PTGDS is a potential marker for lung adenocarcinoma identified in a pancancer analysis
Runzhi Wang, Fengling Shao, Deng Liu, Jun Chen, Qin Zhou
TL;DR
This study finds that PTGDS is a potential marker for lung adenocarcinoma and plays a role in cancer progression and immunity across various tumor types.
Contribution
The study identifies PTGDS as a tumor suppressor in lung adenocarcinoma and explores its pancancer implications.
Findings
PTGDS is dysregulated across various cancers and correlates with patient survival.
PTGDS influences tumor immunity and is associated with immune cell types like natural killer cells and macrophages.
PTGDS inhibits lung adenocarcinoma cell proliferation through fatty acid degradation and cell cycle regulation.
Abstract
Prostaglandin D2 synthase (PTGDS) is the enzyme responsible for synthesizing prostaglandin D2. Despite its crucial role, PTGDS remains relatively understudied in tumor therapy, and comprehensive pancancer analyses are lacking. By leveraging multiomics data integration, this study elucidated the widespread dysregulation of PTGDS across various cancers and its correlation with patient survival. Moreover, PTGDS is significantly associated with stem cell scores, microenvironmental scores, microsatellite instability (MSI) status, tumor mutational burden (TMB), and methylation status in diverse tumor types. Additionally, immune correlations with PTGDS are evident across most cancers, with single-cell transcriptome data indicating predominant associations with natural killer cells and macrophages. Notably, experimental validation revealed the role of PTGDS in inhibiting A549 and H1975 lung…
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Figure 8- —http://dx.doi.org/10.13039/501100001809National Natural Science Foundation of China
- —National Key R&D Program from Ministry of Science and Technology of China
- —https://doi.org/10.13039/501100012166National Key Research and Development Program of China
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Taxonomy
TopicsFerroptosis and cancer prognosis · Cancer, Lipids, and Metabolism · Cancer Immunotherapy and Biomarkers
Introduction
PTGDS (prostaglandin D2 synthase) was initially classified within the lipocalin family and is located in the HSA9q34 region of the human genome^1^. Consequently, early investigations focused primarily on prostaglandin D2 (PGD2), which is derived from prostaglandin H2 (PGH2) via PTGDS catalysis^2,3^. Concurrently, genetic variations in PTGDS have been observed across various nonneoplastic diseases^4–6^. Recent studies have increasingly underscored the pivotal role of PTGDS in cancer. For example, PTGDS was found to promote diffuse large B-cell lymphoma tumorigenesis by regulating the MYH9-mediated Wnt β–catenin STAT3 signaling pathway^7^. Additionally, its downregulation by miRNA-155 leads to prostaglandin reprogramming in breast cancer^8^. PTGDS has also been implicated in counteracting the tumor-promoting effects of YAP in gastric cancer, inhibiting the proliferation and migration of breast cancer cells^9,10^, and participating in the progression of cervical squamous cell carcinoma along with SNX10^11^.
The tumor immune microenvironment (TME) critically influences cancer progression and the therapeutic response. Recent advances in multiomics analysis, exemplified by tools such as the iMLGAM platform^12^, have demonstrated how comprehensive TME profiling can predict immunotherapy outcomes by evaluating immune cell infiltration patterns. Notably, such approaches have identified key regulators, such as CEP55, that modulate T-cell responses, paralleling our interest in the potential immune regulatory functions of PTGDS. Despite these insights, the comprehensive assessment of PTGDS gene expression in tumorigenesis and its prognostic implications across various cancer types remains unexplored.
The advancement of next-generation sequencing (NGS) in recent years has revolutionized clinical cancer management^13^. Notably, data acquired through initiatives such as The Cancer Genome Atlas (TCGA) project have fueled cross-tumor research endeavors^14^. These efforts aim to identify novel prognostic molecules and therapeutic targets via multiomics analysis (including whole transcriptomics, proteomics, and single-cell transcriptomics) of tumor specimens from diverse cancer cohorts^15,16^. For example, leveraging TCGA data, researchers have probed the association between LRRC4C and the tumor immune microenvironment in colon and gastric cancers, alongside its clinical prognostic relevance^17^. Bai et al. investigated the prognostic and immunological significance of FAM72A across multiple cancers, substantiated by functional assays^18^. Furthermore, transcriptomic data can delineate immune infiltration status and tumor mutation burden, serving as pivotal immunotherapy biomarkers and prognostic indicators for patients^19,20^, underscoring the importance of investigating PTGDS within the tumor immune microenvironment, an area that remains largely unexplored to date^21,22^.
Overall, current research on PTGDS remains limited. Therefore, the primary objective of this study was to analyze PTGDS transcriptional levels across 33 cancer types to clarify its potential role. By integrating multiple databases and multi-omics approaches—including TCGA, GTEx, the Clinical Proteomics Tumor Analysis Consortium (CPTAC), and UCSC Xena—we examined aberrant expression, prognostic significance, methylation patterns, potential clinical associations, tumor mutational and immune landscapes, and functional roles of PTGDS in cancer. To strengthen the evidence, we further focused on lung adenocarcinoma for an in-depth analysis of single-cell transcriptomic data and validated the findings through functional experiments, demonstrating that PTGDS suppresses tumor cell proliferation in lung adenocarcinoma cell lines.
