3.1 Phenotypic clusters of B cells according to scRNA-seq
To investigate the variations in the immune microenvironment across all cell subgroups in HNSCC, we collected scRNA-seq datasets of HNSCC. By utilizing these datasets, we aim to gain a more profound comprehension of the molecular mechanisms at the individual cell level (Figure S1-S2).
Through the standard workflow of single-cell analysis, we identified four cell types in the integrated data, including NBs, MBs, GCBs, and PCs. We plotted the distribution of HPV infection status and phase (Fig. 1A). To further elucidate the heterogeneity and intricacy of HNSCC, we have subdivided them into 16 distinct subgroups. Among these subgroups, the population of NBs encompasses C0 TCL1A+ Naive B cells, C5 TCL1A+ Naive B cells, C7 CD83+ Naive B cells, C8 ISG15+ Naive B cells, C10 TCL1A+ Naive B cells, and C15 HSPA1B+ Naive B cells. Additionally, the category of MBs includes C1 CRIP1+ Memory B cells, C2 LINC01781+ Memory B cells, C3 PPP1R15A+ Memory B cells, C4 IFI30+ Memory B cells, C9 CD1C+ Memory B cells, and C13 TLE5+ Memory B cells. Furthermore, the subgroup of GCBs comprises C11 MEF2B+ Germinal center B cells and C14 STMN1+ Germinal center B cells. Lastly, we have identified PCs represented by C6 FKBP11+ IgG Plasma cells and C12 DERL3+ IgA Plasma cells (Fig. 1B). The distribution of CNVscore, nCount_RNA, S.Score, and G2M. The score for each subpopulation is shown in Fig. 1C, in which GCBs on the right side of the UMAP plot have a high CNVscore, a high nCount_RNA, and a more active cell cycle state. In addition, we analyzed the proportion of subgroups derived from peripheral blood (PBL) and tumor tissues. The pie chart displayed the proportion of B and plasma cell subtypes (Fig. 1D). The results showed that most subgroups belonged to malignant cells, with a small proportion of NBs and MBs originating from the PBL (Fig. 1E). Bubble maps and volcano plots demonstrated the expression of differentially expressed genes (DEGs) in each subgroup and the associated features of different HPV infection statuses (HPV+ vs HPV−) (Fig. 1F-G).
3.2 Pseudotime trajectories unveiled the differentiation status of B and plasma cell subgroups
To understand the dynamic transitional processes of B and plasma cells in HNSCC, we further performed a pseudotime analysis to trace the transcriptional trajectory. As shown in Fig. 2A-B, with the progression of pseudotime, the number of NBs gradually decreased, while GCBs and MBs gradually differentiated toward PCs. The decrease in GCBs and MBs may be due to their gradual transformation into PCs, most of PCs were at the end of the cell developmental trajectory. Cellular composition was identified by colouring the pseudotemporal map with tissue of origin. By analysing Fig. 1A, B & Fig. 2C, it was found that the pseudotime of NBs was significantly lower than that of other B cell subgroups, and the pseudotime of PCs represented by C6 FKBP11+ IgG Plasma cells and C12 DERL3+ IgA Plasma cells was the highest. An enhanced graphical representation, a bar plot, elucidated that the NBs subgroups predominantly comprised neoplastic cells originating from State1. Likewise, the MBs subgroups exhibited a preponderance of cells stemming from State2. Furthermore, the GCBs subgroups was characterized by a prevailing presence of neoplastic cells deriving from State4. Lastly, the PCs subgroups exhibited a notable predominance of cells originating from State5 (Fig. 2D, E). These observations collectively unveil that the NBs subgroups is positioned within the incipient stage of cellular differentiation, contrasting with the PCs, which signifies the end of differentiation. Additionally, cells within the GCBs and MBs subgroups display an augmented propensity for proliferation and differentiation capacity, thereby highlighting their distinctive biological characteristics.
