Ze-qi Decoction Inhibits Neutrophil Extracellular Trap Formation to Suppress the Progression and Metastasis of Non-Small Cell Lung Cancer

Abstract

Non-small-cell lung cancer (NSCLC) research has focused on complementary and well-established treatments with clear mechanisms and less toxicity. Immune dysregulation is vital in NSCLC progression and metastasis. Ze-qi decoction (ZQD) exhibits therapeutic effects in patients with NSCLC; however, its pharmacodynamic material basis and specific mechanisms remain unclear. In this study, we integrated UPLC-HRMS, pharmacological analysis, and transcriptomic analysis to identify the potential effective components of ZQD and elucidate its intrinsic mechanisms. ZQD exhibited potent anti-NSCLC activity in the mouse subcutaneous tumor model. A total of 297 bioactive compounds were identified in mouse plasma following ZQD administration. Pharmacological analysis revealed liquiritigenin, vibsanin B, and 11-keto-beta-boswellic acid as the potential active ingredients of ZQD and suggested that ZQD exerted anti-NSCLC effects primarily via immunomodulatory and anti-inflammatory pathways. Integrative analysis of network pharmacology and transcriptomics indicated the neutrophil extracellular trap (NET) formation as a key pathway. Further analysis showed that ZQD disrupted the neutrophil recruitment environment by decreasing hypoxia-inducible factor-1α, CD18, and intercellular adhesion molecule-1 levels and downregulating NET-related markers (citrullinated histone H3, myeloperoxidase, and neutrophil elastase). Finally, these results were confirmed in a lung metastasis model. This is the first study designed to analyze the material basis of ZQD responsible for its effect on NSCLC. Our results indicate that the mechanisms of action of ZQD involve impeding neutrophil recruitment and activation, as well as reducing the levels of NETs-related markers. These suggest the potential of ZQD in suppressing NETs formation or release, inhibiting NSCLC progression and metastasis.

Introduction

Lung cancer is the leading cause of cancer-related mortality, constituting 18% of all cancer fatalities. ¹ Non-small-cell lung cancer (NSCLC) largely contributes (80%-85%) to lung cancer cases. ² Cytotoxic chemotherapy remains the primary treatment regimen for most patients with NSCLC. In addition, radiotherapy, targeted therapy, and immunotherapy are vital treatments. However, the 5-year survival rate of NSCLC is 26.4%, ³ and patients exhibit poor survival outcomes owing to cancer recurrence and metastasis. Hence, well-established therapeutic approaches for NSCLC are crucial.

The pro-oncogenic inflammation ⁴ and highly heterogeneous immune microenvironment ⁴ are vital features of NSCLC, which participate in tumor progression, relapse, and metastasis, causing the poor response to antitumor therapy in many patients with NSCLC. Tumor cells release immunosuppressive factors into their environment, inducing the polarization of innate immune cells (such as macrophages and neutrophils) to pro-oncogenic phenotypes ⁵ and the exhaustion of adaptive immune cells (including CD4⁺ and CD8⁺ T cells); these weaken the immune response and cause tumor immune escape. ⁶ Recent studies have implicated neutrophils in tumor progression and metastasis. By analyzing the immune landscape in the tumor tissues of 31 patients with NSCLC, Guo et al ⁷ revealed that the extent of neutrophil extracellular traps (NETs) infiltration was associated with disease progression after PD-1 antibody treatment, while decreased levels of neutrophil/CD8⁺ T cells and neutrophil/CD3⁺ T ratios were associated with prolonged progression-free survival. In their analysis of the tumor and blood samples of 34 patients diagnosed with NSCLC, including those with and without brain metastasis, Chen et al ⁸ discovered NETs formation within the metastatic niche, which promoted epithelial-mesenchymal transition (EMT) and metastasis. Neutrophils are recruited to the tumor microenvironment (TME) and polarized into the anti-inflammatory N2 phenotype with a prolonged lifespan. ⁹ N2 neutrophils are stimulated by inflammatory factors, activating a stepwise sequence of cell events to release NETs, a web-like structure comprising DNA, contents of granules, and cytokines. This process is otherwise termed “NETosis.” NETosis follows strict cellular mechanisms and is conserved among different species, ¹⁰ implying that strategies targeting this process are translational. NETs inhibit the activity of immune effector cells and induce the differentiation of immunosuppressive cells.^11,12 NETs constitute the “culprits” that induce tumor immune escape. In addition, NETs stimulate dormant cancer cells, ¹³ as well as promote the advancement of orthotopic cancer and its aggressiveness. The granule proteins degrade the extracellular matrix (ECM) and promote tumor cell shedding and metastasis from the primary tumor site. ¹³ Furthermore, patients with lung cancer exhibit elevated blood neutrophil levels and a high neutrophil/lymphocyte ratio. ¹⁴ Therefore, focusing on neutrophil and NET inhibition is an effective approach to boosting the immune system and preventing NSCLC growth and metastasis.

Ze-qi decoction (ZQD) is a classic formula comprising Zeqi (Euphorbia helioscopia), Shijianchuan (Chinese Sage Herb), Huangqin (Scutellariae radix), Baiqian (Cynanchum glaucescens), Guizhi (Cinnamomi ramulus), Renshen (Ginseng radix et rhizoma), Banxia (Pinelliae rhizoma), Shengjiang (Zingiberis rhizoma recens), and Gancao (Glycyrrhizae radix et rhizoma). Clinical research has confirmed the efficacy of ZQD in treating lung cancer, particularly demonstrating its effects in chemotherapy enhancement, pleural effusion reduction, and immune system regulation. Basic studies have demonstrated that ZQD regulates the cell cycle and induces apoptosis through the PI3K/Akt/p53 pathway. ¹⁵ Furthermore, ZQD improves immunosuppression by decreasing the activity of granulocytic myeloid-derived suppressor cells to exert antitumor effects.^16,17 In addition, ZQD upregulates M1 macrophage expression, reverses M1/M2 polarization, and enhances the therapeutic effects of PD-1 antibodies. ¹⁸ However, the specific mechanisms of how ZQD modulates the immune system remain unclear, and the pharmacodynamic basis underlying its efficacy is unknown.

Our previous study confirmed the anti-NSCLC effect of ZQD, highlighting that it inhibited tumor growth and exhibited the potential of hindering metastasis. ¹⁶ In this study, we confirmed the anti-NSCLC efficacy of ZQD in both subcutaneous model and lung metastasis model. Further efforts identified the effective components of ZQD and revealed that ZQD blocked neutrophils recruitment and reduced NETs formation. These findings delivered convincing evidence that ZQD was a potent NETs inhibitor and established the groundwork for applying ZQD in NSCLC therapy.

