Catalase inhibits tumor growth by alleviating oxidative stress and promoting the polarization of tumor-associated macrophage from M2 to M1 phenotype in non-small cell lung cancer

Abstract

Background

Catalase (CAT) plays a crucial role in converting hydrogen peroxide (H₂O₂) into water and oxygen, which can help alleviate oxidative stress in body. However, whether CAT is associated with the prognosis and immunotherapy response in patients with non-small cell lung cancer (NSCLC) requires further investigation.

Methods

This study collected data from over 3,170 NSCLC cases across multiple countries. Thirteen machine learning algorithms were employed to identify the most effective diagnostic model, with performance evaluated based on area under the curve (AUC) values. The prognostic significance of CAT expression was assessed in relation to survival, tumor recurrence, and tumor differentiation in NSCLC patients. Additionally, the effectiveness of immunotherapy in relation to CAT expression was evaluated using an immunotherapy dataset. The GDSC database was utilized to examine the correlation between CAT expression and sensitivity to potential therapeutic agents. A multi-omics approach was then applied to analyze the expression and distribution of CAT in NSCLC. In vitro experiments were conducted to validate CAT expression in lung cancer cell lines, and its impact on cell proliferation and migration was assessed using CCK-8 assays, scratch assays, and colony formation assays following transfection with a CAT overexpression construct. The regulatory role of CAT in oxidative stress was further evaluated by adding hydrogen peroxide. Finally, the xenograft tumor mouse model was established to observe the effect of CAT on macrophage phenotype.

Results

We first observed that CAT exhibited the highest AUC value in the machine learning model. Subsequent analyses revealed that NSCLC patients with high CAT expression had prolonged survival, reduced tumor recurrence, and reduced tumor poor differentiation, as confirmed by data from multiple global national databases. Moreover, these patients showed greater responsiveness to immunotherapy and experienced prolonged progression-free survival (PFS). The high CAT expression cohort also exhibited increased sensitivity to Cisplatin, Savolitinib, and Docetaxel. Additionally, we also verified the low expression of CAT in tumor tissues by RT-qPCR and immunohistochemistry. Furthermore, overexpression of CAT inhibited lung cancer cell proliferation and migration, while significantly enhancing its ability to regulate hydrogen peroxide levels. Notably, in the xenograft tumor mouse model, we observed that CAT may suppress tumor growth by alleviating tissue hypoxia and facilitating the polarization of tumor-associated macrophage from the M2 phenotype to M1.

Conclusion

This study demonstrated the potential of CAT as a prognostic biomarker for NSCLC. Targeting CAT might provide an effective strategy for improving patient survival and the efficacy of immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13062-026-00730-6.

Introduction

Cancer is the leading cause of death and a major barrier to longevity, with rising incidence and mortality worldwide. Among all types of cancer, lung cancer is the second most common malignancy, with an estimated 2.2 million new cases and 1.8 million deaths each year [1]. NSCLC, which accounts for 85% of lung cancer cases, includes lung adenocarcinoma (LUAD) as the most prevalent pathological subtype of primary lung cancer [2]. Despite advances in treatment, the five-year survival rate for lung cancer patients remains as low as 21.7% [3]. It is encouraging to note that significant advances have been made in the field of immunotherapy over the past decade, resulting in a notable improvement in patient survival rates. The development of specific antibodies against the programmed death receptor (PD-1), its ligand (PD-L1), and the cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) in first- or second-line NSCLC treatment strategies has resulted in an unprecedented prolongation of survival in a subset of these patients [4]. Nevertheless, it is evident that not all patients respond favorably to immunotherapy. Consequently, there is a pressing need to identify biomarkers that can accurately assess the efficacy of immunotherapy in different NSCLC patients.

Oxidative stress, defined as an imbalance between oxidants and antioxidants in the body—particularly an excess of reactive oxygen species (ROS)—can disrupt redox signaling and regulation, leading to molecular damage. It has been associated with a variety of diseases, including atherosclerosis, chronic obstructive pulmonary disease, Alzheimer’s disease, and cancer [5]. In the event that antioxidant defenses are unable to provide adequate protection against oxidative stress, ROS may potentially increase the risk of cancer. For example, mice that are genetically defective in antioxidant genes have been observed to exhibit increased oxidative DNA damage and a higher spontaneous incidence of lung cancer in comparison to normal mice [6]. H₂O₂, a type of ROS, can contribute to cancer development by causing DNA damage and genomic instability, thereby driving the accumulation of oncogenic mutations [7]. However, catalase (CAT) is an antioxidant enzyme present in a wide variety of organisms, responsible for catalyzing the conversion of H₂O₂ into water and oxygen [8]. As such, catalase plays a protective role by preventing the buildup of harmful oxidants, which can otherwise promote tumorigenesis and cancer progression [9]. In the tumor microenvironment (TME), hypoxia is a common and significant feature of most solid tumors. Hypoxic conditions promote cancer cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT), while also contributing to resistance against immunotherapy, chemotherapy, and radiotherapy [10]. Additionally, hypoxia in the TME induces macrophage polarization towards the M2 phenotype, which further supports tumor progression and drug resistance. Therefore, catalase may exert its influence by decomposing H₂O₂ to produce oxygen, potentially shifting macrophage polarization towards the M1 phenotype. This shift inhibits tumor growth and improves the efficacy of immunotherapy as well as chemotherapy [11]. Therefore, it is essential to investigate whether the role of the CAT gene in mitigating oxidative stress influences the treatment outcomes and prognosis of lung cancer.

