Immunometabolic interactions in gynecologic cancers: Mechanisms, biomarkers, and therapeutic implications

Abstract

This narrative review synthesizes current evidence on immunometabolic interactions in gynecologic cancers, with emphasis on their implications for tumor biology, therapeutic response, and global cancer care. Emerging data indicate that systemic metabolic dysfunction is closely integrated with tumor microenvironment immune remodeling. Key features include enhanced glycolysis, nutrient competition, lipid accumulation, and hypoxia, which collectively impair cytotoxic T-cell function, promote immune exhaustion, and support immunosuppressive myeloid phenotypes.

Although gynecologic malignancies share common immunometabolic hallmarks, distinct tumor-specific patterns are evident. Cervical cancer is characterized by human papillomavirus-associated metabolic reprogramming and immune checkpoint activation; ovarian cancer demonstrates dependence on lipid metabolism and omental adipose interactions; and endometrial cancer is strongly associated with obesity-driven insulin resistance and endocrine–inflammatory signaling. This review evaluates potential biomarkers and therapeutic strategies, including metformin, statins, and immune checkpoint inhibitor-based combinations, and clearly distinguishes between preclinical findings and clinically established interventions. Current evidence for metabolic targeting remains largely exploratory, with variable degrees of clinical validation across tumor types. In addition, this review highlights implementation challenges in low- and middle-income countries, particularly limited access to advanced molecular diagnostics and immunotherapies, and discusses opportunities for scalable, low-cost metabolic approaches. Finally, we outline future research priorities, including spatial multiomics, longitudinal metabolic profiling, and computational modeling approaches to improve patient stratification and predictive accuracy. Overall, immunometabolism represents a promising but evolving framework for improving understanding and management of gynecologic cancers, with important implications for both precision oncology and global health equity.

Introduction

Gynecologic cancers, including cervical, ovarian, and endometrial malignancies, remain a major cause of cancer-related morbidity and mortality worldwide, with a disproportionately high burden in low- and middle-income countries (LMICs). ¹ Although persistent infection with high-risk human papillomavirus (HR-HPV) is the primary driver of cervical cancer, metabolic disorders such as obesity, insulin resistance, and metabolic syndrome are increasingly recognized as important modifiers of cancer risk and progression across gynecologic malignancies.^2,3 Recent advances in cancer biology emphasize the importance of host–tumor interactions in shaping disease behavior. In this context, immunometabolism has emerged as an integrative framework linking cellular metabolic reprogramming to immune cell function, influencing activation, differentiation, and antitumor effector responses.

Gynecologic cancers exhibit distinct but overlapping immunometabolic characteristics. Cervical cancer is primarily shaped by persistent HPV-driven immune and metabolic reprogramming, ovarian cancer by lipid-rich peritoneal and omental ecosystems that support tumor dissemination, and endometrial cancer by systemic metabolic dysfunction associated with obesity and endocrine alterations.^4–7 Together, these tumor-specific characteristics illustrate how shared immunometabolic principles are adapted to distinct tissue contexts and clinical phenotypes and also reflect organ-specific biological drivers. Despite growing recognition of these mechanisms, their translation into clinically actionable biomarkers and effective therapeutic strategies remains limited, particularly in resource-constrained settings.

Existing literature has largely examined tumor immunology, cancer metabolism, and gynecologic malignancies in isolation. In contrast, this review integrates these domains by synthesizing immunometabolic interactions across cervical, ovarian, and endometrial cancers and critically evaluates emerging therapeutic strategies and translational challenges, particularly in LMIC contexts. ⁴ This review aims to bridge mechanistic insights with clinical application, including biomarker development, immunotherapy, and metabolic targeting. Accordingly, it proposes a unified immunometabolic framework that integrates tumor–immune ecology, systemic metabolic dysfunction, and translational oncology, with particular emphasis on gynecologic cancers and implementation challenges in LMICs. No preprint version of this manuscript has been previously published, posted, or cited.

Methods

This narrative review was executed in accordance with the Scale for the Assessment of Narrative Review Articles (SANRA) guidelines to ensure systematic transparency in literature selection and data synthesis. ⁸

Literature search strategy

A structured literature search was conducted across the PubMed/MEDLINE, Scopus, and Web of Science databases for articles published between January 2018 and March 2026. A selective number of older, foundational studies were also included to capture historical context. The literature search was performed using specific Boolean logic combinations: (“immunometabolism” OR “metabolic reprogramming”) AND (“tumor microenvironment” OR “immune evasion”) AND (“cervical cancer” OR “ovarian cancer” OR “endometrial cancer”). Free-text keywords included “HPV kinetics,” “lipid trafficking,” “CD36,” “insulin resistance,” “lactate accumulation,” and “immune checkpoint inhibitors.” Reference lists of retrieved landmark articles were manually curated to capture foundational mechanistic papers.

