Jingyu Wang
Yuxuan He1,2†
Zhouluo Wang- 1Department of Sport Leisure, Sungshin Women’s University, Seoul, Republic of Korea
- 2College of Education and Arts, Jiujiang Polytechnic University of Science and Technology, Jiujiang, China
- 3School of Philosophy and Sociology, Jilin University, Changchun, China
- 4Department of Exercise Prescription, Dongshin University, Jeollanam-do, Republic of Korea
- 5Department of Exercise Therapy, Gachon University, Seoul, Republic of Korea
- 6Department of Physical Education, College of Education, Korea University, Seoul, Republic of Korea
Objective: Angiogenesis plays a central role in tumor growth, progression, and treatment response. The primary aim of this systematic review and meta-analysis was to determine the effects of regular exercise on angiogenesis biomarkers in patients with cancer.
Methods: Randomized controlled trials (RCTs) were searched across five databases up to July 2025. Eligible studies included adults with cancer (≥18 years), exercise interventions lasting more than four weeks, and at least one reported angiogenesis biomarker. The Cochrane risk of bias tool was used to assess RCT quality, and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach was applied to evaluate evidence certainty. Meta-analysis and meta-regression were performed using robust variance estimation.
Results: Thirteen RCTs were included. Pooled estimates suggested a small, nonsignificant difference in circulating VEGF favoring exercise (d = −0.14, p = 0.36). Similarly small, nonsignificant differences were observed for VCAM-1 (d = 0.24, p = 0.15) and MCP-1 (d = −0.20, p = 0.36).
Conclusion: Regular exercise was associated with only small and nonsignificant changes in angiogenesis biomarkers in patients with cancer. Although these preliminary patterns suggest potential interactions with angiogenic and immune pathways, the low certainty of evidence limits firm conclusions. Future randomized controlled trials are needed to confirm these effects and clarify how exercise prescriptions at different treatment stages influence angiogenesis.
Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420251088092.
1 Introduction
Angiogenesis is an essential physiological process in human embryonic development and tissue repair, but it can be abnormally activated or disrupted in many diseases (1). In tumors, persistent hypoxia and the upregulation of pro-angiogenic factors drive abnormal angiogenic activation (2). As tumor volume increases and metabolic demands rise, angiogenesis not only sustains tumor growth but also promotes invasion, immune evasion, and distant metastasis through aberrant vascular structures (3). Compared with solely inhibiting angiogenesis, current research has shifted toward restoring vascular function to achieve vascular normalization, thereby slowing tumor progression and enhancing the efficacy of anticancer therapies (4).
Multiple molecules within the tumor microenvironment (TME) regulate angiogenesis, including growth factors such as vascular endothelial growth factor (VEGF), adhesion molecules, and matrix metalloproteinases (3). VEGF is one of the key drivers of tumor angiogenesis and metastasis and has become a primary target of anti-angiogenic cancer therapies (5, 6). It is markedly upregulated in many solid tumors and induces a disorganized and highly permeable vascular network that supports tumor growth and progression (7).
Based on this biological foundation, growing evidence has highlighted the clinical significance of VEGF in cancer risk, screening, and prognosis. For cancer risk, Mendelian randomization studies have suggested a causal relationship between circulating VEGF levels and the incidence of colorectal cancer and colon adenocarcinoma (8). For diagnostic purposes, systematic review evidence indicates that circulating VEGF shows moderate diagnostic accuracy for ovarian cancer (9). For prognosis, several systematic reviews and meta-analyses have shown that elevated circulating VEGF is closely associated with overall survival or disease-free survival in patients with colorectal cancer, lung cancer, intrahepatic cholangiocarcinoma, ovarian cancer, breast cancer, and hepatocellular carcinoma (10–15).