Results
Transcriptional and protein expression levels of PTGDS across cancers
Through the integration and analysis of data from the TCGA, GTEx, CPTAC, and HPA databases, we acquired a comprehensive understanding of PTGDS (ENSG00000107317) expression across various cancers. Transcriptionally, relative to normal tissues, PTGDS was significantly downregulated in 21 common malignancies, including LUAD, KIRC, and LIHC (Fig. 1A, Fig. S1A). Upon expanding the sample size of normal tissues, PTGDS was consistently underexpressed in 30 tumors, with OV and PAAD showing notable exceptions with high expression levels (Fig. 1B). Proteomic data from CPTAC revealed low expression of PTGDS in lung adenocarcinoma and breast cancer, albeit without statistical significance in other tumor types (Fig. 1C). Immunohistochemical analysis using the HPA revealed a consistently low expression pattern of PTGDS in digestive tract tumors, including gastric cancer, liver cancer, kidney cancer, and colorectal cancer (Fig. 1D). Additionally, PTGDS was predominantly expressed at low levels in lung cancer (Fig. 1E, Fig. S1B).
Fig. 1. Differential expression of PTGDS in pancancer. (A) Analysis of PTGDS expression in tumor tissues compared with normal tissues across various cancers in the TCGA database. (B) Examination of aberrant PTGDS expression in diverse tumors with additional samples from the GTEx database. (C) Assessment of PTGDS expression abnormalities at the protein level, utilizing data from the CPTAC database. (D,** E)** Histological images depicting PTGDS expression in various tumors and their corresponding normal tissues retrieved from the HPA database. Ns, p ≥ 0.05; *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.
Pancancer analysis of the clinical parameter correlations of the PTGDS
Survival analysis spans four domains—overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), and progression-free survival (PFS)—emphasizing the prognostic relevance of PTGDS across multiple cancers. Cox regression modeling revealed high PTGDS expression as a risk factor for shorter OS in KIPAN, KIRC, and STAD patients, yet it conferred protective effects in the GBMLGG, LUAD, LGG, CESC, HNSC, and DLBC cohorts (Fig. 2A). The Kaplan–Meier (KM) survival curves further illustrated the diagnostic potential of PTGDS across these cancer types (Fig. 2B). Moreover, PTGDS expression was significantly correlated with DSS in various cancers, including KIPAN, KIRC, STAD, COAD, GBMLGG, LUAD, LGG, PRAD, DLBC, CESCH, and HNSC (Fig. 2C). Notably, elevated PTGDS expression was associated with an increased risk of shorter disease-free survival (DFS) in patients with KIRP and KIPAN but demonstrated protective effects in patients with CHOL and BLCA (Fig. S2A). In terms of PFS, PTGDS was associated with a poor outcome in patients with KIPAN, STAD, and KIRP but exhibited protective effects in patients with GBMLGG, LUAD, PRAD, DLBC, HNSC, MESO, CHOL, and LGG (Fig. 2D). The expression pattern of PTGDS appears to be associated with a poor prognosis in kidney cancer, but with a good prognosis in lung and brain tumors. This differential expression warrants further investigation.
Fig. 2. Pancancer analysis of the prognostic significance of PTGDS.** (A)** Univariate Cox regression analysis assessing the correlation between PTGDS transcript levels and patient overall survival (OS) across cancers. (B) Receiver operating characteristic (ROC) curve analysis illustrating the performance of PTGDS across cancers. (C) Univariate Cox regression analysis examining the associations between PTGDS transcript levels and patient disease-specific survival across cancers. (D) Univariate Cox regression analysis examining the associations between PTGDS transcript levels and progression-free interval (PFI) across cancers.*p < 0.01; **p < 0.001.
We further investigated the associations between PTGDS and various clinical and pathological parameters across different cancer types, including T (tumor), N (node), and M (metastasis) stages; overall stage; and age. PTGDS was associated with 12 tumor types, including LUAD, BRCA, STES, and KIRP (Fig. S2B). However, lymph node metastasis was linked to PTGDS in only six tumor types (Fig. S2C). Additionally, PTGDS expression was closely correlated with the stages of LUAD, STES, KIRP, KIPAN, STAD, KIRC, and BLCA (Fig. S2D).
Genomic diversity and correlation with the tumor stemness of PTGDS in pan-cancer
We further investigated the role of PTGDS in tumorigenesis by examining its genomic variation across diverse cancer types. Using comprehensive copy number variation (CNA) data from the cBioPortal database and the extensive sample collection from the “Pancancer Analysis of Whole Genomes” project of the TCGA, we analyzed 2,682 samples from 2,565 patients. Our analysis revealed that diploidy was the predominant mutation type of PTGDS across cancers, closely followed by shallow deletion (Fig. 3A). Notably, PTGDS mutations were most common in hepatobiliary cancer, followed by pancreatic cancer (Fig. 3B). Additionally, we identified COAD, ESCA, UCEC, and ACC as cancer types with notably high PTGDS mutation rates (Fig. 3C).
