Tumor-derived progranulin reprograms immunosuppressive macrophages via cholesterol efflux in oral squamous cell carcinoma

Tumor-derived progranulin reprograms immunosuppressive macrophages via cholesterol efflux in oral squamous cell 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 Research Article Tumor-derived progranulin reprograms immunosuppressive macrophages via cholesterol efflux in oral squamous cell carcinoma Chengzhe Yang, Yijun Luan, Yan Xu, Simin Zhao, Hao Li, Zheming Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7333736/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background The immunosuppressive tumor microenvironment (TME) contributes to poor prognosis in oral squamous cell carcinoma (OSCC), with tumor-associated macrophages (TAMs) playing a pivotal role. However, the underlying metabolic mechanisms that drive TAM polarization remain unclear. Methods We performed integrated single-cell RNA sequencing (scRNA-seq) on primary OSCC tumors (n = 3) and validated findings in 77 head and neck squamous cell carcinoma (HNSCC) samples across five public datasets. The role of tumor-derived progranulin (PGRN) in TAM reprogramming was examined using genetic knockdown, pharmacologic inhibition of PGRN–SORT1 interaction, and activation of downstream PPARγ signaling in vitro and in vivo . Results A malignant epithelial subpopulation highly expressing PGRN was identified, which reprogrammed TAMs via SORT1-mediated dependent signaling. Mechanistically, PGRN–SORT1 interaction induced PPARγ/LXRα activation, upregulated cholesterol efflux transporters ABCA1/ABCG1 (p < 0.001), and reduced intracellular r cholesterol levels in macrophages. This metabolic rewiring led to an immunosuppressive TAM phenotype, with increased secretion of IL-6, IL-10, and TGFβ. PGRN knockdown or SORT1 inhibition restored cholesterol retention, reduced TAM infiltration, and increased the CD86 + /CD206 + ratio in vivo . Notably, PPARγ agonism with rosiglitazone reinstated immunosuppression in PGRN-deficient tumors, confirming the dependence on this signaling axis. Conclusion Tumor-derived PGRN reprograms TAMs via SORT1-mediated cholesterol efflux and downstream PPARγ/LXRα activation, promoting an immunosuppressive TME in OSCC. Targeting the PGRN–SORT1–PPARγ axis may represent a promising immunometabolic approach to overcome immune resistance and improve OSCC outcomes. progranulin (PGRN) SORT1 tumor-associated macrophages (TAMs) cholesterol efflux oral squamous cell carcinoma (OSCC) immunosuppression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Oral cancer, particularly oral squamous cell carcinoma (OSCC), represents a major global health burden, with over 389,000 new cases and 188,000 deaths annually [ 1 , 2 ]. Despite advances in surgical resection, radiotherapy, and chemotherapy, the 5-year survival rate remains disappointingly below 50% [ 3 , 4 ]. This clinical reality underscores the urgent need to elucidate the molecular mechanisms driving OSCC progression and to identify novel therapeutic targets that can improve patient outcomes. Recent advances in cancer biology have reframed tumors as complex ecosystems rather than isolated masses of malignant cells. The tumor microenvironment (TME), composed of various stromal and immune cells, plays a critical role in cancer progression and immune evasion. Among these, tumor-associated macrophages (TAMs) are particularly abundant and exhibit remarkable plasticity, capable of adopting diverse phenotypes that either support or suppress anti-tumor immunity [ 5 – 7 ]. TAMs in OSCC have been associated with tumor growth, metastasis, and treatment resistance, making them compelling candidates for targeted immunomodulation. Beyond cytokine signaling, increasing evidence points to metabolic reprogramming as a fundamental mechanism regulating TAM function. Cholesterol, an essential lipid component of cellular membranes, influences numerous macrophage activities, including phagocytosis, antigen presentation, and cytokine secretion [ 8 – 11 ]. Dysregulated cholesterol metabolism in TAMs not only alters immune responsiveness but also contributes to the formation of an immunosuppressive TME that facilitates tumor progression [ 8 , 12 , 13 ]. Consequently, targeting TAM cholesterol homeostasis represents a promising strategy for enhancing anti-tumor immunity. Emerging evidence suggests that tumor-derived secreted factors may directly modulate macrophage lipid metabolism [ 14 ]. However, the molecular mediators responsible for this regulation remain incompletely defined. One such candidate is progranulin (PGRN; gene symbol GRN ), a multifunctional growth factor implicated in tumor proliferation, angiogenesis, and chemoresistance [ 15 ]. PGRN has also been linked to immune modulation in various malignancies [ 16 – 20 ], yet its specific role in regulating TAM metabolism, particularly cholesterol efflux and immunosuppressive reprogramming, remains unexplored. In this study, we integrate single-cell RNA sequencing (scRNA-seq) and experimental validation to investigate the role of tumor-derived PGRN in TAM polarization in OSCC. We demonstrate that PGRN promotes TAM immunosuppressive reprogramming via SORT1-mediated cholesterol efflux, activating downstream PPARγ/LXRα signaling and inducing ABCA1/ABCG1 expression. These findings reveal a novel immunometabolic axis in OSCC and identify the PGRN–SORT1 pathway as a potential therapeutic target for modulating the tumor immune microenvironment. Materials and Methods Data Acquisition and scRNA-seq Preprocessing The scRNA-seq data were obtained from a cohort of three OSCC patients, cataloged under GSA-Human: HRA007439 at the National Genomics Data Center ( https://ngdc.cncb.ac.cn/gsa-human )[ 21 ]. Detailed protocols for tissue collection and single-cell suspension preparation using the BD Rhapsody™ platform are available in the original publication [ 21 ]. Additionally, we utilized datasets from the Gene Expression Omnibus (GEO) database (GSE103322, GSE164690, GSE181919, GSE195832, GSE215403), comprising 68 HNSCC samples and 9 paracancerous (PCA) samples. Raw count matrices were imported into the Seurat package (v4.1.0) in R and combined into a single object for comprehensive analysis. Quality control was performed to exclude cells with fewer than 200 or more than 5000 genes, abnormal UMI counts, or excessive mitochondrial read percentages (> 10%). Genes related to red blood cells or multiplets were also removed. Data normalization was performed using the functions “NormalizeData,” “FindVariableFeatures,” and “ScaleData.” For clustering and visualization, t-SNE dimensionality reduction was applied, and unsupervised clusters were identified using the “FindClusters” function. Cell types were annotated based on established marker genes, and differentially expressed genes (DEGs) were identified with the “FindAllMarkers” function, utilizing the Wilcoxon rank-sum test for p-value adjustments. Heat maps and violin plots depicting DEGs were generated using ggplot2. Cell-cell communication networks were inferred using the CellChat R package [ 22 ], beginning with the initialization of a CellChat object using the “createCellChat” function. Preprocessing steps included identification of overexpressed genes (“identifyOverExpressedGenes”), ligand-receptor interactions (“identifyOverExpressedInteractions”), and data projection (“projectData”) using default parameters from the human CellChatDB. Interaction probabilities were calculated via “computeCommunProb”, filtered by cell abundance (“filterCommunication”, min.cells = 3), and analyzed at the pathway level with “computeCommunProbPathway”. Aggregated networks were generated using “aggregateNet”. Functional enrichment analysis of Gene Ontology (GO) and KEGG pathways was conducted with the “ClusterProfiler package”, identifying enriched pathways (adjusted p < 0.05) via “compareCluster” and visualized as dot plots. Lastly, tissue distribution for each cluster was evaluated with the STARTRAC-dist index [ 23 ], where Ro/e represents the ratio of observed to expected cell counts, and Re/o indicates whether specific subclusters are enriched or depleted in given tissues. Single-cell copy-number variation (CNV) analysis was conducted using the infercnv R package, with CNVs of epithelial cells calculated relative to immune cells as a reference. The interCNV analysis was executed with parameters including “denoise,” standard hidden Markov model (HMM) settings, and a cutoff value of 0.1. Patients and specimens Clinical specimens were collected from 40 patients diagnosed with oral squamous cell carcinoma (OSCC) at Qilu Hospital of Shandong University from 2006 to 2015, with approval from the Ethics Committee of Qilu Hospital (KYLL-202210-052). All patients underwent surgical resection to ensure the complete removal of visible tumor cells, and none received cancer-specific treatments prior to surgery. Tumor specimens were embedded in paraffin and subjected to histopathological evaluation. Clinical characteristics, including age, gender, tumor size, lymph node involvement, and survival rates, were thoroughly documented. Prognostic correlations with GRN gene expression and OSCC outcomes were evaluated using the GEPIA2 database ( http://gepia2.cancer-pku.cn/#survival ). Immunohistochemistry (IHC) of PGRN and CD68 Tumor and adjacent non-cancerous tissues were sliced into 5-µm thick sections for IHC analysis. Sections were incubated overnight at 4°C with mouse anti-PGRN monoclonal antibody (1:100, 18410-1-AP, Proteintech, Wuhan, China) or mouse anti-CD68 monoclonal antibody (ab955, Abcam, USA). Detection was performed using a non-biotin detection system (PV-9000, ZSGB-bio, Beijing, China). Microscopic examination was conducted using an Olympus BX53 microscope, and images were captured with a full slide scanning system (SLIDEVIEW™ VS200, Olympus, Tokyo, Japan). PGRN expression was evaluated based on staining intensity (scored from 0 to 3) and the proportion of positively stained tumor cells (scored from 0 to 4). PGRN was considered positive if staining intensity was strong (3) and the proportion exceeded 25% (2–4), or if the intensity was moderate (2) with over 75% of the tumor cells positive(4). For CD68 evaluation, positively stained cells were counted in six randomly selected fields at 400× magnification within tumor nests, and the average count of CD68 + cells per field was calculated. Evaluations were conducted by two blinded pathologists and confirmed by an independent experienced pathologist. Cell culture and treatment Human immortalized oral epithelial cells (HIOEC) were obtained from the Shanghai Cell Bank and cultured in Keratinocyte Serum Free Medium supplemented with bovine pituitary extract (Gibco-BRL, 10744019, NY, USA). The CAL27 human OSCC cell line, THP-1 human monocyte line, and RAW264.7 murine monocyte/macrophage line were sourced from ATCC. All cell lines were maintained in a humidified atmosphere at 37°C with 5% CO 2 . THP-1 cells were cultured in RPMI 1640 (Vivacell, C3010-0500, Shanghai, China) supplemented with 10% fetal bovine serum (Gibco 10099-141, NY, USA) and 1% penicillin-streptomycin (Biosharp, BL505A, Anhui, China), while CAL27 and RAW264.7 cells were kept in DMEM (Vivacell, C3103-0500, Shanghai, China) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Transfection assay To generate GRN -knockdown CAL27 cells (shPGRN-CAL27), CAL27 cells were transfected with lentiviral vector-based plasmids expressing shRNA targeting GRN (GeneChem Co., Ltd., Shanghai, China), following the manufacturer's protocol. Control cells were transduced with a scrambled shRNA vector (shNC-CAL27). Infection efficiency was evaluated using qRT-PCR and western blotting, and stable cell lines were selected by treating with puromycin (2 µg/mL) for 7 days. Conditioned medium preparation Conditioned media (CMs) were prepared by plating 5×10 6 tumor cells in 10 mL of complete medium until reaching 80–90% confluence. The medium was then replaced with RPMI 1640, and following a 24-hour incubation, the supernatants were collected. These were centrifuged at 4°C for 10 minutes at 4000 rpm and stored at -80°C. The CMs were designated as HIOEC-CM, CAL27-CM, shPGRN-CAL27-CM, and shNC-CAL27-CM. Indirect co-culture and differentiation of TAMs THP-1 or RAW264.7 cells were seeded at a density of 5×10 5 cells per well in a six-well plate. After 24 hours, the medium was replaced with diluted conditioned media (1:1 in RPMI 1640) for indirect co-culture. Cells and supernatants were collected 48 hours post-incubation. For specific treatments, 2 µM of the SORT1-PGRN interaction inhibitor 1(HY-115213, MCE, NJ, USA) was added 2 hours prior to co-culture with CAL27-CM, and 2 µM of rosiglitazone (RSG, a PPARγ agonist) (HY-17386, MCE, NJ, USA) was used during co-culture with shPGRN-CAL27-CM. Quantitative real-time polymerase chain reaction (qRT-PCR) Total RNA was extracted from cell cultures using the Fastagen Biotech™ RNAfast200 Extreme Extraction Kit (Fastagen Biotech Co., Ltd, Shanghai, China). Reverse transcription was conducted using Hifair® Ⅲ 1st Strand cDNA Synthesis SuperMix for qPCR (gDNA digester plus) (11141ES10, YEASEN, Shanghai, China). cDNA amplification was performed using Hieff® qPCR SYBR Green Master Mix (No Rox) (11201ES08, YEASEN, Shanghai, China), following the manufacturer's protocols. The qPCR conditions included an initial denaturation at 95°C for 5 minutes, followed by 40 cycles of 95°C for 10 seconds and 60°C for 30 seconds. Each sample was run in triplicate, and relative expression levels were calculated using the 2 −ΔΔCt method. Each experiment was repeated independently three times. Western blotting analysis Cell lysates were prepared using ice-cold RIPA buffer with PMSF (R0020, Solarbio, Beijing, China). Protein concentrations were measured using the BCA Protein Assay Kit (PC0020, Solarbio, Beijing, China). Equal amounts of protein were loaded onto SDS-PAGE gels (PAGE Gel Fast Preparation Kit, PG112, Epizyme Biotech, Shanghai, China) and subsequently transferred to PVDF membranes (Millpore, MA, USA), which