Construction of a prognostic risk model based on colorectal cancer-specific ferroptosis genes and preliminary validation of FGFR4

Introduction

Colorectal cancer (CRC) ranks among the most common cancers globally, and notably, its patient population has been gradually shifting towards younger individuals in recent years (1-3). Despite the widespread clinical use of novel chemotherapy drugs, molecular targeted therapies, and immunotherapies, the prognosis for patients with advanced CRC remains unsatisfactory (4-6). Furthermore, tumor tissues are highly heterogeneous and complex (7), and there is a lack of effective biomarkers to diagnose and predict the prognosis of CRC (8). Studies have also suggested that multi-gene prognostic models outperform single biomarkers in terms of prediction accuracy and comprehensiveness (9). In summary, the search for reliable biomarkers or traits and the development of reliable prognostic risk models to accurately predict the prognosis and treatment response of CRC patients remain an urgent issue that need to be addressed in current CRC research and provides the core motivation for this study.

Ferroptosis is a novel form of iron-dependent non-apoptotic cell death characterized by the accumulation of lipid reactive oxygen species (ROS) (4). Studies have shown that inducing ferroptosis can not only enhance the anti-tumor effects of anticancer drugs but also play a role in reversing drug resistance to anticancer agents (5-7). Therefore, this mechanism holds promise as a potential new strategy for cancer treatment. Although increasing evidence suggests that ferroptosis plays an important role in CRC, the specific regulatory network and translational potential of ferroptosis-related genes have not been fully elucidated. In this study, we systematically screened ferroptosis-related genes and constructed a prognostic risk model for CRC. Among the identified genes, fibroblast growth factor receptor 4 (FGFR4) was selected for further functional validation due to its upstream regulatory role and potential translational significance. Based on this, elucidating the association between ferroptosis and CRC, and the potential role of FGFR4 may provide an important theoretical basis for the development of novel CRC therapeutic options. The aim of this study is to investigate the role of ferroptosis in CRC, and to construct a prognostic risk model by screening ferroptosis-related genes; at the same time, we will verify the biological behaviors of the ferroptosis-related gene, FGFR4, in CRC and explore the regulatory mechanism, so as to provide new ideas and experimental bases for prognostic assessment and targeted therapy of CRC. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2850/rc).

Methods

Data collection and analysis

Transcriptomic sequencing data and clinical data of CRC patients were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov). A total of 701 CRC patients’ gene expression profile data (630 tumor samples and 71 normal samples) were included as the training set. A total of 1,839 ferroptosis-related genes were obtained from the GeneCards database (https://www.genecards.org/). Transcriptome sequencing data for the GSE72970 (containing 124 tumor samples, of which 124 had prognostic information) and GSE39582 (containing 585 tumor samples, of which 489 had prognostic information) datasets were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) and clinical data were used for validation (Table 1). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Differential gene screening

Differentially expressed genes (DEGs) between CRC tissues and normal tissues were screened using the Wilcoxon rank-sum test in R software, with the filtering criteria set as false discovery rate (FDR) <0.05 and |log₂ fold change (log₂FC)| >1. Ferroptosis-related genes were identified from the GeneCards database, and the intersection with DEGs was obtained to identify ferroptosis-related DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the “clusterProfiler” R package to conduct GO and KEGG enrichment analysis on ferroptosis-related DEGs, revealing biological functions, including biological processes (BP), molecular functions (MF), and cellular components (CC) pathways.

Construction and validation of prognostic risk model

First, univariate Cox regression analysis was performed to assess the prognostic value of each gene for overall survival (OS). The “glmnet” R package was employed for least absolute shrinkage and selection operator (LASSO) regression analysis to further reduce the dimensionality of the previously screened genes. A 10-fold cross-validation was performed to determine the optimal value of the regularization parameter λ (lambda.min), and genes with non-zero coefficients at the optimal λ were retained for subsequent multivariate Cox regression analysis. Subsequently, multivariate Cox regression analysis was conducted to identify the optimal gene set, and a prognostic model was established by calculating the risk score based on the regression coefficients and expression levels of each gene derived from the multivariate Cox regression analysis. Patients were stratified into high-risk and low-risk groups according to the median risk score. The “survminer” and “timeROC” R packages were used to generate Kaplan-Meier (KM) survival curves and time-dependent receiver operating characteristic (ROC) curves, respectively, for evaluating the predictive performance of the model. Finally, external validation was carried out in the GSE72970 and GSE39582 validation cohorts.

