{"paper_id":"062f1a99-c8ec-458f-99ea-1c213eca8a63","body_text":"Linoleic acid promotes TF expression through PPAR-α, which leads to tumor progression in primary pulmonary lymphoepithelioma-like carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Linoleic acid promotes TF expression through PPAR-α, which leads to tumor progression in primary pulmonary lymphoepithelioma-like carcinoma Hejing Bao, Jiani Zhang, Zhuoyan Chen, Yuhuan Wang, Zhe Wang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5704972/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 Primary pulmonary lymphoepithelioma-like carcinoma (pLELC) is a relatively uncommon variant of primary non-small cell lung cancer, and its etiology is still largely unexplored. Objective: The aim of this study is to investigate the underlying mechanisms and potential therapeutic targets associated with pLELC. The patients diagnosed with advanced pLELC were retrospectively collected and subjected to proteomics and metabonomics analysis. Finally, a patient-derived xenograft (PDX) model of pLELC xenograft was constructed for validation. The results of the data-independent acquisition(DIA) quantitative analysis revealed that the expression of tissue factor (TF) protein was found to be upregulated in pLELC. Furthermore, it was observed that TF protein played a role in iron death, hypoxia-inducible factor-1 (HIF-1) signalling pathway, and leukocyte transendothelial migration. Untargeted metabonomics analysis revealed the presence of major metabolites, namely linoleic acid (LA), free fatty acid (16:0), and histidine. LA has been found to contribute to the progression of tumors by promoting the infiltration of M2 tumor-associated macrophages and inhibiting the infiltration of natural killer(NK) cells. However, this effect can be reversed by the TF inhibitor Tisotumab. LA enhances the expression of TF through peroxisome proliferator-activated receptor (PPAR)-α, and the malignancy caused by LA can be counteracted by TF inhibitors.The findings of this study suggest that LA has the ability to alter the tumor microenvironment in pLELC by upregulating TF expression through PPAR-α. These results indicate that TF could potentially serve as a therapeutic target for pLELC. Biological sciences/Cancer/Lung cancer Biological sciences/Cancer/Tumour biomarkers Primary pulmonary lymphoepithelioid carcinoma Multiomics Linoleic acid Tissue factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Lymphoepithelial carcinoma is an uncommon neoplasm of epithelial origin, primarily found in the nasopharynx. However, it has also been documented in various other anatomical sites including the lungs, thymus, stomach, liver, cervix, and bladder [ 1 – 11 ]. pLELC constitutes a mere 0.4% of all primary lung cancers and 0.9% of all non-small cell lung cancers [ 12 , 13 ]. In 1987, Begin et al. [ 14 ] first reported the discovery of pLELC. Lymphoepithelial-like carcinoma, which was classified under the category of \"other and unclassified cancers\" in the fourth edition of the World Health Organization's lung tumor classification, was renamed as lymphoepithelial carcinoma and reclassified as a type of squamous cell carcinoma in the year 2021. The pathological features of this condition include diffuse positive staining for CK5/6, p40, and p63, prominent syncytial growth patterns, variable infiltration of lymphatic plasma cells, and frequent association with the Epstein-Barr virus (EBV) [ 1 , 2 ]. Studies have demonstrated that pLELC exhibits genetic characteristics similar to nasopharyngeal carcinoma, but displays distinct differences from other types of lung cancers, NK/T-cell lymphoma, or EBV-associated gastric cancers [ 15 ]. pLELC exhibits a relatively low frequency of somatic mutation, yet it displays a broad spectrum of copy number variation. The main components implicated in the host-virus interaction are mutations and frequent loss of type I interferon genes [ 15 ]. Studies have reported an increased presence of pLELC somatic mutant genes and genetic abnormalities. EBV has been observed to integrate into intergenic and intronic regions, specifically showing upregulation of two miR-BamH1-A right-to-right transcripts (Barts), namely Bart5-3p and BART20-3P [ 16 ]. Currently, several studies focus on the genomics, transcriptomics, and EBV integration analysis of pLELC. However, the pathogenesis of pLELC has not yet been investigated at the proteomics and metabolomics levels. The immune microenvironment of pLELC has been minimally studied due to limited tissue availability and the prevalence of advanced disease at the time of diagnosis. Methods Proteomics Identification We adhered to the ethical guidelines set forth in the 1964 Helsinki declaration and its subsequent amendments. The study’s procedures were also compliant with ethical standards comparable to these guidelines. Ten serum samples were collected for the purpose of Proteomics Identification, comprising 5 samples from patients diagnosed with pLELC(pLELC group) and 5 samples from healthy individuals serving as controls(CON group). A volume of 7mL of peripheral blood was obtained from each individual and collected in serum separator tubes one day prior to the commencement of treatment. Serum samples were subjected to centrifugation at a speed of 1,000 g for a duration of 10 minutes at a temperature of 4℃. The resulting samples were then divided into smaller portions and stored at a temperature of -80℃. The subsequent procedures were conducted by Fitgene Biotech Co., LTD. In a nutshell, all samples were processed individually by data-independent acquisition (DIA) to assess proteomic differences. MS1 and MS2 data were collected, and samples were randomly collected. The iRT kit (Ki3002, Biognosys AG, Switzerland) was added to all samples to calibrate the retention time of the extracted peptide peaks. Statistical analysis of the DIA dataset was performed by Spectronaut 16 (Biognosys AG, Switzerland), including data normalization and relative protein quantification. Details can be found in the published literature [ 17 ]. Metabolomics Identification A total of 25 serum samples were chosen for the purpose of metabolomics identification. This sample set comprised 15 samples from individuals with pLELC group and 10 samples from healthy controls. The sample stored at -80℃refrigerator was thawed on ice and vortexed for 10 s. 50µL of sample and 300µL of extraction solution (ACN : Methanol = 1:4, V/V) containing internal standards were added into a 2 mL microcentrifuge tube. The sample was vortexed for 3 min and then centrifuged at 12000 rpm for 10 min (4 ℃). 200 µL of the supernatant was collected and placed in -20 ℃ for 30 min, and then centrifuged at 12000 rpm for 3 min (4 ℃). A 180 µL aliquots of supernatant were transferred for LC-MS analysis. All samples were acquired by the LC-MS system following machine orders. The analytical conditions were as follows: UPLC: column, Waters ACQUITY UPLC HSS T3 C18 (1.8 µm, 2.1 mm*100 mm); column temperature, 40℃; flow rate, 0.4 mL/min; injection volume, 2 µL; solvent system, water (0.1% formic acid): acetonitrile (0.1% formic acid); gradient program, 95:5 V/V at 0 min, 10:90 V/V at 11.0 min, 10:90 V/V at 12.0 min, 95:5 V/V at 12.1 min, and 95:5 V/V at 14.0 min. The original data file acquisition by LC-MS was converted into mzML format by ProteoWizard software. Peak extraction, peak alignment, and retention time correction were respectively performed by the XCMS program. The SVR method was used to correct the peak area. Peaks with a detection rate lower than 50% in each group of samples were discarded. Metabolic identification information was then obtained by searching the laboratory’s self-built database, integrated public database, AI database, and metDNA.Unsupervised PCA (principal component analysis) was performed by statistics function prcomp within R ( www.r-project.org ). The data was scaled by unit variance before unsupervised PCA. The HCA (hierarchical cluster analysis) results of samples and metabolites were presented as heatmaps with dendrograms, while pearson correlation coefficients (PCC) between samples were calculated by the cor function in R and presented as heatmaps only. Both HCA and PCC were carried out using the R package ComplexHeatmap. For HCA, normalised signal intensities of metabolites (unit variance scaling) are visualised as a color spectrum. Next, differential metabolites were selected, and KEGG annotation and enrichment analysis were conducted[ 18 , 19 ]. Immunofluorescence (IF) and immunohistochemistry (IHC) Immunofluorescence was performed on tissues using antibodies against TF (Bioss, P13726, 1:50), CD68 (PTMBIO, JMMR-2659, 1:100), and CD206 (PTMBIO, PTM-5343, 1:100). The tissues were fixed with paraformaldehyde and paraffin-embedded. Antigens were extracted by boiling in citrate buffer (pH 6.0) for 15 minutes, and endogenous peroxidase was blocked with 3% hydrogen peroxide for 20 minutes. The blocking buffer (normal goat serum) was added at 37°C for 30 minutes. The tissues were then incubated with TF, CD68, and CD206 antibodies at 4°C overnight, followed by incubation with conjugated goat anti-rabbit (Invitrogen, DyLight 488 AffiniPure Goat Anti-Rabbit IgG(H + L), A23220, 1:50), goat anti-mouse (Invitrogen, DyLight 488 AffiniPure Goat Anti-Rabbit IgG(H + L), A23210, 1:50), and goat anti-rabbit (Invitrogen, DyLight 649 AffiniPure Goat Anti-Rabbit IgG(H + L), A23630, 1:50) IgG antibodies at 37°C for 90 minutes. Finally, the nuclei were stained with DAPI (blue, C02-04002). The immunohistochemistry steps were the same as before. The tissues and Granzyme B (Bioss, P10144, 1:50) were incubated at 4°C overnight and then conjugated to the secondary antibody (SP-0023) and stained with DAB (C-0010). Enzyme-linked immunosorbent assay(ELISA) We also obtained 24 sera samples from patients with pLELC, as well as 30 samples from healthy individuals serving as controls. ELISA was conducted following the protocol provided by the manufacturer (Table 1 , Supplementary Table 1). The kits utilised in this study comprised the Tissue Factor ELISA (ELISA China), Mouse Interleukin 10 Receptor (IL-10R) ELISA Kit (ELISA China), and Mouse Tumor Necrosis Factor Alpha (TNF-α) ELISA Kit (ELISA China). Mice Feeding Each animal experiment was approved by the Animal Ethics Committee of the Affiliated Panyu Center Hospital, Guangzhou Medical University and followed the guidelines of the animal Experiments Control and Supervision Committee. All methods are implemented in accordance with relevant guidelines and regulations. The research aligns with the Animal Research: Reporting In Vivo Experiments (ARRIVE) Guidelines. The research involved 3–6 week-old male BALB/c nude mic sourced from Laboratory Animal Center, Southern Medical University. Male BALB/c nude mice, aged 3 and 6 weeks, were housed in groups of five to six mice per cage. They were maintained under standard temperature and humidity conditions, with a 12-hour light-dark cycle. Routine health monitoring of the control mice was conducted as a protocol component to maintain specific pathogen-free conditions. After the mice were shipped and acclimated, they were randomly assigned to different experimental diet groups, namely the ω-6 group and the control group. All mice were thoroughly combined within a container and subsequently allocated to diet groups at random. The ω-6 Diet utilised in this study was based on the Lieber-de-Cali control diet (Dyets #710027), whereas the control diet was formulated using a basal diet (Dyets #710028) [ 27 , 28 ]. The two liquid diets supplied 1.0 kilocalories of energy per millilitre of diet, with equivalent caloric contributions from macronutrients (35% from fat, 47% from carbohydrates, and 18% from protein) [ 20 , 21 ]. PDX models and Drug-treated Patient tumor explants were procured from surgical specimens of individuals diagnosed with pulmonary lymphoepithelial carcinoma at Southern Medical University, located in NanFang Hospital in Guangzhou, China. Prior to specimen collection, written informed consent was obtained from the patient. PDX models were generated through the engraftment of lung cancer tissue into immunodeficient mice. The tumor specimens were extracted from the tissue preservation solution, subjected to three washes in PBS, and transferred to a dish containing DMEM culture solution. The specimens were then sectioned into tissue blocks measuring 2mm×2mm×2mm. The excised tissue samples were inserted into a trocar. After skin disinfection was performed on nude mice, tumor tissue blocks were subcutaneously inoculated on the right dorsal region of the mice [ 22 , 23 ]. In addition to the standard hematoxylin and eosin (HE) staining, the remaining tumor tissues were subjected to inoculation through passage. These passages were then labelled as P2, P3, and so forth to ensure stability up to generation P3, which indicates successful modelling. The tumors exfoliated from P3 mice were regularly cryopreserved at a temperature of -80°C, except for cases where further passage was required. The initial administration of Tisotumab(AntibodySystem, France) occurred at a dosage of 4mg/Kg one day following tumor inoculation and was subsequently