In the therapeutic context, VEGF has gradually evolved from a pro-angiogenic factor to a potentially actionable biomarker with predictive value. Emerging phase III trial data suggest that serum VEGF-A and its isoforms may help identify patients with metastatic non-squamous non-small-cell lung cancer who could benefit from bevacizumab combined with immunotherapy (16). Similarly, in breast cancer, VEGF levels have been proposed as a candidate biomarker for bevacizumab treatment (17). Across cancer types, evidence shows that adding VEGF inhibitors to standard regimens can improve survival outcomes in patients with liver metastases (18). Broader data also indicate that anti-VEGF/VEGFR therapy combined with chemotherapy can improve progression-free survival and response rates in ovarian, breast, and prostate cancers, although with some increase in adverse events (19–21). Notably, some studies have reported that the clinical implications of elevated VEGF expression may also be related to paraneoplastic manifestations (22–24). Overall, VEGF possesses a dual role as both a biological driver and a clinically relevant biomarker.Growing evidence indicates that exercise, as a complementary therapy for cancer patients, can enhance the efficacy of anticancer treatments and reduce treatment-related adverse events (25). Exercise also reprograms the TME by reshaping metabolic, immune, and vascular systems (26, 27). In terms of angiogenesis, exercise promotes vascular normalization through mechanisms such as increased pericyte coverage and enhanced vascular maturity (26, 28). Two meta-analysis based on tumor animal models suggests that exercise may improve tumor vascularization (29, 30). Another meta-analysis in cancer patients reported that exercise improved endothelial function in breast and prostate cancer survivors (31). However, most existing systematic reviews have focused on inflammatory biomarkers such as IL-6, CRP, and TNF-α, while few have provided quantitative analyses centered on angiogenesis biomarkers. Only one meta-analysis in cancer patients included VEGF as an inflammatory marker, but it did not provide an in-depth interpretation of changes in VEGF (32). Therefore, the effects of exercise on angiogenesis biomarkers in patients with cancer remain insufficiently understood.
Therefore, this study aimed to include RCTs that reported exercise interventions of at least four weeks in cancer patients to systematically evaluate the effects of regular exercise on angiogenesis biomarkers. VEGF was the primary outcome, other angiogenesis biomarkers as secondary outcomes. Meta-analysis and meta-regression were conducted using the robust variance estimation (RVE) method. Compared with traditional approaches, RVE is better suited for handling the dependency of multiple correlated effect sizes reported within the same study and remains robust under small-sample conditions (33, 34). The specific objectives were (1): to synthesize effect sizes using RVE to determine the impact of exercise interventions on angiogenesis biomarkers in cancer patients; and (2) to conduct prespecified univariable RVE meta-regressions on exercise-related moderators to explore the potential moderating effects of intervention characteristics on angiogenesis biomarkers.
2 Methods
This systematic review was registered in PROSPERO (registration number: CRD420251088092) and conducted in accordance with the PRISMA guidelines (Supplementary Material 1) (35).
2.1 Data sources, literature search, and eligibility criteria
A systematic search of five databases was performed from their inception to July 6, 2025: PubMed (MEDLINE), Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), SPORTDiscus, and Web of Science. In addition, reference lists of included articles and prior systematic reviews or meta-analyses were screened to identify relevant studies. The search strategy was developed based on the PICOS framework (Supplementary Material 2).
The inclusion criteria were as follows: population, cancer patients aged 18 years or older; intervention, exercise interventions lasting at least four weeks, including weight loss programs with explicitly reported exercise prescription parameters; comparator, non-exercise control groups, including usual care, health education, or waitlist controls; outcomes, at least one circulating angiogenesis-related biomarker; study design, randomized controlled trials (RCTs).
The exclusion criteria were as follows: population, cancer patients younger than 18 years; intervention, rehabilitation training, mind-body exercise such as yoga or tai chi, and exergames; comparator, no non-exercise control group; outcomes, biomarkers primarily representing systemic inflammation such as IL-6 and IL-8, and studies that did not report circulating angiogenesis biomarkers; study design, systematic reviews, narrative reviews, animal studies, and non-English publications. Studies were also excluded if the full text or relevant data could not be obtained after contacting the authors.