were blocked and probed with primary antibodies against PGRN (1:500, 10826-RP03, SinoBiological, Beijing, China), ABCA1 (1:1000, #96292, Cell Signaling Technology, MA, USA), ABCG1 (1:1000, A17907, ABclonal, Wuhan, China), SR-B1 (1:800, A0827, ABclonal, Wuhan, China), PPARγ (1:2500, 16643-1-AP, Proteintech, Wuhan, China), LXRα (1:5000, 14351-1-AP, Proteintech, Wuhan, China;), and GAPDH (1:20000, 10494-1-AP, Proteintech, Wuhan, China). After washing, membranes were incubated with HRP-conjugated secondary antibodies (ZB2301, ZSGB-bio, Beijing, China) and detected using an ECL kit (BL520A) Biosharp, Anhui, China). Imaging and analysis were performed using the EasyCL-50 system (Nanjing Zhiheng Intelligent Technology Co., Ltd., Nanjing, China). Each experiment was independently conducted three times. Enzyme-linked immunosorbent assay (ELISA) The concentrations of PGRN (Boster Biological Technology, Wuhan, China), IL-6, IL-10, and TGF-β (all from 4A Biotech, Suzhou, China) in conditioned media and TAM culture supernatants were measured using respective human or mouse ELISA kits following the manufacturers' instructions. Total cholesterol measurement Total cholesterol levels in supernatant and intracellular compartments were determined using a total cholesterol assay kit (A111-1-1, Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Cells were harvested in PBS, sonicated on ice, and homogenates were analyzed according to the kit's instructions. Absorbance values were utilized to calculate total cholesterol content, with results normalized across replicate wells. Filipin III staining RAW264.7 cells were seeded in 35mm glass-bottom dishes at a density of 1×10 5 cells per well and incubated with various CMs for 48 hours. After washing three times with PBS, the cells were fixed with 4% paraformaldehyde (PFA) (Biosharp, BL539A, China) for 30 minutes at room temperature. Cells were then incubated with 50µg/mL Filipin III (SAE0087, Sigma-Aldrich, St. Louis, MO, USA) in the dark at 37°C for 1 hour. Subsequently, the cells were washed three times with PBS and imaged using an Olympus IX73 fluorescence microscope. Xenograft experiments Animal experiments were conducted in accordance ARRIVE guidelines and approved by the Ethics Committee of Experimental Animals of Qilu Hospital, Shandong University (Approval No: DWLL-202400115). Eighteen male BALB/c nude mice (nu/nu, aged 4–5 weeks) were obtained from Jinan Pengyue Laboratory Animal Technology Co., Ltd. (Jinan, China) and housed under specific-pathogen-free (SPF) conditions at the Laboratory Animal Center of Qilu Hospital, Shandong University. The mice (n = 18) were randomly divided into three experimental groups (n = 6/group): the shNC-CAL27 group (inoculated with scramble shRNA-transfected CAL27 cells), the shPGRN-CAL27 group (inoculated with PGRN-knockdown CAL27 cells), and the shPGRN-CAL27 + RSG (inoculated with PGRN-knockdown CAL27 cells + RSG treatment). All mice received subcutaneous injections of 1×10 6 cells into the right flank. Throughout the experiment, four mice (two from shPGRN-CAL27 + RSG group and one from each of the other groups) succumbed to complications unrelated to treatment, resulting in 14 evaluable tumor specimens by the study's end. Following tumor formation, the shPGRN-CAL27 + RSG group received intraperitoneal injections of RSG (10 mg/kg in 0.5% CMC-Na) every 48 hours, while other groups received an equivalent volume of vehicle (0.5% CMC-Na). After 14 days of treatment, mice were euthanized using CO 2 asphyxiation. At the end of the experiment, tumors were excised, weighed, and subjected to multiple immunofluorescence staining. Multiple immunofluorescence staining Multiple immunofluorescence staining (mIFC) of the tissue samples was performed following the manufacturer’s protocol using the Enhanced Polymer Detection System (ZSGB-BIO, Beijing, China). Briefly, tissue sections were incubated with primary antibodies against F4/80 (1:3000, GB113373, Servicebio, Wuhan, China), CD68 (1:1000, GB115630, Servicebio, Wuhan, China), and CD206 (1:5000, GB113497, Servicebio, Wuhan, China). This was followed by incubation with a goat anti-rabbit IgG secondary antibody (1:500, GB23303, Servicebio, Wuhan, China). A fluorophore-conjugated tyramide amplification system (PerkinElmer) was then used for signal amplification, while DAPI was employed for counterstaining the nuclei. Processed sections were scanned using a Panoramic Digital Slide Scanner (3D HISTECH, Hungary), and fluorescence intensity was quantified using ImageJ software (National Institutes of Health, USA). Statistical analysis Statistical analyses were conducted using IBM SPSS (v25.0) and GraphPad Prism (v9.0). Data are presented as means ± standard deviation (SD). Comparisons were performed using Student's t-test or ANOVA, with Kaplan-Meier analysis employed for cumulative survival estimation. A two-tailed P-value < 0.05 was considered statistically significant. Results Single-cell transcriptomics identifies progranulin as a tumor-derived macrophage reprogramming signal via SORT1 in OSCC Integrated analysis of scRNA-seq data from three OSCC patients identified seven epithelial subpopulations (Epi_1–Epi_7) (Supplementary Fig. 1A) and six macrophage subsets (Mac_1–Mac_6) (Supplementary Figure. 1C). Notably, Epi_6 was enriched in tumors compared to paracancerous tissues (Supplementary Figure. 1B) and exhibited significantly overexpression of GRN (Supplementary Figure. 1E). CellChat analysis revealed robust GRN signaling between Epi_6 (ligand sender) and Mac_1 (receiver), the predominant tumor-associated macrophage subset (Supplementary Fig. 1D, F, G). This interaction was mediated primarily through the GRN-SORT1 ligand-receptor pair (Supplementary Fig. 1H), with Mac_1 showing high levels of SORT1 expression (Supplementary Fig. 1I). Gene Set Variation Analysis (GSVA) indicated significant enrichment of cholesterol metabolism and PPAR signaling pathways in Mac_1 (Supplementary Fig. 1J), consistent with SORT1 ’s role in lipid homeostasis. Gene Ontology (GO) analysis further highlighted upregulated pathways involved in lipoprotein lipase activity and chylomicron remodeling (Supplementary Fig. 1K), suggesting that GRN-SORT1 signaling may coordinate lipid processing in tumor-associated macrophages. To corroborate our findings, we analyzed multiple scRNA-seq datasets of HNSCC from GEO databases. Unsupervised clustering partitioned eight epithelial cells subpopulations (Epi_1–Epi_8; Fig. 1 A) and five macrophage subsets (Mac_1–Mac_5; Fig. 1 D). CNV analysis with the Ro/e algorithm ranked Epi_1 as the epithelial subpopulation with the highest malignant potential (Fig. 1 B). Consistently, Epi_1 dominated HNSCC tissues (vs. PCA), comprising 33.4% of total epithelial cells in tumors (Fig. 1 C). Analogous CNV analysis of macrophages identified Mac_4 as the subset with the highest tumor-to-PCA tissue ratio (Fig. 1 E), with Mac_4 showing significantly elevated abundance in HNSCC tissues compared to PCA samples (17.6% vs. 6.6%, p < 0.001; Fig. 1 F). Given PGRN’s oncogenic role, we examined GRN signaling between Epi_1 and Mac_4, finding that Epi_1 exhibited marked GRN overexpression compared to other epithelial subsets (Fig. 1 G). CellChat analysis demonstrated a robust GRN signaling network between Epi_1 (ligand sender) and Mac_4 (receiver), predominantly mediated by the GRN-SORT1 ligand-receptor pair (Fig. 1 H-J). Additionally, Mac_4 expressed highest SORT1 level among macrophage subsets (Fig. 1 K). Furthermore, GSVA indicated significant enrichment of cholesterol metabolism and PPAR signaling pathways in Mac_4 (Fig. 1 L), suggesting that SORT1 may orchestrate macrophage metabolic reprogramming in tumors, aligning with its established role in lipid metabolism across cancers [ 24 ]. The above analyses suggest a potential link between OSCC epithelial cells and TAMs through the GRN-SORT1 signaling pathway, implicating tumor-derived PGRN as a regulator of TAM cholesterol metabolism and function. PGRN overexpression associates with increased CD68⁺ macrophages infiltration and poor prognosis in OSCC To verify the relationship between PGRN expression and macrophage infiltration, as well as the clinical significance of elevated PGRN levels, we performed IHC analysis on 40 OSCC patient samples. Results showed a marked upregulation of PGRN in tumor tissues compared to adjacent normal epithelia (Fig. 2 A). This finding was confirmed in vitro , with consistently higher PGRN protein levels in OSCC cell lines compared to HIOEC (Fig. 2 B) and by ELISA (Fig. 2 C). Clinically, high PGRN expression correlated significantly with lymph node metastasis ( p = 0.037), surrounding issue invasion ( p = 0.034), and reduced overall survival ( p = 0.025; log-rank test, Table 1). To further support these observations, we analyzed GRN expression in the GEPIA2 pan-cancer cohort, which indicated that high GRN mRNA levels predicted poorer overall survival ( p = 0.047; Fig. 2 D) and reduced disease-free survival ( p = 0.015; Fig. 2 E) in HNSCC. Remarkably, IHC staining (Fig. 2 F) revealed a higher density of CD68 + macrophage in the high PGRN expression group compared to the low- expression group (Fig. 2 G, p = 0.0396). OSCC-derived PGRN drives macrophage cholesterol efflux via SORT1 TAMs exhibit significant dysregulation in cholesterol metabolism, with enhanced efflux facilitating IL-4-mediated macrophage reprogramming and TAM differentiation [ 9 ]. GSVA of HNSCC datasets from GEO showed significant enrichment of cholesterol metabolism and PPAR signaling pathways in Mac_4 (Fig. 1 L), suggesting and tumor-derived PGRN may play a crucial role in regulating TAM functionality (Fig. 1 H-L). To investigate whether OSCC-derived PGRN influences macrophage cholesterol efflux, we generated GRN -knockdown CAL27 cells (shPGRN-CAL27) and confirmed the knockdown efficacy (Fig. 3 A-C). Then we prepared conditioned media (CM) from four experimental groups: HIOEC-CM (normal immortalized oral epithelial cells), CAL27-CM (OSCC cells), shNC-CAL27-CM (scrambled shRNA control), and shPGRN-CAL27-CM ( GRN -depleted cells). Further study showed that THP-1 cultured with CAL27-CM exhibited reduced intracellular cholesterol (vs. HIOEC-CM, p = 0.0127) and elevated extracellular cholesterol (vs. HIOEC-CM, p = 0.0275), while shPGRN-CAL27-CM reversed these effects compared to shNC-CAL27-CM (intracellular: p = 0.0124; extracellular: p = 0.0421), with similar trends observed in RAW264.7 cells (Fig. 3 D, E). Filipin III staining further confirmed reduced cholesterol levels in CAL27-CM-treated macrophages compared to HIOEC-CM, while cholesterol levels were restored in the shPGRN-CAL27-CM group (Fig. 3 J). Supplementation with rhPGRN dose-dependently rescued cholesterol efflux in THP-1 cells, leading to increased extracellular cholesterol (p = 0.0465 at 200 ng/mL; p = 0.0027 at 400 ng/mL) and decreased intracellular cholesterol (p = 0.036 - p = 0.0085) (Fig. 3 F). To establish SORT1 as the critical mediator of PGRN-induced cholesterol efflux, we employed a pharmacological approach using the selective SORT1-PGRN interaction inhibitor (HY-115213). Pretreatment with this inhibitor (2 µM, 2 h) abolished CAL27-CM-induced cholesterol efflux in both THP-1 and RAW264.7 macrophages (Fig. 3 G, H), with filipin III staining confirming intracellular cholesterol accumulation (Fig. 3 J). Importantly, the pro-efflux effect of exogenous rhPGRN (200 ng/mL) was entirely dependent on SORT1 activity, as co-treatment with the inhibitor completely blocked rhPGRN-mediated cholesterol export (Fig. 3 I), confirming SORT1 as the essential receptor for PGRN’s pro-cholesterol efflux function. PGRN from OSCC cells activates macrophage cholesterol efflux via the SORT1 mediated ABCA1/ABCG1/LXRα/PPARγ axis Cholesterol efflux is regulated by multiple transporters, including ATP-binding cassette transporters ABCA1 and ABCG1, as well as scavenger receptor class B type I (SR-B1, encoded by SCARB1 ) [ 25 ]. These pathways are transcriptionally regulated by nuclear receptors Liver X receptor alpha (LXRα, encoded by NR1H3 ) and peroxisome proliferator-activated receptor gamma (PPARγ, encoded by PPARG ), which upregulate ABCA1/ABCG1 expression to promote cholesterol export from macrophages [ 26 – 29 ]. To assess whether OSCC-derived PGRN modulates macrophage cholesterol efflux via these mediators, we cultured THP-1 monocytes with CAL27-CM or HIOEC-CM. Strikingly, CAL27-CM significantly elevated both mRNA and protein expression of PPARγ, LXRα, ABCA1, and ABCG1 compared to HIOEC-CM (Fig. 4 A, B). In contrast, shPGRN-CAL27-CM diminished these effects compared to shNC-CAL27-CM, while SR-B1 levels remained unchanged (Fig. 4 A, B). Blockade of SORT1 eliminated CAL27-CM-induced upregulation of PPARγ, LXRα, ABCA1, and ABCG1 (Fig. 4 C, D). Furthermore, activation of PPARγ with rosiglitazone (RSG) restored the expression levels in PGRN-deficient systems (shPGRN-CAL27-CM + RSG; Fig. 4 E, F). Notably, SR-B1 expression remained unaffected across all treatments (Fig. 4 A-F). Functionally, RSG treatment rescued cholesterol efflux in PGRN-depleted systems (Fig. 4 G, H), and reduced intracellular lipid accumulation, as confirmed by Filipin III staining (Fig. 4 I), mimicking the pro-efflux phenotype of wild-type tumors. These results delineate a preferential cholesterol export pathway in macrophages, wherein PGRN triggers ABCA1/ABCG1 upregulation via the LXRα/PPARγ cascade while leaving SR-B1 transport mechanisms unaffected. Tumor-derived PGRN promotes macrophages produce immuno-suppressive mediators To evaluate functional impact of PGRN-mediated cholesterol efflux, we further analyzed cytokine expression in macrophages exposed to conditioned media (HIOEC-CM, CAL27-CM, shNC-CAL27-CM, shPGRN-CAL27-CM, CAL27-CM + inhibitor, and shPGRN-CAL27-CM + RSG). Compared to HIOEC-CM, CAL27-CM significantly increased both mRNA and secreted protein levels of IL-6, IL-10, and TGFβ in both THP-1 and RAW264.7 macrophages (Fig. 5 A-D). In contrast, the shPGRN-CAL27-CM group showed reduced expression of these cytokines compared to the