Introduction

Cancer cells rapidly acquire and utilize energy through various metabolic pathways to support their swift growth and proliferation. Consequently, alterations in cellular metabolism and energy production are recognized as hallmark features of cancer cells1. Lipid metabolism, as a crucial component of cancer cell metabolism, plays a pivotal role in the initiation and progression of cancer. Specifically, pathways such as fatty acid synthesis (FAS), fatty acid oxidation (FAO), cholesterol synthesis, and phospholipid metabolism are highly active in cancer cells. These metabolic pathways not only provide essential energy and biosynthetic precursors for cancer cells but also play significant roles in cell signaling, membrane structure maintenance, and oxidative stress response2,3,4,5,6.

FAS is a primary pathway by which cancer cells meet their rapid proliferation demands. The key enzyme in the FAS pathway, fatty acid synthase (FASN), is overexpressed in various cancers, and inhibition of FASN significantly reduces tumor growth and induces apoptosis7. FAO supplies a critical energy source for tumor cells under hypoxic and nutrient-deprived conditions; inhibiting FAO impairs tumor cell survival and enhances chemotherapy sensitivity4,5,6. Cholesterol synthesis, mediated by HMG-CoA reductase (HMGCR), not only contributes to the structural integrity of cell membranes but also participates in the synthesis of signaling molecules such as steroid hormones. Inhibition of this pathway effectively suppresses cancer cell growth and metastasis8,9,10. Furthermore, de novo lipogenesis protects cancer cells from free radicals and chemotherapeutics by promoting membrane lipid saturation10. Phospholipid metabolism is essential for constructing cell membranes and facilitating signal transduction, and abnormal phospholipid metabolism is often associated with the invasiveness and metastatic potential of cancer cells6.

In addition to these pathways, recent studies have highlighted the importance of lipid metabolic reprogramming within the tumor microenvironment. Adipocyte-induced CD36 expression drives ovarian cancer progression and metastasis, while lipid metabolic reprogramming supports tumor growth and immune evasion11,12. Moreover, a high-fat diet (HFD) can influence cancer development and progression by altering lipid metabolism mechanisms12.

Inhibitors targeting lipid metabolic pathways have shown significant potential in cancer therapy. For instance, FASN inhibitors and HMGCR inhibitors, such as statins, have been demonstrated to effectively inhibit tumor growth and, in some cases, enhance the efficacy of existing treatment modalities8,13,14,15,16. Furthermore, in various cancers such as hepatocellular carcinoma, colorectal cancer, prostate cancer, and breast cancer, statins have been associated with a reduced risk and improved therapeutic outcomes17,18,19,20. Combination therapies, where statins are used alongside traditional chemotherapy drugs like doxorubicin and cyclophosphamide, have shown enhanced therapeutic efficacy4,21. By regulating lipid metabolism, it is possible to not only directly suppress cancer cell growth but also modulate the tumor microenvironment, reducing tumor invasiveness and metastatic capabilities, thereby providing multi-faceted antitumor strategies11,12.

Since their introduction in the 1980s, statins have become the primary drugs for treating hypercholesterolemia and preventing cardiovascular diseases. As potent competitive inhibitors of HMGCR, statins reduce cholesterol synthesis and alter the expression of low-density lipoprotein (LDL) receptors, thereby lowering plasma cholesterol levels13. In addition to their widespread use in cardiovascular diseases, an increasing body of research has shown that statins possess various biological effects, including anti-inflammatory, antioxidant, and antiproliferative properties22,23. These effects provide a theoretical foundation for their potential application in cancer treatment. Through the inhibition of cholesterol synthesis, reduction of isoprenoid compound synthesis, modulation of lipid metabolism-related signaling pathways, and decrease in fatty acid synthesis, statins exert antitumor effects through multiple mechanisms14,15,24.

However, the application of statins in cancer treatment is limited by their low bioavailability and poor drug stability21. To overcome these physicochemical limitations, encapsulating statins in polymeric nanocarriers represents an innovative strategy. Polymeric nanoparticles (PNs), due to their small size, large surface area, and high modifiability, are widely used in drug delivery systems. Encapsulating statins in PNs not only significantly improves their solubility and stability, reducing degradation in the body, but also enables targeted drug delivery through surface modifications23. This increases the drug concentration at tumor sites while minimizing impact on healthy tissues. For example, liposomal encapsulation of doxorubicin (brand name: Doxil) is the first FDA-approved liposomal anticancer drug, demonstrating the success of nanotechnology in prolonging drug circulation time and reducing toxicity to non-target tissues25.