Fig. 3. Gene variations in PTGDS across cancers, genomic heterogeneity, and its correlation with tumor stemness. (A-C) Comprehensive analysis of PTGDS mutation types (A), distributions (B), and frequencies (C) across various cancer types. (D-F) Correlation analysis examining the relationships between PTGDS and tumor mutational burden (TMB) (D), microsatellite instability (MSI) (E), and tumor stemness (F) across cancers.
We also explored the genomic heterogeneity of PTGDS across cancers and its correlation with tumor stemness. The associations of tumor mutational burden (TMB) and microsatellite instability (MSI) with tumor aggressiveness and prognosis have been established^23–25^. Our correlation analysis revealed a significant positive correlation between PTGDS and TMB in KIPAN, whereas a notable negative correlation was observed in 14 tumor types, including GBM, LUAD, and STES (Fig. 3D). Similarly, PTGDS was positively correlated with MSI in GBM and LGG but significantly negatively correlated with 10 tumor types, such as ESCA and SARC, but not with LUAD (Fig. 3E).
Given that tumor growth often depends on cancer stem cells^26^, we examined the tumor stemness index (DNAss) derived from mRNA expression and methylation signatures^27^. Spearman correlation analysis revealed a significant positive association between PTGDS and tumor stemness in three tumor types: KIRP, THCA, and UVM. Conversely, a significant negative correlation was observed in 18 tumor types, including GBMLGG, LUAD, COAD, and BRCA (Fig. 3F).
PTGDS association with immune infiltration and checkpoints
We investigated the impact of PTGDS on the tumor microenvironment (TME) by examining its correlation with immune infiltration levels across various cancers (Fig. 4A–C, Fig. S3A–C). We observed consistent positive correlations between PTGDS and the three immune scores. PTGDS was positively correlated with the stromal score in 16 cancers, including STES, STAD, LUSC, THCA, and BLCA (Fig. 4A, Fig. S3A). Additionally, PTGDS expression in 15 cancers, such as LUAD, BRCA, STES, HNSC, and LUSC, was significantly correlated with the immune score (Fig. 4B, Fig. S3B). Similarly, the ESTIMATEScore indicated a positive correlation between PTGDS expression and immune infiltration in CES, READ, BRCA, LUAD, and PAAD (Fig. 4C, Fig. S3C) (r > 0.3, p < 0.05). Further analysis of potential associations between PTGDS and 60 immune checkpoint genes revealed the top correlations in THCA, GBMLGG, KIPAN, BRCA, and LUAD. PTGDS was positively correlated with most immune checkpoint genes, except in GBMLGG (Fig. S4A). Moreover, we assessed the associations of PTGDS with 150 immune pathways and 44 RNA modification marker genes across different cancers. Notably, BRCA, KIPAN, LIHC, THCA, and LUAD emerged as the top five cancers closely related to immune regulatory genes. PTGDS displayed positive associations with immune regulatory genes in most cancers, except for GBMLGG and LGG (Fig. S4B).
Fig. 4. Pancancer analysis of immune cell infiltration and immune correlates of PTGDS. (A-C) Correlations of PTGDS expression with the stromal score (A), immune score (B), and ESTIMATEScore (C) in LUAD. (D-F) Utilizing the TIMER (D), EPIC (E), and QUANTISEQ (F) algorithms, the associations between PTGDS and immune infiltration in patients with cancer were examined. The top 10 cancer types with the highest correlations are shown. Significance levels are denoted as follows: *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.
Interaction of PTGDS with immune cells
To evaluate the relationship between PTGDS expression and immune cell infiltration across various cancers, we utilized four established algorithms (TIMER, EPIC, QUANTISEQ, and CIBERSORT) to calculate a pancancer immune score. Our analysis revealed significant associations between PTGDS expression and immune cell infiltration in most cancers, particularly CD4 + T cells, macrophages, and B cells, which are closely linked to PTGDS expression levels (Fig. 4D–F, Fig. S5 A–D). Additionally, data from the Human Protein Atlas (HPA) revealed PTGDS-specific expression in normal immune cells, notably in plasmacytoid dendritic cells and natural killer cells (Fig. S5E–G). Consistently, across all four algorithms, PTGDS was positively correlated with immune cell infiltration in malignant tumors such as LUAD, BRCA, and LUSC, contributing to the establishment of an immunosuppressive microenvironment (Fig. 4D–F, Fig.S5A–D). While slight variations were observed among the results of the four algorithms, PTGDS consistently exhibited robust positive or significant negative correlations with cancers such as STES, STAD, HNSC, COAD, and SKCM (Fig. 4D–F, Fig. S5A–D).
