Synergistic Effect of Epigenetic Modulator Decitabine and Metformin in the Battle Against Gastric Cancer: A Potential Therapeutic Strategy 

Synergistic Effect of Epigenetic Modulator Decitabine and Metformin in the Battle Against Gastric Cancer: A Potential Therapeutic Strategy | 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 Synergistic Effect of Epigenetic Modulator Decitabine and Metformin in the Battle Against Gastric Cancer: A Potential Therapeutic Strategy Mohammed AlAli, Saeid Latifi-Navid, Mohammad Reza Khakzad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7382082/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The 5-year global survival rate of patients with gastric cancer (GC) is approximately 20%; however, 74% of these patients survived for up to 1 year with effective therapy. The anticancer effects of metformin, an antidiabetic agent, and 5-Aza-2′-deoxycytidine (5-AZA-CdR, decitabine), a DNA methyltransferase inhibitor that leads to malignant cell differentiation and apoptosis, have been investigated. In this study, we evaluated the synergistic effects of decitabine and metformin to achieve better GC treatment. MTT assay was used to assess the viability of MKN 45 cells, and flow cytometry was employed to evaluate apoptosis induction using Annexin V and propidium iodide (PI) staining. The expression profiles of key proapoptotic genes were compared across five distinct cell groups. Protein-protein interaction (PPI) network construction and pathway enrichment analysis were used to gain new insights into biological pathways. MTT assays demonstrated that metformin and decitabine inhibited cell viability at IC 50 values of 17.37 mM and 3.20 µM, respectively. A synergistic effect in promoting apoptosis compared to single treatments was shown by flow cytometry analysis and the Bliss synergy model (combination index (CI) < 1). All treatments, especially the combination of 5-AZA-CdR and metformin, led to a decrease in the number of cells in the S phase (down to 7.2%), while increasing the percentage of cells in the G2/M phase, indicating that normal cell cycle progression was disrupted and apoptosis was effectively induced. Metformin and decitabine did not affect target gene expression, except for the increased expression of CASP-1 (7.21 ± 0.48; p bon = 0.003) and CASP-3 (7.17 ± 0.19; p bon = 0.007) in response to decitabine exposure. However, the synergistic effect of decitabine and metformin significantly changed the expression of all target genes, downregulating BCL 2 (0.56 ± 0.05; p bon = 0.0005) and significantly increasing the expression of BAX (8.08 ± 0.15; p bon < 0.0001), caspase-1 (8.47 ± 0.34; p bon < 0.0001), caspase-3 (9.45 ± 0.26; p bon < 0.0001), and ATG 7 (1.97 ± 0.15; p bon = 0.001). The BAX/BCL 2 gene expression ratio was significantly increased in the cells treated with metformin (7.18 ± 1.16, p bon = 0.018) and 5Aza + metformin (14.46 ± 1.46, p bon < 0.0001) compared with the control group. Bioinformatics analysis showed that why combining decitabine with metformin may be a game-changer in treatment. This study revealed a notable synergistic effect of the combination of decitabine and metformin in GC cell apoptosis triggering, which may inform future therapeutic strategies for GC management. Biological sciences/Biochemistry Biological sciences/Cancer Biological sciences/Cell biology Biological sciences/Drug discovery Biological sciences/Molecular biology Health sciences/Oncology Antineoplastic Agents Gastric Neoplasms Combination Chemotherapy Programmed Cell Death Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Gastric cancer (GC) is influenced by a complex interplay of genetic and environmental factors. Although most gastric cancers are sporadic, a small percentage 1–3% of cases are attributed to inherited genetic predispositions[ 1 – 4 ]. The overall 5-year relative survival rate for patients with GC varies on their ethnic background, genetic predispositions, stage of diagnosis, and therapeutic regimen. However, globally, the rate is approximately 20%, indicating that individuals diagnosed with stomach cancer are 20% more likely to be alive five years after diagnosis than are similar individuals without the disease[ 5 ]. Recent studies have demonstrated that 74% of these patients survive for up to 1 year after chemotherapy, and 50% of these patients survive two years or more. This report highlights the importance of utilizing effective therapies for GC, including chemotherapy, targeted therapies, or immunotherapy, which significantly improve survival rates compared with those of untreated patients, who have a one-year survival rate of less than 20%[ 6 , 7 ]. Metformin is mainly known as an antidiabetic drug; however, it has also been shown to have anticancer effects by activating the AMPK pathway. When used alone or in combination with other therapeutic agents, metformin plays a significant role in inducing cancer cell apoptosis. This effect is achieved by increasing the expression of proapoptotic genes, such as BAX, and reducing antiapoptotic signals, such as BCL 2 . This modulation increases the sensitivity of GC cells to apoptotic stimuli [ 8 , 9 ]. 5-Aza-2′-deoxycytidine (5-AZA-CdR, decitabine), but it is a drug that functions as a DNA methyltransferase inhibitor. It works by reversing abnormal DNA methylation patterns that can contribute to cancer development. Hypomethylation process can restore the expression of silenced genes that are important for normal cell function. In cancer cells, these genes may be inactivated due to hypermethylation, and their effects lead to the differentiation and apoptosis of malignant cells[ 10 , 11 ]. Decitabine may be more effective when synergistically used with other therapeutic agents. In this study, the efficacy of the combination of metformin and decitabine was evaluated by assessing the expression of key apoptotic genes, including caspase-1 ( CASP-1 ), caspase-3 ( CASP-3 ), BCL 2 , BAX , and ATG 7 , in the MKN 45 human GC cell line derived from poorly differentiated adenocarcinoma of the stomach. Its response to various chemotherapeutic agents has been studied, providing important insights into its sensitivity and resistance mechanisms[ 12 , 13 ]. Results MTT Assay finding This study investigated the effects of 5-AZA-CdR and metformin on the viability of MKN 45 GC cells using the MTT assay. Initially, 10,000 cells per well were treated with varying concentrations of each drug. Metformin significantly reduced cell viability in a dose-dependent manner, with an IC 50 value of 26.21 mM after 48 h. However, 5-AZA-CdR showed no significant effect at standard doses. To optimize the results, experiments were repeated with 1,000 cells per well and a 96-h exposure. Under these conditions, Metformin reduced cell viability with IC 50 values of 13.58 mM and 17.37 mM, depending on the timing of drug administration. For 5-AZA-CdR, extended exposure and lower cell density led to a marked decrease in cell viability, with IC 50 values dropping to 3.20 µM. Finally, the selected concentrations to investigate the effect of metformin was IC 50 = 17.37 mM, and for 5-AZA-CdR, IC 50 = 3.20 µM by culturing 1000 cells per well of a 96-well plate for 96 h (Fig. 1 ). Apoptosis and cell cycle assays by flow cytometry Quantitative assessment of apoptosis and cell cycle was performed via flow cytometry following Annexin V/propidium iodide (PI) dual staining. The apoptotic effects of metformin (17.37 mM), 5-AZA-CdR (3.2 µM), and their concurrent administration were systematically evaluated in MKN45 gastric adenocarcinoma cells (Fig. 3 a-b). Untreated cell populations cultured under identical in vitro conditions served as the negative control. The results revealed that nearly all cells in the control group were viable (99.1%), with negligible early (0.41%) and late apoptosis (0.14%), indicating minimal baseline apoptosis. The cells viability of 5-AZA-CdR treatment group was decreased to 82.1%, with increases in early apoptosis (8.85%) and late apoptosis (7.78%), demonstrating apoptosis induction. Most cells treated with metformin remained viable (92.2%), with modest increases in the early (6.52%) and the late apoptosis (1.31%), suggesting a mild pro-apoptotic effect. However, 5-Aza + Metformin combination decreased the cell viability to 70.6%, with early apoptosis at 13.4% and late apoptosis at 15.7%. This indicates a synergistic or additive effect in promoting apoptosis compared with single treatments. In the cisplatin group as a positive control, the lowest viability (63.9%), with the highest early apoptosis (31.8%) and a moderate increase in late apoptosis (3.38%) was observed, reflecting cisplatin’s potent apoptotic effect. Combination of 5-AZA-CdR (3.20 µM) and metformin (17.37 mM) increased the apoptotic cells and showed synergy according to the Bliss synergy model, indicating Bliss excess (BE) of 0.0594 and combination index (CI) of 0.795 (< 1). The effects of different combinations of 5-AZA-CdR and metformin and their synergistic effects on cell cycle progression in MKN 45 cells were evaluated (Fig. 3 c). The cells were stained with PI and evaluated via flow cytometry. As shown in the control group, most cells were found in the G1 and G2/M phases, with a minimal sub-G1 population, indicating low baseline apoptosis and normal cell cycle progression. The 5-AZA-CdR-treated group showed a marked increase in the sub-G1 population, reflecting enhanced apoptosis. The proportions of cells in the G1 and S phases decreased, whereas the G2/M remained predominant. Treatment with metformin resulted in a modest increase in sub-G1 cells, with a slight reduction in the G1 and S phase populations compared with control, indicating mild induction of apoptosis. In the 5-Aza + metformin group, the combination treatment further increased the sub-G1 fraction, suggesting a synergistic effect on apoptosis induction. The synergistic effect at doses of 3.20 and 17.37 mM led to greater arrest at the G2/M phase in MKN 45 cells. The G1 and S phase populations were further reduced, while the G2/M fraction remained the largest. Finally, the treatment resulted in the highest sub-G1 population in the cisplatin group, indicating a strong apoptosis induction. The G1 and S phases are significantly decreased, with the G2/M phase still present but reduced compared with other treatments. All treatments, especially the combination of 5-AZA-CdR and metformin, led to a decrease in the number of cells in the S phase (down to 7.2%), while increasing the percentage of cells in the G2/M phase, indicating that normal cell cycle progression was disrupted and apoptosis was effectively induced. Gene expression analysis The real-time q-PCR results revealed significant alterations in the expression levels of apoptosis-related genes. Figure 2 shows the influence of various compounds on gene expression in GC lines. The results revealed that the CASP-1 gene expression significantly increased in the metformin (3.67 ± 0.36, p = 0.018), 