Evidence selection and synthesis

Articles were selected using transparent, predefined eligibility criteria optimized for both contextual background and thematic relevance. Studies were included according to the following inclusion criteria: (a) peer-reviewed original or translational research investigating immunometabolic mechanisms; (b) focus on human gynecologic tissues, relevant preclinical models, or clinical trial outcomes; and (c) published in English. Landmark oncology literature, textbook concepts, global epidemiological data, and authoritative clinical practice guidelines were also included to provide clinical and theoretical context where appropriate. Exclusion criteria comprised non-peer-reviewed preprints, conference abstracts without full datasets, and general cancer metabolism reviews lacking specific focus on gynecologic malignancies, unless cited for background information. Evidence was synthesized using a qualitative thematic approach, and studies were categorized as preclinical mechanistic study, observational translational study, or clinical trial.

Immunometabolic crosstalk

Immunometabolism in cancer

Immunometabolism in oncogenesis describes a highly competitive metabolic ecosystem in which tumor cells and infiltrating immune cells compete for limited and shared nutrient resources. Malignant cells sustain uncontrolled proliferation through enhanced glycolytic flux and glutaminolysis, which collectively support rapid biomass accumulation. ⁹ This metabolic reprogramming progressively depletes extracellular glucose and essential amino acids, including tryptophan, thereby restricting the metabolic substrates required for effector T-cell activation, which depends on aerobic glycolysis for clonal expansion and cytokine production. ¹⁰

In contrast, immunosuppressive populations such as regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) exhibit greater metabolic plasticity. These cells preferentially utilize oxidative phosphorylation and fatty acid oxidation (FAO), enabling their persistence and suppressive function within nutrient-deprived tumor microenvironment (TME). ¹⁰ Collectively, these spatial and functional metabolic disparities within the TME directly shape immune cell fate, effector capacity, and therapeutic responsiveness.

TME reprogramming

In addition to nutrient competition, the TME is further reshaped by the accumulation of bioactive metabolic byproducts that act as signaling mediators. ¹¹ Sustained aerobic glycolysis leads to excessive lactate production and proton release, resulting in extracellular acidification and a persistently low-pH microenvironment. ¹²

This acidic microenvironment suppresses cytotoxic immune effector functions, including those of cluster of differentiation 8 (CD8)⁺ T cells and natural killer (NK) cells, and promotes polarization of tumor-associated macrophages (TAMs) toward an M2-like, pro-tumorigenic phenotype. ¹³ In parallel, hypoxic conditions within the TME stabilize hypoxia-inducible factor 1-alpha (HIF-1α), which not only enhances transcription of glycolytic enzymes but also upregulates programmed death-ligand 1 (PD-L1) expression on tumor and myeloid cells. ¹⁴ Together, these interlinked metabolic and hypoxic adaptations couple local metabolic stress with immune checkpoint activation, thereby reinforcing immune evasion and tumor progression.

Metabolic dysregulation and systemic inflammation

In addition to local immunometabolic reprogramming within the TME, cancer development is shaped by systemic metabolic disturbances that exert effects at the organismal level. In particular, obesity-driven inflammation and insulin resistance create a systemic metabolic environment that reinforces tumor-promoting signaling pathways.

Obesity and adipose tissue inflammation

Obesity transforms adipose tissue from an energy storage depot into an immunometabolically active organ characterized by chronic low-grade inflammation, altered adipokine secretion, macrophage recruitment, and systemic metabolic remodeling. ¹⁵ Adipocyte hypertrophy and cellular stress promote progressive immune cell infiltration, especially that of macrophages, which shift toward a proinflammatory phenotype and sustain continuous secretion of cytokines and adipokines. ¹⁶ This persistent inflammatory state disrupts systemic metabolic homeostasis and extends its effects to distant tissue sites. In the context of pelvic malignancies, such as endometrial cancer, these circulating inflammatory mediators contribute to a proproliferative and prohyperplastic tissue environment, thereby facilitating malignant transformation. ¹⁷

