{"paper_id":"2b024c57-1598-4fa3-9bdc-906bf4a25753","body_text":"Multi-Omics Deciphering of the Characteristics, Functional Mechanisms, and Prognostic Value of Tumor-Associated Macrophage Subsets 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 Multi-Omics Deciphering of the Characteristics, Functional Mechanisms, and Prognostic Value of Tumor-Associated Macrophage Subsets in Hepatocellular Carcinoma Qin Yang, Hongjie Qiu, Ruoqin Zhao, Bingbing Lin, Diya Xie, Qiang Zhu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7981845/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hepatocellular carcinoma (HCC) is a highly prevalent malignancy with a poor prognosis and limited response to immunotherapy, largely due to its heterogeneous tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are key regulators in the TME, though their subsets and clinical roles remain incompletely understood. To address this, we integrated multi-omics data and performed single-cell transcriptome clustering, identifying five distinct TAM subsets. Among these, the SPP1+/TREM2 + subset (TAM_0) was highlighted as an independent prognostic risk factor, associated with advanced disease stage, immunosuppressive TME remodeling, and upregulation of immune checkpoint genes. Based on these findings, a robust six-gene prognostic model (including SPP1, SLC11A1, HK2, BCAT1, PHLDA2, and ANP32E) was constructed and validated across multiple cohorts, demonstrating high accuracy in predicting overall survival. Spatial transcriptomics further confirmed that these genes and related metabolic pathways were specifically enriched in tumor regions. This study systematically delineates TAM heterogeneity in HCC, identifies a key immunosuppressive TAM subset, and provides a clinically applicable prognostic model for risk stratification and personalized treatment. Hepatocellular carcinoma Tumor-associated macrophages Single-cell RNA sequencing Tumor microenvironment Prognostic model Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Primary liver cancer is one of the most common cancers worldwide, with a long-maintained high incidence, and its mortality ranks third among all malignant tumors [ 1 , 2 ] . Hepatocellular carcinoma (HCC) is the most important and common type of primary liver cancer, accounting for approximately 85% of all liver cancer cases [ 3 ] . Given that patients typically do not exhibit any specific symptoms in the early stage of tumor development, most patients are already at a relatively advanced stage when clinically diagnosed [ 4 ] , at which point treatment options are limited and therapeutic effects are often suboptimal. Additionally, due to factors such as the inherent heterogeneity of HCC, epigenetic differences, and the complexity of the tumor microenvironment (TME), even immune checkpoint inhibitors, which have emerged in the field of cancer treatment in recent years, can only achieve sustained remission in a small subset of patients [ 5 , 6 ] . Most patients still face issues such as significant variability in therapeutic efficacy and frequent tumor drug resistance. Therefore, comprehensive elucidation of the molecular mechanisms of HCC and in-depth understanding of the subset composition of the tumor microenvironment are of great significance for treating the disease and prolonging patient survival. The TME is closely associated with tumor progression and metastasis, making it a major focus in oncological research. The TME constitutes a complex ecosystem composed of various cell types, including immune cells, cancer-associated fibroblasts, and mesenchymal stem cells [ 7 ] . Among these, tumor-associated macrophages (TAMs), as the most abundant innate immune cell population within the TME, play a critical role in tumor angiogenesis, immune suppression, and therapy resistance [ 8 ] . Previous studies have confirmed that in the pathological microenvironment of HCC, TAMs enhance intercellular communication among tumor cells by secreting various key signaling factors, while also functionally modulating other immune cell populations within the TME [ 9 , 10 ] . These actions promote HCC progression and influence the efficacy of immunotherapy. The classical paradigm categorizes TAMs into the anti-tumor M1 phenotype and the pro-tumor M2 phenotype [ 11 ] . However, with the recent application of technologies such as single-cell RNA sequencing (scRNA-seq) in cancer research, it has become evident that TAMs exhibit substantial heterogeneity in vivo that extends far beyond this dichotomous classification. For instance, Angio-TAMs have been identified as playing a critical role in the recurrence and progression of IDH-mutant glioma, a mechanism potentially associated with the activation of the mTORC1 pathway [ 12 ] . Different TAM subsets may originate from distinct precursor cells, reside in varying differentiation states, and execute unique or even opposing functions. In HCC, although the existence of heterogeneous TAM subpopulations has been previously reported [ 13 , 14 ] , a systematic characterization of their precise compositional architecture, developmental trajectories, biological functions, and exact clinical prognostic significance remains lacking. A key unresolved scientific question in the field is how to identify the critical functional subsets within the highly heterogeneous TAM population and elucidate how they remodel the immune microenvironment to drive HCC malignant progression. Therefore, this study aims to systematically characterize the heterogeneous subpopulations of TAMs in HCC through analysis of single-cell transcriptomic data, with a specific focus on identifying key subsets. Based on these findings, we seek to construct a robust prognostic prediction model and validate its reliability. This work is expected to provide novel insights into the classification of TAM subsets in HCC and offer valuable guidance for the development of personalized therapeutic strategies in clinical practice. Materials and methods 1.1 Quality Control of the Single-Cell Dataset The count matrix of the single-cell RNA sequencing (scRNA-seq) dataset GSE149614 was imported and converted into a Seurat object using the \"CreateSeuratObject\" function from the R package Seurat [ 15 ] , with parameters set to include genes expressed in at least 3 cells and cells expressing at least 250 genes. Since low-quality cells or empty droplets typically contain very few genes, we filtered out cells with < 300 RNA counts, < 250 RNA feature counts, log10GenesPerUMI < 0.8, and mitochondrial gene proportion ≥ 20%. Subsequently, the sequencing depth of the single-cell data was normalized using the \"NormalizeData\" function with the default \"LogNormalize\" method, and the top 2000 highly variable genes in the dataset were detected using the \"vst\" method via the \"FindVariableFeatures\" function. We then scaled the data using \"ScaleData\" to eliminate the impact of sequencing depth. Principal Component Analysis (PCA) was applied to identify significant principal components. The K-nearest neighbor graph based on Euclidean distances in the PCA space was constructed using the default parameters of \"FindNeighbors\" with 50 significant principal component dimensions. Cell clustering into distinct clusters was performed by calling the \"FindClusters\" function, with the \"clustree\" function used to determine a resolution of 0.8. Finally, dimensionality reduction was performed using the \"RunUMAP\" function to enable visualization and exploration of the dataset. 1.2 Cell-Cell Communication and Pseudotime Analysis The cell-cell communication network was inferred by analyzing ligand-receptor interactions among different cell subsets using the CellChat package [ 16 ] . The developmental trajectory of TAM subsets in dataset GSE146409 was predicted through pseudotime analysis implemented with the R package Monocle [ 17 – 19 ] . An expression family object was created using the cell dataset function, with the detection threshold set at 0.5. Cell developmental trajectories were explored through an unsupervised approach based on highly variable genes selected by Monocle. 