Zinc finger protein 296 promotes hepatocellular carcinoma progression via inducing interaction between macrophages and B cells

Zinc finger protein 296 promotes hepatocellular carcinoma progression via inducing interaction between macrophages and B cells | 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 Zinc finger protein 296 promotes hepatocellular carcinoma progression via inducing interaction between macrophages and B cells Nan Xu, Shuai Wang, Huan Chen, Yiyuan Chen, Yijie Yang, Xuyong Wei, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3256244/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 Hepatocellular carcinoma (HCC) is one of the most common malignancies with poor survival. Tumor tissues are heterogeneous, with different cell types in the tumor microenvironment, which play different roles in tumorigenesis and tumor progression attached to the prognosis of HCC.This study analyzed HCC RNA-seq for cell-type identification and prognosis by estimating relative subsets of RNA transcript (CIBERSORTx). Analyzing LIHC RNA-seq (n = 423) from TCGA showed that high infiltration of eosinophils promoted HCC progression.Interaction of B cells and macrophages in HCC was detected by the Hepa1-6 orthotopic transplantation mice model and flow cytometer analysis. B cells were correlated with macrophages (r=-0.24) and could affect the expression of PDL1 in macrophages infiltrating in LIHC. Transcription factor Zinc finger protein 296 (ZNF296) might accelerate HCC progression by regulating PAFAH1B3 and H2AFX. HCC patients with high expression of ZNF296 were in the late pathological stage. Moreover, the expression of ZNF296 was positively associated with the abundance of activated B cells (r = 0.185) and macrophages (r = 0.167). Among the survival and dead phenotype related to immune cells identified by SCISSOR analysis, T cells were most correlated to the excellent prognosis of HCC. The normal function of Liver cells and DC cells were also connected with the good prognosis of HCC.This investigation primarily delves into the intricate interplay between the immune microenvironment and the prognosis of HCC, thereby unveiling ZNF296 as a novel diagnostic and therapeutic target for HCC. Hepatocellular carcinoma immune cells prognosis Zinc finger protein 296 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Hepatocellular carcinoma (HCC) is the the fifth most diagnosed cancer and third leading cause of cancer-related mortality worldwide 1 , 2 . The prognosis in HCC is poor, which represents a major global health-care challenge. To improve the outcomes of patients with HCC, it is essential to decipher how key clinical and molecular characteristics influence disease course and treatment response 3 . Therefore, generating robust tools for prognosis prediction and therapeutic response assessment of HCC patients is urgently needed. Cancer immunotherapy leverages the body's immune response against tumors to identify and eradicate cancer cells by stimulating the host's immune system 4 . Immune checkpoint inhibitors, including nivolumab and pembrolizumab, obstruct the PD-1(PD‐L1) pathway, marking a significant advancement in immune therapy for HCC 5 . Nonetheless, only a small fraction of cancer patients derive benefits from immune checkpoint inhibitors 6 . While immunotherapy for liver cancer holds promise, it poses unique challenges due to the distinct immunological environment and diversity of immune cells in the liver 7 . These cells can either augment or inhibit the anti-cancer immune response, and may either contribute to or hinder cancer immunotherapy. Therefore, it is essential to analyze the immune microenvironment of HCC to improve the prognosis. Thus, CIBERSORTx, a popular tumor-infiltrating lymphocyte (TILs) prediction method, was used in this study to analyze the immune microenvironment of HCC 8 . Meanwhile, to further investigate the relationship between TILs and prognosis, SCISSCOR, a method that identifies cell sub-populations from single-cell data, was performed 9 . In this study, we used two algorithms to determine the immune landscape of HCC. We connected it with prognosis to provide broad prospects for improving the diagnosis and treatment of HCC. RESULTS Higher infiltration of eosinophil cells is correlated with a worse prognosis of HCC in TME. To analyze the tumor immune microenvironment of HCC, we used CIBERSORTx to evaluate the infiltration of immune cells. As shown in Fig. 1 A, M2 macrophages, resting CD4 memory T cells, and CD8 T cells were highly infiltrated in LIHC. Other immune cells exhibited low infiltration. Immune contribution to survival hazard ratio showed that the infiltration of CD8T cells was a protective factor, and the infiltration of M2 macrophages was a risk factor (Fig. 1 B). Meanwhile, immune contribution to the disease process indicates that the infiltration of memory B cells, CD8 T cells, and CD4 T cells were protective factors. Still, the infiltration of M0 macrophages was a risk factor (Fig. 1 C), which might be because the infiltration of M0 macrophages as a risk factor was prone to differentiate into M2 macrophages in HCC 10 . The correlation analysis of 22 types of immune cells infiltrating in LIHC showed that the infiltration of naive B cells were negatively correlated with the infiltration of M0 macrophage (r=-0.24), monocytes (r=-0.26), and mast cells (r=-0.26) (Fig. 1 D). The infiltration of CD8 T cells were positively correlated with the infiltration of activated Memory CD4T cells (r = 0.33) and follicular helper T cells (r = 0.26) while negatively correlated with resting NK cells (r=-0.27). TCGA database analysis indicates that HCC patients with higher infiltration of CD8 T cells have relatively more prolonged Overall Survival. In comparison, HCC patients with lower infiltration of Eosinophils have somewhat shorter Overall Survival (Fig. 1 E-F). These results indicated that CD8 T cells were essential for the prognosis of HCC, and B cells may play an important role in HCC. Interaction of B cells and Macrophages affects HCC progression. To further investigate the correlation of B cells and macrophages in HCC progression, we first constructed the hepa1-6-Luc orthotopic transplantation mice HCC model (Luc-HCC). We evaluated the effect of Clodronate Liposomes (CL), a specific inhibitor for the depletion of macrophages, and the impact of CD20 antibody, a specific antibody for the depletion of CD20 + B cells. CL treatment didn’t evidently affect tumor progression 11 (Fig. 2 A). Furthermore, we found that CL treatment didn’t affect the expression of CD138 in CD20 + cells (Fig. 2 B). Unexpected, CD20 antibody treatment accelerated tumor progression (Fig. 2 C). Moreover, to our surprise, the depletion of CD20 + cells promoted the expression of PDL1 in macrophages but did not promote macrophage differentiation into M2 macrophages (Fig. 2 D-E). Overall, these results indicated that B cells could inhibit HCC progression and suppress the expression of PDL1 in macrophages. Immune analysis of different clusters of immune response for HCC patients To explore the different immune response clusters for HCC patients, HCC patients were divided into nine clusters of immune response via CIBERSORTx analysis (Fig. 3 A). The overall survival of cluster 2 for HCC patients was the worst, while the overall survival of cluster 7 was the best (Fig. 3 B). We then investigated possible reasons for the difference in overall survival by analyzing the infiltration of different immune cells in HCC. As shown in Fig. 3 C, for cluster 2 and 6, M0 and M2 macrophages were highly infiltrated, which is correlated with HCC progression (Fig. 3 C) 12 . M1 macrophages were higher, and M0 macrophages were lower infiltrated in cluster 7 than those in cluster 2 and 6 13 . Meanwhile, cluster 2 and 6 were mainly at the stage T3/T4 of HCC, and cluster 7 was mainly at the early stage of HCC (Fig. 3 D). TF enrichment analysis showed that ZWINT (ZW10 interacting kinetochore protein) and ZSWIM5(zinc finger SWIM-type 5) were enriched in cluster 2, while WDR65 was relatively enriched in cluster 7 (Fig. 3 E). TF enrichment analysis showed that ZNF296 was enriched in cluster 2 and cluster 6 while was not enriched in cluster 7 (Fig. 3 E). Compared with other tumors, ZNF296 was highly correlated with immune subtypes in HCC (Figure S1A). HCC patients with high expression of ZNF296 had poor prognoses and were distributed in the late stage of HCC (Figure S1B-C). Correlation analysis showed that the upregulation of ZNF296 expression was closely related to the upregulation of PAFAH1B3 and H2AFX (Figure S1D). PAFAH1B3 is a platelet-activating factor acetyl Hydrolase (PAF-AH), which is involved in glycolysis and lipid synthesis signaling pathway of HCC progress 14 . At the same time, histone H2AFX is highly correlated with immune cell infiltration, such as B cells, TAM, and neutrophils, which might promote HCC 15 . Besides, the downregulation of ZNF296 expression is closely related to the upregulation of MPDZ and ALDH6A1, which might suppress the progression of HCC (Figure S1E) 16 , 17 . In addition, correlation analysis indicated that the expression of ZNF296 was positively correlated with the abundance of activated B cells (R = 0.185) and macrophages (R = 0.157) (Figure S2A-C). The expression of LAG-3 (Lymphocyte-activation Gene 3) (R = 0.27) was also related to the expression of ZNF296 (Figure S2D), which might be a potential target for HCC patients with high expression of ZNF296 18 . Moreover, small neutral L- and D-amino acids translocator SLC7A10 was highly expressed in cluster 2, while pH regulator SLC9A11 was mainly enriched in cluster 7, indicating that differential biological activities may be involved in cluster 2 and cluster 7 (Fig. 3 F). Immune analysis of infiltrated immune cells in HCC by SCISSOR. To mine the prognosis information correlated with infiltrated immune cells in HCC, we performed SCISSOR analysis by combining HCC scRNA-seq and TCGA bulk RNA-seq 19 (Fig. 4 A). As shown in Fig. 4 B, there was a total of 18 cell subsets, including T cell, dendritic cell (DC), endothelial cell (EC), neutrophil(Neu), liver cell, and Myeloid cell (Fig. 4 B). Furthermore, dead and survival phenotype-associated cells were distributed across these cell subsets (Fig. 4 C). Two kinds of T cells (T1, T2), myeloid cells, and liver cells, were associated with survival phenotype, while two types of DCs (DC1, DC2) were associated with the dead phenotype (Fig. 4 D). Differential gene enrichment analysis indicated that GZMK, GZMA, KLRB1 NKG7 were enriched in T1, HLA-DRA/DRB1/DPB1 and CXCL8 were enriched in T2, and CTSB was explicitly expressed in DC2 (Fig. 4 E). DEGs analysis suggests T1 is activated CTL and T2 has migrated Teff, and these T cell subsets are related to a better prognosis. However, CTSB + DC2 is connected to a worse prognosis, which may be contributed to CTSB promoting cancer metastasis 20 . Differentially expressed genes (DEGs) analysis of bulk cells is performed by survival phenotype -associated cells and dead phenotype -associated cells. Compared to dead phenotype-associated cell subsets, the survival phenotype-associated cell had 42 significant DEGs (21 upregulated and 21 downregulated) (Fig. 5 A). As shown in Fig. 5 B, IGKC, which was highly expressed in survival phenotype-associated cells, was highly expressed in normal tissue compared to tumor tissue (Fig. 5 B). GDF15, which was highly expressed in dead phenotype-associated cells, was highly expressed in tumor tissue compared to normal tissue (Fig. 5 C). TCGA database analysis indicated that HCC patients with higher expression of CD69 had relatively longer overall survival (Fig. 5 D). In comparison, HCC patients with higher terms of BRI3, NDRG1, or GAPDH had relatively more prolonged overall survival (Fig. 5 E-G). CD69 is a type 2 transmembrane protein rapidly induced on T lymphocytes' surface after TCR/CD3 conjugation to activate cytokines and stimulate polyclonal mitosis. CD69 is significantly and positively correlated with T cells. The increased expression of CD69 may change the immunosuppressed tumor microenvironment into an immune state with a better prognosis, which may explain why HCC patients with high expression of CD69 had a better prognosis 21 , 22 . Besides, recent research has shown that BRI3 is identified as a new downstream target of the Wnt/ β-catenin signaling, which may be related to the poorer prognosis of HCC. NDRG1 regulates gene expressions related to transmembrane transporter activity, immune response, cell adhesion, and cell proliferation. NDRG1 protein is associated with HCC metastasis and promotes EpCAM protein stability to enhance the CSC characteristics of HCC 23 . GAPDH is a glyceraldehyde-3-phosphate dehydrogenase that catalyzes the fifth step of glycolysis, and overexpression of GAPDH exacerbates HCC progression by activating cell proliferation and inflammation 24 . Differential gene expression analysis of the immune cell subtype was performed by survival and dead-associated cells. Differential gene expression analysis of the immune cell subtype was performed to find the differential genes of survival phenotype-associated cells and dead phenotype-associated cells. Compared with the slow phenotype associated with the Liver 1 cell, the survival phenotype related to the Liver 1 cell had 45 significantly differential genes (24 upregulated and 21 downregulated) (Fig. 6 A). Compared with the dead phenotype associated with the Liver2 cell, the survival phenotype associated Liver2 cell had 48 significantly differential genes (34 upregulated and 14 downregulated) (Fig. 6 B). Among Liver 1 and Liver 2 cells, ALB, HGF, APOE, and APOC were expressed highly. The survival phenotype associated with liver cells’ protein synthesis function, lipid metabolism, and hepatocyte proliferation was normal. Compared with dead associated phenotype DC1 cell, the survival phenotype associated DC1 cell had 32 significantly differential genes (13 upregulated and 19 downregulated) (Fig. 6 C). Among survival phenotypes associated with DC1 cell genes of antigen presentation were highly expressed, which indicates the normal anti-tumor function of DC and a better prognosis 25 . TF enrichment analysis showed that KLF6 and BATF related to activation of T cells were enriched in T1 cells, indicating T1 cells play a positive role in anti-tumor (Fig. 6 D) 26 , 27 . Discussion HCC is a highly heterogeneous and malignant tumor. Although progress has been made in therapeutic strategies, the prognosis of HCC could be better, especially in advanced patients. Recently, more and more studies have reported that the tumor microenvironment is highly related to the forecast of tumor patients and the treatment resistance of tumor patients. Thus, clarifying the underlying mechanisms of how the tumor microenvironment affects HCC resistance to therapy is essential for improving the therapeutic effect. In the present study, we analyzed the HCC microenvironment via CIBERSORT analysis using the TCGA database. We found that M2 macrophage was the most highly infiltrated in HCC, a risk factor for the overall survival of HCC. Meanwhile, the M0 macrophage was a risk factor for the HCC process, possibly due to the M0 macrophage tending to differentiate into M2-type macrophages under the HCC microenvironment 28 . CD8 T cell is the protective factor for the overall survival and process of HCC. Interestingly, we found that memory B cell was the protective factor for the HCC process, and naïve B cell was a negative correlation with M0 macrophage and Monocyte. In the following experiments, we, for the first time, found that the depletion of B cells could accelerate the development of liver cancer in mice. Simultaneously, B-cell depletion did not promote macrophage polarization into M2 type but increased PDL1 expression on macrophages. This was consistent with other