Skip to main content
Download PDF

Network module analysis and molecular docking-based study on the mechanism of astragali radix against non-small cell lung cancer

BMC Complementary Medicine and Therapies volume 23, Article number: 345 (2023) Cite this article

Abstract

Background

Most lung cancer patients worldwide (stage IV non-small cell lung cancer, NSCLC) have a poor survival: 25%-30% patients die < 3 months. Yet, of those surviving > 3 months, 10%-15% patients survive (very) long. Astragali radix (AR) is an effective traditional Chinese medicine widely used for non-small cell lung cancer (NSCLC). However, the pharmacological mechanisms of AR on NSCLC remain to be elucidated.

Methods

Ultra Performance Liquid Chromatography system coupled with Q-Orbitrap HRMS (UPLC-Q-Orbitrap HRMS) was performed for the qualitative analysis of AR components. Then, network module analysis and molecular docking-based approach was conducted to explore underlying mechanisms of AR on NSCLC. The target genes of AR were obtained from four databases including TCMSP (Traditional Chinese Medicine Systems Pharmacology) database, ETCM (The Encyclopedia of TCM) database, HERB (A high-throughput experiment- and reference-guided database of TCM) database and BATMAN-TCM (a Bioinformatics Analysis Tool for Molecular mechanism of TCM) database. NSCLC related genes were screened by GEO (Gene Expression Omnibus) database. The STRING database was used for protein interaction network construction (PIN) of AR-NSCLC shared target genes. The critical PIN were further constructed based on the topological properties of network nodes. Afterwards the hub genes and network modules were analyzed, and enrichment analysis were employed by the R package clusterProfiler. The Autodock Vina was utilized for molecular docking, and the Gromacs was utilized for molecular dynamics simulations Furthermore, the survival analysis was performed based on TCGA (The Cancer Genome Atlas) database.

Results

Seventy-seven AR components absorbed in blood were obtained. The critical network was constructed with 1447 nodes and 28,890 edges. Based on topological analysis, 6 hub target genes and 7 functional modules were gained. were obtained including TP53, SRC, UBC, CTNNB1, EP300, and RELA. After module analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that AR may exert therapeutic effects on NSCLC by regulating JAK-STAT signaling pathway, PI3K-AKT signaling pathway, ErbB signaling pathway, as well as NFkB signaling pathway. After the intersection calculation of the hub targets and the proteins participated in the above pathways, TP53, SRC, EP300, and RELA were obtained. These proteins had good docking affinity with astragaloside IV. Furthermore, RELA was associated with poor prognosis of NSCLC patients.

Conclusions

This study could provide chemical component information references for further researches. The potential pharmacological mechanisms of AR on NSCLC were elucidated, promoting the clinical application of AR in treating NSCLC. RELA was selected as a promising candidate biomarker affecting the prognosis of NSCLC patients.

Peer Review reports

Introduction

Lung cancer is the leading cause of cancer-related mortality. It is subdivided as Non-Small-Cell Cancers (NSCLC, 80–85% of all) and Small Cell Lung Cancers (SCLC), as SCLC have a very poor prognosis. The global overall 5-year survival rate of non-small cell lung cancer (NSCLC) is also less than 20% and thus poses a serious threat to public health [1].

NSCLCs are typically divided as operable (patients with TNM stage 1, 2 and 3A) and non-operable (stage 3B and 4, patients with metastatic NSCLC) at the time of diagnosis, whose life expectancy is so poor, that surgery in general is not recommended [2,3,4,5]. This leaves systemic platinum-based therapy, radiotherapy, and more recently tyrosine kinase inhibitors target therapy, immune-checkpoint inhibitors, as well as some new frontiers such as Antibody–drug conjugates (ADCs [6]). ADCs are an innovative class of anticancer drugs that combine the strengths of targeted therapy to cytotoxic chemotherapy. Promising results have been demonstrated, but none has been approved for the treatment of NSCLC. The HER2 ADC trastuzumab deruxtecan is the furthest along but other ADCs targeting HER3 and MET will be more valuable as we start to encounter patients with NSCLC harboring EGFR mutations who have developed resistance to the 3rd generation tyrosine kinase inhibitors. ADCs targeting TROP2 holds a particular advantage as TROP2 is known to be expressed in both squamous and nonsquamous NSCLC. NSCLC remains an incurable malignancy resistant to conventional therapy, leading to an overall poor prognosis [7].

Understanding the molecular biological mechanisms of the NSCLC cancer cells have been extensively studied and show geographic/racial variations. For example, in Asian patients, EGFR-mutation status was 51% positive, but in western countries such as Norway, it is much lower (8%). Intercellular signal pathways are also important from the therapeutic point of view. Different signaling pathways mechanisms are known, the EGFR [8], Notch [9], Wnt [10], and Type I IFN signaling pathways [11]. Despite the emerging of new treatment and specific molecular targets, there remains the problem of poor systemic treatment and large toxic and side effects of NSCLC. Developing more efficient therapy to further improve the management of NSCLC is an urgent matter.

Many patients in Asia use Traditional Chinese Medical (TCM) herbs, also because of the serious side effects of western cytotoxic drugs. Indeed, TCM herbs additional to widely used platinum-based chemotherapy, strongly reduces nausea and vomiting [4] and also improve prognosis of stage 3B and 4 NSCLC patients [12]. Over the past decade, the demand for TCM herbs has increased greatly. In 2015, the total production value of the TCM pharma industry in China alone was over 110 billion USD. Due to the significantly increased worldwide interest in the use of TCM herbal medicines, it is expected that the global market value of TCM herbs will significantly increase in the coming years [13, 14].

Astragali radix (AR, Huangqi) is a dominant herb in NSCLC anticancer treatment exhibits immunomodulatory, anti-oxidant, anti-inflammatory and anti-viral properties and shows strong antitumor effects in various tumors including NSCLC [15, 16]. It is speculated that AR mainly exerts its anti-tumor effects by directly inhibiting the proliferation and promoting apoptosis of tumor cells; increasing the efficacy of chemotherapies, potentially preventing tumor cell metastasis and improving TME by enhancing organic or local immunity [16]. McCulloch et al. evaluated the evidence from 34 randomized control trials and found that AR components and AR-based TCM including its combination with platinum-based chemotherapy could reduce the risk of death in 12 months, with improved tumor response data and reduced toxic reaction of chemotherapy [17]. The rapid development of resistance to anticancer agents and their unwanted side effects increase the demand for novel antitumor drugs that can become active against untreatable cancers [18]. Given this background, AR as a popular tonic can play a key role in treatment of NSCLC, because of its effective, safer, and inexpensive than traditional chemotherapeutic drugs. However, the mechanism of AR on NSCLC is unclear. Therefore, it is urgently needed to demonstrate the signaling pathway of AR on NSCLC. As AR exerts therapeutic effects through multi-components, multi-targets and multi-pathways, exploring the mechanisms of TCM against diseases remain a problem due to their complex matrices nature.

Astragaloside IV is one of the main active components from AR, which has attracted much attention in the field of antitumor in recent years [19]. It was found that astragaloside IV can inhibit the proliferation of tumor cells and further inhibit the progression of cancers such as lung cancer [20]. We compared astragaloside IV with Mobocertinib [21], Erlotinib [22], which are marketed for the treatment of NSCLC, and the compound information is shown in Supplementary Table 1.