Single cell sequencing reveals PTGDS expression in specific cell types
Given the specific performance of PTGDS in LUAD (e.g., consistency in transcriptional differences, prognostic efficacy, immune infiltration-related aspects, and genomic heterogeneity) (Figs. 1, 2, 3 and 4), especially in the red box, we further utilized single-cell sequencing data from CancerSEA, we examined the correlation between PTGDS and functional status across 14 cancers. PTGDS was predominantly negatively correlated with DNA damage repair and the cell cycle across most tumors, whereas it was positively associated with inflammation (Fig. 5A). Notably, in LUAD, PTGDS was positively correlated with key biological behaviors, including angiogenesis and differentiation (Fig. 5A, B). Further analysis of the single-cell atlas from the Human Protein Atlas (HPA) database revealed predominant PTGDS expression in fibroblasts, endothelial cells, B cells, and T cells in normal lung (Fig. 5C, D). Additionally, exploration of the normal human lung single-cell atlas by Kyle et al., accessible using the UCSC Cell Browser, highlighted specific PTGDS expression in lipofibroblasts, adventitial fibroblasts, alveolar fibroblasts, natural killer cells, arteries, and veins (Fig. 5E, F).
Fig. 5. Single-cell data analysis of PTGDS expression and biological functions. (A) Correlation of PTGDS expression with functional status across 12 cancers according to the pancancer analysis. (B) Positive correlation between PTGDS expression and angiogenesis and differentiation in lung adenocarcinoma (LUAD). (C) PTGDS expression in the single-cell atlas of the normal human lung from the HPA database. (D) Relationships between PTGDS expression and the expression of various cell marker genes. (E) Single-cell atlas of the normal human lung. (F) PTGDS-specific expression in the single-cell atlas of the normal human lung as reported by Kyle et al.
Integrated analysis of PTGDS methylation and functional enrichment in LUAD
Methylation serves as a critical hallmark distinguishing tumor tissue from its normal counterparts, shaping the genetic landscape of cancer cells and impacting human longevity. Its potential in both tumor diagnosis and therapeutic interventions is substantial. Therefore, we comprehensively investigated the interplay between PTGDS methylation and tumor progression, with a particular focus on LUAD. While PTGDS displayed negative associations with immune regulatory genes in GBMLGG and LGG, it predominantly exhibited positive associations in most other cancer types. Notably, within LUAD, 27 RNA-modifying genes, including DNMT3A, DNMT3B, and LRPPRC, were closely associated with PTGDS. Notably, the negative correlation of LRPPRC with PTGDS appeared consistent across various cancer types, indicating a robust relationship (Fig. S6A).
Using data from the MethSurv platform, we subsequently identified eight methylation sites within the DNA sequence of PTGDS. Intriguingly, utilizing the MEXPRESS tool, we found that five methylation sites (cg18502630, cg02156769, cg13796381, cg13602921, and cg13561390) were positively correlated with PTGDS expression levels (Fig. S6B, C). Finally, our analysis revealed higher PTGDS methylation levels in LUAD tissues than in normal tissues (Fig. S6D-E).
To elucidate the underlying mechanisms, PTGDS was analyzed using the STRING database. This analysis generated an interaction network between PTGDS and its related genes (Fig. 6A), which was subsequently subjected to KEGG and GO functional enrichment analyses to uncover potential pathways. These analyses revealed significant enrichment of PTGDS and its associated genes in arachidonic acid metabolism (Fig. 6B). The subsequent Gene Ontology Biological Process (GO, BP) terms highlighted pathways such as the cyclooxygenase pathway, prostaglandin biosynthetic process, and proteinoid biosynthetic process. Additionally, GO cellular component (GO, CC) terms suggested localization in the endoplasmic reticulum and organelle subcompartments, with involvement in activities such as intramolecular oxidoreductases and isomerases (Fig. 6C–E).
Fig. 6. Functional enrichment analysis of PTGDS. (A) Interaction network of PTGDS and its related genes obtained from the STRING database. (B) KEGG functional enrichment analysis of PTGDS and its associated genes. (C-E) GO functional enrichment analysis of PTGDS and its associated genes. (F) Gene set enrichment analysis (GSEA) of PTGDS in the TCGA-LUAD cohort.
Moreover, leveraging the TCGA LUAD cohort for gene set enrichment analysis (GSEA), we stratified the LUAD cohort into high- and low-PTGDS expression groups. Interestingly, the genes in the low-expression group were enriched in pathways associated with autoimmune thyroid disease and the hematopoietic cell lineage. Oncological features enriched in this group included the Notch, KRAS, and VEGF pathways. The top biological process (BP) terms included adaptive immune response, regulation of cell activation, and B-cell proliferation, whereas the cellular component (CC) terms included receptor complexes and plasma membrane protein complexes. The main enriched molecular function (MF) terms were molecular transducer activity and immune receptor activity (Fig. 6F).
