Multi-omics analyses reveal a distinct tumor microenvironment in microvascular invasion positive hepatocellular carcinoma

Multi-omics analyses reveal a distinct tumor microenvironment in microvascular invasion positive 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 Multi-omics analyses reveal a distinct tumor microenvironment in microvascular invasion positive hepatocellular carcinoma Junwen Hu, Chenglei Yang, Lixin Pan, Zhijian Li, Xi Wang, Yuting Tao, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4479454/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Microvascular invasion (MVI) is a risk factor for the recurrence and poor prognosis of hepatocellular carcinoma (HCC). However, the molecular mechanisms underlying MVI-mediated progression of HCC remain unclear. This study aimed to discuss the background of MVI-positive HCC through multiple approaches. Patients and methods: The cancer genome atlas (TCGA) database analysis, next-generation sequencing (NGS), time-of-flight mass-cytometry (CyTOF), imaging mass cytometry (IMC) and lipidomics were used to analyze the molecular characteristics of MVI-positive HCC. Results Pathway enrichment analysis of TCGA database and NGS showed that the enriched pathways in MVI-positive HCC were significantly involved in the tumor microenvironment (TME) remodeling; moreover, single-sample gene set enrichment (ssGSEA) and xCell analysis indicated that the TME of MVI-positive HCC might be in low immune state. In-depth interrogation of the immune landscapes using CyTOF showed that the ratio of Tregs to CD4 + T cells in MVI-positive HCC was higher than that in MVI-negative HCC. CD4 + T cells, CD8 + T cells, Tregs and tumor-associated macrophages (TAMs) express higher levels of immune exhaustion-related markers in MVI-positive HCC. IMC further showed that in MVI-positive HCC the distance between T cells and CAFs was significantly shortened, and the expression of PD-L1 in T cells was higher. The lipidomics results showed the cholesterol which may cause T cells to be in immune exhaustion was significantly elevated in MVI-positive HCC. Conclusion Through high-dimensional analysis, we found that there was immunosuppression of TME in MVI-positive HCC, which may be the cause of worse prognosis. Microvascular invasion Tumor microenvironment Next-generation sequencing Time-of-flight mass-cytometry Imaging mass cytometry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Hepatocellular carcinoma (HCC) is the sixth most common cancer and the fourth leading cause of cancer-related deaths worldwide, with estimated new cases exceeding 630,000 and related deaths exceeding 586,000 annually [1] . Radical hepatectomy and liver transplantation are considered effective methods for the treatment of HCC and are the most important measures for long-term survival of patients with HCC [2] . Surgical techniques continue to advance, molecular targeted and immunotherapeutic drugs have come out in succession, and treatment methods are becoming increasingly abundant; however, due to the high recurrence rate and high metastasis rate after surgery, long-term survival results are still unsatisfactory [3] . Studies have shown that about 70% of HCC patients relapse within 5 years after surgery, and the 5-year survival rate is less than 15% [4, 5] . HCC commonly invades the blood vessels during its development, which can be divided into macrovascular and microvascular invasion [6] . Macrovascular invasion can be accurately judged by relevant preoperative examination whereas microvascular invasion (MVI), which is a histological feature, can only be determined through postoperative pathological examination. MVI refers to the microscopic observation of invasion of one or more of the portal vein, hepatic vein, arterioles, and lymphatic vessels by micrometastatic HCC emboli [7] . The process of MVI may involve the destruction of vascular endothelial cells by tumor cells through the death necrosis process mediated by death receptor-6, resulting in microvascular tumor thrombus [8] . Many studies have confirmed that MVI is a high-risk factor for postoperative recurrence and metastasis of HCC and it has an extremely important impact on the long-term survival rate of liver cancer patients after surgery. MVI is an effective and independent predictor of early recurrence and poor overall survival after surgery for HCC [9] . However, the molecular mechanisms underlying the poor prognosis of MVI-positive HCC remain unclear. In this study, we first downloaded the transcriptome data of HCC from The Cancer Genome Atlas (TCGA) database, conducted pathway enrichment and immune infiltration analyses, and initially revealed the molecular characteristics of MVI-positive HCC. Multidimensional analysis based on multi-omics technology was used to further explore and verify the molecular characteristics of MVI-positive HCC to provide a new target for clinical treatment. Materials and methods Data acquisition from TCGA Transcriptome data and clinical information of HCC patients, including 87 patients with MVI-positive HCC and 198 patients with MVI-negative HCC, were obtained from TCGA database ( https://portal.gdc.cancer.gov/ ). Bioinformatics analysis The change and significant difference in the folding rate were calculated using the DESeq2 software. P value 2.0 were used as thresholds to screen differentially expressed genes (DEGs). The DEGs were condensed using Gene Ontology (GO) and analyzed using the Kyoto Encyclopedia of Gene and Genome (KEGG) pathway with R statistics software (version 3.6). The GO term and KEGG pathway with correction P < 0.05 were significantly enriched. Single-sample gene set enrichment analysis (ssGSEA) and xCell were further used to detect the enrichment levels or activity of immune cells, functions, or pathways between MVI-positive HCC and MVI-negative HCC. Patients A retrospective study was conducted on 1202 patients with HCC admitted to the Guangxi Medical University Cancer Hospital from January 2012 to December 2017, including 432 MVI-positive HCC patients and 770 MVI-negative HCC patients. Tumor and matched non-tumor liver tissues were collected from patients with HCC, who underwent hepatectomy at the Guangxi Medical University Cancer Hospital. All samples were collected under the supervision of the same pathologist. The pathological diagnosis was performed by two independent expert pathologists. All the tumor tissues were primary HCC. Retrospective data, including demographic data, preoperative laboratory results, and pathological parameters, were collected from the electronic medical records. All the participants provided written informed consent, and the study was approved by the Ethics Committee of the Guangxi Medical University Cancer Hospital. RNA sequencing (RNA-seq) Total RNA was extracted from MVI-positive (n = 49) and MVI-negative (n = 44) HCC patients using TRIzol reagent (Invitrogen). RNA purification, reverse transcription, library preparation, and sequencing were carried out in Wuxi NextCODE (Shanghai) in accordance with the manufacturer's protocol (Illumina). FastQC was used for quality control and salmon was used to compare the reads with the human genome (Hg19). The UpSet plot was drawn using R statistical software (version 5.3). False discovery rate (FDR) or Q values were corrected and multiple hypothesis tests were performed. Time-of-flight mass-cytometry (CyTOF) Cancer tissues were collected from MVI-positive (n = 35) and MVI-negative (n = 33) HCC patients. After the cancer tissues were disintegrated and digested into a single-cell suspension, CyTOF was used to analyze the differences in the tumor microenvironment (TME) between the two groups. Fresh samples were isolated by enzymatic digestion with 20 µg/mL DNAse I (Sigma) and 2 mg/mL collagenase type II (Gibco) at 37°C for 30 min, as detailed in our previous research [10] . The samples were analyzed with 35 metal-conjugated antibodies using CyTOF as previously described. Briefly, the antibody was coupled to isotope labels using the MaxPar X8 antibody labeling kit (Fluidigm) according to the manufacturer's instructions. Single-cell suspensions were thawed and rested in a complete Roswell Park Memorial Institute (RPMI) solution containing 10% fetal bovine serum and 1% penicillin-streptomycin. The living/dead cells were then identified with cisplatin (Fludigm) staining and incubated with metal-coupled surface membrane antibodies. The cells were then fixed in 1.6% paraformaldehyde and permeabilized in 100% methanol to allow staining with metal-coupled antibodies. Finally, the cells were labeled with a DNA intercalator containing iridium and analyzed using a CyTOF-II instrument (Fluidigm). The signal was normalized using EQ™ Four Element Calibration Beads (Fluidigm). After sample data were collected, the flow cytometry standard (FCS) files were normalized and spliced using CyTOF software v6.7. The cells were manually de-coded after the file was standardized as previously described. Before data analysis, we used the online software Cytobank to manually set individual cells and living cell gates for each file ( https://www.cytobank.org/ ). For the spanning-tree progression analysis of density-normalized events (SPADE), we used 25 target nodes and 100% of the downsampled event targets. The clustering channel was selected based on whether they were pedigree markers and the cell population to be clustered. Cell phenotypes were compared in pairs using t-distribution random neighbor embedding (t-SNE) analysis to optimally draw similar cells close to each other. Regarding heat maps, the transformation rate of the median intensity corresponds to a logical data scale. The color in the heat map represents the measured average intensity value of a given marker in a given cluster. The four-color scale was used, wherein blue and white indicate low expression values, white and yellow indicate medium intensity expression markers, and red indicates high expression markers. Imaging mass cytometry The sections were collected from 8 MVI-positive and 10 MVI-negative HCC patients and prepared as mentioned previously. Paraffin-embedded tissue sections were analyzed using imaging mass cytometry (IMC) (Hyperion, Fluidigm). Image acquisition after daily tuning was performed at a laser frequency of 200 Hz, according