Metformin in Colorectal Cancer: Epidemiological Evidence, Predictive Biomarkers, and Implications for Prevention and Treatment
Abstract
1. Introduction
- Identifying and evaluating promising predictive biomarkers that are vital for stratifying patients and forecasting metformin responses in CRC are indispensable tools for future clinical use.
- Analyzing the complex and varied molecular pathways through which metformin exerts its anticancer effects and exploring how understanding these mechanisms, alongside biomarkers, might shed light on the differing patient outcomes.
- Analyze and integrate clinical evidence from epidemiological and intervention studies, specifically considering the variation in observed results as the rationale for adopting biomarker-guided approaches.
- To investigate the effect of metformin on the tumor microenvironment (TME) and discuss how relevant characteristics might interact with biomarkers to influence treatment response.
- To emphasize the implications of the current findings for developing biomarker-based personalized therapeutic strategies as the key to optimizing the use of metformin in CRC.
- To highlight current hurdles and future research priorities in this field, particularly regarding biomarker validation and clinical practice.
2. Biomarkers for Metformin Response in CRC: Essential Tools for Patient Stratification
2.1. KRAS Mutation Status as a Key Predictive Biomarker
2.2. Drug Transporter Expression as Determinants of Metformin Accumulation
2.2.1. MATE1 Expression Predicts Response
2.2.2. OCT and MATE2 Transporter Profiles Influence Sensitivity
2.3. Adenosine Monophosphate-Activated Protein Kinase Pathway Components as Indicators of Sensitivity
2.4. Metabolic Signatures as Predictors of Response and Resistance
2.4.1. Glutamine Metabolism and Metformin Sensitivity
2.4.2. Transcriptomic Profiles for Identifying Responsive Subtypes
2.5. Biomarker Behavior in Diabetic Versus Non-Diabetic Populations: Implications for Patient Stratification
3. Molecular Mechanisms of Metformin Action: The Basis for a Differential Response
3.1. AMPK-Dependent Pathways: A Primary Biomarker-Influenced Mechanism
- Suppression of protein synthesis: Following mTOR inhibition, protein synthesis is reduced, limiting the availability of building materials for rapidly dividing cancer cells [22];
- Cell cycle arrest: Metformin-induced AMPK activation can result in a greater number of cyclin-dependent kinase inhibitors and fewer cyclins, causing the cell cycle to pause, typically in the G0/G1 phase, and thus preventing uncontrolled cell division [23]. Thus, proteins that regulate the cell cycle may serve as potential pharmacodynamic biomarkers;
- Induction of autophagy: AMPK triggers autophagy, a cellular process that removes damaged organelles and proteins [24]. Autophagy can facilitate cancer cell survival. However, in certain situations, it can contribute to the toxic effects of metformin or support cell survival under drug-induced metabolic stress. The context-dependent role of autophagy in the effects of metformin on CRC requires further study, and markers of autophagic flux should be explored as potential biomarkers.
3.2. AMPK-Independent Mechanisms: Contributing to Variable Responses
3.2.1. Reactive Oxygen Species Production: A Context-Dependent Effect
3.2.2. Mitochondrial Complex I Inhibition: A Fundamental Target with Downstream Implications
3.3. Modulation of Oncogenic Signaling Pathways: Intersecting with Biomarkers
3.3.1. Wnt/Beta-Catenin Pathway Modulation
3.3.2. TGF-Beta/PI3K/AKT Signaling Inhibition
3.3.3. Adenosine A1 Receptor-Mediated Apoptosis
4. Clinical Evidence: Highlighting the Need for Patient Stratification
4.1. Meta-Analyses and Systematic Reviews: Evidence for the Association Between Metformin and CRC and the Need for Further Precision
4.2. Nationwide Cohort Studies: Confirming Associations, Limited by Confounding
4.3. Clinical Trials: Variable Outcomes Highlight the Need for Stratification
5. Metformin and the TME: A Source of Biomarkers and Potential Therapeutic Targets
5.1. Immune Modulation Within the TME
5.1.1. Neutrophil Extracellular Traps and Metformin’s Influence
5.1.2. CD39/CD73 Axis Modulation and Immune Evasion
5.2. Metabolic Reprogramming in the TME: Implications for Response
6. Implications for Personalized Medicine: Biomarkers as a Foundation
6.1. Biomarker-Guided Treatment Selection: The Path Forward
- Patients with KRAS-mutant CRC: these patients, particularly those with diabetes, appear to significantly benefit from metformin [10]. Testing for KRAS has already been performed in metastatic CRC to determine eligibility for anti-EGFR monoclonal antibody therapies, such as cetuximab or panitumumab, making it a readily usable predictive marker.
