PTGDS is a potential marker for lung adenocarcinoma identified from multi-omics pan-cancer data

doi:10.21203/rs.3.rs-4965286/v1

Download PDF

Article

PTGDS is a potential marker for lung adenocarcinoma identified from multi-omics pan-cancer data

https://doi.org/10.21203/rs.3.rs-4965286/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Prostaglandin D2 Synthase (PTGDS) is an enzyme responsible for synthesizing prostaglandin D2. Despite its crucial role, PTGDS remains relatively understudied in tumor therapy, lacking comprehensive pan cancer analyses. Leveraging multi omics data integration, this study elucidates PTGDS's widespread dysregulation across various cancers and its correlation with patient survival. Moreover, PTGDS exhibits significant associations with stem cell scores, microenvironmental scores, microsatellite instability (MSI), 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 reveals PTGDS's role in inhibiting lung adenocarcinoma cell A549 proliferation, linked to fatty acid degradation and cell cycle regulation. In summary, PTGDS demonstrates prognostic potential, influences pan cancer tumor immunity, and acts as a tumor suppressor in lung adenocarcinoma (LUAD).

Biological sciences/Biochemistry

Biological sciences/Cancer

Biological sciences/Computational biology and bioinformatics

PTGDS

LUAD

TCGA

Pan-cancer

PTGDS (Prostaglandin D2 Synthase) was initially identified within the lipocalin family, situated in the HSA9q34 region of the human genome ¹. Consequently, early investigations primarily concentrated on prostaglandin D2 (PGD2), derived from prostaglandin H2 (PGH2) via PTGDS catalysis ^2,3. Concurrently, genetic variations in PTGDS were observed across various non neoplastic diseases ^4-6. Recent studies have increasingly underscored PTGDS's pivotal role in cancer. For instance, 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 proliferation and migration in breast cancer cells ^9,10, and participating in the progression of cervical squamous cell carcinoma along with SNX10 ¹¹. 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 ¹². Notably, data amassed through initiatives like The Cancer Genome Atlas (TCGA) project have fueled cross tumor research endeavors ¹³. These efforts aim to unearth novel prognostic molecules and therapeutic targets via multi omics analysis (including whole transcriptomics, proteomics, and single cell transcriptomics) of tumor specimens from diverse cancer cohorts ^14,15. For instance, 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 ¹⁶. Bai et al. delved into the prognostic and immunological significance of FAM72A across multiple cancers, substantiated by functional assays ¹⁷. Furthermore, transcriptomic data can delineate immune infiltration status and tumor mutation burden, serving as pivotal immunotherapy biomarkers and prognostic indicators for patients ^18,19, underscoring the importance of investigating PTGDS within the tumor immune microenvironment, an area that remains largely unexplored to date ^20,21.

Overall, current research on PTGDS remains relatively superficial. Hence, this study conducts a pan cancer analysis of PTGDS across 33 cancer types to elucidate its role. Through an integrative assessment leveraging multiple databases and multi omic approaches (TCGA, Genotype Tissue Expression (GTEx), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and UCSC Xena), we explore PTGDS's aberrant expression, prognostic significance, methylation status, potential clinical associations, tumor mutational immune burden, and its functional role in lung adenocarcinoma (LUAD).

Transcriptional and protein expression levels of PTGDS in pan cancer.

Through integration and analysis of data from TCGA, GTEx, CPTAC, and HPA databases, we acquired a comprehensive understanding of PTGDS (ENSG00000107317) expression across various cancers. Transcriptionally, PTGDS exhibited statistically significant underexpression in 21 common malignancies, including LUAD, KIRC, and LIHC (Fig. 1A). 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 HPA revealed a consistently low expression pattern of PTGDS in digestive tract tumors, including gastric cancer, liver cancer, kidney cancer, and colorectal cancer (Figure 1D). Additionally, PTGDS was predominantly expressed at low levels in lung cancer (Figure 1E, Figure S1A)..

