Myeloid cell-expressed MNDA enhances M2 polarization to facilitate the metastasis of hepatocellular carcinoma

Myeloid cell-expressed MNDA enhances M2 polarization to facilitate the metastasis of hepatocellular carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Myeloid cell-expressed MNDA enhances M2 polarization to facilitate the metastasis of hepatocellular carcinoma Yanru Meng, Mengxin Zhang, Xinli Li, Qian Dong, Hu Zhang, Yuanjun Zhai, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3480636/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Dec, 2023 Read the published version in International Journal of Biological Sciences → Version 1 posted You are reading this latest preprint version Abstract Background The molecular subtypes of hepatocellular carcinoma (HCC) with the worst prognosis are characterized by immune disorders dominated by myeloid cell infiltration, but how to accurately screen these patients for accurate diagnosis and treatment is not clear. In this study, based on HCC proteomic data from two independent centers, we found that Myeloid cell nuclear differentiation antigen (MNDA) could be used as a marker of myeloid lymphocyte especially M2 myeloid cell infiltration, and further analyzed the mechanism and potential clinical value of MNDA in promoting poor prognosis of HCC. Methods We investigated the proteomic molecular subtype of HCC and discovered a significant elevation of the myeloid cell nuclear differentiation antigen (MNDA) in the most aggressive subtype. The association between MNDA and the prognosis of HCC was examined using multi-omics data. Gene expression analysis, multiple immunofluorescence and western blot were used for detecting the localization of MNDA in HCC. Cellular co-culture experiments were conducted for exploring the functions of MNDA in vitro while intravenous injections were used in in vivo study. To elucidate its oncogenic mechanisms, we used RNA-seq combined with mass spectrometry analysis and cellular experiments to identify the related signaling pathway. Results MNDA demonstrated significantly elevated expression in the most aggressive subtype of HCC and exhibited a positively correlation with M2 infiltration and HCC metastasis. Moreover, MNDA also functioned as an independent prognostic predictor and has a good synergistic effect with existing prognostic clinical indicators (such as AFP, tumor size, MVI, etc.). We also found that MNDA was primarily expressed in tumor M2 macrophages and contributed to the enhancement of M2 macrophage polarization by upregulating the expression of the enhancers of M2 polarization. Furthermore, MNDA knockdown inhibited the secretion of M2 macrophage-derived pro-metastasis proteins via the exosome pathway to suppress HCC metastasis both in vivo and in vitro. Conclusions MNDA exerts a protumor role by promoting M2 macrophages polarization and HCC metastasis, and can serve as a potential biomarker and therapeutic target for HCC. MNDA HCC metastasis Potential biomarker M2 polarization Exosome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Liver cancer ranks as the sixth most common cancer in the world and is the third leading cause of tumor-associated death, and HCC accounts for 90% of cases. Despite significant advancements in traditional treatments such as surgery, intervention, and radiation therapy, as well as innovative approaches like targeted therapy and immunotherapy, the management of HCC still faces formidable challenges in terms of prevention, early detection, and limited treatment targets [ 1 , 2 ]. Notably, HCC is a typical inflammation-related cancer, characterized by complex interactions between immune cells and cancer cells within the HCC microenvironment [ 3 ]. Targeted immunotherapy, despite high expectations, exhibits substantial variations in different patients, which can be attributed to the immune microenvironment's heterogeneity and a limited understanding of the specific immune molecular subtypes and driving events in HCC [ 4 , 5 ]. Currently, the molecular classification of the immune microenvironment in HCC is being elucidated using multi-omics techniques. For example, by assessing inflammation-related gene expression, immune cell infiltration, and immune regulatory ligands, the immune class has been classified into two components: (1) Active Immune Response Subtype (∼65%) with overexpressed adaptive immune response genes, and (2) Exhausted Immune Response Subtype (∼35%) with immunosuppressive signals like TGF-ß and M2 macrophages [ 6 ]. Using 10x Genomics and SMART-seq2 single-cell RNA sequencing, immune cells from various HCC tissues were extensively characterized. Researchers have identified two distinct states of macrophages in HCC tumors: TAM-like and MDSC-like states. Survival analysis links the characteristic genes of TAM-like state to poor prognosis, including the expression of two key genes, SLC40A1 and GPNMB, which are also associated with unfavorable prognosis [ 7 ]. From a proteomic perspective, we have previously investigated the heterogeneity in early-stage hepatocellular carcinoma and stratified the cohort into the subtypes S-I, S-II and S-III. Patients with HCC of the S-III subtype have a higher level of immune infiltration (especially myeloid cells, exhausted T cells, et al. ), the lowest overall survival rate and the greatest risk of a poor prognosis after first-line surgery [ 8 ]. However, there is a scarcity of effective immune-related biomarkers and interpretive tools to guide clinical decision-making. Tumor-associated macrophages (TAMs) plays a vital role in tumor microenvironment to facilitate tumor progression [ 9 , 10 ]. Generally, macrophages can be polarized to classical activation state (M1) and the alternative activation state (M2). M1 macrophages are activated by lipopolysaccharides (LPS) and interferon-gamma (IFN-γ) to produce proinflammatory cytokines, such as IL6, CXCL10 etc. However, TAMs are thought to resemble M2 macrophages, which are activated by IL-4, IL-10, IL-13 and produce IL-10, CCL22 etc. Recently, it has been reported that M2 macrophages promote tumor development through exosome-mediated communication with tumor cells [ 11 , 12 ]. Particularly, TAMs, a common immune cells that infiltrate in the TME, highly infiltrate S-III subtype of HCC tissues compared to other subtypes and are positively associated with relapse-free survival rate. Thus, delving into additional biomarkers and therapeutic targets connected to TAM and uncovering their association with HCC would greatly enhance their practical implementation in clinical settings. Myeloid cell nuclear differentiation antigen (MNDA) is a member of the hematopoietic interferon-inducible nuclear proteins containing a 200-amino-acid repeat (HIN-200) family, which can induce cell cycle arrest, apoptosis, senescence, inflammation and recognize foreign double-stranded DNA [ 13 – 16 ]. It has been reported that MNDA restricts HIV-1 and other viral pathogens by interfering with Sp1-dependent gene expression and supports an important role in innate antiviral immunity [ 17 ]. Unlike other HIN-200 factors, MNDA is essential for IFNα induction by regulating the expression of IRF7, rather than acting as a cytosolic PRR for dsDNA [ 18 ]. Moreover, MNDA has become a potential therapeutic target for sepsis and inflammatory pathologies by promoting the apoptosis of neutrophils [ 19 ]. MNDA can effectively inhibit proliferation, induce apoptosis and reduce migration of osteosarcoma cells [ 20 ]. Meanwhile, high expression of MNDA is associated with good overall survival in lung adenocarcinoma [ 21 ]. Nevertheless, the functional and mechanistic roles of MNDA in HCC are not yet understood. In this study, we demonstrated for the first time that MNDA exhibited significantly increased expression in the most aggressive subtype of HCC and acted as an independent prognostic predictor, showing a correlation with myeloid immune response and HCC metastasis. Furthermore, MNDA promoted HCC metastasis through enhancing the polarization of M2 macrophages and the secretion of pro-metastatic proteins via the exosome pathway. Thus, our study uncovered MNDA as a novel biomarker of poor prognosis in TMEs and might be exploited as a new strategy to reprogram M2 macrophages with the aim of reducing progression of HCC. Methods Public Data Acquisition We gathered two distinct sets of Hepatocellular Carcinoma (HCC) proteomic datasets, accompanied by relevant clinical information, from published literature. These datasets are referred to as the "Jiang et al’s Cohort" [ 8 ] and the "Gao et al’s Cohort" [ 22 ]. The Jiang et al’s Cohort comprises 101 tumor samples, 98 paracancerous samples, and the Gao et al’s Cohort comprises 159 pairs of tumor and paracancerous samples. In the Jiang et al’s Cohort, any missing values were addressed by imputing the minimum observed value. Furthermore, to facilitate subsequent analytical procedures, log2 transformation was applied to the data. Survival Analysis The survival curves were calculated with the R function survfit from the R package survival with the formula Surv (time, vitalstatus) ~ categorie and plotted with the R function ggsurvplot from the R package survminer. The “surv_cutpoint” function was employed to classify our sample dataset. Subsequently, the power of the "coxph" function was harnessed to construct a Cox Regression model and compute Hazard Ratios (HR). Biological function and pathway enrichment analysis Pathway enrichment analysis was performed using Metascape Gene Annotation & Analysis Resource [ 23 ] that uses several ontology sources: KEGG Pathway, GO Biological Processes, Reactome Gene Sets, Canonical Pathways and CORUM. Proteins were clustered according to their pathways and processes. Relationships between genes were identified using the network map generated from the Metascape tool and visualized with Cytoscape. Gene set enrichment analysis (GSEA) is a method of analyzing and interpreting microarray and such data using biological knowledge. The data firstly generated an ordered list of logFC based on differential analysis, and then a predefined gene set receives an enrichment score (ES), which was a measure of statistical evidence rejecting the null hypothesis that its members were randomly distributed in the ordered list. The R package clusterProfiler was mainly used for GSEA analysis. Cell lines and cell culture All hepatocellular carcinoma cell lines (Huh7, PLC/PRF/5 (PLC), MHCC-97H) were purchased from the Shanghai Cell Bank, Chinese Academy of Sciences (Shanghai, China), and were subjected to cell identification and mycoplasma detection. All hepatocellular carcinoma cell lines were cultured in DMEM (Gibco, USA) medium supplemented with 10% FBS and 1% penicillin-streptomycin (Gibco, USA). Human monocyte line THP-1 was purchased from Shanghai Zhongqiao Xinzhou Biotechnology Co., Ltd. and cell identification and mycoplasma detection were performed (Shanghai, China). The cell lines were cultured in RPMI-1640 (Gibco, USA) medium supplemented with 10% FBS and 1% penicillin of streptomycin (Gibco, USA). All cells were maintained in a humidified incubator at 37°C with 5% CO2 cells within 50 passages were used for experiments. Macrophage polarization assay THP-1 cells were treated with 162 nM phorbol 12-myristate 13-acetate (PMA, P8139, Sigma) for 24 h to differentiate into M0 macrophages. On the basis of M0, cells were polarized to the M1 phenotype after treatment with 100 ng/mL Lipopolysaccharide (LPS, L2880, Sigma) and 20 ng/mL Interferon-γ (IFN-γ, 300-02, Peprotech) for 24 hours. Based on this M0 state, cells were polarized to the M2 phenotype after treatment with 20 ng/mL interleukin-4 (IL-4, 200-04, Peprotech) for 48 hours. MNDA Knockdown stable cell line generation We used retroviral vectors containing short hairpin (sh) RNA for MNDA CCCAAACAGAATTATCGAAAT (denoted as shRNA1), GCACAATATCAAGTGTGAGAA (denoted as shRNA2), to transfect THP-1 cells. The transfected cells were selected in 4 µg/ml puromycin. Immunostaining Liver cancer tissues were first harvested and fixed overnight in 4% paraformaldehyde (PFA). Thereafter, the tissues were embedded in paraffin and sliced at 5 µm. The sections were blocked with 10% goat serum for 30 min at 37°C and then incubated overnight at 4°C with the following primary antibodies: MNDA (HPA034532, 1:1000; sigma), CD163 (ab182422, 1:100; Abcam), CD3 (ab5690, 1:100; Abcam). Samples were then incubated with the corresponding Alexa Fluor-conjugated secondary antibodies (1:100, Thermo Fisher Scientific) and finally examined using a confocal microscope (LSM 880, Carl Zeiss AG). Isolation and characterization of exosomes When macrophage confluence reached 80–90%, the cell culture medium was removed. Cells were washed twice with pre-warmed PBS and then macrophages were cultured in conditioned medium supplemented with 10% exosome-free fetal bovine serum (FBS, C3801-0050, VivaCell) for 48 hours. Approximately 50 mL of cell culture medium was collected for each cell line, the collected medium was centrifuged at 300 × g for 10 minutes, followed by centrifugation at 2,000 × g for 30 minutes to remove cell debris. Next, the supernatant was filtered using a 0.22-µm filter (Millipore). A 12,000 × g centrifugation was performed for 40 min, the precipitate was discarded, and the supernatant was retained. The exosomes in the supernatant were precipitated by ultracentrifugation at 100,000 × g for 90 min. After washing the exosome precipitate in PBS, the exosomes were precipitated again and resuspended in 500 µL of PBS. The protein content of the exosomes was determined by using the BCA Protein Assay Kit (Thermo Fisher Scientific). The exosomes were stored at -80°C for further use. Expression of exosome-specific marker proteins, such as CD63 and CD81, was detected by Western blotting. Exosomes were identified using transmission electron microscopy. First, 10 µL of exosomes were added dropwise to the copper grid, left for 1 min, and then the liquid was blotted from the side