repeated weekly. The control group was administered an equivalent quantity of IgG. WY-14643(Pirinixic Acid, APExBIO, American) was dissolved in corn oil at a concentration of 100mg/kg/d and administered via intraperitoneal injection daily. The control group received intraperitoneal injections of 0.1 ml/10g corn oil. Real-time quantitative PCR Total RNA was extracted from tissues using the TRIzol kit (Takara Bio, Shiga, Japan) in accordance with the manufacturer's instructions. cDNA synthesis was performed using the Takara RT reagent (Takara Bio). The primers utilised in this study are provided in Supplementary Table 2. qRT-PCR was conducted on a photocycler 480 system (Roche Diagnostics, Basel, Switzerland) utilising the SYBR Premix Ex Taq II kit (Roche). We employed glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as an internal reference. The relative expression of the target gene was analysed using the 2 −ΔΔCT method. Western-blot(WB) An equal amount of proteins was separated using a 10% Sodium dodecyl sulfate–polyacrylamide gel electrophoresis(SDS-PAGE) gel and transferred onto PVDF membranes (Millipore, Bedford, MA, USA). The membranes were then probed with the following primary and secondary antibodies: polyclonal rabbit primary antibodies and fluorescent secondary antibodies, goat anti-rabbit antibodies (LI-COR, Lincoln, NE, USA) against PPAR α (AF7794, beyotim China, 1:1000) and Nuclear Factor kappa B(NF-κB) (AN365, beyotim China, 1:1000). The primary antibody was incubated at 4°C overnight, while the secondary antibody was incubated at 25°C for approximately one hour. The Odyssey infrared imaging system (LI-COR) was employed to analyse the immune response bands. Western blotting was conducted on three separate occasions. Statistical analysis If the econometric data adhere to a normal distribution, they can be assessed using a t-test; if not, a rank sum test should be used. The data was analysed using the chi-square test to determine the counts. The Kaplan-Meier method is commonly employed to estimate overall survival (OS) and progression-free survival (PFS) in various clinical studies. All statistical analyses were performed using IBM SPSS Statistics (Version 19.0, Armonk, New York, USA). The statistical significance of a difference is determined when the P-value is less than 0.05. Results Proteomics identification and verification of TF A total of 259 proteins were identified using the DIA method. Among these proteins, 16 exhibited differential expressions between the two groups. Specifically, 6 proteins were found to be down-regulated, while 10 proteins were up-regulated (Sfigure1, Table 2 and Supplementary Table 3–7). Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis revealed the involvement of these proteins in various biological processes, including ferroptosis, HIF-1 signalling pathway, metabolic pathway, leukocyte transendothelial migration, and cell adhesion. The TF molecule exhibited significant enrichment in the most extensive pathway and demonstrated a 1.55-fold up-regulation in the pLELC group (P < 0.05) (Fig. 1 A-C). The literature has documented that treatment aimed at targeting TF has decreased the infiltration of M2-type tumor-associated macrophages (TAMs). It has been suggested that TF may play a crucial role in inducing the infiltration of M2-type TAMs in pLELC [ 24 ]. Next, the expression of TF in pLELC was validated, and it was observed that 87.5% (21/24) of the samples showed positive TF expression in pLELC, with an IRS score of ≥ 1 (Fig. 2 A). A survival analysis was conducted to gain deeper insights into the prognostic significance of TF. TF expression in pLELC patients was significantly higher than in healthy controls (Fig. 2 B). Based on the TF ELISA results, patients were categorised into high-expression and low-expression groups. The analysis revealed a positive correlation between TF expression in pLELC and PFS (Fig. 2 C). Survival analysis was conducted on the Lung Squamous Cell Carcinoma (LUSC) group using TF mRNA expression data from the Cancer Genome Atlas(TCGA) database. The results indicated that the level of TF mRNA expression did not have a significant impact on the OS of LUSC (P = 0.95). (Fig. 2 D). Metabolomics identification results To investigate potential metabolic pathways as therapeutic targets for pLELC, an untargeted metabolomic identification was conducted. This analysis led to the detection of a total of 3175 metabolites in both experimental groups, with 946 metabolites being further identified as secondary metabolites. A total of 74 differential metabolites were identified, with 34 showing down-regulation and 40 showing up-regulation (Supplementary Table 4–8). The analysis revealed that the concentration primarily focused on the metabolism of fatty and amino acids. The metabolism of fatty acids encompasses LA and free fatty acids (FFA, 16:0), while the metabolites of amino acids include histidine (Fig. 1 D-G,Supplementary Table 9–11). Increasing evidence suggests that unsaturated fat, specifically polyunsaturated fatty acids (PUFAs), contributes to the risk and progression of cancer [ 25 ]. LA, a prominent constituent of the ω-6 family, strongly correlates with the tumor immune microenvironment [ 25 ]. To investigate the involvement of LA and TF in pLELC, a series of follow-up experiments were conducted. The progression of pLELC caused by LA can be reversed by the TF inhibitor Tisotumab Due to the infrequency of pLELC, there is currently a lack of established cell lines representing mature pLELC. In order to conduct the follow-up experiment, we utilised the PDX model. For the construction of the PDX model, we obtained a percutaneous lymph node biopsy and a surgical specimen. Finally, the surgical specimen was successfully prepared for subsequent experimental amplification. The accuracy of the PDX model was confirmed through HE staining, which demonstrated consistency between the patient source and the PDX model (Fig. 3 A-B). LA is a significant constituent of the omega-6 family of polyunsaturated fatty acids. It is metabolised to arachidonic acid(AA) and then further processed by cyclooxygenase(COX)/lipoxygenase (LOX) to produce inflammatory lipid mediators such as prostaglandins(PGs) and leukotrienes(LTs) [ 26 , 27 ] Currently, most studies have focused on an ω-6 diet, leading to the establishment of an ω-6 group and a control diet group[ 28 , 29 ]. Mice were allocated into three groups using randomisation, each consisting of five mice (Fig. 3 C). PDX models were established through tumor inoculation at week 10, following a 3-week period from birth on either a control or ω-6 Diet. The TF inhibitor Tisotumab was administered intraperitoneally to mice at the time of tumor inoculation, followed by weekly doses, to control mice with normal IgG. Changes in tumor volume were documented at regular intervals of 3–4 days, and the mice were euthanised on Day 24 in order to extract the tumors. The findings indicated that the tumor volume of the group receiving the ω-6 Diet + IgG was significantly greater compared to the group receiving the control diet + IgG (P < 0.0001). In the ω-6 Diet + Tisotumab group, a notable reduction in tumor volume was observed following the administration of Tisotumab (P<0.0001) (Fig. 3 D-E). We conducted additional investigations to gain a deeper understanding of the impact of LA and TF modifications on pLELC immune cell infiltration. Our findings revealed that compared to the ω-6 Diet + IgG Group, the control diet + IgG group and ω-6 Diet + Tisotumab group exhibited a decrease in CD68 + and CD206 + cells. The observed phenomenon is the upward trajectory of granzyme B + cells. (Fig. 4 A-C) Furthermore, an investigation was conducted to analyse the alterations in cytokine levels. The results demonstrated a decrease in IL-10 levels and an increase in TNF-α levels in both the control diet + IgG group and the ω-6 Diet + Tisotumab group when compared to the ω-6 Diet + IgG Group (Fig. 4 D). LA causes increased infiltration of M2-type TAMs and decreased infiltration of NK cells through PPAR-α From the aforementioned experiments, it has been confirmed that the LA diet has the potential to induce an increase in M2-type TAM and a decrease in NK cell infiltration. Furthermore, the administration of a TF inhibitor has shown the ability to reverse these observed changes. A comprehensive examination of the existing literature indicates that PPAR and sterol regulatory element-binding proteins(SREBP) are the primary transcription factors responsible for regulating lipid catabolism and anabolism in the context of fatty acid metabolism. Furthermore, it has been reported that the nuclear transcription factor NF-κB plays a role in the transcriptional regulation of lipid metabolism [ 30 ]. The PPAR family consists of three subtypes, namely PPAR-α, PPAR-γ, and PPAR-δ (also referred to as PPAR-β). Notably, the natural receptors for LA are limited to PPAR-α and PPAR-γ. The results showed that, compared with the control diet + IgG group, the ω-6 diet + IgG group and the ω-6 diet + Tisotumab group exhibited significantly increased expression of PPAR-α and NF-κB and significantly decreased expression of PPAR-γ (all P values < 0.0001). There were no statistically significant differences observed in the expression levels of SREBP-1 and early growth response-1(Erg-1) between the ω-6 Diet + IgG group and the ω-6 Diet + Tisotumab group (P > 0.05) (Fig. 4 E). WB analysis revealed a significant increase in PPAR-α expression in both the ω-6 Diet + IgG group and the ω-6 Diet + Tisotumab group compared to the control diet + IgG group. However, no significant difference was observed in NF-κB expression (Fig. 4 F). The tumor progression caused by changes in the immune microenvironment caused by PPAR-α agonists can be reversed by TF inhibitors Subsequently, a total of fifteen six-week-old nude mice were allocated into three groups, each consisting of five mice. The first group received the PPAR-α agonist WY-14643, while the second group received the control solvent, corn oil. Both treatments were administered from the time of tumor inoculation. The third group received daily doses of the TF inhibitor Tisotumab. Mice were administered the initial dose at the tumor inoculation, followed by weekly doses thereafter. The control group received normal IgG. Tumor volume measurements were taken every 3–4 days, and the mice were euthanised on the 24th day (Fig. 5 A). The findings indicated that the tumor volume of the WY-14643 + IgG Group was significantly greater than that of the control group (P < 0.0001). In the WY-14643 + Tisotumab group, a significant reduction in tumor volume was observed following the administration of Tisotumab (P < 0.0001) (Fig. 5 B-D). Compared to the WY-14643 + IgG group, a decrease in CD68 + cells was observed in both the control corn oil + IgG group and the WY-14643 + Tisotumab group. Additionally, a decrease in CD206 + cells was observed in the control corn oil + IgG group and the WY-14643 + Tisotumab group. In contrast, the population of Granzyme B + cells increased in both the control diet + IgG group and the WY-14643 + Tisotumab group compared to the WY-14643 + IgG Group (Fig. 5 E-F). Similarly, the IL-10 content decreased in both the control corn oil + IgG group and the WY-14643 + Tisotumab group compared to the WY-14643 + IgG group. Conversely, the TNF-α content increased in both the control corn oil + IgG group and the WY-14643 + Tisotumab group (Fig. 5 G). Discussion This study aimed to investigate the therapeutic targets of pLELC through proteomic analysis and KEGG pathway enrichment analysis. The differential proteins identified in this study were found to be associated with iron death, HIF-1 pathway signalling, metabolism, leukocyte transendothelial migration, and cell adhesion, among others. Full-length tissue factors serve as transmembrane receptors and cofactors for (F) VII/FVIIa factors. These tissue factors, known as TFs, are expressed by perivascular cells, including adventitial fibroblasts, as well as body surface cells, such as epithelial cells. They play a crucial role in the process of hemostasis. TF can also lead to different types of thrombosis [ 31 , 32 ]. The expression of TF in tumors has been found to be correlated with a negative prognosis, as well as being implicated in tumor growth and metastasis [ 33 ]. TF was found to be significantly increased in non-small cell lung cancer and glioblastoma multiforme cell lines and tumors harboring EGFR mutations. This observation was reported in a human study conducted by et al. [ 24 ], which also revealed a correlation between elevated TF levels and poor prognosis in patients. Both mTOR and TF-targeted therapy elicited complex alterations in the tumor microenvironment. The observed changes included a reduction in hypercoagulable state as indicated by decreased fibrin levels. Additionally, there was a decrease in stromal fibrosis, characterised by alterations in collagen distribution. The study also revealed a decline in vascular density and maturity, as evidenced by decreased expression of CD31 and α-SMA. Furthermore, there was a significant decrease in the infiltration of immunosuppressive M2 tumor-associated