Nanozyme-enabled cancer theranostics: bridging enzyme mimicry and material intelligence

Yupeng Wang† ^a, Xinxin Sun†^a, Shunfeng Wang^a, Zhixiao Zhang^c, Jin Sun^ab, Cong Luo^ab, Zhonggui He*^ab and Shenwu Zhang*^a
aDepartment of Pharmaceutics, Wuya College of Innovation, Shenyang Pharmaceutical University, No. 103 Wenhua Road, Shenyang 110016, PR China. E-mail: hezhonggui@vip.163.com; zhangshenwu@syphu.edu.cn; Fax: +86-024-23986321; Tel: +86-024-23986321
^bJoint International Research Laboratory of Intelligent Drug Delivery Systems of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, PR China
^cDepartment of Pharmacy, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121000, China

First published on 4th August 2025

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Wider impact This review highlights the prominent role of nanozymes, a novel class of nanomaterials with enzyme-like catalytic activity, in advancing cancer theranostics. Unlike natural enzymes, nanozymes combine high stability, tunable physicochemical properties, and tumor-targeting capabilities, enabling precise therapeutic intervention and accurate diagnosis. They offer insights into addressing critical challenges in cancer treatment by leveraging tumor microenvironment features to enhance therapeutic efficacy while minimizing off-target effects. The work systematically explores nanozyme-driven multimodal theranostics, offering innovative strategies for precision oncology. By bridging nanotechnology and biomedicine, this review provides a roadmap for designing next-generation cancer therapies and diagnostics, with notable potential for clinical translation. It appeals to researchers in nanotechnology, oncology, and drug delivery, fostering interdisciplinary advancements in personalized medicine.

1. Introduction

Although significant progress has been made in diagnosis and treatment, cancer remains a major threat to human health.¹ In diagnosis, despite advanced imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI), their sensitivity to early-stage tumors remains insufficient, often resulting in missed diagnoses.² Small tumors (<5 mm in diameter) are difficult to detect, leading to delayed treatment.³ Additionally, liquid biopsy techniques for detecting circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) face challenges due to low abundance and complex separation processes, which limit their clinical utility.⁴ In terms of treatment, chemotherapy lacks selectivity, damaging rapidly proliferating healthy cells and causing severe side effects such as bone marrow suppression and gastrointestinal toxicity.⁵ Radiation therapy (RT), while effective in local tumor control, can damage surrounding normal tissues, especially in sensitive areas like the brain and spinal cord, leading to long-term complications.⁶ Immunotherapy, while promising, benefits only a subset of patients due to the immunosuppressive tumor microenvironment, which hinders immune cell activation and infiltration.⁷ Therefore, continuous exploration of novel treatment strategies and innovative drug formulations is essential to effectively address these challenges.

With the rapid advancement of nanotechnology, the integration of nanotechnology and enzymes has created new opportunities for cancer diagnosis and treatment. Owing to their nanoscale size, nanomaterials can passively accumulate in tumor tissues via the enhanced permeability and retention (EPR) effect.⁸ This passive targeting mechanism offers some tumor selectivity compared to larger molecules. However, passive targeting alone does not equate to “precise” targeting, as non-specific accumulation can still occur in normal tissues with vascular leakage.⁹ Therefore, true precision targeting requires functionalization with antibodies, peptides, aptamers, or small molecules that specifically recognize tumor-associated antigens or receptors.¹⁰

Beyond targeting, some nanomaterials exhibit enzyme-like catalytic activity, significantly improving treatment efficiency. Notably, the term “nanozyme” was first introduced by Manea et al. in 2004 to describe gold nanoparticles (AuNPs) with transphosphorylation activity.¹¹ Later, in 2007, iron oxide nanoparticles (IONPs) were found to exhibit intrinsic peroxidase-like (POD-like) activity, greatly advancing nanozyme research in biomedicine.¹² Specifically, nanozymes bridge enzyme simulation and material intelligence, imitate the functions of natural enzymes, and also possess the intrinsic ability to dynamically respond to and interact with the biological microenvironment. They integrate multiple functionalities and adapt their behavior to achieve specific therapeutic or diagnostic objectives. Composed of carbon-based nanomaterials, metal nanoparticles, and metal oxide nanostructures, nanozymes exhibit superior stability, selectivity, and therapeutic safety, overcoming natural enzyme limitations such as high cost and instability, thereby advancing cancer diagnostics and therapies.¹³

Recently, nanozymes have demonstrated significant potential in cancer diagnosis and treatment, highlighting a promising trajectory in this rapidly evolving field. Thus, it is an opportune time to review the latest advancements (Fig. 1). In this review, we first outline the enzymatic activities of functionalized nanozymes, including POD-like, catalase-like (CAT-like), oxidase-like (OXD-like), and superoxide dismutase-like (SOD-like) nanozymes. Although nanozymes can mimic various enzymes (e.g., hydrolases, nucleases), this review focuses on POD-, CAT-, OXD-, and SOD-like activities due to their direct relevance to tumor microenvironment (TME) modulation and established roles in cancer theranostics. Additionally, their unique physicochemical properties include optical, electromagnetic, and catalytic response characteristics. Next, the review explores nanozyme antitumor mechanisms, detailing how they generate reactive oxygen species (ROS), counteract antioxidants, modulate tumor hypoxia, reverse immunosuppression, disrupt energy metabolism, and ultimately induce tumor suppression and regression. Furthermore, the review elucidates the nanozymes-driven cancer diagnostic paradigm and consolidates a multimodal cancer therapeutic strategy involving nanozymes. It also discusses the rationale and challenges associated with the clinical translation and application of nanozymes. Lastly, we highlight future prospects and unresolved challenges in optimizing nanozymes for clinical use, offering actionable insights for designing more effective therapeutic strategies.

2. Dual functional foundations of nanozymes

Nanozymes integrate enzymatic activity with nanomaterial properties for cancer theranostics (Fig. 2). Catalytic functions, including POD-, CAT-, OXD-, and SOD-like mimicry, exploit tumor microenvironmental aberrations to induce oxidative stress, metabolic disruption, and more.¹⁴ Moreover, their physicochemical properties enable multimodal imaging: optical plasmonic responses for photoacoustic/fluorescence imaging, magnetic contrast for MRI, and redox-responsive signal amplification for dynamic monitoring.¹⁵ Surface engineering enhances target specificity and biocompatibility, creating closed-loop systems for precise therapy and real-time feedback.¹⁶ This dual functionality bridges therapeutic intervention and diagnostic precision, which is helpful in addressing the challenges related to tumor specificity and treatment resistance.

2.1. Catalytic antitumor functions

Nanozymes can mimic the activity of natural enzymes, making them valuable for diagnosing and treating diseases, including cancer.¹⁷ However, the widespread application of nanozymes is still hindered by an unclear catalytic mechanism. Therefore, elucidating the specific mechanisms underlying nanozymes’ catalytic activity is crucial for designing and developing effective applications in the biomedical field. Specifically, the catalytic activities of nanozymes can be categorized into POD-, CAT-, OXD-, and SOD-like activities based on their catalytic substrates (Table 1). This categorization will provide direction for the design of future advanced nanozymes, enhancing their efficacy and expanding their potential in cancer diagnosis and treatment.

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2.2. Diagnostic physicochemical properties

The diagnostic use of nanozymes stems not only from their enzyme-like activity, but also from their unique physicochemical properties, which enable multifunctional integration of real-time tumor biomarker detection, high-resolution imaging, and dynamic therapeutic response monitoring.⁵⁵ At the nanoscale, their architecture, characterized by high surface-area-to-volume ratios and tunable porosity, facilitates efficient interactions with biological molecules, enhancing sensitivity for detecting low-abundance biomarkers such as ctDNA, exosomes, or cell surface receptors.⁵⁶ Moreover, catalytic activity, mimicking natural enzymes like POD or OXD, drives signal amplification through enzymatic reactions (e.g., 3,3′,5,5′-tetramethylbenzidine (TMB) oxidation and H₂O₂ decomposition), enabling colorimetric, electrochemical, or fluorescent readouts for ultrasensitive biomarker quantification.⁵⁷

3. Nanozyme-mediated antitumor mechanisms

The TME exhibits unique and complex characteristics that significantly influence tumor growth and development. During the rapid proliferation of tumors, elevated levels of H₂O₂ are generated, which can enhance cell proliferation and metastasis.⁶⁹ Concurrently, increased concentrations of GSH render tumor cells more resistant to oxidative stress, thereby improving their survival rates.⁷⁰ Moreover, the poorly developed internal vascular system often results in solid tumor tissues being in a state of hypoxia.⁷¹ This oxygen-deficient condition compels tumor cells to rely on aerobic glycolysis for energy metabolism, leading to an accumulation of lactic acid.⁷² In this hypoxic and acidic environment, immunosuppressive mechanisms within the TME enable tumors to effectively evade immune surveillance by recruiting dysfunctional immune cells, ultimately diminishing the efficacy of immunotherapy.⁷³ In this complex and dysregulated microenvironment, nanozymes demonstrate a notable capacity to exploit the unique features of the TME to achieve targeted and site-specific treatment outcomes. This is achieved through a series of antitumor mechanisms specifically designed to target the distinctive characteristics of the TME, as illustrated in Fig. 3.

3.1. ROS generation and amplification

An excess of ROS can cause irreversible damage to cells by compromising intracellular lipids, proteins, and DNA.⁷⁴ Consequently, various types of ROS, including ˙OH, peroxide ions (O₂²⁻), ¹O₂, and O₂˙⁻ can be harnessed for the elimination of tumor cells.⁷⁵ Fortunately, nanozymes exhibit a unique capability to utilize their POD-like activity to catalyze the conversion of H₂O₂ into the particularly harmful ˙OH.⁷⁶ This characteristic makes these nanozymes promising candidates for cancer therapy.

To further enhance the catalytic activity of nanozymes, regulating the size and electronic structure of the active site has emerged as a promising strategy. Ai et al. capitalized on this concept by employing a metal–ligand cross-linking strategy to fabricate ultra-small nanoparticles supported by porous carbon nanorods derived from metal-supramolecular polymers.⁷⁷ The mechanism behind this nanozyme involves the activation of O₂ to generate O₂˙⁻, which subsequently oxidizes ascorbic acid (AA) to produce H₂O₂. This H₂O₂ is then transformed into harmful ˙OH via a POD-like pathway, ultimately inducing tumor cell death. In vivo evaluations using a 4T1 tumor-bearing mice model demonstrated that the combination of AA and the nanozyme exerted a substantial inhibitory effect on tumor growth, achieving a notable inhibition rate of 60%. In addition to the aforementioned strategies, enhancing the enzyme-like activity of nanozymes can be achieved by facilitating redox cycles in the presence of X-rays. Zhang et al. introduced a nanocomposite composed of SnS₂ NPs and Fe₃O₄ quantum dots (QDs), proposing the concept of X-ray-facilitated redox cycling of POD-like nanozymes.⁷⁸ Under X-ray irradiation, SnS₂ cofactors acted as electron donors, promoting electron transfer to Fe₃O₄ and thereby regenerating the Fe²⁺ sites on its surface. These regenerated Fe²⁺ sites reacted with overexpressed H₂O₂, resulting in a continuous production of reactive ROS, which enhanced the efficacy of tumor therapy. When subjected to X-ray treatment, the tumors displayed significant growth arrest, particularly in the group treated with PEG-SF NPs (PEG-SF NPs, where SF denotes SnS₂–Fe₃O₄) combined with X-rays, demonstrating the promising potential of this integrated approach.

