Comprehensive analysis of the role of PTGFRN as a new potential biomarker in lung adenocarcinoma

Comprehensive analysis of the role of PTGFRN as a new potential biomarker in lung adenocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comprehensive analysis of the role of PTGFRN as a new potential biomarker in lung adenocarcinoma Qi Zhao, Xin Chen, Xiyan Zhu, Xing Cui, Xinjian Xu, Nan Mu, Shuping Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5040921/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lung adenocarcinoma, the most prevalent and heterogeneous subtype of lung cancer, presents significant challenges for diagnosis and treatment. Prostaglandin F2 receptor negative regulator (PTGFRN) has recently emerged as a molecule of interest in cancer, but its specific contribution to lung adenocarcinoma pathogenesis remains to be elucidated. This study employed bioinformatics methods to investigate the expression patterns and potential functional roles of PTGFRN in lung adenocarcinoma. We utilized large-scale transcriptome datasets from public repositories to analyze PTGFRN expression levels and prognostic significance in lung adenocarcinoma cohorts. Furthermore, we explored the correlation between PTGFRN and immune cell infiltration to elucidate the potential molecular mechanisms of PTGFRN dysregulation in lung cancer development. Overall, our findings provide insights into the significance of PTGFRN in lung adenocarcinoma pathogenesis and emphasize its potential as a novel biomarker and therapeutic target for precision medicine approaches. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics TCGA POC1A prognostic biomarker pan-cancer tumor-infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Lung cancer, also known as lung carcinoma, is a leading cause of cancer-related mortality among men in industrialized nations, particularly non-small-cell lung cancer (NSCLC), which comprises approximately 85% of cases. Lung adenocarcinoma (LUAD) is the most prevalent pathological subtype [ 1 ] . Despite advancements in treatment strategies such as small-molecule tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), many advanced LUAD patients die from the disease due to drug resistance and insensitivity, leading to poor long-term outcomes [ 2 ] . Early diagnosis remains crucial for effective LUAD management. However, current prognostic predictors predominantly rely on gene chips, which lack specificity and add an economic burden to patients. Thus, there is an urgent need to identify sensitive and cost-effective novel biomarkers for early LUAD diagnosis and prognosis. Tumor angiogenesis, a hallmark of cancer, plays a critical role in the metastasis and progression of solid tumors [ 3 ] . Prostaglandin F2 receptor negative regulator (PTGFRN), a type I transmembrane Ig superfamily protein, is frequently upregulated in various cancers [ 4 ] . Studies suggest that PTGFRN overexpression can induce tumor angiogenesis and facilitate tumor growth [ 5 ] . Furthermore, PTGFRN has been implicated in promoting tumor cell proliferation, migration, invasion, and cell cycle progression [ 6 ] . Brittany et al. reported PTGFRN's involvement in driving the onset and advancement of brain glioma through increased PI3K/AKT signaling and p110β stability [ 7 ] . However, the expression and potential prognostic significance of PTGFRN in LUAD remain unclear. This study leveraged datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and other public cancer databases (e.g., cBioPortal, TIMER 2.0) to comprehensively explore the association between PTGFRN expression and immune infiltration in LUAD. Additionally, we investigate PTGFRN mutations and their regulatory network in LUAD. Through this multidimensional analysis, our findings aim to shed light on PTGFRN as a promising novel biomarker for diagnosing and prognosticating LUAD. Materials and Methods 2.1 Data source RNA sequencing data (transcripts per million) for normal and tumor tissues were obtained from TCGA ( https://www.genome.gov/Funded-Programs-Projects/Cancer-Genome-Atlas ) and Genotype-Tissue Expression (GTEx) project ( https://www.gtexportal.org/home/index.html ). Log transformation was applied to the data to evaluate changes in PTGFRN expression across various cancer types. The analyzed cancer types included adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and cervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon cancer (COAD), rectal adenocarcinoma (READ), lymphomas diffuse large B-cell lymphoma (DLBC), esophageal cancer (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), renal chromophobe (KICH), renal clear cell carcinoma (KIRC), renal papillary cell carcinoma (KIRP), and acute myeloid leukemia (LAML), low-grade glioma (LGG), hepatocellular carcinoma (LIHC), LUAD, lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic cancer (PAAD), pheochromocytoma and paraganglioma (PCPG) Prostate cancer (PRAD), rectal adenocarcinoma (READ), sarcoma (SARC), skin melanoma (SKCM), gastric adenocarcinoma (STAD), gastric and esophageal cancer (STES), testicular germ cell tumor (TGCT), thyroid cancer (THCA), thymoma (THYM), endometrial cancer (UCEC), and uveal melanoma (UVM). 2.2 TIMER2.0 TIMER 2.0 ( http://timer.comp-genomics.org/ ) is an online resource that estimates immune infiltration levels in tumor tissues based on gene expression profiles. It provides six algorithms and multiple modules to explore the association between tumor immunity and genetic or clinical characteristics [ 8 ] . In this study, we utilized TIMER 2.0 to analyze the expression differences of PTGFRN in lung adenocarcinoma tissues compared to adjacent healthy tissues. Additionally, we investigated the correlation between PTGFRN expression and the abundance of various immune cell types infiltrating the tumor microenvironment. 2.3 GEPIA2 GEPIA2 ( http://gepia2.cancer-pku.cn/#index ), an online analysis tool that utilizes data from TCGA and GTEx, was employed in this study [ 9 ] . We leveraged GEPIA2 to analyze the expression differences of PTGFRN in lung adenocarcinoma tissues compared to adjacent normal tissues. Additionally, we investigated the relationship between PTGFRN expression and the overall survival of lung adenocarcinoma patients. 2.4 ULACAN We employed UALCAN ( http://ualcan.path.uab.edu/index.html ), a user-friendly, interactive web portal for analyzing TCGA gene expression data [ 10 ] , to investigate PTGFRN expression differences in LUAD tissues compared to adjacent normal tissues. Additionally, we assessed the correlation between PTGFRN expression and various clinicopathological features, including age, gender, race, smoking history, tumor stage, and metastasis. Welch's T-test was employed to evaluate the statistical significance of PTGFRN expression differences between the two groups. 2.5 Human Protein Atlas To further explore PTGFRN protein expression, we utilized the Human Protein Atlas (HPA) database ( https://www.proteinatlas.org/ ), a comprehensive resource that integrates proteomic, transcriptomic, and systems biology data. HPA facilitates the exploration of protein expression profiles across various tissues, cells, and organs. Notably, the database provides access to immunohistochemical reports and patient survival curves for numerous cancers [ 11 ] . In this study, we specifically examined immunohistochemistry data using the HPA antibody HPA017074. This antibody reports PTGFRN protein levels in selected tissues, categorized as undetected, minimal, moderate, or strong based on staining intensity. By analyzing these images, we aimed to compare PTGFRN protein levels between LUAD tissues and normal tissues. 2.6 Kaplan-Meier plotter analysis The Kaplan-Meier plotter ( http://kmplot.com/analysis/ ), a web-based tool for survival analysis [ 12 ] , was employed to investigate the relationship between PTGFRN expression and overall survival (OS), disease-specific survival (DSS), recurrence-free survival (RFS), and progression-free survival (PFS) in LUAD patients. This resource integrates data from GEO, EGA, and TCGA databases, enabling the evaluation of gene expression correlation with patient survival across over 30,000 samples from 21 tumor types. In our study, we specifically analyzed the association of PTGFRN expression with various clinicopathological features (gender, AJCC stage T, race, grade, stage, alcohol consumption, vascular invasion) in LUAD. Using the median expression value as a cutoff, we assessed the impact of high versus low PTGFRN expression on patient survival outcomes using a total of 364, 370, 316, and 362 samples for OS, DSS, RFS, and PFS, respectively. 2.7 LinkedOmics LinkedOmics can perform multi-omics analysis on TCGA datasets ( http://www.linkedomics.org/login.php ). In this study, the TCGA-LUAD was selected for analysis, including 515 LUAD patients. Differentially expressed genes (DEGs) related to PTGFRN were obtained from the LinkFinder module. Pearson correlation coefficient analysis was performed, and the results were represented using heat maps and volcano maps. Additionally, gene set enrichment analysis (GSEA) was performed using the LinkInterpreter modules for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). 2.8 StarBase StarBase ( http://starbase.sysu.edu.cn/ ), a website specializing in non-coding RNA research, offers predictions and detection of non-coding RNA (lncRNA, circRNA, etc.) and mRNA targets, competing endogenous RNAs (ceRNAs), and RNA binding proteins [ 13 ] . To identify potential regulators of PTGFRN expression in LUAD, we employed StarBase to search for miRNAs targeting LUAD. We then selected four specific miRNAs (hsa-mir-29c-3p, hsa-mir-30b-5p, hsa-mir-30e-5p, and hsa-mir-532-5p) predicted to bind to PTGFRN in more than four cancer types. 2.9 TISIDB TISIDB ( http://cis.hku.hk/TISIDB ) is an online database that integrates tumor immune-related genes, literature, high-throughput screening, immunotherapy, TILs, immune modulators, chemical substances, and other types of data RNA-Seq data of 515 TCGA-LUAD patients in the TISIDB database was used to analyze the correlation between PTGFRN expression and chemokines and immune modulators. P values below 0.05 and correlation coefficients above or below − 0.2 were considered statistically significant. 