Materials and Methods

Chemicals and Reagents

Lewis lung carcinoma (LLC, RRID: CVCL_4358) cells were purchased from the Chinese Academy of Sciences cell bank (Shanghai, China). Cisplatin was supplied by Qilu Pharmaceutical Co., Ltd. (Jinan, China). The primary antibodies, anti-intercellular adhesion molecule-1 (ICAM-1) (Cat# A24648, RRID: AB_3719455) and anti-phospho nuclear factor kappa B (NF-κB) p65-S536 (p-p65, Cat# AP1294, RRID: AB_3099756), were acquired from ABclonal (Wuhan, China). CD18 (Cat# 10554-1-AP, RRID: AB_2877736) and neutrophil elastase (NE, Cat# 27642-1-AP, RRID: AB_2918126) antibodies were obtained from Proteintech (Wuhan, China). Myeloperoxidase (MPO, Cat# AB208670, RRID: AB_2864724), histone H3 (citrulline R17) (CitH3, Cat# AB219407, RRID: AB_3665807), and hypoxia-inducible factor (HIF)-1α (Cat# AB179483, RRID: AB_2732807) were purchased from Abcam (Cambridge, UK). Ly6G (Cat# sc-53515, RRID: AB_783639) was acquired from Santa Cruz Biotechnology (Santa Cruz, CA, USA). The interleukin (IL)-6 (Cat# EK206, RRID: AB_2935866), IL-1β (Cat# EK201B, RRID: AB_2934104), tumor necrosis factor (TNF)-α (Cat# EK282, RRID: AB_2934106), interferon (IFN)-γ (Cat# EK280, RRID:AB_3719457), and MPO (Cat# EK2133, RRID:AB_3719456) enzyme-linked immunosorbent assay (ELISA) kits were obtained from MultiScience (Shanghai, China).

Preparation of ZQD

ZQD comprises 9 traditional Chinese herbs: Herba Euphorbiae helioscopiae (Ze qi) (30 g) (Voucher number: SXTCM0005114), Herba Salviae chinensis (Shi jian chuan) (30 g) (Voucher number: KUN1267389), Scutellariae radix (Huang qin) (15 g) (Voucher number: SXTCM0007484), Pinelliae rhizoma (Ban xia) (15 g) (Voucher number: SXTCM0007539), Cynanchum glaucescens (Bai qian) (15 g) (Voucher number: IBSC0518685), Ginseng radix et rhizoma (Ren shen) (10 g) (Voucher number: CCAU0000697), Zingiberis rhizoma recens (Sheng jiang) (10 g) (Voucher number: CCAU0000612), Cinnamomi ramulus (Gui zhi) (6 g) (Voucher number: GZTM0013239), and Radix Rhizoma Glycyrrhizae (Gan cao) (6 g) (Voucher number: SXTCM0008038). Specific information about these herbs is presented in Table 1. All herbs were provided by the Affiliated Hospital of Shandong University of Traditional Chinese Medicine (TCM) (Jinan, China). After boiling over high heat, the herbs were decocted over low heat for 1 and 0.5 hour to obtain 2 infiltrates. Both infiltrates were mixed and concentrated in a rotary evaporator (N-1300-W, EYELA, Tokyo, Japan).

Table 1.

Herbs Composition of ZQD.

Latin name	Chinese name	English name	Plant part	Composition
Euphorbia helioscopia Linn	Ze qi	Euphorbiae Helioscopiae	Herba	21.9%
Salvia chinensis Benth	Shi jian chuan	Chinese Sage Herb	Herba	21.9%
Scutellaria baicalensis Georgi	Huang qin	Scutellariae Radix	Radix	10.9%
Pinellia ternata (Thunb.) Breit	Ban xia	Pinelliae Rhizoma	Tuber	10.9%
Cynanchum glaucescens (Decne.) Hand.-Mazz	Bai qian	Cynanchum glaucescens	Rhizome	10.9%
Panax ginseng C. A. Meyer	Ren shen	Ginseng Radix Et Rhizoma	Radix	7.3%
Zingiber officinale Rosc	Sheng jiang	Zingiberis Rhizoma Recens	Rhizome	7.3%
Cinnamomum cassia Presl	Gui zhi	Cinnamomi Ramulus	Twig	4.4%
Glycyrrhiza uralensis Fisch	Gan cao	Radix Rhizoma Glycyrrhizae	Rhizome	4.4%

Animals

Jinan Pengyue Experimental Animal Co., Ltd. (Jinan, China) supplied 5-week-old male SD rats (RRID: RGD_70508) and 4-week-old male C57BL/6J mice (RRID: IMSR_JAX:000664). All animal experiments were conducted per the guidelines of the Animal Ethics Committee of the Affiliated Hospital of Shandong University of Traditional Chinese Medicine (Approval number: AWE-2022-080, SDSZYYAWE20230822002). The animals were maintained under standard breeding conditions at 25°C ± 2°C and 50% to 60% humidity with a 12-h light/dark cycle.

Drug Administration and Sample Collection

Male SD rats (5-week-old) were assigned to the blank control or ZQD-treated group. The mass of each herb was converted into a dose according to the body surface area of rats, which was 6.3 times that of humans; the high dose was 2 times the equivalent dose of humans and mice, and the liquid medicine was concentrated to a volume of 2.5 mL per rat. Rats in the ZQD-treated group were administered a high ZQD dose (28.85 g/kg/day) twice daily, whereas those in the blank group were administered double-distilled water of the same volume. On the 21st day after intragastric administration, rats were fasted for 12 hours with access to water only. Blood samples were collected for serum analysis after 1 hour of intragastric administration on the 22nd day.

Furthermore, 6-week-old C57BL/6J mice received a subcutaneous injection of 100 µL of phosphate-buffered saline (PBS, Cat# G4202-100ML, Sevicebio, Wuhan, China) containing 5 × 10⁵ LLC cells into their right axilla. The mass of each herbal medicine was converted into a dosage according to the body surface area of mice, which was 9.1 times that of human; the high dose was 2 times that of human and mouse equivalent dose, the low dose was 1/2 that of human and mouse equivalent dose, and the liquid medicine was concentrated to a volume of 0.2 mL per mouse. After 3 days, the experimental mice were randomly assigned to 4 groups: model, cisplatin (positive control), ZQD-low (L) (10.67 g/kg), or ZQD-high (H) (42.67 g/kg). The mice in the cisplatin group were administered 2 mg/kg cisplatin intraperitoneally every 3 days. The weights and tumor sizes were measured every 3 days. The tumor volume was determined using the following formula:

$$\mathrm{tumor}\mathrm{volume}=\mathrm{length}\times {\mathrm{width}}^2/2$$ (1)

All mice were euthanized on the 22nd day after modeling. Tumor tissues, blood samples, and major organ samples were analyzed.

Survival Analysis

Survival analysis was conducted for the 4 groups of mice to determine whether ZQD prolonged the survival time. Mice were monitored daily in their natural environment, and the time of death was recorded, which was restricted to 60 days after starting the treatment. The survival curve was generated using GraphPad Prism 8.0 (RRID: SCR_002798).

UPLC-HRMS Conditions

We placed 600 μL of ZQD sample into a centrifuge tube (1.5 mL), added 400 μL of pure methanol, and vortexed. Subsequently, we collected 200 μL of the above solution, to which 200 μL of 40% methanol aqueous solution was added. We vortexed the solution and centrifuged it at 16 000 × g and 4°C for 15 min, collected the supernatant, and obtained a ZQD stock solution sample. Furthermore, we collected 200 μL of a serum sample, to which 800 μL of methanol was added. We vortexed the sample for 60 s, allowed it to stand at −20°C for 30 min, and centrifuged it at 16 000 × g and 4°C for 20 min. We dried the supernatant under vacuum, added 100 μL of 40% methanol aqueous solution to the residue, and vortexed and centrifuged the mixture at 16 000 × g and 4°C for 15 min. We collected the supernatant and obtained the serum sample.

