The key m6A methylation regulator IGF2BP1 possesses potential prognostic value in papillary thyroid carcinoma

Abstract

Papillary thyroid carcinoma (PTC) poses a risk of recurrence, and the efficacy of existing treatments is limited. Consequently, there is an urgent need to identify new prognostic markers and potential therapeutic targets. N6-methyladenosine (m6A) mRNA methylation is involved in tumorigenesis and progression, yet the role of m6A RNA methylation regulators in PTC remains unclear. The Cancer Genome Atlas database was utilized to analyze 17 m6A regulators in PTC. Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) was markedly down-regulated in PTC, yet higher IGF2BP1 expression predicts better 5-year survival, acting as an independent prognostic marker with high accuracy. Elevated IGF2BP1 also indicated greater sensitivity to doxorubicin and sunitinib. Clinically, low IGF2BP1 correlated with central lymph-node metastasis and BRAFV600E mutation. Additionally, IGF2BP1 overexpression suppresses thyroid carcinoma cell proliferation, invasion, and migration. In conclusion, High expression of IGF2BP1 was associated with a favorable prognosis in PTC, and it served as an independent prognostic factor and a potential therapeutic target for PTC.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-43501-z.

Introduction

Thyroid cancer (TC) ranks among the most prevalent cancers globally¹, with its incidence rate rising rapidly and accounting for 1–2% of all cancer cases². Based on pathological classification, TC can be categorized into papillary thyroid cancer (PTC), follicular thyroid cancer, medullary thyroid cancer, and anaplastic thyroid cancer. Among these types, PTC is the most frequently occurring, representing over 80% of cases³. For patients diagnosed with PTC, surgical procedures, thyroid-stimulating hormone suppression therapy, and ¹³¹I treatment significantly influence their prognosis⁴. Nevertheless, despite these interventions, more than 10% of patients still experience recurrence⁵. Moreover, current treatment approaches have limited efficacy in managing local tumor progression and distant metastasis in PTC patients. Consequently, identifying effective diagnostic markers, prognostic indicators, and therapeutic targets for PTC is of utmost importance.

N6-methyladenosine (m6A) methylation is the most prevalent post-transcriptional modification of mRNA, participating in various biological processes, such as tumor proliferation, invasion, and epithelial-mesenchymal transition (EMT)⁶. It has been reported that three types of proteins collaborate to maintain the balance of the m6A network: methyltransferase complexes, m6A-binding proteins, and demethylases. Among them, the methyltransferase complex comprises methyltransferase-like 3 (METTL3), methyltransferase-like 14 (METTL14), methyltransferase-like 16 (METTL16), RNA binding motif protein 15 (RBM15), RNA binding motif protein 15B (RBM15B), wilms tumor 1 associating protein (WTAP), and KIAA1429 protein (KIAA1429). The m6A-binding proteins include YTH domain-containing protein 1 (YTHDC1), YTH domain-containing protein 2 (YTHDC2), YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), YTH N6-methyladenosine RNA binding protein 2 (YTHDF2), YTH N6-methyladenosine RNA binding protein 3 (YTHDF3), insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1), insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2), insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3), heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1), and heterogeneous nuclear ribonucleoprotein C (HNRNPC). The demethylases include fat mass and obesity-associated protein (FTO) and AlkB homolog 5, alpha-ketoglutarate-dependent dioxygenase (ALKBH5). This intricate regulatory network is deeply involved in the malignant biological behaviors of tumor cells, such as proliferation, invasion, and EMT⁷. Abnormal m6A modifications have been found to be associated with disease progression in solid tumors⁸, including breast cancer, colorectal cancer, lymphoma, liver cancer, and gastric cancer. However, the prognostic value of m6A methylation-related genes in PTC still requires further clarification.