Insulin resistance and hyperinsulinemia

In parallel with obesity-associated inflammation, insulin resistance promotes compensatory hyperinsulinemia, which exerts potent mitogenic effects through the activation of insulin receptor and insulin-like growth factor-1 (IGF-1) signaling pathways. Downstream activation of the phosphoinositide 3-kinase (PI3-K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway enhances tumor cell proliferation, survival, and metabolic reprogramming and influences immune cell differentiation and functional capacity. ¹⁸ Concurrently, elevated insulin levels suppress hepatic production of sex hormone-binding globulin (SHBG), resulting in increased circulating free estrogen. This hormonal shift amplifies estrogen receptor-mediated proliferative signaling in hormone-sensitive tissues, creating a synergistic interaction between metabolic and endocrine dysregulation. Collectively, these mechanisms link insulin resistance to both direct oncogenic signaling and hormone-driven carcinogenesis, particularly in endometrial cancer. ¹⁸

Dyslipidemia and lipid signaling

Systemic dyslipidemia emerges as a key downstream feature characterized by elevated circulating free fatty acids and lipid-derived intermediates with potent signaling capacity. ¹⁹ Rather than serving solely as metabolic substrates or structural components, these lipids function as bioactive mediators that actively reshape both tumor and immune cell behavior within the TME. ²⁰ Within this context, excessive lipid availability drives metabolic reprogramming of infiltrating immune cells. Increased intracellular lipid accumulation within the TME induces oxidative stress responses in effector lymphocytes, thereby impairing their cytotoxic function and supporting the persistence and survival of immunosuppressive myeloid populations. This results in progressive skewing of the immune balance toward a tumor-permissive state. Notably, lipid-driven immunometabolic remodeling is particularly pronounced in ovarian cancer where the lipid-rich omental microenvironment provides a specialized metabolic environment that facilitates tumor cell colonization and peritoneal metastatic seeding. ¹⁹ Together, these processes integrate systemic lipid dysregulation with local metabolic adaptation within the TME. Figure 1 illustrates the major immunometabolic characteristics of gynecologic cancers.

Figure 1.

Representative dominant immunometabolic characteristics of gynecologic cancers.

The figure illustrates the connection between systemic dysregulation (obesity and hyperinsulinemia) and TME changes (lactate, lipids, and checkpoints) across cervical, ovarian, and endometrial malignancies.

Key mediators of immune–metabolic interactions

Adipokines

Adipokines function as key mediators linking metabolic status to immune regulation. In obesity, dysregulated adipose tissue signaling establishes a chronic proinflammatory and tumor-supportive microenvironment characterized by altered secretion of leptin, adiponectin, interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α). ²¹ Leptin exerts predominantly protumorigenic effects by activating the Janus kinase (JAK)/signal transducer and activator of transcription (STAT), PI3-K/AKT, and mitogen-activated protein kinase (MAPK) signaling pathways, thereby promoting proliferation, angiogenesis, metabolic adaptation, and immune modulation. Research has also indicated that leptin contributes to T-cell dysfunction and immune exhaustion within the TME through sustained inflammatory signaling and metabolic stress. ²¹ In contrast, adiponectin exerts anti-inflammatory and antiproliferative effects by enhancing insulin sensitivity, suppressing nuclear factor kappa B (NF-κB) signaling, and inhibiting oncogenic pathways, including the PI3-K/AKT/mTOR axis. ²² Thus, reduced adiponectin levels in obesity favor tumor progression and immune dysregulation, establishing a high leptin-to-adiponectin ratio as a central systemic driver of tumor-associated immune suppression.

Cytokines and inflammatory signaling

Chronic inflammation in metabolic disorders is sustained by cytokines such as IL-6 and TNF-α, which integrate metabolic stress with immune and oncogenic signaling pathways. ²³ IL-6 promotes tumor progression primarily through JAK/STAT3 activation, driving proliferation, angiogenesis, survival, immune suppression, and metabolic adaptation and contributing to apoptosis resistance within the TME.^23,24 TNF-α reinforces protumorigenic inflammation through NF-κB signaling, sustaining inflammatory transcriptional programs, tumor cell survival, and recruitment of immunosuppressive immune populations. ²³