shNC-CAL27-CM group (Fig. 5 A-D), indicating that tumor-derived PGRN acts as a potent enhancer of immunosuppressive cytokine secretion. However, SORT1 inhibition in CAL27-CM-treated macrophages induced cell-type-specific cytokine responses. RAW264.7 cells exhibited coordinated reductions in both mRNA and protein levels of IL-6, IL-10, and TGFβ (Fig. 5 A, B), suggesting that SORT1-dependent regulation is predominant in this model. Conversely, THP-1 cells showed unchanged IL-6 and TGFβ mRNA/protein levels, but with decreased IL-10 secretion despite stable mRNA expression (Fig. 5 C, D). Additionally, further experiments investigating PPARγ activation (using RSG) in PGRN-depleted macrophages revealed more divergence: while RSG consistently promoted immunosuppressive IL-10 secretion across both macrophage models (Fig. 5 B, D), its effects on IL-6 and TGFβ were cell type-dependent, suggesting context-specific modulation of PPARγ signaling in TAM polarization. This functional profiling reveals that OSCC-derived PGRN orchestrates an immunosuppressive cytokine milieu (IL-6/IL-10/TGFβ) in macrophages, with SORT1 and PPARγ contributing distinct regulatory layers across cellular models. PGRN deficiency reduces immunosuppressive macrophage accumulation in tumor xenografts To further validate PGRN-mediated macrophage reprogramming, we established xenograft models by subcutaneously inoculating nude mice with three experimental groups: shNC-CAL27, shPGRN-CAL27, and shPGRN-CAL27 + RSG. Starting 10 days post-inoculation, the shPGRN-CAL27 + RSG group received intraperitoneal injections of RSG (10 mg/kg in 0.5% CMC-Na) every 48 hours, while the shNC-CAL27 and shPGRN-CAL27 groups were administered the vehicle (0.5% CMC-Na) until sacrifice at day 24. Although tumor weights did not show significant intergroup differences, shPGRN-CAL27 tumors exhibited a trend towards reduced mass compared to shNC-CAL27 controls (Fig. 6 B). Multiplex immunofluorescence evaluations revealed that shPGRN-CAL27 tumors displayed fewer infiltration of F4/80 + macrophages compared to shNC-CAL27 (Fig. 6 C, D) and an elevated ratio of F4/80 + CD86 + to F4/80 + CD206 + macrophages (Fig. 6 C, E). Notably, RSG treatment in the shPGRN-CAL27 + RSG group re-established immunosuppressive TAM profiles, increasing infiltration (Fig. 6 C, D) and reducing the CD86⁺/CD206⁺ ratio to levels comparable to control tumors (Fig. 6 C, E). Collectively, our in vivo models demonstrate that OSCC-derived PGRN mediates macrophage recruitment and drives their acquisition of immunosuppressive markers in the tumor microenvironment. Discussion Dysregulated cholesterol homeostasis in TAMs profoundly impacts immune responses, influencing crucial processes such as antigen presentation, phagocytosis, cytokine secretion and polarization, thus fostering an immunosuppressive TME [ 8 – 13 ]. However, the mechanisms underlying cholesterol metabolism abnormalities in TAMs remain poorly understood. Our study provides the first evidence that OSCC-derived PGRN activates the SORT1/PPARγ/LXRα/ABCA1-ABCG1 cholesterol efflux axis, promoting immunosuppressive TAM polarization, as confirmed by both genetic knockdown and pharmacological inhibition. Cholesterol plays an essential role in cancer cell proliferation [ 30 ]. Targeting cholesterol metabolism represents a potential therapeutic avenue in cancer treatment, although clinical efficacy remains debated [ 31 – 34 ]. Disparities in outcomes often arise from differential impacts on macrophage polarization [ 13 ]. For instance, Goossens et al demonstrated that ovarian cancer cells induce membrane cholesterol efflux in macrophages, enhancing IL4-mediated M2-like reprogramming while suppressing IFNγ responses [ 9 ]. This underscores the necessity for in-depth investigations into the role of cholesterol efflux in TAMs when designing cholesterol-targeting strategies for malignancies. In this study, single-cell RNA-seq indicated that OSCC-derived PGRN may regulate macrophage cholesterol metabolism. Both in vitro and in vivo experiments demonstrated that PGRN mediates cholesterol efflux, reducing intracellular cholesterol levels and fostering the generation of immunosuppressive macrophages. Specifically, culture supernatants from OSCC cells significantly decreased cholesterol levels in macrophages, consistent with previous findings [ 9 , 13 , 35 ]. Conversely, supernatants from CAL27 cells with PGRN knockdown increased macrophage cholesterol content, an effect partially reversible by recombinant human PGRN. PGRN is known to interact with various membrane proteins and receptors, including SORT1, prosaposin, and tumor necrosis factor receptors (TNFR) 1 and 2 [ 36 ]. SORT1, a novel lipid-associated protein receptor located on the cell membrane and in the Golgi apparatus, is expressed in various cell types associated with lipid metabolism, such as hepatocytes and macrophages [ 37 ]. Our DEGs analysis revealed that Mac_4 exhibited high SORT1 expression, whereas Epi_1 expressed high levels of GRN. CellChat analysis indicated a robust GRN signaling network between Epi_1 (the ligand sender) and Mac_4 (the receiver), predominantly mediated by the GRN-SORT1 ligand-receptor interaction (Fig. 1 H-J). To further investigate the role of SORT1 in PGRN-mediated regulation of macrophage cholesterol efflux, we pre-treated macrophages with a SORT1 inhibitor during co-culture with various conditioned media. The results demonstrated that the SORT1 inhibitor reversed the promotive effects of PGRN, indicating that PGRN regulates cholesterol dynamics in macrophages via SORT1. Previous studies have identified several cholesterol transport proteins, including ABCA1, ABCG1, and scavenger receptor class B type I (SR-B1), as key regulators of cholesterol efflux [ 25 ]. Liver X receptor alpha (LXRα) serves as a critical regulator of lipid homeostasis, forming LXR-RXR heterodimers that enhance ABCA1 expression, thereby promoting cholesterol efflux from macrophages [ 21 , 31 ]. Additionally, PPARγ plays a significant role in lipid metabolism within macrophages, stimulating cholesterol efflux through the upregulation of LXRα, ultimately leading to increased ABCA1 expression [ 28 , 32 ]. Our findings demonstrated that in co-culture systems with conditioned media from shPGRN-CAL27 cells, the mRNA and protein levels of PPARγ, LXRα, ABCA1 and ABCG1 were significantly reduced compared to the shNC-CAL27 group, leading to diminished cholesterol efflux from macrophages. Importantly, the application of the PPARγ agonist rosiglitazone re-established these effects. These results indicate that PGRN-SORT1 signaling activates the PPARγ/LXRα/ABCA1/ABCG1 pathway to enhance cholesterol efflux in macrophages. Lipid metabolism appears crucial to the differentiation and function of TAMs [ 33 ]. One study reported that increased ABCA1 activity in TAMs in a mouse ovarian cancer model enhances membrane cholesterol efflux, promoting the loss of cholesterol-rich membrane microdomains that amplify IL-4 receptor activity, thus supporting the M2-like tumor-promoting TAM phenotype [ 9 ]. Cholesterol depletion disrupts membrane raft integrity, impairing TLR/MHC-II signaling and antigen presentation [ 9 , 14 ]. Moreover, LXRα activation by oxidized sterols promotes the production of anti-inflammatory cytokines (e.g., IL-10) while suppressing pro-inflammatory responses [ 19 , 21 ]. Nevertheless, contrasting studies emphasize the importance of lipid accumulation and metabolism in differentiating and functioning pro-cancer TAMs within the TME [ 33 ]. In our in vitro study, we demonstrated that lipid metabolic reprogramming promotes an immunosuppressive phenotype, as evidenced by increased secretion of IL-6, IL-10 and TGFβ. Notably, treatment with the PPARγ agonist RSG restored the expression of IL-6 (in RAW264.7 macrophages), IL-10 (in both THP-1 and RAW264.7 macrophages), and TGFβ (in THP-1 macrophages) in the shPGRN-CAL27-CM group, confirming pathway dependency (Fig. 5 ). In vivo experiments revealed a significant decrease in both the number of F4/80 + macrophages and the proportion of CD206 + macrophages in the shPGRN-CAL27 group compared to the shNC-CAL27 group. Remarkably, the RSG treatment group exhibited an overall trend closely resembling that of the shNC-CAL27 group. Therefore, this study underscores the critical role of PGRN-enhanced cholesterol efflux in the development of macrophages with an immunosuppressive phenotype (Fig. 6 ). Our investigations reveal that targeting the PGRN-SORT1 axis represents a promising therapeutic strategy for OSCC. Specifically, inhibition of SORT1 using HY-115213 effectively counteracted PGRN-mediated immunosuppressive polarization of TAMs, as demonstrated by the restoration of intracellular cholesterol levels (Fig. 3 G, H) and significant reductions in key immunosuppressive cytokines including IL-6, IL-10 and TGFβ (Fig. 5 ). These findings strongly support further development of SORT1-targeted approaches for immunometabolic intervention in OSCC. Importantly, our study uncovered a clinically relevant paradox: while SORT1 inhibition blocked PGRN's immunosuppressive effects, PPARγ activation with rosiglitazone restored the immunosuppressive phenotype in PGRN-deficient models. This dual regulation suggests complex crosstalk within the PGRN-SORT1-PPARγ network that may have important implications for patient management. Of particular concern is the potential for PPARγ-activating antidiabetic drugs to inadvertently promote tumor immune evasion in diabetic OSCC patients with functional PGRN-SORT1 signaling. Building on these findings, we propose several key directions for future research: Firstly, optimization of SORT1 inhibitors to enhance specificity and potency for clinical translation. Secondly, exploration of synergistic combinations with established immunotherapies such as PD-1/PD-L1 blockade. Thirdly, comprehensive characterization of compensatory pathways that may emerge upon disruption of cholesterol efflux in TAMs. Together, these investigations will advance our understanding of immunometabolic regulation in OSCC and facilitate the development of more effective therapeutic strategies. Conclusions Our study identifies tumor-derived PGRN as a critical driver of immunosuppressive TAM polarization in OSCC by promoting cholesterol efflux through the SORT1/PPARγ/LXRα/ABCA1/ABCG1 axis. By elucidating this metabolic-immune linkage, we provide a mechanistic foundation for targeting macrophage cholesterol efflux pathways to enhance anti-tumor immunity. However, several limitations must be acknowledged in the present study. Notably, alternative receptors such as TNFR1, TNFR2 and EphA2 [ 15 , 41 ] also play significant roles in PGRN functionality. Therefore, the mediating roles of these alternative receptors in PGRN-enhanced cholesterol efflux and immunosuppressive cytokine expression in macrophages warrant further comparative investigation, especially considered that SORT1 mediates differential regulation of immunosuppressive cytokines in the two macrophage cell models examined in this study. Moreover, expanding clinical cohorts and utilizing humanized models should be applied to further validate our findings. Abbreviations TME: The immunosuppressive tumor microenvironment OSCC: Oral squamous cell carcinoma TAM: Tumor-associated macrophages scRNA-seq: single-cell RNA sequencing HNSCC: head and neck squamous cell carcinoma PGRN: progranulin GEO: Gene Expression Omnibus PCA: paracancerous DEGs: differentially expressed genes GO: Gene Ontology KEGG: Kyoto Encyclopedia of Genes and Genomes CNV: Single-cell copy-number variation HMM: hidden Markov model IHC: Immunohistochemistry RSG: rosiglitazone mIF: multiple immunofluorescence SR-B1: scavenger receptor class B type I LXRα: Liver X receptor alpha Declarations Ethic approval and consent to participant This study was approved by the Ethics Committee of Qilu Hospital, Shandong University (Ethics Approval Number: KYLL-202210-052). All procedures involving human participants were conducted in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. The raw datasets supporting the findings are available in publicly accessible repositories: Single-cell RNA sequencing data analyzed in this study were derived from oGSA-Human: HRA007439), accessible via the National Genomics Data Center (https://ngdc.cncb.ac.cn/gsa-human) and datasets GSE164690, GSE195832, GSE103322, GSE215403, and GSE181919 from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). Competing interest The authors declare no competing interests. Funding This work was supported by the Natural Science Foundation of Shandong Province (No. ZR2022MH136), the Key R&D Program of Shandong Province, China (No. 2021SFGC0502), the Jinan Clinical Medicine Technology Innovation Plan Project (No. 202328025), Science and Technology Program of Jinan Municipal Health Commission (No. 2023-2-167). Author Contributions: Yijun Luan designed and conducted the experiments, analyzed the data and wrote the manuscript. Yan Xu performed the experiments and wrote the manuscript. Simin Zhao performed the database analysis. Hao Li collected the samples. Zheming Liu participated in the Xenograft Experiments. Pishan Yang designed, revised and commented on the manuscript. Chengzhe Yang designed, supervised and revised the manuscript. All the authors read and approved the final manuscript. Acknowledgements We thank Research Center for Basic Medical Science of Qilu hospital affiliated to Shandong University for consultation and instrument availability that supported this work. Generative AI in scientific writing During the preparation of this work the author used Deepseek in order to improve the readability and language of the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. 