Macrophage infiltration into HNSCC: hub module identification and enrichment analysisIn the TCGA-HNSCC cohort, the blue module was significantly correlated with macrophages (R2 = 0.59, P = 5e−57) (Fig. 1a). In the GSE146771 cohort, the M0, M1, and M2 macrophages had the strongest correlation with the red, tan, and turquoise modules, respectively (M0: R2 = 0.4, P = 8e−12, M1: R2 = 0.59, P = 4e−26, M2: R2 = 0.35, P = 6e−09) (Fig. 1b). And the correlation values between macrophage module membership and gene significance were shown in Fig. 1c–f. The above were all analyzed using Pearson’s test, and for the reliability of result, we further conducted Spearman’ s test, the results of which were exhibited in Supplementary Fig. S2. The tendencies of both analyses were consistent. Subsequently, we conducted a paired difference analysis of cancerous and adjacent tissues from patients with HNSCC in the TCGA database (Fig. 1g). A total of 194 MRGs were identified by intersecting the hub modules and DEGs in the TCGA dataset (Fig. 1h). A functional enrichment analysis of the 194 MRGs revealed that their biological activities predominantly involved cellular immunity. Different databases emphasized different aspects of the immune system, such as “cytokine-cytokine receptor interaction” at the center of the KEGG database, while “inflammatory response” and “allograft rejection” were the focus of the MisgDB database, and maintenance of immune function and cytokine signaling transmission were the primary concerns of the Reactome database (Fig. 1i–k). Meaning that the function of MRGs is mainly related to the immune inflammatory response of cells.Figure 1Construction of a co-expression network involving macrophage-related genes (MRGs) and identification MRGs using weighted gene coexpression network analysis. (a, b) Heatmap demonstrating the correlation between module eigengenes and macrophages in TCGA-HNSCC and GSE146771 datasets. (c) The blue module had a significant correlation with macrophages in the TCGA-HNSCC dataset (Cor = 0.79, p < 1e−200). (d–f) The M0, M1, and M2 macrophages had the strongest correlation with the red, tan, and turquoise modules, respectively (M0: Cor = 0.7, p < 1e−200, M1: Cor = 0.79, p = 4.6e−49, M2: Cor = 0.63, p < 1e−200). (g) The volcano plot showing the genes with significant differences in the top six positions in TCGA-HNSCC dataset. (h) Venn diagram displaying the macrophage-related selected intersection genes from different datasets. (i–k) Functional enrichment analysis on the 194 intersected MRGs using Kyoto Encyclopedia of Genes and Genomes (KEGG), Molecular Signatures Database (MisgDB), and Reactome databases.Construction and prognostic analysis of molecular subtypes of MRGs in HNSCC patientsTo delve deeper into the heterogeneity of macrophages among HNSCC tumors, unsupervised consensus analysis was used to classify the patients in TCGA-HNSCC into two distinct subtypes according to the 194 MRGs (Fig. 2a–d). Patients in cluster 2 had considerably superior overall survival (OS) than that in cluster 1 (Fig. 2e). We presented distribution differences between clusters 1 and 2, in terms of clinicopathological features, and simultaneously visualized the top ten genes with upregulated and downregulated levels between the two clusters (Fig. 2f).Figure 2Cluster analysis of intersected MRGs in the TCGA cohort. (a) Consensus clustering identified two clusters of HNSCC with different macrophages infiltration characteristics. (b) Consensus clustering cumulative distribution function (CDF) for k = 2–6. (c) Relative change in the area under the CDF curve for k = 2–6. (d) The 2D PCA plot demonstrated the two clusters could be easily identified based on the MRGs. (e) The Kaplan–Meier curve survival analysis between different clusters. (f) Heatmap showing the distribution differences between clusters 1 and 2 in terms of clinicopathological features. (g) Heatmap displaying notable disparities between the two clusters in multiple biological processes via gene set variation analysis (GSVA). (h) The bar plot of the KEGG pathways enriched on the differentially expressed genes (DEGs) between different clusters. (i) The cluster plot of the Gene ontology (GO) pathways enriched on the DEGs between different clusters. *P < 0.05; **P < 0.01; ***P < 0.001.GSVA revealed notable disparities between the two clusters in multiple functional pathways, and almost all immune and inflammatory response pathways were enriched in cluster 2 (Fig. 2g). Finally, we performed KEGG and GO enrichment analyses of the DEGs between clusters 1 and 2 (Fig. 2h–i). KEGG analysis suggested that the main pathways were involved in