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Identification of m6 A-regulated ferroptosis biomarkers for prognosis in laryngeal cancer

BMC Cancer volume 25, Article number: 694 (2025) Cite this article

Abstract

Laryngeal cancer (LC) is a malignant tumor that occurs in the larynx. N6-methyladenosine (m6A) RNA methylation, a pivotal and prevalent epigenetic modification in eukaryotic mRNA, intricately intertwines with ferroptosis, and together, they play a crucial role in the development of LC. Accordingly, further research on related molecular mechanisms and pathology of LC is necessary. Weighted gene co-expression network analysis and correlation analysis were used to identify differentially expressed m6A-related ferroptosis genes in LC. The TCGA-HNSC and GSE65858 datasets were obtained from public databases. The TCGA-HNSC dataset consisted of 110 primary tumor oropharynx samples and 12 control oropharynx samples, while the GSE65858 dataset contained forty-eight primary tumor oropharynx samples. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression were utilized for feature selection and risk model construction in the TCGA-HNSC dataset. The risk model was validated in the GSE65858 dataset. Then, a nomogram was built based on the independent prognostic factor identified using univariate and multivariate Cox regression in the TCGA-HNSC dataset. Mutation analysis, immune-related analysis, and drug sensitivity prediction were applied to analyze the utility of the risk model in the TCGA-HNSC dataset. Additionally, qRT-PCR and western blot were performed to detect the TFRC, RGS4, and FTH1 expression. Three biomarkers were identified to build a risk model using the univariate Cox and LASSO regression algorithms. Receiver operating characteristic (ROC) analysis verified the accuracy of the risk model. Tumor Immune Dysfunction and Exclusion (TIDE) and Estimation of STromal and Immune cells in MAlignant Tumors using the Expression data (ESTIMATE) algorithm showed a positive relationship between risk score and TIDE or ESTIMATE score. Furthermore, drug sensitivity prediction found that 19 chemotherapy drugs were strongly correlated with a risk score. TFRC, RGS4, and FTH1 exhibited high expression levels in 30 laryngeal carcinoma tissues and cell lines. Notably, TFRC and FTH1 expression levels were significantly associated with patient prognosis. In Conclusion, TFRC, RGS4, and FTH1, were identified as m6A-regulated ferroptosis biomarkers in LC, providing insights into LC treatment and prognosis.

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Introduction

Laryngeal cancer (LC), a prevalent malignant tumor of the head and neck region, is the third most common malignant tumor in this area. According to data released by Cancer Statistics in 2024, the global incidence of laryngeal cancer is 2.9 per 100,000 in men and 0.7 per 100,000 in women. The prognosis for laryngeal cancer remains poor, with a 5-year relative survival rate of 65.9%. Despite its relatively low incidence, laryngeal cancer poses a significant public health challenge due to its notably lower survival rate compared to many other cancer types. This underscores the necessity for enhanced research into early screening, diagnosis, and treatment of laryngeal cancer, as well as efforts to raise public awareness and vigilance regarding this disease, ultimately aiming to improve patient survival outcomes [1]. The primary symptoms of LC are dyspnea, cough, hoarseness, and hemoptysis [2]. The progression of advanced LC significantly impacts the quality of life and survival of affected individuals [3]. Surgery is a primary therapeutic approach for LC, effectively eliminating lesion tissue and enhancing the short-term prognosis of patients [4, 5]. Nevertheless, it harbors numerous nerve tissues and lymph nodes due to the distinctive anatomical positioning of the larynx, rendering it susceptible to lymph node metastasis and resulting in suboptimal long-term prognostic outcomes [6]. Research has demonstrated that CRISPR screening is an efficient and precise method for identifying gene targets associated with drug resistance [7]. Monoclonal antibodies directed against PD- 1 or PD-L1 have been shown to augment the immune system's capacity to detect and combat lung cancer (LC) [8]. Additionally, certain cytokines, including interferon and interleukin, have been employed in LC treatment due to their ability to stimulate the activation and proliferation of immune cells [9]. As a prospective therapeutic approach in contemporary medicine, immunotherapy is assuming an increasingly significant role in the management of various diseases. This therapeutic approach can enhance tumor drug sensitivity, augment the effectiveness of anti-tumor medications, and mitigate adverse effects. Consequently, investigating novel biomarkers and their underlying mechanisms holds promise for informing clinical treatment strategies [10, 11].

N6-methyladenosine (m6 A) is the most prevalent RNA modification, initially discovered in mRNA and subsequently identified in precursor mRNAs, circular RNA (circRNAs), and long non-coding RNAs (lncRNAs) [12, 13]. m6 A is crucial in various biological processes, including regulating mRNA stability, pre-mRNA splicing, translation, and DNA damage repair [14, 15]. Our previous investigation demonstrated that RBM15-mediated m6 A modification of the transmembrane BAX inhibitor motif- 6 (TMBIM6) mRNA enhances TMBIM6 stability via the IGF2BP3-dependent mechanism. Furthermore, RNA-binding motif protein 15 (RBM15) promotes the malignant progression of LC by upregulating TMBIM6 [16]. Iron is a crucial trace element necessary for the oxidative respiration of mitochondria within biological cells. Research has demonstrated that elevated intracellular iron levels can trigger a distinct type of cellular demise known as ferroptosis, distinguishing it from other cell death mechanisms, such as autophagy and cell death [17, 18]. Ferroptosis, a regulated form of cell death, has been well-characterized in cancer research. Additionally, recent studies have highlighted the role of cuproptosis and disulfidptosis in cancer cell death, which may intersect with ferroptosis in laryngeal cancer [19].