5-Aza (7.21 ± 0.48, p = 0.0003), and 5Aza + metformin groups (8.47 ± 0.34, p = 0.00001). However, after the Bonferroni correction, only the 5-Aza and 5Aza + metformin combination groups remained significant ( p bon = 0.003 and < 0.0001, respectively). The CASP-3 gene expression significantly increased in the metformin (6.67 ± 0.46, p = 0.011), 5-Aza (7.17 ± 0.19, p = 0.001) and 5Aza + metformin groups (9.45 ± 0.26, p = 0.00001) compared with those in the control group. However, after Bonferroni correction, only the 5-Aza and 5Aza + metformin combination groups remained significant ( p bon = 0.007 and < 0.0001, respectively). The results revealed that compared with those in the control (untreated) group, BAX gene expression in the MKN 45 cell line treated with 5-Aza (6.10 ± 0.38, p = 0.005), metformin (6.05 ± 0.34, p = 0.006), and 5Aza + metformin (8.08 ± 0.15, p = 0.00001) significantly increased, but after Bonferroni correction, only the 5Aza + metformin combination group remained significant ( p bon < 0.0001). BCL 2 gene expression in the MKN 45 cell line treated with cisplatin (0.72 ± 0.08, p = 0.007) and 5Aza + metformin (0.56 ± 0.05, p = 0.00005) significantly decreased compared with that in the control group, but after Bonferroni correction, only the 5Aza + metformin combination group remained significant ( p bon < 0.0005). The BAX / BCL 2 gene expression ratio in the MKN 45 cell line treated with metformin (7.18 ± 1.16, p = 0.002), 5-Aza (6.45 ± 0.5, p = 0.005), and 5Aza + metformin (14.46 ± 1.46, p = 0.00001) significantly increased compared with control group, but after Bonferroni correction, only the metformin and 5Aza + metformin combination groups remained significant ( p bon = 0.018 and < 0.0001, respectively). ATG 7 gene expression in the 5-Aza group (1.64 ± 0.28, p = 0.012), the 5Aza + metformin group (1.97 ± 0.15, p = 0.0001), and the cisplatin-positive control group (2.02 ± 0.2, p = 0.00004) significantly increased, but after Bonferroni correction, only the 5Aza + metformin combination and cisplatin groups remained significant ( p bon = 0.001 and 0.0004, respectively). PPI network construction STRING integrates both known and predicted protein‒protein interactions, including physical interactions and functional associations, on the basis of shared pathways or biological processes. Clusters of interacting proteins are visualized with central protein structure circles as nodes, and their connections are shown with edges. Each cluster with the same function is displayed with a distinct color, and the interaction confidence score is based on supporting evidence, prioritizing the most biologically relevant interactions. The scores range from 0 to 1, with higher values indicating stronger confidence[ 14 ]. Figure 4 a displays the interaction network of BAX, BCL 2 , CASP-1, CASP-3, and ATG 7 with the predicted interacting proteins and their specific functions, which reveals critical insights into therapeutic targets in modulating key apoptotic regulators, including caspases, BCL 2 family proteins, and autophagy-related genes. PPI network analysis with k -means clustering (defined number of clusters on the basis of their centroid) and the minimum required interaction score of 0.700 (high confidence) predicted 5 more functional protein partners from the 1st shell in three clusters. The first cluster included the apoptosis network and BCL 2 -related functions (6 red nodes with 11 edges, p value: < 1.0e-16), consisting of the BAX, BCL 2 , and either the BBC3 or PUMA proteins (score: 0.999). BAX is a proapoptotic member of the BCL 2 family[ 15 ], whereas BCL 2 or BCL-W (score: 0.930) is an antiapoptotic member that inhibits apoptosis by binding to the proapoptotic member BAX and is crucial for regulating mitochondrial outer membrane permeabilization[ 16 ]. Additionally, BID (score: 0.998) and BECN2 (score: 0.999) play key roles in lysosomal degradation pathways as regulators of autophagy and regulators of G-protein coupled receptor turnover[ 17 ]. The second cluster displayed 3 green nodes with 3 edges, with a p value of 0.000276, related to the caspase complex, and Legionellosis consisted of CASP-1 and CASP-3. Additionally, it predicted one more related protein (CASP-8 score: 0.999) from the extrinsic apoptosis pathway[ 18 ]. The third cluster predicted autophagy function-interacting proteins and consisted of 2 blue nodes with 1 edge, with a p value of 0.0137. These proteins are ATG 7 and ATG 5 . Network analysis scores and interaction p values revealed that the apoptotic function had the lowest and strongest p value, indicating its greater relevance to specific protein interactions. Functional enrichment in the network of BAX, BCL, CASP-1, CASP-3, and ATG Gene Ontology (GO) is a comprehensive framework used to designate the functions of gene products; it uses a mix of experimental data, computer predictions, and comparisons with other proteins whose functions are already known. This approach allows researchers to make educated guesses about the roles of proteins that have not yet been fully studied on the basis of how similar they are to proteins that are better understood. By classifying protein functions via GO, scientists can gain valuable insights into disease mechanisms, particularly by uncovering how proteins interact with each other and what pathways they are involved[ 19 ]. The pathway analysis uncovered the most important impact of dysregulated proteins on the cellular function, here the KEGG[ 20 ] and STING[ 21 ] enrichment analysis in Fig. 4 b-d showed this. Part B revealed that the most notable pathway here is "Apoptosis-multiple species, and TRAIL signaling," which shows remarkable statistical significance (FDR < 1.0e-13) and has the highest gene count, indicating a strong activation of death receptor-mediated apoptosis. It’s directly related to the increased expression of caspase-1 (CASP-1) and caspase-3 (CASP-3), as TRAIL signaling triggers the extrinsic apoptotic cascade by activating these caspases. Part C, which is related to KEGG pathway functional determination, demonstrated the "Apoptosis-multiple species" pathway shows the highest enrichment (FDR < 1.0e-15) and most significantly enriched. Part D shows the number of genes with a role in a specific function, and it shows similar patterns of pathway enrichment, though the statistical significance levels and gene counts. Discussion The results of this study provided strong evidence for the combined effects of decitabine and metformin to induce apoptosis in MKN45 GC cells. The MTT assay results showed that these agents not only slow down tumor growth but might also make cancer cells more susceptible to apoptosis. Moreover, there was a notable decrease in BCL2 levels and an increase in BAX, CASP-1, CASP-3, and ATG 7 . Specifically, it reduces the BCL2/BAX ratio and boosts the expression of caspase-1 (8.47 ± 0.34 vs. control; p bon < 0.0001) and caspase-3 (9.45 ± 0.26 vs. control; p bon < 0.0001) highlighting the synergistic action of decitabine and metformin. This suggests a shift towards creating an environment that promotes cell death and these significant changes in gene expression could be an important consideration for cancer treatments. A recent study demonstrated that one of the primary mechanisms by which metformin acts against cancer is through AMPK-mediated inhibition of mTORC1, which disrupts cancer cell metabolism and increases their sensitivity to DNA damage. When metformin is combined with 5-Aza-dC—a DNA hypomethylating agent—it appears to enhance the reactivation of tumor suppressor genes silenced by promoter hypermethylation. Our flow cytometry results showed a 2.1-fold increase in late apoptosis (15.7% vs. 7.78% with 5-Aza-dC alone) and G2/M phase arrest (29.1% vs. 22.4% in control), indicating that metformin boosts 5-Aza-dC's ability to create replicative stress and trigger a DNA damage response. These findings support Nguyen et al.'s model of metabolic-epigenetic synergy, in which AMPK activation increases chromatin accessibility, facilitating the incorporation of 5-Aza-dC into DNA[ 22 ]. In this study, we also demonstrated that Caspase-3 activation (9.45 ± 0.26 vs. control), which promotes cell death, likely occurs through both intrinsic (BAX-mediated apoptosis) and extrinsic (TRAIL receptor) pathways, as supported by bioinformatic analysis using the STRING network enrichment assay of death receptor signaling components. Research on autophagy modulation as a therapeutic amplifier revealed that while single-agent treatments had minimal effects on ATG 7 expression (1.32–1.64-fold vs. control), the combination treatment resulted in a significant 1.97-fold increase ( p = 0.001). This suggests that metformin's activation of AMPK synergizes with 5-Aza-dC-induced epigenetic changes to overcome autophagy suppression in cancer cells. This finding aligns with Tong et al.'s report that ATG7 overexpression is associated with improved chemosensitivity in GC, potentially because autophagy facilitates the clearance of damaged organelles and enhances apoptotic signaling[ 23 ]. However, the complex role of autophagy in cancer necessitates further investigation into its temporal dynamics—while early autophagy may promote cell survival, prolonged activation can induce type II programmed cell death[ 24 ]. The observed changes in ATG 7 expression, influenced by the combination of decitabine and metformin, highlight a complex interplay between apoptosis and autophagy during treatment. Further research is essential, as modulating autophagy could have both positive and negative effects on cell survival and death. Notably, KEGG pathway analysis revealed concurrent enrichment in the "Platinum Drug Resistance" and "p53 Signaling Pathway" , shedding light on why combining decitabine with metformin may be a game-changer in treatment. Platinum drug resistance often arises from enhanced DNA repair mechanisms and increased expression of anti-apoptotic proteins, particularly BCL 2 overexpression. The emphasis on these pathways suggests that this drug combo might tackle chemoresistance by hitting two targets at once: it addresses DNA methylation with decitabine and metabolic pathways with metformin, while also promoting apoptosis. This study has certain limitations, such as the use of a single cell line, which may not fully capture the heterogeneity observed in GC overall. Further validation in multiple GC cell types or primary cell cultures is needed. Additionally, the in vitro nature of this assessment may not accurately replicate the complexities of in vivo environments, particularly regarding drug metabolism and systemic interactions. These factors could affect the effectiveness of decitabine and metformin in inducing apoptosis in living organisms compared to controlled laboratory conditions. Conclusions In conclusion, this study demonstrated the synergistic effects of decitabine and metformin in triggering apoptosis in MKN 45 GC cells through comprehensive analyses. These findings