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Table Table 1． Correlation between PGRN expression and clinicopathological parameters in OSCC PGRN (No. patients) Clinicopathological parameters P-value Age at surgery (years) <60 16 5 ≧60 12 7 0.369 Gender Male 18 6 Female 10 6 0.398 Smoking Yes 11 5 No 17 7 0.888 CD68+cell/Total (%) 4.58±5.95 9.34±8.17 0.040 Drinking Yes 12 4 No 16 8 0.573 Tumor size T1+T2 20 9 T3+T4 8 3 0.817 lymph node metastasis N0 23 6 N+ 5 6 0.037 Histological differentiation Well 17 5 Moderate/Poor 11 7 0.267 Clinical stage I+II 16 5 III+IV 12 7 0.369 Disease recurrence Yes 8 4 No 20 8 0.763 Surrounding issue invasion Yes 2 4 No 26 8 0.034 Survival status yes 25 7 no 3 5 0.025 Five-year survival Yes 3 4 No 25 8 0.084 Supplementary Figure 1 Supplementary Figure 1 is not available with this version; the figure title and legend is below. Supplementary Figure 1. Single-cell Sequencing Reveals the Role of Granulin Signaling in Epithelial-Macrophage Interactions within the Tumor Microenvironment of Oral Squamous Cell Carcinoma A. B . UMAP plot depicting the clustering of 7 epithelial cell subtypes (A) and 6 macrophage subtypes (B) among two distinct tissues. C . D. Bar chart displaying the proportions of each cell subtype in the various tissues. E . Violin plot highlighting the variations in GRN expression among different epithelial cell subtypes. F . Heatmap illustrating the communication intensity between epithelial cells and macrophages in the context of the GRN signaling pathway. G . Heatmap representing the roles of various epithelial cell and macrophage clusters within the signaling network of the GRN pathway. H . Dot plot showing the interaction intensity of ligand/receptors between Epi_6 and Mac_1, highlighting the role of GRN and its ligands, according to CellPhoneDB analysis. I . Violin plot representing the expression distribution of GRN signaling pathway-related genes, showing variations between epithelial and macrophage clusters. J . K. Heatmap showing the top 10 enrichment of representative GO (J) and GSVA (K) pathways in gene sets expressed in macrophage subsets, sorted by t-values from largest to smallest. Supplementary Files GraphicalAbstract.png GA SupplementaryMatieral.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 03 Oct, 2025 Reviewers invited by journal 13 Sep, 2025 Editor assigned by journal 12 Aug, 2025 First submitted to journal 09 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7333736","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514631986,"identity":"cef49fb3-25f1-4f7f-b097-253d9ee1d82c","order_by":0,"name":"Chengzhe 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11:58:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7333736/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7333736/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91710477,"identity":"93c80b28-9046-46a2-84cf-e69eca0e0fb8","added_by":"auto","created_at":"2025-09-19 12:26:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7578478,"visible":true,"origin":"","legend":"\u003ch4\u003eSingle-cell transcriptomics identifies \u003cem\u003eGRN-SORT1\u003c/em\u003e-mediated epithelial- macrophage crosstalk in OSCC\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eA.D. \u003c/strong\u003et-SNE analysis categorizing eight major epithelial cell subtypes (A) and five major macrophage subtypes (D). \u003cstrong\u003eB.E. \u003c/strong\u003eRo/e algorithm analysis of tissue distribution of major cell types in each group (epithelial cells (B) and macrophages (E)). \u003cstrong\u003eC.F. \u003c/strong\u003eBar chart displays the proportion of different cells in HNSCC and PCA tissues (epithelial cells (C) and macrophages (F)). \u003cstrong\u003eG. \u003c/strong\u003eHeatmap comparing the expression of \u003cem\u003eGRN\u003c/em\u003e in different subtypes of epithelial cells. \u003cstrong\u003eH. \u003c/strong\u003eHeatmap showing the counts of cell-cell interactions in the \u003cem\u003eGRN\u003c/em\u003e signaling network between epithelial cells and macrophages. \u003cstrong\u003eI. \u003c/strong\u003eHeatmap depicting the network of\u003cem\u003e GRN\u003c/em\u003e signaling regulation between epithelial and macrophage cells. \u003cstrong\u003eJ. \u003c/strong\u003eBar chart illustrating ligand-receptor pairs in the \u003cem\u003eGRN\u003c/em\u003e pathway. \u003cstrong\u003eK. \u003c/strong\u003eViolin plot comparing the expression of \u003cem\u003eSORT1\u003c/em\u003e in different subtypes of macrophages.\u003cstrong\u003e L. \u003c/strong\u003eDot plot presenting the characteristic functional enrichment of each major cell type.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7333736/v1/db73eee3fcb8aadee54b1850.png"},{"id":91711646,"identity":"83eb4042-e6ba-4b61-9bcb-8f87189b5b44","added_by":"auto","created_at":"2025-09-19 12:42:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21693677,"visible":true,"origin":"","legend":"\u003ch4\u003eExpression of PGRN in OSCC and its relationship with CD68+ macrophages\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eImmunohistochemical staining of PGRN in OSCC and PCA tissues. \u003cstrong\u003eB. \u003c/strong\u003eWestern blot analysis of PGRN protein levels in CAL27 and HIOEC. \u003cstrong\u003eC. \u003c/strong\u003eELISA measurement of PGRN levels in the supernatant of CAL27 and HIOEC cells. \u003cstrong\u003eD. E. \u003c/strong\u003eCorrelation between PGRN expression levels and overall survival (OS) (D) as well as disease-free survival (DFS) (E). \u003cstrong\u003eF.\u003c/strong\u003e Relationship Between PGRN expression and CD68\u003csup\u003e+ \u003c/sup\u003emacrophages (black arrow) expression in immunohistochemistry. \u003cstrong\u003eG. \u003c/strong\u003eProportion of CD68\u003csup\u003e+ \u003c/sup\u003emacrophages in OSCC samples with high vs. low PGRN expression (\u003cem\u003ep\u003c/em\u003e= 0.039).\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7333736/v1/6f5192bcf06ac09e0890d30a.png"},{"id":91709576,"identity":"b77868ce-d9be-4858-881a-5f7ea35f823b","added_by":"auto","created_at":"2025-09-19 12:18:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19225658,"visible":true,"origin":"","legend":"\u003ch4\u003eOSCC-derived PGRN drives macrophage cholesterol efflux via SORT1\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eA.B.C\u003c/strong\u003e. RT-PCR, Western blotting, and ELISA results showing PGRN expression changes in shNC-CAL27 and shPGRN-CAL27 cells. \u003cstrong\u003eD.E. \u003c/strong\u003eCo-culture of THP-1 and RAW264.7 cells with supernatants from HIOEC-CM, CAL27-CM, shNC-CAL27-CM, and shPGRN-CAL27-CM, measuring intracellular and extracellular cholesterol concentrations. \u003cstrong\u003eF. \u003c/strong\u003eCo-culture of THP-1 cells with the control group (shNC-CAL27-CM) and shPGRN-CAL27-CM supernatants with added concentrations of recombinant human PGRN (rhPGRN), measuring cholesterol levels. \u003cstrong\u003eG.H. \u003c/strong\u003ePre-treatment of THP-1 and RAW264.7 cells with a SORT-PGRN interaction inhibitor 1 (2 μM) followed by co-culture with CAL27-CM, measuring cholesterol concentrations compared to controls (HIOEC-CM and CAL27-CM). \u003cstrong\u003eI. \u003c/strong\u003eEffects of SORT-PGRN interaction inhibitor 1 on cholesterol levels in THP-1 cells after adding 200 ng/mL rhPGRN, comparing to the control group (NC). \u003cstrong\u003eJ. \u003c/strong\u003eFilipin III staining showing cholesterol changes in RAW264.7 cells treated with HIOEC-CM, CAL27-CM, CAL27-CM + inhibitor， shNC-CAL27-CM and shPGRN-CAL27-CM groups. Data are mean ± SD; *, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05; **,\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; ****, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.0001; ns, not significant. (Student’s t-test).\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7333736/v1/389a651c3d1fd34bf1057a8a.png"},{"id":91710478,"identity":"2e8b0650-9eec-44fe-a318-6baa6c4935ed","added_by":"auto","created_at":"2025-09-19 12:26:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11212359,"visible":true,"origin":"","legend":"\u003ch4\u003eTumor-derived PGRN regulates cholesterol efflux in macrophages via the ABCA1/ABCG1/LXRα/PPARγ pathway\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eA.B. \u003c/strong\u003emRNA (A) and protein (B) expression levels of PPARγ, LXRα, ABCA1, ABCG1, and SR-B1 in THP-1 cells following indirect co-culture with various conditioned media. \u003cstrong\u003eC.D. \u003c/strong\u003emRNA (C) and protein (D) expression levels of PPARγ, LXRα, ABCA1, ABCG1, and SR-B1 in THP-1 cells treated with a SORT-PGRN interaction inhibitor 1. \u003cstrong\u003eE.F. \u003c/strong\u003emRNA (E) and protein (F) expression levels of PPARγ, LXRα, ABCA1, ABCG1, and SR-B1 in THP-1 cells treated with the PPARγ agonist RSG. \u003cstrong\u003eG.H. \u003c/strong\u003eChanges in intracellular and extracellular cholesterol levels in THP-1 (G) and RAW264.7 cells (H) following treatment with RSG. \u003cstrong\u003eI. \u003c/strong\u003eFilipin III staining of RAW264.7 cells treated with RSG to evaluate intracellular cholesterol levels. Data are mean ± SD; *, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05; **,\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; ****, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.0001; ns, not significant. (Student’s t-test).\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7333736/v1/a5ed0091b7dc9ea0230feae5.png"},{"id":91709570,"identity":"2f9876da-4ebe-47da-ac37-9922dd7fd2ec","added_by":"auto","created_at":"2025-09-19 12:18:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2987876,"visible":true,"origin":"","legend":"\u003ch4\u003eTumor-derived PGRN promoted the expression of IL-6, IL-10 and TGFβ in macrophages\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eA.B. \u003c/strong\u003eRT-PCR (A) and ELISA (B) analyses showing the expression changes of IL-6, IL-10, and TGFβ mRNA, as well as their levels in the supernatant, in THP-1 cells indirectly co-cultured with various conditioned media. \u003cstrong\u003eC.D. \u003c/strong\u003eRT-PCR (C) and ELISA (D) analyses indicating the expression changes of IL-6, IL-10, and TGFβ mRNA in RAW264.7 cells indirectly co-cultured with various conditioned media. Data are mean ± SD; *, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05; **,\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; ****, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.0001; ns, not significant. (Student’s t-test).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7333736/v1/f735bba2598c0a272884000c.png"},{"id":91709572,"identity":"bdee72da-00af-420e-9014-8658d3b5f6df","added_by":"auto","created_at":"2025-09-19 12:18:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1450296,"visible":true,"origin":"","legend":"\u003ch4\u003e\u003cem\u003eIn vivo\u003c/em\u003e experiments validate the impact of PGRN on macrophage polarization\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eTumor sizes in each group. \u003cstrong\u003eB. \u003c/strong\u003eTumor weights in each group. \u003cstrong\u003eC. \u003c/strong\u003emultiplex immunofluorescence staining of tumor tissues. DAPI (blue), F4/80(red), CD86(green), CD206(yellow). \u003cstrong\u003eD. \u003c/strong\u003eThe number of F4/80\u003csup\u003e+\u003c/sup\u003emacrophages in three groups. \u003cstrong\u003eE. \u003c/strong\u003eThe ratio of F4/80\u003csup\u003e+\u003c/sup\u003eCD86\u003csup\u003e+\u003c/sup\u003e to F4/80\u003csup\u003e+\u003c/sup\u003eCD206\u003csup\u003e+\u003c/sup\u003e macrophages in the tumors in three groups. Data are mean ± SD; *, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05; **,\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01; ns, not significant. (Student’s t-test).\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7333736/v1/16684bbaf81eb798ee1d7cf7.png"},{"id":91709568,"identity":"a078f670-22c4-43b7-805f-6d4e44a4fa35","added_by":"auto","created_at":"2025-09-19 12:18:04","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":209139,"visible":true,"origin":"","legend":"\u003cp\u003eGA\u003c/p\u003e","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-7333736/v1/e9918927bba9d2e52ac8ce70.png"},{"id":91710476,"identity":"ebcea096-ffa0-4ab3-a136-563ebb2db25c","added_by":"auto","created_at":"2025-09-19 12:26:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20514,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMatieral.docx","url":"https://assets-eu.researchsquare.com/files/rs-7333736/v1/057e26a954be5e3dcf40424b.docx"}],"financialInterests":"","formattedTitle":"Tumor-derived progranulin reprograms immunosuppressive macrophages via cholesterol efflux in oral squamous cell carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOral cancer, particularly oral squamous cell carcinoma (OSCC), represents a major global health burden, with over 389,000 new cases and 188,000 deaths annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite advances in surgical resection, radiotherapy, and chemotherapy, the 5-year survival rate remains disappointingly below 50% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This clinical reality underscores the urgent need to elucidate the molecular mechanisms driving OSCC progression and to identify novel therapeutic targets that can improve patient outcomes.\u003c/p\u003e\u003cp\u003eRecent advances in cancer biology have reframed tumors as complex ecosystems rather than isolated masses of malignant cells. The tumor microenvironment (TME), composed of various stromal and immune cells, plays a critical role in cancer progression and immune evasion. Among these, tumor-associated macrophages (TAMs) are particularly abundant and exhibit remarkable plasticity, capable of adopting diverse phenotypes that either support or suppress anti-tumor immunity [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. TAMs in OSCC have been associated with tumor growth, metastasis, and treatment resistance, making them compelling candidates for targeted immunomodulation.