To clarify this point, we conducted a comprehensive analysis of m6A RNA methylation regulators in PTC by utilizing data from The Cancer Genome Atlas (TCGA) database. Patients were classified into two subgroups through clustering, and clinical outcomes between the two subgroups were compared. Subsequently, a risk prediction model was constructed based on three regulators to predict the prognosis of PTC patients. Meanwhile, the expression of IGF2BP1 in clinical samples was compared, and TC cell lines with IGF2BP1 overexpression were established. The successful construction of the cell lines was verified by quantitative real-time polymerase chain reaction (qRT-PCR). Furthermore, Cell Counting Kit-8 (CCK-8) assay, transwell assay, colony formation assay, and wound healing assay were employed to further substantiate the prognostic value of IGF2BP1 in PTC. This study was committed to exploring potential effective diagnostic markers, prognostic indicators, and therapeutic targets for PTC, aiming to provide theoretical support for the clinical treatment of PTC.

Materials and methods

Cell culture

The normal human thyroid cell line NTHY ORI 3 − 1 (CL-0817, Procell), as well as the TC cell lines B-CPAP (CL-0575, Procell) and TPC-1 (CL-0643, Procell), were all purchased from Wuhan Procell Life Science & Technology Co., Ltd. For the cultivation of the NTHY ORI 3 − 1 cell line, DMEM/F12 medium (PM150312B, Procell) was used, supplemented with 5% horse serum (164215, Procell), 10 µg/mL insulin (PB180432, Procell), 20 ng/mL epidermal growth factor (92713ES, Yeasen), 250 ng/µL hydrocortisone (40109ES, Yeasen), and 100 ng/mL cholera toxin (MX0931, Maokangbio). Within the scope of this study, all TC cell lines were subcultured in DMEM medium (PM150210B, Procell) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Subsequently, the cell cultures were placed in an incubator at 37^°C with 5% CO₂ for incubation.

Data collection

RNA-seq transcriptome data and related prognostic information of 397 patients with PTC were obtained from TCGA database (https://cancergenome.nih.gov/). Drug susceptibility test was according to the largest publicly pharmacogenomics database (Genomics of Drug Sensitivity in Cancer, GDSC, https://www.cancerrxgene.org/).

Patients and samples

A total of 101 patients with PTC who underwent unilateral radical thyroidectomy were included in this study. These patients received surgical treatment at the First Affiliated Hospital of Ningbo University between January 2019 and December 2019. Among them, there were 39 males and 62 females, with an age range of 27–78 years and a mean age of 50.43 ± 14.58 years. The mean tumor diameter was 1.26 ± 0.73 cm. After collection, PTC specimens were placed in RNA preservation solution and stored at -80^°C.

The inclusion criteria for patients were as follows: (1) age ≥ 18 years; (2) diagnosed with PTC and received radical thyroidectomy between January and December 2019; (3) availability of surgical specimens and postoperative pathological reports. The exclusion criteria included: (1) presence of concomitant malignant tumors or severe diseases that might affect prognosis, such as liver or renal insufficiency; (2) incomplete clinical data.

This study adhered to the principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee of the First Affiliated Hospital of Ningbo University (2021-R140). All participants in this study provided written informed consent.

Selection of m6A methylation regulators

Seventeen genes have been identified as crucial m6A RNA methylation regulators, including METTL3, YTHDC1, YTHDC2, METTL14, VIRMA, RBM15, WTAP, ZC3H13, YTHDF3, YTHDF1, HNRNPC, HNRNPA2B1, FTO, IGF2BP1, IGF2BP2, IGF2BP3, and ALKBH5. We utilized data from PTC patients in the TCGA database to explore the associations between m6A methylation regulators and clinicopathological features, as well as overall survival (OS).

All TCGA data used in this study are publicly accessible through the Genomic Data Commons portal of the National Cancer Institute (https://portal.gdc.cancer.gov/).

Bioinformatics analysis

The limma package (http://www.Bioconductor.org/packages/release/bioc/html/limma.) was employed to analyze the associations between m6A methylation regulators and clinicopathological features in PTC, with a P-value cutoff set at 0.05. Subsequently, the expression profiles of 17 m6A regulators in 59 normal tissues and 397 tumor tissues were visualized. Correlations among these genes were explored using Spearman analysis.

