Bioinformatics combined with single-cell analysis reveals the molecular mechanism of pyroptosis in hepatocellular carcinoma

Bioinformatics combined with single-cell analysis reveals the molecular mechanism of pyroptosis in 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 Bioinformatics combined with single-cell analysis reveals the molecular mechanism of pyroptosis in hepatocellular carcinoma Wei Luo, Junxia Wang, Hongfei Wang, Fei Liu, Taiwei Yang, Zhongjun Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5365183/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 Hepatocellular carcinoma (HCC) is the third leading cause of cancer related death, and its molecular mechanisms have not been fully elucidated. The aim of this work is to discover the association between immune microenvironment changes and pyroptosis molecular mechanisms in HCC. Methods Select gene expression profiles from the comprehensive gene expression database, establish protein-protein interaction networks, and perform functional enrichment analysis using databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG). Single cell identification of HCC cell types and malignant cells, trajectory analysis and intercellular signal communication further analyze the molecular mechanisms between immune cells and liver cells. Bioinformatics combined with single-cell analysis to elucidate the immune pyroptosis molecular mechanism underlying the development of HCC. Results The key hub genes of immune pyroptosis were validated through immunohistochemistry and in vitro experiments. Molecular biology has identified six focal death hub genes in HCC. Enrichment analysis shows that intersecting genes are enriched in immune responses, chemokine mediated signaling pathways, and inflammatory responses. The cellular clustering of single cells revealed the infiltration of immune cells, especially the polarization of macrophages, which plays an important role. Immunohistochemistry suggests that hub genes such as HMGB1, CYCS, GSDMD, IL-1β, NLRP3, and IL18 are the link between macrophage polarization and pyroptosis during HCC development. Conclusions In summary, the main molecular mechanisms underlying the pathogenesis of HCC are related to immune cell infiltration, particularly macrophage infiltration polarization that promotes the secretion of inflammatory factors leading to hepatocyte pyroptosis. Our study may guide future research on the macrophage pyroptosis signaling pathway in HCC. Liver cancer macrophages pyroptosis single-cell analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introction Around the world, hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related deaths. The majority of patients are only discovered in advanced stages, which impedes curative procedures such liver resection and transplantation and results in a 5 year survival rate of only 18% [ 1 – 3 ], despite the fact that early discovery is linked to enhanced overall survival. HCC not only seriously damages the physical and mental health of patients, leading to metabolic dysfunction, but also imposes a heavy medical and economic burden on society [ 4 ]. Both viral and non-viral factors have been linked to the pathophysiology of HCC. Changes in cellular signaling, persistent inflammation, and tissue remodeling are important contributors leading to HCC regardless of the type of injury [ 5 , 6 ]. An increasing amount of data points to the commonality of tumor heterogeneity in HCC, which could account for some variations in therapy response and survival outcomes. Therefore, tumor cell heterogeneity plays a key role in its pathogenesis, but its specific pathogenesis is not fully understood [ 7 , 8 ]. Classifying HCC cells based on molecular and cellular characteristics may help guide the discovery of biomarkers and treatment choices, especially for HCC developed on the basis of cirrhosis, which is still under researched in most studies [ 9 ]. Pyroptosis is a newly discovered program that plays a crucial role in tumor related diseases[ 10 ]. At present, most studies believe that cell pyroptosis has a double-edged sword effect on the occurrence and development of liver cancer [ 11 ]. Research has shown that cell pyroptosis can induce rapid death of liver cancer cells, thereby reducing the risk of tumor deterioration and metastasis [ 12 ]. However, during the process of cell pyroptosis, cellular contents are also released from the interior of liver cells to the exterior, acting as endogenous stimuli to mediate different biological changes in different cells within the liver [ 13 ]. Not only that, cells in the liver may also have the potential to undergo or induce pyroptosis in other cells [ 14 ]. Therefore, these subsequent biological changes based on cell pyroptosis are also subtly affecting the prognosis of liver cancer [ 15 ]. The liver is an important immune organ in the human body, containing immune cells such as liver macrophages, natural killer cells, and cytotoxic T cells [ 16 ]. Among them, liver macrophages are macrophages in liver tissue and are the first line of defense against cancer cells. Under physiological conditions, liver macrophages are highly susceptible to activation by endotoxins, complement, and other pathogen related molecular patterns through various signaling pathways to exert their phagocytic activity [ 17 ]. However, when the NLRP3/Caspase-1 pathway inside liver macrophages is activated, it not only causes pyroptosis on its own, but also increases the sensitivity of cells to endotoxins, and can lead to an excessive immune response in the body, exacerbating the inflammatory damage in cancer patients [ 18 ]. Pyroptosis is associated with macrophages, cell resistance to pathogens, and transmission to cell, but its role and mechanism in cancer cells are still unclear [ 19 ]. Research has shown that in sepsis-induced acute kidney injury, pyroptosis mediated by the NLRP3 inflammasome is weakened by downregulating macrophage migration inhibitory factor [ 20 ]. Since macrophages monitor the hepatic immune response, their pyroptosis surely upsets the immunological homeostasis of the liver and incites a potent immune response [ 21 ]. The process of macrophage pyroptosis results in the active release of pro-inflammatory cytokines such as IL-1β and IL-18. Among these, the pleiotropic pro-inflammatory cytokine IL-1β has the ability to increase the inflammatory response by inducing the release of pro-inflammatory cytokines and drawing neutrophils to liver tissue [ 22 ]. Concurrently, IL-1β has the ability to imitate T lymphocytes, trigger Th17 differentiation, and draw in inflammatory cells. By stimulating the synthesis of IFN γ, IL-18 and IL-1 promote the recruitment and differentiation of helper T cell 1 (Th1) cells, as well as the activation of NK cells and innate lymphocytes. While overexpressing Fas on liver cells increases their susceptibility to NK cell toxicity, overexpression of IL-18 stimulates FasL expression on NK cells and CD4 T cells [ 22 , 23 ]. Membrane swelling, rupture, and the release of cellular components such as mitochondrial and nuclear DNA, adenosine triphosphate (ATP), high mobility group box-1 (HMGB-1), and pieces of organelles are the results of macrophage pyroptosis. These structures from isolated cells can be identified as Damage Associated Molecular Patterns (DAMPs) once they are discharged into the extracellular environment [ 24 ]. Through its interactions with cellular receptors, such as pattern recognition receptors (PRRs), DAMP triggers pro-inflammatory responses. They also cause immunological responses in dendritic and bone marrow cells, which controls the activation of innate immune cells and programmed cell death [ 23 ]. These findings imply that the spectrum of immune cell infiltration by macrophage pyroptosis may impact anti-tumor immunity. As a result, HCC and immunological macrophage infiltration are strongly associated. By interfering with macrophage pyroptosis, TME can be changed to prevent tumor cell proliferation and spread. However, more investigation into this intriguing line of inquiry is warranted. In this study, we used bioinformatics analysis combined with single factor analysis to systematically elucidate the immune cell infiltration involved in the occurrence and development of HCC. Furthermore, we conducted gene enrichment analysis to clarify the relevant immune cell pyroptosis and potential molecular mechanisms of HCC. Materials and Methods Based on bulk RNA material information analysis Differential gene analysis According to GSE461, downloaded from Gene Expression Omnibus（GEO）. The gene expression profile of RNA levels in liver cancer tissue and adjacent tissues was measured using the GPL4133 sequencing platform[25]. Use Sangerbox 3.0's simple tSEN dimensionality reduction analysis to perform dimensionality reduction grouping on the data[26], and use the "limma" tool for differential gene analysis. The threshold for differential genes is set to the absolute value of log2 fold change | log2FC |>1.2 and P.value<0.05. Positive numbers indicate upregulation of differentially expressed genes (DEGs). Similarly, | log2FC |<1.2 and P.value<have a value of 0.05, with negative numbers indicating downregulation of DEGs. Use volcano and heatmap to display the results of differential gene expression. Identification of pyroptosis characteristic genes in hepatocellular carcinoma From literature and MSigDB database, pyroptosis related genes were obtained from the database GSEA | MSigDB (gsea-msigdb.org), and liver cancer pyroptosis characteristic genes were obtained by taking the intersection of pyroptosis genes and GSE46408 differentially expressed genes using the online intersection tool Venny plot [27]. In order to further discover prognostic genes for hepatocellular carcinoma, forest pattern maps were used for predicting pyroptosis related genes. Finally, the intersection of pyroptosis characteristic genes in hepatocellular carcinoma and pyroptosis related genes in forest pattern maps was taken to obtain the pyroptosis hub genes involved in hepatocellular carcinoma [28, 29]. Enrichment analysis of pyroptosis hub genes in hepatocellular carcinoma using KEGG and Reactome to explore the potential molecular mechanisms underlying its pathogenesis [29-31]. Construction of a hub gene network for hepatocellular carcinoma PPI network analysis was carried out on the pyroptosis hub genes implicated in hepatocellular carcinoma of species, which was limited to Homo sapiens with a confidence level>0.4, using a string database（ http://string-db.org/. Cytoscape (version 3.9.1) was used to build the PPI network [32, 33]. To determine the interactions between the important apoptotic genes involved in the development of hepatocellular carcinoma, use the co expression analysis module of the string database. Lastly, export these genes' molecular 3D structures from the string database for further verification. Validation of Core Genes in Clinical Samples Clinical Samples Informed consent from the patients' families and approval from the Ethics Committee of The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University (Nos. YJ-KY2024048) were obtained in order to validate the results of the aforementioned analysis. Additionally, cancerous and adjacent tissues were obtained from patients undergoing liver cancer surgery. The following were the exclusion criteria: (1) history of radiation or chemotherapy; (2) diabetes or cardiovascular illness; and (3) patients who refused to follow the study's procedures. The Supplementary Table S1 contains the patients' original data. Before being used, each sample was quickly placed in liquid nitrogen storage. Immunohistochemistry (IHC) staining The sections were hydrated with gradient ethanol and dewaxed with xylene. Subsequently, the slides were treated with hydrogen peroxide to inhibit endogenous peroxidase activity, and goat serum was used to prevent nonspecific binding sites. After that, the proper volume of primary antibody was added, and it was kept at 4 °C for the entire night. The samples were stained with diaminobenzidine and counterstained with hematoxylin on the second day after being treated for 30 minutes at room temperature with the secondary and tertiary antibodies. After that, pictures stained with immunohistochemistry were captured using an upright microscope. Real-time quantitative PCR analysis Sangon Biotech (China) designed and synthesized the gene primer sequences. Table 1 lists the primer sequences for every gene. RNeasy reagents were utilized to extract total RNA from various tissues. The TaqMan microRNA Reverse Transcription Kit (#AM2071; InvitrogenTM) was used to reverse-transcribe and purify RNA samples. A 7500 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA) was used to conduct 40 cycles of qRT-PCR. The relative expression levels of each gene were estimated using the 2 −ΔΔCT technique, with GAPDH serving as the internal control. Every experiment was carried out separately and three times. Table 1 The primer sequences used for PCR amplifcation. Gene Forward primer seguen c e Revere primer sequence HMGB1 CGGACAAGGCCCGTTATGAA GAGGAAGAAGGCCGAAGGA CYCS ATCTGGGGAGAGGATACACTG AAGTCTGCCCTTTCTTCCTTC GSDMD GATGGGCAGATACAGGGCAG CCAGGTGTTAGGGTCCACAC IL1B CCAGGGACAGGATATGGAGCA TTCAACACGCAGGACAGGTA NLRP3 GATCTTCGCTGCGATCAACA GGGATTCGAAACACGTGCATT IL18 GACCAAGTTCTCTTCATTGACC AGATAGTTACAGCCATACCTC GAPDH GCACCGTCAAGGCTGAGAAC TGGTGAAGACGCCAGTGGA Western Blot Following three rounds of cold phosphate-buffered saline (PBS) washing for each batch of samples, tissues were pulverized in liquid nitrogen and proteins were extracted using high-efficiency RIPA lysis buffer (R0010, Solarbio). With a BCA protein assay kit (PC0020, Solarbio), the total protein content was determined. After that, the samples were heated for five minutes at 95°C to denature the proteins. After being separated using 10% SDS-PAGE, a 20 μg protein sample was transferred to a PVDF membrane. The membrane was incubated in non-fat milk for 1 hour to block nonspecific binding, followed by overnight incubation at 4°C with primary antibodies against NLRP3 (IMMUNOWAY, 1:800), IL18 (IMMUNOWAY, 1:1000), GSDMD (IMMUNOWAY, 1:800), IL-1β (IMMUNOWAY, 1:1200), CYCS (IMMUNOWAY, 1:800), HMGB1 (IMMUNOWAY, 1:1000), and GAPDH (IMMUNOWAY, 1:5000). Subsequently, they were incubated with the corresponding secondary antibodies. Chemiluminescent substrates (Invitrogen) were used to visualize the immunoblots, and quantification was performed using