\u003c/p\u003e\u003cp\u003eBeyond cytokine signaling, increasing evidence points to metabolic reprogramming as a fundamental mechanism regulating TAM function. Cholesterol, an essential lipid component of cellular membranes, influences numerous macrophage activities, including phagocytosis, antigen presentation, and cytokine secretion [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Dysregulated cholesterol metabolism in TAMs not only alters immune responsiveness but also contributes to the formation of an immunosuppressive TME that facilitates tumor progression [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Consequently, targeting TAM cholesterol homeostasis represents a promising strategy for enhancing anti-tumor immunity.\u003c/p\u003e\u003cp\u003eEmerging evidence suggests that tumor-derived secreted factors may directly modulate macrophage lipid metabolism [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the molecular mediators responsible for this regulation remain incompletely defined. One such candidate is progranulin (PGRN; gene symbol \u003cem\u003eGRN\u003c/em\u003e), a multifunctional growth factor implicated in tumor proliferation, angiogenesis, and chemoresistance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. PGRN has also been linked to immune modulation in various malignancies [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], yet its specific role in regulating TAM metabolism, particularly cholesterol efflux and immunosuppressive reprogramming, remains unexplored.\u003c/p\u003e\u003cp\u003eIn this study, we integrate single-cell RNA sequencing (scRNA-seq) and experimental validation to investigate the role of tumor-derived PGRN in TAM polarization in OSCC. We demonstrate that PGRN promotes TAM immunosuppressive reprogramming via SORT1-mediated cholesterol efflux, activating downstream PPARγ/LXRα signaling and inducing ABCA1/ABCG1 expression. These findings reveal a novel immunometabolic axis in OSCC and identify the PGRN\u0026ndash;SORT1 pathway as a potential therapeutic target for modulating the tumor immune microenvironment.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Acquisition and scRNA-seq Preprocessing\u003c/h2\u003e\u003cp\u003eThe scRNA-seq data were obtained from a cohort of three OSCC patients, cataloged under GSA-Human: HRA007439 at the National Genomics Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/gsa-human\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/gsa-human\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Detailed protocols for tissue collection and single-cell suspension preparation using the BD Rhapsody\u0026trade; platform are available in the original publication [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, we utilized datasets from the Gene Expression Omnibus (GEO) database (GSE103322, GSE164690, GSE181919, GSE195832, GSE215403), comprising 68 HNSCC samples and 9 paracancerous (PCA) samples.\u003c/p\u003e\u003cp\u003eRaw count matrices were imported into the Seurat package (v4.1.0) in R and combined into a single object for comprehensive analysis. Quality control was performed to exclude cells with fewer than 200 or more than 5000 genes, abnormal UMI counts, or excessive mitochondrial read percentages (\u0026gt;\u0026thinsp;10%). Genes related to red blood cells or multiplets were also removed. Data normalization was performed using the functions \u0026ldquo;NormalizeData,\u0026rdquo; \u0026ldquo;FindVariableFeatures,\u0026rdquo; and \u0026ldquo;ScaleData.\u0026rdquo;\u003c/p\u003e\u003cp\u003eFor clustering and visualization, t-SNE dimensionality reduction was applied, and unsupervised clusters were identified using the \u0026ldquo;FindClusters\u0026rdquo; function. Cell types were annotated based on established marker genes, and differentially expressed genes (DEGs) were identified with the \u0026ldquo;FindAllMarkers\u0026rdquo; function, utilizing the Wilcoxon rank-sum test for p-value adjustments. Heat maps and violin plots depicting DEGs were generated using ggplot2.\u003c/p\u003e\u003cp\u003eCell-cell communication networks were inferred using the CellChat R package [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], beginning with the initialization of a CellChat object using the \u0026ldquo;createCellChat\u0026rdquo; function. Preprocessing steps included identification of overexpressed genes (\u0026ldquo;identifyOverExpressedGenes\u0026rdquo;), ligand-receptor interactions (\u0026ldquo;identifyOverExpressedInteractions\u0026rdquo;), and data projection (\u0026ldquo;projectData\u0026rdquo;) using default parameters from the human CellChatDB. Interaction probabilities were calculated via \u0026ldquo;computeCommunProb\u0026rdquo;, filtered by cell abundance (\u0026ldquo;filterCommunication\u0026rdquo;, min.cells\u0026thinsp;=\u0026thinsp;3), and analyzed at the pathway level with \u0026ldquo;computeCommunProbPathway\u0026rdquo;. Aggregated networks were generated using \u0026ldquo;aggregateNet\u0026rdquo;. Functional enrichment analysis of Gene Ontology (GO) and KEGG pathways was conducted with the \u0026ldquo;ClusterProfiler package\u0026rdquo;, identifying enriched pathways (adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) via \u0026ldquo;compareCluster\u0026rdquo; and visualized as dot plots.\u003c/p\u003e\u003cp\u003eLastly, tissue distribution for each cluster was evaluated with the STARTRAC-dist index [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], where Ro/e represents the ratio of observed to expected cell counts, and Re/o indicates whether specific subclusters are enriched or depleted in given tissues. Single-cell copy-number variation (CNV) analysis was conducted using the infercnv R package, with CNVs of epithelial cells calculated relative to immune cells as a reference. The interCNV analysis was executed with parameters including \u0026ldquo;denoise,\u0026rdquo; standard hidden Markov model (HMM) settings, and a cutoff value of 0.1.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePatients and specimens\u003c/h3\u003e\n\u003cp\u003e Clinical specimens were collected from 40 patients diagnosed with oral squamous cell carcinoma (OSCC) at Qilu Hospital of Shandong University from 2006 to 2015, with approval from the Ethics Committee of Qilu Hospital (KYLL-202210-052). All patients underwent surgical resection to ensure the complete removal of visible tumor cells, and none received cancer-specific treatments prior to surgery. Tumor specimens were embedded in paraffin and subjected to histopathological evaluation. Clinical characteristics, including age, gender, tumor size, lymph node involvement, and survival rates, were thoroughly documented. Prognostic correlations with \u003cem\u003eGRN\u003c/em\u003e gene expression and OSCC outcomes were evaluated using the GEPIA2 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/#survival\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/#survival\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eImmunohistochemistry (IHC) of PGRN and CD68\u003c/h3\u003e\n\u003cp\u003eTumor and adjacent non-cancerous tissues were sliced into 5-\u0026micro;m thick sections for IHC analysis. Sections were incubated overnight at 4\u0026deg;C with mouse anti-PGRN monoclonal antibody (1:100, 18410-1-AP, Proteintech, Wuhan, China) or mouse anti-CD68 monoclonal antibody (ab955, Abcam, USA). Detection was performed using a non-biotin detection system (PV-9000, ZSGB-bio, Beijing, China). Microscopic examination was conducted using an Olympus BX53 microscope, and images were captured with a full slide scanning system (SLIDEVIEW\u0026trade; VS200, Olympus, Tokyo, Japan).\u003c/p\u003e\u003cp\u003ePGRN expression was evaluated based on staining intensity (scored from 0 to 3) and the proportion of positively stained tumor cells (scored from 0 to 4). PGRN was considered positive if staining intensity was strong (3) and the proportion exceeded 25% (2\u0026ndash;4), or if the intensity was moderate (2) with over 75% of the tumor cells positive(4). For CD68 evaluation, positively stained cells were counted in six randomly selected fields at 400\u0026times; magnification within tumor nests, and the average count of CD68\u003csup\u003e+\u003c/sup\u003ecells per field was calculated. Evaluations were conducted by two blinded pathologists and confirmed by an independent experienced pathologist.\u003c/p\u003e\n\u003ch3\u003eCell culture and treatment\u003c/h3\u003e\n\u003cp\u003eHuman immortalized oral epithelial cells (HIOEC) were obtained from the Shanghai Cell Bank and cultured in Keratinocyte Serum Free Medium supplemented with bovine pituitary extract (Gibco-BRL, 10744019, NY, USA). The CAL27 human OSCC cell line, THP-1 human monocyte line, and RAW264.7 murine monocyte/macrophage line were sourced from ATCC. All cell lines were maintained in a humidified atmosphere at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eTHP-1 cells were cultured in RPMI 1640 (Vivacell, C3010-0500, Shanghai, China) supplemented with 10% fetal bovine serum (Gibco 10099-141, NY, USA) and 1% penicillin-streptomycin (Biosharp, BL505A, Anhui, China), while CAL27 and RAW264.7 cells were kept in DMEM (Vivacell, C3103-0500, Shanghai, China) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin.\u003c/p\u003e\n\u003ch3\u003eTransfection assay\u003c/h3\u003e\n\u003cp\u003eTo generate \u003cem\u003eGRN\u003c/em\u003e-knockdown CAL27 cells (shPGRN-CAL27), CAL27 cells were transfected with lentiviral vector-based plasmids expressing shRNA targeting \u003cem\u003eGRN\u003c/em\u003e (GeneChem Co., Ltd., Shanghai, China), following the manufacturer's protocol. Control cells were transduced with a scrambled shRNA vector (shNC-CAL27). Infection efficiency was evaluated using qRT-PCR and western blotting, and stable cell lines were selected by treating with puromycin (2 \u0026micro;g/mL) for 7 days.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eConditioned medium preparation\u003c/h2\u003e\u003cp\u003eConditioned media (CMs) were prepared by plating 5\u0026times;10\u003csup\u003e6\u003c/sup\u003e tumor cells in 10 mL of complete medium until reaching 80\u0026ndash;90% confluence. The medium was then replaced with RPMI 1640, and following a 24-hour incubation, the supernatants were collected. These were centrifuged at 4\u0026deg;C for 10 minutes at 4000 rpm and stored at -80\u0026deg;C. The CMs were designated as HIOEC-CM, CAL27-CM, shPGRN-CAL27-CM, and shNC-CAL27-CM.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIndirect co-culture and differentiation of TAMs\u003c/h3\u003e\n\u003cp\u003eTHP-1 or RAW264.7 cells were seeded at a density of 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells per well in a six-well plate. After 24 hours, the medium was replaced with diluted conditioned media (1:1 in RPMI 1640) for indirect co-culture. Cells and supernatants were collected 48 hours post-incubation. For specific treatments, 2 \u0026micro;M of the SORT1-PGRN interaction inhibitor 1(HY-115213, MCE, NJ, USA) was added 2 hours prior to co-culture with CAL27-CM, and 2 \u0026micro;M of rosiglitazone (RSG, a PPARγ agonist) (HY-17386, MCE, NJ, USA) was used during co-culture with shPGRN-CAL27-CM.\u003c/p\u003e\n\u003ch3\u003eQuantitative real-time polymerase chain reaction (qRT-PCR)\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from cell cultures using the Fastagen Biotech\u0026trade; RNAfast200 Extreme Extraction Kit (Fastagen Biotech Co., Ltd, Shanghai, China). Reverse transcription was conducted using Hifair\u0026reg; Ⅲ 1st Strand cDNA Synthesis SuperMix for qPCR (gDNA digester plus) (11141ES10, YEASEN, Shanghai, China). cDNA amplification was performed using Hieff\u0026reg; qPCR SYBR Green Master Mix (No Rox) (11201ES08, YEASEN, Shanghai, China), following the manufacturer's protocols. The qPCR conditions included an initial denaturation at 95\u0026deg;C for 5 minutes, followed by 40 cycles of 95\u0026deg;C for 10 seconds and 60\u0026deg;C for 30 seconds. Each sample was run in triplicate, and relative expression levels were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. Each experiment was repeated independently three times.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eWestern blotting analysis\u003c/h2\u003e\u003cp\u003eCell lysates were prepared using ice-cold RIPA buffer with PMSF (R0020, Solarbio, Beijing, China). Protein concentrations were measured using the BCA Protein Assay Kit (PC0020, Solarbio, Beijing, China). Equal amounts of protein were loaded onto SDS-PAGE gels (PAGE Gel Fast Preparation Kit, PG112, Epizyme Biotech, Shanghai, China) and subsequently transferred to PVDF membranes (Millpore, MA, USA), which were blocked and probed with primary antibodies against PGRN (1:500, 10826-RP03, SinoBiological, Beijing, China), ABCA1 (1:1000, #96292, Cell Signaling Technology, MA, USA), ABCG1 (1:1000, A17907, ABclonal, Wuhan, China), SR-B1 (1:800, A0827, ABclonal, Wuhan, China), PPARγ (1:2500, 16643-1-AP, Proteintech, Wuhan, China), LXRα (1:5000, 14351-1-AP, Proteintech, Wuhan, China;), and GAPDH (1:20000, 10494-1-AP, Proteintech, Wuhan, China). After washing, membranes were incubated with HRP-conjugated secondary antibodies (ZB2301, ZSGB-bio, Beijing, China) and detected using an ECL kit (BL520A) Biosharp, Anhui, China). Imaging and analysis were performed using the EasyCL-50 system (Nanjing Zhiheng Intelligent Technology Co., Ltd., Nanjing, China). Each experiment was independently conducted three times.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEnzyme-linked immunosorbent assay (ELISA)\u003c/h2\u003e\u003cp\u003eThe concentrations of PGRN (Boster Biological Technology, Wuhan, China), IL-6, IL-10, and TGF-β (all from 4A Biotech, Suzhou, China) in conditioned media and TAM culture supernatants were measured using respective human or mouse ELISA kits following the manufacturers' instructions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eTotal cholesterol measurement\u003c/h2\u003e\u003cp\u003eTotal cholesterol levels in supernatant and intracellular compartments were determined using a total cholesterol assay kit (A111-1-1, Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Cells were harvested in PBS, sonicated on ice, and homogenates were analyzed according to the kit's instructions. Absorbance values were utilized to calculate total cholesterol content, with results normalized across replicate wells.