Pan cancer analysis of clinical parameter correlations of PTGDS

Survival analysis spanning four domains—overall survival (OS), disease specific survival (DSS), disease free survival (DFS), and progression free survival (PFS)—emphasized the prognostic relevance of PTGDS across multiple cancers. Cox regression modeling identified high PTGDS expression as a risk factor for OS in KIPAN, KIRC, and STAD patients, yet it conferred protective effects in GBMLGG, LUAD, LGG, CESC, HNSC, and DLBC cohorts (Fig. 2A). Kaplan Meier (KM) survival curves further illustrated PTGDS's diagnostic potential across these cancer types (Fig. 2B). Moreover, PTGDS expression 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 increased risk for DFS in KIRP and KIPAN, while demonstrating protective effects in CHOL and BLCA (Fig. S2A). In terms of PFS, PTGDS was identified as a risk factor for KIPAN, STAD, and KIRP, while exhibiting protective effects in GBMLGG, LUAD, PRAD, DLBC, HNSC, MESO, CHOL, and LGG (Fig. 2D). The expression pattern of PTGDS appears to be associated with poor prognosis in kidney cancer, but with good prognosis in lung and brain tumors. This differential expression warrants further investigation.

We further investigated the association between PTGDS and various clinical and pathological parameters across different cancer types, including T (Tumor), N (Node), M (Metastasis) stages, overall stage, and age. PTGDS demonstrated associations 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 closely correlated with the stages of LUAD, STES, KIRP, KIPAN, STAD, KIRC, and BLCA (Fig. S2D).

Genomic Diversity and Correlation with 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 "Pan-Cancer Analysis of Whole Genomes" project by TCGA, we analyzed 2683 samples from 2565 patients. Our analysis revealed that diploidy is the predominant mutation type of PTGDS in pan-cancer, closely followed by shallow deletion (Fig. 3A). Notably, PTGDS mutations were most frequent 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).

We also explored the genomic heterogeneity of PTGDS in pan-cancer and its correlation with tumor stemness. Guided by the established associations of tumor mutational burden (TMB) and microsatellite instability (MSI) with tumor aggressiveness and prognosis ^22-24. our correlation analysis revealed a significant positive correlation between PTGDS and TMB in KIPAN, while a notable negative correlation was observed in 14 tumor types, including GBM, LUAD, and STES (Fig. 3D). Similarly, PTGDS showed a positive correlation with MSI in GBMLGG, but a significant negative correlation in 10 tumor types such as ESCA and SARC (Fig. 3E).

Given that tumor growth often depends on cancer stem cells ²⁵, we examined the tumor stemness index (DNAss) derived from mRNA expression and methylation signatures ²⁶. 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). Analyzing PTGDS's associations with three immune scores, we observed consistent positive correlations. PTGDS was positively correlated with the StromalScore in 16 cancers, including STES, STAD, LUSC, THCA, and BLCA (Fig. 4A). Additionally, PTGDS expression in 15 cancers, such as LUAD, BRCA, STES, HNSC, and LUSC, significantly correlated with the ImmuneScore (Fig. 4B). Similarly, the ESTIMATEScore indicated a positive correlation between PTGDS expression and immune infiltration in CES, READ, BRCA, LUAD, and PAAD (Fig. 4C) (r > 0.3, p < 0.05). Further analysis of potential associations between PTGDS and 60 immune checkpoint genes revealed top correlations in THCA, GBMLGG, KIPAN, BRCA, and LUAD. PTGDS exhibited positive correlations with most immune checkpoint genes, except in GBMLGG (Fig. S3A). Moreover, we assessed PTGDS associations 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 GBMLGG and LGG (Fig. S3B). These results suggest that PTGDS has a potential positive regulatory role in the TME.

