{"paper_id":"3da17ffd-72fc-41a6-bbb5-54b70a5f1f86","body_text":"Disclosing the potential mechanisms of Amomi Fructus phenolic compounds inducing ferroptosis in SGC-7901 gastric cancer cells: a joint network pharmacology-based analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Disclosing the potential mechanisms of Amomi Fructus phenolic compounds inducing ferroptosis in SGC-7901 gastric cancer cells: a joint network pharmacology-based analysis Yana Lv, Bin Xia, Wei Shi, Yan Mou, Jing Su, Xuan Ding, Lixia Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5287414/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives This study aimed to elucidate the mechanisms by which Amomi Fructus phenolic compounds (AFPC) induce ferroptosis in gastric cancer (GC) using a combination of network pharmacology and experimental validation. Methods Targets associated with AFPC, ferroptosis, and GC were compiled, and a component-target network was constructed to identify key compounds. Hub targets of AFPC-induced ferroptosis in GC were determined through protein-protein interaction (PPI) network analysis. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were conducted to assess these hub targets. The hub targets' mRNA and protein expression levels were evaluated using UALCAN, GEPIA, Kaplan-Meier Plotter, HPA, and TISCH1. Molecular docking and molecular dynamics (MD) simulations were performed to determine the binding affinity and stability between hub targets and active compounds. Finally, in vitro cell experiments validated the network pharmacology findings. Finally, in vitro cell experiments validated the network pharmacology findings. Results Two main active compounds, gallic acid and quercetin, and five hub targets—TP53, HIF1A, IL6, STAT3, and EGFR—were identified. GO and KEGG analyses indicated that AFPC treatment in GC primarily involves oxidative stress, nuclear localization, p53 binding, and cancer-associated pathways. Molecular docking and simulations suggested that gallic acid and quercetin inhibit GC through modulating hub targets. Experimental results demonstrated that gallic acid and quercetin suppress GC cell viability and induce ferroptosis in SGC7901 cells by elevating Fe 2+ , MDA, and reducing GSH levels. The expressions of p-STAT3, STAT3, and IL-6 were significantly downregulated. Conclusions This study suggests that AFPC inhibits GC cell proliferation and induces ferroptosis by blocking the IL-6/STAT3 pathway. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Gastric cancer Amomi Fructus Network pharmacology Ferroptosis Experimental validation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Gastric cancer (GC) is among the most common malignant tumors of the digestive tract. According to global cancer statistics for 2020, GC accounted for over 1 million new cases and more than 769,000 cancer-related deaths, translating to approximately one death for every 13 cases of GC. In terms of incidence and mortality, GC ranks fifth and fourth globally, respectively [ 1 ]. Current treatment options, including surgical resection and chemotherapy, are limited in efficacy, as evidenced by a dismal 5-year survival rate of less than 20% for advanced GC [ 2 ]. The regulation of cell death plays a fundamental role in tumor onset and progression, with cancer cells exhibiting the unique ability to evade programmed cell death. Consequently, the exploration of novel strategies that induce tumor cell death represents a promising avenue for GC treatment. Ferroptosis is an iron-dependent form of regulated cell death that is distinct from other programmed cell death pathways, such as autophagy, necroptosis, and apoptosis. Key features of ferroptosis include lipid ROS accumulation, GSH depletion, and elevated Fe2 + levels [ 3 – 5 ]. Increasing evidence highlights the relevance of ferroptosis in cancer treatment, particularly in overcoming GC-related drug resistance [ 6 – 8 ]. Inducing ferroptosis in GC cells may reduce their invasion, proliferation, and metastasis potential [ 9 – 11 ]. Moreover, ferroptosis introduces damage-associated molecular patterns (DAMPs) into the tumor microenvironment (TME), triggering immune responses that hinder tumor growth [ 12 , 13 ]. Immune cells involved in tumor immunity, such as T cells, B cells, and macrophages, can also undergo ferroptosis under specific conditions [ 14 – 16 ]. Thus, targeting ferroptosis is a promising strategy for GC treatment. Polyphenols, natural compounds found in plant-based foods, exhibit diverse biological activities, including antioxidant properties. These compounds have demonstrated therapeutic potential for managing various complex diseases, including cardiovascular, neurological, and metabolic disorders, as well as cancer [ 17 , 18 ]. Amomi Fructus (AF), the dried ripe fruit of Amomum villosum, is commonly used in traditional Chinese medicine and dietary supplements for digestive ailments [ 19 , 20 ]. AF comprises both volatile and non-volatile components, with recent studies focusing mainly on volatile oil constituents. However, the biological activities of non-volatile components, particularly polyphenols, remain underexplored. Polyphenols and polysaccharides are the primary non-volatile components of AF, known for their anti-inflammatory, antitumor, antioxidant, and antibacterial properties [ 21 ]. Yue et al. demonstrated that flavonoid extracts from AF possess potent inhibitory effects on GC cell proliferation in vitro, exceeding the effects of volatile oils [ 22 ]. Additionally, traditional Chinese formulations containing AF, such as Aucklandiae Radix-Amomi Fructus and Additive Xiangsha Liujunzi Decoction, have been shown to induce apoptosis and suppress GC cell proliferation [ 23 , 24 ]. Despite evidence suggesting the anti-GC potential of Amomi Fructus phenolic compounds (AFPC), the active components, molecular targets, and mechanisms of action remain largely undefined. Therefore, this study aimed to investigate whether AFPC induces ferroptosis in GC cells and to elucidate the underlying mechanisms using network pharmacology, molecular docking, molecular dynamics simulations, and in vitro experiments. Results Network pharmacology analysis Bioactive compound screening. A total of 41 compounds from AFPC were collected via literature mining. After identifying relevant structures, 20 bioactive compounds were selected using SwissADME based on gastrointestinal (GI) absorption and drug-likeness (Table 1 ). A total of 5,336 potential targets for these compounds were identified using the CTD database. Table 1 Information of 20 bioactive compounds Compound Name Formula GI absorption Lipinski Drug likeness Bioavailability score 3-Hydroxybenzoic Acid C 10 H 12 O 2 high Yes 3 0.85 4-(4-Hydroxyphenyl)-2-Butanone C 10 H 12 O 2 high Yes 4 0.55 4-Hydroxybenzoic acid C 7 H 6 O 3 high Yes 3 0.85 Caffeic acid C 9 H 8 O 4 high Yes 4 0.56 Catechin C 15 H 14 O 6 high Yes 5 0.55 Cinnamic acid C 9 H 8 O 2 high Yes 3 0.85 Ferulic Acid C 10 H 10 O 4 high Yes 4 0.85 Gallic acid C 7 H 6 O 5 high Yes 3 0.56 Isorhamnetin C 16 H 12 O 7 high Yes 5 0.55 p-Coumaric Acid C 9 H 8 O 3 high Yes 4 0.85 Polydatin C 20 H 22 O 8 high Yes 3 0.55 Protocatechualdehyde C 7 H 6 O 3 high Yes 3 0.55 Protocatechuic acid C 7 H 6 O 4 high Yes 3 0.56 Protocatechuic acid methyl ester C 8 H 8 O 4 high Yes 4 0.55 Quercetin C 15 H 10 O 7 high Yes 5 0.55 Salicylic acid C 7 H 6 O 3 high Yes 3 0.85 Shikimic acid C 7 H 10 O 5 high Yes 3 0.56 Sinapinic acid C 11 H 12 O 5 high Yes 5 0.56 Syringic acid C 11 H 12 O 5 high Yes 4 0.85 Vanillic acid C 8 H 8 O 4 high Yes 4 0.85 Collection of targets and construction of the component-target network. After removing duplicates, 5,336 AFPC genes, 340 GC genes, and 494 ferroptosis-related genes were integrated, resulting in 58 shared targets (Fig. 1 A). A component-target network was subsequently constructed to elucidate the potential mechanisms of AFPC bioactive compounds against GC (Fig. 1 B). The two highest-ranked bioactive compounds, gallic acid and quercetin, were identified as the main components involved in AFPC-induced ferroptosis in GC. Construction of the PPI network and screening of hub targets. The PPI network was visualized and analyzed using Cytoscape, encompassing 729 edges and 58 nodes (Fig. 2 ). The top 10 genes were ranked by degree, betweenness centrality (BC), and closeness centrality (CC) as presented in Table 2 . The hub targets were defined as the intersecting genes among the top-ranked genes, which included TP53, HIF1A, IL6, STAT3, and EGFR. Table 2 Top 10 genes in PPI network Genes Degree Genes BC Genes CC MCC TP53 55 TP53 0.08891521 TP53 0.89855072 TP53 HIF1A 51 HIF1A 0.06766567 HIF1A 0.84931507 HIF1A IL6 48 PTGS2 0.03584706 IL6 0.80519481 JUN IL1B 45 ABCC1 0.03243863 IL1B 0.775 STAT3 STAT3 44 IL6 0.03075067 STAT3 0.775 IL6 JUN 44 EGFR 0.02961317 EGFR 0.7654321 SRC EGFR 43 IL1B 0.02936726 JUN 0.7654321 PTEN PTGS2 43 STAT3 0.02366127 PTGS2 0.75609756 MTOR MAPK3 41 KRAS 0.02242708 MAPK3 0.73809524 CDKN1A SRC 40 PPARA 0.02229267 NFE2L2 0.72941176 EGFR GO and KEGG enrichment analysis. GO and KEGG enrichment analyses were performed using the DAVID database to elucidate the molecular mechanisms of AFPC-induced ferroptosis in GC. In GO functional enrichment, a total of 1,842 GO gene annotations were obtained, comprising 295 biological processes (BP), 43 cellular components (CC), and 50 molecular functions (MF). The top 10 GOBP, GOCC, and GOMF terms were plotted in a bar chart based on ascending p-values (Fig. 3 A). BP enrichment primarily included cellular response to reactive oxygen species, oxidative stress, and regulation of gene expression and apoptosis. CC enrichment involved the nucleus, cytosol, nucleoplasm, and focal adhesion, while MF terms included enzyme binding, ubiquitin protein ligase binding, and p53 binding. A total of 136 pathways enriched for shared targets were identified for further study. The pathways were categorized into five primary groups: metabolism, environmental information processing, cellular activities, organismal systems, and human disease (Fig. 3 B). Among these, cancer-related pathways contained the highest number of annotated genes. Hub targets were notably implicated in the HIF-1, PI3K-AKT, MAPK, JAK-STAT, and p53 signaling pathways. A total of 26 GC-related pathways were analyzed, with the \"Pathways in cancer\" being the most prominent (Fig. 4 ). Several pathways affecting gene stability, apoptosis, metabolism, and cellular damage were identified, including PI3K-AKT, MAPK, JAK-STAT, and p53 pathways. Expression of Hub Targets in STAD mRNA expression and survival analysis of hub genes in STAD and paracancerous stomach tissues. The GEPIA database analysis revealed that the mRNA expression levels of TP53 and HIF1A were significantly higher in stomach adenocarcinoma (STAD) tissues compared to paracancerous tissues ( p < 0.05) (Fig. 5 A). To further investigate the mRNA expression levels of the five hub genes, the UALCAN database was utilized. Except for IL6, the mRNA expression levels of the remaining four hub genes—TP53, HIF1A, STAT3, and EGFR—were significantly elevated in STAD tissues compared to paracancerous tissues ( p < 0.05) (Fig. 5 B). These findings suggest that these four hub genes play critical roles in GC diagnosis, progression, and treatment, highlighting their importance in the pathogenesis of GC. Prognostic values of the five hub genes were analyzed using the Kaplan-Meier plotter (Fig. 5 C). The results indicated that TP53 (HR: 1.7, log-rank p = 1.9e -08 ), STAT3 (HR: 1.34, log-rank p = 0.00078), EGFR (HR: 1.35, log-rank p = 0.0092), and HIF1A (HR: 0.73, log-rank p = 0.00032) were significantly correlated with overall survival (OS) in GC patients. OS was higher in patients with low transcript levels of TP53, STAT3, and EGFR, and high levels of HIF1A. These findings suggest that TP53, STAT3, EGFR, and HIF1A may have novel roles in GC and have potential as diagnostic and prognostic biomarkers. Immunohistochemical analysis and single-cell analysis of the hub genes in STAD. The expression of five hub genes in STAD and paracancerous tissues was investigated using the Human Protein Atlas (HPA) database (Fig. 6 A). Results showed that EGFR, STAT3, and TP53 had higher expression levels in STAD compared to paracancerous tissues, whereas HIF1A and IL6 showed no significant difference. These findings suggest that EGFR, STAT3, and TP53 could serve as important targets for GC diagnosis and treatment, implying