Next, the tumor samples were classified into two groups using the Consensus Cluster Plus package, and principal component analysis (PCA) was conducted to validate the clustering results. Survival analysis was performed on the two groups of samples using a survival analysis package. Univariate and multivariate Cox analyses were then employed to investigate the impact of m6A RNA methylation regulators on the prognosis of PTC patients, and a risk signature was established using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm.

Data source and quality control

Sequencing data for 33,448 PTC cells from 5 primary PTC patients were extracted from the GSE184362 dataset in the GeoData database. Cells with factor counts exceeding 200 but less than 25,000, and having a mitochondrial proportion of 10%, were identified. Subsequently, sequencing depth correlation analysis was performed. The data were then normalized, and genes exhibiting a high coefficient of variation across cells were selected for further analysis.

Clustering analysis

Dimensionality reduction was performed using PCA, followed by t-distributed stochastic neighbor embedding (t-SNE) clustering analysis. Clustering results for different cell subsets were obtained. Tools such as “BiocManager”, “GSVA”, and “GSEABase” were utilized during the analysis process.

Pseudo-time analysis

For the different cell subsets obtained above, the average gene expression of each subset was compared with that of other subsets to identify gene sets that were highly expressed in each subset, referred to as marker genes. Subsequently, the previously identified cell marker genes were compared using the “SingleR” and “monocle” packages to obtain the differentiation trajectories of each cell subset.

Immune score calculation

Immune cell infiltration in TC samples was assessed using the immunedeconv R package. Spearman correlation analysis was then performed to evaluate the association between IGF2BP1 expression and immune cell scores, as well as key immune checkpoint genes.

Drug sensitivity assessment

Based on the GDSC database, the R package pRRophetic was used to predict the half-maximal inhibitory concentration of four common chemotherapeutic drugs (doxorubicin, sunitinib, paclitaxel, and sorafenib) for each sample using ridge regression. Subsequently, Spearman correlation analysis was performed to evaluate the association between IGF2BP1 expression and the predicted drug sensitivity.

Overexpression of IGF2BP1

Lentiviral vectors encoding IGF2BP1 (L02621, Beyotime) or an empty vector control (L00017, Beyotime) were transfected into B-CPAP and TPC-1 cells. Subsequently, a 48-hour selection process with puromycin was applied to isolate stably transduced cells.

qRT-PCR

Tumor tissues from 101 patients with PTC and IGF2BP1-overexpressing cell lines were collected. Total RNA was extracted from the tissues or transfected cells using TRIzol reagent (R0016, Beyotime) according to the manufacturer’s instructions. Subsequently, the RNA was reverse-transcribed into complementary DNA (cDNA) using a reverse transcription kit (D7170S, Beyotime). Then, the expression of IGF2BP1 in PTC tissues or cells was detected using the TB Green™ Premix Ex Taq kit (CN830A, Takara) in an SYBR-Green PCR system. The relative expression levels of mRNA were evaluated by the comparative threshold cycle method (2^−ΔΔCt), with GAPDH as the internal reference. Primer sequences are shown in Table 1.

Table 1.

Primer sequences.

	Forward	Reverse
IGF2BP1	GGGCCATCGAGAATTGTTGC	CGGGAGCCTGCATAAAGGAG
GAPDH	GGAGCGAGATCCCTCCAAAAT	GGCTGTTGTCATACTTCTCATGG

CCK-8 assay

B-CPAP and TPC-1 cells were seeded into 96-well plates, with 2,000 cells and 100 µL of culture medium added to each well. From day 1 to day 5 post-seeding, 10 µL of CCK-8 reagent (C0038, Beyotime) was added to each well daily, followed by incubation at 37^°C for 1.5 h. Subsequently, the absorbance at 450 nm was measured using a microplate reader (Infinite M200 pro, TECAN) to assess cell viability.

Transwell assay

Cells infected with oe-NC or oe-IGF2BP1 were suspended in 200 µL of serum-free medium and then placed in the upper chamber of a 24-well plate equipped with an 8-µm pore size non-coated membrane. Subsequently, 600 µL of medium (containing 20% fetal bovine serum) was added to the lower chamber. After incubation at 37^°C for 48 h, the cells were fixed with 4% paraformaldehyde (P0099, Beyotime) for 20 min and then stained with 1.0% crystal violet (C0121, Beyotime) for 15 min. Finally, the cells were observed under a microscope (DM2500, Leica) for recording, and cell counting was performed using ImageJ software.