However, the application of nanozymes in tumor therapy is significantly hampered by poor water stability and biocompatibility.⁷⁹ To address these challenges, Yang et al. explored the potential of silk fibroin (SF) as a solution.⁸⁰ They developed biocompatible AuPt bimetallic nanozymes by employing biocrystals derived from chloroauric and chloroplatinic acids as raw materials. The synthesized AuP@SF (APS) nanozymes demonstrated the capability to sustainably oxidize glucose to produce H₂O₂ and consume intracellular GSH. Furthermore, the APS nanozymes effectively catalyzed the reaction between adsorbed O₂ and endogenous H₂O₂, yielding O₂˙⁻ and ˙OH. Similarly, Zeng et al. designed BON nanospheres, which are biodegradable POD-mimicking enzymes enriched with N–O bonds, to catalyze the production of cytotoxic ˙OH.⁸¹ Their antitumor experiments in vivo illustrated that mice receiving injections of BON samples, as well as phosphate-buffered saline (PBS) as controls, exhibited significant tumor growth inhibition, with BON nanozymes achieving an notable 97% reduction in tumor growth.

Despite the advancements made, traditional ROS nanomaterials continue to encounter significant challenges. These include limitations related to external energy sources for tissue penetration and dependence on O₂ availability, among others.⁵⁸ To overcome these challenges, Liu et al. integrated ultra-small graphene quantum dots (GQDs) with POD-like activity and natural GOx into a pH-sensitive zeolitic imidazolate framework-8 (ZIF-8), resulting in the creation of an ultra-small nanozyme generator (ZIF@GOx/GQDs).⁸² In this innovative approach, GOx catalyzed the oxidation of glucose into H₂O₂ and gluconic acid within the TME, effectively disrupting the energy supply. As gluconic acid gradually induced the dissociation of ZIF-8, the ultra-small GQDs were released, facilitating substantial tumor penetration. Concurrently, the GQDs nanozyme transformed H₂O₂ into the toxic radical ˙OH, enabling the direct induction of endogenous ROS without relying on O₂ or external energy sources. However, it is important to emphasize that the production of ROS by nanomaterials is inherently non-selective, impacting both normal and cancerous cells.⁷⁹ This lack of selectivity can compromise the functionality of healthy tissues. In response to this limitation, Xu et al. designed a novel HNPs@PPy nanozyme with spatiotemporally controllable catalytic activity.⁸³ This innovative nanozyme features a heme nanoparticle (HNPs) core and a protective polypyrrole (PPy) coating, created through the self-assembly of heme in aqueous solution and in situ polymerization of pyrrole. The steady-state catalytic kinetics demonstrated that HNPs@PPy exhibited superior catalytic activity, comparable to that of natural horseradish peroxidase (HRP). Additionally, therapeutic evaluations in 4T1 tumor-bearing mice indicated that HNPs@PPy outperformed HNPs in inhibiting tumor growth. Notably, under NIR laser irradiation, HNPs@PPy could facilitate spatiotemporally controllable production of ROS in cancer cells, achieving selectivity in cancer treatment.

3.2. Antioxidant depletion and redox reprogramming

GSH is a vital endogenous antioxidant and redox regulator predominantly found in cells, particularly tumor cells, where its concentration is often significantly higher than in normal cells.⁸⁴ This small-molecule tripeptide consists of three amino acids: L-glutamic acid, L-cysteine, and glycine. Its distinctive molecular structure, characterized by a thiol (–SH) group, plays a crucial role in conferring its antioxidant properties. Notably, elevated levels of GSH are a hallmark of the TME.⁸⁵ Tumor cells, during periods of rapid proliferation and heightened anabolic metabolic activity, produce significant amounts of ROS.⁸⁶ While these ROS can induce apoptosis to some extent, tumor cells effectively mitigate the potential damage by upregulating GSH synthesis, thereby reducing oxidative stress and enhancing cell survival.⁸⁷ This resilience against oxidative damage renders GSH a promising target for cancer therapy.⁸⁸

To counteract the protective effect of GSH in tumor cells, innovative nanozymes with enzyme-like activity of oxidizing GSH have been developed, thereby enhancing the antitumor efficacy of various ROS-generating therapeutic agents. Zou et al. devised a novel approach by co-encapsulating iridium oxide nanozymes (IrOx) and β-lapachone (Lap) within thermally responsive phase change materials (PCMs) to create phase change cascade nanomedicines known as Lap-IrOx@PCM.⁸⁹ In this system, the POD-like activity of IrOx enables it to react with endogenous H₂O₂, resulting in the release of cytotoxic ˙OH. Concurrently, IrOx exhibits glutathione oxidase-like activity to consume GSH and contributing to the accumulation of harmful ROS. Additionally, metal-based nanozymes can deplete GSH using their intrinsic physicochemical properties. Shen et al. designed a magnetic hyperthermia nanozyme, Ir@MnFe₂O₄ NPs, which targets mitochondria.⁹⁰ Under the influence of an alternating magnetic field (AMF), these nanoparticles induce localized heating, resulting in mitochondrial damage (known as the magnetic hyperthermia effect). Notably, when GSH is overexpressed in cancer cell mitochondria, it reduces Fe(III) on the nanoparticle surface to Fe(II), leading to GSH depletion. Ultimately, these mechanisms disrupt cellular redox homeostasis, as demonstrated in in vivo experiments with mice harboring transplanted HeLa tumors, where the Ir@MnFe₂O₄ NPs + AMF treatment group exhibited a statistically significant reduction in tumor volume over a 14-day period.

In addition to the above innovative design using the characteristics of nanozymes themselves, improving the catalytic activity of nanozymes through structural redesign has yielded promising results. Li et al. developed a well-designed sandwich structure using cobalt ferrite layered double hydroxide (Co–Fe LDHs).⁹¹ This structure can also serve as a nanocore for loading gold nanoparticles (Au NPs) and maleimide (MA). The Co–Fe LDHs@Au@MA compound not only generates ROS and consumes GSH but also alleviates hypoxia through its Co/Fe dual-catalytic cycle, further contributing to the disruption of the established redox equilibrium within TME. Remarkably, the therapeutic efficacy against CT26 tumor-bearing mice reached 75.34% under 808 nm irradiation, showcasing the potential of Co–Fe LDHs@Au@MA to enhance oxidative stress directly at the tumor site and reshape the local TME. Similarly, the concept of defect engineering was introduced into the structural design of the nanozyme to enrich defects and vacancies and promote a better oxidation cycle. Chen et al. employed a nano-casting method to fabricate metallic n-MoSe₂ photothermal nanozymes, characterized by highly ordered nanopores and dispersed selenium vacancies.⁹² The nanoporous structure significantly enhanced the POD-like activity through the ultra-high 1T MoSe₂ phase transition while also increasing the consumption of endogenous antioxidant GSH, thus amplifying oxidative stress within the tumor. Furthermore, the metal MoSe₂ with its ordered nanopores could stimulate SPR effects, enhancing photothermal therapeutic properties and oxidative stress within the NIR biological window. Consequently, under NIR laser irradiation, MoSe₂ effectively disrupted the balance of redox and metabolic homeostasis in tumor regions, significantly improving therapeutic outcomes and highlighting the multifunctional potential of engineered nanozymes in cancer treatment.

3.3. Hypoxia remodeling and reoxygenation

Hypoxic regions in tumor tissues result from two main factors: rapid O₂ consumption by proliferating tumor cells and poor vascularization, leading to inadequate O₂ supply.⁷¹ Hypoxia not only impacts the metabolism and growth of tumor cells but also diminishes the catalytic efficiency of nanozymes.⁹³ This reduction in efficacy is attributed to the fact that a significant proportion of catalytic reactions involving nanozymes depend on the presence of O₂.⁵⁸ To tackle this challenge, the researchers proposed an innovative strategy that employs H₂O₂, a metabolite prevalent in the TME, as a substrate. The essence of this approach lies in recognizing that H₂O₂ is not merely a metabolic byproduct; it can also be decomposed under specific conditions to release O₂. This process provides a continuous and substantial supply of O₂ for catalysis involving nanozymes. As such, nanozymes are capable of exhibiting enhanced catalytic activity even within oxygen-deficient TME environments, thereby amplifying their antitumor effects. In the study conducted by Veroniaina et al., MnO₂ and the anticancer drug doxorubicin (Dox) were co-loaded into the cavity of recombinant heavy-chain ferritin (HFn), resulting in the formation of MnO₂–Dox@HFn.⁹⁴ By leveraging the enzyme-like activity of MnO₂, MnO₂–Dox@HFn can utilize the elevated concentration of H₂O₂ present in the TME as a substrate to generate O₂, thereby addressing tumor hypoxia. Subsequent investigations demonstrated that in a 4T1 tumor mice model, tumor suppression reached a notable 78.5% within the MnO₂–Dox@HFn treatment group. Similarly, Yu et al. developed a novel type of redox-responsive nanozyme known as Fe–MoOV, which achieved highly dispersed catalytic active sites through defect engineering.⁹⁵ In this nanozyme, OVs serve as substrate binding pockets that facilitate nearly unobstructed dissociation of H₂O₂. Concurrently, iron replacement sites exhibit strong binding interactions with oxygen-containing intermediates. These design features endow Fe–MoOV nanozymes with exceptional POD-like and CAT-like catalytic activity. Furthermore, the presence of Mo sites enhances O₂ adsorption; collectively, these factors contribute to enhanced catalytic performance. Further experimental results confirmed that combining Fe–MoOV with second near-infrared (IR-II) laser therapy effectively alleviated hypoxia within tumors and markedly inhibited tumor growth.

3.4. Immunosuppression reversal

The interaction between the TME and adjacent immune cells occurs via the circulatory and lymphatic systems, significantly influencing the antitumor immune response and its clinical outcomes.⁹⁶ Consequently, reshaping the immunosuppressive TME has emerged as a crucial strategy for enhancing immunotherapy.⁹⁷ Recent studies have demonstrated that nanomaterials possess notable potential for modulating immunosuppression within the tumor environment.

In the hypoxic TME, antitumor T-cell activation and effector functions are suppressed, enabling immune evasion by tumor cells.⁷¹ Consequently, it is reasonable to hypothesize that reversing this immunosuppressive state may be achievable through alleviating the hypoxic conditions within tumors. The capacity of nanozymes to enhance O₂ transport via enzyme-like activity has been well established. In this context, Wang et al. utilized this capability to develop a nanozyme reactor termed CDSDM nano-reactor (NR), which comprises a CaO₂/DOX core and a biocompatible SiO₂/DOX–MnO₂ shell.⁹⁸ A key feature of this design is that biocompatible CaO₂ serves as an O₂ storage component capable of efficiently releasing O₂ or H₂O₂, thereby providing substantial oxygenation throughout tumor tissue and mitigating hypoxia. Subsequent in vivo experiments conducted on B16F10 tumor-bearing mice demonstrated that the antitumor efficacy of cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) blockade was statistically more pronounced when used alongside CDSDM NPs compared to CTLA-4 blockade alone. These experimental findings indicate that the CDSDM NP delivery system effectively alleviates tumor hypoxia and reverses immunosuppressive conditions within the TME, thus markedly synergistically enhancing the efficacy of CTLA-4-based immunotherapy.

In addition, nanozymes can be loaded with immunomodulators to enhance their therapeutic efficacy. For instance, Deng et al. integrated Mn and SiO₂ onto CaCO₃ nanoparticles through cation exchange and heterophase nucleation, resulting in the development of a calcium–manganese dual-ion hybrid nanomaterial (CMS).⁹⁹ This innovative material serves as an immune adjuvant that alleviates hypoxia within tumors while simultaneously activating the innate immune response and adaptive immune priming. It induces the activation of the stimulator of interferon genes (STING) signaling pathway via Mn²⁺, promotes the polarization of tumor-associated macrophages (TAMs) from M2 to M1 phenotype, and effectively activates dendritic cells (DCs) for enhanced antigen presentation. The experimental results demonstrated significant lymphocytic infiltration of tumor-specific cytotoxic T lymphocytes (CTLs) into tumor tissues. In studies using a 4T1 triple-negative breast cancer model, the M1/M2 macrophage ratio in the CMS-treated group was significantly higher than in controls, reaching a notable 4.8-fold increase.

