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Abstract

Astragali Radix (AR) and Schisandrae chinensis Fructus (SCF) have been used individually and in traditional Chinese medicine (TCM) formulas for treating non-small cell lung carcinoma (NSCLC). Qi Wei Wan (QWW), a 2-herb TCM formula composed of AR and SCF, is used to treat blood deficiency, fatigue, and metabolic abnormalities. We speculate that QWW may be more effective in treating NSCLC than AR or SCF alone. We identified 28 bioactive compounds in QWW and 322 targets of these compounds from databases. Network pharmacology analysis was used to identify 248 putative NSCLC-related gene targets of the bioactive compounds in QWW. Common target genes were analyzed to build protein–protein interaction networks. Implicated biological functions and pathways (p53, PI3K-Akt, etc) were identified by Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses. Molecular docking of core target proteins with the key active compounds was also performed. This study identified the potential gene targets and mechanisms involved in the anti-NSCLC effects of QWW.

Keywords

TCM network pharmacology non-small cell lung carcinoma flavonoids lignans terpenoids

Introduction

Cancer accounted for 10 million deaths in 2020.¹ Lung cancer is the third most frequently diagnosed form of cancer (age-standardized incidence rate: 22.4/100 000).¹ It is the most commonly diagnosed cancer in China, accounting for 787 000 cases in 2015.² In the United States, 236 740 new cases are estimated in 2022.³ Lung cancer is classified as small-cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC).⁴ NSCLC can be subcategorized into lung squamous cell carcinoma, lung adenocarcinoma, and large cell carcinoma.⁵ NSCLC accounts for more than 85% of lung cancer cases, with wheezing, cough, hemoptysis, chest pain, trouble breathing, and fever being the main clinical symptoms with diagnosis usually being made at late stages.⁶ Treatment methods include surgery, chemotherapy, radiotherapy, and targeted therapy. Although overall cancer survival rates have increased with improvements in technology, survival rates remain relatively low.^3,7 In China, lung cancer's 5-year survival rates (2012-2015) in men and women were 16.8% and 25.1%, respectively.⁸ Worldwide 5-year survival rates ranged from 4% to 17%, depending on stage and region.⁹

Traditional Chinese medicine (TCM) has been practiced for centuries and has been established as a primary medical system in China.^10,11 In the recent past, the consumption of TCM has also gained recognition as complementary and alternative medicine, particularly for the treatment of cancer.¹² Astragali Radix (AR) and Schisandrae chinensis Fructus (SCF) have been used individually or as key components in formulas for thousands of years in China. The functions of AR and SCF have been documented in “Ben Cao Feng Yuan” (The Medicinal Herbs in the Wild, Zhang, 1695). The functions of AR include enriching and nourishing the lung and the spleen system, while SCF strengthens the lung, resolves systemic infections, and invigorates the kidney system. Furthermore, it has been reported that SCF encourages oxygen exchange and reduces coughing and pulmonary inflammation in guinea pigs.¹³ Some ingredients of AR and SCF have been reported to have functions on NSCLC.^14–16 The functions of the Qi Wei Wan (QWW) formula (composed of AR and SCF) have been documented in “Pu Ji Fang” (Prescriptions of Universal Relief, 1406). It is described as a supplement for the treatment of blood deficiency syndrome. However, the mechanisms and pathways involved in the functions of QWW remain elusive and warrant further research. In this study, the potential of QWW as an anticancer medicine for the treatment of NSCLC was investigated using a network pharmacology-based approach.

Network pharmacology involves systems biology, network analysis, protein–protein and protein–compound interactions, redundancy, and pleiotropy.¹⁷ It is a primary data-driven approach, which potentiates drug discovery by incorporating multiple parameters, such as compound bioactivity, application, assimilation, and clinical efficacy to identify drug targets, toxicity, and side effects.¹⁷ Network pharmacology has successfully been used to predict the potential functions of natural products.^18,19 For example, the probability of the compound Matrine being a macropinocytosis inducer was predicted by network pharmacology and then verified by biochemical experiments.¹⁸ Similarly, Danhong Injection, an injectable Chinese compound medicine, was also predicted to specifically target anti-inflammation pathways.¹⁹ Biochemical analysis revealed that Danhong Injection specifically inhibited the NF-kB pathway.¹⁹ Collectively, such research provides explicit evidence for the practical application of network pharmacology.

The QWW formula is a multicomponent and multitarget therapeutic agent that has the potential to comprehensively treat complex disorders including NSCLC. Thus, network pharmacology analysis of QWW can predict target pathways and targets by the bioactive components of QWW, with a high probability that the results can subsequently be verified using biochemical techniques.