their significant roles in GC development. Single-cell analysis was used to examine the distribution of the five hub genes in tumor microenvironment (TME) cells (Fig. 6 B). TP53 showed moderate expression in patient mucous cells and high expression in mast cells. HIF1A was substantially expressed in both malignant cells and myofibroblasts, whereas STAT3 was mainly found in mucous cells. Overall, the single-cell analysis of the STAD_GSE134520 dataset [ 39 ] showed enrichment of three hub genes (TP53, HIF1A, STAT3), excluding EGFR and IL6. Molecular docking validation Molecular docking was performed to evaluate the binding affinity between the five hub targets identified from the PPI network (TP53, HIF1A, IL6, STAT3, EGFR) and the two key compounds (gallic acid, quercetin) identified from the compound-target network. The docking results, visualized as a heatmap, revealed good binding activity between the hub targets and the two compounds (Fig. 7 A). PyMOL visualization showed specific interactions between gallic acid and quercetin with each of the hub targets (Fig. 7 B-K). For TP53, gallic acid formed hydrogen bonds with residues K370, Y240, N206, K17, and E135, and quercetin formed hydrogen bonds with R253, D242, and R306 (Fig. 7 B-C). HIF1A interacted with gallic acid through residues E159, E524, and R156, and quercetin interacted with L273, D404, and M341 (Fig. 7 D-E). IL6 formed hydrogen bonds with L254, S30, and D26 using gallic acid, and quercetin interacted with Q175, R179, G164, and others (Fig. 7 F-G). STAT3 bound to gallic acid at S113, Q3, and A44, while quercetin formed bonds with R13, T118, and others (Fig. 7 H-I). EGFR demonstrated binding with gallic acid to residues L858, A859, and others, and quercetin formed bonds with A743 and K745 (Fig. 7 J-K). In summary, the two representative AFPC compounds, gallic acid and quercetin, demonstrated favorable binding to the five hub targets: EGFR, TP53, HIF1A, IL6, and STAT3. These targets may hold significant therapeutic implications for GC patients. Molecular dynamics (MD) simulation validation Results from MD simulations—including RMSD, RMSF, Rg values, and the number of hydrogen bonds (H-bonds)—were used to evaluate the hydrophobicity of amino acid residues, stability of protein-ligand complexes, and overall stability of protein tertiary structures upon binding of small molecules. RMSD is a key metric for assessing system stability. As shown in Fig. 8 A, the RMSD values for gallic acid-TP53, quercetin-TP53, gallic acid-STAT3, gallic acid-EGFR, and quercetin-EGFR rapidly stabilized at 0.2–0.3 nm after 10 ns. Gallic acid-HIF1A and quercetin-STAT3 stabilized at 0.23–0.3 nm after 20 ns. After 25–30 ns, quercetin-IL6 and gallic acid-IL6 reached stability at 0.24–0.38 nm, while quercetin-HIF1A stabilized at 0.25–0.3 nm after 40 ns. RMSF reflects protein residue flexibility. As depicted in Fig. 8 B, the average fluctuation for most complexes was 0.13 nm, while the gallic acid-HIF1A, quercetin-HIF1A, quercetin-IL6, and gallic acid-STAT3 complexes exhibited slightly higher fluctuations at 0.14 nm. Variations in RMSF values were observed for specific residues, primarily due to differences in the binding degree. Rg was used to evaluate the compactness of receptor-ligand binding. As shown in Fig. 8 C, gallic acid-STAT3 and gallic acid-TP53 stabilized at 1.65–2.03 nm after 10 ns, while quercetin-STAT3 stabilized at 1.98–2.02 nm after 20 ns. Other complexes, including gallic acid-HIF1A, quercetin-HIF1A, and quercetin-IL6, stabilized at values between 1.62 and 2.05 nm after 25–50 ns. A slight increase in Rg for quercetin-TP53 was observed after 10 ns, which may reflect conformational changes in the protein chain. The number of H-bonds reflects protein-ligand binding strength. As illustrated in Fig. 8 D, H-bond numbers for quercetin-TP53 and quercetin-EGFR stabilized at 1–3, while other complexes, such as quercetin-HIF1A, gallic acid-IL6, and quercetin-IL6, varied between 0–4. The H-bond numbers for gallic acid-TP53 decreased gradually, primarily staying between 0 and 2, while those for gallic acid-HIF1A fluctuated considerably between 1–5. These results indicate stable binding of the five hub proteins with both gallic acid and quercetin, consistent with molecular docking results. These results indicate stable binding of the five hub proteins with both gallic acid and quercetin, consistent with molecular docking results. In vitro activity verification Inhibition of SGC7901 Cell Growth by Gallic Acid and Quercetin. SGC7901 cells were treated with different concentrations of gallic acid and quercetin for 24 hours. Both compounds significantly inhibited cell growth in a dose-dependent manner (Fig. 9 A). The IC50 values for gallic acid and quercetin were 24.44 ± 0.54 µg/mL and 33.83 ± 0.31 µg/mL, respectively. Based on these findings, concentrations of 24 µg/mL and 34 µg/mL were used in subsequent experiments. Effects of Gallic Acid and Quercetin on MDA, Fe2 + Production, and GSH Depletion. Following treatment with gallic acid and quercetin, there was a significant increase in Fe 2+ (Fig. 9 B) and malondialdehyde (MDA) levels (Fig. 9 C) and a marked decrease in GSH levels (Fig. 9 D) compared to the control group. Additionally, the addition of Ferrostatin-1 during treatment with gallic acid and quercetin partially or fully reversed these changes, further supporting the hypothesis that gallic acid and quercetin induce ferroptosis in SGC7901 cells. Induction of Ferroptosis via the IL-6/STAT3 Pathway. Western blot analysis revealed a significant reduction in the expression of p-STAT3, STAT3, and IL-6 following 24-hour treatment with gallic acid and quercetin compared to the control group. The concurrent addition of Ferrostatin-1 partially reversed these effects, though not to the same extent as the untreated control group (Fig. 10 ). These findings indicate that gallic acid and quercetin induce ferroptosis in SGC7901 cells through modulation of the IL-6/STAT3 signaling pathway. Discussion Cell death plays an essential role in cancer initiation and progression. The ability of cancer cells to evade death is a hallmark of tumorigenesis [ 25 ]. Ferroptosis, characterized by iron overload and lipid peroxidation, has been implicated in the development, progression, treatment, and prognosis of GC[ 26 , 27 ]. This study aimed to explore the mechanism by which AFPC induce ferroptosis in GC using a combination of network pharmacology and experimental validation. The network pharmacology analysis identified gallic acid and quercetin as the main active components responsible for AFPC-induced ferroptosis in GC. Gallic acid, a natural plant-derived phenolic compound, possesses anti-inflammatory, weight-reducing, and anticancer properties [ 28 ]. It has been shown to promote apoptosis and inhibit metastasis and invasion of GC cells by modulating NF-kappaB and PI3K/AKT/small GTPase signaling [ 29 , 30 ]. Quercetin, a naturally occurring flavonoid, also demonstrates broad anticancer activity [ 31 , 32 ]. Quercetin induces apoptosis in GC and regulates autophagy through the Akt-mTOR/HIF-1α pathway [ 33 ]. Additionally, it has been reported to induce ferroptosis in GC cells. The AFPC-induced ferroptosis in GC was primarily associated with cancer-related pathways, including HIF-1, PI3K-AKT, MAPK, JAK-STAT, and p53 signaling pathways, all of which contribute to GC development and progression. HIF-1α, a key subunit of HIF-1, is overexpressed in GC cells, leading to aberrant gene expression and cancer progression [ 34 , 35 ]. Zhang et al. demonstrated that downregulation of HIF-1α could inhibit GC cell proliferation, migration, and invasion by suppressing the PI3K/AKT pathway and VEGF expression [ 36 ]. Both the PI3K/AKT and MAPK pathways are involved in cellular processes such as proliferation, migration, and apoptosis, and are activated in GC [ 37 – 41 ]. The p53 signaling pathway also contributes to GC progression, with PI3K/Akt affecting p53 through multiple mechanisms [ 42 – 45 ]. The JAK/STAT pathway plays a role in proliferation, differentiation, and apoptosis of GC cells [ 46 – 50 ]. Inhibition of JAK/STAT3 activation has been shown to increase apoptosis and cell cycle arrest while reducing GC cell proliferation and invasion [ 51 , 52 ]. Taken together, AFPC may induce ferroptosis in GC by regulating the crosstalk among multiple signaling pathways, thus achieving its therapeutic effects. The PPI network analysis identified TP53, HIF1A, IL6, STAT3, and EGFR as hub targets involved in AFPC-induced ferroptosis in GC. TP53 is closely associated with gastrointestinal malignancies and positively regulates ferroptosis [ 53 – 55 ]. Suppression of TP53 prevents apoptosis and reduces Bcl-6 expression in GC cells [ 56 ]. HIF1α is a critical transcription factor for cellular responses to hypoxia, and its aberrant activation is linked to GC progression [ 57 – 59 ]. EGFR, a poor prognostic marker, is highly expressed in GC and regulates cell migration, proliferation, and apoptosis [ 60 , 61 ]. IL-6 is a pro-tumor cytokine that activates the JAK/STAT3 pathway, contributing to tumor cell survival and proliferation [ 62 , 63 ]. STAT3 regulates GC cell survival by promoting the expression of anti-apoptotic proteins, and inhibition of STAT3 induces ferroptosis [ 64 – 66 ]. The IL-6/STAT3 signaling pathway is involved in the survival, proliferation, invasion and migration of tumor cells [ 67 , 68 ]. Zheng has shown that by blocking the IL-6/STAT3 signaling pathway, Kang-ai injection can stop the growth of GC cells [ 69 ]. Therefore, targeting the IL-6/STAT3 pathway may represent an effective approach for GC therapy. The present study validated the expression and prognostic value of five hub targets—TP53, HIF1A, IL6, STAT3, and EGFR—using bioinformatics data. High expression levels of TP53, STAT3, and EGFR were observed in GC tissues, making them potential biomarkers for GC diagnosis and prognosis. Additionally, using the STAD_GSE134520 dataset, we explored the cellular function of these genes in the tumor microenvironment (TME). TP53 was found to be expressed in mast cells, while HIF1A was highly expressed in myofibroblasts. Studies have suggested that overexpression of TP53 in mast cells and HIF1A in myofibroblasts plays a role in tumor immune response and GC progression [ 72 , 71 ]. In addition, molecular docking and MD simulations were further performed to validate network pharmacology predictions. The docking results indicated strong binding affinities between gallic acid, quercetin, and the hub targets TP53, HIF1A, IL6, STAT3, and EGFR. The dynamic stability of these interactions was confirmed by RMSD, RMSF, Rg, and H-bond analyses during MD simulations. To further elucidate the link between AFPC and ferroptosis, we investigated the anti-tumor effects of gallic acid and quercetin and their regulatory effects on IL-6 and STAT3. The results demonstrated that both compounds significantly inhibited SGC7901 cell growth in a dose-dependent manner. Typical features of ferroptosis, such as increased Fe 2+ and MDA levels and decreased GSH production, were observed in treated cells. However, these changes were reversed by pre-treatment with Ferrostatin-1, indicating ferroptosis induction. Western blot analysis showed downregulation of p-STAT3, STAT3, and IL-6, which was partially reversed by Ferrostatin-1, suggesting that AFPC components inhibit the IL-6/STAT3 pathway, thereby inducing ferroptosis in GC cells. Materials and Methods Network pharmacology analysis Screening bioactive compounds and their target genes. The phenolic compounds of Amomi Fructus (AFPC) were initially retrieved through literature mining. The molecular structure files of these compounds were then downloaded from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ) [ 72 ] and saved in SDF format. Bioactive compounds were filtered based on high gastrointestinal (GI) absorption and more than three \"yes, 0 violation\" entries in SwissADME ( http://www.swissadme.ch/ ) [ 73 ]. The target genes of the bioactive compounds were predicted using the Comparative Toxicogenomics Database (CTD) ( https://ctdbase.org/ ) [ 74 ], and compounds without identified target genes were excluded. Screening the targets of AFPC induced ferroptosis in GC. The keyword \"gastric cancer\" was used in the CTD to obtain GC-related genes based on an inference score of at least 50. Ferroptosis-related targets were identified using the FerrDb V2 database ( http://www.zhounan.org/ferrdb/current/ ) [ 75 ], selecting the \"human\" tag. To identify shared targets, all potential targets from AFPC, ferroptosis, and GC were analyzed using OmicStudio ( https://www.omicstudio.cn/tool ) [ 76 ]. Cytoscape v3.10.2 [ 77 ] was used to construct and visualize the component-target network, and the network was further analyzed using the CytoHubba plugin [ 78 ]. The two highest-scoring bioactive compounds were selected as the main compounds using the maximal clique centrality (MCC) method. Protein‒protein interaction (PPI) analysis and hub targets screening. Shared targets were input into the STRING database (version 12.0, https://string-db.org/ ) [ 79 ] to extract PPI data with interaction scores for *Homo sapiens* (high: >0.7, medium: >0.4, low: >0.15). Cytoscape v3.10.2 was utilized to visualize the network and calculate node topological properties, including degree, betweenness centrality (BC), and closeness centrality (CC). Nodes with higher scores in all three indices were considered central. The top 10 genes based on degree, BC, and CC, respectively, were identified. The network was also analyzed using CytoHubba, and the top 10 genes were determined based on MCC scores. Overlapping genes among the top 10 by degree, BC, CC, and MCC were defined as the hub targets of AFPC-induced ferroptosis in GC. GO and KEGG enrichment analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID, https://david.ncifcrf.gov/summary.jsp ) [ 80 ] to determine the potential roles of AFPC-induced ferroptosis in GC. GO enrichment analyses included biological processes (BP), cellular components (CC), and molecular functions (MF). Annotation terms with p -values < 0.05 were considered statistically significant. GO terms and KEGG pathways were plotted based on the number of enriched genes using an online tool ( https://www.bioinformatics.com.cn ). Expression analysis of hub targets in gastric adenocarcinoma (STAD) mRNA expression and survival analysis of hub genes in STAD. To verify mRNA expression levels of hub targets in TCGA-STAD, the UALCAN ( http://ualcan.path.uab.edu ) [ 81 ] and GEPIA ( http://gepia.cancer-pku.cn/index.html ) [ 82 ] databases were used. Results from both databases were considered significant with p -values < 0.05. The overall survival (OS) of hub targets in GC was assessed using the Kaplan-Meier Plotter ( http://kmplot.com/analysis/index.php?p=service ) [ 83 ]. High and low hub gene expression groups were compared, and significance was determined using log-rank p -values and hazard ratios (HR) with 95% confidence intervals (CI), with a log-rank p < 0.05 considered significant. Immunohistochemistry and single-cell Analysis of hub targets in GC. Protein expression levels of the five hub targets in STAD were analyzed using the Human Protein Atlas (HPA) database ( http://kmplot.com/analysis/ ). Immunohistochemistry results for the selected targets in GC tissues and normal gastric tissues were obtained by selecting \"Stomach Cancer\" and \"Stomach,\" respectively. Single-cell analysis of the hub targets was performed using the Tumor Immune Single Cell Center 1 (TISCH1) online database ( http://tisch1.comp-genomics.org/ ) [ 84 ], with p < 0.05 considered statistically significant. Molecular docking validation The two main compounds were used as small molecular ligands for molecular docking with the five hub targets to validate the network pharmacology predictions. The PDB IDs for hub targets were EGFR (PDB ID: 4QTB), TP53 (PDB ID: 3TG5), HIF1A (PDB ID: 1FMK), IL6 (PDB ID: 5FUC), and STAT3 (PDB ID: 4ZIA). Compound SDF structures were obtained from PubChem and converted to mol2 format using Chem3D. Hub target PDB structures were downloaded from the RCSB protein databank (PDB, http://www.rcsb.org/ ) [ 85 ]. Small molecule ligands and solvent molecules were removed using PyMOL. Structures were then prepared in AutoDock 1.5.7 and saved in PDBQT format. Molecular docking simulations were conducted using AutoDock Vina v1.1.2, and binding affinities < -5 kJ mol-1 were considered indicative of significant binding activity. The results were visualized using the ggplot2 package (v.1.42.0) ( http://www.bioconductor.org/ ) in R (v.3.6.0). The combinations of docking scores were illustrated with PyMOL 2.5.2. Molecular dynamics (MD) simulation validation To further evaluate the stability of protein-ligand interactions, 10 complexes were subjected to MD simulations using Gromacs 2022 [ 86 ]: gallic acid-TP53, quercetin-TP53, gallic acid-HIF1A, quercetin-HIF1A, gallic acid-IL6, quercetin-IL6, gallic acid-STAT3, quercetin-STAT3, gallic acid-EGFR, and quercetin-EGFR. The simulation system was built using the AMBER14SB force field and TIP3P water model. The LINCS algorithm was used to constrain covalent bond lengths, and the PME algorithm was applied to calculate electrostatic interactions. NVT and NPT simulations were run for 100 ps at a constant temperature of 298 K and pressure of 1 bar to equilibrate the system. Production MD simulations were performed for 100 ns with confirmations saved every 10 ps. The MD simulation results, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and the number of hydrogen bonds (H-bonds), were analyzed and visualized using embedded Gromacs software and VMD. In vitro activity verification Cell culture and viability assay. SGC7901 cells were cultured at 37°C in a humidified incubator with 5% CO2, using specialized culture medium for SGC7901 (Procell Life Science & Technology Co., Ltd.). Gallic acid (purity 99%, J&K Scientific) and quercetin (purity 99.1%, J&K Scientific) were dissolved in dimethyl sulfoxide (DMSO) (Sigma, USA) and subsequently diluted to various concentrations using the culture medium. SGC7901 cells in the logarithmic growth phase were seeded at a density of 1×104 cells per well in a 96-well plate. The cells were treated with varying concentrations of gallic acid and quercetin (100.00, 50.00, 25.00, 12.50, 6.25, 3.12, 1.56, and 0 µg/mL) for 24 h to determine their inhibitory effect on cell proliferation. After treatment, 10 µL of CCK-8 solution (Beyotime, China) was added to each well, followed by incubation for an additional 2 h at 37°C. The optical density (OD) at 450 nm was measured using a SpectraMax i3 microplate reader (Molecular Devices). The the 50% inhibitory concentration (IC50) value was calculated using GraphPad Prism software version 6. Fe2+, reduced glutathione (GSH), and lipid peroxidation (MDA) level detection. SGC7901 cells were seeded at a density of 2×106 cells/well in 6-well plates and incubated overnight at 37°C. After treating the cells for 2 h with 2 µM of the ferroptosis inhibitor Ferrostatin-1 (Ferostatin-1; TargetMol), cell viability was assessed using the CCK-8 assay. According to the manufacturer’s instructions (Servicebio), the treated cells were collected, and the levels of Fe 2+ , GSH, and MDA were measured using specific detection kits. Western blotting (WB) assay. Total protein was extracted from treated cells using a mammalian protein extraction reagent (CW Biotech, China). Protein content was quantified using a BCA Protein Quantification Kit (CW Biotech, China). Protein samples were denatured by adding the protein extraction reagent and loading buffer (CW Biotech). Western blot analysis was performed using primary antibodies for p-STAT3 (OriGene, China), STAT3 (OriGene, China), IL-6 (Zenbio, China), and β-actin (Bioss, China), which were incubated at 4°C overnight. HRP-linked secondary antibodies against mice and rabbits (CW Biotech, China) were subsequently incubated for 2 h at room temperature. Enhanced chemiluminescence (ECL) detection reagents (NCMBiotech, China) were used for signal development, and blot intensity was quantified using ImageJ software. Statistical analysis. All data are presented as mean ± SEM from at least three independent experiments. The Student's t-test was used to determine statistical significance between two groups, with GraphPad Prism 6 used for all statistical analyses. A p -value < 0.05 was considered significant. Additionally, the IC50 value was determined by nonlinear regression using a variable slope (log[inhibitor] vs. normalized response) in GraphPad Prism. Conclusions In conclusion, AFPC induces ferroptosis in GC by modulating multiple crosstalk signaling pathways, including the IL-6/STAT3 pathway. The five hub targets—TP53, HIF1A, IL6, STAT3, and EGFR—play key roles in the disease etiology, diagnosis, and treatment. These findings provide insights into the potential therapeutic effects of AFPC on GC and highlight its promise as a novel approach for targeting ferroptosis in cancer therapy. Declarations Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contribution L.G. and C.X. conceived and designed the study. L.Y.N, X.B, and S.W. performed the experiments. M.Y., S.J.and D.X. analyzed the data. D.X. and Z.L.conducted data analysis; L.N. Y. and L.G.wrote the manuscript. All authors have read and approved this manuscript. Data Availability Data inquiries can be directed to the corresponding author. 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Molecular dynamics simulation of proteins. Methods Mol Biol 2073, 311–327. doi: (2020). 10.1007/978-1-4939-9869-2_17 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5287414\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":376730496,\"identity\":\"4287e473-50c8-4574-a73c-edbd65c8cc1d\",\"order_by\":0,\"name\":\"Yana Lv\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chinese Academy of Medical Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yana\",\"middleName\":\"\",\"lastName\":\"Lv\",\"suffix\":\"\"},{\"id\":376730498,\"identity\":\"c791d631-5913-4ecb-8fb9-d6cbbcdd439d\",\"order_by\":1,\"name\":\"Bin Xia\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Heilongjiang University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bin\",\"middleName\":\"\",\"lastName\":\"Xia\",\"suffix\":\"\"},{\"id\":376730502,\"identity\":\"8fc02f42-0cca-4180-aa36-64ea4a8eccbd\",\"order_by\":2,\"name\":\"Wei Shi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yuxi Normal College\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Wei\",\"middleName\":\"\",\"lastName\":\"Shi\",\"suffix\":\"\"},{\"id\":376730503,\"identity\":\"bccdbd4c-6c78-47cc-9d70-c7e4331749df\",\"order_by\":3,\"name\":\"Yan Mou\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yuxi Normal College\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yan\",\"middleName\":\"\",\"lastName\":\"Mou\",\"suffix\":\"\"},{\"id\":376730504,\"identity\":\"916e7952-cf9d-4daa-9570-d6d40a12f3ce\",\"order_by\":4,\"name\":\"Jing Su\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chinese Academy of Medical Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jing\",\"middleName\":\"\",\"lastName\":\"Su\",\"suffix\":\"\"},{\"id\":376730505,\"identity\":\"28bfb0ed-0bf4-4fe2-a48b-ce76cee3301a\",\"order_by\":5,\"name\":\"Xuan Ding\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chinese Academy of Medical Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xuan\",\"middleName\":\"\",\"lastName\":\"Ding\",\"suffix\":\"\"},{\"id\":376730506,\"identity\":\"107e5560-c34c-4dee-8862-162abae93742\",\"order_by\":6,\"name\":\"Lixia Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chinese Academy of Medical Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lixia\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":376730507,\"identity\":\"4a40a83e-ca0a-4004-9a03-c7ac3f46ee84\",\"order_by\":7,\"name\":\"Guang Li\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYDACdsbGBwkMDDIMDAnEamFmbDYAKuYhRQsDmwQDSVrknZnbKh7m3OHhb09g/PCDwS6PoBbDw4xtNxK3PeOROPOAWbKHIbmYsJZmsJbDPAw3EhikGRgOJDYQo6UApEX+RgLzb6K0yDMztjGAtBjcSGAjzhYDYCBLgPxieOZhm2WPQTIRtrS3P/z4c9sdObnjyYdv/KiwI8KWA2AKRDICFRsQUg+ypQGuZRSMglEwCkYBDgAAKFE9k2p65OsAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Chinese Academy of Medical Sciences\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Guang\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":376730508,\"identity\":\"b6ef2c79-2a28-4675-bf99-de605b2d1216\",\"order_by\":8,\"name\":\"Xi Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chinese Academy of Medical Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xi\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-10-18 07:38:59\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5287414/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5287414/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":68899707,\"identity\":\"2f0d3755-994a-494d-ac6b-c269d682822d\",\"added_by\":\"auto\",\"created_at\":\"2024-11-13 09:24:59\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":125546,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTargets of AFPC-Induced Ferroptosis in GC and the Component-Target Network.