2.10 GSCA Gene Set Cancer Analysis (GSCA) is a web-based platform for in-depth cancer research. It facilitates analysis at various levels, including single genes, multi-gene sets, immune infiltration, mutations, and drug sensitivity. GSCA integrates data from TCGA and GTEx, encompassing 33 cancer types. Additionally, it incorporates clinical information and over 750 small-molecule drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database [ 14 ] . This comprehensive resource empowers researchers to identify potential biomarkers and explore promising drug candidates. We utilized GSCA to investigate the correlation between PTGFRN expression levels and anti-tumor drugs. RESULTS PTGFRN is upregulated in various types of cancer We initially analyzed PTGFRN expression by integrating data from TCGA and GTEx. Tumor and normal tissue samples were compared, with a significance threshold set at log2 (fold change) > 1. This analysis revealed significant upregulation of PTGFRN in 19 tumor types (BLCA, BRCA, CESC, etc.; Fig. 1 A) and downregulation in 5 others (COAD, OV, PRAD, etc.). To further validate pan-cancer overexpression, we examined the expression of three PTGFRN transcripts: one protein-coding and two non-coding (Fig. 1 B). Notably, the protein-coding transcript displayed significant increases across multiple cancers, while non-coding transcripts showed significant decreases in several. Finally, protein-level expression analysis using the CPTAC database identified upregulation of PTGFRN in four cancer types (GBM, HNSC, LSCC, LUAD) and downregulation in 3 others (HCC, OV, PDAC) across 12 datasets(Fig. 1 C). Collectively, these findings indicate PTGFRN upregulation in various cancers, suggesting its potential role in promoting cancer development. PTGFRN methylation and CNV alterations in pan-cancer Since DNA methylation and copy number variations (CNVs) are known to influence gene expression, we investigated their impact on PTGFRN. Promoter region methylation of PTGFRN exhibited a significant negative correlation with PTGFRN expression in most cancer types (Fig. 2 A). Additionally, pan-cancer analysis of PTGFRN CNVs revealed a predominance of high-level amplifications and single-copy deletions(Fig. 2 B). These findings collectively suggest that DNA methylation and CNVs play a role in regulating PTGFRN gene expression. The prognostic value of PTGFRN in pan-cancer To evaluate the prognostic significance of PTGFRN, we analyzed its expression in relation to patient outcomes in the TCGA database. UniCox analysis identified PTGFRN as a risk factor for OS in patients with SKCM, READ, LUAD, LIHC, LGG, KIRC, KICH, and ACC (Fig. 3 ). Furthermore, PTGFRN emerged as a risk factor for DSS, DFI, and PFI in multiple cancer types (Fig. 3 ). Kaplan-Meier analysis for OS revealed that high PTGFRN expression significantly predicted poorer patient outcomes across various cancers, including ACC, BLCA, COAD, GBM, KRIC, LGG, LIHC, LUAD, LUSC, MESO, READ, SARC, and UCEC (Fig. 4 ). Collectively, these findings suggest PTGFRN as a potential novel pan-cancer prognostic biomarker. PTGFRN exhibited significant upregulation in LUAD Given the pan-cancer analysis suggesting PTGFRN as a risk factor for various patient outcomes in LUAD, we further explored its specific role in this cancer type. We initially utilized the TIMER2 database to evaluate PTGFRN expression differences between LUAD tumor tissues and adjacent normal tissues within the TCGA project. Consistent with our pan-cancer analysis (Fig. 5 A), PTGFRN expression was significantly elevated in BRCA, CHOL, ESCA, and other cancer types compared to normal tissues. Notably, LUAD displayed a significantly higher level of PTGFRN expression compared to its corresponding normal tissue (p < 0.001). To strengthen this observation, we further analyzed PTGFRN expression in LUAD using GEPIA2 and UALCAN portals (Fig. 5 B, 5 C). All platforms consistently demonstrated significantly higher PTGFRN expression in LUAD tissues compared to controls. Student's t-test was employed for statistical analysis. Additionally, UALCAN analysis revealed a significant increase in total PTGFRN protein levels within LUAD tissues (p < 0.001) (Fig. 5 D). Finally, immunohistochemical analysis through the Human Protein Atlas portal corroborated these findings, demonstrating elevated PTGFRN expression in LUAD tissues compared to adjacent normal tissues (Fig. 5 E). Collectively, these results strongly suggest the potential of PTGFRN expression as a diagnostic biomarker for LUAD. PTGFRN upregulation is associated with the poor survival of patients with LUAD Our subsequent analysis investigated the correlation between PTGFRN expression and LUAD patient prognosis. We employed data from TCGA, GEO, and other publicly available databases for Kaplan-Meier survival analysis. Four overall survival curves were generated from GEPIA2, Kaplan-Meier plotter, LinkedOmics, and Oncomine databases, all representing TCGA-LUAD datasets (Fig. 6 A-D). The blue curve represents low PTGFRN expression, while the red curve represents high expression. Notably, the blue curve consistently remained above the red curve, indicating significantly better overall survival for LUAD patients with low PTGFRN expression compared to those with high expression (p-value < 0.05). Furthermore, analysis of four additional LUAD datasets (GSE31210, GSE42127, GSE68465, GSE72092) retrieved from GEO yielded consistent results (Fig. 6 E-H). Collectively, these findings strongly suggest that high PTGFRN expression is associated with a poor prognosis in LUAD patients, supporting its potential as a prognostic biomarker. GSEA of PTGFRN LinkedOmics was utilized to perform GSEA on PTGFRN co-expressed genes. A volcano plot (Fig. 7 A) was generated to visualize the genes significantly correlated with PTGFRN. The volcano displays the log2 fold change of gene expression levels, with rows representing genes and columns representing samples. The color scale indicates the degree of upregulation (red) or downregulation (blue) of genes. Bubble plot of Gene Ontology (GO) enrichment analysis for differentially expressed genes(Fig. 7 B). The x-axis represents the -log10(p-adjusted value), with larger values indicating more significant enrichment. The y-axis shows the enriched GO terms, categorized into Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). The size of each bubble corresponds to the number of genes involved in the specific GO term, and the color gradient reflects the p-value significance, with darker colors indicating more significant enrichment. The Fig. 7 C bar chart illustrates the enriched KEGG pathways( www.kegg.jp/kegg/kegg1.html ), with the x-axis representing the -log10(p-adjusted value) and the y-axis showing the pathway names [ 15 – 17 ] . The bar length indicates the significance of pathway enrichment, and the color gradient reflects the p-value significance, with darker colors indicating more significant enrichment. Key pathways such as the Estrogen signaling pathway, Staphylococcus aureus infection, and Neuroactive ligand-receptor interaction are highlighted. Figure 7 D shows the GSEA results. The plot shows the running enrichment score (y-axis) across the ranked list of genes (x-axis). The ranked list metric indicates the position of genes in the ordered dataset. The enrichment score (ES) reflects the degree to which a gene set is overrepresented at the top or bottom of the ranked list. The plot highlights significant enrichment of pathways such as EPITHELIAL_MESENCHYMAL_TRANSITION, G2M_CHECKPOINT, and MITOTIC_SPINDLE in LUAD. Analysis and prediction of miRNAs associated with PTGFRN Given the critical role of non-coding RNAs (ncRNAs) in regulating gene expression, we investigated their potential influence on PTGFRN. StarBase ( https://rnasysu.com/encori/ ) was employed to predict miRNAs that might interact with PTGFRN. This analysis identified four candidate miRNAs. Interestingly, all four miRNAs (hsa-mir-29c-3p, hsa-mir-30b-5p, hsa-mir-30e-5p, and hsa-mir-532-5p) exhibited a significant negative correlation with LUAD expression. Subsequently, survival analysis was performed on these four miRNAs. The results, presented in Fig. 8 A-H, revealed that increased expression of all four miRNAs in LUAD patients correlated with a more favorable prognosis. Correlation between the expression of PTGFRN and the degree of immune cell infiltration in LUAD The number of tumor-infiltrating lymphocytes (TILs) is a crucial prognostic indicator for cancer patient outcomes and their response to immunotherapy. RNA sequencing expression data and corresponding clinical information for LUAD were downloaded from TCGA ( https://portal.gdc.com ). Count data was converted to transcripts per million (TPM) and normalized using log2(TPM + 1) while preserving sample and clinical information. To ensure reliable immune score evaluation, the "immuneconv" R software package was employed. This package integrates six well-established algorithms, including TIMER, xCell, MCP-counter, CIBERSORT, EPIC, and quanTIseq. Each of these algorithms has undergone benchmark testing and possesses unique strengths. Analysis and visualization were performed using the R package ggClusterNet. Our research findings demonstrate that using the CIBERSORT algorithm, high PTGFRN expression exhibited a positive correlation with M0 macrophages and neutrophils while displaying a negative correlation with immune effector cells such as CD8 + T cells and natural killer (NK) cells (Fig. 9 A-B). Similar results were obtained using xCell, MCP-counter, and EPIC algorithms: PTGFRN expression correlated negatively with immune effector cells and positively with immunosuppressive cells (Fig. 9 C-E). Furthermore, the relationship between PTGFRN expression and immune cell function was explored using the TISDB database. A significant negative correlation was observed between PTGFRN and immune enhancers, including KLRK1 (r = − 0.251, p = 8.12e-09), CD48 (r = − 0.221, p = 4.4e-09), TNFSF13 (r = − 0.182, p = 3.1e-05), and TNFRSF14 (r = − 0.233, p = 1.12e-05) (Fig. 10 A). Conversely, PTGFRN displayed a strong positive correlation with immunosuppressive factors like TGFBR1 (r = 0.238, p = 4.5e-08), CTLA4 (r = 0.147, p = 8e-04), PVRL2 (r = 0.143, p = 0.0011), and KDR (r = 0.128, p = 0.00363) (Fig. 10 B). Correlation analysis between PTGFRN expression and drug sensitivity To explore potential therapeutic applications of PTGFRN in LUAD, we utilized the CTRP drug sensitivity database available through the GSCALite online platform. This analysis aimed to identify correlations between PTGFRN gene expression and sensitivity to various small-molecule drugs. The results revealed a significant positive correlation between PTGFRN expression levels and sensitivity to these drugs (Fig. 11 A). Subsequently, computer-aided molecular docking simulations were performed on the drugs exhibiting the strongest positive correlations with PTGFRN protein expression. The four drugs with the highest predicted binding affinity are presented in Fig. 11 B-E: Sotrastaurin, Teniposide, Tacedinaline, and Topotecan. Statistical analysis Statistical analyses were performed using R version 4.3.1 software. Quantitative data are presented as mean ± standard deviation. One-way analysis of variance (ANOVA) or two-tailed Student's t-test were employed for comparisons between groups, depending on the number of groups being compared. Non-parametric methods were avoided in favor of these parametric tests whenever possible. Qualitative data were analyzed using chi-square (χ²) tests. The P-values for each analysis are marked on the chart, and the statistical significance level is defined as P < 0.05 (* P < 0.05; * * P < 0.01; * * P < 0.001; **** P < 0.0001). Discussion This study employed bioinformatics approaches to analyze the expression patterns of PTGFRN in pan-cancer and lung adenocarcinoma (LUAD) and its