Hepatocyte-Derived LRG1 Primes the Liver for Metastasis and Impairs Immunotherapy

Hepatocyte-Derived LRG1 Primes the Liver for Metastasis and Impairs Immunotherapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Hepatocyte-Derived LRG1 Primes the Liver for Metastasis and Impairs Immunotherapy wenyu wang, guojie Long, Bing Cheng, yue Jiang, qiufeng Liu, xiaoming Huang, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7522111/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Cellular & Molecular Immunology → Version 1 posted 12 You are reading this latest preprint version Abstract The liver undergoes active remodeling by the primary tumor prior to metastatic spread. However, the mechanisms by which hepatocytes dictate the liver-specific tropism of tumors remain elusive. Here, we identify hepatocyte-derived leucine-rich alpha-2-glycoprotein 1 (LRG1) as a key mediator of liver pre-metastatic niche (PMN) formation. LRG1 remodels the hepatic microenvironment by driving immunosuppressive neutrophils accumulation, impairing effect T cell and dendritic cell function, and enhancing angiogenesis in the liver, thereby fostering a pro-metastatic landscape. Clinically, elevated serum LRG1 correlates with existing or impending liver metastases in patients and mouse models. Hepatocyte-specific ablation of LRG1 dampens pre-metastatic niche formation and significantly reduces metastatic burden in vivo. Hepatic LRG1 induced by tumor-associated inflammation via STAT3, promotes liver metastasis through LRG1-driven neutrophil extracellular trap (NET) formation. Importantly, therapeutic blockade of LRG1 not only suppressed liver metastasis but also reprogrammed the hepatic niche toward an immune-activated state, sensitizing tumors to anti-PD-1 therapy. Collectively, our findings reveal a hepatocyte–LRG1 axis that drives liver pre-metastatic niche remodeling and highlight LRG1 as a promising target for the prevention and treatment of liver metastasis. Health sciences/Oncology/Cancer/Cancer microenvironment Biological sciences/Cancer/Gastrointestinal cancer/Colorectal cancer/Colon cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The liver is one of the most common sites for metastatic dissemination across multiple cancer types, including colorectal cancer, accounting for a significant proportion of cancer-related mortality worldwide 1 . Emerging evidence highlights the critical role of pre-metastatic niche (PMN) formation, a process wherein primary tumors systemically prime distant organs to favor metastatic seeding and outgrowth 2 – 4 . Rather than being a passive recipient, the liver—with its unique vasculature and immune environment—undergoes active remodeling during PMN development 5 . However, the mechanisms by which hepatic cells, particularly hepatocytes, orchestrate this tumor-supportive microenvironment remain poorly defined, hindering targeted therapeutic strategies. Leucine-rich α-2-glycoprotein 1 (LRG1), a secreted glycoprotein, contributes to a wide range of human diseases 6 , including cancers. Elevated LRG1 in tumors correlate with cancer progression, tumor burden, and poor prognosis 7 – 11 . LRG1 is produced by diverse cellular sources. Tumor cell-derived LRG1 has been shown to enhance tumor proliferation 12 , modulate angiogenesis 13 , and, as demonstrated in our previous work, promote epithelial-mesenchymal transition (EMT) in an autocrine manner 14 . Additionally, endothelial cell-derived LRG1 facilitates lung metastasis by increasing the abundance of neural/glial antigen 2 (NG2) + pericytes and modulating the vascular niche 7 . Among all sources, hepatocytes represent the predominant origin of systemic LRG1. This is particularly significant as the liver—a frequent metastatic target—undergoes extensive remodeling in response to primary tumor signals before metastatic colonization. However, the dynamic expression and role of liver-derived LRG1 in this context remains largely unexplored. LRG1 exerts most of its pathogenic effects on the vasculature, yet it remains unclear whether LRG1 also contributes to liver metastasis via non-vascular mechanisms. In particular, whether hepatocyte-derived LRG1 regulates the formation or function of the pre-metastatic niche (PMN), a critical determinant of metastasis organotropism 3 , is completely unknown. Understanding this axis may reveal novel insights into systemic crosstalk between primary tumors and metastatic organs and uncover potential therapeutic targets for metastatic prevention. Here, we investigate the hypothesis that hepatocyte-derived LRG1 serves as a central mediator of PMN formation in the liver, bridging systemic tumor-derived signals to local immunosuppressive and pro-metastatic alterations. Combining clinical observations with mechanistic studies in murine models, we demonstrate that tumor-induced inflammatory cues drive hepatocytes to secrete LRG1, which in turn recruits myeloid cells and triggers NET formation. We further establish the translational potential of targeting this axis, showing that LRG1 blockade not only inhibits liver metastasis but also synergizes with immune checkpoint inhibitors by reshaping the hepatic immune microenvironment. These findings position the hepatocyte-LRG1 pathway as a critical determinant of liver tropism in metastasis and unveil new opportunities for therapeutic intervention. Results Serum LRG1 Predicts Liver Metastasis and is associated with Pre-Metastatic Niche Formation in the Liver To evaluate the association of serological LRG1 with metastasis, we analyzed serum samples from patients with localized or liver metastatic colorectal cancer (CRC), pancreatic ductal adenocarcinomas (PDAC) or gastric cancer (GC). Results demonstrated that patients with liver metastasis exhibited significantly higher serum LRG1 levels (Fig. 1A-C), suggesting its close association with liver metastasis. To assess its possible predictive capacity for liver metastasis, we retrospectively analyzed serum LRG1 levels in patients with early-stage cancers (TNM stage I/II for CRC, and TNM stage I for GC). Individuals who developed liver metastasis within 5 years displayed significantly elevated baseline LRG1 levels (Fig. 1D, E). Survival analysis further revealed that high LRG1 correlated with reduced liver metastasis-free survival (Fig. 1F and 1G). These findings suggest that elevated LRG1 precedes detectable metastasis, implicating its role in pre-metastatic niche formation. To validate this hypothesis, we employed a CT26 murine cecal orthotopic inoculation model (Fig. 1H) by harvesting liver weekly. By day 28, small hepatic metastatic foci were observed in a subset of mice, while no liver metastasis was detected at earlier timepoints (day 21) (Fig. S1A) which was defined as pre-metastatic phase consistent with our previous reports 15 . This was further corroborated by CD11b and fibronectin staining (Fig. 1I-K) and S100a8 , S100a9 , Mmp9 expression (Fig. S1C-E), which were reported to be significantly enriched in pre-metastatic livers 16 . Synchronously with the formation of the liver pre-metastatic niche, serum LRG1 levels exhibited a marked elevation at the pre-metastatic phase and further increased upon metastatic establishment (Fig. 1L). No such phenomenon was observed in sham-operated controls (Fig. S1F-I). Similarly, in the splenic tumor inoculation model, LRG1 serum levels were also elevated during the pre-metastatic phase and progressively increased with hepatic metastasis progression (Fig. 1M-Q, Fig. S1I-K). This trend was further validated in orthotopic pancreatic cancer (KPC) (Fig. 1R and Fig. S2A-C) and melanoma (B16F10) (Fig. 1S and Fig. S2D-F) models, where the liver represents a common metastatic site. To further investigate whether LRG1 directly induces the pre-metastatic niche and subsequent liver metastasis, we overexpressed LRG1 in the liver via hydrodynamic tail vein injection (HTVi) 17 prior to tumor cell splenic inoculation (Fig. 1T). Indeed, LRG1 expression highly induced fibronectin deposition in the liver (Fig. S2G, H). And after day 12 post-inoculation, mice with LRG1 overexpression displayed significantly more hepatic metastatic foci compared to controls (Fig. 1U-V). Collectively, these findings confirm that serum LRG1 levels are closely associated with the liver pre-metastatic microenvironment thereby promoting hepatic metastasis progression. Hepatocyte-Derived LRG1 Drives Liver Metastasis LRG1 is expressed and secreted by hepatocytes 18 , adipocytes 19 , tumor cells 14 , and vascular endothelial cells 7,20 . To identify the source of circulating LRG1 during liver pre-metastatic niche formation, we analyzed LRG1 expression across tissues in orthotopic, intrasplenic and other tumor models (Fig. 2A). LRG1 expression was markedly elevated in the pre-metastatic liver and further increased with tumor dissemination (Fig. 2B, C and Fig. S3A-C) but not in control model (Fig. S3D, E). This is also found in PDAC PMN model (Fig. S3F). Although LRG1 expression increased modestly in other tissues, levels remained significantly lower than in the liver (Fig. S3G). To determine the primary cellular source of LRG1 in the pre-metastatic liver, we further dissociated hepatic tissues and isolated hepatocytes, immune cells (CD45 + ), and endothelial cells (CD31 + ) (Fig. 2D). Hepatocytes exhibited the highest LRG1 expression, which escalated with metastasis (Fig. 2E). This finding was further confirmed by our previous single-cell RNA sequencing (scRNA-seq) data of pre-metastatic liver tissues 15 (Fig. S3H) and IHC staining of LRG1 in pre-metastatic mice models (Fig. S3I-P). Collectively, these results demonstrate that hepatocyte-derived LRG1 is strongly induced during metastasis and tightly associated with pre-metastatic niche establishment. To investigate the role of hepatocyte-derived LRG1 in metastasis, we generated Lrg1 flox/flox ; Alb-Cre (hereafter referred to as Lrg1 ( Δ/ Δ) Hep ) mice to specifically knock out Lrg1 in the liver (Fig. S4A-C) or Lrg1 wt/wt ; Alb-Cre (hereafter referred to as Lrg1 ( +/+ )Hep ) mice, then orthotopically implanted MC38 colon cancer cells (Fig. 2F). Over time (D19, D35), Lrg1 ( +/+ )Hep mice showed progressive increases in hepatic LRG1 expression and serum LRG1 levels, while hepatocyte-specific Lrg1 knockout dramatically suppressed circulating LRG1 elevation (Fig. 2G, 2H and 2I), confirming hepatocytes as the primary source of serum LRG1 during metastatic progression. Although orthotopic MC38 tumors exhibited low hepatic metastasis rates due to excessive primary tumor burden (2/5 by day 35), Lrg1 deletion completely abolished metastasis (Fig. 2J). In the splenic metastasis model (which induces higher liver metastasis frequency), Lrg1 knockout also significantly reduced metastatic burden and tumor size (Fig. 2K-N). Interestingly, the knockout of hepatocyte-derived LRG1 also significantly inhibited the growth of orthotopic tumors (Fig.S4D), which may be due to the interaction between serum LRG1 levels and the orthotopic tumor. This warrants further investigation. These findings demonstrate that hepatocytes serve as the primary source of serum LRG1 during hepatic metastasis progression, and elevated hepatocyte-derived LRG1 critically drives liver metastasis formation. Hepatic LRG1 Promotes Pre-Metastatic Niche Formation in the liver Hepatocyte-specific Lrg1 deletion abolished the establishment of a tumor-induced pre-metastatic niche, manifested by the absence of fibronectin deposition and reduced myeloid cell infiltration (Fig. 3A, 3B). To validate the impact of LRG1-mediated PMN formation on liver metastasis, we induced liver PMN via orthotopic cecal implantation of MC38 in Lrg1 ( +/+ )Hep and Lrg1 ( Δ/ Δ) Hep mice. Fourteen days later, MC38-luciferase (MC38-luc) cells were intrasplenically injected to assess liver metastasis (Fig. 3C). Primary tumor-induced PMN significantly promoted MC38-luc liver metastasis, whereas hepatocyte Lrg1 KO dramatically reversed this phenomenon (Fig. 3C, D), indicating LRG1-induced PMN is essential for liver metastasis. To define the impact of hepatocyte-specific Lrg1 KO on liver PMN formation, we performed single-cell RNA sequencing (scRNA-seq) on liver tissues from healthy and orthotopic MC38 -bearing mice (day 19, PMN model) of both Lrg1 ( Δ/ Δ) Hep and Lrg1 ( +/+ )Hep genotypes (Fig. 3E). After quality control, 75,053 cells were included for further analysis. 13 major cell populations were identified, including immune cells (B cells, DC1, DC2, macrophages, monocytes, neutrophils, NK cells, pDCs, and T cells), hepatocytes, cholangiocytes, endothelial cells, and fibroblasts (Fig. 3F, Fig. S5A) 21 . Immunosuppression is a key feature of PMN 2 . Within the pre-metastatic liver, myeloid cell proportions increased while lymphoid cells (T/NK, B cells) decreased (Fig. 3F, S5B). Neutrophils showed the most dramatic abundance change, increasing from 2.89% in healthy Lrg1 ( +/+ )Hep mice to 31.68% in Lrg1 ( +/+ )Hep -PMN mice. This increase was significantly reversed to 10.49% in Lrg1 ( Δ/ Δ) Hep mice (Fig. S5B), further validated by flow cytometry (Fig. S5C, D). Pseudotime analysis revealed that Lrg1 ( +/+ )Hep -PMN livers had a higher proportion of neutrophils at an early developmental stage compared to control mice, which was also diminished upon Lrg1 deletion (Fig. 3G). These early neutrophils highly expressed genes associated with differentiating bone marrow–derived neutrophils, such as Camp , Ltf , Ngp , and Chil3 (Fig. S5E) 22,23 . Since immature neutrophils are often more immunosuppressive, we next analyzed the expression of immunosuppressive genes enriched in myeloid-derived suppressor cells (MDSCs), including Wfdc17 , Ifitm1 , Cd14 , Prok2 , Nos2 , Cebpb , Stfa2 , and Asprv1 24-31 . These genes were significantly higher along the neutrophil pseudotime trajectory in tumor-bearing Lrg1 ( +/+ )Hep mice compared to Lrg1 ( Δ/ Δ) Hep counterparts (Fig. 3H). MDSCs are pathologically activated neutrophils and monocytes with strong immunosuppressive capacity 32 . An established MDSC signature score 33 was significantly elevated in neutrophils from Lrg1 ( +/+ )Hep -PMN mice but not in Lrg1 ( Δ/ Δ)Hep -PMN mice (Fig. 3I). Notably, inducible nitric oxide synthase (iNOS, encoded by Nos2 ), known to suppress T cell function 34-37 , was highly expressed by immunosuppressive neutrophils to promote metastasis 30,38 . iNOS + MPO + neutrophil infiltration gradually increased in the liver following metastatic progression in Lrg1 ( +/+ )Hep mice but was also dampened upon Lrg1 KO (Fig. 3J). Correspondingly, CD8+ T cell proportions decreased but PD1 + in CD8 + T cells increased (Fig. S5F, G). Similarly, the MDSC-related immunosuppressive signature in monocytes was elevated in tumor-bearing livers and attenuated by Lrg1 KO (Fig. S5H). Moreover, all dendritic cell (DC1, DC2 and pDC) subsets, responsible for antigen presentation, were reduced in tumor-bearing Lrg1 ( +/+ )Hep mice (Fig. S5B). Tolerogenic DCs promote antigen-specific tolerance by dampening T cell responses and inducing pathogenic T cell exhaustion and regulatory T cells 39,40 . Functional analysis revealed an increase in tolerogenic DC–related genes in Lrg1 ( +/+ )Hep -PMN livers, which was reversed by Lrg1 deletion (Fig. S5I). Angiogenesis, primarily mediated by endothelial cells, is another key feature of PMN formation. Gene ontology analysis showed enrichment of angiogenesis- and inflammation-related pathways in endothelial cells from tumor-bearing Lrg1 ( +/+ )Hep mice (Fig. S5J). Interestingly, the upregulation of genes involved in angiogenesis ( Hgf , Rhob , Lrg1 , Ets1 , Il1a ) 20,41-44 