A Thermo UPLC Vanquish system was used for chemical identification. Chromatography was performed using an ACQUITY UPLC HSS T3 column (2.1 × 100 mm, 1.8 µm). Mobile phases A and B comprised 0.1% formic acid aqueous solution and 0.1% formic acid in acetonitrile, respectively. The gradient elution was performed as follows: 0 to 17 min, 95% A to 2% A; 17 to 17.2 min, 2% A to 95% A; 17.2 to 20 min, 95% A. The flow rate was maintained at 0.3 mL/min.

A Thermo Q-Exactive HFX mass spectrometer was used in positive and negative ion modes. High-energy collision dissociation fragmentation was performed on all parent ions in a specific narrow range to obtain the full scan spectrum of the daughter ions (dd-MS2).

The original data were matched to those in the standard spectral database for structural identification, and ingredients with a score >7 were retained.

Network Pharmacology Analysis

Acquisition of Drug and Disease Targets

The basic information of serum-migrating compounds was retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) (RRID: SCR_004284) and imported into the encyclopedias of the Swiss Target Prediction (http://www.swisstargetprediction.ch/) (RRID: SCR_023756), ETCM (http://www.tcmip.cn/ETCM/), and BATMAN-TCM (http://bionet.ncpsb.org.cn/batman-tcm/) databases to obtain, merge, and remove duplicates, as well as eliminate the redundant targets of these candidate active components.

Genes linked to “non-small cell lung carcinoma” were identified using the databases GeneCards (http://www.genecards.org/) (RRID: SCR_002773), Disease Gene Network (http://www.disgenet.org/) (RRID: SCR_006178), and Comparative Toxicogenomics Database (http://ctdbase.org/) (RRID: SCR_006530). Genes that appeared in at least 2 databases were finally screened as disease targets.

The online VENN tool (https://www.bioinformatics.com.cn/) was used to integrate the targets of disease and components to acquire the therapeutic targets of ZQD in NSCLC. VENN diagrams were subsequently generated.

All protein names were standardized according to the UniProt database (https://www.uniprot.org/) (RRID: SCR_002380).

Identification of Core Targets and Functional Analysis

Using intersection targets, the protein-protein interaction (PPI) network was constructed in the STRING database (https://string-db.org) (RRID: SCR_005223) and subsequently loaded into Cytoscape 3.8.2 (RRID: SCR_003032). The plug “CytoNCA” was used to analyze the PPI network topology using 6 parameters, namely degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), local mean connection-based method (LAC), and network centrality (NC), to filter out nodes with low parameters. Genes with a DC higher than the median were initially used to create a sub-network. Subsequently, genes with DC values exceeding twice the median and with BC, CC, EC, LAC, and NC values greater than their respective medians were further filtered within this sub-network. The items in this sub-network were viewed as the core targets. Next, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (RRID: SCR_001120) and Gene Ontology (GO) enrichment analyses (RRID: SCR_002811) were performed on these targets. Finally, some items and pathways were visualized as bar and bubble charts.

Identification of the Crucial Active Components

The component-target-pathway network was analyzed using the “Network Analyzer” tool. Compounds with topological parameters DC, CC, and BC ranking in the top 50 were identified as crucial ZQD components. Subsequently, the ZQD core component-target-pathway network was visualized using Cytoscape 3.8.2.

Validation of Component-Target Binding Affinity

The crystal structures of the key ZQD compounds were obtained from PubChem, processed using AutoDockTools (RRID: SCR_026401), and saved as PDBQT files. The crystal structures of the target proteins were obtained from the Protein Data Bank (https://www.rcsb.org) (RRID: SCR_012820) and processed using AutoDockTools. The processing involved eliminating water molecules, adding hydrogenation and charges, and saving as PDBQT files. Molecular docking was performed using AutoDock Vina (RRID: SCR_011958). The results were partially visualized using Pymol software (RRID: SCR_000305).

Transcriptomic Analysis

The total RNA was extracted and purified from tumor tissues using a TRIzol reagent (Magen). A Nanodrop ND-2000 (RRID: SCR_018042) was used to assess the purity of the RNA samples (Thermo Scientific, USA), and the integrity was determined using an Agilent 4150 Bioanalyzer. RNA fragments were purified to construct a complementary DNA library, the quality of which was assessed using an Agilent Bioanalyzer 4150, followed by PE150 sequencing on the NovaSeq 6000 (or MGISEQ-T7) platform (RRID: SCR_016387).

Hematoxylin-Eosin (HE), Immunofluorescence, and Immunohistochemical Staining

Tumor tissues were fixed in 4% paraformaldehyde and embedded in paraffin, followed by staining with HE. After being deparaffinized and rehydrated, sections were stained with hematoxylin (Sigma) for 20 s and subsequently with eosin (Sigma) for 20 s. Next, the sections were dehydrated and sealed. Images were obtained using a pathology section scanner (WS-10 digitized panoramic scanner, Wisleap, Beijing).

Mouse tumor and lung tissues were immersed in 4% paraformaldehyde and embedded in paraffin. Following antigen repair and sealing, tissue sections were incubated with a CitH3 rabbit (1:200) or Ly6G rat (1:100) antibody at 4 °C. Subsequently, the slices were incubated for 1 hour at room temperature with goat anti-rat IgG H&L-AF488 (green, 1:200) and goat anti-rabbit IgG (H+L)-Alexa Fluor 594 (red, 1:200) secondary antibodies. Finally, the slices were scanned, and the images obtained were analyzed with a digital slide scanner (Panoramic MIDI II, 3DHISTECH).

After blocking, sections were incubated overnight at 4°C with an MPO rabbit antibody (1:500), followed by a 40-min incubation at room temperature with a secondary antibody (dilution ratio 1:200). Following hematoxylin counterstaining, an appropriate amount of diaminobenzidine working liquid was added for color development. The immunohistochemically stained slides were scanned using a pathology section scanner (WS-10 Digitized Panoramic Scanner, Wisleap, Beijing).

ELISA

Quantitative analyses for IL-6, IL-1β, TNF-α, IFN-γ, and MPO were conducted using ELISA kits, following the corresponding manufacturers’ instructions.

Western Blotting

Tumor tissue proteins were obtained using a radioimmunoprecipitation assay lysis buffer containing phenylmethanesulfonyl fluoride (100 mM) and a phosphatase inhibitor cocktail (100 mM). The proteins were separated using 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred onto 0.2-mm polyvinylidene difluoride membranes. The membranes were soaked in 5% nonfat dry milk at room temperature for 2 hours, followed by overnight incubation with primary antibodies at 4°C. Subsequently, the membranes were incubated with secondary anti-rabbit peroxidase-linked antibodies (1:10000–1:20000, Cat# GAR0072, RRID: AB_2827833, MultiSciences, Hangzhou, China) at room temperature for 1 hour. The chemiluminescent signal was detected using a gel imaging analysis system, and ImageJ software was used to analyze the gray values of the proteins.