With the widespread advancement of gene sequencing, our understanding of tumors has deepened at the genetic level, significantly impacting clinical diagnosis, prognosis, and the prediction of targeted therapy efficacy [12]. Interestingly, catalase levels and their regulation may be linked to longevity. For example, transgenic mice with mitochondrial-targeted catalase showed a 20% increase in lifespan [13]. It was reported that alleviating hypoxia in the TME using CAT-loaded nanoparticles in a mouse model of triple-negative breast cancer led to a remodeled TME that promoted the polarization of tumor-associated macrophage (TAM) M2 phenotype to M1, thereby enhancing the efficacy of immunotherapy [14]. Nevertheless, further investigation is required to ascertain whether catalase can enhance the prognosis of NSCLC patients and their response to immunotherapy. To further investigate this, we compiled data from over 3,170 cases of NSCLC across multiple countries. Our analysis revealed that patients with high CAT expression exhibited longer survival times, lower recurrence rates, and better responses to immunotherapy. Additionally, through the overexpression of CAT in lung cancer cell lines, we demonstrated that CAT inhibited both cell proliferation and migration, while also enhance the regulation of exogenous H₂O₂. In short, CAT might suppress tumor growth by alleviating tissue hypoxia and facilitating the polarization of TAM from the M2 phenotype to M1.

Data, materials and methods

Data collection

In our study, we included a total of more than 3,170 non-small cell lung cancer (NSCLC) patients. The training set consisted of 1,129 NSCLC patients from the UCSC Xena database (https://xenabrowser.net/datapages/) [15]. The validation set comprised 2,041 patients, including those from several cohorts: GSE68465 (442 patients), GSE68571 (86 patients), GSE72094 (398 patients), and GSE42127 (176 patients) from the United States; GSE31210 (226 patients) from Japan; GSE50081 (181 patients) from Canada; GSE37745 (196 patients) from Sweden; and GSE30219 (293 patients) from France. Additionally, the NSCLC immunotherapy dataset included GSE126044 (16 patients) from the United States and GSE135222 (27 patients) from Korea. All data were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) [16]. To eliminate batch effects, we employed the “limma” R package, and log2 transformations were applied for normalization.

Acquisition of differential oxidative stress-related genes

A list of 2,152 oxidative stress-related genes was obtained from the GeneCards database (https://www.genecards.org/) [17]. These genes were available in the Supplementary Data (Supplementary Table S1). Differential gene expression analysis was performed on tumor and normal tissue samples using the “limma” R package [18]. A significance threshold was set with an adjusted p-value (adj. p) of < 0.05 and |log2 fold change (FC)| > 1.5 to identify differentially expressed genes.

Machine learning approach identifies key genes

To identify key genes, we employed 13 machine learning (ML) algorithms. The application of machine learning methods is as previously described [19–21]. These combinations were validated using two independent test cohorts. To assess the predictive performance of each combination, we calculated the area under the curve (AUC) values for each cohort. The ML combination with the highest average AUC across both the training and test cohorts was selected as the best-performing model.

Prognosis assessment of the CAT gene

Patients were stratified into high and low expression groups based on the optimal cutoff value of the CAT gene. We then assessed survival rate, recurrence rate, and tumor differentiation in these groups. And we plotted Kaplan-Meier (KM) survival curves by the “survival” R package. In addition, we assessed the potential impact of CAT expression using publicly available data on patient tumor recurrence rates and the degree of tumor differentiation.

Multi-omics analysis of CAT gene

We first evaluated the transcriptomic expression of the CAT gene in both tumor and normal tissues in the UCSC Xena database. Protein expression levels were then assessed using the CPTAC Protein Database [22]. Single-cell RNA sequencing (scRNA-seq) data from NSCLC tissues were obtained from the Tumor Immunology Single Cell Hub (TISCH) database (http://tisch.comp-genomics.org/home/), with all single cell analyses performed using TISCH, including quality control, batch effect removal, cell clustering, and cell type annotation [23]. Last, spatial transcriptomics data in NSCLC were visualized and analyzed using through the Spatial Integrative Resource for Cancer (SORC, http://bio-bigdata.hrbmu.edu.cn/SORC) [24].

Efficacy of clinical immunotherapy

To comprehensively evaluate the effectiveness of immunotherapy based on different levels of CAT gene expression, we explored multiple immunotherapy databases. We first analyzed the differences in CAT gene expression between responders and non-responders to anti-PD1 immunotherapy. Additionally, we stratified patients into high and low CAT expression groups and compared the immunotherapeutic responses between these two cohorts.

Screening of potential therapeutic drugs

To evaluate the sensitivity of therapeutic drugs, the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerRxgene.org/) was used to assess chemotherapy responses in high CAT patients [25, 26]. The “oncoPredict” R package was employed to analyze drug sensitivity. A p-value of less than 0.05 was considered the threshold for statistical significance to determine whether there was a significant difference in drug sensitivity.

Cell culture and RT-qPCR analysis

The human lung cancer cell line A549, H1299 and normal pulmonary epithelial cells BEAS-2B were purchased from Cell Bank of affiliated to Shanghai Institute of Biochemistry and Cell Biology (SIBCB) (https://cellbank.com.cn/eindex.php), and routinely preserved in our laboratory. Both A549, H1299 and BEAS-2B cell lines were authenticated via short tandem repeat (STR) analysis. Cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Gibco, USA) supplemented with 10% fetal bovine serum (FBS; Gibco, USA) at 37 °C in a 5% CO2 incubator. Total RNA (1 µg) was extracted from each cell line using the RNA-easy Isolation Reagent (Vazyme, Nanjing, China). After reverse transcription of mRNA to cDNA, real-time quantitative polymerase chain reaction (RT-qPCR) was performed using the Applied Biosystems ViiA 7 (Life Technologies, USA). Gene expression was quantified using the 2^ΔΔCT method. Primer sequences were obtained from Sangon Biotech (Shanghai, China), and the primers are listed in Supplementary Data (Supplementary Table S2).