macrophages, as indicated by a decreased CD206/F4/80 ratio. A study conducted on C57BL/6 -derived tumor cells expressing TF revealed that TF plays a role in promoting metastasis. It achieves this by inhibiting the clearance of micrometastasis mediated by NK cells in a manner dependent on ogen and platelets [ 34 ]. The TF has been found to play a significant role in the process of metastasis through a thrombin-dependent mechanism, which operates independently of NK cells [ 34 ]. A recent study has demonstrated that the TF-thrombin pathway plays a crucial role in promoting metastasis by recruiting macrophages [ 35 ]. In conclusion, it can be observed that TF has the potential to influence the progression of tumors through its ability to modify the microenvironment in which the tumor develops. To delve deeper into the role of metabolites in pLELC, we subsequently conducted a non-targeted metabolomic analysis. Enrichment analysis revealed a significant concentration in metabolic pathways related to fatty acids and amino acids. Specifically, there was notable enrichment in fatty acid metabolism, amino acid metabolism, and metabolites such as linolenic acid, free fatty acid (FFA 16:0), and histidine. There is an increasing body of evidence indicating that PUFAs are implicated in the risk and progression of cancer. The n-3/ω-3 family of polyunsaturated fatty acids comprises Alpha-linolenic Acid, Eicosapentaenoic Acid, and Docosahexaenoic Acid, whereas the n-6/ω-6 family consists of LA and AA [ 36 ]. There is compelling evidence suggesting that the ω-6 polyunsaturated fatty acid LA may play a significant role in both carcinogenic and anticancer mechanisms. For instance, in vitro studies have shown that ω-6 polyunsaturated fatty acids promote the proliferation of BT-474 and human A549 cells [ 37 ]. In addition, it has been observed that high doses of LA inhibit the proliferation of Caco-2 cells. Conversely, it has been found that a high intake of LA can exhibit protective effects against the progression of cancer [ 38 ]. The deposition of lipids in the adipose tissue of individuals with obesity has been demonstrated to facilitate the infiltration of M2 -M2-polarised macrophages, whereas lean adipose tissue has been found to contain M1 phenotype macrophages [ 39 , 40 ]. ω-6 PUFAs possess pro-inflammatory characteristics, as they are metabolised into AA and further metabolised by COX and LOX enzymes into inflammatory lipid mediators such as PGs and LTs [ 41 ]. These AA metabolites exhibit a tumor-promoting effect by facilitating the enhancement of tumor growth through the induction of tolerance in dendritic cells (DC) and regulatory T cells (Tregs) via the downstream metabolite prostaglandin E-2 (PGE-2) of COX [ 41 ]. The products of AA metabolism, specifically the leukotriene B4 (LTB4) and lipoxin A4 (LXA4), have been shown to have various effects on myeloid progenitor cells, including myeloid-derived suppressor cells (MDSCs) and macrophages (M2). LTB4 and LXA4 can stimulate the expansion and differentiation of these cells [ 42 ]. Additionally, LXA4 has been found to induce monocyte differentiation through the action of interleukin-4 (IL-4). M2 macrophages, which are activated by LTB4 and LXA4, play a role in tumor angiogenesis, tumor progression, invasion, and metastasis. They also contribute to T cell immune suppression and the differentiation of Th2 cells by secreting IL-10 [ 42 ]. This phenomenon creates a positive feedback mechanism that facilitates the differentiation of other M2 macrophages. It is worth noting that all M2 macrophages have the ability to express programmed death ligand 1, which in turn enhances the process of activated T cell apoptosis [ 42 ]. TF and LA significantly modify the tumor microenvironment, particularly in relation to M2 macrophages, NK cells, and T cells. Hepatocyte growth factor (HGF)/c-Met and EGFR pathways have been documented to stimulate various kinase pathways, including c-Jun N-terminal kinase (JNK), Src, phosphatidylinositol-3 kinase (PI3k)/Akt/mammalian rapamycin target (mTOR), and KRAS/Raf/MEK/ERK. These pathways contribute to the upregulation of TF gene expression by inducing the expression of transcription factors such as activator protein-1 (AP-1), NF-κB, and early growth response protein-1 (Egr-1) [ 31 ]. The primary transcriptional programs that regulate lipid catabolism and anabolic processes consist of PPAR and SREBP. In light of the aforementioned potential mechanisms, the present study has determined that PPAR-α enhances the impact of LA on TF. Furthermore, it has been observed that TF inhibitors can counteract the progression of tumors induced by PPAR-α agonists. Specifically, the expression of TF in pLELC is associated with poor prognosis and is involved in the growth process of tumors. LA promotes the expression of TF through PPAR-α in comparison to the control group, leading to an increase in the infiltration of CD68 + tumor-associated macrophages, mainly CD206 + immunosuppressive M2-type tumor-associated macrophages, a reduction in the infiltration of GranzymeB + NK cells, an increase in the secretion of pro-inflammatory cytokine TNF-α, and a reduction in the secretion of anti-inflammatory cytokine IL-10, thereby causing tumor progression. This effect can be reversed by TF inhibitor Tisotumab. Similar results show that the alteration of the immune microenvironment caused by PPAR-α agonists leads to tumor progression, which can be reversed by TF inhibitors. Therefore, LA promotes the expression of TF through transcription factor PPAR-α, thereby promoting the infiltration of M2-type macrophages, inhibiting the infiltration of NK cells, and affecting cytokine secretion, participating in the remodeling process of the tumor microenvironment, ultimately forming an inhibitory tumor microenvironment and causing tumor progression. Current research on the pathogenesis of pLELC primarily focuses on genomics and transcriptomics. It has been discovered that pLELC shares similar driver mutations in NF-κB, CDKN2A, and JAK/STAT pathways, as well as similar regulatory patterns for p53 and PD-L1. Its expression of latency genes, such as LMP1 and LMP2, also suggests that it has a type II latency program similar to nasopharyngeal carcinoma (NPC) [ 15 , 43 ]. The genomic and molecular profiles of EBV-positive NPC and pLELC show similarities, suggesting potential co-therapeutic strategies for advanced NPC and pLELC[ 44 ]. However, these profiles are distinct from those of other lung cancers, NK/T-cell lymphomas, or EBV-associated gastric cancers regarding genetic signatures[ 15 ]. Currently, immunotherapy combined with chemotherapy and anti-angiogenic therapy combined with chemotherapy has been reported in the treatment of advanced pLELC [ 45 , 46 ]. However, most of these reports are based on retrospective studies with small sample sizes. Their initial roles in anti-tumor immunotherapy and targeted therapy warrant further in-depth research. In this study, proteomic and metabolomic analyses were used for the first time to explore the therapeutic targets of pLELC. A new mechanism was discovered, showing that LA promotes TF expression through PPAR-α, leading to tumor progression. This finding provides new potential targets for the treatment of pLELC. In addition to the mechanisms discussed in this study, the research also reported more possible mechanisms. Conjugated linoleic acid (CLA) can alleviate inflammation and restore the pro-regenerative properties of microglia, ultimately by activating the PPAR-γ pathway, leading to better recovery from demyelination injury and improved spatial learning function[ 47 ]. A low LA/ALA ratio not only regulates endogenous fatty acid levels but also upregulates PPAR-α and ACOX1, downregulates SREBP-1c and FAS gene expression levels, thereby affecting the lipid metabolism and endogenous fatty acid distribution of mice[ 48 ]. CLA induces the endogenous PPARα ligand palmitoleic acid (PA) and oleic acid ethyl ester (OEA) synthesis in brain tissue, through a positive feedback mechanism, activating PPARα and mediating possible anti-neuroinflammatory effects[ 49 ]. Maternal CLA supplementation regulates the fatty acid composition in the yolk sac, mediates embryonic chicken development and liver fat metabolism, which may be related to the AMPK pathway[ 50 ].For the potential mechanisms of how LA promotes TF expression, the regulatory relationship between TF and PPAR-α, and whether Tisotumab counteracts the malignancy induced by LA through downstream effects independent of the expression of PPAR-α and NF-κB, more detailed and sufficient exploration awaits subsequent experiments. There are still certain limitations to this study. The investigation into the immune microenvironment has yet to establish the concurrent expression of multiple immune cells or immune checkpoints as reliable immune prognostic indicators. Due to the infrequency of pLELC, the collection of fresh tissues and adjacent tissues for genomic and transcriptomic identification was not feasible. Additionally, the validation of cell lines for pathways is lacking. In our animal experiment, the use of a relatively small sample size (n = 5 per group) in this study may limit the statistical power of the research results, and a larger sample size validation experiment will be conducted in the future to further confirm the conclusion. Also, we did not utilise classical NOD-scid IL2Rg (null) (NSG) or NSG-SGM3 mice to reinfuse human hematopoietic stem cells to remodel the human immune system. Additionally, we did not include T and B cells in our study to investigate the immune microenvironment. The investigation of the immune microenvironment did not encompass the examination of matrix components, such as angiogenesis. Additionally, the correlation between EBV and LA, TF, and PPAR-α, as well as their impact on pLELC, was not investigated. This study, nonetheless, offers novel perspectives on the immune microenvironment and pathogenesis of pLELC. Conclusions Our study shows that LA can promote the progression of pLELC tumors by upregulating TF expression through PPAR-α, providing a potential target for treatment of pLELC. More research is needed to uncover more possible mechanisms in the future. Abbreviations pLELC lymphoepithelioma-like carcinoma PDX patient-derived xenograft DIA data-independent acquisition TF tissue factor LA linoleic acid NK natural killer PPAR peroxisome proliferator-activated receptors EBV Epstein-Barr virus HE hematoxylin and eosin GAPDH glyceraldehyde 3-phosphate dehydrogenase OS overall survival PFS progression-free survival TAMs tumor-associated macrophages LUSC Lung Squamous Cell Carcinoma TCGA The Cancer Genome Atlas PUFAs polyunsaturated fatty acids AA arachidonic acid COX cyclooxygenase LOX lipoxygenase SREBP sterol regulatory element-binding proteins PGs prostaglandins LTs leukotrienes WB Western blot HIF-1 hypoxia-inducible factor-1 ELISA Enzyme-linked immunosorbent assay DC dendritic cells MDSCs myeloid-derived suppressor cells LTB4 leukotriene B4 LXA4 lipoxin A4 PGE-2 prostaglandin E-2 IL-4 interleukin-4 JNK c-Jun N-terminal kinase mTOR mammalian rapamycin target HGF Hepatocyte growth factor AP-1 activator protein-1 TNF-α Tumor Necrosis Factor Alpha Erg-1 early growth response-1 KEGG Kyoto Encyclopedia of Genes and Genomes NF-κB Nuclear Factor kappa B Declarations Ethical approval and consent to participate This study was approved by the Ethics Committee of the Affiliated Panyu Center Hospital, Guangzhou Medical University (PYRC-2021-189). All participants signed an informed consent form. All animal experimental schemes have been approved by the Animal Ethics Committee of the Affiliated Panyu Center Hospital, Guangzhou Medical University(PYRC-A-2022-18).This research complies with the ARRIVE guidelines (https://arriveguidelines.org) for documenting animal experiments, ensuring all experimental methods adhere to local legal and ethical norms. Consent for publication Not applicable. Funding This work was supported by the Beijing Xishike Clinical Oncology Research Foundation (Y-tongshu2021/ms-0268), the Panyu District Science and Technology Plan Project (2023-Z04-014) and the Guangzhou Health Science and Technology Project (20241A011114). Data Availability statement All the data analysis results obtained during this study are included in the article/Additional file. Further inquiries can be obtained upon request by contacting the corresponding author via email. CRediT authorship contribution statement Hejing Bao: Conceptualization; Data curation; Writing—original draft; Writing—review & editing. Jiani Zhang, Zhuoyan Chen, Yuhuan Wang: Methodology; Visualization; Writing—review & editing. Zhe Wang, Ting Jiang, Zhiting Chen, Baishen Zhang: Resources; Validation; Visualization. Weng Zeng, Hehong Bao.: Formal analysis; Methodology; Software; Writing—original draft. Shudong Ma: Funding acquisition; Project administration; Supervision; Visualization. All authors read and approved the final manuscript. Declaration of competing interest The authors declared that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. Acknowledgements Not applicable. References Travis, W. D., Brambilla, E., Burke, A. P., Marx, A. & Nicholson, A. G. Introduction to The 2015 World Health Organization Classification of Tumors of the Lung, Pleura, Thymus, and Heart. 