Image Lab 3.0 software. Statistical Analysis Software for statistical analysis and graphing was GraphPad Prism 9.0. The data are shown as histograms of the means from three or more separate experiments, plus or minus the standard error of the mean (SEM) values. The t-test was used to compare two groups when the samples were normally distributed; non-parametric tests were used in other cases. ANOVA was utilized to compare samples from several groups in a one-way fashion. The Tukey method was utilized to conduct comparisons between the two groups; a statistically significant result is indicated by *P < 0.05, and a high level of significance is indicated by **P < 0.01. Single cell analysis based on scRNAseq Data preprocessing The sample data was downloaded from the GEO database, and this analysis included 2 cases of liver cancer tissue from dataset GSE125449 and 2 cases of normal liver tissue from dataset GSE136103 [34, 35]. All data processing was done using BioBean (Sheng-Xin-Dou-Ya-Cai) sprout analysis tools. After obtaining the data, select the single-cell analysis tool and import 10 x genomics data. This tool uses the Percentage Feature Set function in the Seurat package of R language to calculate mitochondrial content and rRNA content, and analyzes the relationship between mitochondrial content and ncount (UMI), nFeature (number of genes) through correlation analysis. After data quality control, there are still many miscellaneous cells and excess cell fragments. Set cell filtering criteria based on quality control data. Based on the tsne/UMAP map of the distribution of each sample cell after cell filtration, select the Harmony tool for batch processing[36]. Cell clustering and annotation To use principal component analysis (PCA) for dimensionality reduction, we employed 2000 highly variable genes. Then use Seurat's random neighbor embedding (t-SNE) algorithm, the "FindNeighbors" and "FindClusters" (resolution=0.1) functions to select the best cluster for visualization. Finally, the "SingleR" package, marker gene markers, and literature review were used to annotate cells from different subgroups. After the first clustering, it was found that there were several types of cell populations with insufficient clustering and several identical feature groups. Therefore, these special cell populations were extracted again for secondary subgroup analysis and definition. Still using Seurat's Random Neighbor Embedding (t-SNE) algorithm (resolution=0.1) function to select the best cluster for visualization. Finally, use the "SingleR" package combined with literature search to annotate the cells of the subpopulation. Identification of malignant cells in hepatocellular carcinoma The microenvironment of tumors is made up of both malignant and non-malignant cells, and malignant cells usually have widespread chromosomal abnormalities. To find evidence of extensive chromosomal copy number changes in somatic cells, such as the addition or deletion of large chromosomal segments or complete chromosomes, InferCNV is utilized to examine tumor scRNA data [37]. InferCNV infers chromosomal variations by comparing gene expression intensity at different locations of the tumor genome with a set of reference normal cells. Like inferCNV, CopyKAT is a technique for inferring tumor cell CNV. The idea is also to use single-cell transcriptome data to deduce a cell's chromosome ploidy, which allows one to determine if the cell is diploid or aneuploid—that is, whether it is a tumor or a normal cell [38]. This time, InferCNV and copyKAT were used to identify malignant cells in the sample tissue and assess the degree of tissue malignancy. Analysis of Cell Development Trajectory Single cell pseudo temporal analysis is an analytical tool used to study single-cell RNA Seq data, revealing cellular heterogeneity, function, and developmental processes. Monocle 2 uses a simple, unbiased, and highly scalable statistical program to select cell populations with trajectory progression characteristics[39]. Then, it employed a class of manifold learning algorithms aimed at embedding a main image in high-dimensional single-cell RNA seq data. The selected cells for secondary clustering are mapped according to their cell types, with each point representing a cell. Pseudotime represents the calculated developmental time, with smaller values indicating that the cell is at the beginning of development and larger values indicating that the cell is closer to the end of development. State represents the developmental status of cells, with smaller values indicating early development. CytoTACE is a tool for inferring cell differentiation trajectories based on single-cell expression matrices. CytoTACE packages are used for secondary clustering and cell trajectory analysis. Here, a CytoTACE score is calculated to measure the state of cell differentiation. In addition, CytoTracy can further label target genes in cells at different developmental stages, thus selecting the key apoptotic genes involved in the development of hepatocellular carcinoma for trajectory prediction. Cell to cell communication In multicellular organisms, communication between cells is frequently facilitated by cytokines and membrane proteins, which serve to control biological activity and maintain the organism's effective and organized functioning. Among these, intercellular communication mediated by receptor ligands is essential for coordinating a range of biological activities, including illness, differentiation, and development. Cell communication analysis infers the interactions between different cells by analyzing the expression and pairing of receptors and ligands in different cell types. Use bioinformatics bean sprouts cell communication analysis function for cell communication analysis of hepatocellular carcinoma. Finally, select the hub communication signal pathway for analysis [40]. Joint analysis of bioinformatics and single-cell sequencing Single cell technology can not only identify specific cell types present in different tissues, but also further identify the expression of different regulatory factors and target genes in different cells. Based on transcriptome sequencing, the key apoptotic genes involved in hepatocellular carcinoma were obtained and analyzed in combination with single cells to further validate the expression and potential molecular mechanisms of related genes in different cells. Using single-cell analysis tools, first identify the distribution of different cell types in different sample tissues. Secondly, identify the different cell distributions and expression levels of the focal pyroptosis genes involved in the occurrence of hepatocellular carcinoma in secondary clustering. Use R language package to draw UMAP and violin expression maps of specific gene expression. Human Protein Expression Profile (THPA) Database（ https://www.proteinatlas.org/ ）We summarized and aggregated the expression of common human proteins in different tissues and cells, and searched for the key apoptotic genes involved in hepatocellular carcinoma in this database to obtain the expression of related proteins in liver cancer tissues and cells, further verifying our specific gene expression results [41]. Results Results of differential gene analysis based on different groups After extracting sample data from the GEO dataset, 12 specimens, 6 liver cancer tissues, and 6 adjacent tissues were included. The gene expression profiles of the sample tissues are presented in Supplementary Table S 2 . Simple tSEN dimensionality reduction analysis showed a significant difference between liver cancer samples and adjacent samples, with high tissue reliability, which can be used for subsequent analysis (Figure 1A) . 4725 DEGs were found among 19712 genes in the study comparing cancer samples to control samples, with 3943 genes being upregulated and 782 genes being downregulated (Supplementary Table S 3 , Figure 1B, C). Screening of genes with characteristic features of pyroptosis 57 genes related to cell pyroptosis were obtained from literature and MSigDB database (Supplementary Table S4). The intersection of cell pyroptosis related genes and GSE46408 sample tissue differentially expressed genes was obtained through Venny plot, resulting in 17 intersecting pyroptosis characteristic genes (Figure 2A) . Forest pattern analysis further screened 15 genes related to pyroptosis, which has a certain effect on the prognosis of liver cancer (Supplementary Table S 5 , Figure 2B, C). Finally, the genes in the forest pattern map were intersected with the genes associated with liver cancer necrosis, resulting in significant liver cancer necrosis hub genes (Supplementary Table S 6 , Figure 2D) . The KEGG results showed that the occurrence of liver cancer is closely related to signaling pathways such as Necroptosis, Cytosolic DNA sensing pathway, Pyroptosis multiple species, Hematotoxic cell line, NF kappa B signaling pathway, Autophagy animal, Hepatis C, Hepatis B, MAPK signaling pathway, and pathways in cancer. The results of the Reactome showed that the occurrence of liver cancer was closely associated with signaling pathways such as PYROPTOSIS, REGULATED_NECROSIS, INTERLEUKIN_1-PROCESSING, PROGRAMMED_CELL_deATH, INTERLEUKIN_1-FAMILY_SIGNALING, SIGNALING-BYINTERLEUKINS, INTERLEUKIN_18_SIGNALING, and THE-NLRP3-INFLAMMASOME (Figure 2E, F). Protein analysis of liver cancer necrosis hub genes By taking the intersection of genes in the forest model map and the characteristic genes of liver cancer necrosis, six significant liver cancer necrosis hub genes were obtained, including NLRP3, IL18, GSDMD, IL-1β, CYCS, and HMGB1. To clarify the potential association of six genes, a protein-protein interaction network diagram was constructed using PPI (Figure 3A). The results showed that the six hub genes were closely related, and further co expression analysis showed that GSDMD, IL18, NLRP3, and HMGB1 were significantly co expressed in the hub genes (Figure 3B). Based on this, we downloaded the 3D pattern structure diagrams of these four hub genes (Figure 3C-F). Validation of Hub Genes in Hepatocellular Cancer To further validate the aforementioned results, we employed qRT-PCR, immunohistochemistry, and Western blot to examine the expression of key genes (NLRP3, IL18, GSDMD, IL-1β, CYCS, and HMGB1) in hepatocellular carcinoma (HCC) and paracancerous tissue (PC). We collected cancerous and PC from 10 patients who underwent curative hepatectomy at The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University. Part of the samples was used for immunohistochemistry, while the remainder was used for protein and RNA extraction for Western blot and qRT-PCR analyses. The qRT-PCR results indicated that the expression levels of pyroptosis-related genes NLRP3 (**P<0.01), IL18 (**P<0.01), GSDMD (*P<0.05), IL-1β (**P<0.01), CYCS (*P<0.05), and HMGB1 (**P<0.01) were significantly elevated in HCC tissues compared to PC tissues (Figure 4B). The results of immunohistochemical staining and Western blot analyses were consistent with those of qRT-PCR; significant upregulation of NLRP3, IL18, GSDMD, IL-1β, CYCS, and HMGB1 was detected in HCC tissues (Figure 4A-C). These findings preliminarily validated our previous analyses. Grouping and Definition of Single Cells After preliminary processing of single-cell sample data, both the expression point map and violin plot of cell RNA suggest the need for further cell filtration (Fig S1). Set nFeature-RNA and nCount-RNA to be less than 4000, percent.mt and percent.Ribo to be less than 0.1, select 10 PCs, and use the Harmony batch removal tool to remove miscellaneous cells and excess cell debris. After filtering, the miscellaneous cells and excess cell debris are effectively removed (Figure S2). By using the R language package to perform marker gene labeling on single-cell data after batch removal, we obtained the marker genes for all cells (Supplementary Table S 7 ) . Through marker gene classification, we obtained 11 cell subgroups. Using the singleR package combined with literature search to classify subgroups, the distribution of marker genes in different cell populations was marked along the linear tree axis, and the TOP5 marker gene point maps showed the significantly expressed genes in different subgroups (Figure 5 A, B). Based on singleR and TOP5 marker genes, we define the distribution of cell populations from 0 to 10 as NK_cell, Monocyte, T-cell: CD4+_ effector_memory, Hepatocytes, Endothelialcells, Tissue_stem-cells, B-cell, Epithelialcells, Hepatocytes, T-cell: gamma delta, Hepatocytes (Figure 5 C, D). Among them, the 0 cell group significantly aggregated and had a large number of cells, while group 1 aggregated 4 tissue samples. Groups 3, 8, and 10 were all defined as Hepatocytes. Therefore, we extracted these cell groups again for secondary clustering analysis. After the secondary cell clustering definition, the UMAP plot showed that a total of 8 cell subgroups were clustered, and based on singleR and TOP5 marker genes, subgroups 0 to 7 were defined as macrophage M1, macrophage M0, Hepatocytes, Hepatocytes, Monocyte, Hepatocytes, macrophage M2, and Hepatocytes, respectively (Figure 4E, F) . Identification of malignant cells Cancer tissue is often deformed due to infiltration of malignant cells, so we believe that the number of malignant cells in liver cancer tissue is increasing. We preliminarily believe that the UMAP distribution cells in the two liver cancer samples have a higher proportion of malignant cells. Therefore, we used InferCNV and copyKAT to identify malignant cells in the sample tissues (Figure 6A). Group 0 NK cells belong to normal sample tissue cells, and the UMAP plot shows no significant infiltration of other tissue sample cells in Group 0. Therefore, we used Group 0 cells as normal reference cells for InferCNV analysis. The InferCNV plot showed that all other cell subsets exhibited malignant lesions (Figure 6B). Further copyKAT analysis accurately identified the significant expression of malignant cells in two liver cancer tissue samples, and the cell grouping UMAP map showed that these malignant cells may be Hepatocytes, Endothelial cells, and Epithelial cells (Figure 6C-E). Therefore, InferCNV and copyKAT accurately predicted the distribution of malignant cells in liver cancer tissues, and the main malignant cells may be liver cells, endothelial cells, and epithelial cells. Cell Trajectory Analysis Based on the analysis of single-cell secondary atlas definition, immune cells