Construction of a Mouse Lung Cancer Metastasis Model

C57BL/6J mice (6-week-old) were xenografted via the tail vein with 200 µL of LLC cell suspension containing 1 × 10⁶ cells. The groups and treatments were the same as those used in the subcutaneous tumor model. After 21 days of modeling, the mouse lungs were isolated, and the number of metastatic nodules was determined.

Statistical Analysis

Results were presented as the mean ± standard error. Statistical analyses were performed with GraphPad Prism 8.0 using the one-way analysis of variance for multiple sample group comparisons. Data were compared using a post-event least significant difference test for homogeneous variance; otherwise, Dennett’s test and multiple comparative analyses of minimum significant differences were used. Survival analysis was performed using the Log-rank test, and the Kaplan–Meier curves were plotted to show the survival time. Significance levels were defined as ^*P < 0.05 (statistically significant), ^**P < 0.01 (extremely significant), and ^***P < 0.001 (highly significant).

Results

ZQD Alleviated Tumor Growth and Prolonged Survival in LLC Tumor-Bearing Mice

To assess the therapeutic effects of ZQD, LLC tumor-bearing mice were treated with ZQD, using cisplatin as the positive control. Mice in the ZQD group exhibited more rapid growth than did those treated with cisplatin, suggesting that ZQD exerted lesser effects on body weight than did cisplatin (P < 0.05) (Figure 1A). As expected, subcutaneous tumors in the mice treated with ZQD exhibited relatively slow growth (Figure 1B). After 21 days of treatment, the subcutaneous tumor was peeled off and weighed. The macroscopic observation showed that the subcutaneous tumor volume after ZQD intervention was significantly smaller than that in the model group, and the tumor suppression effect was dose-dependent. The subcutaneous tumor volume of mice in the high-dose group was close to that in the cisplatin treatment group (Figure 1C). Weight measurement showed that the subcutaneous tumor weights of the mice in the high-dose ZQD group were significantly smaller than those in the model (P < 0.001) and low-dose groups (P = 0.0071) and slightly heavier than those in the cisplatin group (Figure 1D). The survival analysis confirmed that the tumor-bearing mice had a prolonged survival time under ZQD and cisplatin treatments (Figure 1E). Following ZQD and cisplatin treatments, the tumor tissues displayed a necrotic area, the tumor cell density and the pleomorphic nuclear division declined, and the nuclei became shallow, as revealed by staining (Figure 1F). These indicated that ZQD exhibited a pronounced antitumor effect against NSCLC.

Figure 1.

Pharmacodynamic validation of the effect of Ze-qi decoction (ZQD) on non-small cell lung cancer (NSCLC). The body weights (A) and tumor volumes (B) of mice were measured every 3 days. ZQD had minor side effects on the mice’s body weights and significantly affected tumor growth. Tumors were obtained after 21 days of treatment (C), and tumor weights (D) were measured. (E) Comparison of survival times among the model, cisplatin, ZQD-low (L), and ZQD-high (H) groups of mice within 60 days after modeling. (F) Hematoxylin-eosin (HE) staining of the tumor tissues in the model, cisplatin, ZQD-L, and ZQD-H groups.

Abbreviation: ns, no significance.

*Cisplatin/ZQD-L/ZQD-H versus Model.

^*P < 0.05, ^**P < 0.01, ^*** P < 0.001.

Serum Pharmacological Analysis of ZQD Using UPLC-HRMS

Given that the material basis for the effect of ZQD remained unelucidated, the relevant serum-migrating components of ZQD were analyzed using UPLC-HRMS to explore its pharmacodynamic material basis against NSCLC. The MS spectra in the positive and negative ion modes (Figure 2A and B) revealed 2335 chemical components in the water extract of ZQD. The 6 most prevalent components were flavonoids, benzene and substituted derivatives, carboxylic acids and their derivatives, fatty acyl groups, organic oxygen compounds, and isoprene lipids. Further details are provided in Supplemental Table S1. The comprehensive analysis and comparison of secondary or tertiary fragment ion-specific compounds and literature search via PubMed (https://pubmed.ncbi.nlm.nih.gov/) revealed 297 serum-migrating components of ZQD (Figure 2C-F and Supplemental Table S2). According to their relative abundance in drug-containing serum, the top 40 serum-migrating compounds with the highest response strengths are presented in Table 2. The compounds included 11-keto-beta-boswellic acid, vibsanin B, quinate, baicalin, liquiritigenin, itaconic acid, and pantothenic acid.

Figure 2.

BPC chromatograms of each sample in the positive and negative ion modes. The BPC chromatograms of the ZQD sample in the positive (A) and negative (B) ion modes. The top 40 serum-migrating compounds with the highest response strength are marked in chromatographic peaks. BPC chromatograms of blank (C) and drug-containing (D) serum samples in the positive ion mode. BPC chromatograms of blank (E) and drug-containing (F) serum samples in the negative ion mode. The top 40 serum-migrating compounds with the highest response strength are marked in chromatographic peaks of drug-containing serum in positive and negative ion modes.

Table 2.

UPLC-HRMS Data of the Identified Components of Ze-qi Decoction Extract Absorbed in Rat Plasma (Top 40).