to the manufacturer's instructions. An area of approximately 500 × 500 µm was selected according to the bright-field image. Two to five regions of interest (ROIs) were randomly selected per slide. The expression intensity of markers related to individual ROI was used as the input for further analysis. lipidomics Total lipid was extracted from 16 MVI-positive and 14 MVI-negative HCC samples, using a improved method of Blig & Dyer, as previously descbibed [11] . HPLC-MS/MS analyses were performed using an Exion UPLC system (Thermo fisher Scientific) coupled with a SCIEX QTRAP 6500 PLUS system in both positive and negative modes. Statistical analysis Continuous variables are expressed as mean ± standard deviation, and categorical variables are expressed as frequencies (n) and percentages (%). The Kaplan–Meier method was used for survival analysis, and the log-rank test was used to analyze the differences between groups. The Student's t-test was used for continuous variables and the Mann–Whitney U test for nonparametric variables to assess the significance of the baseline differences between the two groups. Categorical variables were compared using the chi-square test or Fisher's exact test, and continuous variables were compared using Student’s t-test. The Kaplan–Meier method was used for survival analysis, and the log-rank test was used to analyze differences between groups. All analyses were performed using the SPSS 24.0 statistical software package (IBM SPSS Statistics). P < 0.05 was considered significant. Results Clinical date The baseline characteristics of MVI-positive HCC patients and MVI-negative HCC patients are provided in Table 1. In the baseline data, we were interested and concerned about the significant reduction in lymphocyte counts in MVI-positive HCC patients. DFS and OS in the MVI-positive HCC patients was significantly lower than in the MVI-negative HCC patients (both P < 0.05) (Fig. 1 ). TCGA database analysis We first divided HCC samples into MVI-positive and MVI-negative HCC groups to determine the DEGs (Figs. 2 A and B). Functional enrichment analysis was then applied to analyze the functions of the DEGs. GO and KEGG analyses revealed that many of them are involved in the TME, such as monocyte chemotaxis, chemokine mediated signaling pathway, chemokine activity, CCR10 chemokine receptor binding, cytokine and cytokine receptor interaction, and chemokine signaling pathway (Figs. 2 C, D, and E ). Furthermore, we used TCGA database to compare immune cell infiltration between MVI-positive and MVI-negative HCC. ssGSEA suggested that the enrichment scores of Mast_cells, T_helper_cells and Type_II_IFN_Reponse were significantly lower in the MVI-positive HCC group than in the MVI-negative HCC group (Fig. 3 A). The xCell algorithm demonstrated that the enrichment scores of CD8 + central memory T cells (Tcm) and conventional dendritic cell (cDC) were lower in MVI-positive than in MVI-negative HCC (Fig. 3 B). Distinct transcriptomic signatures in MVI-positive HCC and MVI-negative HCC To verify the results of TCGA database, we performed next-generation sequencing (NGS) on MVI-positive HCC and MVI-negative HCC. Based on the DEGs between the two groups, 864 genes were upregulated and 216 genes were downregulated in MVI-positive HCC compared to those in MVI-negative HCC (Figs. 4 A and 4 B ). Next, we used functional enrichment analysis to characterize the DEGs in MVI-positive HCC. According to gene ontology (GO) analysis, the DEGs of MVI-positive HCC were mainly involved in cell differentiation, adhesion, and extracellular matrix disassembly (Figs. 4 C and 4 D). KEGG analysis showed that the DEGs enriched in MVI-positive HCC mainly included extracellular matrix (ECM) receptor interaction, cell adhesion molecules (CAMs), hypoxia-inducible factor 1 (HIF-1), phosphatidylinositol 3 kinase (PI3K-Akt), wingless-related integration site (Wnt), mitogen-activated protein kinases (MAPK), and Ras signaling pathways (Fig. 4 E), which are closely related to the occurrence and development of tumors and can mediate the remodeling of TME by regulating immune function. Using ssGSEA analysis, we found that the enrichment scores of mast cells (MCs), Th2 cells, and B cells were significantly decreased in MVI-positive HCC, and cytolytic activity and interferon-γ (IFN-γ), also known as type 2 interferon response (Type_II_IFN_REPONSE) activity, were also significantly reduced in MVI-positive HCC (Figs. 5 A and 5 B). xCell analysis showed that the enrichment scores of CD4 + effector memory T cells (CD4 + Tem), CD8 + Tcm, CD8 + Tem, and cDC were significantly decreased in MVI-positive HCC (Figs. 5 C and 5 D). Distinct immune subsets in MVI-positive and MVI-negative HCC Based on the hypothesis that the TME of HCC may affect MVI, we first attempted to determine whether there is a difference in the TME of MVI-positive HCC and MVI-negative HCC. In this study, CyTOF was used to implement a thorough immunoassay of the immune status of the two subsets of HCC. Our CyTOF group consisted of 35 surface and intracellular immune markers (Fig. 6 A), which can detect different immune markers and phenotypes in MVI-positive and MVI-negative HCC. First, based on the expression of immune cell markers, we identified 20 distinct cell clusters in CD45 + HCC cells. Meanwhile, the 20 cell clusters were further classified into CD19 + B cells (clusters 16 and 20), CD14 + human leucocyte antigen DR (HLA-DR) + tumor-associated macrophages (TAM) (cluster 3), CD66b + tumor-associated neutrophils (TAN) (cluster 14), CD4 + T cells (clusters 1,2,4,5,6,7, and 8), CD25 + Foxp3 + CD4 + Treg cells (clusters 2 and 4), CD8 + T cells (clusters 11,12,15,17, and 18), programmed cell death protein 1 (PD1) + CD8 + T cells (clusters 12,15, and 18) and other types of cells (clusters 9,10,13, and 19) (Figs. 6 B and 6 C ). We further investigated the frequency of differential expression clusters in MVI-positive and MVI-negative HCC and found that there was no difference in the proportion of CD4 + T cells, CD8 + T cells, B cells, TAMs, and TANs subsets between the two groups. However, we found that the ratio of immunosuppressive Tregs to CD4 + T cells in MVI-positive HCC was higher than that in non-MVI-positive HCC (P < 0.05), as shown in Fig. 6 D, indicating that there were more immunosuppressive Tregs among CD4 + T cells in MVI-positive HCC. We additionally detected the functional status of immune cells and found that CD4 + T cells from MVI-positive HCC expressed higher levels of T-cell immunoglobulin and mucin domain 3 (TIM3), PD-1, and PD-L1 (Figs. 7 A and 7 B ). CD8 + T cells from MVI-positive HCC cells expressed higher levels of lymphocyte-activation gene 3 (LAG-3), PD-1, and PD-L1 (Figs. 7 C and 7 D ). Tregs from MVI-positive HCC patients expressed higher levels of LAG-3, PD-1, and PD-L1 (Figs. 7 E and 7 F). TAMs from MVI-positive HCC expressed higher levels of TIM3, LAG-3, and PD-L1 (Figs. 7 G and 7 H). Simultaneously, we detected the expression of these immune markers in the two groups of HCC. Interestingly, we found that the expression of immunosuppressive markers, IL-6, TIM3, transforming growth factor beta (TGF-β), CD196, CD223 (LAG-3), CD279 (PD-1), and CD274 (PD-L1) was significantly upregulated in MVI-positive HCC (Figs. 7 I, 7 J, and 7 K ). IMC analysis in MVI-positive and MVI-negative HCC Our RNA-seq data revealed that the ECM receptor interaction pathway, which is mainly secreted by cancer-associated fibroblasts (CAFs), is significantly enriched in MVI-positive HCC. The CyTOF results indicated that T cells were in a state of immune exhaustion in MVI-positive HCC, and studies showed that CAFs could inhibit the tumor-killing ability of T cells by interacting with T cells. Therefore, we hypothesized that there may be a stronger interaction between CAFs and T cells in MVI-positive HCC. Therefore, we used IMC to explore the interaction between CAFs and T cells in MVI-positive HCC. The results of IMC showed that all the cells of MVI-positive HCC and MVI-negative HCC were clustered into 19 subsets based on various cell surface markers, including 1 CD4 + T cell (CD3 + CD4+) (subset 10), 1 CD8 + T cell subpopulation (CD3 + CD8+) (subset 16), 3 smooth muscle alpha-actin (α-SMA) + collagen-1 + CAFs (subsets 9, 13, and 18), 2 fibroblast-activating protein (FAP) + vimentin + CAFs (subsets 17 and 19), and some tumor cell subsets (Figs. 8 A and 8 B ). Compared to MVI-negative HCC, α-SMA + collagen-1 + CAFs and FAP + vimentin + CAFs were significantly increased in MVI-positive HCC (Fig. 8 C ). The distance between T cells and CAFs in MVI-positive HCC was significantly shortened (Fig. 8 D), and the expression of the T cell immune depletion marker PD-L1 was significantly higher than that in MVI-negative HCC (Fig. 8 E). lipidomic analysis in MVI-positive HCC and MVI-negative HCC We tried to understand the underlying mechanisms by which such distinctive local immune statuses are established in the HCC microenvironment. Our transcriptome sequencing results showed that MVI-positive HCC is significantly enriched in lipid metabolism-related pathways and that lipid metabolism plays an important role in tumor development, so we used lipidomics to analyze the mechanisms of poor prognosis of MVI-positive HCC at the lipidomic level. We detected a total of 528 intact lipids originating from 28 major lipid categories (Figure S1 A and 1B ). There were significant up-regulation of cholesterol (Cho), Acylcarnitine, plasmalogenPC, Glucosylceramides (GluCer), Lactosylceramides (LacCer) and Sulfatides (SL) in MVI-positive HCC, however Cardiolipins (CL) was significantly reduced in the MVI-positive HCC group (Figure S2A、S2B and S2C ). There was no significant difference among other lipids. Discussion MVI is a histological feature of HCC and is associated with invasive biological behavior and a poor clinical prognosis. Many studies have confirmed that MVI is a high-risk factor for postoperative recurrence and metastasis of HCC and it has an extremely important impact on the long-term survival rate of HCC patients after surgery. MVI is an effective and independent predictor of early recurrence and poor overall survival after surgery for HCC [12, 13] . Our clinical data also showed that DFS and OS are significantly shorter for MVI-positive HCC compared to