\\u003cstrong\\u003e \\u003c/strong\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e) Venn diagram illustrating the shared targets of AFPC-induced ferroptosis in GC. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) The compound-target network. Yellow diamonds represent bioactive compounds, green circles represent targets, and edges represent interactions between compounds and targets.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/b73e317aecac6e029957c7aa.png\"},{\"id\":68900013,\"identity\":\"a83f57a2-a1b6-48e9-921d-4273ded7c4cd\",\"added_by\":\"auto\",\"created_at\":\"2024-11-13 09:32:59\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":352495,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eProtein-protein interaction Network\\u003cstrong\\u003e. \\u003c/strong\\u003eNodes are colored based on degree value, with darker hues indicating higher degree values.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/95a49e9b7ec1c9b883ddf13d.png\"},{\"id\":68898558,\"identity\":\"8ca43b35-b2af-49d1-abb6-5678424ad33f\",\"added_by\":\"auto\",\"created_at\":\"2024-11-13 09:16:59\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":392288,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eResults of GO and KEGG Enrichment Analysis.\\u003cstrong\\u003e \\u003c/strong\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e) Bar diagram of the top 10 GOBP, GOCC, and GOMF terms. (\\u003cstrong\\u003eB)\\u003c/strong\\u003e Pathway classification. (\\u003cstrong\\u003eC\\u003c/strong\\u003e) Sankey diagram of KEGG pathways showing AFPC-induced ferroptosis targets in GC. The left rectangle represents targets, the right rectangle represents KEGG pathways, and the line represents the interaction between them.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/95247384272e30dc48e55320.png\"},{\"id\":68898555,\"identity\":\"0f38f762-8382-4141-96d6-cdd1f1ca59f9\",\"added_by\":\"auto\",\"created_at\":\"2024-11-13 09:16:59\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":345912,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe distribution of hub targets in Pathway in cancer signaling pathway.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/3df2ebaf74f85164d4c22e6b.png\"},{\"id\":68898553,\"identity\":\"46b89f91-3389-4f39-8d56-b1b25d465478\",\"added_by\":\"auto\",\"created_at\":\"2024-11-13 09:16:59\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":84950,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003emRNA expression levels and OS analysis of the five hub genes.\\u003cstrong\\u003e \\u003c/strong\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e) mRNA expression levels of five hub genes in STAD and normal tissues from GEPIA (*\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.05). (\\u003cstrong\\u003eB\\u003c/strong\\u003e) mRNA expression levels in STAD and paracancerous tissues from UALCAN (*\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.05). (\\u003cstrong\\u003eC\\u003c/strong\\u003e) Kaplan-Meier analysis for OS of five hub genes in GC.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/f3a837aa856602b0f9372c9a.png\"},{\"id\":68899711,\"identity\":\"a08e4625-3ee5-469b-b193-feb2ea528b8e\",\"added_by\":\"auto\",\"created_at\":\"2024-11-13 09:24:59\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":743298,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eImmunohistochemical and Single-Cell Analysis of Hub Genes.\\u003cstrong\\u003e \\u003c/strong\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e) Protein expression levels of five hub genes in GC and normal tissues from the HPA database. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) Single-cell analysis of three hub genes in TME cell subsets from the STAD_GSE134520 dataset. Cells with high hub gene expression are indicated by the red box.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/582e6d3879d9abc87acbc856.png\"},{\"id\":68898557,\"identity\":\"d42774cd-ee15-4331-8582-2177a422a512\",\"added_by\":\"auto\",\"created_at\":\"2024-11-13 09:16:59\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":444635,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMolecular docking results.\\u003cstrong\\u003e \\u003c/strong\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e) Heatmap of binding affinities (binding energy \\u0026lt; −5.0 kcal/mol). (\\u003cstrong\\u003eB\\u003c/strong\\u003e) TP53 with gallic acid. (\\u003cstrong\\u003eC\\u003c/strong\\u003e) TP53 with quercetin. (\\u003cstrong\\u003eD\\u003c/strong\\u003e) HIF1A with gallic acid. (\\u003cstrong\\u003eE\\u003c/strong\\u003e) HIF1A with quercetin. (\\u003cstrong\\u003eF\\u003c/strong\\u003e) IL6 with gallic acid. (\\u003cstrong\\u003eG\\u003c/strong\\u003e) IL6 with quercetin. (H) STAT3 with gallic acid. (\\u003cstrong\\u003eI\\u003c/strong\\u003e) STAT3 with quercetin. (\\u003cstrong\\u003eJ\\u003c/strong\\u003e) EGFR with gallic acid. (\\u003cstrong\\u003eK\\u003c/strong\\u003e) EGFR with quercetin. Active site residues are represented by blue sticks, ligands by green sticks, and hydrogen bonds by yellow dotted lines.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/e6dd6dcf9848da6f8c9b73f6.png\"},{\"id\":68900014,\"identity\":\"38aefcc2-ee67-46f5-be28-57ff1118d23c\",\"added_by\":\"auto\",\"created_at\":\"2024-11-13 09:32:59\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":504371,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMolecular dynamics simulation results.\\u003cstrong\\u003e \\u003c/strong\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e) RMSD values of the 10 complexes. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) RMSF values of the 10 complexes. (\\u003cstrong\\u003eC\\u003c/strong\\u003e) Rg values of the 10 complexes. (\\u003cstrong\\u003eD\\u003c/strong\\u003e) Number of hydrogen bonds in the 10 complexes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/ecd7b47d86f56501db94b5a5.png\"},{\"id\":68900015,\"identity\":\"77bda043-af85-4e82-bc26-f8f73fe3ab01\",\"added_by\":\"auto\",\"created_at\":\"2024-11-13 09:33:00\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":77008,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSGC7901 cells' proliferation was suppressed, and the synthesis of Fe2+, GSH, and MDA was impacted by gallic acid and quercetin. (\\u003cstrong\\u003eA\\u003c/strong\\u003e) IC50 values for gallic acid and quercetin in SGC7901 cells. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) Effects of gallic acid and quercetin treatment on Fe\\u003csup\\u003e2+\\u003c/sup\\u003e levels. (\\u003cstrong\\u003eC\\u003c/strong\\u003e) Effects on GSH levels. (\\u003cstrong\\u003eD\\u003c/strong\\u003e) Effects on MDA levels. The addition of Ferrostatin-1 reversed the changes induced by gallic acid and quercetin. ***\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.001, ****\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.0001, **\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.01, *\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/ecf21ed3e9b5afe0a05e2ab1.png\"},{\"id\":68899705,\"identity\":\"14eb3ec9-eb76-41c6-823c-802c1e9cf89c\",\"added_by\":\"auto\",\"created_at\":\"2024-11-13 09:24:59\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":85806,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGallic acid and quercetin affected the expression of ferroptosis-related proteins. (A) (A) Western blot analysis showing significantly decreased p-STAT3/STAT3 and IL-6 levels in the gallic acid-treated group compared to the control. (B) Western blot analysis showing significantly decreased p-STAT3/STAT3 and IL-6 levels in the quercetin-treated group compared to the control. Co-treatment with ferrostatin-1 reversed the expression trend of these proteins. *\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.05, **\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.01 and ***\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/441cc04aa2aef37404100e5b.png\"},{\"id\":85148286,\"identity\":\"98df01c6-27b3-44f1-acb2-30791141044d\",\"added_by\":\"auto\",\"created_at\":\"2025-06-22 14:31:52\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4516063,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/e7d63ec5-f231-4c00-a5b4-42ac7820aaa5.pdf\"},{\"id\":68898563,\"identity\":\"52b2c0ee-fc42-4f14-9f64-3b04127048cb\",\"added_by\":\"auto\",\"created_at\":\"2024-11-13 09:16:59\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":267837,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryInformation.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5287414/v1/aeab229a506b5e53de34231f.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Disclosing the potential mechanisms of Amomi Fructus phenolic compounds inducing ferroptosis in SGC-7901 gastric cancer cells: a joint network pharmacology-based analysis\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eGastric cancer (GC) is among the most common malignant tumors of the digestive tract. According to global cancer statistics for 2020, GC accounted for over 1\\u0026nbsp;million new cases and more than 769,000 cancer-related deaths, translating to approximately one death for every 13 cases of GC. In terms of incidence and mortality, GC ranks fifth and fourth globally, respectively [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Current treatment options, including surgical resection and chemotherapy, are limited in efficacy, as evidenced by a dismal 5-year survival rate of less than 20% for advanced GC [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. The regulation of cell death plays a fundamental role in tumor onset and progression, with cancer cells exhibiting the unique ability to evade programmed cell death. Consequently, the exploration of novel strategies that induce tumor cell death represents a promising avenue for GC treatment.\\u003c/p\\u003e \\u003cp\\u003eFerroptosis is an iron-dependent form of regulated cell death that is distinct from other programmed cell death pathways, such as autophagy, necroptosis, and apoptosis. Key features of ferroptosis include lipid ROS accumulation, GSH depletion, and elevated Fe2\\u0026thinsp;+\\u0026thinsp;levels [\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Increasing evidence highlights the relevance of ferroptosis in cancer treatment, particularly in overcoming GC-related drug resistance [\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Inducing ferroptosis in GC cells may reduce their invasion, proliferation, and metastasis potential [\\u003cspan additionalcitationids=\\\"CR10\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Moreover, ferroptosis introduces damage-associated molecular patterns (DAMPs) into the tumor microenvironment (TME), triggering immune responses that hinder tumor growth [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Immune cells involved in tumor immunity, such as T cells, B cells, and macrophages, can also undergo ferroptosis under specific conditions [\\u003cspan additionalcitationids=\\\"CR15\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Thus, targeting ferroptosis is a promising strategy for GC treatment.\\u003c/p\\u003e \\u003cp\\u003ePolyphenols, natural compounds found in plant-based foods, exhibit diverse biological activities, including antioxidant properties. These compounds have demonstrated therapeutic potential for managing various complex diseases, including cardiovascular, neurological, and metabolic disorders, as well as cancer [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Amomi Fructus (AF), the dried ripe fruit of Amomum villosum, is commonly used in traditional Chinese medicine and dietary supplements for digestive ailments [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. AF comprises both volatile and non-volatile components, with recent studies focusing mainly on volatile oil constituents. However, the biological activities of non-volatile components, particularly polyphenols, remain underexplored. Polyphenols and polysaccharides are the primary non-volatile components of AF, known for their anti-inflammatory, antitumor, antioxidant, and antibacterial properties [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Yue et al. demonstrated that flavonoid extracts from AF possess potent inhibitory effects on GC cell proliferation in vitro, exceeding the effects of volatile oils [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Additionally, traditional Chinese formulations containing AF, such as Aucklandiae Radix-Amomi Fructus and Additive Xiangsha Liujunzi Decoction, have been shown to induce apoptosis and suppress GC cell proliferation [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Despite evidence suggesting the anti-GC potential of Amomi Fructus phenolic compounds (AFPC), the active components, molecular targets, and mechanisms of action remain largely undefined.