Pancancer landscape analysis of the thymosin family identified TMSB10 as a potential prognostic biomarker and immunotherapy target in glioma

Pancancer genomic alteration landscape, expression pattern, prognostic significance, and immunological correlation of the thymosin family

To investigate the genomic characteristics of the TMSs in cancer, we first showed the location of thymosin genes on chromosomes, and four of them were located on the sex chromosomes (Fig. 1A). We then calculated the somatic mutation (truncating and missense) and CNA (including amplification and deep deletion) frequencies in a pancancer cohort of 10,953 patients with 32 cancer types. The overall DNA alteration frequency of thymosin family genes was less than 2%, which is relatively low for cancers. PTMS had the highest mutation frequency (1.7%), while TMSB4Y a very low mutation frequency. Moreover, TMSB15A and TMSB15A showed a trend of coamplification or codeletion (Fig. 1B). At the genomic level, CNA, rather than gene mutation, is the primary cause of dysregulation of the thymosin gene in cancer. Subsequently, we showed the distribution of the amplification, deep deletion and mutation frequencies in different cancers. We found that among cancers, several cancer types [such as adrenocortical carcinoma (ACC), mesothelioma (MESO), acute myeloid leukemia (LAML), thyoma (THYM) and uveal melanoma (UVM)] have relatively low mutation frequencies for these genes (Fig. 1C). To assess the expression patterns of these 7 TMSs in cancer tissues, we obtained sample data for 19 cancer types with at least 3 matching tumor and normal samples from the TCGA database and found that these 7 TMSs showed heterogeneous expression patterns across cancer types (Fig. 1D). To further explore the association of thymosin genes with survival, we performed Cox regression analysis for all 7 individual thymosin genes and showed the distribution of hazard ratios (HRs) across 33 cancer types via a heatmap. As shown in Fig. 1E, TMSB10 had significant prognostic value in the largest number of cancer types, acting as an adverse prognostic factor in nine cancer types.

Fig. 1figure 1

Pancancer genomic alteration landscape, expression pattern, prognostic significance, and immunological correlation of the thymosin family. A The location of TMSs on 23 chromosomes. B Landscape of genomic aberrations in the TMSs in cancer. Each row represents a gene, and each column represents a patient. Only patients with genomic alterations in the indicated genes are shown. Alteration rates per TMS gene are displayed on the left. C Distribution of (up) mutation and (middle and down) CNA frequencies over cancer types. The darkness of color is proportional to the frequency. D Differential expression of TMS genes in 19 different cancer types. FC and p values were obtained by comparing normal tissue with the corresponding tumor tissue. Color is displayed only when P value < 0.05. Red indicates upregulation, while blue indicates downregulation. E Summary of Cox regression correlation of TMSs with survival. Color is displayed only when P value < 0.05. Red indicates worse survival. F Correlation between TMSB10 expression and (up) immune score, as well as (down) stromal score, across 33 cancer types. G Differences in the TMSs between six different immune molecular subtypes. The Kruskal–Wallis test was used to determine the significance of differences between the six immune molecular subtypes. H Box plot showing the distribution of sample-specific pathway scores across 33 cancer types. The median, interquartile range, and outliers are indicated. I The expression of TMSB10 between GTEx normal tissues and tumor tissues. The statistical significance is indicated as follows: ns > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001

To explore the roles in the immune system and tumor microenvironment, we also investigated the relationship between TMSs and the immune/stromal score, calculated via an estimation algorithm. The expression of TMSB10 and TMSB4X was positively correlated with the immune/stromal score in most cancer types (Fig. 1F). Differences in the expression of thymosin genes between different immune subtypes were further investigated [18]. Expression was significantly different between the C1 (wound healing), C2 (IFN-γ dominant, inflammatory), C3 (lymphocyte depleted), C4 (lymphocyte depletion), C5 (immunologically quiet) and C6 (TGF-β dominant) subtypes, which are characterized by differences in macrophage or lymphocyte signatures. As shown in Fig. 1G, TMSB10 and TMSB4X expression were higher in patients with the C2 and C6 subtypes, which indicate a poorer prognosis, suggesting that TMSB10 and TMSB4X might be involved in tumor immune deregulation.