such as 0, 1, 4, and 6 play an important role in the occurrence and progression of liver cancer. We conducted cell trajectory analysis on these immune cells. In Figure 7A, the black dots represent differentially expressed genes selected based on the classification of four cell populations and used for subsequent monocle 2 analysis; Figure 7B shows the tsne plot of cell clustering distribution in principal components, where different colors represent branches with different cell fates. Group 1 in red represents macrophage M1, green represents macrophage M0, light blue represents monocytes, and purple represents macrophage M0; In the survival state diagram of cell clustering distribution, value 1 represents the early developmental stage, and value 3 represents the late developmental stage (Figure 7C). In the time-series diagram of cell clustering distribution, the intensity of colors indicates the sorting of cells based on pseudo time values. Time series analysis revealed that monocytes are early developing cells that gradually develop into macrophages over time, and then differentiate into M1 and M2 macrophages. This developmental process is consistent with the developmental trajectory of monocytes/macrophages, indicating that the pseudo temporal analysis of monocyte 2 is correct (Figure 7D). In addition, to validate the liver cancer necrosis hub genes identified through bioinformatics analysis, we conducted CytoTracy trajectory analysis. The results showed that these genes were significantly expressed in all four cell populations. Among them, HMGB1, CYCS, and GSMDM are positively correlated, while IL-1β, IL18, and NRRP3 are negatively correlated (Figure 7E). We can infer from the proportion of cell clusters and the developmental trajectory of monocytes 2 that the light blue cell cluster in the upper left corner of the CytoTracy diagram is monocytes, the yellow cell cluster in the middle is M0 macrophages, the light red cell cluster in the upper right corner is M1 macrophages, and the dark red cell cluster in the lower right corner is M2 macrophages. The CytoTracy trajectory of HMGB1 shows that it is mainly distributed in M0 macrophages, while the CytoTracy trajectory of CYCS shows that it is mainly distributed in M0 macrophages and M2 macrophages. The CytoTracy trajectories of GSDMD are mainly distributed in M2 macrophages, IL-1β are mainly distributed in M0 macrophages, IL18 are mainly distributed in monocytes, M0 macrophages, and NRRP3 are mainly distributed in M0 macrophages (Figure 7F-K). Analysis of intercellular communication in cells Inter cellular communication provides us with assistance in understanding the interactions between different cells. Figure 8A shows the communication connections between all cell populations, while Figure 8B shows the communication connections between each type of cell population and other cells. The thicker the line, the tighter the communication between cells. From Figure 8B, it can be seen that the communication connections between cell populations 4, 5, 7, and 8 are relatively close, and these cells correspond exactly to Hepatocytes, Endotheliales, Epitheliales, and Tissue stem cells. Therefore, we found that the communication between these malignant cells exacerbates the occurrence of liver cancer. Further exploration of the MIF signaling pathway network and VEGF signaling pathway network revealed the potential mechanisms underlying their malignant transformation. The MIF signaling pathway network showed significant communication between C3 and C8 Hepatocytes and C0 NK cells, C1 monocytes, and C2 T cells (Figure 8C, D). According to ligand information, CD74 is the main marker of macrophage migration inhibitory factor. Furthermore, we discovered that the two ligand receptor pairings (L-R pairs) that have the greatest influence on the MIF signaling pathway are MIF - (CD74+CXCR4) and MIF - (CD74+CD44). Tumor cells exhibited a considerable increase in the expression of receptors CXCR4 and CD74, suggesting that the MIF signaling pathway is activated in malignancies (Figure 8E). The VEGF signaling pathway network showed C4 Endothelial cells and C3 Hepatocytes, C5 Tissue Stem Cells, C7 Epithelial Cells, and C8 Hepatocytes have strong communication, and VEGF - (VEGFA+VEGFR) is the ligand receptor pair (L-R pair) that contributes the most to the MIF signaling pathway, indicating that the VEGF signaling pathway is activated in tumors (Figure 8F-H). Bioinformatics and Single Cell Combined Analysis The distribution maps of different cells in different sample tissues show that NK cells and monocytes are mainly distributed in normal liver tissue, Hepatocytes, Endothelial cells, Epithelial cells, and Tissue stem cells are mainly distributed in liver cancer tissues (Figure 9A, B). Based on the secondary clustering distribution and violin plot of six hub genes, HMGB1, CYCS, and GSDMD were significantly expressed in all cell populations from 0 to 7, while IL-1β and NLRP3 were mainly expressed in monocytes, M0 macrophages, M1 macrophages, M2 macrophages, and IL18 was mainly expressed in monocytes and M0 macrophages (Figure 9C-I). In summary, inflammatory factors such as IL-1β, NLRP3, and IL18 are mainly regulated by monocytes/macrophages, which is closely related to the early inflammation of pyroptosis. Hub Gene Immunohistochemistry and Cellular Localization The human protein expression profile database provides immunohistochemical analysis and subcellular localization of hub genes in liver cancer-related cells. HMGB1 immunohistochemistry showed that it was mainly expressed in liver cells, and the samples were from patients with liver fibrosis. CYCS immunohistochemistry showed that it was mainly expressed in liver cells and bile duct cells, and the samples were from patients with liver fibrosis. GSDMD immunohistochemistry showed expression in the main bile duct cells, and the samples were from patients with liver fibrosis. IL-1β HE sections showed expression in major liver cells and fibroblasts (HE staining, no immunohistochemical data found). NLRP3 immunohistochemistry shows that it is mainly expressed in liver cells and bile duct cells. IL18 immunohistochemistry showed that it was mainly expressed in bile duct cells (Fig S3 A-F). Discussion Particularly HCC, liver cancer ranks fourth in the world for cancer-related mortality and is the sixth most frequent cancer globally, placing a heavy load on health care systems. Between 85% and 90% of all initial liver malignancies are HCCs [ 42 ]. Even with improvements in adjuvant therapy, liver transplantation, and surgery, the survival rate for people with HCC is still not good enough [ 43 ]. Thus, it is still essential to clarify the molecular causes of HCC and find novel treatment targets [ 23 ]. A significant concentration of macrophages has been seen in HCC liver tissue and mouse models, suggesting that liver macrophages play a complex role in the pathophysiology of HCC. These macrophages suppress anti-tumor immunity because they support tumor growth[ 44 ]. Liver macrophages secrete pro-angiogenic factors, which together stimulate tumor growth, during the evolution of HCC. These factors include transforming growth factor-β (TGF-β), vascular endothelial growth factor (VEGF), and platelet-derived growth factor (PDGF) [ 45 ]. Pyroptosis is a type of programmed cell death linked to inflammation that contributes to the growth of tumors in two distinct manners. On the one hand, pyroptosis induction can stop tumor cells from proliferating and migrating. On the other side, an inflammatory tumor microenvironment (TME), which promotes tumor growth, might result from overactivation of pyroptosis. GSDMD-mediated cell pyroptosis in HCC can activate tumor-infiltrating macrophages, promoting phagocytosis and anti-tumor immunity [ 46 ]. It's interesting to note that GSDMD in HCC shows distinct signs of immune cell infiltration. The majority of GSDMDs have a negative correlation with macrophages and a positive correlation with B cells, neutrophils, and dendritic cells. It can be seen that the tumor immune microenvironment, especially macrophages, is closely related to the occurrence and development of cell pyroptosis, but further exploration is needed in HCC. Thus, by combining single-cell technology with bioinformatics, we were able to uncover the mechanism of action of pyroptosis and macrophages in HCC, offering direction for the clinical diagnosis and therapy of HCC. In this study, we analyzed gene expression between liver cancer tissue and normal tissue adjacent to the cancer using the GEO database, and obtained six HCC related pyroptosis hub genes HMGB1, CYCS, GSDMD, IL-1β, NLRP3, and IL18. Through enrichment analysis, it was determined that the occurrence of HCC is closely associated with signaling pathways such as Necroptosis, Pyroptosis multiple species, NF kappa B signaling pathway, Autophagy animal, Hepatitis C, Hepatitis B, PYROPTOSIS, INTERLEUKIN_18_SIGNALING, and THE-NLRP3-INFLAMMASOME. Through single-cell sequencing data, we found that the main infiltrating cells in liver cancer tissue include NK_cell, Monocyte, T-cell: CD4+_ effector_memory, Hepatocytes, Endothelial-cells, Tissue_stem-cells, B-cell, Epithelial-cells, Hepatocytes, T-cell: gamma delta, macrophage M1, macrophage M0, macrophage M2. As for HCC itself, it is mainly related to the inflammatory processes of Endothelial cells, Tissue_stem-cells, Epithelial cells, and Hepatocytes. Regarding the tumor immune microenvironment, it is mainly related to the differentiation and function of NK_cell, Monocyte, T-cell: CD4 + effector_memory, macrophages. The identification of malignant cells further confirms the close relationship between these cells and the progression of cancer. Further analysis of cell trajectories revealed that polarization of monocytes and macrophages first plays a role in HCC. In addition, key apoptotic targets such as HMGB1, CYCS, GSDMD, IL-1β, NLRP3, and IL18 regulate macrophage polarization and affect HCC. Inter cellular communication indicates that the MIF and VEGF signaling pathways are activated in HCC tumors. Recent studies have shown that HMGB1 is up-regulated in human rectal cancer, breast cancer, cervical cancer, lung cancer and other malignant tumors, which is closely related to the occurrence, growth, invasion and metastasis of tumors, and can be used as a serum marker to provide reference value for tumor progress and prognosis evaluation [ 47 ]. A prerequisite for cellular angiogenesis in HCC has been found to be RAGE, whereas HMGB1 is thought to be a pro-angiogenic factor that causes colon carcinoma to produce VEGF. Given that RAGE is one of HMGB1's receptors, this obliquely implies that HMGB1 may use RAGE to generate angiogenesis in HCC. [ 48 ]. In addition, we also found that the VEGF signaling pathway was activated, which may be related to the role of HMGB1. Research has shown that dysregulation of key proteins involved in GSDMD, IL-1β, and NLRP3 pyroptosis may lead to the occurrence and development of various human diseases, particularly malignant tumors[ 49 ]. Zhang et al.'s work demonstrated that mifepristone reduces HCC cell growth by pyroptosis that is dependent on BAX-caspase-GSDME, and that the ROS-MEK-ERK1/2 pathway is involved in pyroptosis regulation [ 50 ]. Similar research by Yu et al. previously demonstrated that GSDME can promote ROS/JNK/Bax mitochondrial cell pyroptosis pathways and caspase-3/-9 activation in lobaplatin-mediated cell pyroptosis [ 51 ]. Our investigation revealed that patients in high-risk groups (liver cancer tissue) not only had cell necrosis but also an accumulation of immune-suppressive cells including macrophages, T cell regulators (Tregs), T cell CD4+, and cancer-related endothelial cells. It is commonly accepted that the infiltration of macrophages in TME results in an immunosuppressive milieu and treatment resistance, both of which are frequently linked to unfavorable prognoses and outcomes [ 49 ]. Fatty liver and ECM deposition can result from inflammation associated with chronic liver disease. Fibrous scars will eventually build up and cause cirrhosis if they are not treated, which will result in HCC. The primary constituents of the tumor microenvironment are tumor-associated macrophages, which are highly malleable and can differentiate into M1 and M2 phenotypes, which is crucial for the advancement of HCC. TLR2 ligands in HCC have been shown to decrease NF-κ B activity and increase M2 macrophage polarization. Additional investigation reveals that HMGB1 generated from HCC promotes the development of HCC by inducing M2 polarization via the TLR2/NOX2/autophagy axis [ 17 ]. Furthermore, angiogenesis is a key aspect of the growth of tumors. Studies have demonstrated that lncRNA CRNDE can stimulate M2 polarization and angiogenesis; its mechanism involves upregulating the expression of proteins associated to angiogenesis, including JAK1, STAT6, AKT1, and others [ 52 ]. In conclusion, M1 macrophages perform antigen presentation, pathogen removal, and anti-tumor activities. They also provide protection against viral hepatitis and parasite-induced HCC. On the other hand, M2 macrophages can efficiently treat liver illnesses mostly brought on by inflammatory damage, exhibit anti-inflammatory properties, and aid in wound healing. The primary cause of liver cell degeneration is the late stage of pyroptosis, which is accompanied by the production of inflammatory molecules like IL-1β and IL-18. The activation of the NF-κ B signaling pathway in liver cells and macrophages is enhanced by the inflammatory substances released by hepatocyte necrosis, which in turn promotes the inflammatory response. In the meantime, the body may attract mononuclear macrophages through the release of pro-inflammatory cytokines, which could worsen inflammatory responses and eventually cause a significant loss of liver cells. It is clear that numerous signaling pathways are shared by the mechanisms of pyroptosis and macrophage polarization when the signaling pathways are combined. For instance, the TLR/NF-κ B pathway causes cell pyroptosis in addition to being crucial for M1 polarization. Furthermore, pyroptosis and macrophage polarization are mutually regulated, according to a number of studies. Thus, we think that controlling liver cell pyroptosis and macrophage polarization will be a novel therapeutic approach. Conclusion In summary, we have established a predictive model for HCC disease, and the main molecular mechanism of its pathogenesis is immune cell infiltration, especially macrophage polarization promoting the secretion of inflammatory factors leading to cell pyroptosis. We finally confirmed that hub genes such as HMGB1, CYCS, GSDMD, IL-1β, NLRP3, and IL18 are the link between macrophage polarization and pyroptosis during HCC development. Declarations The studies involving humans were approved by the Ethics Committee of The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University (Nos. YJ-KY2024048). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Author Contributions Conceptualization, Methodology, Data analysis, Writing - original draft: W. Lou and J.X. Wang; Images analysis, Providing technical support, Performing the experiments: H.F. Wang, T.W. Yang and F. Liu; Conceptualization, Supervision, Funding, Writing – review & editing: Z.J. Wu andW.B. Guo; All authors participated in these experiments. Funding The following grants supported this work: the Science and Technology Project of Sichuan Provincial Administration of Traditional Chinese Medicine (Nos. 2024MS151), the Southwest Medical University Integrated Traditional Chinese and Western Medicine Special Project (Nos. 2023ZYYJ07), and the Joint Project of Southwest Medical University and Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University (Nos. 2020XYLH- 021). Availability of data and materials All data generated or analyzed during this study are included in this published article. Conflicts of interest The authors declare that they have no competing interests. Acknowledgments We thank the CEO (Home - GEO - NCBI https://www.ncbi.nlm.nih.gov/geo/) database for providing the underlying data for this study and all the patients and families who provided clinical samples for this study. References Jemal, A., et al., Annual Report to the Nation on the Status of Cancer, 1975-2014, Featuring Survival. J Natl Cancer Inst, 2017. 109 (9). Alvarez, M., et al., Human liver single nucleus and single cell RNA sequencing identify a hepatocellular carcinoma-associated cell-type affecting survival. Genome Med, 2022. 14 (1): p. 50. Piñero, F., M. Dirchwolf, and M.G. Pessôa, Biomarkers in Hepatocellular Carcinoma: Diagnosis, Prognosis and Treatment Response Assessment. Cells, 2020. 9 (6). Sung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin, 2021. 71 (3): p. 209-249. 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Wang, X., et al., The Role of HMGB1 Signaling Pathway in the Development and Progression of Hepatocellular Carcinoma: A Review. Int J Mol Sci, 2015. 16 (9): p. 22527-40. Chen, X., et al., Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma. BMC Cancer, 2023. 23 (1): p. 411. Zhang, X., et al., Miltirone induces cell death in hepatocellular carcinoma cell through GSDME-dependent pyroptosis. Acta Pharm Sin B, 2020. 10 (8): p. 1397-1413. Yu, J., et al., Cleavage of GSDME by caspase-3 determines lobaplatin-induced pyroptosis in colon cancer cells. Cell Death Dis, 2019. 10 (3): p. 193. Hou, Z.H., et al., Long non-coding RNA MALAT1 promotes angiogenesis and immunosuppressive properties of HCC cells by sponging miR-140. Am J Physiol Cell Physiol, 2020. 318 (3): p. C649-c663. Additional Declarations No competing interests reported. Supplementary Files FigS1.tif Figure S1 single-cell sample RNA expression data: a sample RNA scatter plot, a sample RNA violin plot. FigS2.tif Figure S2 Cell Filtration and Batch Removal: a. Single Cell Sample PC Scatter Plot, b. Single Cell Sample PC Curve Plot, c. RNA Violin Plot after Single Cell Sample Filtration, d. UMAP Plot before Batch Removal of Single Cell Samples, e. Harmony Batch Quality Control, f. UMAP Plot after Batch Removal of Single Cell Samples. FigS3.tif Figure S3 Histochemistry of Hub Genes: A Exemption from immunohistochemistry for HMGB1, B. CYCS, C. GSDMD, D. IL-1β HE staining, E. NLRP3, and F.IL18. SupplementaryTable.xls Supplementary Table S1 Patient clinical information. Supplementary Table S2 GSE46408 Sample Tissue Expression Profile Data Supplementary Table S3 GSE46408 Sample Tissue Differential Gene Expression Profile Supplementary Table S4 Cellular pyroptosis gene data Supplementary Table S5 Forest Pattern Prediction Genes Supplementary Table S6 Expression data of liver cancer necrosis characteristic genes Supplementary Table S7 Single cell marker gene data Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5365183","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373057368,"identity":"5e090645-d280-4a91-b821-9a6793b85025","order_by":0,"name":"Wei Luo","email":"","orcid":"","institution":"Department of General Surgery, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Luo","suffix":""},{"id":373057370,"identity":"682ff2e0-843e-4474-9d08-233ad0d1e4ed","order_by":1,"name":"Junxia Wang","email":"","orcid":"","institution":"Department of Pediatrics,The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junxia","middleName":"","lastName":"Wang","suffix":""},{"id":373057372,"identity":"0d329ddc-d021-46d0-8095-09cc35d9cb36","order_by":2,"name":"Hongfei Wang","email":"","orcid":"","institution":"Department of Pathology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongfei","middleName":"","lastName":"Wang","suffix":""},{"id":373057373,"identity":"a99b5ceb-cd02-44b0-be44-415e6635baf1","order_by":3,"name":"Fei Liu","email":"","orcid":"","institution":"Department of General Surgery, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Liu","suffix":""},{"id":373057374,"identity":"b3cf1298-4fed-4944-bb31-d946a03ae88b","order_by":4,"name":"Taiwei Yang","email":"","orcid":"","institution":"Department of General Surgery, Xuyong County second name hospital","correspondingAuthor":false,"prefix":"","firstName":"Taiwei","middleName":"","lastName":"Yang","suffix":""},{"id":373057375,"identity":"3158f967-5b1b-44fb-a531-11666789d806","order_by":5,"name":"Zhongjun Wu","email":"","orcid":"","institution":"Department of Hepatobiliary Surgery, the First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhongjun","middleName":"","lastName":"Wu","suffix":""},{"id":373057379,"identity":"973686a2-fa9d-412d-9616-d295f0864509","order_by":6,"name":"Wubin Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYFACHoYDHyr+y0F5zERpYTw44wyzMUlamA/ztjEnNhCtRT4i98ABHja29PkzstMkGCqsExvYzx7Aq8XwRl7CAQkentwNN3K3STCcSU9s4MlLwK9ldo7BAQMJidwN0kAtjG2HExskeAwIa0kwMEiXnw3S8o8ILfLSQC0HEhISGG6DtDQQocVA/l3CwYYDBww33H+72SLhWLpxG08OAVt6zh7+/PffAXkgY+ONDzXWsv3sZwjYcgCZlwDEbHjVg2xpIKRiFIyCUTAKRgEASmBKDgl+E8IAAAAASUVORK5CYII=","orcid":"","institution":"Department of General Surgery, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wubin","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2024-10-31 06:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5365183/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5365183/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68911823,"identity":"8ab553e8-2ae6-4fa1-a1e0-71ea0a8fa9c7","added_by":"auto","created_at":"2024-11-13 11:54:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2133358,"visible":true,"origin":"","legend":"\u003cp\u003eLimma differential analysis: A. Simple tSEN dimensionality reduction analysis shows significant differences between liver cancer samples and adjacent samples. B. Volcanic map of differentially expressed genes in GSE46408 samples, with green triangles indicating downregulated genes and red triangles indicating up-regulated genes, and black origins indicating no significantly different genes. C. Heat map of differentially expressed genes in GSE446408 samples, with blue indicating downregulation and red indicating upregulation.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/eead7ae66bdb85922fc3c01c.png"},{"id":68911820,"identity":"304cdd67-5328-4241-b6d9-98a008086c42","added_by":"auto","created_at":"2024-11-13 11:54:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3528049,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of gene apoptosis: A Intersection diagram of differentially expressed genes and cell pyroptosis genes in GSE46408 sample tissues, B. Forest pattern diagram of pyroptosis related genes in GSE46408 sample tissues, C. K-M survival curve of forest pattern prediction diagram, D. Intersection of genes and pyroptosis characteristic genes in forest pattern diagram, KEGG analysis chord diagram of E-junction pyroptosis genes, F. Reaction analysis chord diagram of junction pyroptosis genes\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/508d181f5b90417e972ef18c.png"},{"id":68911828,"identity":"ed059a5a-949c-4baf-b83d-02f6f38e0b5d","added_by":"auto","created_at":"2024-11-13 11:54:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10206788,"visible":true,"origin":"","legend":"\u003cp\u003eProtein analysis of liver cancer necrosis hub genes: A. Hub gene protein mapping, B. Hub gene co expression pattern diagram, C. 3D structural pattern diagram of GSDMD, D. 3D structural pattern diagram of IL18, E. NLRP3 3D structural pattern diagram, F. 3D structural pattern diagram of HMGB1.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/fa5be131a42469eea1d0adbc.png"},{"id":68911812,"identity":"6a2029ea-c4a4-48c4-a1d5-cc6bc402431a","added_by":"auto","created_at":"2024-11-13 11:54:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2849460,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of Hub Genes in Hepatocellular Cancer A. Immunohistochemical staining images of cancerous and paracancerous tissue（PC） from HCC patients, with a scale bar of 200 μm (n=3). B. Expression of hub gene mRNA (n=10). NLRP3 (**P\u0026lt;0.01) vs PC, IL18 (**P\u0026lt;0.01) vs PC, GSDMD (*P\u0026lt;0.05) vs PC, IL-1β (**P\u0026lt;0.01) vs PC, CYCS (*P\u0026lt;0.05) vs PC, and HMGB1 (**P\u0026lt;0.01). C. Differences in hub gene protein levels between HCC and PC (n=3).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/d69b4f07aec2b2bb194c2a73.png"},{"id":68911821,"identity":"b32fb5d5-d1e6-4739-8e76-e2cb4af636d3","added_by":"auto","created_at":"2024-11-13 11:54:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3201366,"visible":true,"origin":"","legend":"\u003cp\u003eCell Grouping: A. Single cell marker gene TOP5 significantly expressed gene point map, B. Single cell marker gene distribution tree map, C. Single cell UMAP sample tissue distribution, D. Single cell sample grouping UMAP map, E. Secondary clustering sample grouping UMAP map, F. Secondary clustering sample marker gene TOP5 significantly expressed gene point map.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/afe992b5c39a80acd148dcc4.png"},{"id":68911818,"identity":"2908f69a-52f6-4cb1-a5e2-4d1b4d3524d8","added_by":"auto","created_at":"2024-11-13 11:54:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2505313,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of malignant cells: A. Distribution of single-cell samples in different tissues, with circles indicating possible malignant cells, B. InferCNV malignant cell prediction results, 0 cells indicating normal cells, and the rest of the cell population showing malignant lesions. C.copyKAT malignant cell prediction heatmap, D.. CopyKAT malignant cell prediction UMAP map, with red circles representing malignant cells and blue circles representing normal cells. The data shows that malignant cells are mainly expressed in two cases of liver cancer tissues. E. The cell grouping of malignant cells shows that these malignant cells may be Hepatocytes Endothelial_cells、Epithelial_cells。\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/000bed4a7aaa5a6b344a6a5e.png"},{"id":68911827,"identity":"6f78e4fb-d0fa-4671-94b0-90f4cdcc6ea9","added_by":"auto","created_at":"2024-11-13 11:54:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2844161,"visible":true,"origin":"","legend":"\u003cp\u003eCell Trajectory Analysis: A. Scatter plot of pseudo temporal analysis data screening, B. Principal component expression plot of pseudo temporal analysis, C. Survival status plot of pseudo temporal analysis, D. Temporal developmental trajectory plot of pseudo temporal analysis, E. Expression correlation values of pyroptosis hub genes in CytoTRACE trajectory analysis, F. CytoTRACE trajectory expression plot of HMGB1, G. CytoTRACE trajectory expression plot of CYCS, H. CytoTRACE trajectory expression plot of GSDMD, I.IL18 CytoTRACE trajectory expression plot, J.NLRP3 CytoTRACE trajectory expression plot, K.IL-1β CytoTRACE trajectory expression plot.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/1c61d6f8cab9153c49d5ba60.png"},{"id":68911813,"identity":"80fc1f9e-1fbe-4fd7-8af5-d27489ab141b","added_by":"auto","created_at":"2024-11-13 11:54:39","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2845940,"visible":true,"origin":"","legend":"\u003cp\u003eCell communication analysis: A. All intercellular communication diagrams of cell subpopulations, B. Communication connections between different cell subpopulations and other cells, C. MIF signaling pathway network, D. MIF signaling pathway network heatmap, E. MIF signaling pathway network receptor information, F. VEGF signaling pathway network, G. VEGF signaling pathway network heatmap, H. VEGF signaling pathway network receptor information.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/de4ee5fd38d702d4c0e75e87.png"},{"id":68911814,"identity":"706337ac-9a9e-454a-91cd-ca504ad9d6ee","added_by":"auto","created_at":"2024-11-13 11:54:39","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2300900,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of cell populations and hub genes: A. Heatmap of different cell populations in different tissues, B. UMAP map of different cells in different tissues, C. UMAP distribution of GSDMD in the cell population of secondary clustering, D. UMAP distribution of NLRP3 in the cell population of secondary clustering, E.IL-1β UMAP distribution in the cell population of secondary clustering,\u003c/p\u003e\n\u003cp\u003eF. The UMAP distribution of IL18 in the cell population of secondary clustering, the UMAP distribution of G. CYCS in the cell population of secondary clustering, the UMAP distribution of H.HMGB1 in the cell population of secondary clustering, and the violin expression map of I. hub genes in the cell population of secondary clustering.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/42c62083a4c1bc0687563ec7.png"},{"id":71153295,"identity":"139d19f6-b8e7-4f6a-839e-d8637c7e3d73","added_by":"auto","created_at":"2024-12-11 15:17:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":30616397,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/afce3ec8-3dc6-4500-a50f-aeda0d95fe37.pdf"},{"id":68911822,"identity":"1c651907-0f05-47a0-abb5-be44f8831506","added_by":"auto","created_at":"2024-11-13 11:54:42","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32199072,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1 single-cell sample RNA expression data: a sample RNA scatter plot, a sample RNA violin plot.