MVI-negative HCC. However, the differences in cellular ecosystems between MVI-positive and MVI-negative HCC remain unexplained. In our clinical data, we found a significant reduction in the count of lymphocyte, lymphocyte is an important component of the TME, it may suggested that there may be some difference in the TME between MVI-positive HCC and MVI-negative HCC. Our current study provides a comprehensive examination of the intratumoral immune landscapes of MVI-positive HCC compared to MVI-negative HCC using high-dimensional analytical tools such as NGS, CyTOF, and IMC. This approach enabled us to address the fundamental impact of the underlying TME. In our study, we first downloaded the HCC transcriptome data from TCGA database and found that MVI-positive HCC enriched pathways related to multiple immunity; further, through immune infiltration analysis we revealed that MVI-positive HCC had less immune cell infiltration. RNA-seq revealed that compared to MVI-negative HCC, MVI-positive HCC enriched more signaling pathways remolded by regulating TME, which was closely related to tumor progression. ssGSEA and xCell analysis further revealed that the TME may be in a low immune state, suggesting that MVI-positive HCC may have a TME that is more conducive to cancer progression. Therefore, we performed CyTOF to reveal the TEM images of MVI-positive HCC. Interestingly, we observed immunosuppression in patients with MVI-positive HCC. Our CyTOF results showed that there were higher numbers of Tregs among CD4 + T cells in MVI-positive HCC. Tregs are important immunosuppressive factors, and a high density of Tregs is strongly associated with poor prognosis in a variety of tumors, including HCC [14, 15] . LAG-3, TAMs, PD-1, and PD-L1, as classical markers of immune exhaustion, represent the exhaustion of immune cells [16–18] . CD4 + T cells express higher levels of TIM3, PD-1 and PD-L1, while CD8 + T cells express higher levels of LAG-3, PD-1, and PD-L1 in MVI-positive HCC; the co-expression of exhausted genes can increase T cell damage, which is also related to the progression of tumor [19, 20] . Tregs express more LAG-3, PD-1, and PD-L1, and immune depletion markers have been reported to be important for enhancing the suppressive activity of Tregs, suggesting that Tregs in MVI-positive HCC have a stronger immunosuppressive function [21, 22] ; TAMs in MVI-positive HCC also express more TIM3, LAG-3, and PD-L1, which maintain the immunosuppressive function of TAMs [23, 24] . Our IMC data revealed that the proportion of CAFs in MVI-positive HCC was significantly increased. CAFs are among the most abundant and critical components of the TME. It promotes the growth, invasion, and angiogenesis of cancer cells by remodeling the extracellular matrix, inducing tumor angiogenesis, and mediating tumor inflammation. As the main participants of immune regulation in TME, CAFs induce TME to a state of immunosuppression and promote tumor immune escape through the secretion of a variety of cytokines and chemokines, mediating the recruitment and functional differentiation of innate and acquired immune cells [25, 26] . Studies have shown that CAFs can inhibit the tumor-killing ability of T cells by interacting with T cells [27, 28] . Compared to non-MVI-positive HCCs, the distance between T cells and CAFs is significantly shortened, and the expression of PD-L1, an immune exhaustion-related marker of T cells, is higher. CAFs may inhibit T cell function through stronger interactions, leading to the existence of an immunosuppressive TME in MVI-positive HCC. Our lipidomics results showed significant lipid differences between MVI-positive HCC and MVI-negative HCC. Compared with MVI-negative HCC, MVI-positive HCC is rich in more lipids associated with HCC progression, especially the significantly elevated cholesterol of MVI-positive HCC has been shown to cause T cells to be in a state of immune depletion [29] . This further confirmed the existence of immunosuppressive tumor microenvironment in MVI-positive HCC. The immunosuppressive TME can enable tumor cells to escape immune surveillance, which makes them more prone to microvascular invasion and promotes tumor development. In conclusion, our study is the first and most comprehensive analysis of molecular typing of HCC significantly related to MVI, and revealed the real reason for the poor prognosis of MVI-positive HCC at the molecular level. Such deep immunophenotyping strategies are essential for enhancing our understanding of tumor immunity in cancers derived from different etiologies, and will help guide the design of novel immunotherapeutic strategies. Abbreviations MVI，Microvascular invasion；HCC=hepatocellular carcinoma；TCGA，The cancer genome atlas；NGS，next-generation sequencing；CyTOF， time-of-flight mass-cytometry；IMC，imaging mass cytometry；TME，tumor microenvironment；ssGSEA，single-sample gene set enrichment；TAMs，tumor-associated macrophage；CAFs，cancer-associated fibroblasts；DEGs，differentially expressed genes；GO，Gene Ontology；KEGG，Kyoto Encyclopedia of Gene and Genome； Declarations Acknowledgment ： The authors thank the National Natural Science Foundation of China (81960450), the National Major Special Science and Technology Project (2017ZX10203207), the project of Key laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education (GKE2018-KF02 and GKE2019-ZZ10), Open Foundation of Guangxi Key Laboratory of Biological Targeting Diagnosis and Therapy Research & Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy (GXSWBX201503), and ‘Guangxi BaGui Scholars’ Special Fund. Funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Disclosure: Funding Information National Natural Science Foundation of China (81960450), the National Major Special Science and Technology Project (2017ZX10203207), the project of Key laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education (GKE2018-KF02 and GKE2019-ZZ10), Open Foundation of Guangxi Key Laboratory of Biological Targeting Diagnosis and Therapy Research & Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy (GXSWBX201503). Conflict of interest The authors report no conflicts of interest in this work. Ethics Statement The manuscript has been read and approved by all the authors, each author believes the manuscript represents honest work, the information is not provided in another form. All the participants provided written informed consent, and the study was approved by the Ethics Committee of the Guangxi Medical University Cancer Hospital. Informed Consent: All the participants provided written informed consent. Registry and the Registration No. of the study/trial. N/A. Animal Studies. N/A. Author contributions Bangde Xiang and Qiuyan Wang contributed to the conception of the study, provided feedback on the report; Lixin Pan, Zhijian Li, Yuting Tao, Xiaoyin Hu and Jingfei Zhao performed research; Lixin Pan, Zhijian Li, Xi Wang, Yaobang Wang, Zhenxing Wang analyzed data; Junwen Hu, Chenglei Yang and performed the data analyses and wrote the manuscript; All authors gave final approval of the version to be published, and agree to be accountable for all aspects of the work. References Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. 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Zelba H, Bedke J, Hennenlotter J, et al. PD-1 and LAG-3 Dominate Checkpoint Receptor-Mediated T-cell Inhibition in Renal Cell Carcinoma[J]. Cancer Immunol Res. 2019, 7(11): 1891-1899. Lichtenegger F S, Rothe M, Schnorfeil F M, et al. Targeting LAG-3 and PD-1 to Enhance T Cell Activation by Antigen-Presenting Cells[J]. Front Immunol. 2018, 9: 385. Camisaschi C, Casati C, Rini F, et al. LAG-3 expression defines a subset of CD4(+)CD25(high)Foxp3(+) regulatory T cells that are expanded at tumor sites[J]. J Immunol. 2010, 184(11): 6545-6551. Francisco L M, Salinas V H, Brown K E, et al. PD-L1 regulates the development, maintenance, and function of induced regulatory T cells[J]. J Exp Med. 2009, 206(13): 3015-3029. Yan W, Liu X, Ma H, et al. Tim-3 fosters HCC development by enhancing TGF-β-mediated alternative activation of macrophages[J]. Gut. 2015, 64(10): 1593-1604. Rogers T L, Holen I. Tumour macrophages as potential targets of bisphosphonates[J]. J Transl Med. 2011, 9: 177. Monteran L, Erez N. The Dark Side of Fibroblasts: Cancer-Associated Fibroblasts as Mediators of Immunosuppression in the Tumor Microenvironment[J]. Front Immunol. 2019, 10: 1835. Gok Y B, Gunaydin G, Gedik M E, et al. Cancer associated fibroblasts sculpt tumour microenvironment by recruiting monocytes and inducing immunosuppressive PD-1(+) TAMs[J]. Sci Rep. 2019, 9(1): 3172. Kato T, Noma K, Ohara T, et al. Cancer-Associated Fibroblasts Affect Intratumoral CD8(+) and FoxP3(+) T Cells Via IL6 in the Tumor Microenvironment[J]. Clin Cancer Res. 2018, 24(19): 4820-4833. Lakins M A, Ghorani E, Munir H, et al. Cancer-associated fibroblasts induce antigen-specific deletion of CD8 (+) T Cells to protect tumour cells[J]. Nat Commun. 2018, 9(1): 948. Ma X, Bi E, Lu Y, et al. Cholesterol Induces CD8(+) T Cell Exhaustion in the Tumor Microenvironment[J]. Cell Metab. 