Network pharmacology, proposed by Li [23], provides a new research paradigm for systematic illumination of TCM mechanism. With the arrival of biological Big Data, bioinformatics had profound implications on the predictive power and reproducibility of results [24]. We have previously shown by detailed network analysis, that LHQW herbal TCM treatment in COVID-19 disease, modulates the inflammatory process, exerts antiviral effects and repairs lung injury. Moreover, it also relieves the “cytokine storm” and improves ACE2-expression-disorder-caused symptoms. These innovative findings give a rational pharmacological basis for and support treating COVID-19 and possibly other diseases with LHQW.

We therefore have applied Network Pharmacology to unravel the working mechanism of Astragali Radix (Huangqi), in Non-Small Cell Lung Cancer. Moreover, Molecular Complex Detection (MCODE [25]) was applied to find densely connected regions of a given network based on topology. Molecular docking refers to the process that a small molecule is spatially docked into a macromolecule and can score the complementary value at the binding sites, which is used for structure‐based drug design [26].

We firstly employed ultra-high performance liquid chromatography-quadrupole/Orbitrap high resolution mass spectrometry (UPLC-Q-Orbitrap HRMS) to identify the components absorbed in blood of AR. Secondly, we collected information about components, component-related targets, and NSCLC–related genes from extensive databases and identified crucial targets and key pathways based on network module analysis and molecular docking. Finally, the prognostic value of AR-NSCLC hub genes was analyzed using bioinformatics technology. The detailed flowchart of this study is summarized in Fig. 1.

Fig. 1
figure 1

The flowchart of this study was based on UPLC-Q-Orbitrap HRMS, network pharmacology, molecular docking, and molecular dynamics mechanistic simulations to decipher the potential mechanism of astragali radix for the treatment of NSCLC

Full size image

Materials and methods

Preparation of samples for analysis

Experimental animals

SPF grade male Sprague–Dawley (SD) rats (280 ± 20 g, SCXK, Jing, 2019–0010) were purchased from SPF (Beijing) Biotechnology Co., Ltd. (Beijing, China). All rats were housed in the specific-pathogen-free grade animal observation room of Chengdu University of Traditional Chinese Medicine (SYXK, Chuan, 2020–124), which was set at 21 ± 1℃ and 60% relative humidity with a 12 h light/dark cycle. Rats were deprived of food for 12 h before the start of experiment, but permitted of free access to drinking water. This study was performed in accordance with the guidelines of the National Health Institutes of China and approved by the Animal Ethics Committee of Chengdu University of Traditional Chinese Medicine (Chengdu, China, Ethical approval number: 2018–15).

Preparation of plasma samples

Astragali radix was obtained from affiliated hospital of Chengdu University of Traditional Chinese Medicine. Briefly, astragali radix was soaked in 8-fold mass of purified water for 30 min and then decocted for 50 min twice. After being filtered with gauze, the two filtrates were combined and concentrated to 1 g/mL using a rotary evaporator at 40 °C for intragastric administration.

After 7 days of acclimation, SD rats were randomly distributed in two groups including control and AR group (2.7 ml/100 g), nine rats in each group. The control group was given equal volume distilled water.

Blood was collected at 30,60 and 120 min after administration, and then the rats were anesthetized with pentobarbital sodium. Blood samples of 3 rats were collected each time, then the rats were dragged by the neck to death after the experiment. The collected blood was centrifuged for 15 min at 4000 r/min, 4 ℃. 250 µL plasma was mixed with 1000 µL acetonitrile (chromatographically pure) and performed vortex for 4 min, then centrifuged for 15 min at 13,000 r/min, 4 ℃. The supernatant was transferred to another clean Eppendorf tube, and evaporated until dry using a mild nitrogen gas stream at 25 ℃. Add 150 µL 0.01% hydrochloric acid methanol (1:1) mixed solution for redissolution, centrifuge at 13,000 r/min, 4 ℃ for 15 min, and suck the clear solution into the tube. The plasma test samples collected at three time points in the same administration group were mixed and filtered through a 0.22 µm filter for UPLC-Q-Orbitrap HRMS analysis.

Preparation of AR samples for analysis

20 ml of AR decoction was weighed accurately, added with 100 ml of 75% methanol solution, and weighed accurately again. After refluxing for 60 min, 75% methanol solution was used to make up the lost weight and then filtered. 90 ml of filtrate was evaporated using a rotary evaporator at 40 °C. The dried extracts were redissolved in methanol to 10 mL. The redissolved methanolic solution was filtered with a 0.22 μm filter before injecting into the UPLC-Q-Orbitrap HRMS system.

UPLC-Q-Orbitrap HRMS analysis

The qualitative analysis of chemical components was performed using Ultra Performance Liquid Chromatography (UPLC) system coupled with Q-Orbitrap HRMS/MS (Thermo Fisher Scientific, United States). The sample was separated by using Thermo Scientific AccucoreTMC18 (3 mm × 100 mm, 2.6 µm) eluted with a mixture of 0.1% formic acid (A) and acetonitrile (B). The proportion of acetonitrile (B) was increased from 1–20% (0–8 min), 20–30% (8–10 min), 30–35% (10–12 min), 35–40% (12–14 min), 40–55% (14–23 min), 55–70% (23–27 min), 70–80% (27–30 min), 80–99% (30–37 min), 99% (37–38 min). The flow rate was 1 mL min − 1, and the column temperature was set at 40 °C. The injection volume was 5 μL.

Mass detection was performed on a Q-Exactive Orbitrap Mass Spectrometer equipped with an Electron Spray Ionization source in positive and negative ion mode. Mass conditions were as follows: the spray voltage in positive/negative mode was 5.0 kV/3.0 kV; sheath gas (N2) flow rate of 50 L/h Pa; and the probe heating temperature is 350 ℃. In the automatic MSE mode, the CLT-GND voltage (+) and CLT-GND voltage (-) are 20 V and 15.9 V respectively. The full-scan mass spectrum was recorded in m/z 50–2,035 Da. All data were acquired and processed by Compound Discoverer 2.9 software (Thermo Fisher Scientific, FL, United States).

Obtaining the AR targets and NSCLC related genes

Four database were used to search AR’s components targets: TCMSP (Traditional Chinese Medicine Systems Pharmacology) database (https://tcmspw.com/) [27], ETCM (The Encyclopedia of TCM) database (http://www.tcmip.cn/ETCM/index.php/Home/Index/) [28], HERB [29] (A high-throughput experiment- and reference-guided database of TCM) database (http://herb.ac.cn/) and BATMAN-TCM [30] (a Bioinformatics Analysis Tool for Molecular mechanism of TCM) database (http://bionet.ncpsb.org.cn/batman-tcm/). A union of the search results was used to establish an AR related target genes.

NSCLC related genes were the union of differentially expressed genes (DEGs) obtained from the gene expression profiles including GSE1987 [31], GSE33532 [32], GSE7670 [33], GSE103512 [34], GSE134381 [35] and GSE29249 [36] in GEO (Gene Expression Omnibus) database. We firstly downloaded the raw data as MINiML files. And then we normalized the probe expression profile by the preprocessCore package in R software (version 3.4.1). Next, probes were converted to gene symbols according to the platform annotation information of the normalized data. Probes with more than one gene were eliminated using the removeBatchEffect function of limma R package. When multiple probes can be converted to the same gene, the probe with the average expression level is selected to represent that gene. For each gene expression profiles, we performed the differential expression analysis by using the R package Limma 3.42.2. In brief, we firstly fitted linear models from the normalized gene expression matrix by using the ‘lmFit’ function, and then computed the empirical Bayes statistics by using the ‘eBayes’ function. Finally, we selected the genes with sufficient expression difference. For example, the parameters of adjusted p value < 0.05 and logFC cutoff criteria ≥ 0.5 are upregulated and adjusted p value < 0.05 and logFC cutoff criteria ≤ -0.5 are downregulated were selected [37]. The AR-NSCLC shared genes were obtained by intersecting the AR target genes and the NSCLC related genes.

