Network pharmacology-based investigation and experimental validation of the mechanism of metformin in the treatment of acute myeloid leukemia

Abstract

Metformin, a widely used anti-diabetic agent, has shown significant anti-cancer properties as reported in in various cancers, including acute myeloid leukemia. However, the detailed mechanisms by which metformin influences acute myeloid leukemia remain unrevealed. Employing a synergistic approach of network pharmacology and experimental validation, this study systematically identifies and analyzes potential metformin targets and AML-related genes. These findings are then cross-referenced with biomedical databases to construct a target-gene network, providing insights into metformin's pharmacodynamics in AML treatment. Protein–Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses are utilized. Results show metformin's effectiveness in inhibiting AML cell proliferation and inducing apoptosis through the AKT/HIF1A/PDK1 signaling pathway. This research provides insights into metformin's clinical application in AML treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-024-02022-z.

Introduction

Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy, typically diagnosed in individuals aged between 60 and 70 years. It is characterized by a differentiation block in myeloid progenitor cells and uncontrolled proliferation of leukemia cells. The treatment response and overall prognosis of AML vary significantly, influenced by various patient- and tumor-specific factors, including age, performance status, and karyotype. The five-year survival rate for AML patients remains low, at approximately 21% [1, 2]. AML patients used to have limited treatment options, depending solely on cytarabine + anthracycline (7 + 3) intensive chemotherapy and hypomethylating agents [3]. Recent years have witnessed a therapeutic renaissance in AML treatment, spurred by an enhanced understanding of its genomic landscape. Novel therapies such as B-cell leukemia/lymphoma-2 inhibitors, FMS-like tyrosine kinase 3 (FLT3) inhibitors, and chimeric antigen receptor T-cell (CAR-T) therapy have entered clinical use [4, 5]. However, despite these advancements, AML relapse remains common after post-intensive chemotherapy and allogeneic stem cell transplantation (allo-HSCT). Consequently, identifying new therapeutic targets and developing novel treatments continue to be of paramount importance in AML research.

Metformin, a derivative of the biguanide compound found in Galega officinalis (French lilac), is widely recognized for its glucose-lowering effects and is a first-line treatment for type 2 diabetes [6]. Beyond its role in diabetes management, metformin has shown potential in reducing cancer risks, including in AML, by interfering with the proliferation and clonal activity of AML cells [7, 8]. Although preclinical data, clinical trials, reviews, and meta-analyses have reported on metformin's anti-cancer effects in AML, conclusions vary, and its exact mechanism of action remains unclear.

With the rapid advancement of systems biology and bioinformatics, network pharmacology has emerged as a cutting-edge approach to deciphering the complex interplay between drugs, targets, and diseases [9, 10]. This method offers a unique advantage in uncovering previously unexplored relationships between drugs and diseases, employing sophisticated computational tools to map these interactions and identify novel drug candidates that traditional methods might overlook.

In this context, network pharmacology represents an invaluable tool for deepening our understanding of drug actions on diseases. In the current study, network pharmacology was applied to unravel the molecular mechanisms by which metformin may exert therapeutic effects in AML. This approach promises to shed new light on metformin's role in AML treatment and could potentially guide the development of innovative therapeutic strategies (Fig. 1).

Fig. 1.

The workflow of action mechanism of metformin on treating AML in this study

Materials and methods

Acquisition of AML-related target genes

GeneCards (https://www.genecards.org/) is a comprehensive, authoritative compendium of annotative information about human genes. Online Mendelian Inheritance in Man database (OMIM, http://www.omim.org/) is the primary repository of comprehensive, curated information on genes and genetic phenotypes and the relationships between them [11]. AML-related targets were obtained from the GeneCards e and OMIM databases. Among them, targets with a relevance score greater than or equal to 20 were screened from the GeneCards database.

Prediction of targets of metformin

PharmMapper (http://lilab-ecust.cn/pharmmapper/) is an online tool for potential drug target identification. SuperPred (https://prediction.charite.de/) offers state-of-the-art models for drug classification according to ATC classes and target prediction. The Swiss Target Prediction (http://www.swisstargetprediction.ch) provides a portfolio of openly accessible, high-quality databases for target prediction. PharmMapper, SuperPred database, and Swiss Target Prediction databases were used to obtain the targets of metformin. Then the names of target genes were converted into the official symbols using the UniProt database (http://www.uniprot.org/) and types were selected to “Homo sapiens”.

