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Multi-omics analysis reveals that neutrophil extracellular traps related gene TIMP1 promotes CRC progression and influences ferroptosis

Cancer Cell International volume 25, Article number: 31 (2025) Cite this article

Abstract

Background

Previous studies have found that neutrophil extracellular traps (NETs) are highly expressed in colorectal cancer (CRC) and are associated with poor prognosis. Currently, there are few studies on the relationship between NETs and CRC, so we tried to explore new markers based on NETs to assist in the treatment of CRC.

Method

We jointly screened three major NETs genes through machine learning. Large-sample RNA transcriptome and single-cell transcriptome analysis further confirmed that TIMP1 is a core gene in NETs. We used small interfering RNA to knockdown TIMP1, and verified the ability of TIMP1 in CRC proliferation, invasion and migration through western blot, transwell, cell scratch assay, cell clone formation and other experiments.

Result

We screened out three major NETs Genes: TIMP1, F3, and CRISPLD2 based on machine learning. The NETs score constructed based on this not only predicts the prognosis of CRC patients but also shows significant differences in MSI status, chenckpoints expression, and predicted efficacy of PD-L1 targeted therapy. Transcriptome and single-cell data reveal that TIMP1 is highly expressed in neutrophils and is associated with poor prognosis in colorectal cancer patients and the occurrence of ferroptosis. Biological experiments have proven that TIMP1 can promote the proliferation, invasion and migration of CRC.

Conclude

Bioinformatics analysis combined with experimental verification showed that TIMP1 is related to ferroptosis and plays a promoting role in the invasion, migration and proliferation of CRC.

Introduction

Globally, colorectal cancer (CRC) stands as a leading cause of cancer-related deaths, occupying the third spot in terms of incidence and second in mortality [1]. With the deepening understanding of the mechanism of tumor development, more and more therapeutic modalities are gradually emerging, such as immunotherapy, targeted therapy, and so on [2, 3]. Despite this, the outcome of advanced CRC is unsatisfactory [4]. One of the reasons for this may lie in the fact that CRC is a highly heterogeneous tumor with complex biology [5]. Therefore, the development of novel biomarkers is of great significance for the precise individualized treatment of CRC.

Neutrophils are a major line of defense for the body’s innate immune system, releasing neutrophil extracellular traps to capture pathogens [6, 7]. Neutrophil extracellular traps (NETs) are secreted by activated neutrophils and consist of a meshwork of uncondensed chromatin and antimicrobial proteins such as neutrophil elastase (NE) and myeloperoxidase (MPO) [6, 8]. And recent studies have found that the formation of NETs promotes the invasive and migratory ability of tumor cells, exacerbates tumor progression, and helps tumors achieve immune escape [9,10,11]. Therefore, exploring the role of NETs in CRC may help us discover novel markers and explore new modalities for treating CRC.

Tissue inhibitor of metalloproteinase 1 (TIMP-1) acts as a natural antagonist to matrix metalloproteinase (MMP) [12]. The balance between MMP and TIMP1 levels affects processes such as cell migration, proliferation and survival. In addition, TIMP1 has independent oncogenic activity and promotes tumor cell invasion, and these effects are independent of the MMP-inhibitory structure of TIMP1 [13, 14]. Recent studies have reported that in pancreatic cancer patients, TIMP1 is a key trigger for NETs formation and further drives pancreatic cancer progression [15]. In CRC, high TIMP1 expression is highly correlated with poor prognosis [16].

In this study, we employed machine learning to identify key NETs-related genes, including TIMP1, F3, and CRISPLD2, and constructed a NETs score to assess its association with prognosis and immune landscapes. Through the integration of bulk RNA sequencing and single-cell RNA sequencing data, TIMP1 was identified as a pivotal gene. Bioinformatic analysis further elucidated the role of TIMP1 in tumor progression and its involvement in ferroptosis. Finally, the functional significance of TIMP1 in tumor proliferation was validated through gene knockdown experiments in vitro.

Materials and methods

The flowchart of this study is shown in Fig. 1.

Fig. 1
figure 1

Flow diagram

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Data acquisition

157 NETs-related genes were collected from previous literature [17, 18]. RNA matrices were obtained from the GEO database, including 203 CRC samples and 160 control samples (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE87211). Single-cell assays were obtained from the GEO database, including 5 colorectal cancer tissues and 3 adjacent normal tissues (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE231559).

Machine learning screening parameters

The TOP20 differential genes at p < 0.05 were obtained by R package “limma”. 14 genes affecting prognosis were screened by COX regression analysis. 51 genes with weights greater than 0.7 were screened by random forest. 76 best variable genes were screened by SVM-RFE. NETs Genes are obtained by taking the intersection of three machine learning screening results.

Single-sample gene set enrichment analysis (ssGSEA)

ssGSEA estimates the relative enrichment of a particular gene set in each sample by comparing its gene expression data to that set. Quantifying the score for each sample site and thus comparing differences between groups.

Cibersort

CIBERSORT is a computational method for assessing the relative abundance of different immune cell types in a sample. By analyzing the degree of infiltration of various immune cells in conjunction with specific gene set scores, CIBERSORT helps explore the populations of cells affected by the gene set.

Principal component analysis

PCA is an unsupervised algorithm used to reduce the dimensionality of high-dimensional data. The PCA score for each sample is obtained by standardizing the data, calculating the covariance matrix, performing eigenvalue decomposition and projecting the data into the principal component space.

Survival analysis

The median value of the NETs score was used as the threshold to divide the GSE87211 cohort into two groups for survival analysis. KM survival curves were plotted to demonstrate survival differences between the groups. Survival analysis data for TIMP1 were obtained from https://kmplot.com/ and http://gepia.cancer-pku.cn/.

Single-cell analysis data process

Sequencing data from primary CRC and paraneoplastic tissues in the GSE231559 dataset were processed using the Seurat R package. Data were read in the “10x” format and Seurat objects were created based on the following criteria: at least 3 cells, at least 200 features per cell, total RNA counts of at least 1000, number of features between 500 and 2000 and mitochondrial gene content of 10%. Data were normalized using the NormalizeData function and scaled using the ScaleData function. The top 2000 highly variable genes were identified and PCA was performed. The optimal number of principal components (PCs) was determined to be 30. Batch effect correction was performed using the “harmony” method, cell populations were manually annotated based on known markers and visualized using t-distributed stochastic neighbor embedding (tSNE) plots, providing insights into the cellular composition and heterogeneity of the samples. Choose a resolution of 0.8 for the FindClusters function. Use FindMarkers to perform differential gene expression analysis of neutrophils with high and low TIMP1 expression, with the parameters set as pct.1 > 0.25, p < 0.05, and logFC > 0.