The key features of this phenomenon encompass the buildup of intracellular iron, heightened production of reactive oxygen species (ROS), and lipid peroxidation [20]. Ferroptosis impedes the tumor cell growth and enhances the responsiveness to chemotherapeutic agents [21, 22]. Ferroptosis is significantly associated with head and neck squamous cell carcinoma. Suppressing glutaredoxin 5 gene expression induces iron death in cisplatin-resistant cells [23, 24]. Research on rectal and liver cancers has demonstrated that m6 A modifications are pivotal in the regulation of ferroptosis. Specifically, METTL17 has been identified as a key regulator of ferroptosis and tumorigenesis through its role in mitochondrial translation within colorectal cancer. Furthermore, the METTL16-SENP3-LTF axis has been implicated in conferring resistance to ferroptosis and promoting tumorigenesis in hepatocellular carcinoma [25, 26]. A substantial research foundation exists to explore the relationship between m6 A, ferroptosis, and LC progression and prognosis.

This study employed bioinformatics to identify m6 A-related ferroptosis biomarkers in LC using publicly available databases. We developed a risk model based on ferroptosis genes regulated by m6 A. The univariate Cox regression analysis results revealed that transferrin receptor (TFRC), regulator of G protein signaling 4 (RGS4), and ferritin heavy chain 1 (FTH1) genes were significantly associated with the survival status of patients with LC. Additionally, we assessed the risk scores of the clinical characteristics of different LC samples. The risk model demonstrated a predictive relationship between the risk score and the efficacy of immune therapy and chemotherapy drugs. Besides, TFRC, RGS4, and FTH1 expression levels were examined in three laryngeal carcinoma cell lines (TU212, TU686, and AMC-HN- 8) and 30 pairs of laryngeal carcinoma clinical specimens. Our results indicated that TFRC, RGS4, and FTH1 expression levels were significantly higher than normal human bronchial epithelial cells (NHBEC). Furthermore, gene expression levels were significantly higher in laryngeal carcinoma tissues than in the adjacent tissues. These findings hold substantial importance for further elucidating the underlying mechanisms of LC.

Materials and methods

Data source

Two LC datasets, TCGA-HNSC and GSE65858, were acquired from the Cancer Genome Atlas (TCGA) (http://cancergenome.nih.gov/) and the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) databases. The GSE65858 dataset contains gene data sequenced using the GPL10558 Illumina HumanHT- 12 V4.0 expression bead chip platform [27]. Forty-eight primary tumor oropharynx samples from GSE65858 with survival information were enrolled in this study, which was used to validate the risk model. A total of 110 primary tumor oropharynx samples and 12 control oropharynx samples from the larynx were obtained from the TCGA-HNSC dataset [28], which was used as the major source for all analyses. Twenty-three m6 A RNA methylation-related genes were identified based on a previous study [29]. A total of 567 genes related to ferroptosis were sourced from the FerrDb database (http://www.zhounan.org/ferrdb/current/).

Function analysis of differentially expressed m6 A-related ferroptosis genes (DE-MRFG)

First, the ‘DESeq2’ R package (version 1.34.0) [30] was applied to identify differentially expressed genes (DEGs) in LC patients and healthy individuals in DE-MRFG based on the data of the TCGA-HNSC dataset. The filter criteria were adjusted p-value < 0.05 and |log2FC|> 0.5. DEGs are shown in the volcano map and heatmap. Second, LC-related genes were identified using weighted-gene co-expression network analysis (WGCNA) [31]. WGCNA constructed and screened gene modules were strongly correlated to LC using the R package ‘WGCNA’ (version 1.70–3). Third, LC-related genes intersected with DEGs and ferroptosis-related genes, obtaining differentially expressed ferroptosis-related genes (DE-FG) in LC. Finally, DE-MRFG in LC was determined using Pearson’s correlation analysis between m6 A-related genes and DE-FG. A p-value < 0.05 and |R|> 0.3 were the filter criteria. Gene ontology (GO) and the Kyoto encyclopedia of genes and genomes (KEGG) analysis were applied using the ‘clusterProfiler’ package (version 4.2.2) to probe the molecular mechanism of DE-MRFG [32].

Establishment and verification of a risk model

Initially, to screen the biomarkers in the DE-MRFG, based on the DE-MRFG, univariate Cox regression was applied using the ‘survival’ R package (version 3.2–13) to obtain candidate biomarkers (p < 0.05 and HR ≠ 1) in the TCGA-HNSC dataset (n = 108, two cancer samples were filtered, and patient survival was less than 30 days) (Therneau, 2012). Then, the least absolute shrinkage and selection operator (LASSO) regression was performed on the candidate biomarkers using and the ‘glmnet’ R package (version 4.1–2) [33]. After tenfold cross-validation, the optimal model was selected, and the genes with non-zero coefficients at the lambda.min value were regarded as the biomarkers for LC. Then, a risk model was constructed based on the biomarkers. The risk score formula was performed on the candidate biomarkers using calculated as follows:

$$\text{Risk score }= \sum \text{regression coefficient}\left({\text{gene}}_{\text{i}}\right)\times \text{expression value}\left({\text{gene}}_{\text{i}}\right)$$