Immunohistochemistry assay of tissue microarrays

Lung cancer tissue microarray (No. AF-LucSur2201) was obtained from AiFang Biological (Hunan, China), with informed consent from all patients. The array included 80 lung cancer and 80 adjacent normal tissue spots (Supplementary Figure S1). Endogenous peroxidase activity was inhibited with hydrogen peroxide, followed by incubation with 3% BSA for 30 min at room temperature. Primary antibody (G1209, Servicebio, China) was applied and incubated overnight at 4 °C in a humidified chamber. After adding the secondary antibody (AFIHC001, AiFang) and incubating for 50 min, DAB (AFIHC004, AiFang) and hematoxylin (AFIHC005, AiFang) staining were performed. Staining was examined microscopically, and images were analyzed. H-Score was calculated as H-Score = ∑(pi×i), where i represents the intensity grade of positive cells, and pi denotes the percentage of positive cells. H-Score analysis was conducted using QuPath software [27].

Cell transfection

The Catalase-turboRFP plasmid was used to induce CAT gene overexpression, while the Catalase-turboRFP vector plasmids served as the mock control. Transfection was carried out using ExFect Transfection Reagent (Vazyme, China) according to the manufacturer’s instructions.

Western blot

Cells were lysed using ice-cold RIPA lysis buffer (Beyotime Biotechnology, China) to extract total protein. The protein concentration was determined using the BCA Protein Assay Kit (Beyotime Biotechnology, China). Proteins were then separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto a nitrocellulose membrane (NC) (Millipore, USA). After blocking with 5% (w/v) non-fat milk for 1 h at 25 °C, the membranes were incubated overnight at 4 °C with primary antibodies against Catalase (21260-1-AP, 1:1000, Proteintech, China), GAPDH (60004-1-Ig, 1:3000, Proteintech, China), beta-actin (4970 S, 1:1000, CST, USA), and HIF1-alpha (80933-1-RR, 1:1000, Proteintech, China). The membranes were then washed with TBST buffer (10 mM Tris-HCl, pH 8.0, 150 mM NaCl, 0.05% Tween 20) and incubated with the appropriate secondary antibodies: Goat anti-Rabbit IgG (926-32211, 1:10,000, LI-COR, USA) or Goat anti-Mouse IgG (926-68070, 1:10,000, LI-COR, USA) for 1 h at 25 °C. Protein bands were visualized and quantified using Bio-Rad Image Lab software.

Cell proliferation assay

Cell proliferation was assessed using the Cell Counting Kit-8 (CCK8) assay and the colony formation assay. For the CCK8 assay, cells were seeded into 96-well plates at 100 µl per well. After 1, 2, 3, and 4 days of incubation, 10 µl of CCK8 reagent (NCM Biotech, China) was added to each well and incubated for 2 h at 37 °C. The optical density (OD) at 450 nm was then measured using a microplate reader (BioTek, USA). For the colony formation assay, cells in the logarithmic growth phase were trypsinized to generate a single-cell suspension. The cells were seeded into 6-well plates and cultured for 14 days, with regular monitoring of colony growth. At the end of the culture period, colonies were fixed with 4% paraformaldehyde, stained with crystal violet, and imaged using a digital camera.

Wound healing assay

Transfected A549 and H1299 cells were seeded into 6-well plates at a density of 1 × 10⁶ cells per well and cultured for 24 h. Once cells had adhered, a scratch was made using a 200 µl pipette tip. The cells were then washed twice with PBS and cultured in serum-free medium. Wound closure was monitored, and images were captured at 0, 24, 48, and 96 h, respectively.

Exogenous addition of H₂O₂

Using A549 cells overexpressing CAT (A549-CAT-OE) and A549 CAT vector control cells (A549-CAT-VC) cells as experimental models, we prepared different concentrations of H₂O₂ solutions, including 200, 400, and 600, 800, and 1,000 µM, respectively. Each concentration of solution was diluted with sterile PBS. The cells in the petri dishes were washed with PBS to remove the medium. The corresponding concentration of H₂O₂ solution was subsequently added. The optimal concentration was found and the cells were allowed to continue to be cultured at 37 °C, 5% CO₂ after the addition of H₂O₂, with treatment times of 0, 2, 4, and 6 h to assess the effect of H₂O₂ on the activity of the cells.

Nude mouse xenograft studies

The BALB/c nude mice (male, 4–6 weeks old, weighing 16–18 g) were obtained from GemPharmatech Co., Ltd. (China) and housed at the Experimental Animal Center of Shanghai Chest Hospital. For the construction of the xenograft tumor mouse model, 1 × 10⁵ well-cultured A549 cells were resuspended in 100 µl of PBS and subcutaneously inoculated into the right armpits of BALB/c nude mice. Tumor growth was monitored every three days, with tumor volumes calculated using the following formula: volume (mm³) = length × width² × 1/2. All animal procedures were approved by the Animal Care and Use Committee of Shanghai Chest Hospital (KS24001).

The paraffin-embedded samples were sectioned into 4 μm continuous slices. After deparaffinization, the sections were rehydrated and subjected to endogenous peroxidase blocking using a blocking buffer for 10 min. Following this, the sections were blocked with 5% bovine serum albumin (BSA) for 30 min. The samples were then incubated overnight at 4 °C with Catalase Polyclonal antibody (21260-1-AP, Proteintech, China) and HIF-1 alpha Polyclonal antibody (20960-1-AP, Proteintech, China). After washing with PBS, the sections were stained with HRP-conjugated Goat Anti-Rabbit IgG (H + L) secondary antibody (SA00001-2, Proteintech, China) and developed using the DAB Substrate Kit (G1004, Servicebio, China). Finally, the stained sections were observed using a Leica DM2000 microscope (Leica, Germany).