\\u003c/p\\u003e \\u003cp\\u003eTherefore, this study aimed to investigate whether AFPC induces ferroptosis in GC cells and to elucidate the underlying mechanisms using network pharmacology, molecular docking, molecular dynamics simulations, and in vitro experiments.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eNetwork pharmacology analysis\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003e \\u003cb\\u003eBioactive compound screening.\\u003c/b\\u003e A total of 41 compounds from AFPC were collected via literature mining. After identifying relevant structures, 20 bioactive compounds were selected using SwissADME based on gastrointestinal (GI) absorption and drug-likeness (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). A total of 5,336 potential targets for these compounds were identified using the CTD database.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eInformation of 20 bioactive compounds\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCompound Name\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFormula\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGI\\u003c/p\\u003e \\u003cp\\u003eabsorption\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLipinski\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eDrug\\u003c/p\\u003e \\u003cp\\u003elikeness\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBioavailability\\u003c/p\\u003e \\u003cp\\u003escore\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3-Hydroxybenzoic Acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e10\\u003c/sub\\u003eH\\u003csub\\u003e12\\u003c/sub\\u003eO\\u003csub\\u003e2\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4-(4-Hydroxyphenyl)-2-Butanone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e10\\u003c/sub\\u003eH\\u003csub\\u003e12\\u003c/sub\\u003eO\\u003csub\\u003e2\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4-Hydroxybenzoic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e7\\u003c/sub\\u003eH\\u003csub\\u003e6\\u003c/sub\\u003eO\\u003csub\\u003e3\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCaffeic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e9\\u003c/sub\\u003eH\\u003csub\\u003e8\\u003c/sub\\u003eO\\u003csub\\u003e4\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCatechin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e15\\u003c/sub\\u003eH\\u003csub\\u003e14\\u003c/sub\\u003eO\\u003csub\\u003e6\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCinnamic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e9\\u003c/sub\\u003eH\\u003csub\\u003e8\\u003c/sub\\u003eO\\u003csub\\u003e2\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFerulic Acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e10\\u003c/sub\\u003eH\\u003csub\\u003e10\\u003c/sub\\u003eO\\u003csub\\u003e4\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGallic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e7\\u003c/sub\\u003eH\\u003csub\\u003e6\\u003c/sub\\u003eO\\u003csub\\u003e5\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIsorhamnetin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e16\\u003c/sub\\u003eH\\u003csub\\u003e12\\u003c/sub\\u003eO\\u003csub\\u003e7\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ep-Coumaric Acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e9\\u003c/sub\\u003eH\\u003csub\\u003e8\\u003c/sub\\u003eO\\u003csub\\u003e3\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePolydatin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e20\\u003c/sub\\u003eH\\u003csub\\u003e22\\u003c/sub\\u003eO\\u003csub\\u003e8\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eProtocatechualdehyde\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e7\\u003c/sub\\u003eH\\u003csub\\u003e6\\u003c/sub\\u003eO\\u003csub\\u003e3\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eProtocatechuic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e7\\u003c/sub\\u003eH\\u003csub\\u003e6\\u003c/sub\\u003eO\\u003csub\\u003e4\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eProtocatechuic acid methyl ester\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e8\\u003c/sub\\u003eH\\u003csub\\u003e8\\u003c/sub\\u003eO\\u003csub\\u003e4\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQuercetin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e15\\u003c/sub\\u003eH\\u003csub\\u003e10\\u003c/sub\\u003eO\\u003csub\\u003e7\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSalicylic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e7\\u003c/sub\\u003eH\\u003csub\\u003e6\\u003c/sub\\u003eO\\u003csub\\u003e3\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eShikimic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e7\\u003c/sub\\u003eH\\u003csub\\u003e10\\u003c/sub\\u003eO\\u003csub\\u003e5\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSinapinic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e11\\u003c/sub\\u003eH\\u003csub\\u003e12\\u003c/sub\\u003eO\\u003csub\\u003e5\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSyringic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e11\\u003c/sub\\u003eH\\u003csub\\u003e12\\u003c/sub\\u003eO\\u003csub\\u003e5\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVanillic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003csub\\u003e8\\u003c/sub\\u003eH\\u003csub\\u003e8\\u003c/sub\\u003eO\\u003csub\\u003e4\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ehigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eCollection of targets and construction of the component-target network.\\u003c/b\\u003e After removing duplicates, 5,336 AFPC genes, 340 GC genes, and 494 ferroptosis-related genes were integrated, resulting in 58 shared targets (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). A component-target network was subsequently constructed to elucidate the potential mechanisms of AFPC bioactive compounds against GC (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). The two highest-ranked bioactive compounds, gallic acid and quercetin, were identified as the main components involved in AFPC-induced ferroptosis in GC.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eConstruction of the PPI network and screening of hub targets.\\u003c/b\\u003e The PPI network was visualized and analyzed using Cytoscape, encompassing 729 edges and 58 nodes (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The top 10 genes were ranked by degree, betweenness centrality (BC), and closeness centrality (CC) as presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. The hub targets were defined as the intersecting genes among the top-ranked genes, which included TP53, HIF1A, IL6, STAT3, and EGFR.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eTop 10 genes in PPI network\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGenes\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDegree\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGenes\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eBC\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eGenes\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eCC\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMCC\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTP53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTP53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.08891521\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTP53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.89855072\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTP53\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHIF1A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHIF1A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.06766567\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHIF1A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.84931507\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eHIF1A\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIL6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePTGS2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.03584706\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIL6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.80519481\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eJUN\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIL1B\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eABCC1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.03243863\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIL1B\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.775\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eSTAT3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSTAT3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIL6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.03075067\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSTAT3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.775\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eIL6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eJUN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEGFR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.02961317\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eEGFR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.7654321\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eSRC\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEGFR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIL1B\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.02936726\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eJUN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.7654321\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePTEN\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePTGS2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSTAT3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.02366127\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ePTGS2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.75609756\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMTOR\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMAPK3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eKRAS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.02242708\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMAPK3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.73809524\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eCDKN1A\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSRC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePPARA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.02229267\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNFE2L2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.72941176\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eEGFR\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eGO and KEGG enrichment analysis.\\u003c/b\\u003e GO and KEGG enrichment analyses were performed using the DAVID database to elucidate the molecular mechanisms of AFPC-induced ferroptosis in GC. In GO functional enrichment, a total of 1,842 GO gene annotations were obtained, comprising 295 biological processes (BP), 43 cellular components (CC), and 50 molecular functions (MF). The top 10 GOBP, GOCC, and GOMF terms were plotted in a bar chart based on ascending p-values (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). BP enrichment primarily included cellular response to reactive oxygen species, oxidative stress, and regulation of gene expression and apoptosis. CC enrichment involved the nucleus, cytosol, nucleoplasm, and focal adhesion, while MF terms included enzyme binding, ubiquitin protein ligase binding, and p53 binding.