studies on PDL1 expression in myeloid cells promoting the progression of HCC 29 . However, the specific mechanism by which B cells affect PDL1 expression in macrophages has yet to be thoroughly explored. Subsequently, we sub-grouped different immune cells based on their response types and identified a higher infiltration of M0 and M2 macrophages in the population with poor prognosis. On this basis, we compared the cluster 2 and 6 with cluster 7 and first found the ZNF296, which was highly associated with immune subtypes of HCC. This provided a possibility for predicting the prognosis of HCC immunotherapy. Next, correlation analysis showed that ZNF296 might promote the interaction between B cells and macrophages by upregulating H2AFX. At the same time, we found that the high expression of ZNF296 is closely related to the increased expression of immune checkpoint CLTA4 and LAG3. All the evidence proved that ZNF296 was closely associated with the effectiveness of immunotherapy for HCC. Thus, the critical transcript factor ZNF296 might be the promising target of HCC. However, the point of therapy for ZNF296 still needs to be verified. Next, we performed the SCISSOR algorithm to compare RNA seq using the TCGA database and single-cell data from existing data, and for the first time, explored the association of immune cell infiltration with HCC prognosis. T-cell infiltration demonstrated a better prediction of HCC, while infiltration of myeloid cells and neutrophils was associated with a poorer prognosis. By comparison, we found some exciting genes, such as immunoglobulin kappa C (IGKC), a protein related to B cells and plasma cells, expressed at a lower level in HCC and correlated with better prognosis. However, there are currently no relevant studies focusing on this gene. In addition, molecules such as CD69, BRI3, GAPDH, and NDRG1 were also closely related to the prognosis of liver cancer. But our research mainly focuses on analyzing gene transcription without verifying protein expression. Next, we conducted a similar analysis in the relevant cell subpopulations. Those with normal cell function in hepatocytes were mainly related to a good prognosis, while those with glucose and lipid metabolism disorders indicated a significantly poorer prognosis. The prediction of HCC also showed a strong correlation with antigen-presenting genes among DC cells. However, our analysis still needs a dynamic communication process and transformations between cell subpopulations. Besides, the results in the study were mainly obtained through bioinformatic analysis and required to be verified using more clinical samples. MATERIALS AND METHODS Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORTx) The proportions of the 22 TILs from each sample were determined using the “CIBERSORTx” 30 . CIBERSORTx was used to analyze the relative expression levels of 547 genes in individual tissue samples according to their gene expression profiles (GEPs) to predict the proportion of 22 types of TIICs in each tissue, namely: naive B cells (Bn), memory B cells(Bm), plasma cells, CD8 + T cells, naive CD4 + T cells (CD4 + Tn), CD4 + resting memory T cells (CD4 + Tmr), CD4 + memory-activated T cells (CD4 + Tma), Tfh, Tregs, γδT, resting natural killer cells (NKr), activated natural killer cells (NKa), monocytes, M0 macrophages (M0), M1 macrophages (M1), M2 macrophages (M2), resting dendritic cells (DCr), activated dendritic cells (DCa), resting mast cells (Mr), activated mast cells (Ma), eosinophils, and neutrophils. Normalized LIHC GEPs were transformed into the proportion of 22 TILs. The relative expression of 22 TILs in each sample was determined. Significant results (P < 0.05) were selected for subsequent analysis. Single-Cell Identification of Subpopulations with bulk Sample phenOtype coRrelation(SCISSOR) The proportions of each cluster from each sample were determined using the “SCISSOR” (R package). The three input data for Scissor are the single cell expression matrix, batch expression matrix, and target phenotype. Then, the correlation matrix and cell-cell similarity network are calculated according to the input source, and their phenotypes are further integrated into the network regularization sparse regression model to select the most relevant cell subsets. According to the estimated sign of the regression coefficient, it is divided into scissor positive (Scissor+) cells and scissor negative (Scissor -) cells, indicating a positive and negative correlation with the target phenotype, respectively. Subsequently, reliability significance testing was conducted to determine whether the selected data was suitable for phenotype cell association. Finally, the cells selected by the scissors would be further characterized in downstream analysis. Animal Experiments and ethics statement Six to eight weeks old C57BL/6 mice were purchased from Hangzhou Medical College (Hangzhou, China). Animal experiments were undertaken by the Ethics Committee of Zhejiang University School of Medicine (Ethics approval ID: 22031). A total 25 µL mixture of PBS and Matrigel (Corning, USA) (1:1) containing 5 × 10^5 Hepa1-6 cells were injected into the left liver lobe of C57BL/6 mice to establish the HCC orthotopic model. For inhibitors experiments, mice were intraperitoneally injected with CD20 antibody (100 ug/each) or Clodronate Liposomes (CL) (200ul/each) every other day. This study was approved by the Ethics Committee of Zhejiang University School of Medicine. Mouse tissue-derived lymphocytes isolation Liver tissues were cut from C57BL/6 mice which were injected with Hepa1-6 cells. Liver-derived lymphocytes were isolated from HCC tissues using collagenase II/ IV (Solarbio, China) and Lymphocyte Separation Medium (Dakewe Biotech, China). cytometric analysis Briefly, nonspecific binding of cells was blocked using Fc receptor block (clone 2.4G2, BD, USA). After incubating for 10 min at 4°C, cells were stained with surface antigen-antibodies for 30 min at 4°C. After washing using PBS twice; cells were analyzed using BD FACSCanto II. Data were analyzed using Flowjo X software (TreeStar). Statistics analysis Data were analyzed using GraphPad Prism 9.0 software (GraphPad Software Inc., La Jolla, CA, USA). All experiments were repeated at least three times. Statistical significance was determined by unpaired two-tailed Student’s t-test between two groups. One-way ANOVA was used for multiplying groups. Differences were considered to be significant when p < 0.05. Declarations Acknowledgements Not applicable. Authors’ contributions W.Q. and X.X. designed and conceived the study. X.N. conducted most of the experiments and prepared the manuscript. W.S. performed the RNA sequencing data analysis. C.H., C.Y.Y, Y.Y.J, and W.X.Y contributed to the technical supportand animal work. W.Q., X.X., W.S., C.H., C.Y.Y, Y.Y.J, and W.X.Y provided a critical reading of the manuscript.All the authors have given their consent to publish this study. Funding Key Program, National Natural Science Foundation of China (No. 81930016). The National Natural Science Foundation of China (No. 92159202, No.82273177). Key Research & Development Plan of Zhejiang Province (No. 2021C03118). DATA AVAILABILITY STATEMENT The dataset generated during the current study is available from the corresponding author on reasonable request. Ethics approval Human subjects were not used in this study. All preclinical investigations involving animals were carried out in line with ethical standards and according to Ethics Committee of Zhejiang University School of Medicine. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. References Dong ZR, Ke AW, Li T, et al. CircMEMO1 modulates the promoter methylation and expression of TCF21 to regulate hepatocellular carcinoma progression and sorafenib treatment sensitivity. Mol Cancer 2021; 20 (1): 75. Li Q, Cao M, Lei L, et al. Burden of liver cancer: From epidemiology to prevention. Chin J Cancer Res 2022; 34 (6): 554-66. Lin Z, Xiang X, Lu D, Xu X. Targeting tumor microenvironment as a treatment strategy for hepatocellular carcinoma. Hepatobiliary Surg Nutr 2020; 9 (6): 794-6. Bai KH, Zhang YY, Li XP, et al. Comprehensive analysis of tumor necrosis factor-α-inducible protein 8-like 2 (TIPE2): A potential novel pan-cancer immune checkpoint. 