1.3 Gene Set Variation Analysis The reference gene set \"h.all.v2023.2.Hs.symbols.gmt\" and \"h.all.v2025.1.Hs.symbols.gmt\" were downloaded from the MSigDB database [ 20 ] ( https://www.gsea-msigdb.org/gsea/msigdb/ ). GSVA pathway analysis was performed between TAM subsets, respectively, and the results were visualized using heatmaps. 1.4 Acquisition of Transcriptomic Data The liver hepatocellular carcinoma dataset (TCGA-LIHC) was downloaded from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ) using the R package TCGAbiolinks [ 21 ] and analyzed as the test set. After excluding samples with missing or unknown prognostic information, clinical grading, and clinical staging data, a total of 233 liver cancer samples were obtained. Meanwhile, the dataset was normalized to FPKM (Fragments Per Kilobase per Million) format. The liver cancer datasets GSE14520 [ 23 ] and GSE116174 [ 24 ] were downloaded from the GEO database [ 22 ] ( https://www.ncbi.nlm.nih.gov/geo/ ). Both datasets included samples derived from Homo sapiens, and only liver cancer samples were enrolled. 1.5 Construction of the Prognostic Model The R package survival [ 25 ] was used to perform univariate Cox regression analysis based on clinical information, aiming to evaluate the effect of genes on prognosis and determine whether they are independent prognostic factors. Subsequently, variables with a p-value < 0.05 were subjected to LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis. Genes included in the LASSO analysis were then incorporated into a multivariate Cox regression model to construct a prognostic model. The calculation formula for the RiskScore of the prognostic model is as follows: 1.6 Immune Infiltration Analysis ssGSEA (Single-Sample Gene-Set Enrichment Analysis), known as single-sample gene set enrichment analysis, quantifies the relative abundance of each infiltrating immune cell. First, various types of infiltrating immune cells are labeled, such as Activated CD8 T cell, Activated dendritic cell, Gamma delta T cell, Natural killer cell, Regulatory T cell, and other human immune cell subsets. Subsequently, the enrichment scores calculated by ssGSEA analysis are used to represent the relative abundance of each immune cell infiltration in each sample, and the immune cell infiltration matrix in TCGA is obtained. Then, the R package ggplot2 is used to draw group comparison plots to show the expression differences of immune cells between the two groups in the GEO dataset (Combined Datasets). CIBERSORT [ 26 ] deconvolves the transcriptome expression matrix based on the principle of linear support vector regression, thereby estimating the composition and abundance of immune cells in mixed cells. Using the CIBERSORT algorithm, combined with the cell subset signature gene matrix, and filtering out data with cell subset enrichment scores greater than zero, the specific results of the macrophage subset infiltration matrix in all samples of the integrated GEO dataset (Combined Dataset) are finally obtained. Subsequently, the R package ggplot2 is used to draw group comparison plots to show the expression differences of immune cells between the two groups in the GEO dataset (Combined Datasets). 1.7 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis We used the R package clusterProfiler [ 27 ] to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The screening criteria for significant terms were set as a p-value < 0.05 and a false discovery rate (FDR, also referred to as q-value) < 0.25. 1.8 Acquisition of Spatial Transcriptomics Data The idle data were derived from HRA000437, downloaded from the National Center for Bioinformation ( https://www.cncb.ac.cn/ ). Three samples from the same hepatocellular carcinoma patient were selected for analysis: HCC-1N (normal sample), HCC-1L (leading-edge sample), and HCC-1T (tumor sample). 1.9 Statistical Analysis The Wilcoxon test was used to assess differences between groups, and Spearman's rank correlation analysis was applied for correlation assessment. All statistical tests were two-tailed, with significance defined as P < 0.05. Statistical analyses were performed using R software (version 4.4.20). Results 2.1 Characterization of the Single-Cell Multiomics Landscape in HCC Tissues To characterize the chromatin accessibility landscape of TAMs in HCC, the scRNA-seq dataset GSE149614 from the GEO database was selected for analysis. This dataset comprises transcriptomic profiles of over 70,000 single cells obtained from four distinct anatomical sites—primary tumor, portal vein tumor thrombus, metastatic lymph node, and non-tumor liver tissue—across 10 HCC patients. Following quality control and filtration of low-quality cells, 21,478 high-quality cells were retained for subsequent clustering analysis, which identified 19 distinct cell clusters (Fig. 1 A). Cell type annotation was performed using the SingleR package complemented by manual curation, leading to the identification of seven major cell types: macrophages, natural killer cells, T cells, hepatocytes, B cells, fibroblasts, and endothelial cells (Fig. 1 B). This annotation was further validated using gene activity scores (Fig. 1 C). Furthermore, the proportional distribution of each cell type was quantified and compared between normal and tumor samples. The results revealed a notable reduction in the proportions of natural killer cells, T cells, and B cells in tumor samples compared to normal tissues. Conversely, the proportions of macrophages, fibroblasts, and endothelial cells were significantly elevated in tumor samples (Fig. 1 D–F). 2.2 Analysis of Intercellular Communication Network in HCC We employed the CellChat R package to investigate the intercellular communication networks between HCC samples and normal samples. This approach infers potential interactions by mapping the expression of ligand-receptor pairs among various immune cells within the TME. Overall, strong cell-cell signaling was observed among macrophages, endothelial cells, fibroblasts, and NK cells in both the normal and HCC groups (Fig. 2 A-F). Notably, the total number and interaction strength of cell-cell communications were relatively reduced in the HCC group compared to the normal group (Fig. 2 A, D). Specifically, macrophages, B cells, T cells, NK cells, and endothelial cells in the HCC group exhibited weaker signaling activity in both sending and receiving signals compared to the normal group (Fig. 2 G). In contrast, fibroblasts demonstrated stronger signaling activity in the HCC group than in the normal group, particularly in sending signals (Fig. 2 G). We further visualized the interaction strength of ligand-receptor pairs between hepatocytes (including both normal and HCC hepatocytes) and immune cells using a bubble plot. The results showed that the interaction strengths of various ligand-receptor pairs were weaker in HCC than in normal hepatocytes, consistent with the aforementioned findings (Fig. 2 H). Heatmaps depicting outgoing, incoming, and overall signaling patterns further revealed extensively activated intercellular communication signals in both groups. Signaling pathways such as PERIOSTIN, OX40, EDN, PTPRM, NPR1, L1CAM, VEGI, and PRL were identified exclusively in the HCC group but not in the normal group (Fig. 2 J-K, Figure S1 ). Subsequently, we calculated the differential pathway strength within the HCC group and identified the top five significantly enriched pathways in tumors, as shown below (Fig. 2 I). Further comparison of communication patterns and strength between the two groups revealed significant alterations in the communication patterns and strength of macrophages in the HCC group compared to the normal group within the SPP1, CD80, THY1, and SELL pathways (Figure S2 ). 