Wound healing assay

Cells were seeded into 6-well plates (5 × 10⁵ cells/well). Once they covered the bottom of the wells, a scratch was made using a pipette tip, and the detached cells were washed away with PBS (C0221A, Beyotime). Subsequently, initial cell images were recorded using a microscope. The 6-well plates were then incubated at 37^°C for 24 h. After the incubation period, images of the scratch area were captured again using a microscope to assess cell migration.

Colony formation assay

B-CPAP and TPC-1 cells infected with oe-NC or oe-IGF2BP1 were seeded into 6-well plates at a density of 1000 cells per well. Subsequently, they were incubated at 37^°C for 14 days. After the incubation period, the cells were washed with PBS and fixed with 4% paraformaldehyde for 20 min. Then, the cells were stained with 1% crystal violet for 15 min. Finally, images were captured using a microscope, and cell counting was performed using ImageJ software.

Statistical analysis

The Kruskal-Wallis method was used to compare the expression levels of 17 m6A RNA methylation regulators between PTC tumor tissues and normal tissues. The Chi-square test was used to analyze the relationship between clinicopathological characteristics and m6A RNA regulators in PTC patients. Patients were divided into high risk group and low risk group with the median risk score as the cut-off value. In order to further analyze the survival differences between the two groups, Kaplan-Meier survival analysis was used to compare the survival differences between two or more groups. Time receiver operating characteristic (ROC) analysis was used to compare the prediction accuracy. The correlation between genes and pathway scores was analyzed by Spearman correlation. The statistical inferences of intergroup differences were evaluated using the χ² test. Data conforming to a normal distribution were presented as the mean ± standard error of the mean, and one-way analysis of variance followed by the least significant difference post-hoc test was used for multiple comparisons between groups. All statistical analyses were performed with the use of R software (version 3.5.1). A p-value of less than 0.05 was considered statistically significant.

Results

m6A methylation regulators were closely associated with the pathological progression of PTC

To compare the differences in m6A methylation regulators between PTC tissues and normal tissues, the expression levels of m6A methylation regulators (17 differentially expressed genes) in the TCGA database were analyzed. Through heatmap and Violin plot analyses, we observed that IGF2BP2 and HNRNPC were upregulated in cancerous tissues. Furthermore, in comparison with normal tissues, METTL3, VIRMA, METTL14, RBM15, WTAP, YTHDC1, IGF2BP3, YTHDC2, YTHDF3, YTHDF1, ZC3H13, IGF2BP1, HNRNPA2B1, ALKBH5, and FTO were significantly downregulated in tumor tissues (Fig. 1A-D).

Fig. 1.

Expression pattern of m6A methylation regulators in PTC and the relation between m6A methylation regulators and clinicopathological characteristics of PTC. (A) Heatmap analysis was used to compare the expression differences of m6A methylation regulators between PTC patients and normal individuals. (B) Violin plot analysis was performed to investigate the differential expression of m6A methylation regulators. (C–D) The expression differences of m6A methylation regulators across different stages of PTC were examined.

m6A methylation regulators were associated with the prognosis of PTC

Using the Consensus Cluster Plus package, clustering classification was performed on 397 PTC tumor samples. Based on the cumulative distribution function value, the samples were initially divided into two groups. To further determine the optimal clustering stability, we conducted an analysis based on the similarity of m6A regulator expression levels and the proportion of fuzzy clustering measures. It was found that the clustering results were relatively stable when k = 2 (Fig. 2A-B). Therefore, the 397 PTC patients were divided into two subgroups. PCA was then employed to validate the grouping of PTC. The results showed a clear separation between subgroup 1 and subgroup 2 (Fig. 2C-D), and the heatmap analysis also supported this conclusion. The expression levels of m6A methylation regulators were higher in subgroup 1 compared to subgroup 2.

