Unveiling Lipid Metabolism-related Gene PTGDS: A Tumor Suppressor in Lung Adenocarcinoma with Therapeutic Potential

Unveiling Lipid Metabolism-related Gene PTGDS: A Tumor Suppressor in Lung Adenocarcinoma with Therapeutic Potential | 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 Research Article Unveiling Lipid Metabolism-related Gene PTGDS: A Tumor Suppressor in Lung Adenocarcinoma with Therapeutic Potential Boxuan Zhou, Jianwei Shi, Linchuan Liang, Yushun Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7946797/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 Background Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality worldwide. Although the lipid metabolism-associated gene PTGDS has been implicated in tumorigenesis, its functional significance and regulatory mechanisms in LUAD are poorly understood. Methods We integrated multi-omics data from TCGA and GEO cohorts to evaluate PTGDS expression and its clinicopathological relevance. Functional characterization was performed using gain-of-function models in LUAD cell lines and a xenograft mouse model, assessing proliferation, migration, invasion, and immune microenvironment alterations. Results PTGDS expression is markedly reduced in LUAD tissues and correlates strongly with advanced disease stage and unfavorable prognosis. Clinically, low PTGDS expression is associated with specific driver mutations and reduced tumor mutation burden. Notably, PTGDS levels correlate with enhanced cytotoxic T-cell infiltration and suppressed M2 macrophage polarization, suggesting immunomodulatory functions. Ectopic expression of PTGDS significantly suppressed malignant behaviors in vitro and tumor growth in vivo. Mechanistically, PTGDS downregulates key cell cycle regulators CDK1 and PLK1. Conclusions Our findings establish PTGDS as a novel tumor suppressor in LUAD that restrains tumor progression through cell cycle modulation and immune microenvironment remodeling, highlighting its potential as both a prognostic biomarker and a therapeutic target. Cell cycle Immune cell infiltration Lipid metabolism Lung adenocarcinoma Prognostic biomarker PTGDS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. INTRODUCTION Lung cancer is the second most common malignancy globally [ 1 ], responsible for approximately 18% of cancer-related deaths and representing one of the leading causes of cancer mortality [ 2 ]. Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the predominant subtypes of non-small cell lung cancer (NSCLC), accounting for approximately 82% of all lung cancer cases [ 3 ]. LUAD, a major subtype of NSCLC, constitutes approximately 40% of lung cancer cases [ 4 ]. The overall five-year survival rate for patients with LUAD remains poor despite substantial progress in diagnostic and therapeutic approaches for lung cancer over the past decade, particularly for those with late-stage disease, post-treatment recurrence, and metastasis [ 5 , 6 ]. Therefore, it is crucial to investigate the underlying molecular mechanisms driving LUAD development and to identify potential therapeutic targets. Metabolic dysregulation is a hallmark of cancer that allows tumor cells to adapt to their specific energy requirements through metabolic reprogramming. Lipid metabolic reprogramming is a common feature across nearly all types of cancer. Studies have confirmed that metabolic reprogramming is closely associated with tumor initiation, progression, metastasis, remodeling of the immune microenvironment, and chemoresistance [ 7 – 9 ]. Lipids are a primary energy source and essential structural components of cellular membranes [ 10 ]. Lipid metabolism is a dynamic process, with tumor tissues undergoing lipid remodeling via key lipid-metabolizing enzymes, transcription factors, and signaling pathways [ 11 – 13 ]. Studies have revealed that lipid metabolism and associated genes (LMRGs) play a critical role in LUAD development and prognosis [ 14 , 15 ]. Prostaglandin D synthase (PTGDS) is a lipid metabolism-related gene on chromosome 9. PTGDS belongs to the lipocalin superfamily and is involved in prostaglandin metabolism and lipid transport [ 16 ]. Interestingly, PTGDS plays varying roles across different types of cancer. It promotes tumor progression in diffuse large B-cell lymphoma, hepatocellular adenoma, testicular cancer, and ovarian cancer [ 17 – 20 ], whereas its downregulation in prostate, lung, and gastric cancers is associated with poor prognosis [ 21 – 23 ]. Hence, the role of PTGDS in LUAD progression requires further investigation. This study investigated PTGDS expression and its prognostic significance in LUAD using bioinformatics methods. We examined its association with cell cycle regulation, immune cell infiltration, and immune checkpoint genes. The role of PTGDS across multiple cancers was also investigated, revealing its differential expression and association with immune infiltration in various tumors. Additionally, it was confirmed that PTGDS suppressed LUAD cell proliferation, migration, and invasion in both in vitro and in vivo experiments. These findings suggested that PTGDS may serve as a prognostic biomarker and a potential therapeutic target for LUAD. 2. MATERIALS AND METHODS 2.1 Data Collection The gene expression data for the differential analysis of NSCLC was sourced from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo/ ) with the accession number GSE118370. A dataset comprising 743 genes associated with lipid metabolism was obtained from the GeneCards website ( https://www.genecards.org/ ). Gene expression data and clinical information for the analysis of NSCLC prognosis were retrieved from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/repository ). The clinical characteristics and genetic variation data of patients with LUAD were obtained from the TIMER2.0 ( http://timer.cistrome.org/ ) databases. 2.2 Identification of Differentially Expressed Lipid Metabolism-related Genes The R package “limma” was used to analyze genes in LUAD from TCGA and the GSE118370 dataset. Genes with a p-value 1.5 were identified as differentially expressed. A Venn diagram ( https://hiplot-academic.com/basic/Venn ) was used to identify differentially expressed lipid metabolism-related genes by overlapping the two sets with the lipid metabolism-related gene set. 2.3 PTGDS Co-expression Analysis and Functional Enrichment of Differentially Expressed PTGDS-related Genes Pearson’s correlation coefficient was used to determine the co-expression between PTGDS expression and other genes. The R package “limma” was used to analyze gene expression in tumor and adjacent non-tumor tissues from TCGA data of patients with LUAD, categorizing PTGDS expression into high and low groups. Genes with a p-value 1.5 were identified as PTGDS-related differential genes. Differential genes were analyzed for gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment. Statistically significant differences (p-value < 0.05) were visualized using the Cairo and ggplot2 packages of R software. 2.4 Immune Microenvironment Assessment The R software was used to estimate the stromal, immune, and ESTIMATE scores for LUAD. The R package CIBERSORT was used to examine the proportions of 22 common immune cell types. To perform reliable immune score evaluation, we employed the immunedeconvR package and utilized the ggClusterNet R package for analysis and visualization of the results. And we applied the TIDE (Tumor Immune Dysfunction and Exclusion) algorithm to predict potential immunotherapy responses. 2.5 Pan-cancer Analysis of PTGDS The standardized pan-cancer dataset was retrieved from the UCSC database ( https://xenabrowser.net/ ). The expression data of the PTGDS gene in various samples was also extracted. R software was used to estimate the differences in expression between normal and tumor samples according to cancer type, using an unpaired Student’s t-test for significance assessment. The coxph function from the R package survival was used to construct a Cox proportional hazards regression model, examining the correlation between gene expression and prognosis across various cancer types using the Logrank test for statistical significance. Additionally, the clinical relevance of PTGDS across pan-cancer was calculated using the TISIDB database ( http://cis.hku.hk/TISIDB/).W e reassessed the( http://cis.hku.hk/TISIDB/ ). The infiltration scores of T cells, CD8 + T cells, cytotoxic lymphocytes, B lineage, NK cells, monocytic lineage, myeloid dendritic cells, neutrophils, endothelial cells, and fibroblasts in each cancer type were reassessed based on gene expression. The tumor mutational burden (TMB) scores were determined for each cancer type from published studies [ 24 ], and the Pearson correlation was calculated. 2.6 Collection of LUAD Samples Between 2015 and 2018, 31 pairs of LUAD and matched adjacent normal lung tissues were collected from surgeries performed at the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College. The collection of these specimens was approved by the Medical Ethics Committee of the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College. 2.7 Cell Culture and Transfection Human LUAD cell lines (H1299 and A549) were obtained from the Chinese Academy of Sciences. H1299 cells were cultured in RPMI-1640 medium (Gibco) supplemented with 10% FBS (Pricella) and 1% penicillin-streptomycin (Beyotime). A549 cells were cultured in 10-cm dishes (Corning) with DMEM (Gibco) supplemented with 10% FBS (Pricella) and 1% penicillin-streptomycin (Beyotime). Both cell lines were cultured at 37°C under a 5% CO2 atmosphere. PTGDS plasmids (General Biol) were transfected into H1299 and A549 cells using Lipofectamine 8000 (Beyotime) in a serum-free medium according to the manufacturer’s protocol. 2.8 Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) RNA was extracted from tissues, and cells lysed with TRIzol reagent (Invitrogen), followed by precipitation and washing with chloroform, isopropanol, and 75% ethanol. The RNA pellet was dissolved in 30 µL of deionized water, and its concentration was measured for further analysis. Subsequently, cDNA was synthesized using HiScript II Q RT SuperMix for qPCR. Then, cDNA was analyzed by qRT–PCR using AceQ qPCR SYBR Green Master Mix (without ROX) (Vazyme) following the manufacturer’s protocol. The primers used were GAPDH (Forward: 5′-GGAGCGAGATCCCTCCAAAAT-3′, Reverse: 5′-GGCTGTTGTCATACTTCTCATGG-3′) and PTGDS (Forward: 5′-GGCGTTGTCCATGTGCAAG-3′, Reverse: 5′-GGACTCCGGTAGCTGTAGGA-3′). 2.9 Immunohistochemistry (IHC) The paraffin sections were baked, deparaffinized, hydrated, and washed. Antigen retrieval was performed, and peroxidase activity was inhibited. The sections were blocked with an antigen-blocking solution and incubated overnight at 4°C with a polyclonal rabbit anti-PTGDS antibody (1:500, Proteintech). The sections were then incubated with a secondary antibody (1:1000, Huabio) at room temperature for 2 hours. Staining was performed using DAB and hematoxylin. 