2019, 30(1): 143-156. Table 1 Table 1. Comparison of Clinicopathologic Features Between MVI-Positive and MVI-Negative HCCs. Characteristics MVI (+) MVI (-) P N=432 N=770 Sex 0.964 Male 371(85.9%) 662(86.0%) Female 61(14.1%) 108(14.0%) Age(yr) 50.4±10.6 52.1±11.1 0.017 Neutrophil (10 9 /L) 3.9±1.7 3.8±1.8 0.284 Lymphocyte(10 9 /L) 1.7±0.7 1.8±0.7 0.001 Platelets (10 9 /L) 210.3±80.6 203.0±97.0 0.033 HBV viral infection 0.506 Yes 366(84.7%) 641(83.2%) No 66(15.3%) 129(16.8%) Total bilirubin (µmol/L) 0.019 ≤17.1 316(73.1%) 609(79.1%) ＞17.1 116(26.9%) 161(20.9%) AFP (ng/mL) ＜0.001 ≤400 220(50.9%) 489(63.5%) ＞400 212(49.1%) 281(36.5%) Tumor number 0.154 Solitary 308(71.3%) 578(75.1%) Multiple 124(28.7%) 192(24.9%) Tumor size ＜0.001 ≤5cm 166(38.4%) 393(51.0%) ＞5cm 266(61.6%) 377(49.0%) Tumor capsular 0.619 Yes 301(71.3%) 547(71.0%) No 131(28.7%) 223(29.0%) Edmodson-steiner grading ＜0.001 I,II 5(1.2%) 43(5.6%) III,IV 427(98.8%) 727(94.4%) Liver cirrhosis 0.001 Yes 237(54.9%) 342(44.4%) No 195(45.1%) 428(55.6%) Tumor recurrence ＜0.001 Yes 266(61.6%) 360(46.8%) No 166(38.4%) 410(53.2%) Additional Declarations No competing interests reported. Supplementary Files FigureS1andS2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-4479454","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310894180,"identity":"3ed079c4-7e9f-44b8-86cd-9f4fdf8aec0e","order_by":0,"name":"Junwen Hu","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junwen","middleName":"","lastName":"Hu","suffix":""},{"id":310894181,"identity":"2e6a66b9-86b2-4255-8108-6b2b806ce453","order_by":1,"name":"Chenglei Yang","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chenglei","middleName":"","lastName":"Yang","suffix":""},{"id":310894182,"identity":"9c8fecf7-eef2-40d4-a0aa-1d3a2af2f312","order_by":2,"name":"Lixin Pan","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lixin","middleName":"","lastName":"Pan","suffix":""},{"id":310894183,"identity":"4d2a4589-cfcb-4238-80c6-404b8354d596","order_by":3,"name":"Zhijian Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhijian","middleName":"","lastName":"Li","suffix":""},{"id":310894186,"identity":"bb356b95-6098-4ff4-99b9-6b0b24476761","order_by":4,"name":"Xi Wang","email":"","orcid":"","institution":"Guangxi Medical 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyin","middleName":"","lastName":"Hu","suffix":""},{"id":310894198,"identity":"6799351c-1067-4b59-93b4-d59a9fbdf8d9","order_by":8,"name":"Jingfei Zhao","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingfei","middleName":"","lastName":"Zhao","suffix":""},{"id":310894199,"identity":"394b918b-cfeb-40b4-8eba-42576c45a56d","order_by":9,"name":"Zhenxing Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenxing","middleName":"","lastName":"Wang","suffix":""},{"id":310894200,"identity":"743ae870-781c-4ae0-8135-f96549603d79","order_by":10,"name":"Qiuyan Wang","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiuyan","middleName":"","lastName":"Wang","suffix":""},{"id":310894201,"identity":"21db8081-2dd1-4333-8202-f29749a0f16a","order_by":11,"name":"Bangde Xiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACZiBmbACxEhgfQMUMiNbCDFNKQAsDQgubBFFaDI4zP3v4dYddnrx78rHKr23bEhvYm7dJMNTcwalFspnN3Fj2THKx4ZlnabdlztxObOA5VibBcOwZTi38zAxm0pJtzIkbZ+SY3ZaoAGqRyDGTYGw4jFMLGzP7N6CWeqCW/G/FEgZALfJv8GvhZ+Yxk/zYdjhxvkQOG+MHsC08+LVINvOUSTOeOZ64geeZsTTDmdvGbTxpxRYJx3BrMTh/fJvkzx3VifPbkx9+/Nl2W7af/fDGGx9qcGsBAWYekN4DUAYbSCgBrwZgTP4AEvINUMYoGAWjYBSMAnQAAId4V3k/j+zwAAAAAElFTkSuQmCC","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Bangde","middleName":"","lastName":"Xiang","suffix":""}],"badges":[],"createdAt":"2024-05-26 09:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4479454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4479454/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58154717,"identity":"031a05b9-fc16-4cde-ad08-7587b18c48cb","added_by":"auto","created_at":"2024-06-11 20:41:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":223704,"visible":true,"origin":"","legend":"\u003cp\u003eDisease-free survival rate (left) and Overall survival rate (right) of MVI-positive and MVI-negative HCC.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4479454/v1/ee1b6b82bc5f50298e050a69.png"},{"id":58155819,"identity":"4f74dc20-8973-4b55-b332-b3830026d00b","added_by":"auto","created_at":"2024-06-11 20:57:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1128498,"visible":true,"origin":"","legend":"\u003cp\u003eGO term and KEGG pathway analysis for differentially expressed genes (DEGs) between MVI-positive and MVI-negative HCC from TCGA database. (fold change (FC) \u0026gt; 2.0, P \u0026lt; 0.05). (A) Heat map shows the clustering of DEGs; (B) Volcano plot of DEGs; (\u003cstrong\u003eC\u003c/strong\u003e) biological process, (\u003cstrong\u003eD\u003c/strong\u003e) molecular function, and (\u003cstrong\u003eE\u003c/strong\u003e) KEGG pathway.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4479454/v1/b93dc5ad0296ce0aceb21888.png"},{"id":58155379,"identity":"d12d94c1-9e86-4fef-b9e1-9afb12e85108","added_by":"auto","created_at":"2024-06-11 20:49:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1248517,"visible":true,"origin":"","legend":"\u003cp\u003essGSEA and xCell analysis of tumor microenvironment in MVI-positive HCC. (A) ssGSEA analysis showed that the enrichment scores of Mast_cells, T_helper_cells and Type_II_IFN_Reponse were significantly lower in MVI-positive HCC than that in MVI-negative HCC. (B) xCell algorithm demonstrated that the enrichment scores of CD8+ Tcm and cDC were lower in MVI-positive HCC.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4479454/v1/f5352d7e3adac497e9f8e33d.png"},{"id":58155820,"identity":"3318d91f-b596-4fb0-be01-795d7ff7bd24","added_by":"auto","created_at":"2024-06-11 20:57:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1270756,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG analysis of differentially expressed genes (DEGs) from RNA sequencing data (fold change (FC) \u0026gt; 2.0, P \u0026lt; 0.05) in MVI-positive HCC. (A) Heat map shows the clustering of DEGs between MVI-positive and MVI-negative HCC; (B) Volcano plot of DEGs in MVI-positive HCC; (C) GO analysis of DEGs of MVI-positive HCC in biological process；(D) GO analysis of DEGs of MVI-positive HCC in molecular function；(E) KEGG pathway enrichment analysis of DEGs in MVI- positive HCC.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4479454/v1/83835040a9ade0279a493574.png"},{"id":58154723,"identity":"9dd56360-f408-435e-a3a2-c9fdd3d5efd9","added_by":"auto","created_at":"2024-06-11 20:41:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1699722,"visible":true,"origin":"","legend":"\u003cp\u003essGSEA and xCell analysis of tumor microenvironment in MVI-positive HCC. ssGSEA analysis：(A) Heat map and (B) Box plot show that mast cells (MCs), Th2 cells, B cells, cytolytic activity, and IFN-γ (Type_II_IFN_Reponse) activity were significantly decreased in MVI-positive HCC patients, *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001. xCell analysis：Box plot (C) and Heat map (D) show that CD4+ Tem, CD8+ Tcm, D8+ Tem, and cDC were significantly decreased in MVI-positive HCC patients, *P \u0026lt; 0.05, **P \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4479454/v1/f9cfe6c19cda72927a608226.png"},{"id":58155381,"identity":"6f92e7aa-c3bc-43ea-93c6-9e3ae81fe639","added_by":"auto","created_at":"2024-06-11 20:49:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1446971,"visible":true,"origin":"","legend":"\u003cp\u003eTime-of-flight mass-cytometry (CyTOF) revealed the immune landscape of MVI-positive and MVI-negative HCC. (A) A t-SNE diagram of the expression of each immune marker. (B) The t-SNE map shows the total samples of HCC grouped into 20 clusters. (C) Heat map of the expression of all immune-related markers after normalization of 20 immune cell subsets in HCC. (D) The ratio of Treg to CD4+ T cells in MVI-positive HCC patients was higher than that in MVI-negative HCC patients.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4479454/v1/35446ae23823e044fee2eca5.png"},{"id":58154726,"identity":"d68e497f-895f-46c6-bbb1-2f03b883718b","added_by":"auto","created_at":"2024-06-11 20:41:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1414903,"visible":true,"origin":"","legend":"\u003cp\u003eTime-of-flight mass-cytometry (CyTOF) revealed the immune landscape of MVI-positive and MVI-negative HCC. (A) The t-SNE diagram and (B) Box plot show that the immune exhaustion-related markers TIM3, PD-1, and PD-L1 in CD4+ T cells were highly expressed in MVI-positive HCC patients. (C) The t-SNE diagram and (D) Box plot show that the immune exhaustion-related markers LAG-3, PD-1, and PD-L1 in CD8+ T cells were highly expressed in MVI-positive HCC patients. (E) The t-SNE diagram and (F) Box plot show that the immune exhaustion-related markers LAG-3, PD-1, and PD-L1 in Treg were highly expressed in MVI-related HCC patients. (G) The t-SNE diagram and (H) Box plot show that the immune exhaustion-related markers TIM3, LAG-3, and PD-L1 in TAMs were highly expressed in MVI-positive HCC patients. (I) The t-SNE diagram (J) Heat map and (K) Box plot show that the immune-related markers KI67, IL-6, TIM3, TGF-β, CCR6, LAG-3, PD-1, and PD-L1 in TANs were highly expressed in MVI-positive HCC patients.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4479454/v1/1aea0fe7c00063bb96b34408.png"},{"id":58154725,"identity":"3852b7e9-1d3d-460b-883d-fc0bf7aee13e","added_by":"auto","created_at":"2024-06-11 20:41:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":817158,"visible":true,"origin":"","legend":"\u003cp\u003eImaging mass cytometry (IMC) revealed the differences in immune microenvironment of MVI-positive and MVI-negative HCC. (A) The t-SNE diagram and (B) Heat map show the 19 immune cell subsets of all HCC; (C) The relative proportion of α-SMA + collagen-1 + CAFs and FAP + vimentin + CAFs cell subsets were higher in MVI-positive HCC; (D) The median distance between T cells and CAFs was shorter in MVI-positive HCC；(E) The immune exhaustion-related marker PD-L1 was highly expressed in T cells in MVI- positive HCC.