potential clinical implications. The results revealed that PTGFRN was upregulated in multiple cancer types and associated with poor prognosis in LUAD. These findings are consistent with the pro-tumorigenic role of PTGFRN experimentally validated by Marquez et al. [ 18 ] , further supporting its potential value as a tumor biomarker. It should be emphasized that as a bioinformatics analysis, the conclusions of this study require validation through subsequent experimental investigations. In particular, the specific molecular mechanisms of PTGFRN in LUAD and its feasibility as a therapeutic target warrant further confirmation through cellular and animal experiments. Our investigation revealed pan-cancer upregulation of PTGFRN, with high expression significantly associated with a poorer prognosis. These findings suggest PTGFRN as a potential pan-cancer diagnostic and prognostic biomarker. Furthermore, our analysis demonstrated that DNA methylation and copy number amplification influence PTGFRN expression. Focusing on LUAD, we observed significant PTGFRN overexpression compared to normal lung tissues. This upregulation, consistent across multiple datasets, suggests a potential oncogenic role for PTGFRN in LUAD. This finding aligns with previous studies implicating PTGFRN in various cancers, highlighting its broader importance in tumorigenesis [ 19 – 22 ] . Additionally, elevated PTGFRN expression significantly correlated with poorer overall survival in LUAD patients. Collectively, these findings suggest PTGFRN as a potential prognostic biomarker for predicting patient outcomes and informing treatment decisions in LUAD. Functional enrichment analysis identified potential biological processes and signaling pathways associated with PTGFRN dysregulation in LUAD. Our findings suggest that PTGFRN may modulate key oncogenic processes, including protein digestion and absorption, ECM-receptor interaction, focal adhesion, and microRNAs in cancer. Furthermore, PTGFRN may interact with other proteins, participating in protein-protein interaction networks implicated in cancer-related functions. Marquez et al. discovered that silencing PTGFRN expression affects cellular autophagy processes, thereby revealing another potential pathway through which PTGFRN may contribute to the cancer cell phenotype [ 23 ] .This highlights the multifaceted role of PTGFRN in lung adenocarcinoma biology. The immunosuppressive tumor microenvironment has been well-established as a factor contributing to poor patient prognosis [ 24 ] . To investigate the immune landscape within LUAD, we employed various algorithms to estimate immune cell infiltration levels. Our analysis revealed a negative correlation between PTGFRN expression and the abundance of immune effector cells, including CD4 + and CD8 + T cells, as well as activated NK cells. Conversely, a positive correlation was observed between PTGFRN expression and immunosuppressive cells such as M0 macrophages and neutrophils. This trend was consistently observed across multiple analytical methods. These findings collectively suggest that LUAD tissues with high PTGFRN expression exhibit an immunosuppressive microenvironment, potentially contributing to the poorer prognosis observed in LUAD patients with high PTGFRN expression. Furthermore, we investigated the correlation between PTGFRN expression and immune regulatory genes. The results demonstrated a negative correlation between PTGFRN and both immune-activating and immunosuppressive genes in LUAD. These findings suggest a potential immunomodulatory role for PTGFRN in lung adenocarcinoma. Current evidence demonstrates that PTGFRN knockout enhances pathways associated with innate immune responses. PTGFRN has been identified to primarily interact with tetraspanins CD9, CD81, and CD151 (the latter only when complexed with CD9). As a tetraspanin family member, PTGFRN may impair antitumor immune responses by forming membrane protein complexes that disrupt MHC class I surface localization [ 25 ] . Furthermore, as a key component of exosomes [ 26 ] , PTGFRN overexpression potentially increases secretion of immunosuppressive exosomes containing TGF-β and IL-10, thereby promoting M2 macrophage polarization and T-cell suppression. These findings provide a theoretical foundation for targeting PTGFRN in immunotherapy, although the precise molecular mechanisms require further validation through PTGFRN gene-editing models and immune cell co-culture experiments. In conclusion, our data and published studies support a potential immunomodulatory role for PTGFRN, with LUAD patients expressing high levels of PTGFRN potentially existing in an immunosuppressive state. Correlation analysis of gene expression and drug sensitivity revealed a pronounced sensitivity to small molecule drugs, including Sotrastaurin, Teniposide, Tacedinaline, and Topotecan, in the PTGFRN high expression group. While the primary targets of these drugs are well-defined, their structures may possess secondary binding sites with other proteins. This concept has guided the development of multi-targeted anticancer drugs to maximize efficacy, and PTGFRN represents a promising lead for further exploration. Sotrastaurin (AEB071) is a novel and potent PKC inhibitor that regulates PKCδ, thereby inhibiting cancer stem-like characteristics, chemotherapy resistance, and metastasis [ 27 ] . Teniposide acts specifically during the S and G2 cell cycle phases, preventing mitosis [ 28 ] . Tacedinaline, a histone deacetylase (HDAC) inhibitor, induces histone hyperacetylation in tumor cells, causing G1 phase cell cycle arrest [ 29 ] . Finally, Topotecan, an orally effective topoisomerase I inhibitor, promotes tumor cell apoptosis by inducing G0/G1 and S phase arrest [ 30 ] . As a tetraspanin family protein, PTGFRN possesses multiple interaction domains capable of accommodating structurally diverse ligands [ 31 ] . Our molecular docking results identified strong binding sites for these four drugs on the PTGFRN protein structure, suggesting their potential as anti-LUAD drugs with the added benefit of targeting PTGFRN. Further research will involve a more active and in-depth investigation of these promising candidates. Overall, this comprehensive bioinformatics analysis has shed light on the multifaceted role of PTGFRN in LUAD, providing valuable insights into its underlying molecular mechanisms and potential clinical significance. These findings warrant further experimental validation to definitively elucidate the precise role of PTGFRN in LUAD progression. Moreover, targeted therapeutic strategies aimed at modulating PTGFRN activity offer a promising avenue for improving patient outcomes in this aggressive malignancy. This study has potential limitations. Firstly, as our analysis relied on publicly available datasets, we were constrained by variations in data quality across different sequencing platforms and institutions. While we applied rigorous normalization procedures (including ComBat for batch effect correction) and only included datasets with standardized clinical annotations, residual technical biases may persist. Secondly, the inherent heterogeneity in patient populations (e.g., differences in staging, treatment history, and genetic background across cohorts) could influence the generalizability of our results. Thirdly, although we observed consistent patterns across multiple independent datasets (TCGA, GEO: GSE31320), prospective validation in uniformly processed clinical samples would strengthen our conclusions. Finally, the lack of in vitro/in vivo experiments means the causal relationship between PTGFRN and tumorigenesis remains hypothetical. These limitations highlight the need for future studies incorporating standardized multi-center cohorts with matched molecular and clinical data. CONCLUSIONS In conclusion, this study offers compelling evidence supporting PTGFRN as a potential prognostic biomarker and therapeutic target in LUAD. The observed associations between PTGFRN expression and poor prognosis, the immunosuppressive microenvironment, and differential drug sensitivity highlight its multifaceted role in LUAD pathogenesis. Moving forward, efforts should prioritize experimental validation of these findings and the exploration of targeted therapeutic strategies that modulate PTGFRN activity in LUAD. Abbreviations NSCLC Non-small-cell lung cancer LUAD Lung adenocarcinoma TKIs Tyrosine kinase inhibitors PTGFRN Prostaglandin F2 receptor negative regulator TCGA The Cancer Genome Atlas GEO Gene Expression Omnibus GTEx Genotype-Tissue Expression ACC Adrenocortical carcinoma, BLCA Bladder urothelial carcinoma, BRCA Breast invasive carcinoma CESC Cervical squamous cell carcinoma and cervical adenocarcinoma CHOL Cholangiocarcinoma COAD Colon cancer READ Rectal adenocarcinoma DLBC Lymphomas diffuse large B-cell lymphoma ESCA Esophageal cancer GBM Glioblastoma multiforme HNSC Head and neck squamous cell carcinoma KICH Renal chromophobe KIRC Renal clear cell carcinoma KIRP Renal papillary cell carcinoma LAML Acute myeloid leukemia LGG Low-grade glioma LIHC Hepatocellular carcinoma LUSC Lung squamous cell carcinoma MESO Mesothelioma OV Ovarian serous cystadenocarcinoma PAAD Pancreatic cancer PCPG Pheochromocytoma and paraganglioma PRAD Prostate cancer READ Rectal adenocarcinoma SARC Sarcoma SKCM Skin melanoma STAD Gastric adenocarcinoma STES Gastric and esophageal cancer TGCT Testicular germ cell tumor THCA Thyroid cancer THYM Thymoma UCEC Endometrial cancer UVM Uveal melanoma HPA Human Protein Atlas OS Overall survival DSS Disease-specific survival RFS Recurrence-free survival PFS Progression-free survival AJCC American Joint Committee on Cancer DEGs Differentially expressed genes GSEA Gene set enrichment analysis GO Gene Ontology BP Biological processes CC Cellular component MF Molecular function KEGG Kyoto Encyclopedia of Genes and Genomes ceRNAs Competing endogenous RNAs ncRNAs Non-coding RNAs GSCA Gene Set Cancer Analysis GDSC Genomics of Drug Sensitivity in Cancer CPTAC Clinical Proteomic Tumor Analysis Consortium CNVs Copy number variations TILs Tumor-infiltrating lymphocytes TPM Tanscripts per million NK Natural killer ANOVA One-way analysis of variance Declarations DATA AVAILABILITY STATEMENT The datasets for this study can be found in the public databases TCGA, GTEx, TIMER 2.0, GDSA, and cBioportal. AUTHOR CONTRIBUTIONS All authors contributed equally. CONFLICTS OF INTEREST The authors declare that there are no conflicts of interest. FUNDING This study was supported by the medical science research project plan of Hebei Provincial Health Commission (No. 20210980). References Thai, A. A., Solomon, B. J., Sequist, L. V., Gainor, J. F. & Heist, R. S. Lung cancer. Lancet 398 (10299), 535–554 (2021). Wei, X., Li, X., Hu, S., Cheng, J. & Cai, R. Regulation of Ferroptosis in Lung Adenocarcinoma. Int. J. Mol. Sci. 24 (19), 14614 (2023). Hanahan, D. Hallmarks of Cancer: New Dimensions. Cancer Discov . 12 (1), 31–46 (2022). Mala, U., Baral, T. K. & Somasundaram, K. Integrative analysis of cell adhesion molecules in glioblastoma identified prostaglandin F2 receptor inhibitor (PTGFRN) as an essential gene. BMC Cancer . 