Constructing component-target network, protein–protein interaction network (PIN) and critical PIN

Based on the AR-NSCLC shared target genes, a component-target network was constructed using Cytoscape version 3.9.0 [38]. Then, we imported AR-NSCLC shared target genes into the STRING database (http://string-db.org/) to obtained the protein–protein interactions (PPI) used for constructing the AR-NSCLC PIN. The PPI confident score was set at 0.9 to ensure the credibility of the results. Meanwhile, we performed the network analysis by means of Analyze Network plugin in Cytoscape to calculate the betweennesscentrality, closenesscentrality and degree of each node in AR-NSCLC network [39]. Finally, we filtered genes according to the score of betweennesscentrality, closenesscentrality and degree higher than the median value. We used the filtered genes to construct the primary critical PIN based on AR-NSCLC PIN background. The filter process was conducted again to acquire the final critical AR-NSCLC PIN.

Analysis of network topological properties

Topological properties have become very popular to gain an insight into the organization and the structure of the complex networks [40]. Therefore, the topological parameters such as degree distribution and average shortest path were analyzed by Network Analyzer in Cytoscape [40]. Properties of scale-free and small word of the critical AR-NSCLC network were also investigated to assess the biological feasibility.

Hub genes, module and enrichment analysis of the critical AR-NSCLC PIN

CytoHubba plugin in Cytoscape was employed to calculate the degree, closeness and betweenness of nodes in critical AR-NSCLC PIN. We select the nodes as the hub genes whose score of degree, closeness and betweenness was in the top 10. Module analysis was performed by the MCODE plugin in Cytoscape to explore the significant modules in critical AR-NSCLC PIN. The advanced options set as degree cutoff = 2, K-Core = 2, and Node Score Cutoff = 0.2. Enrichment analysis, including GO (gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis, was performed for the genes in modules to reveal the underlying mechanism through biological processes, cellular components, molecular function and key signaling pathways. ClusterProfiler package (version: 3.18.0) in R was employed to analyze enrichment analysis. The box plot was implemented by the R software package ggplot2; PCA graphs were drawn by R software package ggord; the heat map is displayed by the R software package pheatmap. All the above analysis methods and R package were implemented by R foundation for statistical computing (2020) version 4.0.3.

Molecular docking

The molecular docking was performed in Autodock Vina software to evaluate the accuracy of network analysis results. We downloaded the 2D structure for the molecule ligands from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The 3D structure of protein was downloaded in RCSB PDB database (https://www.rcsb.org/). ChemBio 3D software was used to calculate and export the minimized energy stricter. Then the protein receptors were performed to remove water molecules, separate proteins, add nonpolar hydrogens, calculate Gasteiger charges of the structures in AutoDock Tools 1.5.6, and saved as PDBQT files. PyMOL 2.3.0 software was used to set the active pocket sites where molecule ligands bind and visualize the docking results. In addition, analyze all hydrogen bonding and hydrophobic interactions with ProteinPlus [41].

Molecular dynamics simulation

Gromacs 2022.3 [42] (Van Der Spoel et al., 2005) was used to analyze the molecular dynamics (MD) simulation to check the stability of the protein–ligand complexes. We imported the structure files of proteins and ligand into Gromacs separately, and generated topology files and simulation boxes by using the pdb2gmx and gmx editconf commands. The structures of proteins and ligand were minimized and simulated using the gmx grompp and gmx mdrun commands. The molecular dynamics simulation system was first energy minimization using the steepest descent method, followed by 100,000 steps of isothermal isovolumetric (NVT) and isothermal isobaric (NPT) equilibria with coupling constants of 0.1 ps and durations of 100 ps. Finally, a free-fractional dynamics simulation was run, with a step size of 2 fs. Molecular dynamics simulations of proteins and ligand were performed for up to 100 ns using the gmx grompp and gmx mdrun commands, and conformational information during the simulations was recorded. The root mean square deviation (RMSD) of each complex was analyzed to measure the stability of the complex system according to the degree of the molecular structure change. The root mean square fluctuation (RMSF) of the identified complexes was analyzed to understand the relative fluctuation of proteins. The receptor-ligand binding free energy was calculated using the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method in the 30 ns MD simulation trajectory.

Prognostic value of hub genes

To see whether hub genes were related to prognostic significance, survival analysis was performed in the R environment using TCGAbiolinks [43]. The gene expression quantification data and clinical information of NSCLC patients were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) which has generated comprehensive, multi-dimensional maps of the key genomic changes in various types of cancers. RNA-seq data (level 3) including 1017 NSCLC tissues and 108 normal tissues, and corresponding clinical information of NSCLC were obtained.

Univariate and multivariate Cox regression analyses were performed to identify prognostic factors for NSCLC. The forest was used to show the P value, HR and 95% CI of each variable through ‘forestplot’ R package. Thereafter, nomogram was developed based on the results of multivariate cox proportional hazards analysis to predict the 5-year overall recurrence. The nomogram provided a graphical representation of the factors which could be used to calculate the risk of recurrence for an individual patient by the points associated with each risk factor through ‘rms’ R package. The predictive accuracy of the nomogram was assessed by calibration plot [44]. Null hypotheses of no difference were rejected if p-values were less than 0.05.

Results

Components analysis by UPLC-Q-Orbitrap HRMS

The total ion chromatograms of AR and AR absorbed in blood in the positive and negative ion mode is shown in Fig. 2. The tentative identification of components was conducted by comparing retention times to external standards and MS data. Compared to blank plasma data, 77 component peaks were observed from AR plasma. Among the migration components in AR plasma, 35 components were from AR, as shown in Table 1.

Fig. 2
figure 2

Representative UPLC-Q-Orbitrap HRMS total ion chromatograph of AR in positive mode (A) and negative mode (B), and AR absorbed in blood in positive mode (C) and negative mode (D)

Full size image
Table 1 Identification of components in AR and AR absorbed in blood by UPLC-Q-Orbitrap HRMS
Full size table

Identification of AR-NSCLC shared target genes

An overall 688 AR components target genes were gained after removing duplication (Fig. 3A). 294, 266, 162 and 149 AR components target genes were obtained from TCMSP, ETCM, HERB and BATMAN-TAM database, respectively. Besides, 784, 918, 4193, 1667, 59 and 1859 NSCLC-related genes were obtained from GSE1987, GSE33532, GSE7670, GSE103512, GSE134381 and GSE29249, respectively. After combining multiple gene expression profiles results and removing duplication, a total of 7360 NSCLC- related genes were acquired (Fig. 3B). Further, through taking an intersection of the AR-target genes and NSCLC-related genes, we finally gained the AR-NSCLC shared target genes (Fig. 3C).