viral protein interactions with cytokine and cytokine receptors. GO analysis revealed that the DEGs were mostly enriched in the BP functional set, namely, leukocyte cell–cell adhesion and regulation of leukocyte proliferation, and were associated with the MF functional set. These findings indicated that MRGs may influence the interaction between cytokines and tumor cells.Evaluation of TME and biological characteristics of each macrophage-related clusterUsing the ESTIMATE algorithm, we computed the immune score, estimated score, stromal score, and tumor purity to compare the TME and activities of immune-related pathways between the two clusters. Cluster 1 exhibited a significantly lower immune score, stromal score, and estimated score than cluster 2 (Fig. 3a); conversely, cluster 1 demonstrated a substantially higher tumor purity than cluster 2 (Fig. 3b). Based on these findings, it appeared the two clusters had totally distinct TME infiltration patterns. There were significant differences in the immune cell functions involved between the two clusters, with almost all immune pathways enriched in cluster 2 (Fig. 3c). Cluster 2 showed higher expression of HLA-related genes and immune checkpoint genes (NRP1, IDO1, LGALS9, CD40, TNFRSF14, CD274, etc.) and lower expression of CD44, TNFRSF18, TNFSF9, and TNFSF18 (Fig. 3d–e). To assess the predictive power of the clusters associated with macrophages for ICI response, IPS analysis was performed on patients with HNSCC to ascertain their immunotherapeutic sensitivity. As shown in Fig. 3f–i, cluster 2 showed a higher IPS score in CTLA4−_PD1−, CTLA4−_PD1+, and CTLA4+_PD1+, which suggested that individuals in cluster 2 may potentially get more advantages from immunotherapy.Figure 3Immune infiltration and tumor mutation analysis between different clusters. (a) The comparisons of stromal score, immune score, and estimated score between various clusters. (b) The comparisons of tumor purity between distinct clusters. (c) The box plot demonstrating the difference in the immune cell functions involved between the two clusters. (d) The box plot showing the difference in HLA expression between distinct clusters. (e) The box plot displaying the difference in immune checkpoint genes between the two clusters. (f–i) Immunophenoscore (IPS) analysis of CTLA4−_PD1−, CTLA4−_PD1+, CTLA4+_PD1+ and CTLA4+_PD1− groups between various clusters. (j) The macrophage-related DEGs in HNSCC, together with their mutation rates, were displayed in a waterfall plot. (k) The comparison of tumor mutation burden (TMB) between different clusters. (l) The Kaplan–Meier curve showed the survival analysis between high- and low TMB groups. (m) The Kaplan–Meier curve showed the survival analysis combining the cluster with the TMB risk group. *P < 0.05; **P < 0.01; ***P < 0.001.TMB, or non-synonymous variation, is strongly correlated with the infiltration of immune cells and activation of immunological responses28. The somatic cell mutation frequency in patients is 94.18%, with the highest frequencies observed for TP53, TTN, CDKN2A, and FAT1 mutations (Fig. 3j). Comparison of the TMB scores between clusters 1 and 2 revealed that cluster 1 was significantly higher than cluster 2 (Fig. 3k). Furthermore, according to the TMB score, patients were divided into high and low TMB groups; patients with high TMB had a poorer prognosis than those with low TMB (Fig. 3l). Additionally, we conducted a stratification study and found that combining the cluster with the TMB risk group could more accurately predict the prognosis of patients with HNSCC (Fig. 3m).Integrative machine learning algorithms constructed an optimal prognostic MRSWe evaluated the MRGs and created a prognostic MRS, both accurately and stably, using an integrative approach combined with 10 machine-learning-based algorithms. Consequently, a total of 101 distinct prediction models were obtained, as shown in Fig. 4a. The StepCox[both] + CoxBoost method yielded the optimum model, which consisted of APOC1, CTLA4, IGF2BP2, CYP27A1, NTN4, SLC7A5, PPP1R14C, KRT9, and RAC2, as evidenced by an average C-index of 0.6469 (Fig. 4a). We subsequently classified HNSCC cases into high- and low-risk categories based on their risk scores. Differences in age, sex, pathological grade, and clinical stage distribution among patients in the various risk groups are shown in Supplementary Fig. S3a-b. As anticipated, HNSCC patients with high-risk scores had a significantly poor OS rate in TCGA training (p < 0.001, Fig. 4b), TCGA testing (p = 0.002, Fig. 4c), and GSE65858 (p = 0.037, Fig. 4d) cohorts. Analysis of the risk curve revealed that patients in the high-risk