2.10 Western blot (WB) Tissues and cells were lysed using RIPA buffer containing protease and phosphatase inhibitors (CWbio), then boiled for 10 min to denature. SDS-PAGE was performed on 20 µg of protein and 2.5 µL of pre-stained protein marker (Vazyme) and subsequently transferred to a PVDF membrane (Millipore). The membrane was blocked with 5% non-fat milk for 1 h at room temperature, followed by overnight incubation at 4°C with the primary antibody. Subsequently, the membrane was incubated with a goat anti-rabbit secondary antibody for 1 h. Images were captured using an enhanced chemiluminescence reagent (Vazyme) and an imaging system. The antibodies used were PTGDS (1:1000, Proteintech), GAPDH (1:100000, Huabio), β-Actin (1:10000, Huabio), CDK1 (1:2000, Huabio), and PLK1 (1:1000, Immunoway). 2.11 Immunofluorescence LUAD cells subjected to different treatments were fixed on coverslips with 4% paraformaldehyde. Cells were treated with 0.1% Triton X-100 for permeabilization and blocked with 10% goat serum for 30 min at room temperature. After blocking, the cells were incubated at 4°C overnight with either rabbit anti-human CDK1 (1:100, Huabio) or rabbit anti-human PLK1 (1:200, Immunoway). The cells were incubated in the dark with iFluor 594-labeled goat anti-rabbit IgG (1:500, Huabio) for 60 min, followed by DAPI staining of the nuclei for 5 min (Beyotime). Finally, the cells were examined under a confocal microscope at 200x magnification. 2.12 Cell Counting Kit-8 (CCK-8) Assay LUAD cells subjected to different treatments were seeded into 96-well plates. Then, 10 µL of CCK-8 reagent (APE x BIO) was added to each well and incubated for 2 h at 24, 48, or 72 h. The absorbance of each well was measured at 450 nm using a multifunctional microplate reader. 2.13 Colony Formation Assay LUAD cells were seeded into 6-well plates at a density of 1 × 103 cells per well, following various treatments. The medium was replaced every three days. After 10 days of growth, the medium was discarded, and the cells were fixed with 4% paraformaldehyde and washed with PBS. The cells were stained with crystal violet for 10 min. Finally, the plates were air-dried in a ventilated area and photographed. 2.14 EdU Assay LUAD cells were seeded into 6-well plates after various treatments. EdU was added to the medium at 10 µM for 2 h after cell density reached 60%–70%. The media were discarded, and the cells were fixed for 15 min in 4% paraformaldehyde before being permeabilized with 0.1% Triton X-100. Cells were stained with BeyoClick™ EdU-555 and Hoechst dye at room temperature in the dark. Finally, fluorescence was detected using a laser confocal microscope. 2.15 Wound Healing Assay LUAD cells treated with various methods were seeded into 6-well plates until 90% confluence was achieved. A scratch was created by a 200 µL pipette tip in the cell monolayer. The cell scratch was examined under a microscope at 0 and 48 h. ImageJ software was used to estimate the scratch area. 2.16 Transwell Assay The Transwell chambers were optionally coated with Matrigel. After various treatments, LUAD cells (2 x 104 per well) were seeded into the upper chamber of 24-well Transwell inserts (Corning) with serum-free media. The lower chamber was filled with 600 µL of medium containing FBS. After 24 h, the cells in the Transwell inserts were fixed with paraformaldehyde and stained with crystal violet. The Images were captured using a microscope. 2.17 Cell Cycle Analysis H1299 and A549 cells were digested and transfected with PTGDS plasmids after 24 h of culture. The cells were washed with PBS and fixed overnight at 4°C with 70% ethanol. After PBS washing, the cells were resuspended in a PI and RNase solution and analyzed via flow cytometry. FlowJo software was used to analyze the data. 2.18 Establishment of the Nude Mouse Xenograft Tumor Model A PTGDS xenograft model was developed using 4-week-old BALB/c nude mice at the Laidi Biomedical Research Institute. The Ethics Committee of the Laidi Biomedical Research Institute approved the animal study protocol. BALB/c nude mice (n = 5 per group) were subcutaneously injected with 2 x 106 H1299 cells under various treatments. Tumor growth was monitored, and tumor size was determined using calipers every five days. Tumor volume was determined using the formula: V = (a × b2)/2. On day 25, the mice were euthanized, and the tumors were surgically removed. Tumor tissues were collected to determine their weight and volume, and proteins were extracted for subsequent analysis. 2.19 Statistical Analyses Statistical analyses were performed using GraphPad Prism (version 8) or the Statistical Package for the Social Sciences (version 21.0). A two-sided Student’s t-test was used to calculate the p-values for comparing the two groups. For multiple group comparisons, p-values were determined using analysis of variance. The association between PTGDS expression and clinicopathological characteristics was assessed using the two-sided χ² test. Survival curves were generated using the Kaplan–Meier method and compared using the log-rank test. A p-value < 0.05 was considered statistically significant. 3. RESULTS 3.1 Downregulation of the Lipid Metabolism-related Gene PTGDS in LUAD and its Positive Correlation with Prognosis To investigate the role of lipid metabolism-related genes in the progression of lung adenocarcinoma (LUAD), we analyzed LUAD data from the TCGA cohort. By comparing differentially expressed genes (DEGs) between T3 + T4 stage and T1 stage LUAD patients, we identified 281 DEGs, including 106 upregulated and 175 downregulated genes in the T3 + T4 group (Fig. 1 . A-B). Additionally, using sequencing data from 6 paired LUAD tumor and adjacent normal tissues in the GEO dataset GSE118370, we identified 3,221 DEGs, with 1,402 upregulated and 1,819 downregulated in tumors (Fig. 1 C-D). From the GeneCards database, we obtained a lipid metabolism-related gene set comprising 743 genes. Intersection analysis between these DEGs and lipid metabolism-related genes revealed 3 overlapping differentially expressed lipid metabolism-related genes (Fig. 1 E). Subsequently, we performed univariate COX regression analysis to assess the prognostic significance of these 3 genes. The results indicated that PTGDS and CYP27A1 were protective factors, while FHL2 was a risk factor in LUAD (Fig. 1 F). Given that CYP27A1 and FHL2 have been extensively studied in LUAD, we focused on PTGDS for further investigation. TCGA expression analysis showed that PTGDS was downregulated in tumor tissues, with even lower expression in higher T stages (Fig. 1 G). Prognostic analysis further revealed that PTGDS expression was negatively correlated with poor survival in LUAD patients (Fig. 1 H). These findings suggest that low PTGDS expression is closely associated with unfavorable progression in LUAD. 3.2 Clinical and Genetic Characteristics of the Lipid Metabolism-related Gene PTGDS in LUAD We conducted a comprehensive analysis of clinical data from LUAD patients in the TCGA database stratified by PTGDS expression levels. Our findings revealed significant differences in T-stage and N-stage distribution between LUAD with high PTGDS expression and those with low expression, while no significant variations were observed in M-stage or smoking status (Fig. 2 A). To further characterize PTGDS expression patterns in lung adenocarcinoma, we assessed its levels in the context of common driver mutations (EGFR, ALK, KRAS, TP53) using the TIMER2.0 database. This analysis revealed that PTGDS expression was significantly upregulated in tumors harboring EGFR mutations and downregulated in those with KRAS mutations (Fig. 2 B). Our analysis of PTGDS methylation status revealed significantly lower methylation levels in adjacent normal tissues compared to tumor tissues, providing a partial explanation for the higher PTGDS expression observed in normal adjacent tissue (Fig. 2 C). Further investigation of PTGDS mutation profiles in LUAD showed a low mutation rate (0.39%), with all mutations being nonsense variants (Fig. 2 D). Comparative analysis of the top 10 mutated genes between high and low PTGDS expression groups showed no significant differences, with TP53, TTN, and MUC16 being predominant in both groups (Fig. 2 E). Subsequent analysis revealed a negative correlation between PTGDS expression and tumor mutation burden in lung adenocarcinoma (Fig. 2 F). Single-cell RNA sequencing analysis using the EMTAB6149 dataset from TISCH2 database demonstrated predominant PTGDS expression in fibroblasts, malignant cells, endothelial cells, and monocytes (Fig. 2 G-I). 3.3 Co-expression Gene Analysis and Differential Gene Enrichment Analysis of the Lipid Metabolism-Related Gene PTGDS Subsequently, utilizing TCGA-LUAD data, we stratified patients into high and low PTGDS expression groups and performed differential gene expression analysis. Using a threshold of p-value 1.5, we identified a total of 933 differentially expressed genes (DEGs), comprising 788 upregulated genes and 145 downregulated genes (Fig. 3 A-B). We conducted GO, KEGG pathways, and GSEA analyses were used to investigate the pathways and functions associated with PTGDS. Enrichment analysis demonstrated that differentially expressed genes were mainly involved in cell cycle and immune-related pathways (Fig. 3 C-D, and Fig. S1). Further analysis revealed significant correlations between PTGDS expression and key tumorigenic processes: PTGDS expression exhibited a negative correlation with the expression of tumor proliferation-associated genes, while demonstrating a positive correlation with the expression of tumor immunity-associated genes (Fig. 3 E-F). Additionally, correlation analysis of LUAD patient expression data from the TCGA database identified two genes strongly co-expressed with PTGDS (R > 0.7): RASGRP2 and LCNL1 (Fig. 3 G). 3.4 Association between the Lipid Metabolism-Related related Gene PTGDS and Tumor Immune Microenvironment Enrichment analysis indicated the role of PTGDS in immune pathways, prompting further investigation into its relationship with immune infiltration. Tumor microenvironment (TME) scoring scores revealed a positive association between high PTGDS expression and immune infiltration scores (Fig. 4 A), suggesting that elevated PTGDS expression is linked to increased immune infiltration.This study analyzed the variations in immune cell components between LUAD samples exhibiting high and low PTGDS expression (Fig. 4 B). LUAD samples with high PTGDS expression revealed an increased infiltration of CD8 + T cells and mast cells and a reduced proportions of M2 macrophages (Fig. 4 C-E). Further correlation analysis demonstrated that the infiltration of resting mast cells, CD8 + T cells, memory B cells, monocytes, and resting dendritic cells were positively associated with PTGDS expression. In contrast, activated NK cells, activated dendritic cells, M0 macrophages, M2 macrophages, eosinophils, activated mast cells, and neutrophils were negatively associated with PTGDS expression (Fig. 4 F). Subsequently, we analyzed differences in immune response between the high and low PTGDS expression strata. The results revealed no statistically significant differences in immune response between these groups (Fig. 4 G). However, PTGDS expression exhibited a positive correlation with the expression of majority of immune checkpoint-related genes (Fig. 4 H). 