Fig. 3
figure 3

Identification of the AR-NSCLC shared target genes. A The AR–related genes by taking a union of all the results from 4 databases. B The NSCLC–related genes by taking a union of all the results from 7 gene expression profiles. C The AR-NSCLC shared target genes by taking an intersection of AR target genes and NSCLC–related genes

Full size image

Component-target network and critical AR-NSCLC network

The component-target network was visualized by Cytoscape with 467 nodes and 1244 edges (Fig. 4A). Among the 346 genes, PTGS2 is the most targeted gene by AR components. Based on the AR-NSCLC shared target genes, protein–protein interactions derived from STRING database were imported into Cytoscape to visualize the AR-NSCLC network with 4313 nodes and 62,617 edges. After two filtrations of topological properties including betweennesscentrality, closenesscentrality and degree, the critical AR-NSCLC network was constructed with 1447 nodes and 28,890 edges (Fig. 4B).

Fig. 4
figure 4

Protein–protein interaction network. A The component-targets interaction network. Triangles represent the components in AR. Circles represent the AR-NSCLC shared targets. The larger the degree of the node, the darker the color of the node. B The critical AR-NSCLC PIN and eight significant modules identified from the critical AR-NSCLC PIN via MCODE with a score of > 5. The circle represents module1, module 2, module 3, module 4, module 5, module 6 and module 7 respectively, starting from the red in a clockwise direction

Full size image

Topological analysis

All the topological parameters of AR-NSCLC PIN and critical AR-NSCLC PIN were calculated and shown in Table 2.

Table 2 The simple parameters of protein interaction network
Full size table

Biological networks have been proposed to have scale-free topology whose degree distribution follows a power law distribution P(k) ~ k −γ (γ < 3) [45]. As shown in Fig. 5(A and B), the degree distribution of AR-NSCLC PIN and critical AR-NSCLC PIN followed the power law distribution. Their equation are y = 4039.6x −1.491 and y = 394.17x −1.062, respectively. So, they were a scale-free network. Small world networks have a property that mean path length is short [46]. As shown in Fig. 5(C and D), network path length was mostly concentrated in 3–5 steps, which meant that most proteins were closely linked. And AR-NSCLC PIN and critical AR-NSCLC PIN were a small world network.

Fig. 5
figure 5

Topological properties of the network. A The degree distribution of AR-NSCLC PIN; B The degree distribution of critical AR-NSCLC PIN; C The shortest path length distribution of AR-NSCLC PIN; D The shortest path length distribution of critical AR-NSCLC PIN

Full size image

Hub genes, module and enrichment analysis

After the intersection calculation of the nodes in the top 10 of degree, closeness and betweenness, 6 hub target genes were obtained including TP53, SRC, UBC, CTNNB1, EP300, and RELA. 7 functional modules with a score > 5 were obtained after performing the MCODE (Fig. 4B). KEGG enrichment analysis was performed to discover those pathways enriched by each module target genes. The filter was also set an adjusted P-value < 0.05 and q‐value < 0.05. The bubble plot of the top 10 KEGG pathways of each module was shown in Fig. 6. KEGG pathways were significantly enriched in JAK-STAT signaling pathway (module 4), PI3K-AKT signaling pathway (module 5), ErbB signaling pathway (module 6), as well as NFkB signaling pathway (module 7).

Fig. 6
figure 6

KEGG enrichment analysis of each module. Gene ratio refers to the ratio of enriched genes to all target genes. Counts refer to the number of the enriched genes

Full size image

Analysis of core genes affecting prognosis in NSCLC patients

Based on TCGA data and clinical information, 6 core genes were used for survival analysis for NSCLC patients. Their relationship with OS was analyzed by univariate and multivariate Cox regression. RELA was associated with OS (P < 0.05), which was selected as the prognostic marker for NSCLC (Fig. 7A). The univariate analysis revealed that RELA correlated significantly with a poor OS (hazard ratio)  [34]: 1.50253; 95% confidence interval (CI): 1.09973, 2.05286; P = 0.01056). Other clinicopathologic variables including age, pT_stage, pN_stage, and pM_stage associated with poor survival (P < 0.05). Gender, race, and smoking did notcorrelate with the prognosis of NSCLC (P > 0.05). At multivariate analysis, RELA remained independently associated with OS, with a HR of 1.43132 (CI: 1.14128, 1.79506, p = 0.00191), along with age, pT_stage, pN_stage, and pM_stage (Fig. 7B). Thereafter, the difference of RELA expression in NSCLC samples by Kruskal–Wallis test. RELA expression was significantly different in primary tumor size and extent (Fig. 7C), regional lymph nodes (Fig. 7D), and distant metastases (Fig. 7E), further indicating that RELA gene may affect tumor progression and poor prognosis in NSCLC by overexpression. Based on the C-index values, a nomogram integrating the RELA expression, age, pT_stage, pN_stage, and pM_stage was constructed to predict the probability of 1-, 2-, 3- and 5 year OS in NSCLC patients (Fig. 7F). Total points were calculated by adding the points by adding the points of the RELA, age, pT_stage, pN_stage, and pM_stage. The higher the total score, the worse the prognosis of the patient. The calibration plot showed that the nomogram performed well in predicting patient OS according to an ideal model (Fig. 7G).

Fig. 7
figure 7

Screening of core genes affecting the prognosis of NSCLC patients. A Forest plots based on the results of univariate Cox regression of RELA expression and other clinicopathological factors. B Forest plots are based on the results of multivariate Cox regression of RELA expression and other clinicopathological factors. HR and p-values were displayed. C RELA gene expression distribution in primary tissue and normal tissue of NSCLC tumor tissues. D RELA gene expression distribution in lymphoid tissue and normal tissue of NSCLC tumor tissues. E RELA gene expression distribution in metastatic grade and normal tissue of NSCLC tumor tissues. *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001 by Kruskal–Wallis test. F Nomogram predicting the proportion of patients with OS. G Plots depict the calibration of model in terms of agreement between predicted and observed OS. Model performance is shown by the plot, relative to the 45-degree line, which represents perfect prediction

Full size image

Molecular docking analysis

After the intersection calculation of the hub targets and the proteins participated in the JAK-STAT signaling pathway, PI3K-AKT signaling pathway, ErbB signaling pathway, and NFkB signaling pathway, TP53, SRC, EP300, and RELA were obtained. These proteins were used for molecular docking with astragaloside IV. The components with lower binding energy are generally considered to have higher binding affinity with protein targets. It is generally believed that the binding energy is less than—4.25 kcal·mol−1, suggesting that the ligand has a certain binding activity with the receptor, less than—5.0 kcal·mol−1 had good binding activity, below -7.0 kcal·mol−1 had strong binding activity [47]. According to the molecular docking results, the docking binding energies of the four hub target genes with astragaloside IV were all less than -5 kcal·mol-1, indicating that target genes had good binding activity with astragaloside IV. The binding energy information is shown in Table 3, and the specific binding situation is shown in Fig. 8. Finally, astragaloside IV and Mobocertinib, Erlotinib were interacted with RELA by hydrogen bond. The RELA-ligand interaction diagram is shown in Fig. 9.

Table 3 Binding energy between the component and hub target genes
Full size table
Fig. 8
figure 8

The three-dimensional structure of the interaction between astragaloside IV and hub target genes

Full size image
Fig. 9
figure 9

RELA-Ligand interaction diagrams retrieved from Proteins Plus (Pose View). Directed bonds between protein and ligand are drawn as dashed lines and the interacting protein residues and the ligand are visualized as structure diagrams. Hydrophobic contacts are represented more indirectly through spline sections highlighting the hydrophobic parts of the ligand and the label of the contacting residue. A RELA-astragaloside IV; B RELA-Erlotinib; C RELA-Mobocertinib

Full size image

Molecular dynamics simulation

We chose to perform molecular dynamics simulations on the complexes of four proteins (RELA, TP53, EP300, and SRC) bound to Astragaloside IV. The RMSD curve represents positional deviations in the protein. The RELA-Astragaloside IV complex average RMSD value is 0.240771882; the EP300-Astragaloside IV complex average RMSD value is 0.189652057; the SRC-Astragaloside IV complex average RMSD value is 0.283678912; the TP53-Astragaloside IV complex average RMSD value is 0.283678912. As can be seen from Fig. 10, the RELA-Astragaloside IV was in an equilibrium state and tended to balance out the last 10 ns. Meanwhile, the EP300-Astragaloside IV complex had a rise within 80 ns, and tended to balance out the last 10 ns.