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4479454/v1/478f7ea050cd969a369757ba.png"},{"id":73019293,"identity":"c9ae70fe-2e2a-4e2f-adb5-1fdaf3f7a895","added_by":"auto","created_at":"2025-01-06 03:31:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12054250,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4479454/v1/915049a4-4e0c-42f9-b6b3-55c50fa527a3.pdf"},{"id":58154719,"identity":"a08fa3bd-eb42-4ec5-bbc8-afe43943ba6b","added_by":"auto","created_at":"2024-06-11 20:41:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1071519,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1andS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4479454/v1/333daf52ee6e9f7c12ddb5f2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics analyses reveal a distinct tumor microenvironment in microvascular invasion positive hepatocellular carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the sixth most common cancer and the fourth leading cause of cancer-related deaths worldwide, with estimated new cases exceeding 630,000 and related deaths exceeding 586,000 annually\u003csup\u003e[1]\u003c/sup\u003e. Radical hepatectomy and liver transplantation are considered effective methods for the treatment of HCC and are the most important measures for long-term survival of patients with HCC\u003csup\u003e[2]\u003c/sup\u003e. Surgical techniques continue to advance, molecular targeted and immunotherapeutic drugs have come out in succession, and treatment methods are becoming increasingly abundant; however, due to the high recurrence rate and high metastasis rate after surgery, long-term survival results are still unsatisfactory\u003csup\u003e[3]\u003c/sup\u003e. Studies have shown that about 70% of HCC patients relapse within 5 years after surgery, and the 5-year survival rate is less than 15%\u003csup\u003e[4, 5]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHCC commonly invades the blood vessels during its development, which can be divided into macrovascular and microvascular invasion\u003csup\u003e[6]\u003c/sup\u003e. Macrovascular invasion can be accurately judged by relevant preoperative examination whereas microvascular invasion (MVI), which is a histological feature, can only be determined through postoperative pathological examination. MVI refers to the microscopic observation of invasion of one or more of the portal vein, hepatic vein, arterioles, and lymphatic vessels by micrometastatic HCC emboli\u003csup\u003e[7]\u003c/sup\u003e. The process of MVI may involve the destruction of vascular endothelial cells by tumor cells through the death necrosis process mediated by death receptor-6, resulting in microvascular tumor thrombus\u003csup\u003e[8]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMany studies have confirmed that MVI is a high-risk factor for postoperative recurrence and metastasis of HCC and it has an extremely important impact on the long-term survival rate of liver cancer patients after surgery. MVI is an effective and independent predictor of early recurrence and poor overall survival after surgery for HCC\u003csup\u003e[9]\u003c/sup\u003e. However, the molecular mechanisms underlying the poor prognosis of MVI-positive HCC remain unclear. In this study, we first downloaded the transcriptome data of HCC from The Cancer Genome Atlas (TCGA) database, conducted pathway enrichment and immune infiltration analyses, and initially revealed the molecular characteristics of MVI-positive HCC. Multidimensional analysis based on multi-omics technology was used to further explore and verify the molecular characteristics of MVI-positive HCC to provide a new target for clinical treatment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition from TCGA\u003c/h2\u003e \u003cp\u003eTranscriptome data and clinical information of HCC patients, including 87 patients with MVI-positive HCC and 198 patients with MVI-negative HCC, were obtained from TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics analysis\u003c/h2\u003e \u003cp\u003eThe change and significant difference in the folding rate were calculated using the DESeq2 software. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and fold change\u0026thinsp;\u0026gt;\u0026thinsp;2.0 were used as thresholds to screen differentially expressed genes (DEGs). The DEGs were condensed using Gene Ontology (GO) and analyzed using the Kyoto Encyclopedia of Gene and Genome (KEGG) pathway with R statistics software (version 3.6). The GO term and KEGG pathway with correction P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were significantly enriched. Single-sample gene set enrichment analysis (ssGSEA) and xCell were further used to detect the enrichment levels or activity of immune cells, functions, or pathways between MVI-positive HCC and MVI-negative HCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eA retrospective study was conducted on 1202 patients with HCC admitted to the Guangxi Medical University Cancer Hospital from January 2012 to December 2017, including 432 MVI-positive HCC patients and 770 MVI-negative HCC patients.\u003c/p\u003e \u003cp\u003eTumor and matched non-tumor liver tissues were collected from patients with HCC, who underwent hepatectomy at the Guangxi Medical University Cancer Hospital. All samples were collected under the supervision of the same pathologist. The pathological diagnosis was performed by two independent expert pathologists. All the tumor tissues were primary HCC. Retrospective data, including demographic data, preoperative laboratory results, and pathological parameters, were collected from the electronic medical records. All the participants provided written informed consent, and the study was approved by the Ethics Committee of the Guangxi Medical University Cancer Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eRNA sequencing (RNA-seq)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from MVI-positive (n\u0026thinsp;=\u0026thinsp;49) and MVI-negative (n\u0026thinsp;=\u0026thinsp;44) HCC patients using TRIzol reagent (Invitrogen). RNA purification, reverse transcription, library preparation, and sequencing were carried out in Wuxi NextCODE (Shanghai) in accordance with the manufacturer's protocol (Illumina). FastQC was used for quality control and salmon was used to compare the reads with the human genome (Hg19). The UpSet plot was drawn using R statistical software (version 5.3). False discovery rate (FDR) or Q values were corrected and multiple hypothesis tests were performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTime-of-flight mass-cytometry (CyTOF)\u003c/h2\u003e \u003cp\u003eCancer tissues were collected from MVI-positive (n\u0026thinsp;=\u0026thinsp;35) and MVI-negative (n\u0026thinsp;=\u0026thinsp;33) HCC patients. After the cancer tissues were disintegrated and digested into a single-cell suspension, CyTOF was used to analyze the differences in the tumor microenvironment (TME) between the two groups. Fresh samples were isolated by enzymatic digestion with 20 \u0026micro;g/mL DNAse I (Sigma) and 2 mg/mL collagenase type II (Gibco) at 37\u0026deg;C for 30 min, as detailed in our previous research\u003csup\u003e[10]\u003c/sup\u003e. The samples were analyzed with 35 metal-conjugated antibodies using CyTOF as previously described. Briefly, the antibody was coupled to isotope labels using the MaxPar X8 antibody labeling kit (Fluidigm) according to the manufacturer's instructions. Single-cell suspensions were thawed and rested in a complete Roswell Park Memorial Institute (RPMI) solution containing 10% fetal bovine serum and 1% penicillin-streptomycin. The living/dead cells were then identified with cisplatin (Fludigm) staining and incubated with metal-coupled surface membrane antibodies. The cells were then fixed in 1.6% paraformaldehyde and permeabilized in 100% methanol to allow staining with metal-coupled antibodies. Finally, the cells were labeled with a DNA intercalator containing iridium and analyzed using a CyTOF-II instrument (Fluidigm). The signal was normalized using EQ\u0026trade; Four Element Calibration Beads (Fluidigm).\u003c/p\u003e \u003cp\u003eAfter sample data were collected, the flow cytometry standard (FCS) files were normalized and spliced using CyTOF software v6.7. The cells were manually de-coded after the file was standardized as previously described. Before data analysis, we used the online software Cytobank to manually set individual cells and living cell gates for each file (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cytobank.org/\u003c/span\u003e\u003cspan address=\"https://www.cytobank.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the spanning-tree progression analysis of density-normalized events (SPADE), we used 25 target nodes and 100% of the downsampled event targets. The clustering channel was selected based on whether they were pedigree markers and the cell population to be clustered. Cell phenotypes were compared in pairs using t-distribution random neighbor embedding (t-SNE) analysis to optimally draw similar cells close to each other. Regarding heat maps, the transformation rate of the median intensity corresponds to a logical data scale. The color in the heat map represents the measured average intensity value of a given marker in a given cluster. The four-color scale was used, wherein blue and white indicate low expression values, white and yellow indicate medium intensity expression markers, and red indicates high expression markers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImaging mass cytometry\u003c/h2\u003e \u003cp\u003eThe sections were collected from 8 MVI-positive and 10 MVI-negative HCC patients and prepared as mentioned previously. Paraffin-embedded tissue sections were analyzed using imaging mass cytometry (IMC) (Hyperion, Fluidigm). Image acquisition after daily tuning was performed at a laser frequency of 200 Hz, according to the manufacturer's instructions. An area of approximately 500 \u0026times; 500 \u0026micro;m was selected according to the bright-field image. Two to five regions of interest (ROIs) were randomly selected per slide. The expression intensity of markers related to individual ROI was used as the input for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003elipidomics\u003c/h2\u003e \u003cp\u003eTotal lipid was extracted from 16 MVI-positive and 14 MVI-negative HCC samples, using a improved method of Blig \u0026amp; Dyer, as previously descbibed\u003csup\u003e[11]\u003c/sup\u003e. HPLC-MS/MS analyses were performed using an Exion UPLC system (Thermo fisher Scientific) coupled with a SCIEX QTRAP 6500 PLUS system in both positive and negative modes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables are expressed as frequencies (n) and percentages (%). The Kaplan\u0026ndash;Meier method was used for survival analysis, and the log-rank test was used to analyze the differences between groups. The Student's t-test was used for continuous variables and the Mann\u0026ndash;Whitney U test for nonparametric variables to assess the significance of the baseline differences between the two groups. Categorical variables were compared using the chi-square test or Fisher's exact test, and continuous variables were compared using Student\u0026rsquo;s t-test. The Kaplan\u0026ndash;Meier method was used for survival analysis, and the log-rank test was used to analyze differences between groups. All analyses were performed using the SPSS 24.0 statistical software package (IBM SPSS Statistics). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical date\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of MVI-positive HCC patients and MVI-negative HCC patients are provided in Table\u0026nbsp;1. In the baseline data, we were interested and concerned about the significant reduction in lymphocyte counts in MVI-positive HCC patients. DFS and OS in the MVI-positive HCC patients was significantly lower than in the MVI-negative HCC patients (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTCGA database analysis\u003c/h2\u003e \u003cp\u003eWe first divided HCC samples into MVI-positive and MVI-negative HCC groups to determine the DEGs (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). Functional enrichment analysis was then applied to analyze the functions of the DEGs. GO and KEGG analyses revealed that many of them are involved in the TME, such as monocyte chemotaxis, chemokine mediated signaling pathway, chemokine activity, CCR10 chemokine receptor binding, cytokine and cytokine receptor interaction, and chemokine signaling pathway (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D, and E ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, we used TCGA database to compare immune cell infiltration between MVI-positive and MVI-negative HCC. ssGSEA suggested that the enrichment scores of Mast_cells, T_helper_cells and Type_II_IFN_Reponse were significantly lower in the MVI-positive HCC group than in the MVI-negative HCC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The xCell algorithm demonstrated that the enrichment scores of CD8\u0026thinsp;+\u0026thinsp;central memory T cells (Tcm) and conventional dendritic cell (cDC) were lower in MVI-positive than in MVI-negative HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDistinct transcriptomic signatures in MVI-positive HCC and MVI-negative HCC\u003c/h2\u003e \u003cp\u003eTo verify the results of TCGA database, we performed next-generation sequencing (NGS) on MVI-positive HCC and MVI-negative HCC. Based on the DEGs between the two groups, 864 genes were upregulated and 216 genes were downregulated in MVI-positive HCC compared to those in MVI-negative HCC (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB ). Next, we used functional enrichment analysis to characterize the DEGs in MVI-positive HCC. According to gene ontology (GO) analysis, the DEGs of MVI-positive HCC were mainly involved in cell differentiation, adhesion, and extracellular matrix disassembly (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). KEGG analysis showed that the DEGs enriched in MVI-positive HCC mainly included extracellular matrix (ECM) receptor interaction, cell adhesion molecules (CAMs), hypoxia-inducible factor 1 (HIF-1), phosphatidylinositol 3 kinase (PI3K-Akt), wingless-related integration site (Wnt), mitogen-activated protein kinases (MAPK), and Ras signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), which are closely related to the occurrence and development of tumors and can mediate the remodeling of TME by regulating immune function. Using ssGSEA analysis, we found that the enrichment scores of mast cells (MCs), Th2 cells, and B cells were significantly decreased in MVI-positive HCC, and cytolytic activity and interferon-γ (IFN-γ), also known as type 2 interferon response (Type_II_IFN_REPONSE) activity, were also significantly reduced in MVI-positive HCC (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). xCell analysis showed that the enrichment scores of CD4\u0026thinsp;+\u0026thinsp;effector memory T cells (CD4\u0026thinsp;+\u0026thinsp;Tem), CD8\u0026thinsp;+\u0026thinsp;Tcm, CD8\u0026thinsp;+\u0026thinsp;Tem, and cDC were significantly decreased in MVI-positive HCC (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDistinct immune subsets in MVI-positive and MVI-negative HCC\u003c/h2\u003e \u003cp\u003eBased on the hypothesis that the TME of HCC may affect MVI, we first attempted to determine whether there is a difference in the TME of MVI-positive HCC and MVI-negative HCC. In this study, CyTOF was used to implement a thorough immunoassay of the immune status of the two subsets of HCC. Our CyTOF group consisted of 35 surface and intracellular immune markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), which can detect different immune markers and phenotypes in MVI-positive and MVI-negative HCC. First, based on the expression of immune cell markers, we identified 20 distinct cell clusters in CD45\u0026thinsp;+\u0026thinsp;HCC cells. Meanwhile, the 20 cell clusters were further classified into CD19\u0026thinsp;+\u0026thinsp;B cells (clusters 16 and 20), CD14\u0026thinsp;+\u0026thinsp;human leucocyte antigen DR (HLA-DR)\u0026thinsp;+\u0026thinsp;tumor-associated macrophages (TAM) (cluster 3), CD66b\u0026thinsp;+\u0026thinsp;tumor-associated neutrophils (TAN) (cluster 14), CD4\u0026thinsp;+\u0026thinsp;T cells (clusters 1,2,4,5,6,7, and 8), CD25\u0026thinsp;+\u0026thinsp;Foxp3\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;Treg cells (clusters 2 and 4), CD8\u0026thinsp;+\u0026thinsp;T cells (clusters 11,12,15,17, and 18), programmed cell death protein 1 (PD1)\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells (clusters 12,15, and 18) and other types of cells (clusters 9,10,13, and 19) (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further investigated the frequency of differential expression clusters in MVI-positive and MVI-negative HCC and found that there was no difference in the proportion of CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, B cells, TAMs, and TANs subsets between the two groups. However, we found that the ratio of immunosuppressive Tregs to CD4\u0026thinsp;+\u0026thinsp;T cells in MVI-positive HCC was higher than that in non-MVI-positive HCC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, indicating that there were more immunosuppressive Tregs among CD4\u0026thinsp;+\u0026thinsp;T cells in MVI-positive HCC.\u003c/p\u003e \u003cp\u003eWe additionally detected the functional status of immune cells and found that CD4\u0026thinsp;+\u0026thinsp;T cells from MVI-positive HCC expressed higher levels of T-cell immunoglobulin and mucin domain 3 (TIM3), PD-1, and PD-L1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB ). CD8\u0026thinsp;+\u0026thinsp;T cells from MVI-positive HCC cells expressed higher levels of lymphocyte-activation gene 3 (LAG-3), PD-1, and PD-L1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD ). Tregs from MVI-positive HCC patients expressed higher levels of LAG-3, PD-1, and PD-L1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). TAMs from MVI-positive HCC expressed higher levels of TIM3, LAG-3, and PD-L1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH). Simultaneously, we detected the expression of these immune markers in the two groups of HCC. Interestingly, we found that the expression of immunosuppressive markers, IL-6, TIM3, transforming growth factor beta (TGF-β), CD196, CD223 (LAG-3), CD279 (PD-1), and CD274 (PD-L1) was significantly upregulated in MVI-positive HCC (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ, and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eK ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIMC analysis in MVI-positive and MVI-negative HCC\u003c/h2\u003e \u003cp\u003eOur RNA-seq data revealed that the ECM receptor interaction pathway, which is mainly secreted by cancer-associated fibroblasts (CAFs), is significantly enriched in MVI-positive HCC. The CyTOF results indicated that T cells were in a state of immune exhaustion in MVI-positive HCC, and studies showed that CAFs could inhibit the tumor-killing ability of T cells by interacting with T cells. Therefore, we hypothesized that there may be a stronger interaction between CAFs and T cells in MVI-positive HCC. Therefore, we used IMC to explore the interaction between CAFs and T cells in MVI-positive HCC.\u003c/p\u003e \u003cp\u003eThe results of IMC showed that all the cells of MVI-positive HCC and MVI-negative HCC were clustered into 19 subsets based on various cell surface markers, including 1 CD4\u0026thinsp;+\u0026thinsp;T cell (CD3\u0026thinsp;+\u0026thinsp;CD4+) (subset 10), 1 CD8\u0026thinsp;+\u0026thinsp;T cell subpopulation (CD3\u0026thinsp;+\u0026thinsp;CD8+) (subset 16), 3 smooth muscle alpha-actin (α-SMA)\u0026thinsp;+\u0026thinsp;collagen-1\u0026thinsp;+\u0026thinsp;CAFs (subsets 9, 13, and 18), 2 fibroblast-activating protein (FAP)\u0026thinsp;+\u0026thinsp;vimentin\u0026thinsp;+\u0026thinsp;CAFs (subsets 17 and 19), and some tumor cell subsets (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB ). Compared to MVI-negative HCC, α-SMA\u0026thinsp;+\u0026thinsp;collagen-1\u0026thinsp;+\u0026thinsp;CAFs and FAP\u0026thinsp;+\u0026thinsp;vimentin\u0026thinsp;+\u0026thinsp;CAFs were significantly increased in MVI-positive HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC ). The distance between T cells and CAFs in MVI-positive HCC was significantly shortened (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD), and the expression of the T cell immune depletion marker PD-L1 was significantly higher than that in MVI-negative HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003elipidomic analysis in MVI-positive HCC and MVI-negative HCC\u003c/h2\u003e \u003cp\u003eWe tried to understand the underlying mechanisms by which such distinctive local immune statuses are established in the HCC microenvironment. Our transcriptome sequencing results showed that MVI-positive HCC is significantly enriched in lipid metabolism-related pathways and that lipid metabolism plays an important role in tumor development, so we used lipidomics to analyze the mechanisms of poor prognosis of MVI-positive HCC at the lipidomic level. We detected a total of 528 intact lipids originating from 28 major lipid categories (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA and 1B ). There were significant up-regulation of cholesterol (Cho), Acylcarnitine, plasmalogenPC, Glucosylceramides (GluCer), Lactosylceramides (LacCer) and Sulfatides (SL) in MVI-positive HCC, however Cardiolipins (CL) was significantly reduced in the MVI-positive HCC group (Figure S2A、S2B and S2C ). There was no significant difference among other lipids.