Data from the LC samples from TCGA and GSE65858 were used to assess and verify the risk model. First, all LC samples were classified into two groups for use in the following analyses, according to the risk score optimal threshold calculated by the risk model. One group contained LC samples with high-risk scores, and the other contained low scores. Second, the survival state of different groups was analyzed using Kaplan–Meier (KM) survival analysis and the ‘survminer’ package (version 3.3.5) (Kassambara, 2017) (p < 0.05). Survival curves were drawn using'ggplot2'[R package survminer version 0.2.0]). Third, the accuracy of survival prediction was assessed using receiver operating characteristic (ROC) analysis and the ‘survivalROC’ package (version 1.0.3) (Heagerty, 2013) (area under the curve (AUC) > 0.6). SurvivalROC: time-dependent roc curve estimation from censored survival data). Fourth, survival state distribution maps were drawn to investigate the survival time of patients with different risk scores. Finally, a heatmap of the biomarker expression was drawn. In summary, survival, ROC, and expression analyses were used in both TCGA-HNSC and GSE65858 to assess the performance of the risk model.

Enrichment analysis of DEGs in different groups

DEG analyses were performed using the data of the TCGA-HNSC dataset and the ‘DESeq2’ package to research the potential influence of DEGs in high- and low-risk groups. DEGs from the different groups are shown in the volcano map. Genes with an adjusted p-value ≥ 0.05 and |log2FC|≤ 0.5 were filtered. GO and KEGG analyses were performed to investigate the function of the DEGs using the ‘clusterProfiler’ package (p.adj < 0.05). The top five GO items in the three categories of GO analysis and the top 10 KEGG pathways were drawn into bubble plots.

Relationship between risk model and clinical characteristics

Clinical information and samples with survival data from the TCGA-HNSC dataset were obtained to explore the influence of different clinical characteristics in the risk model. First, Wilcoxon (for two groups) and Kruskal–Wallis (for more than two groups) tests were used to compare the risk scores of different clinical features, including gender, age, pathological N stage, pathological T stage, and tumor Stage. Violin plots were used to reveal the distribution of risk scores in different clinical characteristics. Then, a heatmap was used to show the expression of biomarkers with different clinical characteristics. Finally, KM survival analyses of the high- and low-risk groups were used to investigate the different survival states according to different clinical features.

A nomogram model of LC

First, univariate Cox regression analysis was applied for risk score, age, gender, tumor stage, Pathologic_T, and Pathologic_N of LC samples from the TCGA-HNSC dataset. Forest plot demonstrated the result of these factors using ‘forestplot’ (version 2.0.1) (Max and Thomas (2022). Forestplot: Advanced Forest Plot Using'grid'Graphics, https://rdrr.io/cran/forestplot/). The factors with p-value < 0.05 were used for multivariate Cox regression analysis to identify the independent prognosis factor. Then, a nomogram was built to predict the two-, three-, and four-year overall survival (OS) of LC patients based on the independent prognostic factors and verified using calibration curves and ROC analysis.

The relationship of risk model and immunotherapy

The single-sample gene set enrichment analysis (ssGSEA) was utilized to screen out the differences in immune cells between high- and low-risk groups. During this process, expression data from TCGA-HNSC was used. Statistical differences were calculated using Wilcoxon and corrected using Benjamini and Hochberg. Then, correlation analysis of immune infiltration and biomarkers was used. The relationship analysis results were visualized using the ‘ggpubr’ package (version 0.4.0) (Aut, 2017). ggpubr:'ggplot2'Based Publication Ready Plots). Then, the immune infiltration situation was evaluated using the Estimation of STromal and Immune cells in MAlignant Tumors using the Expression data (ESTIMATE) algorithm and the ‘estimate’ package (version 1.0.31) in LC samples [34]. The ESTIMATE results were drawn into violin plots, and the statistical significance was tested using the Wilcoxon test.

The ‘maftools’ package (version 2.10.5) [35] was used to obtain information on mutation in high- and low-risk groups from the TCGA-HNSC dataset and visualize the top 20 gene-mutation results using waterfall plots. Immune checkpoints (PD- 1, IFN-γ, CTLA4, PD-L1, and IL- 2) were analyzed using the TCGA-HNSC dataset to probe immune therapy response in the groups classified by risk score. Initially, the relationship between immune checkpoints and risk scores was studied using Spearman correlation analysis. Then, expression and KM survival analysis of the immune checkpoint were applied. Afterward, the tumor immune dysfunction and exclusion (TIDE) algorithm was used to estimate the tumor's immune escape capability. The TIDE, dysfunction, and exclusion score of each LC sample were calculated. The correlation between them and the risk score was analyzed using Spearman correlation analysis. A higher TIDE score means that the tumor's immune escape ability is higher, and the response to immune therapy is poor in patients with LC.

Prediction of chemotherapy drug sensitivity

The TCGA-HNSC dataset was used to assess the response of chemotherapy drugs in LC and investigate the relationship between drug response and risk score. A total of 150 chemotherapeutics of LC were sourced from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Then, the half maximal inhibitory concentration (IC50) value of each drug in different LC samples was calculated using the ‘pRRophetic’ package (version 0.5) [36]. IC50 reflects the drug response; the smaller the IC50, the stronger the drug's effect. Twenty out of 150 chemotherapeutics of LC were contained in ‘pRRophetic’ package, which was used to conduct difference and correlation analyses between the chemotherapeutics and risk score.