Fig. 10
figure 10

The RSMD simulation results of four complexes

Full size image

In addition, the binding energy of RELA-Astragaloside IV was -37.09 kcal/mol, that of TP53-Astragaloside IV complex was -61.020 kcal/mol, EP300-Astragaloside IV complex was -33.016 kcal/mol, SRC- Astragaloside IV complex was -62.96 kcal/ mol. Molecular dynamics simulations showed that RELA-Astragaloside IV binds tightly to EP300-Astragaloside IV.

Discussion

The results of the GO and KEGG enrichment for modules revealed several crucial pathways (signaling pathway JAK/STAT, PI3K-AKT, ERBB and NFκB) that may explain the underlying mechanisms of AR. More interestingly, the four pathways are interrelated (Fig. 11). It is speculated that AR may have the systematically therapeutical effect on NSCLC by multi-pathway including signaling pathway JAK/STAT, PI3K-AKT, ERBB and NFκB.

Fig. 11
figure 11

The signaling pathways that regulated by AR on NSCLC

Full size image

Module 4 was enriched in JAK-STAT signaling pathway which is closely related to multiple tumorigenesis, progression, invasion, and metastasis [48]. It has been reported that Long noncoding RNA PART1 promotes progression of NSCLC cells via JAK-STAT signaling pathway [49].The genes in this pathway mainly included IL2 (interleukin 2), IL23A (interleukin 23a), LIF (LIF interleukin 6 family cytokine), IL10 (interleukin 10), IL2RA (interleukin 2 receptor subunit alpha), IL2RB (interleukin 2 receptor subunit beta), JAK3 (Janus kinase 3), PIM1 (Moloney murine leukemia virus-1), MYC (MYC proto-oncogene), CDKN1A (cyclin dependent kinase inhibitor 1A).

The JAK/STAT signaling pathway promotes the cytokine-mediated cell activation in a simple and efficient way [50]. Binding of the cytokines to their receptors activates janus kinases(JAKs) [51]. The activated JAK phosphorylates the receptor, activates and phosphorylates its main substrate STAT. Phosphorylated STAT dimerizes with other members of STAT family with conserved SH2 domains. The dimer is then translocated into the nucleus and binds to the specific adjustment zones of DNA sequences to activate PIM1, MYC and CDKN1A [52], leading to anti-apoptosis and cell cycle inhibition. In Richeng Jiang’s observation, nuclear overexpression of PIM1 correlated with LN metastasis, histology and poor survival in NSCLC [53]. The targeting of Myc in lung cancer is potentially an unprecedented opportunity for inhibiting a key player in tumor progression and maintenance and therapeutic resistance [54]. And its aberrant overexpression has been reported in about 30–75% of NSCLC cases [55]. Besides, phosphorylated JAK allows for PI3K activation, which in turn activates the downstream cascade of the PIK3/AKT signaling pathway [56].

Module 5 was related to the PI3K-AKT signaling pathway mainly including PIK3CA, PIK3R1, EGFR(Epidermal growth factor receptor), ERBB2 (epidermal growth factor receptor 2), ERBB3(epidermal growth factor receptor 3), KDR(VEGF receptor 2), EPHA2(Ephrin type-A receptor 2), PDGFRB(platelet-derived growth factor receptor beta), IRS1(insulin receptor substrate 1), CDK2(cyclin-dependent kinase 2), CDK4(cyclin-dependent kinase 4), CDK6(cyclin-dependent kinase 6), GSK3B(glycogen synthase kinase 3 beta), SOS1(Son of sevenless 1), HRAS(HRas proto-oncogene), KRAS(Kirsten rat sarcoma viral oncogene).

In NSCLC, the PI3K-Akt pathway has been heavily implicated in both tumorigenesis and the disease progression [57]. PIK3CA and PIK3R1 belong to class IA PI3K that are typically activated by tyrosine receptor kinases (RTKs) such as KDR, EPHA2, PDGFRB, EGFR, ERBB2, ERBB3 and ERBB4 [58]. EGFR has become an important therapeutic target for the treatment of NSCLC. Inhibitors that target the kinase domain of EGFR have been developed and are clinically active. The presence of PIK3CA gene mutations or amplifications has been found in a diverse range of malignancies [59]. PI3K phosphorylates phosphatidylinositol 4,5‐bisphosphate (PIP2), to produce phosphatidylinositol (3,4,5)–trisphosphate (PIP3 [60]). Then, PIP3 activated Akt directly via phosphorylation [61]. Akt consists of three homologues, Akt1, Akt2 and Akt3 [62]. Akt activation subsequently leads to a number of potential downstream effects. It can result in inhibition of cyclin-dependent kinase (CDK), RAF1 and also GSK3B [63], leading to regulation of cell cycle progression, cell survival and cell proliferation. The Phosphorylate Akt may also active MDM2, which causes downregulation of p53‐mediated apoptosis and forkhead transcription factors that produce cell‐death promoting proteins [58]. The nuclear factor kappa‐light‐chain‐enhancer of activated B cells (NFκB) transcription factor plays a crucial role in the consequences of PI3K/Akt pathway activation. NFκB regulates gene expression of hundreds of genes which are implicated in apoptosis, cell cycle control, immune modulation, cell survival and cell adhesion and differentiation [64]. Akt prevents negative regulation of NFκB by the IκB family, and in particular IκBα. IκBα removes NFκB from DNA and returns it to the cytoplasm [65].

Module 6 was closely related to ErbB signaling pathway. It included EGF (epidermal growth factor), HBEGF (heparin binding EGF like growth factor), NRG1 (neuregulin-1), NRG2 (neuregulin-2), ERBB4 (epidermal growth factor receptor 4), SRC(proto-oncogene tyrosine-protein kinase Src), RAF1(Raf-1 proto-oncogene, serine/threonine kinase).

The ErbB signaling pathway performs important role in the molecular pathogenesis of lung cancer [66]. The pathway was extensively analyzed and found to be key to the unremitting growth and development of carcinoma cells. The ligands (EFG, HBEGF, NRG1 and NRG2) actived the ErbB receptors though the direct or indirect way which included EGFR and ERBB4. Homo- or heterodimerization of these receptors occurs after ligand binding, leads to activation of downstream signaling cascades like SRC, PI3K/AKT and Ras/Raf/MEK/ERK. In ERK cascade, the SHC and GRB2 phosphotyrosine-binding adaptors link phosphorylated receptors, through a guanine nucleotide exchange protein (SOS) and a small GTP-binding protein (RAS), to a linear cascade culminating in ERK1 and ERK2, which translocate to the nucleus to stimulate various transcription factors [67]. NRG1 has been revealed fusions to exist at low frequencies across multiple tumor types, though the largest number of cases have been identified in NSCLC [68]. SRC are frequently expressed and activated in lung cancer which involved in the proliferation, survival, angiogenesis, invasion, and migration of various types of cancer cells [69].