3.3 NBs are in the initial differentiation stage of pseudotime trajectories
According to our research, B and plasma cells could be classified into NBs, GCBs, MBs, and PCs. Each of these subgroups manifests distinct phenotypic characteristics and undertakes specific roles within the tumor microenvironment (TME). NBs can be further classified into 6 subgroups (Fig. 3A), and the expression of top5 marker genes in different subgroups is also shown (Fig. 3B, C). By analyzing the differences in cell cycle, gene expression and CNV among samples with different NBs subgroups and HPV infection states, it was found that the CNV score of C0 TCL1A+ Naive B cells and C15 HSPA1B+ Naive B cells was higher, the proportion of G2M and S phase in the cell cycle was higher, and the expression of differential genes was higher, indicating that their cell proliferation was more active and genetic instability (Fig. 3D, I). The DEGs of 6 NBs subgroups and their corresponding transcription factors and surface proteins were analyzed by GO-BP enrichment analysis, and the active biological process of each tumor cell was obtained. It was found that C0 TCL1A+ Naive B cells was active in the biological process of Cytoplasmic translation, and C8 ISG15+ Naive B cells was active in the biological process of Negative regulation of b cell activation (Fig. 3E, H). Immune landscape studies showed that HPV+ HNSCC were enriched with more NBs, HPV− HNSCC were infiltrated by fewer NBs, and C0 TCL1A+ Naive B cells had the highest percentage of HPV+ cells (Fig. 3F). In the analysis of NBs subgroups and cell cycle, it was found that the proportion of NBs within the G1 phase of cellular growth was the highest, which may be related to the fact that NBs is in the initial stage of B cell differentiation (Fig. 3G). Through GSEA enrichment analysis, it was found that C0 TCL1A+ Naive B cells subgroup was negatively correlated with Negative regulation of metabolic process and Negative regulation of programmed cell death pathway, indicating that cells of C0 TCL1A+ Naive B cells subgroup had higher ability of cell metabolism and proliferation (Fig. 3J, K).
3.4 GCBs subgroups may have a high immune response ability
In the germinal center, follicular helper T cells (Tfh) stimulate the B cell receptor (BCR) and induce the differentiation of NBs into a variety of fates, mainly including GCBs, MBs and PCs24. Complete transcriptional characteristics at the single-cell level would be particularly helpful in determining transcriptional trajectories and heterogeneity in the process of differentiation from NBs to PCs. C11 MEF2B+ Germinal center B cells (C11 GCBs) and C14 STMN1+ Germinal center B cells (C14 GCBs) were obtained by further subgroup analysis of GCBs (Fig. 4A). The bubble chart and UMAP plots showed the expression of GCBs top marker genes (Fig. 4B-C). Among the two GCBs subgroups, the CNVscore of C11 was higher, and the nCount-RNA, G2M.Scorce and S.Score of C14 were higher (P < 0.001) (Fig. 4D-F). The GCBs in patients with HNSCC mainly comes from tumor tissues, and some from PBL (Fig. 4G). The percentage of C11 GCBs in HPV+ samples was higher than in C14 GCBs (Fig. 4H). In C11 GCBs, there were more cells with G1 cell cycle, and most of C14 GCBs were in G2M and S phase (Fig. 4I). The proportion of cells in G1 phase was more in HPV+ group, and the proportion of GCBs cells in G2M and S phase was more in HPV− group (Fig. 4J). The DEGs of C11 GCBs and C14 GCBs were screened (Fig. 4L), and the enrichment results of GO-BP among C11 GCBs and C14 GCBs was shown by heatmap. It was found that GOBP terms of C11 GCBs were up-regulated in biological processes such as cell activation, leukocyte activation, immune response, lymphocyte activation, regulation of immune system process, regulation of immune response, indicating that the DEGs of C11 GCBs may play a promoting role in the regulation of immune system (Fig. 4K). Similarly, in the GO-BP enrichment analysis results of C11 GCBs, C11 GCBs was more active in cytoplasmic translation, B cell activation, B cell proliferation, production of molecular mediator of immune response and immunoglobulin production pathways. The high immune response ability of C11 GCBs subgroup was further verified, which was consistent with the previous research results (Fig. 4M-N).