with filter paper. Next, 10 µL of uranyl acetate was added dropwise, left for 1 min, and then imaged at 100 kv for electron microscopic detection. The particle size and concentration of exosomes were measured using nanoparticle tracking analysis (NTA). First, frozen samples were taken, thawed in a 25°C water bath, and placed on ice. Then, exosome samples were diluted with 1 × PBS and used directly for NTA assay. For exosome uptake experiments, exosomes were labeled with the PKH67 fluorescent Cell adaptor kit (Sigma-Aldrich) according to the manufacturer's instructions. That is, 50 microliter exosomes were mixed with 500 µL Diluent C; 2 µL of PKH67 and 500µL Diluent C were mixed and incubated for 4 min. Then 1mL of 1% BSA was added and incubated for 1min, mainly for termination of staining. Labeled exosomes were washed with PBS, collected by ultracentrifugation, and re-suspended in PBS. Exosomes were incubated with HCC cells and analyzed using confocal microscopy at the indicated time points. Western blot analysis Total proteins from cells or exosomes were extracted using RIPA lysis buffer containing protease inhibitor (A32955, Thermo). Protein lysates were then quantified using the BCA kit (#23227, Thermo Fisher Scientific). Proteins were separated by electrophoresis using 10% sodium dodecyl sulfate polyacrylamide (SDS-PAGE) and transferred to PVDF membranes (Millipore). The membranes were blocked with 5% skim milk for 1 h at room temperature and then incubated overnight at 4°C with the following primary antibodies: CD63 (ab217345, 1:1000; Abcam), CD81 (#66866-1-Ig, 1:1000; Proteintech), Calnexin (ab22595, 1:1000; Abcam), GAPDH (ab8245, 1:5000; Abcam). The membranes were then incubated with horseradish peroxidase-conjugated secondary antibodies of the corresponding species for 1 h at room temperature and subjected to electrochemiluminescence development by chemiluminescence instrumentation. Antibodies and reagents are detailed in Supplementary Table 1. RNA extraction and quantitative reverse transcription PCR (qRT-PCR) Total RNA was extracted using the TRIzol Reagent (#15596018, Life Technologies), according to the manufacturer's instructions, then reverse transcribed into cDNA using reverse transcriptase master mix (R312-00, Vazyme). The cDNA was subjected to qRT-PCR, performed on CFX96 qPCR system (Bio-Rad) using the SYBR Green Real-time PCR Master Mix (Q712-02, Vazyme) according to the manufacturer's instructions. The primers used in this study were synthesized by TsingkeBiotechnologyCo.Ltd. In this experiment, the relative transcript levels of the target genes were calculated by the 2- △△CT method using GAPDH as the internal reference: △△Ct = △Ct experimental group - △Ct control group, where △Ct = Ct target gene - Ct internal reference. The primer sequences are detailed in Supplementary Table 2. Flow cytometry When the macrophage fusion reached 80–90% after induction, the cells were washed twice with 1×PBS. The cells were then digested with 5 mM EDTA and collected into centrifuge tubes for centrifugation (4°C, 500 × g, 5 min), washed twice with PBS containing 1% FBS, and counted. The cells were then incubated in the dark for 30 min at 4°C with the corresponding antibody. Cells were washed 3 times with PBS. The stained cells were analyzed by flow cytometry (Fortessa, BD Biosciences, USA). Antibodies used for flow cytometry: CD163 (#326505, Biolegend), CD206 (#321109, Biolegend). Data were analyzed by FlowJo software 8.7.1 (Treestar Inc., USA). Cell migration assay The migration and invasion ability of HCC cells was determined by using 24-well Transwell chambers with upper and lower culture chambers separated by polycarbonate membranes with 8 µm pores (BD Biosciences, Franklin Lakes, NJ, USA). 1×10 5 cells were resuspended in 400 µL of serum-free DMEM medium and inoculated into the upper chamber. The lower chamber was filled with 600 µL of M2 supernatant or exosomes containing 10% FBS, and PBS was used as a control. After incubation for 24 h in a humidified incubator containing 5% CO2 maintained at 37°C, the remaining cells in the upper chamber were removed, and cells migrating to the surface of the lower chamber were fixed with 4% paraformaldehyde and stained with 0.5% crystal violet. At least five random microscopic views (magnification ×100) were taken and the cells were counted. Three independent experiments were performed. Wound healing assay The cells were seeded in a six-well plate. Once the cells became sub-confluent, a 200 µL pipette tip was used to create a scratch wound, after 48 hours of co-culture with the conditioned medium and HCC cells, the width of the scratch was observed and images were obtained under microscope. The results are expressed as a percentage of wound closure. Cell proliferation assay Cell viability was tested with Cell Counting Kit-8 (CCK8) kit (Beyotime) according to the manufacturer's instructions. HCC cells were trypsinized, counted and then plated at a density of 8,000 cells per well in 96-well plates. After that, they were co-cultured with M2 CM. The cells were incubated at 37°C. The attached cells were treated with the CCK8 dilution for 1 hour, and then the absorbance of each well of the plates was measured at 450 nm. RNA Sequencing and Raw Data Preprocessing Total RNA from the cells was extracted using Trizol. RNA library construction and sequencing were performed by Berry Genomics Co. Ltd. Then the cDNA library was sequenced on an Illumina Hiseq 2500. Quantile normalization, log2 conversion, missing values supplement and differential analysis for the matrix data of RNA-Seq dataset were performed using the “limma” package in R/Bioconductor software (version 4.2.1). Proteomic analysis Peptides (300 ng) obtained from M2 exosomes were used for LC-MS analysis. The LC-MS analysis was performed using an EASY nLC1200 FAIMS Orbitrap Exploris 480 mass spectrometer (Thermo Fisher Scientific, USA). The MS system uses Data Independent Acquisition (DIA) for scanning. The raw LC-MS/MS data files were analyzed using Spectronaut (version 17.2.230208.55965) with the spectra searched against the UniProt human database. Search parameters were set as follows: enzyme digestion: Trypsin/P digestion; variable modifications including: oxidation of methionine (M), protein N-terminal acetylation; immobilised modifications including: urea methylation (C); maximum of two missing trypsin cleavage sites allowed; Filtering for the protein and peptide identification was set at a 1% false discovery rate (FDR). Animal studies The animal care and experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of National Center for Protein Sciences (Beijing), Ethical review number: IACUC-20221209-78MT. NOD-SCID mice were purchased from Charles River, Inc (Beijing, Vital River Laboratory Animal Technology). All mice were 5–6 weeks old males. To examine the metastatic ability of the HCC cells, 6-week-old male NOD SCID mice were intravenously injected via the tail vein with 2 × 10 6 MHCC-97H cells that had been treated with M2 CM. The number of metastatic lungs, the number of clones in the lungs, and small metastatic foci were compared across the four groups. Statistical Analysis R version 4.2.1 was used for displaying and computation of publicly available data, RNA-seq and proteomics graphs using the R-packages ggplot2. Other analysis of data was performed using GraphPad Prism 8.0 software. The Student’s t-test was used for two-group comparisons, and one-way or two-way ANOVA was used for comparisons among more than two groups. All experiments were performed at least three times, and the data are presented as the mean ± SD. A P-value of less than 0.05 indicates statistical significance. Results MNDA abundance positively correlates with poor prognosis, inflammation and metastasis in HCC patients In our quest to delve deeper into the common malignant attributes of HCC, we embarked on an analysis aimed at identifying proteins that were highly expressed specifically in the S-III [ 8 ] and S-Pf [ 22 ] subtypes. Our investigation revealed that, in comparison to the other two subtypes, a total of 22 proteins exhibited significant up-regulation in the S-III/S-Pf subtypes (see schematic overview of the study design in Fig. 1 A). The 22 proteins were subjected to modular analysis using the STRING database ( https://cn.string-db.org/ ). The analysis revealed that a majority of these proteins were associated with myeloid cells (Fig. 1 B), indicating that myeloid cell infiltration could stably predict for poor outcomes in HCC patients. Notably, MNDA showed the highest score indicating a significant prognostic risk (Fig. 1 C). Recent research found that aberrant expression of MNDA occurred in lung adenocarcinoma and nodal marginal zone lymphoma[ 21 ] [ 24 ]. However, the role of MNDA in HCC remained unclear and needed to be elucidated. To explore the function of MNDA in HCC, we divided patients into two groups: the MNDA high group and the MNDA low group, based on the maximum survival difference of MNDA in proteomic cohorts. Subsequently, we performed functional enrichment analysis using the differentially expressed proteins between the two groups in both cohorts (Jiang et al's Cohort: p 2; Gao et al's Cohort: p 1.5; n = 194). The results revealed that the signaling pathways were primarily enriched in inflammatory response, cell migration, integrin-mediated signaling pathway etc (Fig. 1 D). GSEA further revealed that inflammatory response and metastasis were enriched in MNDA high groups in Jiang et al's Cohort and Gao et al's Cohort (Fig. 1 E and Supplementary Fig. 1A). Then, we conducted a rigorous correlation analysis to investigate the relationship between MNDA and a selection of identified immune response and migration-related proteins. Our analysis revealed a robust and significant correlation between MNDA and these proteins, exemplified by ITGAM [ 25 ], ITGB2 [ 26 ], TIMP-1 [ 27 ], S-100A4 [ 28 , 29 ] (Fig. 1 F and G and Supplementary Fig. 1B and C). These findings suggested the potential role of MNDA in shaping the tumor immune microenvironment, particularly in HCC patients with metastasis. MNDA augments the prognostic stratification capability of clinical indicators To investigate the association between MNDA and the prognosis of HCC patients, we first conducted Kaplan-Meier survival analysis using the two proteomic cohorts and The Cancer Genome Atlas (TCGA) dataset. The results revealed that patients with higher MNDA expression, regardless of the protein or mRNA level, exhibited significantly poorer overall survival (OS) (Fig. 2 A-C). Furthermore, higher levels of MNDA expression were notably correlated with shorter disease-free survival (DFS) in HCC patients, as observed in Jiang et al's Cohort (Supplementary Fig. 2A). Consistently, the analysis conducted on https://www.cbioportal.org/ revealed that patients with genomic amplification of MNDA had worse OS and DFS compared to those with unaltered genomic amplification of MNDA (Fig. 2 D and Supplementary Fig. 2C). In the two proteomics cohorts, MNDA exhibited a substantial up-regulation in the S-III/S-Pf subtypes compared with adjacent tissues or other HCC subtypes. This underscored that the distinctive expression patterns of MNDA were closely associated with the degree of malignancy of HCC (Supplementary Fig. 2D). Together, these results suggested that the elevated levels of MNDA were commonly found in highly aggressive HCC cases, correlating with a poor prognosis. Seven factors (i.e., Age, Gender, AFP, Tumor number, Tumor size, BCLC stages, and MVI) were identified as statistically significant factors associated with overall survival (OS) in HCC patients. To further investigate the role of MNDA in clinical diagnosis, we carried out univariate and multivariate analyses. The results indicated that the performance of MNDA as a prognostic marker surpassed most clinical indicators (Fig. 2 E). Furthermore, MNDA had no significant correlation with current clinical indicators that suggest prognosis (such as AFP, tumor size, MVI, etc.), this data implied that MNDA could serve as an independent prognostic factor (Supplementary Fig. 2E). The co-occurrence of high MNDA levels and clinical indicators was associated with a significantly worse overall survival (OS) in HCC patients, as illustrated in Fig. 2 F and Supplementary Fig. 2F. Taken together, these data suggested that MNDA augments the prognostic stratification capability of clinical indicators. MNDA is mainly expressed in M2 macrophages To explore the function of MNDA involved in HCC progression, we firstly examined the expression features of MNDA. MNDA is expressed only in myeloid rather than hepatoma cell lines (Supplementary Fig. 3A). Furthermore, we applied a Smart-seq-based scRNA-seq method to study the expression of MNDA in 7 immune cell types which including B cells, dendritic cells (DCs), mast cells, monocytes/macrophages, natural killer (NK) cells, plasma and T cells, and found that MNDA was mainly expressed in monocytes/macrophages (Fig. 3 A). Moreover, according to the data from the online website (TIMER 2.0), unlike other HIN-200 families, MNDA was found to have the highest correlation with M2 macrophages (Fig. 3 B). To further validate these findings, we also performed multiplexed immunofluorescent staining to provide evidence that MNDA co-localized with CD163 + M2 macrophages rather than CD3 + T cells (Fig. 3 C). The association between MNDA and M2 expression from proteomic cohorts also showed that MNDA was positively correlated with M2 infiltration (Fig. 3 D). The differentiated THP-1 monocytes are widely used as in vitro models of human macrophages [ 30 ], and to unveil the expression of MNDA in polarized macrophages, M0, M1 and M2 macrophages were differentiated from THP-1 cells. The upregulation of the pan macrophage marker CD68 indicated that the M0 macrophage phenotype was successfully differentiated (Supplementary Fig. 3B). In induced M1 macrophages, the markers for M1 ( IL6 , CXCL10 ) rose compared