- Specific metabolic or immune-metabolic signatures: analysis of gene expression patterns (transcriptomics) and metabolic profiles of tumor tissues or blood samples can identify new and complex biomarkers for response [14,15]. Further research is required to validate these complex signatures as predictive markers in the clinical setting.
6.2. Combination Strategies Guided by Molecular and Biomarker Profiles
- Metformin may increase the sensitivity of cancer cells to traditional chemotherapeutic drugs by altering cell metabolism or by affecting survival pathways. This could potentially allow for lower doses or help overcome resistance, particularly in tumors with specific metabolic characteristics that have been identified [61].
- The effects of metformin on the immune system, such as reducing NETs and influencing the CD39/CD73 axis, suggest that metformin could work well in immunotherapies. This could help prevent the TME from becoming immunosuppressive and boost the effects of the immune checkpoint blockade [49,53]. Biomarkers related to the tumor immune microenvironment and immune checkpoint pathways can guide these combinations.
- Targeted therapies: combining metformin with drugs targeting specific molecular changes in a tumor could lead to more effective and lasting responses by simultaneously targeting multiple pathways essential for tumor growth and survival [62]. Testing for targetable changes by using biomarkers is fundamental to this approach.
- With metabolic inhibitors, as shown by Lee et al. [18], combining metformin or related biguanides with other metabolic inhibitors, such as 2-deoxy-D-glucose (2DG), can produce synergistic antitumor effects, particularly in tumors with specific genetic changes such as BRAF and p53 mutations. These genetic markers can guide patient selection using combined approaches.
6.3. Prevention Strategies: Identifying High-Risk Individuals
- High-risk individuals with diabetes: Using metformin in patients with diabetes who are at a high risk for CRC could reduce the incidence, building on existing observational data [50]. Biomarkers related to diabetes control or specific risk factors for CRC in such patients could render this approach more precise.
- In patients with adenomatous polyps, metformin may inhibit the formation or progression of precancerous lesions, which is a key step in CRC development [64]. Biomarkers associated with polyp progression could help to identify individuals who would benefit from metformin for chemoprevention.
- Chemoprevention of hereditary CRC syndromes: Investigating the potential of metformin in individuals with a genetic predisposition to CRC could offer a valuable preventive strategy [65]. The specific genetic mutation that causes this syndrome can serve as a criterion for selecting individuals for metformin chemoprevention.
7. Challenges and Future Directions: Prioritizing Biomarker Validation and Implementation
7.1. Current Limitations: Bridging the Gap to Personalized Clinical Use
- Optimal dosing, duration, and timing of metformin treatment: The optimal dose, duration, and timing of metformin treatment specifically for anticancer effects, which might differ somewhat from those used for blood sugar control, have not been definitively established in large prospective clinical trials designed with cancer outcomes as the primary goal [48].
- Efficacy in patients without diabetes and identification of responders: Much of the most convincing clinical evidence has been derived from studies involving individuals with diabetes [6,7]. Confirming the efficacy of metformin and identifying reliable predictive biomarkers of response in patients without diabetes are critical unmet needs that must be addressed to expand the clinical usefulness of metformin in CRC.
- Limited efficacy as monotherapy for advanced disease: Metformin is generally not considered a cure when used alone for advanced or metastatic CRC. Its main potential is prevention, early disease staging, and combination treatment. Appropriate biomarkers must be identified to select patients for specific applications [49].
- Variability and confounding factors in observational studies: Informative observational studies are subject to influencing factors and biases (such as immortal time bias) that can affect the results and contribute to differing findings. This makes it challenging to draw firm conclusions concerning the efficacy across all subgroups without validating the findings using biomarkers.
- Validation of predictive biomarkers: Promising biomarkers have been identified in laboratory studies and studies examining patient data; however, they require rigorous validation in separate, well-designed clinical trials before they can be routinely used to guide treatment decisions in a personalized manner.
7.2. Future Research Priorities: The Centrality of Biomarkers
- Prospective clinical trials with the required biomarker stratification: designing and undertaking large randomized controlled trials that mandate biomarker testing to divide patients into groups and assess the effectiveness of metformin within these clearly defined subgroups are essential. This approach will validate predictive markers and establish their efficacy in specific populations where the drug is most likely to work.
- Mechanistic studies linking differential responses to biomarkers: further research is needed to fully understand the molecular and cellular reasons why patients respond differently to metformin treatment. Future research should specifically investigate how these mechanisms are affected by and tied to the identified biomarkers (e.g., how KRAS mutations or MATE1 expression levels mechanistically determine sensitivity or resistance) [10].
- Development and validation of novel biomarkers: The continued exploration of new predictive biomarkers is crucial for improving the identification of responsive patients. This could involve markers identified in liquid biopsies, advanced imaging techniques, or comprehensive ‘multi-omics’ profiling (genomics, transcriptomics, proteomics, metabolomics).