Acquisition overlapping targets

The AML was used as the search keyword and the putative targets of metformin and the known therapeutic targets on AML amalgamated.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) functional enrichment analysis

DAVID (https://david.ncifcrf.gov/) database provides quick accessibility to a wide range of heterogeneous annotation data in a centralized location and enriches the level of biological information for an individual gene. In the Enrichment section of the analysis page, DAVID was used to conduct GO and KEGG pathway enrichment analysis to assess gene-related biological processes (BP), molecular functions (MF), cellular components (CC), and gene-related signaling pathways. GO presents the TOP16 and KEGG presented the TOP20 (p < 0.05).

Protein–Protein Interaction (PPI) networks construction

STRING (https://string-db.org/cgi/input.pl) database is a database of known and predicted protein–protein interaction [12]. The overlapping targets were put into the STRING database and select the type "Homo sapiens" to construct and visualize the PPI network.

Hub targets identification

BC, CC, and DC algorithms in cytoNCA plug-in in Cytoscape software are used to filter Hub targets. The TOP 4 targets (HIF1A, HSP90AA1, MMP9, PIK3CA) are defined as Hub targets.

Hub targets expression and prognosis

GEPIA (http://gepia.cancer-pku.cn/index.html), a database for cancer and normal gene expression profiling and interactive analyses, was used to obtain and visualize the expression of Hub targets. Kaplan–Meier plotter (www.kmplot.com) database, including gene expression data and clinical data, was used for prognostic analysis and visualization.

Molecular docking

The 3D structure of the target protein was downloaded from the PDB (https://www.rcsb.org/) database. The target protein is processed by removing ligand and water motifs and adding hydrogen using AutoDockTools. The protein receptors and ligands were converted to PDBQT format. Finally, AutoDock vina software will be used for molecular docking, and the lowest free energy model will be selected for visual analysis by using PyMOL. The docking score is carried out for each docking. The smaller the docking score, the better the docking result.

Detection of apoptosis

THP-1, U937, and MV411 AML cell lines apoptosis were detected by flow cytometry. The cells in suspension (1 × 10⁶ cells) were extracted and washed using phosphate buffer saline. The cells were resuscitated with 100 μL of binding buffer, mixed with 5 μL of Annexin V-BV421 and 5 μL of 7-AAD, and incubated at room temperature in dark for 15 min. The samples were assessed using flow cytometer analysis. Each experiment was repeated 3 times.

Western blot analysis

To determine pro-apoptosis effects of metformin through the AKT/HIF1A/PDK1 signaling pathway, protein phosphorylation was measured by western blotting assay. Briefly, THP-1, HL-60, and MV4-11 cell lysates were prepared in RIPA Lysis buffer after drug administration. The protein concentrations were analyzed by the BCA protein assay kit. Protein extracts were separated by 7.5–12.5% SDS-PAGE according to different molecular weights and then transferred onto the PVDF membrane. The membranes were blocked with 5% nonfat dry milk in TBST for 1 h at room temperature and were then incubated overnight at 4 °C with the following primary antibodies: p-AKT (1:2000), AKT (1:1000), HIF1A (1: 1000), PDK1(1:1000), Caspase-3(1:5000), c-Caspase-3(1:5000) and GAPDH (1:1000). The membranes were then incubated for 1 h with goat anti-rabbit IgG-HRP (1:5000) as the secondary antibody. Antibody-bound protein was detected by the ECL reagent. GAPDH served as the internal control.

Statistical analysis

All the data are expressed as the mean ± SD. The statistical analysis was performed with GraphPad version 9.1. For multiple comparisons, a one-way analysis of variance (ANOVA) was performed, and comparisons between two groups were analyzed using an LSD test. The protein relative expression is calculated by image J version1.54f and GraphPad. A value of p < 0.05 was regarded as statistically significant.

Ethics statements

This study is not an animal study, or a human study, and has no identifiable human images/data.