A recent study has demonstrated that the combination of platinum-based chemotherapeutic agents with Kangai Injection (a Chinese patent medicine, composed of 3 herbs including AR) for the treatment of stage III/IV NSCLC significantly improved treatment outcome and had reduced toxicity compared to single treatment with conventional chemotherapeutic agents.²⁰ Another study has demonstrated that the combination of chemotherapeutic agents with Shengmai Injection (a Chinese patent medicine, composed of 3 herbs including SCF) for the treatment of chemotherapy-induced cardiotoxicity significantly improved the efficacy of the treatment and had significantly less toxicity compared to solitary treatment with chemotherapeutic agents.²¹ Furthermore, a patent TCM named Huangqi Shengmai Yin or Jiawei Shengmai San (adding AR to Shengmai Yin which contains SCF) has also been shown to protect against radiation-induced cardiac fibrosis injury in the process of cancer treatment.²²

Therefore, as QWW is composed of AR and SCF, it can be hypothesized that using this formula may be effective in treating NSCLC. At present, the existing literature does not address this issue. In this paper, the potential gene targets, pathways, and mechanisms of QWW in the treatment of NSCLC were investigated through cross-database network pharmacology analysis.

Results

Overall Strategy

Our initial aim was to construct an interaction network of the compounds in QWW and NSCLC-related genes. The names of the compounds in QWW and their targets were collected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The NSCLC-related genes were collected from the GeneCards and DisGeNet databases. The 2 groups of data were imported into the UniProt KB database to unify target names and identify common targets (genes shared between the 2 groups). A protein–protein interaction (PPI) network of the common targets was then generated, and the core target genes were identified based on the abundance of the interactions. Common targets were also analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) processes to support the prominence of the core targets involved in NSCLC. Finally, molecular docking was performed to further support the interactions of the QWW key compounds with core NSCLC-related targets.

Bioactive Compounds in QWW and Their Targets

The TCMSP database was used to identify all reported compounds in AR and SCF. Some 217 compounds, 87 from AR and 130 from SCF, were found. Among these compounds, key bioactive compounds were identified using 2 thresholds (Drug-likeness [DL] ≥ 0.18 and oral bioavailability [OB] ≥ 30). A total of 28 key bioactive compounds in QWW satisfying the threshold criteria were identified (AR: 20 compounds, 23.0%; SCF: 8 compounds 6.2%). The key bioactive compounds in QWW are shown in Table 1. A total of 322 proteins as the potential targets of the 28 compounds were also collected from the TCMSP database.

Table 1.

Detailed Information of Active Compounds (OB ≥ 30% and DL ≥ 0.18) in QWW.^a

	Mol ID	Molecule name	OB (%)	DL
AR1	MOL000211	Mairin	55.4	0.8
AR2	MOL000239	Jaranol	50.8	0.3
AR3	MOL000296	Hederagenin	36.9	0.8
AR4	MOL000033	(3S,8S,9S,10R,13R,14S,17R)-10,13-dimethyl-17-[(2R,5S)-5-propan-2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol	36.2	0.8
AR5	MOL000354	Isorhamnetin	49.6	0.3
AR6	MOL000371	3,9-di-O-methylnissolin	53.7	0.5
AR7	MOL000374	5′-hydroxyiso-muronulatol-2′,5′-di-O-glucoside	41.7	0.7
AR8	MOL000378	7-O-methylisomucronulatol	74.7	0.3
AR9	MOL000379	9,10-dimethoxypterocarpan-3-O-β-D-glucoside	36.7	0.9
AR10	MOL000380	(6aR,11aR)-9,10-dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol	64.3	0.4
AR11	MOL000387	Bifendate	31.1	0.7
AR12	MOL000392	Formononetin	69.7	0.2
AR13	MOL000398	Isoflavanone	110.0	0.3
AR14	MOL000417	Calycosin	47.8	0.2
AR15	MOL000422	Kaempferol	41.9	0.2
AR16	MOL000433	Folic acid	69.0	0.7
AR17	MOL000438	(3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol	67.8	0.3
AR18	MOL000439	Isomucronulatol 7,2′-di-O-glucoside	49.3	0.6
AR19	MOL000442	1,7-Dihydroxy-3,9-dimethoxy pterocarpene	39.0	0.5
AR20	MOL000098	Quercetin	46.4	0.3
SCF1	MOL008968	Gomisin A	30.7	0.8
SCF2	MOL008957	Schizandrer B	30.7	0.8
SCF3	MOL008956	Angeloylgomisin O	32.0	0.8
SCF4	MOL008974	Gomisin G	32.7	0.8
SCF5	MOL008978	Gomisin R	34.8	0.9
SCF6	MOL005317	Deoxyharringtonine	39.3	0.8
SCF7	MOL008992	Wuweizisu C	46.3	0.8
SCF8	MOL004624	Longikaurin A	47.8	0.5

Abbreviations: QWW, Qi Wei Wan; AR, Astragali Radix; SCF, Schisandrae chinensis Fructus; OB, oral bioavailability; DL, drug-likeness.

QWW, A 2-herb formula composed of AR and SCF.