\u003c/p\u003e","description":"","filename":"FigS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/343605a2442b60556d858555.tif"},{"id":68911826,"identity":"2a665098-9beb-4f06-a683-5ec99f887d8e","added_by":"auto","created_at":"2024-11-13 11:54:43","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":35943472,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2 Cell Filtration and Batch Removal: a. Single Cell Sample PC Scatter Plot, b. Single Cell Sample PC Curve Plot, c. RNA Violin Plot after Single Cell Sample Filtration, d. UMAP Plot before Batch Removal of Single Cell Samples, e. Harmony Batch Quality Control, f. UMAP Plot after Batch Removal of Single Cell Samples.\u003c/p\u003e","description":"","filename":"FigS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/0ea90753469d6eb37b4eba3c.tif"},{"id":68911816,"identity":"6a4231af-c89d-4e6c-983b-a044e99453fa","added_by":"auto","created_at":"2024-11-13 11:54:39","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":40139020,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3 Histochemistry of Hub Genes: A Exemption from immunohistochemistry for HMGB1, B. CYCS, C. GSDMD, D. IL-1β HE staining, E. NLRP3, and F.IL18.\u003c/p\u003e","description":"","filename":"FigS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/7455d32fdf7572aeb109c83f.tif"},{"id":68911819,"identity":"2688f144-dcc2-4fb2-9c76-ef90833cb6db","added_by":"auto","created_at":"2024-11-13 11:54:41","extension":"xls","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":6425088,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S1 Patient clinical information.\u003c/p\u003e\n\u003cp\u003eSupplementary Table S2 GSE46408 Sample Tissue Expression Profile Data\u003c/p\u003e\n\u003cp\u003eSupplementary Table S3 GSE46408 Sample Tissue Differential Gene Expression Profile\u003c/p\u003e\n\u003cp\u003eSupplementary Table S4 Cellular pyroptosis gene data\u003c/p\u003e\n\u003cp\u003eSupplementary Table S5 Forest Pattern Prediction Genes\u003c/p\u003e\n\u003cp\u003eSupplementary Table S6 Expression data of liver cancer necrosis characteristic genes\u003c/p\u003e\n\u003cp\u003eSupplementary Table S7 Single cell marker gene data\u003c/p\u003e","description":"","filename":"SupplementaryTable.xls","url":"https://assets-eu.researchsquare.com/files/rs-5365183/v1/f1dada0c2ed9f722726c6ddb.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatics combined with single-cell analysis reveals the molecular mechanism of pyroptosis in hepatocellular carcinoma","fulltext":[{"header":"Introction","content":"\u003cp\u003eAround the world, hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related deaths. The majority of patients are only discovered in advanced stages, which impedes curative procedures such liver resection and transplantation and results in a 5 year survival rate of only 18% [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], despite the fact that early discovery is linked to enhanced overall survival. HCC not only seriously damages the physical and mental health of patients, leading to metabolic dysfunction, but also imposes a heavy medical and economic burden on society [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Both viral and non-viral factors have been linked to the pathophysiology of HCC. Changes in cellular signaling, persistent inflammation, and tissue remodeling are important contributors leading to HCC regardless of the type of injury [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. An increasing amount of data points to the commonality of tumor heterogeneity in HCC, which could account for some variations in therapy response and survival outcomes. Therefore, tumor cell heterogeneity plays a key role in its pathogenesis, but its specific pathogenesis is not fully understood [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Classifying HCC cells based on molecular and cellular characteristics may help guide the discovery of biomarkers and treatment choices, especially for HCC developed on the basis of cirrhosis, which is still under researched in most studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePyroptosis is a newly discovered program that plays a crucial role in tumor related diseases[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. At present, most studies believe that cell pyroptosis has a double-edged sword effect on the occurrence and development of liver cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Research has shown that cell pyroptosis can induce rapid death of liver cancer cells, thereby reducing the risk of tumor deterioration and metastasis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, during the process of cell pyroptosis, cellular contents are also released from the interior of liver cells to the exterior, acting as endogenous stimuli to mediate different biological changes in different cells within the liver [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Not only that, cells in the liver may also have the potential to undergo or induce pyroptosis in other cells [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, these subsequent biological changes based on cell pyroptosis are also subtly affecting the prognosis of liver cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The liver is an important immune organ in the human body, containing immune cells such as liver macrophages, natural killer cells, and cytotoxic T cells [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Among them, liver macrophages are macrophages in liver tissue and are the first line of defense against cancer cells. Under physiological conditions, liver macrophages are highly susceptible to activation by endotoxins, complement, and other pathogen related molecular patterns through various signaling pathways to exert their phagocytic activity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, when the NLRP3/Caspase-1 pathway inside liver macrophages is activated, it not only causes pyroptosis on its own, but also increases the sensitivity of cells to endotoxins, and can lead to an excessive immune response in the body, exacerbating the inflammatory damage in cancer patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePyroptosis is associated with macrophages, cell resistance to pathogens, and transmission to cell, but its role and mechanism in cancer cells are still unclear [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Research has shown that in sepsis-induced acute kidney injury, pyroptosis mediated by the NLRP3 inflammasome is weakened by downregulating macrophage migration inhibitory factor [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Since macrophages monitor the hepatic immune response, their pyroptosis surely upsets the immunological homeostasis of the liver and incites a potent immune response [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The process of macrophage pyroptosis results in the active release of pro-inflammatory cytokines such as IL-1β and IL-18. Among these, the pleiotropic pro-inflammatory cytokine IL-1β has the ability to increase the inflammatory response by inducing the release of pro-inflammatory cytokines and drawing neutrophils to liver tissue [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Concurrently, IL-1β has the ability to imitate T lymphocytes, trigger Th17 differentiation, and draw in inflammatory cells. By stimulating the synthesis of IFN γ, IL-18 and IL-1 promote the recruitment and differentiation of helper T cell 1 (Th1) cells, as well as the activation of NK cells and innate lymphocytes. While overexpressing Fas on liver cells increases their susceptibility to NK cell toxicity, overexpression of IL-18 stimulates FasL expression on NK cells and CD4 T cells [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Membrane swelling, rupture, and the release of cellular components such as mitochondrial and nuclear DNA, adenosine triphosphate (ATP), high mobility group box-1 (HMGB-1), and pieces of organelles are the results of macrophage pyroptosis. These structures from isolated cells can be identified as Damage Associated Molecular Patterns (DAMPs) once they are discharged into the extracellular environment [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Through its interactions with cellular receptors, such as pattern recognition receptors (PRRs), DAMP triggers pro-inflammatory responses. They also cause immunological responses in dendritic and bone marrow cells, which controls the activation of innate immune cells and programmed cell death [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These findings imply that the spectrum of immune cell infiltration by macrophage pyroptosis may impact anti-tumor immunity. As a result, HCC and immunological macrophage infiltration are strongly associated. By interfering with macrophage pyroptosis, TME can be changed to prevent tumor cell proliferation and spread. However, more investigation into this intriguing line of inquiry is warranted.\u003c/p\u003e \u003cp\u003eIn this study, we used bioinformatics analysis combined with single factor analysis to systematically elucidate the immune cell infiltration involved in the occurrence and development of HCC. Furthermore, we conducted gene enrichment analysis to clarify the relevant immune cell pyroptosis and potential molecular mechanisms of HCC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eBased on bulk RNA material information analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential gene analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to GSE461, downloaded from Gene Expression Omnibus（GEO）. \u0026nbsp;The gene expression profile of RNA levels in liver cancer tissue and adjacent tissues was measured using the GPL4133 sequencing platform[25].\u0026nbsp;Use Sangerbox 3.0\u0026apos;s simple tSEN dimensionality reduction analysis to perform dimensionality reduction grouping on the data[26],\u0026nbsp;and use the \u0026quot;limma\u0026quot; tool for differential gene analysis.\u0026nbsp;The threshold for differential genes is set to the absolute value of log2 fold change | log2FC |\u0026gt;1.2 and P.value\u0026lt;0.05. Positive numbers indicate upregulation of differentially expressed genes (DEGs). Similarly, | log2FC |\u0026lt;1.2 and P.value\u0026lt;have a value of 0.05, with negative numbers indicating downregulation of DEGs. \u0026nbsp;Use volcano and heatmap to display the results of differential gene expression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of pyroptosis characteristic genes in hepatocellular carcinoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom literature and MSigDB database, pyroptosis related genes were obtained from the database GSEA | MSigDB (gsea-msigdb.org), and liver cancer pyroptosis characteristic genes were obtained by taking the intersection of pyroptosis genes and GSE46408 differentially expressed genes using the online intersection tool Venny plot [27]. In order to further discover prognostic genes for hepatocellular carcinoma, forest pattern maps were used for predicting pyroptosis related genes. Finally, the intersection of pyroptosis characteristic genes in hepatocellular carcinoma and pyroptosis related genes in forest pattern maps was taken to obtain the pyroptosis hub genes involved in hepatocellular carcinoma [28, 29]. Enrichment analysis of pyroptosis hub genes in hepatocellular carcinoma using KEGG and Reactome to explore the potential molecular mechanisms underlying its pathogenesis [29-31].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of a hub gene network for hepatocellular carcinoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePPI network analysis was carried out on the pyroptosis hub genes implicated in hepatocellular carcinoma of species, which was limited to Homo sapiens with a confidence level\u0026gt;0.4, using a string database（ http://string-db.org/. Cytoscape (version 3.9.1) was used to build the PPI network [32, 33]. To determine the interactions between the important apoptotic genes involved in the development of hepatocellular carcinoma, use the co expression analysis module of the string database. Lastly, export these genes\u0026apos; molecular 3D structures from the string database for further verification. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Core Genes in Clinical Samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent from the patients\u0026apos; families and approval from the Ethics Committee of The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University (Nos. YJ-KY2024048) were obtained in order to validate the results of the aforementioned analysis. Additionally, cancerous and adjacent tissues were obtained from patients undergoing liver cancer surgery. The following were the exclusion criteria: (1) history of radiation or chemotherapy; (2) diabetes or cardiovascular illness; and (3) patients who refused to follow the study\u0026apos;s procedures. The \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e contains the patients\u0026apos; original data. Before being used, each sample was quickly placed in liquid nitrogen storage. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry (IHC) staining\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sections were hydrated with gradient ethanol and dewaxed with xylene. Subsequently, the slides were treated with hydrogen peroxide to inhibit endogenous peroxidase activity, and goat serum was used to prevent nonspecific binding sites. After that, the proper volume of primary antibody was added, and it was kept at 4 \u0026deg;C for the entire night. The samples were stained with diaminobenzidine and counterstained with hematoxylin on the second day after being treated for 30 minutes at room temperature with the secondary and tertiary antibodies. After that, pictures stained with immunohistochemistry were captured using an upright microscope.