Although numerous studies have reported the anticancer efficacy of statin-loaded nanocapsules (SLCNs) in preclinical rodent models, a systematic review on this subject is still lacking26. Systematic reviews in preclinical research are increasingly valued for addressing the translational challenges in cancer research. By employing rigorous methodological designs and comprehensive data collection, systematic reviews provide comprehensive empirical evidence, offer an in-depth overview of specific topics, reduce research bias, and enhance the reliability and reproducibility of results27. In summary, systematic reviews are widely recognized for addressing specific research questions and providing empirical evidence that offers a comprehensive overview of particular subjects28. Moreover, preclinical systematic reviews hold significant importance in drug development, as they integrate and analyze existing preclinical research data, providing scientific evidence for new drug development and reducing the risks and costs associated with clinical trials28.

Therefore, this study aims to systematically review and meta-analyze the antitumor efficacy of statin-loaded PNs in preclinical studies. By systematically collecting and analyzing existing research data, this study hopes to reveal the therapeutic effects of statin nanocapsules in various tumor models and evaluate their comparative efficacy with non-encapsulated statins. The findings will provide scientific evidence for future clinical research and the development of novel nanomedicines involving statins, holding significant theoretical and practical value.

Methods

Search strategy

This study was conducted as a systematic review and meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines29. Literature searches were performed on May 30, 2025, across PubMed, Scopus, and Web of Science databases without temporal restrictions. Specific search strategies for each database are detailed in Table 1. We utilized the Medical Subject Headings (MeSH) system, a structured and standardized vocabulary developed by the National Library of Medicine.

Table 1 Search strategies for identifying articles in PubMed, Scopus, and Web of science databases.
Full size table

Eligibility criteria

This review included only English-language original studies that reported in vivo tests of tumor growth inhibition by statin-loaded polymeric nanoparticles (SLCNs) in rodent models and demonstrated a significant reduction in tumor lipid metabolism by these nanocapsules. Studies were required to have at least two animal groups: one treated with SLCNs and another receiving unencapsulated statins as a control. Any method of solid tumor induction was acceptable, including chemical induction, genetic modification, or transplantation models. Additionally, studies needed to provide the number of animals per group (n), treatment duration, administration route and dosage, as well as the mean and standard deviation of tumor size for each group.

Excluded studies comprised observational studies, non-interventional studies, review articles, conference abstracts, and comments. Additionally, studies lacking clear or complete information on the number of subjects per cohort, length of therapy, administration route, dosage, or tumor size data (mean and standard deviation) were excluded. Studies that did not assess efficacy within a solid tumor context, or reported tumor induction during or after treatment were also excluded. Furthermore, studies with significant differences in initial tumor volume (TV) between groups, duplicate publications, and studies not focused on the application of statin-loaded polymeric nanoparticles in tumor suppression or not involving changes in lipid metabolism were excluded.

Study selection and data extraction

The literature search was independently conducted by two researchers (CL and AL). Initially, titles and abstracts were screened to identify studies meeting the preliminary inclusion criteria. Subsequently, full-text reviews were performed for secondary screening, and the final studies meeting all eligibility criteria were selected. Any discrepancies between the researchers were resolved through discussion.

Both researchers independently tested the data collection form and extracted outcome data from the selected studies. A third researcher compared the extracted data and resolved any inconsistencies through discussion. If necessary, study authors were contacted to obtain missing data.

CL and AL compiled the relevant data from the selected papers into a table, which included the following information: first author, title, publication year, number of animals per group (n), treatment duration, dosage and administration method, starting and final tumor size and standard deviation for both treatment and control groups, and details of nanoparticle characterization. When tumor measurement data were only available in graphical form, the Getdata Graph Digitizer 2.2.5 program was used to extract the data.

Data integration and statistical evaluation

Meta-analyses were performed using RevMan 5.4 (Review Manager, version 5.4) to assess the mean and standard deviation of tumor volume (TV). The primary outcome measure was inhibition of tumor progression in the experimental group (statin-loaded polymeric nanoparticles) compared to the control group (unencapsulated statins). For continuous data, the standardized mean difference (SMD) was calculated, and sensitivity analyses were conducted using the same software to evaluate the robustness of results. Heterogeneity among included studies was assessed using the I2 test30. Additionally, funnel plots were generated to visually inspect publication bias, and Egger’s regression test was applied for a quantitative assessment of small-study effects31,32. Statistical significance was determined with 95% confidence intervals (CI) and a p-value less than 0.05.