We a priori excluded mind–body exercise (e.g., yoga, tai chi), exergames, and rehabilitation programs. These modalities combine low-intensity activity with substantial meditative or cognitive components, leading to biological mechanisms and dose characteristics that differ from structured aerobic and resistance training. Existing evidence also suggests that mind–body exercise may have limited and uncertain effects on circulating inflammatory biomarkers in cancer populations (36). In addition, their wide variability in movement patterns, device use, and training dose makes key exercise parameters difficult to harmonize for quantitative synthesis. To preserve conceptual and methodological consistency and to align with guideline-based exercise prescriptions, this review focused on conventional aerobic and resistance training.
2.2 Study selection and data extraction
Two authors (YX H and ZQ W) independently reviewed relevant studies to identify potentially eligible trials that met the inclusion criteria. Disagreements were resolved by a third senior author (JY W). The following study characteristics were extracted: basic study information (authors, registration number, etc.), outcome measures (excluding follow-up), and moderators. For studies that reported data in formats other than mean ± SD (such as median, interquartile range, or standard error), data were converted according to the Cochrane Handbook for Systematic Reviews of Interventions (37).
2.3 Risk of bias and quality assessment
The Cochrane risk-of-bias tool (RoB 2) was used to assess the risk of bias in the included RCTs (38). Two authors (YX H and ZQ W) independently conducted the assessment, and disagreements were resolved by a third author (JY W). Inter-rater agreement reached an acceptable level (Kappa = 0.91). Given the nature of exercise interventions, “blinding of participants and personnel” was considered low risk. The certainty of evidence was assessed using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) framework (39).
2.4 Data analysis
All statistical analyses were performed using R (version 4.4.1). Cohen’s d and its variance were used to pool effect sizes. A d value of 0.2 indicated a small effect, 0.5 a medium effect, and 0.8 a large effect (40). When trials reported multiple relevant comparisons (e.g., combined vs. control, aerobic vs. control) or multiple biomarkers within the same construct, we derived separate effect sizes for each. The RVE method was applied using the “robumeta” package. Regression models adopted a correlated effects structure, assuming within-study correlation of 0.5 (model weight = “CORR,” rho = 0.5). The rho parameter represents the assumed correlation among effect sizes derived from the same study and is required to model within-study dependence in the RVE framework. An RVE meta-regression model without covariates was used to estimate the overall mean effect size. Sensitivity analyses were conducted by varying rho from 0.1 to 0.9 in increments of 0.1 (e.g., rho = 0.1, 0.2). Between-study heterogeneity was assessed using the I² statistic, with values above 25% indicating low heterogeneity, above 50% moderate heterogeneity, and above 75% high heterogeneity (41). Twelve potential moderators, including exercise type and session intensity, were tested in univariable RVE meta-regression models, with I² and R² recorded for each model. In RVE models, R² represents the proportion of heterogeneity explained by the moderator, and the model with an R² closest to 1 was considered optimal (42). Publication bias was examined using Egger’s test in the “metafor” package, and the trim-and-fill method was applied to adjust for potential bias and generate corrected funnel plots. Data visualization was performed using the “ggplot2” package.
3 Results
A total of 2347 abstracts were initially identified, and data from 13 RCTs were finally included. The complete screening and selection process is shown in Figure 1.
Figure 1. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram of record identification, screening, and selection processes.
3.1 Overview of included studies and meta-analysis results
Details of the included RCTs and reported outcomes are provided in (Supplementary Material 3). Independent effect sizes were extracted for RCTs with multiple intervention arms. Meta-analysis and meta-regression were conducted for VEGF, while only meta-analyses were performed for ICAM-1, VCAM-1, MCP-1, and VEGF-C. Due to the small number of RCTs (≤3) reporting other angiogenesis biomarkers such as FGF and PlGF, no meta-analyses were performed for these outcomes. (Supplementary Material 4) (Supplementary Table S1) reports the RVE model parameters. Supplementary Material 4 (Supplementary Table S2) presents the results of a sensitivity analysis in which rho was varied from 0.1 to 0.9 in increments of 0.1, showing that changes in rho did not affect the model results. One RCT reported exercise-related adverse events, mainly pain (joint pain, back pain, and muscle pain), flu-like symptoms, and foot blisters (43). No study reported serious exercise-related adverse events.