Potential regulation of PTGDS by miR-3944 in LUAD
To investigate potential miRNAs regulating PTGDS, we analyzed LUAD miRNA data. Differential analysis between normal and tumor tissues revealed 364 differentially expressed miRNAs (DEMs), including 81 downregulated and 283 upregulated DEMs (Fig. 7A, B). We subsequently used the scan database to predict miRNAs that target PTGDS and intersected the resulting DEM list, identifying 11 potential miRNAs (Fig. 7C). Survival analysis highlighted the diagnostic efficacy of miR-3944, miR-296, and miR-542 (Fig. 7D). Spearman’s correlation analysis revealed that miR-3944, miR-5090, miR-939, miR-6777, miR-3622, and miR-4758 were associated with PTGDS, with miR-3944 showing diagnostic efficacy in LUAD and a negative correlation with PTGDS (Fig. 7E). These findings suggest a potential regulatory role of miR-3944 on PTGDS in LUAD.
Fig. 7. Identification of potential miRNAs that target PTGDS.(A) Volcano plot illustrating aberrantly expressed miRNAs in LUAD. (B) Clustering heatmap displaying the top 40 aberrantly expressed miRNAs in LUAD. (C) Kaplan‒Meier analysis of differentially expressed miRNAs (DEMs). (D) Spearman correlation analysis between DEMs and PTGDS.
Effects of PTGDS on metabolism, proliferation, cell cycle, and apoptosis in A549 cells
To explore the biological function of PTGDS in LUAD, we employed the GSVA scoring algorithm to assess its correlation with various biological pathways. Our analysis revealed a positive association between PTGDS and several metabolism-related pathways, including alpha linolenic acid metabolism, arachidonic acid metabolism, fatty acid degradation, glycerophospholipid metabolism, linoleic acid metabolism, and the citrate cycle (Fig. S7A). Additionally, PTGDS was negatively correlated with DNA repair, DNA replication, and the G2M checkpoint pathway but positively correlated with apoptosis (Fig. S7B–D).
The cellular function of PTGDS was further investigated using GFP-PTGDS or siRNA. The gene regulatory efficiency of these was confirmed by Western blotting (Fig. 8A–B). PTGDS overexpression led to increased levels of PPARG, HADH, LDHA, and ACAT1 proteins, while decreasing levels of p-ACLY, ACC, and ACSL1 proteins, indicating a causal relationship between PTGDS and fatty acid degradation (Fig. 8C). Furthermore, PTGDS overexpression also affected the expression of glycolysis-related proteins (Fig. 8D). Regarding cell cycle regulation, PTGDS overexpression inhibited the expression of CDC2 and CCND1, resulting in an increase in the number of cells in G2 and S phases, while decreasing the number of cells in G1 phase (Fig. 8E-F). Although the protein levels of caspase-3 and caspase-9 were increased, no change in the proportion of apoptotic cells was detected by flow cytometry (Fig. 8G-H). The proliferative regulatory capacity of PTGDS was examined using CCK-8 and colony formation assays. The results showed that overexpression of PTGDS inhibited the proliferation of lung adenocarcinoma cell lines (A549 and H1975) (Fig. 8I-L). Conversely, inhibition of PTGDS expression promoted the proliferation of lung adenocarcinoma cells (Fig. 8M-P).
Fig. 8. Cellular biological functions of PTGDS in lung adenocarcinoma cells. (A) Western blot analysis confirmed overexpression of GFP-PTGDS in A549 cells. (B) Western blot analysis confirmed the inhibitory efficiency of siRNA in A549 cells. (C-E) Western blot analysis showed changes in GFP-PTGDS-induced lipid metabolism (C), glycolysis (D), and cell cycle (E) related proteins. (F) Flow cytometry analysis of A549 cell cycle phases using propidium iodide staining. The figure shows the percentage of cells at different cell cycle phases; error bars represent mean ± standard deviation (n = 3). (G) Western blot analysis showed changes in the expression of GFP-PTGDS-induced cell cycle and apoptosis-related proteins. (H) Flow cytometry analysis of apoptosis in A549 cells. (I,** J)** The CCK-8 assay kit was used to evaluate the effect of GFP-PTGDS on the proliferation of A549 (I) and H1975 cells (J). (K,** L)** A colony formation assay was used to evaluate the effect of GFP-PTGDS on the proliferation of A549 (K) and H1975 cells (L). (M,** N)** The CCK-8 assay kit was used to evaluate the effect of si-PTGDS#3 on the proliferation of A549 (M) and H1975 cells (N). (O,** P)** A colony formation assay was used to evaluate the effect of si-PTGDS#3 on the proliferation of A549 (O) and H1975 cells (P). Note: *p < 0.05; **p < 0.01; ***p < 0.001; ns; no statistical significance.
Discussion
PTGDS (prostaglandin D2 synthase) is a glutathione-independent enzyme pivotal in catalyzing the conversion of prostaglandin H2 to prostaglandin D2, a molecule implicated in smooth muscle contraction/relaxation and potent inhibition of platelet aggregation^28^. Moreover, PTGDS is important for the maturation and maintenance of the central nervous system and male reproductive system^28,29^. While prior investigations linked PTGDS to breast cancer susceptibility and testicular, lymphoma, and gastric cancer development, comprehensive pancancer analyses of PTGDS as a potential therapeutic target are currently lacking^3,7,30,31^.
