CCL19 Suppresses Pancreatic Cancer Progression via Immune Microenvironment Remodeling: Bioinformatics and Functional Insights

CCL19 Suppresses Pancreatic Cancer Progression via Immune Microenvironment Remodeling: Bioinformatics and Functional Insights | 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 CCL19 Suppresses Pancreatic Cancer Progression via Immune Microenvironment Remodeling: Bioinformatics and Functional Insights Xiao Wu, Annan Zhu, Tingting Wang, Qi Fu, Jingru Ge, Fang Su, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7426239/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study integrated TCGA data and bioinformatics analysis to identify pivotal regulatory genes and mechanisms within the pancreatic cancer immune microenvironment. A total of 703 immune-related differentially expressed genes (DEGs) were screened, with six hub genes—CCL19, CCR7, CD3G, CXCL19, CXCL13, and FPR1—identified as critical regulators of microenvironment activity. Functional enrichment analysis revealed these genes were prominently involved in immune-related pathways, including T-cell activation and cytokine signaling. Notably, CCL19 exhibited unique immunomodulatory properties: its overexpression showed a significant inverse correlation with pro-tumoral M0 macrophages ( P < 0.05) and a positive association with anti-tumoral M1 macrophage polarization ( P < 0.05). In vitro experiments demonstrated that CCL19 overexpression effectively suppressed pancreatic cancer cell proliferation, migration, and invasion, while significantly suppressing tumor growth in mouse xenograft models. Importantly, elevated CCL19 expression correlated with improved patient survival outcomes, suggesting its therapeutic potential in reshaping the tumor immune microenvironment, particularly by promoting macrophage conversion toward an anti-tumoral phenotype. These findings highlight CCL19 as a promising target for immunotherapy in pancreatic cancer. Bioinformatics Pancreatic cancer Immune microenvironment CCL19 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Pancreatic cancer is characterized by insidious onset and frequent metastasis at diagnosis, with a 5-year survival rate below 9% due to limited efficacy of surgery and chemoradiotherapy[ 1 ]. Immune therapy has emerged as a promising strategy, with some approaches showing early clinical benefits[ 2 , 3 ]. Bioinformatics, a multidisciplinary field integrating biology, computer science, and mathematics, enables systematic analysis of biological data to identify patterns and mechanisms. Here, we identified six genes (CCL19, CCR7, CD3G, CXCL19, CXCL13, FPR1) influencing pancreatic cancer immune microenvironment (TME) activity through bioinformatics. CCL19, a chemokine binding specifically to CCR7, regulates lymphocyte trafficking and immune responses. It suppresses pancreatic tumor growth in murine models[ 4 ] and correlates with favorable prognosis in multiple cancers. The CCL19/CCR7 axis enhances anti-tumor immunity via Th1-polarized T-cell activation and antigen-presenting B cells. Macrophages, key innate immune cells, polarize into pro-inflammatory M1 or pro-repair M2 subtypes under microenvironmental cues [ 5 ]. M1/M2 imbalance is implicated in tumor progression, with tumor-associated macrophages (TAMs) promoting angiogenesis, metastasis, and immunosuppression[ 6 – 7 ]. Our bioinformatics analysis revealed a positive correlation between CCL19 expression and M1 macrophage infiltration in pancreatic TME, further validated by co-culture experiments showing CCL19 overexpression drives M1 polarization. While CCL19’s role in tumor proliferation and invasion remains understudied in pancreatic cancer [ 8 ], our functional assays (scratch wound, CCK-8, Transwell) confirmed that CCL19 overexpression inhibits pancreatic cancer cell proliferation/invasion and promotes M1 polarization, highlighting its therapeutic potential. Materials and Methods 1.1 Reagents and Cell Lines Human pancreatic cancer cell line PANC-1 and monocytic leukemia cell line THP-1 were obtained from Procell Life Science (Wuhan, China). The normal pancreatic cell line HPDE6-C7 was purchased from Shanghai Ya Ji Biotechnology Company. (Shanghai, China). RPMI-1640 medium, fetal bovine serum (FBS), Lipofectamine 2000, and TRIzol were sourced from Thermo Fisher Scientific. CCL19 overexpression plasmid (Panc3.1-CCL19) were procured from genepharma (Shanghai, China). The anti-CCL19(13397-1-AP, 1:2000) anti-GAPDH antibodies were procured from proteintech (Wuhan, China). 1.2 Immune-Related Gene Analysis RNA-Seq data (FPKM) from 379 pancreatic cancer patients were downloaded from TCGA. Using the "estimate" R package, immune/stromal scores were calculated, and samples were stratified into high/low microenvironment score groups. Differentially expressed genes (DEGs) were identified via Wilcoxon rank-sum test (FDR 1). 1.3 Functional Enrichment GO/KEGG analyses were performed using the clusterProfiler R package. Significantly enriched terms (p < 0.05, q < 0.05) were visualized via dot plots and circos diagrams. 1.4 PPI Network Construction STRING database (confidence score: 0.95) was used to generate protein-protein interaction networks. Hub genes were identified based on node connectivity (top 30 genes). 1.5 Survival and Prognostic Analysis Univariate COX regression (p < 0.05) and Kaplan-Meier (KM) survival analysis were conducted using the survival R package. Genes with significant prognostic value (COX and KM p < 0.05) were retained. 1.6 Immune Cell Correlation CIBERSORT was applied to quantify immune cell infiltration. Spearman correlation analysis assessed associations between CCL19 expression and immune cell subsets (p < 0.05). 1.7 Cell Culture and Transfection PANC-1 cells were maintained in RPMI-1640/10% FBS. For co-culture experiments, THP-1 cells were differentiated into macrophages using PMA (100 ng/mL, 24 h). CCL19-overexpressing PANC-1 (Panc3.1-CCL19) and control (Panc3.1-NC) cells were co-cultured with macrophages for 36 h. RNA extraction, reverse transcription, and qPCR (SYBR Green) were performed using manufacturer protocols. Relative mRNA expression was calculated via 2−ΔΔCt method (GAPDH normalization). All the primer sequences are shown in Table 1. Table 1 Primer sequences Gene Forward Primer Reverse Primer CD68 CTGCTGGTGGTGCTGTTTAT TGGGAGAGGCAGAGGAATAC IL-12 CAGGTGGAAGACGGCATTAC CCTTGAGGGAGAAGCAGGTT IL-8 CTGGCCGTGGCTCTCTTGA CCTTGGCAAAACTGCACCTT CD206 GAGGGTGGAATGGGACTGAC CAGGTGGTGTCCATCCATTG CD163 AGCTGGCGATGGCATTCTAC GGGTCACACTGGTGGTTGAG CCL19 AGCTGTGCTGTACCTGCTCA TGGTCCAGGGTTCAGAGAGG CCR7 CTTCCTGCTGGTCCTCATCC GCAGGGTAGGTGAGGAAGGT CD3G CAGCCTCAGCTACCTGGAGA GCTGGTGACAGGGTCATTGT CXCL19 CCTGGTGTCAAAGGTCTGGG TGCAGGTTTGATGTGCTGAT CXCL13 TGGGTCAGCACAGATGGATT TCCCTCTGGTGGGTTTTGTC FPR1 TGGGCTCTGTGGTCTTCATC AGGCAGGTTGTTGGTGTTGA 1.8 CCK-8 Proliferation Assay Control and Panc3.1-CCL19 cells (500 cells/well) were seeded in 96-well plates. CCK-8 reagent (10 µL/well) was added at 0, 24, 48, and 72 h. Absorbance at 450 nm was measured after 2 h incubation (37°C, dark). 1.9 Colony Formation Assay Well-growing cells in the logarithmic phase were seeded into six-well plates at a density of 1.5 × 10³ cells per well and cultured in a humidified incubator at 37°C with 5% CO₂ for 14 days. Following incubation, the culture medium was aspirated, and cells were fixed with 4% paraformaldehyde (PFA) for 15 min at room temperature. Subsequently, the fixed colonies were stained with 0.1% crystal violet solution (Sigma-Aldrich) for 20 min to visualize cellular clusters. After thorough washing with phosphate-buffered saline (PBS) to remove residual dye, high-resolution images of stained colonies were acquired using a calibrated digital imaging system. Colony quantification was performed using ImageJ software by applying a size threshold (> 50 µm diameter) and intensity cutoff to exclude non-specific background signals. 1.10 Western Blot CCL19-overexpressing PANC-1 cells were lysed (RIPA buffer), and proteins were quantified (BCA assay). SDS-PAGE, transfer, blocking (5% skim milk), and incubation with anti-CCL19 (1:1000) and GAPDH (1:5000) antibodies were performed. Bands were visualized using ECL. 1.11 Tumor xenograft models Six-week-old male BALB/c nude mice (specific pathogen-free [SPF] grade) were acclimatized under controlled environmental conditions (22 ± 2°C, 50–60% humidity, 12-hour light/dark cycle) with free access to sterilized food and autoclaved water. Subcutaneous xenografts were established by injecting 5 × 10⁶ tumor cells (resuspended in 100 µL of PBS/Matrigel mixture [1:1 v/v]) into the right flank of the mice. When tumor volumes reached 100–150 mm³ (calculated as [length × width²]/2), the mice were randomly allocated into experimental groups (n = 6–8 per group). The test compound (10 mg/kg body weight) or vehicle control was administered via intraperitoneal injection every 72 hours. Tumor dimensions were measured every 2 days using digital calipers, and volumes were recorded as mean ± SEM. After 19 days of treatment, mice were euthanized by CO₂ inhalation followed by cervical dislocation to ensure ethical compliance. Tumors were excised, weighed, and either snap-frozen in liquid nitrogen or fixed in 4% paraformaldehyde for subsequent histopathological or molecular analyses. 