- Biomarker-driven exploration of the effects of metformin in non-diabetic populations: dedicated prospective clinical trials are needed to evaluate the effectiveness and safety of metformin in preventing or treating CRC in individuals without diabetes who are at high risk or have been diagnosed. These trials should include integrated biomarker analysis to determine which patients without diabetes respond positively.
- Biomarker-guided exploration of novel combination strategies: laboratory and clinical studies investigating suitable combinations of metformin and chemotherapy, targeted therapies, immunotherapy, and other metabolic inhibitors should be guided by preclinical studies and validated biomarker profiles. This will help select patient populations that are most likely to benefit from a specific combination.
- Mechanistic studies of sex-specific differences and potential biomarkers: further research is required to understand the biological basis of the observed differences between the sexes in terms of how metformin affects CRC outcomes. This may involve hormonal or metabolic differences that could serve as biomarkers for tailoring treatment strategies based on sex [6].
- Advanced biomarker discovery technologies: emerging platforms, including multi-omics integration using machine learning [66], liquid biopsy for real-time monitoring [67], AI/machine learning for pattern recognition [68], single-cell sequencing for cellular heterogeneity analysis [69], and spatial transcriptomics for microenvironment mapping [70] offer new opportunities for biomarker discovery and validation by identifying complex signatures, tracking dynamic treatment responses, and providing mechanistic insights beyond traditional approaches.
8. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
CRC | colorectal cancer |
T2DM | type 2 diabetes mellitus |
AMPK | AMP-activated protein kinase |
mTOR | mammalian target of rapamycin |
ROS | reactive oxygen species |
TME | tumor microenvironment |
References
- Giovannucci, E.L.; Harlan, D.M.; Archer, M.C.; Bergenstal, R.M.; Gapstur, S.M.; Habel, L.A.; Pollak, M.; Regensteiner, J.G.; Yee, D. Diabetes and cancer: A consensus report. Diabetes Care 2010, 33, 1674–1685. [Google Scholar] [CrossRef] [PubMed]
- Larsson, S.C.; Orsini, N.; Wolk, A. Diabetes Mellitus and Risk of Colorectal Cancer: A Meta-Analysis. JNCI J. Natl. Cancer Inst. 2005, 97, 1679–1687. [Google Scholar] [CrossRef] [PubMed]
- Bailey, C.J. Metformin: Historical overview. Diabetologia 2017, 60, 1566–1576. [Google Scholar] [CrossRef]
- Dowling, R.J.O.; Goodwin, P.J.; Stambolic, V. Understanding the benefit of metformin use in cancer treatment. BMC Med. 2011, 9, 33. [Google Scholar] [CrossRef]
- Kasznicki, J.; Sliwinska, A.; Drzewoski, J. Metformin in cancer prevention and therapy. Ann. Transl. Med. 2014, 2, 7–57. [Google Scholar] [CrossRef]
- Wang, Y.; Xiao, J.; Zhao, Y.; Du, S.; Du, J. Effect of metformin on the mortality of colorectal cancer patients with T2DM: Meta-analysis of sex differences. Int. J. Color. Dis. 2020, 35, 827–835. [Google Scholar] [CrossRef]
- Ng, C.-A.W.; Jiang, A.A.; Toh, E.M.S.; Ong, Z.H.; Peng, S.; Tham, H.Y.; Sundar, R.; Chong, C.S.; Khoo, C.M. Metformin and colorectal cancer: A systematic review, meta-analysis and meta-regression. Int. J. Color. Dis. 2020, 35, 1501–1512. [Google Scholar] [CrossRef]
- Higurashi, T.; Hosono, K.; Takahashi, H.; Komiya, Y.; Umezawa, S.; Sakai, E.; Uchiyama, T.; Taniguchi, L.; Hata, Y.; Uchiyama, S.; et al. Metformin for chemoprevention of metachronous colorectal adenoma or polyps in post-polypectomy patients without diabetes: A multicentre double-blind, placebo-controlled, randomised phase 3 trial. Lancet Oncol. 2016, 17, 475–483. [Google Scholar] [CrossRef]