PTGDS Interaction 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 pan-cancer immune score. Our analysis revealed significant associations between PTGDS expression and immune cell infiltration in most cancers, particularly highlighting CD4+ T cells, macrophages, and B cells as closely linked to PTGDS expression levels (Fig. 4D-F, Fig. S3C). Additionally, data from the Human Protein Atlas (HPA) showcased PTGDS-specific expression in normal immune cells, notably in plasmacytoid dendritic cells and natural killer cells (Fig. S3D-F). Consistently, across all four algorithms, PTGDS demonstrated positive correlations 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. S3C). 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. S3C).

Single Cell Sequencing Reveals PTGDS Expression in Specific Cell Types

The findings suggest a potential specificity of PTGDS in lung cancer, prompting a focused investigation on LUAD. Utilizing single cell sequencing data from CancerSEA, we examined the correlation between PTGDS and functional status across 14 cancers. PTGDS showed predominantly negative correlations with DNA damage repair and cell cycle across most tumors, contrasting with positive associations with inflammation (Fig. 5A). Notably, in LUAD, PTGDS displayed a positive correlation 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 unveiled predominant PTGDS expression in fibroblasts, endothelial cells, B cells, and T cells (Fig. 5C, D). Additionally, exploration of the normal human lung single cell atlas by Kyle et al., accessible via the UCSC Cell Browser, highlighted specific PTGDS expression in lipofibroblasts, adventitial fibroblasts, alveolar fibroblasts, natural killer cells, arteries, and veins (Fig. 5E, F).

Integrated Analysis of PTGDS Methylation and Functional Enrichment in LUAD

Methylation serves as a critical hallmark distinguishing tumor tissue from their 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 embarked on a comprehensive investigation into the interplay between PTGDS methylation and tumor progression, focusing particularly 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. Remarkably, the negative correlation of LRPPRC with PTGDS appeared consistent across various cancer types, indicating a robust relationship (Supplementary Fig. 4A).

Subsequently, leveraging data from the MethSurv platform, we 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, cg13561390) were positively correlated with PTGDS expression levels (Supplementary Fig. 4B, C). Finally, our analysis unveiled higher PTGDS methylation levels in LUAD compared to normal tissues (Supplementary Fig. 4D).

To elucidate the underlying mechanisms, PTGDS underwent analysis using the STRING database. This analysis generated an interaction network between PTGDS and its related genes (Fig. 6A), 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). 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 part and organelle sub compartment, with involvement in activities such as intramolecular oxidoreductase and isomerase (Fig. 6C-E).

Moreover, leveraging the TCGA LUAD cohort for Gene Set Enrichment Analysis (GSEA), we stratified the LUAD cohort into PTGDS high and low expression groups. Interestingly, the low expression group exhibited enrichment in pathways associated with autoimmune thyroid disease and hematopoietic cell lineage. Oncological features enriched in this group included Notch, KRAS, and VEGF pathways. Top Biological Process (BP) terms included adaptive immune response, regulation of cell activation, and B cell proliferation, while Cellular Component (CC) terms highlighted receptor complexes and plasma membrane protein complexes. Molecular Function (MF) terms mainly enriched in molecular transducer activity and immune receptor activity (Fig. 6F).

Potential Regulation of PTGDS by miR 3944 in LUAD

Investigating potential miRNAs regulating PTGDS, we analyzed LUAD miRNA data. Differential analysis between normal and tumor tissues revealed 364 differentially expressed miRNAs (DEMs), including 81 down regulated and 283 up regulated DEMs (Fig. 7A, B). Subsequently, we used the scan database to predict miRNAs targeting PTGDS and intersected the resulting DEM list, identifying 11 potential miRNAs (Fig. 7C). Survival analysis highlighted miR-3944, miR-296, and miR-542 for their diagnostic efficacy (Fig. 7D). Spearman's correlation analysis revealed miR-3944, miR-5090, miR-939, miR-6777, miR-3622, and miR-4758 associated with PTGDS, with miR-3944 showing diagnostic efficacy in LUAD and a negative correlation with PTGDS (Fig. 6E). These findings suggest a potential regulatory role of miR-3944 on PTGDS in LUAD.

