Revealing the Anti‐Inflammatory Mechanisms of Zingiber officinale Roscoe Through Network Pharmacology and Experimental Validation

ABSTRACT

A comprehensive approach combining network pharmacology and in vitro validation was employed to systematically elucidate the anti‐inflammatory mechanisms of Zingiber officinale Roscoe. First, its components were preliminarily identified via UPLC‐Q‐Exactive Orbitrap MS/MS, whose targets were obtained from the Swiss Target Prediction database and the Traditional Chinese Medicine Systems Pharmacology database. Inflammation‐related targets were retrieved from GeneCards and OMIM databases. Overlapping targets between compound‐related and inflammation‐related genes were identified, followed by the construction of PPI networks and component‐target networks. Subsequent GO and KEGG pathway enrichment analyses were performed. Through network pharmacology analysis, 6‐Shogaol (20), 8‐Shogaol (24), and 8‐Gingerol (19) emerged as core active components, while AKT1, MAPK3, EGFR, SRC, and STAT3 were identified as key targets. KEGG enrichment analysis revealed that the anti‐inflammatory effects were primarily associated with AGE‐RAGE, PI3K‐Akt, MAPK, TNF, and IL‐17 signaling pathways. Subsequently, molecular docking was employed to validate the binding affinity between core components and key targets. Finally, anti‐inflammatory activity was validated in vitro using the LPS‐stimulated RAW 264.7 macrophage model, and gene expression was assessed via qRT‐PCR. Collectively, this study elucidates the active constituents and molecular mechanisms underlying the anti‐inflammatory action of Z. officinale , providing a theoretical basis for its development, utilization, and clinical application.

1. Introduction

Inflammation is a fundamental and complex physiological defense mechanism that is activated in response to harmful stimuli such as pathogens, damaged cells, or irritants. Its primary purpose is to recruit immune cells, eliminate the initial cause of injury, clear out necrotic cells and tissues, and initiate the repair process (Solier et al. 2023). When the inflammatory response becomes excessive or persists chronically, it can cause damage to the body, with clinical manifestations typically including redness, swelling, heat, and pain (Stanke‐Labesque et al. 2020). When a condition deteriorates, inflammatory signals may travel through the bloodstream and lymphatic system, potentially inducing functional disorders in tissues and organs. This can set off a cascade of acute and chronic diseases, including colitis, arthritis, and diabetes, ultimately severely disrupting the body's normal physiological activities (He et al. 2024; Lan et al. 2024; Zhou et al. 2024). The molecular underpinnings of these processes often involve the sustained activation of key signaling pathways, including the nuclear factor kappa B (NF‐κB) pathway, the mitogen‐activated protein kinase (MAPK) cascade, and the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway, which govern the expression of pro‐inflammatory genes (Zhou et al. 2024). Given its central role in pathology, controlling inflammation is a cornerstone strategy for the prevention and management of many chronic diseases. Conventional pharmacotherapy has long relied on synthetic anti‐inflammatory agents. Nonsteroidal anti‐inflammatory drugs (NSAIDs), including ibuprofen and celecoxib, alleviate pain and swelling by inhibiting cyclooxygenase (COX), but may trigger gastrointestinal and cardiovascular complications (Wang, Tang, et al. 2021). These considerable limitations underscore a critical and unmet need for safer, more sustainable, and better‐tolerated therapeutic alternatives. In response to this challenge, scientific inquiry has increasingly turned towards natural products as a rich source of novel bioactive compounds.

Z. officinale Roscoe, a perennial herbaceous plant belonging to the Zingiberaceae family, is native to Southeast Asia and classified as a tropical and subtropical species. It is now widely cultivated in eastern Asian countries, such as China, India, and Indonesia. Z. officinale is characterized in traditional medicine as having a pungent flavor and slightly warm nature. Its pharmacological properties are attributed to a diverse array of bioactive constituents, including gingerols, flavonoids, volatile oils, polysaccharides, and glycoproteins (Zhang et al. 2021). These active components confer the traditional medicinal value of Z. officinale in warming the middle burner to stop vomiting, resolving phlegm to relieve cough, counteracting fish and crab toxins, and dispersing exterior cold. It also provides a significant source of natural compounds for modern clinical development of anti‐inflammatory drugs. Based on its long‐standing medicinal tradition, modern pharmacological research has confirmed that the active components of Z. officinale exhibit significant anti‐inflammatory effects in animal models of inflammatory diseases such as liver fibrosis and asthma (Qiu et al. 2022; Zhu et al. 2021). Notably, it exerts anti‐inflammatory activity through multitarget regulatory mechanisms. It can modulate key inflammatory signaling pathways, including the nuclear factor kappa‐light‐chain‐enhancer of activated B cells (NF‐κB) and mitogen‐activated protein kinase (MAPK) cascades, thereby effectively suppressing the transcription and expression of critical pro‐inflammatory cytokines, such as tumor necrosis factor‐α (TNF‐α) and interleukin‐6 (IL‐6) (Pázmándi et al. 2024). This multipathway inhibition ultimately contributes to a systemic anti‐inflammatory response.

The chemical composition of herbal medicines is inherently complex, making traditional separation and identification approaches laborious and time‐consuming. Ultra‐performance liquid chromatography coupled with UPLC‐Q‐Exactive Orbitrap MS/MS has emerged as a powerful analytical platform. This technology synergizes the high‐resolution separation of liquid chromatography with the exceptional sensitivity, selectivity, and structural elucidation capacity of high‐resolution mass spectrometry. Consequently, it enables the rapid and efficient separation and identification of complex phytochemical constituents and has been widely applied in recent years for the compositional analysis and characterization of Chinese herbal medicines (Riwa et al. 2025). Meanwhile, with the rapid advancement of bioinformatics and computer‐aided drug design technologies, network pharmacology and molecular docking have emerged as crucial tools for deciphering the complex mechanisms of traditional Chinese medicine. Network pharmacology systematically reveals the multicomponent, multitarget, and multipathway synergistic mechanisms of traditional Chinese medicine through the construction of a multidimensional “drug‐component‐target‐disease” network (Qi et al. 2023; Zhang et al. 2023). Nevertheless, a network pharmacology‐based investigation into the anti‐inflammatory effects of Z. officinale remains scarce and warrants in‐depth exploration. This study combines UPLC‐Q‐Exactive Orbitrap MS/MS with network pharmacology, molecular docking, and in vitro experiments to investigate the chemical basis and potential anti‐inflammatory mechanisms of Z. officinale . Despite the established anti‐inflammatory profile of Z. officinale and the availability of these advanced research tools, a comprehensive network pharmacology‐based investigation into its systemic anti‐inflammatory mechanism remains notably underexplored. Therefore, in‐depth study integrating UPLC‐Q‐Exactive Orbitrap MS/MS‐based component analysis with network pharmacology and molecular docking validation is warranted to systematically unravel its multitarget mode of action. In addition, to experimentally validate the predictions derived from network pharmacology and molecular docking, we employed LPS‐stimulated RAW 264.7 macrophage model in vitro to assess the anti‐inflammatory activity of the key candidate compounds and quantified the expression of inflammatory cytokines by qRT‐PCR. Overall, these findings lay a robust theoretical groundwork for the improved understanding, development, and clinical use of Z. officinale as an anti‐inflammatory agent.

2. Materials and Methods

2.1. UPLC‐Q‐Exactive Orbitrap MS/MS Analysis

2.1.1. Sample Preparation

A precisely weighed sample of 100 mg Z. officinale ethanol extract was dissolved in 1 mL of methanol. The mixture was thoroughly vortexed to ensure complete dissolution and subsequently centrifuged at 12,000 rpm for 10 min at 4°C. The supernatant obtained was analyzed by ultra‐performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS).