provide a foundation for future research on combination therapies targeting apoptotic pathways to treat GC. In vivo studies and clinical trials will be crucial for translating these promising findings into effective treatment options for patients with GC. Materials and methods This study employed a quantitative research design to measure apoptotic markers related to the synergistic effects of decitabine (Santa Cruz Biotechnology Inc., Heidelberg, Germany) and metformin (Tehran Chemie Pharmaceutical Company, Tehran, Iran) on the cell viability and apoptosis induction. The study population comprises five distinct groups of MKN 45 GC cells. This study was approved by the Ethics Committee of the University of Mohaghegh Ardabili/IR.UMA.REC.1404.037 (webpage of ethical approval code is: https://ethics.research.ac.ir/form/s1jnx0swghpj423r.pdf ). Cell Cultures and Treatment The MKN 45 poorly differentiated adenocarcinoma cell line was obtained from the Research Institute of Biotechnology (RIB), Ferdowsi University of Mashhad (Mashhad, Iran) and cultured in appropriate media supplements: RPMI 1640, 10% fetal bovine serum, and antibiotics (penicillin‒streptomycin 1%). The samples were placed in an incubator with 5% carbon dioxide, a temperature of 37°C, and 95% humidity. After three passages, the appropriate number of cells was obtained and prepared. Cells were treated with various concentrations of decitabine or metformin, individually or in combination, to determine their effects on cell viability and apoptosis. After exposure to treatment, the cells were harvested for analysis. MTT Assay To assess cell viability, an MTT assay was conducted. This colorimetric assay relies on the reduction of MTT by mitochondrial dehydrogenases in viable cells to form a formazan product. Following treatment, the cells were incubated with 10 µL of MTT solution (10 mg/mL) for 3 h. Subsequently, formazan formed crystals were solubilized in DMSO (100 µL), and the absorbance was measured at 585 nm, providing a cell viability index across treatments. The viability percentages were calculated in five distinct groups of MKN 45 cells: the control group, which was treated with sterile PBS to determine the baseline cell viability; metformin group; decitabine (5-Aza) group; combination of metformin and 5-Aza group; and cisplatin group treated with 20 µg/mL cisplatin as a positive control for apoptosis induction. Flow cytometry analysis of apoptosis To measure apoptosis, flow cytometry was performed using Annexin-V and PI staining. Cells were harvested, washed, and resuspended in a binding buffer. Staining was performed using Annexin-V-FITC and PI for 15 min at room temperature. The stained cells were analyzed using a flow cytometer. Data were processed using FlowJo software. This analysis allowed for the differentiation between viable, early apoptotic, and late apoptotic cells. Synergy was calculated according to the Bliss synergy model. Bliss independence is recognized as one of the most commonly utilized metrics for synergy. The null model posits that the effects of two drugs are independent both mechanistically and probabilistically. Furthermore, Bliss scores are based on the assumption that the individual agents exhibit exponential dose-effect relationships. To compute a Bliss excess, it is necessary to express the activities of Drug 1 (E1) and Drug 2 (E2), along with the observed effect of their combination (E1,2), as probabilities ranging from 0 to 1 (0 ≤ E1 ≤ 1, 0 ≤ E2 ≤ 1, and 0 ≤ E1,2 ≤ 1, respectively). Additive Bliss effect: 𝐸1 + 𝐸2(1 − 𝐸1) = 𝐸1 + 𝐸2 − 𝐸1𝐸2 Bliss excess (BE) is presently determined by calculating the difference between the observed inhibition of the combination and the Bliss additivity of the individual therapies at identical concentrations. Bliss excess: 𝐸1,2 − (𝐸1 + 𝐸2 − 𝐸1𝐸2) Positive BE values suggest a synergistic interaction, while negative BE values indicate an antagonistic behavior. Null BE values signify the absence of any drug interaction. Real-time q-PCR analysis To evaluate the effects of the treatments on apoptosis-related gene expression, total RNA was extracted from the treated cells via Trizol. The extracted RNA was quantified using a NanoDrop spectrophotometer, and the RNA concentration was measured at 260 nm. RNA purity was assessed by examining the 280/260 and 260/230 ratios. cDNA synthesis was performed using a reverse transcriptase kit (Easy cDNA Ultra-TM Synthesis Kit of Parstous). Expression levels of apoptotic markers (caspase-1, caspase-3, BCL2, BAX, and ATG7) were quantified in different groups via real-time PCR with SYBR Green Master Mix. Table 1 shows the specific primers used for each gene. The relative expression levels were calculated via the 2 −ΔΔCt method, where ΔCt is the difference between the Ct values of the gene of interest and those of the housekeeping gene (GAPDH). The magnitude of the change was then determined relative to that of the control treatments. Table 1 The primers used to amplify proapoptotic genes . Genes Sequence (5' → 3') BAX F: GCCCTTTTGCTTCAGGGTTT R: GGAAAAAGACCTCTCGGGGG BCL 2 F: TGGGATTCCTGCGGATTGAC R: ACTTCCTCTGTGATGTTGTATTTTT CASP-1 F: ATCCGTTCCATGGGTGAAGG R: CCTGTGCCCCTTTCGGAATA CASP-3 F: GATGCGTGATGTTTCTAAAG R: CACTGTCTGTCTCAATGC ATG7 F: GAGACCTGTATGTCCTGCGT R: CTGGTGTCCATCAGCTTCAGT GAPDH F: GGAAGGTGAAGGTCGGAGTCA R:GTCATTGATGGCAACAATATCCAT Protein-protein interaction (PPI) network construction and pathway enrichment analysis Protein ̶ protein interaction (PPI) network was used to obtain new insights into protein functionality and related functions interactions that can assist in categorizing key genes. Targeted genes were thus employed to construct a PPI network using the Search Tool for the Retrieval of Interacting Genes (STRING) database. The STRING database provides comprehensive and critical assessment of the interactions among proteins, including both predictive and experimental interaction data, allowing researchers to analyze the interaction networks of specific proteins, which helps to explore the roles of proteins in complex diseases and investigate the mechanisms of action of therapeutic agents[ 20 , 21 , 25]. Statistical analysis Analysis employed the Shapiro-Wilk test to assess data normality. If the data were normally distributed, ANOVA and Tukey pairwise comparisons were used; otherwise, the Kruskal-Wallis test and Posthoc Dunn-Bonferroni pairwise comparisons were performed. Statistical significance was set at p < 0.05. SPSS v.26 and GraphPad Prism v.9 were used for statistical analysis. This approach ensured the selection of appropriate statistical tests based on the distribution of data, maintaining the study findings’ integrity. Declarations Ethics approval and consent to participate The research was performed according to the ethical principles for human research declared in the 1975 Declaration of Helsinki. This study was approved by the Ethics Committee of the University of Mohaghegh Ardabili/IR.UMA.REC.1404.037 (webpage of ethical approval code is: https://ethics.research.ac.ir/form/s1jnx0swghpj423r.pdf ). Conflict of interest No conflicts of interest to declare. Funding This work was supported by the Research Council of the University of Mohaghegh Ardabili. The supporter had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study. Author Contribution S.L-N. provided direction in the preparation of the manuscript; M.A.A. and M.R.K. wrote the first draft of manuscript; M.A.A. and M.R.K. analyzed data; S.L-N discussed and revised the manuscript; M.A.A., M.R.K., and S.L-N. managed the references; S.L-N. approved the version to be published. All authors reviewed the manuscript. Data Availability The data that support the findings of this study are available from the corresponding author, upon reasonable request. References Gullo, I. et al. Precancerous lesions of the stomach, gastric cancer and hereditary gastric cancer syndromes. Pathologica 112 (3), 166 (2020). Abdi, E. et al. Emerging therapeutic targets for gastric cancer from a host-Helicobacter pylori interaction perspective. Expert Opin. Ther. Targets . 25 (8), 685–699 (2021). Abdi, E. et al. Risk factors predisposing to cardia gastric adenocarcinoma: Insights and new perspectives. Cancer Med. 8 (13), 6114–6126 (2019). Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 71 (3), 209–249 (2021). Xu, J. et al. A review of current evidence about lncRNA MEG3: A tumor suppressor in multiple cancers. Front. Cell. Dev. Biology . 10 , 997633 (2022). AZARM, T. & GHANNADI, F. Effectiveness of chemotherapy in 54 cases of advanced gastric cancer in Isfahan. Med. J. Islamic Repub. Iran. (MJIRI) . 4 (4), 261–264 (1990). Sharma, A., Jasrotia, S. & Kumar, A. Effects of chemotherapy on the immune system: implications for cancer treatment and patient outcomes. Naunyn. Schmiedebergs Arch. Pharmacol. 397 (5), 2551–2566 (2024). Kao, H. W., Tsai, K. W. & Lin, W. Synergistic effect of metformin and lansoprazole against gastric cancer through growth inhibition. Int. J. Med. Sci. 20 (6), 717 (2023). Galal, M. A. et al. Metformin: A Dual-Role Player in Cancer Treatment and Prevention. Int. J. Mol. Sci. 25 (7), 4083 (2024). Wang, X. et al. Glutathione promotes the synergistic effects of venetoclax and azacytidine against myelodysplastic syndrome–refractory anemia by regulating the cell cycle. Experimental Therapeutic Med. 26 (6), 1–11 (2023). Sato, T., Issa, J. P. J. & Kropf, P. DNA hypomethylating drugs in cancer therapy. Cold Spring Harbor Perspect. Med. 7 (5), a026948 (2017). Ebert, K. et al. Determining the effects of trastuzumab, cetuximab and afatinib by phosphoprotein, gene expression and phenotypic analysis in gastric cancer cell lines. BMC cancer . 20 , 1–19 (2020). Keller, S. et al. Effects of trastuzumab and afatinib on kinase activity in gastric cancer cell lines. Mol. Oncol. 12 (4), 441–462 (2018). Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51 (D1), D638–D646 (2023). Zheng, S. et al. Effect of the BBC3 Gene on the Proliferation and Apoptosis of Hepatocellular Carcinoma Cells Through p53-Regulated Signaling. J. Biomaterials Tissue Eng. 11 (1), 135–141 (2021). Hartman, M. L. & Czyz, M. BCL-w: apoptotic and non-apoptotic role in health and disease. Cell Death Dis. 11 (4), 260 (2020). Hardwick, J. M. & Soane, L. Multiple functions of BCL-2 family proteins. Cold Spring Harb. Perspect. Biol. 5 (2), a008722 (2013). Jiang, M. et al. Caspase-8: A key protein of cross‐talk signal way in PANoptosis in cancer. Int. J. Cancer . 149 (7), 1408–1420 (2021). Thomas, P. D. The gene ontology and the meaning of biological function. The gene ontology handbook, : pp. 15–24. (2017). Kanehisa, M. et al. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 53 (D1), D672–D677 (2025). Szklarczyk, D. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51 (D1), D638–D646 (2023). Nguyen, J. et al. Phase I trial of 5-aza-4’-thio-2’-deoxycytidine (Aza-TdC) in patients with advanced solid tumors (Wolters Kluwer Health, 2021). Tong, T. et al. Prognostic autophagy-related model revealed by integrating single-cell RNA sequencing data and bulk gene profiles in gastric cancer. Front. Cell. Dev. Biology . 