Immune cell populations

Immune cell function within the TME is tightly regulated by metabolic and inflammatory signals, resulting in distinct pro- or antitumor phenotype. TAMs predominantly acquire an M2-like immunosuppressive phenotype characterized by enhanced FAO and oxidative metabolism, thereby promoting angiogenesis and tissue remodeling and suppressing cytotoxic T-cell responses. ¹³ Lactate accumulation further reinforces this polarization toward tumor-supportive states. ¹³

Tregs suppress effector immunity and display metabolic flexibility that supports survival in glucose-depleted and lipid-rich environments. In contrast, exhausted CD8⁺ T cells exhibit impaired mitochondrial fitness and reduced glycolytic capacity, limiting effective antitumor responses. ¹⁰ MDSCs expand under chronic inflammatory and metabolic stress and inhibit T-cell activation through nutrient depletion, arginine metabolism, reactive oxygen species, and immunosuppressive mediators, with emerging evidence highlighting lipid metabolism as a key driver of their function.^25,26

Collectively, several components of this immune–metabolic network are emerging as candidate biomarkers for risk stratification, prognosis, and prediction of therapeutic response in gynecologic cancers; however, their clinical validation remains inconsistent. Table 1 summarizes representative biomarkers, their biological sources, supporting evidence, and clinical utility.

Table 1.

Representative immunometabolic biomarkers in gynecologic cancers, evidence level, potential clinical applications, and validation status.

Biomarker	Sample type	Cancer	Evidence type	Potential clinical use	Validation status
Leptin/adiponectin ratio	Serum	Endometrial	Observational	Risk stratification and metabolic phenotyping	Exploratory; requires prospective clinical validation
IL-6 and TNF-α	Serum/plasma	Endometrial and ovarian	Observational	Prognostic and inflammatory assessment	Exploratory; limited disease specificity
PD-L1 expression	Tumor tissue	Cervical and endometrial	Clinical	Selection for immune checkpoint inhibitors	Clinically validated in selected indications; guideline-supported where indicated
MSI/MMRd status	Tumor tissue	Endometrial	Clinical	Predict response to immunotherapy	Clinically validated; guideline-supported biomarker
Tumor-infiltrating lymphocytes (TILs)	Tumor tissue	Cervical, ovarian, and endometrial	Translational	Immune profiling and prognosis	Translational; clinical standardization ongoing
Lactate-associated metabolic signatures	Tumor tissue	Cervical	Preclinical/Translational	Assessment of immune suppression and metabolic activity	Preclinical; not yet clinically validated
CD36 expression	Tumor tissue	Ovarian	Translational	Prediction of lipid-dependent metastasis	Translational; prospective validation required
Circulating insulin and IGF-1	Blood	Endometrial	Observational	Assessment of obesity-associated metabolic risk	Observational; not validated as independent clinical biomarkers

IL-6: interleukin-6; TNF-α: tumor necrosis factor-alpha; PD-L1: programmed death-ligand 1; MSI: microsatellite instability; MMRd: mismatch repair-deficient; IGF-1: insulin-like growth factor 1; CD36: cluster of differentiation 36.

Immunometabolic interactions in specific gynecologic cancers

Despite distinct etiologies, gynecologic malignancies share core immunometabolic hallmarks, including PI3-K/AKT/mTOR pathway activation, immune checkpoint upregulation, metabolic competition within the TME, and expansion of immunosuppressive cell populations. However, dominant metabolic substrates and immune evasion strategies vary considerably across tumor types, reflecting tissue-specific metabolic dependencies.

Cervical cancer: HPV-driven metabolic immune reprogramming

Persistent HR-HPV infection uniquely coordinates metabolic and immune remodeling through viral oncoproteins E6 and E7, which enhance glycolytic activity and impair innate immune sensing. These virus-driven alterations intensify the broader immunometabolic mechanisms described above by promoting nutrient competition, immune checkpoint expression, and sustained immune escape within the cervical TME. ²⁷

Concurrently, HPV infection disrupts innate immune sensing pathways, including the cyclic guanosine monophosphate (GMP)–adenosine monophosphate (AMP) synthase-stimulator of interferon genes (cGAS–STING) pathway and type I interferon signaling, thereby linking viral oncogenesis to immune escape. ²⁷ Indoleamine 2, 3-dioxygenase (IDO) is also upregulated, promoting tryptophan depletion and kynurenine accumulation, which collectively expand Tregs and inhibit NK cell activity. ²⁸