group had a greater likelihood of death and a shorter duration of survival. The population of patients in the high-risk group expanded and mortality rates increased with increasing risk scores (Fig. 4e–g). Additionally, the AUCs for 1-, 3-, and 5-year OS were shown in Fig. 4h–j. We further validated the predictive performance of MRS model in two external cohorts, GSE117973 and 40 samples from our hospital, as shown in Supplementary Fig. S4-5.Figure 4Using macrophage-related clusters as a basis, the macrophage-related signature (MRS) was developed and validated. (a) A total of 101 combinations of machine learning algorithms for the MRS via a tenfold cross-validation framework. The Kaplan–Meier curve showed the survival analysis of HNSCC patients in TCGA training (b), TCGA testing (c), which was divided based on the genes in MRS, and GSE65858 cohort (d). (e–g) The distribution of risk scores and survival statuses for patients with HNSCC in the two risk groups as determined by the TCGA training, TCGA test, and GSE65858 cohort. (h–j) The ROC analysis showed the AUCs for 1-, 3-, and 5-year OS of patients with HNSCC in the TCGA training, TCGA testing, and GSE65858 cohort. *P < 0.05; **P < 0.01; ***P < 0.001.As shown in supplementary Fig. S6, age, pathological grade, T status, and N status were significantly associated with risk score in the TCGA training cohort. Supplementary Fig. S7 shows the ROC curves for forecasting patient survival probability. Subsequently, COX analysis suggested that the risk score was an independent prognostic factor for predicting survival and nomogram was constructed to predict the survival duration of patients (Fig. 5a–i). Furthermore, we conducted a paired difference analysis to confirm the expression of nine genes comprising MRS, utilizing data from patients with HNSCC in TCGA (Fig. 5j–r). Additionally, we performed gene set variation analysis (GSVA) on the basis of MRS model, which was shown in Fig. 6a–d.Figure 5Construction and assessment of the survival prediction nomogram. (a, b) In the TCGA-training set, univariate and multivariate Cox regression analyses revealed that the risk score derived from clusters associated with macrophages is an independent prognostic factor that impacts the prognosis of patients with HNSCC. (c, d) In the TCGA testing set, univariate and multivariate Cox regression analyses revealed that the risk score derived from clusters associated with macrophages is an independent prognostic factor that impacts the prognosis of patients with HNSCC. (e, f) In the GSE65858 dataset, univariate and multivariate Cox regression analyses revealed that the risk score derived from clusters associated with macrophages is an independent prognostic factor that impacts the prognosis of patients with HNSCC. (g) The nomogram for the prediction of 1-, 3-, and 5-year OS of patients with HNSCC based on the risk score combined with other clinicopathological characteristics. (h) The decision curve analysis was conducted to assess the net benefit of nomogram and other clinicopathological features for predicting patient OS over the range of clinical threshold. (i) The calibration plot of nomogram exhibited strong consistence of patient OS between predicted and observed probabilities. (j–r) The boxplots showed expression differences of nine genes in MRS between tumor and adjacent normal tissues of patients with HNSCC in TCGA. *P < 0.05; **P < 0.01; ***P < 0.001.Figure 6Identification of hub genes and genetic variation characteristics in high- and low-risk groups. (a–d) Enrichment plots were generated to analyze gene set enrichment in both high- and low-risk groups, depending on the risk score derived from macrophage-related clusters. (e) The Venn diagram illustrated the hub genes that were generated through the intersection of hub genes derived from the three algorithms mentioned earlier. (f–i) The box plot showed how the expression of four hub genes changed in MOC22 tumor mouse models after anti-PD1 treatment was given. (j) The box plot showed frequencies of gain and loss of the four hub genes between high- and low risk groups. (k) Circus plot exhibit the distribution on chromosomes of the four hub genes between high- and low risk groups. (l) Comparison of TMB differences between high- and low risk groups. (m) The correlation analysis revealed the relation among the expression levels of four hub genes, risk scores, and TMB. (n) The Kaplan–Meier curve displayed the survival analysis of patients with HNSCC, categorised based on both TMB groups and risk score. (o, p) Waterfall plot displaying gene mutations in the high- and low-risk groups. *P < 0.05; **P < 0.01; ***P < 