Effects of PTGDS on Metabolism, Proliferation, Cell Cycle, and Apoptosis in A549 Cells

To delve into 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. S5A). Additionally, PTGDS showed negative correlations with DNA repair, DNA replication, and the G2M checkpoint pathways, while positively correlating with apoptosis (Fig. S5B-D).

To investigate the impact of PTGDS overexpression, we transfected the GFP-PTGDS plasmid into A549 cells, which exhibit low PTGDS expression (Fig. 8A, Fig. S5E). This overexpression resulted in increased protein levels of PPARG, HADH, LDHA, and ACAT1, while levels of p-ACLY, ACC, and ACSL1 were decreased. These changes are consistent with the observed positive correlation with fatty acid degradation (Fig. 8B). Additionally, PTGDS overexpression influenced the expression of proteins related to glycolysis (Fig. 8C). Regarding cell cycle regulation, PTGDS overexpression inhibited the expression of CDC2 and CCND1, leading to an increase in cells in the G2 and S phases and a decrease in the G1 phase (Fig. 8D, E). The CCK-8 assay and colony formation experiments confirmed that this regulation was associated with reduced cell proliferation (Fig. 8F, G). Moreover, there was an increase in caspase-3 and caspase-9 proteins, although flow cytometry did not detect a change in the proportion of apoptotic cells (Fig. 8H, I).

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 ²⁷. Moreover, PTGDS holds significance in the maturation and maintenance of the central nervous system and male reproductive system ^27,28. While prior investigations link PTGDS to breast cancer susceptibility, testicular, lymphoma, and gastric cancer development, comprehensive pan cancer analyses of PTGDS as a potential therapeutic target are currently lacking ^3,7,29,30.

This study reveals that PTGDS exhibits abnormal under expression across most cancers, evident both transcriptionally and at the protein level. Cox regression and Kaplan Meier analysis, assessing four prognostic indicators (OS, DSS, DFS, and PFS), suggest PTGDS as a promising biomarker, notably in lung adenocarcinoma (LUAD) and kidney renal clear cell carcinoma (KIRC). Additionally, we note age and sex related variations in PTGDS expression across certain cancer types, offering valuable insights for tailoring treatment strategies based on patient demographics. As tumor mutational burden (TMB) and microsatellite instability (MSI) emerge as pivotal predictive biomarkers in pan cancer research ^31-34, our findings reveal a negative correlation between PTGDS expression and TMB in 14 cancer types and MSI in 18 cancers ^33,34. This suggests that PTGDS expression may influence TMB and MSI in cancer, potentially indicating improved response to immunotherapy in patients exhibiting high PTGDS expression alongside elevated TMB and MSI.

The intricate landscape of the tumor immune microenvironment (TME) presents challenges in realizing the full potential of immunotherapies across diverse tumor types ^35,36. Notably, PTGDS demonstrates positive correlations with stromal and immune cell content in the TME of most cancers. Further analysis, incorporating single cell transcriptome data from LUAD, reveals robust associations between PTGDS and natural killer cells and macrophages, both integral to tumor immune infiltration and therapeutic efficacy ^37,38. While PTGDS's role in cancer metabolic reprogramming garners attention ^39,40, our findings suggest its potential impact on tumor immune status in LUAD through metabolic remodeling ⁴¹. Moreover, co-expression analyses unveil associations between PTGDS and immune regulatory genes and immune checkpoint genes, offering insights into PTGDS's involvement in tumor immune cell infiltration and potential avenues for immunotherapy development.

Enrichment analysis highlights PTGDS's potential role in modulating the tumor immune microenvironment via metabolic remodeling, evidenced by its associations with the cyclooxygenase pathway, endoplasmic reticulum function, and adaptive immune response. Exploring PTGDS promoter methylation in cancer unveils aberrant methylation sites, while experimental evidence demonstrates PTGDS's influence on the cell cycle, particularly its suppression of CDC2 expression. Bioinformatics analyses further underscore PTGDS's associations with the G2/M checkpoint and DNA damage repair, suggesting its capacity to promote DNA repair and prevent cells with DNA damage from entering mitosis.

