Breast cancer remains the leading cause of cancer-related mortality among women worldwide, presenting ongoing challenges despite advances in treatment modalities. Recent research has increasingly focused on combination therapies that could potentially enhance efficacy and overcome drug resistance mechanisms inherent to monotherapies. In this groundbreaking study published in BMC Cancer, researchers Hosseini, Askari, and Yaghoobi explore the synergistic anti-tumor effects of combining metformin, a widely prescribed diabetes medication, with azacitidine, an epigenetic modulator, in combating aggressive breast cancer cell lines.
The rationale behind this combination stems from the distinct yet complementary mechanisms of action these drugs possess. Metformin is well-documented for its antineoplastic properties, primarily through the activation of AMP-activated protein kinase (AMPK) pathways, leading to inhibition of mTOR signaling and subsequent reduction in cancer cell proliferation. Azacitidine, on the other hand, interrupts aberrant DNA methylation patterns characteristic of malignant cells, reactivating tumor suppressor genes and inducing differentiation or apoptosis. The union of these two drugs was posited to amplify therapeutic outcomes in breast cancer treatment by addressing multiple oncogenic pathways simultaneously.
Utilizing the GSE45827 dataset, the authors conducted an extensive bioinformatics analysis to identify differentially expressed genes (DEGs) associated with breast cancer progression. Sophisticated computational tools such as GEO2R and ShinyGO were employed to map out key molecular players, allowing the construction of protein-protein interaction networks through STITCH and Cytoscape platforms. The MCODE algorithm further refined this network to distinguish pivotal clusters that regulate tumorigenic processes, pinpointing critical genes such as CCND1, ELAVL1, and EIF4EBP1 as candidates most involved in the malignancy.
Comparative analyses of these genes’ expression levels in tumor tissues versus matched normal controls, drawn from the GTEx Portal and TNMPlot databases, revealed a distinct upregulation pattern correlating with aggressive breast cancer phenotypes. Such data underlined the biological significance of these targets and established a compelling foundation for investigating their modulation by the drug combination. Moreover, survival outcomes analyzed via Kaplan-Meier plots indicated that alterations in these gene expressions bear prognostic weight, further emphasizing their therapeutic relevance.
In vitro assays on the MDA-MB-231 triple-negative breast cancer cell line validated the bioinformatics predictions. Cell viability assessments using MTT assays demonstrated that metformin and azacitidine, when administered individually, caused a dose-dependent reduction in cancer cell survival. Remarkably, isobologram analyses elucidated that the simultaneous application of both agents resulted in a pronounced synergistic effect, suggesting that lower doses could achieve enhanced antitumor activity while potentially reducing toxic side effects.
Expounding beyond cytotoxicity, the researchers explored the combination’s impact on metastatic potential through wound-healing assays, a proxy for cell migration and invasion ability. Results revealed that co-treatment substantially impaired the motility of MDA-MB-231 cells, an insight with profound implications as metastasis remains the leading cause of mortality in breast cancer patients. This inhibition of migration underscores the potential of the metformin-azacitidine regimen to interfere with not only primary tumor growth but also metastatic dissemination.
At a molecular level, real-time quantitative PCR assays monitored the expression dynamics of CCND1, ELAVL1, and EIF4EBP1 in response to drug treatment. These genes are critically involved in cell cycle progression, mRNA stability, and translation initiation, respectively—fundamental processes commandeered by cancer cells to sustain unchecked proliferation. The combination therapy effectively downregulated these targets, providing mechanistic explanations for the observed phenotypic tumor suppression. This coordinated genetic modulation suggests a multi-layered approach to dismantling cancer cell survival strategies.
The implications of integrating metformin and azacitidine are profound, especially given their individual clinical use histories and safety profiles. Metformin’s extensive application as an anti-diabetic agent presents a low barrier for clinical translation, while azacitidine’s capacity to restore epigenetic normalcy offers a novel angle in cancer pharmacotherapy. By validating their synergistic efficacy in breast cancer cells, this study paves the way for repurposing existing drugs in innovative combinations, potentially expediting new therapeutic options without the prolonged delays often associated with novel drug development.
Such an approach sits at the intersection of precision medicine and drug repurposing, leveraging comprehensive genomic data and robust in vitro experimentation to target cancer hallmarks. Importantly, the study also highlights the value of integrative bioinformatics pipelines for accelerating drug discovery processes, reinforcing the utility of publicly available datasets and analytical tools to identify viable molecular targets with translational potential.
While these results are promising, further investigations are warranted to explore the pharmacodynamics and pharmacokinetics of the metformin-azacitidine duo in vivo, alongside assessments in clinically relevant animal models. Determining optimal dosing regimens, evaluating potential off-target effects, and understanding interactions with existing chemotherapeutics will be vital steps to advancing this therapy toward clinical trials.
Moreover, exploring patient stratification based on gene expression profiles could refine this combination treatment’s application, enabling a more personalized therapeutic strategy that maximizes benefit and minimizes harm. The modulation of CCND1, ELAVL1, and EIF4EBP1 may serve as valuable biomarkers to monitor treatment response and disease progression.
This study ultimately exemplifies the potential of combining metabolic modulators with epigenetic therapies to dismantle complex oncogenic networks in breast cancer. Through meticulous computational analysis and rigorous experimental validation, the authors offer a compelling narrative that reinforces the importance of multidimensional treatment frameworks against formidable cancers.
As breast cancer researchers and clinicians confront the ongoing challenge of treatment resistance and heterogeneous tumor biology, this innovative combination therapy shines as a beacon of hope. It encourages a paradigm shift toward interdisciplinary methods, where repurposed drugs transcend their original indications to deliver impactful anticancer effects.
Looking ahead, the therapeutic horizon appears ever more promising with such integrative approaches gaining momentum. Should subsequent studies confirm these findings in clinical settings, patients battling breast cancer might soon benefit from safer, more effective, and economically accessible treatment options emerging from the synergistic marriage of metformin and azacitidine.
In conclusion, the work by Hosseini and colleagues represents a significant stride in breast cancer therapeutics, binding empirical rigor with translational promise. By deciphering and exploiting the complex gene networks underpinning tumor survival and metastasis, the metformin-azacitidine combination therapy could redefine future oncological practices and improve patient outcomes substantially.
Subject of Research: Combined therapeutic effects of metformin and azacitidine on breast cancer cells, focusing on gene expression regulation and cellular behavior.
Article Title: Combined anti-tumor effects of metformin and azacitidine in breast cancer cells
Article References:
Hosseini, S.S., Askari, N. & Yaghoobi, M.M. Combined anti-tumor effects of metformin and azacitidine in breast cancer cells. BMC Cancer 25, 1487 (2025). https://doi.org/10.1186/s12885-025-14908-0
Image Credits: Scienmag.com