Results

Active components and potential targets of metformin on AML

The molecular formula of metformin is C₄H₁₁N₅, and its molecular structure is shown in Fig. 2A. A total of 189 potential targets for metformin were retrieved from PharmMapper, Super-PRED, and Swiss Target Prediction databases. Similarly, 732 targets related to AML were found in GeneCards and OMIM databases. By matching the targets of metformin with AML-related targets, 30 cross-genes were derived, which were potential targets of metformin in treating AML (Fig. 2B).

Fig. 2.

A Chemical structure of metformin (PubChenm CID:4091). B Venn diagrams showing the number of shared and unique targets by metformin and AML were represented. A total of 30 targets of metformin with therapeutic potential for AML were identified. C Analysis on AML-Related PPI. The construction of protein interaction network of AML target genes induced by metformin. D Four core candidate target genes of metformin for AML treatment.

Conversion of target proteins into network and analysis

We uploaded a total of 30 cross-genes into the STRING database for PPI analysis, with high confidence set to 0.7. Then we imported the results from the STRING database into Cytoscape to estimate the topological properties of the PPI network. From Fig. 2C, a total of 30 nodes and 150 edges were acquired. We selected 30 core target genes based on the Q3 of node degree including HIF1A, MMP9, PIK3CA, HSP90AA1, et al. Figure 2C shows the topological properties results of these 30 core target genes with no isolated nodes. Nodes represented proteins, and each edge represented the interaction relationship between proteins. The more lines, the greater the correlation degree. According to our results, it is suggested that HIF1A, MMP9, PIK3CA, and HSP90AA1 may be the key targets of metformin in the treatment of AML (Fig. 2D).

Gene Ontology enrichment analysis

We imported the selected potential 30 core target genes into the DAVID for GO analysis. A total of 136 biological processes were enrichment, such as negative regulation of apoptotic process, positive regulation of protein phosphorylation, positive regulation of transcription from RNA polymerase II promoter, and et al. (Fig. 3A). A total of 23 cellular components were enrichment, such as macromolecular complex, cytoplasm, nucleus, and et al. (Fig. 3B). A total of 42 molecular functions were enrichment, such as enzyme binding, ATP binding, protein binding, and et al. (Fig. 3C).

Fig. 3.

GO and KEGG pathway enrichment analysis. A–C GO analysis enrichment of candidate target genes of metformin for AML treatment. The number of GO entries in the functional categories of BP (A), CC (B), and MF (C) (p < 0.05). D KEGG pathways enrichment of candidate target genes of metformin against AML. (p < 0.05)

KEGG pathway enrichment analysis

We conducted KEGG pathway enrichment analysis on potential 30 core target genes using DAVID and screened out 20 pathways. Figure 3D shows these 20 pathways, such as pathways in cancer, fluid shear stress and atherosclerosis, PD-L1 expression and PD-1 checkpoint pathway in cancer, progesterone-mediated oocyte maturation, and et al. As shown in Fig. 3D, metformin may play an anti-AML role by regulating cascade reactions of multiple signaling pathways. Interestingly, consistent with the results of the previous GO functional annotation, the KEGG pathway also enriched the apoptotic pathways. It is known that the negative regulation of apoptosis plays an important role in anti-AML. Therefore, it is suggested that metformin may inhibit the growth of AML cells by inhibiting apoptosis in AML cell lines. Based on the enriched pathway of apoptosis, the AKT/HIF1A/PDK1 signaling pathway (https://www.kegg.jp/kegg/) was obtained through further screening of the apoptotic mechanism.

Core target expression and prognosis in AML

To validate the correlation between the 4 core targets and AML, the TCGA database was used to find the 4 core targets expression (Fig. 4) and prognosis in AML (Fig. 5). In AML patients, the HIF1A gene was significantly more highly expressed than in non-AML patients. All 4 core targets were associated with AML prognosis, either positively or negatively.

Fig. 4.

Four core targets gene expression in AML and non-AML, HIF1A expression significantly higher in AML

Fig. 5.

The relation between Four core targets gene and AML prognosis. 4 core targets were associated with AML prognosis, either positively or negatively

Molecular docking analysis

Molecular docking analysis was conducted to evaluate the binding affinity of metformin with key target proteins. To evaluate the biological activity of metformin, the docking simulation of metformin and the key target proteins were implemented by molecular docking analysis. As can be seen from Fig. 6 and Table 1, the results showed that docking scores of colchicine with HIF1A, MMP9, PIK3CA, and HSP90AA1 ranged from − 4.35 to − 6.86, and all the hub proteins showed better binding affinity with metformin. Most of these hub proteins are involved in cell apoptosis.