3.5 MBs play a key role in the proliferation and differentiation of B cells
MBs, the subgroups with the largest number of B cells (Fig. 5A), could be further divided into six subgroups and the expression of top5 marker genes in different subgroups are also shown in Fig. 5B-C. UMAP plots showed that the samples in the C4 IFI30+ Memory B cells (C4 MBs) had higher CNV score, higher nCount_RNA and more active cell cycle state. Among them, the chromosome stability, transcription level and cell cycle state of different subgroups of MBs are also different, which is of great significance for the study of B cell heterogeneity (Fig. 5D, H). The bar charts revealed that the majority of MBs subgroups were associated with HPV + infection, whereas the cell cycle mostly occurred in the G1 and S phases (Fig. 5E-F). The AUCell analysis of the GOBP-positive regulation of B cell proliferation in several subgroups of MBs revealed that the C4 MBs had a higher AUC value for this GOBP term compared to other subgroups (Fig. 5G, left). The CNV score of C13 TLE5+ Memory B cells (C13 MBs) exhibited a lower value compared to the other subgroups (Fig. 5G, right), while the nCount_RNA of C13 MBs was higher than other subgroups (Fig. 5H, left). The G2M.Score of C4 MBs was lower than other subgroups of MBs (Fig. 5H, right). In GO-BP enrichment analysis, C4 MBs were active in cytoplasmic translation, B cell receptor signaling pathway, immune response-regulating cell surface receptor signaling pathway, B cell differentiation, Fc receptor signaling pathway (Fig. 5I). Similarly, in GSEA enrichment analysis, the expression of C4 MBs was up-regulated in positive regulation of cellular biosynthetic process, which was positively correlated with positive regulation of cell communication, positive regulation of cell death, positive regulation of cellular metabolic process (Fig. 5J). These results suggest that the DEGs of C4 MBs plays a key role in the differentiation of B cells and promotes the regulation of immune system process, which may reflect the high immune response ability of tumor cells.
3.6 B cells eventually differentiate into mature PCs
GCBs that have a strong attraction to tumor-associated antigens ultimately develop into fully developed PCs that generate immunoglobulins (IgG, IgA, IgM, and IgE). These immunoglobulins have distinct purposes within the given situation25. According to the difference in the expression of tumor-associated antibodies in PBs, we divided the subgroups into C6 FKBP11+ IgG Plasma cells (C6 IgG), C12 DERL3+ IgA Plasma cells (C12 IgA) (Fig. 6A). The UMAP visualization showed the difference between C6 IgG and C12 IgA in terms of CNV score, nCount_RNA, S.Score, and G2M (Fig. 6B). The bubble chart and UMAP plots displayed the top marker genes for C6 IgG and C12 IgA (Fig. 6C-D). The bar charts depicted the distribution of PCs among various categories (Fig. 6E-G). To investigate the molecular diversity among PCs subgroups and its functionality. The GO-BP enrichment analysis of the DEGs in PCs revealed that C6 IgG was involved in the biosynthesis of amyloid precursor protein and glycoprotein. Additionally, C12 IgA was found to be active in the biological processes of responding to endoplasmic reticulum stress, the ERAD pathway, and topologically incorrect protein (Fig. 6H-I).
3.7 Exploring the intercellular communication between B cells, plasma cells, and tumor cells
Numerous investigations have demonstrated the existence of a diverse spectrum of mutual influence between B cells, plasma cells, and tumor cells. This interplay exerts a profound influence on the progression of cancer25. To elucidate the intricate mechanism of cell-cell interaction between these cells, we established a separate intercellular communication network between B cells, plasma cells, and tumor cells in HNSCC (Fig. 7A). 17 pairs of interactions and 20 significant signaling pathways were identified (Fig. 7B). We generally describe the inferred strengths of incoming and outcoming interaction, in which C11 GCBs and C14 GCBs are the main cell type expressing ligands and receptors that actively participate in cell-cell interaction. These results suggest that GCBs is associated with tumors and plays a central role in TME (Fig. 7C). As the observed C11 GCBs and C14 GCBs played a central role in the intercellular communications at HNSCC, we further explore the interactions between incoming and outgoing communication patterns of target cells and secreting cells. Notably, in outgoing communication patterns of secreting cells of GCBs, CD99 and SEMA4 are shown as the most predominant signaling in C11 GCBs compared to other signaling pathways (Fig. 7D-F). Consistent with these observations, GCBs and MBs are the core of the interaction between B cells and tumor cells (Fig. 7G-H). In summary, the intercellular interaction based on CD99 and SEMA4 signaling pathways is the molecular basis of GCBs-centered TME observed in HNSCC.