22 (1), 642 (2022). Ding, Y. et al. EWI2 and its relatives in Tetraspanin-enriched membrane domains regulate malignancy. Oncogene 42 (12), 861–868 (2023). Marquez, J. et al. 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Marquez, J., Dong, J., Hayashi, J. & Serrero, G. Prostaglandin F2 Receptor Negative Regulator (PTGFRN) Expression Correlates With a Metastatic-like Phenotype in Epidermoid Carcinoma, Pediatric Medulloblastoma, and Mesothelioma. J. Cell. Biochem. 125 (8), e30616 (2024). Janes, P. W., Vail, M. E., Ernst, M. & Scott, A. M. Eph Receptors in the Immunosuppressive Tumor Microenvironment. Cancer Res. 81 (4), 801–805 (2021). Zimmermann, P. & Rubinstein, E. Differential proteomics argues against a general role for CD9, CD81 or CD63 in the sorting of proteins into extracellular vesicles. J. Extracell. Vesicles . 12 (8), e12352 (2023). Wan, Y. et al. Engineered extracellular vesicles efficiently deliver CRISPR-Cas9 ribonucleoprotein (RNP) to inhibit herpes simplex virus1 infection in vitro and in vivo. Acta Pharm. Sin B . 14 (3), 1362–1379 (2024). El-Gamal, D. et al. PKC-β as a therapeutic target in CLL: PKC inhibitor AEB071 demonstrates preclinical activity in CLL. 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Supplementary Files SupplementaryMaterial.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5040921","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":448529240,"identity":"f724cf3f-8ff3-4462-9e03-27799df6adb5","order_by":0,"name":"Qi Zhao","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Zhao","suffix":""},{"id":448529244,"identity":"a21c0fee-8f97-4dcb-af4e-307aa2efebc6","order_by":1,"name":"Xin Chen","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Chen","suffix":""},{"id":448529245,"identity":"fd3e4a17-3b1b-4ed8-9504-ee32c104dda8","order_by":2,"name":"Xiyan Zhu","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiyan","middleName":"","lastName":"Zhu","suffix":""},{"id":448529247,"identity":"06fe6cf5-01a4-475a-94bb-b856cc18d0b5","order_by":3,"name":"Xing Cui","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Cui","suffix":""},{"id":448529248,"identity":"e499b226-4563-44df-bdc4-3a4dad08d0eb","order_by":4,"name":"Xinjian Xu","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinjian","middleName":"","lastName":"Xu","suffix":""},{"id":448529249,"identity":"ba781502-f989-4fe6-8590-44622f84a3ed","order_by":5,"name":"Nan Mu","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Mu","suffix":""},{"id":448529250,"identity":"b191de8e-c95a-41b9-8111-2976fae8913b","order_by":6,"name":"Shuping Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYHACxscMDAfALAlitTAbk6yFTZo0LfL+Z49VF1TciTY4wHzwNg+DXR5BLYY38tJuzzjzLHfDAbZkax6G5GLCWmbwmN3mbTsM1MJjJs3DcCCxgaCW/jNmxbz/QFr4vxGnRZ4hx4yZtwFsCxtxWgwk8pKleY49y515mM3Yco5BMhG29J89+Jmn5k5u3/HmhzfeVNgRYcsBHiiLGcwlpB5kSwMPYUWjYBSMglEwwgEAWaY9y1zQe5cAAAAASUVORK5CYII=","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shuping","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2024-09-06 02:00:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5040921/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5040921/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81539758,"identity":"fd024671-7023-4b74-a28f-aa8a92051af8","added_by":"auto","created_at":"2025-04-28 11:04:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":623884,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePTGFRN is upregulated in multiple cancer types.\u003c/strong\u003e (A) PTGFRN mRNA expression in 33 types of cancer based on TCGA and GTEx databases. Red and blue represent tumor (T) and normal tissue (N), respectively. (B) The PTGFRN protein-coding transcripts based on TCGA and GTEx databases were upregulated in 33 types of cancer. The red and blue squares represent up and down, respectively. (C) Expression of PTGFRN protein in 11 cancer types based on the Clinical Proteomics Cancer Analysis Alliance database. PTGFRN, Prostaglandin F2 Receptor Inhibitor; TCGA, The Cancer Genome Atlas.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/125ec3be17fc5e359748c677.png"},{"id":81539766,"identity":"d9c06730-e30f-4876-b209-c7561b7cec59","added_by":"auto","created_at":"2025-04-28 11:04:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1037988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePTGFRN is regulated by DNA methylation and copy number variation. \u003c/strong\u003e\u0026nbsp;(A) DNA methylation analysis of 33 types of cancer. Green represents the promoter sub-region. (B) DNA copy number variation analysis of 33 types of cancer.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/a642a1e54e742dc487bd32d9.png"},{"id":81540848,"identity":"6411a466-505c-4e31-8787-dad3d7aa4a8e","added_by":"auto","created_at":"2025-04-28 11:12:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1770353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUniCox analysis of PTGFRN\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003eForest plots depicting univariable Cox regression analysis for OS, DSS, DFI, and PFI of PTGFRN expression in 33 TCGA cancer types. Red represents risk factors, and blue represents protective factors.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/808bc2be3c129775e4872443.png"},{"id":81539760,"identity":"69a90ec9-49e8-4f27-922e-2c455f34c901","added_by":"auto","created_at":"2025-04-28 11:04:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":824683,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier analysis of cancer patients with high or low expression of PTGFRN.\u003c/strong\u003e Kaplan-Meier survival analysis of OS with PTGFRN mRNA expression in multiple types of cancer\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/a7c832cebbd7bd536fd7f885.png"},{"id":81539763,"identity":"adecdde9-b729-4474-be5a-e6bfa6e80976","added_by":"auto","created_at":"2025-04-28 11:04:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1048424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePTGFRN expression in LUAD.\u003c/strong\u003e(A) PTGFRN expression between various tumor tissues and adjacent normal tissues in TCGA project. (B-C) The expression difference of PTGFRN in LUAD and corresponding normal tissues through GEPIA2 and Ualcan portal. (D) the difference of PTGFRN protein expression level between LUAD and corresponding normal tissues by Ualcan portal. (E) Representative images of immunohistochemical staining for PTGFRN in LUAD tissues and normal lung. The Scale bars in the image above is 100 μm and the scale bars in the image below is 25 μm.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/890d1dbec0c56d68d601b839.png"},{"id":81542382,"identity":"a7a2447f-4050-4d54-886a-50eb4fed12ad","added_by":"auto","created_at":"2025-04-28 11:20:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4048623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePTGFRN prognostic value was assessed by the Kaplan–Meier plot. \u003c/strong\u003e(A-H) High expression of PTGFRN is associated with poorer OS in different datasets\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/7aaa71532d4b02bd1b11d9f5.png"},{"id":81539762,"identity":"8298a0d8-c27c-4f0e-bc78-d4c0ea781820","added_by":"auto","created_at":"2025-04-28 11:04:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":432187,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA of PTGFRN co-expressed genes. \u003c/strong\u003e(A). PTGFRN co-expressed genes displayed in a volcano plot. (B). Bubble plot of Gene Ontology (GO) enrichment analysis for differentially expressed genes. (C). KEGG pathway enrichment analysis results. (D). Gene Set Enrichment Analysis (GSEA) results for PTGFRN-associated pathways in LUAD\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/975259cf41e9568f148036a7.png"},{"id":81542381,"identity":"eb355de8-4533-4572-824d-507b62904573","added_by":"auto","created_at":"2025-04-28 11:20:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":641324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of potential miRNAs of PTGFRN and their prognostic value in LUAD. \u003c/strong\u003e\u0026nbsp;(A/C/E/G). The expression correlation between hsa-mir-29c-3p, hsa-mir-30b-5p, hsa-mir-30e-5p, and hsa-mir-532-5p and PTGFRN. (B/D/F/H) The OS analysis for hsa-mir-29c-3p (B), hsa-mir-30b-5p (D), hsa-mir-30e-5p (F), and hsa-mir-532-5p (H) in LUAD. Differences in four miRNAs expression between the two groups were compared using hypergeometric tests: *\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/ee7845fb2fda04d29760b521.png"},{"id":81539778,"identity":"6f2312e1-30d8-4dcc-8e19-36aa472eaa5e","added_by":"auto","created_at":"2025-04-28 11:04:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1575502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between immune cell infiltration and the expression level of PTGFRN in LUAD.\u003c/strong\u003e (A) Differences in immune cell infiltration between high and low expression groups of PTGFRN based on the CIBERSORT. (B) Lollipop chart showing the correlation between PTGFRN and immune cell infiltration. (C) Correlation between PTGFRN and immune cell infiltration based on xCell (D) Correlation between PTGFRN and immune cell infiltration based on MCPcounter. (E) Correlation between PTGFRN and immune cell infiltration based on EPIC.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/5f776ce9c40ed67f6872b327.png"},{"id":81539757,"identity":"8de56ae5-35af-481c-8bcc-3a6351552770","added_by":"auto","created_at":"2025-04-28 11:04:45","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":450029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between PTGFRN expression and immunomodulator. \u003c/strong\u003e(A) Immunostimulators, (B)Immunoinhibitors,\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/ee642d7cbb0ad7d28a90b8f8.png"},{"id":81539771,"identity":"9016d5b1-eee0-4b72-828e-d675103f3df3","added_by":"auto","created_at":"2025-04-28 11:04:45","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":453415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis between PTGFRN expression and drug sensitivity. \u003c/strong\u003e(A) Correlation between CTRP drug sensitivity and PTGFRN mRNA expression. (B-E) Molecular docking diagram of Sotrastaurin, Teniposide, Tacedinaline, and Topotecan.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/66acaf00c81676ba603fb740.png"},{"id":82630522,"identity":"dd291e2d-fc6d-423d-8d2c-38e4cf5e143e","added_by":"auto","created_at":"2025-05-13 13:39:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12124992,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/0d4035d7-2a1a-44cd-9e1e-4f2051fa0579.pdf"},{"id":81539759,"identity":"edecd28e-6f59-4b60-be5d-9de8a8373b4b","added_by":"auto","created_at":"2025-04-28 11:04:45","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4292848,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5040921/v1/be695dbf0376a041e49a7e13.