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Cancer Res. Clin. Oncol. 149 (3), 1185–1193 (2023). Epub 2022 Apr 4. PMID: 35377040; PMCID: PMC9984323. Zhou, L. Q. et al. Staged suppression of microglial autophagy facilitates regeneration in CNS demyelination by enhancing the production of linoleic acid. Proc. Natl. Acad. Sci. U S A . 120 (1), e2209990120. 10.1073/pnas.2209990120 (2023). Epub 2022 Dec 28. PMID: 36577069; PMCID: PMC9910603. Wang, Q. & Wang, X. The Effects of a Low Linoleic Acid/α-Linolenic Acid Ratio on Lipid Metabolism and Endogenous Fatty Acid Distribution in Obese Mice. Int. J. Mol. Sci. 24 (15), 12117. 10.3390/ijms241512117 (2023). PMID: 37569494; PMCID: PMC10419107. Murru, E. et al. Conjugated Linoleic Acid and Brain Metabolism: A Possible Anti-Neuroinflammatory Role Mediated by PPARα Activation. Front. Pharmacol. 11 , 587140. 10.3389/fphar.2020.587140 (2021). PMID: 33505308; PMCID: PMC7832089. Fu, C. et al. Maternal conjugated linoleic acid alters hepatic lipid metabolism via the AMPK signaling pathway in chick embryos. Poult. Sci. 99 (1), 224–234. 10.3382/ps/pez462 (2020). Epub 2019 Dec 30. PMID: 32416806; PMCID: PMC7587807. Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tbale1.docx Tbale2.docx sfigure1.pdf stable1.docx stable2.docx stable3.xlsx stable4.xlsx stable5pospLELCvsNOCinfo.xlsx stable6possigMetabolites.xlsx stable7negpLELCvsNOCinfo.xlsx stable8negsigMetabolites.xlsx stable9GO.xlsx stable10Pathway.xlsx stable11Pathway2.xlsx supplementmartial.pdf 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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(A) Volcano plot of differential proteins between pLELC and CON groups. A volcano plot (Volcano plot) was drawn using the protein expression difference fold change between the two groups of samples and the p-value obtained from the T -test to show the significant difference between the two groups of sample data. The abscissa is the difference multiple (logarithmic transformation with base 2), the ordinate is the significance of the difference P-value (logarithmic transformation with base 10), the red represents the up-regulated protein, the blue represents the down-regulated protein, and the blue and red points are considered significant (P \\u0026lt; 0.05), while the black points are not significant (P \\u0026gt; 0.05). (B) Heatmap of differential protein cluster analysis. Each row represents the differential protein to be analysed, and each column represents a comparison group. There are a total of 16 differential proteins between the pLELC and CON groups, of which 6 are down-regulated and 10 are up-regulated. (C) Bubble diagram of KEGG pathway enrichment analysis of differential proteins. A bubble diagram was made for the pathway enrichment results, and the thresholds were set as p.adjust \\u0026lt; 0.05 and the top 20 enrichment rankings to draw two graphs. This figure takes the Rich factor as the abscissa; a larger Rich factor indicates a greater degree of enrichment. The -log10(p.adjust) values are represented by different colors, with red indicating more significant enrichment. The size of the dots represents the number of differential proteins annotated to each item. (D) Principal component analysis diagram of grouping. PC1 represents the first principal component, PC2 represents the second principal component, and the percentage represents the variance interpretation rate of the principal component for the data set; each point in the figure represents a sample, the green point represents the CON group, and the orange point represents the pLELC group. The results show that the variation of the principal components between the two groups is large, while the variation within the groups is small. (E) Volcano plot of differential metabolites between pLELC and CON groups. The upper figure is the negative ion differential metabolite, and the lower figure is the positive ion differential metabolite. Each point in the volcano plot represents a metabolite, among which the green point represents the down-regulated differential metabolite, the red point represents the up-regulated differential metabolite, and the gray represents the metabolite detected but with no significant difference; the abscissa represents the logarithm of the relative content difference multiple of a certain metabolite between the two groups of samples (log2FC), and the larger the absolute value of the abscissa, the greater the relative content difference of the substance between the two groups of samples. Under the triple screening conditions of VIP + FC + P-value, the ordinate represents the significance level of the difference (-log10P-value), and the size of the dot represents the VIP value. (F) Heatmap of positive ion differential metabolite cluster (heatmap). The original relative contents of the differential metabolites identified by the application of screening criteria were subjected to Unit Variance Scaling (UV) standardization treatment row by row, and a heatmap was drawn by the ComplexHeatmap package of R software. The horizontal direction is the sample information, the vertical direction is the differential metabolite information, and the Group indicates grouping. Different colors indicate different numerical values obtained after different relative contents are standardized (red represents high content, and green represents low content). (G) Bubble diagram of KEGG pathway enrichment analysis of positive ion differential metabolites. According to the results of differential metabolites, KEGG pathway enrichment was performed, where Rich Factor is the ratio of the number of differential metabolites in the corresponding pathway to the total number of metabolites annotated to the pathway, and the larger this value, the greater the degree of enrichment. The P-value is the hypergeometric test P-value. The abscissa represents the Rich Factor corresponding to each pathway; the ordinate is the pathway name (sorted by P-value), the color of the point is the size of the P-value, and the redder, the more significant the enrichment. The size of the point represents the number of differential metabolites enriched, and the top 20 pathways ranked by P-value from small to large are displayed.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5704972/v1/299ef51b8d6a20e98e18c3d1.jpg\"},{\"id\":73516428,\"identity\":\"1b6bb4df-e35d-40ee-a020-7c8ad8ab8233\",\"added_by\":\"auto\",\"created_at\":\"2025-01-10 17:47:03\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":764913,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTF is the target of pLELC and is related to prognosis. (\\u003cstrong\\u003eA\\u003c/strong\\u003e) Immunofluorescent staining of TF in pLELC tissues. In cases representative of pLELC patients, stromal cells exhibited positive staining for TF, while tumor cells showed either negative or weakly positive staining for TF. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) The disparity in TF ELISA expression in pLELC patients and healthy control. (\\u003cstrong\\u003eC\\u003c/strong\\u003e) Based on Kaplan-Meier survival analysis, stratified according to differences in TF expression, the subgroup with low expression of pLELC TF exhibited a significantly better survival benefit compared to the subgroup with high expression of TF (P\\u0026lt;0.0001). (\\u003cstrong\\u003eD\\u003c/strong\\u003e) The analysis of the TCGA database revealed no significant impact on survival in LUSC due to differences in mRNA expression of TF (P=0.95).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5704972/v1/1312b95fb7cf94044ca0f15a.jpg\"},{\"id\":73516455,\"identity\":\"c4f7b4f1-6f71-4670-be85-b794181a8a3a\",\"added_by\":\"auto\",\"created_at\":\"2025-01-10 17:47:04\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":800616,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe progression of pLELC caused by LA can be reversed by the TF inhibitor Tisotumab. (A) Schematic diagram of PDX model construction, where tumor tissue specimens were obtained from surgical specimens or lung tumor biopsies. The tumor tissue specimens were cut into 2mm×2mm×2mm tissue blocks, placed in a cannula needle, and then inoculated onto the subcutaneous tissue of the right back of nude mice. The first generation was marked as P1, and the remaining tumor tissue was passaged and marked as P2 until stable passage to P3 was achieved, indicating successful modeling. Finally, the surgical specimen PDX model was successfully constructed. (B) HE staining was used to verify the PDX model, with patient-matched HE staining of surgical specimen tissue and PDX model tissue after model construction. The results show good consistency between the patient and the PDX model. (C) Schematic diagram of mouse feeding and drug administration. The ω-6 diet or control diet was fed at the age of 3 weeks, the pLELC PDX model was implanted at the age of 10 weeks, Tisotumab 4mg/Kg or control IgG was administered every 1 week, and tumors were harvested on the 24th day. (D) Schematic diagram of gross tumors in mice. Tumors were harvested on the 24th day after treatment. (E) Schematic diagram of tumor volume curve in mice. Compared with the control diet + IgG group, the tumor volume was larger in the ω-6 diet + IgG group (P \\u0026lt; 0.0001). However, in the ω-6 diet + Tisotumab group, the tumor volume decreased significantly after the use of Tisotumab (P \\u0026lt; 0.0001). Compared to the control diet + IgG group, the tumor volume in the group receiving ω-6 Diet + IgG was found to be larger (P\\u0026lt;0.0001). However, in the group receiving ω-6 Diet + Tisotumab, the tumor volume showed a significant reduction following Tisotumab treatment (P\\u0026lt;0.0001).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5704972/v1/26b9a6fad7b4347a0398fa0e.jpg\"},{\"id\":73516417,\"identity\":\"946ee359-f1cd-4472-8699-f2a86b076133\",\"added_by\":\"auto\",\"created_at\":\"2025-01-10 17:47:02\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1084815,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLA causes increased infiltration of M2-type TAMs and decreased infiltration of NK cells through PPAR-α.(\\u003cstrong\\u003eA\\u003c/strong\\u003e) Displays the immunofluorescence staining results of CD206 and CD68 in three PDX models. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) Immunohistochemical staining of Granzyme B, which is representative of three PDX models. (\\u003cstrong\\u003eC\\u003c/strong\\u003e) The number of CD68+ cells exhibited a significant decrease in both the control diet +IgG group and the ω-6 Diet +Tisotumab group, as compared to the ω-6 Diet +IgG group (P\\u0026lt;0.0001). CD206+ cells significantly decreased in the control diet +IgG group and ω-6 Diet +Tisotumab group (P\\u0026lt;0.0001). Conversely, there was a notable increase in Granzyme B\\u003csup\\u003e+\\u003c/sup\\u003e cells (P\\u0026lt;0.0001). (\\u003cstrong\\u003eD\\u003c/strong\\u003e) In comparison to the ω-6 Diet + IgG group, the IL-10 content exhibited a decrease in both the control diet +IgG group (P=0.012) and the ω-6 Diet +Tisotumab group (P\\u0026lt;0.0001). TNF-α levels exhibited a significant increase in the control diet + IgG group (P=0.001) and a significant increase in the ω-6 Diet +Tisotumab group (P\\u0026lt;0.0001). (\\u003cstrong\\u003eE\\u003c/strong\\u003e) The results obtained from qPCR analysis revealed significant changes in the expression levels of PPAR-α, PPAR-γ, and NF-κB in the different experimental groups. Specifically, the expression of PPAR-α was found to be significantly increased, while the expression of PPAR-γ was significantly decreased in both the ω-6 Diet +IgG group and the ω-6 Diet +Tisotumab group when compared to the control diet + IgG group (P\\u0026lt;0.0001). Additionally, the expression of NF-κB was significantly increased in both the ω-6 Diet + IgG group and the ω-6 Diet +Tisotumab group, compared to the control diet + IgG group (P\\u0026lt;0.0001). However, no statistically significant difference was observed in the expression levels of SREBP-1 and Erg-1 (P\\u0026gt;0.05). (\\u003cstrong\\u003eF\\u003c/strong\\u003e) WB analysis revealed a significant increase in PPAR-α expression in the ω-6 Diet + IgG group and ω-6 Diet + Tisotumab group compared to the control diet + IgG group. However, there was no significant difference observed in NF-κB expression.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5704972/v1/5b2a48d9642cdc216cea4c0b.jpg\"},{\"id\":73516409,\"identity\":\"9bd38d39-b49e-4235-aead-3f24c5dd244d\",\"added_by\":\"auto\",\"created_at\":\"2025-01-10 17:47:02\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1585984,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe tumor progression caused by changes in the immune microenvironment caused by PPAR-α agonists can be reversed by TF inhibitors.