In summary, our pioneering pan cancer analysis of PTGDS delineates its dysregulation in tumor tissues, unveiling its correlations with clinical prognosis, clinicopathological parameters, and DNA methylation. PTGDS emerges as an independent prognostic factor across multiple tumors, intricately linked with tumor immune infiltration and genomic heterogeneity. Notably, our study underscores PTGDS's tumor suppressor role in LUAD, emphasizing the need for further investigations to elucidate its specific functions across various cancers. These insights contribute to a deeper understanding of PTGDS's implications in tumorigenesis and progression, offering potential avenues for therapeutic intervention.

Data Collection

We obtained a unified and standardized pan cancer 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 platform⁴².

Pan Cancer Prognostic Assessment

Samples lacking complete clinical information were excluded. We evaluated the prognostic value of PTGDS in pan cancer using R packages "limma," "ggplot2," "ggpubr," and "survival." Clinical prognostic parameters assessed included overall survival (OS), disease specific survival (DSS), disease free interval (DFS), and progression free interval (PFS) ⁴³.

Association of PTGDS with Clinicopathological Features, Genomic Heterogeneity, and Tumor Stemness

Clinicopathological features of various cancer types were extracted, including primary tumor status (T), lymph node metastasis (N), distant metastasis (M), tumor grade (G), gender, age, and tumor stage. Nucleotide and copy number variation data were processed using MuTect2 and GISTIC software ⁴⁴. Pan cancer Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI) data were extracted, and their correlation with PTGDS expression was analyzed using Spearman's correlation coefficient ^45,46.

Immune Correlation Analysis of PTGDS in Pan Cancer

We obtained 150 immune regulatory genes and 60 immune checkpoint genes from previous studies ⁴⁶. Immune infiltration scores in 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 ^47-50. 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 HPA, CancerSEA, and the UCSC Cell Browser, to study the specific expression of PTGDS in pan cancer, lung cancer, and normal human lung tissues ^51,52.

Methylation Analysis

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 ⁵³. Additionally, the TCGA LUAD cohort was downloaded, stratified based on PTGDS expression levels, 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 ⁵⁴. MiRNAs with |log2FC| > 1 and false discovery rate (FDR) < 0.05 were selected for subsequent analysis. Kaplan Meier analysis assessed the diagnostic performance of dysregulated miRNAs, while Spearman's correlation coefficient determined their association with PTGDS. The scan v 7.1 database was employed to predict potential miRNAs targeting PTGDS ⁵⁵.

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 based on the CDS sequence (NM_000954.6) of PTGDS obtained from NCBI. Molecular cloning, transfection, and viral infection followed established protocols ⁵⁶.

Western Blot Analysis

Cell lysates were prepared using 1% SDS lysis buffer containing protease and phosphatase inhibitors for western blot analysis. Protein concentrations were determined using a BCA protein assay kit. 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, and β Tubulin. Band visualization was achieved using ECL reagents.

Cell Viability Assay

Forty-eight hours post-transfection with GFP-PTGDS, A549 cells were subjected to a cell viability assay. The CCK-8 assay kit was employed to assess cell viability at 24, 48, and 72 hours from the starting point. Absorbance was measured at 450 nm using a microplate reader, and statistical analysis was performed using GraphPad Prism 8 software.

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 Image J software.

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.

Author Contributions: Conceptualization, writing—original draft preparation, writing—review and editing, W.R.and S.F.; methodology, software, data curation, visualization, W.R.; formal analysis, investigation, resources, C.J. and Z.Q.; supervision, project administra-tion, and funding acquisition, Z.Q. All authors have read and agreed to the published version of the manuscript.

Funding: This work was supported by the National Natural Science Foundation of China (grant number 82030065);

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: The datasets used and analysed during the current study available from the corresponding author on reasonable request.

Acknowledgments: We are grateful to all scientific researchers who have contributed to human life and health.

Conflicts of Interest: The authors declare that they have no conflict of interest.