Evaluation of the quality and design bias in preclinical trials

To evaluate the data quality in the included studies, the investigators independently employed SYRCLE’s risk of bias tool33. The selected studies were examined based on 10 items addressing selection bias, performance bias, detection bias, attrition bias, reporting bias, and other potential biases33. If required information was missing or further clarification was necessary, the corresponding authors were contacted. Any disagreements were resolved through consultation with a third investigator. The tool provides three possible responses, each corresponding to a distinct level of risk, allowing for a comprehensive assessment of bias in each study.

Results

Data

A total of 586 articles were identified through the search strategy (Fig. 1). After removing duplicates and review articles, 448studies were excluded based on their titles and abstracts. Subsequently, two independent investigators (CL and AL) performed a full-text review of the remaining 138 articles against the eligibility criteria. Ultimately, 22 articles met the inclusion criteria and were included in this systematic review.

Fig. 1
figure 1

Study Selection Flow Diagram. This flow diagram illustrates the study selection process for the systematic review. Initially, 586 articles were identified through database searches in PubMed (n = 53), Scopus (n = 183), and Web of Science (n = 350). After removing duplicate articles (n = 133) and review articles (n = 64), 389 potentially relevant articles remained. These articles were further screened by title and abstract, resulting in the exclusion of 251 articles. Following a full-text review of the remaining 138 articles, additional exclusions were made based on the following criteria: observational or non-interventional studies, incomplete or unclear information, non-solid tumors, differences in initial tumor or lipid levels between groups, animal models without tumor induction, and tumor induction conducted either after or during the treatment protocol. Ultimately, 22 papers met the inclusion criteria and were included in this systematic review.

Full size image

Summary of study characteristics

Table 2 outlines the main features of the included studies, detailing the species, strain, gender, and age of the animals; tumor type (with breast cancer being the most prevalent, accounting for 50%, see Fig. 2a); types of statins used (with simvastatin being the most common, comprising 59%, see Fig. 2b); statin dosages administered based on the method of administration; administration routes (oral (n = 1), nasal (n = 1), intravenous (n = 15), intraperitoneal (n = 3), or subcutaneous (n = 2)); study duration (ranging from 9 to 40 days); substances used in the control group (free statin, with most studies using phosphate buffer or normal saline to disperse the non-encapsulated statin); and the author, year, and country of publication, with a high proportion of studies conducted in China.

Table 2 Key Characteristics of the included articles.
Full size table
Fig. 2
figure 2

Distribution of Cancer Types and Statins Used in Studies. A: This chart illustrates the various cancer types examined in the included studies. B: This chart shows the different types of statins utilized in the included studies.

Full size image

The selected articles provided detailed characterizations of polymeric nanoparticles across various parameters, including particle size, zeta potential, polydispersity index (PDI), encapsulation efficiency, and statin loading. Table 3 demonstrates that 32% of formulations comprised solid lipid nanoparticles (SLNs), either alone or blended with other polymers. Most studies also evaluated average particle size, zeta potential, and PDI. Additionally, some articles provided data on stability (n = 15), in vitro statin release profiles (n = 15), and morphological analyses of nanocapsules (n = 17). Standard deviations were excluded from Table 3 where they were not reported in the original publications.

Table 3 Characterization of polymeric nanoparticles (PNs) in selected studies.
Full size table

Beyond the data presented in Tables 2 and 3, additional in vitro and in vivo tests were performed on the polymeric nanoparticles under review. Typical in vitro assessments included cytotoxicity assays (n = 16), studies on cellular uptake (n = 16), and evaluations of cell apoptosis (n = 12). In vivo assessments generally included tumor weight measurements (n = 14), evaluations of lipid levels (n = 6), and pharmacokinetic analyses (n = 5).