Tissue samples and cell culture

A total of 30 pairs of laryngectomy and adjacent non-tumor tissues were collected by the Department of Otorhinolaryngology at the Shaanxi Provincial People's Hospital and the Second Affiliated Hospital of Harbin Medical University. Patients with LC diagnosed before surgery and those without any previous treatment from 2018 to 2019 were included in this study. This study was endorsed by the Harbin Medical University and Shaanxi Provincial People's Hospital Ethics Committee endorsed.

qRT-PCR

Total RNA was extracted from LC tissues and cell lines using Trizol reagent (Invitrogen, Carlsbad, CA, USA) per the manufacturer's instructions. qRT-PCR was conducted as previously described [37]. The 2−ΔΔCt method was used to calculate the data. The primer sequences are listed in Table S1.

Western blot

The cells were harvested and lysed on ice for 30 min. Lysate and cells were collected and centrifuged at 12,000 rpm at 4 °C. The total protein concentration in the supernatant was determined using the BCA method. The remaining sample was added to the SDS loading buffer and heated at 100 ℃ for 5 min. The protein samples were electrophoresed and transferred to the PVDF membrane at 300 mA. The PVDF membrane was blocked with 5% milk for 1 h. The PVDF membrane was incubated with a primary antibody (Abcam, Shanghai, China) at 4 ℃ overnight. Then, it was incubated with a 1:10,000 dilution of HRP-anti-mouse secondary antibody and developed with the ECL substrate.

Statistical analysis

The R programming language was applied for all the analyses. If it was not mentioned, the p-value was less than 0.05 and considered statistically significant. Paired samples were conducted using a two-tailed paired sample t-test.

Results

DE-MRFG in LC was associated with oxidation–reduction and cancer

We screened 14,742 DEGs (Table S2), including 431 up- and 1,065 down-regulated genes, between LC and control samples in the TCGA-HNSC dataset (Fig. 1A). The top 10 DEGs for up- and down-regulated gene expressions were drawn into the heatmap (Fig. 1B). Then, the WGCNA results showed that no outlier samples were found. When the optimal soft threshold was 8, the average connectivity of the co- expression network was close to 0 (R2 = 0.85). After excluding the grey module to which genes that could not be classified belonged, a total of 8 co—expression modules were identified. Among them, the green (cor = − 0.705, p = 1.2e- 19), turquoise (cor = 0.556, p = 3e- 11), and brown (cor = 0.482, p = 1.9e- 08) modules were identified as key modules. There were 971 genes in the green module, 3839 genes in the turquoise module, and 1022 genes in the brown module. After merging the genes in the three modules and removing the duplicate genes, 5832 WGCNA genes were obtained (Fig S1) we obtained 5,832 LC-related genes using WGCNA. Combined with 5,832 WGCNA genes, 14,742 DEGs, and 567 ferroptosis-related genes, an intersection gene, 97 DE-FG in LC, was obtained (Fig. 1C, Table S3). Among them, 83 genes are related to m6 A through correlation analysis between DE-FG and m6 A-related genes, named DE-MRFG. GO and KEGG enrichment analyses demonstrated the molecular mechanism of DE-MRFG. Figure 1D–F show that DE-MRFG is enriched in iron ion transport, NADPH oxidase complex, and superoxide-generating NAD(P)H oxidase activity. Combined with the KEGG analysis results (Fig. 1G), the function and signaling pathways of DE-MRFG were enriched in oxidation–reduction and cancer, such as unsaturated fatty acid biosynthesis and thyroid cancer.

Fig. 1
figure 1

Correlation analysis between m6 A-related and DE-FG genes in LC. A DEGs were screened using TCGA-HNSC dataset. B The top 10 DEG expressions for up- and down-regulated genes are drawn into the heatmap. C Venn diagram of the identified 97 DE-FG from the WGCNA and TCGA-HNSC dataset. D–G Net plot of GO and KEGG pathways of the DEGs

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A risk model based on m6 A-regulated ferroptosis genes

Univariate Cox regression results indicated that three genes, 3 candidate biomarkers in the TCGA-HNSC dataset with survival information samples, including TFRC (HR = 1.31, 95%CI: 1–1.71), p = 0.0499), RGS4 (HR = 1.5, 95%CI: 1.01–2.25, p = 0.0463), and FTH1 (HR = 1.68, 95%CI: 1.11–2.54, p = 0.0148), are related to the survival state of patients with LC (Fig. 2A). Based on these three candidate biomarkers, 3 biomarkers were identified by LASSO analysis including TFRC, RGS4, and FTH1. Based on these biomarkers a risk model was built. The formula was: Risk score = 0.097 expression of × TFRC + 0.269 × expression of RGS4 + 0.303 × expression of FTH1. This formula assigned a risk score to each LC sample in the TCGA-HNSC dataset (108 samples with survival information). We divided all LC samples into two groups with an optimal risk score threshold of 3.44. In GSE65858, the risk score of LC samples was calculated and classified into two groups according to the optimal risk score threshold of 5.88 to verify the risk model. Additionally, KM survival analysis suggested that the low-risk group usually combined with a better survival state in the TCGA (p = 0.00038) and GSE65858 (p = 0.015) datasets (Fig. 2B, C). ROC analysis demonstrated that the risk model accurately predicted OS. The 2-year, 3-year, and 4-year ROC curves for LC patients were plotted. The AUC values were found to be 0.63, 0.691, and 0.678 in TCGA-LSCC dataset, respectively, and 0.607, 0.607, and 0.675 in GSE65858 dataset, respectively (Fig. 2D, E). The survival state distribution maps (Fig. 2F,G) suggest that shorter survival times and more deaths are usually combined with higher risk scores. The three gene expressions are shown in the heatmap. The risk score in this heatmap was sorted ascending from the left to the right (Fig. 2H–I). These three genes showed higher expression in the high-risk groups.