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eFilipin III staining\u003c/h2\u003e\u003cp\u003eRAW264.7 cells were seeded in 35mm glass-bottom dishes at a density of 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells per well and incubated with various CMs for 48 hours. After washing three times with PBS, the cells were fixed with 4% paraformaldehyde (PFA) (Biosharp, BL539A, China) for 30 minutes at room temperature. Cells were then incubated with 50\u0026micro;g/mL Filipin III (SAE0087, Sigma-Aldrich, St. Louis, MO, USA) in the dark at 37\u0026deg;C for 1 hour. Subsequently, the cells were washed three times with PBS and imaged using an Olympus IX73 fluorescence microscope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eXenograft experiments\u003c/h2\u003e\u003cp\u003e Animal experiments were conducted in accordance ARRIVE guidelines and approved by the Ethics Committee of Experimental Animals of Qilu Hospital, Shandong University (Approval No: DWLL-202400115). Eighteen male BALB/c nude mice (nu/nu, aged 4\u0026ndash;5 weeks) were obtained from Jinan Pengyue Laboratory Animal Technology Co., Ltd. (Jinan, China) and housed under specific-pathogen-free (SPF) conditions at the Laboratory Animal Center of Qilu Hospital, Shandong University.\u003c/p\u003e\u003cp\u003eThe mice (n\u0026thinsp;=\u0026thinsp;18) were randomly divided into three experimental groups (n\u0026thinsp;=\u0026thinsp;6/group): the shNC-CAL27 group (inoculated with scramble shRNA-transfected CAL27 cells), the shPGRN-CAL27 group (inoculated with PGRN-knockdown CAL27 cells), and the shPGRN-CAL27\u0026thinsp;+\u0026thinsp;RSG (inoculated with PGRN-knockdown CAL27 cells\u0026thinsp;+\u0026thinsp;RSG treatment). All mice received subcutaneous injections of 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells into the right flank. Throughout the experiment, four mice (two from shPGRN-CAL27\u0026thinsp;+\u0026thinsp;RSG group and one from each of the other groups) succumbed to complications unrelated to treatment, resulting in 14 evaluable tumor specimens by the study's end. Following tumor formation, the shPGRN-CAL27\u0026thinsp;+\u0026thinsp;RSG group received intraperitoneal injections of RSG (10 mg/kg in 0.5% CMC-Na) every 48 hours, while other groups received an equivalent volume of vehicle (0.5% CMC-Na). After 14 days of treatment, mice were euthanized using CO\u003csub\u003e2\u003c/sub\u003e asphyxiation. At the end of the experiment, tumors were excised, weighed, and subjected to multiple immunofluorescence staining.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMultiple immunofluorescence staining\u003c/h2\u003e\u003cp\u003eMultiple immunofluorescence staining (mIFC) of the tissue samples was performed following the manufacturer\u0026rsquo;s protocol using the Enhanced Polymer Detection System (ZSGB-BIO, Beijing, China). Briefly, tissue sections were incubated with primary antibodies against F4/80 (1:3000, GB113373, Servicebio, Wuhan, China), CD68 (1:1000, GB115630, Servicebio, Wuhan, China), and CD206 (1:5000, GB113497, Servicebio, Wuhan, China). This was followed by incubation with a goat anti-rabbit IgG secondary antibody (1:500, GB23303, Servicebio, Wuhan, China). A fluorophore-conjugated tyramide amplification system (PerkinElmer) was then used for signal amplification, while DAPI was employed for counterstaining the nuclei. Processed sections were scanned using a Panoramic Digital Slide Scanner (3D HISTECH, Hungary), and fluorescence intensity was quantified using ImageJ software (National Institutes of Health, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using IBM SPSS (v25.0) and GraphPad Prism (v9.0). Data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Comparisons were performed using Student's t-test or ANOVA, with Kaplan-Meier analysis employed for cumulative survival estimation. A two-tailed P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eSingle-cell transcriptomics identifies progranulin as a tumor-derived macrophage reprogramming signal via SORT1 in OSCC\u003c/h2\u003e\u003cp\u003eIntegrated analysis of scRNA-seq data from three OSCC patients identified seven epithelial subpopulations (Epi_1\u0026ndash;Epi_7) (Supplementary Fig.\u0026nbsp;1A) and six macrophage subsets (Mac_1\u0026ndash;Mac_6) (Supplementary Figure. 1C). Notably, Epi_6 was enriched in tumors compared to paracancerous tissues (Supplementary Figure. 1B) and exhibited significantly overexpression of \u003cem\u003eGRN\u003c/em\u003e (Supplementary Figure. 1E). CellChat analysis revealed robust \u003cem\u003eGRN\u003c/em\u003e signaling between Epi_6 (ligand sender) and Mac_1 (receiver), the predominant tumor-associated macrophage subset (Supplementary Fig.\u0026nbsp;1D, F, G). This interaction was mediated primarily through the \u003cem\u003eGRN-SORT1\u003c/em\u003e ligand-receptor pair (Supplementary Fig.\u0026nbsp;1H), with Mac_1 showing high levels of \u003cem\u003eSORT1\u003c/em\u003e expression (Supplementary Fig.\u0026nbsp;1I). Gene Set Variation Analysis (GSVA) indicated significant enrichment of cholesterol metabolism and \u003cem\u003ePPAR\u003c/em\u003e signaling pathways in Mac_1 (Supplementary Fig.\u0026nbsp;1J), consistent with \u003cem\u003eSORT1\u003c/em\u003e\u0026rsquo;s role in lipid homeostasis. Gene Ontology (GO) analysis further highlighted upregulated pathways involved in lipoprotein lipase activity and chylomicron remodeling (Supplementary Fig.\u0026nbsp;1K), suggesting that \u003cem\u003eGRN-SORT1\u003c/em\u003e signaling may coordinate lipid processing in tumor-associated macrophages.\u003c/p\u003e\u003cp\u003eTo corroborate our findings, we analyzed multiple scRNA-seq datasets of HNSCC from GEO databases. Unsupervised clustering partitioned eight epithelial cells subpopulations (Epi_1\u0026ndash;Epi_8; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and five macrophage subsets (Mac_1\u0026ndash;Mac_5; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). CNV analysis with the Ro/e algorithm ranked Epi_1 as the epithelial subpopulation with the highest malignant potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Consistently, Epi_1 dominated HNSCC tissues (vs. PCA), comprising 33.4% of total epithelial cells in tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Analogous CNV analysis of macrophages identified Mac_4 as the subset with the highest tumor-to-PCA tissue ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), with Mac_4 showing significantly elevated abundance in HNSCC tissues compared to PCA samples (17.6% vs. 6.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Given PGRN\u0026rsquo;s oncogenic role, we examined \u003cem\u003eGRN\u003c/em\u003e signaling between Epi_1 and Mac_4, finding that Epi_1 exhibited marked \u003cem\u003eGRN\u003c/em\u003e overexpression compared to other epithelial subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). CellChat analysis demonstrated a robust \u003cem\u003eGRN\u003c/em\u003e signaling network between Epi_1 (ligand sender) and Mac_4 (receiver), predominantly mediated by the \u003cem\u003eGRN-SORT1\u003c/em\u003e ligand-receptor pair (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH-J). Additionally, Mac_4 expressed highest \u003cem\u003eSORT1\u003c/em\u003e level among macrophage subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK). Furthermore, GSVA indicated significant enrichment of cholesterol metabolism and \u003cem\u003ePPAR\u003c/em\u003e signaling pathways in Mac_4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eL), suggesting that \u003cem\u003eSORT1\u003c/em\u003e may orchestrate macrophage metabolic reprogramming in tumors, aligning with its established role in lipid metabolism across cancers [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe above analyses suggest a potential link between OSCC epithelial cells and TAMs through the \u003cem\u003eGRN-SORT1\u003c/em\u003e signaling pathway, implicating tumor-derived PGRN as a regulator of TAM cholesterol metabolism and function.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003ePGRN overexpression associates with increased CD68⁺ macrophages infiltration and poor prognosis in OSCC\u003c/h2\u003e\u003cp\u003eTo verify the relationship between PGRN expression and macrophage infiltration, as well as the clinical significance of elevated PGRN levels, we performed IHC analysis on 40 OSCC patient samples. Results showed a marked upregulation of PGRN in tumor tissues compared to adjacent normal epithelia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). This finding was confirmed \u003cem\u003ein vitro\u003c/em\u003e, with consistently higher PGRN protein levels in OSCC cell lines compared to HIOEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and by ELISA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Clinically, high PGRN expression correlated significantly with lymph node metastasis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), surrounding issue invasion (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), and reduced overall survival (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025; log-rank test, Table\u0026nbsp;1). To further support these observations, we analyzed \u003cem\u003eGRN\u003c/em\u003e expression in the GEPIA2 pan-cancer cohort, which indicated that high \u003cem\u003eGRN\u003c/em\u003e mRNA levels predicted poorer overall survival (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) and reduced disease-free survival (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) in HNSCC. Remarkably, IHC staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) revealed a higher density of CD68\u003csup\u003e+\u003c/sup\u003e macrophage in the high PGRN expression group compared to the low- expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG, p\u0026thinsp;=\u0026thinsp;0.0396).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eOSCC-derived PGRN drives macrophage cholesterol efflux via SORT1\u003c/h2\u003e\u003cp\u003eTAMs exhibit significant dysregulation in cholesterol metabolism, with enhanced efflux facilitating IL-4-mediated macrophage reprogramming and TAM differentiation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. GSVA of HNSCC datasets from GEO showed significant enrichment of cholesterol metabolism and PPAR signaling pathways in Mac_4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eL), suggesting and tumor-derived PGRN may play a crucial role in regulating TAM functionality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH-L). To investigate whether OSCC-derived PGRN influences macrophage cholesterol efflux, we generated \u003cem\u003eGRN\u003c/em\u003e-knockdown CAL27 cells (shPGRN-CAL27) and confirmed the knockdown efficacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). Then we prepared conditioned media (CM) from four experimental groups: HIOEC-CM (normal immortalized oral epithelial cells), CAL27-CM (OSCC cells), shNC-CAL27-CM (scrambled shRNA control), and shPGRN-CAL27-CM (\u003cem\u003eGRN\u003c/em\u003e-depleted cells). Further study showed that THP-1 cultured with CAL27-CM exhibited reduced intracellular cholesterol (vs. HIOEC-CM, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0127) and elevated extracellular cholesterol (vs. HIOEC-CM, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0275), while shPGRN-CAL27-CM reversed these effects compared to shNC-CAL27-CM (intracellular: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0124; extracellular: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0421), with similar trends observed in RAW264.7 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, E). Filipin III staining further confirmed reduced cholesterol levels in CAL27-CM-treated macrophages compared to HIOEC-CM, while cholesterol levels were restored in the shPGRN-CAL27-CM group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ). Supplementation with rhPGRN dose-dependently rescued cholesterol efflux in THP-1 cells, leading to increased extracellular cholesterol (p\u0026thinsp;=\u0026thinsp;0.0465 at 200 ng/mL; p\u0026thinsp;=\u0026thinsp;0.0027 at 400 ng/mL) and decreased intracellular cholesterol (p\u0026thinsp;=\u0026thinsp;0.036 - p\u0026thinsp;=\u0026thinsp;0.0085) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eTo establish SORT1 as the critical mediator of PGRN-induced cholesterol efflux, we employed a pharmacological approach using the selective SORT1-PGRN interaction inhibitor (HY-115213). Pretreatment with this inhibitor (2 \u0026micro;M, 2 h) abolished CAL27-CM-induced cholesterol efflux in both THP-1 and RAW264.7 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, H), with filipin III staining confirming intracellular cholesterol accumulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ). Importantly, the pro-efflux effect of exogenous rhPGRN (200 ng/mL) was entirely dependent on SORT1 activity, as co-treatment with the inhibitor completely blocked rhPGRN-mediated cholesterol export (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI), confirming SORT1 as the essential receptor for PGRN\u0026rsquo;s pro-cholesterol efflux function.