The following gene sets were used for cell type annotation: for total immune cells, the marker was PTPRC; for dendritic cells (DC), the markers were CD1C, CD1E, and CLEC10A; for neutrophils, G0S2, S100A8, and CSF3R; for macrophages, CD68, C1QC, and C1QB; for B cells, CD79A, MS4A1, and CD79B; for natural killer T cells, GNLY, NKG7, and KLRD1; for endothelial cells, VWF, PLVAP, and CDH5; for epithelial cells, EPCAM, KRT18, and CDX1; for stromal cells, COL1A1, COL1A2, and DCN; and for T cells, TRAC, CD3D, and CD3E [19,20,21].

Pseudotime analysis

Pseudotime analysis was performed using the Monocle R package on preprocessed scRNA sequencing data. Dimensionality reduction was conducted with PCA and UMAP, followed by trajectory inference. Cells were ordered along the pseudotime trajectory and differential gene expression analysis identified key genes associated with cellular transitions and developmental stages [22].

Cell communication analysis

Cell-to-cell communication analysis was conducted utilizing CellChat and CellPhoneDB. Infer receptor-ligand interactions from scRNA-seq data based on known interaction databases. Extract expression data for receptor-ligand pairs and calculate interaction scores. Perform network visualization to illustrate communication patterns between cell types or clusters.

AddModule score

NETs signature and Ferroptosis siganture in individual cells was assessed using the AddModuleScore function in the Seurat R package. The Ferroptosis signature was collected from the GSEA gene sets (WP_FERROPTOSIS).

Small interfering RNA transfection

Cells (150,000 cells/well) were seeded in 6-well plates with 2 ml of complete growth medium 24 h prior to transfection. One hour before transfection, the medium was replaced with FBS-free and antibiotic-free medium. During the transfection experiment, siRNA was prepared according to the manufacturer’s instructions (RIBOBIO, Guangzhou; SiTIMP1#1: GGACTCTTGCACATCACTA; SiTIMP1#2: CTGACATCCGGTTCGTCTA) to a final concentration of 75 nmol. The mixture was gently pipetted to ensure uniformity and incubated at room temperature for 15 min. It was then slowly added dropwise to a complete medium without antibiotics. The cells were incubated at 37 °C with 5% CO2. After 72 h, protein was extracted to evaluate the knockdown efficiency, followed by Transwell and cell clone formation experiment. Cell scratch assay was performed once the cell confluency reached 100%.

Cell culture

HCT116 cell line was purchased from the Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China. Cells were cultured in high-glucose Dulbecco’s Modified Eagle Medium (DMEM, Sigma) supplemented with 10% fetal bovine serum (FBS, Sigma), 100 U/ml penicillin and 100 µg/ml streptomycin (Sigma). Cells were maintained in a humidified incubator at 37 °C with 5% CO2. The medium was replaced every 2–3 days and cells were passaged at 70–80% confluency using 0.25% trypsin-EDTA.

Western blot

Total protein from cells was extracted using RIPA buffer (Cat: P0013B, Beyotime Institute of Biotechnology) supplemented with protease inhibitors (Cat: P6730, Solarbio). Protein were resolved by SDS-PAGE and transferred onto PVDF membranes. Membranes were blocked with 5% non-fat milk in TBST (Tris-buffered saline with 0.1% Tween-20) for 1 h at room temperature and then incubated with primary antibodies (Anti-TIMP1 (16644-1-AP), Proteintech, Wuhan; Anti-Tubulin (ab7291), Abcam, Shanghai.) overnight at 4 °C. After washing, membranes were incubated with the corresponding secondary antibodies for 1 h at room temperature. After three 10-min washes in TBST, protein bands were visualized using an ECL detection system and signals were captured using a chemiluminescence imaging system.

Transwell experiment

Transwell chambers (Cat: 3422, Corning) with or without 1 mg/ml Matrigel were placed in 24-well plates. The lower chambers were filled with 500 µl of complete medium containing 10% FBS. Cells were resuspended in serum-free medium and 200 µl of the cell suspension (containing approximately 50,000 cells) was added to the upper chambers. The plates were incubated at 37 °C with 5% CO2 for 36 h. Following incubation, non-migrated cells on the upper surface of the membrane were gently removed using a cotton swab. The migrated cells on the lower surface were fixed with 4% paraformaldehyde for 10 min, stained with 0.1% crystal violet and rinsed with distilled water. Images of the stained cells were captured using a microscope and the number of migrated cells was counted in three random fields per membrane.

Cell scratch assay

Sterile 200 µl pipette tip was used to create a vertical scratch in the cell monolayer. The wells were washed three times with PBS to remove detached cells and fresh serum-free medium was added. Images of the scratch were captured at 0, 36 h using a phase-contrast microscope. The cell migration rate was calculated as: (Initial scratch area - Scratch area after 36 h) / Initial scratch area × 100%.

Cell clone formation experiment

Cells were harvested and resuspended in complete growth medium. A total of 100 cells were seeded into each well of a 6-well plate containing 2 ml of medium. The cells were cultured for 10 days to allow colony formation. The medium was then removed and the colonies were washed once with PBS. Cells were fixed with 4% paraformaldehyde for 30 min and stained with 0.5% crystal violet for 10 min. Excess dye was washed off with distilled water and the plates were air-dried. Colonies were photographed and counted manually.

Statistical methods

The data are presented as mean ± standard error of the mean (SEM). Kaplan-Meier survival curves were generated to compare survival distributions between different groups, and the log-rank test was employed to determine statistical significance. The Wilcoxon test was used to compare differential gene expression and scores between two groups, utilizing the R package ggpub two-tailed Student’s t-test was performed to examine data differences between two groups in the experiment. The R version used was 4.2, and GraphPad Prism version 9.5 was employed for analysis. A p-value of < 0.05 was considered statistically significant (*p < 0.05, **p < 0.01, ***p < 0.001).

Results

Construction of NETs signature and the relation to immune infiltration in CRC

NETs-related genes were gathered from the existing literature, and a NETs signature was constructed based on these 157 genes. The NETs signature was quantified for 203 CRC patients and 160 normal individuals from the GSE87211 dataset using ssGSEA. Furthermore, we compared the differences in the NETs signature between the two groups using GSEA. The results revealed a significant difference in the NETs signature between CRC and normal tissues, with notably higher expression in CRC (Figure S1A, B).