(A) Schematic diagram of mouse drug administration. The pLELC PDX model was implanted at the age of 6 weeks, Tisotumab 4mg/Kg or control IgG was administered every 1 week, WY-14643 100mg/kg/d, or 0.1ml/10g corn oil was administered intraperitoneally every day, and tumors were harvested on the 24th day. (B) Schematic diagram of gross tumors in mice. Tumors were harvested on the 24th day after treatment. (C) Schematic diagram of mice in general. Mice were sacrificed on the 24th day after treatment. (D) Schematic diagram of tumor volume curve in mice. It was observed that the tumor volume was larger in the WY-14643 + IgG group compared to the control corn oil + IgG group (P\\u0026lt; 0.0001). However, a significant decrease in tumor volume was observed after Tisotumab treatment (P\\u0026lt; 0.0001). (\\u003cstrong\\u003eE\\u003c/strong\\u003e) Representative CD206, CD68 immunofluorescent staining, and Granzyme B immunohistochemical staining were performed in three groups of PDX models. (\\u003cstrong\\u003eF\\u003c/strong\\u003e) The number of CD68\\u003csup\\u003e+\\u003c/sup\\u003e cells and CD206\\u003csup\\u003e+\\u003c/sup\\u003e cells in the control group and WY-14643 + Tisotumab group were significantly lower compared to the WY-14643 + IgG group (P\\u0026lt; 0.0001). The population of granzyme B\\u003csup\\u003e+\\u003c/sup\\u003e cells significantly increased in both the control diet + IgG group and the Wy-14643 + Tisotumab group (P\\u0026lt; 0.0001). (\\u003cstrong\\u003eG\\u003c/strong\\u003e) The concentration of Il-10 was found to be significantly lower in the control maize oil + IgG group and WY-14643 + Tisotumab group compared to the WY-14643 + IgG group (P\\u0026lt; 0.0001). The statistical analysis revealed a significant difference between the Corn oil + IgG group (P= 0.004) and the WY-14643 + Tisotumab group (P\\u0026lt; 0.0001).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5704972/v1/f5fa88452fd8c81dde4e9eae.jpg\"},{\"id\":73659747,\"identity\":\"1def7a8b-ce22-4231-9290-f8bbb1e63b8b\",\"added_by\":\"auto\",\"created_at\":\"2025-01-13 10:54:37\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":6064837,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5704972/v1/ef4fb57e-03c8-4852-bc25-9c5a4d96fea1.pdf\"},{\"id\":73516407,\"identity\":\"152b657c-c5f3-4e29-b255-57b3aba27240\",\"added_by\":\"auto\",\"created_at\":\"2025-01-10 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17:47:04\",\"extension\":\"pdf\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":296247,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"supplementmartial.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5704972/v1/1b62ce2675c9cbd9fd02403c.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Linoleic acid promotes TF expression through PPAR-α, which leads to tumor progression in primary pulmonary lymphoepithelioma-like carcinoma\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eLymphoepithelial carcinoma is an uncommon neoplasm of epithelial origin, primarily found in the nasopharynx. However, it has also been documented in various other anatomical sites including the lungs, thymus, stomach, liver, cervix, and bladder [\\u003cspan additionalcitationids=\\\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. pLELC constitutes a mere 0.4% of all primary lung cancers and 0.9% of all non-small cell lung cancers [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. In 1987, Begin et al. [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e] first reported the discovery of pLELC. Lymphoepithelial-like carcinoma, which was classified under the category of \\\"other and unclassified cancers\\\" in the fourth edition of the World Health Organization's lung tumor classification, was renamed as lymphoepithelial carcinoma and reclassified as a type of squamous cell carcinoma in the year 2021. The pathological features of this condition include diffuse positive staining for CK5/6, p40, and p63, prominent syncytial growth patterns, variable infiltration of lymphatic plasma cells, and frequent association with the Epstein-Barr virus (EBV) [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eStudies have demonstrated that pLELC exhibits genetic characteristics similar to nasopharyngeal carcinoma, but displays distinct differences from other types of lung cancers, NK/T-cell lymphoma, or EBV-associated gastric cancers [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. pLELC exhibits a relatively low frequency of somatic mutation, yet it displays a broad spectrum of copy number variation. The main components implicated in the host-virus interaction are mutations and frequent loss of type I interferon genes [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Studies have reported an increased presence of pLELC somatic mutant genes and genetic abnormalities. EBV has been observed to integrate into intergenic and intronic regions, specifically showing upregulation of two miR-BamH1-A right-to-right transcripts (Barts), namely Bart5-3p and BART20-3P [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eCurrently, several studies focus on the genomics, transcriptomics, and EBV integration analysis of pLELC. However, the pathogenesis of pLELC has not yet been investigated at the proteomics and metabolomics levels. The immune microenvironment of pLELC has been minimally studied due to limited tissue availability and the prevalence of advanced disease at the time of diagnosis.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eProteomics Identification\\u003c/h2\\u003e\\n \\u003cp\\u003eWe adhered to the ethical guidelines set forth in the 1964 Helsinki declaration and its subsequent amendments. The study\\u0026rsquo;s procedures were also compliant with ethical standards comparable to these guidelines. Ten serum samples were collected for the purpose of Proteomics Identification, comprising 5 samples from patients diagnosed with pLELC(pLELC group) and 5 samples from healthy individuals serving as controls(CON group). A volume of 7mL of peripheral blood was obtained from each individual and collected in serum separator tubes one day prior to the commencement of treatment. Serum samples were subjected to centrifugation at a speed of 1,000 g for a duration of 10 minutes at a temperature of 4℃. The resulting samples were then divided into smaller portions and stored at a temperature of -80℃. The subsequent procedures were conducted by Fitgene Biotech Co., LTD. In a nutshell, all samples were processed individually by data-independent acquisition (DIA) to assess proteomic differences. MS1 and MS2 data were collected, and samples were randomly collected. The iRT kit (Ki3002, Biognosys AG, Switzerland) was added to all samples to calibrate the retention time of the extracted peptide peaks. Statistical analysis of the DIA dataset was performed by Spectronaut 16 (Biognosys AG, Switzerland), including data normalization and relative protein quantification. Details can be found in the published literature [\\u003cspan class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003ch3\\u003eMetabolomics Identification\\u003c/h3\\u003e\\n\\u003cp\\u003eA total of 25 serum samples were chosen for the purpose of metabolomics identification. This sample set comprised 15 samples from individuals with pLELC group and 10 samples from healthy controls. The sample stored at -80℃refrigerator was thawed on ice and vortexed for 10 s. 50\\u0026micro;L of sample and 300\\u0026micro;L of extraction solution (ACN : Methanol\\u0026thinsp;=\\u0026thinsp;1:4, V/V) containing internal standards were added into a 2 mL microcentrifuge tube. The sample was vortexed for 3 min and then centrifuged at 12000 rpm for 10 min (4 ℃). 200 \\u0026micro;L of the supernatant was collected and placed in -20 ℃ for 30 min, and then centrifuged at 12000 rpm for 3 min (4 ℃). A 180 \\u0026micro;L aliquots of supernatant were transferred for LC-MS analysis. All samples were acquired by the LC-MS system following machine orders. The analytical conditions were as follows: UPLC: column, Waters ACQUITY UPLC HSS T3 C18 (1.8 \\u0026micro;m, 2.1 mm*100 mm); column temperature, 40℃; flow rate, 0.4 mL/min; injection volume, 2 \\u0026micro;L; solvent system, water (0.1% formic acid): acetonitrile (0.1% formic acid); gradient program, 95:5 V/V at 0 min, 10:90 V/V at 11.0 min, 10:90 V/V at 12.0 min, 95:5 V/V at 12.1 min, and 95:5 V/V at 14.0 min. The original data file acquisition by LC-MS was converted into mzML format by ProteoWizard software. Peak extraction, peak alignment, and retention time correction were respectively performed by the XCMS program. The SVR method was used to correct the peak area. Peaks with a detection rate lower than 50% in each group of samples were discarded. Metabolic identification information was then obtained by searching the laboratory\\u0026rsquo;s self-built database, integrated public database, AI database, and metDNA.Unsupervised PCA (principal component analysis) was performed by statistics function prcomp within R (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ewww.r-project.org\\u003c/span\\u003e\\u003c/span\\u003e). The data was scaled by unit variance before unsupervised PCA. The HCA (hierarchical cluster analysis) results of samples and metabolites were presented as heatmaps with dendrograms, while pearson correlation coefficients (PCC) between samples were calculated by the cor function in R and presented as heatmaps only. Both HCA and PCC were carried out using the R package ComplexHeatmap. For HCA, normalised signal intensities of metabolites (unit variance scaling) are visualised as a color spectrum. Next, differential metabolites were selected, and KEGG annotation and enrichment analysis were conducted[\\u003cspan class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003ch3\\u003eImmunofluorescence (IF) and immunohistochemistry (IHC)\\u003c/h3\\u003e\\n\\u003cp\\u003eImmunofluorescence was performed on tissues using antibodies against TF (Bioss, P13726, 1:50), CD68 (PTMBIO, JMMR-2659, 1:100), and CD206 (PTMBIO, PTM-5343, 1:100). The tissues were fixed with paraformaldehyde and paraffin-embedded. Antigens were extracted by boiling in citrate buffer (pH 6.0) for 15 minutes, and endogenous peroxidase was blocked with 3% hydrogen peroxide for 20 minutes. The blocking buffer (normal goat serum) was added at 37\\u0026deg;C for 30 minutes. The tissues were then incubated with TF, CD68, and CD206 antibodies at 4\\u0026deg;C overnight, followed by incubation with conjugated goat anti-rabbit (Invitrogen, DyLight 488 AffiniPure Goat Anti-Rabbit IgG(H\\u0026thinsp;+\\u0026thinsp;L), A23220, 1:50), goat anti-mouse (Invitrogen, DyLight 488 AffiniPure Goat Anti-Rabbit IgG(H\\u0026thinsp;+\\u0026thinsp;L), A23210, 1:50), and goat anti-rabbit (Invitrogen, DyLight 649 AffiniPure Goat Anti-Rabbit IgG(H\\u0026thinsp;+\\u0026thinsp;L), A23630, 1:50) IgG antibodies at 37\\u0026deg;C for 90 minutes. Finally, the nuclei were stained with DAPI (blue, C02-04002). The immunohistochemistry steps were the same as before. The tissues and Granzyme B (Bioss, P10144, 1:50) were incubated at 4\\u0026deg;C overnight and then conjugated to the secondary antibody (SP-0023) and stained with DAB (C-0010).\\u003c/p\\u003e\\n\\u003ch3\\u003eEnzyme-linked immunosorbent assay(ELISA)\\u003c/h3\\u003e\\n\\u003cp\\u003eWe also obtained 24 sera samples from patients with pLELC, as well as 30 samples from healthy individuals serving as controls. ELISA was conducted following the protocol provided by the manufacturer (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Supplementary Table 1). The kits utilised in this study comprised the Tissue Factor ELISA (ELISA China), Mouse Interleukin 10 Receptor (IL-10R) ELISA Kit (ELISA China), and Mouse Tumor Necrosis Factor Alpha (TNF-\\u0026alpha;) ELISA Kit (ELISA China).\\u003c/p\\u003e\\n\\u003ch3\\u003eMice Feeding\\u003c/h3\\u003e\\n\\u003cp\\u003eEach animal experiment was approved by the Animal Ethics Committee of the Affiliated Panyu Center Hospital, Guangzhou Medical University and followed the guidelines of the animal Experiments Control and Supervision Committee. All methods are implemented in accordance with relevant guidelines and regulations. The research aligns with the Animal Research: Reporting In Vivo Experiments (ARRIVE) Guidelines. The research involved 3\\u0026ndash;6 week-old male BALB/c nude mic sourced from Laboratory Animal Center, Southern Medical University. Male BALB/c nude mice, aged 3 and 6 weeks, were housed in groups of five to six mice per cage. They were maintained under standard temperature and humidity conditions, with a 12-hour light-dark cycle. Routine health monitoring of the control mice was conducted as a protocol component to maintain specific pathogen-free conditions. After the mice were shipped and acclimated, they were randomly assigned to different experimental diet groups, namely the \\u0026omega;-6 group and the control group. All mice were thoroughly combined within a container and subsequently allocated to diet groups at random. The \\u0026omega;-6 Diet utilised in this study was based on the Lieber-de-Cali control diet (Dyets #710027), whereas the control diet was formulated using a basal diet (Dyets #710028) [\\u003cspan class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. The two liquid diets supplied 1.0 kilocalories of energy per millilitre of diet, with equivalent caloric contributions from macronutrients (35% from fat, 47% from carbohydrates, and 18% from protein) [\\u003cspan class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003ePDX models and Drug-treated\\u003c/h2\\u003e\\n \\u003cp\\u003ePatient tumor explants were procured from surgical specimens of individuals diagnosed with pulmonary lymphoepithelial carcinoma at Southern Medical University, located in NanFang Hospital in Guangzhou, China. Prior to specimen collection, written informed consent was obtained from the patient. PDX models were generated through the engraftment of lung cancer tissue into immunodeficient mice. The tumor specimens were extracted from the tissue preservation solution, subjected to three washes in PBS, and transferred to a dish containing DMEM culture solution. The specimens were then sectioned into tissue blocks measuring 2mm\\u0026times;2mm\\u0026times;2mm. The excised tissue samples were inserted into a trocar. After skin disinfection was performed on nude mice, tumor tissue blocks were subcutaneously inoculated on the right dorsal region of the mice [\\u003cspan class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. In addition to the standard hematoxylin and eosin (HE) staining, the remaining tumor tissues were subjected to inoculation through passage. These passages were then labelled as P2, P3, and so forth to ensure stability up to generation P3, which indicates successful modelling. The tumors exfoliated from P3 mice were regularly cryopreserved at a temperature of -80\\u0026deg;C, except for cases where further passage was required.