Abstract
Background: Prostate cancer (PRAD) poses a significant threat to male health. The tumor microenvironment (TME) plays a crucial role in its development process, yet the regulatory significance of specific extracellular matrix proteins such as Dermatopontin (DPT) in PRAD remains poorly understood. Methods: A total of 534 PRAD transcriptome profiles were retrieved from The Cancer Genome Atlas (TCGA) database. CIBERSORT and ESTIMATE computational methods were used to quantify the presence of immune and stromal components. Differentially expressed genes (DEGs) were identified based on ImmuneScore and StromalScore, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) pathway enrichment analyses. DPT expression was analyzed in relation to overall survival, TNM staging, immune-related pathways using Gene Set Enrichment Analysis (GSEA), and tumor-infiltrating immune cells (TICs). Results: A total of 454 DEGs overlapping between high ImmuneScore and StromalScore groups were enriched in immune-related processes and pathways. DPT expression was positively correlated with the survival of PRAD patients, especially the N Stage of PRAD. GSEA revealed that high DPT expression correlated with immune-related activities such as allograft rejection, apical junction, complement, and epithelial mesenchymal transition while low DPT expression was correlated with metabolic pathways such as E2f targets, G2m checkpoint, mitotic spindle, and mitorc1 signaling. Analysis of TICs showed that DPT expression was positively correlated with resting mast cells and neutrophils. Conversely, regulatory T cells, M1 macrophages, M2 macrophages, and resting dendritic cells exhibited negative correlations with DPT expression. Conclusions: DPT may serve as a novel prognostic biomarker in PRAD, potentially affecting the survival of PRAD patients by regulating the immune environment of TME. These findings provide new insights into the immunomodulatory role of DPT and its potential as a therapeutic target for PRAD.
Keywords: DPT, tumor microenvironment, immune infiltration, prostate cancer
Introduction
Prostate cancer (PRAD) is one of the most prevalent malignancies among men worldwide and ranks as the second leading cause of cancer-related mortality, following lung cancer [1,2]. Although many cases progress slowly, advanced PRAD can metastasize to the lymph nodes and bone, significantly compromising patient prognosis [3]. Despite progress in surgery, radiotherapy, and androgen deprivation therapy [4], treatment resistance and disease recurrence remain major clinical challenges. Consequently, the identification of reliable biomarkers for early detection, risk stratification, and immunotherapy prediction has become a priority in PRAD research.
Growing evidence suggests that tumor microenvironment (TME) - a complex milieu comprising immune cells, stromal components, extracellular matrix (ECM), and soluble factors, plays a central role in cancer progression and immune evasion [5-9]. Tumor-infiltrating immune cells (TICs), as key elements of TME, are known to influence therapeutic response and clinical outcome in PRAD [10-12]. Nevertheless, unlike other immunogenic tumors, PRAD exhibits a relatively immunosuppressive TME, often resulting in limited response to immune checkpoint blockade therapies [13-15]. While several computational algorithms such as ESTIMATE and CIBERSORT have been used to quantify immune and stromal cell proportions in tumors, the immunoregulatory roles of specific TME-associated genes remain insufficiently characterized in PRAD.
Dermatopontin (DPT), a small extracellular matrix protein, has been implicated in cell adhesion, matrix remodeling, and immune regulation in various cancer types, including breast and liver cancers [16,17]. However, its role in the immune landscape of PRAD is largely unexplored. In this study, we systematically analyzed transcriptomic data from the Cancer Genome Atlas (TCGA)-PRAD cohort to evaluate the prognostic value and immunological relevance of DPT. By integrating immune scoring, gene set enrichment analysis (GSEA), and immune cell deconvolution via CIBERSORT, we aimed to elucidate the correlation between DPT expression, TICs, and clinical outcomes. Our findings suggest that DPT may serve as a novel prognostic biomarker and a potential modulator of immune infiltration in TME of PRAD.
Materials and methods
Raw data acquisition
We obtained transcriptome RNA-seq data from TCGA database (https://portal.gdc.cancer.gov/) for our study on PRAD patients [18]. A total of 534 PRAD cases were included, consisting of 51 normal samples and 483 tumor samples. Additionally, corresponding clinical data such as stage, TNM classification, survival and outcome were also downloaded from TCGA database [19].
Analysis of ImmuneScore, StromalScore and ESTIMATEScore
Based on the gene expression data of the TCGA-PRAD cohort, the ESTIMATE algorithm was used to calculate the ImmuneScore, StromalScore and ESTIMATEScore to characterize TME of PRAD using R package estimate [20]. Higher scores in ImmuneScore or StromalScore indicate a greater abundance of immune or stromal components in TME, respectively. ESTIMATEScore, derived by summing ImmuneScore and StromalScore, represents the combined proportion of both components in TME.