To verify whether NETs Signature is highly correlated with neutrophils, we calculated the immune cell infiltration of the CRC group and the normal group by CIBERSORT. Figure S1C shows the immune cell infiltration landscape of the two groups, and the proportion of neutrophils in the CRC is much higher than that in the normal group. Figure S1D shows the correlation between neutrophils infiltration status and NETs signature, R = 0.67, which is in line with our expectations. Interestingly, NETs signature was also highly correlated with macrophages, where RM0 = 0.53, RM1 = 0.27, while RM2 = -0.63, second only to neutrophils (Figure. S1E、F、G). This may suggest that neutrophil extracellular traps may be associated with macrophage M2 polarization.

Machine learning screening in NETs signature

After validating the significant correlation between NETs signature and neutrophils, we attempted to screen the key genes in NETs signature by machine learning. Figure 2 A shows the top 20 most significantly upregulated as well as downregulated genes in CRC compared to normal and shows the percentage of ssGSEA scores for each gene. We firstly determined by SVM-RFE that the former 76 gene features were the most important among the NETs signatures(Fig. 2B). Subsequently, we calculated the importance of each gene by Random Forest and filtered out the genes that were bigger than 0.7(Figure. 2 C). Finally, we conducted univariate analysis to identify genes that significantly impact prognosis. Through the intersection of machine learning, we obtained the NETs genes TIMP1, F3, and CRISPLD2 and calculated the HR of the three genes (Fig. 2D、E). We also compared the expression of the 3 NETs genes in CRC with that of the normal group, and the results indicate that TIMP1 is highly expressed in tumors, while the expression of F3 and CRISPLD2 is lower (Fig. 2F).

Fig. 2
figure 2

Machine learning screening in NETs signature. (A) Heatmap shows the TOP 20 differentially expressed genes in NETs signature and their representative ssGSEA scores. (B) SVM-RFE screening of best variable genes in NETs signature. (C) Random forest screening for variable genes with weights greater than 0.7 in NETS signature. (D) Vene plot shows the NETs genes of Unicox analysis, Random Forest Analysis, and SVM-RFE analysis. (E) HRs for NETs genes. (F) Box plot demonstrating the difference in expression of NETs genes between CRC and normal tissue

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Construction of the NETs score and clinical prognostic performance

We performed unsupervised cluster analysis of the data at the gene expression level by consensus clustering, which showed the best clustering value of k = 2. Principal component analysis (PCA) showed that clustering based on the above three genes could clearly distinguish CRC patients into clusters A and B (Fig. 3A). The clustering obtained from PCA analysis shows significant prognostic differences (Fig. 3B).

We set the PCA score of this subgroup as the NETs score, used the median value as the risk threshold, and defined samples with scores above the threshold as the high-risk group and those below the threshold as the low-risk group. Figure 3C, D shows the prognostic differences based on the NETs score.

Currently, immunotherapy is very promising for the treatment of colorectal cancer, especially dMMR CRC. Commonly used predictors of efficacy for immunotherapy are MSI status, expression of immune checkpoints such as PD-L1. Therefore, we analyzed the immune landscape under different NETs score as well as the correlation of NETs score with the above predictors to explore whether NETs score predicted immunotherapy response.

We examined the relationship between NETs score and immune checkpoints, revealing significant differences between high and low risk groups as depicted in Fig. 3E. Further analysis identified distinctions among MSS, MSI-L, and MSI-H classifications through mismatch repair gene expression, highlighting MSI-H was related with lower NETs score (Fig. 3F). Previous studies have shown that MSI-H colorectal cancer patients tend to benefit more from immunotherapy [23, 24]. Survival analysis of patients treated with anti-PD-L1 therapy confirmed this: high-risk patients exhibited significantly lower survival rates compared to low-risk patients (Fig. 3G).

Fig. 3
figure 3

Construction of the NETs score and clinical prognostic performance. (A) Consensus clustering based on NETs Risk Genes and PCA based on clustering results. (B) Survival curve based on PCA grouping. (C) Survival curve based on NETs score. (D) Box plot and bar chart showing the relationship between NETs socre and fustat. (E) Differential expression of immune checkpoints based on NETs score. (F) Correlation of NETs Score with MSS、MSI-L、MSI-H Status. (G) Survival curves with PD-L1 inhibitor treatment

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Immunization landscape associated with TIMP1 and the nomogram construction

To investigate the correlation between TIMP1, F3, CRISPLD2, and neutrophils, we utilized CIBERSORT to assess immune cell infiltration levels and computed the correlation between these immune cells and the three genes. As illustrated in Fig. 4A and B, the correlation between TIMP1 and neutrophils (R = 0.68) was markedly stronger compared to F3 and CRISPLD2. Subsequently, we categorized the samples into high and low expression groups based on the median expression of TIMP1, and calculated the immune cell infiltration scores for each sample using ssGSEA. The results, presented in Fig. 4C, demonstrated significantly higher neutrophil infiltration in the high TIMP1 expression group, suggesting that TIMP1 may play a critical role in regulating neutrophil infiltration.

To further investigate the role of TIMP1 in clinical prognosis, we next performed survival analysis and constructed a nomogram to assess its potential as a prognostic marker. Survival analysis in the GEPIA, GSE39582 and GSE17538 cohorts showed that patients with high TIMP1 expression generally have poorer prognosis (Fig. 4D). We then constructed a nomogram incorporating TIMP1 expression, lymph node metastasis (N), distant metastasis (M), age, and cancer stage to predict the probability of patient outcomes (Fig. 4E). This model also demonstrated a significant correlation between TIMP1 expression and prognosis, with higher TIMP1 expression indicating a higher risk of poor prognosis.

Fig. 4
figure 4

Immunization landscape associated with TIMP1 and the nomogram construction. (A) The correlation between infiltrating immune cells obtained by CIBERSORT and TIMP1, F3, CRISPLD2. (B) Among the NETs Risk Genes, TIMP1 had the highest correlation with neutrophils at 0.68. (C) The box plot of immune cell infiltration obtained by ssGSEA in relation to high and low TIMP1 expression. (D) Survival analysis of GEPIA, GSE39582 and GSE17538 based on TIMP1 expression. (E) Constructing Nomograms based TIMP1 expression, lymph node metastasis (N), distant metastasis (M), age, and cancer stage

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Single-cell analysis of NETs signature

After the previous bioinformatics analysis of RNA-seq, we determined the ability of NETs scores to predict response to immunotherapy. We also determined that, among the three, TIMP1 is most strongly associated with neutrophils and is a negative prognostic factor in CRC.

To further analyze the role of NETs signature as well as TIMP1 in CRC, we performed single-cell analysis. We identified 9 cell types by manual annotation, and the annotated gene expression and cell populations are shown in Fig. 5A. Then, we calculated NETs signature at the single-cell level for tumor tissue compared to normal tissue differences. The results showed that NETs signature in tumor tissue was significantly higher than that in normal tissue(Fig. 5B). Further exploring the characterization of NETs signature in cell populations, as observed in RNA seq, NETs signature was significantly upregulated in neutrophils and macrophages (Fig. 5 C).