No.	RT(min)	Fomula	m/z	Identification	Class	ppm	adduct
1	0.917405833	C7H12O6	191.0550891	Quinate	Organooxygen_compounds	5.1	[M-H]-
2	0.927775	C4H6O6	149.0083637	L-Tartaric acid	Organooxygen_compounds	7.5	[M-H]-
3	2.612833333	C5H6O4	129.0181411	Itaconic acid	Fatty_Acyls	8.1	[M-H]-
4	2.692966667	C6H12O4	147.0649358	Pantoic acid	Fatty_Acyls	6.9	[M-H]-
5	4.043541667	C8H8O5	183.0288073	4-O-Methylgallic acid	Benzene_and_substituted_derivatives	4.2	[M-H]-
6	4.412858333	C9H10O3	165.0547678	Benzenepropanoic acid, 4-hydroxy-	Phenylpropanoic_acids	5.7	[M-H]-
7	4.451975	C7H12O5	175.0603441	alpha-Isopropylmalate	Fatty_Acyls	5	[M-H]-
8	4.639416667	C12H23NO4	246.1697844	Isovalerylcarnitine	Fatty_Acyls	0.9	[M+H]+
9	4.648658333	C7H6O4	153.018354	Gentisic acid	Benzene_and_substituted_derivatives	5.6	[M-H]-
10	4.705833333	C16H20O9	355.1032647	1-O-Feruloylglucose	Cinnamic_acids_and_derivatives	3	[M-H]-
11	4.883258333	C10H10O4	193.0496473	Ferulic acid	Cinnamic_acids_and_derivatives	4.4	[M-H]-
12	4.888841667	C9H9NO3	357.1089593	Hippuric acid	Benzene_and_substituted_derivatives	0.3	[2M-H]-
13	5.42485	C12H18O4	227.1276594	12-Hydroxyjasmonic acid	Fatty_Acyls	0.9	[M+H]+
14	5.539916667	C7H6O4	153.0183558	2,4-Dihydroxybenzoic acid	Benzene_and_substituted_derivatives	6.2	[M-H]-
15	5.54585	C8H8O3	151.0390662	2,4-Cresotic acid	Benzene_and_substituted_derivatives	6.4	[M-H]-
16	5.575166667	C26H28O13	547.1452863	5,7-dihydroxy-2-phenyl-6-[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]-8-(3,4,5-trihydroxyoxan-2-yl)-4H-chromen-4-one	Flavonoids	1.5	[M-H]-
17	5.7782	C15H12O4	257.0805676	Liquiritigenin	Flavonoids	0.6	[M+H]+
18	6.375875	C8H8O3	153.0546484	2-Methoxybenzoic acid	Benzene_and_substituted_derivatives	0.1	[M+H]+
19	6.456583333	C16H22O9	195.0656226	2-O-.beta.-D-Glucosyloxy-4-methoxybenzenepropanoic acid	Organooxygen_compounds	3.5	[M-H-C6H10O5]-
20	6.491866667	C7H6O3	137.0231362	Salicylic acid	Benzene_and_substituted_derivatives	8.3	[M-H]-
21	6.531008333	C9H10O3	165.0548312	2-Hydroxybenzenepropanoic acid	Phenylpropanoic_acids	5.4	[M-H]-
22	6.532916667	C19H19NO3	121.0647503	ML-098	Indoles_and_derivatives	9.2	[M-H-C11H9NO2]-
23	6.6367	C11H16O3	197.117121	Loliolide	NA	0.4	[M+H]+
24	7.178683333	C21H18O11	445.0773716	Baicalin	Flavonoids	0.4	[M-H]-
25	7.436866667	C21H20O10	431.0981242	Cosmosiin	Flavonoids	1.3	[M-H]-
26	7.664216667	C16H12O5	285.0754653	Wogonin	Flavonoids	1.1	[M+H]+
27	7.734883333	C22H20O11	461.1075251	Baicalin methyl ester	Flavonoids	0.7	[M+H]+
28	7.98775	C21H18O11	445.0771775	Apigenin 7-glucuronide	Flavonoids	2	[M-H]-
29	8.0417	C22H20O11	459.0929149	Oroxindin	Flavonoids	1.3	[M-H]-
30	8.093983333	C21H18O10	429.0822363	5-Hydroxy-4-oxo-2-phenyl-4H-chromen-7-yl .beta.-D-glucopyranosiduronic acid	Flavonoids	3.3	[M-H]-
31	8.153233333	C14H10O5	257.0446843	Norlichexanthone	Benzopyrans	1.5	[M-H]-
32	8.199616667	C22H20O12	477.102522	6-O-Methylscutellarin	Flavonoids	0.5	[M+H]+
33	8.412466667	C22H20O11	459.0929875	Oroxyloside	Flavonoids	2	[M-H]-
34	9.374466667	C10H10O3	179.070254	2-Methoxycinnamic acid	Cinnamic_acids_and_derivatives	0.2	[M+H]+
35	11.66670833	C16H12O5	283.0608037	Oroxylin	Flavonoids	1.4	[M-H]-
36	11.97043333	C22H22O10	285.0756256	Prunetinoside	Isoflavonoids	1.8	[M+H-C6H10O5]+
37	14.1929	C17H24O4	291.1600329	Lasiodiplodin	Macrolides_and_analogues	0.6	[M-H]-
38	14.8919	C25H36O5	299.2002943	Vibsanin B	Prenol_lipids	0.8	[M+H-C5H10O3]+
39	14.91605	C30H46O4	453.3360284	11-keto-beta-boswellic acid	Prenol_lipids	0.9	[M+H-H2O]+
40	15.550675	C18H30O3S	325.1837097	4-Dodecylbenzenesulfonic acid	Benzene_and_substituted_derivatives	1.2	[M-H]-

Pharmacological Analysis Predicted the Potential Targets of ZQD Against NSCLC

We identified 1811 protein targets of 297 compounds (Supplemental Table S2) and 6583 NSCLC-related targets (Figure 3A). The intersected compound and disease targets (1124 overlapping targets) were identified as potential therapeutic targets (Figure 3B) for further topological analysis. A PPI network was constructed using 169 core targets identified from the above overlapping targets (Supplemental Table S3 and Figure 3C), and genes (including SRC, AKT1, IL-6, and RELA) showed great significance in this network. The KEGG pathways included NET formation, the TNF signaling pathway, and the NF-κB signaling pathway; the biological processes (BP) comprised immune and inflammatory responses, and the molecular functions (MF) included cytokine activity (Figure 3D and E). Notably, these significant targets showed associations with neutrophils and NETs, serving as upstream or downstream regulators of NETs formation. ZQD possibly regulated the systemic immune response and improved the local microenvironment by targeting NETs.

Figure 3.

Pharmacological investigation and functional enrichment analysis. (A) VENN diagram showing 6583 NSCLC-related genes in total. (B) Overlapping targets of the serum-migrating components of ZQD and NSCLC. (C) Protein-protein interaction network of the core targets of ZQD in NSCLC. (D) Twenty enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of 169 core targets are shown as a bubble plot. (E) The biological process, cell component, and molecular function analyses of 169 core targets are shown as a bar graph.

According to the pharmacological analysis and chemical identification, 16 candidate ingredients with the top 50 values of DC, BC, and CC were filtered as the key active components of ZQD against NSCLC. Table 3 presents the detailed component information, with liquiritigenin, vibsanin B, and 11-keto-beta-boswellic acid exhibiting high response intensity in UPLC-HRMS. Thus, we speculated that the 3 compounds were the primary bioactive ingredients of ZQD mediating its therapeutic effects against NSCLC. A compound-target-pathway network was established based on these key ingredients (Figure 4). The core components of liquiritigenin, vibsanin B, and 11-keto-beta-boswellic acid and the enriched pathway (NET formation) exhibited high degree values. The crystal structures of the 3 components are shown in Figure 4.

Table 3.

Topology Information Table of Core Components.

	PubChem CID	Compound name	Molecular formula	Degree centrality	Betweeness centrality	Closeness centrality
1	Cid_68071	Pinocembrin	C15H12O4	140	0.015945745	0.384719956
2	Cid_114829	Liquiritigenin	C15H12O4	140	0.014984161	0.38388497
3	Cid_7424	3,5-Dihydroxybenzoic acid	C7H6O4	138	0.013362863	0.383468835
4	Cid_75528857	Vibsanin B	C25H36O5	120	0.040236707	0.377938034
5	Cid_6419753	D4476	C23H18N4O3	119	0.046803839	0.376931273
6	Cid_5281166	Jasmonic acid	C12H18O3	114	0.021162761	0.373548046
7	Cid_9890209	Ursonic acid	C30H46O3	109	0.014728683	0.373154008
8	Cid_73309	Echinocystic acid	C30H48O4	107	0.009901687	0.373942918
9	Cid_5280960	Naringenin chalcone	C15H12O5	104	0.010799781	0.375132556
10	Cid_10114	Glycyrrhetic acid	C30H46O4	102	0.011297449	0.373154008
11	Cid_64945	Ursolic acid	C30H46O4	100	0.011328982	0.371976866
12	Cid_10154	Boldine	C19H21NO4	98	0.027778109	0.373548046
13	Cid_6989	Thymol	C10H14O	97	0.052011272	0.373548046
14	Cid_14412557	Feruloyl o-methyldopamine	C19H21NO5	95	0.01811972	0.373350923
15	Cid_480764	Sophoraflavanone B	C20H20O5	94	0.018589853	0.372368421
16	Cid_9847548	11-keto-beta-boswellic acid	C30H46O4	93	0.011793913	0.371781398

Figure 4.