Fig. 6.

Structural interactions of metformin and key target receptors

Table 1.

The docking scores of metformin with key proteins

Targets	Drug	PDB ID	Docking score
HIF1A	Metformin	8HE3	− 4.35
HSP90AA1	Metformin	8B7I	− 5.78
MMP9	Metformin	5TH6	− 6.86
PIK3CA	Metformin	7L1B	− 5.36

Result of CCK-8 cell viability assay

To investigate the cytotoxic effects of metformin, we treated AML cell lines for 48 h with various concentrations of metformin, and a CCK-8 assay was employed to test the cell viability. As illustrated in Fig. 7, metformin dose-dependently reduced the viability of THP-1, U937, and MV411 cells. The IC50 values of metformin on three AML cell lines (THP-1, U937, MV411) were 13.5, 15.0, and 11.8 mM, respectively.

Fig. 7.

A CCK-8 cell viability assay the IC50 values of metformin on three AML cell lines (THP-1,U937,MV411) were 13.5, 15.0, and 11.8 mM, respectively. B Flow cytometry detects the apoptosis-inducing effect of metformin on three AML cell lines, the apoptosis rates on three AML cell lines were significantly increased by metformin treated

Result of apoptosis detection

CCK-8 assay results suggested that metformin can inhibit AML cell line proliferation. To determine whether this anti-AML effect was related to apoptosis, flow cytometry was used to detect the apoptosis-inducing effect of metformin on three AML cell lines and the percentage of apoptotic cells was also detected by Annexin V-BV421 and 7-AAD double staining. As shown in Fig. 7, the apoptosis rates on three AML cell lines were significantly increased by metformin treatment. These results suggested that metformin plays a pro-apoptotic effect partly on AML cells.

Result of western blot analysis

PI3K/AKT is one of the main signal transduction pathways involved in cell proliferation and apoptosis [13]. Hypoxia inducible factor-1A (HIF1A) is an important regulator of the cellular and systemic hypoxia response in both normal tissues and cancer cells. HIF1A is involved in cell survival, apoptosis, cellular metabolic shift, and cancer progression through its downstream effectors [14]. HIF1A/PDK1 is downstream of the PI3K/AKT signaling pathway which is related to the pro-apoptosis effect of metformin based on the result of GO enrichment and KEGG enrichment. To further study the mechanisms underlying the pro-apoptotic effects of metformin and the effects of metformin on the AKT/HIF1A/PDK1 signaling pathway, we detected the apoptosis-related protein Caspase3 and c-Caspase and the key proteins of HIF1A, PDK1, AKT, p-AKT in the signaling pathway. From Fig. 8 and Supplementary Material S1, it could be seen that compared with the control group, IC50 metformin groups observably decreased the phosphorylation levels of AKT(p-AKT) and the expression levels of HIF1A and PDK1. Simultaneously, c-Caspase3 significantly increased expression in metformin-treated AML cell lines. Our results demonstrated that metformin can promote apoptosis of AML cell lines by downregulating the AKT/HIF1A/PDK1 signaling pathway.

Fig. 8.

Metformin promoted apoptosis of AML by regulating the AKT/HIF1A/PDK1 pathway. Protein expression of AKT, p-AKT, HIF1A, PDK1, Caspase-3,c-Caspase-3 and GAPDH were detected by western blot analysis, GAPDH as internal control. (Met = metformin)

Discussion

Acute myeloid leukemia (AML) is a severe malignancy of white blood cells, leading to symptoms associated with bone marrow failure and organ infiltration [15]. Metformin, a widely used anti-diabetic agent, has shown significant anti-cancer properties in various cancers, including AML [16]. However, the precise mechanisms by which metformin influences AML treatment remain largely undefined. Network pharmacology, a novel discipline rooted in systems biology, is employed to dissect biological networks and identify key signal nodes, thereby elucidating potential drug–target interactions. This approach is instrumental in the discovery of new therapeutic strategies [17]. Our study has leveraged network pharmacology to identify potential therapeutic targets and pathways of metformin in AML treatment, offering fresh insights into the drug's pharmacological mechanisms from a network perspective.