3.5 Pan- cancer Analysis of Lipid Metabolism-Related related Gene PTGDS According to the literature, PTGDS is associated with various cancers. PTGDS expression was analyzed using RNA sequencing data from the TCGA database for diverse cancer types and normal tissues in the TCGA database. The results indicated that PTGDS is downregulated in BRCA, KICH, LIHC, LUAD, LUSC, PCPG, and PRAD, while it is upregulated in CHOL (Fig. 5 A). Univariate Cox regression analysis was used to evaluate the prognostic significance of PTGDS in pan-cancer. The results revealed that PTGDS serves as a risk factor for KIPAN, KIRC, STAD, and KICH while acting as a protective factor in GBMLGG, LUAD, LGG, CESC, HNSC, and DLBC (Fig. 5 B). Analysis across multiple tumor types revealed that PTGDS expression positively correlated with most immune checkpoint genes (Fig. 5 C). Further investigation indicated a positive correlation between PTGDS expression and immune cell infiltration in various cancers, notably with CD8 T cells in BRCA, HNSC, and PAAD (Fig. 5 D). Subsequently, the clinical relevance of PTGDS was explored for various cancers using the TISIDB database. Higher PTGDS expression was correlated with longer overall survival (OS) in patients with CESC, HNSC, LGG, and LUAD but with shorter OS in patients with COAD, KIRC, LUSC, and UCS (Fig. 5 E). Higher PTGDS expression was associated with earlier clinical stages in LUAD, LUSC, and TGCT patients, whereas it was associated with later clinical stages in BLCA, KIRC, SKCM, and STAD patients (Fig. 5 F). The association between PTGDS and TMB across various cancer types was also investigated. PTGDS exhibited a significant association with MSI in nine cancers, revealing a positive correlation in KICH and a negative correlation in GBM, UVM, THCA, LGG, SARC, STAD, THYM and PRAD (Fig. 5 G). 3.6 Lipid Metabolism-related Gene PTGDS is Downregulated in LUAD and Correlates with Prognosis Tumors and adjacent normal tissues from 31 patients with LUAD were collected and subjected to IHC staining to detect PTGDS. PTGDS was significantly expressed in normal tissues but was decreased in tumor tissues (Fig. 6 A-B and Supplementary Table 2). WB analysis further validated this finding (Fig. 6 C). The clinical, pathological, and prognosis data revealed a significant association between low PTGDS expression and larger tumor diameter and advanced T stage in patients with LUAD (Table 1 ). Kaplan–Meier survival analysis indicated that elevated PTGDS expression correlated with extended disease-free survival and improved prognoses (Fig. 6 D). 3.7 Lipid Metabolism-related Gene PTGDS Suppresses Proliferation, Migration, and Invasion of LUAD Cells In Vitro PTGDS was overexpressed in the LUAD cell lines A549 and H1299 to investigate further its role (Fig. S2 and 7A). CCK-8 assays demonstrated a significant reduction in absorbance in A549 and H1299 cells overexpressing PTGDS, suggesting that PTGDS inhibits LUAD cell proliferation (Fig. 7 B). Colony formation assays also revealed that the number of colonies formed by PTGDS-overexpressing LUAD cells was significantly reduced, further confirming the suppression effect of PTGDS on cell proliferation (Fig. 7 C). The EdU assay identified a lower ratio of EdU-positive cells in the PTGDS-overexpressing group compared with the control group, supporting the anti-proliferative function of PTGDS in LUAD cells (Fig. 7 D). Wound healing assays revealed a significant decrease in the migratory capacity of A549 and H1299 cells after PTGDS overexpression (Fig. 7 E). The Transwell assays further indicated a significant decrease in the number of invading and migrating cells in PTGDS-overexpressing A549 and H1299 cells. These results demonstrated that PTGDS exerts anti-tumor effects in LUAD by inhibiting proliferation, migration, and invasion in vitro. 3.8 Lipid Metabolism-related Gene PTGDS is Closely Associated with Cell Cycle-related Proteins Prior GO and KEGG enrichment analyses revealed a significant association between PTGDS and the cell cycle. This study used flow cytometry to examine the cell cycle of A549 and H1299 cells overexpressing PTGDS to elucidate the tumor-suppressive effects of PTGDS in LUAD. PTGDS upregulation increased the proportion of cells in the G0/G1 phase and decreased the proportion in the S phase (Fig. 8 A). To understand the impact of PTGDS on the cell cycle, this study examined the correlation between PTGDS and cell cycle-related proteins in patients with LUAD using data from the TCGA database. Analysis from the GEPIA2 website indicated a negative correlation between PTGDS expression and PLK1 and CDK1 in tumor and adjacent normal tissues (Fig. 8 B). Immunofluorescence and WB assays were performed to measure the expression of cell cycle-related proteins in the control and PTGDS-overexpressing groups. The findings indicated a significant reduction in PLK1 and CDK1 expression in LUAD cells with PTGDS overexpression (Fi. 8C-D). 3.9 Lipid Metabolism-related Gene PTGDS Suppresses LUAD Progression In Vivo To further investigate the role of PTGDS in vivo, xenograft experiments were performed using naive-control (NC) H1299 cells and H1299 cells stably overexpressing PTGDS. The results demonstrated that PTGDS significantly inhibited subcutaneous tumor growth in mice (Fig. 9 A). Tumor volume and mass measurements indicated that tumors in the OE-PTGDS group were significantly smaller compared to the NC group (Fig. 9 B-C). Subsequently, WB analysis was performed to detect cell cycle-related proteins, and the results revealed that PTGDS overexpression markedly reduced the expression of PLK1 and CDK1, providing a potential explanation for the tumor-suppressive effects of PTGDS (Fig. 9 D). 4. DISCUSSION Despite numerous studies, LUAD remains a significant global health issue [ 1 , 25 , 26 ]. Metabolic abnormalities in tumors are a key cause of tumorigenesis and progression, with lipid metabolism playing an indispensable role [ 27 ]. Our previous systematic studies examined the involvement of lipid metabolism-related genes in LUAD and developed a risk-scoring model to predict patient prognosis and immunotherapy response [ 28 ]. Our integrated analysis demonstrates that the lipid metabolism gene PTGDS is significantly downregulated in lung adenocarcinoma (LUAD) tumor tissues compared to adjacent normal tissues, with reduced expression correlating strongly with advanced T stages, larger tumor size, lymph node involvement (N-stage), and poor patient survival – establishing it as a key protective factor in LUAD pathogenesis. This downregulation appears driven partly by epigenetic mechanisms, specifically hypermethylation in tumors, while genetic alterations like nonsense mutations are rare. Importantly, PTGDS expression shows distinct associations with driver mutations, being elevated in EGFR-mutant tumors but suppressed in KRAS-mutant contexts. Gene mutations are significant features of LUAD, and common mutations include EGFR, KRAS, ALK, ROS1, and RET [ 29 , 30 ]. Approximately 50% of Asians have activating EGFR mutations [ 31 ]. TMB is anticipated to produce more neoantigens, enhancing cell recognition, and has been clinically linked to improved outcomes with immune checkpoint inhibitors [ 32 ]. This study found that PTGDS is negatively correlated with TMB. Functional enrichment analysis of PTGDS in patients with LUAD revealed its primary enrichment in the cell cycle and immune infiltration pathways. Abnormalities in the cell cycle are an important cause of tumorigenesis, and the malignancy of tumors is often linked to an accelerated abnormal cell cycle [ 33 ]. In LUAD, studies have found that FBXO32 promotes invasion and metastasis by targeting PTEN to promote proteasome-dependent degradation, thereby facilitating EMT and regulating the cell cycle [ 34 ]. DLGAP5 enhances cell proliferation in LUAD by upregulating PLK1 [ 35 ]. The immune microenvironment is another critical factor influencing tumor progression. The complex cellular cross-talk and signaling between tumor cells and immune microenvironment cells affect tumor progression [ 36 – 38 ]. This study examined the relationship between PTGDS and the immune microenvironment in LUAD, along with its clinical significance. Immune cell infiltration in the TME is strongly linked to patient prognosis. The findings revealed a positive correlation between PTGDS expression and the infiltration of various immune cells, particularly an increased proportion of CD8 + T cells. Since PTGDS plays distinct roles in different tumors and exhibits tumor-suppressive and tumor-promoting effects, we conducted a preliminary pan-cancer analysis of PTGDS. The findings indicated that PTGDS is generally underexpressed in tumors and positively correlates with immune infiltration. To further validate the tumor-suppressive role of PTGDS in LUAD, clinical patient tissue samples were used to verify PTGDS expression and its impact on prognosis through WB and IHC. Through the creation of PTGDS-overexpressing LUAD cell lines and subsequent functional experiments, PTGDS overexpression was determined to markedly inhibit LUAD cell proliferation, invasion, and migration, elucidating its role as a protective factor in patients with LUAD. Flow cytometry analysis revealed that PTGDS overexpression increased the proportion of cells in the G0/G1 phase while decreasing their proportion in the S phase, consistent with functional enrichment findings related to the cell cycle pathway. Subsequently, the cell cycle-related proteins PLK1 and CDK1 were detected via WB and immunofluorescence, and the results revealed that PTGDS overexpression reduced the expression of PLK1 and CDK1, thereby inhibiting the progression of LUAD. Finally, a mouse subcutaneous tumor model was established to validate the tumor-inhibitory ability of PTGDS in vivo. 5. CONCLUSIONS This study identified PTGDS, a lipid metabolism-related gene, as being downregulated in LUAD and highlighted its role as a protective factor positively associated with patient prognosis and immune infiltration. In vitro and in vivo experiments revealed that PTGDS inhibits LUAD cell proliferation, invasion, and migration by suppressing the expression of the cell cycle-regulating proteins PLK1 and CDK1. The findings of this study