Not only that, the nanozyme, while serving as a carrier, also demonstrated a synergistic effect when combined with immunomodulators, thereby enhancing its antitumor efficacy. For instance, Xu et al. constructed iron manganese silicate nanoparticles (IMSN) coated with PEG and loaded with transforming growth factor-β (TGF-β) inhibitor (TI) to create IMSN-PEG-TI nanocomposites.¹⁰⁰ The IMSN exhibits enzyme-like activities, including POD and CAT functions, which enable it to generate ˙OH and O₂. In vitro experiments using multicellular tumor spheres (MCTS) and in vivo animal models revealed that the synergistic interaction between IMSN nanomaterials and TI significantly augmented catalytic activity. Notably, both IMSN and TI effectively modulate the TME, alleviate tumor hypoxia, and increase the M1/M2 macrophage ratio and reduce the proportions of CD4⁺ regulatory T cells (Tregs) and CD8⁺ T cells. Another strategy involves combining nanozymes with immune cells to jointly target solid tumors. Zhu et al. synthesized a tumor-specific nanozyme HA@Cu_2−xS-PEG (PHCN) through template chemical transformation.¹⁰¹ This nanozyme possesses dual properties of photothermal nanocatalysis (PNC), which can effectively reverse the immunosuppressive cancer environment while reshaping TME structure and function by disrupting its compact structure. The nanozyme-mediated photothermal effect degrades the tumor extracellular matrix and enhances blood perfusion, thus facilitating increased infiltration of B7-H3 chimeric antigen receptor T cells (B7-H3 CAR T cells). Concurrently, this nanozyme generates substantial amounts of ROS, which heighten tumor cell susceptibility to CAR T cell attack while diminishing their immune resistance. Taken together, these studies highlight the potential for the use of nanozymes in combination with immunomodulators and immune cells, opening up new avenues for antitumor therapeutic strategies.

3.5. Energy metabolism perturbation

Tumor cells typically exhibit a marked increase in glucose uptake due to their substantial energy and biosynthetic material requirements during rapid proliferation.¹⁰² This phenomenon, known as the Warburg effect, indicates that tumor cells engage in anaerobic glycolysis even under aerobic conditions, resulting in the production of lactic acid and energy.¹⁰³ Consequently, glucose levels are generally elevated within these cells, reflecting a significant metabolic dependence on glucose. As an effective biocatalyst, GOx can catalyze the oxidation of glucose to produce gluconic acid and H₂O₂, which can profoundly influence the growth and survival of tumor cells.¹⁰⁴ Therefore, it is reasonable to infer that nanozyme combined with GOx may serve as a promising target for tumor therapy. Wei et al. demonstrated this potential by developing a natural GOx- and PEG-based multi-enzyme active nanozyme system known as IrRu–GOx@PEG NPs.¹⁰⁵ The role of GOx is to degrade glucose into gluconic acid and H₂O₂, thereby depriving tumors of essential nutrients through a “starvation” therapy approach that effectively inhibits tumor growth. Additionally, IrRu NPs can catalyze the conversion of H₂O₂ into highly reactive ¹O₂, promoting apoptosis in tumor cells. Simultaneously, IrRu NPs facilitate the transformation of H₂O₂ into O₂, creating a glucose-depleting nanosystem that reduces O₂ consumption—thereby enhancing the efficacy of starvation therapy. In vivo studies revealed significantly reduced Ki-67 (a cell proliferation marker) expression signals in tumor tissues treated with IrRu–GOx@PEG NPs, indicating that this treatment markedly decreased tumor cell proliferation while increasing apoptosis rates. In addition, the study conducted by Liu et al. also highlighted the impact of TME.¹⁰⁶ They developed an acidic TME-responsive and radiation-mediated cascaded functional nanozyme (TLGp) by conjugating nitrogen-doped titanium nitride (TiN) NPs with pH-responsive PEG-modified GOx-encapsulated liposomes. In the acidic TME, GOx facilitates the release of glucose from PEG and catalyzes the production of substantial amounts of H₂O₂. Subsequently, the POD activity of TiN NPs is markedly enhanced through nitrogen doping, rendering TiN NPs highly effective in catalyzing H₂O₂ and ultimately generating supertoxic ˙OH, which induce tumor cell death. In vivo experiments utilizing a 4T1 tumor-bearing mice model demonstrated that tumor growth inhibition rates in the TLGp + NIR group reached approximately 96%, underscoring the potential application of this nanozyme in tumor therapy.

Notably, the combination of nanozyme with GOx resulted in a reduction in nutrient metabolism and inhibited nucleic acid repair capabilities. Tumor cells metabolize glucose through glycolysis to produce amino acids and other biological macromolecules, which serve as essential substrates for nucleic acid repair.¹⁰⁷ Consequently, diminishing nutrient metabolism can effectively hinder the ability of tumor cells to perform nucleic acid repair.¹⁰⁸

Furthermore, there exists a close relationship between glucose metabolism and adenosine triphosphate (ATP) levels in tumor cells.¹⁰⁹ Although anaerobic glycolysis yields less ATP, tumor cells enhance glucose uptake and consequently increase ATP production by upregulating glucose transporters.¹¹⁰ The elevation of ATP not only promotes the proliferation, migration, and survival of tumor cells but also plays a crucial role in signal transduction, influencing processes such as the cell cycle and apoptosis.¹¹¹ Therefore, employing nanozymes to regulate ATP levels may effectively improve therapeutic outcomes. To this end, Chang et al. developed a porous Cu SAzyme loaded with the glycoside LIK066.¹¹² LIK066 effectively obstructs the energy source required for ATP production by inhibiting the activity of sodium-dependent glucose transporter (SGLT), thereby rendering cancer cells incapable of generating sufficient energy to synthesize heat shock proteins (HSPs). Additionally, Cu SAzyme possesses the capability to convert excess H₂O₂ present in TME into O₂ through catalytic action, thus alleviating tumor hypoxia and activating the catalytic activity of OXD. Moreover, Cu SAzyme can catalyze O₂ to form O₂˙⁻, which react with H⁺ under acidic TME conditions to produce ¹O₂; it can also facilitate the formation of ˙OH via POD activity. Ultimately, researchers successfully eliminated heat shock proteins present in cancer cells by combining SGLT inhibitors with generated ROS. Through this synergistic strategy, Cu SAzyme loaded with LIK066 efficiently removes HSPs while achieving enhanced mild photothermal therapeutic effects.

4. Nanozymes-involved multimodal cancer therapeutic strategies

Nanozymes, as the next generation of artificial enzymes, exhibit unique properties compared to natural enzymes. They exhibit remarkable stability under harsh conditions, are cost-effective, and can be easily synthesized and functionalized, all of which facilitate their diverse applications in biomedicine.¹¹³ Among these applications, nanozyme-catalyzed tumor therapy has experienced significant advancements, particularly due to the enzyme-like activity displayed by nanozymes. However, achieving an effective therapeutic outcome with a single treatment modality presents challenges primarily due to the complexity, diversity, and heterogeneity of the TME.¹¹⁴ To address this issue, researchers frequently combine nanozyme-catalyzed therapy with other therapeutic strategies (Fig. 4). For instance, integrating phototherapy can enhance localized treatment effects; coupling it with chemotherapy allows for a synergistic impact that targets cancer cells from multiple angles. Similarly, radiotherapy can complement nanozyme applications by increasing tumor sensitivity; starvation therapy may further deprive tumors of essential nutrients thereby amplifying overall therapeutic efficacy. Therefore, the strategic integration of these approaches, as illustrated in Table 2, not only improves the effectiveness of nanozyme-catalyzed therapy but also opens up new avenues for advanced cancer treatment paradigms.

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4.1. Oxidative attack strategies

Oxidative attack and redox-modulating strategies leverage nanozymes to perturb the redox homeostasis of tumors by either augmenting cytotoxic ROS production or depleting intracellular antioxidants. This approach capitalizes on the elevated basal ROS levels in the TME, where cancer cells rely heavily on GSH and glutathione peroxidase 4 (GPX4) to counteract oxidative stress.¹³² Nanozymes, such as transition metal-based nanoparticles, exhibit POD-, CAT-, or OXD-like activities that can either decompose H₂O₂ into ˙OH or O₂˙⁻ under TME-specific conditions.¹³³ By disrupting the antioxidant defense systems, these nanozymes induce ferroptosis or apoptosis directly through mitochondrial dysfunction.

4.2. Metabolic disruption strategies

Metabolic disruption strategies target the aberrant metabolic reprogramming in cancer cells, such as aerobic glycolysis or amino acid dependencies.¹⁵² Nanozymes can disrupt energy production or nutrient uptake by mimicking enzymes in key metabolic pathways. For example, GOx-mimicking nanozymes, such as Au or CuO nanoparticles, catalyze the oxidation of glucose to gluconic acid and H₂O₂, thereby depleting extracellular glucose levels and generating intracellular ROS.¹⁵³ This dual effect not only starves cancer cells of their primary energy source but also exacerbates oxidative stress, thereby creating a synergistic cytotoxic microenvironment.

4.3. Physicochemical synergy strategies

Physicochemical synergy strategies integrate nanozyme activity with external stimuli or chemotherapeutics to achieve multi-modal therapy. For instance, photo-responsive nanozymes, such as porphyrin-based MOFs loaded with Pt or Au nanoparticles, can simultaneously generate ROS under NIR light and enhance enzymatic activity through localized photothermal heating.¹⁶⁵ Furthermore, chemo-nanozyme hybrid systems, where nanozymes are combined with conventional chemotherapeutic agents, exploit the TME’s acidic pH or glutathione-rich environment to trigger drug release while augmenting ROS generation.¹⁶⁶ By integrating multiple therapeutic modalities, these strategies provide a promising approach to tackle the heterogeneity and treatment resistance of solid tumors.

5. Nanozyme-enabled precision diagnostics

Tumor markers are defined as substances synthesized and released by tumor cells or produced by the body in response to tumor cells.¹⁸¹ The detection of tumor markers using chemical analysis, immunological assays, and genomic sequencing is of great value for early tumor diagnosis, monitoring the effectiveness of treatment, detecting recurrence, and evaluating prognosis after disease progression.¹⁸² Nanozymes, a novel type of artificial enzyme, exhibit catalytic activity similar to enzymes and have unique physicochemical properties such as photothermal characteristics, superparamagnetic properties, and fluorescence, that are intrinsic to nanomaterials.¹⁸³ These unique features provide the potential for utilizing nanozymes in various applications, especially in in vivo monitoring and disease imaging (Fig. 5).

Nanozymes integrate catalytic activity with diagnostic characteristics. They amplify signals through enzymatic reactions, such as the oxidation of POD-like substrates, thereby enhancing colorimetric or fluorescent signals.⁵⁸ In contrast, traditional contrast agents generally rely solely on passive signal enhancement without such amplification mechanisms.¹⁸⁴ They can perform multimodal imaging within a single nanozyme entity. For example, it is possible to integrate magnetic MRI, CT, and PAI.¹⁸⁵ However, traditional contrast agents are typically restricted to a single imaging modality, necessitating separate administrations for a comprehensive diagnosis.¹⁸⁶ In addition, due to surface modification techniques such as PEGylation, nanozymes generally possess better biocompatibility and longer blood circulation times.⁵⁸ This effectively addresses issues associated with traditional contrast agents, such as the rapid clearance from the body or toxicity problems. For example, some ionic contrast agents used in CT and gadolinium-based MRI contrast agents, which may cause nephrogenic systemic fibrosis, have limitations.¹⁸⁷ In comparison, manganese-based or iron oxide-based nanozymes not only provide comparable or even superior contrast enhancement but also exhibit intrinsic catalytic activity.¹⁸⁸ This catalytic activity can be exploited for therapeutic applications, thus realizing the integration of diagnosis and treatment. Combining tumor marker detection with nanozyme characteristics offers a promising approach for developing novel early tumor diagnostic strategies. This information is presented objectively and impartially and is intended to serve as a reference for related research and clinical applications.