Analysis of expression data from the Cancer Cell Line Encyclopedia (CCLE) and Genotype-Tissue Expression (GTEx) databases revealed that TMSs were also expressed in various cancer cells (Additional file 1: Fig. S1 A–G) and normal tissues (Additional file 1: Fig. S2 A–G). Furthermore, we found that the expression of TMSB10 in the tumor tissues was the highest among these seven TMSs, and TMSB15A, TMSB4Y and TMSB15B showed relatively low expression (Fig. 1H). In paired pancancer samples for 33 cancer types, TMSB10 expression was found to be significantly higher in tumor tissues than in their corresponding normal tissues in all cancers except for KICH (Fig. 1I), while other genes showed heterogeneous expression patterns (Additional file 1: Fig. S3 A–F), suggesting that TMSB10 might play vital roles in regulating cancer hallmarks and the immune response in cancers. Notably, the expression and function of TMSB10 across cancers, including its involvement in immune-related processes, remain largely unknown. Therefore, we selected TMSB10 as a candidate gene for further study.

Pancancer expression pattern and biological and immunomodulatory function of TMSB10

We found that TMSB10 was positively correlated with the enrichment scores of typical cancer hallmarks, the inflammatory response and tumor malignancy markers (PCNA, a proliferation index, and VIM and CD44, markers of invasion) in a wide range of cancer types, especially in lower grade glioma (LGG), glioblastoma (GBM), bladder cancer (BLCA) and liver hepatocellular carcinoma (LIHC) (Fig. 2A). Furthermore, since our results showed that TMSB10 was associated with immunophenotype and inflammatory responses in various tumors, we also focused on its role in immune regulation pathways. Interestingly, we found that a large number of immune-related pathways were positively correlated with TMSB10 in a variety of cancers, such as GBM, LGG, sarcoma (SARC), breast invasive carcinoma (BRCA), skin cutaneous melanoma (SKCM), kidney renal clear cell carcinoma (KIRC), thyroid carcinoma (THCA) and pheochromocytoma and paraganglioma (PCPG) (Fig. 2B). In addition, we observed a statistically positive correlation between TMSB10 expression and the enrichment score of cancer-associated fibroblasts (CAFs), calculated using the EPIC, xCell and MCP-counter algorithms (Fig. 2C), and immunomodulatory molecule analysis (Fig. 2D) in LGG, GBM, LIHC, testicular germ cell tumor (TGCT) and THCA. Cancer-specific univariate prognostic analysis also revealed a significant correlation between TMSB10 and overall survival in multiple cancer types, including ACC, GBM, KIRC, LGG, LIHC, lung adenocarcinoma (LUAD), MESO, pancreatic adenocarcinoma (PAAD) and UVM (Fig. 1E). After a systematic analysis of the relationship between TMSB10 expression, prognosis, biological function and immune regulation, we found a significant relationship between TMSB10 and these variables in gliomas (both in LGG and GBM), suggesting that TMSB10 may be a suitable candidate therapeutic target in glioma.