Flow cytometry

Cells were resuspended in DCFH-DA solution, gently mixed, and incubated. After incubation, the cells were collected, washed, and resuspended in binding buffer for analysis. Fluorescence intensity was detected and analyzed using a Canto II flow cytometer (BD Biosciences, USA). The percentage of cells with elevated ROS was calculated based on the fluorescence intensity of the stained cells. The population of tumor-infiltrating macrophages in the TME was defined by an established flow cytometry gating strategy in Supplementary Data (Supplementary Table S3). The following antibodies were used: CD45 Monoclonal Antibody (30-F11), Alexa Fluor™ 700 (30-F11, Invitrogen, USA), FITC anti-mouse F4/80 (BM8, BioLegend, USA), PE/Cyanine7 anti-mouse CD206 (MMR) (C068C2, BioLegend, USA), APC anti-mouse Ly-6G/Ly-6 C (Gr-1) (RB6-8C5, BioLegend, USA), APC/Cyanine7 anti-mouse/human CD11b (M1/70, BioLegend, USA), and Fixable Viability Dye eFluor™ 506 (Invitrogen, USA).

Statistical analysis

All statistical analyses were performed using R version 4.2.1 and GraphPad Prism 9 software. Flow cytometry data were processed and analyzed using Flowjo10.0 software. T-test was used for comparisons between two groups. The survival curve was constructed utilizing the Kaplan-Meier method. Statistical significance was defined as a P-value < 0.05, with *, **, and *** indicating P-values < 0.05, < 0.01, and < 0.001, respectively.

Results

Machine learning reveals CAT is a valuable biomarker for early NSCLC diagnosis

The analysis presented in Fig. 1 was based on data from the GSE30219, GSE31210, and UCSC Xena cohorts. In order to identify potential key oxidative stress genes, we employed 113 machine learning approaches. The initial set of 90 candidate genes was identified by intersecting oxidative stress-related genes with differentially expressed genes in NSCLC (Fig. 1A). A machine learning model using Enet [alpha = 0.1] was then optimized, achieving the highest average AUC across 113 combinations (Fig. 1B). The ROC curve analysis revealed the highest AUC for the CAT gene (AUC = 0.994) (Fig. 1C), suggesting that CAT could be a key gene involved in oxidative stress and may hold significant potential as an early diagnostic marker for NSCLC.

Fig. 1.

Identification of the CAT gene. (A) Identification of 90 candidate genes by intersecting oxidative stress-related genes with differentially expressed genes in NSCLC; (B) Optimization of a machine learning model using Enet (α = 0.1), achieving the highest average AUC across 113 combinations; (C) AUC values for different genes in the Enet (α = 0.1) machine learning mode; (D-F) Results of the CAT gene in pan-cancer Cox regression analysis for overall survival (OS) (D), disease-specific survival (DSS) (E), and progression-free interval (PFI) (F); (G) Venn diagrams for OS, DSS, and PFI in pan-cancer cohort; (H-J) Kaplan-Meier (KM) survival curves for high CAT and low CAT in LUAD, including OS (H), DSS (I), and PFI (J) ); (K) A schematic diagram of the CAT gene as a potential diagnostic biomarker for NSCLC

To explore its role as an independent prognostic factor, we found that the CAT gene serves as a protective factor in lung adenocarcinoma with respect to overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) (Fig. 1D-G). Survival analysis further demonstrated that higher CAT expression correlates with longer survival in OS, DSS, and PFI (Fig. 1H-J).

In summary, the machine learning model supported CAT as a valuable biomarker for early NSCLC diagnosis, particularly in lung adenocarcinoma, where it might also serve as an independent prognostic factor (Fig. 1K).

High CAT expression is associated with a better clinical prognosis

To further explore the prognostic role of the CAT gene, we analyzed NSCLC patient data from various global datasets (Fig. 2A). We found that patients with high CAT expression generally had a higher survival rate compared to those with low CAT expression in GSE68465 (Fig. 2B), GSE68571 (Fig. 2C), GSE31210 (Fig. 2D), GSE50081 (Fig. 2E), GSE37745 (Fig. 2F), GSE30219 (Fig. 2G), GSE72094 (Fig. 2H), and GSE42127 (Fig. 2I). The findings indicated that NSCLC patients with high CAT expression exhibited a more favorable survival prognosis.

Fig. 2.

Good clinical prognosis in high CAT patients of NSCLC. (A) Flowchart for assessing CAT and prognosis in NSCLC patients; (B-I) Kaplan-Meier survival curves comparing high and low CAT expression in GSE68465 (B), GSE68571 (C), GSE31210 (D), GSE50081 (E), GSE37745 (F), GSE30219 (G), GSE72094 (H), and GSE42127 (I); Flowchart for assessing CAT and tumor recurrence rate in NSCLC patients(J); (K-L) Tumor recurrence rates based on CAT expression in patients from GSE68465 (K) and GSE31210 (L); Flowchart for assessing CAT and tumor differentiation in NSCLC patients(M); (N-O) Tumor differentiation results based on CAT expression in patients from GSE68465 (N) and GSE68571 (O)

Additionally, to further investigate the effect of CAT on tumor recurrence rate, sequencing databases containing tumor recurrence data were collected and analyzed (Fig. 2J). In the GSE68465 dataset, the tumor recurrence rate of patients with high CAT expression was 16.7%, which was lower than that of patients with low CAT expression (34.2%) (Fig. 2K). Similarly, in the GSE31210 dataset, the tumor recurrence rate of patients with high CAT expression was 24.2%, which was lower than that of patients with low CAT expression (37.0%) (Fig. 2L). The results demonstrated that the tumor recurrence rate was lower in patients with high CAT expression.