\\u003c/p\\u003e\\n \\u003cp\\u003eThe initial administration of Tisotumab(AntibodySystem, France) occurred at a dosage of 4mg/Kg one day following tumor inoculation and was subsequently repeated weekly. The control group was administered an equivalent quantity of IgG. WY-14643(Pirinixic Acid, APExBIO, American) was dissolved in corn oil at a concentration of 100mg/kg/d and administered via intraperitoneal injection daily. The control group received intraperitoneal injections of 0.1 ml/10g corn oil.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003ch3\\u003eReal-time quantitative PCR\\u003c/h3\\u003e\\n\\u003cp\\u003eTotal RNA was extracted from tissues using the TRIzol kit (Takara Bio, Shiga, Japan) in accordance with the manufacturer\\u0026apos;s instructions. cDNA synthesis was performed using the Takara RT reagent (Takara Bio). The primers utilised in this study are provided in Supplementary Table\\u0026nbsp;2. qRT-PCR was conducted on a photocycler 480 system (Roche Diagnostics, Basel, Switzerland) utilising the SYBR Premix Ex Taq II kit (Roche). We employed glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as an internal reference. The relative expression of the target gene was analysed using the 2\\u003csup\\u003e\\u0026minus;\\u0026Delta;\\u0026Delta;CT\\u003c/sup\\u003e method.\\u003c/p\\u003e\\n\\u003ch3\\u003eWestern-blot(WB)\\u003c/h3\\u003e\\n\\u003cp\\u003eAn equal amount of proteins was separated using a 10% Sodium dodecyl sulfate\\u0026ndash;polyacrylamide gel electrophoresis(SDS-PAGE) gel and transferred onto PVDF membranes (Millipore, Bedford, MA, USA). The membranes were then probed with the following primary and secondary antibodies: polyclonal rabbit primary antibodies and fluorescent secondary antibodies, goat anti-rabbit antibodies (LI-COR, Lincoln, NE, USA) against PPAR \\u0026alpha; (AF7794, beyotim China, 1:1000) and Nuclear Factor kappa B(NF-\\u0026kappa;B) (AN365, beyotim China, 1:1000). The primary antibody was incubated at 4\\u0026deg;C overnight, while the secondary antibody was incubated at 25\\u0026deg;C for approximately one hour. The Odyssey infrared imaging system (LI-COR) was employed to analyse the immune response bands. Western blotting was conducted on three separate occasions.\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eIf the econometric data adhere to a normal distribution, they can be assessed using a t-test; if not, a rank sum test should be used. The data was analysed using the chi-square test to determine the counts. The Kaplan-Meier method is commonly employed to estimate overall survival (OS) and progression-free survival (PFS) in various clinical studies. All statistical analyses were performed using IBM SPSS Statistics (Version 19.0, Armonk, New York, USA). The statistical significance of a difference is determined when the P-value is less than 0.05.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eProteomics identification and verification of TF\\u003c/h2\\u003e\\n \\u003cp\\u003eA total of 259 proteins were identified using the DIA method. Among these proteins, 16 exhibited differential expressions between the two groups. Specifically, 6 proteins were found to be down-regulated, while 10 proteins were up-regulated (Sfigure1, Table \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Supplementary Table 3\\u0026ndash;7). Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis revealed the involvement of these proteins in various biological processes, including ferroptosis, HIF-1 signalling pathway, metabolic pathway, leukocyte transendothelial migration, and cell adhesion. The TF molecule exhibited significant enrichment in the most extensive pathway and demonstrated a 1.55-fold up-regulation in the pLELC group (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA-C). The literature has documented that treatment aimed at targeting TF has decreased the infiltration of M2-type tumor-associated macrophages (TAMs). It has been suggested that TF may play a crucial role in inducing the infiltration of M2-type TAMs in pLELC [\\u003cspan class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Next, the expression of TF in pLELC was validated, and it was observed that 87.5% (21/24) of the samples showed positive TF expression in pLELC, with an IRS score of \\u0026ge;\\u0026thinsp;1 (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). A survival analysis was conducted to gain deeper insights into the prognostic significance of TF. TF expression in pLELC patients was significantly higher than in healthy controls (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB). Based on the TF ELISA results, patients were categorised into high-expression and low-expression groups. The analysis revealed a positive correlation between TF expression in pLELC and PFS (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC). Survival analysis was conducted on the Lung Squamous Cell Carcinoma (LUSC) group using TF mRNA expression data from the Cancer Genome Atlas(TCGA) database. The results indicated that the level of TF mRNA expression did not have a significant impact on the OS of LUSC (P\\u0026thinsp;=\\u0026thinsp;0.95). (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eMetabolomics identification results\\u003c/h2\\u003e\\n \\u003cp\\u003eTo investigate potential metabolic pathways as therapeutic targets for pLELC, an untargeted metabolomic identification was conducted. This analysis led to the detection of a total of 3175 metabolites in both experimental groups, with 946 metabolites being further identified as secondary metabolites. A total of 74 differential metabolites were identified, with 34 showing down-regulation and 40 showing up-regulation (Supplementary Table 4\\u0026ndash;8). The analysis revealed that the concentration primarily focused on the metabolism of fatty and amino acids. The metabolism of fatty acids encompasses LA and free fatty acids (FFA, 16:0), while the metabolites of amino acids include histidine (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eD-G,Supplementary Table\\u0026nbsp;9\\u0026ndash;11). Increasing evidence suggests that unsaturated fat, specifically polyunsaturated fatty acids (PUFAs), contributes to the risk and progression of cancer [\\u003cspan class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. LA, a prominent constituent of the \\u0026omega;-6 family, strongly correlates with the tumor immune microenvironment [\\u003cspan class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. To investigate the involvement of LA and TF in pLELC, a series of follow-up experiments were conducted.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eThe progression of pLELC caused by LA can be reversed by the TF inhibitor Tisotumab\\u003c/h2\\u003e\\n \\u003cp\\u003eDue to the infrequency of pLELC, there is currently a lack of established cell lines representing mature pLELC. In order to conduct the follow-up experiment, we utilised the PDX model. For the construction of the PDX model, we obtained a percutaneous lymph node biopsy and a surgical specimen. Finally, the surgical specimen was successfully prepared for subsequent experimental amplification. The accuracy of the PDX model was confirmed through HE staining, which demonstrated consistency between the patient source and the PDX model (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA-B). LA is a significant constituent of the omega-6 family of polyunsaturated fatty acids. It is metabolised to arachidonic acid(AA) and then further processed by cyclooxygenase(COX)/lipoxygenase (LOX) to produce inflammatory lipid mediators such as prostaglandins(PGs) and leukotrienes(LTs) [\\u003cspan class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e] Currently, most studies have focused on an \\u0026omega;-6 diet, leading to the establishment of an \\u0026omega;-6 group and a control diet group[\\u003cspan class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Mice were allocated into three groups using randomisation, each consisting of five mice (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). PDX models were established through tumor inoculation at week 10, following a 3-week period from birth on either a control or \\u0026omega;-6 Diet. The TF inhibitor Tisotumab was administered intraperitoneally to mice at the time of tumor inoculation, followed by weekly doses, to control mice with normal IgG. Changes in tumor volume were documented at regular intervals of 3\\u0026ndash;4 days, and the mice were euthanised on Day 24 in order to extract the tumors. The findings indicated that the tumor volume of the group receiving the \\u0026omega;-6 Diet\\u0026thinsp;+\\u0026thinsp;IgG was significantly greater compared to the group receiving the control diet\\u0026thinsp;+\\u0026thinsp;IgG (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). In the \\u0026omega;-6 Diet\\u0026thinsp;+\\u0026thinsp;Tisotumab group, a notable reduction in tumor volume was observed following the administration of Tisotumab (P\\u0026lt;0.0001) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD-E). We conducted additional investigations to gain a deeper understanding of the impact of LA and TF modifications on pLELC immune cell infiltration. Our findings revealed that compared to the \\u0026omega;-6 Diet\\u0026thinsp;+\\u0026thinsp;IgG Group, the control diet\\u0026thinsp;+\\u0026thinsp;IgG group and \\u0026omega;-6 Diet\\u0026thinsp;+\\u0026thinsp;Tisotumab group exhibited a decrease in CD68\\u0026thinsp;+\\u0026thinsp;and CD206\\u0026thinsp;+\\u0026thinsp;cells. The observed phenomenon is the upward trajectory of granzyme B\\u003csup\\u003e+\\u003c/sup\\u003e cells. (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA-C) Furthermore, an investigation was conducted to analyse the alterations in cytokine levels. The results demonstrated a decrease in IL-10 levels and an increase in TNF-\\u0026alpha; levels in both the control diet\\u0026thinsp;+\\u0026thinsp;IgG group and the \\u0026omega;-6 Diet\\u0026thinsp;+\\u0026thinsp;Tisotumab group when compared to the \\u0026omega;-6 Diet\\u0026thinsp;+\\u0026thinsp;IgG Group (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eLA causes increased infiltration of M2-type TAMs and decreased infiltration of NK cells through PPAR-\\u0026alpha;\\u003c/h2\\u003e\\n \\u003cp\\u003eFrom the aforementioned experiments, it has been confirmed that the LA diet has the potential to induce an increase in M2-type TAM and a decrease in NK cell infiltration. Furthermore, the administration of a TF inhibitor has shown the ability to reverse these observed changes. A comprehensive examination of the existing literature indicates that PPAR and sterol regulatory element-binding proteins(SREBP) are the primary transcription factors responsible for regulating lipid catabolism and anabolism in the context of fatty acid metabolism. Furthermore, it has been reported that the nuclear transcription factor NF-\\u0026kappa;B plays a role in the transcriptional regulation of lipid metabolism [\\u003cspan class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. The PPAR family consists of three subtypes, namely PPAR-\\u0026alpha;, PPAR-\\u0026gamma;, and PPAR-\\u0026delta; (also referred to as PPAR-\\u0026beta;). Notably, the natural receptors for LA are limited to PPAR-\\u0026alpha; and PPAR-\\u0026gamma;. The results showed that, compared with the control diet\\u0026thinsp;+\\u0026thinsp;IgG group, the \\u0026omega;-6 diet\\u0026thinsp;+\\u0026thinsp;IgG group and the \\u0026omega;-6 diet\\u0026thinsp;+\\u0026thinsp;Tisotumab group exhibited significantly increased expression of PPAR-\\u0026alpha; and NF-\\u0026kappa;B and significantly decreased expression of PPAR-\\u0026gamma; (all P values\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). There were no statistically significant differences observed in the expression levels of SREBP-1 and early growth response-1(Erg-1) between the \\u0026omega;-6 Diet\\u0026thinsp;+\\u0026thinsp;IgG group and the \\u0026omega;-6 Diet\\u0026thinsp;+\\u0026thinsp;Tisotumab group (P\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE). WB analysis revealed a significant increase in PPAR-\\u0026alpha; expression in both the \\u0026omega;-6 Diet\\u0026thinsp;+\\u0026thinsp;IgG group and the \\u0026omega;-6 Diet\\u0026thinsp;+\\u0026thinsp;Tisotumab group compared to the control diet\\u0026thinsp;+\\u0026thinsp;IgG group. However, no significant difference was observed in NF-\\u0026kappa;B expression (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eF).