3.1.1 Effects of exercise on VEGF
9 RCTs involving 484 cancer patients reported the effects of exercise interventions on VEGF (VEGF-A) (44–52). A total of 11 effect sizes were generated, as two multi-arm RCTs reported separate effects for combined training (aerobic exercise combined with resistance training) and aerobic exercise (AE) (48, 52). Figure 2 shows that the overall effect of exercise interventions on VEGF was small and nonsignificant (ES = -0.14, SE = 0.14, p = 0.36, I² = 45.04%).
Figure 2. Forest plot for VEGF.
Exercise modalities included combined training (n = 6) and AE (n = 5). Timing of intervention was before treatment (n = 2), during treatment (n = 3), and after treatment (n = 6). Breast cancer was the most common cancer type (n = 6). One study included only overweight breast cancer patients (49). One study reported that some participants had hypertension as a comorbidity (47). Two studies did not report cancer stage (47, 48). In the remaining studies, cancer stage was not restricted to a specific category, with stages I–III being the most commonly reported.
3.1.2 Effects of exercise on ICAM-1, VCAM-1, MCP-1, and VEGF-C
4 RCTs, including two multi-arm studies and a total of 172 cancer patients, reported the effects of exercise interventions on ICAM-1 and VCAM-1 (43, 46, 50, 53). 3 RCTs, including one multi-arm study and 115 patients, reported effects on MCP-1 (46, 48, 50). 3 RCTs, including one multi-arm study and 155 patients, reported effects on VEGF-C (46, 52, 54).
As shown in Figure 3, the overall effects of exercise on circulating ICAM-1, VCAM-1, and MCP-1 in cancer patients were small and nonsignificant. Specifically, VCAM-1 (ES = 0.24, SE = 0.09, p = 0.15, I² = 0%) and MCP-1 (ES = -0.20, SE = 0.16, p = 0.36, I² = 0%) reached small effect sizes but were not significant. VEGF-C (ES = 0.07, SE = 0.06, p = 0.38, I² = 0%) and ICAM-1 (ES = 0.05, SE = 0.19, p = 0.81, I² = 47.52%) showed negligible and nonsignificant pooled effect sizes.
Figure 3. Forest plot for other angiogenesis biomarkers.
3.1.3 Other angiogenesis biomarkers
Supplementary Material 4 (Supplementary Figure S1) present the effect sizes (Cohen’s d) for angiogenesis biomarkers that were reported in fewer studies. Within the VEGF family, these included VEGF-D (n = 1), PlGF (n = 3), VEGFR-1 (n = 1), and VEGFR-2 (n = 3). Other angiogenesis-related cytokines included PDGF (n = 2), PDGF subunit B (n = 2), PDGF-BB (n = 1), EGF (n = 2), HGF (n = 2), and FGF (n = 3). The ANG family included ANG (n = 2), ANG-1 (n = 2), and ANG-2 (n = 2).
3.2 Meta-regression results
Meta-regression results for VEGF are reported in Supplementary Material 5, Table S3 details the extraction methods for 12 moderators. Parameters of the univariable RVE regression models are presented in Supplementary Table S4, with corresponding visualizations shown in Supplementary Figure S2. Among these, the supervision model explained 99% of heterogeneity (R² = 0.99, I² = 0.65%). BMI (R² = 0.49, I² = 29.48%), intervention duration (R² = 0.11, I² = 41.98%), and combination with other interventions (R² = 0.11, I² = 42.04%) also partially accounted for between-study heterogeneity. Notably, the treatment-timing model showed that exercise interventions delivered before cancer treatment (ES = -0.17) and during treatment (ES = -0.23) were more effective than those delivered after treatment (ES = 0.14).