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(B) Kaplan–Meier analysis from the TCGA database of overall survival (OS) for patients with high or low expression of ZNF296. (C) The boxplot shows the associations between the expression of ZNF296 and the stage of HCC. The heat map after differential analysis shows the upregulated genes (D) and downregulated genes (E) in the high expression samples of ZNF296. FigureS2.tif Figure S2 High expression of ZNF296 was correlated with the abundance of B cells and macrophages. (A) The heatmap shows the Spearman correlations between the expression of ZNF296 and TILs across human cancers. (B)The scatter plot shows the correlations between the expression of ZNF296 and the abundance of activated B cells. (C) The scatter plot shows the correlations between the expression of ZNF296 and the abundance of macrophages. (D) The heatmap shows the Spearman correlations between the expression of ZNF296 and Immunoinhibitors across human cancers. (E) The scatter plot shows the correlations between the expression of ZNF296 and LAG3. 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-3256244","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":226364057,"identity":"058afeb0-661b-449d-bccd-c430a315ddc7","order_by":0,"name":"Nan Xu","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Xu","suffix":""},{"id":226364058,"identity":"fa77bd6f-20b7-4e9b-bdcb-76755cf1b811","order_by":1,"name":"Shuai Wang","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Wang","suffix":""},{"id":226364059,"identity":"c7a13a79-c571-4c1f-8b2b-8e95c465fd4e","order_by":2,"name":"Huan Chen","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Chen","suffix":""},{"id":226364060,"identity":"3fb5a032-1dd7-4312-84ef-73dc1e887902","order_by":3,"name":"Yiyuan Chen","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiyuan","middleName":"","lastName":"Chen","suffix":""},{"id":226364061,"identity":"24670712-bd08-49ff-ade7-8b0af48b3176","order_by":4,"name":"Yijie Yang","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yijie","middleName":"","lastName":"Yang","suffix":""},{"id":226364062,"identity":"6e4ba96a-52d1-4cac-882f-5089264798e0","order_by":5,"name":"Xuyong Wei","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xuyong","middleName":"","lastName":"Wei","suffix":""},{"id":226364063,"identity":"9dbf607d-2066-4a75-ac98-85de0174af01","order_by":6,"name":"Xiao Xu","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Xu","suffix":""},{"id":226364064,"identity":"b9865f80-52fe-4c3c-a4ea-27dbdb16eec0","order_by":7,"name":"Qiang Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYFCCAwwMHxjYQCwD4rUwziBRCwMDMw+EJlKLfOMZw9s2f/gSG9ibt0kw1NwhrIWx4YyxdQ4PW2IDz7EyCYZjz4hwFMPZbdI5EkAtEjlmEowNhwlrYQNpsTAAapF/Q6QWHpAWhgSQLTxEapFgOP/ZsucAm3EbT1qxRcIxIrTIzziWeOPHn2Oy/eyHN974UEOEFgaJA0CbGI5BIjOBCA0MDPwNIC01RKkdBaNgFIyCEQoAasM1hFnnTTgAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2023-08-11 16:14:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3256244/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3256244/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":41769872,"identity":"18be0dc6-c657-429e-bf42-ff01583d05c7","added_by":"auto","created_at":"2023-08-18 15:19:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1736923,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh infiltration of eosinophils was associated with poor prognosis of HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The heatmap shows the absolute abundance of 22 immune cell subsets. (B)The dot plot shows the immune contribution to the survival hazard ratio. (C) The dot plot shows the immune contribution to the disease process. (D)The correlation of 22 types of immune cells in LIHC was evaluated. Red: positive correlation; blue: negative correlation. (E)Kaplan–Meier analysis from the TCGA database of overall survival (OS) for patients with high or low infiltration of CD8 T cells. (F)Kaplan–Meier analysis from TCGA database of OS for patients with high or low infiltration of Eosinophils.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3256244/v1/4a672f61f3c62730a9b4e403.png"},{"id":41773183,"identity":"7b9f10eb-21e8-4564-a894-8c35978333f2","added_by":"auto","created_at":"2023-08-18 15:35:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1442467,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDeletion of B cells increased the expression of PDL1 from macrophages.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) Luc reporter-carrying hepa1-6 HCC cell line orthotopic transplantation mice model was constructed, and CL was injected every three days. \u003cem\u003eIn vivo,\u003c/em\u003e imaging was performed on the seventh day (A). Tumor-infiltrated immune cells were isolated, and then the percentage of CD138+ cells in CD20+ cells (B) was detected using flow cytometry. The right panel shows statistical analysis. (C-E) Luc reporter-carrying Hepa1-6 HCC cell line orthotopic transplantation mice model was constructed, and CD20 antibody was intraperitoneally injected every three days. \u003cem\u003eIn vivo,\u003c/em\u003e imaging was performed on the seventh day (C). Percentages of PDL1+ cells (D) and CD206+ cells(E) were detected by flow cytometry in F4/80+ cells isolated from mouse splenocytes. The right graph shows statistical analysis. *, p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3256244/v1/16aabca8a5c51d64f084b432.png"},{"id":41769876,"identity":"d1563c47-3275-4772-807f-e9af7132d387","added_by":"auto","created_at":"2023-08-18 15:19:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3143281,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh expression of ZNF296 promoted the progression of HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)The t-Distributed Stochastic Neighbor Embedding（t-SNE）plot shows 9 immune infiltration subtypes of LIHC.(B)The Kaplan–Meier analysis of overall survival (OS) for 9 immune infiltration subtypes of LIHC. (C)The Violin Plot shows 20 types of immune cells infiltrating ratio for 9 immune infiltration subtypes of LIHC. (D) The pathological stage of 9 immune infiltration subtypes of LIHC. (E) The heatmap of 9 immune infiltration subtypes of LIHC. (F)The Markgene heatmap of 9 immune infiltration subtypes of LIHC.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3256244/v1/809f606f2185d587934d0964.png"},{"id":41769877,"identity":"94d1e73b-11bc-4762-b190-b8180400f703","added_by":"auto","created_at":"2023-08-18 15:19:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2535181,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eT cells were related to the survival phenotype of HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)The flow diagram shows the analysis process of SCISSOR. (B)The UMAP visualization of 10000 LIHC cancer cells. (C) The UMAP visualization of the Scissor-selected cells. The red and blue dots are Dead phenotype cells(bad survival) and Survival phenotype cells (good survival). (D)The bar chart shows the cell ratio of 18 cell clusters in LIHC. (E)The heatmap shows the top ten gene expression of 18 cell clusters in LIHC\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3256244/v1/0c08e694480ebe75f8dc9f31.png"},{"id":41771433,"identity":"8f33d8c0-d96b-4b0b-9c8f-afb1c367bba7","added_by":"auto","created_at":"2023-08-18 15:27:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":652066,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh expression of CD69 in T cell infiltrating in HCC was the critical survival factor.