After comparing the characterization of NETs signature in tumor and normal tissues, we further explored the localization of cells expressing NETs genes: TIMP1, F3, and CRISPLD2 (Fig. 5D and E). We found that TIMP1 was highly correlated with neutrophils in scRNA-seq, consistent with our findings in RNA-seq. Meanwhile, CRISPLD2 and F3 were not highly expressed in neutrophils. We then showed the expression of TIMP1 in both tumor and normal tissues, as well as its expression across different cell populations. The results revealed that TIMP1 was significantly overexpressed in CRC, with the highest expression observed in neutrophils (Fig. 5F and G). Both RNA-seq and scRNA-seq showed a significant correlation between TIMP1 and neutrophils in the tumor microenvironment. Therefore, the role played by TIMP1 in neutrophils and tumor progression deserves to be further explored.

Fig. 5
figure 5

Single-cell analysis of NETs signature. (A) Bubble plots demonstrate subgroup differential gene annotation. (B) Violin plot shows the difference between NETs signature in tumor and normal tissues (C) Violin plot showing differences in NETs signature in different cell population. (D) tSNE plot demonstrating cellular compartmentalization. (E) Feature plots demonstrate cellular localization of TIMP1, F3, and CRISPLD2. (F) Violin plot demonstrates differential expression of TIMP1 in tumor and normal groups. (G) Violin plot demonstrates differential expression of TIMP1 in different cell populations

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Pseudotime analysis and cell communication based on TIMP1

We aimed to explore the role of TIMP1 in neutrophils. First, we divided neutrophils into high and low expression groups based on the median TIMP1 expression and calculated the proportion of TIMP1-expressing neutrophils in both tumor and normal tissues. The results showed that in CRC, more than 50% of neutrophils expressed high levels of TIMP1 (Fig. 6A). Next, we simulated the differentiation process of neutrophils using pseudotime analysis (Fig. 6B). As the cells differentiated, the NETs signature gradually enhanced, and the number of TIMP1-high neutrophils increased, suggesting that TIMP1 may play a critical role in the differentiation and functional state of neutrophils (Fig. 6C). These findings imply that TIMP1 may regulate the immune functions of neutrophils in the tumor microenvironment and potentially have a significant impact on tumor progression.

Given the potential role of TIMP1 in the tumor microenvironment, we next explored the possible cell communication mechanisms involved. Specifically, we investigated how neutrophils with differential TIMP1 expression interact with other cells in the tumor microenvironment. The results showed that neutrophils interact with epithelial cells, stromal cells, endothelial cells, immune cells, and other components in CRC (Fig. 6D). Using Secreted Signaling as a target pathway set, we compared pathway differences between the two neutrophil groups (Fig. 6E). The results revealed significant differences in VEGF-related signaling pathways between the two groups. Specifically, neutrophils with high TIMP1 expression exhibited stronger VEGFA expression and were more capable of activating the VEGF pathway in endothelial cells within the microenvironment (Fig. 6F, G).

Current research has found that activation of the VEGF pathway in endothelial cells not only promotes endothelial cell proliferation, migration, lumen formation, and new blood vessel generation to facilitate tumor growth, but also impacts neutrophil migration by increasing vascular permeability, regulating immune factor secretion, and interacting with immune cells [25,26,27]. Our findings provide important insights into understanding the regulatory mechanisms of TIMP1 in tumor progression, as well as the relationship between neutrophils and endothelial cells in the tumor microenvironment.

Fig. 6
figure 6

Pseudotime analysis and cell communication based on TIMP1. (A) Percentage of TIMP1 expression in neutrophils from tumor and normal tissues. (B) The cell trajectory based on pseudotime analysis. (C) Cell trajectory analysis demonstrates changes in NETs signature and TIMP1 expression during tumor development. (D) The chord diagram of the number and strength of cell communication interactions. (E) The heatmap shows the pathway differences in Secreted Signaling between the two neutrophil groups. (F) The heatmap shows the incoming and outgoing signaling of the VEGF pathway across different cell types. (G) The violin plot shows the expression differences of VEGFA between the two neutrophil groups

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TIMP1 affects ferroptosis in neutrophils

To compare the pathway differences between neutrophils with high and low expression of TIMP1, we performed gene enrichment analysis on the differential genes from the two groups. KEGG enrichment analysis revealed that the VEGF signaling pathway was indeed affected in neutrophils with differential TIMP1 expression, which is consistent with the results obtained from cell communication analysis. Interestingly, the ferroptosis pathway was also enriched in these cells (Fig. 7A). GO analysis showed that TIMP1-related differential genes were enriched in pathways such as regulation of cell adhesion and regulation of angiogenesis (Fig. 7B).

Given the significant enrichment of the ferroptosis pathway in neutrophils with differential TIMP1 expression, we next investigated the relationship between TIMP1 and ferroptosis. We first obtained 64 ferroptosis-related genes from msigdb as a ferroptosis signature and calculated the ferroptosis signature scores for different cell subpopulations using the AddModuleScore function. As shown in Fig. 7C, ferroptosis signature scores were higher in neutrophils and macrophages. Moreover, the ferroptosis signature score was also higher in the TIMP1 high expression neutrophil group compared to the low expression group (Fig. 7D). Figure 7E illustrates the expression of all ferroptosis-related genes in the two neutrophil groups, with significant differences observed in several genes, including FTH1, FTL and others. FTH1 and FTL are the heavy and light chains of ferritin, respectively, responsible for storing intracellular iron and maintaining iron homeostasis [28,29,30]. They help neutrophils avoid excessive iron accumulation and the subsequent cellular damage. Therefore, TIMP1 may regulate iron metabolism by influencing iron absorption and storage mechanisms, inhibiting the occurrence of ferroptosis.

Fig. 7
figure 7

TIMP1 affects ferroptosis in neutrophils. (A-B) KEGG and GO analysis of differential genes in TIMP1 high and low expression groups. (C) Violin plot shows ferroptosis signature scores in the different cell type. (D) Violin plots shows ferroptosis signature scores between TIMP1 high and low expression groups. (E) Violin plots show ferroptosis-related genes in the differential gene of TIMP1 high and low expression groups

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Experimental verification of the role of TIMP in CRC

In order to determine the expression of TIMP1, F3, and CRISPLD2 in tumors and normal tissues in colorectal cancer, we displayed typical immunohistochemical images from The Human Protein Atlas (www.proteinatlas.org) (Fig. 8A). To further verify the biological function of TIMP1, we used small interfering RNA to knock down TIMP1 in the HCT116 cell line (Fig. 8B, C). Cell scratch assay and Transwell assay were used to determine whether TIMP1 can affect the invasion and migration ability of cells (Fig. 8D-G). The cell migration rate and the number of invasive and migrated cells in the HCT116-SiNC group were significantly higher than those in the HCT116-SiTIMP1 group. We noticed that in Fig. 7A, the KEGG analysis of differential genes based on TIMP1 showed cell proliferation-related pathways such as the MAPK pathway, so we speculated that TIMP1 may affect cell proliferation. To verify the conjecture, we compared the proliferation abilities of HCT116-SiNC and SiTIMP1 through colony formation experiments. As a result, the cloning ability of cells in the SiTIMP1 group decreased significantly (Fig. 8H、I). In summary, biological experiments have proven that TIMP1 promotes colorectal cancer proliferation, invasion and migration, and aggravates tumor progression.