2.3 Subclassification and Prognostic Evaluation of TAMs To precisely delineate the subsets of tumor-associated macrophages (TAMs), we performed re-clustering of macrophages from HCC samples, yielding five distinct macrophage subpopulations (Clusters 0–4) (Fig. 3 A). Subsequently, based on the expression patterns of SPP1 or TREM2 in macrophages, four TAM subsets were identified: Cluster 0 (TAM_0), Cluster 1 (TAM_1), Cluster 3 (TAM_2), and Cluster 4 (TAM_3). Cluster 2 was excluded from further analysis due to the absence of both SPP1 and TREM2 expression (Fig. 3 B). Using the single-cell dataset, we applied the BayesPrism algorithm for deconvolution analysis (Figures S3 A-D). Kaplan-Meier (KM) curves generated from the TCGA deconvolution results were used to evaluate the prognostic significance of TAM subsets, revealing that TAM_0 and TAM_3 were significantly associated with prognosis (Fig. 3 C). We further validated these findings in two GEO datasets (GSE14520 and GSE116174), which confirmed the significance of only TAM_0 (Figures S3 E-H). This subset was therefore defined as the prognostic TAM subpopulation. Based on the median relative abundance of TAM_0 in the TCGA cohort, we stratified the tumor samples into two groups: TAM_0 Low and TAM_0 High. The association between TAM_0 abundance and clinicopathological features was assessed using the chi-square test. The results indicated that TAM_0 enrichment levels were correlated with pathological T stage, pathological stage, and OS events in HCC patients ( P < 0.05, Table 1 ). Furthermore, TIDE data for the TCGA cohort were obtained from the TIDE web portal, and a comparison between the TAM_0 Low and TAM_0 High groups revealed that high TAM_0 abundance was associated with elevated TIDE scores (Fig. 3 D). This suggests that enrichment of TAM_0 may enhance tumor immune evasion capability, which is linked to the prognosis of HCC patients. Table 1 Relative Abundance of TAM_0 and clinical features of patients with hepatocellular carcinoma Characteristics Relative Abundance of TAM_0 P value Statistic Method Low (n = 116) High (n = 117) OS event, n (%) 0.039 4.236 Chisq test Alive 86 (36.9%) 72 (30.9%) Dead 30 (12.9%) 45 (19.3%) Pathologic T stage, n (%) 0.004 13.473 Chisq test T1 68 (29.2%) 49 (21.0%) T2 18 (7.7%) 32 (13.7%) T3 29 (12.4%) 27 (11.6%) T4 1 (0.4%) 9(3.9%) Pathologic N stage, n (%) 0.317 1.000 Chisq test N0 115 (49.4%) 114 (48.9%) N1 1 (0.4%) 3 (1.3%) Pathologic M stage, n (%) 0.566 0.329 Chisq test M0 115 (49.4%) 115 (49.4%) M1 1 (0.4%) 2 (0.9%) Pathologic stage, n (%) 0.029 9.033 Chisq test Stage Ⅰ 68 (29.2%) 47 (20.2%) Stage Ⅱ 18 (7.7%) 31 (13.3%) Stage Ⅲ 29(12.4%) 36 (15.5%) Stage Ⅳ 1 (0.4%) 3 (1.3%) Histologic grade, n (%) 0.246 4.149 Chisq test G1 19 (8.2%) 10 (4.3%) G2 49 (21%) 52 (22.3%) G3 42 (18%) 51(21.9%) G4 6 (2.6%) 4 (1.7%) Tabular Note : OS (Overall Survival), Chisq test (Chi-Square Test) 2.4 Investigating the Developmental Trajectory and Functional Heterogeneity of TAM Subpopulations Pseudotime analysis revealed the developmental trajectory of TAMs (Fig. 4 A). Cell state clustering based on gene expression profiles further indicated that TAMs transitioned from State 1 to State 2, ultimately differentiating at a branch point into State 3 and State 4, while State 5 potentially represents a rare transitional state (Fig. 4 B). When the predefined TAM subtypes (TAM_0, TAM_1, TAM_2, TAM_3) were overlaid onto the trajectory, TAM_0 was found to be widely distributed across the entire path, suggesting multipotent differentiation potential (Fig. 4 C, D). TAM_1 was concentrated in the upper branch after the bifurcation, implying it is a specialized subtype of this lineage. TAM_2 was primarily confined to the initial segment, potentially representing non-activated macrophages. Subsequently, we employed Gene Set Variation Analysis (GSVA) to further investigate the functional heterogeneity among different TAM subpopulations. A heatmap depicting functional similarity showed that TAM_3 exhibited relatively high similarity with the other three subpopulations. In striking contrast, TAM_0 showed markedly lower similarity to the other three subpopulations, with a particularly significant difference observed between TAM_0 and TAM_1 (Fig. 4 E). Furthermore, an enrichment heatmap illustrated the regulation of Hallmark gene sets by each TAM subpopulation. Overall, TAM_0 displayed a uniquely high activity signature in multiple pathways closely associated with tumor progression, such as angiogenesis and glycolysis, suggesting its potential involvement in tumor vascularization and metabolic reprogramming. TAM_1 was primarily enriched in pathways related to adipogenesis, while TAM_2 and TAM_3 showed high activity in E2F targets and cholesterol homeostasis, respectively (Fig. 4 F). In summary, GSVA analysis elucidated the distinct biological functional heterogeneity among TAM subpopulations, which likely underlies their divergent roles in tumor biology. 2.5 TAM_0 Shapes an Immunosuppressive Tumor Microenvironment by Remodeling Immune Infiltration and Upregulating Immune Checkpoint Genes To elucidate the role of the tumor-associated macrophage subtype TAM_0 in regulating the tumor immune microenvironment (TIME), we analyzed the TCGA dataset using both CIBERSORT and ssGSEA algorithms to compare immune cell infiltration differences between the TAM_0 Low and TAM_0 High groups. Among classical immune subtypes, the infiltration of unpolarized macrophages (Macrophages M0) was significantly higher in the TAM_0 High group compared to the Low group ( P < 0.001), suggesting TAM_0 may be involved in macrophage recruitment or differentiation. Naive B cells were more significantly infiltrated in the Low group ( P < 0.001), whereas no significant differences were observed between the two groups for pro-inflammatory M1 macrophages, pro-tumoral M2 macrophages, cytotoxic CD8 + T cells, or regulatory T cells (Tregs) (P > 0.05) (Fig. 5 A). At a more granular level of immune subpopulations, the TAM_0 High group showed significantly elevated infiltration of activated CD4 + T cells, activated CD8 + T cells, and highly immunosuppressive myeloid-derived suppressor cells (MDSC) (all P < 0.001). Infiltration of other subsets, such as memory B cells and activated dendritic cells, also showed similar increasing trends (Fig. 5 B). Subsequently, we further analyzed the differential expression of immune checkpoint genes between the TAM_0 High and TAM_0 Low groups based on the GSE131793 dataset. We found that key immune checkpoint genes, including CD274 (encoding PD-L1, the primary ligand for PD-1), CTLA4 (encoding CTLA-4, an early T-cell immune checkpoint), and PDCD1 (encoding PD-1, an inhibitory receptor on T cells), were significantly upregulated in samples with high TAM_0 abundance ( P < 0.001, Fig. 5 C). In summary, these findings collectively suggest that TAM_0 shapes an immunosuppressive TIME by reprogramming the immune cell infiltration landscape and upregulating the expression of immune checkpoint genes, thereby promoting tumor immune escape. 