This study revealed that PTGDS is abnormally underexpressed across most cancers, both transcriptionally and at the protein level. Cox regression and Kaplan‒Meier analyses, which assessed four prognostic indicators (OS, DSS, DFS, and PFS), suggest that PTGDS is a promising biomarker, especially for lung adenocarcinoma (LUAD) and kidney renal clear cell carcinoma (KIRC). Notably, PTGDS exhibited divergent prognostic implications across tumor types: it was associated with unfavorable outcomes in clear cell renal cell carcinoma but acted as a protective factor in lung adenocarcinoma. These opposing patterns may reflect tissue-specific dependencies on lipid metabolic pathways, as well as differential activation of the arachidonic acid–PGD2 signaling axis across malignancies^32–34^. Additionally, we noted age- and sex-related variations in PTGDS expression across certain cancer types, offering valuable insights for tailoring treatment strategies on the basis of patient demographics. As tumor mutational burden (TMB) and microsatellite instability (MSI) have emerged as pivotal predictive biomarkers in pancancer research^35–38^, our findings revealed a negative correlation between PTGDS expression and TMB in 14 cancer types and between PTGDS expression and MSI in 18 cancers^37,38^. The lack of associations between PTGDS expression and both tumor mutational burden (TMB) and microsatellite instability (MSI) suggest its potential as a predictive biomarker for immunotherapy response, mirroring the clinical utility of multiomics biomarkers such as the iMLGAM, which stratify patients on the basis of immune microenvironment characteristics^12^.
In addition, the distinct immune microenvironmental landscapes of different tumor types—particularly variations in NK-cell abundance, macrophage composition, and their functional states—may further modulate how PTGDS shapes immune responses through PGD2-mediated signaling^39–41^. For example, heterogeneity in M1/M2 macrophage polarization, the extent of NK-cell exhaustion, and tumor-specific immunometabolic programs can markedly influence both the magnitude and direction of PGD2 signaling effects^41,42^. Furthermore, in single-cell atlases of normal human lungs, PTGDS appear to be enriched in fibroblasts, which may be related to the body’s immune and immune responses^43,44^. In LUAD, fibroblast abnormalities typically indicate tumor microenvironment remodeling, leading to immune resistance and tumor metastasis in LUAD patients^45–47^. Therefore, PTGDS may also affect LUAD through fibroblasts.
The tumor immune microenvironment (TME) plays a pivotal role in therapeutic resistance and disease progression^48^. Its intricate landscape presents significant challenges for realizing the full potential of immunotherapies across diverse tumor types^49,50^. Notably, PTGDS is positively correlated with stromal and immune cell contents in the TME of most cancers. Further single-cell transcriptomic analyses revealed a positive association between PTGDS expression and the immune infiltration of natural killer (NK) cells and macrophages within lung adenocarcinoma. With respect to NK cells, PTGDS does not appear to exert direct regulatory effects on NK-cell function. Instead, its influence is likely mediated through modulation of PGD2 signaling, which in turn governs NK-cell cytotoxicity. This pathway is primarily mediated by the PGD2 receptor PTGDR expressed on NK/T cells and facilitated by the endothelial transporter SLCO2A1 responsible for PGD2 uptake and trafficking^51,52^. In contrast, the regulatory effects of PTGDS on macrophages may involve direct cellular mechanisms. Both in vivo and in vitro studies have shown that PTGDS overexpression can effectively suppress M2 macrophage polarization^53^, potentially through mechanisms involving the modulation of PD-L1 expression, attenuation of macrophage migratory capacity, and inhibition of M2-polarizing programs^54^. While the role of PTGDS in cancer metabolic reprogramming has garnered increasing attention^55,56^, our findings suggest its potential impact on the tumor immune status in LUAD through metabolic remodeling^57^. Furthermore, coexpression analysis revealed significant associations between PTGDS and key immune regulatory genes, including immune checkpoint molecules, mirroring findings observed in immune-excluded tumors (e.g., B4GALT2-mediated CD8 + T-cell exclusion)^58^. These results suggest that PTGDS may play a critical role in modulating immune evasion mechanisms, positioning it as a potential candidate regulator. This is particularly relevant in lung adenocarcinoma (LUAD), where current therapeutic strategies remain limited by low response rates and a lack of reliable predictive biomarkers^48^.
Enrichment analyses suggest that PTGDS may modulate the tumor immune microenvironment through metabolic remodeling, as indicated by its links to the cyclooxygenase pathway, endoplasmic reticulum function, and adaptive immune response^52^. Investigation of PTGDS promoter methylation revealed aberrant sites in cancer^11^, while experimental data demonstrated that PTGDS can influence the cell cycle, notably by suppressing CDC2 expression^59^. Bioinformatic analyses further highlight its associations with the G2/M checkpoint and DNA damage repair, indicating a potential role in maintaining genomic integrity by preventing the progression of cells with DNA damage into mitosis. From a translational perspective, assessing PTGDS expression in patient samples could provide clinical utility. While tumor tissue directly reflects intratumoral PTGDS activity, plasma or exosomal fractions offer less invasive alternatives for biomarker detection^60–62^. In particular, exosomal PTGDS may capture tumor-derived signals while facilitating repeated sampling, suggesting a feasible approach for clinical monitoring and early detection^61^. Future studies should systematically compare these sample types to determine the most informative and practical matrix for clinical applications.