Ovarian cancer: lipid metabolism and the omental environment

Ovarian cancer exemplifies the consequences of lipid-dependent immunometabolism. The omental adipose microenvironment supplies fatty acids that support tumor bioenergetics, whereas lipid accumulation within immune cells reinforces the immunosuppressive metabolic adaptations outlined in previous sections.^19,29 Malignant ascites further amplifies these processes by creating a heterogeneous metabolic environment that promotes immune dysfunction and metastatic dissemination.^30,31

Endometrial cancer: metabolic–inflammatory axis and molecular subtypes

Endometrial cancer represents the clearest clinical manifestation of systemic immunometabolic dysregulation. The obesity-associated inflammatory, endocrine, and metabolic mechanisms described earlier converge to activate PI3-K/AKT/mTOR signaling, enhance estrogen-driven proliferation, and remodel the tumor–immune microenvironment. The relative contribution of these pathways varies across the four molecular subtypes, contributing to their distinct immune landscapes and therapeutic responsiveness. ³²

Therapeutic implications

Immunotherapy and tumor metabolic state

Immune checkpoint inhibitors (ICIs), particularly programmed cell death protein 1 (PD-1)/PD-L1 blockade, produce durable responses; however, they exhibit heterogeneous, tumor-specific efficacy across gynecologic malignancies. They are established as the standard of care in MMRd advanced endometrial cancer and recurrent/metastatic cervical cancer; however, they demonstrate limited benefit in unselected epithelial ovarian cancer cohorts.^35,36

Tumor metabolic state is increasingly recognized as a key determinant of this differential responsiveness. Enhanced glycolysis promotes lactate accumulation and acidosis, which suppress CD8⁺ T-cell cytotoxicity and increase PD-L1 expression via HIF-1α-dependent signaling. ³⁷ Concurrently, nutrient competition for glucose and glutamine impairs T-cell metabolic fitness, leading to mitochondrial dysfunction and exhaustion. ³⁸ However, most immunometabolic interventions remain investigational and should not be considered established standards of care unless explicitly stated.

Metabolic targeting strategies and clinical maturity

Targeting tumor metabolism is an emerging strategy to enhance immunotherapy response, with approaches varying in clinical maturity.

Repurposed metabolic agents

Metformin activates AMP-activated protein kinase (AMPK) and inhibits mitochondrial complex I, reducing glycolytic flux and systemic insulin signaling. Translational evidence suggests potential synergy with ICIs, although results remain heterogeneous and inconclusive.^39,40 In contrast, statins inhibit the mevalonate pathway and demonstrate antiproliferative and proapoptotic effects, with observational studies suggesting possible survival benefits and emerging synergy with poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitors in ovarian cancer, although phase III evidence is lacking.^41,42

Dietary and lifestyle interventions

Caloric restriction and metabolic modulation may reduce glucose and IGF-1 availability, limiting tumor fuel supply. ⁴³ However, these remain non-standard interventions requiring robust clinical validation.

Combination therapeutic strategies and emerging targets

Future progress is likely to depend on rational combination strategies targeting both oncogenic signaling and metabolic reprogramming. The PI3-K/AKT/mTOR axis, frequently dysregulated in endometrial and cervical cancers, integrates metabolic and proliferative signaling; its inhibition in combination with ICIs may restore immune function and limit tumor growth. ⁹

Additional strategies targeting glycolysis and lactate signaling, such as inhibition of glucose transporter 1 (GLUT1), hexokinase 2, or monocarboxylate transporter 4 (MCT4), have reversed acid-mediated immunosuppression in preclinical models; however, their clinical efficacy has not been confirmed gynecologic cancers.^9,44 Similarly, targeting IDO/kynurenine metabolism, FAO pathways, and HIF-1α remains experimental and requires rigorous clinical validation before translation into clinical practice.

Relevance to LMICs

Implementing immunometabolic approaches in LMICs requires a shift from costly multiomic profiling to pragmatic, resource-stratified strategies. Instead of advanced molecular platforms, resource-limited settings can rely on scalable metabolic indicators such as waist-to-hip ratio, body mass index, and point-of-care glucose and lipid testing to identify patients at risk of systemic metabolic dysregulation.