0.001.Identification of hub genes and variation characteristics in high and low risk groupsA PPI network for the DEGs between high and low risk groups was constructed based on the “cytoscape” software.We applied three different algorithms to identify the top 10 hub genes. Further intersections were performed, resulting in the identification of four hub genes: CD80, CTLA4, CCL2, and IFNG (Fig. 6e). We employed TISMO to conduct a comparative analysis of the alterations in the expression of four hub genes in MOC22 tumor mouse models following the administration of anti-PD1 therapy. The findings indicated that the levels of CCL2 and IFNG were significantly increased in the group that responded positively to anti-PD1 therapy compared to the group that did not respond (Fig. 6f–i), which suggest that CCL2 and IFNG can be used as biomarkers to predict the responsiveness of HNSCC patients to anti-PD1 treatment.A copy number variant (CNV) is a DNA segment and is associated with the heterogeneity of the genome and molecular phenotype, which contributes to the development of tumors29. We conducted a mutation frequency analysis and the results showed that IFNG had a greater occurrence of gain-of-function mutations in HNSCC, whereas CTLA4 displayed a higher frequency of loss-of-function mutations (Fig. 6j,l). The TMB scores of the high- and low-risk groups were calculated and compared, and no significant differences were observed (Fig. 6m). Subsequently, we conducted a correlation analysis, which revealed that the expression levels of CD80, CTLA4, and CCL2 were negatively correlated with the TMB score, whereas the expression levels of IFNG and CD80 were positively correlated with the risk score. According to prior research, there is no notable link between risk and TMB scores. There was a possibility of a “chain effect” in the regulation of gene expression among the four hub genes, where their expression levels are positively associated with one another (Fig. 6k).As shown in Fig. 6n, the higher the TMB and risk score, the lower the probability of patient survival. In accordance with the TMB score, individuals classified as high-risk had a greater prevalence of mutations. TP53 had the highest mutation rate in the high-risk group, with a mutation frequency of 80%, whereas the mutation frequency was 60% in the low-risk group. Notably, TTN had a higher mutation rate in the low-risk group (40%) than in the high-risk group (36%). The main types of TP53 and TTN mutations were missense mutations and multiple hits (Fig. 6o–p).The evaluation of immunotherapy for MRS modelWe conducted a comparative analysis of the proportions of the immunological subtypes of HNSCC across various risk groups. While the link between them may not be statistically significant, it was evident that clusters 1 and 2 exhibited a substantial association in terms of survival outcomes. Specifically, patients in cluster 1 predominantly exhibited survival results, whereas those in cluster 2 mainly died (Fig. 7a–b). We observed substantial disparities in the expression of immune checkpoint genes between high- and low-risk groups. CD44, CD276, NRP1, CD274, LAIR1, HAVCR2, CD86, PDCD1LG2, and CD80 were predominantly overexpressed in the high-risk group. Conversely, TNFRSF18, TNFRSF25, and CD200 were highly expressed in the low-risk group (Fig. 7c). In terms of HLA gene expression, except for HLA-DOB, which was significantly overexpressed in the low-risk group, all other differentially expressed genes were enriched in the high-risk group (Fig. 7d).Figure 7Comparison of immune subtypes and response to immunotherapy between different risk groups. (a) The Sankey diagram unveiled the potential correlation among macrophage-related cluster, risk score, and survival status. (b) Examining the variations in immune subtype between various risk categories. (c) The expression disparities of immune checkpoint genes between different risk groups. (d) The expression disparities of HLA genes between different risk groups. (e) Comparison of TIDE score in the low- and high-risk groups. (f) Comparison of positive response rates to immunotherapy between high- and low risk groups. (g) Comparison of risk scores between different immune response groups. (h, i) The composition differences of the proportion of inflammatory immune subtypes or tumor-infiltrating immune cells expressing PD-L1 between the low- and high-risk groups divided based on MRS signature of patients with metastatic urothelial carcinoma in the IMvigor210 cohort. (j) Comparison of risk scores between the CR/PR group and the SD/PD group in IMvigor210 dataset. (h) The Kaplan–Meier curve displayed the survival