Front. Nutr. Frontiers in Nutrition Front. Nutr. 2296-861X Frontiers Media S.A. 10.3389/fnut.2025.1644538 Nutrition Systematic Review Therapeutic effects and safety of resveratrol for lung cancer: an updated preclinical systematic review and meta-analysis Xiao Xiang 1 † Wu Xuanyu 1 † Li Wenyuan 1 2 You Fengming 1 3 * Guo Jing 1 * 1Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China 2Evidence-Based Traditional Chinese Medicine Center of Sichuan Province, Chengdu, China 3Cancer Institute, Chengdu University of Traditional Chinese Medicine, Chengdu, China

Edited by: Eric Gumpricht, Independent Researcher, Gilbert, AZ, United States

Reviewed by: Phiwayinkosi V. Dludla, University of Zululand, South Africa

Sontaya Sookying, University of Phayao, Thailand

 *Correspondence: Fengming You, youfengming@cdutcm.edu.cn; Jing Guo, guojing19910307@sina.com

†These authors have contributed equally to this work and share first authorship

 29 08 2025 2025 12 1644538 12 06 2025 08 08 2025 Copyright © 2025 Xiao, Wu, Li, You and Guo. 2025 Xiao, Wu, Li, You and Guo

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 Background

Lung cancer (LC) is the most common cause of cancer-related death worldwide, while there are limited treatment methods. Resveratrol (RESV), a natural food-derived compound, has attracted attention around the world for its anti-LC effects. However, little is known about the efficacy and safety of RESV for LC.

 Purpose

This study aimed to provide preclinical evidence for the efficacy and safety of RESV for LC, and to find the optimal dose and duration.

 Methods

In vivo studies of RESV against LC, published before 24 July 2024, were retrieved from PubMed, Embase, Web of Science, and Cochrane Library. The CAMARADES checklist was used to assess study quality. Primary outcomes were tumor volume and tumor weight. Secondary outcomes included body weight, lung metastases number, and the apoptotic cell proportion. Statistical analysis was performed using RevMan 5.3 and Stata 16.0. Dose–duration–effect model was conducted to determine the optimal dose and duration, and the toxicology of RESV was predicted through the ProTox 3.0 platform.

 Results

A total of 23 studies involving 425 animals were included. The methodological quality of included studies was medium-to-low. RESV significantly reduced tumor volume, tumor weight, and lung metastases number, and increased apoptotic cell proportion, while having no effect on body weight. High heterogeneity was observed, and subgroup analysis suggested that the heterogeneity was partly attributed to the dose of RESV. The optimal dose and duration of RESV were 30–100 mg/kg and 25–28 days, respectively. The median lethal dose of RESV was 1,560 mg/kg.

 Conclusion

RESV demonstrated a significant inhibitory effect on LC in vivo. However, the lower research quality and high heterogeneity call for more high-quality preclinical studies to be conducted. Before achieving clinical translational research on RESV, the problem of low bioavailability of RESV needs to be solved.

 lung cancer resveratrol preclinical evidence systematic review meta-analysis section-at-acceptance Nutrition and Metabolism Introduction

Lung cancer (LC) led to 1,817,172 deaths in 2022, and remains the most common cause of cancer-related death (18.7% of total deaths), resulting in an enormous global social and economic burden (1). The disastrous prognosis of LC is largely due to factors, including late-stage diagnosis (2), acquired drug resistance, and low treatment tolerance (3), which significantly impede effective treatment and contribute to the high mortality rate. Indeed, various treatment strategies, including surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, have moderately improved survival outcomes in patients with LC. Yet, the overall prognosis remains poor, and the estimated 5-year survival was only 26.4% (4). Additionally, existing pharmacological treatments exhibit significant limitations. Chemotherapy is associated with notable adverse effects, including neurotoxicity, gastrointestinal disturbances, and cardiovascular toxicity (5). Targeted therapies are applicable to a specific subset of patients and are prone to inducing adverse reactions such as irreversible pulmonary fibrosis (6). Immunotherapy is effective for only a limited population and is frequently accompanied by the development of acquired resistance (7). Consequently, there is an urgent need to identify and develop efficacious, tolerable, and safe therapeutic strategies for LC.

Identifying compounds with anticancer activity in natural foods has been a topic of interest for years. Resveratrol (RESV), a phenolic compound found in many plants such as grapes, blueberries, and peanuts, exhibits various pharmacological and biological activities, including anti-aging, anti-inflammatory, antioxidant, antifibrotic, and anticancer effects (8, 9). Indeed, RESV has shown potential in treating a wide range of cancers, including lung, breast, colorectal, renal, liver, and bladder cancers (10–15) (Figure 1). In clinical trials, RESV has demonstrated promising properties for colorectal, breast, and prostate cancers (16). Unfortunately, no clinical study has yet confirmed whether RESV is effective in patients with LC, although numerous in vivo and in vitro studies have explored its anti-LC effects. In vitro, RESV treatment increased the chemical sensitivity of A549 cells to cisplatin (10). In vivo, RESV can inhibit LC progression by suppressing the activation of tumor-associated macrophages (17). Nevertheless, the protective effect of RESV against LC in animals has not been systematically reviewed, and its mechanism remains unknown.

Anti-cancer effects of RESV (created using BioRender.com).

Diagram illustrating the effects of RESV on different organs and cancer types. The top section shows RESV connected to the kidney, colorectal area, breast, liver, and bladder. The bottom section explains RESV’s in-vivo effect in suppressing tumor-associated macrophages to inhibit lung cancer and its in-vitro effect of increasing chemotherapy sensitivity of A549 cells to cisplatin.

A systematic review and meta-analysis of preclinical studies provides valuable insights into the reliability of preclinical studies and facilitates the transition from animal to clinical trials (18). The absence of clinical trials investigating RESV for LC treatment currently precludes robust evaluation of its therapeutic efficacy and safety in human patients. Nevertheless, a comprehensive meta-analysis of preclinical studies may yield valuable therapeutic insights and critical preliminary guidance for future clinical translation. A meta-analysis published in 2016 reported RESV significantly reduced the incidence of LC by 36% in vivo (19). With the publication of numerous studies focusing on RESV against LC, it is necessary to conduct an updated meta-analysis to improve the accuracy of estimated effects. Herein, we systematically reviewed the effects of RESV in LC animals in order to provide preclinical evidence for subsequent clinical trials. We hope RESV may become a promising natural treatment for LC in the future.