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive analysis of the role of PTGFRN as a new potential biomarker in lung adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer, also known as lung carcinoma, is a leading cause of cancer-related mortality among men in industrialized nations, particularly non-small-cell lung cancer (NSCLC), which comprises approximately 85% of cases. Lung adenocarcinoma (LUAD) is the most prevalent pathological subtype\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Despite advancements in treatment strategies such as small-molecule tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), many advanced LUAD patients die from the disease due to drug resistance and insensitivity, leading to poor long-term outcomes\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Early diagnosis remains crucial for effective LUAD management. However, current prognostic predictors predominantly rely on gene chips, which lack specificity and add an economic burden to patients. Thus, there is an urgent need to identify sensitive and cost-effective novel biomarkers for early LUAD diagnosis and prognosis.\u003c/p\u003e \u003cp\u003eTumor angiogenesis, a hallmark of cancer, plays a critical role in the metastasis and progression of solid tumors\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Prostaglandin F2 receptor negative regulator (PTGFRN), a type I transmembrane Ig superfamily protein, is frequently upregulated in various cancers\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Studies suggest that PTGFRN overexpression can induce tumor angiogenesis and facilitate tumor growth\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Furthermore, PTGFRN has been implicated in promoting tumor cell proliferation, migration, invasion, and cell cycle progression\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Brittany et al. reported PTGFRN's involvement in driving the onset and advancement of brain glioma through increased PI3K/AKT signaling and p110β stability\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. However, the expression and potential prognostic significance of PTGFRN in LUAD remain unclear.\u003c/p\u003e \u003cp\u003eThis study leveraged datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and other public cancer databases (e.g., cBioPortal, TIMER 2.0) to comprehensively explore the association between PTGFRN expression and immune infiltration in LUAD. Additionally, we investigate PTGFRN mutations and their regulatory network in LUAD. Through this multidimensional analysis, our findings aim to shed light on PTGFRN as a promising novel biomarker for diagnosing and prognosticating LUAD.\u003c/p\u003e"},{"header":"Materials and Methods","content":" \u003cp\u003e2.1 Data source\u003c/p\u003e \u003cp\u003eRNA sequencing data (transcripts per million) for normal and tumor tissues were obtained from TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.gov/Funded-Programs-Projects/Cancer-Genome-Atlas\u003c/span\u003e\u003cspan address=\"https://www.genome.gov/Funded-Programs-Projects/Cancer-Genome-Atlas\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Genotype-Tissue Expression (GTEx) project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/home/index.html\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/home/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Log transformation was applied to the data to evaluate changes in PTGFRN expression across various cancer types. The analyzed cancer types included adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and cervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon cancer (COAD), rectal adenocarcinoma (READ), lymphomas diffuse large B-cell lymphoma (DLBC), esophageal cancer (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), renal chromophobe (KICH), renal clear cell carcinoma (KIRC), renal papillary cell carcinoma (KIRP), and acute myeloid leukemia (LAML), low-grade glioma (LGG), hepatocellular carcinoma (LIHC), LUAD, lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic cancer (PAAD), pheochromocytoma and paraganglioma (PCPG) Prostate cancer (PRAD), rectal adenocarcinoma (READ), sarcoma (SARC), skin melanoma (SKCM), gastric adenocarcinoma (STAD), gastric and esophageal cancer (STES), testicular germ cell tumor (TGCT), thyroid cancer (THCA), thymoma (THYM), endometrial cancer (UCEC), and uveal melanoma (UVM).\u003c/p\u003e\n\u003ch3\u003e2.2 TIMER2.0\u003c/h3\u003e\n\u003cp\u003eTIMER 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.comp-genomics.org/\u003c/span\u003e\u003cspan address=\"http://timer.comp-genomics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is an online resource that estimates immune infiltration levels in tumor tissues based on gene expression profiles. It provides six algorithms and multiple modules to explore the association between tumor immunity and genetic or clinical characteristics\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. In this study, we utilized TIMER 2.0 to analyze the expression differences of PTGFRN in lung adenocarcinoma tissues compared to adjacent healthy tissues. Additionally, we investigated the correlation between PTGFRN expression and the abundance of various immune cell types infiltrating the tumor microenvironment.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.3 GEPIA2\u003c/h2\u003e \u003cp\u003eGEPIA2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/#index\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/#index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an online analysis tool that utilizes data from TCGA and GTEx, was employed in this study\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. We leveraged GEPIA2 to analyze the expression differences of PTGFRN in lung adenocarcinoma tissues compared to adjacent normal tissues. Additionally, we investigated the relationship between PTGFRN expression and the overall survival of lung adenocarcinoma patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.4 ULACAN\u003c/h3\u003e\n\u003cp\u003eWe employed UALCAN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu/index.html\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a user-friendly, interactive web portal for analyzing TCGA gene expression data\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, to investigate PTGFRN expression differences in LUAD tissues compared to adjacent normal tissues. Additionally, we assessed the correlation between PTGFRN expression and various clinicopathological features, including age, gender, race, smoking history, tumor stage, and metastasis. Welch's T-test was employed to evaluate the statistical significance of PTGFRN expression differences between the two groups.\u003c/p\u003e \u003cp\u003e2.5 Human Protein Atlas\u003c/p\u003e \u003cp\u003eTo further explore PTGFRN protein expression, we utilized the Human Protein Atlas (HPA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a comprehensive resource that integrates proteomic, transcriptomic, and systems biology data. HPA facilitates the exploration of protein expression profiles across various tissues, cells, and organs. Notably, the database provides access to immunohistochemical reports and patient survival curves for numerous cancers\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In this study, we specifically examined immunohistochemistry data using the HPA antibody HPA017074. This antibody reports PTGFRN protein levels in selected tissues, categorized as undetected, minimal, moderate, or strong based on staining intensity. By analyzing these images, we aimed to compare PTGFRN protein levels between LUAD tissues and normal tissues.\u003c/p\u003e \u003cp\u003e2.6 Kaplan-Meier plotter analysis\u003c/p\u003e \u003cp\u003eThe Kaplan-Meier plotter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com/analysis/\u003c/span\u003e\u003cspan address=\"http://kmplot.com/analysis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a web-based tool for survival analysis\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, was employed to investigate the relationship between PTGFRN expression and overall survival (OS), disease-specific survival (DSS), recurrence-free survival (RFS), and progression-free survival (PFS) in LUAD patients. This resource integrates data from GEO, EGA, and TCGA databases, enabling the evaluation of gene expression correlation with patient survival across over 30,000 samples from 21 tumor types. In our study, we specifically analyzed the association of PTGFRN expression with various clinicopathological features (gender, AJCC stage T, race, grade, stage, alcohol consumption, vascular invasion) in LUAD. Using the median expression value as a cutoff, we assessed the impact of high versus low PTGFRN expression on patient survival outcomes using a total of 364, 370, 316, and 362 samples for OS, DSS, RFS, and PFS, respectively.\u003c/p\u003e \u003cp\u003e2.7 LinkedOmics\u003c/p\u003e \u003cp\u003eLinkedOmics can perform multi-omics analysis on TCGA datasets (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.linkedomics.org/login.php\u003c/span\u003e\u003cspan address=\"http://www.linkedomics.org/login.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In this study, the TCGA-LUAD was selected for analysis, including 515 LUAD patients. Differentially expressed genes (DEGs) related to PTGFRN were obtained from the LinkFinder module. Pearson correlation coefficient analysis was performed, and the results were represented using heat maps and volcano maps. Additionally, gene set enrichment analysis (GSEA) was performed using the LinkInterpreter modules for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG).\u003c/p\u003e \u003cp\u003e2.8 StarBase\u003c/p\u003e \u003cp\u003eStarBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://starbase.sysu.edu.cn/\u003c/span\u003e\u003cspan address=\"http://starbase.sysu.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a website specializing in non-coding RNA research, offers predictions and detection of non-coding RNA (lncRNA, circRNA, etc.) and mRNA targets, competing endogenous RNAs (ceRNAs), and RNA binding proteins\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. To identify potential regulators of PTGFRN expression in LUAD, we employed StarBase to search for miRNAs targeting LUAD. We then selected four specific miRNAs (hsa-mir-29c-3p, hsa-mir-30b-5p, hsa-mir-30e-5p, and hsa-mir-532-5p) predicted to bind to PTGFRN in more than four cancer types.\u003c/p\u003e\n\u003ch3\u003e2.9 TISIDB\u003c/h3\u003e\n\u003cp\u003eTISIDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is an online database that integrates tumor immune-related genes, literature, high-throughput screening, immunotherapy, TILs, immune modulators, chemical substances, and other types of data RNA-Seq data of 515 TCGA-LUAD patients in the TISIDB database was used to analyze the correlation between PTGFRN expression and chemokines and immune modulators. P values below 0.05 and correlation coefficients above or below \u0026minus;\u0026thinsp;0.2 were considered statistically significant.