Meta-analyses

By extracting tumor volume (TV) and tumor weight data from the 22 selected articles, the antitumor efficacy of nanoencapsulated statins was evaluated to analyze the primary outcome: tumor growth inhibition. Secondary outcomes, such as tumor weight reduction and the impact of co-encapsulation, were also assessed using data from these same studies. No additional studies were included in the analysis. Meta-analyses were conducted independently to analyze, interpret, and evaluate the collected data for these outcomes, utilizing descriptive statistics and visual aids (forest plots) to clearly illustrate the findings.

Effect of treatment on tumor growth inhibition

Tumor growth inhibition was assessed by comparing TV(mm3) between the control group (receiving free statin) and the treatment group (receiving statin-loaded polymeric nanoparticles, SLCNs). As illustrated in the forest plot (Fig. 3), the meta-analysis involving a cumulative total of 127 animals indicated statistically significant differences between the two groups (SMD −1.79; 95% CI −2.21 to −1.38; p < 0.00001). These results demonstrate that treatment with statin-loaded PNs significantly reduces tumor growth compared to free statin treatment.

Fig. 3
figure 3

Forest Plot Comparing Tumor Growth Inhibition Between Control and Treatment Groups. This forest plot compares tumor growth inhibition between the control group, treated with free statin (STN), and the treatment group, administered with statin-loaded polymeric nanoparticles (STN-PN). The analysis includes data from 109 animals across multiple studies and demonstrates statistically significant differences favoring the treatment group.

Full size image

Effect of treatment on tumor weight reduction

10 of the 22 included articles assessed the variations in tumor weight (g) between the control and experimental groups, as illustrated in the forest plot (Fig. 4). A statistically significant difference was found between the two treatment forms (SMD −3.53; 95% CI −4.75 to −2.31; p < 0.0001). This result indicates that animals treated with SLCNs had lower tumor weights at the end of the experiment compared to those treated with free statin, consistent with the findings of the primary outcome meta-analysis.

Fig. 4
figure 4

Forest Plot Comparing Tumor Weights Between Control and Experimental Groups. This forest plot compares tumor weight (g) between the control group, treated with free statin (STN), and the experimental group, treated with statin-loaded polymeric nanoparticles (STN-PN). The analysis includes data from 67 animals across multiple studies and shows statistically significant differences favoring the treatment group.

Full size image

Effect of co-encapsulation on antitumor activity

Five articles investigated the effect of co-encapsulating statins with other active ingredients on tumor growth inhibition. The meta-analysis evaluated TV (mm3) in animal groups treated with polymeric nanoparticles containing either statins alone or co-encapsulated with other active substances. The co-encapsulation of statin with doxorubicin 55, imatinib 44, tangeretin 46, cholic acid 49 and fenretinide 50 was part of this analysis. As illustrated in the forest plot (Fig. 5), the results indicate a statistically significant reduction in TV (SMD −2.26; 95% CI −3.78 to −0.73; p = 0.004), irrespective of the co-encapsulated substance used.

Fig. 5
figure 5

Forest Plot Comparing TV Reduction Between Different Treatments. This forest plot compares TV reduction between two treatment groups: one group received treatment with statin-loaded polymeric nanoparticles (STN-PN), while the other group was treated with polymeric nanoparticles that co-encapsulated statin and another active substance (STN + AS-PN). The analysis includes data from 29 animals across multiple studies and shows a statistically significant reduction in TV favoring the co-encapsulation treatment group (SMD −2.26; 95% CI −3.78 to −0.73; p = 0.004).

Full size image

Risk of bias assessment summary

The risk of bias assessments for all 22 studies included in this research, according to SYRCLE’s Risk of Bias (RoB) guidelines, are summarized in Table 4. Many issues were marked as “unclear”, indicating that relevant information was inadequately reported or missing. This issue was especially prevalent in areas such as allocation, animal housing, blinding, result evaluation, and handling of incomplete data.

Table 4 Risk of bias assessments for included studies based on SYRCLE’s RoB guidelines
Full size table

Analysis of publication bias

To evaluate the presence of publication bias, a funnel plot was constructed (Fig. 6) based on the standardized mean differences (SMDs) and their corresponding standard errors (SEs) from the included studies. Most of the study data points were clustered in the central region of the funnel plot and displayed a symmetrical distribution, suggesting an overall absence of publication bias. However, slight asymmetry was observed, particularly with a few outlying studies on the lower left and upper right, suggesting potential deviations from the overall trend.

Fig. 6
figure 6

Funnel plot of the included studies assessing publication bias.