3.3 Risk of bias, publication bias, and quality of evidence
Risk of bias was assessed using the RoB 2 tool. The main sources of bias included lack of preregistration, failure to report methods for handling missing data, and insufficient detail regarding exercise interventions (Supplementary Material 6, Figures S3, S4). Egger’s tests for the five outcomes yielded p values > 0.05, suggesting no significant publication bias (Supplementary Material 6, Figure S5). The certainty of evidence was assessed using the GRADE framework, with overall ratings ranging from low to very low (Supplementary Material 6, Table S5).
4 Discussion
This systematic review indicates that regular exercise is associated with only small and statistically nonsignificant changes in circulating angiogenesis biomarkers, including VEGF, VCAM-1, and MCP-1, in patients with cancer. Given the low certainty of evidence and the fact that these biomarkers were secondary outcomes in most included trials, these results should be interpreted cautiously. To our knowledge, this is the first systematic review and meta-analysis to examine the effects of regular exercise on angiogenesis biomarkers in this population. The following sections contextualize these preliminary findings and discuss their potential implications.
4.1 Effects of regular exercise on circulating VEGF in patients with cancer and its clinical implications
Our meta-analysis showed a small and statistically nonsignificant reduction in circulating VEGF among patients with cancer. Notably, this pattern differs from that observed in healthy individuals. Recent meta-analyses report that acute aerobic exercise increases circulating VEGF and VEGF mRNA levels in healthy populations (55, 56), and regular exercise can also elevate circulating VEGF in healthy older adults (57). Although elevated circulating VEGF in cancer is associated with tumor progression and poorer prognosis, it remains unclear whether the small, nonsignificant differences observed here reflect any meaningful biological response. Consequently, any hypothesis that exercise-induced VEGF changes may influence vascular function or treatment response should be regarded as preliminary and requires confirmation in rigorously designed clinical trials.
We further observed that this decrease in VEGF appeared to vary by treatment stage. Regression analyses indicated that exercise implemented before (ES = −0.17) or during cancer treatment (ES = −0.23) showed more negative VEGF estimates, whereas post-treatment survivors demonstrated a response pattern more consistent with healthy individuals (ES = 0.14). Although these differences were small and statistically nonsignificant, they suggest that exercise may exert a stronger modulatory influence on the angiogenic environment during active disease and could potentially interact with standard anticancer therapies. Future adequately powered studies are needed to determine whether treatment phase modifies the VEGF response to exercise.
Regarding exercise prescription parameters, our regression analyses revealed several noteworthy patterns. Combined aerobic and resistance training (effect size = −0.22) showed more negative VEGF estimates than aerobic exercise alone (effect size = −0.04). This aligns with findings from a network meta-analysis in cancer patients showing that combined aerobic and resistance training was superior to single-modality exercise in reducing inflammatory markers such as IL-6, IL-8, IL-10, TNF-α, and CRP (36). Moreover, two three-arm RCTs conducted during and after breast cancer treatment reported that combined aerobic and resistance training had greater effects on VEGF than aerobic exercise alone (48, 52). We also observed that higher exercise intensity may be a key driver of VEGF reduction, whereas increasing duration or total volume did not produce similar effects. This pattern is consistent with another meta-regression examining the effects of exercise on circulating IL-6 concentrations in cancer patients (58).
Although these prescription-related findings require validation in larger trials, they are broadly consistent with current exercise guidelines for cancer patients, which recommend combined aerobic and resistance training and at least 75 minutes of vigorous aerobic exercise per week (59, 60). In addition, our findings suggest that supervised training may yield more stable effects, underscoring the importance of exercise quality and adherence in practice. Importantly, none of the included RCTs reported serious exercise-related adverse events, supporting the safety of structured exercise interventions before and during cancer treatment.