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eA total of 136 pathways enriched for shared targets were identified for further study. The pathways were categorized into five primary groups: metabolism, environmental information processing, cellular activities, organismal systems, and human disease (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB). Among these, cancer-related pathways contained the highest number of annotated genes. Hub targets were notably implicated in the HIF-1, PI3K-AKT, MAPK, JAK-STAT, and p53 signaling pathways. A total of 26 GC-related pathways were analyzed, with the \\\"Pathways in cancer\\\" being the most prominent (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). Several pathways affecting gene stability, apoptosis, metabolism, and cellular damage were identified, including PI3K-AKT, MAPK, JAK-STAT, and p53 pathways.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eExpression of Hub Targets in STAD\\u003c/h3\\u003e\\n\\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003e \\u003cb\\u003emRNA expression and survival analysis of hub genes in STAD and paracancerous stomach tissues.\\u003c/b\\u003e The GEPIA database analysis revealed that the mRNA expression levels of TP53 and HIF1A were significantly higher in stomach adenocarcinoma (STAD) tissues compared to paracancerous tissues (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). To further investigate the mRNA expression levels of the five hub genes, the UALCAN database was utilized. Except for IL6, the mRNA expression levels of the remaining four hub genes\\u0026mdash;TP53, HIF1A, STAT3, and EGFR\\u0026mdash;were significantly elevated in STAD tissues compared to paracancerous tissues (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB). These findings suggest that these four hub genes play critical roles in GC diagnosis, progression, and treatment, highlighting their importance in the pathogenesis of GC.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003ePrognostic values of the five hub genes were analyzed using the Kaplan-Meier plotter (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC). The results indicated that TP53 (HR: 1.7, log-rank \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;1.9e\\u003csup\\u003e-08\\u003c/sup\\u003e), STAT3 (HR: 1.34, log-rank \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.00078), EGFR (HR: 1.35, log-rank \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.0092), and HIF1A (HR: 0.73, log-rank \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.00032) were significantly correlated with overall survival (OS) in GC patients. OS was higher in patients with low transcript levels of TP53, STAT3, and EGFR, and high levels of HIF1A. These findings suggest that TP53, STAT3, EGFR, and HIF1A may have novel roles in GC and have potential as diagnostic and prognostic biomarkers.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eImmunohistochemical analysis and single-cell analysis of the hub genes in STAD.\\u003c/b\\u003e The expression of five hub genes in STAD and paracancerous tissues was investigated using the Human Protein Atlas (HPA) database (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA). Results showed that EGFR, STAT3, and TP53 had higher expression levels in STAD compared to paracancerous tissues, whereas HIF1A and IL6 showed no significant difference. These findings suggest that EGFR, STAT3, and TP53 could serve as important targets for GC diagnosis and treatment, implying their significant roles in GC development.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSingle-cell analysis was used to examine the distribution of the five hub genes in tumor microenvironment (TME) cells (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB). TP53 showed moderate expression in patient mucous cells and high expression in mast cells. HIF1A was substantially expressed in both malignant cells and myofibroblasts, whereas STAT3 was mainly found in mucous cells. Overall, the single-cell analysis of the STAD_GSE134520 dataset [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e] showed enrichment of three hub genes (TP53, HIF1A, STAT3), excluding EGFR and IL6.\\u003c/p\\u003e\\n\\u003ch3\\u003eMolecular docking validation\\u003c/h3\\u003e\\n\\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eMolecular docking was performed to evaluate the binding affinity between the five hub targets identified from the PPI network (TP53, HIF1A, IL6, STAT3, EGFR) and the two key compounds (gallic acid, quercetin) identified from the compound-target network. The docking results, visualized as a heatmap, revealed good binding activity between the hub targets and the two compounds (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA). PyMOL visualization showed specific interactions between gallic acid and quercetin with each of the hub targets (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB-K).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFor TP53, gallic acid formed hydrogen bonds with residues K370, Y240, N206, K17, and E135, and quercetin formed hydrogen bonds with R253, D242, and R306 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB-C). HIF1A interacted with gallic acid through residues E159, E524, and R156, and quercetin interacted with L273, D404, and M341 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eD-E). IL6 formed hydrogen bonds with L254, S30, and D26 using gallic acid, and quercetin interacted with Q175, R179, G164, and others (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eF-G). STAT3 bound to gallic acid at S113, Q3, and A44, while quercetin formed bonds with R13, T118, and others (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eH-I). EGFR demonstrated binding with gallic acid to residues L858, A859, and others, and quercetin formed bonds with A743 and K745 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eJ-K).\\u003c/p\\u003e \\u003cp\\u003eIn summary, the two representative AFPC compounds, gallic acid and quercetin, demonstrated favorable binding to the five hub targets: EGFR, TP53, HIF1A, IL6, and STAT3. These targets may hold significant therapeutic implications for GC patients.\\u003c/p\\u003e\\n\\u003ch3\\u003eMolecular dynamics (MD) simulation validation\\u003c/h3\\u003e\\n\\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eResults from MD simulations\\u0026mdash;including RMSD, RMSF, Rg values, and the number of hydrogen bonds (H-bonds)\\u0026mdash;were used to evaluate the hydrophobicity of amino acid residues, stability of protein-ligand complexes, and overall stability of protein tertiary structures upon binding of small molecules.\\u003c/p\\u003e \\u003cp\\u003eRMSD is a key metric for assessing system stability. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eA, the RMSD values for gallic acid-TP53, quercetin-TP53, gallic acid-STAT3, gallic acid-EGFR, and quercetin-EGFR rapidly stabilized at 0.2\\u0026ndash;0.3 nm after 10 ns. Gallic acid-HIF1A and quercetin-STAT3 stabilized at 0.23\\u0026ndash;0.3 nm after 20 ns. After 25\\u0026ndash;30 ns, quercetin-IL6 and gallic acid-IL6 reached stability at 0.24\\u0026ndash;0.38 nm, while quercetin-HIF1A stabilized at 0.25\\u0026ndash;0.3 nm after 40 ns.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eRMSF reflects protein residue flexibility. As depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eB, the average fluctuation for most complexes was 0.13 nm, while the gallic acid-HIF1A, quercetin-HIF1A, quercetin-IL6, and gallic acid-STAT3 complexes exhibited slightly higher fluctuations at 0.14 nm. Variations in RMSF values were observed for specific residues, primarily due to differences in the binding degree.\\u003c/p\\u003e \\u003cp\\u003eRg was used to evaluate the compactness of receptor-ligand binding. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eC, gallic acid-STAT3 and gallic acid-TP53 stabilized at 1.65\\u0026ndash;2.03 nm after 10 ns, while quercetin-STAT3 stabilized at 1.98\\u0026ndash;2.02 nm after 20 ns. Other complexes, including gallic acid-HIF1A, quercetin-HIF1A, and quercetin-IL6, stabilized at values between 1.62 and 2.05 nm after 25\\u0026ndash;50 ns. A slight increase in Rg for quercetin-TP53 was observed after 10 ns, which may reflect conformational changes in the protein chain.\\u003c/p\\u003e \\u003cp\\u003eThe number of H-bonds reflects protein-ligand binding strength. As illustrated in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eD, H-bond numbers for quercetin-TP53 and quercetin-EGFR stabilized at 1\\u0026ndash;3, while other complexes, such as quercetin-HIF1A, gallic acid-IL6, and quercetin-IL6, varied between 0\\u0026ndash;4. The H-bond numbers for gallic acid-TP53 decreased gradually, primarily staying between 0 and 2, while those for gallic acid-HIF1A fluctuated considerably between 1\\u0026ndash;5. These results indicate stable binding of the five hub proteins with both gallic acid and quercetin, consistent with molecular docking results. These results indicate stable binding of the five hub proteins with both gallic acid and quercetin, consistent with molecular docking results.\\u003c/p\\u003e\\n\\u003ch3\\u003eIn vitro activity verification\\u003c/h3\\u003e\\n\\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003e \\u003cb\\u003eInhibition of SGC7901 Cell Growth by Gallic Acid and Quercetin.\\u003c/b\\u003e SGC7901 cells were treated with different concentrations of gallic acid and quercetin for 24 hours. Both compounds significantly inhibited cell growth in a dose-dependent manner (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003eA). The IC50 values for gallic acid and quercetin were 24.44\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.54 \\u0026micro;g/mL and 33.83\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.31 \\u0026micro;g/mL, respectively. Based on these findings, concentrations of 24 \\u0026micro;g/mL and 34 \\u0026micro;g/mL were used in subsequent experiments.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eEffects of Gallic Acid and Quercetin on MDA, Fe2\\u0026thinsp;+\\u0026thinsp;Production, and GSH Depletion.\\u003c/b\\u003e Following treatment with gallic acid and quercetin, there was a significant increase in Fe\\u003csup\\u003e2+\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003eB) and malondialdehyde (MDA) levels (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003eC) and a marked decrease in GSH levels (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003eD) compared to the control group. Additionally, the addition of Ferrostatin-1 during treatment with gallic acid and quercetin partially or fully reversed these changes, further supporting the hypothesis that gallic acid and quercetin induce ferroptosis in SGC7901 cells.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eInduction of Ferroptosis via the IL-6/STAT3 Pathway.\\u003c/b\\u003e Western blot analysis revealed a significant reduction in the expression of p-STAT3, STAT3, and IL-6 following 24-hour treatment with gallic acid and quercetin compared to the control group. The concurrent addition of Ferrostatin-1 partially reversed these effects, though not to the same extent as the untreated control group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e). These findings indicate that gallic acid and quercetin induce ferroptosis in SGC7901 cells through modulation of the IL-6/STAT3 signaling pathway.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eCell death plays an essential role in cancer initiation and progression. The ability of cancer cells to evade death is a hallmark of tumorigenesis [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. Ferroptosis, characterized by iron overload and lipid peroxidation, has been implicated in the development, progression, treatment, and prognosis of GC[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. This study aimed to explore the mechanism by which AFPC induce ferroptosis in GC using a combination of network pharmacology and experimental validation.\\u003c/p\\u003e \\u003cp\\u003eThe network pharmacology analysis identified gallic acid and quercetin as the main active components responsible for AFPC-induced ferroptosis in GC. Gallic acid, a natural plant-derived phenolic compound, possesses anti-inflammatory, weight-reducing, and anticancer properties [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. It has been shown to promote apoptosis and inhibit metastasis and invasion of GC cells by modulating NF-kappaB and PI3K/AKT/small GTPase signaling [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Quercetin, a naturally occurring flavonoid, also demonstrates broad anticancer activity [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Quercetin induces apoptosis in GC and regulates autophagy through the Akt-mTOR/HIF-1α pathway [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Additionally, it has been reported to induce ferroptosis in GC cells.