Non-Small Cell Lung Carcinoma–Related Genes and Analysis of the Drug-Compound-Target Network

To identify proteins involved in NSCLC that are potential targets of the bioactive compounds in QWW, we searched the GeneCards and DisGeNet databases (keywords: “non-small-cell lung carcinoma” and “NSCLC”) and identified 6223 verified genes that are involved in NSCLC. To analyze the interactions between NSCLC-related proteins and the key bioactive compounds in QWW, the protein targets of the compounds in QWW were compared to the NSCLC-related genes using the UniProt KB database. Among the 6223 NSCLC-related genes and 322 targets of the key bioactive compounds in QWW, 248 genes, referred to as “the common targets” that are both the targets of the QWW compounds and NSCLC-related genes, were identified as the putative NSCLC targets of the 28 key bioactive compounds in QWW (Figure 1).

Figure 1.

Venn diagram of the potential anti-lung cancer targets. Some 6223 NSCLC-related genes (purple) and 322 gene targets of QWW compounds (yellow) were identified. Common gene targets (248) shared between the targets of QWW compounds and NSCLC-related genes were identified and used for PPI network analysis. The remaining 74 targets of QWW are not related to NSCLC. NSCLC, non-small cell lung carcinoma; QWW, Qi Wei Wan; PPI, protein–protein interaction.

A drug-compound-target network was then constructed to further analyze the 248 NSCLC-related genes targeted by the key bioactive compounds in QWW (Figure 2). In the network, most compounds have many overlapping gene targets. The average number of interactions that each compound has with a gene (degree value) is 20.5. A total of 8 compounds in QWW have a degree value greater than 20.5, namely, with the degree value in parentheses, quercetin (124), (3R)-7,2′-dihydroxy-3′,4′-dimethoxyisoflavan (recorded as (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol in the TCMSP database; CAS#64474-51-7) (85), isoflavanone with 4 hydroxyl groups naturally substituted (6,7-dimethoxyl-3-(2,4-dihydroxyl)phenol-benzopyran-4-one, recorded as isoflavanone in the TCMSP database) (80), kaempferol (51), formononetin (33), 7-O-methylisomucronulatol (31), isorhamnetin (30), and calycosin (21). The high-degree values suggest that these compounds may have important roles in NSCLC treatment owing to the higher number of interactions with NSCLC-related genes.

Figure 2.

Compound-target network analysis. The network of 28 QWW key compounds (OB ≥ 30% and DL ≥ 0.18; red circles) and 248 common genes (yellow squares) in NSCLC targeted by QWW compounds. Larger circles mean more interactions in the network. OB, oral bioavailability; DL, drug-likeness; NSCLC, non-small cell lung carcinoma; QWW, Qi Wei Wan.

Protein–Protein Interaction and Network Analysis

Of the 248 common genes that were identified (Figure 2), a PPI network of the gene products was generated (17 proteins without interactions were removed) (Figure 3). A screening threshold, that is, P < .05 and minimum interaction score of 0.700 (“high confidence”), was applied. The network was analyzed using Cytoscape software (231 dots and 3762 edges). Major topological features were also identified (“degree,” “betweenness,” and “closeness”). Nodes were selected that had a median topological feature value (for individual proteins) greater than the threshold (“degree” 12, “betweenness” 0.001681, and “closeness” 0.4054). From this screen, 82 “core targets” were identified, and they were considered to be highly important in the application of QWW for the treatment NSCLC. The names of core targets are shown in Table 2, and the network of the core targets and the QWW bioactive compounds is shown in Figure 4. Targets with the highest degree values were TP53, AKT1, SRC, HSP90AA1, and JUN.

Figure 3.

PPI network analysis. Analysis of the interactions of 248 common genes in NSCLC targeted by the QWW compounds, with a screening threshold of P < .05 and “high confidence (0.700)” from STRING. P < .05 represents the results collected have strong evidence against the null hypothesis. After removing disconnected genes, the remaining 231 common genes have interactions with other genes. NSCLC, non-small cell lung carcinoma; QWW, Qi Wei Wan; PPI, protein–protein interaction.

Figure 4.

Core compound-core target network analysis. The network of 82 core target genes (yellow squares) in NSCLC targeted by QWW compounds (red squares). Core target genes are defined as having a median topological feature value (for individual proteins) greater than the threshold (“degree” 12, “betweenness” 0.001681, and “closeness” 0.4054). NSCLC, non-small cell lung carcinoma; QWW, Qi Wei Wan.

Table 2.

Details of 82 Targets in the Core Network.