Independent prognostic analysis and nomogram construction

A prognostic risk prediction nomogram for 1-, 3-, and 5-year OS was constructed using the “rms” R package (https://cran.r-project.org/web/packages/rms/) to further explore the association between individual factors and survival outcomes. Following this, calibration curves were generated to evaluate the predictive validity of the nomogram.

Target gene analysis

The Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/) and the Human Protein Atlas (HPA) database (http://proteinatlas.org) were used to compare the differences in FGFR4 mRNA and protein expression between CRC and normal tissues. Moreover, gene set enrichment analysis (GSEA) was performed to explore the biological pathways associated with FGFR4.

Cell culture and transfection

The human CRC cell line HCT116 was purchased from Haixing Biotechnology (Jiangsu, China). Cells were cultured in RPMI-1640 medium (Solarbio, Beijing, China) supplemented with 10% fetal bovine serum (FBS, Gibco, USA), 100 U/mL penicillin, and 100 µg/mL streptomycin at 37 °C in a 5% CO₂ atmosphere. FGFR4-specific small interfering RNA (siRNA) and siRNA negative control (NC) group were purchased from Suzhou GenePharma (Suzhou, China). The sequence of siRNA targeting FGFR4 is as follows: forward siRNA: 5’-CCUCCAGCGAUUCUGUCUUTT-3’; reverse siRNA: 5’-AAGACAGAAUCGCUGGAGGTT-3’. FGFR4 siRNA transfection was performed in 6-well plates using Lipofectamine 8000 (Beyotime, Shanghai, China) according to the manufacturer’s protocol.

Quantitative reverse transcription polymerase chain reaction (qRT-PCR)

Total RNA was extracted from cell lines using the Quick RNA Extraction Kit (YALEPIC, Jiangsu, China). Complementary DNA (cDNA) was then synthesized from the extracted RNA using a reverse transcription kit (Transgen, Beijing, China). Then, qRT-PCR reactions (Transgen) were performed to detect the expression level using FGFR4-specific primers (F: CACTGGTACAAGGAGGGCAG; R: ATCGTTGCTGGAGGTCAAGG). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as the internal reference gene. The reaction conditions were set as follows: 94 °C for 30 s, followed by 40 cycles of 94 °C for 5 s and 60 °C for 30 s. The expression level was quantified using the 2^-△△Ct method.

Cell Counting Kit-8 (CCK-8) assay

Cell viability was determined using the CCK-8 (Beyotime). Cells were collected 24 hours after transfection and seeded into 96-well culture plates. Subsequently, 10 µL of CCK-8 was added to each well at different time points, followed by incubation at 37 °C for 1 hour. The optical density (OD) was measured at 450 nm using a microplate reader (Thermo Fisher Scientific, Waltham, MA, USA), and cell viability was calculated.

Cell scratch assay

siRNA-transfected cells were seeded into 6-well plates. When the cells reached over 90% confluence, a 20 µL pipette tip was used to scratch the cell monolayer, and the medium containing 10% FBS was replaced with serum-free medium. Cell images were captured at 0 h and 36 h in corresponding order.

ROS detection

siRNA-transfected cells were seeded into 6-well plates and incubated with erastin. After being thoroughly rinsed twice with PBS under dark conditions, the cells were incubated with DCFH-DA (10 µmol /L) at 37 °C for 30 minutes. Cell observation was performed using a fluorescence microscope (Olympus, Tokyo, Japan), and image analysis was conducted with ImageJ software (NIH Image, Bethesda, MD, USA).

Glutathione (GSH) analysis

GSH levels were measured using a GSH assay kit (G4305-48T) purchased from Servicebio (Wuhan, China). Briefly, adherent HCT116 cells were lysed and centrifuged (10,000 × g, 10 minutes) to collect the supernatant. Intracellular GSH content was then determined according to the manufacturer’s instructions.

Lipid peroxide assay

Lipid peroxides in live cells were detected using a BODIPY 581/591 C11 kit (Beyotime). Cells were seeded into 6-well plates and cultured for 24 hours. Subsequently, HCT116 cells were stained with a 2 mM C11-BODIPY (581/591) probe according to the manufacturer’s instructions. A fluorescence microscope (Olympus) was used to observe the cells. ImageJ software (NIH Image) was then utilized for data analysis. Oxidized BODIPY was observed at excitation/emission wavelengths of 488/510 nm. Meanwhile, reduced BODIPY was observed at excitation/emission wavelengths of 581/591 nm.