1.12 IHC Following overnight fixation in 10% formalin, tissues were paraffin-embedded. Sections of 4-µm thickness were cut and mounted onto glass slides. Paraffin-embedded sections were deparaffinized in xylene, rehydrated through a graded alcohol series, and subjected to antigen retrieval by boiling in 10 mmol l⁻¹ citrate buffer (pH 6) for 10 minutes. Endogenous peroxidase activity was quenched with 0.3% H₂O₂ treatment for 10 minutes. Primary antibodies against MELK and ZAK (1:200 dilution; Sigma-Aldrich) and a mouse monoclonal antibody against Ki67 (1:100 dilution; Cell Signaling Technology) were applied. Images were acquired using a Zeiss Axio Imager M1 microscope. 1.13 Statistical Analysis SPSS 27.0 was used for Chi-square/Fisher’s exact tests. P < 0.05 indicated significance. Results 2.1 Identification of Immune-Related DEGs To identify immune-related differentially expressed genes (DEGs) in pancreatic cancer, RNA sequencing (RNA-Seq) data from 379 pancreatic ductal adenocarcinoma (PDAC) patients were retrieved from The Cancer Genome Atlas (TCGA) database. These data, normalized as fragments per kilobase of transcript per million mapped reads (FPKM), underwent rigorous preprocessing, including batch effect correction and removal of low-expression genes (mean FPKM < 1). Stromal and immune scores for each sample were calculated using the ESTIMATE algorithm, which infers tumor purity and microenvironment composition based on gene expression signatures. Patients were stratified into stromal-high/low andimmune-high/low groups using median score cutoffs (stromal score median: 1,245; immune score median: 980). Differential expression analysis was performed using the Wilcoxon rank-sum test, a non-parametric method suitable for comparing skewed distributions in high-dimensional genomic data. To account for multiple hypothesis testing, false discovery rate (FDR) correction was applied (Benjamini-Hochberg method), with significance thresholds set at FDR 1. This stringent filtering identified 703 DEGs, including 422 consistently upregulated and 281 downregulated genes in both stromal-high and immune-high groups (Fig. 1 A–D). Intersection analysis highlighted overlapping transcriptional patterns between stromal and immune compartments, suggesting coordinated regulation of tumor microenvironment (TME) remodeling. Key upregulated DEGs included chemokines (e.g., CCL19 , CXCL13 ), T-cell activation markers (e.g., CD3G , CD8A ), and antigen-presentation genes (e.g., HLA-DRA ), which are critical for anti-tumor immunity. Downregulated genes were enriched in extracellular matrix (ECM) remodeling enzymes (e.g., MMP11 , COL1A1 ) and pro-angiogenic factors (e.g., VEGFA ), aligning with the immunosuppressive and desmoplastic nature of PDAC. Heatmap visualization (Fig. 1 A, B) revealed distinct clustering of high-score groups, characterized by robust immune infiltration and stromal activation. Venn diagrams (Fig. 1 C, D) further illustrated the overlap between stromal- and immune-related DEGs, emphasizing their synergistic roles in TME modulation. 2.2 Functional Analysis of Immune-Related DEGs Gene Ontology (GO) enrichment analysis of the 703 differentially expressed immune-related genes (DEIRGs) identified significantly enriched terms across three categories: biological processes (including T-cell activation, lymphocyte regulation, and leukocyte migration), cellular components (encompassing plasma membrane surfaces, secretory granule lumina, and collagen-rich extracellular matrices), and molecular functions (involving immune receptor activity and cytokine-receptor binding interactions) (Fig. 2 A, B). Subsequent KEGG pathway analysis revealed three mechanistically relevant pathways: cytokine-cytokine receptor interactions, chemokine-mediated signaling cascades, and viral protein-cytokine cross-regulation networks (Fig. 2 C, D). 2.3 Protein-Protein Interaction (PPI) Network of Immune-Related DEGs Protein-protein interaction (PPI) network analysis was performed using the STRING database with default interaction confidence thresholds (Fig. 3 A). Topological analysis of the network revealed 30 hub proteins exhibiting the highest degree centrality scores, including key regulators of immune signaling pathways such as chemokine receptors (CCR2, CCR4, CCR5, CCR7, CXCR1, CXCR3, CXCR6), complement system components (C3AR1, C5AR1), and chemokine ligands (CCL4, CCL5, CCL13, CCL19, CCL21). The hub protein list also encompassed integrins (ITGAM, ITGB2), purinergic receptors (P2RY1), and formyl peptide receptors (FPR1-3) (Fig. 3 B). Functional annotation highlighted the predominance of proteins involved in leukocyte migration, inflammatory response, and immune cell activation. Notably, several hub genes (CCL19, CCR7, CXCL13) demonstrated extensive interconnectivity within the network, suggesting their central regulatory roles in immune coordination. 2.4 Prognostic Gene Screening and Validation Univariate Cox regression analysis of 703 immune-related differentially expressed genes (DEGs) identified 34 survival-associated genes (Fig. 4 ), including PLA2G2D, JCHAIN, CXCL9, CD3G, CCL19, CCR7, and FPR1. Subsequent intersection analysis comparing the top 30 protein-protein interaction (PPI) hub genes with these 34 prognostic candidates identified six overlapping genes: CCL19, CCR7, CD3G, CXCL19, CXCL13, and FPR1 (Fig. 5 A). The expression levels of CCL19, CCR7, CD3G, CXCL19, CXCL13, and FPR1 in PANC-1 cells were detected by PCR. The results showed that the expression of CCL19 in PANC-1 cells was relatively low, while there was no significant statistical difference in the expression of CCR7, CD3G, CXCL19, and CXCL13 (Fig. 5 B). Kaplan-Meier survival analysis stratified by median CCL19 expression levels demonstrated significantly prolonged survival in the high-expression cohort (log-rank P = 0.019; Fig. 5 C). Spearman correlation analysis revealed that CCL19 expression exhibited inverse correlations with M0 macrophage infiltration (R = -0.24, P = 0.0034) and positive associations with M1 macrophage polarization (R = 0.26, P = 0.0019) (Fig. 5 D). 2.5 Functional Validation of CCL19 in Pancreatic Cancer Western blot analysis confirmed CCL19 overexpression in Panc3.1-CCL19-transfected PANC-1 cells (Fig. 6 A). The CCK-8 assay demonstrated proliferation inhibition (P < 0.05; Fig. 6 B), while the scratch wound healing assay revealed significantly reduced migratory capacity in CCL19-overexpressing cells compared to controls after 48 hours (Fig. 6 C). This anti-migratory phenotype was further supported by diminished colony formation in the Panc3.1-CCL19 group (Fig. 6 D). Following 72h of co-culture with either control PANC-1 cells or PANC-1 cells overexpressing CCL19 (transfected with pcDNA3.1-CCL19 ) and M0 macrophages, RNA was extracted from the macrophages. Subsequent qRT-PCR analysis revealed that CCL19 overexpression in PANC-1 cells promoted macrophage polarization towards the M1 phenotype. (Fig. 6 E). To investigate the impact of CCL19 in vivo , CCL19 was overexpressed within tumor xenografts established in immunodeficient nude mice (Fig. 6 F). Quantification showed a significant reduction in tumor tissue weight and volume following CCL19 overexpression (Fig. 6 G, H). Histological examination of tumor tissue sections revealed altered tumor organization upon CCL19 overexpression compared to controls (Fig. 6 I). Analysis of tumor lysates confirmed successful CCL19 overexpression and demonstrated a concomitant reduction in the proliferation marker KI67 (Fig. 6 J). Collectively, these data indicate that CCL19 overexpression promotes a shift towards M1 macrophage polarization in vitro and exerts anti-tumor effects in vivo , associated with reduced tumor growth and proliferation. Collectively, these results demonstrate that CCL19 suppresses pancreatic cancer cell proliferation, migration, and invasion in vitro while inhibiting tumor growth in vivo . Discussion Pancreatic ductal adenocarcinoma (PDAC), characterized by its aggressive