2.6 Identification of TAM_0 Prognostic Genes and Construction of a Prognostic Model Using all cell types (NK cells, T cells, B cells, endothelial cells, fibroblasts, macrophages, hepatocytes, TAM_0, TAM_1, TAM_2, TAM_3), TAM_0-specific differentially expressed genes were identified via the FindAllMarkers algorithm (p_val < 0.05 and |avg_log2FC| >1). After excluding genes not expressed in the GSE14520 and GSE116174 datasets, a total of 518 genes were retained. Genes with independent prognostic value were subsequently screened through univariable Cox regression analysis using the TCGA dataset and the two aforementioned GEO datasets (Fig. 6 A, Figures S4 A-B). This process identified 9 core genes common to all three datasets (Fig. 6 B). Subsequently, we applied LASSO regression analysis to the TCGA data for further refinement, narrowing the gene set down to 6 genes used to calculate a risk score (Fig. 6 C, D). The risk score was calculated using the following formula: RiskScore = − 3.239 + SPP1∗0.069 + SLC11A1∗0.092 + HK2∗0.0639 + BCAT1∗0.0424 + PHLDA2∗0.219 + ANP32E∗0.573 To enhance clinical applicability, we integrated the risk score with these 6 genes to construct a multivariable Cox model and generated a prognostic nomogram (Fig. 6 E). The calibration curve demonstrated that the model had good predictive accuracy for patient OS (Fig. 6 F). Furthermore, the Kaplan-Meier survival curve and time-dependent ROC curves, stratified based on the nomogram risk groups, showed that patients classified into the high-risk group exhibited significantly poorer survival outcomes, with a markedly reduced median survival time (Fig. 6 G). The area under the curve (AUC) values for predicting 1-, 3-, and 5-year survival were 0.768, 0.731, and 0.740, respectively, indicating favorable diagnostic accuracy (Fig. 6 H). Consistent conclusions were subsequently validated in the GSE14520 and GSE116174 datasets (Figures S4 C-F), suggesting that the model possesses robust stability and external reproducibility, holding potential clinical utility. 2.7 GO/KEGG Enrichment Analysis of Model Genes and Spatial Transcriptomics Validation We performed GO and KEGG enrichment analysis on the 6 genes from the multivariable Cox model. The results for Biological Process (BP) and KEGG pathways are displayed via bubble plots. The model genes were significantly enriched in metabolism-related BP terms, such as \"organic cyclic compound catabolic process,\" \"generation of precursor metabolites and energy,\" and \"glucose metabolic process,\" suggesting a potential core role for these genes in metabolic regulation (Fig. 7 A, B). Subsequently, we analyzed spatial transcriptomics data (HRA000437) from three samples of patient HCC-1: HCC-1N (normal tissue), HCC-1L (adjacent leading-edge tissue), and HCC-1T (tumor tissue) (Fig. 7 C). As the KEGG pathways involved only a single model gene, the study utilized BP terms for spatial data visualization. The spatial expression patterns of all six model genes were simultaneously displayed, and the enrichment scores of five BP pathways were calculated and visualized on the spatial slices.The results revealed that in HCC-1T, the expression signals (color intensity) of most genes (e.g., HK2, SPP1) were significantly higher than those in HCC-1N and HCC-1L. The gene expression levels in HCC-1L were intermediate between normal and tumor tissues, reflecting a molecular phenotypic gradient from \"normal to tumor.\" This indicates tumor region-specific spatial expression of the model genes (Fig. 7 D). Furthermore, the activity of the various metabolic pathways was significantly higher in HCC-1T compared to HCC-1N and HCC-1L (Fig. 7 E), and the areas of high activity showed strong spatial overlap with the tumor tissue regions. This heterogeneity in spatial gene and pathway expression likely reflects intra-tumoral metabolic reprogramming in HCC and further confirms the reliability of the model genes. Discussion By integrating multi-omics data, this study systematically delineated the heterogeneity landscape of tumor-associated macrophages (TAMs) in hepatocellular carcinoma (HCC) and intensively investigated the biological functions and clinical significance of a key subset—TAM_0. We identified several pivotal findings: (1) The intercellular interaction network within the HCC tumor microenvironment (TME) is remodeled, characterized by a general attenuation of communication activity among macrophages in tumor tissues; (2) We successfully identified five TAM subpopulations, among which the TAM_0 subset was validated as an independent prognostic risk factor across multiple independent cohorts; (3) Based on the signature genes of TAM_0, we constructed a risk score system comprising six genes, which demonstrated robust performance in predicting overall survival for HCC patients; (4) Spatial transcriptomics analysis visually confirmed the tumor region-specific expression patterns of these model genes and their association with metabolic reprogramming. TAMs, as the most abundant innate immune cell population within the TME with central roles in functional regulatory networks, have seen their intrinsic heterogeneity become a core research frontier in tumor immunology [ 28 ] . In this study, by re-clustering macrophages from the scRNA-seq dataset and filtering based on the TAM-specific markers SPP1 and TREM2 [ 29 ] , we identified four functional TAM subpopulations (excluding the cluster negative for both markers). Among these, the TAM_0 subset (SPP1+/TREM2+) exhibited the most significant prognostic predictive value. In the TCGA-LIHC dataset, a higher abundance of TAM_0 was significantly associated with poorer overall survival (OS), more advanced Pathologic T stage, and worse overall pathologic stage. This association was further validated in two independent GEO datasets (GSE14520 and GSE116174). These findings resonate with prior reports [ 30 , 31 ] , gradually establishing SPP1+/TREM2 + TAMs as a crucial pro-tumoral population in HCC. Furthermore, pseudotime trajectory analysis revealed that TAM_0 was widely distributed across the developmental path, suggesting diverse differentiation potential and rich biological functionality, which may underpin its ability to adapt to TME signals and execute functions relevant to tumor growth. Another major finding of this study is the elucidation of the critical role played by TAM_0 in shaping an immunosuppressive TME. We noted that high TAM_0 abundance correlated with a higher Tumor Immune Dysfunction and Exclusion (TIDE) score, providing a direct clue to the mechanism underlying its association with poor prognosis [ 32 ] . Further analysis revealed increased infiltration of unpolarized macrophages (M0) and myeloid-derived suppressor cells (MDSCs) in the TAM_0 High group. Both cell types can suppress effector T cell function and contribute to tumor progression [ 33 , 34 ] . Concurrently, we observed significant upregulation of key immune checkpoint genes, including CD274, CTLA-4, and PDCD1, in the TAM_0 High group. These results collectively depict a scenario where TAM_0 potentially fosters an immunosuppressive microenvironment conducive to tumor immune escape by recruiting immature myeloid cells, engaging and \"exhausting\" activated T cells, and upregulating immune checkpoint molecules. This strongly implies that HCC patients with high TAM_0 expression might be potential beneficiaries of immune checkpoint inhibitor therapy, a hypothesis warranting validation in future prospective studies. Based on the specific marker genes of the prognostic TAM subset, we constructed a multi-gene prognostic model comprising six genes (SPP1, SLC11A1, HK2, BCAT1, PHLDA2, ANP32E) via LASSO and Cox regression. This model demonstrated favorable predictive accuracy and stability in both the internal training set (TCGA) and two external validation sets (GSE14520, GSE116174). Notably, the functions of these genes align well with our GSVA results. For instance, SPP1 is a well-known oncogenic factor playing key roles in cell migration, macrophage recruitment, and immune regulation [ 31 , 35 ] . HK2 is a key rate-limiting enzyme in glycolysis, and its upregulation, consistent with the high glycolytic pathway activity in TAM_0 revealed in this study, suggests this subset's role in regulating tumor cell metabolic reprogramming within the TME [ 36 ] . BCAT1 is a key enzyme in branched-chain amino acid metabolism, and its high expression corresponds to the activation of amino acid metabolic pathways in TAM_0. Previous studies have indicated that BCAT1 can promote