Statistical analysis

All statistical analyses were performed using GraphPad Prism version 10.5.0 and R (version 4.2.3). In in vitro experiments, analysis of variance (ANOVA) was used to compare the means of multiple sample groups, while the t-test was employed for comparing the means of two sample groups. All experiments were repeated three times per assay and independently replicated at least three times. A P value <0.05 was considered statistically significant.

Results

Screening and enrichment analysis of prognosis-related ferroptosis DEGs (Figure 1)

Figure 1 DEGs analysis in the TCGA database. (A) A Venn diagram showing a total of 285 ferroptosis-related DEGs in CRC. (B) A volcano plot of DEGs in CRC. Red represents genes with increased expression, and blue represents genes with decreased expression. (C) KEGG enrichment analysis of key genes. (D) A heatmap illustrating the expression levels of DEGs in tumor and normal tissues. (E) GO enrichment analysis of key genes. BP, biological process; CC, cellular component; CRC, colorectal cancer; FDR, false discovery rate; DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function.

Data from 630 CRC patients were obtained from the TCGA database. A total of 3,671 DEGs were identified, including 2,486 upregulated genes and 1,185 downregulated genes (Figure 1B). A heatmap was used to display the top 20 most significantly upregulated and downregulated genes (Figure 1D). A Venn diagram was used to intersect the DEGs of CRC with ferroptosis-related genes, resulting in 285 key genes (Figure 1A). GO analysis and KEGG enrichment analysis were performed on the key genes (Figure 1C,1E). GO analysis highlighted responses to iron ions, hydrogen peroxide, and ROS, while KEGG analysis indicated that the model genes are primarily involved in key mechanisms such as ferroptosis, HIF-1 signaling pathways, and fatty acid metabolism, which are highly consistent with the molecular features of ferroptosis (10,11).

Construction of the prognostic model (Figure 2)

Figure 2 Construction of the risk model using LASSO and Cox regression analysis. (A,B) The optimal λ value was determined using LASSO regression. (C) Forest plot of the multivariate Cox regression analysis. (D) The coefficients of the eight target genes. (E) The distribution of risk scores and survival status for low-risk/high-risk patients. LASSO, least absolute shrinkage and selection operator.

Univariate Cox regression analysis was performed on the key genes identified above (P<0.05), resulting in 23 genes associated with OS of patients (Table 2). To prevent overfitting, LASSO analysis was performed on the above genes, yielding 16 results. The selection process and results are shown in Figure 2A,2B. To construct a robust and biologically representative prognostic model, multivariate Cox regression analysis was then performed. This rigorous method ultimately identified eight ferroptosis-related DEGs associated with CRC prognosis (Figure 2C). To more accurately assess patient prognosis, we created a prognostic risk model, with the following scoring formula:

RiskScore=(0.01837×expALG3)+(0.00123×expCST1)+(0.04492×expRPS10)+(0.08056×expTRPV4)+(0.00213×expSPRR1A)+(−0.00823×expFGFR4)+(−0.01113×expTPM1)+(−0.01237×expKDM1A)[1]

The detailed coefficients of the eight genes are provided in Table 3, and their distribution is shown in Figure 2D. Afterwards, based on the median risk score of each group, CRC patients were divided into high-risk and low-risk groups. The results showed that as the risk score increased, the number of surviving samples decreased and the number of deceased samples increased. The prognosis of the low-risk group was significantly better than that of the high-risk group (Figure 2E).

Validation of the prognostic model (Figure 3)

Figure 3 Survival analysis and model validation of the prognostic model in the training cohort and testing cohort. (A) TCGA-COADREAD, (C) GSE72970, and (E) GSE39582 show the ROC curve at 1-, 3-, and 5-year. The area under the curve indicates the accuracy of the model in predicting survival status. (B) TCGA-COADREAD, (D) GSE72970, and (F) GSE39582 show the Kaplan-Meier analysis results for the high-risk and low-risk groups. Blue represents the low-risk group, and red represents the high-risk group. AUC, area under the curve; OS, overall survival; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas; TCGA-COADREAD, colorectal cancer dataset from the TCGA database.