Fig. 2.

Consensus cluster classification by m6A methylation regulators and the risk signature comprising three m6A methylation regulators. (A) Consensus clustering matrix for k = 2. (B)Relative area change under the cumulative distribution function curve for k = 2 to 6. (C) PCA of the total RNA expression profile of two clusters in the TCGA database. Clusters 1 and 2 were marked red and blue, respectively. (D) Kaplan-Meier curves of overall survival. (E) Heatmap and clinicopathologic features of the two clusters (cluster1/2) (F) Univariate Cox analysis of 17 m6A methylation regulators in PTC patients. HRs and 95% CIs were calculated.

To evaluate the prognostic value of m6A methylation regulators in PTC, the correlation between clustering and OS was explored. The results showed that subgroup 2 had better OS than subgroup 1 (Fig. 2E), indicating a negative correlation between the expression levels of m6A methylation regulators and the prognosis of PTC patients. Subsequently, univariate Cox analysis revealed that the expression of IGF2BP1, YTHDC2, and YTHDF3 might be associated with the survival of PTC patients (Fig. 2F). Therefore, IGF2BP1, YTHDC2, and YTHDF3 were selected to construct a risk signature prediction model (Fig. S1A). To assess the predictive performance of the risk signature, patients were divided into high-risk and low-risk groups based on their median risk scores. The OS curves demonstrated that the low-risk group had a higher survival rate than the high-risk group (Fig. S1B), validating the reliability of the prediction model. Subsequently, by using the coefficients of these three regulators obtained from the LASSO regression algorithm, we could calculate the risk scores for PTC patients in the TCGA database (Fig. S1C-D). Models: lambda.min = 0.0025, Riskscore = (0.6777) × IGF2BP1 + (0.2532) × YTHDF3 + (0.5893) × YTHDC2. This model can be used to assess the prognosis of PTC patients.

IGF2BP1 served as an independent prognostic factor of PTC

To assess the correlation between clinicopathological characteristics and risk scores in PTC patients, a heatmap was generated based on data from the TCGA cohort, and time-dependent ROC analysis was conducted using R software packages to evaluate prognostic classification at different time points (including three, five, and ten years). The results indicated that samples with high-risk scores had significantly lower OS than those with low-risk scores, suggesting a poorer prognosis for samples with high-risk scores (Fig. 3A).

Fig. 3.

IGF2BP1 served as an independent prognostic factor for PTC: Analyses of Sequencing Components, Principal Components, Cluster, and Pseudo-Time. (A) The correlation between risk scores and clinicopathological characteristics in patients with PTC from the TCGA database. (B–C) Univariate and multivariate Cox regression analyses confirmed that IGF2BP1 was an independent prognostic factor for PTC. (D) The nomogram score composed of IGF2BP1 level and age could well reflect the OS of PTC patients, and the C-index is 0.931.

To further investigate whether IGF2BP1, YTHDC2, and YTHDF3 could serve as independent factors for PTC prognosis, we performed univariate and multivariate Cox regression analyses. Univariate Cox regression analysis suggested that IGF2BP1 might be an independent factor for PTC prognosis (Fig. 3B-C). The prognostic nomogram generated from multivariate regression also revealed that IGF2BP1, as an independent factor for PTC prognosis, had a C-index of 0.931 (Fig. 3D), indicating good predictive performance. Additionally, the calibration curve for survival prediction indicated that the prediction model had high accuracy in predicting the 1-year, 3-year, and 5-year survival risks of patients (Fig. S2A). Meanwhile, rigorous quality control was also conducted on the data. The results showed a correlation coefficient of 0.86, suggesting high sequencing data quality (Fig. S2B,C), further confirming the reliability of the prediction model.