HCC progression by activating the AKT signaling pathway and epithelial-mesenchymal transition (EMT) [ 37 ] . Furthermore, using spatial transcriptomics, we visually demonstrated, for the first time in situ, the specific enrichment of these genes within the core tumor regions of HCC and found a high spatial overlap between the activity of related metabolic pathways and the tumor areas, providing strong evidence for the reliability of our model. However, this study has several limitations. First, our findings are primarily based on retrospective bioinformatic analyses of public databases. The relatively limited sample size and lack of multi-center cohort support might not fully capture the TAM heterogeneity across HCC patients from different regions or with varying etiologies (e.g., HBV, HCV, NASH). Future prospective clinical cohorts and experimental studies are needed for confirmation. Second, although we inferred TAM_0 abundance in HCC bulk data using deconvolution algorithms, the most precise quantification still relies on immunohistochemistry or flow cytometry performed on tissue or fresh samples. Finally, the constructed model has not yet been validated in prospective clinical cohorts, and its prognostic predictive efficacy for future patients requires further confirmation. In conclusion, this study systematically reveals the heterogeneity of TAMs in HCC and identifies a key immunosuppressive subset, TAM_0. We elucidate the potential mechanisms by which TAM_0 promotes HCC progression through remodeling the immune microenvironment and upregulating immune checkpoints. Based on these findings, we developed a prognostic model with promising clinical applicability. These discoveries not only deepen the understanding of the mechanisms by which TAMs regulate HCC progression but also provide novel perspectives and a basis for HCC prognosis assessment and targeted therapy. Conclusion This study employed multi-omics technologies to systematically characterize the heterogeneity of tumor-associated macrophages (TAMs) in hepatocellular carcinoma (HCC), building upon prior research to refine the understanding of TAM subtype classification, biological functions, and clinical prognostic value. We elucidated the significant role of the TAM_0 (SPP1+/TREM2+) subset within the HCC immune microenvironment. Based on TAM_0 gene signatures, we constructed a robust prognostic signature comprising six genes (SPP1, SLC11A1, HK2, BCAT1, PHLDA2, ANP32E), which effectively predicts patient outcomes. Furthermore, subsequent spatial transcriptomics analysis provided in situ confirmation of the tumor region-specific expression patterns of these model genes. Future efforts should focus on refining this signature and validating its clinical utility through prospective studies. Our work not only deepens the comprehension of the HCC immune microenvironment but also provides novel biomarkers and a theoretical foundation for patient prognosis prediction and potential targeted immunotherapy strategies. Declarations Acknowledgements Not applicable. Author contributions The original ideas of this manuscript were conceived and designed by Qin Yang and Hongjie Qiu. Hongjie Qiu and Bingbing Lin collected and analyzed the data. The figures and tables were prepared by Ruoqin Zhao, and Diya Xie. Qiang Zhu and Yuekai You assisted in data interpretation. Qin Yang drafted the initial manuscript, Kun Zhang reviewed and edited the initial manuscript. All authors have read and approved the final manuscript. Funding The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by Fuzhou First General Hospital Health Key Research and Development Project(2023-YJ-zd3), Natural Science Foundation of Xiamen Municipality (3502Z20227286), Natural Science Foundation of Fujian Province (2023J01015), Key Clinical Specialty Discipline Construction Program of Fuzhou,Fujian, P.R.C (Grant no.20220301). Data availability The datasets utilized in this study were obtained from publicly available databases. The single-cell RNA sequencing data (GSE149614) and the bulk RNA-seq validation datasets (GSE14520 and GSE116174) were sourced from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The primary transcriptome cohort (TCGA-LIHC) was obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). The spatial transcriptomics data are available at the National Center for Bioinformation under accession number HRA000437 (https://www.cncb.ac.cn/). Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The author(s) declare that there are no potential conflicts of interest in the research, writing, and/or publication of this article. References Yang JD, Hainaut P, Gores GJ, Amadou A, Plymoth A, Roberts LR. A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastroenterol Hepatol. 2019 Oct;16(10):589-604. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-263. Wang Y, Deng B. Hepatocellular carcinoma: molecular mechanism, targeted therapy, and biomarkers. Cancer Metastasis Rev. 2023;42(3):629-652. Yeung YP, Lo CM, Liu CL, Wong BC, Fan ST, Wong J. Natural history of untreated nonsurgical hepatocellular carcinoma. Am J Gastroenterol. 2005;100(9):1995-2004. 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(A)\\u003c/strong\\u003e Comparison of cell-cell interaction counts between HCC and normal groups.\\u003cstrong\\u003e (B, C) \\u003c/strong\\u003eCircle plots showing cell-cell interaction counts for the two groups. \\u003cstrong\\u003e(D) \\u003c/strong\\u003eComparison of cell-cell communication interaction strength between HCC and normal groups. \\u003cstrong\\u003e(E, F)\\u003c/strong\\u003eCircle plots showing communication interaction strength for the two groups. \\u003cstrong\\u003e(G) \\u003c/strong\\u003eCommunication count and strength between specific cell subsets and others in normal/HCC groups. \\u003cstrong\\u003e(H) \\u003c/strong\\u003eTop 5 significantly enriched signaling pathways in tumors (differential pathway strength analysis).\\u003cstrong\\u003e (I) \\u003c/strong\\u003eCommunication signals between hepatocytes and 4 immune cell types. \\u003cstrong\\u003e(J, K)\\u003c/strong\\u003e Heatmaps of cellular outgoing/incoming signals.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Picture2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7981845/v1/a8e2bee3e6693f9bd3c0a205.png\"},{\"id\":96814475,\"identity\":\"4755c30e-c03f-4d78-94f5-469bb927e2c8\",\"added_by\":\"auto\",\"created_at\":\"2025-11-26 10:52:53\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":980927,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eIdentification of distinct TAM subpopulations and evaluation of their prognostic value.\\u003c/strong\\u003e \\u003cstrong\\u003e(A)\\u003c/strong\\u003eMacrophages were extracted from tumor samples only and subjected to re-clustering.\\u003cstrong\\u003e (B)\\u003c/strong\\u003e TAMs were identified based on SPP1 and TREM2 expression. Cells expressing neither marker were defined as conventional macrophages. \\u003cstrong\\u003e(C)\\u003c/strong\\u003e Kaplan-Meier curves for the TAM subpopulations constructed in the TCGA dataset, suggesting TAM_0 and TAM_3 as potential prognostic subpopulations. \\u003cstrong\\u003e(D) \\u003c/strong\\u003eTIDE scores obtained from the TIDE web portal for the TCGA cohort were compared between the TAM_0 Low and TAM_0 High groups to assess significant differences. *\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05, **\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.01, ***\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Picture3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7981845/v1/2034b25407131035f38c44d2.png\"},{\"id\":96814471,\"identity\":\"d4f623b9-8dc0-4c12-8098-175b45e7183f\",\"added_by\":\"auto\",\"created_at\":\"2025-11-26 10:52:53\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":970664,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDevelopmental trajectory and functional heterogeneity of TAM subpopulations.