- Chen, K.; Li, Y.; Guo, Z.; Zeng, Y.; Zhang, W.; Wang, H. Metformin: Current clinical applications in nondiabetic patients with cancer. Aging 2020, 12, 3993–4009. [Google Scholar] [CrossRef]
- Xie, J.; Xia, L.; Xiang, W.; He, W.; Yin, H.; Wang, F.; Gao, T.; Qi, W.; Yang, Z.; Yang, X.; et al. Metformin selectively inhibits metastatic colorectal cancer with the KRAS mutation by intracellular accumulation through silencing MATE1. Proc. Natl. Acad. Sci. USA 2020, 117, 13012–13022. [Google Scholar] [CrossRef]
- Chowdhury, S.; Yung, E.; Pintilie, M.; Muaddi, H.; Chaib, S.; Yeung, M.; Fusciello, M.; Sykes, J.; Pitcher, B.; Hagenkort, A.; et al. MATE2 Expression Is Associated with Cancer Cell Response to Metformin. PLoS ONE 2016, 11, e0165214. [Google Scholar] [CrossRef] [PubMed]
- Morrison, K.R.; Wang, T.; Chan, K.Y.; Trotter, E.W.; Gillespie, A.; Michael, M.Z.; Oakhill, J.S.; Hagan, I.M.; Petersen, J. Elevated basal AMP-activated protein kinase activity sensitizes colorectal cancer cells to growth inhibition by metformin. Open Biol. 2023, 13, 230021. [Google Scholar] [CrossRef] [PubMed]
- Kim, J.H.; Lee, K.J.; Seo, Y.; Kwon, J.-H.; Yoon, J.P.; Kang, J.Y.; Lee, H.J.; Park, S.J.; Hong, S.P.; Cheon, J.H.; et al. Publisher Correction: Effects of metformin on colorectal cancer stem cells depend on alterations in glutamine metabolism. Sci. Rep. 2018, 8, 13111. [Google Scholar] [CrossRef] [PubMed]
- Luo, S.; Zhu, Y.; Guo, Z.; Zheng, C.; Fu, X.; You, F.; Li, X. Exploring biomarkers and molecular mechanisms of Type 2 diabetes mellitus promotes colorectal cancer progression based on transcriptomics. Sci. Rep. 2025, 15, 4086. [Google Scholar] [CrossRef]
- Pedrosa, L.; Foguet, C.; Oliveres, H.; Archilla, I.; de Herreros, M.G.; Rodríguez, A.; Postigo, A.; Benítez-Ribas, D.; Camps, J.; Cuatrecasas, M.; et al. A novel gene signature unveils three distinct immune-metabolic rewiring patterns conserved across diverse tumor types and associated with outcomes. Front. Immunol. 2022, 13, 926304. [Google Scholar] [CrossRef]
- Guchelaar, N.A.D.; Buck, S.A.J.; van Doorn, L.; Hussaarts, K.G.A.M.; Sandberg, Y.; van der Padt-Pruijsten, A.; van Alphen, R.J.; Poppe-Manenschijn, L.; Vleut, I.; de Bruijn, P.; et al. The OCT2/MATE1 Interaction Between Trifluridine, Metformin and Cimetidine: A Crossover Pharmacokinetic Study. Clin. Pharmacokinet. 2024, 63, 1037–1044. [Google Scholar] [CrossRef]
- Ye, H.; Liu, Y.; Wu, K.; Luo, H.; Cui, L. AMPK activation overcomes anti-EGFR antibody resistance induced by KRAS mutation in colorectal cancer. Cell Commun. Signal. 2020, 18, 115. [Google Scholar] [CrossRef]
- Lee, B.; Lee, C.; Moon, H.-M.; Jo, S.-Y.; Jang, S.J.; Suh, Y.-A. Repurposing Metabolic Inhibitors in the Treatment of Colon Adenocarcinoma Patient-Derived Models. Cells 2023, 12, 2859. [Google Scholar] [CrossRef]
- Foretz, M.; Guigas, B.; Bertrand, L.; Pollak, M.; Viollet, B. Metformin: From Mechanisms of Action to Therapies. Cell Metab. 2014, 20, 953–966. [Google Scholar] [CrossRef]
- Kamarudin, M.N.A.; Sarker, M.R.; Zhou, J.-R.; Parhar, I. Metformin in colorectal cancer: Molecular mechanism, preclinical and clinical aspects. J. Exp. Clin. Cancer Res. 2019, 38, 491. [Google Scholar] [CrossRef]
- Mogavero, A.; Maiorana, M.V.; Zanutto, S.; Varinelli, L.; Bozzi, F.; Belfiore, A.; Volpi, C.C.; Gloghini, A.; Pierotti, M.A.; Gariboldi, M. Metformin transiently inhibits colorectal cancer cell proliferation as a result of either AMPK activation or increased ROS production. Sci. Rep. 2017, 7, 15992. [Google Scholar] [CrossRef] [PubMed]
- Saxton, R.A.; Sabatini, D.M. mTOR Signaling in Growth, Metabolism, and Disease. Cell 2017, 168, 960–976. [Google Scholar] [CrossRef] [PubMed]