and inflammation ( Il1r1 , Socs3 ) 45,46 in tumor-bearing mice were relieved when hepatic Lrg1 was knocked out (Fig. S5J, K). Collectively, these findings demonstrate that hepatocyte-derived LRG1 responds to the presence of primary colorectal tumors by reshaping the liver immune microenvironment to support tumor metastasis. LRG1 directs NET formation of neutrophils Single-cell RNA-seq analysis comparing neutrophils from the pre-metastatic liver microenvironment with control neutrophils revealed that, during niche formation, pathways related to pathogen response and chemotaxis were highly upregulated in neutrophils, indicating an inflammatory-activated state (Fig. S6A). Consistent with this, RNA sequencing of bulk liver tissue demonstrated enrichment of neutrophil extracellular trap formation prior to metastasis (Fig. 4A; Fig. S6B). NET formation is closely linked to tumor colonization and metastatic seeding 47-49 . We then examined human liver metastasis specimens and found that LRG1 expression in the liver correlated positively with the NETosis markers (Fig. 4B, 4C). Further, single-cell data comparing Lrg1 ( +/+ )Hep and Lrg1 ( Δ/ Δ) Hep mice showed that deletion of Lrg1 substantially attenuated the upregulation of inflammatory genes in neutrophils during pre-metastatic niche establishment (Fig. 4D), demonstrating that liver-derived LRG1 is essential for neutrophil inflammatory activation. In both orthotopic and splenic injection mouse models of liver metastasis, Lrg1 ( +/+ )Hep mice accumulated NETs progressively, whereas Lrg1 knockout almost completely abolished NET deposition in the liver (Fig. 4E, 4F; Fig. S6C, S6D). Similarly, Lrg1 deletion prevented the increase of circulating NETs during metastatic progression (Fig. 4G). To assess whether LRG1 directly acts on neutrophils, we treated primary human neutrophils with recombinant LRG1. LRG1 significantly enhanced neutrophil chemotaxis (Fig. 4H) and induced NETosis—a process reversed by an inhibitor of PAD4 (a key enzyme in NET formation) (Fig. 4I, 4J). Likewise, LRG1 induced NETosis in differentiated HL60 cells (neutrophil-like) (Fig. S6E), and mouse hepatocytes overexpressing LRG1 triggered NET formation in co-cultured mouse neutrophils (Fig. S6F, S6G). Reported LRG1 receptors include TGFBRⅡ 20 , Endoglin 20 , ADGRL2 50 , and EGFR 12,51 . Single-cell expression profiling of neutrophils revealed that only Tgfbr2 was appreciably expressed (Fig. S6H), suggesting that LRG1-induced NETosis might be mediated via TGFBRⅡ. Indeed, the TGFBRⅡ inhibitor SB431542 significantly blocked LRG1-triggered NET formation in both donor neutrophils and dHL60 cells (Fig. 4K, 4L, 4M). Genetic ablation of TGFBR2 or pharmacological inhibition of downstream AKT signaling similarly suppressed LRG1-driven NETosis (Fig. S6I–S6M). LRG1-induced NETosis is responsible for liver metastasis NETosis has been shown to facilitate tumor cell adhesion and migration. To test whether LRG1-induced NETs enhance tumor cell motility, we performed transwell assays with colorectal cancer cell lines. Co-culture with neutrophils supplied with LRG1 markedly increased migration of DLD1 and HCT116 cells, an effect that was reversed by DNase treatment to degrade NET DNA (Fig. S7A–S7C). Moreover, CCDC25—a receptor on tumor cells that binds extracellular DNA 49 —was required for this enhanced migration, as CCDC25 knockout in DLD1 and HCT116 dramatically reduced their movement under co-culture conditions (Fig. S7D–S7G; Fig. 4N, 4O). In vivo, Lrg1 ( +/ +)Hep and Lrg1 ( Δ/ Δ) Hep mice first received orthotopic MC38 implants to establish a pre-metastatic niche, then were injected intrasplenically with luciferase-tagged MC38-luc cells. Prior to injection, mice were assigned to control, anti-Ly6G antibody (neutrophil depletion), or DNase (NET depletion) groups. Longitudinal bioluminescence imaging showed that Lrg1 ( +/ +)Hep controls developed the heaviest liver metastatic burden, while neutrophil depletion and NET degradation both reduced metastasis; LRG1-deficient mice exhibited minimal liver metastases across all conditions (Fig. 4P, 4Q; Fig. S7H). These results confirm that LRG1-driven liver metastasis depends on its ability to induce neutrophil NETosis. Tumor-associated inflammation promotes the expression of LRG1 in hepatocytes by IL6/STAT3 Tumor-derived secreted factors play pivotal roles in shaping the pre-metastatic niche 3,4 . To determine how LRG1 was upregulated in the presence of tumor burden, we co-cultured mouse AML12 hepatocytes with various tumor cell lines (CT26, MC38, KPC, 4T1, B16F10) or treated them with mouse serum from pre-metastatic or metastatic mice. Only serum from pre-metastatic and metastatic mice significantly increased Lrg1 mRNA and protein level in AML12 cells (Fig. 5A, 5B), indicating that tumor-associated systemic changes , rather than tumor cell-derived factors, drive LRG1 induction. Cytokine array analysis of mouse serum from orthotopic and splenic models revealed 5 common elevated cytokines during metastasis including IL6, G-CSF, CXCL13, CCL12, and TIMP1 (Fig. 5C; Fig. S8A). Among these, only IL6 robustly upregulated hepatocyte Lrg1 transcription and secretion when added individually to AML12 or mice hepatocytes cultures (Fig. 5D–5G). Combinatorial cytokine experiments confirmed that IL6 was indispensable for LRG1 induction, whereas removal of other factors had minimal impact (Fig. 5H, 5I). Furthermore, neutralization of IL6 with antibody or blockade of IL6R with tocilizumab abrogated LRG1 upregulation (Fig. 5J, 5K). In vivo, serum IL6 levels and hepatic phospho-STAT3 (Tyr705) increased during metastatic progression (Fig. 5L–5N). Analysis of colorectal cancer patient sera demonstrated a positive correlation between IL6 and LRG1 levels (Fig. 5O). Finally, in both Lrg1 ( +/+ )Hep and Lrg1 ( Δ/ Δ)Hep mice, hydrodynamic tail vein overexpression of Il6 followed by splenic MC38 injection showed that IL6 greatly promoted liver metastasis (Fig. 5P, 5Q), an effect that was significantly blunted in the absence of hepatocyte Lrg1 (Fig. 5P-S; Fig. S8B–S8D). Targeting LRG1 diminishes liver metastasis and promotes ICB efficacy on metastatic tumor To evaluate LRG1 as a therapeutic target, we administered anti-LRG1 antibody to mice undergoing splenic MC38 injection (Fig. 6A). Antibody treatment effectively abolished LRG1 elevation (Fig. S9A), prevented NETs formation in liver (Fig. S9B, S9C) and significantly reduced both the number and size of liver metastases compared to controls (Fig. 6B–6D). Similarly, AAV8-mediated, hepatocyte-specific Lrg1 knockout prior to metastasis decreased metastatic burden (Fig. 6E–G) and orthotopic tumor size (Fig. S9D). Since liver metastases often confer resistance to immune checkpoint blockades 52,53 , we tested whether LRG1 inhibition could sensitize tumors to anti-PD-1 therapy (Fig. 6H). In a model of direct intrahepatic implantation of MC38 cells, combined treatment with anti-LRG1 and anti-PD-1 antibodies synergistically suppressed tumor growth (Fig. 6I, 6J) and liver tumor burden (Fig. 6K–6L). Flow cytometry revealed that dual blockade markedly increased CD8⁺ T-cell infiltration (Fig. 6M, 6N) and the proportion of cytotoxic T cells within metastatic lesions (Fig. 6O). Discussion Our study identifies that hepatocyte-derived LRG1 as a central systemic mediator that links primary tumors to the liver pre-metastatic niche. Clinically, elevated serum LRG1 levels strongly correlate with both existing and future liver metastases across several gastrointestinal malignancies, and high baseline LRG1 predicts poorer liver-metastasis-free survival. Importantly, hepatocyte-derived LRG1 was both necessary and sufficient for promoting liver PMN formation. These findings extend the recognized role of LRG1 beyond local effects on tumor or endothelium, demonstrating that liver parenchymal cells can be co-opted by distant tumors to create a metastasis-permissive microenvironment (Fig.7). Our data position LRG1 squarely in the emerging paradigm of tumor–host crosstalk. LRG1 was first identified as a promoter of pathological angiogenesis via modulation of endothelial TGFβ signaling, and has since been implicated in tumor EMT, growth, and metastasis. Notably, a recent report demonstrated that liver-secreted LRG1 activates HER3 to sustain metastatic colorectal tumors in the liver, highlighting LRG1 as a liver-to-tumor growth signal. While previous studies reported that IL-6/STAT3 signaling regulates LRG1 in tumor cells, our findings demonstrate that IL-6/LRG1 cascade links systemic inflammation-a common feature of advancing tumors-to hepatic PMN establishment. Our work also adds a complementary insight: beyond tumor-intrinsic LRG1 expression, hepatocyte-released LRG1 profoundly remodels the liver immune landscape to favor metastasis. We show that LRG1 recruits and reprograms myeloid cells in the liver. Strikingly, we found that LRG1 directly induces neutrophil extracellular trap (NET) formation. NETosis has emerged as a key enabler of metastasis by trapping tumor cells and fostering growth. This is the first demonstration that a hepatocyte-derived factor can orchestrate neutrophil NETs during PMN formation. Interestingly, previous studies have shown that LRG1 is itself secreted by activated neutrophils-suggesting a feed-forward loop where neutrophil-released LRG1 may further amplify local niche effects. The immunological consequences of LRG1 in the PMN were profound. LRG1-rich pre-metastatic livers harbored large infiltrates of immature neutrophils and inflammatory monocytes with a suppressive MDSC-like gene signature, while T cell populations contracted and became more exhausted. Whether LRG1 directly functions on other immune cells (such as T cells, dendritic cells) remains to be tested, our data clearly show that LRG1 drives an immunosuppressive microenvironment. From a translational perspective, our data suggests significant implications. First, Elevated LRG1 predicted future metastasis in multiple GI cancers in our cohorts, akin to reports that high LRG1 portends poor outcomes in PDAC and other cancers, underscoring its potential as an early, non-invasive biomarker. Integrating LRG1 into diagnostic panels may improve early detection of occult metastasis. Second, LRG1 itself is a promising therapeutic target. Here, anti-LRG1 antibody treatment prevented NET formation and dramatically reduced liver metastases. Importantly, combining LRG1 blockade with PD-1 checkpoint inhibitors had a synergistic effect, unleashing cytotoxic T cells in liver tumors. This is particularly relevant since liver metastases are notoriously resistant to immunotherapy 52,53 . Recent evidence shows that LRG1 inhibition can enhance the efficacy of chemotherapy and checkpoint blockade by normalizing the tumor vasculature 8 . Our data suggest that targeting the LRG1 can reprogram the metastatic niche from “cold” to “hot,” rendering it more susceptible to ICB. Methods Patients and specimens Serum of patients were obtained from BioBank, The Six Affiliated Hospital, Sun Yat-sen University. Human liver metastases paraffin sections were collected from Sixth Affiliated Hospital of Sun Yat-sen University. The samples were used with informed consent under a protocol approved by the Medical Ethics Committee of Sixth Affiliated Hospital of Sun Yat-sen University. Animal experiments Lrg1 -flox mice (Strain NO. T009577) and Alb-Cre mice (Strain NO. T017784) on a C57BL6/J background were purchased from GemPharmatech (Nanjing, China). Lrg1 wt/wt ; Alb-cre (termed Lrg1 (+/+)Hep ), Lrg1 wt/fl ; Alb-cre (termed Lrg1 (+/∆)Hep ), and Lrg1 fl/fl ; Alb-cre (termed Lrg1 (∆/∆)Hep ) were generated by crossing Lrg1 - flox mice and Alb-Cre mice. Genotyping of mice was performed by polymerase chain reaction (PCR) analysis of genomic DNA extracted from mouse tails using the primers (Table S1). For all tumor models, BALB/c or C57BL/6J mice, between 6-10 weeks of age were purchased from Guangdong GemPharmatech unless indicated otherwise. Mice with similar sex, age and weight were randomized before tumor inoculation. To detect the primary source of LRG1 in tumor models, the mice were orthotopically implanted with 1×10 6 CT26 cells or MC38 cells, 5×10 4 KPC cells, 1×10 6 B16F10 cells and intrasplenically implanted with 5×10 4 CT26 cells. The mice were euthanized at the depicted time point and serum, specific organs and cells were collected to detect the expression of LRG1. To detect liver metastases in Lrg1 (+/+)Hep , Lrg1 (+/∆)Hep and Lrg1 (∆/∆)Hep mice, mice were implanted with 1×10 5 MC38 cells and euthanized. To detect the effect of PMN on liver metastases in Lrg1 (+/+)Hep and Lrg1 (∆/∆)Hep mice, mice were first orthotopically implanted with 1×10 6 MC38 cells. Then, the luciferase-labeled MC38-luc cells were intrasplenically implanted (1×10 5 cells for each mouse). Mice were euthanized, and the liver was rapidly harvested for ex vivo BLI. To detect the effect of neutrophils depletion and Dnase I on liver metastases in Lrg1 (∆/∆)Hep mice, the mice were orthotopically implanted with 1×10 6 MC38 cells. On day 14, mice received initial i.p. injection of anti-Ly6G antibody (200 ug/mouse, Bio Xcell, #BE0075) every 3 days and i.p. injection Dnase I(5mg/kg, daily shots for 7 days from day 14, and then maintenance shots every 3 days). On day 40, mice were euthanized, and the liver were rapidly harvested for ex vivo BLI. To detect the therapeutic efficacy of anti-LRG1(C4, sc-390920) in CRC intrasplenic model, mice were i.p. injected with anti-LRG1(2 ug/mouse) 1 day before cancer cell intrasplenic injection, followed by every 3 days injection of the reagents for 21 days. On day 21, The mice were euthanized for detecting liver metastases. To detect the therapeutic efficacy of AAV8-TGB-shLRG1(5’-TGTCCATCTGTCGGTGGAATT-3’) in CRCLM model, mice were i.v. injected with AAV-TGB-shLRG1 1 day before orthotopic injection of MC38. On day 14, MC38-luc cells were intrasplenically implanted, on day 35, mice were euthanized, and the liver were rapidly harvested for ex vivo BLI. To detect the combined effect of the anti-LRG1 and anti-PD-1 immunotherapy, BALB/c mice were uesd and 2.5×10 5 CT26-luc cells in 25 ul PBS were injected into the left main lobe of the mouse liver. On day 5, mice were grouped according to ex vivo BLI and received an initial i.p. injection of anti-LRG1 every 2 days and anti-PD-1(100ug/mouse) every 3 days until completion of the experiment. Mice were given a standard diet, allowed free access to water, and were housed on 12-hour light/dark cycles. All the animal experiments were approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University and conformed to the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health (National Academies Press, 2011) in China. Ex vivo bioluminescence imaging (BLI) For liver metastasis models used in the present study, the luciferase activities in liver were used to monitor liver metastasis progression. Mice were i.p. injected with 100 ul D-luciferin (150 mg/kg) and were anesthetized for ex vivo BLI. The mice were euthanized, and livers were rapidly dissected and placed in a plate filled with 2 ml D-luciferin (150 mg/ml; diluted in PBS) for ex vivo BLI. BLI results were obtained using the Xenogen IVIS system. Light emission from the region of interest was quantified as photons/second/cm2/steradian (p/sec/cm2/sr) through Living Images software. HTVi animal experiment All these experimental procedures were approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University. To overexpress the LRG1 or IL6 in the liver, we performed hydrodynamic injection of plasmid DNAs