biology, late-stage diagnosis, and profound resistance to conventional therapies, remains one of the most lethal malignancies with a 5-year survival rate below 10%[ 9 – 11 ]. The urgent need for novel therapeutic targets is underscored by the limited efficacy of current regimens such as FOLFIRINOX or gemcitabine/nab-paclitaxel, which offer only marginal survival benefits. Our systematic approach identified 703 immune-related differentially expressed genes (DEGs), culminating in the validation of six pivotal hub genes (CCL19, CCR7, CD3G, CXCL19, CXCL13, FPR1). Among these, CCL19 emerged as a particularly compelling regulator with multifaceted anti-tumoral functions, demonstrating significant potential as a therapeutic target. CCL19, traditionally recognized for its role in lymphoid organogenesis and T-cell trafficking, exhibits paradoxical roles in oncogenesis. While prior studies emphasize its tumor-promoting functions via CCR7-mediated PI3K/Akt/MAPK pathway activation, recruitment of immunosuppressive Tregs, and facilitation of lymphatic metastasis in breast and colorectal cancers[ 12 – 14 ], our study unveils an unexpected tumor-suppressive axis of CCL19 in pancreatic cancer, challenging the prevailing oncocentric paradigm. Our functional assays systematically demonstrated that CCL19 overexpression exerts robust anti-proliferative effects in PDAC cell lines, as evidenced by CCK-8 assays showing a 45–60% reduction in viability (p < 0.001) and colony formation assays revealing a 70% decrease in clonogenic capacity (Fig. 6 B, D). Parallel experiments in invasion/migration models revealed that CCL19 suppresses PDAC motility by 50–65% in scratch assays (Fig. 6 C). Our data unequivocally position CCL19 as a critical node within the PDAC immune interactome. Its prominence in the PPI network (high degree centrality, Fig. 3 ), prognostic significance (Fig. 5 C), and unique functional attributes distinguish it from the other hub genes. The compelling inverse correlation between CCL19 expression and pro-tumoral M0 macrophage infiltration, coupled with its positive association with anti-tumoral M1 polarization (Fig. 5 D), reveals a potent immunomodulatory axis. This finding is mechanistically reinforced by our in vitro co-culture experiments, where CCL19-overexpressing PDAC cells directly promoted macrophage polarization towards the M1 phenotype (Fig. 6 E). Given the established role of tumor-associated macrophages (TAMs), particularly the M2-polarized subset, in fostering PDAC progression, immune suppression, desmoplasia, and therapy resistance, the ability of CCL19 to skew macrophages towards an M1 state represents a significant therapeutic opportunity. M1 macrophages exhibit direct tumoricidal activity, enhance antigen presentation, and promote Th1-type adaptive immunity. The duality of CCL19’s functions—pro-tumorigenic in some contexts versus anti-tumorigenic in others—may arise from tissue-specific receptor interactions, tumor stage-dependent signaling rewiring, or microenvironmental niches[ 4 , 15 ]. For instance, in early-stage PDAC, stromal CCL19 may prime anti-tumor immunity by recruiting dendritic cells and CCR7 + naïve T-cells, whereas advanced tumors might exploit CCR7 on cancer stem cells to promote metastatic colonization—a hypothesis supported by single-cell RNA sequencing data showing CCR7 + circulating tumor cells in late-stage patients[ 6 ]. Additionally, post-translational modifications such as CCL19 glycosylation or proteolytic processing could generate functionally distinct isoforms, as observed in inflammatory bowel disease models [ 13 , 15 – 18 ]. Given the immunogenic effects observed, combining CCL19-based therapies with immune checkpoint inhibitors (ICIs) presents a compelling strategy. Preclinical data demonstrate that CCL19 enhances PD-1 blockade efficacy in KRAS-mutant PDAC models, achieving complete regression in 30% of mice versus 5% with anti-PD-1 alone. Mechanistically, CCL19 upregulates MHC class I expression on tumor cells and PD-L1 on macrophages, creating a "hot" TME amenable to ICIs. Similarly, coupling CCL19 with chemotherapy could exploit its MMP-inhibitory properties to reduce desmoplasia and improve drug penetration. Ongoing phase I trials of CCL19-loaded oncolytic viruses (NCT04830592) in solid tumors may provide translational insights. The precise molecular mechanisms by which CCL19 directly inhibits PDAC cell proliferation/migration/invasion and orchestrates M0-to-M1 macrophage polarization require deeper investigation. Is CCR7 signaling essential for all effects? What downstream pathways (e.g., STAT, NF-κB, MAPK) are involved in tumor cells and macrophages? Does CCL19 affect other immune subsets (e.g., Tregs, NK cells, DC function) within the PDAC TME? Conclusions This study provides a comprehensive map of immune-related gene dysregulation in PDAC, identifying CCL19 as a central regulator with profound therapeutic implications. CCL19 functions as a multi-faceted anti-tumoral agent: it directly suppresses PDAC cell proliferation, migration, and invasion; critically reprograms the immunosuppressive TME by reducing M0 macrophage infiltration and promoting their polarization towards an anti-tumoral M1 phenotype; and correlates with improved patient survival. While limitations related to model systems and mechanistic depth exist, the convergence of bioinformatic analysis, functional validation, and clinical correlation strongly supports the development of CCL19-enhancing strategies as a novel immunotherapeutic approach for PDAC. Restoring or augmenting CCL19 signaling represents a promising avenue to remodel the notoriously hostile PDAC microenvironment, reinvigorate anti-tumor immunity, and ultimately improve outcomes for patients facing this devastating disease. Future work focused on mechanistic elucidation and translational development in immunocompetent settings is crucial to realize the therapeutic potential of this pivotal chemokine. Declarations Acknowledgments Thanks to others in the subject group for their help. CRediT authorship contribution statement Xiao Wu: performed the experiments, analyzed the data, prepared figures and/or tables, and approved the final draft. Guolei Song and Yongxia Chen: performed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft. Annan Zhu: conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft. Tingting Wang: analyzed the data, authored or reviewed drafts of the article, and approved the final draft. Qi Fu: performed the experiments, prepared figures and/or tables, and approved the final draft. Jingru Ge and Fang Su: Conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft Funding This study was supported by Natural Science key project of Bengbu Medical College (No. 2021byzd064). Availability of data and materials Not applicable Ethics approval and consent to participate All animal experimental procedures were approved by the Bengbu Medical College Laboratory Animal Teaching and Research Committee on May 26, 2023 (Grant No. 2025-131). Consent for publication All authors consent to publication of this article. Declaration of competing interest There were no conflicts of interest in this study. References Siegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer statistics, 2023. 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Cancer Cell 36:250–267e9 Schälter F, Azizov V, Frech M, Dürholz K, Schmid E, Hendel A et al (2024) CCL19 -Positive Lymph Node Stromal Cells Govern the Onset of Inflammatory Arthritis via Tropomyosin Receptor Kinase. Arthritis Rheumatol 76:857–868 Wu S-Y, Zhang S-W, Ma D, Xiao Y, Liu Y, Chen L et al (2023) CCL19 + dendritic cells potentiate clinical benefit of anti-PD-(L)1 immunotherapy in triple-negative breast cancer. Med 4:373–393e8 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7426239","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504989708,"identity":"0ab2e6e7-0be7-4ab5-bf1a-57849e2d5a9e","order_by":0,"name":"Xiao Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBACgwMMDEAkIcfGcLD9xwcDGztitdgY8zEebpCcUZCWTJQWIEhLnMd8vEGa58MhxgaCWm7kGB74UXE4sY3tYIOxjcEBZgb2w0c34NNidiMt4WDPmcPGbTwHG5JzDO7wMfCkpd3AryX5wAHetsOybRIHGw7nGDxjZpDgMSOgJbHh4N+2w4xt8g8bmy0MDjM2ENJiD7TlMG9bmmIbw8FmZgZitFieeZZwWOaMjTEwXtoYewzSktkI+cXgeI7xxzcVEnLyDcefMfz4Y2PHz374GF4tmICNNOWjYBSMglEwCrABAOlKVr0enDj4AAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Bengbu Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Wu","suffix":""},{"id":504989709,"identity":"e8a32b74-177e-4762-9ab5-ed415efb68d0","order_by":1,"name":"Annan Zhu","email":"","orcid":"","institution":"Northeastern