analysis of patients in IMvigor210 dataset, categorised based on risk score. (i) The ROC analysis showed the AUCs for 1-, 3-, and 5-year OS of patients in the IMvigor210 dataset. The specimens were categorised as immunohistochemistry IC0, IC1 or IC2 + based on the percentage of PD-L1 positive cells: less than 1%, 1% to less than 5%, or 5% or more, respectively. *P < 0.05; **P < 0.01; ***P < 0.001.Using the TIDE methodology, we found that the risk score was positively correlated with the TIDE score (Fig. 7e). The high-risk group exhibited a considerably lower response rate than the low-risk group. Conversely, the risk score of the immune response group was much lower than that of the non-response group (Fig. 7f–g). Patients with metastatic urothelial carcinoma in the IMvigor210 cohort were divided into high- and low-risk groups. In both groups, the proportions of inflammatory immune subtypes and tumor-infiltrating immune cells expressing PD-L1 differed significantly (Fig. 7h–i). Nevertheless, the treatment outcomes following immunotherapy did not differ considerably between two groups (Fig. 7j, Supplementary Fig. S3c-d). It is worth mentioning that the survival probability of patients in the high-risk group was significantly lower than that in the low-risk group, which is consistent with the conclusion obtained from the HNSCC dataset (Fig. 7k–l). Additionally, the chemotherapy drugs sensitivity analysis was showed in Supplementary Fig. S8.MRS gene expression patterns at single cell resolution.To further investigate the patterns of gene expression in the MRS model at the single-cell level, we analyzed the single-cell dataset GSE1822271. After quality control and dimensional reduction, a grand total of 30 cell clusters were obtained (Fig. 8a–b). The top five differentially expressed marker genes in each cell type are shown as a heatmap (Fig. 8c). We further visualized the distribution of nine genes in MRS across different cell types, among which SLC7A5, RAC2, and APOC1 were enriched in macrophages, with APOC1 being the most highly expressed (Fig. 8d–e).Figure 8Patterns of gene expression in MRS model at the single cell level. (a) The uniform manifold approximation and projection (UMAP) plot showed all the cells in the GSE1822271 dataset can be classified into 30 clusters. (b) The UMAP plot exhibit aforementioned 30 cell clusters can be annotated as 7 major cell lineages. (c) Heatmap displayed the differentially expressed top five marker genes in each cell type. (d) The UMAP plots visualized the distribution of nine genes in MRS across different cell types. (e) Violin plots compared the expression level of nine genes in MRS. (f, g) The bar plot and UMAP plot showed the proportion of cells in distinct cell cycles. (h, i) Cellular functional pathways enriched in different cell types and their inhibition or activation status. (j) Bar plots showing the proportion of cell types in each sample. (k) Bar plots showing the proportion of cell types in patients with different HPV infection status. (l, m) The UMAP plot and bar plot displayed the proportion of cell types in high- and low-expression group divided based on the expression levels of nine genes in MRS. *P < 0.05; **P < 0.01; ***P < 0.001.Every cell type plays a role in the onset and progression of the disease, and follows a distinct cell cycle. Data analysis revealed that macrophages were mostly in the transitional phase from G2 to M, with the lowest cell count observed in the S phase, suggesting that macrophage division and proliferation were more active. Simultaneously, fibroblasts mostly resided in the G2 and S phases and were primarily tasked with duplicating genetic material and preparing for cellular division (Fig. 8f–g). Functional enrichment analysis showed that epithelial mesenchymal transition was significantly upregulated in fibroblasts, but downregulated in macrophages, mast cells, plasma cells, and T cells. We also found that epithelial cells, endothelial cells, fibroblasts, and macrophages were involved in the activation of tumor-related pathways, whereas mast cells, plasma cells, and T cells were mainly involved in the inhibition of related pathways (Fig. 8h–i). It can be seen that macrophages, epithelial cells, and fibroblasts were more abundant in the HPV- group, while endothelial cells and T cells were more abundant in the HPV+ group (Fig. 8k). In addition, the nine genes in MRS were mainly enriched in macrophages, T cells, epithelial cells, and mast cells, with the highest proportion of high expression in macrophages and T cells (Fig. 8l–m). To some extent, this confirms the effectiveness of our MRS prognostic model.Authentication