Although our review did not include mind–body exercise interventions, emerging preliminary evidence suggests that such approaches may also influence angiogenesis-related biomarkers in cancer populations. For example, a randomized controlled trial in men with prostate cancer (NCT02620033) reported that a 12-week perioperative yoga program (two 60-minute sessions per week) significantly reduced MCP-1 and increased VEGF, alongside improvements in quality of life (61). Similarly, a conference abstract in breast cancer survivors described that a 16-week therapeutic yoga program was associated with post-intervention differences in VEGF-A (+45.51%) and MCP-1 (−19.79%) (62). These early findings illustrate the diversity of movement-based approaches that could interact with angiogenesis-related pathways, although their heterogeneity and limited methodological detail preclude direct comparison with structured aerobic or resistance training.
4.2 Mechanisms underlying the regulation of circulating VEGF by regular exercise in patients with cancer
Although direct mechanistic evidence from clinical studies is scarce, preclinical research provides several biologically plausible pathways through which exercise might influence VEGF regulation. Across various tumor models, regular exercise has been shown to downregulate VEGF expression in tumor tissues or in circulation (63–66). Further evidence indicates that regular exercise can reduce VEGF expression within breast tumor tissue, thereby decreasing angiogenesis and tumor growth (67). During chemotherapy, aerobic exercise can enhance tumor perfusion and induce vascular remodeling, improving chemotherapy efficacy (68). However, animal findings remain heterogeneous. Some studies have reported that exercise increases VEGF without altering tumor growth (69), and others have proposed that exercise-induced elevations in VEGF may help alleviate tumor burden (70). These diverse findings point to the existence of multiple interacting mechanisms.
In cancer patients, elevated VEGF primarily originates from tumor cells, tumor-associated macrophages (TAMs), and neutrophils (71, 72). Animal studies consistently show that regular exercise reduces TAM and neutrophil infiltration in tumor tissue and decreases the expression of M2 macrophage–associated markers (73–76). Although human evidence remains limited, one RCT in breast cancer survivors reported changes in adipose tissue macrophage phenotype following combined exercise training (77). These observations suggest that immune-cell pathways may contribute to VEGF modulation.
Inflammatory regulation may represent another potential pathway. Regular exercise reduces circulating pro-inflammatory cytokines such as IL-6 and TNF-α in cancer patients (36, 58). Both IL-6 and TNF-α have been shown to promote tumor angiogenesis by upregulating VEGF through signaling pathways such as STAT3 and NF-κB (78–82). Several human studies also support this inflammation–angiogenesis axis, reporting positive correlations between IL-6 or TNF-α and circulating VEGF levels in patients with breast cancer, small cell lung cancer, and melanoma (83–85). In contrast, the anti-inflammatory cytokine IL-10 suppresses VEGF production from macrophages and tumor cells and inhibits VEGF-dependent endothelial proliferation (86–88). Acute exercise induces a transient IL-6 response that promotes IL-10 secretion, while long-term training is associated with more stable anti-inflammatory adaptations (36). These observations raise the possibility that the anti-inflammatory effects of exercise may contribute to reductions in VEGF.
Adipose tissue, an important source of VEGF, may constitute another mechanism underlying exercise-induced VEGF regulation. Animal evidence shows that obesity increases circulating TNF-α and VEGF levels (89, 90). Moreover, adipose-derived VEGF strongly promotes angiogenesis and tumor growth in breast cancer and reduces the effectiveness of anti-VEGF therapies (91, 92). Exercise-induced improvements in adipose tissue quantity and quality are well established (28). An RCT in overweight women further showed that exercise-related weight loss was significantly associated with reductions in circulating VEGF, with the greatest decreases observed among those who achieved at least 10% weight loss during long-term follow-up (93, 94).