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMVI is a histological feature of HCC and is associated with invasive biological behavior and a poor clinical prognosis. Many studies have confirmed that MVI is a high-risk factor for postoperative recurrence and metastasis of HCC and it has an extremely important impact on the long-term survival rate of HCC patients after surgery. MVI is an effective and independent predictor of early recurrence and poor overall survival after surgery for HCC\u003csup\u003e[12, 13]\u003c/sup\u003e. Our clinical data also showed that DFS and OS are significantly shorter for MVI-positive HCC compared to MVI-negative HCC. However, the differences in cellular ecosystems between MVI-positive and MVI-negative HCC remain unexplained. In our clinical data, we found a significant reduction in the count of lymphocyte, lymphocyte is an important component of the TME, it may suggested that there may be some difference in the TME between MVI-positive HCC and MVI-negative HCC. Our current study provides a comprehensive examination of the intratumoral immune landscapes of MVI-positive HCC compared to MVI-negative HCC using high-dimensional analytical tools such as NGS, CyTOF, and IMC. This approach enabled us to address the fundamental impact of the underlying TME.\u003c/p\u003e \u003cp\u003eIn our study, we first downloaded the HCC transcriptome data from TCGA database and found that MVI-positive HCC enriched pathways related to multiple immunity; further, through immune infiltration analysis we revealed that MVI-positive HCC had less immune cell infiltration. RNA-seq revealed that compared to MVI-negative HCC, MVI-positive HCC enriched more signaling pathways remolded by regulating TME, which was closely related to tumor progression. ssGSEA and xCell analysis further revealed that the TME may be in a low immune state, suggesting that MVI-positive HCC may have a TME that is more conducive to cancer progression. Therefore, we performed CyTOF to reveal the TEM images of MVI-positive HCC. Interestingly, we observed immunosuppression in patients with MVI-positive HCC.\u003c/p\u003e \u003cp\u003eOur CyTOF results showed that there were higher numbers of Tregs among CD4\u0026thinsp;+\u0026thinsp;T cells in MVI-positive HCC. Tregs are important immunosuppressive factors, and a high density of Tregs is strongly associated with poor prognosis in a variety of tumors, including HCC\u003csup\u003e[14, 15]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLAG-3, TAMs, PD-1, and PD-L1, as classical markers of immune exhaustion, represent the exhaustion of immune cells\u003csup\u003e[16\u0026ndash;18]\u003c/sup\u003e. CD4\u0026thinsp;+\u0026thinsp;T cells express higher levels of TIM3, PD-1 and PD-L1, while CD8\u0026thinsp;+\u0026thinsp;T cells express higher levels of LAG-3, PD-1, and PD-L1 in MVI-positive HCC; the co-expression of exhausted genes can increase T cell damage, which is also related to the progression of tumor\u003csup\u003e[19, 20]\u003c/sup\u003e. Tregs express more LAG-3, PD-1, and PD-L1, and immune depletion markers have been reported to be important for enhancing the suppressive activity of Tregs, suggesting that Tregs in MVI-positive HCC have a stronger immunosuppressive function\u003csup\u003e[21, 22]\u003c/sup\u003e; TAMs in MVI-positive HCC also express more TIM3, LAG-3, and PD-L1, which maintain the immunosuppressive function of TAMs\u003csup\u003e[23, 24]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur IMC data revealed that the proportion of CAFs in MVI-positive HCC was significantly increased. CAFs are among the most abundant and critical components of the TME. It promotes the growth, invasion, and angiogenesis of cancer cells by remodeling the extracellular matrix, inducing tumor angiogenesis, and mediating tumor inflammation. As the main participants of immune regulation in TME, CAFs induce TME to a state of immunosuppression and promote tumor immune escape through the secretion of a variety of cytokines and chemokines, mediating the recruitment and functional differentiation of innate and acquired immune cells\u003csup\u003e[25, 26]\u003c/sup\u003e. Studies have shown that CAFs can inhibit the tumor-killing ability of T cells by interacting with T cells\u003csup\u003e[27, 28]\u003c/sup\u003e. Compared to non-MVI-positive HCCs, the distance between T cells and CAFs is significantly shortened, and the expression of PD-L1, an immune exhaustion-related marker of T cells, is higher. CAFs may inhibit T cell function through stronger interactions, leading to the existence of an immunosuppressive TME in MVI-positive HCC. Our lipidomics results showed significant lipid differences between MVI-positive HCC and MVI-negative HCC. Compared with MVI-negative HCC, MVI-positive HCC is rich in more lipids associated with HCC progression, especially the significantly elevated cholesterol of MVI-positive HCC has been shown to cause T cells to be in a state of immune depletion\u003csup\u003e[29]\u003c/sup\u003e. This further confirmed the existence of immunosuppressive tumor microenvironment in MVI-positive HCC. The immunosuppressive TME can enable tumor cells to escape immune surveillance, which makes them more prone to microvascular invasion and promotes tumor development.\u003c/p\u003e \u003cp\u003eIn conclusion, our study is the first and most comprehensive analysis of molecular typing of HCC significantly related to MVI, and revealed the real reason for the poor prognosis of MVI-positive HCC at the molecular level. Such deep immunophenotyping strategies are essential for enhancing our understanding of tumor immunity in cancers derived from different etiologies, and will help guide the design of novel immunotherapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMVI，Microvascular invasion；HCC=hepatocellular carcinoma；TCGA，The cancer genome atlas；NGS，next-generation sequencing；CyTOF，\u0026nbsp;time-of-flight mass-cytometry；IMC，imaging mass cytometry；TME，tumor microenvironment；ssGSEA，single-sample gene set enrichment；TAMs，tumor-associated macrophage；CAFs，cancer-associated fibroblasts；DEGs，differentially expressed genes；GO，Gene Ontology；KEGG，Kyoto Encyclopedia of Gene and Genome；\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe authors thank the National Natural Science Foundation of China (81960450), the National Major Special Science and Technology Project\u0026nbsp;(2017ZX10203207), the project of Key laboratory of High-Incidence-Tumor Prevention \u0026amp; Treatment (Guangxi Medical University), Ministry of Education (GKE2018-KF02 and GKE2019-ZZ10), Open\u0026nbsp;Foundation of Guangxi Key Laboratory of Biological Targeting Diagnosis and Therapy Research \u0026amp; Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy (GXSWBX201503), and \u0026lsquo;Guangxi BaGui Scholars\u0026rsquo; Special Fund. Funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (81960450), the National Major Special Science and Technology Project (2017ZX10203207), the project of Key laboratory of High-Incidence-Tumor Prevention \u0026amp; Treatment (Guangxi Medical University), Ministry of Education (GKE2018-KF02 and GKE2019-ZZ10), Open Foundation of Guangxi Key Laboratory of Biological Targeting Diagnosis and Therapy Research \u0026amp; Collaborative Innovation Center for Targeting Tumor Diagnosis and Therapy (GXSWBX201503).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe manuscript has been read and approved by all the authors, each author believes \u0026nbsp;the manuscript represents honest work, the information is not provided in another form. All the participants provided written informed consent, and the study was approved by the Ethics Committee of the Guangxi Medical University Cancer Hospital.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed Consent: All the participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Registry and the Registration No. of the study/trial. \u0026nbsp;N/A.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Animal Studies. \u0026nbsp;N/A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBangde Xiang and Qiuyan Wang contributed to the conception of the study, provided feedback on the report;\u003c/p\u003e\n\u003cp\u003eLixin Pan, Zhijian Li, Yuting Tao, Xiaoyin Hu and Jingfei Zhao performed research;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLixin Pan, Zhijian Li, Xi Wang, Yaobang Wang, Zhenxing Wang analyzed data;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJunwen Hu, Chenglei Yang and performed the data analyses and wrote the manuscript;\u003c/p\u003e\n\u003cp\u003eAll authors gave final approval of the version to be published, and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin. 2018, 68(6): 394-424.\u003c/li\u003e\n\u003cli\u003eFan S T, Poon R T, Yeung C, et al. Outcome after partial hepatectomy for hepatocellular cancer within the Milan criteria[J]. Br J Surg. 2011, 98(9): 1292-1300.\u003c/li\u003e\n\u003cli\u003eDhir M, Melin A A, Douaiher J, et al. A Review and Update of Treatment Options and Controversies in the Management of Hepatocellular Carcinoma[J]. Ann Surg. 2016, 263(6): 1112-1125.\u003c/li\u003e\n\u003cli\u003eNjei B, Rotman Y, Ditah I, et al. Emerging trends in hepatocellular carcinoma incidence and mortality[J]. Hepatology. 2015, 61(1): 191-199.\u003c/li\u003e\n\u003cli\u003eColecchia A, Schiumerini R, Cucchetti A, et al. Prognostic factors for hepatocellular carcinoma recurrence[J]. World J Gastroenterol. 2014, 20(20): 5935-5950.\u003c/li\u003e\n\u003cli\u003eRoayaie S, Blume I N, Thung S N, et al. A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma[J]. Gastroenterology. 2009, 137(3): 850-855.\u003c/li\u003e\n\u003cli\u003eParfitt J R, Marotta P, Alghamdi M, et al. Recurrent hepatocellular carcinoma after transplantation: use of a pathological score on explanted livers to predict recurrence[J]. Liver Transpl. 2007, 13(4): 543-551.\u003c/li\u003e\n\u003cli\u003eStrilic B, Yang L, Albarr\u0026aacute;n-Ju\u0026aacute;rez J, et al. Tumour-cell-induced endothelial cell necroptosis via death receptor 6 promotes metastasis[J]. Nature. 2016, 536(7615): 215-218.\u003c/li\u003e\n\u003cli\u003eYanhan W, Lianfang L, Hao L, et al. Effect of Microvascular Invasion on the Prognosis in Hepatocellular Carcinoma and Analysis of Related Risk Factors: A Two-Center Study[J]. Front Surg. 2021, 8: 733343.\u003c/li\u003e\n\u003cli\u003eLi Z, Hu J, Qin Z, et al. 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Eur J Immunol. 2019, 49(8): 1140-1146.\u003c/li\u003e\n\u003cli\u003eTanaka A, Sakaguchi S. Regulatory T cells in cancer immunotherapy[J]. Cell Res. 2017, 27(1): 109-118.