Module 7 was associated with the NFKB signaling pathway which is considered the classical, or canonical pathway of NFkB [70]. The proteins included IL1R1(interleukin-1 receptor 1), MYD88(MYD88 innate immune signal transduction adaptor), TRAF6(TNF receptor associated factor 6), IRAK4(interleukin 1 receptor associated kinase 4), IKBKB (inhibitor of nuclear factor kappa B kinase subunit beta), IKBKG(inhibitor of nuclear factor kappa B kinase regulatory subunit gamma), BCL2L1(BCL2 like 1), BCL2(BCL2 apoptosis regulator), BIRC2(baculoviral IAP repeat containing 2).

In this canonical NFkB signaling pathway, the activation of IL1R1 phosphorylated IKBKB in the IKK complex consisting two catalytic subunits (IKBKA and IKBKB) and a regulatory subunit (IKBKG), and this process is mediated by the adapter protein MYD88. Then, IKK phosphorylates IkB, thus tagging it for proteasomal degradation and freeing the NFkB subunit dimers (p50/p65). NFkB dimers translocate to the nucleus where they bind to specific DNA sequences in the promoter region of a wide array of genes [71]. Meta-analysis concluded that NFκB expression may be a potential unfavorable prognostic marker for NSCLC patients [72].

Based on survival analysis, RELA could influence the survival of NSCLC patients. And there was a positive correlation between RELA expression and tumor grade, including pT_stage, pN_stage, and pM_stage. It has prognostic value for patients and can be used to diagnose diseases or indicate the severity of diseases. The RELA played a central role in the NFκB pathway [73]. Lin HC’s research showed that RELA attenuation significantly inhibited the proliferation and induced apoptosis of NSCLC cells in vitro, suggesting the indispensable role it has in lung cancer [74]. More interestingly, AR can inhibit the RELA expression according to western bolting test. RELA might be of greater value to be used for diagnosis, treatment and prognosis of NSCLC patients.

Combined with molecular simulations of RSMD, RSMF, and binding free energy data, we fouce that the RELA binds better to Astragaloside IV, and thus we performed an in-depth analysis of the RELA- Astragaloside IV complex with multiple metrics. The solvent accessible surface area (SASA) is a measure of a protein’s surface area. We determined SASA between the RELA and astragaloside IV (Fig. 12A). The findings demonstrated that the RELA’s SASA value was high prior to its binding to the small molecule but decreased following its binding, indicating that astragaloside IV and RELA binding reduced the protein’s surface area. Hydrogen bonding (HBond) of RELA and astragaloside IV interaction was assessed by the HBond (Fig. 12B). The results showed that many hydrogen bonds were formed between astragaloside IV and RELA, mainly between some key residues in the protein and some important groups in the small molecule. The radius of Gyration (Gyrate) is an indicator of the overall compactness of the protein, the Gyrate between astragaloside IV and RELA showed that the RELA value of proteins combined with small molecules decreased compared with that of individual protein molecules, indicating that the binding of small molecules makes the protein molecules more compact (Fig. 12C).

Fig. 12
figure 12

Simulation analysis of molecular dynamics of RELA-Astragaloside IV complexes. A The SASA between the RELA and astragaloside IV; B The HBond between astragaloside IV and RELA; C The Gyrate between astragaloside IV and RELA proteins

Full size image

The free energy landscape (FEL) of the complex of RELA -Astragaloside IV was created by combining two indicators, RMSD and R(g), as shown in Fig. 13. During the simulation process, there are two low-energy regions on the free-energy maps, indicating that there are two relatively stable states in the molecular structure, which are mainly concentrated around 30 ns and 80 ns.

Fig. 13
figure 13

Free energy morphology of RELA-Astragaloside IV complex

Full size image

Conclusion

Pharmacological mechanisms of AR on NSCLC were explored, promoting the clinical application of AR in treating NSCLC. UPLC-Q-Orbitrap HRMS analysis of AR which provided chemical component information references for further researches. Based on network pharmacology approach, TP53, SRC, UBC, CTNNB1, EP300, and RELA were identified as hub genes. KEGG results showed that AR may produce curative effects on signaling pathway JAK-STAT, PI3K-AKT, ErbB, and NFkB. Of these hub genes, TP53, SRC, EP300, and RELA participated in the above signaling pathways and had great docking energy with the key components of AR. The survival analysis revealed that RELA was proved to have prognostic value on NSCLC patients’ survival. These findings provided references for further researches.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

References

  1. Wu C, Song W, Wang Z, et al. Functions of lncRNA DUXAP8 in non-small cell lung cancer. Mol Biol Rep. 2022;49(3):2531–42.

    CAS  PubMed  Google Scholar 

  2. Baak JPA, Li H, Guo H. Clinical and biological interpretation of survival curves of cancer patients, exemplified with stage IV non-small cell lung cancers with long follow-up. Front Oncol. 2022;12:837419.

    PubMed  PubMed Central  Google Scholar 

  3. Pompili C, Koller M, Velikova G, et al. EORTC QLQ-C30 summary score reliably detects changes in QoL three months after anatomic lung resection for non-small cell lung cancer (NSCLC)[J]. Lung Cancer. 2018;123:149–54.

  4. Guo H, Liu JX, Li H, et al. In metastatic non-small cell lung cancer platinum-based treated patients, herbal treatment improves the quality of life. A prospective randomized controlled clinical trial. Front Pharmacol. 2017;8:454.

  5. Rauma V, Sintonen H, Räsänen J V, et al. Long-term lung cancer survivors have permanently decreased quality of life after surgery[J]. Clin Lung Cancer. 2015;16(1):40–5.

  6. Marks S, Naidoo J. Antibody drug conjugates in non-small cell lung cancer: an emerging therapeutic approach. Lung Cancer. 2022;163:59–68.

    CAS  PubMed  Google Scholar 

  7. de Fraipont F, Gazzeri S, Cho WC, et al. Circular RNAs and RNA splice variants as biomarkers for prognosis and therapeutic response in the liquid biopsies of lung cancer patients. Front Genet. 2019;10:390.

    PubMed  PubMed Central  Google Scholar 

  8. Subramaniyan V, Fuloria S, Gupta G, et al. A review on epidermal growth factor receptor’s role in breast and non-small cell lung cancer. Chem Biol Interact. 2022;351:109735.

    CAS  PubMed  Google Scholar 

  9. Huang S, Lin W, Wang L, et al. SIX1 predicts poor prognosis and facilitates the progression of non-small lung cancer via activating the notch signaling pathway. J Cancer. 2022;13(2):527–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Cheng L, Tong Q. Interaction of FLNA and ANXA2 promotes gefitinib resistance by activating the Wnt pathway in non-small-cell lung cancer. Mol Cell Biochem. 2021;476(10):3563–75.

    CAS  PubMed  Google Scholar 

  11. Wang H, Xia L, Yao CC, et al. NLRP4 negatively regulates type I interferon response and influences the outcome in anti-programmed cell death protein (PD)-1/PD-ligand 1 therapy. Cancer Sci. 2022;113(3):838–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Tian G, Guo L, Gao W. Use of compound Chinese medicine in the treatment of lung cancer. Curr Drug Discov Technol. 2010;7(1):32–6.

    CAS  PubMed  Google Scholar 

  13. Lin AX, Chan G, Hu Y, et al. Internationalization of traditional Chinese medicine: current international market, internationalization challenges and prospective suggestions. Chin Med. 2018;13:9.