to M0 and M2 macrophages, while the markers for M2 ( IL-10 , CCL22 and CD206 ) rose only in induced M2 macrophages (Supplementary Fig. 3C and D). The flow cytometry analysis also showed a higher level of CD206 and CD163 in M2 macrophages (Supplementary Fig. 3E and F). These results confirmed the success of the cellular M1 and M2 polarization model establishment we used. In differentiated and induced M0, M1 and M2 macrophages, the expression of MNDA was examined and the results showed that MNDA was significantly highly expressed in M2 macrophages (Fig. 3 E). Taken together, these results confirmed the high expression of MNDA in M2 macrophages. M2 macrophages share numerous characteristics with TAMs and exhibit several key features associated with malignant tumors, including angiogenesis, invasiveness, metastasis, regulation of the TME, and therapeutic resistance [ 31 ]. Indeed, we found M2 macrophage score had a higher value in S-III subtype, which was associated with the poorest outcome and featured immune dysregulation and tumor metastasis (Supplementary Fig. 3G). Consistently, GSEA analysis further revealed the enrichment of the inflammatory response and metastasis pathways in the M2 high group by analyzing two independent proteomics datasets (Supplementary Fig. 3H). MNDA enhances M2 macrophages polarization To clarify the role of MNDA in M2 macrophages, we specifically knocked down MNDA in THP-1 cells using lentivirus-mediated gene transfer and established two MNDA stable knockdown THP-1 cell lines (shMNDA1 and shMNDA2) and its control cell (shCtrl) (Fig. 4 A). The shMNDA and its control THP-1 cells were then induced for M2 polarization, and RNA-seq analyses were performed to compare the transcriptome of these M2 macrophages The transcriptome data showed that 606 genes (329 downregulated and 277 upregulated) were differentially expressed with statistical significance (Fig. 4 B and supplementary Table 3). Heatmap showed the significantly downregulation of CCL22 [ 32 ], TGFB1 [ 33 ], ETV1 [ 34 ], CSF1 [ 35 ], BMP7 [ 36 ], SGMS2 [ 37 ], CXCL14 [ 38 ] and S100A4 [ 39 ] which have been reported to enhance protumor macrophage polarization via various pathways. (Fig. 4 C). We validated our transcriptomics data by qRT-PCR (Fig. 4 D). Consistently, flow cytometry analysis also revealed a close correlation between MNDA deficiency and decreased expression of CD163 and CD206 (Fig. 4 E). Overall, these findings suggested that MNDA might play a regulatory role in the expression of M2-related molecules and contribute to the enhancement of M2 macrophage polarization. MNDA drives the secretion of pro-metastatic proteins by M2 macrophages We next proceeded to unveil the modulators of MNDA in M2 macrophages using the transcriptome data. KEGG analysis was performed for genes significantly down-regulated after knockdown of MNDA in M2 macrophages, and the results showed that signaling pathways were mainly enriched in extracellular matrix organization, cell-cell adhesion, cytokine production, inflammatory response, etc (Fig. 5 A). Strikingly, among the 329 downregulated genes, 45 genes were identified as secretory proteins, which were closely associated with extracellular matrix organization, wound healing, and cell migration (Fig. 5 B and C). Heatmap showed 17 proteins that associated with tumor metastasis in the RNA-Seq data, were significantly down-regulated with the knockdown of MNDA (Fig. 5 D). Consistently, the proteome data indicated that high expression of most of the proteins was associated with a poor prognosis for HCC patients (Fig. 5 E). qRT-PCR confirmed that MNDA positively affected the expression of some pro-metastasis genes ( TIMP1, ITGB5, MMP14, COL6A1, COL6A2 and COL6A3 ) in M2 macrophages (Fig. 5 F). These data demonstrated that MNDA positively regulated the secretion of pro-metastasis proteins by M2 macrophages. Therefore, we hypothesized that MNDA might promote invasion and migration of HCC cells by regulating the expression of pro-metastatic secretory proteins from M2 macrophages. MNDA promotes HCC cells invasion and migration via serum-derived from M2 To confirm the assumption, next, we collected the conditioned medium (CM) from the control and MNDA knockdown M2 macrophages. The results of transwell and matrigel assays showed that the incubation with CM from the control M2 macrophages increased the migration and invasion abilities of tumor cells (Huh7, PLC, MHCC-97H), whereas the promotion of the CM from the MNDA knockdown cells on tumor cell migration and invasion was weakened (Fig. 6 A and Supplementary Fig. 4A). Similarly, in scratch test, CM derived from M2 macrophage-treated HCC cells showed increased mobility, and the culture with shMNDA M2 CM led to a significant reduction in the HCC cell mobility potential compared with M2 CM–treated cells (Fig. 6 B and Supplementary Fig. 4B). We subsequently co-cultured with the HCC cells with CM from M2 macrophages, and the proliferation displayed no clear alterations (Supplementary Fig. 4C). To further elucidate the effect of MNDA on in vivo metastasis ability of HCC cells, we intravenously injected MHCC-97H cells that had been pre-incubated with M2 conditioned media (CM) into NOD-SCID mice (Fig. 6 C). Subsequently, we monitored the development of the lung metastasis in these mice. 8 weeks later, histological analysis of the lung tissues revealed a significant increase in the incidence of lung metastases in mice inoculated with MHCC-97H cells treated with M2 CM. However, the promotion of CM from MNDA downregulated M2 macrophages on tumor cell lung metastases was weakened (Fig. 6 D and E). Notably, there was no significant difference in body weight between the different groups of mice (Supplementary Fig. 4D). These findings indicated that MNDA had the potential to enhance HCC cell metastasis both in vivo and in vitro via regulating the expression of secretory proteins from M2 macrophages. MNDA promotes HCC cells migration via exosome-derived from M2 Emerging evidence suggests the central role of exosomes in intercellular communication in tumor metastasis [ 40 ]. To further study the effect of exosomes on the biological functions of MNDA on HCC cells in vitro , we added a specific exosome secretion inhibitor GW4869 to the M2 macrophage culture medium. Transwell assays revealed that treatment with GW4869 decreased the promotion of M2 CM on the migration abilities of Huh7, PLC and MHCC-97H HCC cells (Supplementary Fig. 5A). These results indicated that M2 exosomes might be accountable for the effects exerted by M2 macrophages on HCC cells. Subsequently, we extracted exosomes from M2 macrophage supernatants by ultracentrifugation, and western blot results showed that protein levels of the exosome markers CD63 and CD81 were significantly increased in the extracted exosomes compared with cell lysis, whereas the exosome-negative marker calnexin was not detectable in the extracted exosomes (Supplementary Fig. 5B). Transmission electron microscopy results showed that the shape of the extracted exosomes had a typical two-layer membrane structure, and nanoparticle tracking analysis (NTA) results indicated that the average diameter of exosomes was 132 nm (Supplementary Fig. 5C and D). The above results confirmed the success of exosome extraction. We next detected whether these exosomes could be internalized by HCC cells. Exosomes were labeled with PKH67, a green fluorescent carbocyanine dye, which was followed by the treatment of HCC cells with these labeled exosomes. We used a fluorescence microscope to confirm that PLC cells and Huh7 cells could take up exosomes derived from M2 macrophage with a robust, time-dependent method (Supplementary Fig. 5E). To gain insight into the molecular mechanism of MNDA, we studied the regulation of MNDA on the M2 derived exosome. The exosomes from shCtrl and shMNDA of M2 macrophages were isolated and the protein expression profiles in M2 exosomes were analyzed by using mass spectrometry assay (supplementary Table 4). The results showed dramatic differential expression of proteins between the two sets, and the differential proteins were closely related to cytokine signaling, β-catenin independent WNT signaling, extracellular matrix organization and cell migration (Fig. 7 A). In particular, a panel of proteins, including NRP2, TIMP1, ITGB2, ITGAM, COL6A1, COL6A2, COL6A3, CCL5 and LCP1, were significantly down-regulated in M2 shMNDA exosomes (Fig. 7 B). As shown in Supplementary Table 5, the higher expression of these proteins predicted poorer OS and DFS. We explored whether MNDA has a critical role in promoting the migratory potential of HCC cells via M2-derived exosomes. The exosomes derived from shCtrl, shMNDA1 and shMNDA2 of M2 macrophages were collected and used in the function experiments in vitro . Transwell assays showed that the exosomes derived from MNDA knock down M2 macrophages attenuated the migration of Huh7 cells and PLC cells (Fig. 7 C). Importantly, the addition of M2 exosomes partially restored the weakened promotion of cell migration resulting from the downregulation of MNDA in M2 macrophages (Fig. 7 D). The aforementioned results suggested that MNDA promoted the migration of HCC cells through exosomes derived from M2 macrophages. Discussion Liver cancer is the third-highest cause of cancer-related deaths worldwide, and HCC is the predominant form of liver cancer [ 1 ]. Despite treatments with sorafenib, regorafenib and immune checkpoint blockades (ICBs) benefits for other cancer indications, the response rates in HCC were unsatisfactory [ 41 ]. The complexity and heterogeneity of TME is one of the important reasons. Nevertheless, HCC is an inflammation-driven disease with underlying chronic liver inflammation and cirrhosis, a quarter of HCC cases express markers of inflammatory response [ 42 ]. It has been reported that the immunosuppressive features of tumor lesions participate not only as one of the major players inducing cancer progression, but also a major challenge for effective immunotherapy resistance [ 43 ]. Consequently, there is an urgent need to characterize the molecular characteristics of tumor immunosuppressive environment to explore biomarkers and targets of tumor-infiltrating immune cells in HCC. In this study, based on HCC proteomic data from two independent centers, we found that the most malignant HCC (S-III/S-Pf) were characterized by immune disturbances dominated by myeloid cell infiltration. Particularly, TAMs represent the most abundant immune population in the myeloid cells and are widely considered to be M2-like macrophages that promote tumor progression and suppress local immunity [ 41 ]. To further explore the potential biomarkers and molecular mechanisms of high M2 infiltration and the crosstalk of M2 with HCC, MNDA attracted our attention. MNDA is a nuclear factor initially identified in normal and transformed cells of the human myelo-monocytic lineage (granulocytes, monocytes, and macrophages) and its use as a marker of myeloid cell differentiation has been proposed [ 42 ]. Increasing evidence shows that MNDA expression is associated with clinicopathological features in patients with tumors, yet the function of MNDA in HCC has not been reported. In this study, we found that MNDA as one of the representative proteins of myeloid cells was elevated significantly in the most malignant S-III/S-Pf subtype HCC and positively correlated with M2 infiltration. To explore the association between MNDA and poor prognosis in HCC patients, multiple omics data had been used. We observed patients with high MNDA expression had shorter OS than those with low MNDA expression in both DNA, mRNA and protein levels. In addition, MNDA as an independent prognostic indicator was superior to most clinical markers (AFP level, tumor diameter, number of tumors, BCLC stage, and presence of MVI). To further evaluate the potential diagnostic value of MNDA in HCC, we found that combining MNDA with clinical indicators could further predict the adverse prognosis of HCC. Interestingly, we found that mainly M2 macrophages-expressed MNDA could promote M2 macrophages polarization and HCC cells metastasis. These data suggested that a higher expression of MNDA might serve as a therapeutic target for the TME of HCC. To decipher the mechanisms underlying how MNDA modulates TAMs with protumor functions, our RNA-seq analysis revealed that reducing macrophagic MNDA could reverse the typical M2-like gene expression signature. Meanwhile, the decreased expression of MNDA also reduced the factors associated with alternative polarization toward a protumor phenotype, such as CSF1 , S100A4 . Therefore, it is logical that any intrinsic or extrinsic factors that can induce MNDA upregulation may induce TAM protumor polarization and the poor prognosis of tumor. On the other hand, it has been well-illustrated that TAMs release a wide range of chemokines and cytokines, which potentiate tumor invasion and metastasis [ 44 ]. In this study, MNDA regulated the expression of 45 secretory genes in M2 macrophage, which were associated with extracellular matrix organization and cell migration. A large number of secretory factors were significantly up-regulated in the proteome cohorts. Furthermore, we demonstrated that MNDA promoted the invasion and migration of HCC cells via serum-derived M2 macrophages. Exosomes are usually constituted by small vesicles that are released from a variety of cell types [ 45 ]. Functionally, exosomes can transfer biomolecules (such as proteins, RNAs and DNAs), which have intriguing and elaborate roles in TMEs [ 46 ]. The dysfunction of exosomes has been widely investigated in HCC development [ 47 ]. Here, we show the evidence of M2 macrophage-derived exosomes in malignant progression of HCC. For example, macrophages-derived exosomes transmit miR-92a-2-5p to liver cancer cells to increase the invasion capacity of liver cancer [ 48 ]. In addition, Xu et al. found that macrophage-mediated regulation of the metabolic reprogramming in HCC