Survival analysis
For survival analysis, we utilized R language with the survival and survminer packages. A total of 483 tumor samples had available records of detailed survival time, spanning from 0 to 13.8 years. To plot the survival curve, we employed the Kaplan-Meier method. Additionally, we used the log-rank test as the statistical significance test to evaluate the differences in survival outcomes.
DEGs between high-score and low-score groups
We categorized the 483 tumor samples into high-score or low-score groups based on the median score of ImmuneScore and StromalScore. Subsequently, limma package was used to identify differentially expressed genes (DEGs) between high-score and low-score groups. Specifically, the DEGs with fold changes greater than 1 and a false discovery rate (FDR) less than 0.05, following log2 transformation of the expression levels in the high and low score groups, were considered statistically significant. Additionally, heatmaps of the DEGs were generated using R package pheatmap to visualise the expression patterns of these genes across different samples.
GO and KEGG enrichment analysis
As previously described [21-23], Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis were performed on 454 DEGs. These analysis were carried out in R with the assistance of packages such as clusterProfiler, enrichplot, and ggplot2 [24]. Significantly enriched terms were identified based on both P-value and q-value thresholds less than 0.05.
Difference analysis of clinical stages
The clinicopathological characteristics data corresponding to the PRAD samples were obtained from TCGA. The Wilcoxon rank sum test or the Kruskal-Wallis rank sum test was used to assess differences in different clinical stages of PRAD patients, providing valuable insights into the associations between clinical variables and molecular profiles.
Gene set enrichment analysis
We downloaded the Hallmark and C7 gene sets v6.2 collections from the Molecular Signatures Database to serve as the target sets for GSEA as previously described [25,26], GSEA enrichment analysis was performed using the R package clusterpofiler. GSVA scores were generated for all tumor samples based on transcriptomic data. Gene sets with a nominal P-value (NOM P-value) less than 0.05 and a FDR q-value less than 0.06 were considered statistically significant.
TICs profile
The CIBERSORT computational method was utilized to estimate the TICs abundance profile in all tumor samples. Subsequently, a quality filtering step was implemented, where only tumor samples with a P-value less than 0.05 were retained for further analysis. This rigorous selection criterion ensured that only samples with reliable estimations of immune cell abundance were included in the subsequent analysis, enhancing the robustness and accuracy of the findings related to the tumor immune microenvironment.
Statistical analysis
Wilcoxon test and one-way analysis of variance were used to compare the DPT expression level of PRAD in cancer tissue and normal tissue. For survival comparisons between high and low DPT expression, the Logrank test will be used. P<0.05 was considered statistically significant.
Results
Analysis process of this study
The analysis process of our study is shown in Figure 1. Firstly, we retrieved 534 transcriptome RNA-seq data from the TCGA database and processed them using the CIBERSORT and ESTIMATE algorithms to calculate Immune and Stromal scores. Subsequently, we utilized the Gene profile analysis and functional enrichment analysis in PRAD samples. Lastly, our focus shifted towards survival analysis, clinicopathological correlation analysis, Cox regression, GSEA and correlation with TICs specifically for DPT.
Figure 1.
Analysis workflow of this study.
Analysis of ImmuneScore, StromalScore and ESTIMATEScore
All PRAD samples were divided into high and low score groups based on the median of each score (ImmuneScore, StromalScore, EstimatScore). To assess the correlation between the proportions of Immune and Stromal components and survival outcomes, Kaplan-Meier survival analysis was conducted on ImmuneScore, StromalScore, and ESTIMATEScore. As shown in Figure 2A-C, ImmuneScore, StromalScore and ESTIMATEScore did not exhibit significant correlations with overall survival rate.
Figure 2.
Correlation of scores with the survival of patients with PRAD. A. Kaplan-Meyer survival analysis for PRAD patients divided by ImmuneScore into high or low score groups (P=0.77, log-rank test). B. Kaplan-Meyer survival analysis for PRAD patients divided by StromalScore into high or low score groups (P=0.413, log-rank test). C. Kaplan-Meyer survival analysis for PRAD patients with high and low ESTIMATEScore (P=0.394, log-rank test).
Gene profile analysis and functional enrichment analysis
As shown in Figure 3A, 3B, we found that compared to the median, ImmuneScore displayed 1122 DEGs in both high and low scoring samples. Among them, 1089 genes were upregulated and 33 genes were downregulated. Similarly, StromalScore analysis generated 1277 DEGs, of which 1236 genes were upregulated and 41 genes were downregulated (Figure 3A, 3B). The Venn plot intersection analysis showed that 442 identical genes were up-regulated in the ImmuneScore and StromalScore groups, while 12 identical genes were down-regulated. Furthermore, GO enrichment analysis highlighted that the DEGs predominantly mapped to immune-related GO terms, encompassing functions such as leukocyte cell-cell adhesion, leukocyte proliferation, lymphocyte proliferation, mononuclear cell proliferation, and regulation of T cell activation (Figure 3C). Additionally, KEGG enrichment analysis underscored the enrichment of pathways including cell adhesion molecules, cytokine-cytokine receptor interaction, hematopoietic cell lineage, intestinal immune network for IgA production, and viral protein interaction with cytokine and cytokine receptor (Figure 3D).