Full size image

To further assess small-study effects quantitatively, Egger’s regression test was performed. The regression analysis yielded a p value of 0.0404, which is not statistically significant (p < 0.05), indicating no substantial evidence of small-study effects or publication bias. This result aligns with the visual inspection of the funnel plot and supports the robustness of the meta-analysis findings.

Discussion

Lipid metabolism includes the processes of fatty acid uptake, synthesis, breakdown, storage, and cholesterol metabolism. Essential fatty acids (such as alpha-linolenic acid and linoleic acid) must be obtained through diet. Fatty acids can enter cells via diffusion, fatty acid transport proteins (such as CD36), or low-density lipoproteins (LDL) and very-low-density lipoproteins (VLDL). The starting point of fatty acid synthesis is citrate, which is converted into acetyl-CoA and oxaloacetate by ATP-citrate lyase (ACLY). Acetyl-CoA carboxylases (ACACA and ACACB) then convert acetyl-CoA to malonyl-CoA, the rate-limiting step in fatty acid synthesis. FASN produces fatty acid chains, ultimately generating palmitate (16:0), a common saturated fatty acid, which can then be converted to monounsaturated fatty acids (MUFA) by desaturases (such as SCD and FADS2). Triacylglycerol (TAG) is the main storage form of fat, stored in lipid droplets (LDs). Lipases, including lipoprotein lipase (LPL), hydrolyze TAG to release free fatty acids (FFA). The breakdown of endogenous TAG begins with adipose triglyceride lipase (ATGL), which produces diacylglycerol (DAG). DAG is then further hydrolyzed by hormone-sensitive lipase (HSL) to produce monoacylglycerol (MAG). MAG is subsequently hydrolyzed by monoacylglycerol lipase (MGL), releasing glycerol and free fatty acids. FFAs are transported into the mitochondria via carnitine palmitoyltransferase (CPT1) for oxidation, producing acetyl-CoA, which enters the tricarboxylic acid (TCA) cycle. This process generates NADH and FADH2, which are used in the electron transport chain to produce ATP. Acetyl-CoA is also the key starting material for cholesterol synthesis, converted to mevalonate by HMG-CoA reductase (HMGCR), the rate-limiting step in cholesterol synthesis, essential for cell membrane structure2,3.

In recent years, significant attention has been directed towards the critical role of lipid metabolism in tumor development and progression. Research has demonstrated that tumor cells undergo metabolic reprogramming to meet their rapid proliferation and high energy demands, with lipid metabolism playing a crucial role in this process. For instance, FASN catalyzes the production of fatty acids, and overactive FASN leads to increased construction of tumor cell membranes, thereby promoting cell proliferation and survival. Its inhibition can significantly reduce tumor growth and induce apoptosis7. Cholesterol, which is not only an essential component of cell membranes but also plays a role in cell signaling, is also implicated in cancer, with key enzymes in the cholesterol synthesis pathway such as HMG-CoA reductase being highly expressed in many cancers8,10. Cholesterol forms lipid rafts in the cell membrane, which are aggregation sites for signaling molecules and receptors that can influence cell proliferation, differentiation, and survival. Additionally, cholesterol metabolism is associated with drug resistance in tumor cells. Excess cholesterol can promote tumor cell resistance to chemotherapy drugs by altering the permeability of the cell membrane to drugs or by activating drug efflux pumps56. Furthermore, fatty acid oxidation (FAO) serves as a crucial energy source for tumor cells under hypoxic and nutrient-deprived conditions, and inhibiting FAO can impair tumor cell survival and enhance chemotherapy sensitivity9,57. FAO not only provides ATP for tumor cells but also generates NADPH, an important antioxidant that helps tumor cells resist oxidative stress. Studies have shown that certain tumors, such as prostate cancer, rely on FAO to sustain their growth and invasive capabilities58. Lipid metabolism in the tumor microenvironment also significantly impacts tumor progression and treatment, with tumor-associated macrophages (TAMs) promoting tumor growth and immune evasion through lipid metabolic pathways11,59. TAMs support tumor cells by secreting cytokines and chemokines that inhibit the activity of immune cells. Additionally, TAMs enhance their support for tumor cells by reprogramming their own lipid metabolism60. In summary, lipid metabolism plays a crucial role in tumor development and progression, and therapeutic strategies targeting lipid metabolic pathways have shown significant antitumor effects. Key regulatory points in lipid metabolism are emerging as novel targets for anticancer therapy.