9 , 729485 (2022). Fitzwalter, B. E. & Thorburn, A. Recent insights into cell death and autophagy. FEBS J. 282 (22), 4279–4288 (2015). Nayak, C. & Singh, S. K. Integrated transcriptome profiling identifies prognostic hub genes as therapeutic targets of glioblastoma: evidenced by bioinformatics analysis. ACS omega . 7 (26), 22531–22550 (2022). Tomczak, A. et al. Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations. Sci. Rep. 8 (1), 5115 (2018). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers invited by journal 04 Sep, 2025 Editor assigned by journal 04 Sep, 2025 Editor invited by journal 04 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 02 Sep, 2025 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. 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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-7382082","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":510328264,"identity":"e49e23e6-91fa-4ab7-ae19-4ce35103e0c0","order_by":0,"name":"Mohammed AlAli","email":"","orcid":"","institution":"Ardabil University of Mohaghegh Ardabili","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"AlAli","suffix":""},{"id":510328265,"identity":"f3ef9325-6a0b-41d9-b719-796892f353f5","order_by":1,"name":"Saeid Latifi-Navid","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYFAC5gYGBgM5Hn4Yn42wFsYGhgMGxjySDaRpYTBmMDhArLPkGxgbP38oMJAxvpGd+IGhxo6BT5qAZoMDjM0SBwwMeMxu5G6WYDiWzMDGl0BAC9BhQC1/QFo2SDCwHWBg4yHssOYfIFuMZ+Ru/sHwjwgtDAcY28AOM5DI3SbB2EaEFqBf2izOALVInHm7zSKxL5mHCIcxH75R8cfAnr89d/OND9/s5OR7CDlM/gESJ4GBgaBPRsEoGAWjYBQQAQCliTnE9AS9zQAAAABJRU5ErkJggg==","orcid":"","institution":"Ardabil University of Mohaghegh Ardabili","correspondingAuthor":true,"prefix":"","firstName":"Saeid","middleName":"","lastName":"Latifi-Navid","suffix":""},{"id":510328266,"identity":"354c7be2-8696-4be4-8299-dcca92e75f0f","order_by":2,"name":"Mohammad Reza Khakzad","email":"","orcid":"","institution":"MMS.C, Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Reza","lastName":"Khakzad","suffix":""}],"badges":[],"createdAt":"2025-08-15 14:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7382082/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7382082/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-42417-y","type":"published","date":"2026-03-25T16:11:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91120746,"identity":"c6aef26c-744b-4fbd-933b-62c1e252d8f7","added_by":"auto","created_at":"2025-09-11 19:06:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":740773,"visible":true,"origin":"","legend":"\u003cp\u003eDose-dependent cytotoxic effect of metformin and 5-Aza-deoxycytidine on MKN45 cell line. A: cytotoxic efficacy of metformin with 10,000 cells per well in 2.5-80 mM exposure after 48-hour treatment, demonstrating IC₅₀ value of 26.21 mM, B: metformin effect after 72-hour treatment (1.25-40 mM range in 1,000 cells per well) yielding IC₅₀value of 17.37 mM, C: 5-Aza-deoxycytidine effect after 96-hour treatment (1.25-40 μM range in 1,000 cells per well) establishing IC₅₀ of 3.20 μM. D: Dual-treatment protocol with media refreshment after 48 hours showing IC₅₀ of 1.643 μM. *. \u003cem\u003ep\u003c/em\u003e ≤ 0.05 ; **. \u003cem\u003ep\u003c/em\u003e≤ 0.01 ; ***. \u003cem\u003ep\u003c/em\u003e ≤ 0.001 ; ****. \u003cem\u003ep\u003c/em\u003e ≤ 0.0001\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7382082/v1/b981a4f046a7ebdaa28195f1.jpg"},{"id":91120743,"identity":"c2fb89f5-c360-4f16-b337-41c7740786a2","added_by":"auto","created_at":"2025-09-11 19:06:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":223892,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of the effects of compounds on proapoptotic and necrotic gene expression in the MKN\u003csub\u003e45\u003c/sub\u003e cell line. A: \u003cem\u003eCASP-1\u003c/em\u003e gene expression, B: \u003cem\u003eCASP-3\u003c/em\u003e gene expression, C: \u003cem\u003eBAX\u003c/em\u003e gene expression, D: \u003cem\u003eBCL\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e \u003c/em\u003egene expression, E: \u003cem\u003eBAX\u003c/em\u003e/\u003cem\u003eBCL\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e gene expression, F: \u003cem\u003eATG7\u003c/em\u003e gene expression.\u003cstrong\u003e \u003c/strong\u003eThe relative expression levels were calculated via the 2\u003csup\u003e−ΔΔCt\u003c/sup\u003e method, where ΔCt is the difference between the Ct values of the gene of interest and the housekeeping gene (GAPDH).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7382082/v1/a641bd209cb03d56d9e00506.jpg"},{"id":91122158,"identity":"f7983925-4c41-4727-b667-9b6d821ecfaa","added_by":"auto","created_at":"2025-09-11 19:15:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":206858,"visible":true,"origin":"","legend":"\u003cp\u003eA: These diagrams display flow cytometry dot plots assessing apoptosis in cells treated with different agents, control, 5-Aza, metformin, 5-Aza \u0026amp; metformin, and cisplatin. Each plot is divided into four quadrants: Q4 (lower left): Viable cells (Annexin V-/PI-), Q3 (lower right): Early apoptotic cells (Annexin V+/PI-), Q2 (upper right): Late apoptotic/necrotic cells (Annexin V+/PI+), Q1 (upper left): Necrotic cells (Annexin V-/PI+). B:\u003cstrong\u003e \u003c/strong\u003eThe diagram presents Comparison of flow cytometry analysis of the apoptotic cells’ percentages after treatment with different compounds: control, 5-Aza, metformin, 5-Aza+metformin, and cisplatin. Three cell populations are quantified for each condition: viable cells (orange bars), early apoptotic cells (blue bars), and late apoptotic cells (green bars). C: The bar graph illustrates the effects of different treatments—control, 5-Aza, metformin, 5-Aza plus metformin, and cisplatin—on cell cycle disruption and apoptosis, as indicated by the proportion of cells in each cell cycle phase: sub-G1 (apoptotic, light blue), G1 (green), S (red), and G2/M (gray).\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7382082/v1/98cedbbce50ca95db52afea5.jpg"},{"id":91122597,"identity":"38e6b0e0-8293-497d-98f2-ebc252c1f842","added_by":"auto","created_at":"2025-09-11 19:22:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1171330,"visible":true,"origin":"","legend":"\u003cp\u003eprotein-protein interaction (PPI) network analysis and its Pathway Enrichment Analysis\u003cstrong\u003e \u003c/strong\u003evia the STRING database, \u003cstrong\u003e(A)\u003c/strong\u003e PPI with the same node color represents specific cluster proteins. Edges represent protein‒protein associations (shared functions; not necessarily physical interactions), and the edges between clusters are shown with dotted lines. Different colored lines are illustrated here, (B) This dot plot displays STRING protein network cluster enrichment analysis where apoptosis-related pathways, particularly those involving Bcl-2 family proteins and TRAIL signaling, show significant enrichment with low false discovery rates (FDR \u0026lt; 1.0e-13), (C) KEGG pathway enrichment analysis revealing strong statistical significance (FDR \u0026lt; 1.0e-15) for apoptosis pathways, indicating robust involvement of the analyzed gene set in programmed cell death mechanisms, (D) KEGG pathway enrichment with notable enrichment on the basis of gene account involvement which revealed apoptosis path at the top.\u003cstrong\u003e FDR \u003c/strong\u003e(\u003cstrong\u003eFalse Discovery Rate\u003c/strong\u003e) is a statistical measure used to assess the proportion of false positives among all significant results in multiple hypothesis testing, represented as the −log10 ​(\u003cem\u003ep\u003c/em\u003e value), and extremely low \u003cem\u003ep\u003c/em\u003e values (e.g., \u0026lt; 1.0e-16) indicate statistically significant biological process. \u003cstrong\u003eSignal\u003c/strong\u003e in Gene Ontology (GO) enrichment analysis refers to the statistical strength of the associations between gene sets and specific biological processes[26].\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7382082/v1/fe6da3ba3a29d87763703165.jpg"},{"id":105756065,"identity":"45b607d5-2096-470e-a668-84981f9e2d70","added_by":"auto","created_at":"2026-03-30 16:35:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3075104,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7382082/v1/6f5b4106-30b9-4163-b7bb-9d224c10720b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Synergistic Effect of Epigenetic Modulator Decitabine and Metformin in the Battle Against Gastric Cancer: A Potential Therapeutic Strategy ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is influenced by a complex interplay of genetic and environmental factors. Although most gastric cancers are sporadic, a small percentage 1\u0026ndash;3% of cases are attributed to inherited genetic predispositions[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The overall 5-year relative survival rate for patients with GC varies on their ethnic background, genetic predispositions, stage of diagnosis, and therapeutic regimen. However, globally, the rate is approximately 20%, indicating that individuals diagnosed with stomach cancer are 20% more likely to be alive five years after diagnosis than are similar individuals without the disease[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recent studies have demonstrated that 74% of these patients survive for up to 1 year after chemotherapy, and 50% of these patients survive two years or more. This report highlights the importance of utilizing effective therapies for GC, including chemotherapy, targeted therapies, or immunotherapy, which significantly improve survival rates compared with those of untreated patients, who have a one-year survival rate of less than 20%[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMetformin is mainly known as an antidiabetic drug; however, it has also been shown to have anticancer effects by activating the AMPK pathway. When used alone or in combination with other therapeutic agents, metformin plays a significant role in inducing cancer cell apoptosis. This effect is achieved by increasing the expression of proapoptotic genes, such as BAX, and reducing antiapoptotic signals, such as BCL\u003csub\u003e2\u003c/sub\u003e. This modulation increases the sensitivity of GC cells to apoptotic stimuli [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. 