    PubMed  PubMed Central  Google Scholar 

  14. Xu J, Xia Z. Traditional Chinese Medicine (TCM)–does its contemporary business booming and globalization really reconfirm its medical efficacy & safety? Med Drug Discov. 2019;1:100003.

    Google Scholar 

  15. Shao BM, Xu W, Dai H, et al. A study on the immune receptors for polysaccharides from the roots of Astragalus membranaceus, a Chinese medicinal herb. Biochem Biophys Res Commun. 2004;320(4):1103–11.

    CAS  PubMed  Google Scholar 

  16. Li S, Sun Y, Huang J, et al. Anti-tumor effects and mechanisms of Astragalus membranaceus (AM) and its specific immunopotentiation: status and prospect. J Ethnopharmacol. 2020;258:112797.

    CAS  PubMed  Google Scholar 

  17. McCulloch M, See C, Shu XJ, et al. Astragalus-based Chinese herbs and platinum-based chemotherapy for advanced non-small-cell lung cancer: meta-analysis of randomized trials. J Clin Oncol. 2006;24(3):419–30.

    CAS  PubMed  Google Scholar 

  18. Khalifa SAM, Elias N, Farag MA, et al. Marine natural products: a source of novel anticancer drugs. Mar Drugs. 2019;17(9):491.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Min L, Wang H, Qi H. Astragaloside IV inhibits the progression of liver cancer by modulating macrophage polarization through the TLR4/NF-κB/STAT3 signaling pathway. Am J Transl Res. 2022;14(3):1551–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Xing L, Fang J, Zhu B, et al. Astragaloside IV protects against podocyte apoptosis by inhibiting oxidative stress via activating PPARγ-Klotho-FoxO1 axis in diabetic nephropathy. Life Sci. 2021;269:119068.

    CAS  PubMed  Google Scholar 

  21. Riely GJ, Neal JW, Camidge DR, et al. Activity and safety of mobocertinib (TAK-788) in previously treated non-small cell lung cancer with EGFR Exon 20 insertion mutations from a phase I/II trial. Cancer Discov. 2021;11(7):1688–99.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhou Q, Xu CR, Cheng Y, et al. Bevacizumab plus erlotinib in Chinese patients with untreated, EGFR-mutated, advanced NSCLC (ARTEMIS-CTONG1509): a multicenter phase 3 study. Cancer Cell. 2021;39(9):1279-91.e3.

    CAS  PubMed  Google Scholar 

  23. Li S. Possible association of Chinese medicine evidence with molecular network regulatory mechanisms[C] China Association for Science and Technology,Zhejiang Provincial People's Government. Scientific and technological progress and socio-economic development for the 21st century (Previous). China Science and Technology Press; 1999. p. 1.

  24. Gauthier J, Vincent AT, Charette SJ, et al. A brief history of bioinformatics. Brief Bioinform. 2019;20(6):1981–96.

    PubMed  Google Scholar 

  25. Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2.

    PubMed  PubMed Central  Google Scholar 

  26. Saikia S, Bordoloi M. Molecular docking: challenges, advances and its use in drug discovery perspective. Curr Drug Targets. 2019;20(5):501–21.

    CAS  PubMed  Google Scholar 

  27. Ru J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13.

    PubMed  PubMed Central  Google Scholar 

  28. Xu HY, Zhang YQ, Liu ZM, et al. ETCM: an encyclopaedia of traditional Chinese medicine. Nucleic Acids Res. 2019;47(D1):D976–82.

    CAS  PubMed  Google Scholar 

  29. Fang S, Dong L, Liu L, et al. HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine. Nucleic Acids Res. 2021;49(D1):D1197–206.

    CAS  PubMed  Google Scholar 

  30. Liu Z, Guo F, Wang Y, et al. BATMAN-TCM: a bioinformatics analysis tool for molecular mechANism of traditional Chinese medicine. Sci Rep. 2016;6:21146.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Dehan E, Ben-Dor A, Liao W, et al. Chromosomal aberrations and gene expression profiles in non-small cell lung cancer. Lung Cancer. 2007;56(2):175–84.

    CAS  PubMed  Google Scholar 

  32. Meister M, Belousov A, Xu E C, et al. Intra-tumor heterogeneity of gene expression profiles in early stage non-small cell lung cancer[J]. J Bioinf Res Stud. 2014;1:1.

  33. Chen CH, Lai JM, Chou TY, et al. VEGFA upregulates FLJ10540 and modulates migration and invasion of lung cancer via PI3K/AKT pathway. PLoS One. 2009;4(4):e5052.

    PubMed  PubMed Central  Google Scholar 

  34. Brouwer-Visser J, Cheng WY, Bauer-Mehren A, et al. Regulatory T-cell genes drive altered immune microenvironment in adult solid cancers and allow for immune contextual patient subtyping. Cancer Epidemiol Biomarkers Prev. 2018;27(1):103–12.

    CAS  PubMed  Google Scholar 

  35. Hoang LT, Domingo-Sabugo C, Starren ES, et al. Metabolomic, transcriptomic and genetic integrative analysis reveals important roles of adenosine diphosphate in haemostasis and platelet activation in non-small-cell lung cancer. Mol Oncol. 2019;13(11):2406–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Chen T, Yang Z, Liu C, et al. Circ_0078767 suppresses non-small-cell lung cancer by protecting RASSF1A expression via sponging miR-330-3p. Cell Prolif. 2019;52(2):e12548.

    PubMed  Google Scholar 

  37. Chinnappan J, Ramu A, V VR, et al. Integrative bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin. Sci Rep. 2021;11(1):22036.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin J Nat Med. 2013;11(2):110–20.

    PubMed  Google Scholar 

  40. Zhu X, Gerstein M, Snyder M. Getting connected: analysis and principles of biological networks. Genes Dev. 2007;21(9):1010–24.

    CAS  PubMed  Google Scholar 

  41. Schöning-Stierand K, Diedrich K, Ehrt C, et al. ProteinsPlus: a comprehensive collection of web-based molecular modeling tools. Nucleic Acids Res. 2022;50(W1):W611–5.

    PubMed  PubMed Central  Google Scholar 

  42. van der Spoel D, Lindahl E, Hess B, et al. GROMACS: fast, flexible, and free. J Comput Chem. 2005;26(16):1701–18.

    PubMed  Google Scholar 

  43. Colaprico A, Silva TC, Olsen C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44(8):e71.

    PubMed  Google Scholar 

  44. Iasonos A, Schrag D, Raj GV, et al. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364–70.

    PubMed  Google Scholar 

  45. Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999;286(5439):509–12.

    CAS  PubMed  Google Scholar 

  46. Strogatz SH. Exploring complex networks. Nature. 2001;410(6825):268–76.

    CAS  PubMed  Google Scholar 

  47. Hsin KY, Ghosh S, Kitano H. Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PLoS One. 2013;8(12):e83922.

    PubMed  PubMed Central  Google Scholar 

  48. Li HX, Zhao W, Shi Y, et al. Retinoic acid amide inhibits JAK/STAT pathway in lung cancer which leads to apoptosis. Tumour Biol. 2015;36(11):8671–8.

    CAS  PubMed  Google Scholar 

  49. Zhu D, Yu Y, Wang W, et al. Long noncoding RNA PART1 promotes progression of non-small cell lung cancer cells via JAK-STAT signaling pathway. Cancer Med. 2019;8(13):6064–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Yan Z, Gibson SA, Buckley JA, et al. Role of the JAK/STAT signaling pathway in regulation of innate immunity in neuroinflammatory diseases. Clin Immunol. 2018;189:4–13.