\\u003c/p\\u003e \\u003cp\\u003eThe AFPC-induced ferroptosis in GC was primarily associated with cancer-related pathways, including HIF-1, PI3K-AKT, MAPK, JAK-STAT, and p53 signaling pathways, all of which contribute to GC development and progression. HIF-1α, a key subunit of HIF-1, is overexpressed in GC cells, leading to aberrant gene expression and cancer progression [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. Zhang et al. demonstrated that downregulation of HIF-1α could inhibit GC cell proliferation, migration, and invasion by suppressing the PI3K/AKT pathway and VEGF expression [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. Both the PI3K/AKT and MAPK pathways are involved in cellular processes such as proliferation, migration, and apoptosis, and are activated in GC [\\u003cspan additionalcitationids=\\\"CR38 CR39 CR40\\\" citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. The p53 signaling pathway also contributes to GC progression, with PI3K/Akt affecting p53 through multiple mechanisms [\\u003cspan additionalcitationids=\\\"CR43 CR44\\\" citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. The JAK/STAT pathway plays a role in proliferation, differentiation, and apoptosis of GC cells [\\u003cspan additionalcitationids=\\\"CR47 CR48 CR49\\\" citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. Inhibition of JAK/STAT3 activation has been shown to increase apoptosis and cell cycle arrest while reducing GC cell proliferation and invasion [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]. Taken together, AFPC may induce ferroptosis in GC by regulating the crosstalk among multiple signaling pathways, thus achieving its therapeutic effects.\\u003c/p\\u003e \\u003cp\\u003eThe PPI network analysis identified TP53, HIF1A, IL6, STAT3, and EGFR as hub targets involved in AFPC-induced ferroptosis in GC. TP53 is closely associated with gastrointestinal malignancies and positively regulates ferroptosis [\\u003cspan additionalcitationids=\\\"CR54\\\" citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e]. Suppression of TP53 prevents apoptosis and reduces Bcl-6 expression in GC cells [\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]. HIF1α is a critical transcription factor for cellular responses to hypoxia, and its aberrant activation is linked to GC progression [\\u003cspan additionalcitationids=\\\"CR58\\\" citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e]. EGFR, a poor prognostic marker, is highly expressed in GC and regulates cell migration, proliferation, and apoptosis [\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e]. IL-6 is a pro-tumor cytokine that activates the JAK/STAT3 pathway, contributing to tumor cell survival and proliferation [\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e]. STAT3 regulates GC cell survival by promoting the expression of anti-apoptotic proteins, and inhibition of STAT3 induces ferroptosis [\\u003cspan additionalcitationids=\\\"CR65\\\" citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e]. The IL-6/STAT3 signaling pathway is involved in the survival, proliferation, invasion and migration of tumor cells [\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e]. Zheng has shown that by blocking the IL-6/STAT3 signaling pathway, Kang-ai injection can stop the growth of GC cells [\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e]. Therefore, targeting the IL-6/STAT3 pathway may represent an effective approach for GC therapy.\\u003c/p\\u003e \\u003cp\\u003eThe present study validated the expression and prognostic value of five hub targets\\u0026mdash;TP53, HIF1A, IL6, STAT3, and EGFR\\u0026mdash;using bioinformatics data. High expression levels of TP53, STAT3, and EGFR were observed in GC tissues, making them potential biomarkers for GC diagnosis and prognosis. Additionally, using the STAD_GSE134520 dataset, we explored the cellular function of these genes in the tumor microenvironment (TME). TP53 was found to be expressed in mast cells, while HIF1A was highly expressed in myofibroblasts. Studies have suggested that overexpression of TP53 in mast cells and HIF1A in myofibroblasts plays a role in tumor immune response and GC progression [\\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e]. In addition, molecular docking and MD simulations were further performed to validate network pharmacology predictions. The docking results indicated strong binding affinities between gallic acid, quercetin, and the hub targets TP53, HIF1A, IL6, STAT3, and EGFR. The dynamic stability of these interactions was confirmed by RMSD, RMSF, Rg, and H-bond analyses during MD simulations.\\u003c/p\\u003e \\u003cp\\u003eTo further elucidate the link between AFPC and ferroptosis, we investigated the anti-tumor effects of gallic acid and quercetin and their regulatory effects on IL-6 and STAT3. The results demonstrated that both compounds significantly inhibited SGC7901 cell growth in a dose-dependent manner. Typical features of ferroptosis, such as increased Fe\\u003csup\\u003e2+\\u003c/sup\\u003e and MDA levels and decreased GSH production, were observed in treated cells. However, these changes were reversed by pre-treatment with Ferrostatin-1, indicating ferroptosis induction. Western blot analysis showed downregulation of p-STAT3, STAT3, and IL-6, which was partially reversed by Ferrostatin-1, suggesting that AFPC components inhibit the IL-6/STAT3 pathway, thereby inducing ferroptosis in GC cells.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eNetwork pharmacology analysis\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003e \\u003cb\\u003eScreening bioactive compounds and their target genes.\\u003c/b\\u003e The phenolic compounds of Amomi Fructus (AFPC) were initially retrieved through literature mining. The molecular structure files of these compounds were then downloaded from PubChem (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://pubchem.ncbi.nlm.nih.gov/\\u003c/span\\u003e\\u003cspan address=\\\"https://pubchem.ncbi.nlm.nih.gov/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e] and saved in SDF format. Bioactive compounds were filtered based on high gastrointestinal (GI) absorption and more than three \\\"yes, 0 violation\\\" entries in SwissADME (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.swissadme.ch/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.swissadme.ch/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e73\\u003c/span\\u003e]. The target genes of the bioactive compounds were predicted using the Comparative Toxicogenomics Database (CTD) (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://ctdbase.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://ctdbase.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e74\\u003c/span\\u003e], and compounds without identified target genes were excluded.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eScreening the targets of AFPC induced ferroptosis in GC.\\u003c/b\\u003e The keyword \\\"gastric cancer\\\" was used in the CTD to obtain GC-related genes based on an inference score of at least 50. Ferroptosis-related targets were identified using the FerrDb V2 database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.zhounan.org/ferrdb/current/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.zhounan.org/ferrdb/current/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e], selecting the \\\"human\\\" tag. To identify shared targets, all potential targets from AFPC, ferroptosis, and GC were analyzed using OmicStudio (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.omicstudio.cn/tool\\u003c/span\\u003e\\u003cspan address=\\\"https://www.omicstudio.cn/tool\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e76\\u003c/span\\u003e]. Cytoscape v3.10.2 [\\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e77\\u003c/span\\u003e] was used to construct and visualize the component-target network, and the network was further analyzed using the CytoHubba plugin [\\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e78\\u003c/span\\u003e]. The two highest-scoring bioactive compounds were selected as the main compounds using the maximal clique centrality (MCC) method.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eProtein‒protein interaction (PPI) analysis and hub targets screening.\\u003c/b\\u003e Shared targets were input into the STRING database (version 12.0, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://string-db.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://string-db.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e79\\u003c/span\\u003e] to extract PPI data with interaction scores for *Homo sapiens* (high: \\u0026gt;0.7, medium: \\u0026gt;0.4, low: \\u0026gt;0.15). Cytoscape v3.10.2 was utilized to visualize the network and calculate node topological properties, including degree, betweenness centrality (BC), and closeness centrality (CC). Nodes with higher scores in all three indices were considered central. The top 10 genes based on degree, BC, and CC, respectively, were identified. The network was also analyzed using CytoHubba, and the top 10 genes were determined based on MCC scores. Overlapping genes among the top 10 by degree, BC, CC, and MCC were defined as the hub targets of AFPC-induced ferroptosis in GC.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eGO and KEGG enrichment analysis.\\u003c/b\\u003e Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://david.ncifcrf.gov/summary.jsp\\u003c/span\\u003e\\u003cspan address=\\\"https://david.ncifcrf.gov/summary.jsp\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e] to determine the potential roles of AFPC-induced ferroptosis in GC. GO enrichment analyses included biological processes (BP), cellular components (CC), and molecular functions (MF). Annotation terms with \\u003cem\\u003ep\\u003c/em\\u003e-values\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 were considered statistically significant. GO terms and KEGG pathways were plotted based on the number of enriched genes using an online tool (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.bioinformatics.com.cn\\u003c/span\\u003e\\u003cspan address=\\\"https://www.bioinformatics.com.cn\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eExpression analysis of hub targets in gastric adenocarcinoma (STAD)\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003e \\u003cb\\u003emRNA expression and survival analysis of hub genes in STAD.\\u003c/b\\u003e To verify mRNA expression levels of hub targets in TCGA-STAD, the UALCAN (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://ualcan.path.uab.edu\\u003c/span\\u003e\\u003cspan address=\\\"http://ualcan.path.uab.edu\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e81\\u003c/span\\u003e] and GEPIA (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://gepia.cancer-pku.cn/index.html\\u003c/span\\u003e\\u003cspan address=\\\"http://gepia.cancer-pku.cn/index.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e82\\u003c/span\\u003e] databases were used. Results from both databases were considered significant with \\u003cem\\u003ep\\u003c/em\\u003e-values\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003cp\\u003eThe overall survival (OS) of hub targets in GC was assessed using the Kaplan-Meier Plotter (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://kmplot.com/analysis/index.php?p=service\\u003c/span\\u003e\\u003cspan address=\\\"http://kmplot.com/analysis/index.php?p=service\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e83\\u003c/span\\u003e]. High and low hub gene expression groups were compared, and significance was determined using log-rank \\u003cem\\u003ep\\u003c/em\\u003e-values and hazard ratios (HR) with 95% confidence intervals (CI), with a log-rank \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 considered significant.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eImmunohistochemistry and single-cell Analysis of hub targets in GC.