnetin49.60.310.0042817117MOL000371Methylnissolin53.740.480.0030240914MOL000296Hederagenin36.910.750.0038683913Note. DDCT, disease-drug-components-targets; OB, oral bioavailability; DL, drug likeness; BC, betweenness centrality.Table 3
Basic information of 28 key targets in the PPI network diagram.
SymbolTargetBCDegreeAKT1RAC-alpha serine/threonine-protein kinase0.0039251627TP53Cellular tumor antigen p530.0039251627CASP3Caspase-30.0039251627MAPK1Mitogen-activated protein kinase 140.0039251627PTGS2Prostaglandin G/H synthase 20.0039251627MMP9Matrix metalloproteinase-90.0039251627FOSProto-oncogenec-Fos0.0033617526JUNTranscription factor AP-10.0033617526VEGFAVascular endothelial growth factor A0.0033617526CXCL8Interleukin-80.0033617526MAPK8Mitogen-activated protein kinase 80.0033617526IL6Interleukin-60.0033617526TNFTumor necrosis factor0.0033617526EGFProepidermal growth factor0.0033617526MYCMyc proto-oncogene protein0.0023667626EGFREpidermal growth factor receptor0.0016279425CCND1G1/S-specific cyclin-D10.0019504925PPARGPeroxisome proliferator-activated receptor gamma0.0023778925IL4Interleukin-40.0023778925IL1BInterleukin-1 beta0.0019429124RELATranscription factor p650.0012135424STAT1Signal transducer and activator of transcription 1-alpha/beta0.0012260524ESR1Estrogen receptor0.0010840423SIRT1NAD-dependent deacetylase sirtuin-10.0014065923MPOMyeloperoxidase0.0010289722ERBB2Receptor tyrosine-protein kinase ERBB20.0006860022HMOX1Heme oxygenase 10.0010289722ARAndrogen receptor0.0003960021Note. PPI, protein-protein interaction; BC, betweenness centrality.Figure 4
PPI network diagram. PPI, protein-protein interaction; BC, betweenness centrality.


## 3.5. GO and KEGG Enrichment Analysis

GO enrichment analysis mainly describes genes from three aspects, namely, biological process (BP), cellular component (CC), and molecular function (MF). A total of 1850 entries withp<0.05 of GO enrichment analysis were obtained finally, which included 1752 BP entries, 21 CC entries, and 77 MF entries (Supplementary Material 5). The 15 entries closely related to the disease were selected for visualization, respectively. As is shown in Figure 5(a), the results of BP mainly involved epithelial cell proliferation and regulation, regulation of angiogenesis, regulation of the DNA metabolic process, and regulation of macro-autophagy. CC mainly included nuclear chromatin, membrane microdomain, membrane region, and vesicle lumen (Figure 5(b)). MF mainly contained phosphatase binding, cytokine activity, growth factor receptor binding, and growth factor activity (Figure 5(c)). A total of 145 entries with p<0.05 of KEGG enrichment analysis were screened (Supplementary Material 6). The results of KEGG enrichment analysis indicated that multiple signaling pathways were associated with NSCLC, which included the MAPK signaling pathway, the IL-17 signaling pathway, and PI3K/AKT signaling pathway, etc. The difference in the MAPK signaling pathway is the most obvious. The 15 entries that were closely related to NSCLC were selected for visualization (Figure 6). The key genes in these signaling pathways mainly included TP53, AKT1, CASP3, MMP9, MAPK1, and PTGS2. The results of the above enrichment analysis suggested that ADI plays an anti-NSCLC role by affecting cell apoptosis, proliferation, migration, and up-regulating or down-regulating the expression of key genes in these signaling pathways.Figure 5
The results of GO enrichment analysis. GO, gene ontology; BP, biological process; CC, cellular component; MF, molecular function.
(a)(b)(c)Figure 6
The results of KEGG enrichment analysis. KEGG, Kyoto Encyclopedia of Genes and Genomes.