Furthermore, to explore the effect of CAT expression on tumor differentiation, we analyzed a sequencing dataset containing information on tumor differentiation (Fig. 2M). In the GSE68465 dataset, 21.5% of patients with high CAT expression exhibited poorly differentiated tumors, a lower proportion compared to 50.0% in patients with low CAT expression (Fig. 2N). Similarly, in the GSE68571 dataset, 16.7% of patients with high CAT expression had poorly differentiated tumors, which was lower than the 34.2% observed in patients with low CAT expression (Fig. 2O). These findings suggest that high CAT expression may be associated with a reduced likelihood of tumor poor differentiation.

Overall, these findings, based on survival time, tumor recurrence, and tumor malignancy, suggested that high CAT expression was associated with a better clinical prognosis.

Patients with high CAT expression have better potential immunotherapeutic response

In order to evaluate the correlation between CAT and immunotherapy response, a dataset comprising patients with NSCLC treated with anti-PD1 as a therapeutic regimen was assembled. Patients were classified as responders or non-responders based on their response to immunotherapy (Fig. 3A). The GSE126044 dataset, comprising 16 NSCLC patients treated with nivolumab or pembrolizumab, was analyzed to assess the relationship between CAT expression and response to anti-PD1 therapy. We found that patients who responded to therapy had higher CAT expression levels, while non-responders showed lower CAT expression (Fig. 3B). Dividing patients into two groups based on median CAT expression, we observed that 71.4% of patients in the high-expression group responded positively to immunotherapy, whereas none in the low-expression group did (Fig. 3C). Further analysis of the GSE13522 dataset, which included 27 patients with advanced NSCLC receiving anti-PD1 therapy, revealed similar findings. The median CAT expression was higher in the treatment-responsive subgroup compared to the non-responsive subgroup (Fig. 3D). In the high CAT expression group, 38.5% of patients showed a favorable response, compared to 21.4% in the low expression group (Fig. 3E). Additionally, survival analysis using publicly available progression-free survival (PFS) data showed a significantly longer PFS in the high CAT expression group (Fig. 3F-G).

Fig. 3.

Patients with high CAT expression exhibited better potential for immunotherapeutic response. (A) Flowchart for assessing CAT and immunotherapy response in NSCLC patients; (B) Comparison of CAT expression levels between responders and non-responders to immunotherapy in GSE126044; (C) Comparison of the percentage of responders and non-responders to immunotherapy based on high and low CAT expression in GSE126044; (D) Comparison of CAT levels between responders and non-responders to immunotherapy in GSE135222; (E) Comparison of the percentage of responders and non-responders to immunotherapy based on high and low CAT expression in GSE135222; (F-G) Progression-free survival (PFS) curves for immunotherapy in patients with high and low CAT expression in GSE126044 (F) and GSE135222 (G)

In general, these results suggested that high CAT expression was associated with a better therapeutic response to anti-PD1 immunotherapy, positioning CAT as a potential biomarker for evaluating immunotherapy responsiveness in clinical settings.

Potential highly sensitive therapeutic drugs in high CAT patients

To assess the correlation between CAT expression and therapeutic drug sensitivity, we conducted a screening using the GDSC database. This analysis evaluated the efficacy of various therapeutic drugs in treating NSCLC based on CAT expression levels. We identified a list of drugs with p-values less than 0.05. In the high CAT group, three drugs—Cisplatin, Savolitinib, and Docetaxel—were found to be more effective (Supplementary Figure S2A-C). These drugs were commonly used in the treatment of NSCLC.

Taken together, these findings suggested that patients with high CAT expression in NSCLC might have increased sensitivity to these conventional therapeutic agents, which could guide the selection of optimal treatment regimens in clinical practice.

Multi-omics analysis suggests CAT is involved in macrophage polarization

Firstly, our analysis revealed CAT showed low expression at the transcriptome level in NSCLC tumor tissues in the UCSC Xena Database (Fig. 4A). Secondly, similar under-expression was observed in the proteomic data, confirming the reduced presence of CAT at the protein level in tumor tissues (Fig. 4B). Further investigation into single-cell datasets, including EMTAB6149 and GSE117570, revealed that CAT expression was predominantly localized to macrophages within the TME (Fig. 4C-F). Moreover, two spatial transcriptome datasets also demonstrated high expression of CAT specifically in macrophages (Fig. 4G-L), further supporting the notion that CAT may exert its effects by targeting macrophages. This finding is consistent with the hypothesis that CAT plays a role in modulating macrophage function in the TME.

Fig. 4.

Multi-omics analysis of CAT expression. (A) CAT mRNA expression in tumor tissues and normal tissues from the UCSC Xena NSCLC Database; (B) Protein expression of CAT in tumor tissues and normal tissues from the CPTAC NSCLC Protein Database; (C-D) Classification of cell clusters in the EMTAB6149 single-cell dataset (C) and distribution of CAT expression in different clusters (D); (E-F) Classification of cell clusters in the GSE117570 single-cell dataset (E) and distribution of CAT expression in macrophages (F); (G-I) The first slice of NSCLC is used to demonstrate the classification (G) and annotation (H) of CAT in the spatial transcriptome, with comparative plots of CAT expression across different clusters (I); (J-L) The second slice of NSCLC is used to demonstrate the classification (J) and annotation (K) of CAT in the spatial transcriptome, with comparative plots of CAT expression across different clusters (L)

Specifically, CAT expression was suggested to alleviate hypoxia-induced reprogramming of TAM to the M1 phenotype by generating large amounts of oxygen. This mechanism could enhance the efficacy of immunotherapy and improve anti-tumor immune responses [11].