Limitations
This study has several limitations. First, although the pancancer analyses integrated multiple large-scale datasets, the findings remain associative and cannot establish causal relationships. Second, mechanistic validation was restricted to LUAD, despite the observation that PTGDS shows tumor-type-specific prognostic patterns, particularly in kidney cancers. Whether PTGDS plays distinct biological roles across malignancies requires further investigation. Third, the in vitro experiments were limited to two LUAD cell lines. Although both overexpression and knockdown approaches were included, the absence of rescue assays and in vivo models reduces the strength of mechanistic inference. Moreover, single-cell analyses indicated that PTGDS is mainly expressed in fibroblasts and other stromal or immune cells, suggesting that the tumor-cell-intrinsic effects observed in vitro may not fully reflect its functions within the tumor microenvironment. Finally, the proposed regulation of PTGDS by miR-3944 was based on bioinformatic prediction and correlation analyses without experimental validation. Additional molecular and functional studies are required to clarify upstream regulators, downstream pathways, and the translational potential of PTGDS in LUAD and other cancers.
Materials and methods
Data collection
We obtained a unified and standardized pancancer dataset from the UCSC Xena database, comprising TCGA GTEx (N = 19131, G = 60499), encompassing mRNA sequencing data of ENSG00000107317 (PTGDS) across 34 tumor tissues and 31 normal tissues, alongside clinical data (including survival status, clinical, and pathological stage). Samples with zero PTGDS expression and cancer types with fewer than 3 samples were excluded. PTGDS expression across different clinical stages of specific cancer types was visualized using the UALCAN platform^63^.
Pancancer prognostic assessment
Samples lacking complete clinical information were excluded. We evaluated the prognostic value of PTGDS across cancers using the R packages “limma,” “ggplot2,” “ggpubr,” and “survival.” The clinical prognostic parameters assessed included overall survival (OS), disease-specific survival (DSS), the disease-free interval (DFS), and the progression-free interval (PFS)^64^.
Association of PTGDS with clinicopathological features, genomic heterogeneity, and tumor stemness
Clinicopathological features of various cancer types, including primary tumor status (T), lymph node metastasis (N), distant metastasis (M), tumor grade (G), sex, age, and tumor stage, were extracted. Nucleotide and copy number variation data were processed using MuTect2 (version 4.3.0.0, https://github.com/broadinstitute/gatk/blob/master/scripts/mutect2_wdl/mutect2.wdl) and GISTIC (version 2.0, https://broadinstitute.github.io/gistic2/)^65,66^. Pancancer tumor mutational burden (TMB) and microsatellite instability (MSI) data were extracted, and their correlations with PTGDS expression were analyzed using Spearman’s correlation coefficient^67,68^.
Immune correlation analysis of PTGDS in Pan cancer
We obtained 150 immune regulatory genes and 60 immune checkpoint genes from previous studies^68^. The immune infiltration scores of different samples of various cancer types were scored using the R package “ESTIMATE.” Immune cell infiltration scores were obtained from the TIMER 2.0 database, EPIC, QUANTISEQ, and CIBERSORT^69–72^. The correlation of PTGDS expression with immune parameters was analyzed using Spearman’s correlation coefficient.
Specific expression of PTGDS in the LUAD single cell dataset
We analyzed single-cell datasets from open research sources, including the HPA, CancerSEA, and UCSC Cell Browser, to study the specific expression of PTGDS in pancancer, lung cancer, and normal human lung tissues^73,74^.
Methylation analysis
The methylation patterns of key genes in hepatocellular carcinoma (HCC) and normal tissues were assessed using MEXPRESS and the MethSurv database, with a specific focus on PTGDS and LUAD.
Functional enrichment analysis
To elucidate the underlying mechanisms of PTGDS, KEGG and GO enrichment analyses were conducted using the “clusterProfiler” R package^75^. Additionally, the TCGA LUAD cohort was downloaded, stratified on the basis of PTGDS expression level, and subjected to gene set enrichment analysis. Terms with adjusted p values < 0.05 were considered indicative of potential pathways associated with PTGDS in LUAD.
Screening potential MiRNAs targeting PTGDS in LUAD
Differential expression analysis was performed on miRNA expression data from the TCGA LUAD cohort, with the R package “limma” utilized to identify dysregulated miRNAs in tumor tissues^76^. miRNAs with a |log2FC| > 1 and a false discovery rate (FDR) < 0.05 were selected for subsequent analysis. Kaplan‒Meier analysis was used to assess the diagnostic performance of the dysregulated miRNAs, and Spearman’s correlation coefficient was used to determine their association with PTGDS. The scan v 7.1 database was used to predict potential miRNAs that target PTGDS^77^.