IGF2BP1 can be used to predict the sensitivity of therapeutic drugs for PTC

Cytotoxic T lymphocytes (CTL) cells play a central role in tumor immune surveillance and killing⁹, and are closely associated with tumor prognosis¹⁰. Twenty major component branches of CTL cells were selected for further analysis. t-SNE clustering analysis divided CTL cells into 13 subgroups, and 3373 markers were identified from these cell subgroups through differential expression analysis (Fig. 4A-B). Based on the expression of marker genes, the Monocle 2 algorithm was employed to analyze the sequence and loci of the cell subgroups, revealing high expression levels of genes related to B lymphocytes and CD4⁺ T cells (Fig. 4C), suggesting a relatively high proportion of B lymphocytes and CD4⁺ T cells within TC cells.

Fig. 4.

IGF2BP1 had the potential to predict the sensitivity of therapeutic drugs for PTC. (A) PCA analysis of CTL cells. (B) Cell proportion distribution in different groups. (C) Utilize the Monocle 2 algorithm to analyze sequences and loci of 17 tumor cell subsets. (D) The correlations between IGF2BP1 and immune score. (E)The relation between IGF2BP1 and immune checkpoint-related genes.

Subsequently, Spearman correlation analysis demonstrated a significant correlation between IGF2BP1 and the immune score of CD4 + T cells (Fig. 4D). Given that TC cells are regulated by immune checkpoints, the expression levels of IGF2BP1 and immune checkpoint-related genes were analyzed to explore the potential association between IGF2BP1 and immune checkpoints. The results indicated that IGF2BP1 was closely correlated with immune checkpoint proteins such as cytotoxic T-lymphocyte-associated protein 4 (CTLA4) (Fig. 4E). Based on this, we speculated that this association may have the potential for application in pharmacodynamic model prediction. Therefore, commonly used chemotherapy drugs and targeted drugs for advanced PTC treatment were screened, and the therapeutic effects of each drug were analyzed based on the largest publicly available pharmacogenomic database. The analysis revealed that the expression of IGF2BP1 could predict the sensitivity of PTC patients to doxorubicin and sunitinib, whereas it was not predictive for sensitivity to paclitaxel and sorafenib (Fig. S3A-D).

IGF2BP1 was underexpressed in PTC patients

This study enrolled a total of 101 PTC patients who underwent unilateral radical thyroidectomy. Through comparison, it was found that the expression of IGF2BP1 in cancerous tissues was lower than that in adjacent non-cancerous tissues (Fig. 5). Among them, 37 patients exhibited central lymph node metastasis. Clinical and pathological factor analysis revealed that the expression of IGF2BP1 was correlated with central lymph node metastasis and BRAFV600E mutation, but not with tumor size, age, number of lesions, presence of Hashimoto’s thyroiditis, or gender (Table 2). During the follow-up period, 13 patients developed lateral cervical lymph node metastasis, and some patients were lost to follow-up. Therefore, further research and validation are needed regarding the association between the expression level of IGF2BP1 and the prognosis of PTC.

Fig. 5.

The expression of IGF2BP1 in 101 PTC patients who underwent unilateral radical thyroidectomy.

Table 2.

Correlation between the level of IGF2BP1 and clinical pathological characteristics of patients with PTC.

		N	IGF2BP1	P
Sex	Male	39	24.48 ± 4.42	0.321
	Female	62	24.84 ± 3.81
Age (y)	< 55	38	25.32 ± 4.21	0.597
	≥ 55	63	24.3.2 ± 3.92
T	T1	88	24.95 ± 3.96	0.585
	T2	13	23.01 ± 4.30
N	N0	64	24.95 ± 3.38	0.049
	N1	37	24.27 ± 5.01
BRAFV600E	Mutation	28	24.53 ± 4.98	0.044
	No mutation	73	24.77 ± 3.65
TGAb/TPOAb	Within normal range	49	24.63 ± 3.91	0.883
	Outside normal range	52	24.77 ± 4.19
Focus	1	90	24.70 ± 4.04	0.256
	2	8	24.73 ± 3.85
	3	2	24.60 ± 3.68
	5	1	26.37

Overexpression of IGF2BP1 inhibited the migration and invasion of TC cells

To validate the correlation between the level of IGF2BP1 and PTC prognosis, qRT-PCR was utilized to separately detect the level of IGF2BP1 in normal thyroid cell line (NTHY ORI 3 − 1) and TC cell lines (B-CPAP, TPC-1). The results indicated that the level of IGF2BP1 in B-CPAP and TPC-1 cell lines was significantly lower than that in NTHY ORI 3 − 1 cell line, and this phenomenon was particularly evident in TPC-1 (Fig. 6A).