indicated that PTGDS may serve as a therapeutic target for LUAD, laying the groundwork for future strategies that integrate targeted and immunotherapy for lung cancer treatment. Declarations CONFLICT OF INTEREST STATEMENT The authors have no conflict of interest. ETHICS STATEMENT The collection of specimens in this study was approved by the Medical Ethics Committee of the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College. This study was conducted in strict accordance with the ethical principles of the Helsinki Declaration. All procedures involving animals adhered to the Basel Declaration and were performed under the guidelines of the Chinese Animal Welfare and Ethics Review Regulations. Every effort was made to minimize suffering, including optimized sample sizes and humane endpoints. FUNDING INFORMATION This work was supported by National Key R&D Program of China (Grant No. 2020YFE02022200). The funder had no role in the design, data collection, analysis, experiments and reporting of this study. Author Contribution Boxuan Zhou, Jianwei Shi and Linchuan Liang jointly completed the experimental design, writing, typesetting and modification of the paper. Yushun Gao served as supervisors for examination and review. All authors read and approved the final manuscript. Acknowledgement Thanks to Dr. Liang for the guidance. Data Availability The gene expression data for differential analysis of NSCLC was sourced from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) with the accession number GSE29249. A dataset comprising 743 genes associated with lipid metabolism was obtained from the GeneCards website (https://www.genecards.org/). Gene expression data as well as clinical information for NSCLC prognosis analysis were retrieved from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/repository). 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Association between PTGDS expression and clinicopathological features in LUAD patients. low expression high expression total p gender 0.001 0.981 female 11 8 19 male 7 5 12 age 1.873 0.171 ≤60 10 4 14 >60 8 9 17 grade fisher 0.237 I-II 11 11 22 III 7 2 9 diameter fisher 0.008 > 2 cm 10 1 11 ≤ 2 cm 8 12 20 TNM stage fisher 0.659 I 15 11 26 II-III 3 2 5 T classification fisher 0.010 T1 10 13 23 T2-3 8 0 8 N classification fisher 1.000 N0 15 11 26 N1-3 3 2 5 blood vessel invasion fisher 1.000 no 14 11 25 yes 4 2 6 Pleural invasion fisher 0.058 no 13 13 26 yes 5 0 5 Spread through air spaces fisher 1.000 no 13 9 22 yes 5 4 9 Additional Declarations No competing interests reported. Supplementary Files SFGTAB.docx 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. 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1","display":"","copyAsset":false,"role":"figure","size":5102121,"visible":true,"origin":"","legend":"\u003cp\u003eA. Volcano plot of differential genes between T3+T4 and T1 LUAD in TCGA. B. Heatmap of differential genes between T3+T4 LUAD and T1 LUAD in TCGA. C. Volcano plot of differential genes between LUAD tumors and adjacent normal tissues in the GEO dataset(GSE118370). D. Heatmap of differential genes between LUAD tumors and adjacent normal tissues in the GEO dataset(GSE118370). E. Venn diagram of differentially expressed genes and lipid metabolism-related genes. F. Univariable Cox regression analysis of FHL2, PTGDS, and CYP27A1 in LUAD. G. Expression of PTGDS in different stages of LUAD and adjacent normal tissues. H. Analysis of PTGDS expression and its association with LUAD prognosis.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7946797/v1/51843025a5ae39b0a7b8c480.png"},{"id":95524676,"identity":"63569859-5d7b-4764-b650-96d17671e5a6","added_by":"auto","created_at":"2025-11-10 10:03:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3276159,"visible":true,"origin":"","legend":"\u003cp\u003eA. Correlation between PTGDS expression and clinical features of LUAD in TCGA (* p \u0026lt; 0.05). B. Expression of PTGDS in patients with LUAD harboring common gene mutations (EGFR, ALK, KRAS and TP53). C. Expression of PTGDS in patients with LUAD harboring common gene mutations (EGFR, ALK, KRAS and TP53). D. Mutation profile of PTGDS in LUAD from TCGA. E. Mutational landscape of LUAD stratified by PTGDS expression levels. F. Correlation between PTGDS expression and tumor mutational burden in LUAD. G-I. Single-cell analysis of PTGDS expression distribution in lung cancer.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7946797/v1/db6ba926f95141bf9a562034.png"},{"id":95325460,"identity":"863d5731-0e0f-4f35-894c-99cb6b5c45cc","added_by":"auto","created_at":"2025-11-06 17:38:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8027790,"visible":true,"origin":"","legend":"\u003cp\u003eA. Volcano plot of differentially expressed genes between PTGDS-high and PTGDS-low groups in TCGA-LUAD. B. Heatmap of differentially expressed genes between PTGDS-high and PTGDS-low groups in TCGA-LUAD. C. KEGG pathway enrichment results of differential genes. D. GO pathway enrichment results of differential genes. E. Correlation analysis between PTGDS expression and tumor proliferation-related gene expression in LUAD. F. Correlation analysis between PTGDS expression and tumor inflammation-related gene expression in LUAD. G. Volcano plot of correlation coefficients between PTGDS and all genes.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7946797/v1/2e2ef9a42b9c2ed8b86b80e8.png"},{"id":95524331,"identity":"ceec3548-9a28-4f3f-a1d5-279302b1f5a9","added_by":"auto","created_at":"2025-11-10 10:02:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2705646,"visible":true,"origin":"","legend":"\u003cp\u003eA. Correlation between PTGDS expression and immune-related scores (StromalScore, ImmuneScore, and ESTIMATEScore) in patients with LUAD. B. Relationship between PTGDS expression and the composition of immune cell infiltration in patients with LUAD. C-E. Association of PTGDS expression with CD8+ T-cell, M2 macrophage and Mast cell infiltration in patients with LUAD. F. Heatmap of correlation between PTGDS expression and immune cell infiltration in LUAD. G. Differential analysis of immunotherapy response between PTGDS-high and PTGDS-low expression groups. H. Differential expression analysis of immune checkpoint genes between PTGDS-high and PTGDS-low groups. (*** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7946797/v1/361c55ed50e7e577d143feff.png"},{"id":95325465,"identity":"fd405f2d-75ee-4cc9-9ef5-04a822e73011","added_by":"auto","created_at":"2025-11-06 17:38:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4268961,"visible":true,"origin":"","legend":"\u003cp\u003eA. Pan-cancer analysis of PTGDS expression. B. Univariate risk model of PTGDS and prognosis across cancers. C. Correlation between PTGDS expression and immune checkpoint-related genes in pan-cancer. D. Correlation between PTGDS expression and immune cell infiltration in pan-cancer. E. Association between PTGDS expression and OS across different cancers. F. Correlation between PTGDS expression and clinical staging in pan-cancer analysis. G. Bar plot of PTGDS expression correlation with tumor mutational burden (TMB) across pan-cancer. (* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7946797/v1/e754b2d57ceef97329b1be63.png"},{"id":95325463,"identity":"24b67df5-962e-4e5c-a85d-c383bdc71ddd","added_by":"auto","created_at":"2025-11-06 17:38:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3447394,"visible":true,"origin":"","legend":"\u003cp\u003eA. IHC staining of PTGDS in tumor and adjacent normal tissues of patients with LUAD. IHC scoring of PTGDS was performed on tumor tissues and paired adjacent normal tissues from 31 patients with LUAD. C. WB analysis of PTGDS expression in tumor tissues versus adjacent normal tissues in patients with LUAD. Correlation between PTGDS expression and progression-free survival and OS in patients with LUAD. (*: p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7946797/v1/463c0a6de32165e88a46bda2.png"},{"id":95524061,"identity":"3643e035-6866-49bb-b280-671208e237a3","added_by":"auto","created_at":"2025-11-10 10:02:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":8956777,"visible":true,"origin":"","legend":"\u003cp\u003eA. Analysis of PTGDS overexpression in A549 and H1299 LUAD cells. CCK-8 assay to evaluate the proliferation of NC and OE A549 and H1299 cells. C. Colony formation assay indicating the colony count in NC, PTGDS-OE A549, and H1299 cells. Quantification of EDU-positive cells in NC and PTGDS-OE A549 and H1299 cells. E. Scratch wound-healing assay assessing the migration capacity of NC and PTGDS-OE A549 and H1299 cells. F. Transwell assay evaluating the migration and invasion capabilities of NC and PTGDS-OE A549 and H1299 cells. (* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7946797/v1/17fa0702e95fc83784318bfd.png"},{"id":95325469,"identity":"c6bd40e8-c46e-4070-aa85-bf6f0a86d128","added_by":"auto","created_at":"2025-11-06 17:38:58","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1227179,"visible":true,"origin":"","legend":"\u003cp\u003eA. Flow cytometry analysis of cell cycle alterations in NC and PTGDS-OE A549 and H1299 cells. B. Correlation between PTGDS expression and the cell cycle-related proteins PLK1 and CDK1 in tumor and adjacent normal tissues of patients with LUAD. Immunofluorescence analysis of PLK1 and CDK1 expression in NC and PTGDS-OE A549 and H1299 cells. WB analysis of PLK1 and CDK1 expression in NC and PTGDS-OE A549 and H1299 cells. (*** : p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7946797/v1/5a6b474476b3f4e882dac7a5.png"},{"id":95523689,"identity":"9aeaa4f2-bd90-4836-a2cf-25cfd4a892e7","added_by":"auto","created_at":"2025-11-10 10:00:04","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1809216,"visible":true,"origin":"","legend":"\u003cp\u003eA. Subcutaneous tumors from NC and PTGDS-OE H1299 cells in mice. B-C. Tumor volume (B) and weight (C) comparison between NC and PTGDS-OE H1299 subcutaneous tumors. WB analysis of PLK1 and CDK1 expression in NC and PTGDS-OE H1299 subcutaneous tumors. (** : p \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7946797/v1/6cb1c6e6a1101c4e64e3d4c1.png"},{"id":95530793,"identity":"830919fd-d6bf-48cb-9d2b-861010093f88","added_by":"auto","created_at":"2025-11-10 10:22:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":38331420,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7946797/v1/92785f3d-2acf-4656-8aa7-d5f66f3457ab.pdf"},{"id":95325459,"identity":"6a2494dc-ea28-4a26-af25-9e825aed0314","added_by":"auto","created_at":"2025-11-06 17:38:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":144167,"visible":true,"origin":"","legend":"","description":"","filename":"SFGTAB.docx","url":"https://assets-eu.researchsquare.com/files/rs-7946797/v1/6596134e412df515af356356.