 Materials and methods Study registration

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline (20), and has been registered on PROSPERO (CRD42024569393).

 Search strategy

PubMed, Embase, Web of Science, and the Cochrane Library were searched by two authors (Xiang Xiao and Xuanyu Wu) to identify relevant studies. The search period was from the establishment of the database to 24 July 2024. The search was carried out by using MeSH combined with free words (Supplementary Table 1). Additionally, we searched the references of included studies to collect other potential studies.

 Study selection

Two authors (Xiang Xiao and Wenyuan Li) selected studies following the PRISMA guidelines using EndNote X9 software. Selection results were cross-checked to ensure consistency, and a third investigator (Jing Guo) was consulted to resolve disagreements. We screened titles and abstracts to exclude irrelevant and non-English studies after duplicate papers were eliminated through electronic and manual-based steps. Full texts of the remaining studies were then reviewed to confirm final eligibility.

Based on the PICOS principle, the inclusion criteria were as follows: (1) participants (animals): LC model animals, including orthotopic tumors and ectopic transplanted tumors, without limitation of species, sex, or age; (2) intervention: the intervention group was treated with RESV, and the dose and duration were clarified; (3) comparator: the control group was treated with placebo or saline; (4) outcome measure: the primary outcomes were tumor volume and tumor weight, and the secondary outcomes were lung metastases number, body weight, and apoptotic cell proportion; and (5) study design: only in vivo animal studies with separate treatment groups were eligible.

The exclusion criteria were as follows: (1) studies that were not in vivo animal studies, such as clinical studies, in vitro studies, in silico studies, reviews, letters, conference papers, abstracts, and editorials; (2) studies with missing data; (3) studies in which the intervention group received RESV derivatives or analogs; and (4) studies without any pre-set outcomes.

 Data extraction

Two authors (Xiang Xiao and Xuanyu Wu) extracted the following information independently using Excel 2021 software: (1) article information, including first author’s name, publication year, and country; (2) animal information, including species, sex, age, and weight; (3) modeling information, including modeling method, drug/cell, route, dose, duration, and anesthetic; (4) intervention information, including dose, duration, and route; (5) sample size of both intervention and control groups; and (6) mean and standard deviation (SD) of outcome measures (final time point result). The WebPlotDigitizer1 was employed to measure graphic values. The p-values not reported in studies were calculated using the independent samples t-test (summarized data) function in the SPSS 26.0 software. An investigator (Xiang Xiao) contacted the corresponding authors to get missing data.

 Risk of bias assessment

The 10-item CAMARADES checklist (21) was employed by two authors (Xiang Xiao and Wenyuan Li) independently to evaluate the quality of included studies. The assessment criteria were as follows: (1) peer reviewed publication; (2) control of temperature; (3) random allocation to treatment or control; (4) blinded induction of ischemia; (5) blinded assessment of outcome; (6) use of anesthetic without significant intrinsic neuroprotective activity; (7) animal model (aged, diabetic, or hypertensive); (8) sample size calculation; (9) compliance with animal welfare regulations; and (10) statement of potential conflict of interests. Each item was evaluated at “high,” “low,” or “unclear” risk based on the information reported in the full text and Supplementary materials.

 Dose–duration–effect 3D model

The Origin 2021 software was used to display 3D models of the dose and duration of RESV and the primary outcome measures (tumor volume and tumor weight).

 RESV toxicology prediction

The toxicity assessment of RESV was insufficient in the included studies. Herein, ProTox 3.0,2 an online platform, was employed to predict the toxicology of RESV. The organ toxicity, carcinogenicity, immunotoxicity, mutagenicity, cytotoxicity, blood–brain barrier (BBB) permeability, ecotoxicity, clinical toxicity, and nutritional toxicity were predicted.

 Statistics analysis

The RevMan 5.3 and Stata 16.0 were used for meta-analysis. The weighted mean difference (WMD) and 95% confidence interval (CI) were used when comparing continuous variables with consistent measurement methods and units across studies; otherwise, the standardized mean difference (SMD) and 95% CI were applied.

Heterogeneity was assessed using the I2 statistic and Cochrane’s Q test. Heterogeneity was graded as high (I2 > 75%), moderate (50% ≤ I2 ≤ 75%), or low (I2 < 50%). For Cochrane’s Q test, a p-value < 0.05 indicates significant heterogeneity. When high or moderate grade heterogeneity was observed, we first performed sensitivity analysis by sequentially removing each trial to determine if any single study contributed to the heterogeneity. If substantial heterogeneity in tumor volume persisted and its source remained unexplained after sensitivity analysis, subgroup analysis or meta-regression was performed to explore potential causes. Subgroup analysis was stratified by the dose of RESV (22). Meta-regression analysis identified eight variables as potential sources of heterogeneity: publication year, dose, duration, administration, modeling method, species, gender, and region. Variables with a p-value < 0.05 were considered significant contributors to heterogeneity. For low-grade heterogeneity, we used a fixed-effect model to combine effect sizes; otherwise, a random-effects model was employed. Funnel plots and Egger’s regression test were conducted to explore publication bias when there were more than 10 studies.

 Results Search and selection results

A total of 2,261 records were obtained through database screening, and 1,690 records were obtained after eliminating duplicates. After reading titles and abstracts, 260 records remained. After reviewing the full text of the 260 articles, 23 studies were ultimately included for meta-analysis. For different subgroups present in a study, we treated them as different experiments, and among the 23 studies, there were 30 experiments (Figure 2).

Flow chart of the study selection process.