Fig. 6.

Biological role of IGF2BP1 in PTC cells. (A) The mRNA expression levels of IGF2BP1 in NTHY cells and TC lines. (B) The efficiency of B-CPAP, TPC-1 cell lines transfected with oe-NC and oe- IGF2BP1 was validated by qRT-PCR. (C) CCK-8 assays were conducted to observe B-CPAP, TPC-1 cells’ viability after IGF2BP1 overexpression. (D) Comparison of the number of invasive. (E) Clonogenic activities of B-CPAP, TPC-1 by colony formation assays. (F) Comparison of the number of migrated. *represents p < 0.05; ** represents p < 0.01; *** represents p < 0.001.

Subsequently, to further elucidate the association between IGF2BP1 and PTC prognosis, TC cell lines overexpressing IGF2BP1 were constructed via lentiviral vector-mediated transduction. qRT-PCR was then employed to measure the level of IGF2BP1 in the two TC cell lines, which was substantially higher than that in the NC group, demonstrating the successful establishment of IGF2BP1-overexpressing cell lines (Fig. 6B). Overexpression of IGF2BP1 reduced the viability of TC cells (Fig. 6C), decreased the formation of cell colonies, and inhibited the growth of TC cells (Fig. 6D). Transwell assay and cell wound-healing assay also yielded similar findings, showing that overexpression of IGF2BP1 significantly suppressed the invasion and migration of TC cells (Fig. 6E,F), further confirming that a high expression level of IGF2BP1 was closely related to a favorable prognosis of PTC.

Discussion

In this study, we demonstrated that IGF2BP1 served as an independent prognostic factor and a potential therapeutic target for PTC. IGF2BP1 is an RNA-binding protein involved in the stabilization, localization, and translational regulation of RNA¹¹. As an m6A methylation reader protein, it can recognize and bind to m6A-modified mRNA, thereby enhancing its stability¹². Multiple previous reports have indicated that IGF2BP1 was highly expressed in various types of tumors and was closely associated with poor prognosis in patients^13,14. However, in this study, we observed a different phenomenon: among 101 PTC patients, the level of IGF2BP1 in cancer tissues was lower than that in adjacent non-cancerous tissues. A retrospective study confirmed that IGF2BP1 was almost negative in poorly and well-differentiated thyroid carcinoma tissue samples, whereas it was positive in 75% of anaplastic thyroid carcinoma (ATC) samples, suggesting its potential as a distinguishing marker for ATC¹⁵. Similarly, this study confirmed its low expression in PTC. Together, these data indicate that IGF2BP1 expression is tightly linked to the differentiation status of thyroid tumors and is characteristically low in PTC.

Further analysis also revealed a correlation between low IGF2BP1 expression and lymph node metastasis as well as BRAFV600E mutation. It is noteworthy that BRAFV600E mutation is a major driving force for PTC¹⁶, and the higher the number of metastatic central lymph nodes, the higher the proportion of BRAFV600E mutation¹⁷. Additionally, BRAFV600E mutation is also associated with clinical pathological features of PTC such as primary tumor size, capsular invasion, and lymph node metastasis¹⁸. This further underscored the possibility of IGF2BP1 serving as a prognostic factor for PTC patients.

Notably, although the expression levels of m6A methylation regulators are negatively correlated with the prognosis of PTC patients, IGF2BP1 may still serve as a potential protective factor. This discrepancy may be attributed to the complexity of the m6A regulatory network, in which different regulators can exert distinct or even opposite effects in cancer¹⁹. Previous studies have also explored prognostic models based on m6A RNA methylation regulators in PTC. For example, Hou’s team developed a three-gene signature comprising RBM15, KIAA1429, and FTO to predict overall survival, demonstrating that m6A regulators can serve as independent prognostic indicators²⁰. In our study, we constructed a novel prognostic model using IGF2BP1, YTHDC2, and YTHDF3, which integrates Consensus Clustering, LASSO regression, and Cox analyses, achieving high predictive accuracy (C-index = 0.931) and providing stratification for 1-, 3-, and 5-year survival. Furthermore, we linked IGF2BP1 expression to tumor immune infiltration, highlighting additional biological insights beyond previous models.