3.8 GCBs based regulatory network detect tumor-specific signaling pathways in HNSCC
To unveil the intricate signaling pathways that contribute to the multifaceted intercellular communications, we computed the CD99 and SEMA4 signaling pathways (Fig. 8A, F). Tumor-specific C11 MEF2B+ Germinal center B cells could express CD99 and SEMA4 to interact with some receptors of tumor cells to promote tumor progression (Fig. 8B, G). Regarding the CD99 signaling pathways, we observed that C11 MEF2B+ Germinal center B cells and C14 STMN1+ Germinal center B cells highly expressed CD99 to interact with the tumor cell receptors, C4 and C13 of memory B cells (Fig. 8C-E). In SEMA4 signaling pathway networks, the specific interaction networks of C11 MEF2B+ Germinal center B cells and C14 STMN1+ Germinal center B cells with tumor cells were also observed (Fig. 8H-J). These phenomena show that CD99 and SEMA4 could potentially serve as pivotal pathways mediating the interplay between GCBs and tumor cells. Furthermore, these signaling pathways possess the capacity to regulate B cell differentiation, consequently exercising control over tumor growth. Overall, our findings underscore that the C11 MEF2B+ Germinal center B cells manifest intricate intercellular interplays that exacerbate the virulence of the tumor. Targeting tumor-specific cell interaction networks could be a promising strategy for treating HNSCC.
3.9 Screening and validation of prognostic genes in GCBs and MBs
Considering the presence of tumor-specific signaling pathways in MBs and GCBs mentioned earlier, we will conduct an in-depth examination of these subgroups. A total of 499 HNSCC patients from the TCGA database were finally included in this study. Univariate Cox regression analysis was performed to identify the prognosis-related genes in MBs and GCBs (Fig. 9A, 9F, 10A, 10F). The candidate genes were recruited into the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model to screen out the robust prognosticators (Fig. 9B, 9G, 10B, 10G). Ultimately, using multivariate Cox regression analysis, we were able to construct four prognostic risk scores consisting of prognostic genes specific to the MBs and GCBs subgroups (Fig. 9C, 9D, 9H, 9I, 10C, 10D, 10H, 10I). The individual genes that constituted risk scores, which showed an important association with overall survival in MBs and GCBs, were thoroughly shown (Fig. 9E, 9J, 10E, 10J, 10K). The expression of CD38, SMIM14, and TCL1A in C11 MEF2B+ Germinal center B cells varied considerably over distinct N stages (Fig. 10L, P < 0.05).
3.10 MEF2B+ GCB score based on the prognosis-related genes in C11 GCBs
We then investigated the possible mechanisms of prognostic effects of 18 prognosis-related genes that comprised the MEF2B+ GCB score. Bubble maps demonstrated the expression of 18 prognosis-related genes in each GCBs subgroup and the associated features of different HPV infection statuses (HPV+ vs HPV−), Phase (G1, S, G2M), G2M.Scorce, S.Score, nCount_RNA, CNVscore (Fig. 11A ). The expression of 18 prognostic genes in C11 and C14 GCBs subgroups was further visualized in Fig. 11B and 11C. Risk scores were computed for individuals diagnosed with HNSCC within the TCGA datasets. The collected data were subsequently partitioned into cohorts of low and high-risk groups, demarcated by the median risk score (Fig. 11D above). The heatmap specifically showed the expression of genes in high and low risk groups (Fig. 11D below). More HNSCC patients had higher survival rates (p < 0.05) observed in the low-risk group, while the distribution of stage and TNM phases was not statistically different (Fig. 11E). The Kaplan-Meier survival curves showed that HNSCC patients in the high-risk group had a lower probability of survival and shorter survival time (Fig. 11F). Moreover, the time-dependent ROC curve demonstrated the proficient prognostic capacity of this model concerning the OS of HNSCC patients. The calculated Area Under the ROC Curve (AUC) values stood at 0.690 (1 year), 0.711 (3 years) and 0.677 (5 years), as evidenced in Fig. 11G. Then, we further assessed the correlation between gene expression and risk score in the prognostic model (Fig. 11H). The expression of five genes, ACTB, BASP1, EZR, PRDX6, and RRAS2, was positively correlated with the risk scores (Fig. 11I), which was in agreement with the results of the previous prognostic studies (Fig. 10J), indicating that these five genes may be tumor-related risk genes in HNSCC. Meanwhile, the five genes of ACTB, BASP1, EZR, PRDX6, RRAS2 were highly expressed in the high-risk group, and the difference between the high and low risk groups was statistically significant (Fig. 11J, P < 0.001). They were negatively correlated with OS (Fig. 11K). Both EZR and RRAS2 expressions were all positively correlated with the other four genes (Fig. 11L-M, P < 0.05)Figure. We further investigated the differences of EZR and RRAS2 in high- and low-risk groups, different ages, different races, and different TNM stages, and found that the gene expression was higher in all high-risk groups (Fig. 11N).