into tail vein of mice following a previously published protocol. Each mouse received 10% of its body weight of saline containing the 25-50ug plasmid DNAs (pcDNA3.4-mLRG1 or pcDNA3.4-mIL6). Mice were maintained on standard diet and sacrificed at indicated time points. Cell culture CT26 colon adenocarcinoma cells and B16F10 melanoma cells were obtained from American Type Culture Collection. MC38 colon adenocarcinoma cells were purchased from Kerafast. The murine pancreatic tumor KPC cell line was derived from the pancreatic tumors of KrasG12D/+; Trp53R172H/+; Pdx1-Cre C57BL/6 mice. The human CRC cell lines DLD1 and HCT116 were obtained from American Type Culture Collection. The murine hepatocyte AML12 cell line and HL60 cell line were obtained from American Type Culture Collection. CT26 cells were cultured in RPMI-1640 media containing 10% fetal bovine serum and penicillin/streptomycin. MC38, KPC, DLD1 and HCT116 cells were cultured in DMEM with 10% fetal bovine serum and penicillin/streptomycin. AML12 cells were cultured in DMEM/F12 with 10% fetal bovine serum, 1 × ITS(Sigma-Aldrich), 40 ng/ml dexamethasone (Sigma-Aldrich) and penicillin/streptomycin. HL60 cells were cultured in IMDM with 10% fetal bovine serum and penicillin/streptomycin. To diferentiate HL60 cells into neutrophil-like cells, the cells were incubated in culture medium containing 1% DMSO for 7 days. All cells were cultured in a humidified incubator at 37 °C with 5% CO2. RNA extraction and quantitative real-time PCR (qRT-PCR) Total RNA was extracted from cells and tissues using TRIzol Reagent (Invitrogen) and ethanol precipitation. RNA reverse transcription was performed using KAPA SYBR® FAST Universal kit. Then, qPCR was conducted using Roche Light-Cycler 480. Primers are summarized in Table S1. Plasmids construction and lentiviral vector transduction To generate the LRG1 overexpression plasmids, the full-length cDNA of mouse LRG1 were cloned into plenti-CMV vector. And the sgRNA targeting CCDC25 were synthesized and cloned into Lenti-CRISPR-V2-Puro plasmids. For stable transfection, the above constructs were respectively cotransfected with pMD2.G, pRSV-REV and pMDLg/pRRE packaging plasmids into HEK293T cells in accordance with the manufacture’s protocols. Lentiviral vector supernatants were collected and used to knock out CCDC25 in DLD1 and HCT116 cells. Primers are summarized in Table S2. Western blot Cells and tissues were lysed in radioimmunoprecipitation assay (RIPA) buffer (Sigma-Aldrich) containing protease inhibitor (Roche) and quantified using a BCA kit (Thermo Scientific). Proteins were separated by SDS-PAGE and transferred to NC membranes. The membranes were subsequently blocked with milk, followed by incubation with specific primary antibodies against LRG1, H3cit, p-AKT, t-AKT, p-ERK, t-ERK, p-p38, p38, CCDC25 and Actin overnight at 4 °C. The details of antibodies used are listed in table S3. Then, the membranes were washed with TBST and incubated with HRP-conjugated secondary antibodies (1:5000, Sigma). The bands were obtained by chemiluminescence using StarSignal Western ECL Substrate (GeneStar). Immunofluorescence The tissue was firstly fixed in 4% paraformaldehyde, embedded in paraffin and sectioned at 4 μm thickness. Then those paraffin-embedded tissue sections were deparaffinized, rehydrated and microwave antigen retrieved in EDTA buffer. Sections were blocked with 5% BSA for 30min at room temperature. Then those samples were incubated with specific primary antibodies against CD11b, LRG1, MPO and H3cit overnight at 4 °C. After rinsing by PBS, fluorochromeconjugated secondary antibodies were added for incubation 1h at room temperature. Slides were counterstained with DAPI (D1306, Invitrogen). The details of antibodies used are listed in table S3. Observation and photographing were performed with the confocal microscopy Cell Observer (ZEISS, Germany). The analysis of fluorescence images primarily relies on ImageJ. IHC staining Paraffin-embedded tissue sections were deparaffinized with dimethylbenzene, dehydrated in an ethanol gradient, followed by antigen retrieval with EDTA buffer. Next, the tissues were blocked with normal goat serum and incubated with specific primary antibodies against Fibronectin, LRG1 and p-Stat3 overnight at 4 °C. The tissues sections were then incubated with secondary antibodies for 1 h, and positive staining was visualized with a HRP DAB substrate kit and nuclear counterstained with hematoxylin. expression of LRG1 were quantified based on the intensity of staining and the percentage of positive cell. In brief, the proportion of positive cells was estimated and given a score ranging from 1 to 4 (1, less than 5%; 2, 5–25%; 3, 26–50%; 4, > 51%). The average intensity of the positively stained cells was also given a score on a scale from 1 to 4 (1, no staining; 2, weak staining; 3, moderate staining; 4, strong staining). A final IHC score of each tissue was then calculated via multiplying the positive percentage score by the intensity score. For LRG1, MPO, H3cit, CD8a, and GZMb tissue staining was performed with TSA (tyramide signal amplification) according to the manufacturer’s instructions. Slides were counterstained with DAPI (D1306, Invitrogen). The details of antibodies used are listed in table S3 ELISA ELISA kits were used to assay the levels of human LRG1(Ray Bio), human IL6(Boster, EK0410) mouse LRG1(ELK Biotechnology), mouse IL6(Boster, EK0411) in cell culture supernatants or sera according to the manufacturer’s instruction. We detected plasma MPO-DNA using a previously described sandwich ELISA method. Briefly, 96-well microtiter plates were coated with 5 ug/ml anti-MPO monoclonal antibody (R&D, AF3667) as the capturing antibody overnight at 4 °C. After blocking by 1% BSA buffer for 1 h, 50 µl samples were added per well and incubated for 2h at room temperature. Quant-iTTM PicoGreenTM dsDNA Reagent (ThermoFisher, P7589) and kit was used to assay the levels of MPO-DNA following the manufacturer’s instructions.The absorbance was measured using a microplate reader. Tissue dissociation For flow cytometry of liver cells, Single cell suspensions were prepared from freshly excised mouse livers by mechanical trituration and samples were then passed through a 70 mm steel mesh and hepatocytes were isolated from cell suspensions by centrifugation 50g for 5 min. The remaining cells were used for flow cytometry. For flow sorting of liver cells, livers were extracted and minced, hepatocytes were isolated by mechanical trituration and centrifugation. And remaining liver tissues were digested with collagenase II(1mg/ml) and Dnase(100ug/ml) in DMEM medium for 20 min at 37 °C. The cells were then filtered through 70-mm strainers to remove small fragments of undigested tissue for subsequent experiments. Flow cytometry and sorting Prepared single cell suspensions from mouse tissues were first stained with anti-mouse CD16/32 (BioLegend, #101320) to block the IgG Fc receptor, then cells were stained with surface fluorescent antibodies on ice for 30 min. The details of antibodies used are listed in table S3. Flow cytometry was performed on a Beckman CytoFLEX flow cytometer. Isolation and culture of primary mouse hepatocytes Mouse Primary hepatocytes were isolated by liver perfusion medium using a 2-step retrograde procedure. Under terminal anaesthesia, mice underwent a laparotomy, the inferior vena cava was then cannulated, and the superior vena cava was clamped to achieve retro-perfusion of the liver using the portal vein as an outlet. The liver was perfused sequentially with buffer A(HBSS + 0.2 mg/ml EDTA + 1 mg/ml glucose) and then buffer B(HBSS + 0.75 mg/ml collagenase Ⅳ + 0.02 mg/ml Dnase + 1 mg/ml glucose). Post perfusion, the liver capsule was removed, and the liver was gently swirled in PBS to yield a cell suspension. Hepatocytes were collected by three rounds of centrifugation (50g for 3 minutes) and cultured in DMEM/F12 with 10% fetal bovine serum, 1 × ITS(Sigma-Aldrich), 40 ng/ml dexamethasone (Sigma-Aldrich) and penicillin/streptomycin. Neutrophil isolation Human neutrophils were isolated from the peripheral blood of healthy volunteers by density gradient separation using Ficoll(Cytiva, 17544202) and centrifuging at 400 g for 40 min at room temperature. To isolate neutrophils from bone marrow, bone marrow cells from 8 to 12-weeks-old BALB/c mice were harvested in PBS, and the extraction of neutrophils from bone marrow cells was performed using the Mouse neutrophil Isolation Kit (Solarbio)according to the manufacturer’s instruction. Two-chamber neutrophil migration assays Neutrophil migration assays were performed using a Transwell migration assay. Briefly, 5×10 5 freshly isolated neutrophils in RPMI 1640 were added to the upper chamber, and rhLRG1(20ug/ml) added to the lower chamber as the chemoattractant. The migrated cells in the lower chamber were counted after 4 h. In vitro NET analysis To assess NET formation, neutrophils (1×10 6 cells) were seeded on coverslips coated with poly-L-lysine in 24-well plates for 30 min before adding rhLRG1(20ug/ml), PAD4i and SB431542. After 6 to 8 h at 37 °C, neutrophils were fixed with 4% paraformaldehyde (PFA) for 10 min at room temperature, washed twice with PBS and were blocked in PBS containing 2% BSA for 30 min, then incubated with anti-H3cit(1:100, ab5103, abcam) and anti-MPO (10 ug/ml, AF3667, R&D) in blocking buffer overnight at 4 °C. After three washes in PBS, cells were incubated with fluorochrome-conjugated secondary antibodies for 1 h, and then counter stained with DAPI. Observation and photographing were performed with the confocal microscopy Cell Observer (ZEISS, Germany). Transwell migration assays For DLD1 cells and HCT116 cells (5×10 4 ) transwell migration assays. DLD1 cells and HCT116 cells were plated on upper wells for 24h. Then after cell adherence, the medium was replaced by 300 ul serum-free conditioned medium with neutrophils (1×10 5 ), rhLRG1 (20ug/ml) or Dnase (0.25 mg/ml). Complete medium with 10% FBS was added into the bottom as chemotaxis. For cells counting after 48 h, the cells from the upper surface of the membrane were wiped off, and penetrated cells that crossed the membrane were fixed with 4% paraformaldehyde and stained with crystal violet. The number of penetrated cells were counted under a light microscope in three fields of view, and the average number of cells was calculated. Cytokine array analyses The Serum from two CRC models was obtained by centrifugation and stored at −80 °C for cytokine assays. This was performed using the Mouse Inflammation Array GS1 (Raybiotech, Peachtree Corners, GA, USA) following the instruction of the manufacturer (Wayen Biotechnologies Inc., Shanghai, China). RNA-seq Data Analysis Raw RNA-seq sequencing data (FASTQ format) were aligned to the mouse reference genome mm10 using HISAT2 (v2.1.0) with default parameters to ensure alignment accuracy. Exon-aligned uniquely mapped reads were quantified using featureCounts (Subread package v2.0.6) to generate the raw count matrix. Gene expression data were normalized to Transcripts Per Million (TPM), followed by hierarchical clustering analysis and heatmap visualization using pheatmap (v1.0.12). For the public dataset GSE109480 (retrieved from the GEO database), its raw expression matrix underwent identical TPM normalization, and differential gene expression bar plots were generated using ggplot2 (v3.5.0). Single-Cell Sequencing Data Analysis Single-cell sequencing data were processed using the Seurat (v4.2.0) pipeline. Strict quality control was applied: low-quality cells with total UMI counts <1,000 or detected genes 25%. Red blood cells with hemoglobin gene expression (e.g., Hba-a1, Hbb-bt) >1% were excluded. To mitigate doublet interference, DoubletFinder (v2.0.3) was employed to predict doublets. Differential Analysis and Pathway Enrichment Differentially expressed genes (DEGs) were identified using the Wilcoxon rank-sum test via Seurat’s FindMarkers function, focusing on neutrophil subsets with significant upregulation in the Lrg1 (+/+)Hep -PMN and Lrg1 (+/+)Hep -Ctrl group (adjusted p-value 0.5). For these DEGs, Gene Ontology (GO) functional enrichment analysis was performed using clusterProfiler (v4.7.1), with Benjamini-Hochberg-adjusted q-values <0.05 as the significance threshold. The top 20 enriched terms were ranked by enrichment factors. For PMN-MDSC Signature Score, individual cells were scored using the AddModuleScore function, which calculated the average expression levels of selected genes at the single-cell level and subtracted by the aggregated expression of control feature sets. Pseudo-time Analysis We used Monocle3 (v1.3.1) to analyze pseudotime distribution in scRNA-seq data and construct cell trajectories to uncover state transitions within neutrophil populations. During trajectory and pseudotime computation, cells with high differentiation potential predicted by CytoTRACE (v0.3.3) were selected as the root node. Statistical analysis Statistical tests were carried out using GraphPad Prism (v9.5.0). Unless otherwise stated, Experimental data are presented as the mean ± standard deviation (SD) of at least three biologically independent replicates. For comparing parametric data, a two-tailed unpaired Student’s t test was used to determine statistical significance. A p value less than 0.05 was considered statistically significant. Data and code availability Raw data of the murine liver scRNA-seq, bulk RNA-seq of liver have been deposited at GEO. This paper does not report the original code. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Declarations Acknowledgement This work was supported by the National Natural Science Foundation of China (82273359, 82473022 to W.W.; 82003163 to B.C.), Guangzhou Science and Technology Projects (2023B01J1004), the Fundamental Research Funds for the Central Universities, Sun Yat-Sen University (23ptpy145 to B.C.), the Discipline Construction Funding for Pancreatic Hepatobiliary Surgery Department of the Sixth Affiliated Hospital of Sun Yat-Sen University (No. X202102172026091184 to W. P.) and Guangdong Basic and Applied Basic Research Foundation (2024A1515012862 to W. P.). The authors also acknowledge the support from the National Key Clinical Discipline of China. Author contributions W.W., and B.C. conceived the study. W.W., W.P. and B.C. supervised the project, and secured the fundings. G.L. and B.C. contributed to designing and conducting in vitro and in vivo experiments. Y.J. did the bioinformatic analysis and helped with in vivo assay. Q.L., Q.X., C.W., J.C., Y.W., contributed to technical assistance, analysis and mouse models. X.H., X.Q., L.G., D.L., D.Z., Z.Z., D.L., T.M., T.S., Q.T. organized clinical sample collection, patient information and analysis. W.P., Y.C., J.T., R.Z., Q.Y. and L.Z. provided crucial resources or conceptual advice. W.W., and G.L. wrote the manuscript with input from other co-authors. Declaration of Interests B.C., W.W., W.P., and G.L. are inventors of patent applications related to the technology described in this paper. The other authors declare no competing interests. References Tsilimigras, D. I. et al. 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03:05:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7522111/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7522111/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41423-026-01408-9","type":"published","date":"2026-04-10T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91486601,"identity":"9f9d6c1d-c012-4ef3-9546-ac3920a36204","added_by":"auto","created_at":"2025-09-17 04:57:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":638500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerological LRG1 predicts liver metastasis and is associated with pre-metastatic niche formation in liver\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-C) Serological LRG1 levels in patients with or without liver metastasis. (A) Colorectal cancer patients. (B) PDAC patients. (C) Gastric cancer patients. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(D-E) Serological LRG1 levels in TNM stage I-II CRC patients (D) and TNM stage I GC patients (E) with or without subsequent metastasis during follow-up.\u003c/p\u003e\n\u003cp\u003e(F-G) Metastasis-free survival analysis of TNM stage I-II CRC cohort (F) and TNM stage I GC cohort (G).\u003c/p\u003e\n\u003cp\u003e(H-K) Schematic of CRC orthotopic model in Balb/c (H). Representative images of IHC staining of FN and immunofluorescence staining of CD11b (arrows indicate CD11b+ cells) in the liver. Scale bars, 50 μm (I). Relative quantification of FN (J) and quantification of CD11b (K), n=3 mice per time point, and Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(L) ELISA analysis of serum LRG1 levels from CRC orthotopic mouse model on days 7, 14, 21, and 28. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(M-P) Schematic of CRC intrasplenic model in Balb/c (M). Representative images of IHC staining of FN and immunofluorescence staining of CD11b (Arrows indicate CD11b+ cells) in the liver. Scale bars, 50 μm (N). Relative quantification of FN (O) and quantification of CD11b (P), n=3 mice time point. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(Q) ELISA analysis of serum LRG1 levels from CRC intrasplenic model on days 5, 10, 15, and 21. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(R) ELISA analysis of serum LRG1 levels from PDAC orthotopic model on days 5, 10, 15, and 21. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(S) ELISA analysis of serum LRG1 levels from melanoma orthotopic model on days 5, 10, and 15. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(T-V) Schematic of experimental design(T). ELISA analysis of serum LRG1 levels 3 days after HTVi plasmid delivery (U). Representative images and quantification of liver metastases (number and maximum tumor size) in HTVi-LRG1 and HTVi-Ctrl groups(V). Scale bars, 1 cm. Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003eStatistical significance was determined using two-tailed unpaired Student’s t test or log-rank test (F-G).\u003c/p\u003e","description":"","filename":"mainfigures1.png","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/6c7605f8467a282e64fbd472.png"},{"id":91486605,"identity":"9efb2436-e44f-47f5-952e-6329766fc1ee","added_by":"auto","created_at":"2025-09-17 04:57:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":632574,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLRG1 is derived from hepatocytes and promotes CRLM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-C) Quantification of Lrg1 expression in different organs from CRC mouse models. \u003cem\u003eLrg1 \u003c/em\u003eexpression in organs from the CRC orthotopic model on days 7, 14, 21, and 28 (B) or from the CRC intrasplenic model on days 5, 10, 15, and 21, measured by qRT-PCR and normalized to Actin (C). Dots represent individual samples. Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003e(D-E) Western blot analysis of LRG1 expression in hepatocytes, CD45+ cells, and CD31+ cells isolated from liver at indicated time points.\u003c/p\u003e\n\u003cp\u003e(F) Schematic of CRC orthotopic model using \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(∆/∆)Hep\u003c/sup\u003e mice.\u003c/p\u003e\n\u003cp\u003e(G-H) Representative images and quantification of IHC staining for LRG1 in liver. n = 5. Scale bars, 50 μm.\u003c/p\u003e\n\u003cp\u003e(I) ELISA analysis of serum LRG1 levels. n=5. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(J) Representative images and percentage of liver metastases at day 35. n = 5. Scale bars, 1 cm.\u003c/p\u003e\n\u003cp\u003e(K) Schematic of CRC intrasplenic model using \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e, \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/∆)Hep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(∆/∆)Hep \u003c/sup\u003emice.\u003c/p\u003e\n\u003cp\u003e(L) Representative images of liver metastases.\u003c/p\u003e\n\u003cp\u003e(M) Quantification of liver metastasis number.\u003c/p\u003e\n\u003cp\u003e(N) Quantification of maximum tumor size. n = 6-9 per group. Scale bars, 1 cm. Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003eStatistical significance was determined using two-tailed unpaired Student’s t test. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"mainfigures2.png","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/961817d191eee17c6dc5c6c9.png"},{"id":91487932,"identity":"6c05d58d-0a0d-445f-9538-bbbe54fb761e","added_by":"auto","created_at":"2025-09-17 05:05:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":643913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLRG1 promotes the formation of a pre-metastatic niche in the liver\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Representative images and quantification of IHC staining for FN in the liver. n=5. Scale bars, 50 μm. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(B) Representative images and quantification of immunofluorescence staining for CD11b (arrows indicate CD11b+ cells). n = 5. Scale bars, 50 μm. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(C) Schematic of bioluminescent imaging (BLI) assay in \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(∆/∆)Hep\u003c/sup\u003e mice.\u003c/p\u003e\n\u003cp\u003e(D) Representative bioluminescence images and quantification of liver metastases. Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003e(E) Schematic of single-cell RNA-seq analysis of liver tissues from \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(∆/∆)Hep\u003c/sup\u003e mice with orthotopic injection of MC38 or PBS.\u003c/p\u003e\n\u003cp\u003e(F) UMAP plot of liver cells from all groups (left), and cell type proportions in different groups (right).\u003c/p\u003e\n\u003cp\u003e(G-H) Neutrophil pseudotime trajectory: (G) cell density estimates; (H) gene expression dynamics.\u003c/p\u003e\n\u003cp\u003e(I) PMN-MDSC signature score in neutrophils of each group.\u003c/p\u003e\n\u003cp\u003e(J) Representative images and quantification of immunofluorescence staining for MPO (red) and iNOS (green). White arrows indicate MPO+ iNOS+ cells. n = 5. Scale bars, 50 μm. Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003eStatistical significance was determined using two-tailed unpaired Student’s t test. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"mainfigures3.png","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/82916102e178df5e80db6589.png"},{"id":91489415,"identity":"6e87952d-3b7b-49b3-8a3d-2a0a96eac818","added_by":"auto","created_at":"2025-09-17 05:13:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1188044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLRG1 promotes liver metastasis by directing NET formation of neutrophils\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) KEGG pathway analysis from bulk RNA-seq of PMN versus sham liver tissue, highlighting significant changes in NET-related pathways.\u003c/p\u003e\n\u003cp\u003e(B-C)Representative images (B) and quantification (C) of Immunofluorescence staining of MPO (green) and H3cit (red) in liver from \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(∆/∆)Hep\u003c/sup\u003e mice. n=5 per group. DAPI is shown in blue. Scale bars, 100 μm. Dots represent field of view (FOV). Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003e(D)Heatmap of selected genes of enriched pathways in (Figure S6A).\u003c/p\u003e\n\u003cp\u003e(E-F)Representative images (E) and quantification (F) of Immunofluorescence staining of MPO (green) and H3cit (red) in liver from \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(∆/∆)Hep \u003c/sup\u003emice. n=5 per group. DAPI is shown in blue. Scale bars, 100 μm. Dots represent field of view (FOV). Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003e(G)Serological levels of MPO-DNA in mice (from fig 2F). Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003e(H)Migration of human neutrophils recruited by rhLRG1. n=3 independent experiments, Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(I-L) Immunofluorescence analyses of NETs formed by human neutrophils treated with rhLRG1 and/or PAD4i and SB431542 (an inhibitor of TGFβR2). Scale bars, 100 μm. Dots represent field of view (FOV). Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(M)Western blot analysis of the expression H3cit and actin in dHL60 cells after the indicated treatments.\u003c/p\u003e\n\u003cp\u003e(N-O) Transwell migration assays for DLD-1-sgctrl or sgCCDC25 cells treated with rhLRG1 and/or neutrophils. n=3 independent experiments. Scale bars, 50 μm. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(P) As depicted in the schematic (left), liver metastases were determined in \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(∆/∆)Hep \u003c/sup\u003emice treated with or without anti-Ly6G and Dnase. Shown are representative images of each group (right).\u003c/p\u003e\n\u003cp\u003e(Q) Shown are BLI analyses (n=5 for each group). Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003eStatistical significance was determined using two-tailed unpaired Student’s t test. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"mainfigures4.png","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/921e046939736b106f0fbddf.png"},{"id":91486610,"identity":"5ba7073e-2c4a-48f8-abdf-56a5b002a089","added_by":"auto","created_at":"2025-09-17 04:57:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":482755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTumor-associated inflammation promotes the expression of LRG1 in hepatocytes by IL6/STAT3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The relative expression of \u003cem\u003eLrg1 \u003c/em\u003ein AML12 cells co-cultured with various mouse tumor cell lines or treated with serum (5%) from CRC model mice. n = 3 independent experiments. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(B) Western blot analysis of the expression LRG1 and β-actin in AML12 cells after the indicated treatments.\u003c/p\u003e\n\u003cp\u003e(C) Cytokine array analyses of serum from CRC orthotopic mice model (day 7, day 14, day 21 and day 28) and from CRC intrasplenic mice model (day 5, day 10, day 15 and day 21).\u003c/p\u003e\n\u003cp\u003e(D-G) Quantitative real-time PCR analyses of the expression \u003cem\u003eLrg1\u003c/em\u003e and ELISA analyses of media of AML12 (D-E) and murine primary hepatocytes (F-G) treated with vehicle or recombinant IL6/G-CSF/CXCL13/CCL12/TIMP1. n=3 independent experiments. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(H-I) qRT-PCR and Western blot analyses of LRG1 in AML12 cells treated with cytokine combinations. n=3 independent experiments. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(J-K) Western blot analysis and qRT-PCR of the expression LRG1 and actin in AML12 cells treated with indicated serum and anti-IL6 or tocilizumab. n=3 independent experiments. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(L)ELISA analyses of plasma samples for IL6 from sham-group mice, CRC orthotopic mice model at day 21(PMN) and CRC intrasplenic mice model at day 21(Met). n=4 sample. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(M-N) Representative images of IHC staining(M) and quantification of p-stat3 in liver (N). Scale bars 10 μm. Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003e(O)Correlation between serological IL6 levels and serological LRG1 levels of CRC patients is shown using Pearson’s correlation analysis. Dots represent individual samples.\u003c/p\u003e\n\u003cp\u003e(P-S) Schematic of the experimental design(P). Representative images of liver metastases (Q) and quantification of the number (R) and maximum tumor size (S) of liver metastases in each group. Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003ep values were obtained by two-tailed unpaired Student’s t test, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001 and ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"mainfigures5.png","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/529d5d9714fa932b3c46f914.png"},{"id":91487934,"identity":"a0caeb23-78d4-42c2-a2a2-762b2436baef","added_by":"auto","created_at":"2025-09-17 05:05:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1089726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTargeting LRG1 reduces colorectal cancer liver metastasis and enhances the efficacy of immunotherapy for metastatic liver tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) \u0026nbsp;Schematic of CRC intrasplenic model treated with anti-LRG1 antibody.\u003c/p\u003e\n\u003cp\u003e(B-D) Representative images of liver metastases (B) and quantification of the number (C) and maximum tumor size (D) of liver metastases in control and anti-LRG1-treated mice. Red arrows indicate liver metastases. n=8 for mice in control group, n=11 for mice treated with anti-LRG1. Scale bars, 1 cm. Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003e(E-G) As depicted in the schematic (E), liver metastases in mice were measured by ex vivo bioluminescent imaging (BLI). Shown are representative images of each group (right)(F). Shown are BLI analyses (G). n=6 in AAV8-ctrl and AAV8-shLRG1 group. Data are means ± SEM.\u003c/p\u003e\n\u003cp\u003e(H) Schematic of CRCLM hepatic model treated with or without anti-LRG1 and anti-PD1. Tumors in liver were measured by ex vivo bioluminescent imaging (BLI). Shown are images of each group (I) and BLI analyses (J). n=6.\u003c/p\u003e\n\u003cp\u003e(K-L) Representative images of tumors in the liver and H\u0026amp;E staining of tumor burden in liver (K). Scale bars, 1 cm. Scale bars, 10 μm. Quantification of tumor area in H\u0026amp;E staining of tumor burden in liver (L). n=6. Data are means ± SD.\u003c/p\u003e\n\u003cp\u003e(M-O) Representative images and quantification of immunofluorescence staining of CD8a and GZMB in tumor. n=6 per group, Scale bars, 50 μm. Data are means ± SD (N). Data are means ± SEM (O).\u003c/p\u003e\n\u003cp\u003ep values were obtained by two-tailed unpaired Student’s t test, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001 and ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"mainfigures6.png","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/9f7994c65c8cce3c19206738.png"},{"id":91487935,"identity":"c44471bd-b539-4cff-8bdd-e001b776f8cb","added_by":"auto","created_at":"2025-09-17 05:05:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":222020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe schematic model illustrating the mechanisms of Hepatocyte-derived LRG1 promote liver PMN formation and targeting approach.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn response to IL-6-driven inflammation induced by primary tumors, hepatocytes secrete LRG1, which promotes the formation of a pre-metastatic niche (PMN) in the liver. This process includes the recruitment of immunosuppressive myeloid cells—predominantly neutrophils—and the induction of NET formation via TGFβ receptor II signaling. These mechanisms facilitate tumor colonization and create an immunosuppressive hepatic environment. Therapeutic targeting of hepatocyte-derived LRG1 or the use of neutralizing antibodies may attenuate this process and offer a promising clinical strategy.