Fig. 8
figure 8

Experimental verification of the role of TIMP in CRC. (A) Immunohistochemical expression of TIMP1, F3, and CRISPLD2 in normal tissue and cancer tissues. (B) Western Blot showed the knockdown of TIMP1. (C) The bar graphs show the relative quantification of protein expression. (D) Cell scratch experiment to verify the effect of TIMP1 on cell migration rate. (E) The bar graphs show that the migration ability of HCT116 is significantly reduced after SiTIMP1. (F) Transwell experiment to verify the effect of TIMP1 on cell invasion and migration. (G) The bar graphs show that the invasion and migration ability of HCT116 is significantly reduced after SiTIMP1. (H) Cloning experiments show reduced proliferative capacity after SiTIMP1. (I) The bar graphs show that the Cell proliferation ability of HCT116 is significantly reduced after SiTIMP1

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Discussion

Since studies have found that the presence of cancer causes neutrophils to form NETs, which are a poor prognostic factor for tumor patients, more and more studies are focusing on the relationship between NETs and tumors, including tumor immunotherapy, tumor progression and metastasis, etc [31,32,33]. Evidence suggests that NETs are highly expressed and exacerbate tumor progression in a variety of tumors, including breast, liver, and colorectal cancers [11, 34, 35]. In CRC, the formation of NETs induces the elevation of IL-8, which in turn activates neutrophils to produce more NETs, exacerbating the progression and metastasis of CRC [10, 36, 37]. However, there is no specific and standardized test for NETs, so exploring possible markers in CRC would be helpful in the treatment of CRC [38]. Our study confirmed that the NETs-associated gene TIMP1 is an adverse prognostic factor in colorectal cancer, and its expression is associated with ferroptosis, potentially serving as a biomarker.

TIMP1 is a metalloproteinase (MMP) tissue inhibitor, and the main function of MMP is to regulate cellular interactions with the ECM, including cell growth, tumor invasion and metastasis [39, 40]. Dynamic regulation of MMP and TIMP levels determines the degree of degradation of the ECM at the cell periphery, thereby affecting cell migration. However, in cancer patients, the expression of TIMP1 and MMP family is upregulated, which may be due to the fact that TIMP1 not only has MMP dependence, but also has a MMP independent mechanism [41]. As a natural regulator of MMP, TIMP1’s role extends beyond inhibiting MMP activity and may also affect immune responses, angiogenesis, and tumor cell migration within the tumor microenvironment. Future research could focus on developing drugs or therapeutic strategies targeting TIMP1 to regulate MMP activity while minimizing the potential side effects of traditional MMP inhibitors. Furthermore, TIMP1 is closely related to the formation of NETs and ferroptosis, and balancing these factors in cancer treatment to achieve optimal therapeutic benefits will be an important direction for future drug development.

Ferroptosis, a form of iron-dependent regulated cell death triggered by the toxic accumulation of lipid peroxides on cell membranes, often exerts an inhibitory effect on tumor growth [42, 43]. The formation of NETs releases components such as MPO, which induce oxidative stress and increase intracellular lipid peroxidation. These changes are considered key factors in ferroptosis [44, 45]. Additionally, the relationship between NETs and ferroptosis may be regulated through various cellular processes and pathways: In intestinal ischemia-reperfusion injury, NETs formation inhibits mitochondrial autophagy in intestinal microvascular endothelial cells, affecting the level of ferroptosis through Fundc1-dependent mitochondrial autophagy [46]. In abdominal aortic aneurysm-related studies, NETs formation induces ferroptosis by inhibiting the PI3K/AKT pathway and reducing the stability and dimerization of SLC25A11, leading to the depletion of mitochondrial glutathione [47, 48]. In alveolar epithelial cells, NETs induce ferroptosis through METTL3-mediated methylation modifications [49].In triple-negative breast cancer, NETs mediate tumor cell ferroptosis through the TLR9/Merlin signaling axis [50]. Although these studies reveal potential associations between NETs and ferroptosis, the underlying mechanisms in colorectal cancer require further experimental validation and in-depth research.

TIMP1, as a secreted protein, does not directly regulate lipid peroxidation, but it participates in ferroptosis through multiple pathways, including affecting cellular metabolism, iron metabolism, and antioxidant systems. Specifically, TIMP1 influences extracellular matrix remodeling by inhibiting MMP. The structure and composition of the extracellular matrix play a crucial role in maintaining cellular homeostasis and antioxidant capacity, so TIMP1 may regulate ferroptosis by altering intracellular oxidative stress levels [51, 52]. Moreover, research has shown that TIMP1’s binding to TFRC may influence TFRC ubiquitination, regulating cellular Fe2+ uptake, and ultimately altering intracellular iron metabolism, thus affecting ferroptosis [53]. Furthermore, some studies have shown that in tumor cells, TIMP1 inhibits sorafenib-induced ferroptosis by activating the PI3K/AKT pathway. And the overexpression of TIMP1 can lead to a significant increase in the expression of ferroptosis-related proteins GPX4 and SLC7A11 in tumor cells [54, 55]. Further experimental studies will help clarify the specific mechanisms of TIMP1 in NETs-mediated ferroptosis and provide new insights for TIMP1-targeted therapeutic strategies.

Admittedly, our study constructed a NETs score based on sequencing data, but this is a retrospective analysis, and there is a lack of prospective evidence to validate the feasibility of this classification. Secondly, although we have demonstrated the role of TIMP1 in tumor proliferation and invasion, we have not delved into the specific mechanisms by which TIMP1 affects tumor progression. Finally, although we have shown that TIMP1 may influence neutrophil ferroptosis and discussed the potential mechanisms between TIMP1 and ferroptosis, further analysis and validation of the specific mechanisms of TIMP1 in NETs-mediated ferroptosis are still required.