2.1.2. UPLC‐Q‐Exactive Orbitrap MS/MS Analysis

The column is a Sepax GP‐C18 column. The mobile phase consisted of solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in acetonitrile). A gradient elution program was employed (0–1 min, 95% A; 1–6 min, 95%–70% A; 6–20 min, 70%–5% A). The flow rate was set at 0.3 mL/min, column temperature at 40°C, and autosampler temperature at 4°C. Total run time was 20 min. Mass spectrometry conditions were shown in Table S1.

2.1.3. Compound Identification Process

Mass spectrometry analysis was conducted on a Q‐Exactive Orbitrap system (Thermo Fisher Scientific). The data files obtained via UPLC‐Q‐Exactive Orbitrap MS/MS were imported into Compound Discoverer software for preliminary analysis. The software identified putative compounds from the mz Cloud, ChemSpider, and mz Vault databases, followed by fragment and molecular weight matching. The error margin was set to within 5 ppm (Lin et al. 2022). Preliminary compound identification was performed by integrating retention time, mass‐to‐charge ratio, and mass spectrometry fragment information. To validate the analytical method, one representative bioactive compound was selected for a comprehensive validation study, encompassing linearity, precision, repeatability, stability, spike recovery, and content determination (detailed procedures and results were provided in the Supporting Information) (Jiang et al. 2024). Ultimately, we achieved efficient identification and analysis of the compounds in the samples. The total ion chromatogram (TIC) obtained in both positive and negative ion modes demonstrated excellent chromatographic separation and strong mass spectrometry response. The chemical constituents of Z. officinale ethanol extract were identified by integrating database searches, literature data, and spectral information from isolated reference compounds.

2.2. Network Pharmacology Analysis

2.2.1. Establishment of a Database of Active Components

Based on literature reviews and experimental data, this study systematically constructed a database of active components, aiming to provide comprehensive and precise data support for network pharmacology research. The constituents were systematically collected and integrated from authoritative databases, including PubMed (https://pubmed.ncbi.nlm.nih.gov/), TCMSP (http://tcmspw.com/tcmsp.php), and SymMap (http://www.symmap.org/). By searching keywords such as Z. officinale , all known active components of ginger were systematically organized. Chemical constituents identified using UPLC‐Q‐Exactive Orbitrap MS/MS technology were incorporated into the database to ensure its completeness and scientific rigor.

2.2.2. Collection of Targets for Active Components in Z. officinale

Following the establishment of the bioactive component database, the identified active constituents were systematically processed for target prediction. The molecular structure files of these compounds were uploaded to the Swiss Target Prediction platform (http://www.swisstargetprediction.ch/), with the species parameter specified as “ Homo sapiens .” Potential functional targets were then screened by selecting all predictions with a probability score greater than 0.1 for subsequent analysis.

2.2.3. Screening of Disease Targets

Using “inflammation” as the keyword, disease targets were retrieved from the GeneCards (https://www.genecards.org/) and OMIM (https://www.omim.org/) databases. Targets with a relevance score ≥ the average value were selected, overlapping targets were removed, and ultimately, disease targets closely associated with inflammation were obtained.

2.2.4. Identification of Common Targets Between Active Constituents and Disease

The targets of the drug active ingredients and those associated with the disease were uploaded to the Venny 2.1 online platform (https://bioinfogp.cnb.csic.es/tools/venny/) to generate a Venn diagram, providing a visual representation of the relationship between the two target sets. Through in‐depth analysis of these two gene sets, genes shared between drug targets, disease targets were successfully identified. These overlapping genes were ultimately determined to represent the core anti‐inflammatory targets of Z. officinale .

2.2.5. Protein–Protein Interaction Network Construction

Common targets of Z. officinale's components and inflammation were imported into STRING 12.0 (https://cn.string‐db.org/) for PPI analysis under the following settings: interaction type “multiple proteins,” organism “ H. sapiens, ” and confidence score ≥ 0.900. The results were visualized in Cytoscape 3.10.3 to generate a PPI network, with nodes representing targets and edges indicating interactions. Node degrees were calculated using the Network Analyzer plugin, and the top five targets by degree were defined as core targets. This network visually elucidated the interactions between components and inflammatory targets, providing key insights for understanding anti‐inflammatory mechanisms and guiding subsequent experimental studies.

2.2.6. Construction of Drug Component‐Target Network

The active components of Z. officinale and their common anti‐inflammatory targets were imported into Cytoscape 3.10.3 to construct a “Component‐Target” network. In this network, nodes represent either Z. officinale's components or their corresponding targets, edges indicate interactions between them. The Network Analyzer plugin was used to calculate degree values, based on which the core anti‐inflammatory components were identified.

2.2.7. GO Enrichment and KEGG Pathway Analysis

The common anti‐inflammatory targets were analyzed using the DAVID database (https://davidbioinformatics.nih.gov) for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. The analysis was restricted to H. sapiens , with a significance threshold of p < 0.05. GO analysis showed significant enrichment in three categories: biological processes (BPs), cellular components (CCs), and molecular functions (MFs). KEGG pathway analysis indicated that these targets are involved in key inflammatory signaling pathways, suggesting a multitarget therapeutic mechanism. Bar plots and enrichment bubble charts were generated using the online bioinformatics platform (https://www.bioinformatics.com.cn/) to visualize and interpret the enrichment results.

2.2.8. Molecular Docking

The two‐dimensional structures of the core ligand compounds were retrieved in SDF format from the PubChem database. After energy minimization using Chem3D, they were converted and saved in mol2 format. The three‐dimensional structures of the core targets were downloaded in PDB format from the RCSB Protein Data Bank (https://www.rcsb.org/). PyMOL was used to remove water molecules and original ligands from the protein structures. AutoDockTools was then employed to add hydrogen atoms to both proteins and ligands, and the prepared files were saved in pdbqt format. Molecular docking was performed using AutoDock Vina software. After docking, results containing binding free energy, binding conformation, and interaction details were generated and visualized.

2.3. Experimental Verification

2.3.1. Regents and Materials

The RAW 264.7 cell line was obtained from the Cell Bank of the Chinese Academy of Sciences Committee on Type Culture Collection. Lipopolysaccharide (LPS) and the nitric oxide (NO) detection kit were purchased from Beyotime Biotechnology Co. Ltd. (Shanghai, China). DMEM medium was acquired from Gibco Life Technologies (Grand Island, NY, USA), and fetal bovine serum (FBS) was sourced from Thermo Fisher Scientific (Waltham, MA, USA).

2.3.2. Cell Culture

RAW264.7 cells were cultured in high‐glucose DMEM medium supplemented with 10% FBS and 1% penicillin–streptomycin at 37°C in a humidified incubator with 5% CO₂.