\\u003c/strong\\u003e \\u003cstrong\\u003e(A) \\u003c/strong\\u003ePseudotime analysis revealing the developmental trajectory of TAMs. \\u003cstrong\\u003e(B) \\u003c/strong\\u003eThe five distinct states identified during TAM development. \\u003cstrong\\u003e(C, D)\\u003c/strong\\u003eEvaluation of the developmental trajectories for different TAM subtypes.\\u003cstrong\\u003e (E) \\u003c/strong\\u003eHeatmap showing the similarity among TAM subpopulations. The color gradient from dark blue to dark red indicates decreasing similarity. \\u003cstrong\\u003e(F) \\u003c/strong\\u003eFunctional enrichment heatmap of Hallmark gene sets across TAM subpopulations.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Picture4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7981845/v1/e047dbae73c4bbbb52719ba3.png\"},{\"id\":96814472,\"identity\":\"481c1d5e-bece-4847-a1fd-909bb1c618e4\",\"added_by\":\"auto\",\"created_at\":\"2025-11-26 10:52:53\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1731449,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTAM_0 Shapes an Immunosuppressive Tumor Immune Microenvironment.\\u003c/strong\\u003e \\u003cstrong\\u003e(A)\\u003c/strong\\u003e Differences in immune cell infiltration between the TAM_0 Low and High groups, as analyzed by the CIBERSORT algorithm. \\u003cstrong\\u003e(B) \\u003c/strong\\u003eDifferences in immune cell infiltration between the TAM_0 Low and High groups, as analyzed by the ssGSEA algorithm. \\u003cstrong\\u003e(C)\\u003c/strong\\u003eDifferential expression of immune checkpoint genes between the TAM_0 High and Low groups based on analysis of the GSE131793 dataset. *\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05, **\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt; 0.01, ***\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Picture5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7981845/v1/1c8f7fd9ab39cb317b76b9b7.png\"},{\"id\":96917733,\"identity\":\"5f8872f4-bd6e-4876-9d5f-0c25eb5b18c4\",\"added_by\":\"auto\",\"created_at\":\"2025-11-27 14:10:27\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1772827,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eIdentification of TAM_0 prognostic genes and construction of a corresponding prognostic model.\\u003c/strong\\u003e \\u003cstrong\\u003e(A) \\u003c/strong\\u003eUnivariable Cox regression analysis of the TCGA-LIHC dataset. \\u003cstrong\\u003e(B) \\u003c/strong\\u003eVenn diagram showing the 9 core genes (SPP1, BCAT1, HK2, PHLDA2, SNAPC1, CCL20, SLC11A1, ANP32E, and FNDC3B) shared across the three datasets.\\u003cstrong\\u003e (C)\\u003c/strong\\u003e The 6 genes selected by the LASSO regression algorithm. \\u003cstrong\\u003e(D)\\u003c/strong\\u003e LASSO coefficient profile derived from repeated sampling tests. \\u003cstrong\\u003e(E)\\u003c/strong\\u003e Nomogram constructed by combining the risk score and the 6 identified genes. \\u003cstrong\\u003e(F)\\u003c/strong\\u003e Calibration curve of the nomogram for predicting overall survival. \\u003cstrong\\u003e(G) \\u003c/strong\\u003eKaplan-Meier survival analysis based on risk groups stratified by the nomogram. (H) ROC curves evaluating the prognostic prediction efficiency of the nomogram.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Picture6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7981845/v1/5257144546342fe736eb14e1.png\"},{\"id\":96814529,\"identity\":\"833d53a5-f049-47b8-8844-628345431b6e\",\"added_by\":\"auto\",\"created_at\":\"2025-11-26 10:52:55\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":7303283,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGO/KEGG enrichment analysis of model genes and spatial transcriptomics validation. (A, B)\\u003c/strong\\u003e Bubble plots showing the results of GO and KEGG enrichment analysis for the 6 genes in the multivariable Cox model, depicting significantly enriched Biological Process (BP) terms and KEGG pathways, respectively. \\u003cstrong\\u003e(C)\\u003c/strong\\u003e Representative tissue sections of HCC-1N (normal liver tissue), HCC-1L (adjacent leading-edge tissue), and HCC-1T (tumor tissue). Darker colors indicate higher gene expression or pathway activity. \\u003cstrong\\u003e(D)\\u003c/strong\\u003eSpatial expression patterns of the six model genes across the tissue sections. \\u003cstrong\\u003e(E) \\u003c/strong\\u003eSpatial enrichment scores of the five significantly enriched BP pathways identified in the GO analysis.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Picture7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7981845/v1/865f38a96dfc7e3c0e659e6b.png\"},{\"id\":99310954,\"identity\":\"db214ccf-a820-4c3e-9d88-11db38297af5\",\"added_by\":\"auto\",\"created_at\":\"2025-12-31 16:13:36\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":16533270,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7981845/v1/060be48e-0efd-4d76-91d8-a8f27aa44add.pdf\"},{\"id\":96916680,\"identity\":\"1ac14b49-ea23-41d2-b1e2-c8b214bf0ae0\",\"added_by\":\"auto\",\"created_at\":\"2025-11-27 14:08:49\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":36165164,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supportinginformation.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7981845/v1/b0aad61f65d7fc8befc37e9f.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Multi-Omics Deciphering of the Characteristics, Functional Mechanisms, and Prognostic Value of Tumor-Associated Macrophage Subsets in Hepatocellular Carcinoma\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003ePrimary liver cancer is one of the most common cancers worldwide, with a long-maintained high incidence, and its mortality ranks third among all malignant tumors\\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/sup\\u003e. Hepatocellular carcinoma (HCC) is the most important and common type of primary liver cancer, accounting for approximately 85% of all liver cancer cases\\u003csup\\u003e[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/sup\\u003e. Given that patients typically do not exhibit any specific symptoms in the early stage of tumor development, most patients are already at a relatively advanced stage when clinically diagnosed\\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e, at which point treatment options are limited and therapeutic effects are often suboptimal. Additionally, due to factors such as the inherent heterogeneity of HCC, epigenetic differences, and the complexity of the tumor microenvironment (TME), even immune checkpoint inhibitors, which have emerged in the field of cancer treatment in recent years, can only achieve sustained remission in a small subset of patients\\u003csup\\u003e[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/sup\\u003e. Most patients still face issues such as significant variability in therapeutic efficacy and frequent tumor drug resistance. Therefore, comprehensive elucidation of the molecular mechanisms of HCC and in-depth understanding of the subset composition of the tumor microenvironment are of great significance for treating the disease and prolonging patient survival.\\u003c/p\\u003e\\u003cp\\u003eThe TME is closely associated with tumor progression and metastasis, making it a major focus in oncological research. The TME constitutes a complex ecosystem composed of various cell types, including immune cells, cancer-associated fibroblasts, and mesenchymal stem cells \\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e. Among these, tumor-associated macrophages (TAMs), as the most abundant innate immune cell population within the TME, play a critical role in tumor angiogenesis, immune suppression, and therapy resistance \\u003csup\\u003e[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/sup\\u003e. Previous studies have confirmed that in the pathological microenvironment of HCC, TAMs enhance intercellular communication among tumor cells by secreting various key signaling factors, while also functionally modulating other immune cell populations within the TME \\u003csup\\u003e[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e. These actions promote HCC progression and influence the efficacy of immunotherapy.