Hypoxia-inducible factors (HIFs) may represent another potential pathway. HIFs are key transcription factors mediating cellular responses to hypoxia, upregulating VEGF expression to promote angiogenesis under hypoxic conditions (95, 96). HIF-1α and HIF-2α are widely overexpressed in multiple tumor types (97, 98). Animal studies indicate that TNF-α can activate HIF through the NF-κB pathway, and HIF upregulation further drives VEGF production (96). Inhibition of HIF-1α reduces VEGF expression and suppresses tumor cell proliferation and invasion (99). Some animal models have shown that aerobic exercise decreases HIF-1α and VEGF-A expression in tumor and adipose tissues (67, 100, 101). However, almost no clinical trials have directly evaluated whether exercise modulates HIF activity in patients with cancer.
Taken together, these pathways offer biologically plausible frameworks through which exercise might influence VEGF regulation. However, given the limited mechanistic evidence in humans, these explanations should be regarded as exploratory and require targeted clinical investigation.
4.3 Effects of regular exercise on other angiogenesis biomarkers in patients with cancer
Although VEGF was the primary focus of this study, our findings indicate that regular exercise may also be associated with small shifts in other angiogenesis-related molecules.
We observed a slight increase in circulating vascular cell adhesion molecule-1 (VCAM-1) following regular exercise in patients with cancer. VCAM-1 is a key adhesion molecule involved in endothelial activation and angiogenesis (102). Traditionally, VCAM-1 upregulation has been thought to promote tumor angiogenesis and metastasis (103). However, emerging evidence challenges this view. Some studies have shown that VEGF may suppress VCAM-1 expression to facilitate tumor immune evasion (104). In pancreatic ductal adenocarcinoma, restoring VCAM-1 expression enhances T-cell infiltration, inhibits tumor growth, and is associated with longer survival (105). Additionally, recent animal evidence reports that aerobic exercise improves tumor vasculature and increases VCAM-1 expression in two melanoma models, accompanied by enhanced immune mobilization (106). Taken together, these findings suggest that exercise-related changes in VCAM-1 may reflect immune-activating rather than purely pro-angiogenic processes.
We also observed a reduction in circulating monocyte chemoattractant protein-1 (MCP-1) following regular exercise. MCP-1 is an important regulator within the chemotactic–inflammatory–angiogenic axis, although its value as a clinical biomarker remains inconsistent across studies (107, 108). Recent animal research indicates that MCP-1 promotes the upregulation of HIF-1α, thereby driving VEGF expression (109). In cancer patients, MCP-1 levels are positively correlated with both VEGF and TNF-α concentrations (110). Our findings align with those of a clinical study in esophageal adenocarcinoma, which reported that exercise attenuated the chemotherapy-related rise in MCP-1 (111). Given MCP-1’s upstream position in inflammatory–angiogenic signaling, its reduction could plausibly contribute to the VEGF patterns observed in our analysis.
Overall, these response patterns suggest that exercise may influence angiogenesis-related and immune-regulatory pathways beyond VEGF alone. However, these findings are preliminary, and confirmation will require rigorously designed, mechanism-oriented clinical trials.
4.4 Limitations and future directions
This study is not without limitations. First, due to the limited number of available RCTs, we excluded angiogenesis biomarkers originally planned for analysis, such as angiopoietins (ANG), which constrained our ability to characterize the broader angiogenesis network. Second, variability in outcome measurement and reporting of exercise prescription parameters (intensity, frequency, supervision, etc.) across studies may have affected the comparability of results. Third, given the limited number of eligible trials, random-effects estimates in both the primary analysis and RVE models may be unstable, potentially affecting the precision of the pooled effects. Fourth, part of our mechanistic interpretation was based on animal evidence, which should be regarded as exploratory and hypothesis-generating rather than confirmatory. Fifth, heterogeneity in treatment regimens and patient characteristics across cancer types may have contributed to differences in intervention effects. Sixth, in most RCTs included in this review, angiogenesis biomarkers were not reported as primary outcomes, this may have led to unstable effect estimates and limited interpretability. In addition, mind–body interventions (e.g., yoga, tai chi) were not included, which may limit the comprehensiveness of the exercise modalities evaluated. Therefore, given that the certainty of evidence based on our GRADE assessment was rated as “Low” or “Very Low,” these findings should be interpreted with caution in clinical practice.