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReal-time quantitative PCR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSangon Biotech (China) designed and synthesized the gene primer sequences. Table 1 lists the primer sequences for every gene. RNeasy reagents were utilized to extract total RNA from various tissues. The TaqMan microRNA Reverse Transcription Kit (#AM2071; InvitrogenTM) was used to reverse-transcribe and purify RNA samples. A 7500 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA) was used to conduct 40 cycles of qRT-PCR. The relative expression levels of each gene were estimated using the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;CT\u003c/sup\u003e technique, with GAPDH serving as the internal control. Every experiment was carried out separately and three times.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 The primer sequences used for PCR amplifcation.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5026%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.1005%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward primer seguen\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.3969%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRevere primer sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5026%;\"\u003e\n \u003cp\u003eHMGB1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.1005%;\"\u003e\n \u003cp\u003eCGGACAAGGCCCGTTATGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.3969%;\"\u003e\n \u003cp\u003eGAGGAAGAAGGCCGAAGGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5026%;\"\u003e\n \u003cp\u003eCYCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.1005%;\"\u003e\n \u003cp\u003eATCTGGGGAGAGGATACACTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41.3969%;\"\u003e\n \u003cp\u003eAAGTCTGCCCTTTCTTCCTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5026%;\"\u003e\n \u003cp\u003eGSDMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.1005%;\"\u003e\n \u003cp\u003eGATGGGCAGATACAGGGCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.3969%;\"\u003e\n \u003cp\u003eCCAGGTGTTAGGGTCCACAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5026%;\"\u003e\n \u003cp\u003eIL1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.1005%;\"\u003e\n \u003cp\u003eCCAGGGACAGGATATGGAGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.3969%;\"\u003e\n \u003cp\u003eTTCAACACGCAGGACAGGTA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5026%;\"\u003e\n \u003cp\u003eNLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.1005%;\"\u003e\n \u003cp\u003eGATCTTCGCTGCGATCAACA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.3969%;\"\u003e\n \u003cp\u003eGGGATTCGAAACACGTGCATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5026%;\"\u003e\n \u003cp\u003eIL18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.1005%;\"\u003e\n \u003cp\u003eGACCAAGTTCTCTTCATTGACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.3969%;\"\u003e\n \u003cp\u003eAGATAGTTACAGCCATACCTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5026%;\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.1005%;\"\u003e\n \u003cp\u003eGCACCGTCAAGGCTGAGAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.3969%;\"\u003e\n \u003cp\u003eTGGTGAAGACGCCAGTGGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eWestern Blot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing three rounds of cold phosphate-buffered saline (PBS) washing for each batch of samples, tissues were pulverized in liquid nitrogen and proteins were extracted using high-efficiency RIPA lysis buffer (R0010, Solarbio). With a BCA protein assay kit (PC0020, Solarbio), the total protein content was determined. After that, the samples were heated for five minutes at 95\u0026deg;C to denature the proteins. After being separated using 10% SDS-PAGE, a 20 \u0026mu;g protein sample was transferred to a PVDF membrane. The membrane was incubated in non-fat milk for 1 hour to block nonspecific binding, followed by overnight incubation at 4\u0026deg;C with primary antibodies against NLRP3 (IMMUNOWAY, 1:800), IL18 (IMMUNOWAY, 1:1000), GSDMD (IMMUNOWAY, 1:800), IL-1\u0026beta; (IMMUNOWAY, 1:1200), CYCS (IMMUNOWAY, 1:800), HMGB1 (IMMUNOWAY, 1:1000), and GAPDH (IMMUNOWAY, 1:5000). Subsequently, they were incubated with the corresponding secondary antibodies. Chemiluminescent substrates (Invitrogen) were used to visualize the immunoblots, and quantification was performed using Image Lab 3.0 software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoftware for statistical analysis and graphing was GraphPad Prism 9.0. The data are shown as histograms of the means from three or more separate experiments, plus or minus the standard error of the mean (SEM) values. The t-test was used to compare two groups when the samples were normally distributed; non-parametric tests were used in other cases. ANOVA was utilized to compare samples from several groups in a one-way fashion. The Tukey method was utilized to conduct comparisons between the two groups; a statistically significant result is indicated by *P \u0026lt; 0.05, and a high level of significance is indicated by **P \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle cell analysis based on scRNAseq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample data was downloaded from the GEO database, and this analysis included 2 cases of liver cancer tissue from dataset GSE125449 and 2 cases of normal liver tissue from dataset GSE136103\u0026nbsp;[34, 35].\u0026nbsp;All data processing was done using BioBean (Sheng-Xin-Dou-Ya-Cai) sprout analysis tools. After obtaining the data, select the single-cell analysis tool and import 10 x genomics data. \u0026nbsp;This tool uses the Percentage Feature Set function in the Seurat package of R language to calculate mitochondrial content and rRNA content, and analyzes the relationship between mitochondrial content and ncount (UMI), nFeature (number of genes) through correlation analysis. \u0026nbsp;After data quality control, there are still many miscellaneous cells and excess cell fragments. \u0026nbsp;Set cell filtering criteria based on quality control data. \u0026nbsp;Based on the tsne/UMAP map of the distribution of each sample cell after cell filtration, select the Harmony tool for batch processing[36].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell clustering and annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo use principal component analysis (PCA) for dimensionality reduction, we employed 2000 highly variable genes. \u0026nbsp; Then use Seurat\u0026apos;s random neighbor embedding (t-SNE) algorithm, the \u0026quot;FindNeighbors\u0026quot; and \u0026quot;FindClusters\u0026quot; (resolution=0.1) functions to select the best cluster for visualization. \u0026nbsp;Finally, the \u0026quot;SingleR\u0026quot; package, marker gene markers, and literature review were used to annotate cells from different subgroups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter the first clustering, it was found that there were several types of cell populations with insufficient clustering and several identical feature groups. Therefore, these special cell populations were extracted again for secondary subgroup analysis and definition. \u0026nbsp; Still using Seurat\u0026apos;s Random Neighbor Embedding (t-SNE) algorithm (resolution=0.1) function to select the best cluster for visualization. \u0026nbsp;Finally, use the \u0026quot;SingleR\u0026quot; package combined with literature search to annotate the cells of the subpopulation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of malignant cells in hepatocellular carcinoma\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe microenvironment of tumors is made up of both malignant and non-malignant cells, and malignant cells usually have widespread chromosomal abnormalities. To find evidence of extensive chromosomal copy number changes in somatic cells, such as the addition or deletion of large chromosomal segments or complete chromosomes, InferCNV is utilized to examine tumor scRNA data\u0026nbsp;[37].\u0026nbsp;InferCNV infers chromosomal variations by comparing gene expression intensity at different locations of the tumor genome with a set of reference normal cells. \u0026nbsp;Like inferCNV, CopyKAT is a technique for inferring tumor cell CNV. The idea is also to use single-cell transcriptome data to deduce a cell\u0026apos;s chromosome ploidy, which allows one to determine if the cell is diploid or aneuploid\u0026mdash;that is, whether it is a tumor or a normal cell\u0026nbsp;[38].\u0026nbsp;This time, InferCNV and copyKAT were used to identify malignant cells in the sample tissue and assess the degree of tissue malignancy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Cell Development Trajectory\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle cell pseudo temporal analysis is an analytical tool used to study single-cell RNA Seq data, revealing cellular heterogeneity, function, and developmental processes. \u0026nbsp;Monocle 2 uses a simple, unbiased,\u0026nbsp;and highly scalable statistical program to select cell populations with trajectory progression characteristics[39].\u0026nbsp;Then, it employed a class of manifold learning algorithms aimed at embedding a main image in high-dimensional single-cell RNA seq data. \u0026nbsp;The selected cells for secondary clustering are mapped according to their cell types, with each point representing a cell. Pseudotime represents the calculated developmental time, with smaller values indicating that the cell is at the beginning of development and larger values indicating that the cell is closer to the end of development. \u0026nbsp;State represents the developmental status of cells, with smaller values indicating early development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCytoTACE is a tool for inferring cell differentiation trajectories based on single-cell expression matrices. CytoTACE packages are used for secondary clustering and cell trajectory analysis. Here, a CytoTACE score is calculated to measure the state of cell differentiation. \u0026nbsp;In addition, CytoTracy can further label target genes in cells at different developmental stages, thus selecting the key apoptotic genes involved in the development of hepatocellular carcinoma for trajectory prediction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell to cell communication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn multicellular organisms, communication between cells is frequently facilitated by cytokines and membrane proteins, which serve to control biological activity and maintain the organism\u0026apos;s effective and organized functioning. Among these, intercellular communication mediated by receptor ligands is essential for coordinating a range of biological activities, including illness, differentiation, and development. \u0026nbsp;Cell communication analysis infers the interactions between different cells by analyzing the expression and pairing of receptors and ligands in different cell types. \u0026nbsp; Use bioinformatics bean sprouts cell communication analysis function for cell communication analysis of hepatocellular carcinoma. \u0026nbsp;Finally, select the hub communication signal pathway for analysis\u0026nbsp;[40].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJoint analysis of bioinformatics and single-cell sequencing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle cell technology can not only identify specific cell types present in different tissues, but also further identify the expression of different regulatory factors and target genes in different cells. \u0026nbsp;Based on transcriptome sequencing, the key apoptotic genes involved in hepatocellular carcinoma were obtained and analyzed in combination with single cells to further validate the expression and potential molecular mechanisms of related genes in different cells. \u0026nbsp;Using single-cell analysis tools, first identify the distribution of different cell types in different sample tissues. \u0026nbsp;Secondly, identify the different cell distributions and expression levels of the focal pyroptosis genes involved in the occurrence of hepatocellular carcinoma in secondary clustering. \u0026nbsp;Use R language package to draw UMAP and violin expression maps of specific gene expression. \u0026nbsp;Human Protein Expression Profile (THPA) Database（ \u0026nbsp; https://www.proteinatlas.org/ \u0026nbsp;）We summarized and aggregated the expression of common human proteins in different tissues and cells, and searched for the key apoptotic genes involved in hepatocellular carcinoma in this database to obtain the expression of related proteins in liver cancer tissues and cells, further verifying our specific gene expression results [41].\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eResults of differential gene analysis based on different groups\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter extracting sample data from the GEO dataset, 12 specimens, 6 liver cancer tissues, and 6 adjacent tissues were included. The gene expression profiles of the sample tissues are presented in \u003cstrong\u003eSupplementary Table S\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e. \u0026nbsp;Simple tSEN dimensionality reduction analysis showed a significant difference between liver cancer samples and adjacent samples, with high tissue reliability, which can be used for subsequent analysis\u003cstrong\u003e\u0026nbsp;(Figure 1A)\u003c/strong\u003e. \u0026nbsp;4725 DEGs were found among 19712 genes in the study comparing cancer samples to control samples, with 3943 genes being upregulated and 782 genes being downregulated \u003cstrong\u003e(Supplementary Table S\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e, Figure 1B, C).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening of genes with characteristic features of pyroptosis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e57 genes related to cell pyroptosis were obtained from literature and MSigDB database\u0026nbsp;\u003cstrong\u003e(Supplementary Table S4).