Fig. 2figure 2

Pancancer expression pattern and biological and immunomodulatory function of TMSB10. A (Top) The distribution of sample-specific TMSB10 expression across each cancer type; (middle) bubble plots showing the correlation between TMSB10 and classical cancer pathways or genes. The color of the circle represents the correlation coefficient: red indicates a positive correlation, and blue indicates a negative correlation. (Down) The scatter plot shows the correlation between TMSB10 and classical cancer pathways or genes in LGG. Correlation between TMSB10 and B 28 tumor-associated immune cells calculated with the ssGSEA algorithm and correlation with (C) CAFs and D immunomodulators (immunoinhibitory, immunostimulatory and MHC molecules). The color indicates the correlation coefficient. The asterisks indicate a statistically significant p value calculated using Spearman correlation analysis. The statistical significance is indicated as follows: *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001

Expression signature and prognosis value of TMSB10 in glioma

Considering the heterogeneity of different grades of gliomas, we compared the expression levels of TMSB10 in different grades of gliomas in the Chinese Glioma Genome Atlas (CGGA), TCGA and Gravendeel datasets and found that TMSB10 expression was positively correlated with tumor grade, and the expression level was highest in GBM (WHO grade IV glioma) (Fig. 3A). Kaplan–Meier survival curve analysis showed that the prognosis of LGG and GBM patients with high WEE2-AS1 expression was significantly poorer than that of those with low expression in all three glioma datasets (Fig. 3B). Since biological heterogeneity exists between GBM and LGG, we further investigated the prognostic value of TMSB10 in three other GBM datasets. Similarly, we found that the prognosis of patients with high TMSB10 mRNA and protein expression was significantly worse than that of patients with low expression (Additional file 1: Fig. S4 A). Moreover, receiver operating characteristic (ROC) curve analysis of the CGGA, TCGA and Gravendeel datasets showed that TMSB10 expression level could predict glioma survival with AUC values of 0.796, 0.808 and 0.770, respectively, and the survival prediction performance was the better than that of other clinicopathological factors in all three datasets (Fig. 3C). Considering that the prognosis of glioma patients is influenced by various factors, we performed univariate Cox regression analysis to further evaluate the prognostic value of TMSB10 in glioma patients in the CGGA, TCGA and Gravendeel datasets. As shown in Fig. 3D, TMSB10 expression, glioma grade, age, and IDH mutation status were significantly associated with OS. Consequently, we performed multivariate Cox regression analysis and found that the expression level of TMSB10 was an independent prognostic factor for glioma patients in the CGGA, TCGA and Gravendeel cohorts (Fig. 3D). These results suggested that TMSB10 may be a novel prognostic biomarker for glioma.

Fig. 3figure 3

Expression signature and prognostic value of TMSB10 in glioma. A The mRNA expression level of TMSB10 increased with tumor grade in the (left) CGGA, (middle) TCGA and (right) Gravendeel datasets. B Kaplan–Meier curves for the OS of (upper) LGG and (lower) GBM patients with high TMSB10 expression and low TMSB10 expression in three glioma datasets; the log-rank test was used to calculate the p value. C ROC curve showing that, among other clinical characteristics, the expression level of TMSB10 had the largest AUC in predicting the survival rate of glioma patients in three glioma datasets. D Univariate and multivariate analyses of TMSB10 expression and clinicopathological characteristics in three glioma datasets

Biological pathways and immunological characteristics of TMSB10 in glioma

To further investigate the biological function of TMSB10 in glioma, we performed gene set variation analysis (GSVA) to identify cancer hallmarks that are closely associated with TMSB10 expression. The results showed that carcinogenic processes such as inflammatory responses, stromal activation, cell proliferation and invasion-related pathways were positively correlated with TMSB10 expression in both LGG and GBM in these three glioma datasets (Fig. 4A). Subsequent analysis of TME cell infiltration, calculated by the single-sample gene set enrichment analysis (ssGSEA) algorithm, showed that TMSB10 was positively correlated with the abundance of most immune cells (Fig. 4B). In addition, TMSB10 was also positively correlated with the expression of most immunomodulatory molecules in these three glioma datasets (Fig. 4C). We also showed similar results in the other three GBM datasets (Additional file 1: Fig. S4B, C). Gene set enrichment analysis (GSEA) showed that pathways involved in tumor pathogenesis, including cell proliferation regulation pathways (such as cell cycle checkpoint and apoptosis), pathways associated with tumor invasion and migration (such as focal adhesion and ECM-receptor interaction), and classical signaling pathways, including p53 signaling and PI3K AKT mTOR signaling pathways and immune-related signatures, were significantly enriched in the high TMSB10 expression group (Fig. 4D). In addition, Metascape database [19] analysis revealed that genes with a significant positive association with TMSB10 (Additional file 2: Table S1, Pearson r > 0.3, P < 0.05) were significantly enriched in pathways related to the regulation of cell biological functions, stromal activation and immunity (Additional file 1: Fig. S4D). Further PaGenBase [20] enrichment analysis showed that these genes were mainly specifically expressed in peripheral immune organs such as the spleen, blood and bone marrow (Additional file 1: Fig. S4E).