- Alimova, I.N.; Liu, B.; Fan, Z.; Edgerton, S.M.; Dillon, T.; Lind, S.E.; Thor, A.D. Metformin inhibits breast cancer cell growth, colony formation and induces cell cycle arrest in vitro. Cell Cycle 2009, 8, 909–915. [Google Scholar] [CrossRef]
- Kim, J.; Kundu, M.; Viollet, B.; Guan, K.-L. AMPK and mTOR regulate autophagy through direct phosphorylation of Ulk1. Nat. Cell Biol. 2011, 13, 132–141. [Google Scholar] [CrossRef]
- Lee, D.E.; Lee, H.M.; Jun, Y.; Choi, S.Y.; Lee, S.J.; Kwon, O.-S. Metformin induces apoptosis in TRAIL-resistant colorectal cancer cells. Biochim. Biophys. Acta (BBA)—Mol. Cell Res. 2024, 1872, 119873. [Google Scholar] [CrossRef]
- Andrzejewski, S.; Gravel, S.-P.; Pollak, M.; St-Pierre, J. Metformin directly acts on mitochondria to alter cellular bioenergetics. Cancer Metab. 2014, 2, 12. [Google Scholar] [CrossRef]
- Algire, C.; Moiseeva, O.; Deschênes-Simard, X.; Amrein, L.; Petruccelli, L.; Birman, E.; Viollet, B.; Ferbeyre, G.; Pollak, M.N. Metformin Reduces Endogenous Reactive Oxygen Species and Associated DNA Damage. Cancer Prev. Res. 2012, 5, 536–543. [Google Scholar] [CrossRef]
- Saisho, Y. Metformin and Inflammation: Its Potential Beyond Glucose-lowering Effect. Endocr. Metab. Immune Disord.-Drug Targets 2015, 15, 196–205. [Google Scholar] [CrossRef]
- Chuang, H.; Chan, H.; Shih, K. Suppression of colorectal cancer growth: Interplay between curcumin and metformin through DMT1 downregulation and ROS-mediated pathways. BioFactors 2024, 51, e2137. [Google Scholar] [CrossRef]
- Wheaton, W.W.; Weinberg, S.E.; Hamanaka, R.B.; Soberanes, S.; Sullivan, L.B.; Anso, E.; Glasauer, A.; Dufour, E.; Mutlu, G.M.; Budigner, G.S.; et al. Metformin inhibits mitochondrial complex I of cancer cells to reduce tumorigenesis. eLife 2014, 3, e02242. [Google Scholar] [CrossRef]
- Bridges, H.R.; Jones, A.J.Y.; Pollak, M.N.; Hirst, J. Effects of metformin and other biguanides on oxidative phosphorylation in mitochondria. Biochem. J. 2014, 462, 475–487. [Google Scholar] [CrossRef] [PubMed]
- Deng, X.-S.; Wang, S.; Deng, A.; Liu, B.; Edgerton, S.M.; Lind, S.E.; Wahdan-Alaswad, R.; Thor, A.D. Metformin targets Stat3 to inhibit cell growth and induce apoptosis in triple-negative breast cancers. Cell Cycle 2012, 11, 367–376. [Google Scholar] [CrossRef] [PubMed]
- Clevers, H.; Nusse, R. Wnt/β-catenin signaling and disease. Cell 2012, 149, 1192–1205. [Google Scholar] [CrossRef] [PubMed]
- Zhang, C.; Wang, Y. Metformin attenuates cells stemness and epithelial-mesenchymal transition in colorectal cancer cells by inhibiting the Wnt3a/β-catenin pathway. Mol. Med. Rep. 2018, 19, 1203–1209. [Google Scholar] [CrossRef]
- Massagué, J. TGFβ signalling in context. Nat. Rev. Mol. Cell Biol. 2012, 13, 616–630. [Google Scholar] [CrossRef]
- Fruman, D.A.; Chiu, H.; Hopkins, B.D.; Bagrodia, S.; Cantley, L.C.; Abraham, R.T. The PI3K Pathway in Human Disease. Cell 2017, 170, 605–635. [Google Scholar] [CrossRef]
- Xiao, Q.; Xiao, J.; Liu, J.; Liu, J.; Shu, G.; Yin, G. Metformin suppresses the growth of colorectal cancer by targeting INHBA to inhibit TGF-β/PI3K/AKT signaling transduction. Cell Death Dis. 2022, 13, 202. [Google Scholar] [CrossRef]
- Lan, B.; Zhang, J.; Zhang, P.; Zhang, W.; Yang, S.; Lu, D.; Li, W.; Dai, Q. Metformin suppresses CRC growth by inducing apoptosis via ADORA1. Front. Biosci. 2017, 22, 248–257. [Google Scholar] [CrossRef]
- Evans, J.M.M.; Donnelly, L.A.; Emslie-Smith, A.M.; Alessi, D.R.; Morris, A.D. Metformin and reduced risk of cancer in diabetic patients. Br. Med. J. 2005, 330, 1304–1305. [Google Scholar] [CrossRef]
- Gandini, S.; Puntoni, M.; Heckman-Stoddard, B.M.; Dunn, B.K.; Ford, L.; DeCensi, A.; Szabo, E. Metformin and Cancer Risk and Mortality: A Systematic Review and Meta-analysis Taking into Account Biases and Confounders. Cancer Prev. Res. 2014, 7, 867–885. [Google Scholar] [CrossRef]