Flowchart illustrating the study selection process. Initially, five previous studies are included. New records from databases such as PubMed, Embase, and others total 2,261. After removing 571 duplicates, 1,690 records are screened. Post-title and abstract review, 1,430 are excluded for reasons like irrelevance and non-English language. Of 260 reports sought, none are excluded for retrieval issues. Finally, 237 reports are excluded for full-text issues, leaving 23 new studies included alongside the previous five, totaling 23 studies in the review. Characteristics of included studies

Table 1 shows basic characteristics of the 23 included studies. These studies were published from 2001 to 2024. There were 211 and 214 animals in the intervention and control groups, respectively. In total, 7 studies used BALB/c mice (23–29), 6 studies used C57BL/6 J mice (17, 30–34), 5 studies used nude mice (35–39), 2 studies used laka mice (40, 41), 1 study used severe combined immunodeficient mice (42), 1 study used A/J mice (43), and 1 study used the Rowett nude rat (44). A total of 10 studies (23–25, 28, 30, 31, 35, 37, 42, 43) used female mice, and 7 studies (29, 33–35, 40, 41, 44) used male mice, while the other 7 studies (17, 26, 27, 32, 36, 38, 39) did not report the sex of animals. In addition, 4 studies established mouse models of orthotopic xenograft (40, 41, 43, 44), while the remaining studies used ectopic transplanted tumor mouse models. The dose range of RESV was from 0.23 to 3,000 mg/kg. The duration ranged from 6 to 84 days. The routes of RESV included oral gavage, intratumor injection, intranasal infusion, intravenous injection, and intraperitoneal injection.

Basic characteristics of the included studies.