Fig. 2
figure 2

Risk model based on m6 A-regulated ferroptosis genes. A Cox regression for data analysis. B, C KM survival analysis results are based on the TCGA and GSE65858 datasets. D, E ROC analysis of the risk model. F, G Survival state distribution maps showing the survival of patients with LC in the risk model. H, I Heatmap showing that genes have higher expression in high-risk groups

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Risk model was related to skin and protein interaction

To further investigate the potential influence of DEGs in the high- and low-risk groups, 5,754 DEGs, including 2,225 up- and 3,529 down-regulated genes, were found (Fig. 3A, Table S4). In the TCGA-HNSC dataset. A total of 697 results were obtained through GO enrichment analysis. Among them, 538 were enriched in BP terms like epidermis development, 101 in MF terms lke receptor ligand activity, and 58 in CC terms lke collagen-containing extracellular matrix. A total of 58 results were obtained through KEGG enrichment analysis, such as neuroactive ligand-receptor interaction’ (Fig. 3B, C).

Fig. 3
figure 3

Enrichment analysis of DEGs. A Volcano plot of DEGs. B–C GO enrichment and KEGG pathway analyses

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The performance of the risk model in different clinical characteristics

In the TCGA-HNSC dataset, the distribution of LC samples with different risk scores in clinical characteristics indicated that the risk scores of patients with LC have no significant differences in most features, such as gender, tumor stage, pathological N stage, and pathological T stage, except for age (Fig. 4A). Figure 4B displays the TFRC, RGS4, and FTH1 expressions in different clinical characteristics. Furthermore, the KM survival curve of different groups with different clinical characteristics demonstrated that people in the low-risk group had better OS in males (p = 0.00023), older age (p = 0.00063), tumor stage III–IV (p = 0.0013), pathological N2_N3 (p = 0.00059), pathological T2 (p = 0.015), and pathological T3 (p = 0.0037) (Fig. 4C–H). Fig. S1 depicts the results without significant differences.

Fig. 4
figure 4

Model performance and clinical characteristics. A Risk scores with a significant difference in the age of patients with LC. B TFRC, RGS4, and FTH1 expression according to different clinical characteristics. C–H KM survival curves of different groups with different clinical characteristics

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Establishment of a nomogram

In the TCGA-HNSC dataset, univariate Cox regression analysis suggested that risk score (HR = 3.463, 95%CI: 1.544–7.77, p = 0.0026), gender (HR = 0.275, 95%CI: 0.139–0.543, p < 0.001), and Pathologic_N (N2-N3 (HR = 2.63, 95%CI: 1.155–5.988, p = 0.0213)) were associated with the OS of patients with LC (Fig. 5A). According to multivariate Cox regression analysis (Fig. 5B), risk score, gender, and Pathologic_N were considered independent prognostic factors to build a nomogram (Fig. 5C) that can predict the OS of patients with LC. Calibration curves and ROC analyses proclaimed the good comportment of nomograms for predicting OS. The AUC values of the ROC for the nomograms in the 2-year, 3-year, and 4-year were 0.753, 0.776, and 0.715 respectively (Fig. 5D–G). The above results showed that the nomogram was demonstrated to have good prognostic value for LC.

Fig. 5
figure 5

Model performance and clinical characteristics. A, B Univariate and multivariate Cox regression models. C Nomogram predicting the OS probability. D–G Calibration curves and ROC analyses for predicting OS

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The risk model predicted the effect of immune therapy

In the TCGA-HNSC dataset, ssGSEA was used to determine immune infiltration in the LC. Figure 6A reveals that the ssGSEA scores of six immune cells, including neutrophils, mast cells, and Memory B cells, differed significantly between the high- and low-risk groups. Additionally, Fig. 6B–D present the relationship between the three biomarkers (TFRC, RGS4, and FTH1) and the six immune cells. Biomarkers were positively related to the other five immune cells, except for neutrophils. Finally, stromal, immune, and ESTIMATE scores were calculated (Fig. 6E, F). The stromal and ESTIMATE scores were dramatically higher in the high-risk group than in the low-risk group (p < 0.05).

Fig. 6
figure 6

Construction of the immune‐related risk score model. A Enrichment score of immune functions in high- and low-risk immune cells in ssGSEA. B–D Bubble chart showing the relationship between biomarkers and six immune cells. E, F Stromal and ESTIMATE scores were calculated using the ESTIMATE algorithm. G, H The top 20 genes mutated in the high- and low-risk groups. I Correlation between risk scores and immune checkpoints. J PD-L1 had a significantly positive relationship with the risk score. K PD-L1 expression analysis in the TCGA dataset. L Relationship between risk and TIDE scores. M Relationship between the risk and exclusion scores

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To probe the mutation in different groups, the ‘maftools’ package was used. Figure 6G–H depict the top 20 mutant genes. The High-risk group had 3,905 mutated genes, which were present in all samples (Fig. 6G). The low-risk group contained 9,030 genes, with mutations occurring in 98.85% of the samples (Fig. 6H). TP53 had the highest mutation rate in both groups.