\u003c/p\u003e\n\u003ch3\u003e2.10 GSCA\u003c/h3\u003e\n\u003cp\u003eGene Set Cancer Analysis (GSCA) is a web-based platform for in-depth cancer research. It facilitates analysis at various levels, including single genes, multi-gene sets, immune infiltration, mutations, and drug sensitivity. GSCA integrates data from TCGA and GTEx, encompassing 33 cancer types. Additionally, it incorporates clinical information and over 750 small-molecule drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. This comprehensive resource empowers researchers to identify potential biomarkers and explore promising drug candidates. We utilized GSCA to investigate the correlation between PTGFRN expression levels and anti-tumor drugs.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePTGFRN is upregulated in various types of cancer\u003c/h2\u003e \u003cp\u003eWe initially analyzed PTGFRN expression by integrating data from TCGA and GTEx. Tumor and normal tissue samples were compared, with a significance threshold set at log2 (fold change)\u0026thinsp;\u0026gt;\u0026thinsp;1. This analysis revealed significant upregulation of PTGFRN in 19 tumor types (BLCA, BRCA, CESC, etc.; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and downregulation in 5 others (COAD, OV, PRAD, etc.). To further validate pan-cancer overexpression, we examined the expression of three PTGFRN transcripts: one protein-coding and two non-coding (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Notably, the protein-coding transcript displayed significant increases across multiple cancers, while non-coding transcripts showed significant decreases in several. Finally, protein-level expression analysis using the CPTAC database identified upregulation of PTGFRN in four cancer types (GBM, HNSC, LSCC, LUAD) and downregulation in 3 others (HCC, OV, PDAC) across 12 datasets(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Collectively, these findings indicate PTGFRN upregulation in various cancers, suggesting its potential role in promoting cancer development.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePTGFRN methylation and CNV alterations in pan-cancer\u003c/h3\u003e\n\u003cp\u003eSince DNA methylation and copy number variations (CNVs) are known to influence gene expression, we investigated their impact on PTGFRN. Promoter region methylation of PTGFRN exhibited a significant negative correlation with PTGFRN expression in most cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Additionally, pan-cancer analysis of PTGFRN CNVs revealed a predominance of high-level amplifications and single-copy deletions(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These findings collectively suggest that DNA methylation and CNVs play a role in regulating PTGFRN gene expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eThe prognostic value of PTGFRN in pan-cancer\u003c/h3\u003e\n\u003cp\u003eTo evaluate the prognostic significance of PTGFRN, we analyzed its expression in relation to patient outcomes in the TCGA database. UniCox analysis identified PTGFRN as a risk factor for OS in patients with SKCM, READ, LUAD, LIHC, LGG, KIRC, KICH, and ACC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, PTGFRN emerged as a risk factor for DSS, DFI, and PFI in multiple cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Kaplan-Meier analysis for OS revealed that high PTGFRN expression significantly predicted poorer patient outcomes across various cancers, including ACC, BLCA, COAD, GBM, KRIC, LGG, LIHC, LUAD, LUSC, MESO, READ, SARC, and UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Collectively, these findings suggest PTGFRN as a potential novel pan-cancer prognostic biomarker.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePTGFRN exhibited significant upregulation in LUAD\u003c/h2\u003e \u003cp\u003eGiven the pan-cancer analysis suggesting PTGFRN as a risk factor for various patient outcomes in LUAD, we further explored its specific role in this cancer type. We initially utilized the TIMER2 database to evaluate PTGFRN expression differences between LUAD tumor tissues and adjacent normal tissues within the TCGA project. Consistent with our pan-cancer analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), PTGFRN expression was significantly elevated in BRCA, CHOL, ESCA, and other cancer types compared to normal tissues. Notably, LUAD displayed a significantly higher level of PTGFRN expression compared to its corresponding normal tissue (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). To strengthen this observation, we further analyzed PTGFRN expression in LUAD using GEPIA2 and UALCAN portals (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). All platforms consistently demonstrated significantly higher PTGFRN expression in LUAD tissues compared to controls. Student's t-test was employed for statistical analysis. Additionally, UALCAN analysis revealed a significant increase in total PTGFRN protein levels within LUAD tissues (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Finally, immunohistochemical analysis through the Human Protein Atlas portal corroborated these findings, demonstrating elevated PTGFRN expression in LUAD tissues compared to adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Collectively, these results strongly suggest the potential of PTGFRN expression as a diagnostic biomarker for LUAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePTGFRN upregulation is associated with the poor survival of patients with LUAD\u003c/h2\u003e \u003cp\u003eOur subsequent analysis investigated the correlation between PTGFRN expression and LUAD patient prognosis. We employed data from TCGA, GEO, and other publicly available databases for Kaplan-Meier survival analysis. Four overall survival curves were generated from GEPIA2, Kaplan-Meier plotter, LinkedOmics, and Oncomine databases, all representing TCGA-LUAD datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-D). The blue curve represents low PTGFRN expression, while the red curve represents high expression. Notably, the blue curve consistently remained above the red curve, indicating significantly better overall survival for LUAD patients with low PTGFRN expression compared to those with high expression (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, analysis of four additional LUAD datasets (GSE31210, GSE42127, GSE68465, GSE72092) retrieved from GEO yielded consistent results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-H). Collectively, these findings strongly suggest that high PTGFRN expression is associated with a poor prognosis in LUAD patients, supporting its potential as a prognostic biomarker.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGSEA of PTGFRN\u003c/h2\u003e \u003cp\u003eLinkedOmics was utilized to perform GSEA on PTGFRN co-expressed genes. A volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA) was generated to visualize the genes significantly correlated with PTGFRN. The volcano displays the log2 fold change of gene expression levels, with rows representing genes and columns representing samples. The color scale indicates the degree of upregulation (red) or downregulation (blue) of genes. Bubble plot of Gene Ontology (GO) enrichment analysis for differentially expressed genes(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The x-axis represents the -log10(p-adjusted value), with larger values indicating more significant enrichment. The y-axis shows the enriched GO terms, categorized into Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). The size of each bubble corresponds to the number of genes involved in the specific GO term, and the color gradient reflects the p-value significance, with darker colors indicating more significant enrichment. The Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC bar chart illustrates the enriched KEGG pathways(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://www.genome.gov/Funded-Programs-Projects/Cancer-Genome-Atlas\" target=\"_blank\"\u003ewww.kegg.jp/kegg/kegg1.html\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.kegg.jp/kegg/kegg1.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with the x-axis representing the -log10(p-adjusted value) and the y-axis showing the pathway names\u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The bar length indicates the significance of pathway enrichment, and the color gradient reflects the p-value significance, with darker colors indicating more significant enrichment. Key pathways such as the Estrogen signaling pathway, Staphylococcus aureus infection, and Neuroactive ligand-receptor interaction are highlighted. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD shows the GSEA results. The plot shows the running enrichment score (y-axis) across the ranked list of genes (x-axis). The ranked list metric indicates the position of genes in the ordered dataset. The enrichment score (ES) reflects the degree to which a gene set is overrepresented at the top or bottom of the ranked list. The plot highlights significant enrichment of pathways such as EPITHELIAL_MESENCHYMAL_TRANSITION, G2M_CHECKPOINT, and MITOTIC_SPINDLE in LUAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis and prediction of miRNAs associated with PTGFRN\u003c/h2\u003e \u003cp\u003eGiven the critical role of non-coding RNAs (ncRNAs) in regulating gene expression, we investigated their potential influence on PTGFRN. StarBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to predict miRNAs that might interact with PTGFRN. This analysis identified four candidate miRNAs. Interestingly, all four miRNAs (hsa-mir-29c-3p, hsa-mir-30b-5p, hsa-mir-30e-5p, and hsa-mir-532-5p) exhibited a significant negative correlation with LUAD expression. Subsequently, survival analysis was performed on these four miRNAs. The results, presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-H, revealed that increased expression of all four miRNAs in LUAD patients correlated with a more favorable prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between the expression of PTGFRN and the degree of immune cell infiltration in LUAD\u003c/h2\u003e \u003cp\u003eThe number of tumor-infiltrating lymphocytes (TILs) is a crucial prognostic indicator for cancer patient outcomes and their response to immunotherapy. RNA sequencing expression data and corresponding clinical information for LUAD were downloaded from TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.com\u003c/span\u003e\u003cspan address=\"https://portal.gdc.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Count data was converted to transcripts per million (TPM) and normalized using log2(TPM\u0026thinsp;+\u0026thinsp;1) while preserving sample and clinical information. To ensure reliable immune score evaluation, the \"immuneconv\" R software package was employed. This package integrates six well-established algorithms, including TIMER, xCell, MCP-counter, CIBERSORT, EPIC, and quanTIseq.