Chan, P., Simon-Chazottes, D., Mattei, M. G., Guenet, J. L. & Salier, J. P. Comparative mapping of lipocalin genes in human and mouse: the four genes for complement C8 gamma chain, prostaglandin-D-synthase, oncogene-24p3, and progestagen-associated endometrial protein map to HSA9 and MMU2. Genomics 23, 145-150, doi:10.1006/geno.1994.1470 (1994).
Garza, L. A. et al. Prostaglandin D2 inhibits hair growth and is elevated in bald scalp of men with androgenetic alopecia. Sci Transl Med 4, 126ra134, doi:10.1126/scitranslmed.3003122 (2012).
Shyu, R. Y. et al. H-rev107 regulates prostaglandin D2 synthase-mediated suppression of cellular invasion in testicular cancer cells. J Biomed Sci 20, 30, doi:10.1186/1423-0127-20-30 (2013).
Marin-Mendez, J. J. et al. Differential expression of prostaglandin D2 synthase (PTGDS) in patients with attention deficit-hyperactivity disorder and bipolar disorder. J Affect Disord 138, 479-484, doi:10.1016/j.jad.2012.01.040 (2012).
Nelson, A. M. et al. Prostaglandin D2 inhibits wound-induced hair follicle neogenesis through the receptor, Gpr44. J Invest Dermatol 133, 881-889, doi:10.1038/jid.2012.398 (2013).
Thompson, V. C. et al. A gene signature identified using a mouse model of androgen receptor-dependent prostate cancer predicts biochemical relapse in human disease. Int J Cancer 131, 662-672, doi:10.1002/ijc.26414 (2012).
Hu, S. et al. Glycoprotein PTGDS promotes tumorigenesis of diffuse large B-cell lymphoma by MYH9-mediated regulation of Wnt-beta-catenin-STAT3 signaling. Cell Death Differ 29, 642-656, doi:10.1038/s41418-021-00880-2 (2022).
Kim, S. et al. Targeted eicosanoids profiling reveals a prostaglandin reprogramming in breast Cancer by microRNA-155. J Exp Clin Cancer Res 40, 43, doi:10.1186/s13046-021-01839-4 (2021).
Bie, Q. et al. YAP promotes self-renewal of gastric cancer cells by inhibiting expression of L-PTGDS and PTGDR2. Int J Clin Oncol 25, 2055-2065, doi:10.1007/s10147-020-01771-1 (2020).
Pan, J., Zhang, L. & Huang, J. Prostaglandin D2 synthase/prostaglandin D2/TWIST2 signaling inhibits breast cancer proliferation. Anticancer Drugs 32, 1029-1037, doi:10.1097/CAD.0000000000001111 (2021).
Jiang, P. et al. SNX10 and PTGDS are associated with the progression and prognosis of cervical squamous cell carcinoma. BMC Cancer 21, 694, doi:10.1186/s12885-021-08212-w (2021).
Hussen, B. M. et al. The emerging roles of NGS in clinical oncology and personalized medicine. Pathol Res Pract 230, 153760, doi:10.1016/j.prp.2022.153760 (2022).
Hutter, C. & Zenklusen, J. C. The Cancer Genome Atlas: Creating Lasting Value beyond Its Data. Cell 173, 283-285, doi:10.1016/j.cell.2018.03.042 (2018).
Mosele, F. et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group. Ann Oncol 31, 1491-1505, doi:10.1016/j.annonc.2020.07.014 (2020).
Chen, M. & Zhao, H. Next-generation sequencing in liquid biopsy: cancer screening and early detection. Hum Genomics 13, 34, doi:10.1186/s40246-019-0220-8 (2019).
Yang, X. et al. Prognostic value of LRRC4C in Colon and Gastric Cancers correlates with Tumour Microenvironment Immunity. Int J Biol Sci 17, 1413-1427, doi:10.7150/ijbs.58876 (2021).
Bai, Y., Cao, K., Zhang, P., Ma, J. & Zhu, J. Prognostic and Immunological Implications of FAM72A in Pan-Cancer and Functional Validations. Int J Mol Sci 24, doi:10.3390/ijms24010375 (2022).
Finotello, F. & Trajanoski, Z. Quantifying tumor-infiltrating immune cells from transcriptomics data. Cancer Immunol Immunother 67, 1031-1040, doi:10.1007/s00262-018-2150-z (2018).