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003ePGRN from OSCC cells activates macrophage cholesterol efflux via the SORT1 mediated ABCA1/ABCG1/LXRα/PPARγ axis\u003c/h2\u003e\u003cp\u003eCholesterol efflux is regulated by multiple transporters, including ATP-binding cassette transporters ABCA1 and ABCG1, as well as scavenger receptor class B type I (SR-B1, encoded by \u003cem\u003eSCARB1\u003c/em\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These pathways are transcriptionally regulated by nuclear receptors Liver X receptor alpha (LXRα, encoded by \u003cem\u003eNR1H3\u003c/em\u003e) and peroxisome proliferator-activated receptor gamma (PPARγ, encoded by \u003cem\u003ePPARG\u003c/em\u003e), which upregulate ABCA1/ABCG1 expression to promote cholesterol export from macrophages [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To assess whether OSCC-derived PGRN modulates macrophage cholesterol efflux via these mediators, we cultured THP-1 monocytes with CAL27-CM or HIOEC-CM. Strikingly, CAL27-CM significantly elevated both mRNA and protein expression of PPARγ, LXRα, ABCA1, and ABCG1 compared to HIOEC-CM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). In contrast, shPGRN-CAL27-CM diminished these effects compared to shNC-CAL27-CM, while SR-B1 levels remained unchanged (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). Blockade of SORT1 eliminated CAL27-CM-induced upregulation of PPARγ, LXRα, ABCA1, and ABCG1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). Furthermore, activation of PPARγ with rosiglitazone (RSG) restored the expression levels in PGRN-deficient systems (shPGRN-CAL27-CM\u0026thinsp;+\u0026thinsp;RSG; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, F). Notably, SR-B1 expression remained unaffected across all treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-F). Functionally, RSG treatment rescued cholesterol efflux in PGRN-depleted systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG, H), and reduced intracellular lipid accumulation, as confirmed by Filipin III staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI), mimicking the pro-efflux phenotype of wild-type tumors. These results delineate a preferential cholesterol export pathway in macrophages, wherein PGRN triggers ABCA1/ABCG1 upregulation via the LXRα/PPARγ cascade while leaving SR-B1 transport mechanisms unaffected.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eTumor-derived PGRN promotes macrophages produce immuno-suppressive mediators\u003c/h2\u003e\u003cp\u003eTo evaluate functional impact of PGRN-mediated cholesterol efflux, we further analyzed cytokine expression in macrophages exposed to conditioned media (HIOEC-CM, CAL27-CM, shNC-CAL27-CM, shPGRN-CAL27-CM, CAL27-CM\u0026thinsp;+\u0026thinsp;inhibitor, and shPGRN-CAL27-CM\u0026thinsp;+\u0026thinsp;RSG). Compared to HIOEC-CM, CAL27-CM significantly increased both mRNA and secreted protein levels of IL-6, IL-10, and TGFβ in both THP-1 and RAW264.7 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D). In contrast, the shPGRN-CAL27-CM group showed reduced expression of these cytokines compared to the shNC-CAL27-CM group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D), indicating that tumor-derived PGRN acts as a potent enhancer of immunosuppressive cytokine secretion.\u003c/p\u003e\u003cp\u003eHowever, SORT1 inhibition in CAL27-CM-treated macrophages induced cell-type-specific cytokine responses. RAW264.7 cells exhibited coordinated reductions in both mRNA and protein levels of IL-6, IL-10, and TGFβ (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B), suggesting that SORT1-dependent regulation is predominant in this model. Conversely, THP-1 cells showed unchanged IL-6 and TGFβ mRNA/protein levels, but with decreased IL-10 secretion despite stable mRNA expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D). Additionally, further experiments investigating PPARγ activation (using RSG) in PGRN-depleted macrophages revealed more divergence: while RSG consistently promoted immunosuppressive IL-10 secretion across both macrophage models (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, D), its effects on IL-6 and TGFβ were cell type-dependent, suggesting context-specific modulation of PPARγ signaling in TAM polarization.\u003c/p\u003e\u003cp\u003eThis functional profiling reveals that OSCC-derived PGRN orchestrates an immunosuppressive cytokine milieu (IL-6/IL-10/TGFβ) in macrophages, with SORT1 and PPARγ contributing distinct regulatory layers across cellular models.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003ePGRN deficiency reduces immunosuppressive macrophage accumulation in tumor xenografts\u003c/h2\u003e\u003cp\u003eTo further validate PGRN-mediated macrophage reprogramming, we established xenograft models by subcutaneously inoculating nude mice with three experimental groups: shNC-CAL27, shPGRN-CAL27, and shPGRN-CAL27\u0026thinsp;+\u0026thinsp;RSG. Starting 10 days post-inoculation, the shPGRN-CAL27\u0026thinsp;+\u0026thinsp;RSG group received intraperitoneal injections of RSG (10 mg/kg in 0.5% CMC-Na) every 48 hours, while the shNC-CAL27 and shPGRN-CAL27 groups were administered the vehicle (0.5% CMC-Na) until sacrifice at day 24. Although tumor weights did not show significant intergroup differences, shPGRN-CAL27 tumors exhibited a trend towards reduced mass compared to shNC-CAL27 controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eMultiplex immunofluorescence evaluations revealed that shPGRN-CAL27 tumors displayed fewer infiltration of F4/80\u003csup\u003e+\u003c/sup\u003e macrophages compared to shNC-CAL27 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, D) and an elevated ratio of F4/80\u003csup\u003e+\u003c/sup\u003eCD86\u003csup\u003e+\u003c/sup\u003e to F4/80\u003csup\u003e+\u003c/sup\u003eCD206\u003csup\u003e+\u003c/sup\u003e macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, E). Notably, RSG treatment in the shPGRN-CAL27\u0026thinsp;+\u0026thinsp;RSG group re-established immunosuppressive TAM profiles, increasing infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, D) and reducing the CD86⁺/CD206⁺ ratio to levels comparable to control tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, E).\u003c/p\u003e\u003cp\u003eCollectively, our \u003cem\u003ein vivo\u003c/em\u003e models demonstrate that OSCC-derived PGRN mediates macrophage recruitment and drives their acquisition of immunosuppressive markers in the tumor microenvironment.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDysregulated cholesterol homeostasis in TAMs profoundly impacts immune responses, influencing crucial processes such as antigen presentation, phagocytosis, cytokine secretion and polarization, thus fostering an immunosuppressive TME [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the mechanisms underlying cholesterol metabolism abnormalities in TAMs remain poorly understood. Our study provides the first evidence that OSCC-derived PGRN activates the SORT1/PPARγ/LXRα/ABCA1-ABCG1 cholesterol efflux axis, promoting immunosuppressive TAM polarization, as confirmed by both genetic knockdown and pharmacological inhibition.\u003c/p\u003e\u003cp\u003eCholesterol plays an essential role in cancer cell proliferation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Targeting cholesterol metabolism represents a potential therapeutic avenue in cancer treatment, although clinical efficacy remains debated [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Disparities in outcomes often arise from differential impacts on macrophage polarization [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For instance, Goossens \u003cem\u003eet al\u003c/em\u003e demonstrated that ovarian cancer cells induce membrane cholesterol efflux in macrophages, enhancing IL4-mediated M2-like reprogramming while suppressing IFNγ responses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This underscores the necessity for in-depth investigations into the role of cholesterol efflux in TAMs when designing cholesterol-targeting strategies for malignancies.\u003c/p\u003e\u003cp\u003eIn this study, single-cell RNA-seq indicated that OSCC-derived PGRN may regulate macrophage cholesterol metabolism. Both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments demonstrated that PGRN mediates cholesterol efflux, reducing intracellular cholesterol levels and fostering the generation of immunosuppressive macrophages. Specifically, culture supernatants from OSCC cells significantly decreased cholesterol levels in macrophages, consistent with previous findings [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Conversely, supernatants from CAL27 cells with PGRN knockdown increased macrophage cholesterol content, an effect partially reversible by recombinant human PGRN.\u003c/p\u003e\u003cp\u003ePGRN is known to interact with various membrane proteins and receptors, including SORT1, prosaposin, and tumor necrosis factor receptors (TNFR) 1 and 2 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. SORT1, a novel lipid-associated protein receptor located on the cell membrane and in the Golgi apparatus, is expressed in various cell types associated with lipid metabolism, such as hepatocytes and macrophages [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our DEGs analysis revealed that Mac_4 exhibited high \u003cem\u003eSORT1\u003c/em\u003e expression, whereas Epi_1 expressed high levels of \u003cem\u003eGRN.\u003c/em\u003e CellChat analysis indicated a robust \u003cem\u003eGRN\u003c/em\u003e signaling network between Epi_1 (the ligand sender) and Mac_4 (the receiver), predominantly mediated by the \u003cem\u003eGRN-SORT1\u003c/em\u003e ligand-receptor interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH-J).\u003c/p\u003e\u003cp\u003eTo further investigate the role of SORT1 in PGRN-mediated regulation of macrophage cholesterol efflux, we pre-treated macrophages with a SORT1 inhibitor during co-culture with various conditioned media. The results demonstrated that the SORT1 inhibitor reversed the promotive effects of PGRN, indicating that PGRN regulates cholesterol dynamics in macrophages via SORT1.\u003c/p\u003e\u003cp\u003ePrevious studies have identified several cholesterol transport proteins, including ABCA1, ABCG1, and scavenger receptor class B type I (SR-B1), as key regulators of cholesterol efflux [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Liver X receptor alpha (LXRα) serves as a critical regulator of lipid homeostasis, forming LXR-RXR heterodimers that enhance ABCA1 expression, thereby promoting cholesterol efflux from macrophages [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, PPARγ plays a significant role in lipid metabolism within macrophages, stimulating cholesterol efflux through the upregulation of LXRα, ultimately leading to increased ABCA1 expression [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our findings demonstrated that in co-culture systems with conditioned media from shPGRN-CAL27 cells, the mRNA and protein levels of PPARγ, LXRα, ABCA1 and ABCG1 were significantly reduced compared to the shNC-CAL27 group, leading to diminished cholesterol efflux from macrophages. Importantly, the application of the PPARγ agonist rosiglitazone re-established these effects. These results indicate that PGRN-SORT1 signaling activates the PPARγ/LXRα/ABCA1/ABCG1 pathway to enhance cholesterol efflux in macrophages.\u003c/p\u003e\u003cp\u003eLipid metabolism appears crucial to the differentiation and function of TAMs [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. One study reported that increased ABCA1 activity in TAMs in a mouse ovarian cancer model enhances membrane cholesterol efflux, promoting the loss of cholesterol-rich membrane microdomains that amplify IL-4 receptor activity, thus supporting the M2-like tumor-promoting TAM phenotype [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Cholesterol depletion disrupts membrane raft integrity, impairing TLR/MHC-II signaling and antigen presentation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, LXRα activation by oxidized sterols promotes the production of anti-inflammatory cytokines (e.g., IL-10) while suppressing pro-inflammatory responses [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Nevertheless, contrasting studies emphasize the importance of lipid accumulation and metabolism in differentiating and functioning pro-cancer TAMs within the TME [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our \u003cem\u003ein vitro\u003c/em\u003e study, we demonstrated that lipid metabolic reprogramming promotes an immunosuppressive phenotype, as evidenced by increased secretion of IL-6, IL-10 and TGFβ. Notably, treatment with the PPARγ agonist RSG restored the expression of IL-6 (in RAW264.7 macrophages), IL-10 (in both THP-1 and RAW264.7 macrophages), and TGFβ (in THP-1 macrophages) in the shPGRN-CAL27-CM group, confirming pathway dependency (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). \u003cem\u003eIn vivo\u003c/em\u003e experiments revealed a significant decrease in both the number of F4/80\u003csup\u003e+\u003c/sup\u003e macrophages and the proportion of CD206\u003csup\u003e+\u003c/sup\u003e macrophages in the shPGRN-CAL27 group compared to the shNC-CAL27 group. Remarkably, the RSG treatment group exhibited an overall trend closely resembling that of the shNC-CAL27 group. Therefore, this study underscores the critical role of PGRN-enhanced cholesterol efflux in the development of macrophages with an immunosuppressive phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur investigations reveal that targeting the PGRN-SORT1 axis represents a promising therapeutic strategy for OSCC. Specifically, inhibition of SORT1 using HY-115213 effectively counteracted PGRN-mediated immunosuppressive polarization of TAMs, as demonstrated by the restoration of intracellular cholesterol levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, H) and significant reductions in key immunosuppressive cytokines including IL-6, IL-10 and TGFβ (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These findings strongly support further development of SORT1-targeted approaches for immunometabolic intervention in OSCC.\u003c/p\u003e\u003cp\u003eImportantly, our study uncovered a clinically relevant paradox: while SORT1 inhibition blocked PGRN's immunosuppressive effects, PPARγ activation with rosiglitazone restored the immunosuppressive phenotype in PGRN-deficient models. This dual regulation suggests complex crosstalk within the PGRN-SORT1-PPARγ network that may have important implications for patient management. Of particular concern is the potential for PPARγ-activating antidiabetic drugs to inadvertently promote tumor immune evasion in diabetic OSCC patients with functional PGRN-SORT1 signaling.