Statins, widely recognized as the most commonly used lipid-lowering agents, can inhibit lipid metabolism through multiple mechanisms, exerting significant antitumor effects. These effects have been demonstrated across various types of cancer, including breast cancer, prostate cancer, liver cancer, and colorectal cancer61,62,63,64. Statins primarily exert their effects by inhibiting HMG-CoA reductase, a key enzyme in the cholesterol synthesis pathway, thereby lowering cholesterol levels in the body and impacting cancer cell growth and proliferation13. Additionally, statins disrupt lipid rafts in the cell membrane, interfering with cancer cell signaling pathways such as Ras, PI3K/Akt, and MAPK, thereby inhibiting cell proliferation and survival65. Statins block the production of small GTPases (such as Ras and Rho) by inhibiting intermediates of the mevalonate pathway (such as farnesyl pyrophosphate (FPP) and geranylgeranyl pyrophosphate (GGPP)), thereby interfering with cell proliferation and migration signals, inhibiting tumor growth56. Statins also induce apoptosis by activating p53 and other apoptosis-related proteins and by triggering the endoplasmic reticulum stress response pathway66. Furthermore, statins inhibit the expression of vascular endothelial growth factor (VEGF) and other pro-angiogenic factors, disrupting the formation of new blood vessels in tumors, limiting their nutrient supply and inhibiting growth67. Simultaneously, statins reduce inflammatory responses and oxidative stress, decreasing pro-inflammatory cytokines in the tumor microenvironment, such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), thus inhibiting tumor-associated chronic inflammation and slowing tumor progression68. Research also indicates that statins enhance the expression of autophagy-related genes (such as Beclin-1 and LC3), promoting the formation and maturation of autophagosomes, thereby inducing autophagic cell death and further inhibiting tumor cell survival69. In summary, statins demonstrate potential antitumor therapeutic value through multiple mechanisms in both preclinical and clinical studies.

Statins, while demonstrating significant potential in cancer therapy, face several physicochemical limitations that impede their clinical application. Primarily, statins exhibit low bioavailability, meaning that only a small fraction of the orally administered drug reaches systemic circulation at effective concentrations. This is largely due to incomplete absorption in the gastrointestinal tract and significant first-pass metabolism in the liver70. Additionally, statins suffer from poor stability, being prone to degradation in both in vivo and in vitro environments, which affects their therapeutic efficacy71. Their low solubility in water, a consequence of their lipophilic nature, further complicates their absorption and distribution within the body72. Moreover, statins are rapidly metabolized by liver enzymes, primarily cytochrome P450, resulting in a short half-life that necessitates frequent dosing to maintain effective drug levels73. These limitations underscore the need for advanced drug delivery systems, such as NPs and other novel carriers, to enhance the bioavailability, stability, and overall efficacy of statins in cancer treatment.

With the advancement of nanotechnology, various nanomaterials have been developed to encapsulate statins, extending the drug’s half-life, enhancing its targeted delivery capabilities, prolonging its release time, and increasing its local drug concentration in various tumor models to improve efficacy37,38,44,51. Gaber et al. designed a statin-loaded lipid nanoparticle (LNP) delivery system to evaluate its antitumor activity against breast cancer in both in vitro and in vivo models. The study found that these nanocapsules were effectively taken up by MCF-7 cells and exhibited a synergistic inhibitory effect on the proliferation of hepatocellular carcinoma cells. In vivo experiments further demonstrated that, compared to unencapsulated statins, the nanoparticles showed higher accumulation in target organs, enhanced antitumor activity, and reduced toxicity39. This case illustrates that nanoparticle encapsulation not only improves the bioavailability of the drug but also enhances its antitumor effects through targeted delivery. He Ba et al. developed an RGD peptide-modified nanoparticle targeting delivery system that can specifically deliver statins to tumor sites. Experimental results showed that this nanoparticle-encapsulated statin significantly outperformed the unencapsulated form in inhibiting tumor growth. This enhanced efficacy is attributed to the RGD peptide’s ability to recognize and bind to integrin receptors on the surface of tumor cells, thereby increasing the concentration of the drug within tumor tissues and reducing its impact on healthy tissues. Consequently, the targeted delivery system significantly enhances the antitumor effect46. Therefore, our group conducted a literature review on the impacts of statin encapsulation in treating various tumor models, with numerous studies reporting significant findings in preclinical research. Nevertheless, as far as we know, these animal studies have yet to be subjected to meta-analysis to support future clinical research.