Morganti, S. et al. Complexity of genome sequencing and reporting: Next generation sequencing (NGS) technologies and implementation of precision medicine in real life. Crit Rev Oncol Hematol 133, 171-182, doi:10.1016/j.critrevonc.2018.11.008 (2019).
Lei, X. et al. Immune cells within the tumor microenvironment: Biological functions and roles in cancer immunotherapy. Cancer letters 470, 126-133, doi:10.1016/j.canlet.2019.11.009 (2020).
Jardim, D. L., Goodman, A., de Melo Gagliato, D. & Kurzrock, R. The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker. Cancer cell 39, 154-173, doi:10.1016/j.ccell.2020.10.001 (2021).
Wang, J. et al. Mutational analysis of microsatellite-stable gastrointestinal cancer with high tumour mutational burden: a retrospective cohort study. The Lancet. Oncology 24, 151-161, doi:10.1016/S1470-2045(22)00783-5 (2023).
Landen, C. N. et al. Influence of Genomic Landscape on Cancer Immunotherapy for Newly Diagnosed Ovarian Cancer: Biomarker Analyses from the IMagyn050 Randomized Clinical Trial. Clinical cancer research : an official journal of the American Association for Cancer Research, doi:10.1158/1078-0432.CCR-22-2032 (2023).
Kendre, G. et al. Charting co-mutation patterns associated with actionable drivers in intrahepatic cholangiocarcinoma. J Hepatol 78, 614-626, doi:10.1016/j.jhep.2022.11.030 (2023).
Lee, T. K., Guan, X. Y. & Ma, S. Cancer stem cells in hepatocellular carcinoma - from origin to clinical implications. Nat Rev Gastroenterol Hepatol 19, 26-44, doi:10.1038/s41575-021-00508-3 (2022).
Malta, T. M. et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 173, 338-354 e315, doi:10.1016/j.cell.2018.03.034 (2018).
Zhou, Y. et al. Structure-function analysis of human l-prostaglandin D synthase bound with fatty acid molecules. FASEB J 24, 4668-4677, doi:10.1096/fj.10-164863 (2010).
Tokugawa, Y. et al. Lipocalin-type prostaglandin D synthase in human male reproductive organs and seminal plasma. Biol Reprod 58, 600-607, doi:10.1095/biolreprod58.2.600 (1998).
Abraham, J. E. et al. Common polymorphisms in the prostaglandin pathway genes and their association with breast cancer susceptibility and survival. Clinical cancer research : an official journal of the American Association for Cancer Research 15, 2181-2191, doi:10.1158/1078-0432.CCR-08-0716 (2009).
Zhang, B. et al. PGD2/PTGDR2 Signaling Restricts the Self-Renewal and Tumorigenesis of Gastric Cancer. Stem Cells 36, 990-1003, doi:10.1002/stem.2821 (2018).
Fumet, J. D., Truntzer, C., Yarchoan, M. & Ghiringhelli, F. Tumour mutational burden as a biomarker for immunotherapy: Current data and emerging concepts. Eur J Cancer 131, 40-50, doi:10.1016/j.ejca.2020.02.038 (2020).
Boland, C. R. & Goel, A. Microsatellite instability in colorectal cancer. Gastroenterology 138, 2073-2087 e2073, doi:10.1053/j.gastro.2009.12.064 (2010).
Steuer, C. E. & Ramalingam, S. S. Tumor Mutation Burden: Leading Immunotherapy to the Era of Precision Medicine? J Clin Oncol 36, 631-632, doi:10.1200/JCO.2017.76.8770 (2018).
Gryfe, R. et al. Tumor microsatellite instability and clinical outcome in young patients with colorectal cancer. N Engl J Med 342, 69-77, doi:10.1056/NEJM200001133420201 (2000).
Binnewies, M. et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med 24, 541-550, doi:10.1038/s41591-018-0014-x (2018).
Fu, T. et al. Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response. J Hematol Oncol 14, 98, doi:10.1186/s13045-021-01103-4 (2021).