Conclusion

Our study constructed a prognostic classification for colorectal cancer based on NETs through machine learning and bioinformatics analysis to predict clinical prognosis. We have identified a correlation between high expression of the NETs related gene TIMP1 and poor prognosis in CRC and validated the role of TIMP1 in proliferation and invasion of CRC. We also found the correlation between TIMP1 and ferroptosis in neutrophils in colorectal cancer, which may help us target TIMP1 for the treatment of CRC.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

CRC:

Colorectal cancer

NETs:

Neutrophil extracellular traps

NE:

Neutrophil elastase

MPO:

Myeloperoxidase

TIMP-1:

Tissue inhibitor of metalloproteinase 1

MMP:

Matrix metalloproteinase

TCGA:

The Cancer Genome Atlas

KM:

Kaplan-Meier

GSEA:

Gene set enrichment analysis

PCA:

Principal Component Analysis

MSI:

Microsatellite instability

TMB:

Tumor Mutation Burden

CRISPLD2:

Cysteine Rich Secretory Protein LCCL Domain Containing 2

F3:

Coagulation Factor III

References

  1. Global cancer statistics 2018. GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries - PubMed. https://pubmed.ncbi.nlm.nih.gov/30207593/ [Accessed February 23, 2024].

  2. Immunotherapy in. colorectal cancer: rationale, challenges and potential - PubMed. https://pubmed.ncbi.nlm.nih.gov/30886395/ [Accessed February 23, 2024].

  3. Pp FDN. Precision oncology in metastatic colorectal cancer - from biology to medicine. Nat Reviews Clin Oncol. 2021;18. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41571-021-00495-z.

  4. Colorectal cancerstatistics. 2017 - PubMed. https://pubmed.ncbi.nlm.nih.gov/28248415/ [Accessed February 23, 2024].

  5. Sottoriva A, Kang H, Ma Z, Graham TA, Salomon MP, Zhao J, Marjoram P, Siegmund K, Press MF, Shibata D, et al. A Big Bang model of human colorectal tumor growth. Nat Genet. 2015;47:209–16. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/ng.3214.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Castanheira FVS, Kubes P. Neutrophils and NETs in modulating acute and chronic inflammation. Blood. 2019;133:2178–85. https://doiorg.publicaciones.saludcastillayleon.es/10.1182/blood-2018-11-844530.

    Article  CAS  PubMed  Google Scholar 

  7. Liew PX, Kubes P. The Neutrophil’s role during Health and Disease. Physiol Rev. 2019;99:1223–48. https://doiorg.publicaciones.saludcastillayleon.es/10.1152/physrev.00012.2018.

    Article  CAS  PubMed  Google Scholar 

  8. Dömer D, Walther T, Möller S, Behnen M, Laskay T. Neutrophil Extracellular traps activate proinflammatory functions of human neutrophils. Front Immunol. 2021;12:636954. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fimmu.2021.636954.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Teijeira Á, Garasa S, Gato M, Alfaro C, Migueliz I, Cirella A, de Andrea C, Ochoa MC, Otano I, Etxeberria I, et al. CXCR1 and CXCR2 chemokine receptor agonists produced by tumors induce Neutrophil Extracellular traps that interfere with Immune cytotoxicity. Immunity. 2020;52:856–e8718. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.immuni.2020.03.001.

    Article  CAS  PubMed  Google Scholar 

  10. Yang L, Liu L, Zhang R, Hong J, Wang Y, Wang J, Zuo J, Zhang J, Chen J, Hao H. IL-8 mediates a positive loop connecting increased neutrophil extracellular traps (NETs) and colorectal cancer liver metastasis. J Cancer. 2020;11:4384–96. https://doiorg.publicaciones.saludcastillayleon.es/10.7150/jca.44215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Martins-Cardoso K, Almeida VH, Bagri KM, Rossi MID, Mermelstein CS, König S, Monteiro RQ. Neutrophil Extracellular traps (NETs) promote pro-metastatic phenotype in human breast Cancer cells through epithelial-mesenchymal transition. Cancers (Basel). 2020;12:1542. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/cancers12061542.

    Article  CAS  PubMed  Google Scholar 

  12. Grünwald B, Schoeps B, Krüger A. Recognizing the Molecular Multifunctionality and Interactome of TIMP-1. Trends Cell Biol. 2019;29:6–19. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.tcb.2018.08.006.

    Article  CAS  PubMed  Google Scholar 

  13. Cytokine functions of TIMP-1 | Cellular and Molecular Life Sciences. https://link.springer.com/article/10.1007/s00018-013-1457-3 [Accessed February 23, 2024].

  14. Jung YS, Liu X-W, Chirco R, Warner RB, Fridman R, Kim H-RC. TIMP-1 induces an EMT-like phenotypic conversion in MDCK cells independent of its MMP-inhibitory domain. PLoS ONE. 2012;7:e38773. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0038773.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Schoeps B, Eckfeld C, Prokopchuk O, Böttcher J, Häußler D, Steiger K, Demir IE, Knolle P, Soehnlein O, Jenne DE, et al. TIMP1 triggers Neutrophil Extracellular trap formation in pancreatic Cancer. Cancer Res. 2021;81:3568–79. https://doiorg.publicaciones.saludcastillayleon.es/10.1158/0008-5472.CAN-20-4125.

    Article  CAS  PubMed  Google Scholar 

  16. Ma B, Ueda H, Okamoto K, Bando M, Fujimoto S, Okada Y, Kawaguchi T, Wada H, Miyamoto H, Shimada M, et al. TIMP1 promotes cell proliferation and invasion capability of right-sided colon cancers via the FAK/Akt signaling pathway. Cancer Sci. 2022;113:4244–57. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/cas.15567.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Shi H, Pan Y, Xiang G, Wang M, Huang Y, He L, Wang J, Fang Q, Li L, Liu Z. A novel NET-related gene signature for predicting DLBCL prognosis. J Transl Med. 2023;21:630. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12967-023-04494-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhang Y, Guo L, Dai Q, Shang B, Xiao T, Di X, Zhang K, Feng L, Shou J, Wang Y. A signature for pan-cancer prognosis based on neutrophil extracellular traps. J Immunother Cancer. 2022;10:e004210. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/jitc-2021-004210.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Chu X, Li X, Zhang Y, Dang G, Miao Y, Xu W, Wang J, Zhang Z, Cheng S. Integrative single-cell analysis of human colorectal cancer reveals patient stratification with distinct immune evasion mechanisms. Nat Cancer. 2024;5:1409–26. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s43018-024-00807-z.