5-Aza-2\u0026prime;-deoxycytidine (5-AZA-CdR, decitabine), but it is a drug that functions as a DNA methyltransferase inhibitor. It works by reversing abnormal DNA methylation patterns that can contribute to cancer development. Hypomethylation process can restore the expression of silenced genes that are important for normal cell function. In cancer cells, these genes may be inactivated due to hypermethylation, and their effects lead to the differentiation and apoptosis of malignant cells[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Decitabine may be more effective when synergistically used with other therapeutic agents. In this study, the efficacy of the combination of metformin and decitabine was evaluated by assessing the expression of key apoptotic genes, including caspase-1 (\u003cem\u003eCASP-1\u003c/em\u003e), caspase-3 (\u003cem\u003eCASP-3\u003c/em\u003e), \u003cem\u003eBCL\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eBAX\u003c/em\u003e, and \u003cem\u003eATG\u003c/em\u003e\u003csub\u003e\u003cem\u003e7\u003c/em\u003e\u003c/sub\u003e, in the MKN\u003csub\u003e45\u003c/sub\u003e human GC cell line derived from poorly differentiated adenocarcinoma of the stomach. Its response to various chemotherapeutic agents has been studied, providing important insights into its sensitivity and resistance mechanisms[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMTT Assay finding\u003c/h2\u003e\u003cp\u003eThis study investigated the effects of 5-AZA-CdR and metformin on the viability of MKN\u003csub\u003e45\u003c/sub\u003e GC cells using the MTT assay. Initially, 10,000 cells per well were treated with varying concentrations of each drug. Metformin significantly reduced cell viability in a dose-dependent manner, with an IC\u003csub\u003e50\u003c/sub\u003e value of 26.21 mM after 48 h. However, 5-AZA-CdR showed no significant effect at standard doses. To optimize the results, experiments were repeated with 1,000 cells per well and a 96-h exposure. Under these conditions, Metformin reduced cell viability with IC\u003csub\u003e50\u003c/sub\u003e values of 13.58 mM and 17.37 mM, depending on the timing of drug administration. For 5-AZA-CdR, extended exposure and lower cell density led to a marked decrease in cell viability, with IC\u003csub\u003e50\u003c/sub\u003e values dropping to 3.20 \u0026micro;M. Finally, the selected concentrations to investigate the effect of metformin was IC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;17.37 mM, and for 5-AZA-CdR, IC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.20 \u0026micro;M by culturing 1000 cells per well of a 96-well plate for 96 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eApoptosis and cell cycle assays by flow cytometry\u003c/h3\u003e\n\u003cp\u003eQuantitative assessment of apoptosis and cell cycle was performed via flow cytometry following Annexin V/propidium iodide (PI) dual staining. The apoptotic effects of metformin (17.37 mM), 5-AZA-CdR (3.2 \u0026micro;M), and their concurrent administration were systematically evaluated in MKN45 gastric adenocarcinoma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b). Untreated cell populations cultured under identical \u003cem\u003ein vitro\u003c/em\u003e conditions served as the negative control. The results revealed that nearly all cells in the control group were viable (99.1%), with negligible early (0.41%) and late apoptosis (0.14%), indicating minimal baseline apoptosis. The cells viability of 5-AZA-CdR treatment group was decreased to 82.1%, with increases in early apoptosis (8.85%) and late apoptosis (7.78%), demonstrating apoptosis induction. Most cells treated with metformin remained viable (92.2%), with modest increases in the early (6.52%) and the late apoptosis (1.31%), suggesting a mild pro-apoptotic effect. However, 5-Aza\u0026thinsp;+\u0026thinsp;Metformin combination decreased the cell viability to 70.6%, with early apoptosis at 13.4% and late apoptosis at 15.7%. This indicates a synergistic or additive effect in promoting apoptosis compared with single treatments. In the cisplatin group as a positive control, the lowest viability (63.9%), with the highest early apoptosis (31.8%) and a moderate increase in late apoptosis (3.38%) was observed, reflecting cisplatin\u0026rsquo;s potent apoptotic effect. Combination of 5-AZA-CdR (3.20 \u0026micro;M) and metformin (17.37 mM) increased the apoptotic cells and showed synergy according to the Bliss synergy model, indicating Bliss excess (BE) of 0.0594 and combination index (CI) of 0.795 (\u0026lt;\u0026thinsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe effects of different combinations of 5-AZA-CdR and metformin and their synergistic effects on cell cycle progression in MKN\u003csub\u003e45\u003c/sub\u003e cells were evaluated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The cells were stained with PI and evaluated via flow cytometry. As shown in the control group, most cells were found in the G1 and G2/M phases, with a minimal sub-G1 population, indicating low baseline apoptosis and normal cell cycle progression. The 5-AZA-CdR-treated group showed a marked increase in the sub-G1 population, reflecting enhanced apoptosis. The proportions of cells in the G1 and S phases decreased, whereas the G2/M remained predominant. Treatment with metformin resulted in a modest increase in sub-G1 cells, with a slight reduction in the G1 and S phase populations compared with control, indicating mild induction of apoptosis. In the 5-Aza\u0026thinsp;+\u0026thinsp;metformin group, the combination treatment further increased the sub-G1 fraction, suggesting a synergistic effect on apoptosis induction. The synergistic effect at doses of 3.20 and 17.37 mM led to greater arrest at the G2/M phase in MKN\u003csub\u003e45\u003c/sub\u003e cells. The G1 and S phase populations were further reduced, while the G2/M fraction remained the largest. Finally, the treatment resulted in the highest sub-G1 population in the cisplatin group, indicating a strong apoptosis induction. The G1 and S phases are significantly decreased, with the G2/M phase still present but reduced compared with other treatments. All treatments, especially the combination of 5-AZA-CdR and metformin, led to a decrease in the number of cells in the S phase (down to 7.2%), while increasing the percentage of cells in the G2/M phase, indicating that normal cell cycle progression was disrupted and apoptosis was effectively induced.\u003c/p\u003e\n\u003ch3\u003eGene expression analysis\u003c/h3\u003e\n\u003cp\u003eThe real-time q-PCR results revealed significant alterations in the expression levels of apoptosis-related genes. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the influence of various compounds on gene expression in GC lines. The results revealed that the \u003cem\u003eCASP-1\u003c/em\u003e gene expression significantly increased in the metformin (3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), 5-Aza (7.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003), and 5Aza\u0026thinsp;+\u0026thinsp;metformin groups (8.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00001). However, after the Bonferroni correction, only the 5-Aza and 5Aza\u0026thinsp;+\u0026thinsp;metformin combination groups remained significant (\u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;=\u0026thinsp;0.003 and \u0026lt;\u0026thinsp;0.0001, respectively). The \u003cem\u003eCASP-3\u003c/em\u003e gene expression significantly increased in the metformin (6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), 5-Aza (7.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and 5Aza\u0026thinsp;+\u0026thinsp;metformin groups (9.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00001) compared with those in the control group. However, after Bonferroni correction, only the 5-Aza and 5Aza\u0026thinsp;+\u0026thinsp;metformin combination groups remained significant (\u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;=\u0026thinsp;0.007 and \u0026lt;\u0026thinsp;0.0001, respectively).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results revealed that compared with those in the control (untreated) group, \u003cem\u003eBAX\u003c/em\u003e gene expression in the MKN\u003csub\u003e45\u003c/sub\u003e cell line treated with 5-Aza (6.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), metformin (6.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), and 5Aza\u0026thinsp;+\u0026thinsp;metformin (8.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00001) significantly increased, but after Bonferroni correction, only the 5Aza\u0026thinsp;+\u0026thinsp;metformin combination group remained significant (\u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). \u003cem\u003eBCL\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e gene expression in the MKN\u003csub\u003e45\u003c/sub\u003e cell line treated with cisplatin (0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and 5Aza\u0026thinsp;+\u0026thinsp;metformin (0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00005) significantly decreased compared with that in the control group, but after Bonferroni correction, only the 5Aza\u0026thinsp;+\u0026thinsp;metformin combination group remained significant (\u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;\u0026lt;\u0026thinsp;0.0005). The \u003cem\u003eBAX\u003c/em\u003e/\u003cem\u003eBCL\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e gene expression ratio in the MKN\u003csub\u003e45\u003c/sub\u003e cell line treated with metformin (7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), 5-Aza (6.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), and 5Aza\u0026thinsp;+\u0026thinsp;metformin (14.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00001) significantly increased compared with control group, but after Bonferroni correction, only the metformin and 5Aza\u0026thinsp;+\u0026thinsp;metformin combination groups remained significant (\u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;=\u0026thinsp;0.018 and \u0026lt;\u0026thinsp;0.0001, respectively). \u003cem\u003eATG\u003c/em\u003e\u003csub\u003e\u003cem\u003e7\u003c/em\u003e\u003c/sub\u003e gene expression in the 5-Aza group (1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), the 5Aza\u0026thinsp;+\u0026thinsp;metformin group (1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001), and the cisplatin-positive control group (2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00004) significantly increased, but after Bonferroni correction, only the 5Aza\u0026thinsp;+\u0026thinsp;metformin combination and cisplatin groups remained significant (\u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;=\u0026thinsp;0.001 and 0.0004, respectively).