Dual burden of infection and metabolic disease

The immunometabolic landscape of gynecologic cancers in LMICs is shaped by the convergence of persistent infectious diseases, rapidly increasing metabolic disorders, and constrained health systems. Chronic infections, particularly HPV and human immunodeficiency virus (HIV) infections, promote sustained immune activation and dysfunction that may synergize with obesity, insulin resistance, and malnutrition to accelerate tumor progression through immunometabolic mechanisms.^1,45

Cervical cancer exemplifies this interaction, where HPV-driven metabolic reprogramming and immune evasion are compounded by HIV-associated immune impairment, contributing to persistent infection, delayed diagnosis, and poorer outcomes in sub-Saharan African populations. ⁴⁶ Simultaneously, urbanization and lifestyle transitions are driving increasing rates of obesity and metabolic syndrome, particularly among women, thereby expanding the burden of obesity-associated endometrial cancer and potentially influencing ovarian cancer biology.

Translational and implementation challenges

Translation of immunometabolic discoveries into clinical practice remains particularly challenging in LMICs. Although the coverage of HPV vaccination and cervical screening programs has expanded, access to molecular diagnostics, including microsatellite instability (MSI)/MMRd testing, PD-L1 assessment, and other biomarker assays, remains limited owing to shortage of molecular pathology laboratories, trained personnel, and laboratory infrastructure. Limited pathology capacity further delays accurate diagnosis and patient stratification.

Access to ICIs and targeted therapies is additionally constrained by high costs, limited reimbursement, unreliable procurement systems, and cold-chain requirements for biologic agents. Restricted clinical trial infrastructure further limits participation in studies evaluating emerging immunometabolic therapies, reducing opportunities to generate context-specific evidence for LMIC populations.

Opportunities for scalable implementation

Despite these barriers, several interventions are immediately applicable within LMICs health systems. Expanded coverage of HPV vaccination, obesity prevention strategies, and metabolic risk reduction may decrease the future burden of gynecologic cancers and address underlying immunometabolic dysfunction. Repurposing affordable metabolic agents, such as metformin and statins, represents a pragmatic translational strategy deserving further evaluation in well-designed clinical trials. ³⁹ Strengthening pathology services, expanding molecular diagnostic capacity, establishing regional biomarker testing networks, and increasing participation in international collaborative clinical trials will be essential for equitable implementation of precision immunometabolic oncology in resource-constrained settings. Table 2 summarizes biomarker candidates, therapeutic vulnerabilities, and their current stage of clinical development in major gynecologic malignancies.

Table 2.

Cancer-specific immunometabolic phenotypes, therapeutic vulnerabilities, and clinical maturity across gynecologic malignancies.

Malignancy	Primary metabolic phenotype	Targetable biomarker candidate	Combined therapeutic vulnerability	Current clinical maturity/level of evidence
Cervical cancer	HPV-driven accelerated glycolysis, tryptophan depletion, and extracellular acidification	GLUT1/MCT4 overexpression; indoleamine 2,3-dioxygenase 1 (IDO1)/kynurenine axis activation	Immune checkpoint inhibition (PD-1/PD-L1) + glycolysis inhibition (GLUT1/MCT4 targeting); IDO1 inhibition strategies	High clinical maturity: pembrolizumab in recurrent/metastatic disease. Preclinical/early-phase: IDO1 inhibitors and lactate transport blockade combinations
Ovarian cancer	Omental lipid trafficking, fatty acid oxidation (FAO) dependence, and ascites-driven immunosuppression	CD36 lipid transporter; fatty acid-binding protein 4 (FABP4)	Statins (mevalonate pathway inhibition); CPT1A/FAO inhibitors combined with platinum-based chemotherapy	Moderate clinical maturity: observational and Phase II evidence for statin–chemotherapy combinations. Preclinical: CD36 or FABP4 inhibition to block metastatic lipid uptake
Endometrial cancer	Obesity-driven insulin/IGF-1 signaling, hyperestrogenism, and PI3-K/AKT/mTOR pathway activation	Elevated fasting glucose/HbA1c, insulin resistance, and reduced adiponectin; POLE, MMRd/MSI-H), and TP53 status	Metformin (AMPK activation); mTOR inhibitors (e.g. temsirolimus); hormonal therapy; immune checkpoint inhibitors	High clinical maturity: pembrolizumab + lenvatinib for advanced disease; single-agent immunotherapy in MMRd/MSI-H tumors. Phase II/III: metformin combined with hormonal or cytotoxic therapy

GLUT1: glucose transporter 1; MCT4: monocarboxylate transporter 4; PD-L1: programmed death-ligand 1; CPT1A: carnitine palmitoyltransferase 1A; IGF-1: insulin-like growth factor 1; PI3-K: phosphoinositide 3-kinase; AKT: protein kinase B; mTOR: mammalian target of rapamycin; MSI-H: microsatellite instability-high; MMRd: mismatch repair-deficient; POLE: polymerase epsilon exonuclease; AMPK: adenosine monophosphate-activated protein kinase; TP53: tumor protein p53; HbA1c: glycosylated hemoglobin; CD36: cluster of differentiation 36; HPV: human papillomavirus.