## 3.6. Differently Expressed Analysis of Key Genes

The results of the differently expressed analysis of key genes showed that TP53, CASP3, MMP9, AKT1, PTGS2, and MAPK1 had statistical differences in LUSC compared with normal tissuep<0.001 (Figures 7(a)–7(f)). In LUAD, the expression of TP53, CASP3, MMP9, AKT1, and PTGS2 had statistical differences compared with normal tissue p<0.001 (Figures 7(g)–7(k)), while, the expression of MAPK1 had no statistical difference p>0.05 (Figure 7(l)). Compared with normal tissues, TP53, CASP3, and MMP9 were up-regulated in LUSC and LUAD (Figures 7(a)–7(c) and 7(g)–7(i)). Similarly, MAPK1 was up-regulated in LUSC compared with normal tissues (Figure 7(f)). Conversely, AKT1 and PTGS2 were down-regulated in LUSC and LUAD compared with normal tissues (Figures 7(d)–7(e) and 7(j)–7(k)). These results indicated that ADI exerted its therapeutic effect by affecting the expression of these key genes in tumor tissue.Figure 7
The results of key gene expression analysis. LUSC, lung squamous cell carcinoma; LUAD, lung adenocarcinoma.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)


## 3.7. Survival Analysis of Key Genes

In our study, the results of the survival analysis of key genes showed that AKT1, MAPK1, CASP3, MMP9, TP53, and PTGS2 had statistical differences in the OS or RFS of NSCLC patientsp<0.05 (Figure 8). Patients with higher expression of AKT1 had longer OS in LUAD (Figure 8(a)), and MAPK1 had longer OS in LUSC (Figure 8(b)). Patients with lower expression of CASP3 and MMP9 had longer RFS in LUAD (Figures 8(c) and 8(d)), and similarly, in LUSC, patients with lower expression of TP53 had longer RFS (Figure 8(e)). Conversely, patients with higher expression of PTGS2 had longer RFS in LUSC (Figure 8(f)). The results of the above survival analysis were consistent with the results of the differential analysis of key gene expression, which further illustrated the accuracy of the results of this study.Figure 8
The results of key genes survival analysis. LUSC, lung squamous cell carcinoma; LUAD, lung adenocarcinoma; OS, overall survival; RFS, relapse-free survival.
(a)(b)(c)(d)(e)(f)


## 3.8. Molecular Docking

To further verify the accuracy of the results, we performed molecular docking technique on the top 10 compounds in terms of degree in the DDCT network diagram and the top 5 key genes (AKT1, CASP3, MAPK1, PTGS2, and TP53). As shown in Table4, the results of molecular docking indicated that the lowest binding energy was stigmasterol and AKT1 with a binding energy was −10 kcal/mol. The most stable binding to CASP3 was kaempferol, and the binding energy was −8.3 kcal/mol. MAPK1 and stigmasterol had the lowest binding energy, which was stigmasterol. The most stable compounds that bind to PTGS2 and TP53 were quercetin and isorhamnetin, with binding energies of −7.5 kcal/mol and −5.8 kcal/mol, respectively. In addition, we also found that the top 6 binding energies from low to high were AKT1 and stigmasterol, quercetin, kaempferol, hederagenin, calycosin, and beta-sitosterol, respectively, which demonstrated that AKT1 may be a very critical target for ADI in the treatment of NSCLC. These results showed that the core compounds and key genes had good binding activity, which further verified that ADI exerted its therapeutic effect through these key genes. The top 6 molecular docking patterns in terms of binding energies were shown in Figure 9.Table 4
Molecular docking results of the top 10 components and the top 5 genes.
Molecule IDCompoundBinding energy (kcal/mol)AKT1CASP3MAPK1PTGS2TP53MOL000098Quercetin−9.7−8.0−7.7−7.5−5.6MOL000392Formononetin−9.0−7.4−7.8−6.6−5.3MOL000422Kaempferol−9.6−8.3−7.8−7.2−5.7MOL000358Beta-sitosterol−9.1−6.9−8.3−6.4−5.0MOL000449Stigmasterol−10−7.3−8.8−7.1−4.8MOL000417Calycosin−9.2−7.9−7.7−6.7−5.5MOL000354Isorhamnetin9.6−8.2−7.9−7.4−5.8MOL000371Methylnissolin−8.7−6.9−7.6−7.0−5.0MOL000296Hederagenin−9.4−7.7−8.0−6.3−5.3MOL0003787-O-Methylisomucronulatol−8.2−6.6−7.4−6.2−4.6Figure 9
Molecular docking pattern diagram of the top 6 binding energies. (a) Quercetin and AKT1, (b) beta-sitosterol and AKT1, (c) kaempferol and AKT1, (d) hederagenin and AKT1, (e) calycosin and AKT1, and (f) stigmasterol and AKT1.
(a)(b)(c)(d)(e)(f)