ZQD core compound-core target-pathway network. The network diagram of 16 core components, relevant core targets, and pathways constructed using Cytoscape 3.8.2. The crystal structures of the 3 components are indicated in the lower part of the network.

Transcriptomics Revealed that NETs Formation Is a Significant Molecular Mechanism Underlying the Effect of ZQD on NSCLC

Transcriptomic analysis was performed to identify differentially expressed genes (DEGs) between the mice in the model and ZQD groups. A total of 253 DEGs were identified in the ZQD-H and model groups, including 167 upregulated and 86 downregulated genes (Figure 5A). The heatmap demonstrated that the gene expression patterns differed significantly between the 2 groups. Functional exploration was conducted using GO and KEGG analyses. Based on the P-values, the top 30 GO terms (for BP and MF) were selected and presented as bar plots (Figure 5B). Most entries were immune-associated and included the regulation of TNF and cytokine production, as well as immune receptor activity. KEGG enrichment analysis revealed 29 significant KEGG pathways (Figure 5C) that are mostly associated with cancer, immunology, and metabolism, consistent with the GO analysis. NETs formation was equally enriched in the KEGG analysis, confirming its potential role as a targeted signaling pathway for ZQD in NSCLC.

Figure 5.

Transcriptome sequencing and functional enrichment analyses. (A) The hierarchical clustering heatmap of differentially expressed genes (DEGs) between the model and ZQD-H groups. DEGs were identified using fold-change (FC) ≥ 1.5 or ≤ 0.67 and P ≤ 0.05. Visualization of Gene Ontology (GO) (B) and KEGG (C) enrichment analyses of the DEGs.

Molecular Docking Confirmed that the Core Components of ZQD Effectively Regulated NETs Formation

The integration of the pharmacological and transcriptomic analyses suggested the signaling pathway of NETs formation as the anti-NSCLC mechanism of ZQD. The crucial targets screened in the pharmacological analysis and the DEGs identified in transcriptomics are shown in yellow in Figure 6A. Concurrently, molecular docking was conducted to predict the interaction of 16 core components with proteins HIF1A, CD18, and MPO that are associated with neutrophil recruitment and NETs formation. The affinities of docking results were below -5 kcal/mol (Figure 6B). In particular, 11-keto-β-boswellic acid exhibited excellent binding energies (<−12 kcal/mol) with MPO, HIF-1α, and CD18, with the binding sites demonstrating clear functional relevance. This compound (11-keto-β-boswellic acid) bound to key residues within the active channel of MPO (eg, ARG-489), potentially directly inhibiting its enzymatic activity. The binding of 11-keto-β-boswellic acid to the C-terminal transcription activation domain of HIF-1α (eg, SER-751) interfered with transcription complex assembly, while its binding to the ligand-binding interface of the βI domain in CD18 (eg, ARG-256 and LYS-280) blocked the interaction of CD18 with ICAM-1. This multi-target, multi-site precision binding pattern structurally underpins the mechanism of action ZQD in inhibiting metastasis by suppressing neutrophil recruitment, activation, and NETs formation. The Root Mean Square Deviation values of < 2Å (Figure 6C) suggest a high accuracy of binding results. The binding modes of liquiritigenin, vibsanin B, and 11-keto-beta-boswellic acid with corresponding proteins were visualized (Figure 6D).

Figure 6.

Molecular results of neutrophil extracellular trap (NET)-related proteins and the core components of ZQD. (A) The pathway diagram of NETs formation. The targets and pathways acquired from pharmacological and transcriptomic analysis are marked in yellow. Heatmap matrices showing the affinities (B) and Root Mean Square Deviation values (C) between NET-associated proteins and core components. (D) Visualization of the docking results of 3 target proteins with liquiritigenin, vibsanin B, and 11-keto-beta-boswellic acid.

ZQD Disrupted the Neutrophil Recruitment Environment and Reduced NET-Related Markers in NSCLC

HIF1A overexpression in the TME promoted the remodeling of the phenotype of tumor-associated neutrophils (TANs) (from N1 to N2), contributing to NETs formation and release. Combining the pharmacological and molecular docking results, the protein levels of adhesion molecule ICAM-1, β2 integrin subunit CD18, and HIF-1α were detected in the tumor tissues of NSCLC. Compared with those in the model group, the drug intervention groups showed inhibited ICAM-1 and CD18 expression and significantly downregulated HIF-1α levels (Compared with model, P < 0.001) (Figure 7A), with the high-dose ZQD exerting the highest inhibitory effect on these factors.

Figure 7.

ZQD inhibited pro-oncogenic neutrophil activation and NETs formation in NSCLC. (A) Western blotting showing that high-dose ZQD significantly downregulated the protein levels of HIF-1α, CD18, and ICAM-1, which are related to neutrophil activation, adhesion, and recruitment. (B) Immunofluorescence micrographs (200×) of the DAPI (blue fluorescence), Ly6G (green fluorescence), CitH3 (red fluorescence), and merge in model, cisplatin, ZQD-L, and ZQD-H groups. (C) Immunohistochemistry micrograph (200×) of myeloperoxidase (MPO) in the model, cisplatin, ZQD-L, and ZQD-H groups. (D) Enzyme-linked immunosorbent analysis (ELISA) of MPO showing consistent results with immunohistochemistry. (E) Western blotting showing that ZQD downregulated the expression of MPO, CitH3, and neutrophil elastase proteins. ZQD inhibited the expression of these proteins. Data are presented as mean ± SEM.

Abbreviations: ns, no significance; SEM, standard error of the mean.

*Cisplatin/ZQD-L/ZQD-H versus Model.

^*P < 0.05, ^**P < 0.01, ^*** P < 0.001.

Subsequently, we validated NETs formation and release in tumor tissues. Ly6G is a representative biomarker expressed in mouse neutrophils, while CitH3, MPO, and NE are crucial components of NETs. The fluorescence intensity of CitH3 and Ly6G became weak, and their co-location was reduced following ZQD treatment (Figure 7B), suggesting decreased neutrophil infiltration and NETs formation. The protein expression levels of CitH3, MPO, and NE in the ZQD-H group were markedly downregulated compared with those in the model group (Figure 7C-E). Moreover, the inhibitory effect was stronger than that in the cisplatin group. These results support the hypothesis that ZQD inhibits NETs formation in the TME.

ZQD Regulated the Systemic Inflammatory Landscape in NSCLC

The NETs released by neutrophils remodeled the local inflammatory microenvironment, favoring tumor growth. Therefore, we further assessed the landscape of inflammatory factors in mouse serum and the expression of p-p65. ZQD reduced pro-tumorigenic cytokines IL-6 and IL-1β and upregulated the expression of antitumor factors IFN-γ and TNF-α (Figure 8A). Concurrently, ZQD decreased the p-p65 levels, inhibiting tumor progression (Figure 8B).

Figure 8.

ZQD regulated the secretion of serum cytokines and the inflammatory pathway. (A) ELISA of interleukin (IL)-1β, IL-6, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α suggesting that high-dose ZQD inhibited the secretion of cancer-promoting inflammatory cytokines IL-1β and IL-6, as well as elevated levels of tumor suppressors IFN-γ and TNF-α. (B) The western blotting of p-p65 showing the alteration caused by ZQD: ZQD decreased the pro-inflammatory factor p-p65 protein levels. Data are presented as mean ± SEM.