5.1. Enzyme-like activity-based diagnostic probes

Nanozymes have revolutionized cancer diagnostics by enabling ultrasensitive, multiplexed detection of tumor-derived biomarkers such as ctDNA, CTCs, and extracellular vesicles (EVs).⁵⁶ These probes leverage nanozyme catalytic activities to amplify biomarker signals, overcome biological noise in complex matrices, and achieve unprecedented precision for early diagnosis and therapeutic monitoring.⁵⁸

CtDNA, carrying tumor-specific mutations, serves as a critical biomarker for non-invasive cancer screening. Li et al. engineered a photoelectrochemical (PEC) biosensor using Co₃O₄ nanozymes to achieve triple-signal quenching mechanism for ctDNA detection.¹⁸⁹ By integrating enzyme-free target recycling amplification with the POD-like activity of Co₃O₄, the system generated insoluble 3-amino-9-ethylcarbazole (AEC) deposits that suppressed MgIn₂S₄ photoelectric response through competitive light absorption, electron transfer blocking, and hindered ascorbic acid donation. This approach achieved a detection limit of 0.15 fM and a linear range of 0.4 fM–400 pM in human serum, with recoveries of 97–103% (RSD ≤ 3.5%), demonstrating robust clinical applicability for liquid biopsies.

While ctDNA provides genetic insights, CTCs offer direct evidence of metastatic potential. Li et al. addressed CTC detection challenges through a trimetallic AuIrPt nanozyme-based electrochemical cytosensor.¹⁹⁰ The AuIrPt nanozymes exhibited multi-enzymatic activities (SOD, POD, CAT) and amplified signals for CTCs captured via a triple-site recognition strategy (anti-mucin 1 (MUC1), anti-epidermal growth factor receptor (EGFR), and epithelial cell adhesion molecule (EpCAM) aptamers). This system achieved a detection limit of 2 cells per mL in whole blood with 90.76–106.4% recovery, minimizing false positives by requiring concurrent antigen expression.

Beyond CTCs, tumor-derived extracellular EVs, including exosomes, carry proteins and nucleic acids critical for cancer subtyping. Gong et al. designed a dual-mode electrochemical/colorimetric sensor using MoS₂–Au@Pt nanozymes for exosome analysis.¹⁹¹ The core–shell Au@Pt nanoparticles catalyzed TMB oxidation, generating electrochemical and colorimetric signals, while anti-CD63 aptamers ensured specific exosome capture. The platform achieved detection limits of 9.3 particles per mL (electrochemical) and 4.2 × 10³ particles per mL (colorimetric), with 93–106% recovery in clinical serum samples.

5.2. Physicochemical property-driven multimodal imaging

Due to their unique physicochemical characteristics, nanozymes open up innovative technological possibilities for in vivo monitoring and disease imaging (Table 3). For instance, their superparamagnetic properties enable controllability under an external magnetic field, facilitating precise localization and tracking of these nanosystems.¹⁹² This capability is particularly valuable for applications requiring real-time tracking of therapeutic agents in vivo. In addition to magnetism, the fluorescence properties of nanozymes enable the observation of biochemical reactions in cells and pathological changes in tissues through the detection of fluorescent signals.¹⁹³ This optical characteristic not only enhances our understanding of cellular processes but also aids in visualizing disease states, providing a complementary approach to monitoring biological activities. Moreover, the photothermal properties of nanozymes facilitate heat generation when exposed to specific wavelengths of light. This feature enables targeted photothermal ablation strategies that selectively destroy pathological tissues while sparing healthy ones.¹⁹⁴ Collectively, these physicochemical properties offer valuable insights into early disease diagnosis and treatment. They empower healthcare professionals to perform real-time monitoring of disease progression and evaluate treatment efficacy, ultimately enhancing the precision and effectiveness of medical interventions.

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6. Closed-loop theranostic systems

The closed-loop theranostic system is an integrated platform that continuously monitors treatment responses and uses feedback mechanisms to adjust adaptive therapeutic interventions, forming a self-regulating cycle. This core feature of real-time feedback and dynamic modulation sets them apart from traditional multimodal therapies, which typically combine multiple treatment modalities such as chemotherapy with phototherapy in a preprogrammed, non-adaptive manner without continuous monitoring of real-time biological responses. Specifically, closed-loop systems leverage nanozymes’ unique ability to simultaneously act as therapeutic agents and diagnostic probes. For example, a nanozyme may generate cytotoxic ROS for therapy while emitting imaging signals proportional to tumor oxidative stress levels, allowing clinicians to assess treatment efficacy in real time.²¹⁵ If the signal indicates an insufficient response, the system can autonomously amplify catalytic activity via NIR-triggered enhancement of POD-like activity or release additional therapeutic payloads, ensuring optimal therapeutic outcomes. In contrast, traditional multimodal strategies rely on fixed combinations of treatments, with adjustments made only based on delayed, external evaluations such as post-treatment imaging or biomarker assays rather than real-time, in situ feedback.

CDT efficacy is often limited by fluctuating H₂O₂ levels and tumor heterogeneity.²¹⁶ Zhang et al. engineered ROS-responsive Fe₃O₄-based nanoparticles (FGTL) integrating GOx and immunomodulatory Tuftsin.²¹⁷ The system disassembles in H₂O₂-rich TMEs, releasing GOx to amplify Fenton reactions for enhanced ferroptosis, while Tuftsin polarizes immunosuppressive M2 macrophages to antitumor M1 phenotypes and activates cytotoxic T cells. Real-time ¹²⁹Xe MRI tracked therapeutic efficacy by mapping restored lung ventilation and reduced alveolar septal thickness in murine lung metastasis models. This dual-function platform achieved significant tumor suppression while providing non-invasive assessment of treatment response, highlighting the potential of MRI-guided feedback to optimize CDT-immunotherapy synergy.

CDT leverages ROS dynamics as an intrinsic biological readout, allowing the system to self-regulate catalytic activity in response to changes in oxidative stress levels within the tumor microenvironment.²¹⁶ In contrast, phototherapy, which depends on external light stimuli, requires precise spatiotemporal control of light delivery to ensure that therapeutic activation aligns with real-time imaging feedback, such as tumor localization or response markers.²¹⁸ Liu et al. developed core/shell Au@Cu₂S nanocrystals encapsulated in denatured bovine serum albumin (dBSA) for optical coherence tomography (OCT)-guided phototherapy.²¹⁹ The nanocrystals exhibited strong NIR absorption and low cytotoxicity. In vivo OCT imaging revealed time-dependent tumor accumulation, with peak intratumoral signals at 20 minutes post-injection, enabling spatiotemporal control of light delivery. The dBSA coating ensured colloidal stability and enhanced tumor penetration, as confirmed by confocal microscopy. This work underscores the potential of OCT-guided systems to personalize phototherapy dosing, minimizing collateral damage while maximizing tumor ablation.

7. Nanozymes in clinical trials

The introduction of nanozymes has illuminated the biological potential of inorganic nanomaterials, demonstrating their ability to catalyze substrates similar to natural enzymes under physiological conditions through mechanisms and kinetics closely resembling those of their natural counterparts.²²⁰ These engineered nanozymes present a distinct set of advantages over traditional enzymes, including enhanced catalytic activity, cost-effectiveness, exceptional stability, ease of mass production, and tunable catalytic activities.⁵⁸ Furthermore, as a novel category of artificial enzymes, nanozymes not only exhibit enzyme-like catalytic properties but also possess unique physicochemical characteristics such as photothermal effects, superparamagnetism, and fluorescence. The therapeutic potential of nanozymes has been evidenced in numerous animal studies, but the application of these in clinical settings is still relatively rare.¹⁸³ As a point of reference, FDA-approved IONPs, such as Resovist (Ferucarbotran), have undergone evaluation in clinical trials and have shown promise in in vivo monitoring and disease imaging due to their exceptional physicochemical properties. For example, determining the status of sentinel lymph nodes (SLN) is crucial for devising appropriate treatment strategies for breast cancer patients.²²¹ Currently, the standard procedure for SLN detection employs a dual technique involving technetium 99m (Tc99) and blue dye (BD).²²² However, studies have demonstrated that superparamagnetic IONPs can serve as equally reliable alternatives.²²³ Furthermore, Resovist (Ferucarbotran) has the unique ability to label specific cells within the body, allowing for real-time imaging of their natural movements in vivo.²²⁴ This capacity is particularly beneficial in the context of stem cell therapy, where non-invasive monitoring of transplanted cells is essential for tracking their biodistribution and biological functions.²²⁵ While Resovist (ferucarbotran)’s primary application remains MRI imaging, its iron oxide core demonstrates POD-like properties exploitable for future theranostic designs.^226–228

As nanozymes have advanced from preclinical research to early-stage clinical evaluation, their potential to integrate enzyme mimicry with material-based intelligent properties in cancer theranostics has increasingly attracted attention, though translating these innovations into clinical practice presents unique challenges. First, biocompatibility remains a critical concern. While nanozymes like IONPs have shown promise in preclinical studies, their long-term accumulation in organs such as the liver and spleen can trigger immune responses.²²⁹ Additionally, surface modifications such as PEGylation intended to improve circulation can sometimes elicit anti-PEG antibodies, reducing efficacy over repeated administrations.²³⁰ This challenge has been observed in trials of PEGylated cerium oxide nanozymes for radioprotection. Second, large-scale manufacturing faces significant challenges. The synthesis of single-atom nanozymes, which exhibit high catalytic specificity, requires precise control over atomic dispersion.²³¹ Similarly, metal–organic framework-based nanozymes, while versatile, face scalability issues due to their sensitivity to reaction conditions.²³² This results in high production costs that hinder widespread clinical adoption. Third, regulatory challenges persist, particularly in defining standardized toxicity evaluation metrics for nanozymes, which differ from small-molecule drugs.²³³

8. Conclusions and outlooks

This review highlights the transformative potential of nanozymes as a cutting-edge and versatile platform in biomedicine. By highlighting their unique enzyme-like properties, physicochemical characteristics, tunable catalytic activity, and inherent biocompatibility, we illustrate their promising applications across a spectrum of fields, from cancer therapy to tumor imaging. As research in nanomaterials continues to evolve, the exploration of synergies with traditional therapies and the integration of nanozymes into innovative diagnostic platforms herald significant advancements in precision medicine. This review also delves into the fundamental mechanisms underlying nanozyme function and therapeutic effects, emphasizing the necessity for sustained interdisciplinary collaboration and innovative strategies to unlock their full potential for enhancing human health and well-being. Advancements in synthetic technology have significantly enhanced the diverse properties of nanozymes, driving the development of a wide range of materials.

Nonetheless, several challenges must be addressed to realize the full potential of nanozymes in cancer treatment. These include optimizing biocompatibility and stability within complex biological environments, mitigating off-target effects, and ensuring precise control over controlled therapeutic payload release dynamics.⁷⁹ Additionally, thorough evaluations of safety, efficacy, and long-term effects are crucial for transitioning these therapies from preclinical studies to clinical applications.²³⁴ Looking to the future, the potential of nanozymes is poised to expand even further, opening up numerous avenues for advancements in cancer diagnosis and treatment. Future research should prioritize enhancing the catalytic efficiency and specificity of nanozymes, while also developing novel diagnostic techniques that leverage nanozyme technology. Notably, AI-driven knowledge bases enable transparent prediction of nanozymes’ multiple catalytic activities by integrating multi-source data on material properties, catalytic mechanisms, and environmental factors, allowing researchers to identify structure–activity relationships that guide the design of multifunctional nanozymes with tailored POD-, CAT-, or SOD-like activities.²³⁵ Additionally, machine learning models have been trained on large datasets to predict catalytic efficiency, substrate specificity, and stability under physiological conditions.²³⁶ Moreover, exploring the possibilities of personalized medicine through tailored nanozyme-based treatments could revolutionize patient care. The combination of nanozymes with other nanomaterials or therapeutic agents is expected to yield remarkable therapeutic synergies, thereby significantly improving treatment outcomes. This review not only provides a comprehensive overview of the current state of nanozymes but also emphasizes the critical need to address existing challenges. By doing so, we can fully harness the transformative power of nanozymes in advancing cancer diagnosis and treatment, paving the way for innovative approaches in the future of biomedicine.

Conflicts of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

This review did not generate any new data or original data.