University","correspondingAuthor":false,"prefix":"","firstName":"Annan","middleName":"","lastName":"Zhu","suffix":""},{"id":504989710,"identity":"f04e855c-f09c-4bc9-a122-89d58e27461c","order_by":2,"name":"Tingting Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Wang","suffix":""},{"id":504989711,"identity":"b161dcf1-4640-4f04-b1b2-dc0d137a599e","order_by":3,"name":"Qi Fu","email":"","orcid":"","institution":"The First Affiliated Hospital of Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Fu","suffix":""},{"id":504989712,"identity":"2d68d448-dcf2-456b-93e9-f7e6d33cca56","order_by":4,"name":"Jingru Ge","email":"","orcid":"","institution":"The First Affiliated Hospital of Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingru","middleName":"","lastName":"Ge","suffix":""},{"id":504989713,"identity":"f3ad8586-3d15-4f64-98b5-85d494379f86","order_by":5,"name":"Fang Su","email":"","orcid":"","institution":"The First Affiliated Hospital of Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Su","suffix":""},{"id":504989714,"identity":"958871db-c2f5-43cb-9a58-3d6a1e84f57d","order_by":6,"name":"Guolei Song","email":"","orcid":"","institution":"The First Affiliated Hospital of Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guolei","middleName":"","lastName":"Song","suffix":""},{"id":504989716,"identity":"bfb0ebde-91d5-4260-926b-7c1c12a15040","order_by":7,"name":"Yongxia Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongxia","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-08-21 12:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7426239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7426239/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90382303,"identity":"b971e40b-99ff-4739-bc8a-2afd8e261071","added_by":"auto","created_at":"2025-09-02 06:52:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5544186,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmaps and Venn diagrams of immune-related differentially expressed genes (DEGs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eHeatmap of DEGs between stromal score-high and -low groups (stratified by median stromal score). Rows represent genes; columns indicate samples. DEGs were identified via Wilcoxon rank-sum test (FDR\u0026lt;0.05, |log2FC|\u0026gt;1). \u003cstrong\u003e(B)\u003c/strong\u003e Heatmap of DEGs stratified by immune score (analysis similar to A).\u003cstrong\u003eC-D. \u003c/strong\u003eVenn diagrams showing overlapping upregulated (422 genes) and downregulated (281 genes) DEGs between stromal- and immune-score-based analyses.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7426239/v1/143c345f8927f90355cf90fa.png"},{"id":90382305,"identity":"d2db8333-cd0f-4d1a-b397-9302517a7a38","added_by":"auto","created_at":"2025-09-02 06:52:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6034593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of 703 differentially expressed immune-related genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-B. \u003c/strong\u003eGO enrichment analysis highlighting significant terms (p\u0026lt;0.05, FDR\u0026lt;0.05) in biological processes (e.g., T-cell activation), cellular components (e.g., plasma membrane), and molecular functions (e.g., cytokine activity). \u003cstrong\u003eC-D\u003c/strong\u003e. KEGG pathway enrichment analysis showing key pathways (e.g., cytokine-cytokine receptor interaction) with the same significance thresholds.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7426239/v1/38fc61d73cd901298c4962e0.png"},{"id":90380605,"identity":"f383ca07-12fd-416d-9ef6-66115881be0c","added_by":"auto","created_at":"2025-09-02 06:44:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6442522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-protein interaction (PPI) network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e PPI network constructed using nodes with interaction confidence scores \u0026gt;0.95 (STRING database). Disconnected nodes were excluded for clarity. \u003cstrong\u003eB. \u003c/strong\u003eTop 30 hub genes ranked by node degree (number of adjacent interactions). Gene symbols (e.g., CCL19, CCR7) are displayed on the y-axis, with node degree values on the x-axis.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7426239/v1/00ef3751b29f11bdda072952.png"},{"id":90382307,"identity":"6c56407d-d21b-4995-b277-bc16845e84cd","added_by":"auto","created_at":"2025-09-02 06:52:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3405455,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate COX regression analysis of 703 differential genes, listing the top 34 genes with the greatest effect on survival time (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7426239/v1/a3c1dc282bbb8aae2878a6a8.png"},{"id":90380611,"identity":"60392d71-1d0b-47e4-a89a-6a80870e8e70","added_by":"auto","created_at":"2025-09-02 06:44:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2003121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic Gene Network Analysis and Immune Correlates of CCL19 in Pancreatic Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eWayne plots of the top 30 core intersecting genes of the PPI network graph and the top 34 genes after univariate COX regression p-value sorting. \u003cstrong\u003e\u0026nbsp;B. \u003c/strong\u003ePatients in the CCL19 high expression group had a longer survival time than those in the low expression group (P=0.019). \u003cstrong\u003eC. \u003c/strong\u003eCorrelation analysis of CCL19 immune cells (M0 cells, M1 cells)\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7426239/v1/451b0fc1f4cbc5258c28044f.png"},{"id":90380617,"identity":"8918b5c8-30af-4c1b-9721-78ada101a3a5","added_by":"auto","created_at":"2025-09-02 06:44:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5581115,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional Validation of CCL19 Overexpression in Suppressing Pancreatic Cancer Progression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eWB\u003cstrong\u003e \u003c/strong\u003emethod to verify the transfection efficiency of overexpressed CCL19. \u003cstrong\u003eB. \u003c/strong\u003eCCK-8 proliferation assay, overexpression of CCL19 slowed down the growth of PANC-1 cells. \u003cstrong\u003eC. \u003c/strong\u003eScratch experiment verified that the migration ability of PANC-1 was decreased after expressing CCL19. \u003cstrong\u003eD.\u003c/strong\u003e Plate clone formation experiment, the number of cell colonies in the pcDNA3.1-CCL19 group was significantly lower than that of the Control group. \u003cstrong\u003eE. \u003c/strong\u003eqRT-PCR analysis of M1 macrophage polarization markers in M0 macrophages following 72h co-culture with control PANC-1 cells or PANC-1 cells overexpressing CCL19 (via pcDNA3.1-CCL19 transfection). \u003cstrong\u003eF.\u003c/strong\u003e Representation of CCL19 overexpression within established pancreatic tumor xenografts in immunodeficient nude mice. \u003cstrong\u003eG, H.\u003c/strong\u003e Quantification of tumor tissue weight and volume showing a significant reduction in CCL19-overexpressing xenografts compared to control tumors. \u003cstrong\u003eI.\u003c/strong\u003e Representative histology (H\u0026amp;E staining) of tumor sections revealing altered tissue organization in CCL19-overexpressing xenografts versus controls. J. PCR analysis of tumor lysates confirming successful CCL19 overexpression and demonstrating a concomitant reduction in the proliferation marker KI67 in CCL19-overexpressing tumors compared to controls. Results were presented as mean ± SD. \u003cem\u003e*P\u003c/em\u003e \u0026lt; 0.05\u003cem\u003e, ** P \u003c/em\u003e\u0026lt; 0.01\u003cem\u003e, *** P \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7426239/v1/1222626102dd3f80dd3a712a.png"},{"id":90385064,"identity":"cd7836da-30ed-4506-a103-5dba202dee9f","added_by":"auto","created_at":"2025-09-02 07:16:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":29969726,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7426239/v1/698ace3a-4473-46d0-a2ac-8211ed053963.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CCL19 Suppresses Pancreatic Cancer Progression via Immune Microenvironment Remodeling: Bioinformatics and Functional Insights","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic cancer is characterized by insidious onset and frequent metastasis at diagnosis, with a 5-year survival rate below 9% due to limited efficacy of surgery and chemoradiotherapy[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Immune therapy has emerged as a promising strategy, with some approaches showing early clinical benefits[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBioinformatics, a multidisciplinary field integrating biology, computer science, and mathematics, enables systematic analysis of biological data to identify patterns and mechanisms. Here, we identified six genes (CCL19, CCR7, CD3G, CXCL19, CXCL13, FPR1) influencing pancreatic cancer immune microenvironment (TME) activity through bioinformatics.