\u003c/p\u003e\n\u003ch3\u003ePPI network construction\u003c/h3\u003e\n\u003cp\u003eSTRING integrates both known and predicted protein‒protein interactions, including physical interactions and functional associations, on the basis of shared pathways or biological processes. Clusters of interacting proteins are visualized with central protein structure circles as nodes, and their connections are shown with edges. Each cluster with the same function is displayed with a distinct color, and the interaction confidence score is based on supporting evidence, prioritizing the most biologically relevant interactions. The scores range from 0 to 1, with higher values indicating stronger confidence[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea displays the interaction network of BAX, BCL\u003csub\u003e2\u003c/sub\u003e, CASP-1, CASP-3, and ATG\u003csub\u003e7\u003c/sub\u003e with the predicted interacting proteins and their specific functions, which reveals critical insights into therapeutic targets in modulating key apoptotic regulators, including caspases, BCL\u003csub\u003e2\u003c/sub\u003e family proteins, and autophagy-related genes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePPI network analysis with \u003cem\u003ek\u003c/em\u003e-means clustering (defined number of clusters on the basis of their centroid) and the minimum required interaction score of 0.700 (high confidence) predicted 5 more functional protein partners from the 1st shell in three clusters. The first cluster included the apoptosis network and BCL\u003csub\u003e2\u003c/sub\u003e-related functions (6 red nodes with 11 edges, \u003cem\u003ep\u003c/em\u003e value: \u0026lt; 1.0e-16), consisting of the BAX, BCL\u003csub\u003e2\u003c/sub\u003e, and either the BBC3 or PUMA proteins (score: 0.999). BAX is a proapoptotic member of the BCL\u003csub\u003e2\u003c/sub\u003e family[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], whereas BCL\u003csub\u003e2\u003c/sub\u003e or BCL-W (score: 0.930) is an antiapoptotic member that inhibits apoptosis by binding to the proapoptotic member BAX and is crucial for regulating mitochondrial outer membrane permeabilization[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, BID (score: 0.998) and BECN2 (score: 0.999) play key roles in lysosomal degradation pathways as regulators of autophagy and regulators of G-protein coupled receptor turnover[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The second cluster displayed 3 green nodes with 3 edges, with a \u003cem\u003ep\u003c/em\u003e value of 0.000276, related to the caspase complex, and Legionellosis consisted of CASP-1 and CASP-3. Additionally, it predicted one more related protein (CASP-8 score: 0.999) from the extrinsic apoptosis pathway[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The third cluster predicted autophagy function-interacting proteins and consisted of 2 blue nodes with 1 edge, with a \u003cem\u003ep\u003c/em\u003e value of 0.0137. These proteins are ATG\u003csub\u003e7\u003c/sub\u003e and ATG\u003csub\u003e5\u003c/sub\u003e. Network analysis scores and interaction p values revealed that the apoptotic function had the lowest and strongest \u003cem\u003ep\u003c/em\u003e value, indicating its greater relevance to specific protein interactions.\u003c/p\u003e\n\u003ch3\u003eFunctional enrichment in the network of BAX, BCL, CASP-1, CASP-3, and ATG\u003c/h3\u003e\n\u003cp\u003eGene Ontology (GO) is a comprehensive framework used to designate the functions of gene products; it uses a mix of experimental data, computer predictions, and comparisons with other proteins whose functions are already known. This approach allows researchers to make educated guesses about the roles of proteins that have not yet been fully studied on the basis of how similar they are to proteins that are better understood. By classifying protein functions via GO, scientists can gain valuable insights into disease mechanisms, particularly by uncovering how proteins interact with each other and what pathways they are involved[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe pathway analysis uncovered the most important impact of dysregulated proteins on the cellular function, here the KEGG[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and STING[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] enrichment analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-d showed this. Part B revealed that the most notable pathway here is \"Apoptosis-multiple species, and TRAIL signaling,\" which shows remarkable statistical significance (FDR\u0026thinsp;\u0026lt;\u0026thinsp;1.0e-13) and has the highest gene count, indicating a strong activation of death receptor-mediated apoptosis. It\u0026rsquo;s directly related to the increased expression of caspase-1 (CASP-1) and caspase-3 (CASP-3), as TRAIL signaling triggers the extrinsic apoptotic cascade by activating these caspases. Part C, which is related to KEGG pathway functional determination, demonstrated the \"Apoptosis-multiple species\" pathway shows the highest enrichment (FDR\u0026thinsp;\u0026lt;\u0026thinsp;1.0e-15) and most significantly enriched. Part D shows the number of genes with a role in a specific function, and it shows similar patterns of pathway enrichment, though the statistical significance levels and gene counts.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study provided strong evidence for the combined effects of decitabine and metformin to induce apoptosis in MKN45 GC cells. The MTT assay results showed that these agents not only slow down tumor growth but might also make cancer cells more susceptible to apoptosis. Moreover, there was a notable decrease in BCL2 levels and an increase in BAX, CASP-1, CASP-3, and ATG\u003csub\u003e7\u003c/sub\u003e. Specifically, it reduces the BCL2/BAX ratio and boosts the expression of caspase-1 (8.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 vs. control; \u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and caspase-3 (9.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 vs. control; \u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) highlighting the synergistic action of decitabine and metformin. This suggests a shift towards creating an environment that promotes cell death and these significant changes in gene expression could be an important consideration for cancer treatments.\u003c/p\u003e\u003cp\u003eA recent study demonstrated that one of the primary mechanisms by which metformin acts against cancer is through AMPK-mediated inhibition of mTORC1, which disrupts cancer cell metabolism and increases their sensitivity to DNA damage. When metformin is combined with 5-Aza-dC\u0026mdash;a DNA hypomethylating agent\u0026mdash;it appears to enhance the reactivation of tumor suppressor genes silenced by promoter hypermethylation. Our flow cytometry results showed a 2.1-fold increase in late apoptosis (15.7% vs. 7.78% with 5-Aza-dC alone) and G2/M phase arrest (29.1% vs. 22.4% in control), indicating that metformin boosts 5-Aza-dC's ability to create replicative stress and trigger a DNA damage response. These findings support Nguyen et al.'s model of metabolic-epigenetic synergy, in which AMPK activation increases chromatin accessibility, facilitating the incorporation of 5-Aza-dC into DNA[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we also demonstrated that Caspase-3 activation (9.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 vs. control), which promotes cell death, likely occurs through both intrinsic (BAX-mediated apoptosis) and extrinsic (TRAIL receptor) pathways, as supported by bioinformatic analysis using the STRING network enrichment assay of death receptor signaling components. Research on autophagy modulation as a therapeutic amplifier revealed that while single-agent treatments had minimal effects on ATG\u003csub\u003e7\u003c/sub\u003e expression (1.32\u0026ndash;1.64-fold vs. control), the combination treatment resulted in a significant 1.97-fold increase (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). This suggests that metformin's activation of AMPK synergizes with 5-Aza-dC-induced epigenetic changes to overcome autophagy suppression in cancer cells. This finding aligns with Tong et al.'s report that ATG7 overexpression is associated with improved chemosensitivity in GC, potentially because autophagy facilitates the clearance of damaged organelles and enhances apoptotic signaling[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the complex role of autophagy in cancer necessitates further investigation into its temporal dynamics\u0026mdash;while early autophagy may promote cell survival, prolonged activation can induce type II programmed cell death[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The observed changes in ATG\u003csub\u003e7\u003c/sub\u003e expression, influenced by the combination of decitabine and metformin, highlight a complex interplay between apoptosis and autophagy during treatment. Further research is essential, as modulating autophagy could have both positive and negative effects on cell survival and death. Notably, KEGG pathway analysis revealed concurrent enrichment in the \u003cem\u003e\"Platinum Drug Resistance\"\u003c/em\u003e and \u003cem\u003e\"p53 Signaling Pathway\"\u003c/em\u003e, shedding light on why combining decitabine with metformin may be a game-changer in treatment. Platinum drug resistance often arises from enhanced DNA repair mechanisms and increased expression of anti-apoptotic proteins, particularly BCL\u003csub\u003e2\u003c/sub\u003e overexpression. The emphasis on these pathways suggests that this drug combo might tackle chemoresistance by hitting two targets at once: it addresses DNA methylation with decitabine and metabolic pathways with metformin, while also promoting apoptosis.\u003c/p\u003e\u003cp\u003eThis study has certain limitations, such as the use of a single cell line, which may not fully capture the heterogeneity observed in GC overall. Further validation in multiple GC cell types or primary cell cultures is needed. Additionally, the \u003cem\u003ein vitro\u003c/em\u003e nature of this assessment may not accurately replicate the complexities of \u003cem\u003ein vivo\u003c/em\u003e environments, particularly regarding drug metabolism and systemic interactions. These factors could affect the effectiveness of decitabine and metformin in inducing apoptosis in living organisms compared to controlled laboratory conditions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study demonstrated the synergistic effects of decitabine and metformin in triggering apoptosis in MKN\u003csub\u003e45\u003c/sub\u003e GC cells through comprehensive analyses. These findings provide a foundation for future research on combination therapies targeting apoptotic pathways to treat GC. \u003cem\u003eIn vivo\u003c/em\u003e studies and clinical trials will be crucial for translating these promising findings into effective treatment options for patients with GC.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThis study employed a quantitative research design to measure apoptotic markers related to the synergistic effects of decitabine (Santa Cruz Biotechnology Inc., Heidelberg, Germany) and metformin (Tehran Chemie Pharmaceutical Company, Tehran, Iran) on the cell viability and apoptosis induction. The study population comprises five distinct groups of MKN\u003csub\u003e45\u003c/sub\u003e GC cells. This study was approved by the Ethics Committee of the University of Mohaghegh Ardabili/IR.UMA.REC.1404.037 (webpage of ethical approval code is: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ethics.research.ac.ir/form/s1jnx0swghpj423r.pdf\u003c/span\u003e\u003cspan address=\"https://ethics.research.ac.ir/form/s1jnx0swghpj423r.