Abbreviation: ns, no significance; SEM, standard error of the mean.

*Cisplatin/ZQD-L/ZQD-H versus Model.

^*P < 0.05, ^**P < 0.01, ^*** P < 0.001.

ZQD Hindered Tumor Metastasis in Lung Metastasis Models

NETs promote tumor metastasis through multiple pathways. A tail vein-injection lung metastasis model was established to confirm the blockage of NETs formation by ZQD. After 21 days of drug treatment, few nodules were observed on the surface of the lungs of the mice treated with ZQD (Figure 9A). The HE staining of the nodules confirmed higher metastases in the model group mice (Figure 9B), while the drug (cisplatin, ZQD-L, and ZQD-H)-treated groups had fewer foci of cancer cells and many inflammatory nodules (Figure 9B). NET-related proteins CitH3 and Ly6G, as well as MPO, showed decreased expression levels in the lungs after ZQD intervention compared with those in the model group (Figure 9C-E).

Figure 9.

ZQD inhibited NETs formation in lung metastasis models. (A) Representative graphs of lung metastasis nodules. ZQD efficiently inhibited the occurrence of positive pulmonary nodules in lung metastasis models. (B) Representative images of the HE staining of lung metastases in the model, cisplatin, ZQD-L, and ZQD-H groups. The rectangular box represents metastases, and the arrow indicates inflammatory nodules. (C) The immunofluorescence micrograph (200×) of NET-related biomarkers in the lung tissues from the model, cisplatin, ZQD-L, and ZQD-H groups. (D) The immunohistochemistry micrograph (200×) of MPO expressed in the lung tissues in the model, cisplatin, ZQD-L, and ZQD-H groups. (E) ELISA of MPO showing consistent results with immunohistochemistry. Data are presented as mean ± SEM.

Abbreviation: ns, no significance; SEM, standard error of the mean.

*Cisplatin/ZQD-L/ZQD-H versus Model.

^*P < 0.05.

Furthermore, a toxicological assessment was performed. HE staining revealed no evident pathological changes in the visceral organs, including the heart, lungs, kidneys, spleen, and liver (Figure 10), confirming the safety and availability of the compound.

Figure 10.

Drug toxicity evaluation of ZQD. HE staining of mouse hearts, livers, spleens, lungs, and kidneys indicating the minor toxic side effects of ZQD on the viscera of mice.

Discussion

The high morbidity and mortality rates of NSCLC have prompted the development of diverse therapeutic strategies. In contrast to targeting tumor cells with destabilized genomes alone, antitumor drugs that regulate the immune microenvironment, attenuate the immune suppressors, and enhance immune effectors to exert indirect anti-neoplastic effects have a promising clinical application. ZQD reduces malignant pleural effusion in advanced NSCLC and strengthens chemotherapy while inhibiting tumor cell growth, invasion, and metastasis and promoting apoptosis.^15,16 Notably, ZQD regulates the immune system in patients with NSCLC.^17,18 This study confirmed the promising anticancer effect of ZQD in cancer-prone mice without notable toxic side effects through the assessment of animal weights, tumor volumes, tumor weights, tumor pathology, and mouse overall survival time.

Pharmacological analysis indicated that the potential active ingredients of ZQD screened from UPLC-HRMS primarily targeted pathways associated with immune regulation, particularly neutrophil-involved immune processes. Notably, STAT3, SRC, AKT1, JUN, and RELA showed high significance and were closely associated with neutrophils or NETs formation. SRC is activated in NETs formation and contributes to their formation and release through the production of reactive oxygen species via the RAF/MEK/ERK signaling pathway. ¹⁹ The activation of PI3K/AKT is essential for MPO translocation and degranulation during NETosis. ²⁰ TLR4/NF-κB participates in neutrophil activation and neutrophil immune function; thus, its silence would hinder NETs formation. ²¹ NETs promote EMT and activate the NF-κB/NLRP3 signaling pathway to facilitate NSCLC metastasis. ²² The STAT3 signaling pathway in neutrophils contributes to the prolonged survival time of neutrophils in TME and promotes the immune checkpoint expression on CD8⁺ T cells. ²³ Transcriptomics reached the consistent conclusion that NETs formation was identified in ZQD-mediated anti-NSCLC effects. These results emphasize that neutrophil-related immune regulation is possibly an essential mechanism of action of ZQD in NSCLC and that NETs formation was a breakthrough to elucidate this process.

The UPLC-HRMS and network analysis indicated that liquiritigenin, vibsanin B, and 11-keto-beta-boswellic acid most likely mediate the anti-NSCLC activity of ZQD. Liquiritigenin, as a natural flavonoid, alleviates MMP9 expression and MPO activity, decreases the pro-inflammatory factors, and diminishes NF-κB nuclear accumulation by triggering the antioxidative defense of Nrf2. ²⁴ Vibsanin B is a macrocyclic diterpenoid natural product that significantly suppresses leukocyte transendothelial migration, a promising approach to treating inflammatory diseases. ²⁵ Additionally, 11-keto-beta-boswellic acid is a bioactive pentacyclic triterpene isolated from boswellic acids and presents an anti-inflammatory activity that targets cathepsin G in neutrophils. ²⁶ The above functions indicate involvement in the regulation of neutrophils and NETs. Molecular docking simulated the interaction between these 3 bioactive components and proteins related to NETs formation. The favorable docking results revealed the vital roles of liquiritigenin, vibsanin B, and 11-keto-beta-boswellic acid in NETs inhibition by ZQD.

In stressed microenvironments under conditions such as inflammation and cancer, immature neutrophils exhibit high plasticity. ²⁷ Neutrophils play a complex role in the TME,^28,29 particularly in the advanced stage, where they are modulated by cytokines in the TME and polarize towards the oncogenic N2 phenotype that secretes various pro-oncogenic cytokines and promotes tumor growth and metastasis. High neutrophil counts in the TME predict unfavorable survival rates. Hypermetabolism in tumor cells causes hypoxia in the TME. ³⁰ The hypoxic environment with upregulated HIF-1α expression drives neutrophil recruitment and prolongs neutrophil lifespan. ⁹ The integrin β2 protein on the neutrophil surface binds to ICAM-1 on the vascular endothelium, promoting the transmigration of neutrophils to the TME. ³¹ Based on the molecular docking results, we conducted validations across the processes of recruitment, mobilization, and NETs formation. As anticipated, ZQD inhibited HIF-1α to improve the hypoxic environment and reduced ICAM-1 and CD18 levels, disrupting neutrophil accumulation and mobilization.