cells via the exosomal delivery of oncogenic lncMMPA [ 49 ]. In this study, we firstly revealed that the level of some proteins (such as NRP2, TIMP1, ITGB2, ITGAM) associated with tumor metastasis promotion was decreased in the exosomes derived from MNDA knockdown M2 macrophages, which could be taken up by HCC cells. These findings provided the groundwork for macrophage exosomes research toward a further understanding of their clinical and pathological importance. Conclusions In conclusion, our study delineated that MNDA enhanced M2 macrophage polarization by upregulating the enhancers of protumor macrophage polarization and promoted HCC cell metastasis by delivering the pro-metastasis proteins via M2 derived exosomes (Fig. 8 ). Moreover, MNDA could serve as a stratification biomarker and potential therapeutic target for HCC with higher M2 infiltration and poorer prognosis, in attempts to improve the survival rates of these patients. Abbreviations MNDA: Myeloid cell nuclear differentiation antigen HCC: Hepatocellular carcinoma TAM: Tumor-associated macrophages TIME: Tumor immune microenvironment HR: Hazard ratios GSEA: Gene set enrichment analysis ES: Enrichment score TCGA: The Cancer Genome Atlas OS: Overall survival DFS: Disease-free survival NTA: Nanoparticle tracking analysis CM: conditioned media S-Mb: metabolism subgroup S-Me: Microenvironment dysregulated subgroup S-Pf: Proliferation subgroup IL4: Interleukin-4 IFN-γ: Interferon-γ PMA: Phorbol 12-myristate 13-acetate Declarations Ethics approval and consent to participate The animal care and experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of National Center for Protein Sciences (Beijing), Ethical review number: IACUC-20221209-78MT. Consent for publication All authors have read and approved the final version of this manuscript. Availability of data and materials Data related to this paper may be requested from the corresponding author. Competing interests The authors declare that they have no competing interests. Funding Funding was provided by National Key R&D Program of China (No. 2021YFA1301604), National Natural Science Foundation of China (32088101, 82372835), National Science Foundation for Young Scientists of China (82203601,82303197), Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS) (2019-I2M-5-063), The Specific Research Fund for TCM Science and Technology of Guangdong Provincial Hospital of Chinese Medicine (No. YN2022DB04). Authors’ Contributions AH.S, CY.T, MX.Z and FC.H designed and supervised the study. YR.M, MX.Z and XL.L performed the major experiments and analyzed data. Q.D and YJ.Z assisted with bioinformatics. H.Z helped with the animal studies. XX.W provided pathologic tissue sections of liver cancer patients. MX.Z, YR.M and XL.L wrote the paper. 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Supplementary Files Supplementaryfigurelegends.pdf SupplementaryTable3.xlsx SupplementaryTable4.xlsx Cite Share Download PDF Status: Published Journal Publication published 31 Dec, 2023 Read the published version in International Journal of Biological Sciences → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3480636","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":242154890,"identity":"a8361a98-3d7f-49de-8377-ab47a04c7b37","order_by":0,"name":"Yanru Meng","email":"","orcid":"","institution":"Medical College of Qingdao University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanru","middleName":"","lastName":"Meng","suffix":""},{"id":242154892,"identity":"a1617c80-0a25-4cec-9871-4f71eaa3a54c","order_by":1,"name":"Mengxin Zhang","email":"","orcid":"","institution":"National Center for Protein 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07:59:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3480636/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3480636/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.7150/ijbs.91877","type":"published","date":"2024-01-01T04:01:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":45363560,"identity":"1617c5f2-110e-4425-8ccd-4587afad7ff2","added_by":"auto","created_at":"2023-10-28 13:33:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1237581,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMNDA abundance positively correlates with poor prognosis, inflammation and metastasis in HCC patients.\u003c/strong\u003e (A) Workflow for selecting potential prognostic proteins for S-III/S-Pf HCC. Green represents Jiang et al’s Cohort while yellow represents Gao et al’s Cohort. (B) 22 proteins in the string database for modular analysis. Yellow circles represent myeloid cells; red circles represent secretion by cells; green circles represent Extracellular matrix organization; purple circles represent Positive regulation of cell migration. (C) The heatmap according to HR ordering of significantly differentially expressed 15 proteins (FDR q value \u0026lt; 0.05, t test). HR, hazard ratio (an HR of greater than 1 indicates that the observed variable increases the risk of death). (D) Functional enrichment analysis using the differential proteins between MNDA\u003csup\u003elow\u003c/sup\u003e group and MNDA\u003csup\u003ehigh\u003c/sup\u003e group (Jiang et al.'s cohort: p\u0026lt;0.05\u0026amp;FC\u0026gt;2; Gao et al.'s cohort: p\u0026lt;0.05\u0026amp;FC\u0026gt;1.5; n=194). (E) GSEA of inflammatory response and Liao metastasis pathways in the MNDA\u003csup\u003elow\u003c/sup\u003e versus MNDA\u003csup\u003ehigh\u003c/sup\u003e groups in the Gao et al’s Cohort. (F) The heatmap of MNDA associated with inflammatory response and metastasis-related proteins in the Gao et al’s Cohort. (G) Pearson correlation was used to analyze the relationship between MNDA and ITGAM, ITGB2, TIMP1 and S100A4 in the Gao et al’s Cohort.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/b435c694695b1de5d84471a4.png"},{"id":45365436,"identity":"1455f4bf-b8b7-4446-bb1d-691d0ff78d3f","added_by":"auto","created_at":"2023-10-28 13:49:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":390739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMNDA augments the prognostic stratification capability of clinical indicators. \u003c/strong\u003e(A-C) Kaplan–Meier analysis of the OS probability for HCC patients with different MNDA expression intensities under the optimal cut-off value in Jiang et al’s Cohort, Gao et al’s Cohort and TCGA-LIHC cohort. (D) Kaplan–Meier analysis of the OS probability for HCC patients with different genomic amplification of MNDA (cBioPortal). (E) Univariate and multivariate variable Cox regression model analysis of MNDA and clinical factors in two proteome cohorts. (F) Kaplan-Meier curves of Gao et al. 's Cohort demonstrating differences in overall survival among patients with AFP\u0026lt;200 ng/mL \u0026amp; MNDA\u003csup\u003elow\u003c/sup\u003e, AFP\u0026lt;200 ng/mL \u0026amp; MNDA\u003csup\u003ehigh\u003c/sup\u003e, AFP≥200 ng/mL \u0026amp; MNDA\u003csup\u003elow\u003c/sup\u003e, AFP≥200 ng/mL \u0026amp; MNDA\u003csup\u003ehigh\u003c/sup\u003e; Diameter\u0026lt;5 cm \u0026amp; MNDA\u003csup\u003elow\u003c/sup\u003e, Diameter\u0026lt;5 cm \u0026amp; MNDA\u003csup\u003ehigh\u003c/sup\u003e, Diameter≥5 cm \u0026amp; MNDA\u003csup\u003elow\u003c/sup\u003e, Diameter≥5 cm \u0026amp; MNDA\u003csup\u003ehigh\u003c/sup\u003e; Tumor number\u0026lt;1 \u0026amp; MNDA\u003csup\u003elow\u003c/sup\u003e, Tumor number\u0026lt;1 \u0026amp; MNDA\u003csup\u003ehigh\u003c/sup\u003e, Tumor number≥1 \u0026amp; MNDA\u003csup\u003elow\u003c/sup\u003e, Tumor number≥1 \u0026amp; MNDA\u003csup\u003ehigh\u003c/sup\u003e; BCLC A\u0026amp; MNDA\u003csup\u003elow\u003c/sup\u003e, BCLC A\u0026amp; MNDA\u003csup\u003ehigh\u003c/sup\u003e, BCLC B-C\u0026amp; MNDA\u003csup\u003elow\u003c/sup\u003e, BCLC B-C\u0026amp; MNDA\u003csup\u003ehigh\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/bd3f731ffe17d31ce8c77bb8.png"},{"id":45364330,"identity":"5243d606-a3eb-4ea5-8782-a19b9a6204a0","added_by":"auto","created_at":"2023-10-28 13:41:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":958826,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMNDA is mainly localized in M2 macrophages. \u003c/strong\u003e(A) Clustering of cell clusters in the LIHC microenvironment using the uniform manifold approximation and projection (UMAP) method in the GSE140228 database. (B) Analysis of MNDA and its family members in TCGA data using the CIBERSORT-ABS method with 22 immune cell correlations. (C) Confocal microscope image of MNDA (green), CD163 (red), and CD3 (white) immunostaining in human liver cancer tissue. Scale bar, 50 µm. (D) Pearson correlation was used to analyze the relationship between MNDA and M2 in two proteome cohorts. (E) Western blot analysis of MNDA expression in M0/M1/M2 macrophages. Cell experiments were repeated three times independently.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/2268ae10c4ce30de74c5a473.png"},{"id":45363555,"identity":"0f3807cb-ddb7-494f-a4cf-74ce1e715d06","added_by":"auto","created_at":"2023-10-28 13:33:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":459077,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMNDA enhances M2 macrophages polarization.\u003c/strong\u003e (A) Western blotting assay of MNDA expression levels in THP-1 cells after MNDA silencing. (B) A volcano plot of the differential genes in MNDA knockdown and control M2 macrophages in the RNA-Seq data. Blue dots represent the downregulated genes, red dots represent the upregulated genes, and gray dots indicate genes with no significant difference. n = 3 (C) A heatmap of the expression of enhancers of protumor macrophage polarization in M2 macrophages with and without MNDA silencing in the RNA-Seq data. (D) qRT-PCR analysis of MNDA, CCL2, TGFB1, ETV1, CSF1, BMP7, SGMS2, CXCL14 and S100A4 in shCtrl, shMNDA1 and shMNDA2 M2 macrophages. (E) Flow cytometry analysis of CD163 and CD206 in shCtrl, shMNDA1 and shMNDA2 M2 macrophages. Cell experiments were repeated three times independently. (*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/68966fce552e3eb96f9dfdbe.png"},{"id":45363552,"identity":"51f5b6d0-ed0c-4fbe-8488-c6d493968a4c","added_by":"auto","created_at":"2023-10-28 13:33:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":969720,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMNDA drives the secretion of pro-metastatic proteins by M2 macrophages.\u003c/strong\u003e (A) Bubble plot of pathway enrichment analysis of differentially expressed genes in M2 macrophages with and without MNDA silencing identified by RNA-Seq. (log (Ctrl/shMNDA)\u0026gt;1). (B) Venn diagram showed 45 secretion-related genes in the 329 down-regulated genes among shMNDA vs shCtrl M2 macrophages. (C) Pathway chord plot of enrichment analysis of secreted proteins in M2 macrophages with and without MNDA silencing identified by RNA-Seq. (D) A heat map of the expression of metastasis-associated genes in shCtrl, shMNDA1 and shMNDA2 M2 macrophages identified by RNA-Seq. (E) Forest plot of survival analysis of secreted proteins in two proteome cohorts. (F) qRT-PCR analysis of \u003cem\u003eTIMP1\u003c/em\u003e,\u003cem\u003eITGB5\u003c/em\u003e, \u003cem\u003eMMP14\u003c/em\u003e, \u003cem\u003eCOL6A1\u003c/em\u003e, \u003cem\u003eCOL6A2\u003c/em\u003e and \u003cem\u003eCOL6A3\u003c/em\u003e in shCtrl, shMNDA1 and shMNDA2 M2 macrophages. Cell experiments were repeated three times independently. (*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/fbbd21edd2dd33e4475c12c4.png"},{"id":45363559,"identity":"dfdca470-824b-49fa-b877-65bebdbd415b","added_by":"auto","created_at":"2023-10-28 13:33:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3231853,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMNDA promotes HCC cells invasion and migration via serum-derived from M2.\u003c/strong\u003e(A) Transwell chamber assay was used to test cell migration and invasion of Huh7 and MHCC-97H cells co-cultured with Ctrl, M2 CM-shCtrl, M2 CM-shMNDA1 and M2 CM-shMNDA2. Scale bar, 200μm. (B) Effects of treatment with Ctrl, M2 CM-shCtrl, M2 CM-shMNDA1 and M2 CM-shMNDA2 on migration of Huh7 and MHCC-97H cells, as assessed by wound-healing assay. Images were taken at 0 and 48 hours after scratching (left panel represents representative images; right panel shows percentage of migrated distance quantitatively). Scale bar, 200 µm. (C) MHCC-97H cells co-cultured with Ctrl, M2 CM-shCtrl, M2 CM-shMNDA1 and M2 CM-shMNDA2 were injected into NOD-SCID mice through the tail vein, and the number of pulmonary metastatic nodules was calculated 8 weeks later (n=5 each group). (D) Hematoxylin and eosin (H\u0026amp;E) staining representative images of lung metastasis sites in the tissue samples. Scale bar, 200 μm. (E) The number of lung metastatic nodules and the percent of nodules/total areas in the tissue samples. Cell experiments were repeated three times independently. (*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001; ns, no significance). Ctrl: It refers to the co-culture of liver cancer cells with DMEM medium containing 10% serum.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/eecda6c8f6c269775de632c1.png"},{"id":45364328,"identity":"234a9091-20d8-43d7-a889-cdb80e7c474e","added_by":"auto","created_at":"2023-10-28 13:41:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2201207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMNDA promotes HCC cells migration via exosome-derived from M2.\u003c/strong\u003e (A) GO analysis showed the biological pathways enriched by differentially expressed proteins in M2 macrophages with and without MNDA silencing. (B) Heatmap showed the metastasis-related protein expression in Ctrl and shMNDA M2 exosomes in mass spectrometry data. (C) Transwell chamber assay was used to test cell migration of Huh7, PLC and MHCC-97H cells co-cultured with M2 exosomes derived from shCtrl, shMNDA1 and shMNDA2. Scale bar, 200 μm. (D) Effects of treatment with Ctrl, M2 CM-shCtrl, M2 CM-shMNDA1 and M2 CM-shMNDA1+ M2 macrophage-derived exosomes on the migration ability of Huh7, PLC and MHCC-97H cells, as assessed by a transwell assay. Scale bar, 200 μm. Cell experiments were repeated three times independently. (*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001). Ctrl: It refers to the co-culture of liver cancer cells with DMEM medium containing 10% serum.