Number	Name	UniProt ID	Protein name	Degree
1	TP53	P04637	Cellular tumor antigen p53	80
2	AKT1	P31749	RAC-alpha serine/threonine-protein kinase	76
3	SRC	P12931	Proto-oncogene tyrosine-protein kinase Src	70
4	HSP90AA1	P07900	Heat shock protein HSP 90-alpha	68
5	JUN	P05412	Transcription factor AP-1	66
6	EGFR	P00533	Epidermal growth factor receptor	62
7	EP300	Q09472	Histone acetyltransferase p300	57
8	TNF	P01375	Tumor necrosis factor	55
9	RELA	Q04206	Transcription factor p65	54
10	MAPK1	P28482	Mitogen-activated protein kinase 1	53
11	MYC	P01106	Myc proto-oncogene protein	53
12	IL6	P05231	Interleukin-6	48
13	ESR1	P03372	Estrogen receptor	46
14	CCND1	P24385	G1/S-specific cyclin-D1	44
15	CASP3	P42574	Caspase-3	44
16	PTEN	P60484	Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN	42
17	PIK3R1	P27986	Phosphatidylinositol 3-kinase regulatory subunit alpha	42
18	NFKBIA	P25963	NF-kappa-B inhibitor alpha	41
19	IL1B	P01584	Interleukin-1 beta	41
20	HIF1A	Q16665	Hypoxia-inducible factor 1-alpha	40
21	STAT1	P42224	Signal transducer and activator of transcription 1-alpha/beta	39
22	PIK3CA	P42336	Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform	38
23	AR	P10275	Androgen receptor	37
24	MAPK14	Q16539	Mitogen-activated protein kinase 14	37
25	ERBB2	P04626	Receptor tyrosine-protein kinase erbB-2	36
26	RB1	P06400	Retinoblastoma-associated protein	36
27	MMP9	P14780	Matrix metalloproteinase-9	34
28	PRKACA	P17612	cAMP-dependent protein kinase catalytic subunit alpha	34
29	PPARG	P37231	Peroxisome proliferator-activated receptor gamma	34
30	FOS	P01100	Proto-oncogene c-Fos	34
31	SIRT1	Q96EB6	NAD-dependent protein deacetylase sirtuin-1	33
32	CDK1	P06493	Cyclin-dependent kinase 1	33
33	RUNX2	Q13950	Runt-related transcription factor 2	32
34	NCOR2	Q9Y618	Nuclear receptor corepressor 2	32
35	MTOR	P42345	Serine/threonine-protein kinase mTOR	32
36	CXCL8	P10145	Interleukin-8	31
37	CDKN1A	P38936	Cyclin-dependent kinase inhibitor 1	31
38	CAV1	Q03135	Caveolin-1	30
39	PRKCA	P17252	Protein kinase C alpha type	30
40	CCL2	P13500	C-C motif chemokine 2	29
41	IL2	P60568	Interleukin-2	29
42	CDK2	P24941	Cyclin-dependent kinase 2	28
43	PTGS2	P35354	Prostaglandin G/H synthase 2	26
44	BCL2L1	Q07817	Bcl-2-like protein 1	26
45	HDAC3	O15379	Histone deacetylase 3	26
46	IL10	P22301	Interleukin-10	26
47	CCNA2	P20248	Cyclin-A2	26
48	ICAM1	P05362	Intercellular adhesion molecule 1	25
49	RAF1	P04049	RAF proto-oncogene serine/threonine-protein kinase	25
50	CCNB1	P14635	G2/mitotic-specific cyclin-B1	25
51	PRKCB	P05771	Protein kinase C beta type	24
52	RXRA	P19793	Retinoic acid receptor RXR-alpha	24
53	NCOR1	O75376	Nuclear receptor corepressor 1	24
54	MAP2K1	Q02750	Dual specificity mitogen-activated protein kinase kinase 1	24
55	GSK3B	P49841	Glycogen synthase kinase-3 beta	24
56	IFNG	P01579	Interferon gamma	24
57	IL4	P05112	Interleukin-4	23
58	MMP2	P08253	72 kDa type IV collagenase	22
59	NOS2	P35228	Nitric oxide synthase, inducible	22
60	NOS3	P29474	Nitric oxide synthase, endothelial	22
61	PGR	P06401	Progesterone receptor	22
62	ABL1	P00519	Tyrosine-protein kinase ABL1	22
63	PPARA	Q07869	Peroxisome proliferator-activated receptor alpha	21
64	HDAC2	Q92769	Histone deacetylase 2	20
65	TGFB1	P01137	Transforming growth factor beta-1 proprotein [Cleaved into: Latency-associated peptide	20
66	F2	P00734	Prothrombin	19
67	KDR	P35968	Vascular endothelial growth factor receptor 2	19
68	ESR2	Q92731	Estrogen receptor beta	19
69	AURKA	O14965	Aurora kinase A	17
70	HMOX1	P09601	Heme oxygenase 1	16
71	AHR	P35869	Aryl hydrocarbon receptor	15
72	KIT	P10721	Mast/stem cell growth factor receptor Kit	15
73	EZR	P15311	Ezrin	15
74	PIK3CG	P48736	Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform	15
75	SYK	P43405	Tyrosine-protein kinase SYK	15
76	RASA1	P20936	Ras GTPase-activating protein 1	15
77	BCL2	P10415	Apoptosis regulator Bcl-2	15
78	NFE2L2	Q16236	Nuclear factor erythroid 2-related factor 2	14
79	HSPA5	P11021	Endoplasmic reticulum chaperone BiP	13
80	EPHB2	P29323	Ephrin type-B receptor 2	13
81	BAX	Q07812	Apoptosis regulator BAX	13
82	IGFBP3	P17936	Insulin-like growth factor-binding protein 3	12