- Tsilidis, K.K.; Capothanassi, D.; Allen, N.E.; Rizos, E.C.; Lopez, D.S.; van Veldhoven, K.; Sacerdote, C.; Ashby, D.; Vineis, P.; Tzoulaki, I.; et al. Metformin Does Not Affect Cancer Risk: A Cohort Study in the U.K. Clinical Practice Research Datalink Analyzed Like an Intention-to-Treat Trial. Diabetes Care 2014, 37, 2522–2532. [Google Scholar] [CrossRef] [PubMed]
- Suissa, S.; Azoulay, L. Metformin and Cancer: Mounting Evidence Against an Association. Diabetes Care 2014, 37, 1786–1788. [Google Scholar] [CrossRef] [PubMed]
- Zakikhani, M.; Dowling, R.; Fantus, I.G.; Sonenberg, N.; Pollak, M. Metformin Is an AMP Kinase–Dependent Growth Inhibitor for Breast Cancer Cells. Cancer Res. 2006, 66, 10269–10273. [Google Scholar] [CrossRef]
- Algire, C.; Amrein, L.; Bazile, M.; David, S.; Zakikhani, M.; Pollak, M. Diet and tumor LKB1 expression interact to determine sensitivity to anti-neoplastic effects of metformin in vivo. Oncogene 2010, 30, 1174–1182. [Google Scholar] [CrossRef]
- Birsoy, K.; Possemato, R.; Lorbeer, F.K.; Bayraktar, E.C.; Thiru, P.; Yucel, B.; Wang, T.; Chen, W.W.; Clish, C.B.; Sabatini, D.M. Metabolic determinants of cancer cell sensitivity to glucose limitation and biguanides. Nature 2014, 508, 108–112. [Google Scholar] [CrossRef]
- Pollak, M.N. Investigating Metformin for Cancer Prevention and Treatment: The End of the Beginning. Cancer Discov. 2012, 2, 778–790. [Google Scholar] [CrossRef]
- Viollet, B.; Guigas, B.; Garcia, N.S.; Leclerc, J.; Foretz, M.; Andreelli, F. Cellular and molecular mechanisms of metformin: An overview. Clin. Sci. 2012, 122, 253–270. [Google Scholar] [CrossRef]
- Huang, W.; Chang, S.; Hsu, H.; Chou, W.; Yang, T.; Chen, J.; Chang, J.W.; Lin, Y.; Kuo, C.; See, L. Postdiagnostic metformin use and survival of patients with colorectal cancer: A Nationwide cohort study. Int. J. Cancer 2020, 147, 1904–1916. [Google Scholar] [CrossRef]
- Akce, M.; Farran, B.; Switchenko, J.M.; Rupji, M.; Kang, S.; Khalil, L.; Ruggieri-Joyce, A.; Olson, B.; Shaib, W.L.; Wu, C.; et al. Phase II trial of nivolumab and metformin in patients with treatment-refractory microsatellite stable metastatic colorectal cancer. J. Immunother. Cancer 2023, 11, e007235. [Google Scholar] [CrossRef]
- Xiang, J.-C.; An, Y.; Sun, J.-X.; Xu, J.-Z.; Xiong, Y.-F.; Wang, S.-G.; Xia, Q.-D. Unravelling the association between metformin and pan-cancers: Mendelian randomization combined with NHANES database analysis. Discov. Oncol. 2025, 16, 279. [Google Scholar] [CrossRef]
- Brown, J.C.; Spielmann, G.; Yang, S.; Compton, S.L.E.; Jones, L.W.; Irwin, M.L.; Ligibel, J.A.; Meyerhardt, J.A. Effects of exercise or metformin on myokine concentrations in patients with breast and colorectal cancer: A phase II multi-centre factorial randomized trial. J. Cachex- Sarcopenia Muscle 2024, 15, 1520–1527. [Google Scholar] [CrossRef]
- Eikawa, S.; Nishida, M.; Mizukami, S.; Yamazaki, C.; Nakayama, E.; Udono, H. Immune-mediated antitumor effect by type 2 diabetes drug, metformin. Proc. Natl. Acad. Sci. USA 2015, 112, 1809–1814. [Google Scholar] [CrossRef] [PubMed]
- Scharping, N.E.; Menk, A.V.; Whetstone, R.D.; Zeng, X.; Delgoffe, G.M. Efficacy of PD-1 Blockade Is Potentiated by Metformin-Induced Reduction of Tumor Hypoxia. Cancer Immunol. Res. 2017, 5, 9–16. [Google Scholar] [CrossRef]
- Saito, A.; Koinuma, K.; Kawashima, R.; Miyato, H.; Ohzawa, H.; Horie, H.; Yamaguchi, H.; Kawahira, H.; Mimura, T.; Kitayama, J.; et al. Metformin may improve the outcome of patients with colorectal cancer and type 2 diabetes mellitus partly through effects on neutrophil extracellular traps. BJC Rep. 2023, 1, 20. [Google Scholar] [CrossRef] [PubMed]