    Article  CAS  PubMed  Google Scholar 

  20. Salcher S, Sturm G, Horvath L, Untergasser G, Kuempers C, Fotakis G, Panizzolo E, Martowicz A, Trebo M, Pall G, et al. High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell. 2022;40:1503–e15208. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ccell.2022.10.008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Q YS, D Z ZHBMLYT. Decoding the multicellular ecosystem of vena caval tumor thrombus in clear cell renal cell carcinoma by single-cell RNA sequencing. Genome Biol. 2022;23. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13059-022-02651-9.

  22. Reversed graph embedding. resolves complex single-cell trajectories | Nature Methods. https://www.nature.com/articles/nmeth.4402 [Accessed February 20, 2024].

  23. Sidaway P, MSI-H/dMMR. mCRC: ICIs in the first line? Nat Rev Clin Oncol. 2021;18:748. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41571-021-00576-z.

    Article  CAS  PubMed  Google Scholar 

  24. Overman MJ, McDermott R, Leach JL, Lonardi S, Lenz H-J, Morse MA, Desai J, Hill A, Axelson M, Moss RA, et al. Nivolumab in patients with metastatic DNA mismatch repair deficient/microsatellite instability–high colorectal cancer (CheckMate 142): results of an open-label, multicentre, phase 2 study. Lancet Oncol. 2017;18:1182–91. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S1470-2045(17)30422-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhou W, Liu K, Zeng L, He J, Gao X, Gu X, Chen X, Jing Li J, Wang M, Wu D, et al. Targeting VEGF-A/VEGFR2 Y949 signaling-mediated vascular permeability alleviates hypoxic pulmonary hypertension. Circulation. 2022;146:1855–81. https://doiorg.publicaciones.saludcastillayleon.es/10.1161/CIRCULATIONAHA.122.061900.

    Article  CAS  PubMed  Google Scholar 

  26. Simons M, Gordon E, Claesson-Welsh L. Mechanisms and regulation of endothelial VEGF receptor signalling. Nat Rev Mol Cell Biol. 2016;17:611–25. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nrm.2016.87.

    Article  CAS  PubMed  Google Scholar 

  27. Pérez-Gutiérrez L, Ferrara N. Biology and therapeutic targeting of vascular endothelial growth factor A. Nat Rev Mol Cell Biol. 2023;24:816–34. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41580-023-00631-w.

    Article  CAS  PubMed  Google Scholar 

  28. Zhang X-Y, Li S-S, Gu Y-R, Xiao L-X, Ma X-Y, Chen X-R, Wang J-L, Liao C-H, Lin B-L, Huang Y-H, et al. CircPIAS1 promotes hepatocellular carcinoma progression by inhibiting ferroptosis via the miR-455-3p/NUPR1/FTH1 axis. Mol Cancer. 2024;23:113. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12943-024-02030-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. X Y, Z YDLSMSPZ. Ferritin light chain deficiency-induced ferroptosis is involved in preeclampsia pathophysiology by disturbing uterine spiral artery remodelling. Redox Biol. 2022;58. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.redox.2022.102555.

  30. Dixon SJ, Olzmann JA. The cell biology of ferroptosis. Nat Rev Mol Cell Biol. 2024;25:424–42. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41580-024-00703-5.

    Article  CAS  PubMed  Google Scholar 

  31. Demers M, Krause DS, Schatzberg D, Martinod K, Voorhees JR, Fuchs TA, Scadden DT, Wagner DD. Cancers predispose neutrophils to release extracellular DNA traps that contribute to cancer-associated thrombosis. Proc Natl Acad Sci U S A. 2012;109:13076–81. https://doiorg.publicaciones.saludcastillayleon.es/10.1073/pnas.1200419109.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Teijeira A, Garasa S, Ochoa MC, Villalba M, Olivera I, Cirella A, Eguren-Santamaria I, Berraondo P, Schalper KA, de Andrea CE, et al. IL8, neutrophils, and NETs in a collusion against Cancer Immunity and Immunotherapy. Clin Cancer Res. 2021;27:2383–93. https://doiorg.publicaciones.saludcastillayleon.es/10.1158/1078-0432.CCR-20-1319.

    Article  CAS  PubMed  Google Scholar 

  33. De Meo ML, Spicer JD. The role of neutrophil extracellular traps in cancer progression and metastasis. Semin Immunol. 2021;57:101595. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.smim.2022.101595.

    Article  CAS  PubMed  Google Scholar 

  34. Guan X, Lu Y, Zhu H, Yu S, Zhao W, Chi X, Xie C, Yin Z. The crosstalk between Cancer cells and neutrophils enhances Hepatocellular Carcinoma Metastasis via Neutrophil Extracellular traps-Associated cathepsin G component: a potential therapeutic target. J Hepatocell Carcinoma. 2021;8:451–65. https://doiorg.publicaciones.saludcastillayleon.es/10.2147/JHC.S303588.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Li D, Shao J, Cao B, Zhao R, Li H, Gao W, Chen P, Jin L, Cao L, Ji S, et al. The significance of Neutrophil Extracellular traps in Colorectal Cancer and Beyond: from bench to Bedside. Front Oncol. 2022;12:848594. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fonc.2022.848594.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Adrover JM, McDowell SAC, He X-Y, Quail DF, Egeblad M. NETworking with cancer: the bidirectional interplay between cancer and neutrophil extracellular traps. Cancer Cell. 2023;41:505–26. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ccell.2023.02.001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Alfaro C, Teijeira A, Oñate C, Pérez G, Sanmamed MF, Andueza MP, Alignani D, Labiano S, Azpilikueta A, Rodriguez-Paulete A, et al. Tumor-produced Interleukin-8 attracts human myeloid-derived suppressor cells and elicits extrusion of Neutrophil Extracellular traps (NETs). Clin Cancer Res. 2016;22:3924–36. https://doiorg.publicaciones.saludcastillayleon.es/10.1158/1078-0432.CCR-15-2463.

    Article  CAS  PubMed  Google Scholar 

  38. Cristinziano L, Modestino L, Antonelli A, Marone G, Simon H-U, Varricchi G, Galdiero MR. Neutrophil extracellular traps in cancer. Sem Cancer Biol. 2022;79:91–104. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.semcancer.2021.07.011.

    Article  CAS  Google Scholar 

  39. Kessenbrock K, Plaks V, Werb Z. Matrix metalloproteinases: regulators of the tumor microenvironment. Cell. 2010;141:52–67. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.cell.2010.03.015.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. M G, N H. Progress in matrix metalloproteinase research. Mol Aspects Med. 2008;29. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.mam.2008.05.002.

  41. Cytokine functions of TIMP-1 | Cellular and Molecular Life Sciences. https://link.springer.com/article/10.1007/s00018-013-1457-3 [Accessed February 25, 2024].