Gene Ontology and KEGG Enrichment Analysis

GO and KEGG pathway analyses were carried out to identify the key potential pathways that are targeted by QWW in the treatment of NSCLC, using Metascape software. The 248 NSCLC-related genes targeted by QWW were screened in the GO analysis process (P < .05). The pathways that had the highest degree values (representing the top 10 biological processes, cellular components, and molecular functions) are listed (Figure 5A). KEGG pathway analysis was carried out to further refine the search. The top 20 enrichment pathways are shown in Figure 5B.

Figure 5.

Gene Ontology (GO) analysis and KEGG Pathway Enrichment Analyses. (A) GO analysis showing the top 10 biological processes, cellular components, and molecular functions. The Y-axis shows the count of the targets (enrichment score), and the X-axis shows highly enriched GO categories of the target genes. (B) KEGG Pathway Enrichment Analysis. The Y-axis shows the top 20 significantly enriched KEGG pathways of the target genes, and the X-axis is the fold enrichment. The fold enrichment represents the ratio of the number of target genes in a pathway to the total number of annotated genes in the pathway. The size of dots represents the number of genes in the pathway. The colors of the dots correspond to the different p value ranges. KEGG, Kyoto Encyclopedia of Genes and Genomes.

Interestingly, several pathways that are known to be involved in carcinogenesis were identified, such as the p53 signaling pathway, thyroid cancer, IL17 signaling pathway, and mitogen-activated protein kinase (MAPK) signaling pathway. Consolidation of the KEGG analysis and PPI network analysis identified the IL17, MAPK/ERK, PI3K-Akt, and p53 signaling pathways as being high-degree value targets. In addition, platinum drug resistance pathways were also identified as one of the top 20 KEGG enrichment pathways, suggesting that QWW treatment may reduce the resistance to platinum-based drugs in NSCLC treatment.

Molecular Docking Analysis

Molecular docking was carried out using the AutoDockTool application and processed with PyMol and BIOVIA Discovery Studio software. The proteins identified with the highest degree values (Figure 3) and the 8 compounds in QWW that have a degree value greater than 20.5 (Figure 2) were used to perform molecular docking (Table 3). Complexes with the lowest affinity energy were considered to be high probability structures. Three-dimensional diagrams of the interactions were generated (Figure 6). The binding energy of the top 3 active ingredients with each target protein was mostly less than −7 kcal/mol, indicating relatively high affinity.²³ Especially, the interactions of Akt1 and Src with quercetin and (3R)-7,2′-Dihydroxy-3′,4′-dimethoxyisoflavan have the lowest binding energy, at lower than −8 kcal/mol.

Figure 6.

Molecular docking of the core compounds with core targets. Docking results showing the structures and affinity of p53, Akt1, Src, and HSP90AA1 with (3R)-7,2′-Dihydroxy-3′,4′-dimethoxyisoflavan; Src with quercetin; and c-Jun with calycosin.

Table 3.

Binding Energy Between the Core Targets and Core Compounds.

Proteins compounds	p53 (1TUP)	Akt1 (3O96)	Src (2BDF)	Hsp90 (2CCU)	c-Jun (1JNM)
Quercetin	−6.1	−9.1	−8.4	−6.7	−5.1
(3R)-7,2′-Dihydroxy-3′,4′-dimethoxyisoflavan	−6.5	−10.1	−8.4	−7.1	−5.1
Isoflavanone (Hydroxyl substituents)	−6.3	−8.7	−7.9	−7.2	−5.0
Kaempferol	−6.0	−8.6	−8.0	−6.9	−4.9
Formononetin	−5.6	−8.4	−8.0	−6.7	−5.2
7-O-methylisomucronulatol	−6.3	−8.9	−8.2	−6.6	−5.0
Isorhamnetin	−5.5	−9.2	−7.8	−5.8	−5.1
Calycosin	−6.0	−9.0	−8.3	−6.9	−5.4

Figure S2 shows putative protein-ligand interactions and the specific amino acids that bind to the compounds. The result for the p53 interaction with (3R)-7,2′-Dihydroxy-3′,4′-dimethoxyisoflavan (Figure S2A) indicated that the interactions occur at the 98P, 156R, 158R, and 258E residues. The docking result for the Akt1 interaction with (3R)-7,2′-Dihydroxy-3′,4′-dimethoxyisoflavan (Figure S2B) shows that the interaction occurs at the 79Q, 80W, 268K, and 270V residues. The Src interactions with (3R)-7,2′-Dihydroxy-3′,4′-dimethoxyisoflavan (Figure S2C) occur at the 273L, 281V, 293A, 295K, 323V, 348D, 393L, and 403A residues. Src interacts with quercetin through 273L, 281V, 293A, 295K, and 393L (Figure S2D). HSP90AA1 and (3R)-7,2′-Dihydroxy-3′,4′-dimethoxyisoflavan interact mostly by van der Waals forces but also with binding sites, which are 98M and 102D (Figure S2E). Calycosin and c-Jun interact through 300M, 301L, 305V, 306A, and 309K (Figure S2F). The molecular docking results support the protein-ligand interactions of the core targets with the core compounds.