Study Region Species (sex, age, n = experimental/control group) Weight (g) (experimental/control group) Model method (drug/cell, route, dose, duration) Anesthetic Intervention (experimental/control group, route) Dose, duration Outcomes p-value
Kimura et al. (30) Japan C57BL/6 mice (Female, 5, 7/7) 18.8 ± 0.40/18.1 ± 2.51 HTT (LLC, Sc, 5 × 105, once) NA RESV+LLC/LLC, Ip 0.6 mg/kg,QD, 21 days 1; 2; 3; 4 1. P > 0.05; 2. p < 0.05;3. p > 0.05; 4. p < 0.05
Kimura et al. (30) Japan C57BL/6 mice(Female, 5, 7/7) 18.5 ± 1.09/18.1 ± 2.51 HTT (LLC, Sc, 5 × 105, once) NA RESV+LLC/LLC, Ip 2.5 mg/kg,QD, 21 days 1; 2; 3; 4 1. p > 0.05; 2. p < 0.05;3. p > 0.05; 4. p < 0.05
Kimura et al. (30) Japan C57BL/6 mice(Female, 5, 7/7) 18.9 ± 0.74/18.1 ± 2.51 HTT (LLC, Sc, 5 × 105, once) NA RESV+LLC/LLC, Ip 10 mg/kg,QD, 21 days 1; 2; 3; 4 1. p < 0.05; 2. p < 0.053. p > 0.05; 4. p < 0.05
Lee et al. (31) Korea C57BL/6 J mice(Female, 5, 6/6) NA HTT (LLC, Sc, 3 × 105/100 μL, once) NA RESV+LLC/LLC, Ip 20 mg/kg,QD, 21 days 1; 2; 5 1. p < 0.001; 2. p < 0.01;5. p < 0.05
Busquets et al. (32) Spain C57BL/6 mice(NA, 12, 6/6) 24.7 ± 2.45/25.7 ± 2.69 HTT (LLC, Im, 5 × 105, once) Ketamine andxylazine RESV+LLC/LLC, Ip 5 mg/kg,QD, 15 days 1; 2; 3; 4 1. p > 0.05; 2. p > 0.053. p > 0.05; 4. p < 0.05
Busquets et al. (32) Spain C57BL/6 mice(NA, 12, 6/6) 23.9 ± 1.96/25.7 ± 2.69 HTT (LLC, Im, 5 × 105, once) Ketamine andxylazine RESV+LLC/LLC, Ip 25 mg/kg,QD, 15 days 1; 2; 3; 4 1. p > 0.05; 2. p > 0.053. p > 0.05; 4. p < 0.05
Malhotra et al. (40) India Laka mice(Male, NA, 8/9) 18–20 In situ (BaP, Ip, 100 mg/kg, once) Ether RESV+BaP/BaP, Ga 5.7 mg/kg,Q2D, 35 days 3 3. p < 0.05
Zhao et al. (23) China BALB/c mice(Female, NA, 6/6) 18–22 HTT (SPC-A-1-CDDP, Sc, 1 × 108, once) NA RESV+SPC-A-1-CDDP/SPC-A-1-CDDP, NA 1,000 mg/kg,QD, 28 days 1; 2 1. p < 0.05; 2. p < 0.05
Zhao et al. (23) China BALB/c mice(Female, NA, 6/6) 18–22 HTT (SPC-A-1-CDDP, Sc, 1 × 108, once) NA RESV+SPC-A-1-CDDP/SPC-A-1-CDDP, NA 3,000 mg/kg,QD, 28 days 1; 2 1. p < 0.05; 2. p < 0.05
Malhotra et al. (41) India Laka mice(Male, NA, 8/9) 18–20 In situ (BaP, Ip, 100 mg/kg, once) Ether RESV+BaP/BaP, Ga 5.7 mg/kg,TW, 22 days 3 3. p < 0.001
Vetvicka et al. (24) USA BALB/c mice(Female, 8, 5/5) NA HTT (LLC, Im, 5 × 105, once) CO2 RESV+LLC/LLC, Ip 5 mg/kg,QD, 15 days 4 4. p > 0.05
Yin et al. (35) China Nude mice(Male & Female, 6–8, 6/6) 18–22 HTT (A549, Sc, 5 × 106, once) NA RESV+A549/A549, Iv 15 mg/kg,QD, 15 days 1; 3 1. p < 0.05; 3. p > 0.05
Yin et al. (35) China Nude mice(Male and Female, 6–8, 6/6) 18–22 HTT (A549, Sc, 5 × 106, once) NA RESV+A549/A549, Iv 30 mg/kg,QD, 15 days 1; 3 1. p < 0.01; 3. p > 0.05
Yin et al. (35) China Nude mice(Male and Female, 6–8, 6/6) 18–22 HTT (A549, Sc, 5 × 106, once) NA RESV+A549/A549, Iv 60 mg/kg,QD, 15 days 1; 3 1. p < 0.01; 3. p > 0.05
Yu et al. (42) China SCI mice(Female, 4–6, 10/10) NA HTT (A549-FOXC2, Sc, 2 × 106, once) NA RESV+A549-FOXC2/ A549-FOXC2, Ip 20 mg/kg,QD, 42 days 1 1. p < 0.05
Yu et al. (42) China SCI mice(Female, 4–6, 10/10) NA HTT (A549, Sc, 2 × 106, once) NA RESV+A549/A549, Ip 20 mg/kg,QD, 42 days 1 1. p > 0.05
Bai et al. (36) China Nude mice(NA, 6–8, 15/15) NA HTT (H460, Sc, 3 × 107/200 μl, once) CO2 RESV+H460/H460, Ii 200 μl,B2D, 15 days 1; 2 1. p < 0.05; 2. p < 0.05
Vetvicka et al. (25) USA BALB/c mice(Female, 8, 5/5) NA HTT (LLC, Im, 5 × 106, once) CO2 RESV+LLC/LLC, Ga 5 mg/kg,QD, 14 days 4 4. p < 0.05
Li et al. (37) China Nude mice(Female, 5, 5/5) NA HTT (H460, Sc, 1 × 106/100 μl, once) NA RESV+H460/H460, Ip 30 mg/kg,Q3D, 8 days 1; 2 1. p < 0.05; 2. p < 0.05
He et al. (38) China Nude mice(NA, NA, 5/5) NA HTT (A549, Iv, 1 × 107/200 μl, once) NA RESV+A549/A549, Ip 10 mg/kg,QD, 14 days 4 4. p > 0.05
Sun et al. (17) China C57BL/6 mice(NA, 4–5, 5/5) NA HTT (LLC, Sc, 1 × 106, once) Ketamine andxylazine RESV+LLC/LLC, Ip 100 mg/kg,QD, 28 days 1; 2 1. p < 0.01; 2. p < 0.01
Monteillier et al. (43) Switzerland A/J mice(Female, 5–6, 14/14) 14–16 In situ (NNK, Ip, 50 mg/kg, QW, 2doses) NA RESV+NNK/NNK, Nasal 80 mg/kg,TW, 26 days 1; 3 1. p < 0.01; 3. p < 0.05
Zhao et al. (33) China C57BL/6 mice(Male, 6–8, 6/6) NA HTT (LLC, Sc, 1 × 106/200 μl, once) NA RESV+LLC/LLC, Ga 50 mg/kg,QD, 21 days 2 2. p < 0.01
Song et al. (26) China BALB/c mice(NA, 6–8, 8/8) NA HTT (HCC827, Sc, 1 × 106/100 μl, once) NA RESV+HCC827/ HCC827, Iv 50 mg/kg,Q3D, 6 days 1; 2 1. p < 0.05; 2. p < 0.05
Zheng et al. (27) China BALB/c mice(NA, 4–6, 5/5) NA HTT (A549, Sc, 2 × 106, once) NA RESV+A549/A549, Iv 15 mg/kg,Q3D, 15 days 1; 2 1. p < 0.001; 2. p > 0.05
Qin et al. (39) China Nude mice(NA, NA, 5/5) NA HTT (HCC827, Sc, 2 × 106/200 μl, once) NA RESV+HCC827/ HCC827, Iv 0.23 mg/kg, 5 times/week, 25 days 1; 2 1. p < 0.01; 2. p > 0.05
Wang et al. (44) China Rowett nude rats(Male, 8, 8/8) 240–260 In situ (A549, Nasal, 2 × 107, once) Sodium pentobarbital and isoflurane RESV+A549/A549, Ga 250 mg/kg,QD, 84 days 3; 5 3. p > 0.05; 5. p < 0.05
Antonio et al., 2021 Spain BALB/c mice(Female, 10, 10/10) 20 HTT (LP07, Sc, 4 × 105/200 μl, once) Sodium pentobarbital RESV+LP07/LP07, Ip 20 mg/kg,QD, 15 days 2; 3 2. p < 0.05; 3. p < 0.001
Savio et al. (34) Italy C57BL/6 J mice(Male, 4, 5/6) NA HTT (LLC, Sc, 1 × 106/400 μL, once) NA RESV+LLC/LLC, Ga 125 mg/kg,QD, 21 days 1; 2; 5 1. p < 0.05; 2. p > 0.05;5. p < 0.001
Liang et al. (29) China BALB/c mice(Male, 8–10, 5/5) 20 ± 1 HTT (A549, Sc, 5 × 106/200 μl, once) CO2 RESV+A549/A549, Ip 10 mg/kg,QD, 28 days 1; 2 1. p < 0.001; 2. p < 0.01

B2D, Twice every 2 days; BaP, benzo[a]pyrene; FOXC2, Forkhead Box C2; Ga, Gavage; HTT, Heterotopic tumor transplantation; Ii, Intratumor injection; Ip, Intraperitoneal; Iv, Intravenous injection; LC, Lung cancer; LLC, Lewis lung cancer; NA, Not available; NNK, 4-(N-methyl-N-nitrosamino)-1-(3-pyridyl)-1-butanone; QD, Quaque die; Q2D, Quaque secunda die; Q3D, Quaque tertia die; RESV, Resveratrol; Sc, Subcutaneous injection; SCI, Severe combined Immunodeficient; TW, Three times a week; 1, Tumor volume; 2. Tumor weight; 3. Body weight; 4. Lung metastases number; 5. Apoptotic cell proportion.

 Quality of included studies

According to the 10-item CAMARADES checklist, the quality scores of included studies ranged from 3 to 7, with an average score of 4.7 (Figure 3). Two studies received (29, 44) 7 points, three studies received 6 points (28, 34, 36), six studies received 5 points (17, 25, 32, 39–41), 10 studies received 4 points (23, 24, 26, 27, 31, 33, 35, 38, 42, 43), and two studies received 3 points (30, 37). All studies reported detailed information on animal models, and 22 studies reported compliance with animal welfare regulations and statements of potential conflicts of interest. However, no studies have reported the application of blindness and sample size calculation.