Figure 3.
Screening of DEGs and enrichment analysis of GO and KEGG. A, B. Venn plots showing common up-regulated and down-regulated DEGs shared by ImmuneScore and StromalScore. C, D. GO and KEGG enrichment analysis for 455 DEGs, terms with p and q<0.05 were considered to be enriched significantly.
The correlation of DPT expression with the survival and TNM stages
Survival analysis showed that PRAD patients with high DPT expression had longer overall survival than did PRAD patients with low DPT expression (Figure 4C). Furthermore, the Wilcoxon rank sum test demonstrated that the expression of DPT in tumor samples was significantly lower than that in normal samples (Figure 4A). Similar results were observed in the paired analysis between normal and tumor tissues from the same patient (Figure 4B). In addition, there was a significant correlation between DPT expression and the N Stage of PRAD (Figure 4D).
Figure 4.
The differentiated expression of DPT in samples and correlation with survival and clinicopathological staging characteristics of PRAD patients. A. Differentiated expression of DPT in the normal and tumor samples. B. Paired differentiation analysis for expression of DPT in the normal and tumor samples deriving from the same one patient. C. Survival analysis for PRAD patients with high and low DPT expression (P=0.026, log-rank test). D. The correlation of DPT expression with clinicopathological staging characteristics.
GSEA enrichment analysis
Figure 5A indicated that genes in the DPT high-expression group are primarily enriched in immune-related activities, such as allograft rejection, apical junction, complement, and epithelial mesenchymal transition. Conversely, genes in the DPT low-expression group are enriched in E2f targets, G2m checkpoint, mitotic spindle, and mitorc1 signaling, as depicted in Figure 5B. Additionally, when considering the C7 collection defined by MSigDB, immune-related gene sets were found to be enriched in the high DPT expression group, including macrophage, B cell, and CD4+ T cell pathways (Figure 5C). On the other hand, only a few gene sets were enriched in the low DPT expression group, such as CD8+ T cell (Figure 5D).
Figure 5.
GSEA for samples with high DPT expression and low expression. A. The enriched gene sets in HALLMARK collection by the high DPT expression PRAD sample. Each line representing one particular gene set with unique color. Only gene sets with NOM P<0.05 and FDR q<0.06 were considered significant. B. Enriched gene sets in HALLMARK by PRAD samples with low DPT expression. C. Enriched immunologic gene sets in C7 collection by high DPT expression PRAD patients. D. Enriched gene sets in C7 collection by the low DPT expression PRAD patients.
Correlation of DPT with the proportion of TICs
We analyzed the proportion of TICs subsets in PRAD samples using the CIBERSORT algorithm, constructing immune cell profiles for 21 distinct immune cell types (Figure 6). The results from the difference and correlation analysis revealed that a total of 6 TICs types were correlated with the expression of DPT (Figure 7). Among these TICs, the expression of DPT demonstrated positive correlations with 2 types of TICs, including resting mast cells and neutrophils. Conversely, 4 TICs types exhibited negative correlations with DPT expression, comprising regulatory T cells, M1 macrophages, M2 macrophages, and resting dendritic cells.
Figure 6.
TICs profile in PRAD samples and correlation analysis. A. Barplot showing the proportion of TICs in PRAD samples. Column names of plot were sample ID. B. Heatmap showing the correlation between 21 kinds of TICs and numeric in each tiny box indicating the p value of correlation between two kinds of cells.
Figure 7.
Correlation of TICs proportion with DPT expression. A. Violin plot showed the ratio differences of 21 kinds of immune cells between PRAD samples with low or high DPT expression, and Wilcoxon rank sum was used for the significance test. B. Scatter plot showed the correlation of 6 kinds of TICs proportion with the DPT expression (P<0.001), and Pearson coefficient was used for the correlation test. C. Venn plot displayed 17 kinds of TICs correlated with DPT expression codetermined by difference and correlation tests displayed in violin and scatter plots, respectively.
Discussion
In recent years, increasing attention has been directed toward the role of TME in PRAD progression and therapeutic resistance. As a dynamic ecosystem composed of immune cells, stromal cells, ECM, and signaling molecules, the TME not only shapes tumor behavior but also influences treatment outcomes, particularly in the context of immunotherapy.