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)Differential expression was performed to identify genes enriched in bulk cells. For each subtype, the average log fold change and the percentage of cells that express the gene above the background are compared between the 2 clusters (Right). (B)Box plot of IGKC3 expression in HCC tumors and the adjacent tissues in TCGA database. (C)Box plot of GDF15 expression in HCC tumors and the adjacent tissues in TCGA database. (D-G) Kaplan–Meier analysis from the TCGA database of overall survival (OS) for patients with high or low expression of CD69 (D), BRI3 (E), NDRG1 (F), and GAPDH (G).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3256244/v1/aaa7a102c198d39ae77a6b0b.png"},{"id":41769875,"identity":"5a488e51-37f6-42c0-a822-9b26d805253f","added_by":"auto","created_at":"2023-08-18 15:19:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2508471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival of HCC patients depended on the liver and DC cells’ normal function.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-C) Differential expression was performed to identify genes enriched in Liver 1 cells(A), Liver 2 cells(B), and DC1 cells(C). For each subtype, the average log fold change and the percentage of cells that express the gene above the background are compared between the 2 clusters (Right). (D) The heatmap shows the top expressed TFs of 18 cell clusters.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3256244/v1/f8feab08d24d81c8407835c9.png"},{"id":42541740,"identity":"a79e4fb0-e0f4-43e4-a22c-cdb7dba712ee","added_by":"auto","created_at":"2023-09-02 20:52:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5149855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3256244/v1/f40090ed-3751-44e5-80c9-55d1547502bd.pdf"},{"id":41769878,"identity":"370eb90d-deff-415b-9a2b-ad35f6d70e08","added_by":"auto","created_at":"2023-08-18 15:19:25","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22613260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1 ZNF296 was the specific high-expression molecule of HCC with poor prognosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The histogram shows the associations between ZNF296 expression and immune subtypes across human cancers. (B) Kaplan–Meier analysis from the TCGA database of overall survival (OS) for patients with high or low expression of ZNF296. (C) The boxplot shows the associations between the expression of ZNF296 and the stage of HCC. The heat map after differential analysis shows the upregulated genes (D) and downregulated genes (E) in the high expression samples of ZNF296.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3256244/v1/03b8ecb1654b8f949e4f6dbb.tif"},{"id":41769879,"identity":"3d834712-0286-49d3-abbb-6d17eb1ad5f0","added_by":"auto","created_at":"2023-08-18 15:19:25","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28253996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2 High expression of ZNF296 was correlated with the abundance of B cells and macrophages.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The heatmap shows the Spearman correlations between the expression of ZNF296 and TILs across human cancers. (B)The scatter plot shows the correlations between the expression of ZNF296 and the abundance of activated B cells. (C) The scatter plot shows the correlations between the expression of ZNF296 and the abundance of macrophages. (D) The heatmap shows the Spearman correlations between the expression of ZNF296 and Immunoinhibitors across human cancers. (E) The scatter plot shows the correlations between the expression of ZNF296 and LAG3.\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3256244/v1/9b0bf399c31150a7c565cc2b.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Zinc finger protein 296 promotes hepatocellular carcinoma progression via inducing interaction between macrophages and B cells","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the the fifth most diagnosed cancer and third leading cause of cancer-related mortality worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The prognosis in HCC is poor, which represents a major global health-care challenge. To improve the outcomes of patients with HCC, it is essential to decipher how key clinical and molecular characteristics influence disease course and treatment response\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Therefore, generating robust tools for prognosis prediction and therapeutic response assessment of HCC patients is urgently needed.\u003c/p\u003e \u003cp\u003eCancer immunotherapy leverages the body's immune response against tumors to identify and eradicate cancer cells by stimulating the host's immune system\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Immune checkpoint inhibitors, including nivolumab and pembrolizumab, obstruct the PD-1(PD‐L1) pathway, marking a significant advancement in immune therapy for HCC\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Nonetheless, only a small fraction of cancer patients derive benefits from immune checkpoint inhibitors\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. While immunotherapy for liver cancer holds promise, it poses unique challenges due to the distinct immunological environment and diversity of immune cells in the liver\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These cells can either augment or inhibit the anti-cancer immune response, and may either contribute to or hinder cancer immunotherapy.\u003c/p\u003e \u003cp\u003eTherefore, it is essential to analyze the immune microenvironment of HCC to improve the prognosis. Thus, CIBERSORTx, a popular tumor-infiltrating lymphocyte (TILs) prediction method, was used in this study to analyze the immune microenvironment of HCC\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Meanwhile, to further investigate the relationship between TILs and prognosis, SCISSCOR, a method that identifies cell sub-populations from single-cell data, was performed\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In this study, we used two algorithms to determine the immune landscape of HCC. We connected it with prognosis to provide broad prospects for improving the diagnosis and treatment of HCC.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eHigher infiltration of eosinophil cells is correlated with a worse prognosis of HCC in TME.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo analyze the tumor immune microenvironment of HCC, we used CIBERSORTx to evaluate the infiltration of immune cells. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, M2 macrophages, resting CD4 memory T cells, and CD8 T cells were highly infiltrated in LIHC. Other immune cells exhibited low infiltration. Immune contribution to survival hazard ratio showed that the infiltration of CD8T cells was a protective factor, and the infiltration of M2 macrophages was a risk factor (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Meanwhile, immune contribution to the disease process indicates that the infiltration of memory B cells, CD8 T cells, and CD4 T cells were protective factors. Still, the infiltration of M0 macrophages was a risk factor (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), which might be because the infiltration of M0 macrophages as a risk factor was prone to differentiate into M2 macrophages in HCC\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The correlation analysis of 22 types of immune cells infiltrating in LIHC showed that the infiltration of naive B cells were negatively correlated with the infiltration of M0 macrophage (r=-0.24), monocytes (r=-0.26), and mast cells (r=-0.26) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The infiltration of CD8 T cells were positively correlated with the infiltration of activated Memory CD4T cells (r\u0026thinsp;=\u0026thinsp;0.33) and follicular helper T cells (r\u0026thinsp;=\u0026thinsp;0.26) while negatively correlated with resting NK cells (r=-0.27). TCGA database analysis indicates that HCC patients with higher infiltration of CD8 T cells have relatively more prolonged Overall Survival. In comparison, HCC patients with lower infiltration of Eosinophils have somewhat shorter Overall Survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F). These results indicated that CD8 T cells were essential for the prognosis of HCC, and B cells may play an important role in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInteraction of B cells and Macrophages affects HCC progression.