Future research should address several directions. First, upcoming RCTs should designate angiogenesis biomarkers as primary or key secondary outcomes. Second, mediation analyses are needed to test mechanistic pathways linking inflammatory cytokines and HIF signaling to VEGF regulation. Third, studies should investigate whether the exercise-related increase in VCAM-1 reflects enhanced immune mobilization and contributes to a more favorable tumor microenvironment. Fourth, the independent and interactive effects of adipose tissue quantity and function on VEGF should be clarified. Fifth, future trials should stratify participants by cancer type, treatment stage, regimen, and metabolic phenotype (e.g., BMI) to identify treatable subgroups. Sixth, dose–response relationships of exercise intensity on angiogenesis biomarkers warrant investigation. Seventh, integrating exercise with conventional therapies may help evaluate the translational potential of exercise-induced vascular normalization. Additionally, harmonizing biomarker assay procedures would improve comparability across studies. Future work should also assess mind–body exercise modalities within the same analytical framework.
5 Conclusion
Regular exercise was associated with only small and nonsignificant changes in angiogenesis biomarkers in patients with cancer. These preliminary patterns suggest possible interactions with angiogenic and immune pathways, but the low certainty of evidence limits firm conclusions. Future randomized controlled trials are needed to confirm these changes and examine how exercise prescriptions at different treatment stages influence these biomarkers.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.
Author contributions
JW: Conceptualization, Data curation, Formal Analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. YH: Conceptualization, Data curation, Formal Analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. ZQW: Data curation, Formal Analysis, Software, Validation, Writing – original draft. ZLW: Methodology, Writing – review & editing. YS: Methodology, Writing – review & editing. K-HL: Methodology, Writing – review & editing. J-YC: Conceptualization, Methodology, Supervision, Writing – review & editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Acknowledgments
We would like to thank all the experts and scholars who provided valuable advice and help for this study.
Conflict of interest
The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2025.1705472/full#supplementary-material
Abbreviations
TME, tumor microenvironment; VEGF, vascular endothelial growth factor; ICAM-1, intercellular adhesion molecule-1; VCAM-1, vascular cell adhesion molecule-1; MCP-1, monocyte chemoattractant protein-1; RVE, robust variance estimation; RCTs, randomized controlled trials; GRADE, Grading of Recommendations, Assessment, Development, and Evaluation; TAMs, tumor-associated macrophages; FGF, fibroblast growth factor; HIF, hypoxia-inducible factor; AE, aerobic exercise; RT, resistance training.
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Keywords: angiogenesis, biomarkers, cancer, exercise, exercise prescription
Citation: Wang J, He Y, Wang Z, Wang Z, Sun Y, Lee K-H and Choi J-Y (2026) Effects of exercise on angiogenesis biomarkers in cancer patients: a systematic review and meta-analysis. Front. Immunol. 16:1705472. doi: 10.3389/fimmu.2025.1705472
Received: 15 September 2025; Accepted: 17 December 2025; Revised: 08 December 2025;
Published: 09 January 2026.
Edited by:
Leandro J Carreno, University of Chile, ChileReviewed by:
Luca Paolo Ardigò, NLA University College, NorwayEleni Kokkotou, Metropolitan Hospital, Greece
Copyright © 2026 Wang, He, Wang, Wang, Sun, Lee and Choi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jae-Young Choi, Y2hvaXlhZXlvdW5nLnAuaC5kLjE5OTRAZ21haWwuY29t; Jingyu Wang, d2FuZy5qaW5neXUuOThAcXEuY29t
†These authors have contributed equally to this work and share first authorship