\u0026nbsp;Each of these algorithms has undergone benchmark testing and possesses unique strengths. Analysis and visualization were performed using the R package ggClusterNet. Our research findings demonstrate that using the CIBERSORT algorithm, high PTGFRN expression exhibited a positive correlation with M0 macrophages and neutrophils while displaying a negative correlation with immune effector cells such as CD8\u003csup\u003e+\u003c/sup\u003e T cells and natural killer (NK) cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-B). Similar results were obtained using xCell, MCP-counter, and EPIC algorithms: PTGFRN expression correlated negatively with immune effector cells and positively with immunosuppressive cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC-E). Furthermore, the relationship between PTGFRN expression and immune cell function was explored using the TISDB database. A significant negative correlation was observed between PTGFRN and immune enhancers, including KLRK1 (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.251, p\u0026thinsp;=\u0026thinsp;8.12e-09), CD48 (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.221, p\u0026thinsp;=\u0026thinsp;4.4e-09), TNFSF13 (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.182, p\u0026thinsp;=\u0026thinsp;3.1e-05), and TNFRSF14 (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.233, p\u0026thinsp;=\u0026thinsp;1.12e-05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Conversely, PTGFRN displayed a strong positive correlation with immunosuppressive factors like TGFBR1 (r\u0026thinsp;=\u0026thinsp;0.238, p\u0026thinsp;=\u0026thinsp;4.5e-08), CTLA4 (r\u0026thinsp;=\u0026thinsp;0.147, p\u0026thinsp;=\u0026thinsp;8e-04), PVRL2 (r\u0026thinsp;=\u0026thinsp;0.143, p\u0026thinsp;=\u0026thinsp;0.0011), and KDR (r\u0026thinsp;=\u0026thinsp;0.128, p\u0026thinsp;=\u0026thinsp;0.00363) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis between PTGFRN expression and drug sensitivity\u003c/h2\u003e \u003cp\u003eTo explore potential therapeutic applications of PTGFRN in LUAD, we utilized the CTRP drug sensitivity database available through the GSCALite online platform. This analysis aimed to identify correlations between PTGFRN gene expression and sensitivity to various small-molecule drugs. The results revealed a significant positive correlation between PTGFRN expression levels and sensitivity to these drugs (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA). Subsequently, computer-aided molecular docking simulations were performed on the drugs exhibiting the strongest positive correlations with PTGFRN protein expression. The four drugs with the highest predicted binding affinity are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB-E: Sotrastaurin, Teniposide, Tacedinaline, and Topotecan.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R version 4.3.1 software. Quantitative data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. One-way analysis of variance (ANOVA) or two-tailed Student's t-test were employed for comparisons between groups, depending on the number of groups being compared. Non-parametric methods were avoided in favor of these parametric tests whenever possible. Qualitative data were analyzed using chi-square (χ\u0026sup2;) tests. The P-values for each analysis are marked on the chart, and the statistical significance level is defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (* \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * * \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; * * \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ****\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employed bioinformatics approaches to analyze the expression patterns of PTGFRN in pan-cancer and lung adenocarcinoma (LUAD) and its potential clinical implications. The results revealed that PTGFRN was upregulated in multiple cancer types and associated with poor prognosis in LUAD. These findings are consistent with the pro-tumorigenic role of PTGFRN experimentally validated by Marquez et al. \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, further supporting its potential value as a tumor biomarker. It should be emphasized that as a bioinformatics analysis, the conclusions of this study require validation through subsequent experimental investigations. In particular, the specific molecular mechanisms of PTGFRN in LUAD and its feasibility as a therapeutic target warrant further confirmation through cellular and animal experiments.\u003c/p\u003e \u003cp\u003eOur investigation revealed pan-cancer upregulation of PTGFRN, with high expression significantly associated with a poorer prognosis. These findings suggest PTGFRN as a potential pan-cancer diagnostic and prognostic biomarker. Furthermore, our analysis demonstrated that DNA methylation and copy number amplification influence PTGFRN expression. Focusing on LUAD, we observed significant PTGFRN overexpression compared to normal lung tissues. This upregulation, consistent across multiple datasets, suggests a potential oncogenic role for PTGFRN in LUAD. This finding aligns with previous studies implicating PTGFRN in various cancers, highlighting its broader importance in tumorigenesis\u003csup\u003e[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Additionally, elevated PTGFRN expression significantly correlated with poorer overall survival in LUAD patients. Collectively, these findings suggest PTGFRN as a potential prognostic biomarker for predicting patient outcomes and informing treatment decisions in LUAD.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFunctional enrichment analysis identified potential biological processes and signaling pathways associated with PTGFRN dysregulation in LUAD. Our findings suggest that PTGFRN may modulate key oncogenic processes, including protein digestion and absorption, ECM-receptor interaction, focal adhesion, and microRNAs in cancer. Furthermore, PTGFRN may interact with other proteins, participating in protein-protein interaction networks implicated in cancer-related functions. Marquez et al. discovered that silencing PTGFRN expression affects cellular autophagy processes, thereby revealing another potential pathway through which PTGFRN may contribute to the cancer cell phenotype\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.This highlights the multifaceted role of PTGFRN in lung adenocarcinoma biology.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe immunosuppressive tumor microenvironment has been well-established as a factor contributing to poor patient prognosis\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. To investigate the immune landscape within LUAD, we employed various algorithms to estimate immune cell infiltration levels. Our analysis revealed a negative correlation between PTGFRN expression and the abundance of immune effector cells, including CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells, as well as activated NK cells. Conversely, a positive correlation was observed between PTGFRN expression and immunosuppressive cells such as M0 macrophages and neutrophils. This trend was consistently observed across multiple analytical methods. These findings collectively suggest that LUAD tissues with high PTGFRN expression exhibit an immunosuppressive microenvironment, potentially contributing to the poorer prognosis observed in LUAD patients with high PTGFRN expression. Furthermore, we investigated the correlation between PTGFRN expression and immune regulatory genes. The results demonstrated a negative correlation between PTGFRN and both immune-activating and immunosuppressive genes in LUAD. These findings suggest a potential immunomodulatory role for PTGFRN in lung adenocarcinoma. Current evidence demonstrates that PTGFRN knockout enhances pathways associated with innate immune responses. PTGFRN has been identified to primarily interact with tetraspanins CD9, CD81, and CD151 (the latter only when complexed with CD9). As a tetraspanin family member, PTGFRN may impair antitumor immune responses by forming membrane protein complexes that disrupt MHC class I surface localization \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Furthermore, as a key component of exosomes\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, PTGFRN overexpression potentially increases secretion of immunosuppressive exosomes containing TGF-β and IL-10, thereby promoting M2 macrophage polarization and T-cell suppression. These findings provide a theoretical foundation for targeting PTGFRN in immunotherapy, although the precise molecular mechanisms require further validation through PTGFRN gene-editing models and immune cell co-culture experiments. In conclusion, our data and published studies support a potential immunomodulatory role for PTGFRN, with LUAD patients expressing high levels of PTGFRN potentially existing in an immunosuppressive state.\u003c/p\u003e \u003cp\u003eCorrelation analysis of gene expression and drug sensitivity revealed a pronounced sensitivity to small molecule drugs, including Sotrastaurin, Teniposide, Tacedinaline, and Topotecan, in the PTGFRN high expression group. While the primary targets of these drugs are well-defined, their structures may possess secondary binding sites with other proteins. This concept has guided the development of multi-targeted anticancer drugs to maximize efficacy, and PTGFRN represents a promising lead for further exploration. Sotrastaurin (AEB071) is a novel and potent PKC inhibitor that regulates PKCδ, thereby inhibiting cancer stem-like characteristics, chemotherapy resistance, and metastasis\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Teniposide acts specifically during the S and G2 cell cycle phases, preventing mitosis\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Tacedinaline, a histone deacetylase (HDAC) inhibitor, induces histone hyperacetylation in tumor cells, causing G1 phase cell cycle arrest\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Finally, Topotecan, an orally effective topoisomerase I inhibitor, promotes tumor cell apoptosis by inducing G0/G1 and S phase arrest\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. As a tetraspanin family protein, PTGFRN possesses multiple interaction domains capable of accommodating structurally diverse ligands\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Our molecular docking results identified strong binding sites for these four drugs on the PTGFRN protein structure, suggesting their potential as anti-LUAD drugs with the added benefit of targeting PTGFRN. Further research will involve a more active and in-depth investigation of these promising candidates.\u003c/p\u003e \u003cp\u003eOverall, this comprehensive bioinformatics analysis has shed light on the multifaceted role of PTGFRN in LUAD, providing valuable insights into its underlying molecular mechanisms and potential clinical significance. These findings warrant further experimental validation to definitively elucidate the precise role of PTGFRN in LUAD progression. Moreover, targeted therapeutic strategies aimed at modulating PTGFRN activity offer a promising avenue for improving patient outcomes in this aggressive malignancy.