    CAS  PubMed  Google Scholar 

  51. Marrero MB. Introduction to JAK/STAT signaling and the vasculature. Vascul Pharmacol. 2005;43(5):307–9.

    CAS  PubMed  Google Scholar 

  52. Xin P, Xu X, Deng C, et al. The role of JAK/STAT signaling pathway and its inhibitors in diseases. Int Immunopharmacol. 2020;80:106210.

    CAS  PubMed  Google Scholar 

  53. Jiang R, Wang X, Jin Z, et al. Association of nuclear PIM1 expression with lymph node metastasis and poor prognosis in patients with lung adenocarcinoma and squamous cell carcinoma. J Cancer. 2016;7(3):324–34.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Massó-Vallés D, Beaulieu ME, Soucek L. MYC, MYCL, and MYCN as therapeutic targets in lung cancer. Expert Opin Ther Targets. 2020;24(2):101–14.

    PubMed  Google Scholar 

  55. Dong J, Sutor S, Jiang G, et al. c-Myc regulates self-renewal in bronchoalveolar stem cells. PLoS One. 2011;6(8):e23707.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Gao Q, Liang X, Shaikh AS, et al. JAK/STAT signal transduction: promising attractive targets for immune, inflammatory and hematopoietic diseases. Curr Drug Targets. 2018;19(5):487–500.

    CAS  PubMed  Google Scholar 

  57. Tan AC. Targeting the PI3K/Akt/mTOR pathway in non-small cell lung cancer (NSCLC). Thorac Cancer. 2020;11(3):511–8.

    PubMed  PubMed Central  Google Scholar 

  58. Cantley LC. The phosphoinositide 3-kinase pathway. Science. 2002;296(5573):1655–7.

    CAS  PubMed  Google Scholar 

  59. Samuels Y, Wang Z, Bardelli A, et al. High frequency of mutations of the PIK3CA gene in human cancers. Science. 2004;304(5670):554.

    CAS  Google Scholar 

  60. Alessi DR, James SR, Downes CP, et al. Characterization of a 3-phosphoinositide-dependent protein kinase which phosphorylates and activates protein kinase Balpha. Curr Biol. 1997;7(4):261–9.

    CAS  PubMed  Google Scholar 

  61. Myers MP, Pass I, Batty IH, et al. The lipid phosphatase activity of PTEN is critical for its tumor supressor function. Proc Natl Acad Sci USA. 1998;95(23):13513–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Murthy SS, Tosolini A, Taguchi T, et al. Mapping of AKT3, encoding a member of the Akt/protein kinase B family, to human and rodent chromosomes by fluorescence in situ hybridization. Cytogenet Cell Genet. 2000;88(1–2):38–40.

    CAS  PubMed  Google Scholar 

  63. Chuffa LGA, Reiter RJ, Lupi LA. Melatonin as a promising agent to treat ovarian cancer: molecular mechanisms. Carcinogenesis. 2017;38(10):945–52.

    CAS  PubMed  Google Scholar 

  64. Sonenshein GE. Rel/NF-kappa B transcription factors and the control of apoptosis. Semin Cancer Biol. 1997;8(2):113–9.

    CAS  PubMed  Google Scholar 

  65. Duronio V. The life of a cell: apoptosis regulation by the PI3K/PKB pathway. Biochem J. 2008;415(3):333–44.

    CAS  PubMed  Google Scholar 

  66. Hynes NE, Macdonald G. ErbB receptors and signaling pathways in cancer. Curr Opin Cell Biol. 2009;21(2):177–84.

    CAS  PubMed  Google Scholar 

  67. Yarden Y, Shilo BZ. SnapShot: EGFR signaling pathway. Cell. 2007;131(5):1018. e1-1018. e2.

    Google Scholar 

  68. Jonna S, Feldman RA, Swensen J, et al. Detection of NRG1 gene fusions in solid tumors. Clin Cancer Res. 2019;25(16):4966–72.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Rothschild SI, Gautschi O, Haura EB, et al. Src inhibitors in lung cancer: current status and future directions. Clin Lung Cancer. 2010;11(4):238–42.

    CAS  PubMed  Google Scholar 

  70. Hoesel B, Schmid JA. The complexity of NF-κB signaling in inflammation and cancer. Mol Cancer. 2013;12(1):1–15.

    Google Scholar 

  71. Nennig SE, Schank JR. The role of NFkB in drug addiction: beyond inflammation. Alcohol Alcohol. 2017;52(2):172–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Gu L, Wang Z, Zuo J, et al. Prognostic significance of NF-κB expression in non-small cell lung cancer: a meta-analysis. PLoS One. 2018;13(5):e0198223.

    PubMed  PubMed Central  Google Scholar 

  73. Jimi E, Huang F, Nakatomi C. NF-κB signaling regulates physiological and pathological chondrogenesis. Int J Mol Sci. 2019;20(24):6275.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Lin HC, Li J, Cheng DD, et al. Nuclear export protein CSE1L interacts with P65 and promotes NSCLC growth via NF-κB/MAPK pathway. Mol Ther Oncolytics. 2021;21:23–36.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank our research team, all the researchers for their dedication, support and hard work.

Funding

This work was supported by the National Natural Science Foundation of China [81703825], the Sichuan Science and Technology Program [2021YJ0254] and the Chengdu University of Traditional Chinese Medicine “Apricot Grove Scholars” Discipline Talent Research Enhancement Program [QJRC2022051].

Author information

Author notes

  1. Wenke Xiao and Yaxin Xu are contributed equally to this work and share first authorship.

Authors and Affiliations

  1. School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China

    Wenke Xiao, Yaxin Xu, Jinrong Dai, Hongxia Zhu, Yanxiong Gan & Shichao Zheng

  2. Stavanger University Hospital, Stavanger, 4068, Norway

    Jan P. Baak

  3. Dr. Med Jan Baak AS, Tananger, 4056, Norway

    Jan P. Baak

  4. School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China

    Lijia Jing

Authors
  1. Wenke Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaxin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan P. Baak
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinrong Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lijia Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongxia Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yanxiong Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Shichao Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Wenke Xiao: Investigation, Data curation, Visualization, Writing–original draft. Yaxin Xu: Methodology, Validation, Formal analysis, Data curation. Jinrong Dai: Investigation, Validation. Lijia Jing: Investigation, Data curation. Hongxia Zhu: Investigation, Data curation. Yanxiong Gan: SupervisionSupervising, Writing–review & editing. Shichao Zheng: Resources, SupervisionSupervising, Writing – review & editing. Jan P Baak: Molecular dynamics simulation and article revision.

Corresponding authors

Correspondence to Yanxiong Gan or Shichao Zheng.

Ethics declarations

Ethics approval and consent to participate

The experiment complied with ARRIVE guidelines. This study was performed in accordance with the guidelines of the National Health Institutes of China and approved by the Animal Ethics Committee of Chengdu University of Traditional Chinese Medicine (Chengdu, China, Ethical approval number: 2018–15).

Consent for publication

Not applicable (NA).

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Supplementary Table 1. Pubchem compound information of drugs.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, W., Xu, Y., Baak, J.P. et al. Network module analysis and molecular docking-based study on the mechanism of astragali radix against non-small cell lung cancer. BMC Complement Med Ther 23, 345 (2023). https://doi.org/10.1186/s12906-023-04148-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12906-023-04148-9

Keywords

BMC Complementary Medicine and Therapies

ISSN: 2662-7671

Contact us