\u003c/p\u003e\u003cp\u003eBuilding on these findings, we propose several key directions for future research: Firstly, optimization of SORT1 inhibitors to enhance specificity and potency for clinical translation. Secondly, exploration of synergistic combinations with established immunotherapies such as PD-1/PD-L1 blockade. Thirdly, comprehensive characterization of compensatory pathways that may emerge upon disruption of cholesterol efflux in TAMs. Together, these investigations will advance our understanding of immunometabolic regulation in OSCC and facilitate the development of more effective therapeutic strategies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study identifies tumor-derived PGRN as a critical driver of immunosuppressive TAM polarization in OSCC by promoting cholesterol efflux through the SORT1/PPARγ/LXRα/ABCA1/ABCG1 axis. By elucidating this metabolic-immune linkage, we provide a mechanistic foundation for targeting macrophage cholesterol efflux pathways to enhance anti-tumor immunity. However, several limitations must be acknowledged in the present study. Notably, alternative receptors such as TNFR1, TNFR2 and EphA2 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] also play significant roles in PGRN functionality. Therefore, the mediating roles of these alternative receptors in PGRN-enhanced cholesterol efflux and immunosuppressive cytokine expression in macrophages warrant further comparative investigation, especially considered that SORT1 mediates differential regulation of immunosuppressive cytokines in the two macrophage cell models examined in this study. Moreover, expanding clinical cohorts and utilizing humanized models should be applied to further validate our findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTME: The immunosuppressive tumor microenvironment\u003c/p\u003e\n\u003cp\u003eOSCC: Oral squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eTAM: Tumor-associated macrophages\u003c/p\u003e\n\u003cp\u003escRNA-seq: single-cell RNA sequencing\u003c/p\u003e\n\u003cp\u003eHNSCC: head and neck squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003ePGRN: progranulin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGEO: Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003ePCA: paracancerous\u003c/p\u003e\n\u003cp\u003eDEGs: differentially expressed genes\u003c/p\u003e\n\u003cp\u003eGO: Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eCNV: Single-cell copy-number variation\u003c/p\u003e\n\u003cp\u003eHMM: hidden Markov model\u003c/p\u003e\n\u003cp\u003eIHC: Immunohistochemistry\u003c/p\u003e\n\u003cp\u003eRSG: rosiglitazone\u003c/p\u003e\n\u003cp\u003emIF: multiple immunofluorescence\u003c/p\u003e\n\u003cp\u003eSR-B1: scavenger receptor class B type I\u003c/p\u003e\n\u003cp\u003eLXR\u0026alpha;: Liver X receptor alpha\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthic approval and consent to participant\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Qilu Hospital, Shandong University (Ethics Approval Number: KYLL-202210-052). All procedures involving human participants were conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files. The raw datasets supporting the findings are available in publicly accessible repositories: Single-cell RNA sequencing data analyzed in this study were derived from oGSA-Human: HRA007439), accessible via the National Genomics Data Center (https://ngdc.cncb.ac.cn/gsa-human) and datasets GSE164690, GSE195832, GSE103322, GSE215403, and GSE181919 from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Shandong Province (No. ZR2022MH136), the Key R\u0026amp;D Program of Shandong Province, China (No. 2021SFGC0502), the Jinan Clinical Medicine Technology Innovation Plan Project (No.\u0026nbsp;202328025), Science and Technology Program of Jinan Municipal Health Commission (No. 2023-2-167).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYijun Luan designed and conducted the experiments, analyzed the data and wrote the manuscript. Yan Xu performed the experiments and wrote the manuscript. Simin Zhao performed the database analysis. Hao Li collected the samples. Zheming Liu participated in the Xenograft Experiments. Pishan Yang designed, revised and commented on the manuscript. Chengzhe Yang designed, supervised and revised the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Research Center for Basic Medical Science of Qilu hospital affiliated to Shandong University for consultation and instrument availability that supported this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI in scientific writing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author used Deepseek in order to improve the readability and language of the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A, Cancer Statistics. 2021. CA Cancer J Clin. 2021;71(1):7\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLo Nigro C, Denaro N, Merlotti A, Merlano M. Head and neck cancer: improving outcomes with a multidisciplinary approach. Cancer Manag Res. 2017;9:363\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChi AC, Day TA, Neville BW. 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Drug Des Devel Ther. 2019;13:405\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEl-Kenawi A, Dominguez-Viqueira W, Liu M, Awasthi S, Abraham-Miranda J, Keske A, et al. Macrophage-Derived Cholesterol Contributes to Therapeutic Resistance in Prostate Cancer. Cancer Res. 2021;81(21):5477\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao A, Cayabyab FS, Chen X, Yang J, Wang L, Peng T, et al. Implications of Sortilin in Lipid Metabolism and Lipid Disorder Diseases. DNA Cell Biol. 2017;36(12):1050\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArechavaleta-Velasco F, Perez-Juarez CE, Gerton GL, Diaz-Cueto L. Progranulin and its biological effects in cancer. Med Oncol. 2017;34(12):194.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChan NN, Yamazaki M, Maruyama S, Ab\u0026eacute; T, Haga K, Kawaharada M, et al. Cholesterol Is a Regulator of CAV1 Localization and Cell Migration in Oral Squamous Cell Carcinoma. Int J Mol Sci. 2023;24(7):6035.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDickinson A, Saraswat M, Joenv\u0026auml;\u0026auml;r\u0026auml; S, Agarwal R, Jyllikoski D, Wilkman T, et al. Mass spectrometry-based lipidomics of oral squamous cell carcinoma tissue reveals aberrant cholesterol and glycerophospholipid metabolism - A Pilot study. Transl Oncol. 2020;13(10):100807.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu P, Wang Q, Bi E, Ma X, Liu L, Yang M, et al. Enhanced Lipid Accumulation and Metabolism Are Required for the Differentiation and Activation of Tumor-Associated Macrophages. Cancer Res. 2020;80(7):1438\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Zhang Z, Feng D, Xu L, Li F, Liu J, et al. PGRN exerts inflammatory effects via SIRT1-NF-κB in adipose insulin resistance. J Mol Endocrinol. 2020;64(3):181\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table","content":"\u003ch4\u003eTable 1． Correlation between PGRN expression and clinicopathological parameters in OSCC\u003c/h4\u003e\n\u003cp\u003ePGRN (No. patients)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinicopathological parameters P-value\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"673\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eAge at surgery (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003e≧60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eCD68+cell/Total (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e4.58\u0026plusmn;5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e9.34\u0026plusmn;8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eT1+T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eT3+T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003elymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eN+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eHistological differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eWell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eModerate/Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eClinical stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eI+II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eIII+IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eDisease recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eSurrounding issue invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eSurvival status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eFive-year survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.9747%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.68648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.6137%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1159%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3046%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Supplementary Figure 1","content":"\u003cp\u003eSupplementary Figure 1 is not available with this version; the figure title and legend is below. \u003c/p\u003e\n\u003ch4\u003eSupplementary Figure 1. Single-cell Sequencing Reveals the Role of Granulin Signaling in Epithelial-Macrophage Interactions within the Tumor Microenvironment of Oral Squamous Cell Carcinoma\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e UMAP plot depicting the clustering of 7 epithelial cell subtypes (A) and 6 macrophage subtypes (B) among two distinct tissues.\u0026nbsp;\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003eD.\u003c/strong\u003e Bar chart displaying the proportions of each cell subtype in the various tissues.\u0026nbsp;\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Violin plot highlighting the variations in \u003cem\u003eGRN\u003c/em\u003e expression among different epithelial cell subtypes.\u0026nbsp;\u003cstrong\u003eF\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Heatmap illustrating the communication intensity between epithelial cells and macrophages in the context of the \u003cem\u003eGRN\u003c/em\u003e signaling pathway.\u0026nbsp;\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Heatmap representing the roles of various epithelial cell and macrophage clusters within the signaling network of the \u003cem\u003eGRN\u003c/em\u003e pathway.\u0026nbsp;\u003cstrong\u003eH\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Dot plot showing the interaction intensity of ligand/receptors between Epi_6 and Mac_1, highlighting the role of \u003cem\u003eGRN\u0026nbsp;\u003c/em\u003eand its ligands, according to CellPhoneDB analysis.\u0026nbsp;\u003cstrong\u003eI\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eViolin plot representing the expression distribution of \u003cem\u003eGRN\u003c/em\u003e signaling pathway-related genes, showing variations between epithelial and macrophage clusters.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eJ\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eK.\u003c/strong\u003e Heatmap showing the top 10 enrichment of representative GO (J) and GSVA (K) pathways in gene sets expressed in macrophage subsets, sorted by t-values from largest to smallest.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"progranulin (PGRN), SORT1, tumor-associated macrophages (TAMs), cholesterol efflux, oral squamous cell carcinoma (OSCC), immunosuppression","lastPublishedDoi":"10.21203/rs.3.rs-7333736/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7333736/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe immunosuppressive tumor microenvironment (TME) contributes to poor prognosis in oral squamous cell carcinoma (OSCC), with tumor-associated macrophages (TAMs) playing a pivotal role. However, the underlying metabolic mechanisms that drive TAM polarization remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed integrated single-cell RNA sequencing (scRNA-seq) on primary OSCC tumors (n = 3) and validated findings in 77 head and neck squamous cell carcinoma (HNSCC) samples across five public datasets. The role of tumor-derived progranulin (PGRN) in TAM reprogramming was examined using genetic knockdown, pharmacologic inhibition of PGRN–SORT1 interaction, and activation of downstream PPARγ signaling \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA malignant epithelial subpopulation highly expressing PGRN was identified, which reprogrammed TAMs via SORT1-mediated dependent signaling. Mechanistically, PGRN–SORT1 interaction induced PPARγ/LXRα activation, upregulated cholesterol efflux transporters ABCA1/ABCG1 (p \u0026lt; 0.001), and reduced intracellular r cholesterol levels in macrophages. This metabolic rewiring led to an immunosuppressive TAM phenotype, with increased secretion of IL-6, IL-10, and TGFβ. PGRN knockdown or SORT1 inhibition restored cholesterol retention, reduced TAM infiltration, and increased the CD86\u003csup\u003e+\u003c/sup\u003e/CD206\u003csup\u003e+\u003c/sup\u003e ratio \u003cem\u003ein vivo\u003c/em\u003e. Notably, PPARγ agonism with rosiglitazone reinstated immunosuppression in PGRN-deficient tumors, confirming the dependence on this signaling axis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor-derived PGRN reprograms TAMs via SORT1-mediated cholesterol efflux and downstream PPARγ/LXRα activation, promoting an immunosuppressive TME in OSCC. Targeting the PGRN–SORT1–PPARγ axis may represent a promising immunometabolic approach to overcome immune resistance and improve OSCC outcomes.\u003c/p\u003e","manuscriptTitle":"Tumor-derived progranulin reprograms immunosuppressive macrophages via cholesterol efflux in oral squamous cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-19 12:17:59","doi":"10.21203/rs.3.rs-7333736/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-10-03T07:41:19+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-13T16:05:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-12T15:00:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-08-09T07:57:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4aefb3c-cdcb-47e2-b9e7-d697360f5542","owner":[],"postedDate":"September 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T15:11:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-19 12:17:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7333736","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7333736","identity":"rs-7333736","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}