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eThe tumor progression caused by changes in the immune microenvironment caused by PPAR-\\u0026alpha; agonists can be reversed by TF inhibitors\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eSubsequently, a total of fifteen six-week-old nude mice were allocated into three groups, each consisting of five mice. The first group received the PPAR-\\u0026alpha; agonist WY-14643, while the second group received the control solvent, corn oil. Both treatments were administered from the time of tumor inoculation. The third group received daily doses of the TF inhibitor Tisotumab. Mice were administered the initial dose at the tumor inoculation, followed by weekly doses thereafter. The control group received normal IgG. Tumor volume measurements were taken every 3\\u0026ndash;4 days, and the mice were euthanised on the 24th day (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). The findings indicated that the tumor volume of the WY-14643\\u0026thinsp;+\\u0026thinsp;IgG Group was significantly greater than that of the control group (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). In the WY-14643\\u0026thinsp;+\\u0026thinsp;Tisotumab group, a significant reduction in tumor volume was observed following the administration of Tisotumab (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB-D). Compared to the WY-14643\\u0026thinsp;+\\u0026thinsp;IgG group, a decrease in CD68\\u003csup\\u003e+\\u003c/sup\\u003e cells was observed in both the control corn oil\\u0026thinsp;+\\u0026thinsp;IgG group and the WY-14643\\u0026thinsp;+\\u0026thinsp;Tisotumab group. Additionally, a decrease in CD206\\u003csup\\u003e+\\u003c/sup\\u003e cells was observed in the control corn oil\\u0026thinsp;+\\u0026thinsp;IgG group and the WY-14643\\u0026thinsp;+\\u0026thinsp;Tisotumab group. In contrast, the population of Granzyme B\\u0026thinsp;+\\u0026thinsp;cells increased in both the control diet\\u0026thinsp;+\\u0026thinsp;IgG group and the WY-14643\\u0026thinsp;+\\u0026thinsp;Tisotumab group compared to the WY-14643\\u0026thinsp;+\\u0026thinsp;IgG Group (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eE-F). Similarly, the IL-10 content decreased in both the control corn oil\\u0026thinsp;+\\u0026thinsp;IgG group and the WY-14643\\u0026thinsp;+\\u0026thinsp;Tisotumab group compared to the WY-14643\\u0026thinsp;+\\u0026thinsp;IgG group. Conversely, the TNF-\\u0026alpha; content increased in both the control corn oil\\u0026thinsp;+\\u0026thinsp;IgG group and the WY-14643\\u0026thinsp;+\\u0026thinsp;Tisotumab group (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eG).\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study aimed to investigate the therapeutic targets of pLELC through proteomic analysis and KEGG pathway enrichment analysis. The differential proteins identified in this study were found to be associated with iron death, HIF-1 pathway signalling, metabolism, leukocyte transendothelial migration, and cell adhesion, among others. Full-length tissue factors serve as transmembrane receptors and cofactors for (F) VII/FVIIa factors. These tissue factors, known as TFs, are expressed by perivascular cells, including adventitial fibroblasts, as well as body surface cells, such as epithelial cells. They play a crucial role in the process of hemostasis. TF can also lead to different types of thrombosis [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. The expression of TF in tumors has been found to be correlated with a negative prognosis, as well as being implicated in tumor growth and metastasis [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTF was found to be significantly increased in non-small cell lung cancer and glioblastoma multiforme cell lines and tumors harboring EGFR mutations. This observation was reported in a human study conducted by et al. [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e], which also revealed a correlation between elevated TF levels and poor prognosis in patients. Both mTOR and TF-targeted therapy elicited complex alterations in the tumor microenvironment. The observed changes included a reduction in hypercoagulable state as indicated by decreased fibrin levels. Additionally, there was a decrease in stromal fibrosis, characterised by alterations in collagen distribution. The study also revealed a decline in vascular density and maturity, as evidenced by decreased expression of CD31 and α-SMA. Furthermore, there was a significant decrease in the infiltration of immunosuppressive M2 tumor-associated macrophages, as indicated by a decreased CD206/F4/80 ratio. A study conducted on C57BL/6 -derived tumor cells expressing TF revealed that TF plays a role in promoting metastasis. It achieves this by inhibiting the clearance of micrometastasis mediated by NK cells in a manner dependent on ogen and platelets [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. The TF has been found to play a significant role in the process of metastasis through a thrombin-dependent mechanism, which operates independently of NK cells [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. A recent study has demonstrated that the TF-thrombin pathway plays a crucial role in promoting metastasis by recruiting macrophages [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. In conclusion, it can be observed that TF has the potential to influence the progression of tumors through its ability to modify the microenvironment in which the tumor develops.\\u003c/p\\u003e \\u003cp\\u003eTo delve deeper into the role of metabolites in pLELC, we subsequently conducted a non-targeted metabolomic analysis. Enrichment analysis revealed a significant concentration in metabolic pathways related to fatty acids and amino acids. Specifically, there was notable enrichment in fatty acid metabolism, amino acid metabolism, and metabolites such as linolenic acid, free fatty acid (FFA 16:0), and histidine. There is an increasing body of evidence indicating that PUFAs are implicated in the risk and progression of cancer. The n-3/ω-3 family of polyunsaturated fatty acids comprises Alpha-linolenic Acid, Eicosapentaenoic Acid, and Docosahexaenoic Acid, whereas the n-6/ω-6 family consists of LA and AA [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. There is compelling evidence suggesting that the ω-6 polyunsaturated fatty acid LA may play a significant role in both carcinogenic and anticancer mechanisms. For instance, in vitro studies have shown that ω-6 polyunsaturated fatty acids promote the proliferation of BT-474 and human A549 cells [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. In addition, it has been observed that high doses of LA inhibit the proliferation of Caco-2 cells. Conversely, it has been found that a high intake of LA can exhibit protective effects against the progression of cancer [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe deposition of lipids in the adipose tissue of individuals with obesity has been demonstrated to facilitate the infiltration of M2 -M2-polarised macrophages, whereas lean adipose tissue has been found to contain M1 phenotype macrophages [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. ω-6 PUFAs possess pro-inflammatory characteristics, as they are metabolised into AA and further metabolised by COX and LOX enzymes into inflammatory lipid mediators such as PGs and LTs [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. These AA metabolites exhibit a tumor-promoting effect by facilitating the enhancement of tumor growth through the induction of tolerance in dendritic cells (DC) and regulatory T cells (Tregs) via the downstream metabolite prostaglandin E-2 (PGE-2) of COX [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. The products of AA metabolism, specifically the leukotriene B4 (LTB4) and lipoxin A4 (LXA4), have been shown to have various effects on myeloid progenitor cells, including myeloid-derived suppressor cells (MDSCs) and macrophages (M2). LTB4 and LXA4 can stimulate the expansion and differentiation of these cells [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. Additionally, LXA4 has been found to induce monocyte differentiation through the action of interleukin-4 (IL-4). M2 macrophages, which are activated by LTB4 and LXA4, play a role in tumor angiogenesis, tumor progression, invasion, and metastasis. They also contribute to T cell immune suppression and the differentiation of Th2 cells by secreting IL-10 [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. This phenomenon creates a positive feedback mechanism that facilitates the differentiation of other M2 macrophages. It is worth noting that all M2 macrophages have the ability to express programmed death ligand 1, which in turn enhances the process of activated T cell apoptosis [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTF and LA significantly modify the tumor microenvironment, particularly in relation to M2 macrophages, NK cells, and T cells. Hepatocyte growth factor (HGF)/c-Met and EGFR pathways have been documented to stimulate various kinase pathways, including c-Jun N-terminal kinase (JNK), Src, phosphatidylinositol-3 kinase (PI3k)/Akt/mammalian rapamycin target (mTOR), and KRAS/Raf/MEK/ERK. These pathways contribute to the upregulation of TF gene expression by inducing the expression of transcription factors such as activator protein-1 (AP-1), NF-κB, and early growth response protein-1 (Egr-1) [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. The primary transcriptional programs that regulate lipid catabolism and anabolic processes consist of PPAR and SREBP. In light of the aforementioned potential mechanisms, the present study has determined that PPAR-α enhances the impact of LA on TF. Furthermore, it has been observed that TF inhibitors can counteract the progression of tumors induced by PPAR-α agonists.\\u003c/p\\u003e \\u003cp\\u003eSpecifically, the expression of TF in pLELC is associated with poor prognosis and is involved in the growth process of tumors. LA promotes the expression of TF through PPAR-α in comparison to the control group, leading to an increase in the infiltration of CD68\\u0026thinsp;+\\u0026thinsp;tumor-associated macrophages, mainly CD206\\u0026thinsp;+\\u0026thinsp;immunosuppressive M2-type tumor-associated macrophages, a reduction in the infiltration of GranzymeB\\u0026thinsp;+\\u0026thinsp;NK cells, an increase in the secretion of pro-inflammatory cytokine TNF-α, and a reduction in the secretion of anti-inflammatory cytokine IL-10, thereby causing tumor progression. This effect can be reversed by TF inhibitor Tisotumab. Similar results show that the alteration of the immune microenvironment caused by PPAR-α agonists leads to tumor progression, which can be reversed by TF inhibitors. Therefore, LA promotes the expression of TF through transcription factor PPAR-α, thereby promoting the infiltration of M2-type macrophages, inhibiting the infiltration of NK cells, and affecting cytokine secretion, participating in the remodeling process of the tumor microenvironment, ultimately forming an inhibitory tumor microenvironment and causing tumor progression.\\u003c/p\\u003e \\u003cp\\u003eCurrent research on the pathogenesis of pLELC primarily focuses on genomics and transcriptomics. It has been discovered that pLELC shares similar driver mutations in NF-κB, CDKN2A, and JAK/STAT pathways, as well as similar regulatory patterns for p53 and PD-L1. Its expression of latency genes, such as LMP1 and LMP2, also suggests that it has a type II latency program similar to nasopharyngeal carcinoma (NPC) [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. The genomic and molecular profiles of EBV-positive NPC and pLELC show similarities, suggesting potential co-therapeutic strategies for advanced NPC and pLELC[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. However, these profiles are distinct from those of other lung cancers, NK/T-cell lymphomas, or EBV-associated gastric cancers regarding genetic signatures[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Currently, immunotherapy combined with chemotherapy and anti-angiogenic therapy combined with chemotherapy has been reported in the treatment of advanced pLELC [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. However, most of these reports are based on retrospective studies with small sample sizes. Their initial roles in anti-tumor immunotherapy and targeted therapy warrant further in-depth research. In this study, proteomic and metabolomic analyses were used for the first time to explore the therapeutic targets of pLELC. A new mechanism was discovered, showing that LA promotes TF expression through PPAR-α, leading to tumor progression. This finding provides new potential targets for the treatment of pLELC.