Fig. 4figure 4

Biological pathways and immunological characteristics of TMSB10 in glioma. A Spearman correlation analysis of TMSB10 and classical signaling pathways in three glioma cohorts. Red indicates positive correlations, and the darkness of color is proportional to the correlation coefficient. The size of the circle represents the statistical P value, with larger circles representing greater statistical significance. Correlation between TMSB10 expression and B the infiltration of 28 tumor-associated TME cells calculated with the ssGSEA algorithm and C immunomodulators. The color indicates the correlation coefficient. The asterisks indicate a statistically significant p value calculated using Spearman correlation analysis. D GSEA showing the (left) classical cancer-promoting pathways and (right) immune-related pathways in the high TMSB10 expression group. E Dot plot of the log2FC (mRNA expression) versus the log2FC (protein expression), showing a positive correlation between the overall mRNA and protein expression level (Pearson’s r = 0.6527) and the distribution of genes with significant changes in both the mRNA expression (|FC| > 2, P < 0.05) and corresponding protein expression (|FC| > 1.2, P < 0.05) in the high TMSB10 expression group compared with the low TMSB10 expression group. F GO BP enrichment analysis of 93 genes that were significantly upregulated at both the mRNA and protein levels. The statistical significance is shown as P < 0.05; **P < 0.01; ***P < 0.001

Further joint analysis of the protein and RNA-seq data revealed a positive correlation between the differentially expressed mRNAs and proteins; 93 mRNA/protein were simultaneously upregulated, and 26 mRNA/protein were simultaneously downregulated (Fig. 4E, Additional file 2: Table S2). Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis showed that the genes upregulated at both levels were mainly enriched in some classical oncogenic pathways, metabolic pathways and immune response-related pathways (Additional file 1: Fig. S5A). Further enrichment analysis by gene ontology (GO) database showed that these genes were significantly enriched in the following terms: immune cell migration, angiogenesis, stromal remodeling, macrophage activation and ERK signaling pathways (Fig. 4F). Overall, these results revealed that TMSB10 could be a potential biomarker for alterations in TME cell infiltration and carcinogenic pathways in glioma.

Multiomics regulatory profile of TMSB10 in glioma

To better understand the determinants of glioma evolution and treatment resistance, we then analyzed the differences in somatic mutation distribution between the high and low TMSB10 expression groups in the TCGA-LGG and GBM cohorts and found that the rate of IDH mutation (low: 90%, high: 63%), a factor associated with a better prognosis, was obviously decreased in the high-TMSB10 group compared to the low-TMSB10 group in the TCGA-LGG cohort. For GBM samples, the mutation rates of PTEN (low: 24%, high: 30%) and NF1 (low: 8%, high: 10%) were notably higher in the high expression group than in the TMSB10 low expression group (Fig. 5A). Similar results were also shown in another GBM dataset. Additionally, BRAF1 mutation, which causes activation of the downstream MAPK signaling pathway, also occurred at a higher rate in the high expression group (Additional file 1: Fig. S5B).