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (No. 82204317), the Excellent Youth Science Foundation of Liaoning Province (2024JH3/10200046) and the Basic Scientific Research Project of Liaoning Provincial Department of Education (No. LJ212410163015).

References

1. R. L. Siegel, A. N. Giaquinto and A. Jemal, Ca-Cancer J. Clin., 2024, 74, 12–49 CrossRef .
2. M. Khalifa and M. Albadawy, Comput. Methods Programs Biomed. Update, 2024, 5, 100146 CrossRef .
3. R. Bergman, I. Katz, C. Lichtig, Y. Ben-Arieh, A. R. Moscona and R. Friedman-Birnbaum, J. Am. Acad. Dermatol., 1992, 26, 462–466 CrossRef CAS .
4. T. Velpula and V. Buddolla, J. Liq. Biopsy, 2025, 8, 100297 CrossRef PubMed .
5. U. Anand, A. Dey, A. K. S. Chandel, R. Sanyal, A. Mishra, D. K. Pandey, V. De Falco, A. Upadhyay, R. Kandimalla, A. Chaudhary, J. K. Dhanjal, S. Dewanjee, J. Vallamkondu and J. M. Pérez De La Lastra, Genes Dis., 2023, 10, 1367–1401 CrossRef CAS PubMed .
6. H. Chen, Y. Li, Q. Shen, G. Guo, Z. Wang, H. Pan, M. Wu, X. Yan and G. Yang, Mol. Med., 2024, 30, 284 CAS .
7. S. Taefehshokr, A. Parhizkar, S. Hayati, M. Mousapour, A. Mahmoudpour, L. Eleid, D. Rahmanpour, S. Fattahi, H. Shabani and N. Taefehshokr, Pathol., Res. Pract., 2022, 229, 153723 CrossRef CAS PubMed .
8. V. R. Shinde, N. Revi, S. Murugappan, S. P. Singh and A. K. Rengan, Photodiagn. Photodyn. Ther., 2022, 39, 102915 CrossRef CAS PubMed .
9. Z. Chen, R. K. Kankala, L. Long, S. Xie, A. Chen and L. Zou, Coord. Chem. Rev., 2023, 481, 215051 CrossRef CAS .
10. B. Todaro, E. Ottalagana, S. Luin and M. Santi, Pharmaceutics, 2023, 15, 1648 CrossRef CAS PubMed .
11. F. Manea, F. B. Houillon, L. Pasquato and P. Scrimin, Angew. Chem., Int. Ed., 2004, 43, 6165–6169 CrossRef CAS PubMed .
12. L. Gao, J. Zhuang, L. Nie, J. Zhang, Y. Zhang, N. Gu, T. Wang, J. Feng, D. Yang, S. Perrett and X. Yan, Nat. Nanotechnol., 2007, 2, 577–583 CrossRef CAS PubMed .
13. N. Baig, I. Kammakakam and W. Falath, Mater. Adv., 2021, 2, 1821–1871 RSC .
14. X. Xu, Y. Zhang, C. Meng, W. Zheng, L. Wang, C. Zhao and F. Luo, J. Mater. Chem. B, 2024, 12, 9111–9143 RSC .
15. S. Liu, W. Zhang, Q. Chen, J. Hou, J. Wang, Y. Zhong, X. Wang, W. Jiang, H. Ran and D. Guo, Nanoscale, 2021, 13, 14049–14066 RSC .
16. J. Almeida-Pinto, M. R. Lagarto, P. Lavrador, J. F. Mano and V. M. Gaspar, Adv. Sci., 2023, 10, 2304040 CrossRef CAS PubMed .
17. S. Ji, B. Jiang, H. Hao, Y. Chen, J. Dong, Y. Mao, Z. Zhang, R. Gao, W. Chen, R. Zhang, Q. Liang, H. Li, S. Liu, Y. Wang, Q. Zhang, L. Gu, D. Duan, M. Liang, D. Wang, X. Yan and Y. Li, Nat. Catal., 2021, 4, 407–417 CrossRef CAS .
18. N. V. S. Vallabani, A. Vinu, S. Singh and A. Karakoti, J. Colloid Interface Sci., 2020, 567, 154–164 CrossRef CAS PubMed .
19. F. Gong, N. Yang, Y. Wang, M. Zhuo, Q. Zhao, S. Wang, Y. Li, Z. Liu, Q. Chen and L. Cheng, Small, 2020, 16, 2003496 CrossRef CAS .
20. Z. Li, W. Liu, P. Ni, C. Zhang, B. Wang, G. Duan, C. Chen, Y. Jiang and Y. Lu, Chem. Eng. J., 2022, 428, 131396 CrossRef CAS .
21. Q. Liang, J. Xi, X. J. Gao, R. Zhang, Y. Yang, X. Gao, X. Yan, L. Gao and K. Fan, Nano Today, 2020, 35, 100935 CrossRef CAS .
22. D. Wang, J. Wang, X. J. Gao, H. Ding, M. Yang, Z. He, J. Xie, Z. Zhang, H. Huang, G. Nie, X. Yan and K. Fan, Adv. Mater., 2024, 36, 2310033 CrossRef .
23. S. Xia, F. Wu, L. Cheng, H. Bao, W. Gao, J. Duan, W. Niu and G. Xu, Small, 2023, 19, 2205997 CrossRef PubMed .
24. X. Cheng, X. Zhou, Z. Zheng and Q. Kuang, Chem. Eng. J., 2022, 430, 133079 CrossRef .
25. K. Xiang, H. Wu, Y. Liu, S. Wang, X. Li, B. Yang, Y. Zhang, L. Ma, G. Lu, L. He, Q. Ni and L. Zhang, Theranostics, 2023, 13, 2721–2733 CrossRef .
26. Z. Wang, G. Li, Y. Gao, Y. Yu, P. Yang, B. Li, X. Wang, J. Liu, K. Chen, J. Liu and W. Wang, Chem. Eng. J., 2021, 404, 125574 CrossRef .
27. L. Li, L. Cao, X. Xiang, X. Wu, L. Ma, F. Chen, S. Cao, C. Cheng, D. Deng and L. Qiu, Adv. Funct. Mater., 2022, 32, 2107530 CrossRef .
28. Q. Zhao, L. Zheng, Y. Gao, J. Li, J. Wei, M. Zhang, J. Sun, J. Ouyang and N. Na, J. Am. Chem. Soc., 2023, 145, 12586–12600 CrossRef PubMed .
29. C. Liu, M. Zhang, H. Geng, P. Zhang, Z. Zheng, Y. Zhou and W. He, Appl. Catal., B, 2021, 295, 120317 CrossRef .
30. B. Yu, W. Sun, J. Lin, C. Fan, C. Wang, Z. Zhang, Y. Wang, Y. Tang, Y. Lin and D. Zhou, Adv. Sci., 2024, 11, 2307798 CrossRef CAS PubMed .
31. F. Meng, M. Peng, Y. Chen, X. Cai, F. Huang, L. Yang, X. Liu, T. Li, X. Wen, N. Wang, D. Xiao, H. Jiang, L. Xia, H. Liu and D. Ma, Appl. Catal., B, 2022, 301, 120826 CrossRef CAS .
32. X. Hu, F. Li, F. Xia, X. Guo, N. Wang, L. Liang, B. Yang, K. Fan, X. Yan and D. Ling, J. Am. Chem. Soc., 2020, 142, 1636–1644 CrossRef CAS PubMed .
33. W. Yang, C. Zhou, C. He, Y. Yang, W. Aiyiti, L. Xu and C. Shuai, J. Colloid Interface Sci., 2025, 678, 260–271 CrossRef CAS PubMed .
34. F. Cui, L. Li, D. Wang, L. Ren, J. Li, Y. Lu, Y. Meng, R. Ma, S. Wang, X. Li, T. Li and J. Li, Chem. Eng. J., 2023, 473, 145291 CrossRef CAS .
35. L. Yao, M.-M. Zhao, Q.-W. Luo, Y.-C. Zhang, T.-T. Liu, Z. Yang, M. Liao, P. Tu and K.-W. Zeng, ACS Nano, 2022, 16, 9228–9239 CrossRef CAS PubMed .
36. Z. Jiang, W. Wang, Y. Zhao, T. Li, D. Xin, C. Gai, D. Liu and Z. Wang, J. Controlled Release, 2024, 365, 1074–1088 CrossRef CAS PubMed .
37. Y. Qin, S. Li, L. Liang, J. Wu, Y. Zhu, S. Zhao and F. Ye, Sens. Actuators, B, 2024, 412, 135782 CrossRef CAS .
38. S. Li, Z. Chen, F. Yang and W. Yue, Chin. Chem. Lett., 2024, 35, 108793 CrossRef CAS .
39. H. Dong, W. Du, J. Dong, R. Che, F. Kong, W. Cheng, M. Ma, N. Gu and Y. Zhang, Nat. Commun., 2022, 13, 5365 CrossRef CAS PubMed .
40. Q. Wang, C. Chiu, H. Zhang, X. Wang, Y. Chen, X. Li and J. Pan, Adv. Healthcare Mater., 2024, 13, 2303390 CrossRef CAS PubMed .
41. R. Zeng, Y. Li, X. Hu, W. Wang, Y. Li, H. Gong, J. Xu, L. Huang, L. Lu, Y. Zhang, D. Tang and J. Song, Nano Lett., 2023, 23, 6073–6080 CrossRef CAS PubMed .
42. Y. Ye, J. Zou, W. Wu, Z. Wang, S. Wen, Z. Liang, S. Liu, Y. Lin, X. Chen, T. Luo, L. Yang, Q. Jiang and L. Guo, Nanoscale, 2024, 16, 3324–3346 RSC .
43. S. Wei, W. Zhang, Z. Wang, G. Zhang, W.-W. Li, G. Zeng and S. Zhang, ACS Catal., 2025, 15, 6439–6449 CrossRef CAS .
44. Y. Guo, Y. Xue, B. Shen, Y. Dong, H. Zhang, J. Yuan, Z. Liu, L. Li and K. Ren, ACS Appl. Mater. Interfaces, 2024, 16, 27511–27522 CrossRef CAS PubMed .
45. H. Dong, W. Du, J. Dong, R. Che, F. Kong, W. Cheng, M. Ma, N. Gu and Y. Zhang, Nat. Commun., 2022, 13, 5365 CrossRef CAS PubMed .
46. D. Xu, L. Wu, H. Yao and L. Zhao, Small, 2022, 18, 2203400 CrossRef CAS PubMed .
47. S. Gao, H. Lin, H. Zhang, H. Yao, Y. Chen and J. Shi, Adv. Sci., 2019, 6, 1801733 CrossRef PubMed .
48. Y. Su, F. Wu, Q. Song, M. Wu, M. Mohammadniaei, T. Zhang, B. Liu, S. Wu, M. Zhang, A. Li and J. Shen, Biomaterials, 2022, 281, 121325 CrossRef CAS PubMed .
49. Y. Chong, Q. Liu and C. Ge, Nano Today, 2021, 37, 101076 CrossRef CAS .
50. X. Hu, N. Wang, X. Guo, Z. Liang, H. Sun, H. Liao, F. Xia, Y. Guan, J. Lee, D. Ling and F. Li, Nano-Micro Lett., 2022, 14, 101 CrossRef CAS PubMed .
51. H. Zhao, R. Zhang, X. Yan and K. Fan, J. Mater. Chem. B, 2021, 9, 6939–6957 RSC .
52. H. W. Kroto, J. R. Heath, S. C. O’Brien, R. F. Curl and R. E. Smalley, Nature, 1985, 318, 162–163 CrossRef CAS .
53. Z. Wang, G. Li, Y. Gao, Y. Yu, P. Yang, B. Li, X. Wang, J. Liu, K. Chen, J. Liu and W. Wang, Chem. Eng. J., 2021, 404, 125574 CrossRef CAS .
54. J. Lu, L. Song, S. Feng, K. Wang, Y. Mao, Y. Gao, Q. Zhao and S. Wang, Chem. Eng. J., 2024, 481, 148270 CrossRef CAS .