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling Lipid Metabolism-related Gene PTGDS: A Tumor Suppressor in Lung Adenocarcinoma with Therapeutic Potential","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eLung cancer is the second most common malignancy globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], responsible for approximately 18% of cancer-related deaths and representing one of the leading causes of cancer mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the predominant subtypes of non-small cell lung cancer (NSCLC), accounting for approximately 82% of all lung cancer cases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. LUAD, a major subtype of NSCLC, constitutes approximately 40% of lung cancer cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The overall five-year survival rate for patients with LUAD remains poor despite substantial progress in diagnostic and therapeutic approaches for lung cancer over the past decade, particularly for those with late-stage disease, post-treatment recurrence, and metastasis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, it is crucial to investigate the underlying molecular mechanisms driving LUAD development and to identify potential therapeutic targets.\u003c/p\u003e\u003cp\u003eMetabolic dysregulation is a hallmark of cancer that allows tumor cells to adapt to their specific energy requirements through metabolic reprogramming. Lipid metabolic reprogramming is a common feature across nearly all types of cancer. Studies have confirmed that metabolic reprogramming is closely associated with tumor initiation, progression, metastasis, remodeling of the immune microenvironment, and chemoresistance [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Lipids are a primary energy source and essential structural components of cellular membranes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Lipid metabolism is a dynamic process, with tumor tissues undergoing lipid remodeling via key lipid-metabolizing enzymes, transcription factors, and signaling pathways [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies have revealed that lipid metabolism and associated genes (LMRGs) play a critical role in LUAD development and prognosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eProstaglandin D synthase (PTGDS) is a lipid metabolism-related gene on chromosome 9. PTGDS belongs to the lipocalin superfamily and is involved in prostaglandin metabolism and lipid transport [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Interestingly, PTGDS plays varying roles across different types of cancer. It promotes tumor progression in diffuse large B-cell lymphoma, hepatocellular adenoma, testicular cancer, and ovarian cancer [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], whereas its downregulation in prostate, lung, and gastric cancers is associated with poor prognosis [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Hence, the role of PTGDS in LUAD progression requires further investigation.\u003c/p\u003e\u003cp\u003eThis study investigated PTGDS expression and its prognostic significance in LUAD using bioinformatics methods. We examined its association with cell cycle regulation, immune cell infiltration, and immune checkpoint genes. The role of PTGDS across multiple cancers was also investigated, revealing its differential expression and association with immune infiltration in various tumors. Additionally, it was confirmed that PTGDS suppressed LUAD cell proliferation, migration, and invasion in both in vitro and in vivo experiments. These findings suggested that PTGDS may serve as a prognostic biomarker and a potential therapeutic target for LUAD.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Collection\u003c/h2\u003e\u003cp\u003eThe gene expression data for the differential analysis of NSCLC was sourced from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the accession number GSE118370. A dataset comprising 743 genes associated with lipid metabolism was obtained from the GeneCards website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gene expression data and clinical information for the analysis of NSCLC prognosis were retrieved from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/repository\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/repository\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The clinical characteristics and genetic variation data of patients with LUAD were obtained from the TIMER2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Identification of Differentially Expressed Lipid Metabolism-related Genes\u003c/h2\u003e\u003cp\u003eThe R package \u0026ldquo;limma\u0026rdquo; was used to analyze genes in LUAD from TCGA and the GSE118370 dataset. Genes with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |FoldChange| \u0026gt;1.5 were identified as differentially expressed. A Venn diagram (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hiplot-academic.com/basic/Venn\u003c/span\u003e\u003cspan address=\"https://hiplot-academic.com/basic/Venn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to identify differentially expressed lipid metabolism-related genes by overlapping the two sets with the lipid metabolism-related gene set.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 PTGDS Co-expression Analysis and Functional Enrichment of Differentially Expressed PTGDS-related Genes\u003c/h2\u003e\u003cp\u003ePearson\u0026rsquo;s correlation coefficient was used to determine the co-expression between PTGDS expression and other genes. The R package \u0026ldquo;limma\u0026rdquo; was used to analyze gene expression in tumor and adjacent non-tumor tissues from TCGA data of patients with LUAD, categorizing PTGDS expression into high and low groups. Genes with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |FoldChange| \u0026gt;1.5 were identified as PTGDS-related differential genes. Differential genes were analyzed for gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment. Statistically significant differences (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were visualized using the Cairo and ggplot2 packages of R software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Immune Microenvironment Assessment\u003c/h2\u003e\u003cp\u003eThe R software was used to estimate the stromal, immune, and ESTIMATE scores for LUAD. The R package CIBERSORT was used to examine the proportions of 22 common immune cell types. To perform reliable immune score evaluation, we employed the immunedeconvR package and utilized the ggClusterNet R package for analysis and visualization of the results. And we applied the TIDE (Tumor Immune Dysfunction and Exclusion) algorithm to predict potential immunotherapy responses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Pan-cancer Analysis of PTGDS\u003c/h2\u003e\u003cp\u003eThe standardized pan-cancer dataset was retrieved from the UCSC database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The expression data of the PTGDS gene in various samples was also extracted. R software was used to estimate the differences in expression between normal and tumor samples according to cancer type, using an unpaired Student\u0026rsquo;s t-test for significance assessment. The coxph function from the R package survival was used to construct a Cox proportional hazards regression model, examining the correlation between gene expression and prognosis across various cancer types using the Logrank test for statistical significance. Additionally, the clinical relevance of PTGDS across pan-cancer was calculated using the TISIDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB/).W\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB/).W\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee reassessed the(\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). The infiltration scores of T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, cytotoxic lymphocytes, B lineage, NK cells, monocytic lineage, myeloid dendritic cells, neutrophils, endothelial cells, and fibroblasts in each cancer type were reassessed based on gene expression. The tumor mutational burden (TMB) scores were determined for each cancer type from published studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and the Pearson correlation was calculated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Collection of LUAD Samples\u003c/h2\u003e\u003cp\u003eBetween 2015 and 2018, 31 pairs of LUAD and matched adjacent normal lung tissues were collected from surgeries performed at the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College. The collection of these specimens was approved by the Medical Ethics Committee of the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Cell Culture and Transfection\u003c/h2\u003e\u003cp\u003eHuman LUAD cell lines (H1299 and A549) were obtained from the Chinese Academy of Sciences. H1299 cells were cultured in RPMI-1640 medium (Gibco) supplemented with 10% FBS (Pricella) and 1% penicillin-streptomycin (Beyotime). A549 cells were cultured in 10-cm dishes (Corning) with DMEM (Gibco) supplemented with 10% FBS (Pricella) and 1% penicillin-streptomycin (Beyotime). Both cell lines were cultured at 37\u0026deg;C under a 5% CO2 atmosphere. PTGDS plasmids (General Biol) were transfected into H1299 and A549 cells using Lipofectamine 8000 (Beyotime) in a serum-free medium according to the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR)\u003c/h2\u003e\u003cp\u003eRNA was extracted from tissues, and cells lysed with TRIzol reagent (Invitrogen), followed by precipitation and washing with chloroform, isopropanol, and 75% ethanol. The RNA pellet was dissolved in 30 \u0026micro;L of deionized water, and its concentration was measured for further analysis. Subsequently, cDNA was synthesized using HiScript II Q RT SuperMix for qPCR. Then, cDNA was analyzed by qRT\u0026ndash;PCR using AceQ qPCR SYBR Green Master Mix (without ROX) (Vazyme) following the manufacturer\u0026rsquo;s protocol. The primers used were GAPDH (Forward: 5\u0026prime;-GGAGCGAGATCCCTCCAAAAT-3\u0026prime;, Reverse: 5\u0026prime;-GGCTGTTGTCATACTTCTCATGG-3\u0026prime;) and PTGDS (Forward: 5\u0026prime;-GGCGTTGTCCATGTGCAAG-3\u0026prime;, Reverse: 5\u0026prime;-GGACTCCGGTAGCTGTAGGA-3\u0026prime;).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Immunohistochemistry (IHC)\u003c/h2\u003e\u003cp\u003eThe paraffin sections were baked, deparaffinized, hydrated, and washed. Antigen retrieval was performed, and peroxidase activity was inhibited. The sections were blocked with an antigen-blocking solution and incubated overnight at 4\u0026deg;C with a polyclonal rabbit anti-PTGDS antibody (1:500, Proteintech). The sections were then incubated with a secondary antibody (1:1000, Huabio) at room temperature for 2 hours. Staining was performed using DAB and hematoxylin.