2.3.3. Nitric Oxide Production Assay in RAW 264.7 Cells

To evaluate the anti‐inflammatory effects of Z. officinale ethanol extract and its potential active compounds (6‐shogaol, 8‐shogaol and 8‐gingerol, as predicted by network pharmacology), NO production was measured in LPS‐stimulated RAW 264.7 cells. First, RAW 264.7 cells in the logarithmic growth phase were seeded at a density of 2 × 10⁴ cells per well into a 96‐well plate and incubated at 37°C in a 5% CO₂ incubator for 24 h. After aspirating the supernatant, fresh culture medium containing crude extracts at different concentrations (200, 100, 50, 25 and 12.5 μg/mL) or individual compounds (6‐shogaol, 8‐shogaol and 8‐gingerol; each tested at 25, 12.5, 6.25, 3.125 and 1.5625 μM) was added to respective wells (50 μL per well). An LPS group (cells treated with LPS only) and a control group (cells treated with medium only) were also established, each receiving 50 μL of medium. After 3 h of incubation, 50 μL of 1 μg/mL LPS was added to all groups except the control group, followed by a further 24 h of incubation. Cell supernatants were then collected. According to the Griess reagent kit instructions, NaNO₂ standard solutions were prepared in a 96‐well plate. Subsequently, 50 μL of each supernatant was transferred to a separate plate. Griess Reagent I (50 μL) was added to each well, mixed and incubated in the dark at room temperature for 5 min. Next, 50 μL of Griess Reagent II was added, and the reaction proceeded in the dark for another 5 min. Absorbance was measured at 540 nm using a microplate reader, and NO concentrations were calculated based on a standard curve. Each experimental group included three replicate wells, with the average value serving as the final result. The degree of LPS‐induced inflammation was assessed by comparing NO release levels across different treatment groups.

2.3.4. qRT‐PCR Analysis of the Compounds

Following the procedure described in Section 2.3.3, cells were washed with ice‐cold PBS and lysed with RNA extraction buffer. The lysate was mixed with chloroform substitute, centrifuged for phase separation, and the aqueous phase was combined with isopropanol for RNA precipitation at low temperature. After washing with 75% ethanol, the RNA pellet was air‐dried and resuspended in RNase‐free buffer. RNA concentration and purity were determined using a Nanodrop 2000, and samples were diluted to 200 ng/μL. Reverse transcription was performed in a 20 μL reaction containing 10 μL total RNA, 4 μL 5× SweScript All‐in‐One SuperMix, and 1 μL gDNA Remover, with incubation at 25°C for 5 min, 42°C for 30 min, and 85°C for 5 s. For qPCR amplification, each 15 μL reaction mixture (run in triplicate) contained 7.5 μL 2× SYBR Green master mix, 1.5 μL 2.5 μM mixed primers, 2 μL cDNA template, and 4 μL nuclease‐free water. The PCR program included an initial denaturation at 95°C for 30 s, followed by 40 cycles of 95°C for 15 s and 60°C for 30 s, and a melting curve analysis from 65°C to 95°C. Gene expression was quantified using the 2^(‐ΔΔCt) method, where fold change was calculated as 2^(‐ΔΔCt) based on Ct values of target and reference genes in experimental and control samples.

2.3.5. Statistical Analysis

Statistical analyses were performed using one‐way analysis of variance (ANOVA) and Dunnett's multiple comparison test, completed with GraphPad Prism 10.0 software (GraphPad Software, San Diego, California, USA). Results were expressed as the mean ± standard deviation (SD) from three independent experiments. p value < 0.05 was considered statistically significant (Tatar et al. 2016).

3. Results and Discussion

3.1. Identification of Z. officinale 's Chemical Constituents

Using UPLC‐Q‐Exactive Orbitrap MS/MS mass spectrometer under the mass spectrometry detection conditions described in Section 2.1, the Z. officinale ethanol extract was analyzed in both positive and negative ion modes. Total ion current (TIC) chromatograms were obtained in each mode (Figure S1), demonstrating satisfactory chromatographic separation and robust mass spectrometric response for all detected compounds. Tentative compound identification was initially performed using Compound Discoverer software, supplemented by literature review and careful interpretation of the MS data. Further structural assignments were made on the basis of precursor ion peaks, retention times, and MS and MS/MS fragmentation information. A total of 32 compounds were identified in Z. officinale (Table S2), including volatile oils, gingerols, diphenylheptanes, and flavonoids. The chemical composition was identified as shown in Table 1.

TABLE 1.

Chemical composition mass spectrometry information.