\u003c/strong\u003e The\u0026nbsp;intersection of cell pyroptosis related genes and GSE46408 sample tissue differentially expressed genes was obtained through Venny plot, resulting in 17 intersecting pyroptosis characteristic genes \u003cstrong\u003e(Figure 2A)\u003c/strong\u003e. Forest pattern analysis further screened 15 genes related to pyroptosis, which has a certain effect on the prognosis of liver cancer\u003cstrong\u003e\u0026nbsp;(Supplementary Table S\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e, Figure 2B, C). \u0026nbsp;\u003c/strong\u003eFinally, the genes in the forest pattern map were intersected with the genes associated with liver cancer necrosis, resulting in significant liver cancer necrosis hub genes \u003cstrong\u003e(Supplementary Table S\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e, Figure 2D)\u003c/strong\u003e. \u0026nbsp; The KEGG results showed that the occurrence of liver cancer is closely related to signaling pathways such as Necroptosis, Cytosolic DNA sensing pathway, Pyroptosis multiple species, Hematotoxic cell line, NF kappa B signaling pathway, Autophagy animal, Hepatis C, Hepatis B, MAPK signaling pathway, and pathways in cancer. \u0026nbsp;The results of the Reactome showed that the occurrence of liver cancer was closely associated with signaling pathways such as PYROPTOSIS, REGULATED_NECROSIS, INTERLEUKIN_1-PROCESSING, PROGRAMMED_CELL_deATH, INTERLEUKIN_1-FAMILY_SIGNALING, SIGNALING-BYINTERLEUKINS, INTERLEUKIN_18_SIGNALING, and THE-NLRP3-INFLAMMASOME\u003cstrong\u003e\u0026nbsp;(Figure 2E, F).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein analysis of liver cancer necrosis hub genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy taking the intersection of genes in the forest model map and the characteristic genes of liver cancer necrosis, six significant liver cancer necrosis hub genes were obtained, including NLRP3, IL18, GSDMD, IL-1\u0026beta;, CYCS, and HMGB1. \u0026nbsp;To clarify the potential association of six genes, a protein-protein interaction network diagram was constructed using PPI\u003cstrong\u003e\u0026nbsp;(Figure 3A).\u003c/strong\u003e\u0026nbsp; \u0026nbsp;The results showed that the six hub genes were closely related, and further co expression analysis showed that GSDMD, IL18, NLRP3, and HMGB1 were significantly co expressed in the hub genes\u003cstrong\u003e\u0026nbsp;(Figure 3B).\u003c/strong\u003e\u0026nbsp; Based on this, we downloaded the 3D pattern structure diagrams of these four hub genes\u003cstrong\u003e\u0026nbsp;(Figure 3C-F).\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Hub Genes in Hepatocellular Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further validate the aforementioned results, we employed qRT-PCR, immunohistochemistry, and Western blot to examine the expression of key genes (NLRP3, IL18, GSDMD,\u0026nbsp;IL-1\u0026beta;, CYCS, and HMGB1) in hepatocellular carcinoma (HCC) and paracancerous tissue (PC). We collected cancerous and\u0026nbsp;PC\u0026nbsp;from 10 patients who underwent curative hepatectomy at\u0026nbsp;The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University. Part of the samples was used for immunohistochemistry, while the remainder was used for protein and RNA extraction for Western blot and qRT-PCR analyses. The qRT-PCR results indicated that the expression levels of pyroptosis-related genes NLRP3 (**P\u0026lt;0.01), IL18 (**P\u0026lt;0.01), GSDMD (*P\u0026lt;0.05),\u0026nbsp;IL-1\u0026beta;\u0026nbsp;(**P\u0026lt;0.01), CYCS (*P\u0026lt;0.05), and HMGB1 (**P\u0026lt;0.01) were significantly elevated in HCC tissues compared to\u0026nbsp;PC\u0026nbsp;tissues\u0026nbsp;(Figure\u0026nbsp;4B).\u0026nbsp;The results of immunohistochemical staining and Western blot analyses were consistent with those of qRT-PCR; significant upregulation of NLRP3, IL18, GSDMD,\u0026nbsp;IL-1\u0026beta;, CYCS, and HMGB1 was detected in HCC tissues\u0026nbsp;(Figure\u0026nbsp;4A-C).\u0026nbsp;These findings preliminarily validated our previous analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrouping and Definition of Single Cells\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter preliminary processing of single-cell sample data, both the expression point map and violin plot of cell RNA suggest the need for further cell filtration\u0026nbsp;(Fig S1).\u0026nbsp; Set nFeature-RNA and nCount-RNA to be less than 4000, percent.mt and percent.Ribo to be less than 0.1, select 10 PCs, and use the Harmony batch removal tool to remove miscellaneous cells and excess cell debris. After filtering, the miscellaneous cells and excess cell debris are effectively removed\u0026nbsp;(Figure S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy using the R language package to perform marker gene labeling on single-cell data after batch removal, we obtained the marker genes for all cells\u003cstrong\u003e\u0026nbsp;(Supplementary Table S\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e. \u0026nbsp;Through marker gene classification, we obtained 11 cell subgroups. \u0026nbsp; Using the singleR package combined with literature search to classify subgroups, the distribution of marker genes in different cell populations was marked along the linear tree axis, and the TOP5 marker gene point maps showed the significantly expressed genes in different subgroups \u003cstrong\u003e(Figure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003eA, B).\u003c/strong\u003e\u0026nbsp; Based on singleR and TOP5 marker genes, we define the distribution of cell populations from 0 to 10 as NK_cell, Monocyte, T-cell: CD4+_ effector_memory, Hepatocytes, Endothelialcells, Tissue_stem-cells, B-cell, Epithelialcells, Hepatocytes, T-cell: gamma delta, Hepatocytes \u003cstrong\u003e(Figure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003eC, D).\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Among them, the 0 cell group significantly aggregated and had a large number of cells, while group 1 aggregated 4 tissue samples. Groups 3, 8, and 10 were all defined as Hepatocytes. Therefore, we extracted these cell groups again for secondary clustering analysis. \u0026nbsp; After the secondary cell clustering definition, the UMAP plot showed that a total of 8 cell subgroups were clustered, and based on singleR and TOP5 marker genes, subgroups 0 to 7 were defined as macrophage M1, macrophage M0, Hepatocytes, Hepatocytes, Monocyte, Hepatocytes, macrophage M2, and Hepatocytes, respectively\u003cstrong\u003e\u0026nbsp;(Figure 4E, F)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of malignant cells\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCancer tissue is often deformed due to infiltration of malignant cells, so we believe that the number of malignant cells in liver cancer tissue is increasing. \u0026nbsp;We preliminarily believe that the UMAP distribution cells in the two liver cancer samples have a higher proportion of malignant cells. Therefore, we used InferCNV and copyKAT to identify malignant cells in the sample tissues\u0026nbsp;(Figure\u0026nbsp;6A). \u0026nbsp; Group 0 NK cells belong to normal sample tissue cells, and the UMAP plot shows no significant infiltration of other tissue sample cells in Group 0. Therefore, we used Group 0 cells as normal reference cells for InferCNV analysis. \u0026nbsp;The InferCNV plot showed that all other cell subsets exhibited malignant lesions\u0026nbsp;(Figure\u0026nbsp;6B).\u0026nbsp; Further copyKAT analysis accurately identified the significant expression of malignant cells in two liver cancer tissue samples, and the cell grouping UMAP map showed that these malignant cells may be Hepatocytes, Endothelial cells, and Epithelial cells\u0026nbsp;(Figure\u0026nbsp;6C-E).\u0026nbsp; Therefore, InferCNV and copyKAT accurately predicted the distribution of malignant cells in liver cancer tissues, and the main malignant cells may be liver cells, endothelial cells, and epithelial cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Trajectory Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the analysis of single-cell secondary atlas definition, immune cells such as 0, 1, 4, and 6 play an important role in the occurrence and progression of liver cancer. \u0026nbsp; We conducted cell trajectory analysis on these immune cells. \u0026nbsp;In\u0026nbsp;Figure\u0026nbsp;7A,\u0026nbsp;the black dots represent differentially expressed genes selected based on the classification of four cell populations and used for subsequent monocle 2 analysis;\u0026nbsp;Figure\u0026nbsp;7B\u0026nbsp;shows the tsne plot of cell clustering distribution in principal components, where different colors represent branches with different cell fates. Group 1 in red represents macrophage M1, green represents macrophage M0, light blue represents monocytes, and purple represents macrophage M0; In the survival state diagram of cell clustering distribution, value 1 represents the early developmental stage, and value 3 represents the late developmental stage\u0026nbsp;(Figure\u0026nbsp;7C).\u0026nbsp;In the time-series diagram of cell clustering distribution, the intensity of colors indicates the sorting of cells based on pseudo time values. \u0026nbsp;Time series analysis revealed that monocytes are early developing cells that gradually develop into macrophages over time, and then differentiate into M1 and M2 macrophages. \u0026nbsp;This developmental process is consistent with the developmental trajectory of monocytes/macrophages, indicating that the pseudo temporal analysis of monocyte 2 is correct\u0026nbsp;(Figure\u0026nbsp;7D).\u0026nbsp;In addition, to validate the liver cancer necrosis hub genes identified through bioinformatics analysis, we conducted CytoTracy trajectory analysis. \u0026nbsp;The results showed that these genes were significantly expressed in all four cell populations. \u0026nbsp;Among them, HMGB1, CYCS, and GSMDM are positively correlated, while\u0026nbsp;IL-1\u0026beta;, IL18, and NRRP3 are negatively correlated\u0026nbsp;(Figure\u0026nbsp;7E).\u0026nbsp; We can infer from the proportion of cell clusters and the developmental trajectory of monocytes 2 that the light blue cell cluster in the upper left corner of the CytoTracy diagram is monocytes, the yellow cell cluster in the middle is M0 macrophages, the light red cell cluster in the upper right corner is M1 macrophages, and the dark red cell cluster in the lower right corner is M2 macrophages. \u0026nbsp;The CytoTracy trajectory of HMGB1 shows that it is mainly distributed in M0 macrophages, while the CytoTracy trajectory of CYCS shows that it is mainly distributed in M0 macrophages and M2 macrophages. \u0026nbsp;The CytoTracy trajectories of GSDMD are mainly distributed in M2 macrophages,\u0026nbsp;IL-1\u0026beta;\u0026nbsp;are mainly distributed in M0 macrophages, IL18 are mainly distributed in monocytes, M0 macrophages, and NRRP3 are mainly distributed in M0 macrophages\u0026nbsp;(Figure\u0026nbsp;7F-K).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of intercellular communication in cells\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInter cellular communication provides us with assistance in understanding the interactions between different cells.\u0026nbsp;Figure\u0026nbsp;8A\u0026nbsp;shows the communication connections between all cell populations, while\u0026nbsp;Figure\u0026nbsp;8B\u0026nbsp;shows the communication connections between each type of cell population and other cells. \u0026nbsp;The thicker the line, the tighter the communication between cells. From\u0026nbsp;Figure\u0026nbsp;8B,\u0026nbsp;it can be seen that the communication connections between cell populations 4, 5, 7, and 8 are relatively close, and these cells correspond exactly to Hepatocytes, Endotheliales, Epitheliales, and Tissue stem cells. \u0026nbsp;Therefore, we found that the communication between these malignant cells exacerbates the occurrence of liver cancer. \u0026nbsp;Further exploration of the MIF signaling pathway network and VEGF signaling pathway network revealed the potential mechanisms underlying their malignant transformation. \u0026nbsp;The MIF signaling pathway network showed significant communication between C3 and C8 Hepatocytes and C0 NK cells, C1 monocytes, and C2 T cells\u0026nbsp;(Figure\u0026nbsp;8C, D). \u0026nbsp;According to ligand information, CD74 is the main marker of macrophage migration inhibitory factor. Furthermore, we discovered that the two ligand receptor pairings (L-R pairs) that have the greatest influence on the MIF signaling pathway are MIF - (CD74+CXCR4) and MIF - (CD74+CD44). Tumor cells exhibited a considerable increase in the expression of receptors CXCR4 and CD74, suggesting that the MIF signaling pathway is activated in malignancies\u0026nbsp;(Figure\u0026nbsp;8E).\u0026nbsp;\u0026nbsp;The VEGF signaling pathway network showed C4 Endothelial cells and C3 Hepatocytes, \u0026nbsp;C5 Tissue Stem Cells, C7 Epithelial Cells, and C8 Hepatocytes have strong communication, and VEGF - (VEGFA+VEGFR) is the ligand receptor pair (L-R pair) that contributes the most to the MIF signaling pathway, indicating that the VEGF signaling pathway is activated in tumors\u0026nbsp;(Figure\u0026nbsp;8F-H).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatics and Single Cell Combined Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution maps of different cells in different sample tissues show that NK cells and monocytes are mainly distributed in normal liver tissue, \u0026nbsp;Hepatocytes, Endothelial cells, Epithelial cells, and Tissue stem cells are mainly distributed in liver cancer tissues\u0026nbsp;(Figure\u0026nbsp;9A, B).\u0026nbsp; Based on the secondary clustering distribution and violin plot of six hub genes, HMGB1, CYCS, and GSDMD were significantly expressed in all cell populations from 0 to 7, while\u0026nbsp;IL-1\u0026beta;\u0026nbsp;and NLRP3 were mainly expressed in monocytes, M0 macrophages, M1 macrophages, M2 macrophages, and IL18 was mainly expressed in monocytes and M0 macrophages\u0026nbsp;(Figure\u0026nbsp;9C-I).\u0026nbsp; In summary, inflammatory factors such as\u0026nbsp;IL-1\u0026beta;, NLRP3, and IL18 are mainly regulated by monocytes/macrophages, which is closely related to the early inflammation of pyroptosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHub Gene Immunohistochemistry and Cellular Localization\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe human protein expression profile database provides immunohistochemical analysis and subcellular localization of hub genes in liver cancer-related cells. \u0026nbsp;HMGB1 immunohistochemistry showed that it was mainly expressed in liver cells, and the samples were from patients with liver fibrosis. \u0026nbsp;CYCS immunohistochemistry showed that it was mainly expressed in liver cells and bile duct cells, and the samples were from patients with liver fibrosis. \u0026nbsp;GSDMD immunohistochemistry showed expression in the main bile duct cells, and the samples were from patients with liver fibrosis. \u0026nbsp; IL-1\u0026beta; HE sections showed expression in major liver cells and fibroblasts (HE staining, no immunohistochemical data found). \u0026nbsp;NLRP3 immunohistochemistry shows that it is mainly expressed in liver cells and bile duct cells. \u0026nbsp;IL18 immunohistochemistry showed that it was mainly expressed in bile duct cells (Fig S3 A-F).