In vitro validation experiments reveals that low CAT expression is associated with tumorigenesis

Our in vitro analysis demonstrated that CAT mRNA expression was significantly lower in A549 cells compared to BEAS-2B cells (Fig. 5A), as confirmed by RT-qPCR. Additionally, we validated the low expression of CAT in lung cancer tissues using immunohistochemical staining (Fig. 5B-C). To further corroborate these findings, we assessed CAT expression in 80 pairs of lung cancer tissues using the H-Score method, which also indicated low levels of CAT expression (Fig. 5D).

Fig. 5.

In vitro validation of CAT expression. (A) Comparison of CAT mRNA expression between BEAS-2B and A549 cells; (B-C) Immunohistochemical staining of CAT expression in two NSCLC cases; (D) Comparison of CAT H-Score between tumor and normal tissue in tissue microarrays; (E) Western blot analysis and protein quantification of CAT in A549 and H1299 cell lines following cell transfection; (F-G) CCK-8 assay results for CAT-overexpressing A549 (F) and H1299 (G) cells

Overall, these results collectively suggested that CAT gene expression was reduced in tumor tissues, and its downregulation might be associated with tumor development.

Overexpression of CAT reduces proliferation and migration

The expression of CAT was significantly increased in A549 and H1299 cells through transfection with Catalase-turboRFP (Fig. 5E). This overexpression led to a marked decrease in the proliferation and migration capabilities of CAT-overexpressing cells (H1299-CAT-OE and A549-CAT-OE), as confirmed by the CCK-8 assay (Fig. 5F-G), scratch assay (Fig. 6A-B), and colony formation assay (Fig. 6C-D).

Fig. 6.

Cell scratch assay and cell colony formation assay. (A-B) Results of the cell scratch assay in CAT-overexpressing A549 (A) and H1299 (B) cells; (C-D) Results of the cell colony formation assay in CAT-overexpressing A549 (C) and H1299 (D) cells

Collectively, these findings suggested that CAT overexpression inhibited both the proliferation and migration of LUAD cells, indicating that CAT might play a key role in regulating cancer progression.

CAT overexpression enhances regulation of H₂O₂

To investigate the regulatory effect of CAT on H₂O₂, solutions of 200, 400, 600, 800, and 1,000 µM H₂O₂ were applied to A549-CAT-OE and A549-VC cells (Fig. 7A). The most significant difference between the A549-CAT-OE and A549-VC cells was observed at the 400 µM concentration, and the response of A549-CAT-OE cells essentially stabilized at higher concentrations. Therefore, the 400 µM H₂O₂ concentration was chosen as the optimal condition for subsequent experiments (Fig. 7B). Over time, the regulatory capacity of the A549-CAT-OE cells at this concentration was evaluated. The results showed that A549-CAT-OE cells exhibited a significantly superior ability to regulate H₂O₂ levels compared to the A549-VC cells (Fig. 7C). These findings were confirmed by flow-through detection of H₂O₂ (Fig. 7D-E).

Fig. 7.

The regulatory effect of H₂O₂ in A549 cells overexpressing CAT. (A) Flowchart illustrating the effects of CAT on H₂O₂ by applying 200, 400, 600, 800, and 1,000 µM H₂O₂ to A549-CAT-OE and A549-VC cells; (B) Identification of 400 µM H₂O₂ as the optimal concentration for subsequent experiments; (C) ROS flow cytometry results comparing A549-CAT-OE and A549-VC cells; (D-E) Flow cytometry plots of ROS levels in A549-VC (D) and A549-CAT-OE (E) cells treated with 400 µM H₂O₂ for 0, 2, 4, and 6 h

In brief, overexpression of CAT enhanced the ability of cells to regulate H₂O₂ levels, suggesting that CAT may contribute to tumor suppression by modulating oxidative stress.

CAT overexpression promotes the polarization of M2 macrophages to the M1 phenotype

To investigate whether CAT overexpression inhibits tumor growth in vitro, we established a xenograft mouse model of NSCLC by subcutaneously injecting A549-CAT-OE and A549-CAT-VC cell lines into the right armpit of mice (Fig. 8A). We found that, compared to the A549-CAT-VC group, the mice in the A549-CAT-OE group exhibited significantly reduced growth rates and tumor volumes (Fig. 8B-D). Additionally, to further assess whether CAT overexpression induces macrophage polarization within the TME, we analyzed the changes in M1 and M2 macrophages in tumor tissues from each group using flow cytometry. The experimental results showed that, in the CAT-VC group, M1 macrophages accounted for only 6.03% of the total macrophages in the TME, while in the CAT-OE group, the proportion of M1 macrophages increased to 34.1%, consistent across all three flow cytometry results (Fig. 8E-F). Furthermore, in the CAT-VC group, M2 macrophages made up 56.9% of the total macrophages in the TME, whereas in the CAT-OE group, this proportion decreased to 8.87%, with consistent trends observed across all three flow cytometry results (Fig. 8G-H). In addition, we aimed to assess whether CAT overexpression improves tissue hypoxia by detecting HIF1α (Fig. 8I). Through immunohistochemical staining, we observed a significant reduction in HIF1α following CAT overexpression (Fig. 8J-K), suggesting that CAT overexpression can significantly improve the hypoxic conditions within the TME.

Fig. 8.