Rt (min)	Adduct	Precursor (m/z)	Theoretical (m/z)	Mass error (ppm)	Formula	Identification	(m/z)	References
1.173	[M + H]+	217.0678	216.0606	−1.76	${\mathrm{C}}_{12}{\mathrm{H}}_8{\mathrm{O}}_4$	Methoxsalen	365.1047 314.0839 163.0388	(Jia et al. 2025)
3.410	[M‐H]−	433.1716	434.1789	0.49	${\mathrm{C}}_{23}{\mathrm{H}}_{30}{\mathrm{O}}_8$	1‐(3,4‐Dihydroxy‐5‐methoxypheny)‐3‐hydroxy‐7‐(4‐hydroxy‐3‐methoxyphenyl)heptan‐5‐ylacetate	433.0840 431.8418 215.0092	(Jiang et al. 2007)
3.869	[M‐H]−	367.1138	368.1210	0.92	${\mathrm{C}}_{21}{\mathrm{H}}_{20}{\mathrm{O}}_6$	Curcumin	365.8881 146.9650	(Kao et al. 2020)
4.168	[M+H]+	447.1747	446.1674	−4.27	${\mathrm{C}}_{24}{\mathrm{H}}_{30}{\mathrm{O}}_8$	1‐(3,4‐Dihydroxyphenyl)‐7‐(4‐hydroxy‐3‐me‐thoxyphenyl)heptane‐3,5‐diyldiacetate	447.1376 446.2136 217.0469	(Jiang et al. 2007)
4.523	[M‐H]−	403.1725	404.1798	1.31	${\mathrm{C}}_{22}{\mathrm{H}}_{28}{\mathrm{O}}_7$	2‐(4‐Hydroxy‐3,5‐dimethoxyphenyl)‐6‐(4‐hydrox‐y‐3‐methoxyphenethyl)tetrahydro‐2H‐pyran‐4‐ol	447.1376 446.2136 217.0469	(Jiang et al. 2007)
5.847	[M‐H]−	609.1457	610.1525	−1.47	${\mathrm{C}}_{27}{\mathrm{H}}_{30}{\mathrm{O}}_{16}$	Rutin	248.9601 129.9446 112.9844	(Ivane et al. 2022)
6.096	[M‐H+HAc]+	521.18793	462.1740	4.69	${\mathrm{C}}_{24}{\mathrm{H}}_{30}{\mathrm{O}}_9$	3,5‐Diacetoxy‐1‐(3,4‐dihydroxyphenyl)‐7‐(3,4‐dihydroxy‐5‐methoxyphenyl) heptane	514.3248 493.2295 461.1668	(Jiang et al. 2007)
6.890	[M‐H]−	415.1971	416.2043	−0.73	${\mathrm{C}}_{23}{\mathrm{H}}_{28}{\mathrm{O}}_7$	3,5‐Diacetoxy‐7‐(4‐hydroxyphenyl)‐1‐(3,4‐di‐hydroxyphenyl) heptane	146.9648 129.9746 122.9843	(Jiang et al. 2007)
6.715	[M+H]+	398.1219	397.1147	2.57	${\mathrm{C}}_{23}{\mathrm{H}}_{28}{\mathrm{O}}_8$	1,7‐Bis(3,4‐dihydroxyphenyl) heptane‐3,5‐diyl‐diacetate	397.2929 379.2187 261.1091	(Jiang et al. 2007)
7.103	[M‐H]−	417.1752	418.1825	−4.55	${\mathrm{C}}_{23}{\mathrm{H}}_{30}{\mathrm{O}}_7$	3‐Acetoxy‐5‐hydroxy‐1,7‐bis(4‐hydroxy‐3‐me‐thoxyphenyl) heptane	377.1817 146.9649 129.9749	(Ma et al. 2004)
8.518	[M+H]+	493.1667	492.1594	2.31	${\mathrm{C}}_{25}{\mathrm{H}}_{32}{\mathrm{O}}_{10}$	3,5‐Diacetoxy‐1,7‐bis(3,4‐dihydroxy‐5‐mthoxy‐phenyl)heptane	492.2237 174.1275 167.0385	(Jiang et al. 2007)
8.558	[M+H]+	177.0908	176.0836	−1.00	${\mathrm{C}}_{11}{\mathrm{H}}_{14}{\mathrm{O}}_3$	Zingerone	481.2399 194.1175 163.0388	(Ivane et al. 2022)
9.674	[M‐H]−	295.1916	296.1986	−0.54	${\mathrm{C}}_{17}{\mathrm{H}}_{28}{\mathrm{O}}_4$	6‐Gingerdiol	279.0913 261.0472 177.0547 163.0776	(Asamenew et al. 2018)
9.772	[M‐H]−	289.1446	290.1519	0.31	${\mathrm{C}}_{17}{\mathrm{H}}_{22}{\mathrm{O}}_4$	1Dehydro‐6‐gingerdione	289.0080 288.9918 288.0702	(Asamenew et al. 2018)
10.220	[M+H]+	373.1638	372.1565	−2.09	${\mathrm{C}}_{21}{\mathrm{H}}_{24}{\mathrm{O}}_6$	Tetrahydrocurcumin	372.1855 174.1275 163.0387	(Kao et al. 2020)
10.430	[M+H]+	274.2737	273.2663	−1.79	${\mathrm{C}}_{16}{\mathrm{H}}_{32}{\mathrm{O}}_2$	Palmitic acid	296.2568 163.0388	(Anisha and Radhakrishnan 2017)
10.553	[M‐H]−	475.1925	476.1997	−2.24	${\mathrm{C}}_{25}{\mathrm{H}}_{32}{\mathrm{O}}_9$	3,5‐Diacetoxy‐7‐(4‐dihydroxy‐3‐methoxyphenyl)‐1‐(3,4‐dihydroxy‐5‐methoxyphenyl)heptane	248.8601 215.0091 198.9355	(Jiang et al. 2007)
12.524	[M+Na]+	325.1765	302.1872	−3.18	${\mathrm{C}}_{19}{\mathrm{H}}_{26}{\mathrm{O}}_3$	4‐(2‐Hexyl‐6‐methyl‐4H‐pyran4‐yl)‐2‐methoxy‐phenol	318.1748 317.1714 303.1945	(Asamenew et al. 2018)
12.660	[M+H]+	291.1949	290.1877	−1.88	${\mathrm{C}}_{17}{\mathrm{H}}_{26}{\mathrm{O}}_3$	6‐Gingerone	273.1844 191.1064 163.0388	(Asamenew et al. 2018)
13.958	[M+H‐${\mathrm{H}}_2\mathrm{O}$]+	305.2104	322.2138	−2.02	${\mathrm{C}}_{19}{\mathrm{H}}_{30}{\mathrm{O}}_4$	8‐Gingerol	345.2028 163.0387	(Jiang et al. 2005)
14.342	[M+H]+	277.1792	276.1719	−2.26	${\mathrm{C}}_{17}{\mathrm{H}}_{24}{\mathrm{O}}_3$	6‐Shogaol	176.1430 174.1274 137.0595	(Asamenew et al. 2018)
14.850	[M+H]+	203.1789	202.1717	−2.19	${\mathrm{C}}_{15}{\mathrm{H}}_{22}$	Curcumene	177.0705 163.0386 137.0594	(Jia et al. 2025)
15.062	[M+H]+	375.2498	374.2426	−4.26	${\mathrm{C}}_{23}{\mathrm{H}}_{34}{\mathrm{O}}_4$	1Dehydro‐12‐gingerdione	375.1773 374.2015 163.0388	(Asamenew et al. 2018)
15.487	[M‐H]−	363.2169	364.2242	−2.14	${\mathrm{C}}_{21}{\mathrm{H}}_{32}{\mathrm{O}}_5$	Acetoxy‐8‐gingerol	287.1901 179.0681 137.0595	(Lu et al. 2022)
15.563	[M+Na]+	347.1717	318.1800	−2.00	${\mathrm{C}}_{19}{\mathrm{H}}_{26}{\mathrm{O}}_4$	1‐Dehydro‐8‐gingerdione.	403.2085 163.0388	(Asamenew et al. 2018)
15.647	[M+H]+	277.1793	276.1720	−1.81	${\mathrm{C}}_{17}{\mathrm{H}}_{26}{\mathrm{O}}_4$	6‐Gingerol	497.3423 496.3390 403.2084	(Jiang et al. 2005)
16.333	[M+H]+	205.1948	204.1875	−1.09	${\mathrm{C}}_{15}{\mathrm{H}}_{24}$	(E,E)‐α‐farnesene	246.2214 174.1276 163.0388	(Jia et al. 2025)
16.349	[M+H]+	461.2865	460.2793	−3.97	${\mathrm{C}}_{25}{\mathrm{H}}_{32}{\mathrm{O}}_8$	3,5‐Diacetoxy‐1,7‐bis(4‐hydroxy‐3‐methoxyphe‐nyl)heptane	205.1948 174.1275 163.0388 137.0396	(Ma et al. 2004)
16.590	[M+H]+	305.2106	304.2033	−1.70	${\mathrm{C}}_{19}{\mathrm{H}}_{28}{\mathrm{O}}_3$	8‐Shogaol	205.1948 174.1276 163.0388 137.0596	(Li et al. 2019)
18.061	[M+H]+	347.2571	346.2498	−2.78	${\mathrm{C}}_{21}{\mathrm{H}}_{30}{\mathrm{O}}_4$	1Dehydro‐10‐gingerdione	174.1274 163.0387 137.0595	(Asamenew et al. 2018)
18.348	[M+H]+	333.2416	332.2343	−2.54	${\mathrm{C}}_{21}{\mathrm{H}}_{32}{\mathrm{O}}_3$	10‐Shogaol	174.1275 163.0387 137.0595	(Li et al. 2019)
18.486	[M+H]+	351.2520	350.2447	−2.77	${\mathrm{C}}_{21}{\mathrm{H}}_{34}{\mathrm{O}}_4$	10‐Gingerol	449.2652 163.0387 137.0595	(Jiang et al. 2005)

3.2. Network Pharmacology Analysis

3.2.1. Active Components Database

Based on UPLC‐Q‐Exactive Orbitrap MS/MS results and supplemented by the TCMSP database, 32 active components of Z. officinale were identified and compiled from the literature. Their names and structural information are provided in Table S2.

3.2.2. Targets of Active Components

The molecular structures of these 32 compounds were submitted to the Swiss Target Prediction database for target prediction. After removing duplicates, 461 unique targets associated with the bioactive compounds were obtained.

3.2.3. Screening of Inflammation‐Related Targets

Inflammation‐related targets were retrieved from the GeneCards and OMIM databases, and 2761 inflammation‐related gene targets were screened based on the criteria outlined in Section 2.2.3.

3.2.4. Acquisition of Intersection Targets

The compound‐related targets and inflammation‐related targets were uploaded to the Venny 2.1 platform to generate a Venn diagram. As shown in Figure 1, 242 intersection targets were identified as the core targets for anti‐inflammatory effects.

FIGURE 1.

Venn diagram of intersection target genes between Zingiber officinale components and inflammation.