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCell Cultures and Treatment\u003c/h2\u003e\u003cp\u003eThe MKN\u003csub\u003e45\u003c/sub\u003e poorly differentiated adenocarcinoma cell line was obtained from the Research Institute of Biotechnology (RIB), Ferdowsi University of Mashhad (Mashhad, Iran) and cultured in appropriate media supplements: RPMI 1640, 10% fetal bovine serum, and antibiotics (penicillin‒streptomycin 1%). The samples were placed in an incubator with 5% carbon dioxide, a temperature of 37\u0026deg;C, and 95% humidity. After three passages, the appropriate number of cells was obtained and prepared. Cells were treated with various concentrations of decitabine or metformin, individually or in combination, to determine their effects on cell viability and apoptosis. After exposure to treatment, the cells were harvested for analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMTT Assay\u003c/h2\u003e\u003cp\u003eTo assess cell viability, an MTT assay was conducted. This colorimetric assay relies on the reduction of MTT by mitochondrial dehydrogenases in viable cells to form a formazan product. Following treatment, the cells were incubated with 10 \u0026micro;L of MTT solution (10 mg/mL) for 3 h. Subsequently, formazan formed crystals were solubilized in DMSO (100 \u0026micro;L), and the absorbance was measured at 585 nm, providing a cell viability index across treatments. The viability percentages were calculated in five distinct groups of MKN\u003csub\u003e45\u003c/sub\u003e cells: the control group, which was treated with sterile PBS to determine the baseline cell viability; metformin group; decitabine (5-Aza) group; combination of metformin and 5-Aza group; and cisplatin group treated with 20 \u0026micro;g/mL cisplatin as a positive control for apoptosis induction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eFlow cytometry analysis of apoptosis\u003c/h2\u003e\u003cp\u003eTo measure apoptosis, flow cytometry was performed using Annexin-V and PI staining. Cells were harvested, washed, and resuspended in a binding buffer. Staining was performed using Annexin-V-FITC and PI for 15 min at room temperature. The stained cells were analyzed using a flow cytometer. Data were processed using FlowJo software. This analysis allowed for the differentiation between viable, early apoptotic, and late apoptotic cells. Synergy was calculated according to the Bliss synergy model. Bliss independence is recognized as one of the most commonly utilized metrics for synergy. The null model posits that the effects of two drugs are independent both mechanistically and probabilistically. Furthermore, Bliss scores are based on the assumption that the individual agents exhibit exponential dose-effect relationships. To compute a Bliss excess, it is necessary to express the activities of Drug 1 (E1) and Drug 2 (E2), along with the observed effect of their combination (E1,2), as probabilities ranging from 0 to 1 (0\u0026thinsp;\u0026le;\u0026thinsp;E1\u0026thinsp;\u0026le;\u0026thinsp;1, 0\u0026thinsp;\u0026le;\u0026thinsp;E2\u0026thinsp;\u0026le;\u0026thinsp;1, and 0\u0026thinsp;\u0026le;\u0026thinsp;E1,2\u0026thinsp;\u0026le;\u0026thinsp;1, respectively).\u003c/p\u003e\u003cp\u003eAdditive Bliss effect:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e\u0026#119864;1 + \u0026#119864;2(1 \u0026minus; \u0026#119864;1) = \u0026#119864;1 + \u0026#119864;2 \u0026minus; \u0026#119864;1\u0026#119864;2\u003c/h2\u003e\u003cp\u003eBliss excess (BE) is presently determined by calculating the difference between the observed inhibition of the combination and the Bliss additivity of the individual therapies at identical concentrations.\u003c/p\u003e\u003cp\u003eBliss excess:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e\u0026#119864;1,2 \u0026minus; (\u0026#119864;1 + \u0026#119864;2 \u0026minus; \u0026#119864;1\u0026#119864;2)\u003c/h2\u003e\u003cp\u003ePositive BE values suggest a synergistic interaction, while negative BE values indicate an antagonistic behavior. Null BE values signify the absence of any drug interaction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eReal-time q-PCR analysis\u003c/h2\u003e\u003cp\u003eTo evaluate the effects of the treatments on apoptosis-related gene expression, total RNA was extracted from the treated cells via Trizol. The extracted RNA was quantified using a NanoDrop spectrophotometer, and the RNA concentration was measured at 260 nm. RNA purity was assessed by examining the 280/260 and 260/230 ratios. cDNA synthesis was performed using a reverse transcriptase kit (Easy cDNA Ultra-TM Synthesis Kit of Parstous). Expression levels of apoptotic markers (caspase-1, caspase-3, BCL2, BAX, and ATG7) were quantified in different groups via real-time PCR with SYBR Green Master Mix. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the specific primers used for each gene. The relative expression levels were calculated via the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method, where ΔCt is the difference between the Ct values of the gene of interest and those of the housekeeping gene (GAPDH). The magnitude of the change was then determined relative to that of the control treatments.\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\u003e\u003cb\u003eThe primers used to amplify proapoptotic genes\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\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\u003eSequence (5' \u0026rarr; 3')\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBAX\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: GCCCTTTTGCTTCAGGGTTT\u003c/p\u003e\u003cp\u003eR: GGAAAAAGACCTCTCGGGGG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBCL\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: TGGGATTCCTGCGGATTGAC\u003c/p\u003e\u003cp\u003eR: ACTTCCTCTGTGATGTTGTATTTTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCASP-1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: ATCCGTTCCATGGGTGAAGG\u003c/p\u003e\u003cp\u003eR: CCTGTGCCCCTTTCGGAATA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCASP-3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: GATGCGTGATGTTTCTAAAG\u003c/p\u003e\u003cp\u003eR: CACTGTCTGTCTCAATGC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eATG7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: GAGACCTGTATGTCCTGCGT\u003c/p\u003e\u003cp\u003eR: CTGGTGTCCATCAGCTTCAGT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: GGAAGGTGAAGGTCGGAGTCA\u003c/p\u003e\u003cp\u003eR:GTCATTGATGGCAACAATATCCAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eProtein-protein interaction (PPI) network construction and pathway enrichment analysis\u003c/h2\u003e\u003cp\u003eProtein ̶ protein interaction (PPI) network was used to obtain new insights into protein functionality and related functions interactions that can assist in categorizing key genes. Targeted genes were thus employed to construct a PPI network using the Search Tool for the Retrieval of Interacting Genes (STRING) database. The STRING database provides comprehensive and critical assessment of the interactions among proteins, including both predictive and experimental interaction data, allowing researchers to analyze the interaction networks of specific proteins, which helps to explore the roles of proteins in complex diseases and investigate the mechanisms of action of therapeutic agents[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, 25].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAnalysis employed the Shapiro-Wilk test to assess data normality. If the data were normally distributed, ANOVA and Tukey pairwise comparisons were used; otherwise, the Kruskal-Wallis test and Posthoc Dunn-Bonferroni pairwise comparisons were performed. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. SPSS v.26 and GraphPad Prism v.9 were used for statistical analysis. This approach ensured the selection of appropriate statistical tests based on the distribution of data, maintaining the study findings\u0026rsquo; integrity.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eThe research was performed according to the ethical principles for human research declared in the 1975 Declaration of Helsinki. This study was approved by the Ethics Committee of the University of Mohaghegh Ardabili/IR.UMA.REC.1404.037 (webpage of ethical approval code is: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ethics.research.ac.ir/form/s1jnx0swghpj423r.pdf\u003c/span\u003e\u003cspan address=\"https://ethics.research.ac.ir/form/s1jnx0swghpj423r.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cp\u003eNo conflicts of interest to declare.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Research Council of the University of Mohaghegh Ardabili. The supporter had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.L-N. provided direction in the preparation of the manuscript; M.A.A. and M.R.K. wrote the first draft of manuscript; M.A.A. and M.R.K. analyzed data; S.L-N discussed and revised the manuscript; M.A.A., M.R.K., and S.L-N. managed the references; S.L-N. approved the version to be published. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGullo, I. et al. Precancerous lesions of the stomach, gastric cancer and hereditary gastric cancer syndromes. \u003cem\u003ePathologica\u003c/em\u003e \u003cb\u003e112\u003c/b\u003e (3), 166 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdi, E. et al. Emerging therapeutic targets for gastric cancer from a host-Helicobacter pylori interaction perspective. \u003cem\u003eExpert Opin. Ther. Targets\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e (8), 685\u0026ndash;699 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdi, E. et al. Risk factors predisposing to cardia gastric adenocarcinoma: Insights and new perspectives. \u003cem\u003eCancer Med.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (13), 6114\u0026ndash;6126 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e (3), 209\u0026ndash;249 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu, J. et al. A review of current evidence about lncRNA MEG3: A tumor suppressor in multiple cancers. \u003cem\u003eFront. Cell. Dev. Biology\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e, 997633 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAZARM, T. \u0026amp; GHANNADI, F. Effectiveness of chemotherapy in 54 cases of advanced gastric cancer in Isfahan. \u003cem\u003eMed. J. Islamic Repub. Iran. (MJIRI)\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e (4), 261\u0026ndash;264 (1990).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharma, A., Jasrotia, S. \u0026amp; Kumar, A. Effects of chemotherapy on the immune system: implications for cancer treatment and patient outcomes. \u003cem\u003eNaunyn. Schmiedebergs Arch. Pharmacol.\u003c/em\u003e \u003cb\u003e397\u003c/b\u003e (5), 2551\u0026ndash;2566 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKao, H. W., Tsai, K. W. \u0026amp; Lin, W. Synergistic effect of metformin and lansoprazole against gastric cancer through growth inhibition. \u003cem\u003eInt. J. Med. Sci.