The Role of Insulin Resistance in Cancer
Simple Summary
Abstract
1. Introduction
2. Search Strategy
3. Core Molecular Mechanisms Linking Insulin Resistance and Cancer
3.1. The Insulin/IGF System: An Integrated Signaling Axis
- Direct Mitogenic Signaling: Sustained hyperinsulinemia can directly activate the IR and, at high concentrations, the IGF-1R. Upon binding, these receptors activate two major signaling cascades:
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- The Ras/Mitogen-Activated Protein Kinase (MAPK) Pathway: This pathway primarily drives cell proliferation, migration, and differentiation, contributing to tumor expansion and metastasis [18].
- Altered IGF-Axis Signaling: Hyperinsulinemia suppresses the hepatic production of IGF-binding proteins (IGFBP-1 and IGFBP-2), which may increase the bioavailability of IGF-1 [20,21]. However, the direct link between circulating IGF-1 levels and cancer risk is a subject of ongoing scientific debate. Bloodstream levels of IGF-1 have not been consistently reproducible as prognostic markers, and the clinical failure of all IGF-1R drug blockers has called into question the role of the IGF-1/IGF-1R axis as a primary driver of malignancy (as reviewed in [22,23]). A more scientifically supported view is that the potent cellular and tissue growth-promoting signals (e.g., via the PI3K and MAPK pathways) induced by hyperinsulinemia contribute to the benign proliferative stages of tumor development. These signals can create a permissive environment that synergizes with other intra-tumoral events necessary for the switch to a malignant phenotype. To date, no definitive evidence suggests that activation of the insulin or IGF-1 receptor by itself can trigger the “benign versus malignant transformation switch.” In contrast, a more direct role in tumor progression has been demonstrated for IGF-II, which is often secreted by cancer cells and acts as a key autocrine/paracrine factor in the tumor microenvironment, promoting the angiogenic switch and malignant progression [24].
3.2. Chronic Inflammation and Dysregulated Adipokines
- Promoting angiogenesis through factors like VEGF [30].
- Suppressing anti-tumor immune surveillance [31].
3.3. Metabolic Reprogramming, Hypoxia, and Plasticity
3.4. Mitochondrial Dysfunction
- Impaired Apoptosis: Mitochondria are central to programmed cell death (apoptosis). In cancer, this process is often dysregulated by the upregulation of anti-apoptotic proteins like Bcl-2, which prevents the release of cytochrome c and blocks cell death. This not only promotes tumor survival but is also a key mechanism of resistance to many chemotherapies [46,47,48]. Targeting this pathway with BH3 mimetics (e.g., Venetoclax) is a promising strategy to restore apoptotic sensitivity [49].
4. Insulin Resistance and Specific Cancers
- Liver and Pancreatic Cancers: IR is a pivotal factor in the pathogenesis of non-alcoholic fatty liver disease (NAFLD), which can progress to hepatocellular carcinoma (HCC) [55,56]. Chronic hyperinsulinemia is also a major risk factor for pancreatic cancer, potentially by promoting local inflammation and directly stimulating pancreatic cell proliferation [57,58].
5. Therapeutic Strategies and Future Outlook
5.1. Lifestyle Interventions
- Physical Activity: Regular physical activity, including both aerobic and resistance training, improves insulin sensitivity, reduces visceral fat, and lowers systemic inflammation. A minimum of 150 min of moderate-intensity activity per week is associated with a reduced risk for multiple cancer types [67,68,69].
5.2. Pharmacological Approaches
- Metformin: This first-line drug for T2DM improves insulin sensitivity, primarily by activating AMP-activated protein kinase (AMPK), which in turn inhibits the pro-growth mTOR pathway [71]. By reducing systemic hyperinsulinemia and inflammation, metformin has shown promise in pre-clinical models and some clinical studies. For instance, one trial found that low-dose metformin could suppress the formation of colorectal aberrant crypt foci, suggesting a potential role in chemoprevention [72]. However, the clinical evidence is mixed, highlighting significant limitations. The large, phase III MA.32 trial, which evaluated metformin as an adjuvant therapy in early-stage breast cancer, found no significant improvement in invasive disease-free survival or overall survival compared to placebo [73]. These mixed results underscore the critical need to identify the specific patient populations and cancer types that are most likely to benefit from metformin therapy.
- Targeting the insulin/IGF system: Given its central role in driving cell proliferation and survival, the insulin/IGF system is an attractive therapeutic target. Strategies have included IGF-1R monoclonal antibodies (e.g., Cixitumumab) and dual IR/IGF-1R tyrosine kinase inhibitors (e.g., Linsitinib) [74,75]. While promising in preclinical studies, these agents have shown only modest efficacy in clinical trials and face significant challenges, including
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- Metabolic Side Effects: Cross-reactivity with the insulin receptor’s metabolic functions can lead to hyperglycemia, complicating treatment.
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- Resistance: Tumors often develop resistance by activating alternative growth factor pathways, limiting the long-term effectiveness of these inhibitors [76].