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the correlation of B cells and macrophages in HCC progression, we first constructed the hepa1-6-Luc orthotopic transplantation mice HCC model (Luc-HCC). We evaluated the effect of Clodronate Liposomes (CL), a specific inhibitor for the depletion of macrophages, and the impact of CD20 antibody, a specific antibody for the depletion of CD20\u0026thinsp;+\u0026thinsp;B cells. CL treatment didn\u0026rsquo;t evidently affect tumor progression\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Furthermore, we found that CL treatment didn\u0026rsquo;t affect the expression of CD138 in CD20\u0026thinsp;+\u0026thinsp;cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Unexpected, CD20 antibody treatment accelerated tumor progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Moreover, to our surprise, the depletion of CD20\u0026thinsp;+\u0026thinsp;cells promoted the expression of PDL1 in macrophages but did not promote macrophage differentiation into M2 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-E). Overall, these results indicated that B cells could inhibit HCC progression and suppress the expression of PDL1 in macrophages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eImmune analysis of different clusters of immune response for HCC patients\u003c/h2\u003e \u003cp\u003eTo explore the different immune response clusters for HCC patients, HCC patients were divided into nine clusters of immune response via CIBERSORTx analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The overall survival of cluster 2 for HCC patients was the worst, while the overall survival of cluster 7 was the best (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). We then investigated possible reasons for the difference in overall survival by analyzing the infiltration of different immune cells in HCC. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, for cluster 2 and 6, M0 and M2 macrophages were highly infiltrated, which is correlated with HCC progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. M1 macrophages were higher, and M0 macrophages were lower infiltrated in cluster 7 than those in cluster 2 and 6\u003csup\u003e13\u003c/sup\u003e. Meanwhile, cluster 2 and 6 were mainly at the stage T3/T4 of HCC, and cluster 7 was mainly at the early stage of HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). TF enrichment analysis showed that ZWINT (ZW10 interacting kinetochore protein) and ZSWIM5(zinc finger SWIM-type 5) were enriched in cluster 2, while WDR65 was relatively enriched in cluster 7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). TF enrichment analysis showed that ZNF296 was enriched in cluster 2 and cluster 6 while was not enriched in cluster 7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Compared with other tumors, ZNF296 was highly correlated with immune subtypes in HCC (Figure S1A). HCC patients with high expression of ZNF296 had poor prognoses and were distributed in the late stage of HCC (Figure S1B-C). Correlation analysis showed that the upregulation of ZNF296 expression was closely related to the upregulation of PAFAH1B3 and H2AFX (Figure S1D). PAFAH1B3 is a platelet-activating factor acetyl Hydrolase (PAF-AH), which is involved in glycolysis and lipid synthesis signaling pathway of HCC progress \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. At the same time, histone H2AFX is highly correlated with immune cell infiltration, such as B cells, TAM, and neutrophils, which might promote HCC\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Besides, the downregulation of ZNF296 expression is closely related to the upregulation of MPDZ and ALDH6A1, which might suppress the progression of HCC (Figure S1E)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In addition, correlation analysis indicated that the expression of ZNF296 was positively correlated with the abundance of activated B cells (R\u0026thinsp;=\u0026thinsp;0.185) and macrophages (R\u0026thinsp;=\u0026thinsp;0.157) (Figure S2A-C). The expression of LAG-3 (Lymphocyte-activation Gene 3) (R\u0026thinsp;=\u0026thinsp;0.27) was also related to the expression of ZNF296 (Figure S2D), which might be a potential target for HCC patients with high expression of ZNF296\u003csup\u003e18\u003c/sup\u003e. Moreover, small neutral L- and D-amino acids translocator SLC7A10 was highly expressed in cluster 2, while pH regulator SLC9A11 was mainly enriched in cluster 7, indicating that differential biological activities may be involved in cluster 2 and cluster 7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eImmune analysis of infiltrated immune cells in HCC by SCISSOR.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo mine the prognosis information correlated with infiltrated immune cells in HCC, we performed SCISSOR analysis by combining HCC scRNA-seq and TCGA bulk RNA-seq\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, there was a total of 18 cell subsets, including T cell, dendritic cell (DC), endothelial cell (EC), neutrophil(Neu), liver cell, and Myeloid cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, dead and survival phenotype-associated cells were distributed across these cell subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Two kinds of T cells (T1, T2), myeloid cells, and liver cells, were associated with survival phenotype, while two types of DCs (DC1, DC2) were associated with the dead phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Differential gene enrichment analysis indicated that GZMK, GZMA, KLRB1 NKG7 were enriched in T1, HLA-DRA/DRB1/DPB1 and CXCL8 were enriched in T2, and CTSB was explicitly expressed in DC2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). DEGs analysis suggests T1 is activated CTL and T2 has migrated Teff, and these T cell subsets are related to a better prognosis. However, CTSB\u0026thinsp;+\u0026thinsp;DC2 is connected to a worse prognosis, which may be contributed to CTSB promoting cancer metastasis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferentially expressed genes (DEGs) analysis of bulk cells is performed by survival phenotype -associated cells and dead phenotype -associated cells.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCompared to dead phenotype-associated cell subsets, the survival phenotype-associated cell had 42 significant DEGs (21 upregulated and 21 downregulated) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, IGKC, which was highly expressed in survival phenotype-associated cells, was highly expressed in normal tissue compared to tumor tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). GDF15, which was highly expressed in dead phenotype-associated cells, was highly expressed in tumor tissue compared to normal tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). TCGA database analysis indicated that HCC patients with higher expression of CD69 had relatively longer overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In comparison, HCC patients with higher terms of BRI3, NDRG1, or GAPDH had relatively more prolonged overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-G). CD69 is a type 2 transmembrane protein rapidly induced on T lymphocytes' surface after TCR/CD3 conjugation to activate cytokines and stimulate polyclonal mitosis. CD69 is significantly and positively correlated with T cells. The increased expression of CD69 may change the immunosuppressed tumor microenvironment into an immune state with a better prognosis, which may explain why HCC patients with high expression of CD69 had a better prognosis\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Besides, recent research has shown that BRI3 is identified as a new downstream target of the Wnt/ β-catenin signaling, which may be related to the poorer prognosis of HCC. NDRG1 regulates gene expressions related to transmembrane transporter activity, immune response, cell adhesion, and cell proliferation. NDRG1 protein is associated with HCC metastasis and promotes EpCAM protein stability to enhance the CSC characteristics of HCC\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. GAPDH is a glyceraldehyde-3-phosphate dehydrogenase that catalyzes the fifth step of glycolysis, and overexpression of GAPDH exacerbates HCC progression by activating cell proliferation and inflammation\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferential gene expression analysis of the immune cell subtype was performed by survival and dead-associated cells.