To validate the function of IGF2BP1 in PTC, PTC cell lines with IGF2BP1 overexpression were constructed. The overexpression of IGF2BP1 reduced the viability of PTC cells and inhibited their migration and invasion capabilities. This finding diverged from the typical behavior of IGF2BP1 in other tumor types, suggesting that IGF2BP1 may function as a tumor suppressor gene in PTC. He’s team noted that IGF2BP1 was scarcely expressed in normal tissues but was highly expressed in some tumor tissues, though not universally across all tumor types²¹. Additionally, both Chatterji’s team²² and Wang’s team²³ have emphasized that IGF2BP1 acted as an anti-cancer gene in breast cancer and inflammation-associated colon cancer, and our study aligned with their conclusions. More importantly, as an independent prognostic factor for PTC, IGF2BP1 can predict the 5-year survival risk of patients with PTC. However, with regard to prognosis, due to the limited follow-up duration and small sample size, further validation is still required to confirm the relationship between IGF2BP1 and PTC prognosis.

It is noteworthy that the expression level of IGF2BP1 is positively correlated with the number of tumor-infiltrating immune cells, especially showing a particularly significant correlation with CD8⁺ T cells, CD4⁺ T cells, natural killer (NK) cells, and monocytes. In the presence of TC, the body’s immune system can recognize tumor cells as “non-self” components²⁴, thereby triggering an immune response. Therefore, immunotherapy plays a vital role in the clinical treatment strategies for advanced TC²⁵. Further research has revealed that IGF2BP1 was closely associated with immune checkpoint proteins such as CTLA4, and there existed intricate interactions between these immune checkpoint molecules and the tumor progression process. For example, high expression of CTLA4 may be associated with tumor immune escape and poor prognosis²⁶. Based on the aforementioned findings, we speculated that the level of IGF2BP1 may serve as a potential biomarker for predicting drug resistance in patients with PTC tumors. The results indicated that the expression level of IGF2BP1 may be used to predict patient sensitivity to doxorubicin and sunitinib. However, for predicting sensitivity to paclitaxel and sorafenib, the level of IGF2BP1 may not provide effective predictive information.

Although our study demonstrated that IGF2BP1 may serve as an independent prognostic factor for PTC, the current research primarily relied on the existing TCGA database. Due to the limited clinical sample size, there may be an issue of insufficient sample diversity, which could introduce selection bias into the research findings. Therefore, to validate our research discoveries, larger-scale, multi-center sample studies are required in the future. Additionally, although we demonstrated that overexpression of IGF2BP1 can inhibit the proliferation and growth of PTC cells, the specific downstream molecular signaling pathways still require further in-depth research and clarification.

In conclusion, our research has revealed that high expression of IGF2BP1 was associated with a favorable prognosis in PTC, and it served as an independent prognostic predictor and a potential therapeutic target for PTC.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Jinqiu Wang: investigation, data curation, methodology, writing-original draft, and funding acquisition. Chen Dai: data curation, visualization, and conceptualization. Mingze Wei: methodology, validation, and investigation. Weida Fu: supervision, writing-review and editing, and visualization. Jin Luo: project administration, supervision. Yongping Dai: supervision, writing-review, and editing.

Funding

This work was supported by the Zhejiang Provincial Medical and Health Science and Technology Program (2022KY1110) and the “New Feather” Talent Program of the First Affiliated Hospital of Ningbo University (XYJH-1-WJQ).

Data availability

All data supporting the findings of this study are available within the paper and its Supplementary Information.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the First Affiliated Hospital of Ningbo University (Approval No. 2021-R140, dated December 30, 2021). All TCGA data used in this study are publicly available through the National Cancer Institute’s Genomic Data Commons portal (https://portal.gdc.cancer.gov/).