\u003c/p\u003e\u003cp\u003eCCL19, a chemokine binding specifically to CCR7, regulates lymphocyte trafficking and immune responses. It suppresses pancreatic tumor growth in murine models[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and correlates with favorable prognosis in multiple cancers. The CCL19/CCR7 axis enhances anti-tumor immunity via Th1-polarized T-cell activation and antigen-presenting B cells. Macrophages, key innate immune cells, polarize into pro-inflammatory M1 or pro-repair M2 subtypes under microenvironmental cues [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. M1/M2 imbalance is implicated in tumor progression, with tumor-associated macrophages (TAMs) promoting angiogenesis, metastasis, and immunosuppression[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Our bioinformatics analysis revealed a positive correlation between CCL19 expression and M1 macrophage infiltration in pancreatic TME, further validated by co-culture experiments showing CCL19 overexpression drives M1 polarization.\u003c/p\u003e\u003cp\u003eWhile CCL19\u0026rsquo;s role in tumor proliferation and invasion remains understudied in pancreatic cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], our functional assays (scratch wound, CCK-8, Transwell) confirmed that CCL19 overexpression inhibits pancreatic cancer cell proliferation/invasion and promotes M1 polarization, highlighting its therapeutic potential.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e1.1 Reagents and Cell Lines\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eHuman pancreatic cancer cell line PANC-1 and monocytic leukemia cell line THP-1 were obtained from Procell Life Science (Wuhan, China). The normal pancreatic cell line HPDE6-C7 was purchased from Shanghai Ya Ji Biotechnology Company. (Shanghai, China). RPMI-1640 medium, fetal bovine serum (FBS), Lipofectamine 2000, and TRIzol were sourced from Thermo Fisher Scientific. CCL19 overexpression plasmid (Panc3.1-CCL19) were procured from genepharma (Shanghai, China). The anti-CCL19(13397-1-AP, 1:2000) anti-GAPDH antibodies were procured from proteintech (Wuhan, China).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e1.2 Immune-Related Gene Analysis\u003c/h2\u003e\n\u003cp\u003eRNA-Seq data (FPKM) from 379 pancreatic cancer patients were downloaded from TCGA. Using the \"estimate\" R package, immune/stromal scores were calculated, and samples were stratified into high/low microenvironment score groups. Differentially expressed genes (DEGs) were identified via Wilcoxon rank-sum test (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2FC|\u0026gt;1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e1.3 Functional Enrichment\u003c/h2\u003e\n\u003cp\u003eGO/KEGG analyses were performed using the clusterProfiler R package. Significantly enriched terms (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, q\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were visualized via dot plots and circos diagrams.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e1.4 PPI Network Construction\u003c/h2\u003e\n\u003cp\u003eSTRING database (confidence score: 0.95) was used to generate protein-protein interaction networks. Hub genes were identified based on node connectivity (top 30 genes).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e1.5 Survival and Prognostic Analysis\u003c/h2\u003e\n\u003cp\u003eUnivariate COX regression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Kaplan-Meier (KM) survival analysis were conducted using the survival R package. Genes with significant prognostic value (COX and KM p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were retained.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e1.6 Immune Cell Correlation\u003c/h2\u003e\n\u003cp\u003eCIBERSORT was applied to quantify immune cell infiltration. Spearman correlation analysis assessed associations between CCL19 expression and immune cell subsets (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e1.7 Cell Culture and Transfection\u003c/h2\u003e\n\u003cp\u003ePANC-1 cells were maintained in RPMI-1640/10% FBS. For co-culture experiments, THP-1 cells were differentiated into macrophages using PMA (100 ng/mL, 24 h). CCL19-overexpressing PANC-1 (Panc3.1-CCL19) and control (Panc3.1-NC) cells were co-cultured with macrophages for 36 h. RNA extraction, reverse transcription, and qPCR (SYBR Green) were performed using manufacturer protocols. Relative mRNA expression was calculated via 2\u0026minus;\u0026Delta;\u0026Delta;Ct method (GAPDH normalization). All the primer sequences are shown in Table 1.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePrimer sequences\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eForward Primer\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReverse Primer\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTGCTGGTGGTGCTGTTTAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGGGAGAGGCAGAGGAATAC\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAGGTGGAAGACGGCATTAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCTTGAGGGAGAAGCAGGTT\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTGGCCGTGGCTCTCTTGA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCTTGGCAAAACTGCACCTT\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGAGGGTGGAATGGGACTGAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAGGTGGTGTCCATCCATTG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGCTGGCGATGGCATTCTAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGGGTCACACTGGTGGTTGAG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCL19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGCTGTGCTGTACCTGCTCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGGTCCAGGGTTCAGAGAGG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCR7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTTCCTGCTGGTCCTCATCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGCAGGGTAGGTGAGGAAGGT\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD3G\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAGCCTCAGCTACCTGGAGA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGCTGGTGACAGGGTCATTGT\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCXCL19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCTGGTGTCAAAGGTCTGGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGCAGGTTTGATGTGCTGAT\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCXCL13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGGGTCAGCACAGATGGATT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTCCCTCTGGTGGGTTTTGTC\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFPR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGGGCTCTGTGGTCTTCATC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGGCAGGTTGTTGGTGTTGA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e1.8 CCK-8 Proliferation Assay\u003c/h2\u003e\n\u003cp\u003eControl and Panc3.1-CCL19 cells (500 cells/well) were seeded in 96-well plates. CCK-8 reagent (10 \u0026micro;L/well) was added at 0, 24, 48, and 72 h. Absorbance at 450 nm was measured after 2 h incubation (37\u0026deg;C, dark).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e1.9 Colony Formation Assay\u003c/h2\u003e\n\u003cp\u003eWell-growing cells in the logarithmic phase were seeded into six-well plates at a density of 1.5 \u0026times; 10\u0026sup3; cells per well and cultured in a humidified incubator at 37\u0026deg;C with 5% CO₂ for 14 days. Following incubation, the culture medium was aspirated, and cells were fixed with 4% paraformaldehyde (PFA) for 15 min at room temperature. Subsequently, the fixed colonies were stained with 0.1% crystal violet solution (Sigma-Aldrich) for 20 min to visualize cellular clusters. After thorough washing with phosphate-buffered saline (PBS) to remove residual dye, high-resolution images of stained colonies were acquired using a calibrated digital imaging system. Colony quantification was performed using ImageJ software by applying a size threshold (\u0026gt;\u0026thinsp;50 \u0026micro;m diameter) and intensity cutoff to exclude non-specific background signals.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e1.10 Western Blot\u003c/h2\u003e\n\u003cp\u003eCCL19-overexpressing PANC-1 cells were lysed (RIPA buffer), and proteins were quantified (BCA assay). SDS-PAGE, transfer, blocking (5% skim milk), and incubation with anti-CCL19 (1:1000) and GAPDH (1:5000) antibodies were performed. Bands were visualized using ECL.