3.2.5. PPI Network Analysis of Anti‐Inflammatory Targets

To construct the PPI network diagram, 242 anti‐inflammatory potential targets were loaded into the STRING database. Using Cytoscape 3.10.3 software, node colors and sizes were adjusted based on degree values. As shown in Figure 2A, the resulting PPI network contains 241 nodes and 3166 edges. Nodes closer to the center, larger in size, and darker in color indicated higher degree values and greater importance within the network. Based on network topology and algorithmic analysis, the five nodes with the highest degree values were identified as key hubs, representing the core anti‐inflammatory targets of Z. officinale : AKT1, MAPK3, EGFR, SRC and STAT3. In the core target PPI network, AKT1 (protein kinase B) ranked first among the core targets, highlighting its pivotal role in inflammatory responses and related diseases. AKT1 regulates cell survival, proliferation, metabolism and inflammatory responses through the PI3K/AKT/mTOR signaling pathway, which plays an important role in the development and progression of various chronic inflammatory diseases (Zhu et al. 2023). MAPK3 (mitogen‐activated protein kinase 3) was the second core target, a class of serine/threonine protein kinases widely distributed in mammals. Ginger‐derived compounds such as gingerol may alleviate cartilage degeneration by modulating the p38 and c‐Jun N‐terminal kinase pathways within the mitogen‐activated protein kinase signaling pathway. It leads to amplified pro‐inflammatory cytokine effects and decreased synthesis of cartilage‐degrading enzymes. This mechanism supports the potential pharmacological benefit of ginger in the management of osteoarthritis (Ruangsuriya et al. 2017).

FIGURE 2.

(A) Protein–protein interaction (PPI) network diagram (The size and color of nodes in the diagram correspond to their degree values in the network). (B) Component‐Target Network Diagram. Blue diamond nodes represent bioactive compounds, green circular nodes denote intersecting genes.

3.2.6. Construction of Component‐Target Networks

Using Cytoscape 3.10.3, a component‐target network was established by linking anti‐inflammatory active components with the common intersection targets. Following the procedure in Section 2.2.2, 26 active components were retained after excluding compounds lacking corresponding target information or showing no overlap with the shared key targets. As shown in Figure 2B, this network diagram comprises 260 nodes and 744 edges. The top five active components by degree value were as follows: 6‐shogaol (20), 8‐shogaol (24), 8‐gingerol (19), 1‐dehydro‐8‐gingerdione (12) and 6‐gingerone (17). These compounds were presumed to be the core components responsible for anti‐inflammatory effects. In LPS‐stimulated RAW 264.7 macrophage model, 6‐shogaol (10 μmol/L) significantly inhibited the production of NO and prostaglandin E₂ (PGE₂), with activity exceeding that of 6‐gingerol at the same concentration, indicating its potential as a key anti‐inflammatory agent (Pan et al. 2008). A mouse model of house dust mite antigen‐induced asthma revealed that chronic intraperitoneal injection of 6‐shogaol significantly reduced eosinophil and lymphocyte infiltration in bronchoalveolar lavage fluid and decreased IL‐4 concentration in lung tissue by 51% (Yocum et al. 2020). Upon identifying 8‐shogaol as an effective molecule against synovitis, it demonstrated inhibitory effects on inflammation and migration mediated by inflammatory factors such as TNF‐α, IL‐1β and IL‐17 in rheumatoid arthritis patients and in a 3D synovial culture system (Jo et al. 2022). In a CCl₄‐induced liver fibrosis mouse model, it was found that shogaol (20 mg·kg⁻¹·d⁻¹ administered via intraperitoneal injection) effectively suppressed macrophage infiltration and NLRP3 inflammasome activation in liver tissue. This mechanism involved inhibiting IκBα phosphorylation and p65 nuclear translocation, reducing the expression levels of key NF‐κB pathway proteins by 42%–57%, while significantly downregulating proinflammatory factors such as TNF‐α (68% reduction) and IL‐1β (51% reduction). This resulted in diminished inflammatory responses and reduced fibrosis severity in liver tissue (Qiu et al. 2022).

3.2.7. GO and KEGG Pathway Enrichment Analysis

GO and KEGG enrichment analysis was performed on the intersection targets using the David database. The GO analysis of anti‐inflammatory effects, with a screening criterion of p < 0.05, yielded 1212 entries, including 854 BP entries, 108 CC entries, and 250 MF entries. As shown in Figure 3A, the top 10 entries for BPs, MFs and CCs were visualized as bar charts, providing an intuitive representation of the enrichment significance across functional categories for CCs, MFs and BPs. GO analysis indicated that BP primarily participated in processes such as the epidermal growth factor receptor signaling pathway and inflammatory response. CC mainly encompassed the plasma membrane, receptor complexes, cytoplasm, and cytosol, while MF primarily involved processes including protein tyrosine kinase activity, protein kinase activity, and ATP binding. These functions were consistent with roles in signal transduction and protein phosphorylation. Notably, enrichment in histone H3‐Y41 and H2A‐X‐Y142 kinase activity suggested potential epigenetic modulation of inflammation‐related gene expression.

FIGURE 3.

GO and KEGG enrichment analysis. (A) Bar chart of GO functional enrichment analysis. (B) Bubble chart of 20 pathways identified by KEGG enrichment analysis.

In the KEGG enrichment analysis, 166 pathways were identified, and the top 20 were selected for visualization (Figure 3B). Enrichment significance was assessed by the number of enriched targets and the ‐lgP value. The five pathways with the lowest p value were the AGE‐RAGE signaling pathway in diabetic complications, PI3K‐Akt signaling pathway, MAPK signaling pathway, TNF signaling pathway and IL‐17 signaling pathway. The PI3K‐Akt pathway is a crucial inflammatory signaling pathway. Upon PI3K activation, its catalytic product PIP3 recruits Akt to the cell membrane and phosphorylates it. Activated Akt further regulates downstream molecules (such as mTOR, GSK‐3β and NF‐κB), promoting the transcription and release of inflammatory mediators. For instance, pretreatment with quercetin (20 μmol/L) significantly suppressed TNF‐α, IL‐6 and IL‐1β expression at both mRNA and protein levels in LPS‐stimulated RAW264.7 cells. Its core anti‐inflammatory mechanism involved effective inhibition of Akt phosphorylation, thereby regulating the activity of the PI3K/Akt signaling pathway within macrophages. Molecular docking further revealed that quercetin specifically binds to the active site of Akt1, a key target in macrophages, providing new mechanistic evidence for its therapeutic potential in inflammatory diseases (Zhang et al. 2022). In the AGE‐RAGE signaling pathway involved in diabetic complications, AGEs—which are highly abundant in the diabetic renal microenvironment—upregulate RAGE expression. Ligand‐induced RAGE stimulation leads to the activation of intracellular signaling pathways, including JAK/STAT, MAPK/ERK, PI3K/AKT/mTOR and NF‐κB. A common feature of these pathways is the activation of nuclear transcription factors involved in inflammatory and fibrotic processes (Sanajou et al. 2018). These five pathways are considered to be the primary mechanisms through which Z. officinale exerts its anti‐inflammatory effects.