\u003c/p\u003e","description":"","filename":"mainfigures7.png","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/b75b5742eaebf7e39ec74d28.png"},{"id":106662950,"identity":"1a72ca22-d581-4ee6-bc30-b090444a7ba7","added_by":"auto","created_at":"2026-04-11 07:05:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6493446,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/efface99-3a01-4a8e-a6ef-39c070c97e2e.pdf"},{"id":91486600,"identity":"b17e06f7-d318-4ec7-8887-295b9c8af565","added_by":"auto","created_at":"2025-09-17 04:57:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24460,"visible":true,"origin":"","legend":"supplementary figure legends","description":"","filename":"supplementaryfigurelegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/6ce9e40fdd241e05216998b9.docx"},{"id":91486602,"identity":"fd1c74dd-18a0-4726-8e24-09e943e218d1","added_by":"auto","created_at":"2025-09-17 04:57:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17021,"visible":true,"origin":"","legend":"supplementary tables","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/8734bbb37293a3893ca0a00c.docx"},{"id":91486612,"identity":"562e3b2a-f0c1-47f9-876a-1261f7c75cae","added_by":"auto","created_at":"2025-09-17 04:57:56","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2628397,"visible":true,"origin":"","legend":"\u003cp\u003esupplementary figure 1-9\u003c/p\u003e","description":"","filename":"supplementaryfigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/da0feec0bf10ba36bfbbdf02.pdf"},{"id":91486619,"identity":"7f7aad97-cfcc-4f4c-b941-c8f0e7b68130","added_by":"auto","created_at":"2025-09-17 04:57:56","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":6349446,"visible":true,"origin":"","legend":"\u003cp\u003eunprocessed images\u003c/p\u003e","description":"","filename":"unprocessedoriginalimages.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7522111/v1/f2d3ae87d1a763815a4e9e46.pdf"}],"financialInterests":"(Not answered)","formattedTitle":"Hepatocyte-Derived LRG1 Primes the Liver for Metastasis and Impairs Immunotherapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe liver is one of the most common sites for metastatic dissemination across multiple cancer types, including colorectal cancer, accounting for a significant proportion of cancer-related mortality worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Emerging evidence highlights the critical role of pre-metastatic niche (PMN) formation, a process wherein primary tumors systemically prime distant organs to favor metastatic seeding and outgrowth\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Rather than being a passive recipient, the liver\u0026mdash;with its unique vasculature and immune environment\u0026mdash;undergoes active remodeling during PMN development\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, the mechanisms by which hepatic cells, particularly hepatocytes, orchestrate this tumor-supportive microenvironment remain poorly defined, hindering targeted therapeutic strategies.\u003c/p\u003e\u003cp\u003eLeucine-rich α-2-glycoprotein 1 (LRG1), a secreted glycoprotein, contributes to a wide range of human diseases\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, including cancers. Elevated LRG1 in tumors correlate with cancer progression, tumor burden, and poor prognosis\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. LRG1 is produced by diverse cellular sources. Tumor cell-derived LRG1 has been shown to enhance tumor proliferation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, modulate angiogenesis\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and, as demonstrated in our previous work, promote epithelial-mesenchymal transition (EMT) in an autocrine manner\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Additionally, endothelial cell-derived LRG1 facilitates lung metastasis by increasing the abundance of neural/glial antigen 2 (NG2)\u003csup\u003e+\u003c/sup\u003e pericytes and modulating the vascular niche\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Among all sources, hepatocytes represent the predominant origin of systemic LRG1. This is particularly significant as the liver\u0026mdash;a frequent metastatic target\u0026mdash;undergoes extensive remodeling in response to primary tumor signals before metastatic colonization. However, the dynamic expression and role of liver-derived LRG1 in this context remains largely unexplored.\u003c/p\u003e\u003cp\u003eLRG1 exerts most of its pathogenic effects on the vasculature, yet it remains unclear whether LRG1 also contributes to liver metastasis via non-vascular mechanisms. In particular, whether hepatocyte-derived LRG1 regulates the formation or function of the pre-metastatic niche (PMN), a critical determinant of metastasis organotropism\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, is completely unknown. Understanding this axis may reveal novel insights into systemic crosstalk between primary tumors and metastatic organs and uncover potential therapeutic targets for metastatic prevention.\u003c/p\u003e\u003cp\u003eHere, we investigate the hypothesis that hepatocyte-derived LRG1 serves as a central mediator of PMN formation in the liver, bridging systemic tumor-derived signals to local immunosuppressive and pro-metastatic alterations. Combining clinical observations with mechanistic studies in murine models, we demonstrate that tumor-induced inflammatory cues drive hepatocytes to secrete LRG1, which in turn recruits myeloid cells and triggers NET formation. We further establish the translational potential of targeting this axis, showing that LRG1 blockade not only inhibits liver metastasis but also synergizes with immune checkpoint inhibitors by reshaping the hepatic immune microenvironment. These findings position the hepatocyte-LRG1 pathway as a critical determinant of liver tropism in metastasis and unveil new opportunities for therapeutic intervention.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSerum LRG1 Predicts Liver Metastasis and is associated with Pre-Metastatic Niche Formation in the Liver\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the association of serological LRG1 with metastasis, we analyzed serum samples from patients with localized or liver metastatic colorectal cancer (CRC), pancreatic ductal adenocarcinomas (PDAC) or gastric cancer (GC). Results demonstrated that patients with liver metastasis exhibited significantly higher serum LRG1 levels (Fig. 1A-C), suggesting its close association with liver metastasis. To assess its possible predictive capacity for liver metastasis, we retrospectively analyzed serum LRG1 levels in patients with early-stage cancers (TNM stage I/II for CRC, and TNM stage I for GC). Individuals who developed liver metastasis within 5 years displayed significantly elevated baseline LRG1 levels (Fig. 1D, E). Survival analysis further revealed that high LRG1 correlated with reduced liver metastasis-free survival (Fig. 1F and 1G). These findings suggest that elevated LRG1 precedes detectable metastasis, implicating its role in pre-metastatic niche formation. To validate this hypothesis, we employed a CT26 murine cecal orthotopic inoculation model (Fig. 1H) by harvesting liver weekly. By day 28, small hepatic metastatic foci were observed in a subset of mice, while no liver metastasis was detected at earlier timepoints (day 21) (Fig. S1A) which was defined as pre-metastatic phase consistent with our previous reports \u003csup\u003e15\u003c/sup\u003e. This was further corroborated by CD11b and fibronectin staining (Fig. 1I-K) and \u003cem\u003eS100a8\u003c/em\u003e, \u003cem\u003eS100a9\u003c/em\u003e, \u003cem\u003eMmp9\u003c/em\u003e expression (Fig. S1C-E), which were reported to be significantly enriched in pre-metastatic livers \u003csup\u003e16\u003c/sup\u003e. Synchronously with the formation of the liver pre-metastatic niche, serum LRG1 levels exhibited a marked elevation at the pre-metastatic phase and further increased upon metastatic establishment (Fig. 1L). No such phenomenon was observed in sham-operated controls (Fig. S1F-I). Similarly, in the splenic tumor inoculation model, LRG1 serum levels were also elevated during the pre-metastatic phase and progressively increased with hepatic metastasis progression (Fig. 1M-Q, Fig. S1I-K). This trend was further validated in orthotopic pancreatic cancer (KPC) (Fig. 1R and Fig. S2A-C) and melanoma (B16F10) (Fig. 1S and Fig. S2D-F) models, where the liver represents a common metastatic site.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further investigate whether LRG1 directly induces the pre-metastatic niche and subsequent liver metastasis, we overexpressed LRG1 in the liver via hydrodynamic tail vein injection (HTVi)\u003csup\u003e17\u003c/sup\u003e prior to tumor cell splenic inoculation (Fig. 1T). Indeed, LRG1 expression highly induced fibronectin deposition in the liver (Fig. S2G, H). And after day 12 post-inoculation, mice with LRG1 overexpression displayed significantly more hepatic metastatic foci compared to controls (Fig. 1U-V). Collectively, these findings confirm that serum LRG1 levels are closely associated with the liver pre-metastatic microenvironment thereby promoting hepatic metastasis progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHepatocyte-Derived LRG1 Drives Liver Metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLRG1 is expressed and secreted by hepatocytes\u003csup\u003e18\u003c/sup\u003e, adipocytes\u003csup\u003e19\u003c/sup\u003e, tumor cells\u003csup\u003e14\u003c/sup\u003e, and vascular endothelial cells\u003csup\u003e7,20\u003c/sup\u003e. To identify the source of circulating LRG1 during liver pre-metastatic niche formation, we analyzed LRG1 expression across tissues in orthotopic, intrasplenic and other tumor models (Fig. 2A). LRG1 expression was markedly elevated in the pre-metastatic liver and further increased with tumor dissemination (Fig. 2B, C and Fig. S3A-C) but not in control model (Fig. S3D, E). This is also found in PDAC PMN model (Fig. S3F). Although LRG1 expression increased modestly in other tissues, levels remained significantly lower than in the liver (Fig. S3G). To determine the primary cellular source of LRG1 in the pre-metastatic liver, we further dissociated hepatic tissues and isolated hepatocytes, immune cells (CD45\u003csup\u003e+\u003c/sup\u003e), and endothelial cells (CD31\u003csup\u003e+\u003c/sup\u003e) (Fig. 2D). Hepatocytes exhibited the highest LRG1 expression, which escalated with metastasis (Fig. 2E). This finding was further confirmed by our previous single-cell RNA sequencing (scRNA-seq) data of pre-metastatic liver tissues\u003csup\u003e15\u003c/sup\u003e (Fig. S3H) and IHC staining of LRG1 in pre-metastatic mice models (Fig. S3I-P). Collectively, these results demonstrate that hepatocyte-derived LRG1 is strongly induced during metastasis and tightly associated with pre-metastatic niche establishment.\u003c/p\u003e\n\u003cp\u003eTo investigate the role of hepatocyte-derived LRG1 in metastasis, we generated \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003eflox/flox\u003c/sup\u003e; Alb-Cre (hereafter referred to as \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e\u0026Delta;/\u003c/sup\u003e\u003csup\u003e\u0026Delta;)\u003c/sup\u003e\u003csup\u003eHep\u0026nbsp;\u003c/sup\u003e) mice to specifically knock out \u003cem\u003eLrg1\u003c/em\u003e in the liver (Fig. S4A-C) or \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003ewt/wt\u003c/sup\u003e; Alb-Cre (hereafter referred to as \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u0026nbsp;\u003c/sup\u003e) mice, then orthotopically implanted MC38 colon cancer cells (Fig. 2F). Over time (D19, D35), \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e mice showed progressive increases in hepatic LRG1 expression and serum LRG1 levels, while hepatocyte-specific \u003cem\u003eLrg1\u003c/em\u003e knockout dramatically suppressed circulating LRG1 elevation (Fig. 2G, 2H and 2I), confirming hepatocytes as the primary source of serum LRG1 during metastatic progression. Although orthotopic MC38 tumors exhibited low hepatic metastasis rates due to excessive primary tumor burden (2/5 by day 35), \u003cem\u003eLrg1\u003c/em\u003e deletion completely abolished metastasis (Fig. 2J). In the splenic metastasis model (which induces higher liver metastasis frequency), \u003cem\u003eLrg1\u003c/em\u003e knockout also significantly reduced metastatic burden and tumor size (Fig. 2K-N). Interestingly, the knockout of hepatocyte-derived LRG1 also significantly inhibited the growth of orthotopic tumors (Fig.S4D), which may be due to the interaction between serum LRG1 levels and the orthotopic tumor. This warrants further investigation. These findings demonstrate that hepatocytes serve as the primary source of serum LRG1 during hepatic metastasis progression, and elevated hepatocyte-derived LRG1 critically drives liver metastasis formation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHepatic LRG1 Promotes Pre-Metastatic Niche Formation in the liver\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHepatocyte-specific \u003cem\u003eLrg1\u003c/em\u003e deletion abolished the establishment of a tumor-induced pre-metastatic niche, manifested by the absence of fibronectin deposition and reduced myeloid cell infiltration (Fig. 3A, 3B). To validate the impact of LRG1-mediated PMN formation on liver metastasis, we induced liver PMN via orthotopic cecal implantation of MC38 in \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e and\u003cem\u003e\u0026nbsp;Lrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e\u0026Delta;/\u003c/sup\u003e\u003csup\u003e\u0026Delta;)\u003c/sup\u003e\u003csup\u003eHep\u003c/sup\u003e mice. Fourteen days later, MC38-luciferase (MC38-luc) cells were intrasplenically injected to assess liver metastasis (Fig. 3C). Primary tumor-induced PMN significantly promoted MC38-luc liver metastasis, whereas hepatocyte \u003cem\u003eLrg1\u003c/em\u003e KO dramatically reversed this phenomenon (Fig. 3C, D), indicating LRG1-induced PMN is essential for liver metastasis.\u003c/p\u003e\n\u003cp\u003eTo define the impact of hepatocyte-specific \u003cem\u003eLrg1\u003c/em\u003e KO on liver PMN formation, we performed single-cell RNA sequencing (scRNA-seq) on liver tissues from healthy and orthotopic MC38 -bearing mice (day 19, PMN model) of both\u003cem\u003e\u0026nbsp;Lrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e\u0026Delta;/\u003c/sup\u003e\u003csup\u003e\u0026Delta;)\u003c/sup\u003e\u003csup\u003eHep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e genotypes (Fig. 3E). After quality control, 75,053 cells were included for further analysis. 