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDifferential gene expression analysis of the immune cell subtype was performed to find the differential genes of survival phenotype-associated cells and dead phenotype-associated cells. Compared with the slow phenotype associated with the Liver 1 cell, the survival phenotype related to the Liver 1 cell had 45 significantly differential genes (24 upregulated and 21 downregulated) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Compared with the dead phenotype associated with the Liver2 cell, the survival phenotype associated Liver2 cell had 48 significantly differential genes (34 upregulated and 14 downregulated) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Among Liver 1 and Liver 2 cells, ALB, HGF, APOE, and APOC were expressed highly. The survival phenotype associated with liver cells\u0026rsquo; protein synthesis function, lipid metabolism, and hepatocyte proliferation was normal. Compared with dead associated phenotype DC1 cell, the survival phenotype associated DC1 cell had 32 significantly differential genes (13 upregulated and 19 downregulated) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Among survival phenotypes associated with DC1 cell genes of antigen presentation were highly expressed, which indicates the normal anti-tumor function of DC and a better prognosis\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. TF enrichment analysis showed that KLF6 and BATF related to activation of T cells were enriched in T1 cells, indicating T1 cells play a positive role in anti-tumor (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eD)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHCC is a highly heterogeneous and malignant tumor. Although progress has been made in therapeutic strategies, the prognosis of HCC could be better, especially in advanced patients. Recently, more and more studies have reported that the tumor microenvironment is highly related to the forecast of tumor patients and the treatment resistance of tumor patients. Thus, clarifying the underlying mechanisms of how the tumor microenvironment affects HCC resistance to therapy is essential for improving the therapeutic effect.\u003c/p\u003e \u003cp\u003eIn the present study, we analyzed the HCC microenvironment via CIBERSORT analysis using the TCGA database. We found that M2 macrophage was the most highly infiltrated in HCC, a risk factor for the overall survival of HCC. Meanwhile, the M0 macrophage was a risk factor for the HCC process, possibly due to the M0 macrophage tending to differentiate into M2-type macrophages under the HCC microenvironment\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. CD8 T cell is the protective factor for the overall survival and process of HCC. Interestingly, we found that memory B cell was the protective factor for the HCC process, and na\u0026iuml;ve B cell was a negative correlation with M0 macrophage and Monocyte.\u003c/p\u003e \u003cp\u003eIn the following experiments, we, for the first time, found that the depletion of B cells could accelerate the development of liver cancer in mice. Simultaneously, B-cell depletion did not promote macrophage polarization into M2 type but increased PDL1 expression on macrophages. This was consistent with other studies on PDL1 expression in myeloid cells promoting the progression of HCC\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, the specific mechanism by which B cells affect PDL1 expression in macrophages has yet to be thoroughly explored. Subsequently, we sub-grouped different immune cells based on their response types and identified a higher infiltration of M0 and M2 macrophages in the population with poor prognosis. On this basis, we compared the cluster 2 and 6 with cluster 7 and first found the ZNF296, which was highly associated with immune subtypes of HCC. This provided a possibility for predicting the prognosis of HCC immunotherapy. Next, correlation analysis showed that ZNF296 might promote the interaction between B cells and macrophages by upregulating H2AFX. At the same time, we found that the high expression of ZNF296 is closely related to the increased expression of immune checkpoint CLTA4 and LAG3. All the evidence proved that ZNF296 was closely associated with the effectiveness of immunotherapy for HCC. Thus, the critical transcript factor ZNF296 might be the promising target of HCC. However, the point of therapy for ZNF296 still needs to be verified.\u003c/p\u003e \u003cp\u003eNext, we performed the SCISSOR algorithm to compare RNA seq using the TCGA database and single-cell data from existing data, and for the first time, explored the association of immune cell infiltration with HCC prognosis. T-cell infiltration demonstrated a better prediction of HCC, while infiltration of myeloid cells and neutrophils was associated with a poorer prognosis. By comparison, we found some exciting genes, such as immunoglobulin kappa C (IGKC), a protein related to B cells and plasma cells, expressed at a lower level in HCC and correlated with better prognosis. However, there are currently no relevant studies focusing on this gene. In addition, molecules such as CD69, BRI3, GAPDH, and NDRG1 were also closely related to the prognosis of liver cancer. But our research mainly focuses on analyzing gene transcription without verifying protein expression. Next, we conducted a similar analysis in the relevant cell subpopulations. Those with normal cell function in hepatocytes were mainly related to a good prognosis, while those with glucose and lipid metabolism disorders indicated a significantly poorer prognosis. The prediction of HCC also showed a strong correlation with antigen-presenting genes among DC cells. However, our analysis still needs a dynamic communication process and transformations between cell subpopulations. Besides, the results in the study were mainly obtained through bioinformatic analysis and required to be verified using more clinical samples.