Fig. 5figure 5

Multiomics regulatory profile of TMSB10 in glioma. A waterfall plot of the tumor somatic mutation landscape in the low-TMSB10 and high-TMSB10 samples in the TCGA-LGG (left) and GBM (right) datasets. Each bar represents the mutation information for an individual patient. The top bar plot shows TMB, and the numbers on the right indicate the mutation frequency of each gene. The bar plot on the right shows the proportion of each mutation type. B KEGG enrichment analysis of proteins with significantly different phosphorylation levels in GBM samples with high TMSB10 expression. The right represents the upregulated pathway, and the left represents the downregulated pathway. C Heatmap showing somatic mutation-based alterations in specific proteins and their downstream protein phosphorylation sites. Yellow represents a high level, and blue represents a low level. D KEGG enrichment analysis of proteins with significantly different acetylation levels in GBM samples with high TMSB10 expression. The right represents the upregulated pathway, and the left represents the downregulated pathway. E Interaction of TMSB10-associated miRNAs, mRNA, protein and transcription factor (TF). Upregulated mRNAs (claybank bars in the upper panel) and proteins (blue bars in the lower panel) in TMSB10-high GBM were positively correlated (purple lines) with the expression of 19 TFs (Pearson R > 0.5, p < 0.05). Downregulated miRNAs (red bars, left panel) negatively correlated (green lines) with TF expression (Pearson R< − 0.3, p < 0.05). The targeted pathways of TFs are listed in the panel to the right. The blue line indicates miRNA-targeted mRNAs

Exploring the panoramic map of phosphorylomic and acetylationomic differences between the high and low TMSB10 expression groups may provide more valuable clues to explore the mechanism of malignant progression of glioma. The analysis of differentially phosphorylated proteins showed that phosphorylation was markedly upregulated at 1146 phosphorylation sites of 590 proteins and markedly downregulated at 498 phosphorylation sites of 265 proteins in the high-TMSB10 group (Additional file 2:  Table S3, |FC| > 1.2, adj.P.Val < 0.05). KEGG enrichment analysis showed that proteins with upregulated phosphorylation were significantly enriched in oncogenic signaling pathways, stromal activation pathways and immune-related signaling pathways, while downregulated proteins were mainly involved in metabolism-related pathways, the Rap1 signaling pathway, and so on (Fig. 5B). Further analysis of the combined protein quantification data showed a significant positive correlation between phosphorylation levels and protein expression levels (Additional file 1: Fig. S5C, Additional file 2: Table S4). Analysis of the mutation distribution in the TCGA and Wang datasets showed that the PTEN (which inhibited AKT signaling activation) and NF1 (a mesenchymal (MES) subtype GBM marker) mutation rates were increased in both datasets, and BRAF (which could cause MAPK downstream pathway activation) mutation rates were obviously increased in the Wang dataset (Fig. 5A, Additional file 1: Fig. S5 A). We next explored the specific signaling pathways induced by these three mutations and the altered phosphorylation of their downstream proteins. As shown in the heatmap, the expression levels of the tumor suppressor proteins NF1 and BRAF tended to decrease with increasing TMSB10 mRNA expression, and we also observed a gradual increase in the phosphorylation levels of downstream signaling pathway proteins (Fig. 5C). These results support that TMSB10 contributes to the development of GBM through the dysregulation of protein expression and protein phosphorylation.

The proteome-level differential acetylation analysis showed that compared to the low-TMSB10 group, the high group showed significantly upregulated acetylation at 139 acetylation sites in 93 proteins and significantly downregulated acetylation at 38 acetylation sites in 28 proteins (Additional file 2: Table S5, FC > 1.2, adj.P.Val < 0.05). KEGG functional annotation analysis showed that proteins with different acetylation levels were mainly enriched in metabolic-related signaling pathways, HIF-1 signaling pathways and immune-related signaling pathways (Fig. 5D). Further correlation analysis with proteomics data also showed that the acetylation levels were positively correlated with the protein expression levels (Additional file 1: Fig. S5D, Additional file 2: Table S6).