Risk of bias assessment of included studies.

Grid displaying methodological criteria across several studies, indicated by green circles with plus signs for fulfilled criteria and yellow circles with question marks for unclear criteria. Criteria include peer-reviewed publication, temperature control, and more. Each column represents a different study. Intervention effects Primary outcomes Tumor volume

Data from 313 mice (156 in the intervention group and 157 in the control group) from 22 experiments showed that RESV significantly reduced tumor volume of LC [SMD = −2.44, 95% CI (−3.16, −1.71), p < 0.00001]. High heterogeneity was observed among the experiments (I2 = 80%, p < 0.00001; Figure 4A).

The meta-analysis result of the effect of RESV on primary outcomes of LC. (A) Tumor volume (mm3); (B) Tumor weight (g).

Forest plot displaying two meta-analyses comparing RESV and control groups across different studies. Panel A shows studies with a total sample size of 156 in the RESV group and 157 in the control group, revealing an overall effect size of -2.44. Panel B includes a smaller sample size, with an overall effect size of -1.30. Both panels indicate statistical significance with heterogeneity values and confidence intervals. The diagrams illustrate individual study results and pooled estimates. Tumor weight

A total of 18 experiments with 241 mice (120 in the intervention group and 121 in the control group) revealed a significant reduction of tumor weight of LC [SMD = −1.30, 95% CI (−2.07, −0.52), p = 0.001]. The heterogeneity analysis showed high heterogeneity among the 18 experiments (I2 = 83%, p < 0.00001; Figure 4B).

 Secondary outcomes Lung metastases number

A total of 8 experiments with 96 mice (48 in the intervention group and 48 in the control group) indicated that RESV significantly decreased the lung metastases number of LC [SMD = −1.15, 95% CI (−1.61, −0.69), p < 0.00001]. There was low heterogeneity among the experiments (I2 = 0%, p = 0.50; Figure 5A).

The meta-analysis result of the effect of RESV on secondary outcomes of LC. (A) Lung metastases number; (B) Body weight (g); (C) Apoptotic cell proportion (%).

Forest plot with three sections (A, B, and C) comparing studies of RESV versus control. Section A shows standardized mean differences favoring control. Section B shows varied results near zero effect. Section C favors RESV. Confidence intervals and weights are displayed alongside each study. Statistical summaries for heterogeneity and overall effects are included. Body weight

A total of 13 experiments with 200 mice (99 in the intervention group and 101 in the control group) indicated that RESV increased the body weight of LC mice, while the difference was not statistically significant [SMD = 0.37, 95% CI (−0.25, 0.99), p = 0.25]. There was moderate heterogeneity among the experiments (I2 = 75%, p < 0.00001; Figure 5B).

 Apoptotic cell proportion

A total of 3 experiments with 39 mice (19 in the intervention group and 20 in the control group) indicated that RESV significantly increased apoptotic cell proportion of LC cells [SMD = 5.97, 95% CI (0.87, 11.06), p = 0.02]. High heterogeneity was observed (I2 = 88%, p = 0.0003; Figure 5C).

 Exploration of heterogeneity sources

Due to the observed heterogeneity across studies, we used tumor volume as the standardized measurement indicator. First, to assess the robustness of our findings, we performed a leave-one-out sensitivity analysis by sequentially excluding individual trials. The results indicated that after omitting each experiment one by one, the pooled effect estimate did not change significantly, suggesting that the observed heterogeneity could not be attributed to any specific experiment (Figure 6A). Subsequently, we performed subgroup analysis on 21 experiments [excluding Bai et al. (36), due to unreported RESV dosage] stratified by dose ranges, which reduced the overall heterogeneity from high (I2 = 80%, p < 0.00001) to medium (I2 = 71%, p < 0.01). The results showed that the heterogeneities were moderate in the ≤10 mg/kg (I2 = 53%, p = 0.06), 11–20 mg/kg (I2 = 64%, p = 0.02), and >100 mg/kg groups (I2 = 71%, p = 0.04), and the heterogeneities were high in the 21–30 mg/kg (I2 = 81%, p < 0.01) and the 50–100 mg/kg groups (I2 = 82%, p < 0.01; Figure 6B). We observed that the heterogeneity in the ≤10 mg/kg group was mainly caused by the result of Liang et al. (29) and the heterogeneity decreased after deleting this experiment (I2 = 0%, p = 0.77; Figure 6C). Furthermore, we conducted meta-regression analysis to explore the potential heterogeneity source (Table 2). The results suggested that the eight factors (publication year, dose, duration, administration, modeling method, species, gender, and region) were not a source of heterogeneity (p > 0.05). Consequently, the observed heterogeneity across studies may be partially attributable to variations in RESV dosage.

The subgroup and sensitivity analysis of RESV on tumor volume (mm3) of LC. (A) Forest plot of leave-one-out sensitivity analysis; (B) Forest plot of tumor volume [subgroup analysis by dose of RESV after removing Bai et al. (36)]; (C) Forest plot of tumor volume [subgroup analysis by dose of RESV after removing Liang et al. (29)].

Forest plot showing meta-analysis results. Panel A presents individual studies with their p-values, standard mean differences, and confidence intervals. Panel B categorizes studies by dosage groups with summary statistics. Panel C further breaks down results by subgroup analyses, indicating heterogeneity and overall effects. The plots feature blue squares for study estimates and diamond shapes for overall effect sizes.

Elucidate the source of the heterogeneity using meta-regression.