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/39c502fabd992f1453073ee8.png"},{"id":45363557,"identity":"83caa55d-837f-4e54-b578-ff3edd541a23","added_by":"auto","created_at":"2023-10-28 13:33:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":337104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic diagram summarising the role of MNDA in HCC lung metastasis.\u003c/strong\u003e MNDA was primarily expressed in tumor M2 macrophages and contributed to the enhancement of M2 macrophage polarization by upregulating the expression of the enhancers of M2 polarization. MNDA knockdown inhibited the secretion of M2 macrophage-derived pro-metastasis proteins via the exosome pathway to suppress HCC metastasis. MVB: multivesicular body.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/8d98c1fa8ca7a2b226aed4eb.png"},{"id":56140577,"identity":"c963604d-0059-445f-87e4-d85b1ce9a975","added_by":"auto","created_at":"2024-05-09 04:36:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6247534,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/148ff4c0-0d53-468f-86ba-428199e36408.pdf"},{"id":45363561,"identity":"e042b1de-0d10-415f-84b5-41c91ef29e10","added_by":"auto","created_at":"2023-10-28 13:33:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8828360,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigurelegends.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/5dfd9417c93bfe6105100d6b.pdf"},{"id":45363563,"identity":"08da62e7-4217-41c8-af31-eb0f9b278e72","added_by":"auto","created_at":"2023-10-28 13:33:53","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11702282,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/2175baa275934a73115850b2.xlsx"},{"id":45363558,"identity":"578f0f28-6f06-4b77-9f1c-226f11fab02e","added_by":"auto","created_at":"2023-10-28 13:33:52","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":255564,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3480636/v1/17736242b08c68ebb221bf48.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Myeloid cell-expressed MNDA enhances M2 polarization to facilitate the metastasis of hepatocellular carcinoma","fulltext":[{"header":"Background","content":"\u003cp\u003eLiver cancer ranks as the sixth most common cancer in the world and is the third leading cause of tumor-associated death, and HCC accounts for 90% of cases. Despite significant advancements in traditional treatments such as surgery, intervention, and radiation therapy, as well as innovative approaches like targeted therapy and immunotherapy, the management of HCC still faces formidable challenges in terms of prevention, early detection, and limited treatment targets [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Notably, HCC is a typical inflammation-related cancer, characterized by complex interactions between immune cells and cancer cells within the HCC microenvironment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Targeted immunotherapy, despite high expectations, exhibits substantial variations in different patients, which can be attributed to the immune microenvironment's heterogeneity and a limited understanding of the specific immune molecular subtypes and driving events in HCC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, the molecular classification of the immune microenvironment in HCC is being elucidated using multi-omics techniques. For example, by assessing inflammation-related gene expression, immune cell infiltration, and immune regulatory ligands, the immune class has been classified into two components: (1) Active Immune Response Subtype (\u0026sim;65%) with overexpressed adaptive immune response genes, and (2) Exhausted Immune Response Subtype (\u0026sim;35%) with immunosuppressive signals like TGF-\u0026szlig; and M2 macrophages [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Using 10x Genomics and SMART-seq2 single-cell RNA sequencing, immune cells from various HCC tissues were extensively characterized. Researchers have identified two distinct states of macrophages in HCC tumors: TAM-like and MDSC-like states. Survival analysis links the characteristic genes of TAM-like state to poor prognosis, including the expression of two key genes, SLC40A1 and GPNMB, which are also associated with unfavorable prognosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. From a proteomic perspective, we have previously investigated the heterogeneity in early-stage hepatocellular carcinoma and stratified the cohort into the subtypes S-I, S-II and S-III. Patients with HCC of the S-III subtype have a higher level of immune infiltration (especially myeloid cells, exhausted T cells, et al. ), the lowest overall survival rate and the greatest risk of a poor prognosis after first-line surgery [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, there is a scarcity of effective immune-related biomarkers and interpretive tools to guide clinical decision-making.\u003c/p\u003e \u003cp\u003eTumor-associated macrophages (TAMs) plays a vital role in tumor microenvironment to facilitate tumor progression [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Generally, macrophages can be polarized to classical activation state (M1) and the alternative activation state (M2). M1 macrophages are activated by lipopolysaccharides (LPS) and interferon-gamma (IFN-γ) to produce proinflammatory cytokines, such as IL6, CXCL10 etc. However, TAMs are thought to resemble M2 macrophages, which are activated by IL-4, IL-10, IL-13 and produce IL-10, CCL22 etc. Recently, it has been reported that M2 macrophages promote tumor development through exosome-mediated communication with tumor cells [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Particularly, TAMs, a common immune cells that infiltrate in the TME, highly infiltrate S-III subtype of HCC tissues compared to other subtypes and are positively associated with relapse-free survival rate. Thus, delving into additional biomarkers and therapeutic targets connected to TAM and uncovering their association with HCC would greatly enhance their practical implementation in clinical settings.\u003c/p\u003e \u003cp\u003eMyeloid cell nuclear differentiation antigen (MNDA) is a member of the hematopoietic interferon-inducible nuclear proteins containing a 200-amino-acid repeat (HIN-200) family, which can induce cell cycle arrest, apoptosis, senescence, inflammation and recognize foreign double-stranded DNA [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It has been reported that MNDA restricts HIV-1 and other viral pathogens by interfering with Sp1-dependent gene expression and supports an important role in innate antiviral immunity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Unlike other HIN-200 factors, MNDA is essential for IFNα induction by regulating the expression of IRF7, rather than acting as a cytosolic PRR for dsDNA [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, MNDA has become a potential therapeutic target for sepsis and inflammatory pathologies by promoting the apoptosis of neutrophils [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. MNDA can effectively inhibit proliferation, induce apoptosis and reduce migration of osteosarcoma cells [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Meanwhile, high expression of MNDA is associated with good overall survival in lung adenocarcinoma [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Nevertheless, the functional and mechanistic roles of MNDA in HCC are not yet understood.\u003c/p\u003e \u003cp\u003eIn this study, we demonstrated for the first time that MNDA exhibited significantly increased expression in the most aggressive subtype of HCC and acted as an independent prognostic predictor, showing a correlation with myeloid immune response and HCC metastasis. Furthermore, MNDA promoted HCC metastasis through enhancing the polarization of M2 macrophages and the secretion of pro-metastatic proteins via the exosome pathway. Thus, our study uncovered MNDA as a novel biomarker of poor prognosis in TMEs and might be exploited as a new strategy to reprogram M2 macrophages with the aim of reducing progression of HCC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePublic Data Acquisition\u003c/h2\u003e \u003cp\u003eWe gathered two distinct sets of Hepatocellular Carcinoma (HCC) proteomic datasets, accompanied by relevant clinical information, from published literature. These datasets are referred to as the \"Jiang et al\u0026rsquo;s Cohort\" [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and the \"Gao et al\u0026rsquo;s Cohort\" [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The Jiang et al\u0026rsquo;s Cohort comprises 101 tumor samples, 98 paracancerous samples, and the Gao et al\u0026rsquo;s Cohort comprises 159 pairs of tumor and paracancerous samples. In the Jiang et al\u0026rsquo;s Cohort, any missing values were addressed by imputing the minimum observed value. Furthermore, to facilitate subsequent analytical procedures, log2 transformation was applied to the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis\u003c/h2\u003e \u003cp\u003eThe survival curves were calculated with the R function survfit from the R package survival with the formula Surv (time, vitalstatus)\u0026thinsp;~\u0026thinsp;categorie and plotted with the R function ggsurvplot from the R package survminer.\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;surv_cutpoint\u0026rdquo; function was employed to classify our sample dataset. Subsequently, the power of the \"coxph\" function was harnessed to construct a Cox Regression model and compute Hazard Ratios (HR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBiological function and pathway enrichment analysis\u003c/h2\u003e \u003cp\u003ePathway enrichment analysis was performed using Metascape Gene Annotation \u0026amp; Analysis Resource [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] that uses several ontology sources: KEGG Pathway, GO Biological Processes, Reactome Gene Sets, Canonical Pathways and CORUM. Proteins were clustered according to their pathways and processes. Relationships between genes were identified using the network map generated from the Metascape tool and visualized with Cytoscape.\u003c/p\u003e \u003cp\u003eGene set enrichment analysis (GSEA) is a method of analyzing and interpreting microarray and such data using biological knowledge. The data firstly generated an ordered list of logFC based on differential analysis, and then a predefined gene set receives an enrichment score (ES), which was a measure of statistical evidence rejecting the null hypothesis that its members were randomly distributed in the ordered list. The R package clusterProfiler was mainly used for GSEA analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCell lines and cell culture\u003c/h2\u003e \u003cp\u003eAll hepatocellular carcinoma cell lines (Huh7, PLC/PRF/5 (PLC), MHCC-97H) were purchased from the Shanghai Cell Bank, Chinese Academy of Sciences (Shanghai, China), and were subjected to cell identification and mycoplasma detection. All hepatocellular carcinoma cell lines were cultured in DMEM (Gibco, USA) medium supplemented with 10% FBS and 1% penicillin-streptomycin (Gibco, USA). Human monocyte line THP-1 was purchased from Shanghai Zhongqiao Xinzhou Biotechnology Co., Ltd. and cell identification and mycoplasma detection were performed (Shanghai, China). The cell lines were cultured in RPMI-1640 (Gibco, USA) medium supplemented with 10% FBS and 1% penicillin of streptomycin (Gibco, USA). All cells were maintained in a humidified incubator at 37\u0026deg;C with 5% CO2 cells within 50 passages were used for experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMacrophage polarization assay\u003c/h2\u003e \u003cp\u003eTHP-1 cells were treated with 162 nM phorbol 12-myristate 13-acetate (PMA, P8139, Sigma) for 24 h to differentiate into M0 macrophages. On the basis of M0, cells were polarized to the M1 phenotype after treatment with 100 ng/mL Lipopolysaccharide (LPS, L2880, Sigma) and 20 ng/mL Interferon-γ (IFN-γ, 300-02, Peprotech) for 24 hours. Based on this M0 state, cells were polarized to the M2 phenotype after treatment with 20 ng/mL interleukin-4 (IL-4, 200-04, Peprotech) for 48 hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMNDA Knockdown stable cell line generation\u003c/h2\u003e \u003cp\u003eWe used retroviral vectors containing short hairpin (sh) RNA for MNDA CCCAAACAGAATTATCGAAAT (denoted as shRNA1), GCACAATATCAAGTGTGAGAA (denoted as shRNA2), to transfect THP-1 cells. The transfected cells were selected in 4 \u0026micro;g/ml puromycin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eImmunostaining\u003c/h2\u003e \u003cp\u003eLiver cancer tissues were first harvested and fixed overnight in 4% paraformaldehyde (PFA). Thereafter, the tissues were embedded in paraffin and sliced at 5 \u0026micro;m. The sections were blocked with 10% goat serum for 30 min at 37\u0026deg;C and then incubated overnight at 4\u0026deg;C with the following primary antibodies: MNDA (HPA034532, 1:1000; sigma), CD163 (ab182422, 1:100; Abcam), CD3 (ab5690, 1:100; Abcam). Samples were then incubated with the corresponding Alexa Fluor-conjugated secondary antibodies (1:100, Thermo Fisher Scientific) and finally examined using a confocal microscope (LSM 880, Carl Zeiss AG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIsolation and characterization of exosomes\u003c/h2\u003e \u003cp\u003eWhen macrophage confluence reached 80\u0026ndash;90%, the cell culture medium was removed. Cells were washed twice with pre-warmed PBS and then macrophages were cultured in conditioned medium supplemented with 10% exosome-free fetal bovine serum (FBS, C3801-0050, VivaCell) for 48 hours. Approximately 50 mL of cell culture medium was collected for each cell line, the collected medium was centrifuged at 300 \u0026times; g for 10 minutes, followed by centrifugation at 2,000 \u0026times; g for 30 minutes to remove cell debris. Next, the supernatant was filtered using a 0.22-\u0026micro;m filter (Millipore). A 12,000 \u0026times; g centrifugation was performed for 40 min, the precipitate was discarded, and the supernatant was retained. The exosomes in the supernatant were precipitated by ultracentrifugation at 100,000 \u0026times; g for 90 min. After washing the exosome precipitate in PBS, the exosomes were precipitated again and resuspended in 500 \u0026micro;L of PBS. The protein content of the exosomes was determined by using the BCA Protein Assay Kit (Thermo Fisher Scientific). The exosomes were stored at -80\u0026deg;C for further use. Expression of exosome-specific marker proteins, such as CD63 and CD81, was detected by Western blotting. Exosomes were identified using transmission electron microscopy. First, 10 \u0026micro;L of exosomes were added dropwise to the copper grid, left for 1 min, and then the liquid was blotted from the side with filter paper. Next, 10 \u0026micro;L of uranyl acetate was added dropwise, left for 1 min, and then imaged at 100 kv for electron microscopic detection. The particle size and concentration of exosomes were measured using nanoparticle tracking analysis (NTA). First, frozen samples were taken, thawed in a 25\u0026deg;C water bath, and placed on ice. Then, exosome samples were diluted with 1 \u0026times; PBS and used directly for NTA assay.