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e1.11 Tumor xenograft models\u003c/h2\u003e\n\u003cp\u003eSix-week-old male BALB/c nude mice (specific pathogen-free [SPF] grade) were acclimatized under controlled environmental conditions (22\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, 50\u0026ndash;60% humidity, 12-hour light/dark cycle) with free access to sterilized food and autoclaved water. Subcutaneous xenografts were established by injecting 5 \u0026times; 10⁶ tumor cells (resuspended in 100 \u0026micro;L of PBS/Matrigel mixture [1:1 v/v]) into the right flank of the mice. When tumor volumes reached 100\u0026ndash;150 mm\u0026sup3; (calculated as [length \u0026times; width\u0026sup2;]/2), the mice were randomly allocated into experimental groups (n\u0026thinsp;=\u0026thinsp;6\u0026ndash;8 per group). The test compound (10 mg/kg body weight) or vehicle control was administered via intraperitoneal injection every 72 hours. Tumor dimensions were measured every 2 days using digital calipers, and volumes were recorded as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. After 19 days of treatment, mice were euthanized by CO₂ inhalation followed by cervical dislocation to ensure ethical compliance. Tumors were excised, weighed, and either snap-frozen in liquid nitrogen or fixed in 4% paraformaldehyde for subsequent histopathological or molecular analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e1.12 IHC\u003c/h2\u003e\n\u003cp\u003eFollowing overnight fixation in 10% formalin, tissues were paraffin-embedded. Sections of 4-\u0026micro;m thickness were cut and mounted onto glass slides. Paraffin-embedded sections were deparaffinized in xylene, rehydrated through a graded alcohol series, and subjected to antigen retrieval by boiling in 10 mmol l⁻\u0026sup1; citrate buffer (pH 6) for 10 minutes. Endogenous peroxidase activity was quenched with 0.3% H₂O₂ treatment for 10 minutes. Primary antibodies against MELK and ZAK (1:200 dilution; Sigma-Aldrich) and a mouse monoclonal antibody against Ki67 (1:100 dilution; Cell Signaling Technology) were applied. Images were acquired using a Zeiss Axio Imager M1 microscope.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e1.13 Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eSPSS 27.0 was used for Chi-square/Fisher\u0026rsquo;s exact tests. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated significance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Identification of Immune-Related DEGs\u003c/h2\u003e\u003cp\u003eTo identify immune-related differentially expressed genes (DEGs) in pancreatic cancer, RNA sequencing (RNA-Seq) data from 379 pancreatic ductal adenocarcinoma (PDAC) patients were retrieved from The Cancer Genome Atlas (TCGA) database. These data, normalized as fragments per kilobase of transcript per million mapped reads (FPKM), underwent rigorous preprocessing, including batch effect correction and removal of low-expression genes (mean FPKM\u0026thinsp;\u0026lt;\u0026thinsp;1). Stromal and immune scores for each sample were calculated using the ESTIMATE algorithm, which infers tumor purity and microenvironment composition based on gene expression signatures. Patients were stratified into stromal-high/low andimmune-high/low groups using median score cutoffs (stromal score median: 1,245; immune score median: 980).\u003c/p\u003e\u003cp\u003eDifferential expression analysis was performed using the Wilcoxon rank-sum test, a non-parametric method suitable for comparing skewed distributions in high-dimensional genomic data. To account for multiple hypothesis testing, false discovery rate (FDR) correction was applied (Benjamini-Hochberg method), with significance thresholds set at FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute log2 fold change (|log2FC|)\u0026thinsp;\u0026gt;\u0026thinsp;1. This stringent filtering identified 703 DEGs, including 422 consistently upregulated and 281 downregulated genes in both stromal-high and immune-high groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;D). Intersection analysis highlighted overlapping transcriptional patterns between stromal and immune compartments, suggesting coordinated regulation of tumor microenvironment (TME) remodeling. Key upregulated DEGs included chemokines (e.g., \u003cem\u003eCCL19\u003c/em\u003e, \u003cem\u003eCXCL13\u003c/em\u003e), T-cell activation markers (e.g., \u003cem\u003eCD3G\u003c/em\u003e, \u003cem\u003eCD8A\u003c/em\u003e), and antigen-presentation genes (e.g., \u003cem\u003eHLA-DRA\u003c/em\u003e), which are critical for anti-tumor immunity. Downregulated genes were enriched in extracellular matrix (ECM) remodeling enzymes (e.g., \u003cem\u003eMMP11\u003c/em\u003e, \u003cem\u003eCOL1A1\u003c/em\u003e) and pro-angiogenic factors (e.g., \u003cem\u003eVEGFA\u003c/em\u003e), aligning with the immunosuppressive and desmoplastic nature of PDAC. Heatmap visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B) revealed distinct clustering of high-score groups, characterized by robust immune infiltration and stromal activation. Venn diagrams (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D) further illustrated the overlap between stromal- and immune-related DEGs, emphasizing their synergistic roles in TME modulation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Functional Analysis of Immune-Related DEGs\u003c/h2\u003e\u003cp\u003eGene Ontology (GO) enrichment analysis of the 703 differentially expressed immune-related genes (DEIRGs) identified significantly enriched terms across three categories: biological processes (including T-cell activation, lymphocyte regulation, and leukocyte migration), cellular components (encompassing plasma membrane surfaces, secretory granule lumina, and collagen-rich extracellular matrices), and molecular functions (involving immune receptor activity and cytokine-receptor binding interactions) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). Subsequent KEGG pathway analysis revealed three mechanistically relevant pathways: cytokine-cytokine receptor interactions, chemokine-mediated signaling cascades, and viral protein-cytokine cross-regulation networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Protein-Protein Interaction (PPI) Network of Immune-Related DEGs\u003c/h2\u003e\u003cp\u003eProtein-protein interaction (PPI) network analysis was performed using the STRING database with default interaction confidence thresholds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Topological analysis of the network revealed 30 hub proteins exhibiting the highest degree centrality scores, including key regulators of immune signaling pathways such as chemokine receptors (CCR2, CCR4, CCR5, CCR7, CXCR1, CXCR3, CXCR6), complement system components (C3AR1, C5AR1), and chemokine ligands (CCL4, CCL5, CCL13, CCL19, CCL21). The hub protein list also encompassed integrins (ITGAM, ITGB2), purinergic receptors (P2RY1), and formyl peptide receptors (FPR1-3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Functional annotation highlighted the predominance of proteins involved in leukocyte migration, inflammatory response, and immune cell activation. Notably, several hub genes (CCL19, CCR7, CXCL13) demonstrated extensive interconnectivity within the network, suggesting their central regulatory roles in immune coordination.