Patients were divided into low-risk and high-risk groups based on the median risk score in the training cohort and testing cohort to evaluate the usability of the prognostic model. KM analysis showed that patients with high-risk scores had significantly lower OS in both the training cohort and testing cohort compared to patients with low-risk scores, consistently indicating that the OS rate of the high-risk group was significantly lower (Figure 3B,3D,3F). The effectiveness of the risk score in predicting OS was assessed using ROC curve analysis. The area under the curve (AUC) values for the training cohort at 1-, 3-, and 5 years were sequentially 0.857, 0.728, and 0.808. The ROC curve for the testing cohort also demonstrated considerable accuracy (Figure 3A,3C,3E). In conclusion, this study proposes a robust ferroptosis-related prognostic model for CRC patients, with consistent validation results across all cohorts, further confirming its sensitivity as a prognostic tool.

Construction of prognostic risk prediction model of nomogram (Figure 4)

Figure 4 Independent prognostic factor analysis for CRC patients. (A) The forest plot of univariate Cox regression analysis. (B) The forest plot of multivariate Cox regression analysis. (C) The nomogram based on risk score and stage, predicting the 1-, 3-, and 5-year survival probabilities of CRC patients. (D-F) Calibration curves of the nomogram predicting the 1-, 3-, and 5-year survival probabilities. **, P<0.01; ***, P<0.001. CRC, colorectal cancer; OS, overall survival.

Next, univariate and multivariate Cox regression analyses were performed to test whether the risk score serves as an independent prognostic factor for CRC patients, independent of other clinical factors (T, N, M, and stage). The results of the univariate (P<0.05) and multivariate Cox regression analysis (P<0.05) indicated that stage and risk score are prognostic factors (Figure 4A,4B). To quantify individual risk assessment in CRC patients, this study combined clinical features (stage) with the risk score to create a nomogram to predict the 1-, 3-, and 5-year survival probabilities (Figure 4C). The calibration curves suggest that the nomogram performs well in predicting the 1-, 3-, and 5-year survival probabilities for CRC patients (Figure 4D-4F).

Validation of FGFR4 Expression and GSEA (Figure 5)

Figure 5 Validation of mRNA and protein levels of the target gene and GSEA. (A) Transcriptional level of FGFR4 in the GEPIA database. (B) GSEA illustrating the biological pathways associated with FGFR4. (C,D) Protein expression of FGFR4 in CRC tissues (https://www.proteinatlas.org/ENSG00000160867-FGFR4/cancer/colorectal+cancer#img) and normal colorectal tissues (https://www.proteinatlas.org/ENSG00000160867-FGFR4/tissue/colon#img) from the Human Protein Atlas database. Scale bar: 200 µm. *, P<0.05. COAD, colon adenocarcinoma; GSEA, Gene set enrichment analysis; GEPIA, Gene Expression Profiling Interactive Analysis; HPA, Human Protein Atlas; READ, rectal adenocarcinoma; NOS, not otherwise specified.

To further investigate the importance of FGFR4 in CRC, we found that FGFR4 expression was significantly upregulated in CRC tissues compared to normal tissues in the GEPIA database (P<0.05, Figure 5A). Furthermore, the HPA database provided immunohistochemistry (IHC) staining pattern information for FGFR4, showing significant expression differences between normal tissues and CRC tissues (Figure 5C,5D). We performed GSEA to investigate the significant associations of the FGFR4 gene with multiple KEGG pathways. The analysis revealed that FGFR4 was significantly enriched in pathways such as neutrophil extracellular trap formation, systemic lupus erythematosus, alcoholism, IL-17 signaling pathway, and Wnt signaling pathway (Figure 5B).

Knockdown of FGFR4 gene inhibited the malignant biological behaviors of CRC cells and induced ferroptosis

We constructed FGFR4-knockdown cells and their corresponding controls for subsequent experiments (Figure 6A). The CCK-8 assay results showed that HCT116 cells with FGFR4 knockdown had reduced viability compared to the NC group (Figure 6B). The scratch assay showed that the knockdown group significantly inhibited the migration ability of tumor cells (Figure 6C,6D). To further evaluate whether FGFR4 is involved in the regulation of ferroptosis, we examined ferroptosis-related biochemical indicators under erastin treatment. The results showed that cell viability was further decreased in the si-FGFR4 group compared with the NC group under erastin induction (Figure S1A). Meanwhile, intracellular ROS levels were increased (Figure 6E,6F), glutathione (GSH) levels were decreased, and lipid peroxidation levels were increased in the si-FGFR4 group (Figure S1B,S1C). These results indicate that FGFR4 knockdown increases ferroptosis-related biochemical changes induced by erastin, thereby enhancing the sensitivity of CRC cells to ferroptosis.