The xenograft nude mice model with A549 cell-burdened. (A) Schematic diagram of the construction of the xenograft tumor mouse model; (B) Images of xenograft tumors with scale bars, comparing tumor sizes between the A549-CAT-VC and A549-CAT-OE groups; (C) Tumor growth curve comparison between the A549-CAT-VC and A549-CAT-OE groups; (D) Bar chart comparing the tumor volumes of mice in the A549-CAT-VC and A549-CAT-OE groups; (E-F) Flow cytometry proportion plot (E) and bar chart (F) of M1 macrophages in the A549-CAT-VC and A549-CAT-OE groups; (G-H) Flow cytometry proportion plot (G) and bar chart (H) of M2 macrophages in the A549-CAT-VC and A549-CAT-OE groups; (I) Schematic diagram of the immunohistochemistry; (J) CAT histochemical staining images of tumors in the A549-CAT-VC and A549-CAT-OE groups; (K) HIF1α histochemical staining images of tumors in the A549-CAT-VC and A549-CAT-OE groups

Generally speaking, these results confirmed that CAT overexpression alleviated tissue hypoxia and promoted the conversion of TAM from the M2 phenotype to M1, thereby reshaping TME and inhibiting tumor growth.

Discussion

CAT is an enzyme found in nearly all living organisms, playing a critical role in protecting cells from the harmful effects of ROS. It catalyzes the conversion of H₂O₂ into water and oxygen, acting as a primary defense mechanism against oxidative stress [28]. By maintaining this balance, catalase helps shield cells from oxidative damage, which is implicated in aging, inflammation, and a range of diseases, including cancer, neurodegenerative disorders, and cardiovascular diseases [29]. The antioxidant properties of catalase play a crucial role in inhibiting tumorigenesis by preventing ROS-induced DNA damage and mutagenesis. As a result, catalase is increasingly being explored as a potential therapeutic target for cancer treatment [30].

Furthermore, our analysis revealed that CAT exhibited the highest AUC value among oxidative stress-related genes when evaluated using machine learning techniques. This suggested that CAT played a crucial role in the development of disease diagnosis models. In the context of NSCLC, patients with high CAT expression demonstrated significantly better prognoses across various countries worldwide. Specifically, these patients had improved overall survival rates, lower tumor recurrence, and better tumor differentiation compared to those with low CAT expression. These findings suggested that CAT might have a protective effect in NSCLC. Interestingly, studies in animal models had shown that targeting mitochondrial catalase could delay aging and reduce the incidence and severity of cancer. In particular, mice with the mitochondrial catalase genotype exhibited a notable reduction in both the incidence and severity of lung cancer [31].

Our findings indicated that patients with high CAT expression had a higher response rate to immunotherapy and experience longer PFS. These results suggested that CAT could serve as a valuable biomarker for assessing the effectiveness of immunotherapy. A study demonstrated that the antioxidant capacity of CAT was significantly enhanced when co-expressed with tumor-specific chimeric antigen receptors (CAR). This co-expression boosted CAT’s ability to metabolize H₂O₂, thereby improving its antioxidant capacity and enhancing the cytotoxic effects of tumor-infiltrating immune cells on cancer cells [32]. In another study, the delivery of active CAT to melanoma tumors via multifunctional immunoliposomes helped alleviate tumor tissue hypoxia, which, in turn, further improved the effectiveness of immunotherapy [33]. Although our analysis demonstrated that CAT expression could predict responses to immunotherapy, the lack of PD-L1 data in these cohorts prevented correlation analysis with PD-L1 status. Future studies should integrate PD-L1 data to evaluate its potential as a combinatorial biomarker.

Similarly, in the case of therapeutic drugs for NSCLC, our findings indicated that the high CAT population exhibited greater sensitivity to Cisplatin, Savolitinib, and Docetaxel. This might provide valuable insights for the selection of subsequent therapeutic drugs. The observed sensitivity to cisplatin, savolitinib, and docetaxel in patients with high CAT expression might be linked to reduced oxidative stress and enhanced drug efficacy, but further experimental validation is essential. Due to the constraints of the available datasets, our study did not evaluated CAT expression within specific genomic subgroups, such as those with EGFR or KRAS mutations. Future studies incorporating comprehensive molecular profiling are necessary to determine whether CAT’s prognostic and predictive value is preserved across all molecular subtypes of NSCLC. Research suggested that H2O2 might enhance VEGFR2 signaling and angiogenesis through the activation of Nox2 and the subsequent elevation of mtROS via the pSer36-p66Shc pathway. Since CAT can break down H2O2, it can be hypothesized that tumors with high CAT expression may demonstrate increased sensitivity to anti-angiogenic therapies. However, this hypothesis requires further validation through in vitro experiments [34]. Through multi-omics analysis, we observed that CAT predominantly accumulated in macrophages. In the xenograft tumor mouse model, we found that CAT may inhibit tumor growth by alleviating tissue hypoxia promoting the conversion of TAM from the M2 phenotype to M1, thereby reshaping TME. Interestingly, it was also proposed that CAT could alleviate hypoxia-induced M1 polarization of TAMs by generating large amounts of oxygen. This process promoted the conversion of TAM from the M2 phenotype to M1 within the TME, which might enhance the efficacy of chemotherapy and immunotherapy, thereby improving the anti-tumor immune response [11, 14]. Notably, single-cell data indicated that CAT expression was higher in M2 macrophages than in M1 macrophages, which might relate to the antioxidant properties associated with the M2 polarized state. However, our mouse model demonstrated that in the A549-CAT-VC group, CAT expression was indeed higher in M2 macrophages. In contrast, in the A549-CAT-OE group, the proportion of M1 cells significantly increased. This finding suggested that restoring CAT levels in tumor cells could promote the conversion of M2 macrophages to M1 macrophages, highlighting the potential role of CAT in exerting anti-tumor effects through macrophage polarization within the tumor microenvironment.