\u003c/p\u003e \u003cp\u003eFor exosome uptake experiments, exosomes were labeled with the PKH67 fluorescent Cell adaptor kit (Sigma-Aldrich) according to the manufacturer's instructions. That is, 50 microliter exosomes were mixed with 500 \u0026micro;L Diluent C; 2 \u0026micro;L of PKH67 and 500\u0026micro;L Diluent C were mixed and incubated for 4 min. Then 1mL of 1% BSA was added and incubated for 1min, mainly for termination of staining. Labeled exosomes were washed with PBS, collected by ultracentrifugation, and re-suspended in PBS. Exosomes were incubated with HCC cells and analyzed using confocal microscopy at the indicated time points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003eTotal proteins from cells or exosomes were extracted using RIPA lysis buffer containing protease inhibitor (A32955, Thermo). Protein lysates were then quantified using the BCA kit (#23227, Thermo Fisher Scientific). Proteins were separated by electrophoresis using 10% sodium dodecyl sulfate polyacrylamide (SDS-PAGE) and transferred to PVDF membranes (Millipore). The membranes were blocked with 5% skim milk for 1 h at room temperature and then incubated overnight at 4\u0026deg;C with the following primary antibodies: CD63 (ab217345, 1:1000; Abcam), CD81 (#66866-1-Ig, 1:1000; Proteintech), Calnexin (ab22595, 1:1000; Abcam), GAPDH (ab8245, 1:5000; Abcam). The membranes were then incubated with horseradish peroxidase-conjugated secondary antibodies of the corresponding species for 1 h at room temperature and subjected to electrochemiluminescence development by chemiluminescence instrumentation. Antibodies and reagents are detailed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and quantitative reverse transcription PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using the TRIzol Reagent (#15596018, Life Technologies), according to the manufacturer's instructions, then reverse transcribed into cDNA using reverse transcriptase master mix (R312-00, Vazyme). The cDNA was subjected to qRT-PCR, performed on CFX96 qPCR system (Bio-Rad) using the SYBR Green Real-time PCR Master Mix (Q712-02, Vazyme) according to the manufacturer's instructions. The primers used in this study were synthesized by TsingkeBiotechnologyCo.Ltd. In this experiment, the relative transcript levels of the target genes were calculated by the 2- △△CT method using GAPDH as the internal reference: △△Ct = △Ct experimental group - △Ct control group, where △Ct\u0026thinsp;=\u0026thinsp;Ct target gene - Ct internal reference. The primer sequences are detailed in Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry\u003c/h2\u003e \u003cp\u003eWhen the macrophage fusion reached 80\u0026ndash;90% after induction, the cells were washed twice with 1\u0026times;PBS. The cells were then digested with 5 mM EDTA and collected into centrifuge tubes for centrifugation (4\u0026deg;C, 500 \u0026times; g, 5 min), washed twice with PBS containing 1% FBS, and counted. The cells were then incubated in the dark for 30 min at 4\u0026deg;C with the corresponding antibody. Cells were washed 3 times with PBS. The stained cells were analyzed by flow cytometry (Fortessa, BD Biosciences, USA). Antibodies used for flow cytometry: CD163 (#326505, Biolegend), CD206 (#321109, Biolegend). Data were analyzed by FlowJo software 8.7.1 (Treestar Inc., USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCell migration assay\u003c/h2\u003e \u003cp\u003eThe migration and invasion ability of HCC cells was determined by using 24-well Transwell chambers with upper and lower culture chambers separated by polycarbonate membranes with 8 \u0026micro;m pores (BD Biosciences, Franklin Lakes, NJ, USA). 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells were resuspended in 400 \u0026micro;L of serum-free DMEM medium and inoculated into the upper chamber. The lower chamber was filled with 600 \u0026micro;L of M2 supernatant or exosomes containing 10% FBS, and PBS was used as a control. After incubation for 24 h in a humidified incubator containing 5% CO2 maintained at 37\u0026deg;C, the remaining cells in the upper chamber were removed, and cells migrating to the surface of the lower chamber were fixed with 4% paraformaldehyde and stained with 0.5% crystal violet. At least five random microscopic views (magnification \u0026times;100) were taken and the cells were counted. Three independent experiments were performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eWound healing assay\u003c/h2\u003e \u003cp\u003eThe cells were seeded in a six-well plate. Once the cells became sub-confluent, a 200 \u0026micro;L pipette tip was used to create a scratch wound, after 48 hours of co-culture with the conditioned medium and HCC cells, the width of the scratch was observed and images were obtained under microscope. The results are expressed as a percentage of wound closure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCell proliferation assay\u003c/h2\u003e \u003cp\u003eCell viability was tested with Cell Counting Kit-8 (CCK8) kit (Beyotime) according to the manufacturer's instructions. HCC cells were trypsinized, counted and then plated at a density of 8,000 cells per well in 96-well plates. After that, they were co-cultured with M2 CM. The cells were incubated at 37\u0026deg;C. The attached cells were treated with the CCK8 dilution for 1 hour, and then the absorbance of each well of the plates was measured at 450 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRNA Sequencing and Raw Data Preprocessing\u003c/h2\u003e \u003cp\u003eTotal RNA from the cells was extracted using Trizol. RNA library construction and sequencing were performed by Berry Genomics Co. Ltd. Then the cDNA library was sequenced on an Illumina Hiseq 2500.\u003c/p\u003e \u003cp\u003eQuantile normalization, log2 conversion, missing values supplement and differential analysis for the matrix data of RNA-Seq dataset were performed using the \u0026ldquo;limma\u0026rdquo; package in R/Bioconductor software (version 4.2.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eProteomic analysis\u003c/h2\u003e \u003cp\u003ePeptides (300 ng) obtained from M2 exosomes were used for LC-MS analysis. The LC-MS analysis was performed using an EASY nLC1200 FAIMS Orbitrap Exploris 480 mass spectrometer (Thermo Fisher Scientific, USA). The MS system uses Data Independent Acquisition (DIA) for scanning. The raw LC-MS/MS data files were analyzed using Spectronaut (version 17.2.230208.55965) with the spectra searched against the UniProt human database. Search parameters were set as follows: enzyme digestion: Trypsin/P digestion; variable modifications including: oxidation of methionine (M), protein N-terminal acetylation; immobilised modifications including: urea methylation (C); maximum of two missing trypsin cleavage sites allowed; Filtering for the protein and peptide identification was set at a 1% false discovery rate (FDR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAnimal studies\u003c/h2\u003e \u003cp\u003e The animal care and experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of National Center for Protein Sciences (Beijing), Ethical review number: IACUC-20221209-78MT. NOD-SCID mice were purchased from Charles River, Inc (Beijing, Vital River Laboratory Animal Technology). All mice were 5\u0026ndash;6 weeks old males. To examine the metastatic ability of the HCC cells, 6-week-old male NOD SCID mice were intravenously injected via the tail vein with 2 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e MHCC-97H cells that had been treated with M2 CM. The number of metastatic lungs, the number of clones in the lungs, and small metastatic foci were compared across the four groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eR version 4.2.1 was used for displaying and computation of publicly available data, RNA-seq and proteomics graphs using the R-packages ggplot2. Other analysis of data was performed using GraphPad Prism 8.0 software. The Student\u0026rsquo;s t-test was used for two-group comparisons, and one-way or two-way ANOVA was used for comparisons among more than two groups. All experiments were performed at least three times, and the data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. A P-value of less than 0.05 indicates statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMNDA abundance positively correlates with poor prognosis, inflammation and metastasis in HCC patients\u003c/h2\u003e \u003cp\u003eIn our quest to delve deeper into the common malignant attributes of HCC, we embarked on an analysis aimed at identifying proteins that were highly expressed specifically in the S-III [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and S-Pf [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] subtypes. Our investigation revealed that, in comparison to the other two subtypes, a total of 22 proteins exhibited significant up-regulation in the S-III/S-Pf subtypes (see schematic overview of the study design in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The 22 proteins were subjected to modular analysis using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The analysis revealed that a majority of these proteins were associated with myeloid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), indicating that myeloid cell infiltration could stably predict for poor outcomes in HCC patients. Notably, MNDA showed the highest score indicating a significant prognostic risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eRecent research found that aberrant expression of MNDA occurred in lung adenocarcinoma and nodal marginal zone lymphoma[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, the role of MNDA in HCC remained unclear and needed to be elucidated. To explore the function of MNDA in HCC, we divided patients into two groups: the MNDA\u003csup\u003ehigh\u003c/sup\u003e group and the MNDA\u003csup\u003elow\u003c/sup\u003e group, based on the maximum survival difference of MNDA in proteomic cohorts. Subsequently, we performed functional enrichment analysis using the differentially expressed proteins between the two groups in both cohorts (Jiang et al's Cohort: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026amp;FC\u0026thinsp;\u0026gt;\u0026thinsp;2; Gao et al's Cohort: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026amp;FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5; n\u0026thinsp;=\u0026thinsp;194). The results revealed that the signaling pathways were primarily enriched in inflammatory response, cell migration, integrin-mediated signaling pathway etc (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). GSEA further revealed that inflammatory response and metastasis were enriched in MNDA\u003csup\u003ehigh\u003c/sup\u003e groups in Jiang et al's Cohort and Gao et al's Cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and Supplementary Fig.\u0026nbsp;1A). Then, we conducted a rigorous correlation analysis to investigate the relationship between MNDA and a selection of identified immune response and migration-related proteins. Our analysis revealed a robust and significant correlation between MNDA and these proteins, exemplified by ITGAM [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], ITGB2 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], TIMP-1 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], S-100A4 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF and G and Supplementary Fig.\u0026nbsp;1B and C). These findings suggested the potential role of MNDA in shaping the tumor immune microenvironment, particularly in HCC patients with metastasis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMNDA augments the prognostic stratification capability of clinical indicators\u003c/h2\u003e \u003cp\u003eTo investigate the association between MNDA and the prognosis of HCC patients, we first conducted Kaplan-Meier survival analysis using the two proteomic cohorts and The Cancer Genome Atlas (TCGA) dataset. The results revealed that patients with higher MNDA expression, regardless of the protein or mRNA level, exhibited significantly poorer overall survival (OS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). Furthermore, higher levels of MNDA expression were notably correlated with shorter disease-free survival (DFS) in HCC patients, as observed in Jiang et al's Cohort (Supplementary Fig.