13 major cell populations were identified, including immune cells (B cells, DC1, DC2, macrophages, monocytes, neutrophils, NK cells, pDCs, and T cells), hepatocytes, cholangiocytes, endothelial cells, and fibroblasts (Fig. 3F, Fig. S5A) \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eImmunosuppression is a key feature of PMN\u003csup\u003e2\u003c/sup\u003e. Within the pre-metastatic liver, myeloid cell proportions increased while lymphoid cells (T/NK, B cells) decreased (Fig. 3F, S5B). Neutrophils showed the most dramatic abundance change, increasing from 2.89% in healthy \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e mice to 31.68% in \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e-PMN mice. This increase was significantly reversed to 10.49% in \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e\u0026Delta;/\u003c/sup\u003e\u003csup\u003e\u0026Delta;)\u003c/sup\u003e\u003csup\u003eHep\u003c/sup\u003e mice (Fig. S5B), further validated by flow cytometry (Fig. S5C, D). Pseudotime analysis revealed that \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e-PMN livers had a higher proportion of neutrophils at an early developmental stage compared to control mice, which was also diminished upon \u003cem\u003eLrg1\u003c/em\u003e deletion (Fig. 3G). These early neutrophils highly expressed genes associated with differentiating bone marrow\u0026ndash;derived neutrophils, such as \u003cem\u003eCamp\u003c/em\u003e, \u003cem\u003eLtf\u003c/em\u003e, \u003cem\u003eNgp\u003c/em\u003e, and \u003cem\u003eChil3\u003c/em\u003e (Fig. S5E)\u0026nbsp;\u003csup\u003e22,23\u003c/sup\u003e. Since immature neutrophils are often more immunosuppressive, we next analyzed the expression of immunosuppressive genes enriched in myeloid-derived suppressor cells (MDSCs), including \u003cem\u003eWfdc17\u003c/em\u003e, \u003cem\u003eIfitm1\u003c/em\u003e, \u003cem\u003eCd14\u003c/em\u003e, \u003cem\u003eProk2\u003c/em\u003e, \u003cem\u003eNos2\u003c/em\u003e, \u003cem\u003eCebpb\u003c/em\u003e, \u003cem\u003eStfa2\u003c/em\u003e, and \u003cem\u003eAsprv1\u003c/em\u003e\u003cem\u003e\u003csup\u003e24-31\u003c/sup\u003e\u003c/em\u003e. These genes were significantly higher along the neutrophil pseudotime trajectory in tumor-bearing \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e mice compared to \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e\u0026Delta;/\u003c/sup\u003e\u003csup\u003e\u0026Delta;)\u003c/sup\u003e\u003csup\u003eHep\u003c/sup\u003e counterparts (Fig. 3H). MDSCs are pathologically activated neutrophils and monocytes with strong immunosuppressive capacity\u0026nbsp;\u003csup\u003e32\u003c/sup\u003e. An established MDSC signature score\u003csup\u003e33\u003c/sup\u003e was significantly elevated in neutrophils from \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e-PMN mice but not in \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e\u0026Delta;/\u003c/sup\u003e\u003csup\u003e\u0026Delta;)Hep\u003c/sup\u003e-PMN mice (Fig. 3I). Notably, inducible nitric oxide synthase (iNOS, encoded by \u003cem\u003eNos2\u003c/em\u003e), known to suppress T cell function\u003csup\u003e34-37\u003c/sup\u003e, was highly expressed by immunosuppressive neutrophils to promote metastasis\u0026nbsp;\u003csup\u003e30,38\u003c/sup\u003e. iNOS\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eMPO\u003csup\u003e+\u003c/sup\u003e neutrophil infiltration gradually increased in the liver following metastatic progression in \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e mice but was also dampened upon \u003cem\u003eLrg1\u003c/em\u003e KO (Fig. 3J). Correspondingly, CD8+ T cell proportions decreased but PD1\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003eT cells increased (Fig. S5F, G). Similarly, the MDSC-related immunosuppressive signature in monocytes was elevated in tumor-bearing livers and attenuated by \u003cem\u003eLrg1\u003c/em\u003e KO (Fig. S5H). Moreover, all dendritic cell (DC1, DC2 and pDC) subsets, responsible for antigen presentation, were reduced in tumor-bearing \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e mice (Fig. S5B). Tolerogenic DCs promote antigen-specific tolerance by dampening T cell responses and inducing pathogenic T cell exhaustion and regulatory T cells\u0026nbsp;\u003csup\u003e39,40\u003c/sup\u003e. Functional analysis revealed an increase in tolerogenic DC\u0026ndash;related genes in \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e-PMN livers, which was reversed by \u003cem\u003eLrg1\u003c/em\u003e deletion (Fig. S5I).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAngiogenesis, primarily mediated by endothelial cells, is another key feature of PMN formation. Gene ontology analysis showed enrichment of angiogenesis- and inflammation-related pathways in endothelial cells from tumor-bearing \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e mice (Fig. S5J). Interestingly, the upregulation of genes involved in angiogenesis (\u003cem\u003eHgf\u003c/em\u003e, \u003cem\u003eRhob\u003c/em\u003e, \u003cem\u003eLrg1\u003c/em\u003e, \u003cem\u003eEts1\u003c/em\u003e, \u003cem\u003eIl1a\u003c/em\u003e) \u003csup\u003e20,41-44\u003c/sup\u003e and inflammation (\u003cem\u003eIl1r1\u003c/em\u003e, \u003cem\u003eSocs3\u003c/em\u003e)\u003csup\u003e45,46\u003c/sup\u003e in tumor-bearing mice were relieved when hepatic \u003cem\u003eLrg1\u003c/em\u003e was knocked out (Fig. S5J, K).\u003c/p\u003e\n\u003cp\u003eCollectively, these findings demonstrate that hepatocyte-derived LRG1 responds to the presence of primary colorectal tumors by reshaping the liver immune microenvironment to support tumor metastasis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLRG1 directs NET formation of neutrophils\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell RNA-seq analysis comparing neutrophils from the pre-metastatic liver microenvironment with control neutrophils revealed that, during niche formation, pathways related to pathogen response and chemotaxis were highly upregulated in neutrophils, indicating an inflammatory-activated state (Fig. S6A). Consistent with this, RNA sequencing of bulk liver tissue demonstrated enrichment of neutrophil extracellular trap formation prior to metastasis (Fig. 4A; Fig. S6B). NET formation is closely linked to tumor colonization and metastatic seeding\u003csup\u003e47-49\u003c/sup\u003e. We then examined human liver metastasis specimens and found that LRG1 expression in the liver correlated positively with the NETosis markers (Fig. 4B, 4C). Further, single-cell data comparing \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e and\u003cem\u003e\u0026nbsp;Lrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e\u0026Delta;/\u003c/sup\u003e\u003csup\u003e\u0026Delta;)\u003c/sup\u003e\u003csup\u003eHep\u003c/sup\u003e mice showed that deletion of \u003cem\u003eLrg1\u003c/em\u003e substantially attenuated the upregulation of inflammatory genes in neutrophils during pre-metastatic niche establishment (Fig. 4D), demonstrating that liver-derived LRG1 is essential for neutrophil inflammatory activation. In both orthotopic and splenic injection mouse models of liver metastasis, \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e mice accumulated NETs progressively, whereas \u003cem\u003eLrg1\u003c/em\u003e knockout almost completely abolished NET deposition in the liver (Fig. 4E, 4F; Fig. S6C, S6D). Similarly, \u003cem\u003eLrg1\u003c/em\u003e deletion prevented the increase of circulating NETs during metastatic progression (Fig. 4G).\u003c/p\u003e\n\u003cp\u003eTo assess whether LRG1 directly acts on neutrophils, we treated primary human neutrophils with recombinant LRG1. LRG1 significantly enhanced neutrophil chemotaxis (Fig. 4H) and induced NETosis\u0026mdash;a process reversed by an inhibitor of PAD4 (a key enzyme in NET formation) (Fig. 4I, 4J). Likewise, LRG1 induced NETosis in differentiated HL60 cells (neutrophil-like) (Fig. S6E), and mouse hepatocytes overexpressing LRG1 triggered NET formation in co-cultured mouse neutrophils (Fig. S6F, S6G). Reported LRG1 receptors include TGFBRⅡ\u003csup\u003e20\u003c/sup\u003e, Endoglin\u003csup\u003e20\u003c/sup\u003e, ADGRL2\u003csup\u003e50\u003c/sup\u003e, and EGFR\u003csup\u003e12,51\u003c/sup\u003e. Single-cell expression profiling of neutrophils revealed that only\u0026nbsp;\u003cem\u003eTgfbr2\u003c/em\u003e was appreciably expressed (Fig. S6H), suggesting that LRG1-induced NETosis might be mediated via TGFBRⅡ. Indeed, the TGFBRⅡ\u0026nbsp;inhibitor SB431542 significantly blocked LRG1-triggered NET formation in both donor neutrophils and dHL60 cells (Fig. 4K, 4L, 4M). Genetic ablation of \u003cem\u003eTGFBR2\u003c/em\u003e or pharmacological inhibition of downstream AKT signaling similarly suppressed LRG1-driven NETosis (Fig. S6I\u0026ndash;S6M).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLRG1-induced NETosis is responsible for liver metastasis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNETosis has been shown to facilitate tumor cell adhesion and migration. To test whether LRG1-induced NETs enhance tumor cell motility, we performed transwell assays with colorectal cancer cell lines. Co-culture with neutrophils supplied with LRG1 markedly increased migration of DLD1 and HCT116 cells, an effect that was reversed by DNase treatment to degrade NET DNA (Fig. S7A\u0026ndash;S7C). Moreover, CCDC25\u0026mdash;a receptor on tumor cells that binds extracellular DNA \u003csup\u003e49\u003c/sup\u003e\u0026mdash;was required for this enhanced migration, as \u003cem\u003eCCDC25\u003c/em\u003e knockout in DLD1 and HCT116 dramatically reduced their movement under co-culture conditions (Fig. S7D\u0026ndash;S7G; Fig. 4N, 4O).\u003c/p\u003e\n\u003cp\u003eIn vivo,\u0026nbsp;\u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/\u003c/sup\u003e\u003csup\u003e+)Hep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e\u0026Delta;/\u003c/sup\u003e\u003csup\u003e\u0026Delta;)\u003c/sup\u003e\u003csup\u003eHep\u003c/sup\u003e mice first received orthotopic MC38 implants to establish a pre-metastatic niche, then were injected intrasplenically with luciferase-tagged MC38-luc cells. Prior to injection, mice were assigned to control, anti-Ly6G antibody (neutrophil depletion), or DNase (NET depletion) groups. Longitudinal bioluminescence imaging showed that \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/\u003c/sup\u003e\u003csup\u003e+)Hep\u003c/sup\u003e controls developed the heaviest liver metastatic burden, while neutrophil depletion and NET degradation both reduced metastasis; LRG1-deficient mice exhibited minimal liver metastases across all conditions (Fig. 4P, 4Q; Fig. S7H). These results confirm that LRG1-driven liver metastasis depends on its ability to induce neutrophil NETosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor-associated inflammation promotes the expression of LRG1 in hepatocytes by IL6/STAT3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor-derived secreted factors play pivotal roles in shaping the pre-metastatic niche \u003csup\u003e3,4\u003c/sup\u003e. To determine how LRG1 was upregulated in the presence of tumor burden, we co-cultured mouse AML12 hepatocytes with various tumor cell lines (CT26, MC38, KPC, 4T1, B16F10) or treated them with mouse serum from pre-metastatic or metastatic mice. Only serum from pre-metastatic and metastatic mice significantly increased \u003cem\u003eLrg1\u003c/em\u003e mRNA and protein level in AML12 cells (Fig. 5A, 5B), indicating that tumor-associated systemic changes , rather than tumor cell-derived factors, drive LRG1 induction.\u003c/p\u003e\n\u003cp\u003eCytokine array analysis of mouse serum from orthotopic and splenic models revealed 5 common elevated cytokines during metastasis including IL6, G-CSF, CXCL13, CCL12, and TIMP1 (Fig. 5C; Fig. S8A). Among these, only IL6 robustly upregulated hepatocyte \u003cem\u003eLrg1\u003c/em\u003e transcription and secretion when added individually to AML12 or mice hepatocytes cultures (Fig. 5D\u0026ndash;5G). Combinatorial cytokine experiments confirmed that IL6 was indispensable for LRG1 induction, whereas removal of other factors had minimal impact (Fig. 5H, 5I). Furthermore, neutralization of IL6 with antibody or blockade of IL6R with tocilizumab abrogated LRG1 upregulation (Fig. 5J, 5K). In vivo, serum IL6 levels and hepatic phospho-STAT3 (Tyr705) increased during metastatic progression (Fig. 5L\u0026ndash;5N). Analysis of colorectal cancer patient sera demonstrated a positive correlation between IL6 and LRG1 levels (Fig. 5O). Finally, in both \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e+/+\u003c/sup\u003e\u003csup\u003e)Hep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(\u003c/sup\u003e\u003csup\u003e\u0026Delta;/\u003c/sup\u003e\u003csup\u003e\u0026Delta;)Hep\u003c/sup\u003e mice, hydrodynamic tail vein overexpression of \u003cem\u003eIl6\u003c/em\u003e followed by splenic MC38 injection showed that IL6 greatly promoted liver metastasis (Fig. 5P, 5Q), an effect that was significantly blunted in the absence of hepatocyte \u003cem\u003eLrg1\u003c/em\u003e (Fig. 5P-S; Fig. S8B\u0026ndash;S8D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTargeting LRG1 diminishes liver metastasis and promotes ICB efficacy on metastatic tumor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate LRG1 as a therapeutic target, we administered anti-LRG1 antibody to mice undergoing splenic MC38 injection (Fig. 6A). Antibody treatment effectively abolished LRG1 elevation (Fig. S9A), prevented NETs formation in liver (Fig. S9B, S9C) and significantly reduced both the number and size of liver metastases compared to controls (Fig. 6B\u0026ndash;6D). Similarly, AAV8-mediated, hepatocyte-specific \u003cem\u003eLrg1\u003c/em\u003e knockout prior to metastasis decreased metastatic burden (Fig. 6E\u0026ndash;G) and orthotopic tumor size (Fig. S9D).\u003c/p\u003e\n\u003cp\u003eSince liver metastases often confer resistance to immune checkpoint blockades\u003csup\u003e52,53\u003c/sup\u003e, we tested whether LRG1 inhibition could sensitize tumors to anti-PD-1 therapy (Fig. 6H). In a model of direct intrahepatic implantation of MC38 cells, combined treatment with anti-LRG1 and anti-PD-1 antibodies synergistically suppressed tumor growth (Fig. 6I, 6J) and liver tumor burden (Fig. 6K\u0026ndash;6L). Flow cytometry revealed that dual blockade markedly increased CD8⁺ T-cell infiltration (Fig. 6M, 6N) and the proportion of cytotoxic T cells within metastatic lesions (Fig. 6O).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study identifies that hepatocyte-derived LRG1 as a central systemic mediator that links primary tumors to the liver pre-metastatic niche. Clinically, elevated serum LRG1 levels strongly correlate with both existing and future liver metastases across several gastrointestinal malignancies, and high baseline LRG1 predicts poorer liver-metastasis-free survival. Importantly, hepatocyte-derived LRG1 was both necessary and sufficient for promoting liver PMN formation. These findings extend the recognized role of LRG1 beyond local effects on tumor or endothelium, demonstrating that liver parenchymal cells can be co-opted by distant tumors to create a metastasis-permissive microenvironment (Fig.7).\u003c/p\u003e\n\u003cp\u003eOur data position LRG1 squarely in the emerging paradigm of tumor\u0026ndash;host crosstalk. LRG1 was first identified as a promoter of pathological angiogenesis via modulation of endothelial TGF\u0026beta; signaling, and has since been implicated in tumor EMT, growth, and metastasis. Notably, a recent report demonstrated that liver-secreted LRG1 activates HER3 to sustain metastatic colorectal tumors in the liver, highlighting LRG1 as a liver-to-tumor growth signal. While previous studies reported that IL-6/STAT3 signaling regulates LRG1 in tumor cells, our findings demonstrate that IL-6/LRG1 cascade links systemic inflammation-a common feature of advancing tumors-to hepatic PMN establishment. Our work also adds a complementary insight: beyond tumor-intrinsic LRG1 expression, hepatocyte-released LRG1 profoundly remodels the liver immune landscape to favor metastasis.