3.2.8. Molecular Docking Validation

Molecular docking technology is a widely employed computer‐aided drug design (CADD) method that plays a crucial role in modern drug development. By simulating and predicting the binding conformation and affinity between small‐molecule ligands and target protein binding sites, this technique facilitates the rapid screening of potential therapeutic compounds, the optimization of drug candidates, and the elucidation of molecular interaction mechanisms (Dong et al. 2018; Li et al. 2022). The five core targets identified from the protein–protein interaction (PPI) network were selected as receptors, and the five top‐ranking core components from the “Component‐Target” network were used as ligands. After removing water molecules and ligands using pymol, the three‐dimensional structures of AKT1 (6S9X), MAPK3 (4QTB), EGFR (7U9A), SRC (2BDF) and STAT3 (6NJS) were obtained. Subsequently, AutoDock Vina was employed for molecular docking between the five core compounds (6‐shogaol [20], 8‐shogaol [24], 8‐gingerol [19], 1‐dehydro‐8‐gingerdione [12] and 6‐gingerone [17]) and the five core targets (AKT1, MAPK3, EGFR, SRC, and STAT3). Molecular docking is commonly employed to predict and evaluate the binding interactions between compounds and target proteins (Nguyen et al. 2020). The binding affinity is typically reflected by the AutoDock Vina scoring function, with lower scores indicating stronger interactions (Huang, Cheng, et al. 2020). Lower binding energies indicate stronger binding affinity; binding energies < −5 kcal·mol⁻¹ indicate good binding activity, while binding energies < −7 kcal·mol⁻¹ indicate strong binding activity (Song et al. 2022). The molecular docking results (Figure 4) showed that eight compound‐target pairs had binding energies < −7 kcal·mol⁻¹, and one pair exhibited a binding energy < −9.0 kcal·mol⁻¹, indicating favorable binding. Compared to other target proteins, STAT3 and EGFR exhibited lower binding energies. Among these, 1‐dehydro‐8‐gingerdione showed the lowest binding energy (−9.1 kcal·mol⁻¹) with AKT1, suggesting this protein may be a potential anti‐inflammatory binding target. As shown in Figure 5 and Figure S2, visual analysis of the high‐free‐binding‐energy score components and target binding sites revealed that the ligands can form hydrogen bonds with the receptor. As shown in Figure 5, various types of intermolecular forces existed between ligands and receptors, such as conventional hydrogen bonds, carbon‐hydrogen bonds, and π bonds. Specifically, 1‐dehydro‐8‐gingerdione established hydrogen bonds with AKT1 residues GLU‐85 and CYS‐296, a carbon‐hydrogen bond with ASN‐54, and a π‐π conjugation interaction with TYR‐18, while also forming a π‐cation interaction with CYS‐310.8‐shogaol formed a hydrogen bond with the LYS‐131 residue of MAPK3, while 6‐shogaol interacted with THR‐211 of AKT1 via a hydrogen bond and with TRP‐80 through π‐π stacking. As shown in Figure S2, although each ligand was completely enclosed within the pocket, it readily dissociated from the receptor when exposed to water due to the weak hydrophobic effects.

FIGURE 4.

Heatmap of molecular docking scores of core proteins with constituents.

FIGURE 5.

Docking patterns of core targets with core constituents. (A‐D) Interaction force of 6‐shogaol‐AKT1, 8‐shogaol‐MAPK, 1‐dehydro‐8‐gingerdione‐AKT1, and 1‐dehydro‐8‐gingerdione‐MAPK3 in 2D.

3.3. Experimental Verification In Vitro

3.3.1. Effect of Ethanol Extract on NO Production in RAW 264.7 Cells

Z. officinale ethanol extracts at various concentrations (200, 100, 50, 25, and 12.5 μg/mL) were co‐incubated with lipopolysaccharide (LPS, 1 μg/mL) in RAW264.7 cells. Nitric oxide (NO) levels in the culture supernatants were measured to evaluate the anti‐inflammatory activity of the extract. Experimental results (Figure 6A) showed that LPS (1 μg/mL) treatment significantly increased NO release to 39.08 μM, exhibiting a significant difference compared to the normal control group (5.23 μM), indicating successful LPS‐induced inflammation. Compared to the LPS group, ethanol extract groups at different concentrations significantly reduced NO levels in a dose‐dependent manner. All concentration groups exhibited statistically significant differences compared to the LPS group, indicating the anti‐inflammatory potential of the Z. officinale ethanol extract.

FIGURE 6.

(A) Determination of NO content in ethanol extract extracts. (B) Measurement of NO content in active compounds. Data are expressed as the mean ± SD, n = 3. ^# p < 0.05, ^## p < 0.01, ^### p < 0.001 vs. control group. *p < 0.05, **p < 0.01, ***p < 0.001 vs. LPS group.

3.3.2. Effect of Selected Compounds on NO Production in RAW 264.7 Cells

Given the notable anti‐inflammatory activity observed for the Z. officinale ethanol extract, this study further evaluated the inhibitory effects of three candidate compounds 6‐shogaol, 8‐shogaol, and 8‐gingerol, identified via network pharmacology. These compounds were tested on LPS‐stimulated RAW 264.7 cells at concentrations of 25, 12.5, 6.25, 3.125, and 1.5625 μM. Nitric oxide (NO) levels in the culture supernatants were measured to assess their anti‐inflammatory potential (Chen et al. 2018). Compounds at different concentrations were co‐incubated with lipopolysaccharide (LPS, 1 μg/mL) in RAW264.7 cells. The effects of the three compounds on NO production were observed by measuring NO concentrations in cell supernatants, thereby evaluating their anti‐inflammatory activity. Experimental results showed (Figure 6B) that NO release in the LPS (1 μg/mL) treatment group significantly increased to 14.47 μM, exhibiting a significant difference compared to the normal control group (5.23 μM), indicating that LPS successfully induced an inflammatory response. Compared with the LPS group, all three compounds significantly reduced NO concentrations at different concentrations in a dose‐dependent manner. At every concentration tested, the NO levels in compound‐treated groups were significantly lower than those in the LPS‐only group, indicating that 6‐shogaol, 8‐shogaol, and 8‐gingerol possess clear anti‐inflammatory activity.

3.3.3. qRT‐PCR Analysis of the Compounds

To further validate the effects of the core bioactive components of Z. officinale (6‐shogaol, 8‐shogaol, and 8‐gingerol) on key anti‐inflammatory targets (AKT1, MAPK3, EGFR, SRC, and STAT3), mRNA expression levels were detected using qRT‐PCR (primer sequences were provided in Table S3). As shown in Figure 7, LPS stimulation significantly upregulated the mRNA levels of AKT1, MAPK3, EGFR, SRC, and STAT3 compared with the control group. Treatment with the three compounds at concentrations of 6.25, 12.5, and 25 μM all reversed these LPS‐induced aberrations to varying degrees, with the most pronounced inhibitory effects observed at the highest concentration of 25 μM. Specifically, as depicted in Figure 7A, all three compounds dose‐dependently attenuated the LPS‐induced upregulation of AKT1. A similar pattern was observed for EGFR (Figure 7C), all three compounds dose‐dependently attenuated the LPS‐induced upregulation of EGFR. For MAPK3 (Figure 7B), both 8‐shogaol and 8‐gingerol exhibited significant dose‐dependent inhibitory effects, whereas 6‐shogaol significantly suppressed MAPK3 expression across all tested concentrations but without a clear dose–response relationship. A similar pattern was observed for SRC (Figure 7D), 8‐shogaol and 8‐gingerol showed dose‐dependent suppression, while 6‐shogaol did not display a dose‐dependent effect. For STAT3 (Figure 7E), all three compounds significantly reversed the LPS‐induced upregulation in a dose‐dependent manner. These experimental findings are corroborated by previous studies that collectively support the pivotal roles of these targets in inflammatory regulation. In RAW 264.7 macrophages, quercetin‐3,7‐dirhamnoside (QDR) was reported to exert anti‐inflammatory effects via modulation of the AKT1/mTOR pathway and activation of NOS3 (He et al. 2023). Resveratrol was shown to reverse TNF‐α‐induced AKT1 upregulation in urothelial cells (Li et al. 2025). Curculigoside A exerts antirheumatic and antiosteoporotic effects by inhibiting the expression of EGFR, MAP 2 K1, MMP2, FGFR1, and MCL1 (Han et al. 2020). In models of acute pancreatitis and hyperuricemia, XCHD and its constituent quercetin, as well as cortex phellodendri, were found to act on MAPK3, TP53, and AKT1 to alleviate inflammation (Xu et al. 2022; Zhan et al. 2021). Furthermore, luteolin and asiaticoside–nitric oxide hydrogel (ACNO) were demonstrated to inhibit SRC, EGFR, and STAT3 expression in macrophages and diabetic wound models, respectively (Huang, Zhang, et al. 2020; Mu et al. 2025). Collectively, these lines of evidence strongly support our target selection and mechanistic interpretation, further consolidating the anti‐inflammatory potential of the identified Z. officinale constituents.