NETosis represents a distinct form of cell death observed in neutrophils and differs fundamentally from apoptosis and necrosis. This process is initiated following the stimulation of neutrophils by extracellular cytokines, immune complexes, albicans, or phorbol 12-myristate 13-acetate, leading to a stepwise sequence of cellular events. ³² The process starts with the degradation of the actin cytoskeleton, followed by the release of microvesicles containing compounds, such as NE, MMP9, and MPO, creating a conducive intracellular environment for DNA escape. Subsequently, the citrullination of histone H3, chromatin decondensation, nuclear lamin meshwork disassembly, and DNA exposure are promoted by peptidylarginine deiminase 4 (PAD4).^33,34 In addition, protein kinase C alpha and cyclin-dependent kinases 4/6 (CDK4/6) phosphorylate nuclear lamins to disrupt the nuclear envelope and promote the nuclear translocation of NE to mediate histone cleavage.^35,36 In response to a specific signal, the permeability of the plasma membrane is increased, molecules of different sizes enter the cell, and the membrane ruptures to release NETs. Notably, NETs have been identified as crucial components of the TME and exhibit higher levels in the plasma samples of patients with cancer than in those of healthy individuals.^37,38 In this study, the downregulation of the NET-related biomarkers, such as CitH3, MPO, and NE, was observed in ZQD-treated tumor tissues. This implies that ZQD potentially inhibited NETs formation and remodeled the TME. However, further explorations of the upstream-specific mechanisms are required.

NETs release tumor-associated inflammatory cytokines, such as IL-6, IL-8, and IL-1β, in the TME to accelerate tumor growth. These cytokines remodel the non-inflammatory N2 phenotype of TANs ³⁹ and stimulate them to produce reactive oxygen species, initiating NETs formation ⁴⁰ and creating a vicious circle. In this study, ZQD treatment caused significantly decreased IL-6 and IL-1β levels, which blocked the above feedback loop and altered the pro-oncogenic “soil.” As pro-inflammatory cytokines, serum TNF-α and IFN-γ levels were upregulated by ZQD, which is somewhat paradoxical. This shift suggests that the increased TNF-α and IFN-γ in the antitumor event of ZQD was not a pro-tumorigenic event but rather a sign of restored antitumor immunity. Tumor cells would pervert NETosis to their advantage. In TME, NETs decrease the antitumor cytotoxicity of immune cells such as CD8⁺ T and natural killer (NK) cells by isolating, limiting, and weakening these cells. TNF-α and IFN-γ are secreted by NK cells and cytotoxic Tlymphocytes^41,42 to attack tumor cells and facilitate the recruitment of immune effector cells to amplify the tumor-killing effects. In the TME of advanced cancers, NETs induce the upregulation of T cell surface immune checkpoints TIM-3 and LAG-3 ⁴³ and downregulate the activating receptor NKp46 to inhibit the NK cell activation, ⁴⁴ attenuating the organismal immune response mediated by IL-2, TNF-α, and IFN-γ and restricting immune-mediated cytotoxicity. Our results suggest that the inhibition of NETs formation by ZQD reduced the release of pro-oncogenic inflammatory cytokines in TME and eliminated the inhibitory effect of NETs on immune effector cells, restoring the normal antitumor immune function. Neutrophils release NETs to activate the NF-κB signaling pathway, facilitating epithelial-to-mesenchymal transition and resulting in tumor metastasis. Additionally, NF-κB is activated in a hypoxic environment and is closely associated with hypoxia-induced neutrophil survival. ⁴⁵ ICAM-1 activation is positively related to NF-κB stimulation, ⁴⁶ which is essential for acid-induced NETs formation. ⁴⁷ High-dose ZQD treatment inhibited the phosphorylation of the p65 subunit, blocking the NETs formation and remodeling the local immune microenvironment.

Extruded DNAs comprise diverse granule proteins, including NE, MPO, cathepsin G, and MMP9. ⁴⁸ These granule proteins stimulate tumor growth and enhance its migration and invasive abilities. ¹³ The web-like structure forms a protective barrier on the tumor surface against the attack of effector T and NK cells, promoting immune evasion. As identified in our previous study, high-dose ZQD exhibited the potential to inhibit NSCLC metastasis by downregulating MMP9 and MMP2, which participate in the digestion of the ECM and stimulation of cancer cells for migration. ¹³ NET-related MMP9 activates pro-MMP2 by damaging vascular endothelial cells. Cathepsin G in cancer models activates VEGF through the cleavage of pro-MMP9. ⁴⁹ Consistently, ZQD reduced the expression of NET-related biomarkers and attenuated the lung colonization of NSCLC cells in the lung metastasis model. These results indicated that ZQD blocked NETs formation, hindering tumor metastasis (Figure 11).

Figure 11.

Mechanism diagram elucidating the specific action of ZQD against NSCLC.

Cisplatin promotes N1 neutrophil polarization and reduces the N2 neutrophil by inducing tumor ferroptosis. ⁵⁰ Thus, NET-related proteins were downregulated in cisplatin-treated tumor tissues. However, the long-term utilization of chemotherapeutic drugs, such as cisplatin and doxorubicin, has the risks of drug resistance and metastatic relapse, which are triggered by the induction of NETs. ⁵¹ This shift from inhibition to induced risk is a limitation of the long-term clinical application of cisplatin. DNase I cleaves phosphodiester bonds in DNA strands and hydrolyzes the structural backbone of NETs, inhibiting NETs formation in the microenvironment. Nevertheless, a pre-clinical study revealed that long-term treatment with DNase I reduced the overall survival time of mice, which was possibly associated with the immunosuppression. ⁵² In contrast, this study provides a novel, effective and safe strategy for inhibiting NETs. ZQD effectively inhibited NETs formation through multi-target regulations, which is expected to suppress its mediated tumor recurrence and metastasis pathways. In addition, ZQD exhibited a good safety profile compared with that of existing NETs inhibitors such as DNase I, showing better clinical application safety prospects.

However, this study is subject to some limitations. First, although ZQD downregulated some key components of NETs (CitH3, MPO, and NE), the absence of direct structural evidence—such as high-resolution immunofluorescence microscopy to visualize NETs architecture or quantitative DNase I sensitivity assays—limits the proof for the physical disruption of NETs. Future studies should consider these advanced imaging and enzymatic digestion assays to provide definitive visual and functional proof of NETs dissolution. Our pharmacological analysis suggests that the effect of ZQD on NETosis involves various mechanisms, such as the inhibition of CDK4/6 and PAD4. Further investigation is required to clarify the specific pathways. Subsequent investigations will implement targeted genetic silencing or selective pharmacological inhibitors of these candidate targets within NETosis-competent systems to identify their indispensable roles in the observed phenotype. Moreover, whether ZQD augments the immune function of effector T cells by reducing NETs formation warrants further exploration. We plan to use an ex vivo co-culture model integrating tumor cells, NETs, and T cells to examine whether the efficacy of ZQD depends on restoring T cell-mediated tumor clearance. Additionally, potential ZQD monomers were not selected for follow-up studies which are a fundamental prerequisite for transforming a traditional herbal formulation into a defined therapeutic agent compatible with modern drug development paradigms. Further investigations are required for new drug development.

Conclusion

We confirmed the effect of ZQD against NSCLC from a different perspective by integrating UPLC-HRMS technology, network pharmacology, and transcriptomics. The regulation of immune function by ZQD was verified in vivo. Our findings indicate that ZQD affects tumor function and neutrophil recruitment by improving the hypoxic environment and subsequently suppressing NET-associated proteins to improve the immune microenvironment, inhibiting tumorigenesis and metastasis. These findings introduce innovative methods based on previous studies and enhance the understanding of the antitumor mechanisms of ZQD in NSCLC.