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Western blot (WB)\u003c/h2\u003e\u003cp\u003eTissues and cells were lysed using RIPA buffer containing protease and phosphatase inhibitors (CWbio), then boiled for 10 min to denature. SDS-PAGE was performed on 20 \u0026micro;g of protein and 2.5 \u0026micro;L of pre-stained protein marker (Vazyme) and subsequently transferred to a PVDF membrane (Millipore). The membrane was blocked with 5% non-fat milk for 1 h at room temperature, followed by overnight incubation at 4\u0026deg;C with the primary antibody. Subsequently, the membrane was incubated with a goat anti-rabbit secondary antibody for 1 h. Images were captured using an enhanced chemiluminescence reagent (Vazyme) and an imaging system.\u003c/p\u003e\u003cp\u003eThe antibodies used were PTGDS (1:1000, Proteintech), GAPDH (1:100000, Huabio), β-Actin (1:10000, Huabio), CDK1 (1:2000, Huabio), and PLK1 (1:1000, Immunoway).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Immunofluorescence\u003c/h2\u003e\u003cp\u003eLUAD cells subjected to different treatments were fixed on coverslips with 4% paraformaldehyde. Cells were treated with 0.1% Triton X-100 for permeabilization and blocked with 10% goat serum for 30 min at room temperature. After blocking, the cells were incubated at 4\u0026deg;C overnight with either rabbit anti-human CDK1 (1:100, Huabio) or rabbit anti-human PLK1 (1:200, Immunoway). The cells were incubated in the dark with iFluor 594-labeled goat anti-rabbit IgG (1:500, Huabio) for 60 min, followed by DAPI staining of the nuclei for 5 min (Beyotime). Finally, the cells were examined under a confocal microscope at 200x magnification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Cell Counting Kit-8 (CCK-8) Assay\u003c/h2\u003e\u003cp\u003eLUAD cells subjected to different treatments were seeded into 96-well plates. Then, 10 \u0026micro;L of CCK-8 reagent (APE x BIO) was added to each well and incubated for 2 h at 24, 48, or 72 h. The absorbance of each well was measured at 450 nm using a multifunctional microplate reader.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Colony Formation Assay\u003c/h2\u003e\u003cp\u003eLUAD cells were seeded into 6-well plates at a density of 1 \u0026times; 103 cells per well, following various treatments. The medium was replaced every three days. After 10 days of growth, the medium was discarded, and the cells were fixed with 4% paraformaldehyde and washed with PBS. The cells were stained with crystal violet for 10 min. Finally, the plates were air-dried in a ventilated area and photographed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 EdU Assay\u003c/h2\u003e\u003cp\u003eLUAD cells were seeded into 6-well plates after various treatments. EdU was added to the medium at 10 \u0026micro;M for 2 h after cell density reached 60%\u0026ndash;70%. The media were discarded, and the cells were fixed for 15 min in 4% paraformaldehyde before being permeabilized with 0.1% Triton X-100. Cells were stained with BeyoClick\u0026trade; EdU-555 and Hoechst dye at room temperature in the dark. Finally, fluorescence was detected using a laser confocal microscope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Wound Healing Assay\u003c/h2\u003e\u003cp\u003eLUAD cells treated with various methods were seeded into 6-well plates until 90% confluence was achieved. A scratch was created by a 200 \u0026micro;L pipette tip in the cell monolayer. The cell scratch was examined under a microscope at 0 and 48 h. ImageJ software was used to estimate the scratch area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.16 Transwell Assay\u003c/h2\u003e\u003cp\u003eThe Transwell chambers were optionally coated with Matrigel. After various treatments, LUAD cells (2 x 104 per well) were seeded into the upper chamber of 24-well Transwell inserts (Corning) with serum-free media. The lower chamber was filled with 600 \u0026micro;L of medium containing FBS. After 24 h, the cells in the Transwell inserts were fixed with paraformaldehyde and stained with crystal violet. The Images were captured using a microscope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e2.17 Cell Cycle Analysis\u003c/h2\u003e\u003cp\u003eH1299 and A549 cells were digested and transfected with PTGDS plasmids after 24 h of culture. The cells were washed with PBS and fixed overnight at 4\u0026deg;C with 70% ethanol. After PBS washing, the cells were resuspended in a PI and RNase solution and analyzed via flow cytometry. FlowJo software was used to analyze the data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e2.18 Establishment of the Nude Mouse Xenograft Tumor Model\u003c/h2\u003e\u003cp\u003eA PTGDS xenograft model was developed using 4-week-old BALB/c nude mice at the Laidi Biomedical Research Institute. The Ethics Committee of the Laidi Biomedical Research Institute approved the animal study protocol. BALB/c nude mice (n\u0026thinsp;=\u0026thinsp;5 per group) were subcutaneously injected with 2 x 106 H1299 cells under various treatments. Tumor growth was monitored, and tumor size was determined using calipers every five days. Tumor volume was determined using the formula: V = (a \u0026times; b2)/2. On day 25, the mice were euthanized, and the tumors were surgically removed. Tumor tissues were collected to determine their weight and volume, and proteins were extracted for subsequent analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e2.19 Statistical Analyses\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using GraphPad Prism (version 8) or the Statistical Package for the Social Sciences (version 21.0). A two-sided Student\u0026rsquo;s t-test was used to calculate the p-values for comparing the two groups. For multiple group comparisons, p-values were determined using analysis of variance. The association between PTGDS expression and clinicopathological characteristics was assessed using the two-sided χ\u0026sup2; test. Survival curves were generated using the Kaplan\u0026ndash;Meier method and compared using the log-rank test. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Downregulation of the Lipid Metabolism-related Gene PTGDS in LUAD and its Positive Correlation with Prognosis\u003c/h2\u003e\u003cp\u003eTo investigate the role of lipid metabolism-related genes in the progression of lung adenocarcinoma (LUAD), we analyzed LUAD data from the TCGA cohort. By comparing differentially expressed genes (DEGs) between T3\u0026thinsp;+\u0026thinsp;T4 stage and T1 stage LUAD patients, we identified 281 DEGs, including 106 upregulated and 175 downregulated genes in the T3\u0026thinsp;+\u0026thinsp;T4 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A-B). Additionally, using sequencing data from 6 paired LUAD tumor and adjacent normal tissues in the GEO dataset GSE118370, we identified 3,221 DEGs, with 1,402 upregulated and 1,819 downregulated in tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D). From the GeneCards database, we obtained a lipid metabolism-related gene set comprising 743 genes. Intersection analysis between these DEGs and lipid metabolism-related genes revealed 3 overlapping differentially expressed lipid metabolism-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Subsequently, we performed univariate COX regression analysis to assess the prognostic significance of these 3 genes. The results indicated that PTGDS and CYP27A1 were protective factors, while FHL2 was a risk factor in LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Given that CYP27A1 and FHL2 have been extensively studied in LUAD, we focused on PTGDS for further investigation. TCGA expression analysis showed that PTGDS was downregulated in tumor tissues, with even lower expression in higher T stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Prognostic analysis further revealed that PTGDS expression was negatively correlated with poor survival in LUAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). These findings suggest that low PTGDS expression is closely associated with unfavorable progression in LUAD.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Clinical and Genetic Characteristics of the Lipid Metabolism-related Gene PTGDS in LUAD\u003c/h2\u003e\u003cp\u003eWe conducted a comprehensive analysis of clinical data from LUAD patients in the TCGA database stratified by PTGDS expression levels. Our findings revealed significant differences in T-stage and N-stage distribution between LUAD with high PTGDS expression and those with low expression, while no significant variations were observed in M-stage or smoking status (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). To further characterize PTGDS expression patterns in lung adenocarcinoma, we assessed its levels in the context of common driver mutations (EGFR, ALK, KRAS, TP53) using the TIMER2.0 database. This analysis revealed that PTGDS expression was significantly upregulated in tumors harboring EGFR mutations and downregulated in those with KRAS mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Our analysis of PTGDS methylation status revealed significantly lower methylation levels in adjacent normal tissues compared to tumor tissues, providing a partial explanation for the higher PTGDS expression observed in normal adjacent tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Further investigation of PTGDS mutation profiles in LUAD showed a low mutation rate (0.39%), with all mutations being nonsense variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Comparative analysis of the top 10 mutated genes between high and low PTGDS expression groups showed no significant differences, with TP53, TTN, and MUC16 being predominant in both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Subsequent analysis revealed a negative correlation between PTGDS expression and tumor mutation burden in lung adenocarcinoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Single-cell RNA sequencing analysis using the EMTAB6149 dataset from TISCH2 database demonstrated predominant PTGDS expression in fibroblasts, malignant cells, endothelial cells, and monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-I).