We observed that the MEF2B+ GCB score had independent prognostic value and had stronger predictive power than other clinical features (P < 0.001), which increases its potential for clinical applications (Fig. 12A). A nomogram plot was established to get a better clinical application ability by integrating the risk score and clinical features (Fig. 12B). The time-dependent ROC curve demonstrated the proficient prognostic capacity of this model. The AUC values were 0.738 (1 year), 0.769 (3 years) and 0.634 (5 years) (Fig. 12C, D). For the 1-, 3-, and 5-year survival, a satisfactory fit was observed between the survival predicted by the nomogram and the actual survival (Fig. 12E).
3.11 Immune cells play a dual role in the dynamic ecosystem of the HNSCC microenvironment
The initiation, progression, and invasion of HNSCC are controlled by the complex network of signaling pathways in the TME as well as cell-cell interactions between the tumor stroma, immune cells, and tumor cells26. HNSCC microenvironment contained multiple types of immune cells, most of which are T cells and macrophages (Fig. 12F). The high-risk group had lower immune scores and higher tumor purity than the low-risk group (both P < 0.001), which may be related to the fact that immune cells infiltrate into the tumor stroma within the HNSCC and interact with tumor cells (Fig. 12G-H). By analyzing the principal components of immune cells in HNSCC, it was found that there were significant differences in the infiltration of many kinds of immune cells between the two risk subgroups (P < 0.05). There was more CD8 T cell infiltration in the low-risk group and more macrophage M2 infiltration in the high-risk group (Fig. 12I-J). In the correlation analysis between risk score and immune cells, mast cells, NK cells, and macrophages were significantly positively correlated with the risk score, whereas various T cells such as T cells regulatory (Tregs), T cells follicular helper, and CD8 T cells were significantly negatively correlated with the risk score (Fig. 12K). Combined with the results of the previous survival analysis of the two subgroups (Fig. 11F), this suggests that tumor-associated immunocompetent cells may be the key immunosuppressive population of TME cells. In addition, we also analyzed the correlation between the expression of 18 prognostic genes and the proportion of immune cells in HNSCC. The results showed that the risk score, the gene expression of ACTB, BASP1, PRDX6 was positively correlated with macrophage M2 (all P < 0.05), and the risk score, the gene expression of ACTB, BASP1, RRAS2 was negatively correlated with CD8 T cells (all P < 0.01) (Fig. 12L-N). The gene expression of RRAS2 was negatively correlated with naive B cells, plasma cells, stromal score, and immunity score (all P < 0.05), and was positively correlated with tumor purity (P < 0.001) (Fig. 12O-P). Furthermore, RRAS2 was positively correlated with dendritic cells activated, macrophages M0, CD4 memory T cells resting, and NK cells resting (all P < 0.05). RRAS2 was negatively correlated with CD4 memory T cells activated, Mast cells resting, CD8 T cells, and Tregs (Fig. 12Q).
3.12 Functional enrichment and GSEA analysis of differentially expressed genes in HNSCC patients
To scrutinize the molecular heterogeneity and explore the biological mechanism distinguishing the high-risk from the low-risk subgroups, we discerned DEGs within the TCGA cohort [\(\left|{\text{log}}_{2}\left(fold change\right)\right|\)>1, fdr < 0.05]. The results of Gene Ontology (GO) enrichment analysis revealed that the DEGs primarily participated in biological processes such as immunoglobulin complex, circulating immunoglobulin complex, and external side of plasma membrane. Their products were predominantly engaged in immune components, encompassing immunoglobulin production, production of molecular mediator of immune response, B cell activation, etc. Furthermore, these genes played a pivotal role in biological molecular functions, which covered antigen binding and immunoglobulin receptor binding, etc. (Fig. 13A-C). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways mainly included the B cell receptor signaling pathway, Ras signaling pathway, PI3K − Akt signaling pathway, Cytokine − cytokine receptor interaction, Primary immunodeficiency, Hematopoietic cell lineage, etc. (Fig. 13D). Our GSEA results, presented in Fig. 13E, revealed that Ribosomal large subunit biogenesis, Mitochondrial translation, Ncrna 3 End processing, Maturation of ssu Rrna from tricistronic Rrna transcript ssu Rrna 5 8s Rrna lsu Rrna, amino acid activation were activated, while antigen receptor mediated signaling, regulation of B cell activation, B cell mediated immunity, positive regulation of B cell activation, B cell receptor signaling pathway were suppressed in the GCBs subgroup. According to the results of GSEA, many B-cell-associated pathways were suppressed in GCBs, highlighting the presence of a B-cell response in patients with HNSCC.