To further investigate the effect of TMSB10 on miRNA expression, we identified 3 differentially downregulated miRNAs (miR-9-5p, miR-181c-5p and miR-125b-5p) in high TMSB10 and low-TMSB10 GBM samples in the Wang dataset. The mRNA and protein expression levels of multiple target genes of these three miRNAs were significantly upregulated in the TMSB high group. Further correlation and functional enrichment analysis revealed multiple transcription factors (TFs) that were negatively correlated with the expression of these three miRNAs, positively correlated with the expression of their target genes, and significantly enriched in immune regulation, stromal activation and MAPK signaling pathways (Fig. 5E). Taken together, our findings suggest that TMSB10 affects the regulatory network of glioma at multiple levels, providing a comprehensive overview of how TMSB10 affects glioma pathogenesis.

TMSB10 expression is associated with glioma pathological malignant progression and immune phenotype in the Qilu dataset

In our cohort, TMSB10 positively correlated with glioma grade, and the results showed that the GBM samples had the highest TMSB10 expression (Fig. 6A). ROC analysis for TMSB10 expression and glioma grade showed that the AUC was 0.929 (Fig. 6B), suggesting that TMSB10 has the potential to serve as a biomarker for predicting glioma grade. Furthermore, IHC analysis of our local glioma clinical samples validated that TMSB10 expression was highest in GBM samples (Fig. 6C), consistent with the results of the database analyses. To explore the relationship between biological functions and TMSB10, GSVA analysis was conducted using the glioma samples in our cohort. The results showed that the TMSB10-high group demonstrated a marked enrichment of immune-related pathways, stromal activation pathways, and pathways in cancer (Additional file 1: Fig. S6A). GSEA also showed that these functional gene set signatures were enriched in TMSB10-high glioma samples (Additional file 1: Fig. S6B). For TME immune regulation in our cohort, we found that TMSB10-high group samples have higher immune/stromal score, lower tumor purity, and remarkably higher innate immune cell scores (Additional file 1: Fig. S6C). Further Pearson correlation analysis also showed that TMSB10 was positively correlated with these cancer hallmarks and related immune cell scores (Fig. 6D), paralleling the above results. We then performed Pearson correlation analysis to identify the genes that tightly correlated with TMSB10 expression (Pearson |R| > 0.3, P < 0.05 Additional file 2: Table S7). Further gene enrichment analysis using Cytoscape dayabase revealed that genes that were significantly positively associated with TMSB10 were significantly enriched in immune-related, stromal activation-related and cancer malignant progression-related signaling pathways (Additional file 1: Fig. 6D). Further PaGenBase database analysis also showed that these genes were mainly specifically expressed in peripheral immune organs such as the spleen, blood and bone marrow (Additional file 1: Fig. S6E), similar to the above results.