\u003c/p\u003e\n\u003cp\u003eWe show that LRG1 recruits and reprograms myeloid cells in the liver. Strikingly, we found that LRG1 directly induces neutrophil extracellular trap (NET) formation. NETosis has emerged as a key enabler of metastasis by trapping tumor cells and fostering growth. This is the first demonstration that a hepatocyte-derived factor can orchestrate neutrophil NETs during PMN formation. Interestingly, previous studies have shown that LRG1 is itself secreted by activated neutrophils-suggesting a feed-forward loop where neutrophil-released LRG1 may further amplify local niche effects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe immunological consequences of LRG1 in the PMN were profound. LRG1-rich pre-metastatic livers harbored large infiltrates of immature neutrophils and inflammatory monocytes with a suppressive MDSC-like gene signature, while T cell populations contracted and became more exhausted. Whether LRG1 directly functions on other immune cells (such as T cells, dendritic cells) remains to be tested, our data clearly show that LRG1 drives an immunosuppressive microenvironment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom a translational perspective, our data suggests significant implications. First, Elevated LRG1 predicted future metastasis in multiple GI cancers in our cohorts, akin to reports that high LRG1 portends poor outcomes in PDAC and other cancers, underscoring its potential as an early, non-invasive biomarker. Integrating LRG1 into diagnostic panels may improve early detection of occult metastasis. Second, LRG1 itself is a promising therapeutic target. Here, anti-LRG1 antibody treatment prevented NET formation and dramatically reduced liver metastases. Importantly, combining LRG1 blockade with PD-1 checkpoint inhibitors had a synergistic effect, unleashing cytotoxic T cells in liver tumors. This is particularly relevant since liver metastases are notoriously resistant to immunotherapy\u003csup\u003e52,53\u003c/sup\u003e. Recent evidence shows that LRG1 inhibition can enhance the efficacy of chemotherapy and checkpoint blockade by normalizing the tumor vasculature\u003csup\u003e8\u003c/sup\u003e. Our data suggest that targeting the LRG1 can reprogram the metastatic niche from \u0026ldquo;cold\u0026rdquo; to \u0026ldquo;hot,\u0026rdquo; rendering it more susceptible to ICB.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatients and specimens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum of patients were obtained from BioBank, The Six Affiliated Hospital, Sun Yat-sen University. Human liver metastases paraffin sections were collected from Sixth Affiliated Hospital of Sun Yat-sen University. The samples were used with informed consent under a protocol approved by the Medical Ethics Committee of Sixth Affiliated Hospital of Sun Yat-sen University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLrg1\u003c/em\u003e-flox mice (Strain NO. T009577) and \u003cem\u003eAlb-Cre\u003c/em\u003e mice (Strain NO. T017784) on a C57BL6/J background were purchased from GemPharmatech (Nanjing, China). \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003ewt/wt\u003c/sup\u003e; \u003cem\u003eAlb-cre\u003c/em\u003e (termed \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e), \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003ewt/fl\u003c/sup\u003e; \u003cem\u003eAlb-cre\u003c/em\u003e (termed \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/∆)Hep\u003c/sup\u003e), and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003efl/fl\u003c/sup\u003e; \u003cem\u003eAlb-cre\u003c/em\u003e (termed \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(∆/∆)Hep\u003c/sup\u003e) were generated by crossing \u003cem\u003eLrg1\u003c/em\u003e-\u003cem\u003eflox\u003c/em\u003e mice and \u003cem\u003eAlb-Cre\u003c/em\u003e mice. Genotyping of mice was performed by polymerase chain reaction (PCR) analysis of genomic DNA extracted from mouse tails using the primers (Table S1).\u003c/p\u003e\n\u003cp\u003eFor all tumor models, BALB/c or C57BL/6J mice, between 6-10 weeks of age were purchased from Guangdong GemPharmatech unless indicated otherwise. Mice with similar sex, age and weight were randomized before tumor inoculation. To detect the primary source of LRG1 in tumor models, the mice were orthotopically implanted with 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e CT26 cells or MC38 cells, 5\u0026times;10\u003csup\u003e4\u003c/sup\u003e KPC cells, 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e B16F10 cells and intrasplenically implanted with 5\u0026times;10\u003csup\u003e4\u003c/sup\u003e CT26 cells. The mice were euthanized at the depicted time point and serum, specific organs and cells were collected to detect the expression of LRG1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo detect liver metastases in \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e , \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/∆)Hep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(∆/∆)Hep\u003c/sup\u003e mice, mice were implanted with 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e MC38 cells and euthanized. To detect the effect of PMN on liver metastases in \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(∆/∆)Hep\u003c/sup\u003e mice, mice were first orthotopically implanted with 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e MC38 cells. Then, the luciferase-labeled MC38-luc cells were intrasplenically implanted (1\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells for each mouse). Mice were euthanized, and the liver was rapidly harvested for ex vivo BLI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo detect the effect of neutrophils depletion and Dnase I on liver metastases in \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(∆/∆)Hep\u003c/sup\u003e mice, the mice were orthotopically implanted with 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e MC38 cells. On day 14, mice received initial i.p. injection of anti-Ly6G antibody (200 ug/mouse, Bio Xcell, #BE0075) every 3 days and i.p. injection Dnase I(5mg/kg, daily shots for 7 days from day 14, and then maintenance shots every 3 days). On day 40, mice were euthanized, and the liver were rapidly harvested for ex vivo BLI.\u003c/p\u003e\n\u003cp\u003eTo detect the therapeutic efficacy of anti-LRG1(C4, sc-390920) in CRC intrasplenic model, mice were i.p. injected with anti-LRG1(2 ug/mouse) 1 day before cancer cell intrasplenic injection, followed by every 3 days injection of the reagents for 21 days. On day 21, The mice were euthanized for detecting liver metastases. To detect the therapeutic efficacy of AAV8-TGB-shLRG1(5\u0026rsquo;-TGTCCATCTGTCGGTGGAATT-3\u0026rsquo;) in CRCLM model, mice were i.v. injected with AAV-TGB-shLRG1 1 day before orthotopic injection of MC38. On day 14, MC38-luc cells were intrasplenically implanted, on day 35, mice were euthanized, and the liver were rapidly harvested for ex vivo BLI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo detect the combined effect of the anti-LRG1 and anti-PD-1 immunotherapy, BALB/c mice were uesd and 2.5\u0026times;10\u003csup\u003e5\u003c/sup\u003e CT26-luc cells in 25 ul PBS were injected into the left main lobe of the mouse liver. On day 5, mice were grouped according to ex vivo BLI and received an initial i.p. injection of anti-LRG1 every 2 days and anti-PD-1(100ug/mouse) every 3 days until completion of the experiment.\u003c/p\u003e\n\u003cp\u003eMice were given a standard diet, allowed free access to water, and were housed on 12-hour light/dark cycles. All the animal experiments were approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University and conformed to the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health (National Academies Press, 2011) in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEx vivo bioluminescence imaging (BLI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor liver metastasis models used in the present study, the luciferase activities in liver were used to monitor liver metastasis progression. Mice were i.p. injected with 100 ul D-luciferin (150 mg/kg) and were anesthetized for ex vivo BLI. The mice were euthanized, and livers were rapidly dissected and placed in a plate filled with 2 ml D-luciferin (150 mg/ml; diluted in PBS) for ex vivo BLI. BLI results were obtained using the Xenogen IVIS system. Light emission from the region of interest was quantified as photons/second/cm2/steradian (p/sec/cm2/sr) through Living Images software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHTVi animal experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll these experimental procedures were approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University. To overexpress the LRG1 or IL6 in the liver, we performed hydrodynamic injection of plasmid DNAs into tail vein of mice following a previously published protocol. Each mouse received 10% of its body weight of saline containing the 25-50ug plasmid DNAs (pcDNA3.4-mLRG1 or pcDNA3.4-mIL6). Mice were maintained on standard diet and sacrificed at indicated time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCT26 colon adenocarcinoma cells and B16F10 melanoma cells were obtained from American Type Culture Collection. MC38 colon adenocarcinoma cells were purchased from Kerafast. The murine pancreatic tumor KPC cell line was derived from the pancreatic tumors of KrasG12D/+; Trp53R172H/+; Pdx1-Cre C57BL/6 mice. The human CRC cell lines DLD1 and HCT116 were obtained from American Type Culture Collection. The murine hepatocyte AML12 cell line and HL60 cell line were obtained from American Type Culture Collection. CT26 cells were cultured in RPMI-1640 media containing 10% fetal bovine serum and penicillin/streptomycin. MC38, KPC, DLD1 and HCT116 cells were cultured in DMEM with 10% fetal bovine serum and penicillin/streptomycin. AML12 cells were cultured in DMEM/F12 with 10% fetal bovine serum, 1 \u0026times; ITS(Sigma-Aldrich), 40 ng/ml dexamethasone (Sigma-Aldrich) and penicillin/streptomycin. HL60 cells were cultured in IMDM with 10% fetal bovine serum and penicillin/streptomycin. To diferentiate HL60 cells into neutrophil-like cells, the cells were incubated in culture medium containing 1% DMSO for 7 days. All cells were cultured in a humidified incubator at 37 \u0026deg;C with 5% CO2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA extraction and quantitative real-time PCR (qRT-PCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from cells and tissues using TRIzol Reagent (Invitrogen) and ethanol precipitation. RNA reverse transcription was performed using KAPA SYBR\u0026reg; FAST Universal kit. Then, qPCR was conducted using Roche Light-Cycler 480. Primers are summarized in Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlasmids construction and lentiviral vector transduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo generate the LRG1 overexpression plasmids, the full-length cDNA of mouse LRG1 were cloned into plenti-CMV vector. And the sgRNA targeting CCDC25 were synthesized and cloned into Lenti-CRISPR-V2-Puro plasmids. For stable transfection, the above constructs were respectively cotransfected with pMD2.G, pRSV-REV and pMDLg/pRRE packaging plasmids into HEK293T cells in accordance with the manufacture\u0026rsquo;s protocols. Lentiviral vector supernatants were collected and used to knock out CCDC25 in DLD1 and HCT116 cells. Primers are summarized in Table S2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells and tissues were lysed in radioimmunoprecipitation assay (RIPA) buffer (Sigma-Aldrich) containing protease inhibitor (Roche) and quantified using a BCA kit (Thermo Scientific). Proteins were separated by SDS-PAGE and transferred to NC membranes. The membranes were subsequently blocked with milk, followed by incubation with specific primary antibodies against LRG1, H3cit, p-AKT, t-AKT, p-ERK, t-ERK, p-p38, p38, CCDC25 and Actin overnight at 4 \u0026deg;C. The details of antibodies used are listed in table S3. Then, the membranes were washed with TBST and incubated with HRP-conjugated secondary antibodies (1:5000, Sigma). The bands were obtained by chemiluminescence using StarSignal Western ECL Substrate (GeneStar).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe tissue was firstly fixed in 4% paraformaldehyde, embedded in paraffin and sectioned at 4 \u0026mu;m thickness. Then those paraffin-embedded tissue sections were deparaffinized, rehydrated and microwave antigen retrieved in EDTA buffer. Sections were blocked with 5% BSA for 30min at room temperature. Then those samples were incubated with specific primary antibodies against CD11b, LRG1, MPO and H3cit overnight at 4 \u0026deg;C. After rinsing by PBS, fluorochromeconjugated secondary antibodies were added for incubation 1h at room temperature. Slides were counterstained with DAPI (D1306, Invitrogen). The details of antibodies used are listed in table S3. Observation and photographing were performed with the confocal microscopy Cell Observer (ZEISS, Germany). The analysis of fluorescence images primarily relies on ImageJ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIHC staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParaffin-embedded tissue sections were deparaffinized with dimethylbenzene, dehydrated in an ethanol gradient, followed by antigen retrieval with EDTA buffer. Next, the tissues were blocked with normal goat serum and incubated with specific primary antibodies against Fibronectin, LRG1 and p-Stat3 overnight at 4 \u0026deg;C. The tissues sections were then incubated with secondary antibodies for 1 h, and positive staining was visualized with a HRP DAB substrate kit and nuclear counterstained with hematoxylin. expression of LRG1 were quantified based on the intensity of staining and the percentage of positive cell. In brief, the proportion of positive cells was estimated and given a score ranging from 1 to 4 (1, less than 5%; 2, 5\u0026ndash;25%; 3, 26\u0026ndash;50%; 4, \u0026gt; 51%). The average intensity of the positively stained cells was also given a score on a scale from 1 to 4 (1, no staining; 2, weak staining; 3, moderate staining; 4, strong staining). A final IHC score of each tissue was then calculated via multiplying the positive percentage score by the intensity score.\u003c/p\u003e\n\u003cp\u003eFor LRG1, MPO, H3cit, CD8a, and GZMb tissue staining was performed with TSA (tyramide signal amplification) according to the manufacturer\u0026rsquo;s instructions. Slides were counterstained with DAPI (D1306, Invitrogen). The details of antibodies used are listed in table S3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eELISA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eELISA kits were used to assay the levels of human LRG1(Ray Bio), human IL6(Boster, EK0410) mouse LRG1(ELK Biotechnology), mouse IL6(Boster, EK0411) in cell culture supernatants or sera according to the manufacturer\u0026rsquo;s instruction. We detected plasma MPO-DNA using a previously described sandwich ELISA method. Briefly, 96-well microtiter plates were coated with 5 ug/ml anti-MPO monoclonal antibody (R\u0026amp;D, AF3667) as the capturing antibody overnight at 4 \u0026deg;C. After blocking by 1% BSA buffer for 1 h, 50 \u0026micro;l samples were added per well and incubated for 2h at room temperature. Quant-iTTM PicoGreenTM dsDNA Reagent (ThermoFisher, P7589) and kit was used to assay the levels of MPO-DNA following the manufacturer\u0026rsquo;s instructions.The absorbance was measured using a microplate reader.