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (6), 717 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGalal, M. A. et al. Metformin: A Dual-Role Player in Cancer Treatment and Prevention. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (7), 4083 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, X. et al. Glutathione promotes the synergistic effects of venetoclax and azacytidine against myelodysplastic syndrome\u0026ndash;refractory anemia by regulating the cell cycle. \u003cem\u003eExperimental Therapeutic Med.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (6), 1\u0026ndash;11 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSato, T., Issa, J. P. J. \u0026amp; Kropf, P. DNA hypomethylating drugs in cancer therapy. \u003cem\u003eCold Spring Harbor Perspect. Med.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (5), a026948 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEbert, K. et al. Determining the effects of trastuzumab, cetuximab and afatinib by phosphoprotein, gene expression and phenotypic analysis in gastric cancer cell lines. \u003cem\u003eBMC cancer\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e, 1\u0026ndash;19 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeller, S. et al. Effects of trastuzumab and afatinib on kinase activity in gastric cancer cell lines. \u003cem\u003eMol. Oncol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (4), 441\u0026ndash;462 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSzklarczyk, D. et al. The STRING database in 2023: protein\u0026ndash;protein association networks and functional enrichment analyses for any sequenced genome of interest. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e (D1), D638\u0026ndash;D646 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng, S. et al. Effect of the BBC3 Gene on the Proliferation and Apoptosis of Hepatocellular Carcinoma Cells Through p53-Regulated Signaling. \u003cem\u003eJ. Biomaterials Tissue Eng.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (1), 135\u0026ndash;141 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHartman, M. L. \u0026amp; Czyz, M. BCL-w: apoptotic and non-apoptotic role in health and disease. \u003cem\u003eCell Death Dis.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (4), 260 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHardwick, J. M. \u0026amp; Soane, L. Multiple functions of BCL-2 family proteins. \u003cem\u003eCold Spring Harb. Perspect. Biol.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (2), a008722 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang, M. et al. Caspase-8: A key protein of cross‐talk signal way in PANoptosis in cancer. \u003cem\u003eInt. J. Cancer\u003c/em\u003e. \u003cb\u003e149\u003c/b\u003e (7), 1408\u0026ndash;1420 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThomas, P. D. \u003cem\u003eThe gene ontology and the meaning of biological function.\u003c/em\u003e The gene ontology handbook, : pp. 15\u0026ndash;24. (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanehisa, M. et al. KEGG: biological systems database as a model of the real world. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e (D1), D672\u0026ndash;D677 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSzklarczyk, D. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e (D1), D638\u0026ndash;D646 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen, J. et al. \u003cem\u003ePhase I trial of 5-aza-4\u0026rsquo;-thio-2\u0026rsquo;-deoxycytidine (Aza-TdC) in patients with advanced solid tumors\u003c/em\u003e (Wolters Kluwer Health, 2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTong, T. et al. Prognostic autophagy-related model revealed by integrating single-cell RNA sequencing data and bulk gene profiles in gastric cancer. \u003cem\u003eFront. Cell. Dev. Biology\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, 729485 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFitzwalter, B. E. \u0026amp; Thorburn, A. Recent insights into cell death and autophagy. \u003cem\u003eFEBS J.\u003c/em\u003e \u003cb\u003e282\u003c/b\u003e (22), 4279\u0026ndash;4288 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNayak, C. \u0026amp; Singh, S. K. Integrated transcriptome profiling identifies prognostic hub genes as therapeutic targets of glioblastoma: evidenced by bioinformatics analysis. \u003cem\u003eACS omega\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e (26), 22531\u0026ndash;22550 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTomczak, A. et al. Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (1), 5115 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antineoplastic Agents, Gastric Neoplasms, Combination Chemotherapy, Programmed Cell Death","lastPublishedDoi":"10.21203/rs.3.rs-7382082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7382082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe 5-year global survival rate of patients with gastric cancer (GC) is approximately 20%; however, 74% of these patients survived for up to 1 year with effective therapy. The anticancer effects of metformin, an antidiabetic agent, and 5-Aza-2\u0026prime;-deoxycytidine (5-AZA-CdR, decitabine), a DNA methyltransferase inhibitor that leads to malignant cell differentiation and apoptosis, have been investigated. In this study, we evaluated the synergistic effects of decitabine and metformin to achieve better GC treatment. MTT assay was used to assess the viability of MKN\u003csub\u003e45\u003c/sub\u003e cells, and flow cytometry was employed to evaluate apoptosis induction using Annexin V and propidium iodide (PI) staining. The expression profiles of key proapoptotic genes were compared across five distinct cell groups. Protein-protein interaction (PPI) network construction and pathway enrichment analysis were used to gain new insights into biological pathways. MTT assays demonstrated that metformin and decitabine inhibited cell viability at IC\u003csub\u003e50\u003c/sub\u003e values of 17.37 mM and 3.20 \u0026micro;M, respectively. A synergistic effect in promoting apoptosis compared to single treatments was shown by flow cytometry analysis and the Bliss synergy model (combination index (CI)\u0026thinsp;\u0026lt;\u0026thinsp;1). All treatments, especially the combination of 5-AZA-CdR and metformin, led to a decrease in the number of cells in the S phase (down to 7.2%), while increasing the percentage of cells in the G2/M phase, indicating that normal cell cycle progression was disrupted and apoptosis was effectively induced. Metformin and decitabine did not affect target gene expression, except for the increased expression of CASP-1 (7.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48; \u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;=\u0026thinsp;0.003) and CASP-3 (7.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19; \u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;=\u0026thinsp;0.007) in response to decitabine exposure. However, the synergistic effect of decitabine and metformin significantly changed the expression of all target genes, downregulating BCL\u003csub\u003e2\u003c/sub\u003e (0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05; \u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;=\u0026thinsp;0.0005) and significantly increasing the expression of BAX (8.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15; \u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), caspase-1 (8.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34; \u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), caspase-3 (9.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26; \u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and ATG\u003csub\u003e7\u003c/sub\u003e (1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15; \u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;=\u0026thinsp;0.001). The BAX/BCL\u003csub\u003e2\u003c/sub\u003e gene expression ratio was significantly increased in the cells treated with metformin (7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16, \u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;=\u0026thinsp;0.018) and 5Aza\u0026thinsp;+\u0026thinsp;metformin (14.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46, \u003cem\u003ep\u003c/em\u003ebon\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared with the control group. Bioinformatics analysis showed that why combining decitabine with metformin may be a game-changer in treatment. This study revealed a notable synergistic effect of the combination of decitabine and metformin in GC cell apoptosis triggering, which may inform future therapeutic strategies for GC management.\u003c/p\u003e","manuscriptTitle":"Synergistic Effect of Epigenetic Modulator Decitabine and Metformin in the Battle Against Gastric Cancer: A Potential Therapeutic Strategy ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 19:06:50","doi":"10.21203/rs.3.rs-7382082/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-19T06:02:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T23:20:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43137331931255811328627098012009354326","date":"2025-12-08T14:23:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288182917878681029881447240363965054150","date":"2025-11-12T14:42:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T16:46:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60920006723025891677060371903981959099","date":"2025-09-04T14:52:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139012410008388700323675141207370283214","date":"2025-09-04T13:25:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-04T13:01:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-04T12:59:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T09:38:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T15:31:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-02T22:25:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3243c784-2ede-43d9-89da-64d35d0635ae","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54207467,"name":"Biological sciences/Biochemistry"},{"id":54207468,"name":"Biological sciences/Cancer"},{"id":54207469,"name":"Biological sciences/Cell biology"},{"id":54207470,"name":"Biological sciences/Drug discovery"},{"id":54207471,"name":"Biological sciences/Molecular biology"},{"id":54207472,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-30T16:32:02+00:00","versionOfRecord":{"articleIdentity":"rs-7382082","link":"https://doi.org/10.1038/s41598-026-42417-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-25 16:11:42","publishedOnDateReadable":"March 25th, 2026"},"versionCreatedAt":"2025-09-11 19:06:50","video":"","vorDoi":"10.1038/s41598-026-42417-y","vorDoiUrl":"https://doi.org/10.1038/s41598-026-42417-y","workflowStages":[]},"version":"v1","identity":"rs-7382082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7382082","identity":"rs-7382082","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}