5.3. Future Directions and Emerging Research
- Predictive Biomarkers: A key priority is the identification of biomarkers to predict which patients will respond to metabolically targeted therapies. This could include circulating markers of IR (e.g., C-peptide and HOMA-IR) or tumor-specific markers like the expression level of the IR-A isoform. Understanding how a patient’s metabolic state influences their response to immune checkpoint inhibitors is another critical area for future investigation.
- Metabolic Plasticity as a Resistance Mechanism: Cancer cells can evade therapy by adapting their metabolism, for instance by shifting their reliance from glucose to alternative fuel sources like fatty acids or glutamine [38]. This metabolic plasticity is a key mechanism of treatment resistance. As a result, targeting these metabolic escape routes, for example, with glutaminase inhibitors (e.g., Telaglenastat), is a promising strategy to block this adaptation and overcome resistance [39,40].
- High-Risk Populations: The IR–cancer link has profound implications for specific high-risk groups that warrant further study. This includes investigating the long-term cancer predisposition of newborns of mothers with gestational diabetes, who are exposed to hyperinsulinemia in utero. Similarly, rare tumors that ectopically secrete IGF-2, inducing severe hypoglycemia, represent a unique clinical model of IGF-driven malignancy and can provide valuable mechanistic insights.
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Cancer Type | Key Associated Mechanisms | Strength of Epidemiological Evidence |
---|---|---|
Endometrial Cancer | Hyperinsulinemia, Increased Bioavailable Estrogens (reduced SHBG), Chronic Inflammation | Very Strong |
Liver Cancer (HCC) | NAFLD/NASH Progression, Chronic Inflammation, Direct Mitogenic Effects of Insulin/IGF System | Very Strong |
Pancreatic Cancer | Hyperinsulinemia, Local Pancreatic Inflammation, Direct Mitogenic Effects | Strong |
Colorectal Cancer | Hyperinsulinemia, IGF-1 Signaling, Chronic Inflammation | Strong |
Breast Cancer (Postmenopausal) | Hyperinsulinemia, IGF-1 Signaling, Increased Bioavailable Estrogens, Inflammation | Strong |
Cancer | Number (n) of Examined Studies | Relative Risk (CI 95%) |
---|---|---|
Liver | Case–control (n = 13) | 2.50 (1.80–3.50) |
Cohort (n = 7) | 2.51 (1.90–3.20) | |
Cohort (n = 18) | 2.01 (1.61–2.51) | |
Endometrium | Case–control (n = 13) | 2.22 (1.80–2.74) |
Cohort (n = 3) | 1.62 (1.21–2.16) | |
Pancreas | Case–control (n = 17) | 1.94 (1.53–2.36) |
Cohort (n = 19) | 1.73 (1.59–1.88) | |
Cohort (n = 3) | 1.80 (1.50–2.10) | |
Cohort (n = 35) | 1.94 (1.66–2.27) | |
Kidney | Case–control (n = 9) | 1.42 (1.06–1.91) |
Biliary Tract | Case–control (n = 8) and cohort (n = 13) | 1.43 (1.18–1.72) |
Case–control (n = 10) and cohort (n = 5) | 1.60 (1.38–1.87) | |
Bladder | Case–control (n = 7) | 1.37 (1.04–1.80) |
Cohort (n = 3) | 1.43 (1.18–1.74) | |
Colorectal | Case–control (n = 6) | 1.36 (1.23–1.50) |
Cohort (n = 9) | 1.29 (1.16–1.43) | |
Case–control + cohort (n = 14) | 1.38 (1.26–1.51) | |
Esophagus | Case–control (n = 6) and cohort (n = 11) | 1.30 (1.12–1.50) |
Non-Hodgkin’s Lymphoma | Case–control (n = 5) | 1.12 (0.95–1.31) |
Cohort (n = 11) | 1.41 (1.07–1.88) | |
Cohort (n = 10) | 1.18 (0.99–1.42) | |
Cohort (n = 3) | 1.79 (1.30–2.47) | |
Breast | Case–control (n = 5) | 1.18 (1.05–1.32) |
Cohort (n = 15) | 1.20 (1.11–1.30) |
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Subedi, B.K.; Bhimineni, C.; Modi, S.; Jahanshahi, A.; Quiza, K.; Bitetto, D. The Role of Insulin Resistance in Cancer. Curr. Oncol. 2025, 32, 477. https://doi.org/10.3390/curroncol32090477
Subedi BK, Bhimineni C, Modi S, Jahanshahi A, Quiza K, Bitetto D. The Role of Insulin Resistance in Cancer. Current Oncology. 2025; 32(9):477. https://doi.org/10.3390/curroncol32090477
Chicago/Turabian StyleSubedi, Bal Krishna, Charishma Bhimineni, Shivani Modi, Atousa Jahanshahi, Katherine Quiza, and Daniel Bitetto. 2025. "The Role of Insulin Resistance in Cancer" Current Oncology 32, no. 9: 477. https://doi.org/10.3390/curroncol32090477
APA StyleSubedi, B. K., Bhimineni, C., Modi, S., Jahanshahi, A., Quiza, K., & Bitetto, D. (2025). The Role of Insulin Resistance in Cancer. Current Oncology, 32(9), 477. https://doi.org/10.3390/curroncol32090477