Mao, X. et al. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Molecular cancer 20, 131, doi:10.1186/s12943-021-01428-1 (2021).
Laskowski, T. J., Biederstadt, A. & Rezvani, K. Natural killer cells in antitumour adoptive cell immunotherapy. Nature reviews. Cancer 22, 557-575, doi:10.1038/s41568-022-00491-0 (2022).
Orr, S. K. et al. Gene Expression of Proresolving Lipid Mediator Pathways Is Associated With Clinical Outcomes in Trauma Patients. Crit Care Med 43, 2642-2650, doi:10.1097/CCM.0000000000001312 (2015).
Shao, B. et al. Albuminuria, the High-Density Lipoprotein Proteome, and Coronary Artery Calcification in Type 1 Diabetes Mellitus. Arterioscler Thromb Vasc Biol 39, 1483-1491, doi:10.1161/ATVBAHA.119.312556 (2019).
Xia, L. et al. The cancer metabolic reprogramming and immune response. Molecular cancer 20, 28, doi:10.1186/s12943-021-01316-8 (2021).
Chandrashekar, D. S. et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia 25, 18-27, doi:10.1016/j.neo.2022.01.001 (2022).
Wang, Q. et al. NCAPG2 could be an immunological and prognostic biomarker: From pan-cancer analysis to pancreatic cancer validation. Front Immunol 14, 1097403, doi:10.3389/fimmu.2023.1097403 (2023).
Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899-905, doi:10.1038/nature08822 (2010).
Bonneville, R. et al. Landscape of Microsatellite Instability Across 39 Cancer Types. JCO Precis Oncol 2017, doi:10.1200/PO.17.00073 (2017).
Thorsson, V. et al. The Immune Landscape of Cancer. Immunity 48, 812-830 e814, doi:10.1016/j.immuni.2018.03.023 (2018).
Li, T. et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic acids research 48, W509-W514, doi:10.1093/nar/gkaa407 (2020).
Racle, 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, doi:10.7554/eLife.26476 (2017).
Finotello, F. et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med 11, 34, doi:10.1186/s13073-019-0638-6 (2019).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12, 453-457, doi:10.1038/nmeth.3337 (2015).
Karlsson, M. et al. A single-cell type transcriptomics map of human tissues. Sci Adv 7, doi:10.1126/sciadv.abh2169 (2021).
Kim, K. T. et al. Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol 16, 127, doi:10.1186/s13059-015-0692-3 (2015).
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284-287, doi:10.1089/omi.2011.0118 (2012).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research 43, e47, doi:10.1093/nar/gkv007 (2015).
Agarwal, V., Bell, G. W., Nam, J. W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. Elife 4, doi:10.7554/eLife.05005 (2015).
Xie, Y. et al. HPD degradation regulated by the TTC36-STK33-PELI1 signaling axis induces tyrosinemia and neurological damage. Nat Commun 10, 4266, doi:10.1038/s41467-019-12011-0 (2019).

No competing interests reported.

SupportingInformation.pdf

Download PDF

Reviews received at journal
21 Sep, 2024
Reviewers agreed at journal
14 Sep, 2024
Reviewers invited by journal
12 Sep, 2024
Editor assigned by journal
12 Sep, 2024
Editor invited by journal
05 Sep, 2024
Submission checks completed at journal
03 Sep, 2024
First submitted to journal
23 Aug, 2024

You are reading this latest preprint version

PTGDS is a potential marker for lung adenocarcinoma identified from multi-omics pan-cancer data

Status:

Version 1

Abstract

Figures

Introduction

Results

Discussion

Materials and Methods

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1