3.13 Relationship of the OS in HNSCC patients among the TMB subgroups and drug sensitivity
In the gene mutation spectrum of the risk and age groups, 466 (91.37%) of 510 HNSCC patients had the gene mutation (Fig. 13F left). Among the 18 prognostic genes, 31 (6.08%) had gene mutations in 510 HNSCC patients (Fig. 13F right). We further investigated the frequency of CNV in a total of 526 samples. As shown in Fig. 13G, ACTB, RRAS2, SERPINA9, TCL1A, PKM, EZR, LCK, CD38, and BIK had significant CNV amplification frequencies. In addition, the probability of simultaneous mutations in two groups of genes, PKM and SERPINA9, TCL1A and STAG3, was higher (Fig. 13H, P < 0.05). We selected the top 6 genes of somatic mutation rate for single nucleotide polymorphism (SNP) analysis and found that SERPINA9, STAG3, ACTB, CD38, LCK, and PKM were dominated by missense mutation (Fig. 13I). In the two risk subgroups, the TMB of the high-risk group was higher than that of the low-risk group (Fig. 13J, P < 0.001), and TMB was positively correlated with the risk score (Fig. 13K, P < 0.001). Combined with the survival analysis in Fig. 13L, the survival time of patients in high risk-high TMB group was the shortest, while that in low risk-low TMB group was the longest, and the OS of patients in each group was significantly different (P < 0.0001). The AUC values were 0.541 (1 year), 0.532 (3 years), and 0.5I2 (5 years) (Fig. 13M). This indicates that the TMB model has a discernible predictive ability for the prognosis of HNSCC patients. Furthermore, in drug sensitivity analysis, HNSCC patients in the low-risk subgroup demonstrated enhanced responsiveness to cisplatin and docetaxel in chemotherapy (Fig. 13N). In contrast, the high-risk subgroup was more sensitive to Nilotinib and Rapamycin, both targeted drugs (Fig. 13O). The IC50 values of ABT.263, AKT.inhibitor.Ⅷ, CCT007093 were higher in the high-risk subgroup, and Bosutinib, Epothilone. B and Pazopanib were higher in the low-risk subgroup (Fig. 13P). These discoveries not only furnish invaluable insights into selecting suitable chemotherapy and targeted drugs following the risk score of HNSCC patients afflicted with HNSCC but also facilitate informed clinical therapeutic deliberations.
3.14 Immunofluorescence detection of MEF2B+ B cells
To validate the existence of MEF2B + B cells, immunofluorescence assays were executed on three human HNSCC tissues (Figure S3-S5). In the multiple immunofluorescence assay, MEF2B and CD79a were subjected to staining. A conspicuous co-localization was discerned between MEF2B+ cells and CD79a+ cells, thereby affirming the presence of MEF2B+ B cells. To further elucidate the interplay among MEF2B+ B cells, CD8+ T cells, and tumor cells. Strikingly, spatial co-localization between CD79a and MEF2B in CD8+ and EPCAM+ regions was identified, decisively establishing a substantial correlation between MEF2B+ B cells and CD8+ T cells and tumor cells.
3.15 In vitro experiment
Based on the cell communication analysis findings using cellchat between MEF2B+ B cells and tumor cells, we conducted in vitro knockdown experiments targeting the PLXNB2 gene in the head and neck cancer cell line. The results, illustrated in Fig. 14A and B, manifest a conspicuous reduction in the proliferation capacity of both cell lines, discernibly contrasting with the control group. Furthermore, plate cloning experiments unveiled a pronounced suppression in both the quantity and size of cell colonies after the knockdown of the PLXNB2 gene in both cell lines (Fig. 14C, D). The inhibitory impact of PLXNB2 knockdown on the proliferation of the two head and neck cancer cell lines was further substantiated, indirectly suggesting that the interaction between B cells and tumor cells can modulate the biological functions of head and neck cancer cells.
Subsequent wound healing experiments showcased a statistically significant decrease in the cell migration rate within the PLXNB2 knockdown group (Fig. 14E, F). Moreover, Transwell experiments demonstrated a marked reduction in the number of cells penetrating the lower chamber following PLXNB2 gene knockout, indicative of a substantial inhibition of the migration and invasion capabilities of head and neck cancer cells (Fig. 14G, H)