Discussion

Network pharmacology is a powerful approach that has been used to identify potential biological targets of bioactive compounds in TCM, within the context of specific diseases.²⁴ In this study, network pharmacology analysis identified quercetin, (3R)-7,2′-Dihydroxy-3′,4′-dimethoxyisoflavan, isoflavanone, kaempferol, and formononetin as leading compounds (with high-degree values) that interact with NSCLC-related proteins. Some compounds that were identified in this study with lower degree values (relative to the leading key compounds), such as isorhamnetin, 7-O-methylisomucronulatol, and calycosin, have also been reported to act on NSCLC-related proteins.^25,26 Molecular docking results support the structural interactions between the leading key compounds and the major hub genes (Figure 6).

Important functions of the key compounds analyzed by network pharmacology in this study have been shown in previous studies. (1) Quercetin: It has been reported that quercetin treatment inactivates Akt-1 and alters the expression of proteins in the Bcl-2 family, leading to apoptosis in A549 lung carcinoma cells.²⁷ The consumption of quercetin-rich foods has been associated with decreased risk of developing lung carcinoma.²⁸ (2) Isoflavanone: AR is rich in isoflavanone and isoflavone, as are many other food sources such as soy, cereals, and meat. Research has shown that isoflavone inhibits NF-kB and Akt signaling, to abrogate cancer cell growth.²⁹ (3) Kaempferol: Kaempferol is known to inhibit cell progression in many cancer cell types, such as breast, liver, and lung.³⁰ Studies have shown that kaempferol treatment suppresses the PI3K/AKT and ERK pathways, leading to cell death in NSCLC.^31,32 (4) Formononetin: Formononetin treatment has been shown to activate p53, leading to cell cycle arrest and apoptosis in NSCLC.³³ (5) Isorhamnetin: Isorhamnetin treatment has also been reported to inhibit proliferation and induce apoptosis in lung cancer cells.³⁴ (6) Calycosin: Calycosin treatment inhibits the PKC-α/ERK1/2 pathway, impairing NSCLC proliferation and metastasis.²⁶ Moreover, treatment with (3R)-7,2′-Dihydroxy-3′,4′-dimethoxyisoflavan has been proved to inhibit tumor growth in preclinical studies.³⁵ Although most functional compounds in the QWW formula are attributed to the AR herb, a bioactive compound in SCF, Gomisin R, has been shown to inhibit the development of preneoplastic lesions in rat livers.³⁶ Furthermore, Gomisin R treatment has been reported to activate AMPK and p38 to induce G₀/G₁-phase arrest in colorectal cancer cells and prevent the formation of metastatic tumors in the lung.³⁷

Aside from key compounds, hub genes identified by network pharmacology in this study have been shown to play important roles in cancer treatment. The PPI network analysis (Figure 3) identified TP53, AKT1, SRC, HSP90AA1, and JUN as major hub genes essential in the application of QWW for the treatment of NSCLC. PPI network analysis identified TP53 as having the highest degree value. TP53 is a tumor suppressor gene whose mutations play important roles in the tumorigenesis of lung epithelial cells.³⁸ The inactivation of TP53 may lead to increased cancer malignancy, poor patient survival, and resistance to treatment.³⁹ In NSCLC patients, somatic TP53 mutations and increased TP53 expression were 23% and 65%, respectively.⁴⁰ Moreover, TP53 mutations occur at relatively early stages of lung cancer development and are retained during tumor progression and metastasis.⁴¹

Our study identified AKT as having the second-highest degree value and lowest affinity energy. AKT is an essential kinase in the PI3k-AKT pathway activated by PI3K to regulate the proliferation, growth, and apoptosis of cells. Akt/PKB activity is constitutive in NSCLC cells and PI3K dependent, and it promotes NSCLC cell survival.⁴² The activation of Akt/PKB can also protect NSCLC cells from chemotherapy and radiation-induced apoptosis.⁴² In addition, research has shown that AKT1 silencing decreases MEK/ERK1/2 activity and IκB-α expression, leading to NSCLC cell death.⁴³ AKT1 is altered in 0.91% of NSCLC patients with AKT1 mutation present in 0.63% of all NSCLC patients.⁴⁴ Therefore, AKT is considered to be an attractive anticancer drug target. Another study has reported that AKT1 inactivation is associated with increased NSCLC cell metastatic potential, while activation inhibits migration and invasion.⁴⁵