  42. Jiang X, Stockwell BR, Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol. 2021;22:266–82. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41580-020-00324-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Lei G, Zhuang L, Gan B. Targeting ferroptosis as a vulnerability in cancer. Nat Rev Cancer. 2022;22:381–96. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41568-022-00459-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Yee PP, Wei Y, Kim S-Y, Lu T, Chih SY, Lawson C, Tang M, Liu Z, Anderson B, Thamburaj K, et al. Neutrophil-induced ferroptosis promotes tumor necrosis in glioblastoma progression. Nat Commun. 2020;11:5424. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41467-020-19193-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Boonpraman N, Yoon S, Kim CY, Moon J-S, Yi SS. NOX4 as a critical effector mediating neuroinflammatory cytokines, myeloperoxidase and osteopontin, specifically in astrocytes in the hippocampus in Parkinson’s disease. Redox Biol. 2023;62:102698. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.redox.2023.102698.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chu C, Wang X, Yang C, Chen F, Shi L, Xu W, Wang K, Liu B, Wang C, Sun D, et al. Neutrophil extracellular traps drive intestinal microvascular endothelial ferroptosis by impairing Fundc1-dependent mitophagy. Redox Biol. 2023;67:102906. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.redox.2023.102906.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Chen L, Liu Y, Wang Z, Zhang L, Xu Y, Li Y, Zhang L, Wang G, Yang S, Xue G. Mesenchymal stem cell-derived extracellular vesicles protect against abdominal aortic aneurysm formation by inhibiting NET-induced ferroptosis. Exp Mol Med. 2023;55:939–51. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s12276-023-00986-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Qi Y, Chen L, Ding S, Shen X, Wang Z, Qi H, Yang S. Neutrophil extracellular trap-induced ferroptosis promotes abdominal aortic aneurysm formation via SLC25A11-mediated depletion of mitochondrial glutathione. Free Radic Biol Med. 2024;221:215–24. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.freeradbiomed.2024.05.036.

    Article  CAS  PubMed  Google Scholar 

  49. Zhang H, Liu J, Zhou Y, Qu M, Wang Y, Guo K, Shen R, Sun Z, Cata JP, Yang S, et al. Neutrophil extracellular traps mediate m6A modification and regulates sepsis-associated acute lung injury by activating ferroptosis in alveolar epithelial cells. Int J Biol Sci. 2022;18:3337–57. https://doiorg.publicaciones.saludcastillayleon.es/10.7150/ijbs.69141.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yao L, Sheng X, Dong X, Zhou W, Li Y, Ma X, Song Y, Dai H, Du Y. Neutrophil extracellular traps mediate TLR9/Merlin axis to resist ferroptosis and promote triple negative breast cancer progression. Apoptosis. 2023;28:1484–95. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10495-023-01866-w.

    Article  CAS  PubMed  Google Scholar 

  51. Watson WH, Ritzenthaler JD, Roman J. Lung extracellular matrix and redox regulation. Redox Biol. 2016;8:305–15. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.redox.2016.02.005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Labrousse-Arias D, Martínez-Ruiz A, Calzada MJ. Hypoxia and Redox Signaling on Extracellular Matrix Remodeling: from mechanisms to pathological implications. Antioxid Redox Signal. 2017;27:802–22. https://doiorg.publicaciones.saludcastillayleon.es/10.1089/ars.2017.7275.

    Article  CAS  PubMed  Google Scholar 

  53. Peng B, Feng Z, Yang A, Liu J, He J, Xu L, Tian C, Sheng X, Wang Y, Chen R, et al. TIMP1 regulates ferroptosis in osteoblasts by inhibiting TFRC ubiquitination: an in vitro and in vivo study. Mol Med. 2024;30:226. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s10020-024-01000-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Li M, Ni Q-Y, Yu S-Y. Integration of single-cell transcriptomics and epigenetic analysis reveals enhancer-controlled TIMP1 as a regulator of ferroptosis in colorectal cancer. Genes Genomics. 2024;46:121–33. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s13258-023-01474-7.

    Article  CAS  PubMed  Google Scholar 

  55. Wang L, Wang J, Chen L. TIMP1 represses sorafenib-triggered ferroptosis in colorectal cancer cells by activating the PI3K/Akt signaling pathway. Immunopharmacol Immunotoxicol. 2023;45:419–25. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/08923973.2022.2160731.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported by Key Research Foundation of Zhejiang (2022C03015 to J.Z.) and Natural Science Foundation Exploratory Youth Program of Zhejiang (LQ23H220003 to Q.C.) and China Postdoctoral Science Foundation (2022M723204 to Q.C.)and National Natural Science Foundation of China Youth Project (82303688 to Q.C.) and Hangzhou Institute of Medicine, Chinese Academy of Sciences (2024ZZBS11) and BeijingXisike Clinical Oncology Research Foundation (Y-Gilead2024-PT-0092).

Funding

This study was supported by Key Research Foundation of Zhejiang (2022C03015 to J.Z.) and Natural Science Foundation Exploratory Youth Program of Zhejiang (LQ23H220003 to Q.C.) and China Postdoctoral Science Foundation (2022M723204 to Q.C.)and National Natural Science Foundation of China Youth Project (82303688 to Q.C.) and Hangzhou Institute of Medicine, Chinese Academy of Sciences (2024ZZBS11) and BeijingXisike Clinical Oncology Research Foundation (Y-Gilead2024-PT-0092).

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  1. Yuzhao Jin and Luyu Liao contributed equally to this work.

Authors and Affiliations

  1. Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, 310022, China

    Yuzhao Jin, Luyu Liao, Qianping Chen, Ji Zhu & Minghua Bai

  2. Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences Hangzhou, Hangzhou, 310000, China

    Yuzhao Jin, Luyu Liao, Qianping Chen, Ji Zhu & Minghua Bai

  3. Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, 310000, China

    Yuzhao Jin, Luyu Liao, Qianping Chen, Ji Zhu & Minghua Bai

  4. Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Bufu Tang

  5. Department of Oncology, Affiliated Hospital of Jiaxing University, The First Hospital of Jiaxing, Jiaxing, Zhejiang, 31400, China

    Jin Jiang

  6. Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, 310000, China

    Ji Zhu & Minghua Bai

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Contributions to the conception and design of the study were made by all involved authors. The tasks of material preparation, data collection, and analysis were specifically undertaken by Yuzhao Jin, Qianping Chen, and Bufu Tang. Yuzhao Jin prepared the initial draft, which was then refined through the comments and suggestions of all authors. The final manuscript has been reviewed and approved by each author.

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Jin, Y., Liao, L., Chen, Q. et al. Multi-omics analysis reveals that neutrophil extracellular traps related gene TIMP1 promotes CRC progression and influences ferroptosis. Cancer Cell Int 25, 31 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03643-y

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Cancer Cell International

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