Based on the association analyses of immune checkpoints and risk score, only PD-L1 had a significantly positive relationship with risk score (cor = 0.34, p < 0.001) (Fig. 6I), with dramatically up-regulated expression in a high-risk group (p < 0.001) (Fig. 6J). KM survival analysis indicated better OS of patients with high-expressed PD-L1, although the result did not have statistical significance (p = 0.027) (Fig. 6K). Then, the TIDE, dysfunction, and exclusion scores of all LC samples were calculated. Moreover, the relationship between immunotherapy response and risk score was analyzed. Figure 6L-M indicate a significantly positive relationship between the risk score, TIDE, and exclusion score.

Chemotherapy drugs were related to the risk model

In the TCGA-HNSC dataset, the IC50 values of 20 chemotherapeutics were calculated for each LC sample to estimate the sensitivity of chemotherapy drugs in different groups. Nine drugs, such as Cisplatin, had a significantly higher IC50 in the high-risk group than in the low-risk group, while 10 drugs, such as Erlotinib, had a lower one (p < 0.05) (Fig. 7). Moreover, the correlation analysis between risk score and IC50 in 19 drugs indicated a strong relationship (Fig. 8). For example, Bosutinib was positively related to the risk score, while Docetaxel was negatively related to the risk score. These drugs might have affected the expression of m6 A-related ferroptosis genes (TFRC, RGS4, and FTH1), which were thus influenced the prognosis of LC patients.

Fig. 7
figure 7

Chemotherapy drug sensitivity analysis based on IC50 scoring

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Fig. 8
figure 8

Correlation analysis of the risk score and IC50

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Expression of TFRC, RGS4 and FTH1 in laryngeal carcinoma

In this study, we analyzed the TFRC, RGS4, and FTH1 expression levels in LC cell lines and NHBEC. Our findings revealed a significant increase in TFRC, RGS4, and FTH1 expressions in these cell lines than in NHBEC (Fig. 9A, B). Additionally, we examined 30 pairs of laryngeal carcinomas and paracancerous tissues and utilized qRT-PCR to assess TFRC, RGS4, and FTH1 expression. The results demonstrated significantly higher TFRC, RGS4, and FTH1 expression in laryngeal carcinoma tissues than in paracancerous tissues (Fig. 9C). Furthermore, KM analysis demonstrated a significant correlation between TFRC and FTH1 expression and patient prognosis, whereas RGS4 expression was not significantly correlated with patient prognosis (Fig. 9D).

Fig. 9
figure 9

Expression of TFRC, RGS4, and FTH1 in laryngeal carcinoma. A, B TFRC, RGS4, and FTH1 expressions were detected using qRT-PCR and western blotting. C TFRC, RGS4, and FTH1 levels in 30 pairs of laryngeal carcinoma and adjacent tissues were detected using qRT-PCR. D KM survival analysis of TFRC, RGS4, and FTH1

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Discussion

m6 A modification has recently received significant attention in cell biology [38]. Extensive research has demonstrated that m6 A modification is pivotal for regulating RNA stability, transcription, and translation [39]. Moreover, recent investigations have revealed a strong association between m6 A modification and ferroptosis, offering a novel framework for comprehending cellular iron metabolism and regulation [40]. Furthermore, m6 A modification has been identified as intimately linked to tumorigenesis and disease progression in LC research [16, 41]. Subsequent research has revealed that the m6 A modification may influence the incidence and therapeutic efficacy of LC by regulating pivotal gene expression [42, 43]. In summary, m6 A modification is important in cellular biology and cancer investigation, providing valuable insights into the etiology and treatment approaches for associated diseases [44]. Our study identified three biomarkers (TFRC, RGS4, and FTH1) from the DE-MRFG dataset to construct a risk model using the univariate Cox and LASSO regression algorithms. The analysis of immune reaction in LC revealed a significant association between the immune checkpoint PD-L1 and the risk score, with PD-L1 up-regulated in the high-risk group. Additionally, the TIDE and ESTIMATE algorithms demonstrated a positive correlation between risk and the TIDE or ESTIMATE scores.

TFRC is a receptor for transferrin and has a growth-regulating function in some malignant cells [45]. Previous studies have found that the LINC00888/miR- 378 g/TFRC pathway may lead to the development of LC cells by inducing ferroptosis, making it a potential therapeutic target for LC [46]. The alteration in TFRC expression has a direct impact on cellular iron uptake and may indirectly modulate the m6 A modification system [47]. In tumor cells, elevated TFRC expression results in an increased intracellular iron ion concentration, potentially enhancing the methyltransferase activity of METTL3, thereby leading to a greater extent of m6 A modification in mRNA [48]. TFRC knockdown strengthened the inhibitory effect of si-LINC00888 on LC cells'malignant properties. Moreover, elevated levels of TFRC in some malignancies (eg., endometrial cancer, colorectal cancer, breast cancer, and oral squamous cell carcinoma) make TFRC a potential molecular target for cancer imaging or therapy [49,50,51,52]. In our study, the TFRC expression level was much lower in LC cell lines than that of NHBEC (Fig. 9A, B), and the higher the TFRC expression, the worse the prognosis.