\\u003c/p\\u003e \\u003cp\\u003eIn addition to the mechanisms discussed in this study, the research also reported more possible mechanisms. Conjugated linoleic acid (CLA) can alleviate inflammation and restore the pro-regenerative properties of microglia, ultimately by activating the PPAR-γ pathway, leading to better recovery from demyelination injury and improved spatial learning function[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]. A low LA/ALA ratio not only regulates endogenous fatty acid levels but also upregulates PPAR-α and ACOX1, downregulates SREBP-1c and FAS gene expression levels, thereby affecting the lipid metabolism and endogenous fatty acid distribution of mice[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. CLA induces the endogenous PPARα ligand palmitoleic acid (PA) and oleic acid ethyl ester (OEA) synthesis in brain tissue, through a positive feedback mechanism, activating PPARα and mediating possible anti-neuroinflammatory effects[\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. Maternal CLA supplementation regulates the fatty acid composition in the yolk sac, mediates embryonic chicken development and liver fat metabolism, which may be related to the AMPK pathway[\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e].For the potential mechanisms of how LA promotes TF expression, the regulatory relationship between TF and PPAR-α, and whether Tisotumab counteracts the malignancy induced by LA through downstream effects independent of the expression of PPAR-α and NF-κB, more detailed and sufficient exploration awaits subsequent experiments.\\u003c/p\\u003e \\u003cp\\u003eThere are still certain limitations to this study. The investigation into the immune microenvironment has yet to establish the concurrent expression of multiple immune cells or immune checkpoints as reliable immune prognostic indicators. Due to the infrequency of pLELC, the collection of fresh tissues and adjacent tissues for genomic and transcriptomic identification was not feasible. Additionally, the validation of cell lines for pathways is lacking. In our animal experiment, the use of a relatively small sample size (n\\u0026thinsp;=\\u0026thinsp;5 per group) in this study may limit the statistical power of the research results, and a larger sample size validation experiment will be conducted in the future to further confirm the conclusion. Also, we did not utilise classical NOD-scid IL2Rg (null) (NSG) or NSG-SGM3 mice to reinfuse human hematopoietic stem cells to remodel the human immune system. Additionally, we did not include T and B cells in our study to investigate the immune microenvironment. The investigation of the immune microenvironment did not encompass the examination of matrix components, such as angiogenesis. Additionally, the correlation between EBV and LA, TF, and PPAR-α, as well as their impact on pLELC, was not investigated. This study, nonetheless, offers novel perspectives on the immune microenvironment and pathogenesis of pLELC.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eOur study shows that LA can promote the progression of pLELC tumors by upregulating TF expression through PPAR-α, providing a potential target for treatment of pLELC. More research is needed to uncover more possible mechanisms in the future.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003epLELC \\u0026nbsp;lymphoepithelioma-like carcinoma\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003ePDX \\u0026nbsp; \\u0026nbsp;patient-derived xenograft\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eDIA \\u0026nbsp; \\u0026nbsp;data-independent acquisition\\u003c/p\\u003e\\n\\u003cp\\u003eTF \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;tissue factor\\u003c/p\\u003e\\n\\u003cp\\u003eLA \\u0026nbsp; \\u0026nbsp; linoleic acid\\u003c/p\\u003e\\n\\u003cp\\u003eNK \\u0026nbsp; \\u0026nbsp; natural killer\\u003c/p\\u003e\\n\\u003cp\\u003ePPAR \\u0026nbsp; peroxisome proliferator-activated receptors\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eEBV \\u0026nbsp; \\u0026nbsp;Epstein-Barr virus\\u003c/p\\u003e\\n\\u003cp\\u003eHE \\u0026nbsp; \\u0026nbsp; hematoxylin and eosin\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eGAPDH \\u0026nbsp;glyceraldehyde 3-phosphate dehydrogenase\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eOS \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; overall survival\\u003c/p\\u003e\\n\\u003cp\\u003ePFS \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;progression-free survival\\u003c/p\\u003e\\n\\u003cp\\u003eTAMs \\u0026nbsp; \\u0026nbsp;tumor-associated macrophages\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eLUSC \\u0026nbsp; \\u0026nbsp;Lung Squamous Cell Carcinoma\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTCGA \\u0026nbsp; \\u0026nbsp;The Cancer Genome Atlas\\u003c/p\\u003e\\n\\u003cp\\u003ePUFAs \\u0026nbsp; polyunsaturated fatty acids\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAA \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;arachidonic acid\\u003c/p\\u003e\\n\\u003cp\\u003eCOX \\u0026nbsp; \\u0026nbsp;cyclooxygenase\\u003c/p\\u003e\\n\\u003cp\\u003eLOX \\u0026nbsp; \\u0026nbsp; lipoxygenase\\u003c/p\\u003e\\n\\u003cp\\u003eSREBP \\u0026nbsp; sterol regulatory element-binding proteins\\u003c/p\\u003e\\n\\u003cp\\u003ePGs \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;prostaglandins\\u003c/p\\u003e\\n\\u003cp\\u003eLTs \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;leukotrienes\\u003c/p\\u003e\\n\\u003cp\\u003eWB \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;Western blot\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eHIF-1 \\u0026nbsp; \\u0026nbsp;hypoxia-inducible factor-1\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eELISA \\u0026nbsp; Enzyme-linked immunosorbent assay\\u003c/p\\u003e\\n\\u003cp\\u003eDC \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;dendritic cells\\u003c/p\\u003e\\n\\u003cp\\u003eMDSCs \\u0026nbsp; myeloid-derived suppressor cells\\u003c/p\\u003e\\n\\u003cp\\u003eLTB4 \\u0026nbsp; \\u0026nbsp; leukotriene B4\\u003c/p\\u003e\\n\\u003cp\\u003eLXA4 \\u0026nbsp; \\u0026nbsp; lipoxin A4\\u003c/p\\u003e\\n\\u003cp\\u003ePGE-2 \\u0026nbsp; \\u0026nbsp; prostaglandin E-2\\u003c/p\\u003e\\n\\u003cp\\u003eIL-4 \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; interleukin-4\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eJNK \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;c-Jun N-terminal kinase\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003emTOR \\u0026nbsp; \\u0026nbsp;mammalian rapamycin target \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eHGF \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;Hepatocyte growth factor\\u003c/p\\u003e\\n\\u003cp\\u003eAP-1 \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;activator protein-1\\u003c/p\\u003e\\n\\u003cp\\u003eTNF-\\u0026alpha; Tumor Necrosis Factor Alpha\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eErg-1 \\u0026nbsp;early growth response-1\\u003c/p\\u003e\\n\\u003cp\\u003eKEGG\\u0026nbsp; \\u0026nbsp;\\u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\\u003c/p\\u003e\\n\\u003cp\\u003eNF-\\u0026kappa;B \\u0026nbsp; \\u0026nbsp;Nuclear Factor kappa B\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthical approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was approved by the Ethics Committee of the Affiliated Panyu Center Hospital, Guangzhou Medical University (PYRC-2021-189). All participants signed an informed consent form. All animal experimental schemes have been approved by the Animal Ethics Committee of the Affiliated Panyu Center Hospital, Guangzhou Medical University(PYRC-A-2022-18).This research complies with the ARRIVE guidelines (https://arriveguidelines.org) for documenting animal experiments, ensuring all experimental methods adhere to local legal and ethical norms.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by the Beijing Xishike Clinical Oncology Research Foundation (Y-tongshu2021/ms-0268), the Panyu District Science and Technology Plan Project (2023-Z04-014) and the Guangzhou Health Science and Technology Project (20241A011114).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability statement\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll the data analysis results obtained during this study are included in the article/Additional file. Further inquiries can be obtained upon request by contacting the corresponding author via email.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCRediT authorship contribution statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHejing Bao: Conceptualization; Data curation; Writing\\u0026mdash;original draft; Writing\\u0026mdash;review \\u0026amp; editing. Jiani Zhang, Zhuoyan Chen, Yuhuan Wang: Methodology; Visualization; Writing\\u0026mdash;review \\u0026amp; editing. Zhe Wang, Ting Jiang, Zhiting Chen, Baishen Zhang: Resources; Validation; Visualization. Weng Zeng, Hehong Bao.: Formal analysis; Methodology; Software; Writing\\u0026mdash;original draft. Shudong Ma: Funding acquisition; Project administration; Supervision; Visualization. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of competing interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declared that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eTravis, W. D., Brambilla, E., Burke, A. P., Marx, A. \\u0026amp; Nicholson, A. G. Introduction to The 2015 World Health Organization Classification of Tumors of the Lung, Pleura, Thymus, and Heart. J Thorac Oncol. ;10(9):1240\\u0026ndash;1242. 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PMID: 32416806; PMCID: PMC7587807.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003eTable 1 and 2 are available in the Supplementary Files section.\\u003c/p\\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\":\"info@researchsquare.com\",\"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\":\"Primary pulmonary lymphoepithelioid carcinoma, Multiomics, Linoleic acid, Tissue factors \",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5704972/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5704972/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003ePrimary pulmonary lymphoepithelioma-like carcinoma (pLELC) is a relatively uncommon variant of primary non-small cell lung cancer, and its etiology is still largely unexplored. Objective: The aim of this study is to investigate the underlying mechanisms and potential therapeutic targets associated with pLELC. The patients diagnosed with advanced pLELC were retrospectively collected and subjected to proteomics and metabonomics analysis. Finally, a patient-derived xenograft (PDX) model of pLELC xenograft was constructed for validation. The results of the data-independent acquisition(DIA) quantitative analysis revealed that the expression of tissue factor (TF) protein was found to be upregulated in pLELC. Furthermore, it was observed that TF protein played a role in iron death, hypoxia-inducible factor-1 (HIF-1) signalling pathway, and leukocyte transendothelial migration. Untargeted metabonomics analysis revealed the presence of major metabolites, namely linoleic acid (LA), free fatty acid (16:0), and histidine. LA has been found to contribute to the progression of tumors by promoting the infiltration of M2 tumor-associated macrophages and inhibiting the infiltration of natural killer(NK) cells. However, this effect can be reversed by the TF inhibitor Tisotumab. LA enhances the expression of TF through peroxisome proliferator-activated receptor (PPAR)-α, and the malignancy caused by LA can be counteracted by TF inhibitors.The findings of this study suggest that LA has the ability to alter the tumor microenvironment in pLELC by upregulating TF expression through PPAR-α. These results indicate that TF could potentially serve as a therapeutic target for pLELC.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Linoleic acid promotes TF expression through PPAR-α, which leads to tumor progression in primary pulmonary lymphoepithelioma-like carcinoma\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-01-10 17:46:56\",\"doi\":\"10.21203/rs.3.rs-5704972/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"9a09ce1d-d236-405d-85db-1506ae9de9a1\",\"owner\":[],\"postedDate\":\"January 10th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":42494466,\"name\":\"Biological sciences/Cancer/Lung cancer\"},{\"id\":42494467,\"name\":\"Biological sciences/Cancer/Tumour biomarkers\"}],\"tags\":[],\"updatedAt\":\"2025-01-13T10:54:14+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-01-10 17:46:56\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5704972\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5704972\",\"identity\":\"rs-5704972\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}