## 4. Discussion

NSCLC has no obvious clinical symptoms, and most NSCLC patients are in the advanced stage once diagnosed, with poor prognosis, which seriously threatens human health. In recent years, there have been more and more clinical trials and basic researches on ADI in the treatment of NSCLC, providing a new method for the treatment of NSCLC. Several studies have shown that ADI combined with conventional western medicine treatment can significantly improve clinical efficacy and reduce the occurrence of adverse reactions [14–16]. In order to promote the visualization and evidence-based research and development of the mechanism, this study systematically analyzed the potential mechanism of ADI in the treatment of NSCLC by using bioinformatics methods.We found that the main components of ADI were quercetin, formononetin, kaempferol, beta-sitosterol, and stigmasterol by using the method of network pharmacology. Basic research showed that quercetin could trigger BCL2/BAX-mediated apoptosis, necrosis, and mitotic mutation, and inhibit the migration potential of A549 cells, showing good anti-NSCLC tumor activity [17]. Formononetin inhibited tumor growth by inhibiting the EGFR-Akt-Mcl-1 axis in NSCLC [18]. In addition, kaempferol could induce apoptosis of NSCLC cells by down-regulating the expression of Nrf2 [19]. Gourav Kumar found that beta-sitosterol has good anticancer activity [20]. Studies illustrated that stigmasterol effectively inhibited the activity of A549 nonsmall cell lung cancer [21]. All the above studies had demonstrated that the main components of ADI could exert its antitumor effect via inhibiting the growth of NSCLC tumors or promoting tumor cell apoptosis.Through the PPI network map, we found that there were 28 key targets of ADI in the treatment of NSCLC, including 6 key genes that existed in the main signaling pathways, which were AKT1, TP53, CASP3, MAPK1, MMP9, and PTGS2. AKT1 was a member of the AKT kinase family, and research showed that AKT1 plays an antimetastatic role in the NSCLC cells with KRAS or EGFR mutations [22]. In addition, AKT1 down-regulation could also increase the sensitivity of NSCLC cells to chemotherapeutic agents [23]. The TP53 mutation is one of the most common mutated genes in human lung cancer. A large number of studies have shown that TP53 mutation was not only a poor prognostic factor for NSCLC but also increases the resistance of patients with EGFR mutation to EGFR-TKI therapy [24–26]. CASP3, which played a crucial role in apoptosis, had been well known. However, it had been found that CASP3 was also an oncogene in recent studies. Hence, target therapy of CASP3 not only increased the sensitivity of cancer cells to chemotherapy and radiotherapy but also inhibited the invasion and metastasis of cancer cells [27, 28]. MAPK1 could mediate cell proliferation and metastasis [29, 30]. The MAPK1/ERK2 pathway was activated in the early stage of lung cancer formation [31], which suggested that MAPK1 is a potentially important target for the early prevention and treatment of NSCLC. In addition, overexpression of MMP9 led to the occurrence of NSCLC, and inhibition of MMP9 could prevent the growth, migration, and invasion of NSCLC cells, so MMP9 is expected to become a therapeutic target for the prevention of NSCLC metastasis [32–34]. Studies have also shown that the mechanism of chemotherapy resistance in NSCLC was related to PTGS2 [35]. The results of these studies confirmed that these key targets were closely related to nonsmall cell lung cancer, which was consistent with the results of the differential expression analysis of key genes in this study.The results of KEGG enrichment analysis indicated that multiple signaling pathways were involved in the ADI treatment of NSCLC, which included the MAPK signaling pathway, the IL-17 signaling pathway, and PI3K/AKT signaling pathway, etc. Mitogen-activated protein kinase (MAPK) is an important transmitter of signals from the cell surface to the nucleus, which is a group that can be activated by different extracellular stimuli, such as cytokines, neurotransmitters, hormones, cell stress, and cell adhesion. The basic component of the MAPK pathway is a tertiary kinase pattern that is conserved from yeast to humans, including MAP kinase kinase kinase (MKKK), MAP kinase kinase (MKK), and MAPK, which can be activated in turn to jointly regulate cells growth, differentiation and response to inflammation and other important cellular physiological or pathological processes [36]. MAPK signaling pathway is one of the common junction pathways of cell proliferation, stress, inflammation, differentiation, functional synchronization, transformation, apoptosis, and other signal transduction pathways. It transmits extracellular signals to cells through receptors, G proteins, protein kinases, and transcription factors, and participates in cell proliferation, differentiation, canceration, metastasis and apoptosis, etc. [37]. Besides, several studies have shown that the MAPK signaling pathway played a vital role in multicell processes; it is closely related to tumors [38, 39]. Overexpression of an epithelial membrane protein could inhibit the