Regulators of G-protein signaling (RGS) family members are believed to be involved in tumor initiation and metastasis [53]. Generally, RGS4 is considered to be a tumor suppressor with prognostic value in a variety of cancers, such as breast cancer and non-small cell lung cancer [54, 55]. However, elevated levels of RGS4 can also cause a variety of diseases, including pediatric nephroblastoma, tumorigenic glioma cancer, and Parkinson's disease [56, 57]. Especially, the expression of RGS4 in LC cell lines was much higher in qRT-PCR and western blot assay in our study. Currently, limited research exists on RGS4's impact on m6 A modification-related molecules. Some extracellular signals can alter the phosphorylation of m6 A-related enzymes via specific pathways, potentially involving RGS4 in this process and affecting m6 A modification and LC [58]. RGS4 negatively regulates ferroptosis and promotes apoptosis in LC cells, possibly by activating caspase proteins, thereby reducing tumor cells and inhibiting LC progression. It also influences LC cell migration and invasion, making RGS4 a significant factor in LC development and a potential therapeutic target [59]. The specific mechanisms of RGS4 deserve further exploration in LC.

As we know, ferritin consists of 24 middle-hollow round subunits, comprising heavy chain (FTH1) and light chain (FTL1) [60]. FTH1 plays a vital role in ferroptosis resistance in head and neck squamous cell carcinomas (HNSCCs) and provides clues to target HNSCCs resistant to ferroptosis induction and/or epidermal growth factor receptor (EGFR) inhibition. The over-expression of FTH1 is an unfavorable prognostic factor for the prognosis of HNSCC patients [61]. Likewise, the FTH1 expression level was much lower in LC cell lines than that of NHBEC (Fig. 9A, B), and the higher the FTH1 expression, the worse the prognosis. Furthermore, FTH1 is also a direct target of METTL14 in cervical cancer, METTL14 can reduce FTH1 mRNA stability through m6 A methylation, further enhancing sorafenib-induced ferroptosis [62]. In neurodegenerative diseases, aberrant expression of FTH1 can disrupt intracellular iron homeostasis, subsequently influencing the activity of enzymes associated with m6 A modification. This disruption may lead to dysregulation of m6 A modifications on mRNA of certain neuro-related genes, ultimately impairing normal neuronal function [63]. However, whether a similar molecular mechanism exists in LC remains to be further explored.

Overall, we unveiled the differential expression of ferroptosis genes (TFRC, RGS4, and FTH1) associated with m6 A in laryngeal carcinoma and these genes are potential prognostic indicators of patient survival. Using multivariate Cox regression analysis, we verified that the risk score, gender, and Pathologic_N are independent prognostic factors that can be utilized to construct a nomogram to predict the OS of patients with laryngeal carcinoma. The effectiveness of this nomogram for OS prediction was demonstrated using calibration curves and ROC analyses.

GO enrichment analysis revealed that DEGs in high and low risk groups were linked to receptor-ligand activity associated with collagen-containing extracellular matrix. KEGG analysis indicated enrichment in the neuroactive ligand-receptor interaction pathway. Growth factor receptors, like EGFR, are crucial in cell growth regulation and are linked to non-small cell lung cancer development [64]. Extracellular matrix remodeling is essential for tumor cell adhesion, migration, and angiogenesis. Abnormal neuroactive ligand-receptor interactions can disrupt communication between the nervous system and tumor cells. Upon binding, these interactions can activate pathways like PI3 K-AKT, potentially promoting cell cycle progression and accelerating proliferation in laryngeal cancer cells [65]. These enrichment analysis results offer valuable insights into the biological differences between high- and low-risk LC groups, potentially identifying new biomarkers and therapeutic targets for precise diagnosis, prognosis, and treatment of LC.

Recent investigations have suggested that these genes may influence immune cell response to immunotherapy and infiltration. For instance, heightened TFRC expression may be associated with unfavorable outcomes in immunotherapy [49, 66]. Similarly, aberrant RGS4 and FTH1 expression may disrupt the infiltration of immune cells, consequently affecting the immune response against tumors [67, 68]. Further research is warranted to elucidate the underlying mechanisms and their implications for treating LC.

In the diagnosis and treatment of laryngeal cancer (LC), early and accurate diagnosis coupled with effective monitoring is of paramount importance. Emerging diagnostic methodologies offer novel opportunities to exploit potential biomarkers such as TFRC, RGS4, and FTH1. Liquid biopsy presents a viable method for the detection of these biomarkers. Tumor cells release exosomes containing relevant markers into the bloodstream, enabling the detection of aberrant gene expression in the plasma exosomes of various cancer patients. Circulating tumor DNA (ctDNA) detection represents an advanced technique in this context. The ctDNA released by LC cells may harbor mutations or abnormal methylation patterns in TFRC, RGS4, and FTH1. Utilizing second-generation sequencing technologies, including whole-genome and targeted deep sequencing, allows for the analysis of a patient's plasma ctDNA. Identification of specific site mutations in TFRC or abnormal methylation patterns in RGS4 and FTH1 may indicate the presence of laryngeal cancer. The detection of gene methylation markers is increasingly recognized as crucial for cancer diagnosis. Research has demonstrated that gene-specific methylation serves as a potential marker for lymph node micrometastasis in gastric cancer. The methylation status of TFRC, RGS4, and FTH1 may undergo alterations during the progression of laryngeal carcinoma, detectable through bisulfite sequencing and methylation-specific PCR. Research on head and neck tumors has demonstrated that aberrant methylation of certain genes is associated with disease stage and prognosis. Should the methylation changes of these three genes be validated as being linked to laryngeal cancer, they could serve as novel biomarkers for diagnostic purposes.