55. M. Liang and X. Yan, Acc. Chem. Res., 2019, 52, 2190–2200 CrossRef CAS PubMed .
56. R. Du, L. Zhu, J. Gan, Y. Wang, L. Qiao and B. Liu, Anal. Chem., 2016, 88, 6767–6772 CrossRef CAS PubMed .
57. C. Zhu, Z. Zhou, X. J. Gao, Y. Tao, X. Cao, Y. Xu, Y. Shen, S. Liu and Y. Zhang, Chem. Sci., 2023, 14, 6780–6791 RSC .
58. W. Yang, X. Yang, L. Zhu, H. Chu, X. Li and W. Xu, Coord. Chem. Rev., 2021, 448, 214170 CrossRef CAS .
59. J. Lermé, H. Baida, C. Bonnet, M. Broyer, E. Cottancin, A. Crut, P. Maioli, N. Del Fatti, F. Vallée and M. Pellarin, J. Phys. Chem. Lett., 2010, 1, 2922–2928 CrossRef .
60. R. Wei, G. Fu, Z. Li, Y. Liu, L. Qi, K. Liu, Z. Zhao and M. Xue, J. Colloid Interface Sci., 2024, 663, 644–655 CrossRef CAS PubMed .
61. Q. Fu, X. Zhou, M. Wang and X. Su, Anal. Chim. Acta, 2022, 1216, 339993 CrossRef CAS PubMed .
62. J. Wu, S. Li and H. Wei, Nanoscale Horiz., 2018, 3, 367–382 RSC .
63. H. Li, X. Cai, T. Yi, Y. Zeng, J. Ma, L. Li, L. Pang, N. Li, H. Hu and Y. Zhan, J. Nanobiotechnol., 2022, 20, 240 CrossRef CAS PubMed .
64. J.-H. Zhu, H. Gou, T. Zhao, L.-P. Mei, A.-J. Wang and J.-J. Feng, Biosens. Bioelectron., 2022, 203, 114048 CrossRef CAS PubMed .
65. H. Dong, Y. Fan, W. Zhang, N. Gu and Y. Zhang, Bioconjugate Chem., 2019, 30, 1273–1296 CrossRef CAS PubMed .
66. Q. Mao, J. Fang, A. Wang, Y. Zhang, C. Cui, S. Ye, Y. Zhao, Y. Feng, J. Li and H. Shi, Angew. Chem., Int. Ed., 2021, 60, 23805–23811 CrossRef CAS PubMed .
67. C. Lu, Z. Li, N. Wu, D. Lu, X.-B. Zhang and G. Song, Chem, 2023, 9, 3185–3211 CAS .
68. Y. Wang, G. Zhao, Y. Liu, R. Wang, Y. Xing, K. Dou and F. Yu, Sens. Actuators, B, 2025, 426, 137005 CrossRef CAS .
69. Z. Chu, J. Yang, W. Zheng, J. Sun, W. Wang and H. Qian, Coord. Chem. Rev., 2023, 481, 215049 Search PubMed .
70. A. Bansal and M. C. Simon, J. Cell Biol., 2018, 217, 2291–2298 Search PubMed .
71. C. Liao, X. Liu, C. Zhang and Q. Zhang, Semin. Cancer Biol., 2023, 88, 172–186 CrossRef CAS PubMed .
72. Z. Chen, F. Han, Y. Du, H. Shi and W. Zhou, Signal Transduction Targeted Ther., 2023, 8, 70 CrossRef PubMed .
73. F. R. Greten and S. I. Grivennikov, Immunity, 2019, 51, 27–41 Search PubMed .
74. B. Wang, Y. Wang, J. Zhang, C. Hu, J. Jiang, Y. Li and Z. Peng, Arch. Toxicol., 2023, 97, 1439–1451 CrossRef CAS PubMed .
75. Singlet Oxygen: Applications in Biosciences and Nanosciences, ed. S. Nonell and C. Flors, Royal Society of Chemistry, Cambridge, 2016 Search PubMed .
76. X. Yuan, X. He, J. Fan, Y. Tai, Y. Yao, Y. Luo, J. Chen, H. Luo, X. Zhou, F. Luo, Q. Niu, W. (Walter) Hu, X. Sun and B. Ying, J. Mater. Chem. B, 2025, 13, 1599–1618 RSC .
77. Y. Ai, H. Sun, Z. Gao, C. Wang, L. Guan, Y. Wang, Y. Wang, H. Zhang and Q. Liang, Adv. Funct. Mater., 2021, 31, 2103581 Search PubMed .
78. C. Zhang, X. Wang, X. Dong, L. Mei, X. Wu, Z. Gu and Y. Zhao, Biomaterials, 2021, 276, 121023 CrossRef CAS PubMed .
79. L. Shao, X. Wang, X. Du, S. Yin, Y. Qian, Y. Yao and L. Yang, ACS Omega, 2024, 9, 15753–15767 CrossRef CAS PubMed .
80. R. Yang, S. Fu, R. Li, L. Zhang, Z. Xu, Y. Cao, H. Cui, Y. Kang and P. Xue, Theranostics, 2021, 11, 107–116 CrossRef CAS PubMed .
81. L. Zeng, Y. Han, Z. Chen, K. Jiang, D. Golberg and Q. Weng, Adv. Sci., 2021, 8, 2101184 CrossRef CAS PubMed .
82. X. Liu, Z. Liu, K. Dong, S. Wu, Y. Sang, T. Cui, Y. Zhou, J. Ren and X. Qu, Biomaterials, 2020, 258, 120263 CrossRef CAS PubMed .
83. Z. Xu, L. Zhang, M. Pan, Q. Jiang, Y. Huang, F. Wang and X. Liu, Adv. Funct. Mater., 2021, 31, 2104100 CrossRef CAS .
84. S. Wang, L. Zhang, J. Zhao, M. He, Y. Huang and S. Zhao, Sci. Adv., 2021, 7, eabe3588 CrossRef CAS PubMed .
85. Y. Park and E. M. Jeong, Int. J. Stem Cells, 2024, 17, 270–283 CrossRef CAS PubMed .
86. M. A. Shah and H. A. Rogoff, Semin. Oncol., 2021, 48, 238–245 CrossRef CAS PubMed .
87. X. An, W. Yu, J. Liu, D. Tang, L. Yang and X. Chen, Cell Death Dis., 2024, 15, 556 CrossRef CAS PubMed .
88. B. Niu, K. Liao, Y. Zhou, T. Wen, G. Quan, X. Pan and C. Wu, Biomaterials, 2021, 277, 121110 CrossRef CAS PubMed .
89. W. Zou, L. Wang, J. Hao, L. Jiang, W. Du, T. Ying, X. Cai, H. Ran, J. Wu and Y. Zheng, Composites, Part B, 2022, 234, 109707 CrossRef CAS .
90. J. Shen, T. W. Rees, Z. Zhou, S. Yang, L. Ji and H. Chao, Biomaterials, 2020, 251, 120079 CrossRef CAS PubMed .
91. S. Li, X. Zhao, H. Ding, J. Chang, X. Qin, F. He, X. Gao, S. Gai and P. Yang, Chem. Eng. J., 2023, 473, 145414 CrossRef CAS .
92. L. Chen, C. Ding, K. Chai, B. Yang, W. Chen, J. Zeng, W. Xu and Y. Huang, ACS Nano, 2023, 17, 18148–18163 CrossRef CAS PubMed .
93. R. Zhang, Y. Shen, X. Zhou, J. Li, H. Zhao, Z. Zhang, J. Zhao, H. Jin, S. Guo, H. Ding, G. Nie, Z. Zhang, Y. Wang, X. Yan and K. Fan, Nat. Commun., 2025, 16, 890 CrossRef CAS PubMed .
94. H. Veroniaina, Z. Wu and X. Qi, J. Adv. Res., 2021, 33, 201–213 CrossRef CAS PubMed .
95. B. Yu, W. Wang, W. Sun, C. Jiang and L. Lu, J. Am. Chem. Soc., 2021, 143, 8855–8865 CrossRef CAS PubMed .
96. Y. Wang, H. Li, G. Niu, Y. Li, Z. Huang, S. Cheng, K. Zhang, H. Li, Q. Fu and Y. Jiang, ACS Nano, 2024, 18, 5828–5846 CAS .
97. Y. Wang, H. Li, J. Lin, Y. Li, K. Zhang, H. Li, Q. Fu and Y. Jiang, Nat. Commun., 2025, 16, 1876 CrossRef CAS PubMed .
98. J. Wang, L. Fang, P. Li, L. Ma, W. Na, C. Cheng, Y. Gu and D. Deng, Nano-Micro Lett., 2019, 11, 74 CrossRef CAS PubMed .
99. X. Deng, T. Liu, Y. Zhu, J. Chen, Z. Song, Z. Shi and H. Chen, Bioact. Mater., 2024, 33, 483–496 CAS .
100. B. Xu, Y. Cui, W. Wang, S. Li, C. Lyu, S. Wang, W. Bao, H. Wang, M. Qin, Z. Liu, W. Wei and H. Liu, Adv. Mater., 2020, 32, 2003563 CrossRef CAS PubMed .
101. L. Zhu, J. Liu, G. Zhou, T. Liu, Y. Dai, G. Nie and Q. Zhao, Small, 2021, 17, 2102624 CrossRef CAS PubMed .
102. M. Jang, S. S. Kim and J. Lee, Exp. Mol. Med., 2013, 45, e45 CrossRef PubMed .
103. R. J. DeBerardinis and N. S. Chandel, Nat. Metab., 2020, 2, 127–129 CrossRef PubMed .
104. S. Li, Q. Wang, Z. Jia, M. Da, J. Zhao, R. Yang and D. Chen, Heliyon, 2023, 9, e20407 CrossRef CAS PubMed .
105. C. Wei, Y. Liu, X. Zhu, X. Chen, Y. Zhou, G. Yuan, Y. Gong and J. Liu, Biomaterials, 2020, 238, 119848 CrossRef CAS PubMed .
106. J. Liu, A. Wang, S. Liu, R. Yang, L. Wang, F. Gao, H. Zhou, X. Yu, J. Liu and C. Chen, Angew. Chem., Int. Ed., 2021, 60, 25328–25338 CrossRef CAS PubMed .
107. S. Paul, S. Ghosh and S. Kumar, Semin. Cancer Biol., 2022, 86, 1216–1230 CrossRef CAS PubMed .
108. Z.-N. Ling, Y.-F. Jiang, J.-N. Ru, J.-H. Lu, B. Ding and J. Wu, Signal Transduction Targeted Ther., 2023, 8, 1–32 CrossRef PubMed .
109. S. M. Ghiasi, N. M. Christensen, P. A. Pedersen, E. Z. Skovhøj and I. Novak, Cell. Signalling, 2024, 117, 111109 CrossRef PubMed .
110. Z. Kooshan, L. Cárdenas-Piedra, J. Clements and J. Batra, Cancer Lett., 2024, 600, 217156 CrossRef PubMed .
111. S. Shukla, P. Dalai and R. Agrawal-Rajput, Cell. Signalling, 2024, 121, 111281 CrossRef PubMed .
112. M. Chang, Z. Hou, M. Wang, D. Wen, C. Li, Y. Liu, Y. Zhao and J. Lin, Angew. Chem., Int. Ed., 2022, 61, e202209245 CrossRef PubMed .
113. T. Maity, S. Jain, M. Solra, S. Barman and S. Rana, ACS Sustainable Chem. Eng., 2022, 10, 1398–1407 CrossRef .
114. B. Liu, H. Zhou, L. Tan, K. T. H. Siu and X.-Y. Guan, Signal Transduction Targeted Ther., 2024, 9, 175 CrossRef PubMed .
115. Z. Gao, Y. Li, Y. Zhang, K. Cheng, P. An, F. Chen, J. Chen, C. You, Q. Zhu and B. Sun, ACS Appl. Mater. Interfaces, 2020, 12, 1963–1972 CrossRef PubMed .
116. M. Wang, M. Chang, Q. Chen, D. Wang, C. Li, Z. Hou, J. Lin, D. Jin and B. Xing, Biomaterials, 2020, 252, 120093 CrossRef PubMed .