FIGURE 7.

Inhibitory effects of compounds on the expression of AKT1, MAPK3, EGFR, SRC, and STAT3 in LPS‐induced RAW 264.7 cells. Data are expressed as the mean ± SD, n = 3. ^# p < 0.05, ^## p < 0.01, ^### p < 0.001 vs. control group. *p < 0.05, **p < 0.01, ***p < 0.001 vs. LPS group.

4. Discussion

As the body's defensive response to various injuries and pathogens, inflammation helps remove damaged cells and pathogens from the host. However, excessive inflammation can damage normal tissue cells while eliminating pathogens, subsequently triggering multiple inflammatory diseases and adversely affecting human health (Liu et al. 2022). Z. officinale is a globally used spice and medicinal plant traditionally applied for colds, gastrointestinal discomfort, nausea, and migraines. Recent studies have further revealed its anti‐inflammatory, antioxidant, anticancer, and neuroprotective properties, largely attributed to bioactive compounds such as gingerols, shogaols, zingerone, and paradols (Pázmándi et al. 2024). To further explore the potential anti‐inflammatory compounds in Z. officinale and their mechanisms of action, this study employed UPLC‐Q‐Exactive Orbitrap MS/MS technology to identify 32 compounds from ginger ethanol extracts. Subsequently, network pharmacology and molecular docking techniques were utilized to investigate potential anti‐inflammatory effects. Network pharmacology results indicated that 6‐shogaol, 8‐shogaol, 8‐gingerol, 1‐dehydro‐8‐gingerdione, and 6‐gingerone were the primary compounds responsible for anti‐inflammatory effects, acting through core targets including AKT1, MAPK3, EGFR, SRC, and STAT3. Key pathways involved include AGE‐RAGE in diabetic complications, PI3K‐Akt, MAPK, TNF, and IL‐17 signaling pathways, which play crucial roles in inflammation, cell proliferation, apoptosis, and immune modulation. Molecular docking results revealed that the five core chemical components exhibited strong binding affinity with five key targets, with 1‐dehydro‐8‐gingerdione demonstrating the lowest binding energy (−9.1 kcal/mol) toward AKT1. In vitro validation demonstrated that Z. officinale ethanol extract and its core components (6‐shogaol, 8‐shogaol, and 8‐gingerol) significantly inhibited NO release in an LPS‐induced RAW264.7 macrophage inflammation model in a dose‐dependent manner, further confirming their anti‐inflammatory activity. Additionally, qRT‐PCR results suggested that these components may exert anti‐inflammatory effects by regulating the expression of inflammation‐related genes. Overall, this multimethod study provides preliminary insights into the material basis and molecular mechanisms of anti‐inflammatory effects, supporting its further development as an anti‐inflammatory agent or functional food.

LC–MS, with its high separation efficiency, selectivity, and sensitivity, is a key tool for the qualitative and quantitative analysis of natural products. In recent years, this technology has rapidly emerged as a crucial method for determining trace component compositions in food and pharmaceuticals (Wang, Chu, et al. 2021). UPLC‐Q‐Exactive‐HRMS has been utilized to analyze eight ginger‐derived compounds 6‐gingerol, 6‐shogaol, 8‐gingerol, 8‐shogaol, 10‐gingerol, 10‐shogaol, zingerone, and 6‐isodehydrogingenone in rat plasma and tissues, enabling systematic investigation of their pharmacokinetics and tissue distribution (Li et al. 2019). In this work, UPLC‐Q‐Exactive Orbitrap MS/MS identified 32 compounds from ethanol extract, mainly volatile oils, gingerols, and diarylheptanoids, clarifying the material foundation of Z. officinale 's pharmacological activities. To validate the analytical method for Z. officinale ethanolic extract using UPLC‐Q‐Exactive Orbitrap MS/MS, we performed a comprehensive validation on 6‐shogaol, covering linearity, precision, repeatability, stability, spike recovery, and content determination (detailed methods in Supporting Information) (Yudthavorasit et al. 2014). The results showed that the regression equation for 6‐shogaol was y = 0.1885⋇ + 71.804 (R ² = 0.9976). This confirmed that the method was suitable for the quantitative analysis of Z. officinale reference standards (Li et al. 2019). The RSD% for the repeatability test was 2.5%, while the RSDs% for the intraday and interday precision tests were 1.10% and 3.00%, respectively. All results met the predefined acceptance criterion of ≤ 5% (Che et al. 2026), indicating that the quantitative method for Z. officinale exhibits good reproducibility and interday precision. The stability test results showed an RSD% of 3.4%. The spiking recovery test results indicated that the recovery rate of 6‐shogaol in the sample was 97%, with a relative standard deviation (RSD%) of 1.8%, the recovery rate fell within the acceptable range of 95%–105% (Bai et al. 2025). This method has been successfully applied to pharmacokinetic studies of eight ginger constituents (6‐gingerol, 8‐gingerol, 10‐gingerol, and their corresponding shogaols, plus zingerone and 6‐isodehydrogingenone) in rat plasma and tissues, with good linearity (R ² > 0.991), precision (RSD < 12.2%), recovery (91.4%–107.4%), and matrix effects (86.3%–113.4%) (Li et al. 2019). The content of 6‐shogaol in Z. officinale was found to be relatively high (up to 7.94 mg/g), while concentrations of 8‐gingerol and 6‐shogaol in three ginger varieties ranged from 0.52 to 3.07 mg/g and 1.24 to 3.53 mg/g, respectively (Rafi et al. 2013). Extraction and drying methods significantly influence gingerol and shogaol profiles. Subcritical water extraction converts 6‐gingerol to 6‐shogaol via pyrolysis, with the latter exhibiting stronger bioactivity (Sakdasri et al. 2025). Ultrasound‐assisted extraction increased 6‐gingerol (7.72–8.62 mg/g DE) and 8‐shogaol (6.13–6.92 mg/g DE) with increasing amplitude (Sulejmanović et al. 2024), while sun‐drying and hot‐air drying yielded markedly distinct levels of 6‐/8‐gingerol and 6‐/8‐shogaol (Ghasemzadeh et al. 2018). Collectively, the validated method offers excellent stability, reproducibility, and precision, enabling rapid qualitative and quantitative analysis of active components in Z. officinale under standard laboratory conditions.