Factor P > |t| 95% CIs
Publication year 1.00 −0.08 0.08
Dose 1.00 −0.22 0.22
Duration 1.00 −0.23 0.23
Administration 1.00 −0.52 0.52
Modeling method 0.39 −1.41 3.41
Species 1.00 −0.33 0.33
Sex 1.00 −0.43 0.43
Region 1.00 −0.28 0.28
Publication bias

We conducted funnel plots and Egger’s test to assess publication bias of three outcomes: (1) tumor volume; (2) tumor weight; and (3) body weight. The results showed significant publication bias in 22 studies focusing on tumor volume (Pegger = 0.028; Figure 7A), and no significant bias in 18 studies focusing on tumor weight (Pegger = 0.262; Figure 7B) and 13 studies focusing on body weight (Pegger = 0.066; Figure 7C).

The publication bias analysis of RESV on LC. (A) Tumor volume (mm3); (B) Tumor weight (g); (C) Body weight (g).

Three pairs of Egger test and funnel plot graphs labeled A, B, and C. Each pair shows the relationship between study precision and effect estimate. The Egger tests include regression lines and confidence intervals, while the funnel plots display standard error against Hedges’s g with studies marked as dots. Dose–duration–effect 3D model

To identify the most effective RESV dose and duration for preclinical LC, we conducted a dose–duration–effect 3D model of primary outcomes. One study (23) with a significantly large RESV dose (1,000 and 3,000 mg/kg), which was quite different from other studies, was excluded. Additionally, one study (44) that used rats and another study (36) that reported unclear RESV dose (200 μL) were also excluded. Hence, the RESV dose used for the 3D model was from 0.23 to 125 mg/kg. The tumor volume was significantly suppressed when the dose of RESV was no less 30 mg/kg, and the treatment lasted for 25–28 days (Figure 8A). Furthermore, in studies where tumor weight was significantly reduced, the duration of RESV treatment was predominantly concentrated within 21–28 days, with doses typically ranging from 30 to 100 mg/kg (Figure 8B). Thus, the optimized dose and duration of RESV for LC were 30–100 mg/kg and 25–28 days.

The dose–duration–effect 3D model of RESV treating LC. (A) Tumor volume (mm3); (B) tumor weight (g).

Three-dimensional scatter plots labeled A and B display data points as bubbles, varying in size and color based on p-values and mean ratios (Control/RESV). The x-axis lists different studies, the y-axis represents duration in days, and the z-axis indicates dose in mg/kg. Red dots signify p-values greater than 0.05, while blue indicates less than 0.05. Bubble sizes correlate with mean ratio values, demonstrating differences across studies. Toxicology of RESV

RESV demonstrates potential nephrotoxicity (59% probability) and cardiotoxicity (51% probability), with no other organ toxicity and no toxicity endpoint shown (Table 3). Furthermore, the predicted toxicity class was 4, and the predicted LD50 of RESV was 1,560 mg/kg, which was higher than most of the doses in available studies.

Evaluation of toxicological parameters of RESV through ProTox-3.0.

Classification Target Prediction Probability
Organ toxicity Hepatotoxicity Inactive 0.74
Organ toxicity Neurotoxicity Inactive 0.77
Organ toxicity Nephrotoxicity Active 0.59
Organ toxicity Respiratory toxicity Inactive 0.57
Organ toxicity Cardiotoxicity Active 0.51
Toxicity endpoints Carcinogenicity Inactive 0.71
Toxicity endpoints Immunotoxicity Inactive 0.86
Toxicity endpoints Mutagenicity Inactive 0.92
Toxicity endpoints Cytotoxicity Inactive 0.98
Toxicity endpoints BBB Inactive 0.55
Toxicity endpoints Ecotoxicity Inactive 0.55
Toxicity endpoints Clinical toxicity Inactive 0.60
Toxicity endpoints Nutritional toxicity Inactive 0.89
Discussion Primary findings

Previous meta-analysis evaluated the effect of RESV on the incidence of LC tumors (19). However, it failed to comprehensively evaluate the efficacy and safety of RESV on LC. With the publication of a large number of preclinical studies in recent years, we conducted this updated meta-analysis to systematically evaluate the efficacy and safety of RESV for LC in multiple dimensions, including tumor volume, tumor weight, lung metastases number, body weight, and apoptotic cell proportion. Moreover, the toxicological characteristics and optimal dose and duration of RESV were explored. To be specific, 30 experiments from 23 studies were synthesized, and the quality of the included studies was moderate. RESV significantly reduced tumor volume, tumor weight, and lung metastases number, and increased the apoptotic cell proportion, while it could not improve the body weight of mice. High heterogeneities were observed in primary outcomes, and the dose of RESV may be a potential heterogeneity source. The suggested dose and duration of RESV for LC mice were 30–100 mg/kg and 25–28 days, while the LD50 of RESV was 1,560 mg/kg, indicating that RESV was safe for LC mice.

 Heterogeneity

The considerable heterogeneity observed in tumor volume may be attributable to multiple confounding variables across the included trials, including variations in experimental methodologies (modeling techniques, administration protocols, and treatment duration), pharmacological parameters (RESV dosage and formulation), and biological factors (animal species and sex), in addition to potential temporal trends reflected by publication year. A priori, we anticipated substantial heterogeneity given these methodological variations. Subsequent subgroup stratification by RESV dose partly reduced heterogeneity metrics, suggesting dosage variation as a partial explanatory factor. However, residual heterogeneity persisted despite comprehensive meta-regression analyses, implying the potential influence of unmeasured covariates. These may include inter-laboratory environmental differences, instrumentation variability, and technical artifacts in image-based data extraction processes.

 Potential mechanism of RESV for LC Decrease the viability of the LC cells

Figure 9 shows the mechanism of RESV for LC. RESV decreased the viability of LC cells through regulating cell cycle, aging, and epithelial–mesenchymal transition (EMT). RESV maintained the A549 cell cycle in the G1 phase and altered the expression of cyclin A, Chk1, CDC27, and Eg5 (45). In another study, RESV induced a concentration-dependent stagnation of the A549 cell cycle in the S phase, which was associated with the inhibition of retinoblastoma protein phosphorylation and the upregulation of the cyclin-dependent kinase inhibitor p21WAF1/CIP (46). Furthermore, RESV induced the expression of NADPH oxidase-5 in A549 and H460 cells, which promoted reactive oxygen species (ROS) production and upregulated senescence-associated β-galactosidase (SA-β-gal), p53, and p21, resulting in DNA double-strand breaks of LC cells (47). In addition, RESV increased ROS in LC cells by inducing mitochondrial dysfunction, leading to the upregulation of aging-related molecules, including p38MAPK, p27, p21, and RB (48). Moreover, RESV reduced TGF-β-induced EMT by increasing E-cadherin and decreasing fibronectin, vimentin, SNAIL, and SLUG (49). Meanwhile, RESV suppressed FOXC2, a critical regulator of EMT, by influencing miRNA-520 h-mediated signaling pathways (42).