\u003c/li\u003e\n\u003cli\u003eHan Y, Liu D, Li L. PD-1/PD-L1 pathway: current researches in cancer[J]. Am J Cancer Res. 2020, 10(3): 727-742.\u003c/li\u003e\n\u003cli\u003eFriedlaender A, Addeo A, Banna G. New emerging targets in cancer immunotherapy: the role of TIM3[J]. ESMO Open. 2019, 4(Suppl 3): e497.\u003c/li\u003e\n\u003cli\u003eRuffo E, Wu R C, Bruno T C, et al. Lymphocyte-activation gene 3 (LAG3): The next immune checkpoint receptor[J]. Semin Immunol. 2019, 42: 101305.\u003c/li\u003e\n\u003cli\u003eZelba H, Bedke J, Hennenlotter J, et al. PD-1 and LAG-3 Dominate Checkpoint Receptor-Mediated T-cell Inhibition in Renal Cell Carcinoma[J]. Cancer Immunol Res. 2019, 7(11): 1891-1899.\u003c/li\u003e\n\u003cli\u003eLichtenegger F S, Rothe M, Schnorfeil F M, et al. Targeting LAG-3 and PD-1 to Enhance T Cell Activation by Antigen-Presenting Cells[J]. Front Immunol. 2018, 9: 385.\u003c/li\u003e\n\u003cli\u003eCamisaschi C, Casati C, Rini F, et al. LAG-3 expression defines a subset of CD4(+)CD25(high)Foxp3(+) regulatory T cells that are expanded at tumor sites[J]. J Immunol. 2010, 184(11): 6545-6551.\u003c/li\u003e\n\u003cli\u003eFrancisco L M, Salinas V H, Brown K E, et al. PD-L1 regulates the development, maintenance, and function of induced regulatory T cells[J]. J Exp Med. 2009, 206(13): 3015-3029.\u003c/li\u003e\n\u003cli\u003eYan W, Liu X, Ma H, et al. Tim-3 fosters HCC development by enhancing TGF-\u0026beta;-mediated alternative activation of macrophages[J]. Gut. 2015, 64(10): 1593-1604.\u003c/li\u003e\n\u003cli\u003eRogers T L, Holen I. Tumour macrophages as potential targets of bisphosphonates[J]. J Transl Med. 2011, 9: 177.\u003c/li\u003e\n\u003cli\u003eMonteran L, Erez N. The Dark Side of Fibroblasts: Cancer-Associated Fibroblasts as Mediators of Immunosuppression in the Tumor Microenvironment[J]. Front Immunol. 2019, 10: 1835.\u003c/li\u003e\n\u003cli\u003eGok Y B, Gunaydin G, Gedik M E, et al. Cancer associated fibroblasts sculpt tumour microenvironment by recruiting monocytes and inducing immunosuppressive PD-1(+) TAMs[J]. Sci Rep. 2019, 9(1): 3172.\u003c/li\u003e\n\u003cli\u003eKato T, Noma K, Ohara T, et al. Cancer-Associated Fibroblasts Affect Intratumoral CD8(+) and FoxP3(+) T Cells Via IL6 in the Tumor Microenvironment[J]. Clin Cancer Res. 2018, 24(19): 4820-4833.\u003c/li\u003e\n\u003cli\u003eLakins M A, Ghorani E, Munir H, et al. Cancer-associated fibroblasts induce antigen-specific deletion of CD8 (+) T Cells to protect tumour cells[J]. Nat Commun. 2018, 9(1): 948.\u003c/li\u003e\n\u003cli\u003eMa X, Bi E, Lu Y, et al. Cholesterol Induces CD8(+) T Cell Exhaustion in the Tumor Microenvironment[J]. Cell Metab. 2019, 30(1): 143-156.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTable 1. Comparison of Clinicopathologic Features Between MVI-Positive and MVI-Negative HCCs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eMVI (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eMVI (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eN=432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eN=770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e371(85.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e662(86.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e61(14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e108(14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eAge(yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e50.4\u0026plusmn;10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e52.1\u0026plusmn;11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNeutrophil\u0026nbsp;(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e3.9\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e3.8\u0026plusmn;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eLymphocyte(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e1.7\u0026plusmn;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e1.8\u0026plusmn;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003ePlatelets\u0026nbsp;(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e210.3\u0026plusmn;80.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e203.0\u0026plusmn;97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHBV viral infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e366(84.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e641(83.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e66(15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e129(16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eTotal bilirubin\u0026nbsp;(\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026le;17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e316(73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e609(79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e＞17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e116(26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e161(20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eAFP\u0026nbsp;(ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026le;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e220(50.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e489(63.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e＞400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e212(49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e281(36.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eTumor number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eSolitary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e308(71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e578(75.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e124(28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e192(24.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026le;5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e166(38.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e393(51.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e＞5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e266(61.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e377(49.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eTumor capsular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e301(71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e547(71.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e131(28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e223(29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eEdmodson-steiner grading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eI,II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e5(1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e43(5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eIII,IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e427(98.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e727(94.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eLiver cirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e237(54.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e342(44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e195(45.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e428(55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eTumor recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e266(61.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e360(46.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e166(38.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e410(53.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[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":"Microvascular invasion, Tumor microenvironment, Next-generation sequencing, Time-of-flight mass-cytometry, Imaging mass cytometry","lastPublishedDoi":"10.21203/rs.3.rs-4479454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4479454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eMicrovascular invasion (MVI) is a risk factor for the recurrence and poor prognosis of hepatocellular carcinoma (HCC). However, the molecular mechanisms underlying MVI-mediated progression of HCC remain unclear. This study aimed to discuss the background of MVI-positive HCC through multiple approaches.\u003c/p\u003e\u003ch2\u003ePatients and methods:\u003c/h2\u003e \u003cp\u003eThe cancer genome atlas (TCGA) database analysis, next-generation sequencing (NGS), time-of-flight mass-cytometry (CyTOF), imaging mass cytometry (IMC) and lipidomics were used to analyze the molecular characteristics of MVI-positive HCC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePathway enrichment analysis of TCGA database and NGS showed that the enriched pathways in MVI-positive HCC were significantly involved in the tumor microenvironment (TME) remodeling; moreover, single-sample gene set enrichment (ssGSEA) and xCell analysis indicated that the TME of MVI-positive HCC might be in low immune state. In-depth interrogation of the immune landscapes using CyTOF showed that the ratio of Tregs to CD4\u0026thinsp;+\u0026thinsp;T cells in MVI-positive HCC was higher than that in MVI-negative HCC. CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, Tregs and tumor-associated macrophages (TAMs) express higher levels of immune exhaustion-related markers in MVI-positive HCC. IMC further showed that in MVI-positive HCC the distance between T cells and CAFs was significantly shortened, and the expression of PD-L1 in T cells was higher. The lipidomics results showed the cholesterol which may cause T cells to be in immune exhaustion was significantly elevated in MVI-positive HCC.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThrough high-dimensional analysis, we found that there was immunosuppression of TME in MVI-positive HCC, which may be the cause of worse prognosis.\u003c/p\u003e","manuscriptTitle":"Multi-omics analyses reveal a distinct tumor microenvironment in microvascular invasion positive hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 20:40:58","doi":"10.21203/rs.3.rs-4479454/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":"b918dd48-88c1-41c6-82bd-b29742a697f1","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T03:23:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-11 20:40:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4479454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4479454","identity":"rs-4479454","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}