growth, migration, and invasion of NSCLC tumor cells, while knockdown of an epithelial membrane protein can enhance cell migration. Besides, epithelial membrane protein overexpression was closely related to the MAPK signaling pathway [40]. It was well known that the overexpression of ETS-homologous factor was associated with poor prognosis of NSCLC patients, and studies shew that the overexpression of ETS-homologous factor caused the occurrence of NSCLC through the AKT signaling pathway and the MAPK/ERK signaling pathway [41]. The TNF signaling pathway could not only induce the occurrence of NSCLC but also promote tumor cell proliferation and metastasis [42]. It was confirmed that CD47 upregulation in refractory lung tumor models was mediated by the TNF-α/NF-κB1 signaling pathway, and blocking CD47 could enhance anti-tumor effects [43]. Several studies had shown that the PI3K/AKT signaling pathway, not only promoted the proliferation and migration of NSCLC cells but also increased the resistance of NSCLC to cisplatin [44]. Therefore, the PI3K/AKT signaling pathway was also a very important for the treatment of NSCLC. MiR-496 suppresses tumorigenesis via targeting the BDNF-mediated PI3K/Akt signaling pathway in NSCLC had been confirmed [45]. Dieckol inhibiting NSCLC cell proliferation and migration by regulating the PI3K/AKT signaling pathway had also been found [46]. It was well known that immune cells were closely related to the occurrence and development of NSCLC [47]. T cells and B cells are important immune cells, and studies showed immunotherapy targeting T cells or B cells was a very important method of treatment for NSCLC [48, 49]. Therefore, ADI may play a therapeutic role through the T cell receptor signaling pathway or the B cell receptor signaling pathway. NF-κB was widely used by eukaryotic cells as a gene regulator to control cell proliferation and cell survival, which can regulate anti-apoptotic genes and play an indispensable role in the process of apoptosis. Research showed that miR-449a suppresses tumor growth, migration, and invasion in NSCLC by targeting the HMGB1-mediated NF-κB signaling pathway [50]. VEGF was a highly specific vascular endothelial cell growth factor that promoted vascular permeability, vascular endothelial cell migration, proliferation, and angiogenesis, providing nutrients and excreting metabolites for tumor growth. Therefore, inhibiting the activation of the VEGF signaling pathway was a very important approach in the treatment of NSCLC. Bevacizumab, the world’s first VEGF inhibitor, had been widely used in the treatment of NSCLC [51, 52]. The signaling pathways involved in all the above research results were closely related to NSCLC, which demonstrated the accuracy of the enrichment analysis results in this study.It is necessary to use the method of survival analysis for the analysis of the occurrence, development, and prognosis of chronic diseases, especially malignant tumors. OS and RFS are two important reference indicators to describe the survival rate. In our study, the results of survival analysis showed that the expression of key genes in the signaling pathway had a significant impact on the OS or RFS of NSCLC patients, which verified the accuracy of the network pharmacology results, and indicated that the antitumor effect of Aidi injection was by upregulating or downregulating the expression of these key genes in tumor tissue. Molecular docking results showed that the main components of ADI and the key genes in the signaling pathway had good binding activity. It is well known that AKT1 plays an indispensable role in regulating tumor cell proliferation and apoptosis [53]. Interestingly, from the results of molecular docking, we found that the genes involved in the top 6 binding energies are all AKT1. Besides, the components with the lowest binding energies to CASP3, MAPK1, PTGS2, and TP53 were kaempferol, stigmasterol, quercetin, and isorhamnetin. CASP3 as an apoptosis-related gene played a central role in executing cell apoptosis and thus in carcinogenesis [54]. Both MAPK1 and PTGS2 were involved in cell proliferation and invasion, which made them closely related to tumorigenesis [55]. TP53 is well-known as a tumor suppressor gene that induces cell cycle arrest, apoptosis, or senescence [56]. These results demonstrated that the main components of ADI work together to exert antitumor effects by inhibiting tumor cell growth, invasion, and metastasis and promoting tumor cell senescence and apoptosis, which perfectly interpreted the overall concept of TCM.


## 5. Conclusion

Our results revealed the mechanism of ADI in the treatment of NSCLC may be through the targets of AKT1, TP53, CASP3, MAPK1, and PTGS2, regulating the MAPK signaling pathway, PI3K-AKT signaling pathway, and TNF signaling pathway to exert its therapeutic effect. In conclusion, this study revealed the mechanism of ADI against NSCLC through multipathways and multitargets, which provided new insights for clarifying the molecular mechanism of ADI and also provided a reference for the research and development of anti-NSCLC  drugs.



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*Source: 8350218-2022-12-06.xml*