- Liu, G.; Wang, F.; Feng, Y.; Tang, H. Metformin Inhibits NLRP3 Inflammasome Expression and Regulates Inflammatory Microenvironment to Delay the Progression of Colorectal Cancer. Recent Patents Anti-Cancer Drug Discov. 2025, 20, 213–222. [Google Scholar] [CrossRef]
- Vigano, S.; Alatzoglou, D.; Irving, M.; Ménétrier-Caux, C.; Caux, C.; Romero, P.; Coukos, G. Targeting Adenosine in Cancer Immunotherapy to Enhance T-Cell Function. Front. Immunol. 2019, 10, 925. [Google Scholar] [CrossRef]
- Roliano, G.G.; Azambuja, J.H.; Brunetto, V.T.; Butterfield, H.E.; Kalil, A.N.; Braganhol, E. Colorectal Cancer and Purinergic Signalling: An Overview. Cancers 2022, 14, 4887. [Google Scholar] [CrossRef]
- Wu, L.; Xie, W.; Li, Y.; Ni, Q.; Timashev, P.; Lyu, M.; Xia, L.; Zhang, Y.; Liu, L.; Yuan, Y.; et al. Biomimetic Nanocarriers Guide Extracellular ATP Homeostasis to Remodel Energy Metabolism for Activating Innate and Adaptive Immunity System. Adv. Sci. 2022, 9, e2105376. [Google Scholar] [CrossRef]
- Pearce, E.L.; Pearce, E.J. Metabolic Pathways in Immune Cell Activation and Quiescence. Immunity 2013, 38, 633–643. [Google Scholar] [CrossRef]
- Morales, D.R.; Morris, A.D. Metformin in Cancer Treatment and Prevention. Annu. Rev. Med. 2015, 66, 17–29. [Google Scholar] [CrossRef]
- Goodwin, P.J.; Stambolic, V.; Lemieux, J.; Chen, B.E.; Parulekar, W.R.; Gelmon, K.A.; Hershman, D.L.; Hobday, T.J.; Ligibel, J.A.; Mayer, I.A.; et al. Evaluation of metformin in early breast cancer: A modification of the traditional paradigm for clinical testing of anti-cancer agents. Breast Cancer Res. Treat. 2010, 126, 215–220. [Google Scholar] [CrossRef] [PubMed]
- Coyle, C.; Cafferty, F.H.; Vale, C.; Langley, R.E. Metformin as an adjuvant treatment for cancer: A systematic review and meta-analysis. Ann. Oncol. 2016, 27, 2184–2195. [Google Scholar] [CrossRef] [PubMed]
- Hirsch, H.A.; Iliopoulos, D.; Struhl, K. Metformin inhibits the inflammatory response associated with cellular transformation and cancer stem cell growth. Proc. Natl. Acad. Sci. USA 2013, 110, 972–977. [Google Scholar] [CrossRef] [PubMed]
- Bradley, M.C.; Ferrara, A.; Achacoso, N.; Ehrlich, S.F.; Quesenberry, C.P.; Habel, L.A. A Cohort Study of Metformin and Colorectal Cancer Risk among Patients with Diabetes Mellitus. Cancer Epidemiol. Biomark. Prev. 2018, 27, 525–530. [Google Scholar] [CrossRef]
- Fransgaard, T.; Thygesen, L.C.; Gögenur, I. Metformin Increases Overall Survival in Patients with Diabetes Undergoing Surgery for Colorectal Cancer. Ann. Surg. Oncol. 2015, 23, 1569–1575. [Google Scholar] [CrossRef]
- Patel, S.K.; George, B.; Rai, V. Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology. Front. Pharmacol. 2020, 11, 1177. [Google Scholar] [CrossRef]
- Eledkawy, A.; Hamza, T.; El-Metwally, S. Towards precision oncology: A multi-level cancer classification system integrating liquid biopsy and machine learning. BioData Min. 2025, 18, 29. [Google Scholar] [CrossRef]
- Guo, Y.; Li, T.; Gong, B.; Hu, Y.; Wang, S.; Yang, L.; Zheng, C. From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients. Adv. Sci. 2024, 12, e2408069. [Google Scholar] [CrossRef]
- Lin, P.-C.; Tsai, Y.-S.; Yeh, Y.-M.; Shen, M.-R. Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care. Biomolecules 2022, 12, 1133. [Google Scholar] [CrossRef]
- Tharmaseelan, H.; Hertel, A.; Rennebaum, S.; Nörenberg, D.; Haselmann, V.; Schoenberg, S.O.; Froelich, M.F. The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization. Cancers 2022, 14, 3349. [Google Scholar] [CrossRef]
Biomarker | Type | Proposed Mechanism | Predicted Response | Reference |
---|---|---|---|---|
KRAS Mutation Status | Genetic Mutation | Silencing of MATE1 expression in mutant KRAS tumors → Increased intracellular metformin accumulation | KRAS-mutant tumors are more sensitive to metformin | [10] |