This study heavily relies on TCGA data, which has technical and biological biases due to varying sample collection standards across institutions, leading to errors like RNA integrity affecting gene expression analysis. Future efforts should standardize sample collection, validate biomarkers across diverse demographics, and integrate serum miRNA profiles to enhance research accuracy and applicability. TCGA research has identified significant biomarkers in cancers like breast, head and neck squamous cell carcinoma, and renal clear cell carcinoma [69]. The research findings are influenced by the experimental environment and reagents, with systematic errors present, necessitating large samples and standardized procedures for accuracy. Additionally, the study lacks functional experimental validation. Future research will involve using various laryngeal cancer cell lines, applying gene knockdown and ferroptosis inducers, and measuring ferroptosis incidence via flow cytometry. The expression of ferroptosis-related proteins like FTH1 will be assessed using Western blot to explore new treatment methods.

In our study, we identified three genes (TFRC, RGS4, and FTH1) associated with LC prognosis by LASSO-Cox regression analysis. Afterward, a risk model was constructed and validated, followed by the low-risk group having a better prognosis. qPCR analysis of 30 pairs of LC and paracancerous tissue samples revealed consistently high expression of TFRC, RGS4, and FTH1 in LC tissues and cell lines. Despite its dynamic nature, current cancer research has some limitations. However, we have addressed these shortcomings by connecting with clinical research, validating our findings in 30 clinical LC cases and three LC cell lines, and analyzing the correlation between target gene expression and prognosis. Our findings strongly suggest that the expression patterns of FTH1 and TFRC in laryngeal carcinoma align with previous research, while the expression of RGS4 in this context remains unexplored in the literature [46, 70]. In future investigations, we will prioritize the examination of TFRC, RGS4, and FTH1 to delve deeper into their underlying mechanisms.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

Not applicable.

Funding

This work was supported by the National Natural Science Foundation of China (No. 82203114), Shaanxi Provincial Association for Science and Technology Young Talents Promotion Program (20240342), the Key Research and development program of Shaanxi Province (2024SF-YBXM- 342), the Technology Incubation Fund and Talent Program Projects of Shaanxi Provincial People’s Hospital (2023 JY- 02), and the Shaanxi Provincial Special Fund for Talents Introduction Plan for Three Qin Talents.

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Authors and Affiliations

  1. Department of Otorhinolaryngology, Head and Neck Surgery, Shaanxi Provincial People’s Hospital, The Third Affiliated Hospital of Xi’an Jiaotong University, 256 Youyi Road, Xi’an, 710000, China

    Xin Wang, Wen Zhang, Kun Liang, Yujuan Wang, Jin Zhang, Jinping Wang, An Li, Yonggang Yun & Hiu Liu

  2. Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People’s Hospital, The Third Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710068, China

    Xin Wang & Wen Zhang

  3. Shaanxi Engineering Research Center of Cell Immunology, Shaanxi Provincial People’s Hospital, The Third Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710068, China

    Xin Wang & Wen Zhang

  4. Department of Otorhinolaryngology, Head and Neck Surgery, The Second Affiliated Hospital, Harbin Medical University, 246 Xuefu Road, Harbin, 150000, China

    Yanan Sun

Authors
  1. Xin Wang
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  2. Wen Zhang
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  3. Kun Liang
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  4. Yujuan Wang
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  5. Jin Zhang
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  6. Jinping Wang
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  7. An Li
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  8. Yonggang Yun
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  9. Hiu Liu
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  10. Yanan Sun
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Contributions

XW: Conceptualization, Methodology, Funding acquisition, Writing–original draft, Writing–review & editing. HL: Conceptualization, Data curation, Project administration, Writing–review & editing. WZ: Investigation, Writing–review & editing. KL: Data curation, Writing–review & editing. YW: Formal Analysis, Methodology, Writing–review & editing. JZ: Methodology, Writing–review & editing. JW: Data curation, Supervision, Writing–review & editing. AL: Supervision, Validation, Writing–review & editing. YY: Data curation, Writing–review & editing. YS: Investigation, Writing–review & editing.

Corresponding authors

Correspondence to Hiu Liu or Yanan Sun.

Ethics declarations

Ethics approval and consent to participate

According to the protocol approved by the Ethical Review Committee of the Second Affiliated Hospital of Harbin Medical University and Shaanxi Provincial People's Hospital, patient tissue samples were obtained with informed consent. All patients provided signed informed consent (2022–K268).

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Not applicable.

Competing interests

The authors declare no competing interests.

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Supplementary Information

12885_2025_14134_MOESM1_ESM.zip

Supplementary Material 1: Fig. S1 Results without significant differences. Fig. S2 Identification of WGCNA genes. Fig. S3 Partial results from further verification of ferroptosis mechanisms (not for publication)

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Wang, X., Zhang, W., Liang, K. et al. Identification of m6 A-regulated ferroptosis biomarkers for prognosis in laryngeal cancer. BMC Cancer 25, 694 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-14134-8

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-14134-8

Keywords

BMC Cancer

ISSN: 1471-2407

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