In our research, we identified 30 genes overlapping between metformin targets and AML targets. Protein–Protein Interaction (PPI) network analysis revealed that AML-associated targets such as HIF1A, MMP9, PIK3CA, and HSP90AA1 exhibit significant affinity for metformin, as evidenced by molecular docking experiments. Most of these hub targets are implicated in apoptotic pathways, both external and internal. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment suggest that metformin's anti-AML effects are mediated through the apoptotic signaling pathway, with the AKT/HIF1A/PDK1 signaling pathway identified as a key component in the apoptotic mechanism [].

To validate the mechanisms underlying metformin's anti-AML effects, we focused on key proteins of the AKT/HIF1A/PDK1 signaling pathway and the apoptosis-related protein Caspase-3. The PI3K/Akt pathway is known to regulate various cellular processes including migration, proliferation, differentiation, apoptosis, and metabolism, all of which are linked to AML apoptosis [18]. Activation of PI3K leads to the production of phosphatidylinositol 3,4,5-trisphosphate, which in turn phosphorylates AKT [19]. Phosphorylated AKT activates hypoxia-inducible factor-1α (HIF1A), which directly transactivates the gene encoding PDK1, a kinase that inactivates the PDC [20, 21]. Caspase-3, a crucial executioner caspase in apoptosis, catalyzes the cleavage of key cellular proteins, playing a pivotal role in AML cell apoptosis. The activation of Caspase-3 involves its cleavage into smaller subunits [22]. This cleaved form, known as c-Caspase-3, is the active form of the enzyme. The transition from Caspase-3 to c-Caspase-3 is a hallmark of apoptosis, and it is often used as a marker to detect and quantify apoptotic cells [23]. Our findings indicate that metformin enhances the levels of c-Caspase-3 in AML cell lines, with western blot analyses confirming that metformin induces AML apoptosis by modulating the AKT/HIF1A/PDK1 signaling pathway. These results are in alignment with the identified signaling pathway and apoptotic targets, thereby substantiating the anti-AML role of metformin through the promotion of apoptosis. This study not only deepens our understanding of metformin's pharmacological mechanisms in AML treatment, but also lays a solid foundation for future research into its anti-AML pharmacological targets and molecular mechanisms.

Conclusion

In summary, this study employed a synergistic approach combining network pharmacology and empirical evaluation to elucidate the pharmacological action of metformin against acute myeloid leukemia (AML). Our experimental investigations demonstrated that metformin notably promotes apoptosis in AML cell lines. This effect is mediated by the modulation of the AKT/HIF1A/PDK1 signaling pathway and the enhanced expression of the pro-apoptotic protein, c-Caspase-3. These findings provide valuable insights that may guide further pharmacological research into the therapeutic potential of metformin for AML treatment and could potentially lead to the discovery of novel therapeutic agents for AML.

Nevertheless, it is important to recognize the limitations inherent in this study. The methodology of network pharmacology, while innovative and promising, is still in its developmental stages and requires further refinement and validation. Moreover, the necessity of corroborating these in vitro findings through in vivo experimental studies is imperative. Future research efforts should be directed towards confirming the efficacy and elucidating the mechanisms of action of metformin in AML treatment using live animal models. This will not only validate the current findings, but also contribute to a more comprehensive understanding of metformin's potential in AML therapy.

Author contributions

DW, XT and SL were responsible for the study concept and design.SL and MX collected and analyzed the data and wrote the first draft of the manuscript.SL, XM and ZY provided input for the figures. SL and YL finished the experimental validation. XT, LS MX and ZY wrote the final draft of the manuscript. All authors read and approved the final manuscript.SL, MX and ZY contributed equally to this manuscript.

Funding

This work was supported by research grants from National Natural Science Foundation of China (81873443, 82070162), Translational Research Grant of NCRCH (2020ZKZC04) and Natural Science Foundation of Jiangsu Province (BK20201169), The Key Science Research Project of Jiangsu Commission of Health (K2019022), Frontier Clinical Technical Project of Suzhou Science and Technology plan (SKY2022001), Bethune Charitable Foundation (BCF-IBW-XY-20220930-13), Suzhou diagnosis and treatment project of Clinical Key Diseases (LCZX202201), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.