TME plays a crucial role in the progression of tumors. The influence of TME extends to the metabolic reprogramming of cancer cells. The interaction between cancer cells and stromal fibroblasts can enhance the expression of metabolic enzymes, promoting a glycolytic switch that supports tumor growth [27]. The regulatory T cells (Tregs) in TME help tumor survival and progression through IL-2/IL-2 receptor-dependent and CTLA-4-dependent mechanisms [28]. However, the immunological characteristics of TME in PRAD remain incompletely understood, and effective immune biomarkers are still lacking. In this study, we identified DPT as a TME-associated molecule with potential prognostic value and regulatory relevance in PRAD.
Our results demonstrated that DPT expression is significantly decreased in tumor tissues compared to normal prostate tissues, and its low expression was associated with shorter overall survival. These findings suggest that DPT may function as a tumor suppressor in prostate cancer. Importantly, GSEA analysis revealed that high DPT expression was positively correlated with immune-related signaling pathways, including allograft rejection, complement activation, and apical junction pathways - implying that DPT may contribute to an immunologically active TME. In contrast, low DPT expression was associated with proliferative and metabolic pathways such as E2F targets and G2M checkpoint signaling, indicating a potential shift toward immune escape and tumor aggressiveness. These findings suggest that DPT may play a tumor suppressive role in PRAD through immune regulation of TME. From a mechanistic perspective, DPT is known to participate in ECM organization and cell adhesion, potentially influencing immune cell trafficking and spatial architecture within TME. Previous studies in melanoma, hepatocellular carcinoma, and breast cancer have indicated that DPT may regulate macrophage polarization, inhibit tumor growth, and modulate drug resistance through ECM-immune cell interactions [16,17,29-31].
Immune cell infiltration is a critical prognostic factor in solid tumors, including PRAD. The presence and activity of different TICs can significantly influence tumor behavior, disease progression, and response to therapy. Our study demonstrates that high levels of DPT expression are significantly associated with several positively correlated TICs, including dormant mast cells and neutrophils. However, its mechanism of the immune infiltration of patients with PRAD is still unclear. Studies have shown mast cells play a complex role in tumor immunity, promoting local immune responses and potentially improving TME [32]. In PRAD, the presence of dormant mast cells may correlate with enhanced immune surveillance, contributing to better overall survival [33]. Neutrophils are also believed to have protective roles in tumor immunity by activating T cells through the release of reactive oxygen species and cytokines [34]. Therefore, the positive correlation between high DPT levels and these TICs likely reflects a favorable immune environment that enhances patient survival. Conversely, our findings reveal significant associations between DPT expression and various negatively correlated TICs, including Tregs, M1 and M2 macrophages, and dormant dendritic cells. While Tregs are essential for maintaining immune tolerance, their increased presence often correlates with the suppression of effective anti-tumor immune responses, leading to disease progression [35,36]. Typically, high levels of Tregs infiltration in tumors are associated with immune evasion and poorer clinical outcomes [37]. Although M1 macrophages are usually associated with anti-tumor immunity, the negative correlation of DPT in PRAD may indicate that higher DPT levels help inhibit M1 polarization, thus avoiding a detrimental imbalance in anti-tumor immune responses [38]. Additionally, M2 macrophages are known for promoting tumor progression [39]. Thus, the negative correlation between DPT and M2 macrophage infiltration may suggest that elevated DPT levels reduce M2 activity, promoting a more favorable tumor prognosis [40]. The decrease in dormant dendritic cells, which are crucial for T cell activation, alongside increased DPT levels, may indicate a shift toward a lower immunogenic environment [41]. Therefore, the inverse relationship between DPT and immunosuppressive cell types further supports its role in maintaining a favorable immune microenvironment.
Nevertheless, this study is not without limitations. First, all analyses were conducted based on bulk RNA-seq data from TCGA, which may obscure spatial and cellular heterogeneity within TME. Second, the findings remain correlative in nature, and functional validation - such as knockdown or overexpression experiments in vitro and in vivo - is required to confirm the mechanistic roles of DPT in modulating immune cell infiltration and tumor progression. Third, the cohort lacked detailed treatment records, limiting our ability to assess the predictive value of DPT for therapeutic response.
In conclusion, our findings highlight DPT as a potential prognostic biomarker and immunomodulator in PRAD. By influencing immune cell infiltration and reshaping TME, DPT may offer new avenues for stratifying patients and designing immunotherapy strategies. Future studies incorporating single-cell analysis, spatial transcriptomics, and experimental validation will be essential to fully delineate the functional role of DPT and translate these findings into clinical practice.
Acknowledgements
We thank all the families for participating in this research project. This work was supported by Science Foundation of Jiangsu Province Grant BK20240371; National Science Foundation of China Grants 81901632, and the Jiangsu Province College Students’ Innovation and Entrepreneurship Training Program Project (202410285273Y).
Disclosure of conflict of interest
None.
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