Cell culture, lentiviral infection, and generation of stable cell lines
The A549 lung adenocarcinoma cell line was generously provided by Professor Shi of Chongqing Medical University. The CMV Flag PTGDS plasmid was constructed using the coding DNA sequence (CDS) (NM_000954.6) of PTGDS obtained from NCBI. Molecular cloning, transfection, and viral infection followed established protocols^78^.
For gene knockdown, three small interfering RNAs (siRNAs) targeting PTGDS were designed and synthesized (GenePharma, China). The target sequences were as follows:
si-PTGDS#1-F: 5′-GCUUCAUCCUGCACAAUAAAC-3′,
si-PTGDS#1-R: 5′-UUAUUGUGCAGGAUGAAGCCU-3′.
si-PTGDS#2-F: 5′-GGGCUGAGUUAAAGGAGAAAU-3′,
si-PTGDS#2-R: 5′-UUCUCCUUUAACUCAGCCCUG-3′.
si-PTGDS#3-F: 5′-AGUGCAUGACGGAACAAUAGG-3′,
si-PTGDS#3-R: 5′-UAUUGUUCCGUCAUGCACUUA-3′.
Western blot analysis
For Western blot analysis, cell lysates were prepared with 1% SDS lysis buffer containing protease and phosphatase inhibitors. Protein concentrations were determined using a BCA protein assay kit. The blots were probed with primary antibodies against GFP, PPARG, LDHA, p ACLY, ACC, ACSL1, ACAT1, BAX, p65, CCND1, CDC2, CDK2, CDK4, CDK7, CDK6, HK2, PDHK1, ENO2, HADH, caspase3, cleaved caspase3, caspase9, PGAM1, β Actin and β tubulin. Band visualization was achieved via ECL reagents.
Cell viability assay
CCK-8 assays and colony formation assays were carried out according to standard protocols as described in previous studies^79^.
Colony formation assay
Long-term cell survival was assessed through a colony formation assay. Briefly, 1000 cells were seeded into 6-well plates and allowed to grow for 2 weeks. Colonies were fixed, stained with crystal violet, scanned, and quantified using ImageJ 1.48 (NIH, Bethesda, Maryland, https://imagej.net/ij/index.htmltec) (Java 1.8.9 66).
Flow cytometry analysis
Upon reaching 80% 90% confluence, A549 cells overexpressing GFP-CTL or GFP PTGDS were harvested and subjected to cell cycle and apoptosis analysis using a cell cycle and apoptosis analysis kit. Single cells were analyzed by flow cytometry following the manufacturer’s instructions, with apoptosis detection performed according to established protocols at the College of Life Sciences, Chongqing Medical University.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary Material 1
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1Bai, Y., Cao, K., Zhang, P., Ma, J. & Zhu, J. Prognostic and immunological implications of FAM 72A in Pan-Cancer and functional validations. Int. J. Mol. Sci.24(1), 375 (2022).10.3390/ijms 24010375 PMC 982059736613817 · doi ↗ · pubmed ↗
- 2Landen, C. N. et al. Influence of genomic landscape on cancer immunotherapy for newly diagnosed ovarian cancer: biomarker analyses from the I Magyn 050 randomized clinical trial. Clin. Cancer Research: Official J. Am. Association Cancer Res.29(9), 1698–1707 (2023).10.1158/1078-0432.CCR-22-2032 PMC 1015025036595569 · doi ↗ · pubmed ↗
- 3Chen, Z. & Gao, F. The dual role of macrophage extracellular traps in host defense and disease: mechanisms and therapeutic implications. Biomolecules 15(9), 1220 (2025).10.3390/biom 15091220 PMC 1246723241008527 · doi ↗ · pubmed ↗
- 4Correction Novel post-translational modification learning signature reveals B 4GALT 2 as an immune exclusion regulator in lung adenocarcinoma. J. Immunother Cancer 13(6), e 010787 (2025).10.1136/jitc-2024-010787 corr 1PMC 1216464740514072 · doi ↗ · pubmed ↗
- 5Bonneville, R. et al. Landscape of Microsatellite Instability Across 39 Cancer Types. JCO Precis Oncol. ;2017.10.1200/PO.17.00073 PMC 597202529850653 · doi ↗ · pubmed ↗
- 6Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E. & Gfeller, D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. Elife. 6, e 26476 (2017).10.7554/e Life.26476 PMC 571870629130882 · doi ↗ · pubmed ↗
- 7Karlsson, M. et al. A single-cell type transcriptomics map of human tissues. Sci. Adv.7 (31), eabh 2169 (2021).10.1126/sciadv.abh 2169 PMC 831836634321199 · doi ↗ · pubmed ↗
- 8Agarwal, V., Bell, G. W., Nam, J. W. & Bartel, D. P. Predicting effective Micro RNA target sites in mammalian m RN As. Elife.4, e 05005 (2015).10.7554/e Life.05005 PMC 453289526267216 · doi ↗ · pubmed ↗