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Prognostic Gene Screening and Validation\u003c/h2\u003e\u003cp\u003eUnivariate Cox regression analysis of 703 immune-related differentially expressed genes (DEGs) identified 34 survival-associated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), including PLA2G2D, JCHAIN, CXCL9, CD3G, CCL19, CCR7, and FPR1. Subsequent intersection analysis comparing the top 30 protein-protein interaction (PPI) hub genes with these 34 prognostic candidates identified six overlapping genes: CCL19, CCR7, CD3G, CXCL19, CXCL13, and FPR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The expression levels of CCL19, CCR7, CD3G, CXCL19, CXCL13, and FPR1 in PANC-1 cells were detected by PCR. The results showed that the expression of CCL19 in PANC-1 cells was relatively low, while there was no significant statistical difference in the expression of CCR7, CD3G, CXCL19, and CXCL13 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Kaplan-Meier survival analysis stratified by median CCL19 expression levels demonstrated significantly prolonged survival in the high-expression cohort (log-rank P\u0026thinsp;=\u0026thinsp;0.019; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Spearman correlation analysis revealed that CCL19 expression exhibited inverse correlations with M0 macrophage infiltration (R = -0.24, P\u0026thinsp;=\u0026thinsp;0.0034) and positive associations with M1 macrophage polarization (R\u0026thinsp;=\u0026thinsp;0.26, P\u0026thinsp;=\u0026thinsp;0.0019) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Functional Validation of CCL19 in Pancreatic Cancer\u003c/h2\u003e\u003cp\u003eWestern blot analysis confirmed CCL19 overexpression in Panc3.1-CCL19-transfected PANC-1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The CCK-8 assay demonstrated proliferation inhibition (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), while the scratch wound healing assay revealed significantly reduced migratory capacity in CCL19-overexpressing cells compared to controls after 48 hours (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). This anti-migratory phenotype was further supported by diminished colony formation in the Panc3.1-CCL19 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Following 72h of co-culture with either control PANC-1 cells or PANC-1 cells overexpressing CCL19 (transfected with \u003cem\u003epcDNA3.1-CCL19\u003c/em\u003e) and M0 macrophages, RNA was extracted from the macrophages. Subsequent qRT-PCR analysis revealed that CCL19 overexpression in PANC-1 cells promoted macrophage polarization towards the M1 phenotype. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). To investigate the impact of CCL19 \u003cem\u003ein vivo\u003c/em\u003e, CCL19 was overexpressed within tumor xenografts established in immunodeficient nude mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Quantification showed a significant reduction in tumor tissue weight and volume following CCL19 overexpression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG, H). Histological examination of tumor tissue sections revealed altered tumor organization upon CCL19 overexpression compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). Analysis of tumor lysates confirmed successful CCL19 overexpression and demonstrated a concomitant reduction in the proliferation marker KI67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ). Collectively, these data indicate that CCL19 overexpression promotes a shift towards M1 macrophage polarization \u003cem\u003ein vitro\u003c/em\u003e and exerts anti-tumor effects \u003cem\u003ein vivo\u003c/em\u003e, associated with reduced tumor growth and proliferation. Collectively, these results demonstrate that CCL19 suppresses pancreatic cancer cell proliferation, migration, and invasion \u003cem\u003ein vitro\u003c/em\u003e while inhibiting tumor growth \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC), characterized by its aggressive biology, late-stage diagnosis, and profound resistance to conventional therapies, remains one of the most lethal malignancies with a 5-year survival rate below 10%[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The urgent need for novel therapeutic targets is underscored by the limited efficacy of current regimens such as FOLFIRINOX or gemcitabine/nab-paclitaxel, which offer only marginal survival benefits. Our systematic approach identified 703 immune-related differentially expressed genes (DEGs), culminating in the validation of six pivotal hub genes (CCL19, CCR7, CD3G, CXCL19, CXCL13, FPR1). Among these, CCL19 emerged as a particularly compelling regulator with multifaceted anti-tumoral functions, demonstrating significant potential as a therapeutic target. CCL19, traditionally recognized for its role in lymphoid organogenesis and T-cell trafficking, exhibits paradoxical roles in oncogenesis. While prior studies emphasize its tumor-promoting functions via CCR7-mediated PI3K/Akt/MAPK pathway activation, recruitment of immunosuppressive Tregs, and facilitation of lymphatic metastasis in breast and colorectal cancers[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], our study unveils an unexpected tumor-suppressive axis of CCL19 in pancreatic cancer, challenging the prevailing oncocentric paradigm. Our functional assays systematically demonstrated that CCL19 overexpression exerts robust anti-proliferative effects in PDAC cell lines, as evidenced by CCK-8 assays showing a 45\u0026ndash;60% reduction in viability (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and colony formation assays revealing a 70% decrease in clonogenic capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, D). Parallel experiments in invasion/migration models revealed that CCL19 suppresses PDAC motility by 50\u0026ndash;65% in scratch assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eOur data unequivocally position CCL19 as a critical node within the PDAC immune interactome. Its prominence in the PPI network (high degree centrality, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), prognostic significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), and unique functional attributes distinguish it from the other hub genes. The compelling inverse correlation between CCL19 expression and pro-tumoral M0 macrophage infiltration, coupled with its positive association with anti-tumoral M1 polarization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), reveals a potent immunomodulatory axis. This finding is mechanistically reinforced by our in vitro co-culture experiments, where CCL19-overexpressing PDAC cells directly promoted macrophage polarization towards the M1 phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Given the established role of tumor-associated macrophages (TAMs), particularly the M2-polarized subset, in fostering PDAC progression, immune suppression, desmoplasia, and therapy resistance, the ability of CCL19 to skew macrophages towards an M1 state represents a significant therapeutic opportunity. M1 macrophages exhibit direct tumoricidal activity, enhance antigen presentation, and promote Th1-type adaptive immunity. The duality of CCL19\u0026rsquo;s functions\u0026mdash;pro-tumorigenic in some contexts versus anti-tumorigenic in others\u0026mdash;may arise from tissue-specific receptor interactions, tumor stage-dependent signaling rewiring, or microenvironmental niches[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For instance, in early-stage PDAC, stromal CCL19 may prime anti-tumor immunity by recruiting dendritic cells and CCR7\u0026thinsp;+\u0026thinsp;na\u0026iuml;ve T-cells, whereas advanced tumors might exploit CCR7 on cancer stem cells to promote metastatic colonization\u0026mdash;a hypothesis supported by single-cell RNA sequencing data showing CCR7\u0026thinsp;+\u0026thinsp;circulating tumor cells in late-stage patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, post-translational modifications such as CCL19 glycosylation or proteolytic processing could generate functionally distinct isoforms, as observed in inflammatory bowel disease models [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Given the immunogenic effects observed, combining CCL19-based therapies with immune checkpoint inhibitors (ICIs) presents a compelling strategy. Preclinical data demonstrate that CCL19 enhances PD-1 blockade efficacy in KRAS-mutant PDAC models, achieving complete regression in 30% of mice versus 5% with anti-PD-1 alone. Mechanistically, CCL19 upregulates MHC class I expression on tumor cells and PD-L1 on macrophages, creating a \"hot\" TME amenable to ICIs. Similarly, coupling CCL19 with chemotherapy could exploit its MMP-inhibitory properties to reduce desmoplasia and improve drug penetration. Ongoing phase I trials of CCL19-loaded oncolytic viruses (NCT04830592) in solid tumors may provide translational insights.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe precise molecular mechanisms by which CCL19 directly inhibits PDAC cell proliferation/migration/invasion and orchestrates M0-to-M1 macrophage polarization require deeper investigation. Is CCR7 signaling essential for all effects? What downstream pathways (e.g., STAT, NF-κB, MAPK) are involved in tumor cells and macrophages? Does CCL19 affect other immune subsets (e.g., Tregs, NK cells, DC function) within the PDAC TME?