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue dissociation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor flow cytometry of liver cells, Single cell suspensions were prepared from freshly excised mouse livers by mechanical trituration and samples were then passed through a 70 mm steel mesh and hepatocytes were isolated from cell suspensions by centrifugation 50g for 5\u0026thinsp;min. The remaining cells were used for flow cytometry. For flow sorting of liver cells, livers were extracted and minced, hepatocytes were isolated by mechanical trituration and centrifugation. And remaining liver tissues were digested with collagenase II(1mg/ml) and Dnase(100ug/ml) in DMEM medium for 20 min at 37 \u0026deg;C. The cells were then filtered through 70-mm strainers to remove small fragments of undigested tissue for subsequent experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow cytometry and sorting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrepared single cell suspensions from mouse tissues were first stained with anti-mouse CD16/32 (BioLegend, #101320) to block the IgG Fc receptor, then cells were stained with surface fluorescent antibodies on ice for 30 min. The details of antibodies used are listed in table S3. Flow cytometry was performed on a Beckman CytoFLEX flow cytometer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsolation and culture of primary mouse hepatocytes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMouse Primary hepatocytes were isolated by liver perfusion medium using a 2-step retrograde procedure. Under terminal anaesthesia, mice underwent a laparotomy, the inferior vena cava was then cannulated, and the superior vena cava was clamped to achieve retro-perfusion of the liver using the portal vein as an outlet. The liver was perfused sequentially with buffer A(HBSS + 0.2 mg/ml EDTA + 1 mg/ml glucose) and then buffer B(HBSS + 0.75 mg/ml collagenase\u0026nbsp;Ⅳ\u0026nbsp;+ 0.02 mg/ml Dnase + 1 mg/ml glucose). Post perfusion, the liver capsule was removed, and the liver was gently swirled in PBS to yield a cell suspension. Hepatocytes were collected by three rounds of centrifugation (50g for 3 minutes) and cultured in DMEM/F12 with 10% fetal bovine serum, 1 \u0026times; ITS(Sigma-Aldrich), 40 ng/ml dexamethasone (Sigma-Aldrich) and penicillin/streptomycin.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeutrophil isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman neutrophils were isolated from the peripheral blood of healthy volunteers by density gradient separation using Ficoll(Cytiva, 17544202) and centrifuging at 400 g for 40 min at room temperature. To isolate neutrophils from bone marrow, bone marrow cells from 8 to 12-weeks-old BALB/c mice were harvested in PBS, and the extraction of neutrophils from bone marrow cells was performed using the Mouse neutrophil Isolation Kit (Solarbio)according to the manufacturer\u0026rsquo;s instruction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTwo-chamber neutrophil migration assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeutrophil migration assays were performed using a Transwell migration assay. Briefly, 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e freshly isolated neutrophils in RPMI 1640 were added to the upper chamber, and rhLRG1(20ug/ml) added to the lower chamber as the chemoattractant. The migrated cells in the lower chamber were counted after 4 h.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn vitro NET analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess NET formation, neutrophils (1\u0026times;10\u003csup\u003e6\u003c/sup\u003ecells) were seeded on coverslips coated with poly-L-lysine in 24-well plates for 30 min before adding rhLRG1(20ug/ml), PAD4i and SB431542. After 6 to 8 h at 37 \u0026deg;C, neutrophils were fixed with 4% paraformaldehyde (PFA) for 10 min at room temperature, washed twice with PBS and were blocked in PBS containing 2% BSA for 30 min, then incubated with anti-H3cit(1:100, ab5103, abcam) and anti-MPO (10 ug/ml, AF3667, R\u0026amp;D) in blocking buffer overnight at 4 \u0026deg;C. After three washes in PBS, cells were incubated with fluorochrome-conjugated secondary antibodies for 1 h, and then counter stained with DAPI. Observation and photographing were performed with the confocal microscopy Cell Observer (ZEISS, Germany).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranswell migration assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor DLD1 cells and HCT116 cells (5\u0026times;10\u003csup\u003e4\u003c/sup\u003e) transwell migration assays. DLD1 cells and HCT116 cells were plated on upper wells for 24h. Then after cell adherence, the medium was replaced by 300 ul serum-free conditioned medium with neutrophils (1\u0026times;10\u003csup\u003e5\u003c/sup\u003e), rhLRG1 (20ug/ml) or Dnase (0.25 mg/ml). Complete medium with 10% FBS was added into the bottom as chemotaxis. For cells counting after 48 h, the cells from the upper surface of the membrane were wiped off, and penetrated cells that crossed the membrane were fixed with 4% paraformaldehyde and stained with crystal violet. The number of penetrated cells were counted under a light microscope in three fields of view, and the average number of cells was calculated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCytokine array analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Serum from two CRC models was obtained by centrifugation and stored at \u0026minus;80 \u0026deg;C for cytokine assays. This was performed using the Mouse Inflammation Array GS1 (Raybiotech, Peachtree Corners, GA, USA) following the instruction of the manufacturer (Wayen Biotechnologies Inc., Shanghai, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA-seq Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw RNA-seq sequencing data (FASTQ format) were aligned to the mouse reference genome mm10 using HISAT2 (v2.1.0) with default parameters to ensure alignment accuracy. Exon-aligned uniquely mapped reads were quantified using featureCounts (Subread package v2.0.6) to generate the raw count matrix. Gene expression data were normalized to Transcripts Per Million (TPM), followed by hierarchical clustering analysis and heatmap visualization using pheatmap (v1.0.12). For the public dataset GSE109480 (retrieved from the GEO database), its raw expression matrix underwent identical TPM normalization, and differential gene expression bar plots were generated using ggplot2 (v3.5.0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-Cell Sequencing Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell sequencing data were processed using the Seurat (v4.2.0) pipeline. Strict quality control was applied: low-quality cells with total UMI counts \u0026lt;1,000 or detected genes \u0026lt;200 were filtered out, along with apoptotic or damaged cells exhibiting mitochondrial gene content \u0026gt;25%. Red blood cells with hemoglobin gene expression (e.g.,\u0026nbsp;Hba-a1,\u0026nbsp;Hbb-bt) \u0026gt;1% were excluded. To mitigate doublet interference, DoubletFinder (v2.0.3) was employed to predict doublets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential Analysis and Pathway Enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) were identified using the Wilcoxon rank-sum test via Seurat\u0026rsquo;s FindMarkers function, focusing on neutrophil subsets with significant upregulation in the \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e-PMN and \u003cem\u003eLrg1\u003c/em\u003e\u003csup\u003e(+/+)Hep\u003c/sup\u003e-Ctrl group (adjusted\u0026nbsp;p-value \u0026lt;0.05, Benjamini-Hochberg correction; log-fold change [logFC] \u0026gt;0.5). For these DEGs, Gene Ontology (GO) functional enrichment analysis was performed using clusterProfiler (v4.7.1), with Benjamini-Hochberg-adjusted\u0026nbsp;q-values \u0026lt;0.05 as the significance threshold. The top 20 enriched terms were ranked by enrichment factors. For PMN-MDSC Signature Score, individual cells were scored using the AddModuleScore function, which calculated the average expression levels of selected genes at the single-cell level and subtracted by the aggregated expression of control feature sets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePseudo-time Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used Monocle3 (v1.3.1) to analyze pseudotime distribution in scRNA-seq data and construct cell trajectories to uncover state transitions within neutrophil populations. During trajectory and pseudotime computation, cells with high differentiation potential predicted by CytoTRACE (v0.3.3) were selected as the root node.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical tests were carried out using GraphPad Prism (v9.5.0). Unless otherwise stated, Experimental data are presented as the mean \u0026plusmn; standard deviation (SD) of at least three biologically independent replicates. For comparing parametric data, a two-tailed unpaired Student\u0026rsquo;s t test was used to determine statistical significance. A p value less than 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and code availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data of the murine liver scRNA-seq, bulk RNA-seq of liver have been deposited at GEO. This paper does not report the original code. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82273359, 82473022 to W.W.; 82003163 to B.C.), Guangzhou Science and Technology Projects (2023B01J1004), the Fundamental Research Funds for the Central Universities, Sun Yat-Sen University (23ptpy145 to B.C.), the Discipline Construction Funding for Pancreatic Hepatobiliary Surgery Department of the Sixth Affiliated Hospital of Sun Yat-Sen University (No. X202102172026091184 to W. P.) and Guangdong Basic and Applied Basic Research Foundation (2024A1515012862 to W. P.). The authors also acknowledge the support from the National Key Clinical Discipline of China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eW.W., and B.C. conceived the study. W.W., W.P. and B.C. supervised the project, and secured the fundings. G.L. and B.C. contributed to designing and conducting \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments. Y.J. did the bioinformatic analysis and helped with\u003cem\u003e\u0026nbsp;in vivo\u003c/em\u003e assay. Q.L., Q.X., C.W., J.C., Y.W., contributed to technical assistance, analysis and mouse models. X.H., X.Q., L.G., D.L., D.Z., Z.Z., D.L., T.M., T.S., Q.T. organized clinical sample collection, patient information and analysis. W.P., Y.C., J.T., R.Z., Q.Y. and L.Z. provided crucial resources or conceptual advice. W.W., and G.L. wrote the manuscript with input from other co-authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB.C., W.W., W.P., and G.L. are inventors of patent applications related to the technology described in this paper. The other authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTsilimigras, D. I.\u003cem\u003e et al.\u003c/em\u003e Liver metastases. \u003cem\u003eNature Reviews Disease Primers\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 27, doi:10.1038/s41572-021-00261-6 (2021).\u003c/li\u003e\n\u003cli\u003ePatras, L., Shaashua, L., Matei, I. \u0026amp; Lyden, D. 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C.\u003cem\u003e et al.\u003c/em\u003e Regulatory T cell control of systemic immunity and immunotherapy response in liver metastasis. \u003cem\u003eSci Immunol\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, doi:10.1126/sciimmunol.aba0759 (2020). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cellular-and-molecular-immunology","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cmi","sideBox":"Learn more about [Cellular \u0026 Molecular Immunology](http://www.nature.com/cmi/)","snPcode":"41423","submissionUrl":"https://mts-cmi.nature.com/cgi-bin/main.plex","title":"Cellular \u0026 Molecular Immunology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7522111/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7522111/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe liver undergoes active remodeling by the primary tumor prior to metastatic spread. However, the mechanisms by which hepatocytes dictate the liver-specific tropism of tumors remain elusive. Here, we identify hepatocyte-derived leucine-rich alpha-2-glycoprotein 1 (LRG1) as a key mediator of liver pre-metastatic niche (PMN) formation. LRG1 remodels the hepatic microenvironment by driving immunosuppressive neutrophils accumulation, impairing effect T cell and dendritic cell function, and enhancing angiogenesis in the liver, thereby fostering a pro-metastatic landscape. Clinically, elevated serum LRG1 correlates with existing or impending liver metastases in patients and mouse models. Hepatocyte-specific ablation of LRG1 dampens pre-metastatic niche formation and significantly reduces metastatic burden in vivo. Hepatic LRG1 induced by tumor-associated inflammation via STAT3, promotes liver metastasis through LRG1-driven neutrophil extracellular trap (NET) formation. Importantly, therapeutic blockade of LRG1 not only suppressed liver metastasis but also reprogrammed the hepatic niche toward an immune-activated state, sensitizing tumors to anti-PD-1 therapy. Collectively, our findings reveal a hepatocyte\u0026ndash;LRG1 axis that drives liver pre-metastatic niche remodeling and highlight LRG1 as a promising target for the prevention and treatment of liver metastasis.\u003c/p\u003e","manuscriptTitle":"Hepatocyte-Derived LRG1 Primes the Liver for Metastasis and Impairs Immunotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 04:57:51","doi":"10.21203/rs.3.rs-7522111/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-09-25T10:24:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-20T07:14:33+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-14T07:03:47+00:00","index":1,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-13T20:48:01+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-06T06:45:32+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-06T06:36:21+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-06T05:48:38+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-09-06T05:46:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T07:18:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T06:35:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cellular \u0026 Molecular Immunology","date":"2025-09-05T03:48:53+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-09-03T07:44:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cellular-and-molecular-immunology","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cmi","sideBox":"Learn more about [Cellular \u0026 Molecular Immunology](http://www.nature.com/cmi/)","snPcode":"41423","submissionUrl":"https://mts-cmi.nature.com/cgi-bin/main.plex","title":"Cellular \u0026 Molecular Immunology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b5421201-9b18-45a6-8120-154aa8a5da3f","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54289544,"name":"Health sciences/Oncology/Cancer/Cancer microenvironment"},{"id":54289545,"name":"Biological sciences/Cancer/Gastrointestinal cancer/Colorectal cancer/Colon cancer"}],"tags":[],"updatedAt":"2026-04-11T07:05:22+00:00","versionOfRecord":{"articleIdentity":"rs-7522111","link":"https://doi.org/10.1038/s41423-026-01408-9","journal":{"identity":"cellular-and-molecular-immunology","isVorOnly":false,"title":"Cellular \u0026 Molecular Immunology"},"publishedOn":"2026-04-10 04:00:00","publishedOnDateReadable":"April 10th, 2026"},"versionCreatedAt":"2025-09-17 04:57:51","video":"","vorDoi":"10.1038/s41423-026-01408-9","vorDoiUrl":"https://doi.org/10.1038/s41423-026-01408-9","workflowStages":[]},"version":"v1","identity":"rs-7522111","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7522111","identity":"rs-7522111","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}