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORTx)\u003c/h2\u003e \u003cp\u003eThe proportions of the 22 TILs from each sample were determined using the \u0026ldquo;CIBERSORTx\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. CIBERSORTx was used to analyze the relative expression levels of 547 genes in individual tissue samples according to their gene expression profiles (GEPs) to predict the proportion of 22 types of TIICs in each tissue, namely: naive B cells (Bn), memory B cells(Bm), plasma cells, CD8\u0026thinsp;+\u0026thinsp;T cells, naive CD4\u0026thinsp;+\u0026thinsp;T cells (CD4\u0026thinsp;+\u0026thinsp;Tn), CD4\u0026thinsp;+\u0026thinsp;resting memory T cells (CD4\u0026thinsp;+\u0026thinsp;Tmr), CD4\u0026thinsp;+\u0026thinsp;memory-activated T cells (CD4\u0026thinsp;+\u0026thinsp;Tma), Tfh, Tregs, γδT, resting natural killer cells (NKr), activated natural killer cells (NKa), monocytes, M0 macrophages (M0), M1 macrophages (M1), M2 macrophages (M2), resting dendritic cells (DCr), activated dendritic cells (DCa), resting mast cells (Mr), activated mast cells (Ma), eosinophils, and neutrophils. Normalized LIHC GEPs were transformed into the proportion of 22 TILs. The relative expression of 22 TILs in each sample was determined. Significant results (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were selected for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSingle-Cell Identification of Subpopulations with bulk Sample phenOtype coRrelation(SCISSOR)\u003c/h2\u003e \u003cp\u003eThe proportions of each cluster from each sample were determined using the \u0026ldquo;SCISSOR\u0026rdquo; (R package). The three input data for Scissor are the single cell expression matrix, batch expression matrix, and target phenotype. Then, the correlation matrix and cell-cell similarity network are calculated according to the input source, and their phenotypes are further integrated into the network regularization sparse regression model to select the most relevant cell subsets. According to the estimated sign of the regression coefficient, it is divided into scissor positive (Scissor+) cells and scissor negative (Scissor -) cells, indicating a positive and negative correlation with the target phenotype, respectively. Subsequently, reliability significance testing was conducted to determine whether the selected data was suitable for phenotype cell association. Finally, the cells selected by the scissors would be further characterized in downstream analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnimal Experiments and ethics statement\u003c/h2\u003e \u003cp\u003eSix to eight weeks old C57BL/6 mice were purchased from Hangzhou Medical College (Hangzhou, China). Animal experiments were undertaken by the Ethics Committee of Zhejiang University School of Medicine (Ethics approval ID: 22031). A total 25 \u0026micro;L mixture of PBS and Matrigel (Corning, USA) (1:1) containing 5 \u0026times; 10^5 Hepa1-6 cells were injected into the left liver lobe of C57BL/6 mice to establish the HCC orthotopic model. For inhibitors experiments, mice were intraperitoneally injected with CD20 antibody (100 ug/each) or Clodronate Liposomes (CL) (200ul/each) every other day. This study was approved by the Ethics Committee of Zhejiang University School of Medicine.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMouse tissue-derived lymphocytes isolation\u003c/h2\u003e \u003cp\u003eLiver tissues were cut from C57BL/6 mice which were injected with Hepa1-6 cells. Liver-derived lymphocytes were isolated from HCC tissues using collagenase II/ IV (Solarbio, China) and Lymphocyte Separation Medium (Dakewe Biotech, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ecytometric analysis\u003c/h2\u003e \u003cp\u003eBriefly, nonspecific binding of cells was blocked using Fc receptor block (clone 2.4G2, BD, USA). After incubating for 10 min at 4\u0026deg;C, cells were stained with surface antigen-antibodies for 30 min at 4\u0026deg;C. After washing using PBS twice; cells were analyzed using BD FACSCanto II. Data were analyzed using Flowjo X software (TreeStar).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistics analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using GraphPad Prism 9.0 software (GraphPad Software Inc., La Jolla, CA, USA). All experiments were repeated at least three times. Statistical significance was determined by unpaired two-tailed Student\u0026rsquo;s t-test between two groups. One-way ANOVA was used for multiplying groups. Differences were considered to be significant when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.Q. and X.X. designed and conceived the study. X.N. conducted most of the experiments and prepared the manuscript. W.S. performed the RNA sequencing data analysis. C.H., C.Y.Y, Y.Y.J, and W.X.Y contributed to the technical supportand animal work. W.Q., X.X., W.S., C.H., C.Y.Y, Y.Y.J, and W.X.Y provided a critical reading of the manuscript.All the authors have given their consent to publish this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKey Program, National Natural Science Foundation of China (No. 81930016). The National Natural Science Foundation of China (No. 92159202, No.82273177). Key Research \u0026amp; Development Plan of Zhejiang Province (No. 2021C03118). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated during the current study is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman subjects were not used in this study. All preclinical investigations involving animals were carried out in line with ethical standards and according to Ethics Committee of Zhejiang University School of Medicine.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDong ZR, Ke AW, Li T, et al. CircMEMO1 modulates the promoter methylation and expression of TCF21 to regulate hepatocellular carcinoma progression and sorafenib treatment sensitivity. \u003cem\u003eMol Cancer\u003c/em\u003e 2021; \u003cstrong\u003e20\u003c/strong\u003e(1): 75.\u003c/li\u003e\n\u003cli\u003eLi Q, Cao M, Lei L, et al. 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Determining cell type abundance and expression from bulk tissues with digital cytometry. \u003cem\u003eNat Biotechnol\u003c/em\u003e 2019; \u003cstrong\u003e37\u003c/strong\u003e(7): 773-82. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, immune cells, prognosis, Zinc finger protein 296","lastPublishedDoi":"10.21203/rs.3.rs-3256244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3256244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHepatocellular carcinoma (HCC) is one of the most common malignancies with poor survival. Tumor tissues are heterogeneous, with different cell types in the tumor microenvironment, which play different roles in tumorigenesis and tumor progression attached to the prognosis of HCC.This study analyzed HCC RNA-seq for cell-type identification and prognosis by estimating relative subsets of RNA transcript (CIBERSORTx). Analyzing LIHC RNA-seq (n\u0026thinsp;=\u0026thinsp;423) from TCGA showed that high infiltration of eosinophils promoted HCC progression.Interaction of B cells and macrophages in HCC was detected by the Hepa1-6 orthotopic transplantation mice model and flow cytometer analysis. B cells were correlated with macrophages (r=-0.24) and could affect the expression of PDL1 in macrophages infiltrating in LIHC. Transcription factor Zinc finger protein 296 (ZNF296) might accelerate HCC progression by regulating PAFAH1B3 and H2AFX. HCC patients with high expression of ZNF296 were in the late pathological stage. Moreover, the expression of ZNF296 was positively associated with the abundance of activated B cells (r\u0026thinsp;=\u0026thinsp;0.185) and macrophages (r\u0026thinsp;=\u0026thinsp;0.167). Among the survival and dead phenotype related to immune cells identified by SCISSOR analysis, T cells were most correlated to the excellent prognosis of HCC. The normal function of Liver cells and DC cells were also connected with the good prognosis of HCC.This investigation primarily delves into the intricate interplay between the immune microenvironment and the prognosis of HCC, thereby unveiling ZNF296 as a novel diagnostic and therapeutic target for HCC.\u003c/p\u003e","manuscriptTitle":"Zinc finger protein 296 promotes hepatocellular carcinoma progression via inducing interaction between macrophages and B cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-18 15:19:19","doi":"10.21203/rs.3.rs-3256244/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":"0ad42441-74f7-425b-b477-0c55121ce9e0","owner":[],"postedDate":"August 18th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-09-02T20:44:15+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-18 15:19:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3256244","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3256244","identity":"rs-3256244","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}