This meta-analysis aimed to statistically assess the capability of polymeric nanoparticles to enhance the ability to inhibit tumor growth of statins compared to their non-encapsulated form. Tumor growth inhibition, measured by volume or weight, was assessed. The findings indicate that statin-loaded PNs can significantly suppress tumor growth and decrease tumor weight across various solid tumor models, with one exception.Malak et al.’s study demonstrated that simvastatin significantly reduced TV and cell proliferation, but simvastatin-NP did not show better efficacy compared to simvastatin alone. The researchers attributed this to several factors: the dosing frequency of simvastatin-NP was five times a week instead of daily, possibly leading to insufficient drug concentration in the body and affecting efficacy; the lipid nanoparticle formulation might not have effectively increased the drug concentration and stability in the tumor tissue, resulting in suboptimal therapeutic effects; despite the theoretical benefits of lipid NPs in enhancing drug bioavailability and stability, practical applications might face issues such as incomplete or uneven drug release in the body; most importantly, the study did not measure simvastatin concentration in tumor tissues and plasma, making it impossible to determine if the NPs effectively increased drug concentration in the target tissues24. Another key outcome highlighted in this review is the enhanced tumor growth inhibition observed in rodents treated with statin co-encapsulated with another biologically active substance in PNs, in comparison to those treated solely with statin-loaded PNs. For example, doxorubicin kills tumor cells through multiple mechanisms, including hindering DNA synthesis to disrupt tumor cell proliferation and promoting the increase of reactive oxygen species (ROS), which leads to lipid peroxidation and cell membrane destabilization55. Combining statins with drugs that have well-known mechanisms of action supports the evidence that statins can act synergistically or additively to reduce tumor growth. Marwaha and colleagues developed PNs containing both statins and imatinib as a treatment approach for breast cancer. This co-encapsulation demonstrated enhanced antitumor activity in both in vitro and in vivo models. Additionally, compared to single SLCNs, this method enables sustained drug release from the polymeric nanoparticles and increases the local drug concentration44. Our meta-analysis confirmed that co-encapsulating statins in PNs effectively inhibits tumor growth. Co-encapsulation of drugs can coordinate multiple medications to achieve better therapeutic outcomes, alleviate adverse effects, and reduce tumor resistance, making it a promising new strategy for cancer treatment.

Despite the encouraging results of our meta-analysis, several limitations must be acknowledged, particularly the substantial heterogeneity indicated by the I2 values. Specifically, the anti-tumor efficacy of statin-loaded nanoparticles exhibited an I2 value of 85%, while those co-encapsulated with other drugs had an I2 value of 74%. This high heterogeneity is anticipated in preclinical meta-analyses due to significant experimental variability and the lack of standardized protocols in animal studies. The primary sources of heterogeneity include differences in animal models, tumor types, statin dosages, treatment durations and frequencies, administration routes, as well as the characteristics of the nanoparticles and variations in study design. These factors contribute to the uncertainty in the pooled effect sizes, thereby reducing the confidence in the results. Additionally, different tumors exhibit varying growth behaviors and proliferation rates across animal models, further exacerbating heterogeneity and complicating result interpretation. This necessitates the use of subgroup analyses or sensitivity analyses to explore the sources and impacts of heterogeneity. Petersen et al. have highlighted the challenges in interpreting preclinical studies, emphasizing that the lack of standardization in animal experimental procedures significantly impedes clinical translation27. Despite the high heterogeneity, preclinical studies remain crucial for advancing drugs to clinical applications. Systematic reviews can scientifically evaluate drug efficacy and safety, overcoming the limitations of individual studies33. Moreover, from an ethical standpoint, preclinical research allows for the assessment of new drugs based on the latest ethical standards and aids in identifying promising clinical trial strategies. Meta-analyses of animal studies provide statistically reliable results with high value. Despite the challenges posed by diversity and complexity, the benefits of preclinical research clearly outweigh its limitations. Therefore, when interpreting meta-analysis results, the presence of heterogeneity should be carefully considered, and efforts should be made to minimize its impact through systematic approaches.