\u003c/p\u003e \u003cp\u003eThis study has potential limitations. Firstly, as our analysis relied on publicly available datasets, we were constrained by variations in data quality across different sequencing platforms and institutions. While we applied rigorous normalization procedures (including ComBat for batch effect correction) and only included datasets with standardized clinical annotations, residual technical biases may persist. Secondly, the inherent heterogeneity in patient populations (e.g., differences in staging, treatment history, and genetic background across cohorts) could influence the generalizability of our results. Thirdly, although we observed consistent patterns across multiple independent datasets (TCGA, GEO: GSE31320), prospective validation in uniformly processed clinical samples would strengthen our conclusions. Finally, the lack of in vitro/in vivo experiments means the causal relationship between PTGFRN and tumorigenesis remains hypothetical. These limitations highlight the need for future studies incorporating standardized multi-center cohorts with matched molecular and clinical data.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, this study offers compelling evidence supporting PTGFRN as a potential prognostic biomarker and therapeutic target in LUAD. The observed associations between PTGFRN expression and poor prognosis, the immunosuppressive microenvironment, and differential drug sensitivity highlight its multifaceted role in LUAD pathogenesis. Moving forward, efforts should prioritize experimental validation of these findings and the exploration of targeted therapeutic strategies that modulate PTGFRN activity in LUAD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNSCLC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Non-small-cell lung cancer\u003c/p\u003e\n\u003cp\u003eLUAD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Lung adenocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTKIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tyrosine kinase inhibitors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePTGFRN \u0026nbsp; \u0026nbsp; \u0026nbsp; Prostaglandin F2 receptor negative regulator\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTCGA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;The Cancer Genome Atlas\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGEO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Expression Omnibus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGTEx \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Genotype-Tissue Expression\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eACC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Adrenocortical carcinoma,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBLCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Bladder urothelial carcinoma,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBRCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Breast invasive carcinoma \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCESC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cervical squamous cell carcinoma and cervical adenocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHOL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cholangiocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOAD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Colon cancer\u003c/p\u003e\n\u003cp\u003eREAD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Rectal adenocarcinoma\u003c/p\u003e\n\u003cp\u003eDLBC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lymphomas diffuse large B-cell lymphoma\u003c/p\u003e\n\u003cp\u003eESCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Esophageal cancer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGBM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Glioblastoma multiforme\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHNSC \u0026nbsp; \u0026nbsp; \u0026nbsp; Head and neck squamous cell carcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKICH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Renal chromophobe\u003c/p\u003e\n\u003cp\u003eKIRC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Renal clear cell carcinoma\u003c/p\u003e\n\u003cp\u003eKIRP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Renal papillary cell carcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLAML \u0026nbsp; \u0026nbsp; \u0026nbsp; Acute myeloid leukemia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLGG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Low-grade glioma\u003c/p\u003e\n\u003cp\u003eLIHC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hepatocellular carcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLUSC \u0026nbsp; \u0026nbsp; \u0026nbsp; Lung squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eMESO \u0026nbsp; \u0026nbsp; \u0026nbsp;Mesothelioma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ovarian serous cystadenocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePAAD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Pancreatic cancer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCPG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Pheochromocytoma and paraganglioma\u003c/p\u003e\n\u003cp\u003ePRAD \u0026nbsp; \u0026nbsp; \u0026nbsp; Prostate cancer\u003c/p\u003e\n\u003cp\u003eREAD \u0026nbsp; \u0026nbsp; \u0026nbsp; Rectal adenocarcinoma\u003c/p\u003e\n\u003cp\u003eSARC \u0026nbsp; \u0026nbsp; \u0026nbsp; Sarcoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSKCM \u0026nbsp; \u0026nbsp; \u0026nbsp;Skin melanoma\u003c/p\u003e\n\u003cp\u003eSTAD \u0026nbsp; \u0026nbsp; \u0026nbsp; Gastric adenocarcinoma\u003c/p\u003e\n\u003cp\u003eSTES \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gastric and esophageal cancer\u003c/p\u003e\n\u003cp\u003eTGCT \u0026nbsp; \u0026nbsp; \u0026nbsp; Testicular germ cell tumor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTHCA \u0026nbsp; \u0026nbsp; \u0026nbsp; Thyroid cancer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTHYM \u0026nbsp; \u0026nbsp; \u0026nbsp;Thymoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUCEC \u0026nbsp; \u0026nbsp; \u0026nbsp; Endometrial cancer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUVM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Uveal melanoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHPA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Human Protein Atlas\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Overall survival\u003c/p\u003e\n\u003cp\u003eDSS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Disease-specific survival\u003c/p\u003e\n\u003cp\u003eRFS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Recurrence-free survival\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePFS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Progression-free survival\u003c/p\u003e\n\u003cp\u003eAJCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;American Joint Committee on Cancer\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Differentially expressed genes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene set enrichment analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Ontology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Biological processes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cellular component\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Molecular function\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eceRNAs \u0026nbsp; \u0026nbsp; \u0026nbsp;Competing endogenous RNAs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003encRNAs \u0026nbsp; \u0026nbsp; Non-coding RNAs\u003c/p\u003e\n\u003cp\u003eGSCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Gene Set Cancer Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGDSC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Genomics of Drug Sensitivity in Cancer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCPTAC \u0026nbsp; \u0026nbsp; Clinical Proteomic Tumor Analysis Consortium\u003c/p\u003e\n\u003cp\u003eCNVs \u0026nbsp; \u0026nbsp; \u0026nbsp; Copy number variations\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTILs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Tumor-infiltrating lymphocytes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTPM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tanscripts per million\u003c/p\u003e\n\u003cp\u003eNK \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Natural killer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eANOVA \u0026nbsp; One-way analysis of variance\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets for this study can be found in the public databases TCGA, GTEx, TIMER 2.0, GDSA, and cBioportal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICTS OF INTEREST\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the medical science research project plan of Hebei Provincial Health Commission (No. 20210980).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThai, A. 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Biol.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e (6), 509\u0026ndash;522 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TCGA, POC1A, prognostic biomarker, pan-cancer, tumor-infiltration","lastPublishedDoi":"10.21203/rs.3.rs-5040921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5040921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLung adenocarcinoma, the most prevalent and heterogeneous subtype of lung cancer, presents significant challenges for diagnosis and treatment. Prostaglandin F2 receptor negative regulator (PTGFRN) has recently emerged as a molecule of interest in cancer, but its specific contribution to lung adenocarcinoma pathogenesis remains to be elucidated. This study employed bioinformatics methods to investigate the expression patterns and potential functional roles of PTGFRN in lung adenocarcinoma. We utilized large-scale transcriptome datasets from public repositories to analyze PTGFRN expression levels and prognostic significance in lung adenocarcinoma cohorts. Furthermore, we explored the correlation between PTGFRN and immune cell infiltration to elucidate the potential molecular mechanisms of PTGFRN dysregulation in lung cancer development. Overall, our findings provide insights into the significance of PTGFRN in lung adenocarcinoma pathogenesis and emphasize its potential as a novel biomarker and therapeutic target for precision medicine approaches.\u003c/p\u003e","manuscriptTitle":"Comprehensive analysis of the role of PTGFRN as a new potential biomarker in lung adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 11:04:40","doi":"10.21203/rs.3.rs-5040921/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be9f8614-344c-49b1-84eb-5c170f290cda","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47733806,"name":"Biological sciences/Cancer"},{"id":47733807,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2025-05-13T13:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-28 11:04:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5040921","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5040921","identity":"rs-5040921","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}