Figure 6 Cellular phenotype after FGFR4 knockdown. (A) qRT-PCR was used to detect the mRNA expression of FGFR4. (B) The CCK8 assay was used to assess cell viability; (C,D) scratch test to assess cell migration (magnification: ×40); (E,F) after exposing HCT116 cells to erastin, ROS levels were measured using a commercial kit (DCFH-DA staining; Scale bar: 50 μm). *, P<0.05; **, P<0.01; ***, P<0.001. NC, negative control; qRT-PCR, quantitative reverse transcription polymerase chain reaction; ROS, reactive oxygen species; TCGA, The Cancer Genome Atlas; TCGA-COADREAD, colorectal cancer dataset from the TCGA database.

Discussion

CRC, as one of the most common malignant tumors, still faces numerous challenges in non-surgical treatment, including drug resistance, recurrence, and distant metastasis (12-14). Ferroptosis, due to its ability to promote cancer cell death, overcome drug resistance, and enhance the effectiveness of anti-tumor treatments, is currently considered to have potential in the treatment of CRC (15-17).

In constructing the eight-gene risk model, we first performed an integrated analysis based on the ferroptosis database and TCGA CRC data, identifying 285 ferroptosis-related DEGs, which were further enriched in pathways closely associated with ferroptosis. Subsequently, through univariate Cox regression, LASSO regression, and multivariate Cox regression analysis, we identified eight ferroptosis-related DEGs with prognostic value in CRC (ALG3, CST1, RPS10, TRPV4, SPRR1A, FGFR4, TPM1, KDM1A). Based on the median risk score, patients were divided into high-risk and low-risk groups. In the training cohort, KM survival analysis showed that patients in the high-risk group had significantly worse prognosis than those in the low-risk group. Within this cohort, the 1-year, 3-year, and 5-year AUC values were 0.857, 0.728, and 0.808. The feasibility of the model was also validated in independent external cohorts, GSE72970 and GSE39582. To further validate the clinical relevance of the model, we incorporated clinical variables (such as stage and TNM) and constructed a nomogram. The calibration curves indicated that the nomogram’s predictive performance was reliable. Based on database information and previous studies (18,19) showing high expression of FGFR4 in CRC, we validated its biological function through in vitro experiments, suggesting that knockdown of FGFR4 inhibited CRC cell proliferation and migration. Notably, under the condition of ferroptosis inducer erastin, the si-FGFR4 group showed further decreased cell viability. It also presented higher ROS accumulation, lower GSH levels, and increased lipid peroxidation, suggesting that FGFR4 knockdown significantly enhanced the cells’ sensitivity to ferroptosis.

In recent years, ferroptosis has shown great potential in the field of cancer treatment. Xu et al. (20) found that inducing ferroptosis in colorectal cancer stem cells (CSCs) can alleviate CRC progression and chemoresistance. Wang et al. (21) confirmed that promoting ferroptosis in tumor cells enhances the anti-tumor effect of immunotherapy and enhances chemosensitivity. These studies suggest that ferroptosis could serve as an important target in cancer treatment.