Potential mechanism of RESV for LC (created using BioRender.com).

Diagram illustrating the effects of Resveratrol on lung cancer (LC) cells. It includes mechanisms such as decreased viability, promotion of apoptosis, regulation of the tumor immune microenvironment, angiogenesis, and enhancement of other therapies. Key pathways and interactions are represented through interconnected molecules and processes, accompanied by graphical representations of cellular activities like apoptosis, mitochondrial dysfunction, and epithelial-mesenchymal transition. A visual of lungs with a cancerous section is depicted on the right, indicating the context of lung cancer treatment. Promote apoptosis and autophagy of LC cell

Apoptosis and autophagy are gene-controlled programmed cell death and degradation pathways that contribute to maintaining cell homeostasis (50). RESV inhibited Nrf2, NQO1, and SOD1 in a dose-and time-dependent way to destroy the antioxidant pool and induce ROS, thereby promoting the apoptosis of LC cells (51). In addition, RESV promoted the transfer of apoptosis-inducing factor from cytoplasm to nucleus by increasing ROS and decreasing mitochondrial membrane potential, thereby promoting the apoptosis of LC cells (52). Meanwhile, RESV induced apoptosis by decreasing protein arginine methyltransferase 5 through inhibiting the Akt/GSK3β pathway (53). Furthermore, RESV activated caspase 8 and decreased c-FLIP, p-EGFR, and p-Akt in H460 cells, thereby promoting apoptosis (54). RESV increased NGFR mRNA expression and prolonged NGFR mRNA and protein survive, then activated AMPK-mTOR pathway to promote autophagy and apoptosis of A549 cells (55). Additionally, RESV inhibited the Akt/mTOR pathway and activated the p38-MAPK pathway by upregulating SIRT1, thereby inducing apoptosis and autophagy of LC cells (56).

 Regulate tumor immune microenvironment

The heterogeneity of the immune microenvironment is related to the progression and treatment responsiveness of cancer. RESV significantly increased the expression of programmed cell death ligand 1 in NCI-H358 cells, which plays a vital role in suppressing T-cell-mediated immune responses (57). Triacetylresveratrol, a RESV analog, is a potent SIRT2 agonist and is associated with the infiltration of multiple immune cells in LC, including CD8 + T cells, CD4 + T cells, CD4 + memory resting, regulatory T cells, and natural killer cells (58). Furthermore, RESV enhanced the cytotoxic of CD8 + T cells, the most important cytotoxic immune cells, by modulating the SLC7A11-HMMR interaction and activating ferroptosis (59). Moreover, RESV downregulated STAT3 in vitro, thereby inhibiting M2-like polarization of tumor-associated macrophages (TAMs) and suppressing the proliferation of LC cells (17). In another study, TAM (CD68+) infiltration in LC tumors, including tumor-promoting M2 macrophages (CD163+) and lymphocytes (CD3+), was significantly reduced after RESV treatment (34). In addition, RESV promoted the secretion of IL-18 by regulating TAMs in LC, and IL-18 was a key cytokine that promoted the activation of CD8 + T cells (60). RESV prevented the transformation of normal fibroblasts into cancer-associated fibroblasts (CAFs) through regulating autophagy, thus disrupting the promotion of LC by CAFs (34).

 Regulate angiogenesis

Angiogenesis is a crucial mechanism by which tumor cells acquire nutrients and metastasize. However, angiogenesis has been proven to be beneficial for enhancing the efficacy of anti-LC treatment. For instance, RESV reduced endorphins and increased phosphorylated ERK 1/2, thereby promoting microvessel growth and tumor blood perfusion. Under such conditions, the cytotoxic effect of gemcitabine on LC cells was significantly enhanced (39). In H460 cells, RESV downregulates VEGF expression, which is an important regulator of microvascular production (54). In another study, RESV regulated angiogenesis by inhibiting the STAT3/HIF-1α/VEGF pathway (44). In addition, RESV significantly inhibited the secretion of cytokines IL-6 and VEGF in co-cultured A549 cells and mesenchymal stem cells (61). In vivo studies showed that RESV reduced the expression of the angiogenic marker CD31 in tumor tissues (34). In small-cell LC, IL-23-induced inflammatory microenvironment activated the STAT3/VEGF pathway, whereas RESV inhibited this activation (62).

 Enhance the therapeutic effect of other anti-LC therapies

A large number of studies showed that RESV was a promising anti-LC adjuvant. RESV inhibited autophagy of A549 cells by regulating ZFAS1/miR-150-5p/PINK1 pathway, thereby enhancing the sensitivity of LC to paclitaxel (63). Meanwhile, RESV enhanced the susceptibility of A549 cells to paclitaxel through miR-671-5p-mediated inhibition of STOML2 (64). RESV enhanced the effect of cisplatin on mitochondrial apoptosis of small-cell LC by promoting mitochondrial depolarization and ROS production (65). RESV regulated the SPDEF-MUC5AC axis by inhibiting ERK and Akt signaling, thereby increasing the sensitivity of LC to cisplatin (10). Moreover, RESV enhanced the inhibitory effect of gemcitabine on tumor growth by promoting tumor microvascular growth (39). Meanwhile, RESV decreased SIRT1, MMP2, MMP9, E-cadherin, and integrin in A549 cells to enhance the cytotoxic of adriamycin (66). RESV induced ROS production by downregulating survivin and upregulating PUMA, thereby promoting erlotinib-mediated LC cell apoptosis (67). RESV promoted ADAM9 degradation in LC cells through the ubiquitin–proteasome pathway, thereby enhancing the therapeutic effect of dasatinib on LC cells (68). In A549 and HCC-15 cells, RESV reduced LC resistance to tumor necrosis factor-related apoptosis-inducing ligand by inhibiting the Akt/NF-κB pathway (69). Finally, RESV increased the sensitivity of LC to radiotherapy by upregulating ROS and SA-β-gal in A549 and H460 cells and promoting DNA double-strand breaks (70).