\\u003c/p\\u003e\\u003cp\\u003eThe classical paradigm categorizes TAMs into the anti-tumor M1 phenotype and the pro-tumor M2 phenotype \\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]\\u003c/sup\\u003e. However, with the recent application of technologies such as single-cell RNA sequencing (scRNA-seq) in cancer research, it has become evident that TAMs exhibit substantial heterogeneity in vivo that extends far beyond this dichotomous classification. For instance, Angio-TAMs have been identified as playing a critical role in the recurrence and progression of IDH-mutant glioma, a mechanism potentially associated with the activation of the mTORC1 pathway \\u003csup\\u003e[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/sup\\u003e. Different TAM subsets may originate from distinct precursor cells, reside in varying differentiation states, and execute unique or even opposing functions. In HCC, although the existence of heterogeneous TAM subpopulations has been previously reported \\u003csup\\u003e[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]\\u003c/sup\\u003e, a systematic characterization of their precise compositional architecture, developmental trajectories, biological functions, and exact clinical prognostic significance remains lacking. A key unresolved scientific question in the field is how to identify the critical functional subsets within the highly heterogeneous TAM population and elucidate how they remodel the immune microenvironment to drive HCC malignant progression.\\u003c/p\\u003e\\u003cp\\u003eTherefore, this study aims to systematically characterize the heterogeneous subpopulations of TAMs in HCC through analysis of single-cell transcriptomic data, with a specific focus on identifying key subsets. Based on these findings, we seek to construct a robust prognostic prediction model and validate its reliability. This work is expected to provide novel insights into the classification of TAM subsets in HCC and offer valuable guidance for the development of personalized therapeutic strategies in clinical practice.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1.1 Quality Control of the Single-Cell Dataset\\u003c/h2\\u003e\\u003cp\\u003eThe count matrix of the single-cell RNA sequencing (scRNA-seq) dataset GSE149614 was imported and converted into a Seurat object using the \\\"CreateSeuratObject\\\" function from the R package Seurat \\u003csup\\u003e[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]\\u003c/sup\\u003e, with parameters set to include genes expressed in at least 3 cells and cells expressing at least 250 genes. Since low-quality cells or empty droplets typically contain very few genes, we filtered out cells with \\u0026lt;\\u0026thinsp;300 RNA counts, \\u0026lt; 250 RNA feature counts, log10GenesPerUMI\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.8, and mitochondrial gene proportion\\u0026thinsp;\\u0026ge;\\u0026thinsp;20%. Subsequently, the sequencing depth of the single-cell data was normalized using the \\\"NormalizeData\\\" function with the default \\\"LogNormalize\\\" method, and the top 2000 highly variable genes in the dataset were detected using the \\\"vst\\\" method via the \\\"FindVariableFeatures\\\" function. We then scaled the data using \\\"ScaleData\\\" to eliminate the impact of sequencing depth. Principal Component Analysis (PCA) was applied to identify significant principal components. The K-nearest neighbor graph based on Euclidean distances in the PCA space was constructed using the default parameters of \\\"FindNeighbors\\\" with 50 significant principal component dimensions. Cell clustering into distinct clusters was performed by calling the \\\"FindClusters\\\" function, with the \\\"clustree\\\" function used to determine a resolution of 0.8. Finally, dimensionality reduction was performed using the \\\"RunUMAP\\\" function to enable visualization and exploration of the dataset.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1.2 Cell-Cell Communication and Pseudotime Analysis\\u003c/h2\\u003e\\u003cp\\u003eThe cell-cell communication network was inferred by analyzing ligand-receptor interactions among different cell subsets using the CellChat package \\u003csup\\u003e[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e. The developmental trajectory of TAM subsets in dataset GSE146409 was predicted through pseudotime analysis implemented with the R package Monocle \\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR18\\\" citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]\\u003c/sup\\u003e. An expression family object was created using the cell dataset function, with the detection threshold set at 0.5. Cell developmental trajectories were explored through an unsupervised approach based on highly variable genes selected by Monocle.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1.3 Gene Set Variation Analysis\\u003c/h2\\u003e\\u003cp\\u003eThe reference gene set \\\"h.all.v2023.2.Hs.symbols.gmt\\\" and \\\"h.all.v2025.1.Hs.symbols.gmt\\\" were downloaded from the MSigDB database \\u003csup\\u003e[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]\\u003c/sup\\u003e (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.gsea-msigdb.org/gsea/msigdb/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). GSVA pathway analysis was performed between TAM subsets, respectively, and the results were visualized using heatmaps.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1.4 Acquisition of Transcriptomic Data\\u003c/h2\\u003e\\u003cp\\u003eThe liver hepatocellular carcinoma dataset (TCGA-LIHC) was downloaded from The Cancer Genome Atlas (TCGA) database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://portal.gdc.cancer.gov/\\u003c/span\\u003e\\u003cspan address=\\\"https://portal.gdc.cancer.gov/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) using the R package TCGAbiolinks \\u003csup\\u003e[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]\\u003c/sup\\u003e and analyzed as the test set. After excluding samples with missing or unknown prognostic information, clinical grading, and clinical staging data, a total of 233 liver cancer samples were obtained. Meanwhile, the dataset was normalized to FPKM (Fragments Per Kilobase per Million) format. The liver cancer datasets GSE14520 \\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e and GSE116174 \\u003csup\\u003e[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]\\u003c/sup\\u003e were downloaded from the GEO database \\u003csup\\u003e[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]\\u003c/sup\\u003e (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ncbi.nlm.nih.gov/geo/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.ncbi.nlm.nih.gov/geo/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). Both datasets included samples derived from Homo sapiens, and only liver cancer samples were enrolled.