Among the eight ferroptosis-related genes identified in the prognostic model, we selected FGFR4 for experimental validation. We based this choice on its upstream regulatory role and clinical targetability, aiming to further explore the biological relevance of the prognostic model. FGFR4 is a tyrosine kinase receptor that binds to fibroblast growth factors (FGFs) and is involved in various cellular processes, including cell proliferation, differentiation, migration, and the regulation of bile acid biosynthesis (22). Studies have confirmed that FGFR4 acts as an oncogene, driving tumorigenesis and progression in digestive system cancers, such as CRC (19,23,24). This study confirmed the regulatory role of FGFR4 in the malignant phenotype of tumor cells by knocking down FGFR4 in HCT116 cells, further validating its oncogenic function. Additionally, GSEA revealed significant enrichment of the Wnt signaling pathway in FGFR4 high expression samples, suggesting an association between FGFR4 and the Wnt pathway (25), providing clues at the population level for subsequent mechanistic exploration. The study by Ye et al. (26) further corroborated this regulatory relationship at the cellular level: they confirmed in SW620 cells that silencing FGFR4 can reverse epithelial-mesenchymal transition (EMT) by inhibiting the Wnt/β-catenin pathway, suggesting that FGFR4 may participate in the regulation of CRC invasion and metastasis by activating this pathway. Beyond Wnt signaling, other potential downstream pathways of FGFR4 that may intersect with ferroptosis-related regulatory networks are summarized in Table S1. Building on these observations, we further explored whether FGFR4 might participate in ferroptosis regulation through Wnt-associated mechanisms. Previous studies have found that inhibiting FGFR4 in breast cancer can reduce GSH synthesis and Fe²⁺ efflux efficiency through the Wnt/β-catenin/TCF4-SLC7A11/FPN1 axis, further exacerbating ROS accumulation, thereby inducing ferroptosis (27). Considering that the Wnt/β-catenin pathway is a core regulatory pathway in the occurrence and development of CRC, and based on this study’s findings in HCT116 cells where FGFR4 was knocked down, significant alterations in lipid peroxidation and other ferroptosis markers were observed in the erastin-treated group, further confirming the inhibitory effect of FGFR4 on ferroptosis. Taken together, we hypothesize that in CRC, FGFR4 activation of the Wnt pathway leads to sustained activation of β-catenin, which regulates the transcription of downstream SLC7A11 and FPN1, promoting GSH synthesis and Fe²⁺ efflux to eliminate ROS, thereby synergistically providing CRC cells with resistance to ferroptosis; whereas knocking down FGFR4 inhibits Wnt/β-catenin pathway activity, disrupting this resistance barrier and making cells more sensitive to erastin-induced ferroptosis. This study provides a new perspective on the functional research of FGFR4 in CRC, suggesting that it may simultaneously be involved in the regulation of tumor progression and ferroptosis resistance. These findings demonstrate that the current signature model serves as more than a simple prognostic classification tool. In contrast to previously reported ferroptosis-related models that rely primarily on statistical associations, the present study integrates prognostic stratification with functional and mechanistic validation of a core regulatory gene. Therefore, the inclusion of a biologically well-characterized and pharmacologically targetable molecule such as FGFR4 has, to a certain extent, enhanced the translational interpretability of the model, suggesting that it can be used not only for prognostic prediction but also to provide a reference for the exploration of future therapeutic strategies. From a therapeutic perspective, our results indicate that inhibition of FGFR4 may enhance ferroptotic stress by attenuating pro-survival signaling and disrupting redox homeostasis-related pathways. Although further in vivo and clinical validation is still required, our findings provide a biologically reasonable rationale for exploring therapeutic strategies targeting FGFR4 combined with ferroptosis inducers in CRC.

It is noteworthy that, to date, the regulatory role of FGFR4 in ferroptosis in CRC has not been reported in the literature. This gap in clinical data highlights a new research direction and underscores the translational significance of our findings. Future studies involving large scale clinical cohorts and multi-omics approaches are crucial. These studies aim to determine whether FGFR4 can serve not only as a molecular regulator of ferroptosis, but also as a potential prognostic or predictive biomarker for CRC.

This study has several limitations. First, it is a retrospective study, which may have sample selection bias, data heterogeneity, missing clinical information, and limitations inherent in retrospective design. Further prospective validation and multicenter data are needed to confirm the results. Second, although recent studies have extensively characterized the general oncogenic role of FGFR4 in promoting tumor growth and metastasis in vivo, our future research will use xenograft models to specifically verify the regulatory function of FGFR4 in ferroptosis in vivo. Third, future studies should further validate the specific molecular mechanism by which FGFR4 activates the Wnt pathway in CRC. It is also necessary to clarify the downstream target role of SLC7A11 and FPN1 in this regulatory network. Additionally, this study only performed in vitro functional validation of FGFR4, and the biological functions and related molecular mechanisms of the other 7 feature genes require further in depth investigation and experimental validation.

Conclusions

We constructed an eight-gene ferroptosis-related prognostic model for CRC, which exhibits favorable prognostic performance. Additionally, we found that FGFR4 may be involved in regulating both tumor progression and ferroptosis resistance, suggesting its potential as a novel therapeutic target.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2850/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2850/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2850/prf

Funding: This study was supported by the Science and Technology Program of the Joint Fund of Scientific Research for the Public Hospitals of Inner Mongolia Academy of Medical Sciences (No. 2023GLLH0315).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2850/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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