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Co-expression Gene Analysis and Differential Gene Enrichment Analysis of the Lipid Metabolism-Related Gene PTGDS\u003c/h2\u003e\u003cp\u003eSubsequently, utilizing TCGA-LUAD data, we stratified patients into high and low PTGDS expression groups and performed differential gene expression analysis. Using a threshold of p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |FoldChange| \u0026gt;1.5, we identified a total of 933 differentially expressed genes (DEGs), comprising 788 upregulated genes and 145 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). We conducted GO, KEGG pathways, and GSEA analyses were used to investigate the pathways and functions associated with PTGDS. Enrichment analysis demonstrated that differentially expressed genes were mainly involved in cell cycle and immune-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D, and Fig. S1). Further analysis revealed significant correlations between PTGDS expression and key tumorigenic processes: PTGDS expression exhibited a negative correlation with the expression of tumor proliferation-associated genes, while demonstrating a positive correlation with the expression of tumor immunity-associated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F). Additionally, correlation analysis of LUAD patient expression data from the TCGA database identified two genes strongly co-expressed with PTGDS (R\u0026thinsp;\u0026gt;\u0026thinsp;0.7): RASGRP2 and LCNL1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Association between the Lipid Metabolism-Related related Gene PTGDS and Tumor Immune Microenvironment\u003c/h2\u003e\u003cp\u003eEnrichment analysis indicated the role of PTGDS in immune pathways, prompting further investigation into its relationship with immune infiltration. Tumor microenvironment (TME) scoring scores revealed a positive association between high PTGDS expression and immune infiltration scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), suggesting that elevated PTGDS expression is linked to increased immune infiltration.This study analyzed the variations in immune cell components between LUAD samples exhibiting high and low PTGDS expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). LUAD samples with high PTGDS expression revealed an increased infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells and mast cells and a reduced proportions of M2 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-E). Further correlation analysis demonstrated that the infiltration of resting mast cells, CD8\u0026thinsp;+\u0026thinsp;T cells, memory B cells, monocytes, and resting dendritic cells were positively associated with PTGDS expression. In contrast, activated NK cells, activated dendritic cells, M0 macrophages, M2 macrophages, eosinophils, activated mast cells, and neutrophils were negatively associated with PTGDS expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Subsequently, we analyzed differences in immune response between the high and low PTGDS expression strata. The results revealed no statistically significant differences in immune response between these groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). However, PTGDS expression exhibited a positive correlation with the expression of majority of immune checkpoint-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Pan- cancer Analysis of Lipid Metabolism-Related related Gene PTGDS\u003c/h2\u003e\u003cp\u003eAccording to the literature, PTGDS is associated with various cancers. PTGDS expression was analyzed using RNA sequencing data from the TCGA database for diverse cancer types and normal tissues in the TCGA database. The results indicated that PTGDS is downregulated in BRCA, KICH, LIHC, LUAD, LUSC, PCPG, and PRAD, while it is upregulated in CHOL (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Univariate Cox regression analysis was used to evaluate the prognostic significance of PTGDS in pan-cancer. The results revealed that PTGDS serves as a risk factor for KIPAN, KIRC, STAD, and KICH while acting as a protective factor in GBMLGG, LUAD, LGG, CESC, HNSC, and DLBC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Analysis across multiple tumor types revealed that PTGDS expression positively correlated with most immune checkpoint genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Further investigation indicated a positive correlation between PTGDS expression and immune cell infiltration in various cancers, notably with CD8 T cells in BRCA, HNSC, and PAAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Subsequently, the clinical relevance of PTGDS was explored for various cancers using the TISIDB database. Higher PTGDS expression was correlated with longer overall survival (OS) in patients with CESC, HNSC, LGG, and LUAD but with shorter OS in patients with COAD, KIRC, LUSC, and UCS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Higher PTGDS expression was associated with earlier clinical stages in LUAD, LUSC, and TGCT patients, whereas it was associated with later clinical stages in BLCA, KIRC, SKCM, and STAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). The association between PTGDS and TMB across various cancer types was also investigated. PTGDS exhibited a significant association with MSI in nine cancers, revealing a positive correlation in KICH and a negative correlation in GBM, UVM, THCA, LGG, SARC, STAD, THYM and PRAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Lipid Metabolism-related Gene PTGDS is Downregulated in LUAD and Correlates with Prognosis\u003c/h2\u003e\u003cp\u003eTumors and adjacent normal tissues from 31 patients with LUAD were collected and subjected to IHC staining to detect PTGDS. PTGDS was significantly expressed in normal tissues but was decreased in tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B and Supplementary Table\u0026nbsp;2). WB analysis further validated this finding (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The clinical, pathological, and prognosis data revealed a significant association between low PTGDS expression and larger tumor diameter and advanced T stage in patients with LUAD (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Kaplan\u0026ndash;Meier survival analysis indicated that elevated PTGDS expression correlated with extended disease-free survival and improved prognoses (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Lipid Metabolism-related Gene PTGDS Suppresses Proliferation, Migration, and Invasion of LUAD Cells In Vitro\u003c/h2\u003e\u003cp\u003ePTGDS was overexpressed in the LUAD cell lines A549 and H1299 to investigate further its role (Fig. S2 and 7A). CCK-8 assays demonstrated a significant reduction in absorbance in A549 and H1299 cells overexpressing PTGDS, suggesting that PTGDS inhibits LUAD cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Colony formation assays also revealed that the number of colonies formed by PTGDS-overexpressing LUAD cells was significantly reduced, further confirming the suppression effect of PTGDS on cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The EdU assay identified a lower ratio of EdU-positive cells in the PTGDS-overexpressing group compared with the control group, supporting the anti-proliferative function of PTGDS in LUAD cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Wound healing assays revealed a significant decrease in the migratory capacity of A549 and H1299 cells after PTGDS overexpression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). The Transwell assays further indicated a significant decrease in the number of invading and migrating cells in PTGDS-overexpressing A549 and H1299 cells. These results demonstrated that PTGDS exerts anti-tumor effects in LUAD by inhibiting proliferation, migration, and invasion in vitro.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Lipid Metabolism-related Gene PTGDS is Closely Associated with Cell Cycle-related Proteins\u003c/h2\u003e\u003cp\u003ePrior GO and KEGG enrichment analyses revealed a significant association between PTGDS and the cell cycle. This study used flow cytometry to examine the cell cycle of A549 and H1299 cells overexpressing PTGDS to elucidate the tumor-suppressive effects of PTGDS in LUAD. PTGDS upregulation increased the proportion of cells in the G0/G1 phase and decreased the proportion in the S phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). To understand the impact of PTGDS on the cell cycle, this study examined the correlation between PTGDS and cell cycle-related proteins in patients with LUAD using data from the TCGA database. Analysis from the GEPIA2 website indicated a negative correlation between PTGDS expression and PLK1 and CDK1 in tumor and adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Immunofluorescence and WB assays were performed to measure the expression of cell cycle-related proteins in the control and PTGDS-overexpressing groups. The findings indicated a significant reduction in PLK1 and CDK1 expression in LUAD cells with PTGDS overexpression (Fi. 8C-D).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e3.9 Lipid Metabolism-related Gene PTGDS Suppresses LUAD Progression In Vivo\u003c/h2\u003e\u003cp\u003eTo further investigate the role of PTGDS in vivo, xenograft experiments were performed using naive-control (NC) H1299 cells and H1299 cells stably overexpressing PTGDS. The results demonstrated that PTGDS significantly inhibited subcutaneous tumor growth in mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Tumor volume and mass measurements indicated that tumors in the OE-PTGDS group were significantly smaller compared to the NC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB-C). Subsequently, WB analysis was performed to detect cell cycle-related proteins, and the results revealed that PTGDS overexpression markedly reduced the expression of PLK1 and CDK1, providing a potential explanation for the tumor-suppressive effects of PTGDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eDespite numerous studies, LUAD remains a significant global health issue [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Metabolic abnormalities in tumors are a key cause of tumorigenesis and progression, with lipid metabolism playing an indispensable role [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our previous systematic studies examined the involvement of lipid metabolism-related genes in LUAD and developed a risk-scoring model to predict patient prognosis and immunotherapy response [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur integrated analysis demonstrates that the lipid metabolism gene PTGDS is significantly downregulated in lung adenocarcinoma (LUAD) tumor tissues compared to adjacent normal tissues, with reduced expression correlating strongly with advanced T stages, larger tumor size, lymph node involvement (N-stage), and poor patient survival \u0026ndash; establishing it as a key protective factor in LUAD pathogenesis. This downregulation appears driven partly by epigenetic mechanisms, specifically hypermethylation in tumors, while genetic alterations like nonsense mutations are rare. Importantly, PTGDS expression shows distinct associations with driver mutations, being elevated in EGFR-mutant tumors but suppressed in KRAS-mutant contexts. Gene mutations are significant features of LUAD, and common mutations include EGFR, KRAS, ALK, ROS1, and RET [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Approximately 50% of Asians have activating EGFR mutations [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. TMB is anticipated to produce more neoantigens, enhancing cell recognition, and has been clinically linked to improved