Furthermore, the expression of CAT in NSCLC tumor tissues was confirmed through in our vitro experiments. The findings indicated that CAT gene expression was diminished in tumor tissues, and its down-regulation might be linked to tumor progression. Additionally, it was demonstrated that overexpression of CAT in lung cancer cell lines inhibited both cell proliferation and migration. And we found that overexpression of CAT enhanced the cells’ ability to regulate H₂O₂ levels upon exogenous addition of H₂O₂. This further underscored the functional characteristics of CAT, demonstrating its enhanced capacity to manage oxidative stress in the cellular environment. H₂O₂ is a major type of ROS that can induce oxidative damage to form hydroxyl radicals, leading to DNA damage and genomic instability, which in turn promotes cancer through the accumulation of carcinogenic mutations [35]. Therefore, if antioxidant defenses fail to provide sufficient protection against oxidative stress, ROS levels may significantly increase the risk of cancer [36]. For example, mice with genetic defects in antioxidant genes showed higher levels of oxidative DNA damage and an increased incidence of lung cancer compared to normal mice [37]. However, CAT, an important antioxidant enzyme in the body, played a crucial role in regulating ROS levels by catalyzing the conversion of H₂O₂ into oxygen [38]. Therefore, targeting CAT might hold promise for enhancing the body’s antioxidant stress levels and inhibiting tumor initiation and progression. Consistent with previous reports, our multi-omics data indicated that CAT could alleviate hypoxia and promote M1 polarization. However, we would further extend this to its prognostic and therapeutic relevance in NSCLC [39]. The review by Glorieux et al. systematically summarized strategies for targeting catalase [30], while earlier work by Tsai et al. revealed the fundamental cellular functions of CAT [40]. Additionally, Chen et al. confirmed the prognostic value of CAT in concurrent studies [28]. Although the effects of CAT overexpression in A549 cells have been previously reported, our study integrated this with machine learning, multi-omics analysis, and animal models, establishing CAT’s role as a comprehensive biomarker.

However, several of our experimental findings warrant further discussion. Although our single-cell analysis revealed that CAT is predominantly expressed in macrophages within the tumor microenvironment, we deliberately overexpressed CAT in tumor cells to investigate the cell-autonomous consequences of enhancing intrinsic antioxidant defenses. The intriguing increase in sensitivity to acute, high-dose H₂O₂ in CAT-overexpressing cells, while seemingly paradoxical, may reflect an altered redox stress threshold in cells adapted to a lower basal ROS state. This highlights the complex and context-dependent role of ROS in determining cell survival. Importantly, the slower tumor growth observed in vivo is likely attributable mainly to the intrinsically reduced proliferative capacity of the CAT-overexpressing cells. The consequent reduction in tumor volume and alleviation of tissue hypoxia—which we also confirmed via immunohistochemistry to be significantly improved in CAT-overexpressing tumors—likely created a microenvironment less favorable for immunosuppressive M2 polarization, thereby promoting a shift toward an M1-like macrophage phenotype, independent of adaptive immunity. Thus, while our model demonstrates that tumor-intrinsic CAT expression can initiate a cascade of tumor microenvironment remodeling, we acknowledge that the immunodeficient mouse model cannot be used to evaluate adaptive anti-tumor immunity. Future studies using immunocompetent models will be essential to fully elucidate the interplay between CAT-driven redox regulation and adaptive immune responses. It is important to note that although the macrophage function in nude mice is always as same as or even higher than that in normal mice [41], immunodeficient mouse models cannot evaluate adaptive immunity; therefore, the observed correlation of immunotherapy responses in patients cannot be directly simulated in this context. Future validation will be conducted through clinical trials.

Conclusion

In conclusion, the findings of this study highlight CAT may suppress tumor growth by alleviating tissue hypoxia and facilitating the polarization of TAM from the M2 phenotype to M1 (Fig. 9). Targeting CAT holds promise for improving patient survival and enhancing the efficacy of immunotherapy. In summary, CAT expression might serve as a prognostic biomarker, and future research could explore targeting CAT through nanotechnology-based delivery of the catalase enzyme to potentially improve treatment outcomes.

Fig. 9.

Graphical abstract

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Yi Tian and Yi-ru Liu contributed equally to the article. Yi Tian: Writing – original draft, Methodology, Data curation, Conceptualization. Yi-ru Liu: Methodology, Investigation, Data curation. Hai-zhen Jin: Visualization. Qiao-xin Lin: Resources. Wen-ya Zhao: Software. Wen-wen Song: Validation. Yan-na Gong: Investigation. Yi-ting Deng: Formal analysis. Shan-shan Wang: Visualization. Kai Wang: Methodology. Ling Tian: Writing – review & editing, Funding acquisition. Dian-na Gu: Supervision, Project administration.

Funding

This study was supported by the funding of the National Natural Science Foundation of China (No. 82073203), Wenzhou Science and Technology Project (No. Y2023900), Zhejiang Provincial Health Commission (No. 2024KY1253) and the Fundamental Research Funds of Wenzhou Medical University (No. KYYW202209).

Data availability

The data supporting the findings of this study are given in the main manuscript and supplementary files. The publicly available NSCLC dataset utilized in the manuscript was obtained from https://xenabrowser.net/datapages/ and https://www.ncbi.nlm.nih.gov/geo/. All data analyzed during the present study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

All experimental procedures were conducted according to the guidelines set forth by the National Institutes of Health (NIH) and were approved by the Animal Care and Use Committee of Shanghai Chest Hospital (KS24001).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.