Fig. 6figure 6

TMSB10 promotes the proliferation, migration and invasion of glioma cells in vitro and in vivo. A The mRNA expression level of TMSB10 increased with tumor grade in the Qilu dataset. B ROC curve showing that the expression level of TMSB10 had a high AUC in predicting the grade of glioma in the Qilu dataset. C IHC analysis showing TMSB10 protein expression in normal brain tissues (NBTs) and glioma tissues with different WHO grades. Histogram representing statistical data of IHC. The FTO-positive ratio was defined as the ratio of the FTO-positive area to the total area; n = 3. D Correlations between TMSB10 and the enrichment scores of (left) cancer hallmark pathways and (right) immune cells in the Qilu cohort. CCK-8 assays showing the proliferation ability of GBM cells transfected with (E) sh-NC or sh-TMSB10 and (F) ov-NC or ov-TMSB10; n = 3. G Representative Transwell migration and invasion assays showing the migration and invasion ability of GBM cells transfected with (G) sh-NC or sh-TMSB10 and (H) ov-NC or ov-TMSB10; scale bar, 200 μm. The quantification histogram represents the relative cell numbers. Data represent the mean ± SD from at least three independent experiments. I Representative tumor sphere formation images of GSCs transfected with sh-NC or sh-TMSB10; scale bar, 100 μm. The quantification histogram represents the average sphere diameter. Data represent the mean ± SD from at least three independent experiments. J Bioluminescent image showing the tumor size of mice implanted with luciferase-labeled U87MG cells expressing sh-TMSB10 or sh-NC at the indicated times. The quantification histogram represents the bioluminescent flux. Data represent the mean ± SD; n = 5 for each group. K Kaplan–Meier survival curves for mice implanted with luciferase-labeled U87MG cells expressing sh-TMSB10 or sh-NC. Log-rank analysis was used; n = 5 for each group. L Representative CD44 and KI67 immunohistochemistry images for a subgroup of animals sacrificed simultaneously in each group; n = 5 for each group, scale bar, 20 μm. M Representative H&E staining images for a subgroup of animals sacrificed simultaneously in each group; n = 5 for each group, scale bar, 10 μm. All data are presented as the mean ± SD. The statistical significance is shown as *P < 0.05; **P < 0.01; ****P < 0.0001

TMSB10 promotes the proliferation, migration and invasion of glioma cells in vitro and in vivo

To validate the function of TMSB10 in glioma proliferation, metastasis and invasion, we performed several related experiments in vitro and in vivo. We assessed the TMSB10 expression level of the two primary cell lines and other cell lines and found there was no remarkable difference among these cell lines (Additional file 1: Fig. S8D). The results showed that knockdown of TMSB10 significantly inhibited the proliferation of U87MG and U251 GBM cells (Fig. 6E, Additional file 1: Fig. S7A–C), as detected by Cell Counting Kit-8 (CCK-8), colony formation and EdU assays. Overexpression of TMSB10 remarkably promoted these cellular behaviors in GBM cells (Fig. 6F, Additional file 1: Fig. S7D). Moreover, Transwell and wound healing assays showed that knockdown of TMSB10 impaired the migration and invasion abilities of U87MG and U251 GBM cells (Fig. 6G, Additional file 1: Fig. S7E), while overexpression of TMSB10 notably promoted these cellular behaviors (Fig. 6H). Western blot results also showed that knockdown of TMSB10 increased the expression of P21 (a tumor suppressor protein) but decreased the expression of carcinogenic proteins involved in cell proliferation and invasion and the result was consistent in mice tumor samples (Additional file 1: Fig. S7F). Flow cytometric analysis showed that TMSB10 knockdown could induce apoptosis.and G1/S arrest in glioma cells (Additional file 1: Fig. S8A–C). Glioma stem cells (GSCs) are considered to be the drivers of GBM growth and progression [21]. We then performed neurosphere formation assays of GSC20 and GSC267 GSCs, which showed that knockdown of TMSB10 significantly inhibited the tumorsphere expansion ability (Fig. 6I).

Additionally, in vivo experiments also showed that the downregulation of TMSB10 significantly reduced tumor size and prolonged the survival time of tumor-bearing mice (Fig. 6J, K). Subsequently, immunohistochemical (IHC) results showed that compared with the NC group, the expression of Ki67 (proliferation marker) and CD44 (invasion marker) in tumor tissue sections was lower in the TMSB10 knockdown group (Fig. 6L). H&E staining also showed that the invasive ability of tumors following TMSB10 knockdown was weaker than that of the NC group (Fig. 6M). In conclusion, these results suggested that TMSB10 promotes cell proliferation, migration and invasion, playing oncogenic roles in glioma.