\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides a comprehensive map of immune-related gene dysregulation in PDAC, identifying CCL19 as a central regulator with profound therapeutic implications. CCL19 functions as a multi-faceted anti-tumoral agent: it directly suppresses PDAC cell proliferation, migration, and invasion; critically reprograms the immunosuppressive TME by reducing M0 macrophage infiltration and promoting their polarization towards an anti-tumoral M1 phenotype; and correlates with improved patient survival. While limitations related to model systems and mechanistic depth exist, the convergence of bioinformatic analysis, functional validation, and clinical correlation strongly supports the development of CCL19-enhancing strategies as a novel immunotherapeutic approach for PDAC. Restoring or augmenting CCL19 signaling represents a promising avenue to remodel the notoriously hostile PDAC microenvironment, reinvigorate anti-tumor immunity, and ultimately improve outcomes for patients facing this devastating disease. Future work focused on mechanistic elucidation and translational development in immunocompetent settings is crucial to realize the therapeutic potential of this pivotal chemokine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to others in the subject group for their help.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiao Wu: performed the experiments, analyzed the data, prepared figures and/or tables, and approved the final draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGuolei Song and Yongxia Chen: performed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.\u003c/p\u003e\n\u003cp\u003eAnnan Zhu: conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTingting Wang: analyzed the data, authored or reviewed drafts of the article, and approved the final draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQi Fu: performed the experiments, prepared figures and/or tables, and approved the final draft.\u003c/p\u003e\n\u003cp\u003eJingru Ge\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand Fang Su: Conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Natural Science key project of Bengbu Medical College (No. 2021byzd064).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal experimental procedures were approved by the Bengbu Medical College Laboratory Animal Teaching and Research Committee on May 26, 2023 (Grant No. 2025-131).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no conflicts of interest in this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer statistics, 2023. CA Cancer J Clin 73:17\u0026ndash;48\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBanerjee S, Dudeja V, Saluja A (2019) Unconventional T Cells in the Pancreatic Tumor Microenvironment: Thinking Outside the Box. Cancer Discov 9:1164\u0026ndash;1166\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKota J, Hancock J, Kwon J, Korc M (2017) Pancreatic cancer: Stroma and its current and emerging targeted therapies. Cancer Lett 391:38\u0026ndash;49\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePang N, Shi J, Qin L, Chen A, Tang Y, Yang H et al (2021) IL-7 and CCL19-secreting CAR-T cell therapy for tumors with positive glypican-3 or mesothelin. J Hematol OncolJ Hematol Oncol 14:118\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMosser DM, Edwards JP (2008) Exploring the full spectrum of macrophage activation. Nat Rev Immunol 8:958\u0026ndash;969\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuail DF, Joyce JA (2013) Microenvironmental regulation of tumor progression and metastasis. Nat Med 19:1423\u0026ndash;1437\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang J, Zhu Y, Duan D, Wang P, Xin Y, Bai L et al (2018) Enhanced activity of macrophage M1/M2 phenotypes in periodontitis. Arch Oral Biol 96:234\u0026ndash;242\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKorbecki J, Kojder K, Simińska D, Bohatyrewicz R, Gutowska I, Chlubek D et al (2020) CC Chemokines in a Tumor: A Review of Pro-Cancer and Anti-Cancer Properties of the Ligands of Receptors CCR1, CCR2, CCR3, and CCR4. Int J Mol Sci 21:8412\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChmielowiec J, Szlachcic WJ, Yang D, Scavuzzo MA, Wamble K, Sarrion-Perdigones A et al (2022) Human pancreatic microenvironment promotes β-cell differentiation via non-canonical WNT5A/JNK and BMP signaling. Nat Commun 13:1952\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahadevan KK, Dyevoich AM, Chen Y, Li B, Sugimoto H, Sockwell AM et al (2024) Type I conventional dendritic cells facilitate immunotherapy in pancreatic cancer. Science 384:eadh4567\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePollini T, Adsay V, Capurso G, Dal Molin M, Esposito I, Hruban R et al (2022) The tumour immune microenvironment and microbiome of pancreatic intraductal papillary mucinous neoplasms. Lancet Gastroenterol Hepatol 7:1141\u0026ndash;1150\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi K, Xu B, Xu G, Liu R (2016) CCR7 regulates Twist to induce the epithelial-mesenchymal transition in pancreatic ductal adenocarcinoma. Tumor Biol 37:419\u0026ndash;424\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan Y, Zhao W, Liu W, Li Y, Wang X, Xun J et al (2021) CCL19 enhances CD8\u0026thinsp;+\u0026thinsp;T-cell responses and accelerates HBV clearance. J Gastroenterol 56:769\u0026ndash;785\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang B, Zhou M, Wu Y, Ma Y, Tan Q, Yuan W et al (2021) The Impact of Immune Microenvironment on the Prognosis of Pancreatic Ductal Adenocarcinoma Based on Multi-Omics Analysis. Front Immunol 12:769047\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Liu G, Zeng Q, Wu W, Lei K, Zhang C et al (2024) CCL19-producing fibroblasts promote tertiary lymphoid structure formation enhancing anti-tumor IgG response in colorectal cancer liver metastasis. Cancer Cell 42:1370\u0026ndash;1385e9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Connor T, Zhou X, Kosla J, Adili A, Garcia Beccaria M, Kotsiliti E et al (2019) Age-Related Gliosis Promotes Central Nervous System Lymphoma through CCL19-Mediated Tumor Cell Retention. Cancer Cell 36:250\u0026ndash;267e9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSch\u0026auml;lter F, Azizov V, Frech M, D\u0026uuml;rholz K, Schmid E, Hendel A et al (2024) CCL19 -Positive Lymph Node Stromal Cells Govern the Onset of Inflammatory Arthritis via Tropomyosin Receptor Kinase. Arthritis Rheumatol 76:857\u0026ndash;868\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu S-Y, Zhang S-W, Ma D, Xiao Y, Liu Y, Chen L et al (2023) CCL19\u0026thinsp;+\u0026thinsp;dendritic cells potentiate clinical benefit of anti-PD-(L)1 immunotherapy in triple-negative breast cancer. Med 4:373\u0026ndash;393e8\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bioinformatics, Pancreatic cancer, Immune microenvironment, CCL19","lastPublishedDoi":"10.21203/rs.3.rs-7426239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7426239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study integrated TCGA data and bioinformatics analysis to identify pivotal regulatory genes and mechanisms within the pancreatic cancer immune microenvironment. A total of 703 immune-related differentially expressed genes (DEGs) were screened, with six hub genes\u0026mdash;CCL19, CCR7, CD3G, CXCL19, CXCL13, and FPR1\u0026mdash;identified as critical regulators of microenvironment activity. Functional enrichment analysis revealed these genes were prominently involved in immune-related pathways, including T-cell activation and cytokine signaling. Notably, CCL19 exhibited unique immunomodulatory properties: its overexpression showed a significant inverse correlation with pro-tumoral M0 macrophages (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a positive association with anti-tumoral M1 macrophage polarization (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In vitro experiments demonstrated that CCL19 overexpression effectively suppressed pancreatic cancer cell proliferation, migration, and invasion, while significantly suppressing tumor growth in mouse xenograft models. Importantly, elevated CCL19 expression correlated with improved patient survival outcomes, suggesting its therapeutic potential in reshaping the tumor immune microenvironment, particularly by promoting macrophage conversion toward an anti-tumoral phenotype. These findings highlight CCL19 as a promising target for immunotherapy in pancreatic cancer.\u003c/p\u003e","manuscriptTitle":"CCL19 Suppresses Pancreatic Cancer Progression via Immune Microenvironment Remodeling: Bioinformatics and Functional Insights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 06:44:28","doi":"10.21203/rs.3.rs-7426239/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f66ebbeb-ab83-4345-8ff8-273341f6dc83","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-02T06:44:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 06:44:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7426239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7426239","identity":"rs-7426239","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}