outcomes with immune checkpoint inhibitors [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This study found that PTGDS is negatively correlated with TMB. Functional enrichment analysis of PTGDS in patients with LUAD revealed its primary enrichment in the cell cycle and immune infiltration pathways. Abnormalities in the cell cycle are an important cause of tumorigenesis, and the malignancy of tumors is often linked to an accelerated abnormal cell cycle [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In LUAD, studies have found that FBXO32 promotes invasion and metastasis by targeting PTEN to promote proteasome-dependent degradation, thereby facilitating EMT and regulating the cell cycle [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. DLGAP5 enhances cell proliferation in LUAD by upregulating PLK1 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The immune microenvironment is another critical factor influencing tumor progression. The complex cellular cross-talk and signaling between tumor cells and immune microenvironment cells affect tumor progression [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This study examined the relationship between PTGDS and the immune microenvironment in LUAD, along with its clinical significance. Immune cell infiltration in the TME is strongly linked to patient prognosis. The findings revealed a positive correlation between PTGDS expression and the infiltration of various immune cells, particularly an increased proportion of CD8\u0026thinsp;+\u0026thinsp;T cells. Since PTGDS plays distinct roles in different tumors and exhibits tumor-suppressive and tumor-promoting effects, we conducted a preliminary pan-cancer analysis of PTGDS. The findings indicated that PTGDS is generally underexpressed in tumors and positively correlates with immune infiltration.\u003c/p\u003e\u003cp\u003eTo further validate the tumor-suppressive role of PTGDS in LUAD, clinical patient tissue samples were used to verify PTGDS expression and its impact on prognosis through WB and IHC. Through the creation of PTGDS-overexpressing LUAD cell lines and subsequent functional experiments, PTGDS overexpression was determined to markedly inhibit LUAD cell proliferation, invasion, and migration, elucidating its role as a protective factor in patients with LUAD. Flow cytometry analysis revealed that PTGDS overexpression increased the proportion of cells in the G0/G1 phase while decreasing their proportion in the S phase, consistent with functional enrichment findings related to the cell cycle pathway. Subsequently, the cell cycle-related proteins PLK1 and CDK1 were detected via WB and immunofluorescence, and the results revealed that PTGDS overexpression reduced the expression of PLK1 and CDK1, thereby inhibiting the progression of LUAD. Finally, a mouse subcutaneous tumor model was established to validate the tumor-inhibitory ability of PTGDS in vivo.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eThis study identified PTGDS, a lipid metabolism-related gene, as being downregulated in LUAD and highlighted its role as a protective factor positively associated with patient prognosis and immune infiltration. In vitro and in vivo experiments revealed that PTGDS inhibits LUAD cell proliferation, invasion, and migration by suppressing the expression of the cell cycle-regulating proteins PLK1 and CDK1. The findings of this study indicated that PTGDS may serve as a therapeutic target for LUAD, laying the groundwork for future strategies that integrate targeted and immunotherapy for lung cancer treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCONFLICT OF INTEREST STATEMENT\u003c/h2\u003e\u003cp\u003eThe authors have no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eETHICS STATEMENT\u003c/h2\u003e\u003cp\u003e The collection of specimens in this study was approved by the Medical Ethics Committee of the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College.\u003c/p\u003e\u003cp\u003eThis study was conducted in strict accordance with the ethical principles of the Helsinki Declaration.\u003c/p\u003e\u003cp\u003e All procedures involving animals adhered to the Basel Declaration and were performed under the guidelines of the Chinese Animal Welfare and Ethics Review Regulations. Every effort was made to minimize suffering, including optimized sample sizes and humane endpoints.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING INFORMATION\u003c/h2\u003e\u003cp\u003eThis work was supported by National Key R\u0026amp;D Program of China (Grant No. 2020YFE02022200). The funder had no role in the design, data collection, analysis, experiments and reporting of this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBoxuan Zhou, Jianwei Shi and Linchuan Liang jointly completed the experimental design, writing, typesetting and modification of the paper. Yushun Gao served as supervisors for examination and review. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThanks to Dr. Liang for the guidance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe gene expression data for differential analysis of NSCLC was sourced from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) with the accession number GSE29249. A dataset comprising 743 genes associated with lipid metabolism was obtained from the GeneCards website (https://www.genecards.org/). Gene expression data as well as clinical information for NSCLC prognosis analysis were retrieved from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/repository). Clinical characteristics and genetic variation data of lung adenocarcinoma (LUAD) patients were obtained from the UALCAN (https://ualcan.path.uab.edu/) and TIMER2.0 (http://timer.cistrome.org/) databases.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSmith RA, Andrews KS, Brooks D, Fedewa SA, Manassaram-Baptiste D, Saslow D et al (2019) Cancer screening in the United States, 2019: A review of current American Cancer Society guidelines and current issues in cancer screening. CA Cancer J Clin 69(3):184\u0026ndash;210\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I et al (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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J Hematol Oncol 14(1):98\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Association between PTGDS expression and clinicopathological features in LUAD patients.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e\u003cstrong\u003elow expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ehigh expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u003cstrong\u003etotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003egender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e1.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026le;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003egrade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003efisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eI-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ediameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003efisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026gt; 2 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026le; 2 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNM stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003efisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eII-III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT classification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003efisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eT2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN classification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003efisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eN1-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eblood vessel invasion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003efisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePleural invasion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003efisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpread through air spaces\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003efisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.2022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9675%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7726%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2888%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1913%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"Cell cycle, Immune cell infiltration, Lipid metabolism, Lung adenocarcinoma, Prognostic biomarker, PTGDS","lastPublishedDoi":"10.21203/rs.3.rs-7946797/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7946797/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality worldwide. Although the lipid metabolism-associated gene PTGDS has been implicated in tumorigenesis, its functional significance and regulatory mechanisms in LUAD are poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe integrated multi-omics data from TCGA and GEO cohorts to evaluate PTGDS expression and its clinicopathological relevance. Functional characterization was performed using gain-of-function models in LUAD cell lines and a xenograft mouse model, assessing proliferation, migration, invasion, and immune microenvironment alterations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePTGDS expression is markedly reduced in LUAD tissues and correlates strongly with advanced disease stage and unfavorable prognosis. Clinically, low PTGDS expression is associated with specific driver mutations and reduced tumor mutation burden. Notably, PTGDS levels correlate with enhanced cytotoxic T-cell infiltration and suppressed M2 macrophage polarization, suggesting immunomodulatory functions. Ectopic expression of PTGDS significantly suppressed malignant behaviors in vitro and tumor growth in vivo. Mechanistically, PTGDS downregulates key cell cycle regulators CDK1 and PLK1.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings establish PTGDS as a novel tumor suppressor in LUAD that restrains tumor progression through cell cycle modulation and immune microenvironment remodeling, highlighting its potential as both a prognostic biomarker and a therapeutic target.\u003c/p\u003e","manuscriptTitle":"Unveiling Lipid Metabolism-related Gene PTGDS: A Tumor Suppressor in Lung Adenocarcinoma with Therapeutic Potential","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 17:38:53","doi":"10.21203/rs.3.rs-7946797/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":"baafa430-7ebd-4043-a4e4-c58150aa595d","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-24T21:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 17:38:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7946797","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7946797","identity":"rs-7946797","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}