MATE1 (Multidrug and Toxic Compound Extrusion 1) Expression | Protein Expression | Silencing of MATE1 expression (via DNMT1-mediated hypermethylation) → Decreased metformin efflux → Increased intracellular metformin accumulation | Low MATE1 expression is associated with increased metformin efficacy | [10] |
OCT and MATE2 Transporter Profiles | Protein Expression | Regulate intracellular metformin concentration (OCTs for uptake, MATEs for efflux) | High MATE2 expression may be associated with resistance. High OCT3 expression may be associated with increased sensitivity | [11] |
AMPK Pathway Components | Protein Activity/Status | High basal AMPK activity increases sensitivity to metformin-induced growth inhibition | High AMPK activity is associated with increased metformin effects | [12] |
Glutamine Metabolism | Metabolic Profile | Glutamine-dependent CRC cells show resistance to metformin | Glutamine dependency is associated with resistance | [13] |
Transcriptomic Profiles | Gene Expression Pattern | Specific gene expression patterns, potentially reflecting metabolic or signaling signatures (e.g., COX11, immune-metabolic patterns), correlate with response | Specific gene expression patterns predict response | [14,15] |
Study Type | Patient Population | Key Findings (Metformin Effect) | Implication for Patient Stratification/Biomarkers | Reference |
---|---|---|---|---|
Meta-analyses & Systematic Reviews | CRC, particularly in patients with diabetes | Reduced CRC incidence and adenoma formation; improved overall survival and CRC-specific survival (especially in diabetic patients); Sex-specific differences in survival benefit (stronger in women). | Heterogeneity in effect sizes across studies/cohorts highlights the need for predictive biomarkers to select responsive patients. | [6,7] |
Nationwide Cohort Studies | Patients with diabetes and post-diagnostic CRC | Metformin use after CRC diagnosis is associated with reduced all-cause mortality and CRC-specific mortality. | The observational nature limits definitive conclusions across all subgroups without biomarker validation. Highlights potential therapeutic role in diagnosed patients. | [48] |
Clinical Trials (Phase II) | Treatment refractory microsatellite-stable metastatic CRC | Combination of nivolumab and metformin showed limited clinical efficacy; Correlative studies suggested potential immune-modulatory effects (e.g., increased tumor-infiltrating lymphocytes). | Limited response underscores the complexity of advanced disease and the critical need for better patient selection based on predictive biomarkers for combination therapies. | [49] |
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Myung, S.; Park, Y.Y.; Kim, M.S. Metformin in Colorectal Cancer: Epidemiological Evidence, Predictive Biomarkers, and Implications for Prevention and Treatment. Int. J. Mol. Sci. 2025, 26, 6040. https://doi.org/10.3390/ijms26136040
Myung S, Park YY, Kim MS. Metformin in Colorectal Cancer: Epidemiological Evidence, Predictive Biomarkers, and Implications for Prevention and Treatment. International Journal of Molecular Sciences. 2025; 26(13):6040. https://doi.org/10.3390/ijms26136040
Chicago/Turabian StyleMyung, Seokho, Youn Young Park, and Man S. Kim. 2025. "Metformin in Colorectal Cancer: Epidemiological Evidence, Predictive Biomarkers, and Implications for Prevention and Treatment" International Journal of Molecular Sciences 26, no. 13: 6040. https://doi.org/10.3390/ijms26136040
APA StyleMyung, S., Park, Y. Y., & Kim, M. S. (2025). Metformin in Colorectal Cancer: Epidemiological Evidence, Predictive Biomarkers, and Implications for Prevention and Treatment. International Journal of Molecular Sciences, 26(13), 6040. https://doi.org/10.3390/ijms26136040