117. L. Feng, B. Liu, R. Xie, D. Wang, C. Qian, W. Zhou, J. Liu, D. Jana, P. Yang and Y. Zhao, Adv. Funct. Mater., 2021, 31, 2006216 CrossRef .
118. S. Dong, Y. Dong, T. Jia, S. Liu, J. Liu, D. Yang, F. He, S. Gai, P. Yang and J. Lin, Adv. Mater., 2020, 32, 2002439 CrossRef PubMed .
119. Q. You, K. Zhang, J. Liu, C. Liu, H. Wang, M. Wang, S. Ye, H. Gao, L. Lv, C. Wang, L. Zhu and Y. Yang, Adv. Sci., 2020, 7, 1903341 CrossRef PubMed .
120. F. Liu, L. Lin, Y. Zhang, Y. Wang, S. Sheng, C. Xu, H. Tian and X. Chen, Adv. Mater., 2019, 31, 1902885 CrossRef PubMed .
121. C. Ren, X. Hu and Q. Zhou, Adv. Sci., 2018, 5, 1700595 CrossRef PubMed .
122. L. Li, H. Liu, J. Bian, X. Zhang, Y. Fu, Z. Li, S. Wei, Z. Xu, X. Liu, Z. Liu, D. Wang and D. Gao, Chem. Eng. J., 2020, 397, 125438 CrossRef .
123. Z. Wang, B. Liu, Q. Sun, L. Feng, F. He, P. Yang, S. Gai, Z. Quan and J. Lin, ACS Nano, 2021, 15, 12342–12357 CrossRef PubMed .
124. X. Yang, Y. Yang, F. Gao, J.-J. Wei, C.-G. Qian and M.-J. Sun, Nano Lett., 2019, 19, 4334–4342 CrossRef PubMed .
125. C. Wei, Y. Liu, X. Zhu, X. Chen, Y. Zhou, G. Yuan, Y. Gong and J. Liu, Biomaterials, 2020, 238, 119848 CrossRef PubMed .
126. X. Zhu, Y. Gong, Y. Liu, C. Yang, S. Wu, G. Yuan, X. Guo, J. Liu and X. Qin, Biomaterials, 2020, 242, 119923 CrossRef PubMed .
127. L. Yang, C. Ren, M. Xu, Y. Song, Q. Lu, Y. Wang, Y. Zhu, X. Wang and N. Li, Nano Res., 2020, 13, 2246–2258 CrossRef .
128. J.-X. Fan, M.-Y. Peng, H. Wang, H.-R. Zheng, Z.-L. Liu, C.-X. Li, X.-N. Wang, X.-H. Liu, S.-X. Cheng and X.-Z. Zhang, Adv. Mater., 2019, 31, 1808278 CrossRef PubMed .
129. S. Gao, H. Lin, H. Zhang, H. Yao, Y. Chen and J. Shi, Adv. Sci., 2019, 6, 1801733 CrossRef PubMed .
130. W. Zhang, J. Liu, X. Li, Y. Zheng, L. Chen, D. Wang, M. F. Foda, Z. Ma, Y. Zhao and H. Han, ACS Nano, 2021, 15, 19321–19333 CrossRef PubMed .
131. L. Zhu, J. Liu, G. Zhou, T. Liu, Y. Dai, G. Nie and Q. Zhao, Small, 2021, 17, 2102624 CrossRef PubMed .
132. N. S. Aboelella, C. Brandle, T. Kim, Z.-C. Ding and G. Zhou, Cancers, 2021, 13, 986 CrossRef PubMed .
133. F. Attar, M. G. Shahpar, B. Rasti, M. Sharifi, A. A. Saboury, S. M. Rezayat and M. Falahati, J. Mol. Liq., 2019, 278, 130–144 CrossRef .
134. X. Wang, X. Zhong, Z. Liu and L. Cheng, Nano Today, 2020, 35, 100946 CrossRef .
135. Y. Wang, F. Gao, X. Li, G. Niu, Y. Yang, H. Li and Y. Jiang, J. Nanobiotechnol., 2022, 20, 69 CrossRef PubMed .
136. B. Halliwell, A. Adhikary, M. Dingfelder and M. Dizdaroglu, Chem. Soc. Rev., 2021, 50, 8355–8360 RSC .
137. M. Schieber and N. S. Chandel, Curr. Biol., 2014, 24, R453–R462 CrossRef PubMed .
138. A. Ayala, M. F. Muñoz and S. Argüelles, Oxid. Med. Cell. Longev., 2014, 2014, 1–31 CrossRef PubMed .
139. Y. Sang, F. Cao, W. Li, L. Zhang, Y. You, Q. Deng, K. Dong, J. Ren and X. Qu, J. Am. Chem. Soc., 2020, 142, 5177–5183 CrossRef PubMed .
140. C. Cao, H. Zou, N. Yang, H. Li, Y. Cai, X. Song, J. Shao, P. Chen, X. Mou, W. Wang and X. Dong, Adv. Mater., 2021, 33, 2106996 CrossRef PubMed .
141. Y. Qian, J. Zhang, J. Zou, X. Wang, X. Meng, H. Liu, Y. Lin, Q. Chen, L. Sun, W. Lin and H. Wang, Theranostics, 2022, 12, 3690 CrossRef .
142. S. Sheng, F. Liu, L. Lin, N. Yan, Y. Wang, C. Xu, H. Tian and X. Chen, J. Controlled Release, 2020, 328, 631–639 CrossRef PubMed .
143. Z. Zhang, X. Liu, D. Chen and J. Yu, Signal Transduction Targeted Ther., 2022, 7, 1–34 CrossRef PubMed .
144. D. Schaue and W. H. McBride, Nat. Rev. Clin. Oncol., 2015, 12, 527–540 CrossRef PubMed .
145. S. Leo, N. M. Carigga Gutierrez, A.-L. Bulin, J.-L. Coll, L. Sancey, B. Habermeyer and M. Broekgaarden, Eur. J. Med. Chem., 2025, 296, 117861 CrossRef PubMed .
146. M. Redza-Dutordoir and D. A. Averill-Bates, Biochim. Biophys. Acta, Mol. Cell Res., 1863, 2016, 2977–2992 Search PubMed .
147. R. Li, Y. Xu, J. Zhao, L. Zhang, W. Zhong, X. Gao, X. Liu, M. Chen and M. Wang, Lung Cancer, 2024, 194, 107884 CrossRef PubMed .
148. C. Huang, Z. Liu, M. Chen, L. Du, C. Liu, S. Wang, Y. Zheng and W. Liu, J. Nanobiotechnol., 2021, 19, 457 CrossRef PubMed .
149. Z. Yuan, X. Liu, J. Ling, G. Huang, J. Huang, X. Zhu, L. He and T. Chen, Biomaterials, 2022, 287, 121620 CrossRef .
150. R. Kumar, P. Kumari and R. Kumar, Mol. Neurobiol., 2025, 62, 7268–7295 CrossRef PubMed .
151. X. Han, B. Li, W. Wang, B. Feng, Q. Tang, Y. Qi, R. Zhao, W. Qiu, S. Zhao, Z. Pan, X. Guo, H. Du, J. Qiu, H. Liu, G. Li and H. Xue, ACS Nano, 2024, 18, 19836–19853 Search PubMed .
152. X. Xu, Q. Peng, X. Jiang, S. Tan, Y. Yang, W. Yang, Y. Han, Y. Chen, L. Oyang, J. Lin, L. Xia, M. Peng, N. Wu, Y. Tang, J. Li, Q. Liao and Y. Zhou, Exp. Mol. Med., 2023, 55, 1357–1370 CrossRef PubMed .
153. C. Guo, D. Liu, W. Xu, L. He and S. Liu, Colloids Surf., A, 2023, 658, 130555 CrossRef .
154. B. T. Finicle, V. Jayashankar and A. L. Edinger, Nat. Rev. Cancer, 2018, 18, 619–633 CrossRef PubMed .
155. F. Püschel, F. Favaro, J. Redondo-Pedraza, E. Lucendo, R. Iurlaro, S. Marchetti, B. Majem, E. Eldering, E. Nadal, J.-E. Ricci, E. Chevet and C. Muñoz-Pinedo, Proc. Natl. Acad. Sci. U. S. A., 2020, 117, 9932–9941 CrossRef PubMed .
156. G. P. Lobel, Y. Jiang and M. C. Simon, Cell Chem. Biol., 2023, 30, 1015–1032 CrossRef .
157. S. Fu, R. Yang, L. Zhang, W. Liu, G. Du, Y. Cao, Z. Xu, H. Cui, Y. Kang and P. Xue, Biomaterials, 2020, 257, 120279 CrossRef PubMed .
158. X. Zeng, Y. Ruan, Q. Chen, S. Yan and W. Huang, Chem. Eng. J., 2023, 452, 138422 CrossRef .
159. J. Zhang, X. Cao, H. Wen, Q. T. H. Shubhra, X. Hu, X. He and X. Cai, Coord. Chem. Rev., 2025, 539, 216746 CrossRef .
160. D. Liang, Q. Zhang, X. Chen, J. Lu, X. Shen and W. Sun, Biomed. Technol., 2024, 6, 46–60 CrossRef .
161. K. Wang, Y. Li, X. Wang, Z. Zhang, L. Cao, X. Fan, B. Wan, F. Liu, X. Zhang, Z. He, Y. Zhou, D. Wang, J. Sun and X. Chen, Nat. Commun., 2023, 14, 2950 CrossRef PubMed .
162. L. Chen, S.-F. Zhou, L. Su and J. Song, ACS Nano, 2019, 13, 10887–10917 CrossRef PubMed .
163. S. Ning, Y. Zheng, K. Qiao, G. Li, Q. Bai and S. Xu, J. Nanobiotechnol., 2021, 19, 344 CrossRef PubMed .
164. T. Chen, X. Luo, L. Zhu, J. Xiang, C. Fang, D. Zhu, G. Li and Y. Duo, Chem. Eng. J., 2023, 467, 143386 CrossRef .
165. C. Wang, H. Wang, B. Xu and H. Liu, View, 2021, 2, 20200045 CrossRef .
166. Y. Zhang, N. Zhang, S.-P. Gong, Z.-S. Chen and H.-L. Cao, Drug Discovery Today, 2025, 30, 104292 CrossRef PubMed .
167. M. Lu, M. Huang, J. Chen, X. Xu, S. Liu, W. Wang, W. Si, X. Huang and X. Dong, Coord. Chem. Rev., 2025, 535, 216635 CrossRef .
168. Z. Guo, A. T. Zhu, R. H. Fang and L. Zhang, Small Methods, 2023, 7, e2300252 CrossRef PubMed .
169. Y. Cao, X. Qian, M. Shi, X. Sun, J. Zou and X. Chen, Chin. J. Chem., 2025, 43, 1731–1755 CrossRef .
170. M. Overchuk, R. A. Weersink, B. C. Wilson and G. Zheng, ACS Nano, 2023, 17, 7979–8003 CrossRef PubMed .
171. J. H. Correia, J. A. Rodrigues, S. Pimenta, T. Dong and Z. Yang, Pharmaceutics, 2021, 13, 1332 CrossRef PubMed .
172. M. Chen, J. Song, J. Zhu, G. Hong, J. An, E. Feng, X. Peng and F. Song, Adv. Healthcare Mater., 2021, 10, 2101049 CrossRef PubMed .
173. N. Yin, Y. Wang, Y. Huang, Y. Cao, L. Jin, J. Liu, T. Zhang, S. Song, X. Liu and H. Zhang, Adv. Sci., 2023, 10, 2204937 CrossRef PubMed .
174. H. Yang, B. Xu, S. Li, Q. Wu, M. Lu, A. Han and H. Liu, Small, 2021, 17, 2007090 CrossRef PubMed .
175. M. Xu, R. Zhao, B. Liu, F. Geng, X. Wu, F. Zhang, R. Shen, H. Lin, L. Feng and P. Yang, Chem. Eng. J., 2024, 491, 151776 CrossRef .