To bridge the gap between chemical composition and bioactivity, a “Component‐Target” network was constructed based on the identified compounds. Network analysis revealed five core components, including 6‐shogaol (20), 8‐shogaol (24), 8‐gingerol (19), 1‐dehydro‐8‐gingerdione (12), and 6‐gingerone (17). Notably, all these core components belong to the gingerol structural class and have been previously reported to exhibit anti‐inflammatory activity in various experimental models. In a mouse model of CCl₄‐induced liver fibrosis, shogaol inhibited macrophage infiltration and NLRP3 inflammasome activation via suppression of IκBα phosphorylation and NF‐κB nuclear translocation (Qiu et al. 2022). Similarly, 6‐shogaol and 8‐shogaol have been reported to suppress LPS‐induced pro‐inflammatory mediators, including PGE₂, iNOS, COX‐2, TNF‐α, IL‐6, and IL‐1β, in both RAW 264.7 macrophages and murine inflammatory models (Attallah et al. 2025; Kim and Lee 2024). Moreover, in a DSS‐induced colitis rat model, intraperitoneal administration of 6‐gingerol, 8‐gingerol, and 10‐gingerol (30 mg·kg⁻¹·d⁻¹ for 7 days) attenuated colonic injury, reduced oxidative stress (elevated SOD and lowered MDA and MPO), and decreased serum TNF‐α and IL‐1β levels (Zhang et al. 2017). KEGG pathway enrichment analysis revealed that the anti‐inflammatory effects of Z. officinale were primarily associated with AGE‐RAGE, PI3K‐Akt, MAPK, TNF, and IL‐17 signaling pathways. Among these, the IL‐17, TNF, and PI3K‐Akt pathways are recognized as central regulators of inflammatory responses. The PI3K‐Akt pathway mediates extracellular signals via serine/threonine phosphorylation, regulating metabolism, proliferation, cell survival, growth, and angiogenesis (Qiao et al. 2025). Activation of the PI3K‐Akt signaling pathway is crucial for cell proliferation and migration, and also serves as a key factor in ulcer healing and re‐epithelialization (Zhou et al. 2023). Upon activation, PI3K recruits AKT to the plasma membrane; activated AKT subsequently promotes pro‐inflammatory cytokine expression and secretion through NF‐κB activation, contributing to cytokine imbalance and inflammatory cascades (Koorella et al. 2014; Shi et al. 2016). Conversely, inhibition of PI3K signaling during TLR‐mediated inflammation suppresses pro‐inflammatory cytokine secretion by macrophages and dendritic cells while enhancing IL‐10 production (Bai et al. 2014; Fallah et al. 2011). The IL‐17 signaling pathway has garnered considerable attention for its diverse roles in immune regulation, including host defense, autoimmunity, and tissue inflammation (Liu et al. 2024). It exerts pro‐inflammatory effects by activating NF‐κB p65 to induce IL‐1β, IL‐6, and TNF‐α production, and synergizes with these cytokines to amplify inflammation and recruit additional immune cells (Liu et al. 2024; Onishi and Gaffen 2010). Meanwhile, the AGE‐RAGE pathway is associated with oxidative stress and inflammation, while the TNF signaling pathway plays a crucial role in regulating immune responses and apoptosis (Wang, Chu, et al. 2021). In summary, these five pathways are closely associated with anti‐inflammatory effects and may represent potential key mechanisms underlying its action.

Macrophages are central mediators of inflammatory responses, capable of producing and secreting a range of pro‐inflammatory mediators and cytokines upon activation. Nitric oxide (NO), acting as a pro‐inflammatory molecule, plays a crucial role in inflammatory responses and is produced by inducible nitric oxide synthase (iNOS) (Kim et al. 2016). Based on network pharmacology analysis, the top three core compounds (6‐shogaol, 8‐shogaol, and 8‐gingerol) and Z. officinale ethanol extract were ultimately selected for in vitro anti‐inflammatory validation studies. Lipopolysaccharide‐induced macrophage inflammation models were employed to detect pro‐inflammatory factor NO expression in RAW264.7 cells. In vitro validation demonstrated that ethanol extract and its core components significantly inhibited LPS‐induced NO release in the RAW264.7 macrophage inflammation model in a dose‐dependent manner, further confirming their anti‐inflammatory activity. The qRT‐PCR results further demonstrated that treatment with these compounds at three concentrations (6.25, 12.5, and 25 μM) reversed the LPS‐induced upregulation of AKT1, MAPK3, EGFR, SRC, and STAT3 mRNA levels, with the most pronounced inhibitory effects observed at 25 μM.

By integrating UPLC‐Q‐Exactive Orbitrap MS/MS, network pharmacology, molecular docking, and in vitro functional assays, this study systematically delineated the bioactive constituents and mechanistic basis underlying the anti‐inflammatory effects of Z. officinale . Nevertheless, several inherent limitations of this study should be acknowledged. Network pharmacology, a computational strategy relying on public databases (e.g., TCMSP and GeneCards), is inherently incomplete, potentially omitting certain bioactive compounds and target genes (Liu et al. 2023; Tan et al. 2022). In addition, the LPS‐stimulated RAW 264.7 macrophage model, while widely used, cannot fully recapitulate the complex in vivo micro‐environment, involving immune organs, other immune cells, gut microbiota, and neuroendocrine regulation (Wang, Wang, et al. 2021). Therefore, the inflammatory responses and signaling pathway activations observed in in vitro experiments cannot fully mimic the pathological states of acute and chronic inflammatory diseases in vivo. To verify its efficacy and safety, future studies will utilize in vivo animal models to further validate the anti‐inflammatory effects of the core components and their mechanisms of action in vivo (Raj et al. 2020; Ryu et al. 2022; Zhang et al. 2025). Moreover, although our in vitro findings provide compelling evidence that 6‐shogaol, 8‐shogaol, and 8‐gingerol downregulate key inflammatory genes and inhibit NO release, the absence of specific inhibitors or gene‐silencing techniques precludes definitive confirmation that these effects are strictly dependent on AKT1, MAPK3, EGFR, SRC, and STAT3 modulation. Future studies incorporating in vivo animal models and targeted interventions (e.g., pharmacological inhibitors or siRNA) are warranted to further validate these core targets and mechanisms (Chang et al. 2022; Theofilatos et al. 2018). Despite these limitations, this study has preliminarily identified the anti‐inflammatory bioactive components of Z. officinale and elucidated their molecular mechanisms, laying a foundation for novel anti‐inflammatory drug development.

5. Conclusions

This study employed UPLC‐Q‐Exactive Orbitrap MS/MS technology to identify 32 chemical constituents in Z. officinale , preliminarily elucidating its pharmacological basis. Through network pharmacology and molecular docking analysis, the study revealed the anti‐inflammatory mechanism involving multicomponent, multitarget, and multipathway synergistic effects. Molecular docking between the core active components and key inflammatory targets predicted by network pharmacology supported the reliability of the computational predictions. Subsequent in vitro experiments further validated the anti‐inflammatory activity of Z. officinale ethanol extract and its primary bioactive compounds, confirming their ability to inhibit inflammatory responses in a cellular model. Overall, the findings of this study not only enhance the understanding of Z. officinale 's anti‐inflammatory mechanisms but also provide a theoretical foundation for its further development, comprehensive utilization, and potential clinical application. The integrated methodology presented here offers a systematic framework for studying the complex bioactive profiles of other multicomponent natural products.

Supporting information

TABLE S1: Mass spectrometry conditions.

TABLE S2: Active ingredient structures.

FIGURE S1: Total ion chromatograms (TIC) of Zingiber officinale ethanol extract in positive and negative ion modes. (A) Positive ion mode. (B) Negative ion mode.

FIGURE S2: Docking patterns of some core targets with core constituents. (A‐D) Hydrophobic interaction of 6‐shogaol‐AKT1, 8‐shogaol‐MAPK3, 1‐dehydro‐8‐gingerdione‐AKT1, and 1‐dehydro‐8‐gingerdione‐MAPK3 in 3D.

TABLE S3: Primer sequences (F stands for the forward primer, and R stands for the reverse primer).

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.