\u0026nbsp;2A). Consistently, the analysis conducted on \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e revealed that patients with genomic amplification of MNDA had worse OS and DFS compared to those with unaltered genomic amplification of MNDA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and Supplementary Fig.\u0026nbsp;2C). In the two proteomics cohorts, MNDA exhibited a substantial up-regulation in the S-III/S-Pf subtypes compared with adjacent tissues or other HCC subtypes. This underscored that the distinctive expression patterns of MNDA were closely associated with the degree of malignancy of HCC (Supplementary Fig.\u0026nbsp;2D). Together, these results suggested that the elevated levels of MNDA were commonly found in highly aggressive HCC cases, correlating with a poor prognosis.\u003c/p\u003e \u003cp\u003eSeven factors (i.e., Age, Gender, AFP, Tumor number, Tumor size, BCLC stages, and MVI) were identified as statistically significant factors associated with overall survival (OS) in HCC patients. To further investigate the role of MNDA in clinical diagnosis, we carried out univariate and multivariate analyses. The results indicated that the performance of MNDA as a prognostic marker surpassed most clinical indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Furthermore, MNDA had no significant correlation with current clinical indicators that suggest prognosis (such as AFP, tumor size, MVI, etc.), this data implied that MNDA could serve as an independent prognostic factor (Supplementary Fig.\u0026nbsp;2E). The co-occurrence of high MNDA levels and clinical indicators was associated with a significantly worse overall survival (OS) in HCC patients, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF and Supplementary Fig.\u0026nbsp;2F. Taken together, these data suggested that MNDA augments the prognostic stratification capability of clinical indicators.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eMNDA is mainly expressed in M2 macrophages\u003c/h2\u003e \u003cp\u003eTo explore the function of MNDA involved in HCC progression, we firstly examined the expression features of MNDA. MNDA is expressed only in myeloid rather than hepatoma cell lines (Supplementary Fig.\u0026nbsp;3A). Furthermore, we applied a Smart-seq-based scRNA-seq method to study the expression of MNDA in 7 immune cell types which including B cells, dendritic cells (DCs), mast cells, monocytes/macrophages, natural killer (NK) cells, plasma and T cells, and found that MNDA was mainly expressed in monocytes/macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Moreover, according to the data from the online website (TIMER 2.0), unlike other HIN-200 families, MNDA was found to have the highest correlation with M2 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To further validate these findings, we also performed multiplexed immunofluorescent staining to provide evidence that MNDA co-localized with CD163\u003csup\u003e+\u003c/sup\u003e M2 macrophages rather than CD3\u003csup\u003e+\u003c/sup\u003e T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The association between MNDA and M2 expression from proteomic cohorts also showed that MNDA was positively correlated with M2 infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eThe differentiated THP-1 monocytes are widely used as \u003cem\u003ein vitro\u003c/em\u003e models of human macrophages [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and to unveil the expression of MNDA in polarized macrophages, M0, M1 and M2 macrophages were differentiated from THP-1 cells. The upregulation of the pan macrophage marker \u003cem\u003eCD68\u003c/em\u003e indicated that the M0 macrophage phenotype was successfully differentiated (Supplementary Fig.\u0026nbsp;3B). In induced M1 macrophages, the markers for M1 (\u003cem\u003eIL6\u003c/em\u003e, \u003cem\u003eCXCL10\u003c/em\u003e) rose compared to M0 and M2 macrophages, while the markers for M2 (\u003cem\u003eIL-10\u003c/em\u003e, \u003cem\u003eCCL22\u003c/em\u003e and \u003cem\u003eCD206\u003c/em\u003e) rose only in induced M2 macrophages (Supplementary Fig.\u0026nbsp;3C and D). The flow cytometry analysis also showed a higher level of CD206 and CD163 in M2 macrophages (Supplementary Fig.\u0026nbsp;3E and F). These results confirmed the success of the cellular M1 and M2 polarization model establishment we used. In differentiated and induced M0, M1 and M2 macrophages, the expression of MNDA was examined and the results showed that MNDA was significantly highly expressed in M2 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Taken together, these results confirmed the high expression of MNDA in M2 macrophages.\u003c/p\u003e \u003cp\u003eM2 macrophages share numerous characteristics with TAMs and exhibit several key features associated with malignant tumors, including angiogenesis, invasiveness, metastasis, regulation of the TME, and therapeutic resistance [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Indeed, we found M2 macrophage score had a higher value in S-III subtype, which was associated with the poorest outcome and featured immune dysregulation and tumor metastasis (Supplementary Fig.\u0026nbsp;3G). Consistently, GSEA analysis further revealed the enrichment of the inflammatory response and metastasis pathways in the M2\u003csup\u003ehigh\u003c/sup\u003e group by analyzing two independent proteomics datasets (Supplementary Fig.\u0026nbsp;3H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMNDA enhances M2 macrophages polarization\u003c/h2\u003e \u003cp\u003eTo clarify the role of MNDA in M2 macrophages, we specifically knocked down MNDA in THP-1 cells using lentivirus-mediated gene transfer and established two MNDA stable knockdown THP-1 cell lines (shMNDA1 and shMNDA2) and its control cell (shCtrl) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The shMNDA and its control THP-1 cells were then induced for M2 polarization, and RNA-seq analyses were performed to compare the transcriptome of these M2 macrophages The transcriptome data showed that 606 genes (329 downregulated and 277 upregulated) were differentially expressed with statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and supplementary Table\u0026nbsp;3). Heatmap showed the significantly downregulation of \u003cem\u003eCCL22\u003c/em\u003e [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], \u003cem\u003eTGFB1\u003c/em\u003e [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], \u003cem\u003eETV1\u003c/em\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], \u003cem\u003eCSF1\u003c/em\u003e [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], \u003cem\u003eBMP7\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], \u003cem\u003eSGMS2\u003c/em\u003e [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], \u003cem\u003eCXCL14\u003c/em\u003e [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and \u003cem\u003eS100A4\u003c/em\u003e [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] which have been reported to enhance protumor macrophage polarization via various pathways. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). We validated our transcriptomics data by qRT-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Consistently, flow cytometry analysis also revealed a close correlation between MNDA deficiency and decreased expression of CD163 and CD206 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Overall, these findings suggested that MNDA might play a regulatory role in the expression of M2-related molecules and contribute to the enhancement of M2 macrophage polarization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eMNDA drives the secretion of pro-metastatic proteins by M2 macrophages\u003c/h2\u003e \u003cp\u003eWe next proceeded to unveil the modulators of MNDA in M2 macrophages using the transcriptome data. KEGG analysis was performed for genes significantly down-regulated after knockdown of MNDA in M2 macrophages, and the results showed that signaling pathways were mainly enriched in extracellular matrix organization, cell-cell adhesion, cytokine production, inflammatory response, etc (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Strikingly, among the 329 downregulated genes, 45 genes were identified as secretory proteins, which were closely associated with extracellular matrix organization, wound healing, and cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and C). Heatmap showed 17 proteins that associated with tumor metastasis in the RNA-Seq data, were significantly down-regulated with the knockdown of MNDA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Consistently, the proteome data indicated that high expression of most of the proteins was associated with a poor prognosis for HCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). qRT-PCR confirmed that MNDA positively affected the expression of some pro-metastasis genes (\u003cem\u003eTIMP1, ITGB5, MMP14, COL6A1, COL6A2\u003c/em\u003e and \u003cem\u003eCOL6A3\u003c/em\u003e) in M2 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). These data demonstrated that MNDA positively regulated the secretion of pro-metastasis proteins by M2 macrophages. Therefore, we hypothesized that MNDA might promote invasion and migration of HCC cells by regulating the expression of pro-metastatic secretory proteins from M2 macrophages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eMNDA promotes HCC cells invasion and migration via serum-derived from M2\u003c/h2\u003e \u003cp\u003eTo confirm the assumption, next, we collected the conditioned medium (CM) from the control and MNDA knockdown M2 macrophages. The results of transwell and matrigel assays showed that the incubation with CM from the control M2 macrophages increased the migration and invasion abilities of tumor cells (Huh7, PLC, MHCC-97H), whereas the promotion of the CM from the MNDA knockdown cells on tumor cell migration and invasion was weakened (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and Supplementary Fig.\u0026nbsp;4A). Similarly, in scratch test, CM derived from M2 macrophage-treated HCC cells showed increased mobility, and the culture with shMNDA M2 CM led to a significant reduction in the HCC cell mobility potential compared with M2 CM\u0026ndash;treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and Supplementary Fig.\u0026nbsp;4B). We subsequently co-cultured with the HCC cells with CM from M2 macrophages, and the proliferation displayed no clear alterations (Supplementary Fig.\u0026nbsp;4C). To further elucidate the effect of MNDA on \u003cem\u003ein vivo\u003c/em\u003e metastasis ability of HCC cells, we intravenously injected MHCC-97H cells that had been pre-incubated with M2 conditioned media (CM) into NOD-SCID mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Subsequently, we monitored the development of the lung metastasis in these mice. 8 weeks later, histological analysis of the lung tissues revealed a significant increase in the incidence of lung metastases in mice inoculated with MHCC-97H cells treated with M2 CM. However, the promotion of CM from MNDA downregulated M2 macrophages on tumor cell lung metastases was weakened (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD and E). Notably, there was no significant difference in body weight between the different groups of mice (Supplementary Fig.\u0026nbsp;4D). These findings indicated that MNDA had the potential to enhance HCC cell metastasis both \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e via regulating the expression of secretory proteins from M2 macrophages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eMNDA promotes HCC cells migration via exosome-derived from M2\u003c/h2\u003e \u003cp\u003eEmerging evidence suggests the central role of exosomes in intercellular communication in tumor metastasis [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. To further study the effect of exosomes on the biological functions of MNDA on HCC cells \u003cem\u003ein vitro\u003c/em\u003e, we added a specific exosome secretion inhibitor GW4869 to the M2 macrophage culture medium. Transwell assays revealed that treatment with GW4869 decreased the promotion of M2 CM on the migration abilities of Huh7, PLC and MHCC-97H HCC cells (Supplementary Fig.\u0026nbsp;5A). These results indicated that M2 exosomes might be accountable for the effects exerted by M2 macrophages on HCC cells.\u003c/p\u003e \u003cp\u003eSubsequently, we extracted exosomes from M2 macrophage supernatants by ultracentrifugation, and western blot results showed that protein levels of the exosome markers CD63 and CD81 were significantly increased in the extracted exosomes compared with cell lysis, whereas the exosome-negative marker calnexin was not detectable in the extracted exosomes (Supplementary Fig.\u0026nbsp;5B). Transmission electron microscopy results showed that the shape of the extracted exosomes had a typical two-layer membrane structure, and nanoparticle tracking analysis (NTA) results indicated that the average diameter of exosomes was 132 nm (Supplementary Fig.\u0026nbsp;5C and D). The above results confirmed the success of exosome extraction. We next detected whether these exosomes could be internalized by HCC cells. Exosomes were labeled with PKH67, a green fluorescent carbocyanine dye, which was followed by the treatment of HCC cells with these labeled exosomes. We used a fluorescence microscope to confirm that PLC cells and Huh7 cells could take up exosomes derived from M2 macrophage with a robust, time-dependent method (Supplementary Fig.\u0026nbsp;5E). To gain insight into the molecular mechanism of MNDA, we studied the regulation of MNDA on the M2 derived exosome. The exosomes from shCtrl and shMNDA of M2 macrophages were isolated and the protein expression profiles in M2 exosomes were analyzed by using mass spectrometry assay (supplementary Table\u0026nbsp;4). The results showed dramatic differential expression of proteins between the two sets, and the differential proteins were closely related to cytokine signaling, β-catenin independent WNT signaling, extracellular matrix organization and cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In particular, a panel of proteins, including NRP2, TIMP1, ITGB2, ITGAM, COL6A1, COL6A2, COL6A3, CCL5 and LCP1, were significantly down-regulated in M2 shMNDA exosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). As shown in Supplementary Table\u0026nbsp;5, the higher expression of these proteins predicted poorer OS and DFS.