Src, a nonreceptor tyrosine kinase protein involved in processes such as proliferation, differentiation, and adhesion, was identified as having the second-lowest binding energy with the core bioactive compounds in our study. The Src-family of kinase is activated in NSCLC and has been reported to promote survival in various NSCLC cell lines.⁴⁶ In addition, inhibition of Src-kinases induces apoptosis in NSCLC cell lines.⁴⁶ The critical role of Src in tumor growth, metastasis, and angiogenesis makes it a promising therapeutic target for the treatment of solid tumors.⁴⁷

Heat shock protein 90 (Hsp90)-alpha, also known as heat-associated protein 90, is also considered to be an attractive cancer drug target as it can simultaneously inhibit multiple signaling pathways.⁴⁸ Clinical studies have shown that average plasma levels of Hsp90α in lung cancer patients are significantly high, potentiating its use as a diagnostic marker for lung cancer.⁴⁹ The JUN gene encodes the c-Jun protein. The transcription of JUN can be regulated by c-Jun.⁵⁰ Extracellular stimuli, such as peptide growth factors and UV radiation, induce c-Jun protein synthesis.⁵¹ Also, the c-Jun protein is reported to be overexpressed in aggressive, invasive, and metastatic cancers.⁵²

The target genes identified in this study are implicated in the development of various cancers. Therefore, it is rational to conclude that these genes are likely involved in the anti-NSCLC properties of the QWW formula. KEGG pathway enrichment analysis further strengthens this hypothesis (Figure 5B). Thyroid cancer and IL17 signaling pathways had the highest enrichment. Moreover, other highly enriched pathways, such as the regulation of lipolysis in adipocytes and MAPK signaling pathways, are also known to be involved in carcinogenesis.^53,54 Consolidation of the KEGG analysis and PPI network analysis identified the IL17, MAPK/ERK, PI3K-Akt, and p53 signaling pathways as being high-degree value targets. Platinum drug resistance pathways were identified as one of the top 20 KEGG enrichment pathways. In addition, Kangai Injection (which contains SCF) has been proved to reduce adverse effects and regulate tumor immune function,²⁰ suggesting that QWW treatment may reduce the resistance and the associated toxicity of platinum-based drugs in NSCLC treatment. Moreover, our molecular docking models indicate the possibility of interactions between the QWW compounds and NSCLC-related proteins. Further biochemical experiments are warranted to fully elucidate the targets and mechanisms of the bioactive compounds of QWW in the treatment of NSCLC.

Conclusions

QWW may play the role in inhibiting NSCLC by targeting some cancer-related proteins such as p53, Akt, and MEK/ERK The therapeutic effects of QWW involve the IL17, MAPK/ERK, PI3K-Akt, and p53 signaling pathways. The results of this study lay a foundation for future studies to examine the clinical application of QWW in NSCLC.

Materials and Methods

Data Construction of Active Compounds and Prediction of Potential Targets of QWW

The data of the chemical ingredients in the 2 herbs in QWW were collected from TCMSP database and platform (https://tcmsp-e.com/, Ver.2.3). TCMSP contains 499 herbs registered in the Chinese pharmacopoeia (2010), with a total of 12 144 chemicals.⁵⁵ The chemicals were screened according to criteria that the OB is equal to or greater than 30%, and DL is equal to or greater than 0.18. To predict potential targets of the bioactive compounds of QWW, target information was obtained from TCMSP and transformed into UniProt gene identifiers based on the UniProt KB database (https://www.uniprot.org/). If the target information of certain components was not available in the TCMSP database, the 2D structure of the components was downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and submitted to the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) to retrieve the target information. After collecting target protein information from TCMSP and Swiss Target Prediction databases, data were compared with NSCLC-related genes using the common gene name database, UniProt KB. Finally, the targets (322 targets of the QWW compounds and 6223 targets of NSCLC) obtained from the databases (TCMSP, Genecards, and DisGeNet) were imported into Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) and combined after removing duplicates.

Construction of Drug-Compound-Target Network

To analyze the network of all known compounds of QWW and their targets, we used Cytoscape 3.8.2 software for diagram construction. The information was imported into Cytoscape, and the degrees, edges, and nodes were integrated.

Screening of Potential Targets of QWW Compounds in NSCLC

NSCLC-related protein targets were identified from Genecards (https://www.genecards.org/) and DisGeNET (https://www.disgenet.org/) databases. The Genecards database integrates the genomic, proteomic, and transcriptomic information of human genes.⁵⁶ DisGeNET is a discovery platform that contains one of the most comprehensive public collections of genes and variants associated with human diseases based on data integration from expert-curated repositories, GWAS catalogs, animal experiments, and scientific literature.⁵⁷

To analyze the interactions between NSCLC-related proteins and the key bioactive compounds in QWW, the protein targets of the compounds in QWW were compared to the NSCLC-related genes using the UniProt KB database. The key word, “Non-Small Cell Lung Carcinoma,” and relevant target proteins were standardized according to the Uniprot.