 Limitations

Several noteworthy limitations should be acknowledged in this study. First, the generalizability of our findings is constrained by substantial heterogeneity and moderate-to-low methodological quality across included studies. While subgroup analyses and meta-regression identified RESV dosage as a potential source of heterogeneity, residual variability may be explained by additional confounding factors such as experimental conditions and measurement instrumentation. Second, the toxicity predictions generated by ProTox-3.0 require empirical validation through experimental approaches, such as histopathological examination and serum biomarker analysis in preclinical models. Third, despite demonstrating promising clinical potential, the therapeutic application of RESV remains limited by its unfavorable pharmacokinetic properties, notably poor oral bioavailability and rapid systemic metabolism, which pose significant challenges for clinical translation.

 Mechanistic investigation and clinical translation challenges of RESV in LC treatment

The mechanistic underpinnings of RESV in LC treatment necessitate further preclinical investigation. Notably, the therapeutic efficacy of RESV against LC is modulated by multiple variables, including dosage, duration, and administration route. Herein, we systematically evaluated the interrelationships among quantifiable parameters (dose, duration, and therapeutic effect) to establish the potentially optimal dosage and duration of RESV in LC-bearing mouse models. The administration route was excluded due to its inherent non-quantifiable nature and inability to account for heterogeneity sources. However, subsequent researches are encouraged to comprehensively apply our findings to explore more appropriate administration routes, which would significantly contribute to systematically optimizing preclinical protocols for RESV-based LC therapy. More importantly, there are other crucial issues that must be addressed prior to clinical translation. RESV exhibits rapid metabolic clearance in humans, undergoing extensive first-pass metabolism in the liver and intestine, where it is converted to inactive sulfate conjugates, resulting in notably low systemic bioavailability (16). To optimize RESV’s anti-LC efficacy in human applications, key pharmacological parameters, including formulation, dosage regimen, and administration route, require systematic optimization. While our study identified 30–100 mg/kg as the optimal dose range in animal models, subsequent studies must establish the human equivalent dose through appropriate scaling methods. Furthermore, since mouse models are unable to fully replicate the human tumor microenvironment or pharmacokinetics, it must be recognized that the optimal dose and duration proposed in this study cannot simply be applied to human research through equivalent dose conversion. Previous clinical investigations have demonstrated that micronized formulations can enhance RESV bioavailability by approximately threefold compared to conventional preparations, achieved through increased surface area and improved suspension properties (71). Current preclinical research has focused extensively on RESV nanoparticle development for LC therapy. These engineered formulations improve bioavailability through multiple mechanisms, such as enhancing aqueous solubility, improving chemical stability, controlling release kinetics, and targeted delivery (16). Although still in preclinical development, RESV nanoparticles demonstrate promising translational potential. While clinical evaluation of RESV for LC treatment represents a crucial next step, rigorous resolution of these pharmacological challenges is imperative to ensure patient safety and therapeutic efficacy. The transition to human trials must be predicated on comprehensive preclinical data addressing these fundamental issues.

 Conclusion

As a safe food-derived compound, RESV can effectively inhibit LC tumor growth in vivo, especially to control tumor volume, tumor weight, lung metastasis number, and tumor cell apoptosis rate. Its action mechanism is complex and may be related to regulating cell cycle, cell senescence, EMT, apoptosis, autophagy, angiogenesis, and immune microenvironment. However, the clinical research on RESV treatment of LC is limited, which is the key research field to promote the clinical application of RESV.

 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 authors.

 Author contributions

XX: Formal analysis, Conceptualization, Validation, Writing – original draft, Data curation, Investigation, Writing – review & editing. XW: Methodology, Data curation, Investigation, Conceptualization, Writing – review & editing, Writing – original draft, Formal analysis. WL: Investigation, Writing – review & editing, Writing – original draft, Methodology, Data curation. FY: Writing – original draft, Writing – review & editing, Supervision. JG: Investigation, Funding acquisition, Validation, Project administration, Writing – review & editing, Supervision, Writing – original draft, Methodology.

 Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the National Natural Science Foundation of China (No. 82305188); the China Postdoctoral Science Foundation (No. 2022MD723715); and the Natural Science Foundation of Sichuan Science and Technology Department (No. 23NSFSC6246).

The authors thank BioRender for their outstanding contributions to biological picture rendering.

 Conflict of interest

The authors declare that the research 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 authors declare that no Gen AI was used in the creation of this manuscript.

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 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2025.1644538/full#supplementary-material

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2https://comptox.charite.de/protox3/

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twice every 2 days

 BaP

benzo[a]pyrene

 BBB

blood–brain barrier

 CAFs

cancer-associated fibroblasts

 CI

confidence interval

 EMT

epithelial–mesenchymal transition

 FOXC2

forkhead box c2

 Ga

gavage

 HTT

heterotopic tumor transplantation

 Ii

intratumor injection

 Ip

intraperitoneal

 Iv

intravenous injection

 LC

lung cancer

 LD50

median lethal dose

 LLC

lewis lung cancer

 NA

not available

 NNK

4-(N-methyl-N-nitrosamino)-1-(3-pyridyl)-1-butanone

 Q2D

quaque secunda die

 Q3D

quaque tertia die

 QD

quaque die

 RESV

resveratrol

 ROS

reactive oxygen species

 SA-β-gal

senescence-associated β-galactosidase

 Sc

subcutaneous injection

 SCI

severe combined Immunodeficient

 SMD

standardized mean difference

 TAMs

tumor-associated macrophages

 TW

three times a week

 WMD

weighted mean difference