Gaps and future directions

Need for integrated immunometabolic modeling

Existing studies have mainly investigated immune dysregulation and metabolic reprogramming in isolation, resulting in fragmented mechanistic insights into tumor–immune interactions. This compartmentalization limits the understanding of the TME as an integrated, adaptive system. Emerging spatial multiomics platforms, including spatial transcriptomics and metabolomics, provide an opportunity to overcome this limitation by simultaneously mapping immune localization, metabolic gradients, and functional states within intact tumor architecture. ⁴⁷

Limited temporal and functional resolution of tumor metabolism

A key limitation of existing studies is their predominantly static design, which fails to capture dynamic metabolic adaptation and immune evolution during tumor progression and therapy. Immunometabolic states are inherently time-dependent; however, most datasets rely on single time-point analyses that may overlook therapy-induced metabolic rewiring and immune escape trajectories. ³⁷ Future work should prioritize longitudinal sampling strategies using minimally invasive platforms such as circulating tumor DNA (ctDNA) and exosome-based liquid biopsies. When integrated with functional metabolic imaging and patient-derived organoid co-cultures, these approaches could enable real-time tracking of metabolic flux and immune exhaustion under therapeutic pressure.

Toward predictive and precision immunometabolic frameworks

An important future direction is the development of predictive models that integrate metabolic, immune, and molecular tumor features into unified frameworks for therapy guidance. Although still investigational, artificial intelligence and machine learning approaches offer powerful tools for integrating heterogeneous datasets, including genomic, metabolomic, and immunologic profiles, to predict treatment response and resistance. ⁴⁸

Similarly, digital twin models, computational simulations that replicate an individual patient's tumor biology using multiomics and clinical data, may enable in silico testing of therapeutic scenarios and improve treatment stratification.^49,50

Crucially, future biomarker-driven trials should also define optimal sequencing and timing of metabolic interventions, including whether low-cost metabolic agents can enhance response when used as priming strategies before or alongside immune checkpoint blockade.

Limitations

This review has certain limitations. As a narrative review, this synthesis is subject to selection bias and does not include formal methodological quality assessment or quantitative meta-analysis. The included evidence is based on mechanistic studies, preclinical models, observational cohorts, and clinical trials with considerable heterogeneity across gynecologic cancer subtypes. In addition, many proposed biomarkers and immunometabolic therapeutic strategies remain insufficiently validated for routine clinical application.

Key clinical messages

• Immunometabolic dysregulation is a shared hallmark across gynecologic cancers.

• Cervical cancer is predominantly driven by HPV-mediated metabolic immune remodeling.

• Ovarian cancer depends heavily on lipid metabolism within the omental microenvironment.

• Endometrial cancer is strongly linked to obesity-associated insulin and inflammatory signaling.

• Most metabolic therapeutic strategies remain investigational.

• Future precision oncology will require integrated immune–metabolic biomarkers.

Conclusion

Gynecologic cancers are increasingly recognized as diseases driven by coordinated immunometabolic reprogramming rather than genetic alterations alone. Across cervical, ovarian, and endometrial cancers, metabolic adaptations, including enhanced glycolysis, altered lipid utilization, and hypoxia-driven signaling, interact with immune dysfunction to promote tumor progression, immune escape, and therapeutic resistance. This review highlights immunometabolism as a unifying framework linking tumor–intrinsic metabolic remodeling with systemic host metabolic status and TME dynamics. Repurposed metabolic interventions, including metformin administration, along with lifestyle-based strategies, may offer scalable adjunctive approaches, particularly in resource-limited settings.

Future progress will require the integration of static datasets into prospective studies incorporating spatial and longitudinal profiling. Real-time assessment of metabolic flux combined with immune phenotyping will be essential to define predictive biomarkers and clinically actionable pathways. Validating low-cost, system-level metabolic strategies may help reduce global disparities and support more precise, biology-driven cancer care.

Data availability statement

No new data were generated or analyzed in this study. All information reviewed and discussed in this article was derived from previously published literature, which is appropriately cited in the manuscript.