176. L. DeRidder, D. A. Rubinson, R. Langer and G. Traverso, J. Controlled Release, 2022, 352, 840–860 CrossRef PubMed .
177. S. Senapati, A. K. Mahanta, S. Kumar and P. Maiti, Signal Transduction Targeted Ther., 2018, 3, 7 CrossRef PubMed .
178. Y. Li, F. Liu, Q. Cai, L. Deng, Q. Ouyang, X. H.-F. Zhang and J. Zheng, Signal Transduction Targeted Ther., 2025, 10, 57 CrossRef PubMed .
179. L. Li, H. Liu, J. Bian, X. Zhang, Y. Fu, Z. Li, S. Wei, Z. Xu, X. Liu, Z. Liu, D. Wang and D. Gao, Chem. Eng. J., 2020, 397, 125438 CrossRef .
180. M. Zhang, B. Li, Y. Du, G. Zhou, Y. Tang, Y. Shi, B. Zhang, Z. Xu and Q. Huang, Chem. Eng. J., 2021, 424, 130356 CrossRef .
181. Y. Zhou, L. Tao, J. Qiu, J. Xu, X. Yang, Y. Zhang, X. Tian, X. Guan, X. Cen and Y. Zhao, Signal Transduction Targeted Ther., 2024, 9, 132 CrossRef PubMed .
182. U. Laraib, S. Sargazi, A. Rahdar, M. Khatami and S. Pandey, Int. J. Biol. Macromol., 2022, 195, 356–383 CrossRef PubMed .
183. H. Wang, K. Wan and X. Shi, Adv. Mater., 2019, 31, 1805368 CrossRef PubMed .
184. C. Gosée, C. Cadiou, J. Moreau, M. Callewaert, C. Kowandy, C. Henoumont, L. Larbanoix, S. Laurent and F. Chuburu, Coord. Chem. Rev., 2025, 532, 216523 CrossRef .
185. N. Mhlanga, N. Mphuthi, H. Van Der Walt, S. Nyembe, T. Mokhena and L. Sikhwivhilu, Mater. Today Chem., 2024, 40, 102233 CrossRef .
186. L. Caschera, A. Lazzara, L. Piergallini, D. Ricci, B. Tuscano and A. Vanzulli, Pharmacol. Res., 2016, 110, 65–75 CrossRef PubMed .
187. N. Iyad, M. S. Ahmad, S. G. Alkhatib and M. Hjouj, Eur. J. Radiol. Open, 2023, 11, 100503 CrossRef PubMed .
188. A. Ghazzy, H. Nsairat, R. Said, O. A. Sibai, A. AbuRuman, A. S. Shraim and A. Al Hunaiti, Nanoscale Adv., 2024, 6, 1611–1642 RSC .
189. H. Li, Q. Cai, J. Du, G. Jie and G. Jie, Biosens. Bioelectron., 2024, 263, 116611 CrossRef PubMed .
190. B. Li, H. Shen, Q. Liu, X. Liu, J. Cai, L. Zhang, D. Wu, Y. Xie, G. Xie and W. Feng, Sens. Actuators, B, 2023, 386, 133762 CrossRef .
191. L. Gong, B. Chen, Y. Tong, Y. Luo, D. Zhu, J. Chao, L. Wang and S. Su, Sens. Actuators, B, 2024, 398, 134686 CrossRef .
192. L. Feng, B. Liu, R. Xie, D. Wang, C. Qian, W. Zhou, J. Liu, D. Jana, P. Yang and Y. Zhao, Adv. Funct. Mater., 2021, 31, 2006216 CrossRef .
193. S. Gao, H. Lin, H. Zhang, H. Yao, Y. Chen and J. Shi, Adv. Sci., 2019, 6, 1801733 CrossRef PubMed .
194. Z. Ma, M. F. Foda, H. Liang, Y. Zhao and H. Han, Adv. Funct. Mater., 2021, 31, 2103765 CrossRef .
195. S. Liang, X. Deng, Y. Chang, C. Sun, S. Shao, Z. Xie, X. Xiao, P. Ma, H. Zhang, Z. Cheng and J. Lin, Nano Lett., 2019, 19, 4134–4145 CrossRef PubMed .
196. F. Liu, L. Lin, Y. Zhang, Y. Wang, S. Sheng, C. Xu, H. Tian and X. Chen, Adv. Mater., 2019, 31, e1902885 CrossRef PubMed .
197. Z. Wang, Z. Li, Z. Sun, S. Wang, Z. Ali, S. Zhu, S. Liu, Q. Ren, F. Sheng, B. Wang and Y. Hou, Sci. Adv., 2020, 6, eabc8733 CrossRef PubMed .
198. X. Ma, X. Ren, X. Guo, C. Fu, Q. Wu, L. Tan, H. Li, W. Zhang, X. Chen, H. Zhong and X. Meng, Biomaterials, 2019, 214, 119223 CrossRef PubMed .
199. Q. Huang, Y. Pan, M. Wang, Z. Liu, H. Chen, J. Wang, Z. Zhao and Y. Zhang, Acta Biomater., 2022, 147, 270–286 CrossRef PubMed .
200. H. Yuan, M. Q. Wilks, M. D. Normandin, G. El Fakhri, C. Kaittanis and L. Josephson, Nat. Protoc., 2018, 13, 392–412 CrossRef PubMed .
201. F. Gong, N. Yang, Y. Wang, M. Zhuo, Q. Zhao, S. Wang, Y. Li, Z. Liu, Q. Chen and L. Cheng, Small, 2020, 16, 2003496 CrossRef .
202. S. Dong, Y. Dong, T. Jia, S. Liu, J. Liu, D. Yang, F. He, S. Gai, P. Yang and J. Lin, Adv. Mater., 2020, 32, 2002439 CrossRef .
203. A. Mukhatov, T. Le, T. T. Pham and T. D. Do, Nano Select, 2023, 4, 213–230 CrossRef .
204. D. Wang, H. Wu, S. Z. F. Phua, G. Yang, W. Qi Lim, L. Gu, C. Qian, H. Wang, Z. Guo, H. Chen and Y. Zhao, Nat. Commun., 2020, 11, 357 CrossRef PubMed .
205. H. Zhu, X. Yin, Y. Zhou, S. Xu, T. D. James and L. Wang, Chem, 2022, 8, 2498–2513 Search PubMed .
206. J. Du, S. Yang, Y. Qiao, H. Lu and H. Dong, Biosens. Bioelectron., 2021, 191, 113478 CrossRef .
207. Z. Ma, M. Foda, H. Liang, Y. Zhao and H.-Y. Han, Adv. Funct. Mater., 2021, 31, 2103765 CrossRef .
208. Z. Wang, R. Zhang, X. Yan and K. Fan, Mater. Today, 2020, 41, 81–119 CrossRef .
209. H. Ye, L. Yongquan, W. Zhaodi, L. Guizhen, Q. Hua and W. Zhu, Anal. Methods, 2025 10.1039/D5AY00997A .
210. Y. Gao, Z. Zhu, Z. Chen, M. Guo, Y. Zhang, L. Wang and Z. Zhu, Biomater. Sci., 2024, 12, 2229–2243 RSC .
211. H. Wang, Y. Wang, L. Lu, Q. Ma, R. Feng, S. Xu, T. D. James and L. Wang, Adv. Funct. Mater., 2022, 32, 2200331 CrossRef CAS .
212. T. Jia, D. Li, J. Du, X. Fang, V. Gerasimov, H. Ågren and G. Chen, J. Nanobiotechnol., 2022, 20, 424 CrossRef CAS .
213. M. Wang, M. Chang, Q. Chen, D. Wang, C. Li, Z. Hou, J. Lin, D. Jin and B. Xing, Biomaterials, 2020, 252, 120093 CrossRef CAS .
214. Z. Zheng, Z. Jia, Y. Qin, R. Dai, X. Chen, Y. Ma, X. Xie and R. Zhang, Small, 2021, 17, 2103252 CrossRef CAS PubMed .
215. Z. Yu, R. Lou, W. Pan, N. Li and B. Tang, Chem. Commun., 2020, 56, 15513–15524 RSC .
216. X. Wang, X. Zhong, Z. Liu and L. Cheng, Nano Today, 2020, 35, 100946 CrossRef CAS .
217. L. Zhang, M. Qiu, R. Wang, S. Li, X. Liu, Q. Xu, L. Xiao, Z. Jiang, X. Zhou and S. Chen, Angew. Chem., Int. Ed., 2024, 63, e202403771 CrossRef CAS PubMed .
218. P. K. Hashim, A. T. Shaji, A. S. Amrutha and S. Ahmad, RSC Med. Chem., 2025, 16, 2360–2372 RSC .
219. L. Liu, S. Hu, Y. Wang, S. Yang and J. Qu, Sci. Rep., 2018, 8, 8866 CrossRef .
220. H. Wei, L. Gao, K. Fan, J. Liu, J. He, X. Qu, S. Dong, E. Wang and X. Yan, Nano Today, 2021, 40, 101269 CrossRef CAS .
221. E. Drouin, J.-M. Classe and P. Hautecoeur, Lancet Oncol., 2022, 23, 340 CrossRef PubMed .
222. A. Cykowska, L. Marano, A. D’Ignazio, D. Marrelli, M. Swierblewski, J. Jaskiewicz, F. Roviello and K. Polom, Surg. Oncol., 2020, 34, 324–335 CrossRef PubMed .
223. A. Karakatsanis, K. Daskalakis, P. Stålberg, H. Olofsson, Y. Andersson, S. Eriksson, L. Bergkvist and F. Wärnberg, Br. J. Surg., 2017, 104, 1675–1685 CrossRef .
224. P. Reimer and T. Balzer, Eur. Radiol., 2003, 13, 1266–1276 CrossRef PubMed .
225. L. Li, W. Jiang, K. Luo, H. Song, F. Lan, Y. Wu and Z. Gu, Theranostics, 2013, 3, 595–615 CrossRef PubMed .
226. J. He, X. Yang, B. Men and D. Wang, J. Environ. Sci., 2016, 39, 97–109 CrossRef .
227. L. Gao, J. Zhuang, L. Nie, J. Zhang, Y. Zhang, N. Gu, T. Wang, J. Feng, D. Yang, S. Perrett and X. Yan, Nat. Nanotechnol., 2007, 2, 577–583 CrossRef PubMed .
228. Y. Chen, C. J. Miller and T. D. Waite, Environ. Sci. Technol., 2021, 55, 14414–14425 CrossRef PubMed .
229. S. Kraus, S. Arbib, P. Rukenstein, I. Shoval, R. Khandadash and O. Shalev, ACS Omega, 2025, 10, 3535–3550 CrossRef .
230. J. S. Suk, Q. Xu, N. Kim, J. Hanes and L. M. Ensign, Adv. Drug Delivery Rev., 2016, 99, 28–51 CrossRef PubMed .
231. Z. Guo, J. Hong, N. Song and M. Liang, Acc. Mater. Res., 2024, 5, 347–357 CrossRef CAS .
232. Z. Chi, J. Gu, H. Li and Q. Wang, Analyst, 2024, 149, 1416–1435 RSC .
233. I. Csóka, R. Ismail, O. Jójárt-Laczkovich and E. Pallagi, Curr. Med. Chem., 2021, 28, 7461–7476 CrossRef PubMed .
234. N. Tagaras, H. Song, S. Sahar, W. Tong, Z. Mao and T. Buerki-Thurnherr, Adv. Sci., 2024, 11, 2407816 CrossRef CAS .
235. J. Razlivina, A. Dmitrenko and V. Vinogradov, J. Phys. Chem. Lett., 2024, 15, 5804–5813 CrossRef CAS .
236. Y. Yu, M. Zhang and K. Fan, Mater. Horiz., 2025 10.1039/D5MH00719D .

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Footnote

† These authors contributed equally to this work.

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