TMSB10 promotes GBM MES transformation and facilitates macrophage infiltration

The TCGA program uses bulk RNA sequencing to identify GBM into three subtypes: proneural (PN), classical (CL) subtypes and MES subtypes. The MES subtype, accompanied by NF1 mutations, which are more common among TMSB10-high glioma samples (Fig. 5A, Additional file 1: Fig. S5A), was correlated with an abundance of macrophages, stromal activation and the worst prognosis [22, 23]. Our data showed that both the mRNA and protein expression of TMSB10 was highest in the MES GBM subtype in all three GBM datasets (Fig. 7A). Pearson correlation analysis also showed that TMSB10 was positively correlated with immune checkpoints, macrophage-related immunosuppressive molecules and stromal-related genes in six public glioma datasets and the Qilu dataset (Fig. 7B, Additional file 1: Fig. S8A, B). Further correlation analysis also showed that TMSB10 was significantly positively correlated with CD44 expression, a MES marker gene (Additional file 1: Fig. S8B). Additionally, GSEA showed that the MES gene signature was significantly enriched in TMSB10-high GBM samples (Fig. 7C, Additional file 1: Fig. S8A), implying that TMSB10 may promote the MES transformation of GBM. Chen et al. [24] found that PTEN deficiency, which was also upregulated in TMSB10-high GBM samples, could increase macrophage infiltration via the YAP1-LOX-β1 integrin-PYK2 axis, and the infiltrated macrophages in turn secreted SPP1 to support GBM survival. Our GSEA also showed that YAP1 signaling (CORDENONSI_YAP_CONSERVED_SIGNATURE) was significantly enriched in the TMSB10-high samples (Fig. 7B, Additional file 1: Fig. S8C). Furthermore, our Western blot assays verified that TMSB10 promoted the protein expression of MES-related genes and YAP1 and activated AKT and MAPK signaling (Fig. 7C), consistent with the above results, prompting us to conclude that TMSB10 may be associated with the infiltration and immunosuppressive transformation of macrophages. TAMs, including tumor-associated bone marrow-derived macrophages (BMDMs) and brain-resident microglial cells (MGs), are the most abundant cell population in the glioma TME, [25, 26]. We found that TMSB10 was positively correlated with the enrichment of tumor-associated BMDMs (hereinafter referred to as macrophages) but negatively correlated with the enrichment of tumor-associated MGs (Figs. 5B and 6D), suggesting that TMSB10 is involved in the recruitment of macrophages. To further confirm the role of TMSB10 in promoting macrophage migration and immunosuppressive transformation, we found that conditioned medium (CM) from TMSB10-overexpressing GBM cells significantly promoted THP-1-differentiated macrophage migration and significantly upregulated the protein expression of CD163 and SPP1, two well-known macrophage activation markers [24, 26,27,28], compared to that in the NC group, while knockdown of TMSB10 significantly inhibited these effects (Fig. 7D–G). Moreover, we then performed an immunofluorescence staining assay with our local clinical glioma patient tissues, and the results showed that the expression of CD68 (a human macrophage marker) and SPP1 (a key inflammatory protein produced by TAMs that express the pattern recognition receptor MARCO and part of the largest group of proteins with cell type-specific expression) [29], was enhanced in TMSB10-high glioma tissues compared to TMSB10-low tissues (Fig. 7H). Overall, these results suggested that TMSB10 promotes macrophage infiltration, thereby enabling macrophages to acquire angiogenic and immunosuppressive properties, highlighting its potential as a therapeutic target.

Fig. 7figure 7

TMSB10 promotes GBM MES transformation and facilitates macrophage infiltration. A The mRNA or protein expression level of TMSB10 in different subtypes of GBM. B Correlation between TMSB10 and suppressive immunomodulators. The color indicates the correlation coefficient. GSEA showing the C MES signature and D CORDENONSI_YAP_CONSERVED_SIGNATURE in the high TMSB10 expression group in the TCGA GBM cohort. E Western blot assays showing the protein expression of CD44, YAP1, LOX, as well as phosphorylation levels of

留言 (0)

沒有登入
gif