\\u003c/b\\u003e Protein expression levels of the five hub targets in STAD were analyzed using the Human Protein Atlas (HPA) database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://kmplot.com/analysis/\\u003c/span\\u003e\\u003cspan address=\\\"http://kmplot.com/analysis/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). Immunohistochemistry results for the selected targets in GC tissues and normal gastric tissues were obtained by selecting \\\"Stomach Cancer\\\" and \\\"Stomach,\\\" respectively.\\u003c/p\\u003e \\u003cp\\u003eSingle-cell analysis of the hub targets was performed using the Tumor Immune Single Cell Center 1 (TISCH1) online database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://tisch1.comp-genomics.org/\\u003c/span\\u003e\\u003cspan address=\\\"http://tisch1.comp-genomics.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e84\\u003c/span\\u003e], with \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 considered statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMolecular docking validation\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eThe two main compounds were used as small molecular ligands for molecular docking with the five hub targets to validate the network pharmacology predictions. The PDB IDs for hub targets were EGFR (PDB ID: 4QTB), TP53 (PDB ID: 3TG5), HIF1A (PDB ID: 1FMK), IL6 (PDB ID: 5FUC), and STAT3 (PDB ID: 4ZIA). Compound SDF structures were obtained from PubChem and converted to mol2 format using Chem3D. Hub target PDB structures were downloaded from the RCSB protein databank (PDB, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.rcsb.org/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.rcsb.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) [\\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e85\\u003c/span\\u003e]. Small molecule ligands and solvent molecules were removed using PyMOL. Structures were then prepared in AutoDock 1.5.7 and saved in PDBQT format. Molecular docking simulations were conducted using AutoDock Vina v1.1.2, and binding affinities \\u0026lt; -5 kJ mol-1 were considered indicative of significant binding activity. The results were visualized using the ggplot2 package (v.1.42.0) (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.bioconductor.org/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.bioconductor.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) in R (v.3.6.0). The combinations of docking scores were illustrated with PyMOL 2.5.2.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMolecular dynamics (MD) simulation validation\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eTo further evaluate the stability of protein-ligand interactions, 10 complexes were subjected to MD simulations using Gromacs 2022 [\\u003cspan citationid=\\\"CR86\\\" class=\\\"CitationRef\\\"\\u003e86\\u003c/span\\u003e]: gallic acid-TP53, quercetin-TP53, gallic acid-HIF1A, quercetin-HIF1A, gallic acid-IL6, quercetin-IL6, gallic acid-STAT3, quercetin-STAT3, gallic acid-EGFR, and quercetin-EGFR. The simulation system was built using the AMBER14SB force field and TIP3P water model. The LINCS algorithm was used to constrain covalent bond lengths, and the PME algorithm was applied to calculate electrostatic interactions. NVT and NPT simulations were run for 100 ps at a constant temperature of 298 K and pressure of 1 bar to equilibrate the system. Production MD simulations were performed for 100 ns with confirmations saved every 10 ps. The MD simulation results, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and the number of hydrogen bonds (H-bonds), were analyzed and visualized using embedded Gromacs software and VMD.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eIn vitro activity verification\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003e \\u003cb\\u003eCell culture and viability assay.\\u003c/b\\u003e SGC7901 cells were cultured at 37\\u0026deg;C in a humidified incubator with 5% CO2, using specialized culture medium for SGC7901 (Procell Life Science \\u0026amp; Technology Co., Ltd.). Gallic acid (purity 99%, J\\u0026amp;K Scientific) and quercetin (purity 99.1%, J\\u0026amp;K Scientific) were dissolved in dimethyl sulfoxide (DMSO) (Sigma, USA) and subsequently diluted to various concentrations using the culture medium.\\u003c/p\\u003e \\u003cp\\u003eSGC7901 cells in the logarithmic growth phase were seeded at a density of 1\\u0026times;104 cells per well in a 96-well plate. The cells were treated with varying concentrations of gallic acid and quercetin (100.00, 50.00, 25.00, 12.50, 6.25, 3.12, 1.56, and 0 \\u0026micro;g/mL) for 24 h to determine their inhibitory effect on cell proliferation. After treatment, 10 \\u0026micro;L of CCK-8 solution (Beyotime, China) was added to each well, followed by incubation for an additional 2 h at 37\\u0026deg;C. The optical density (OD) at 450 nm was measured using a SpectraMax i3 microplate reader (Molecular Devices). The the 50% inhibitory concentration (IC50) value was calculated using GraphPad Prism software version 6.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eFe2+, reduced glutathione (GSH), and lipid peroxidation (MDA) level detection.\\u003c/b\\u003e SGC7901 cells were seeded at a density of 2\\u0026times;106 cells/well in 6-well plates and incubated overnight at 37\\u0026deg;C. After treating the cells for 2 h with 2 \\u0026micro;M of the ferroptosis inhibitor Ferrostatin-1 (Ferostatin-1; TargetMol), cell viability was assessed using the CCK-8 assay. According to the manufacturer\\u0026rsquo;s instructions (Servicebio), the treated cells were collected, and the levels of Fe\\u003csup\\u003e2+\\u003c/sup\\u003e, GSH, and MDA were measured using specific detection kits.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eWestern blotting (WB) assay.\\u003c/b\\u003e Total protein was extracted from treated cells using a mammalian protein extraction reagent (CW Biotech, China). Protein content was quantified using a BCA Protein Quantification Kit (CW Biotech, China). Protein samples were denatured by adding the protein extraction reagent and loading buffer (CW Biotech). Western blot analysis was performed using primary antibodies for p-STAT3 (OriGene, China), STAT3 (OriGene, China), IL-6 (Zenbio, China), and β-actin (Bioss, China), which were incubated at 4\\u0026deg;C overnight. HRP-linked secondary antibodies against mice and rabbits (CW Biotech, China) were subsequently incubated for 2 h at room temperature. Enhanced chemiluminescence (ECL) detection reagents (NCMBiotech, China) were used for signal development, and blot intensity was quantified using ImageJ software.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eStatistical analysis.\\u003c/b\\u003e All data are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SEM from at least three independent experiments. The Student's t-test was used to determine statistical significance between two groups, with GraphPad Prism 6 used for all statistical analyses. A \\u003cem\\u003ep\\u003c/em\\u003e-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered significant. Additionally, the IC50 value was determined by nonlinear regression using a variable slope (log[inhibitor] vs. normalized response) in GraphPad Prism.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eIn conclusion, AFPC induces ferroptosis in GC by modulating multiple crosstalk signaling pathways, including the IL-6/STAT3 pathway. The five hub targets\\u0026mdash;TP53, HIF1A, IL6, STAT3, and EGFR\\u0026mdash;play key roles in the disease etiology, diagnosis, and treatment. These findings provide insights into the potential therapeutic effects of AFPC on GC and highlight its promise as a novel approach for targeting ferroptosis in cancer therapy.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eConflict of interest\\u003c/h2\\u003e \\u003cp\\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eL.G. and C.X. conceived and designed the study. L.Y.N, X.B, and S.W. performed the experiments. M.Y., S.J.and D.X. analyzed the data. D.X. and Z.L.conducted data analysis; L.N. Y. and L.G.wrote the manuscript. All authors have read and approved this manuscript.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eData inquiries can be directed to the corresponding author.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eFundin\\u003c/strong\\u003e\\u003cstrong\\u003eg\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by National Key Research and Development Program of China (No. 2022YFD1600301), CAMS Innovation Fund for Medical Sciences (CIFMS, No. 2021-I2M-1031), Yunnan Science and Technology Talents and Platform Program (No. 202105AG070011), Yunnan Fundamental Research Projects (No. 202201AT070286), Xishuangbanna Prefecture Science and Technology Plan Project (No. 202401001), Joint Special Project of Local Undergraduate Universities in Yunnan Province (No. 2018FH001-020).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eSung, H. et al. 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Molecular dynamics simulation of proteins. \\u003cem\\u003eMethods Mol Biol\\u003c/em\\u003e 2073, 311\\u0026ndash;327. doi: (2020). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/978-1-4939-9869-2_17\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/978-1-4939-9869-2_17\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Gastric cancer, Amomi Fructus, Network pharmacology, Ferroptosis, Experimental validation\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5287414/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5287414/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eObjectives\\u003c/h2\\u003e \\u003cp\\u003eThis study aimed to elucidate the mechanisms by which Amomi Fructus phenolic compounds (AFPC) induce ferroptosis in gastric cancer (GC) using a combination of network pharmacology and experimental validation.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eTargets associated with AFPC, ferroptosis, and GC were compiled, and a component-target network was constructed to identify key compounds. Hub targets of AFPC-induced ferroptosis in GC were determined through protein-protein interaction (PPI) network analysis. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were conducted to assess these hub targets. The hub targets' mRNA and protein expression levels were evaluated using UALCAN, GEPIA, Kaplan-Meier Plotter, HPA, and TISCH1. Molecular docking and molecular dynamics (MD) simulations were performed to determine the binding affinity and stability between hub targets and active compounds. Finally, in vitro cell experiments validated the network pharmacology findings. Finally, in vitro cell experiments validated the network pharmacology findings.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eTwo main active compounds, gallic acid and quercetin, and five hub targets\\u0026mdash;TP53, HIF1A, IL6, STAT3, and EGFR\\u0026mdash;were identified. GO and KEGG analyses indicated that AFPC treatment in GC primarily involves oxidative stress, nuclear localization, p53 binding, and cancer-associated pathways. Molecular docking and simulations suggested that gallic acid and quercetin inhibit GC through modulating hub targets. Experimental results demonstrated that gallic acid and quercetin suppress GC cell viability and induce ferroptosis in SGC7901 cells by elevating Fe\\u003csup\\u003e2+\\u003c/sup\\u003e, MDA, and reducing GSH levels. The expressions of p-STAT3, STAT3, and IL-6 were significantly downregulated.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eThis study suggests that AFPC inhibits GC cell proliferation and induces ferroptosis by blocking the IL-6/STAT3 pathway.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Disclosing the potential mechanisms of Amomi Fructus phenolic compounds inducing ferroptosis in SGC-7901 gastric cancer cells: a joint network pharmacology-based analysis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-11-13 09:16:54\",\"doi\":\"10.21203/rs.3.rs-5287414/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"69cf1498-f179-4b46-8bfa-1ba2985d45cb\",\"owner\":[],\"postedDate\":\"November 13th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":40102213,\"name\":\"Biological sciences/Cancer\"},{\"id\":40102214,\"name\":\"Biological sciences/Computational biology and bioinformatics\"},{\"id\":40102215,\"name\":\"Biological sciences/Drug discovery\"}],\"tags\":[],\"updatedAt\":\"2025-06-22T14:23:43+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-11-13 09:16:54\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5287414\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5287414\",\"identity\":\"rs-5287414\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}