of MRS-related genes via qRT-PCR and HPA platformWe conducted RT-qPCR to quantify mRNA expression levels of the nine genes in the MRS model to confirm our bioinformatic findings. Supplementary Fig. S9a showing our experimental findings, consistent with earlier findings, all genes except CYP27A1 and NTN4 showed elevated expression levels in cancer. Additionally, we used the HPA database to obtain immunohistochemical images to evaluate the protein expression of MRS-related genes. As shown in Supplementary Fig. S9b, IGF2BP2, PPP1R14C, KRT9, RAC2, SLC7A5, and APOC1 protein expression was substantially higher in tumor tissues than in normal tissues, whereas CYP27A1 and NTN4 showed lower expression levels. Thus, the experimental outcomes of our study effectively confirmed the bioinformatics-based conclusions, thereby strengthening the importance of our work.Knockdown of IGF2BP2 and SLC7A5 inhibits malignant biological behavior of HNSCC cellsTo investigate the biological role of MRGs, we selected two genes with the most significant expression differences: IGF2BP2 and SLC7A5. We used targeted siRNAs to knockdown the expression of IGF2BP2 and SLC7A5 in AMC-HN-8 and CAL 27 cells. The efficiency of knockdown of two genes were validated by RT-qPCR, which exhibited obvious decrease of mRNA levels (Fig. 9a–d). The CCK-8 and colony formation assays demonstrated the knockdown of IGF2BP2 and SLC7A5 apparently inhibited the proliferative ability of AMC-HN-8 and CAL 27 cells compared with the normal group (Fig. 9e–h,m–n). The reduction of IGF2BP2 and SLC7A5 expression resulted in a notable decline in the migration rate in both AMC-HN-8 and CAL 27 cells, as depicted in Fig. 9i–l. Also, the results of transwell assays suggested that the migration ability of HNSCC cells was significantly decreased when IGF2BP2 and SLC7A5 were suppressed (Fig. 9o–r). The above results indicate that knockdown of IGF2BP2 and SLC7A5 markedly inhibits malignant biological behavior of HNSCC cells.Figure 9IGF2BP2 and SLC7A5 knockdown considerably inhibited proliferation and migration abilities of HNSCC cells. (a–d) Confirmation of IGF2BP2 and SLC7A5 knockdown by RT-qPCR in AMC-HN-8 and CAL 27 cells. (e–h) CCK-8 assays were conducted to investigate the proliferation ability of HNSCC cells after transfection at 24, 48, 72 and 96 h. (i–l) Wound healing assays were performed to evaluate the migratory ability of HNSCC cells after knockdown of IGF2BP2 and SLC7A5. (m, n) Colony formation assays were conducted in transfected HNSCC cells. (o–r) Transwell assays were performed to detect the migration ability of HNSCC cells after IGF2BP2 and SLC7A5 knockdown. *P < 0.05; **P < 0.01; ***P < 0.001.Knockdown of IGF2BP2 and SLC7A5 in HNSCC cells co-cultured with macrophages inhibits macrophage polarization to M2 type and migrationWe searched to explore the influence of IGF2BP2 and SLC7A5 on macrophage polarization. To accomplish this, we utilized supernatants from AMC-HN-8 and CAL 27 cells to culture THP-1. The expression levels of macrophage surface markers were examined using immunofluorescence and flow cytometry experiments in different groups. The results revealed an obvious increase in the expression of the M1 type macrophage marker CD86 and a significant reduction in the expression of M2 type macrophage marker CD206, when THP-1 were cultured with IGF2BP2 and SLC7A5 knocked down HNSCC cell supernatants (Fig. 10a–f). Furthermore, we placed THP-1 into the upper chamber and HNSCC cells with or without knockdown of IGF2BP2 and SLC7A5 in the lower chamber to investigate the migratory ability of macrophages. The findings demonstrated a notable attenuation in the capacity of macrophages in the upper compartment to migrate to the lower compartment when co-cultured with HNSCC cells with knockdown of IGF2BP2 and SLC7A5 (Fig. 10g–h). In general, the knockdown of IGF2BP2 and SLC7A5 in HNSCC cells co-cultured with THP-1 inhibits macrophages migration and promotes macrophage polarization to M1 type.Figure 10Suppression of IGF2BP2 and SLC7A5 expression in HNSCC cells attenuates macrophage migration and polarization to M2 type. THP-1 cells were cultured with supernatants from AMC-HN-8 and CAL 27 cells with or without knockdown of IGF2BP2 and SLC7A5, and the expression levels of CD86 and CD206 on the surface of THP-1 cells were examined by immunofluorescence (a–d) and flow cytometry (e, f). (g, h) AMC-HN-8 and CAL 27 cells with or without knockdown of IGF2BP2 and SLC7A5 were co-cultured with THP-1 cells using the transwell co-culture system to detect and the migratory ability of THP-1 cells was detected after 24 h. *P < 0.05; **P < 0.01; ***P < 0.001.