Construction of PPI Network

STRING (https://www.string-db.org/) database was used to predict the interactions among the common target genes of the QWW compounds in NSCLC. The “organism” was limited to “Homo sapiens,” and the minimum required score was “high confidence (0.700),” and disconnected genes were removed.

Gene Ontology and KEGG Pathway Enrichment Analysis and Network Construction of Targets Pathways

Gene ontology function enrichment and KEGG pathway enrichment analysis of common targets were implemented in the Metascape (http://metascape.org). Histogram and bubble chart were generated from the data.

Molecular Docking

A structure analysis software, AutoDockTools, was used for molecular docking based on the Vina docking algorithm.⁵⁸ The structures of proteins were collected from RSCBPDB (https://www.rcsb.org/), which contains tens of thousands of protein structures. PyMol and BIOVIA Discovery Studio graphic tools were used to lay out the detailed connections and bonds of the interaction between proteins and ligands.^23,59

Candidate genes identified by the PPI network analysis were imported into the RSCBPDB database to download the specific proteins in PDB format. The structures of the bioactive compounds from AR and SCF were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Proteins and chemicals were imported into AutoDockTools to obtain the best docking results. The results were further analyzed by PyMol and BIOVIA Discovery Studio software to render the 3D and 2D structures of the related proteins residues and binding bonds.

Supplemental Material

sj-xlsx-1-npx-10.1177_1934578X221120215 - Supplemental material for Network Pharmacology Analysis of Bioactive Components and Mechanisms of Action of Qi Wei Wan Formula for Treating Non-Small Cell Lung Carcinoma

Supplemental material, sj-xlsx-1-npx-10.1177_1934578X221120215 for Network Pharmacology Analysis of Bioactive Components and Mechanisms of Action of Qi Wei Wan Formula for Treating Non-Small Cell Lung Carcinoma by Minghe Zhang, Ye Wang, Aftab Amin, Muhammad Ajmal Khan, Zhiling Yu and Chun Liang in Natural Product Communications

Supplemental Material

sj-xlsx-2-npx-10.1177_1934578X221120215 - Supplemental material for Network Pharmacology Analysis of Bioactive Components and Mechanisms of Action of Qi Wei Wan Formula for Treating Non-Small Cell Lung Carcinoma

Supplemental material, sj-xlsx-2-npx-10.1177_1934578X221120215 for Network Pharmacology Analysis of Bioactive Components and Mechanisms of Action of Qi Wei Wan Formula for Treating Non-Small Cell Lung Carcinoma by Minghe Zhang, Ye Wang, Aftab Amin, Muhammad Ajmal Khan, Zhiling Yu and Chun Liang in Natural Product Communications

Supplemental Material

sj-xlsx-3-npx-10.1177_1934578X221120215 - Supplemental material for Network Pharmacology Analysis of Bioactive Components and Mechanisms of Action of Qi Wei Wan Formula for Treating Non-Small Cell Lung Carcinoma

Supplemental material, sj-xlsx-3-npx-10.1177_1934578X221120215 for Network Pharmacology Analysis of Bioactive Components and Mechanisms of Action of Qi Wei Wan Formula for Treating Non-Small Cell Lung Carcinoma by Minghe Zhang, Ye Wang, Aftab Amin, Muhammad Ajmal Khan, Zhiling Yu and Chun Liang in Natural Product Communications

Supplemental Material

sj-docx-4-npx-10.1177_1934578X221120215 - Supplemental material for Network Pharmacology Analysis of Bioactive Components and Mechanisms of Action of Qi Wei Wan Formula for Treating Non-Small Cell Lung Carcinoma

Supplemental material, sj-docx-4-npx-10.1177_1934578X221120215 for Network Pharmacology Analysis of Bioactive Components and Mechanisms of Action of Qi Wei Wan Formula for Treating Non-Small Cell Lung Carcinoma by Minghe Zhang, Ye Wang, Aftab Amin, Muhammad Ajmal Khan, Zhiling Yu and Chun Liang in Natural Product Communications

Footnotes

Authors’ Note

Minghe Zhang and Ye Wang contributed equally. Data are contained within the article.

Acknowledgements

This work was supported by the Guangzhou Innovation and Entrepreneurship Leading Team Project [202009020005] and the Science and Technology Innovation Bureau of Guangzhou Development District [CY2019-005].

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Science and Technology Innovation Bureau of Guangzhou Development District, the Guangzhou Innovation and Entrepreneurship Leading Team Project (grant number CY2019-005, 202009020005).

ORCID iDs

Minghe Zhang

Zhiling Yu

Supplemental Material

Supplemental material for this article is available online.

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