\u003c/p\u003e \u003cp\u003eWe explored whether MNDA has a critical role in promoting the migratory potential of HCC cells via M2-derived exosomes. The exosomes derived from shCtrl, shMNDA1 and shMNDA2 of M2 macrophages were collected and used in the function experiments \u003cem\u003ein vitro\u003c/em\u003e. Transwell assays showed that the exosomes derived from MNDA knock down M2 macrophages attenuated the migration of Huh7 cells and PLC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Importantly, the addition of M2 exosomes partially restored the weakened promotion of cell migration resulting from the downregulation of MNDA in M2 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). The aforementioned results suggested that MNDA promoted the migration of HCC cells through exosomes derived from M2 macrophages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLiver cancer is the third-highest cause of cancer-related deaths worldwide, and HCC is the predominant form of liver cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite treatments with sorafenib, regorafenib and immune checkpoint blockades (ICBs) benefits for other cancer indications, the response rates in HCC were unsatisfactory [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The complexity and heterogeneity of TME is one of the important reasons. Nevertheless, HCC is an inflammation-driven disease with underlying chronic liver inflammation and cirrhosis, a quarter of HCC cases express markers of inflammatory response [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. It has been reported that the immunosuppressive features of tumor lesions participate not only as one of the major players inducing cancer progression, but also a major challenge for effective immunotherapy resistance [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Consequently, there is an urgent need to characterize the molecular characteristics of tumor immunosuppressive environment to explore biomarkers and targets of tumor-infiltrating immune cells in HCC.\u003c/p\u003e \u003cp\u003eIn this study, based on HCC proteomic data from two independent centers, we found that the most malignant HCC (S-III/S-Pf) were characterized by immune disturbances dominated by myeloid cell infiltration. Particularly, TAMs represent the most abundant immune population in the myeloid cells and are widely considered to be M2-like macrophages that promote tumor progression and suppress local immunity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. To further explore the potential biomarkers and molecular mechanisms of high M2 infiltration and the crosstalk of M2 with HCC, MNDA attracted our attention.\u003c/p\u003e \u003cp\u003eMNDA is a nuclear factor initially identified in normal and transformed cells of the human myelo-monocytic lineage (granulocytes, monocytes, and macrophages) and its use as a marker of myeloid cell differentiation has been proposed [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Increasing evidence shows that MNDA expression is associated with clinicopathological features in patients with tumors, yet the function of MNDA in HCC has not been reported. In this study, we found that MNDA as one of the representative proteins of myeloid cells was elevated significantly in the most malignant S-III/S-Pf subtype HCC and positively correlated with M2 infiltration. To explore the association between MNDA and poor prognosis in HCC patients, multiple omics data had been used. We observed patients with high MNDA expression had shorter OS than those with low MNDA expression in both DNA, mRNA and protein levels. In addition, MNDA as an independent prognostic indicator was superior to most clinical markers (AFP level, tumor diameter, number of tumors, BCLC stage, and presence of MVI). To further evaluate the potential diagnostic value of MNDA in HCC, we found that combining MNDA with clinical indicators could further predict the adverse prognosis of HCC.\u003c/p\u003e \u003cp\u003eInterestingly, we found that mainly M2 macrophages-expressed MNDA could promote M2 macrophages polarization and HCC cells metastasis. These data suggested that a higher expression of MNDA might serve as a therapeutic target for the TME of HCC. To decipher the mechanisms underlying how MNDA modulates TAMs with protumor functions, our RNA-seq analysis revealed that reducing macrophagic MNDA could reverse the typical M2-like gene expression signature. Meanwhile, the decreased expression of MNDA also reduced the factors associated with alternative polarization toward a protumor phenotype, such as \u003cem\u003eCSF1\u003c/em\u003e, \u003cem\u003eS100A4\u003c/em\u003e. Therefore, it is logical that any intrinsic or extrinsic factors that can induce MNDA upregulation may induce TAM protumor polarization and the poor prognosis of tumor. On the other hand, it has been well-illustrated that TAMs release a wide range of chemokines and cytokines, which potentiate tumor invasion and metastasis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In this study, MNDA regulated the expression of 45 secretory genes in M2 macrophage, which were associated with extracellular matrix organization and cell migration. A large number of secretory factors were significantly up-regulated in the proteome cohorts. Furthermore, we demonstrated that MNDA promoted the invasion and migration of HCC cells via serum-derived M2 macrophages.\u003c/p\u003e \u003cp\u003eExosomes are usually constituted by small vesicles that are released from a variety of cell types [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Functionally, exosomes can transfer biomolecules (such as proteins, RNAs and DNAs), which have intriguing and elaborate roles in TMEs [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The dysfunction of exosomes has been widely investigated in HCC development [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Here, we show the evidence of M2 macrophage-derived exosomes in malignant progression of HCC. For example, macrophages-derived exosomes transmit miR-92a-2-5p to liver cancer cells to increase the invasion capacity of liver cancer [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In addition, Xu et al. found that macrophage-mediated regulation of the metabolic reprogramming in HCC cells via the exosomal delivery of oncogenic lncMMPA [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In this study, we firstly revealed that the level of some proteins (such as NRP2, TIMP1, ITGB2, ITGAM) associated with tumor metastasis promotion was decreased in the exosomes derived from MNDA knockdown M2 macrophages, which could be taken up by HCC cells. These findings provided the groundwork for macrophage exosomes research toward a further understanding of their clinical and pathological importance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our study delineated that MNDA enhanced M2 macrophage polarization by upregulating the enhancers of protumor macrophage polarization and promoted HCC cell metastasis by delivering the pro-metastasis proteins via M2 derived exosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Moreover, MNDA could serve as a stratification biomarker and potential therapeutic target for HCC with higher M2 infiltration and poorer prognosis, in attempts to improve the survival rates of these patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMNDA: Myeloid cell nuclear differentiation antigen HCC: Hepatocellular carcinoma TAM: Tumor-associated macrophages TIME: Tumor immune microenvironment HR: Hazard ratios GSEA: Gene set enrichment analysis ES: Enrichment score TCGA: The Cancer Genome Atlas OS: Overall survival DFS: Disease-free survival NTA: Nanoparticle tracking analysis CM: conditioned media S-Mb: metabolism subgroup S-Me: Microenvironment dysregulated subgroup S-Pf: Proliferation subgroup IL4: Interleukin-4 IFN-\u0026gamma;: Interferon-\u0026gamma; PMA: Phorbol 12-myristate 13-acetate\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal care and experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of National Center for Protein Sciences (Beijing), Ethical review number: IACUC-20221209-78MT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData related to this paper may be requested from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding was provided by National Key R\u0026amp;D Program of China (No. 2021YFA1301604), National Natural Science Foundation of China (32088101, 82372835), National Science Foundation for Young Scientists of China (82203601,82303197), Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS) (2019-I2M-5-063), The Specific Research Fund for TCM Science and Technology of Guangdong Provincial Hospital of Chinese Medicine (No. YN2022DB04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAH.S, CY.T, MX.Z and FC.H\u0026nbsp;designed and supervised the study. YR.M, MX.Z and XL.L\u0026nbsp;performed the major experiments\u0026nbsp;and analyzed data. Q.D and YJ.Z assisted with bioinformatics. H.Z helped with the animal studies.\u0026nbsp;XX.W provided pathologic tissue sections of liver cancer patients.\u0026nbsp;MX.Z, YR.M and XL.L wrote the paper.\u0026nbsp;CY.T\u0026nbsp;and\u0026nbsp;AH.S revised the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the National Center for Protein Sciences (Beijing) Animal Platform, the FCM platform, the imaging \u0026nbsp; platform, and the MS platform of the for their assistance with mouse tumor models, FCM analysis, microscopy imaging, and MS analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLlovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, Lencioni R, Koike K, Zucman-Rossi J, Finn RS: 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\u003cstrong\u003e41\u003c/strong\u003e(1):253.\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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MNDA, HCC metastasis, Potential biomarker, M2 polarization, Exosome","lastPublishedDoi":"10.21203/rs.3.rs-3480636/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3480636/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe molecular subtypes of hepatocellular carcinoma (HCC) with the worst prognosis are characterized by immune disorders dominated by myeloid cell infiltration, but how to accurately screen these patients for accurate diagnosis and treatment is not clear. In this study, based on HCC proteomic data from two independent centers, we found that Myeloid cell nuclear differentiation antigen (MNDA) could be used as a marker of myeloid lymphocyte especially M2 myeloid cell infiltration, and further analyzed the mechanism and potential clinical value of MNDA in promoting poor prognosis of HCC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe investigated the proteomic molecular subtype of HCC and discovered a significant elevation of the myeloid cell nuclear differentiation antigen (MNDA) in the most aggressive subtype. The association between MNDA and the prognosis of HCC was examined using multi-omics data. Gene expression analysis, multiple immunofluorescence and western blot were used for detecting the localization of MNDA in HCC. Cellular co-culture experiments were conducted for exploring the functions of MNDA in \u003cem\u003evitro\u003c/em\u003e while intravenous injections were used in in \u003cem\u003evivo\u003c/em\u003e study. To elucidate its oncogenic mechanisms, we used RNA-seq combined with mass spectrometry analysis and cellular experiments to identify the related signaling pathway.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMNDA demonstrated significantly elevated expression in the most aggressive subtype of HCC and exhibited a positively correlation with M2 infiltration and HCC metastasis. Moreover, MNDA also functioned as an independent prognostic predictor and has a good synergistic effect with existing prognostic clinical indicators (such as AFP, tumor size, MVI, etc.). We also found that MNDA was primarily expressed in tumor M2 macrophages and contributed to the enhancement of M2 macrophage polarization by upregulating the expression of the enhancers of M2 polarization. Furthermore, MNDA knockdown inhibited the secretion of M2 macrophage-derived pro-metastasis proteins via the exosome pathway to suppress HCC metastasis both in vivo and in vitro.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMNDA exerts a protumor role by promoting M2 macrophages polarization and HCC metastasis, and can serve as a potential biomarker and therapeutic target for HCC.\u003c/p\u003e","manuscriptTitle":"Myeloid cell-expressed MNDA enhances M2 polarization to facilitate the metastasis of hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-28 13:33:47","doi":"10.21203/rs.3.rs-3480636/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e1dfdc6-2b7a-433c-9cfd-b61fcd38982e","owner":[],"postedDate":"October 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-09T04:01:31+00:00","versionOfRecord":{"articleIdentity":"rs-3480636","link":"https://doi.org/10.7150/ijbs.91877","journal":{"identity":"international-journal-of-biological-sciences","isVorOnly":true,"title":"International Journal of Biological Sciences"},"publishedOn":"2024-01-01 04:01:30","publishedOnDateReadable":"January 1st, 2024"},"versionCreatedAt":"2023-10-28 13:33:47","video":"","vorDoi":"10.7150/ijbs.91877","vorDoiUrl":"https://doi.org/10.7150/ijbs.91877","workflowStages":[]},"version":"v1","identity":"rs-3480636","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3480636","identity":"rs-3480636","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}