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eParticularly HCC, liver cancer ranks fourth in the world for cancer-related mortality and is the sixth most frequent cancer globally, placing a heavy load on health care systems. Between 85% and 90% of all initial liver malignancies are HCCs [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Even with improvements in adjuvant therapy, liver transplantation, and surgery, the survival rate for people with HCC is still not good enough [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Thus, it is still essential to clarify the molecular causes of HCC and find novel treatment targets [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A significant concentration of macrophages has been seen in HCC liver tissue and mouse models, suggesting that liver macrophages play a complex role in the pathophysiology of HCC. These macrophages suppress anti-tumor immunity because they support tumor growth[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Liver macrophages secrete pro-angiogenic factors, which together stimulate tumor growth, during the evolution of HCC. These factors include transforming growth factor-β (TGF-β), vascular endothelial growth factor (VEGF), and platelet-derived growth factor (PDGF) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Pyroptosis is a type of programmed cell death linked to inflammation that contributes to the growth of tumors in two distinct manners. On the one hand, pyroptosis induction can stop tumor cells from proliferating and migrating. On the other side, an inflammatory tumor microenvironment (TME), which promotes tumor growth, might result from overactivation of pyroptosis. GSDMD-mediated cell pyroptosis in HCC can activate tumor-infiltrating macrophages, promoting phagocytosis and anti-tumor immunity [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. It's interesting to note that GSDMD in HCC shows distinct signs of immune cell infiltration. The majority of GSDMDs have a negative correlation with macrophages and a positive correlation with B cells, neutrophils, and dendritic cells. It can be seen that the tumor immune microenvironment, especially macrophages, is closely related to the occurrence and development of cell pyroptosis, but further exploration is needed in HCC. Thus, by combining single-cell technology with bioinformatics, we were able to uncover the mechanism of action of pyroptosis and macrophages in HCC, offering direction for the clinical diagnosis and therapy of HCC.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed gene expression between liver cancer tissue and normal tissue adjacent to the cancer using the GEO database, and obtained six HCC related pyroptosis hub genes HMGB1, CYCS, GSDMD, IL-1β, NLRP3, and IL18. Through enrichment analysis, it was determined that the occurrence of HCC is closely associated with signaling pathways such as Necroptosis, Pyroptosis multiple species, NF kappa B signaling pathway, Autophagy animal, Hepatitis C, Hepatitis B, PYROPTOSIS, INTERLEUKIN_18_SIGNALING, and THE-NLRP3-INFLAMMASOME. Through single-cell sequencing data, we found that the main infiltrating cells in liver cancer tissue include NK_cell, Monocyte, T-cell: CD4+_ effector_memory, Hepatocytes, Endothelial-cells, Tissue_stem-cells, B-cell, Epithelial-cells, Hepatocytes, T-cell: gamma delta, macrophage M1, macrophage M0, macrophage M2. As for HCC itself, it is mainly related to the inflammatory processes of Endothelial cells, Tissue_stem-cells, Epithelial cells, and Hepatocytes. Regarding the tumor immune microenvironment, it is mainly related to the differentiation and function of NK_cell, Monocyte, T-cell: CD4\u0026thinsp;+\u0026thinsp;effector_memory, macrophages. The identification of malignant cells further confirms the close relationship between these cells and the progression of cancer. Further analysis of cell trajectories revealed that polarization of monocytes and macrophages first plays a role in HCC. In addition, key apoptotic targets such as HMGB1, CYCS, GSDMD, IL-1β, NLRP3, and IL18 regulate macrophage polarization and affect HCC. Inter cellular communication indicates that the MIF and VEGF signaling pathways are activated in HCC tumors.\u003c/p\u003e \u003cp\u003eRecent studies have shown that HMGB1 is up-regulated in human rectal cancer, breast cancer, cervical cancer, lung cancer and other malignant tumors, which is closely related to the occurrence, growth, invasion and metastasis of tumors, and can be used as a serum marker to provide reference value for tumor progress and prognosis evaluation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. A prerequisite for cellular angiogenesis in HCC has been found to be RAGE, whereas HMGB1 is thought to be a pro-angiogenic factor that causes colon carcinoma to produce VEGF. Given that RAGE is one of HMGB1's receptors, this obliquely implies that HMGB1 may use RAGE to generate angiogenesis in HCC. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In addition, we also found that the VEGF signaling pathway was activated, which may be related to the role of HMGB1. Research has shown that dysregulation of key proteins involved in GSDMD, IL-1β, and NLRP3 pyroptosis may lead to the occurrence and development of various human diseases, particularly malignant tumors[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Zhang et al.'s work demonstrated that mifepristone reduces HCC cell growth by pyroptosis that is dependent on BAX-caspase-GSDME, and that the ROS-MEK-ERK1/2 pathway is involved in pyroptosis regulation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Similar research by Yu et al. previously demonstrated that GSDME can promote ROS/JNK/Bax mitochondrial cell pyroptosis pathways and caspase-3/-9 activation in lobaplatin-mediated cell pyroptosis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Our investigation revealed that patients in high-risk groups (liver cancer tissue) not only had cell necrosis but also an accumulation of immune-suppressive cells including macrophages, T cell regulators (Tregs), T cell CD4+, and cancer-related endothelial cells. It is commonly accepted that the infiltration of macrophages in TME results in an immunosuppressive milieu and treatment resistance, both of which are frequently linked to unfavorable prognoses and outcomes [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Fatty liver and ECM deposition can result from inflammation associated with chronic liver disease. Fibrous scars will eventually build up and cause cirrhosis if they are not treated, which will result in HCC. The primary constituents of the tumor microenvironment are tumor-associated macrophages, which are highly malleable and can differentiate into M1 and M2 phenotypes, which is crucial for the advancement of HCC. TLR2 ligands in HCC have been shown to decrease NF-κ B activity and increase M2 macrophage polarization. Additional investigation reveals that HMGB1 generated from HCC promotes the development of HCC by inducing M2 polarization via the TLR2/NOX2/autophagy axis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, angiogenesis is a key aspect of the growth of tumors. Studies have demonstrated that lncRNA CRNDE can stimulate M2 polarization and angiogenesis; its mechanism involves upregulating the expression of proteins associated to angiogenesis, including JAK1, STAT6, AKT1, and others [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In conclusion, M1 macrophages perform antigen presentation, pathogen removal, and anti-tumor activities. They also provide protection against viral hepatitis and parasite-induced HCC. On the other hand, M2 macrophages can efficiently treat liver illnesses mostly brought on by inflammatory damage, exhibit anti-inflammatory properties, and aid in wound healing.\u003c/p\u003e \u003cp\u003eThe primary cause of liver cell degeneration is the late stage of pyroptosis, which is accompanied by the production of inflammatory molecules like IL-1β and IL-18. The activation of the NF-κ B signaling pathway in liver cells and macrophages is enhanced by the inflammatory substances released by hepatocyte necrosis, which in turn promotes the inflammatory response. In the meantime, the body may attract mononuclear macrophages through the release of pro-inflammatory cytokines, which could worsen inflammatory responses and eventually cause a significant loss of liver cells. It is clear that numerous signaling pathways are shared by the mechanisms of pyroptosis and macrophage polarization when the signaling pathways are combined. For instance, the TLR/NF-κ B pathway causes cell pyroptosis in addition to being crucial for M1 polarization. Furthermore, pyroptosis and macrophage polarization are mutually regulated, according to a number of studies. Thus, we think that controlling liver cell pyroptosis and macrophage polarization will be a novel therapeutic approach.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we have established a predictive model for HCC disease, and the main molecular mechanism of its pathogenesis is immune cell infiltration, especially macrophage polarization promoting the secretion of inflammatory factors leading to cell pyroptosis. We finally confirmed that hub genes such as HMGB1, CYCS, GSDMD, IL-1β, NLRP3, and IL18 are the link between macrophage polarization and pyroptosis during HCC development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe studies involving humans were approved by the Ethics Committee of The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University (Nos. YJ-KY2024048). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Methodology, Data analysis, Writing - original draft: W. Lou and J.X. Wang; \u0026nbsp; Images analysis, Providing technical support, Performing the experiments: H.F. Wang, T.W. Yang and F. Liu; Conceptualization, Supervision, Funding, Writing \u0026ndash; review \u0026amp; editing: Z.J. Wu andW.B. Guo; \u0026nbsp;All authors participated in these experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following grants supported this work: the Science and Technology Project of Sichuan Provincial Administration of Traditional Chinese Medicine (Nos. 2024MS151), the Southwest Medical University Integrated Traditional Chinese and Western Medicine Special Project (Nos. 2023ZYYJ07), and the Joint Project of Southwest Medical University and Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University (Nos. 2020XYLH- 021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the CEO (Home - GEO - NCBI \u0026nbsp;https://www.ncbi.nlm.nih.gov/geo/) database for providing the underlying data for this study and all the patients and families who provided clinical samples for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJemal, A., et al., \u003cem\u003eAnnual Report to the Nation on the Status of Cancer, 1975-2014, Featuring Survival.\u003c/em\u003e J Natl Cancer Inst, 2017. \u003cstrong\u003e109\u003c/strong\u003e(9).\u003c/li\u003e\n\u003cli\u003eAlvarez, M., et al., \u003cem\u003eHuman liver single nucleus and single cell RNA sequencing identify a hepatocellular carcinoma-associated cell-type affecting survival.\u003c/em\u003e Genome Med, 2022. \u003cstrong\u003e14\u003c/strong\u003e(1): p. 50.\u003c/li\u003e\n\u003cli\u003ePi\u0026ntilde;ero, F., M. 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C649-c663.\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":"Liver cancer, macrophages, pyroptosis, single-cell analysis","lastPublishedDoi":"10.21203/rs.3.rs-5365183/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5365183/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eHepatocellular carcinoma (HCC) is the third leading cause of cancer related death, and its molecular mechanisms have not been fully elucidated. The aim of this work is to discover the association between immune microenvironment changes and pyroptosis molecular mechanisms in HCC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSelect gene expression profiles from the comprehensive gene expression database, establish protein-protein interaction networks, and perform functional enrichment analysis using databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG). Single cell identification of HCC cell types and malignant cells, trajectory analysis and intercellular signal communication further analyze the molecular mechanisms between immune cells and liver cells. Bioinformatics combined with single-cell analysis to elucidate the immune pyroptosis molecular mechanism underlying the development of HCC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe key hub genes of immune pyroptosis were validated through immunohistochemistry and in vitro experiments. Molecular biology has identified six focal death hub genes in HCC. Enrichment analysis shows that intersecting genes are enriched in immune responses, chemokine mediated signaling pathways, and inflammatory responses. The cellular clustering of single cells revealed the infiltration of immune cells, especially the polarization of macrophages, which plays an important role. Immunohistochemistry suggests that hub genes such as HMGB1, CYCS, GSDMD, IL-1β, NLRP3, and IL18 are the link between macrophage polarization and pyroptosis during HCC development.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn summary, the main molecular mechanisms underlying the pathogenesis of HCC are related to immune cell infiltration, particularly macrophage infiltration polarization that promotes the secretion of inflammatory factors leading to hepatocyte pyroptosis. Our study may guide future research on the macrophage pyroptosis signaling pathway in HCC.\u003c/p\u003e","manuscriptTitle":"Bioinformatics combined with single-cell analysis reveals the molecular mechanism of pyroptosis in hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-13 11:54:24","doi":"10.21203/rs.3.rs-5365183/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":"d5614e76-4418-4414-9cec-10fc4be75917","owner":[],"postedDate":"November 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-11T15:08:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-13 11:54:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5365183","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5365183","identity":"rs-5365183","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}