This review and meta-analysis are subject to additional potential limitations, primarily concerning study quality and methodological aspects. We employed SYRCLE’s Risk of Bias tool to assess the 22 included studies, revealing that multiple domains exhibited an “unclear risk” of bias. Specifically, many studies lacked detailed descriptions of random sequence generation, allocation concealment, and blinding of outcome assessments. This deficiency may lead to selection bias, performance bias, and detection bias. For instance, studies that did not explicitly state their randomization methods and allocation concealment procedures might have systematic differences between groups, thereby affecting the accuracy of the results. Furthermore, some studies did not implement blinding during treatment administration and outcome evaluation, increasing the risk of performance bias and detection bias, which could result in the overestimation or underestimation of treatment effects. Additionally, certain studies exhibited attrition bias and reporting bias, particularly when not all predefined outcomes were reported or when missing data were inadequately addressed, potentially distorting the overall effect estimates.

Methodological heterogeneity also constitutes a significant limitation of this study. The included studies demonstrated considerable variability in experimental design, selection of animal models, statin dosages, and administration routes. Such differences not only contribute to the observed heterogeneity but also may impact the consistency and comparability of the results. Moreover, inconsistencies in data processing and reporting, such as the lack of appropriate handling of missing data, could further compromise the accuracy of the meta-analysis. These deficiencies in study quality and methodology may, to some extent, undermine our confidence in the pooled effect sizes. Therefore, future research should focus on enhancing the quality of experimental design and ensuring methodological transparency and standardization to minimize the occurrence of bias, thereby improving the reliability and reproducibility of study findings27.

The findings of this study hold significant clinical implications, demonstrating the potential application of statin-loaded polymeric nanoparticles (SLCNs) in cancer therapy. Firstly, SLCNs markedly increase the concentration of statins within tumor tissues while reducing adverse effects on healthy tissues. This characteristic positions SLCNs as a promising novel drug delivery system, with potential applications in the treatment of various common cancers, including breast and prostate cancer. Additionally, the co-encapsulation strategy of statins with other chemotherapeutic agents observed in this study further enhances the antitumor efficacy. This suggests that, in clinical settings, the combined use of statins and other chemotherapeutic drugs may achieve synergistic effects, not only improving therapeutic outcomes but also potentially reducing the dosage of individual drugs to minimize toxic side effects, thereby enhancing patients’ quality of life.

However, translating these nanoparticles from preclinical studies to clinical applications presents several challenges. Issues such as the stability of the drug in vivo, the biocompatibility of the nanoparticles, and the feasibility of large-scale production require further investigation and resolution. Moreover, the design of clinical trials must consider dose optimization and long-term safety assessments to ensure their efficacy and safety in humans. Future research should focus on addressing these critical aspects of clinical translation while exploring the application of SLCNs across a broader range of cancer models to validate their extensive applicability.

By overcoming these challenges and further optimizing the design and functionality of SLCNs, statin-loaded polymeric nanoparticles have the potential to become an efficient and safe cancer treatment strategy. This advancement would provide cancer patients with more effective therapeutic options, improve overall treatment outcomes, and reduce the adverse effects associated with traditional chemotherapy. Furthermore, given the widespread use of statins in cardiovascular diseases, investigating their potential in combination therapies represents another crucial direction. This could offer new solutions for the integrated treatment of multiple diseases.

Conclusion

This review systematically demonstrated that statin-loaded PNs exhibit significantly greater antitumor efficacy compared to nonencapsulated statins in rodent models across various types of solid tumors. Specifically, the meta-analysis revealed that SLCNs significantly reduced tumor growth (SMD −1.79; 95% CI −2.21 to −1.38; p < 0.00001) and decreased tumor weight (SMD −3.53; 95% CI −4.75 to −2.31; p < 0.0001). Moreover, the efficacy of statins can be further enhanced by co-encapsulating them with other biologically active substances, suggesting a synergistic effect. Despite these promising findings, there remains a critical need to standardize preclinical studies to ensure consistency and reproducibility of results, including uniformity in experimental design, dosage, and evaluation methods. Future research should also explore the long-term effects and safety of SLCNs in larger and more diverse animal models. The findings from this meta-analysis provide a strong empirical foundation for the advancement of SLCNs to clinical applications, suggesting a promising future for the development of new nanomedicines that could lead to more effective and targeted cancer treatments for humans. Future clinical studies are needed to confirm these preclinical results and to assess the potential of these nanocapsules for enhancing cancer treatment outcomes.