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1.5 Construction of the Prognostic Model\\u003c/h2\\u003e\\u003cp\\u003eThe R package survival \\u003csup\\u003e[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u003c/sup\\u003e was used to perform univariate Cox regression analysis based on clinical information, aiming to evaluate the effect of genes on prognosis and determine whether they are independent prognostic factors. Subsequently, variables with a p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 were subjected to LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis. Genes included in the LASSO analysis were then incorporated into a multivariate Cox regression model to construct a prognostic model. The calculation formula for the RiskScore of the prognostic model is as follows:\\u003c/p\\u003e\\u003cp\\u003e\\u003cimg 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\\\" width=\\\"451\\\" height=\\\"54\\\"\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1.6 Immune Infiltration Analysis\\u003c/h2\\u003e\\u003cp\\u003essGSEA (Single-Sample Gene-Set Enrichment Analysis), known as single-sample gene set enrichment analysis, quantifies the relative abundance of each infiltrating immune cell. First, various types of infiltrating immune cells are labeled, such as Activated CD8 T cell, Activated dendritic cell, Gamma delta T cell, Natural killer cell, Regulatory T cell, and other human immune cell subsets. Subsequently, the enrichment scores calculated by ssGSEA analysis are used to represent the relative abundance of each immune cell infiltration in each sample, and the immune cell infiltration matrix in TCGA is obtained. Then, the R package ggplot2 is used to draw group comparison plots to show the expression differences of immune cells between the two groups in the GEO dataset (Combined Datasets). CIBERSORT \\u003csup\\u003e[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]\\u003c/sup\\u003e deconvolves the transcriptome expression matrix based on the principle of linear support vector regression, thereby estimating the composition and abundance of immune cells in mixed cells. Using the CIBERSORT algorithm, combined with the cell subset signature gene matrix, and filtering out data with cell subset enrichment scores greater than zero, the specific results of the macrophage subset infiltration matrix in all samples of the integrated GEO dataset (Combined Dataset) are finally obtained. Subsequently, the R package ggplot2 is used to draw group comparison plots to show the expression differences of immune cells between the two groups in the GEO dataset (Combined Datasets).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1.7 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis\\u003c/h2\\u003e\\u003cp\\u003eWe used the R package clusterProfiler \\u003csup\\u003e[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The screening criteria for significant terms were set as a p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 and a false discovery rate (FDR, also referred to as q-value)\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.25.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1.8 Acquisition of Spatial Transcriptomics Data\\u003c/h2\\u003e\\u003cp\\u003eThe idle data were derived from HRA000437, downloaded from the National Center for Bioinformation (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.cncb.ac.cn/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.cncb.ac.cn/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). Three samples from the same hepatocellular carcinoma patient were selected for analysis: HCC-1N (normal sample), HCC-1L (leading-edge sample), and HCC-1T (tumor sample).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1.9 Statistical Analysis\\u003c/h2\\u003e\\u003cp\\u003eThe Wilcoxon test was used to assess differences between groups, and Spearman's rank correlation analysis was applied for correlation assessment. All statistical tests were two-tailed, with significance defined as P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. Statistical analyses were performed using R software (version 4.4.20).\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.1 Characterization of the Single-Cell Multiomics Landscape in HCC Tissues\\u003c/h2\\u003e\\n \\u003cp\\u003eTo characterize the chromatin accessibility landscape of TAMs in HCC, the scRNA-seq dataset GSE149614 from the GEO database was selected for analysis. This dataset comprises transcriptomic profiles of over 70,000 single cells obtained from four distinct anatomical sites\\u0026mdash;primary tumor, portal vein tumor thrombus, metastatic lymph node, and non-tumor liver tissue\\u0026mdash;across 10 HCC patients. Following quality control and filtration of low-quality cells, 21,478 high-quality cells were retained for subsequent clustering analysis, which identified 19 distinct cell clusters (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). Cell type annotation was performed using the SingleR package complemented by manual curation, leading to the identification of seven major cell types: macrophages, natural killer cells, T cells, hepatocytes, B cells, fibroblasts, and endothelial cells (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). This annotation was further validated using gene activity scores (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC). Furthermore, the proportional distribution of each cell type was quantified and compared between normal and tumor samples. The results revealed a notable reduction in the proportions of natural killer cells, T cells, and B cells in tumor samples compared to normal tissues. Conversely, the proportions of macrophages, fibroblasts, and endothelial cells were significantly elevated in tumor samples (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eD\\u0026ndash;F).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.2 Analysis of Intercellular Communication Network in HCC\\u003c/h2\\u003e\\n \\u003cp\\u003eWe employed the CellChat R package to investigate the intercellular communication networks between HCC samples and normal samples. This approach infers potential interactions by mapping the expression of ligand-receptor pairs among various immune cells within the TME. Overall, strong cell-cell signaling was observed among macrophages, endothelial cells, fibroblasts, and NK cells in both the normal and HCC groups (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA-F). Notably, the total number and interaction strength of cell-cell communications were relatively reduced in the HCC group compared to the normal group (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA, D). Specifically, macrophages, B cells, T cells, NK cells, and endothelial cells in the HCC group exhibited weaker signaling activity in both sending and receiving signals compared to the normal group (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eG). In contrast, fibroblasts demonstrated stronger signaling activity in the HCC group than in the normal group, particularly in sending signals (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eG). We further visualized the interaction strength of ligand-receptor pairs between hepatocytes (including both normal and HCC hepatocytes) and immune cells using a bubble plot. The results showed that the interaction strengths of various ligand-receptor pairs were weaker in HCC than in normal hepatocytes, consistent with the aforementioned findings (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eH). Heatmaps depicting outgoing, incoming, and overall signaling patterns further revealed extensively activated intercellular communication signals in both groups. Signaling pathways such as PERIOSTIN, OX40, EDN, PTPRM, NPR1, L1CAM, VEGI, and PRL were identified exclusively in the HCC group but not in the normal group (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eJ-K, Figure \\u003cspan class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Subsequently, we calculated the differential pathway strength within the HCC group and identified the top five significantly enriched pathways in tumors, as shown below (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eI). Further comparison of communication patterns and strength between the two groups revealed significant alterations in the communication patterns and strength of macrophages in the HCC group compared to the normal group within the SPP1, CD80, THY1, and SELL pathways (Figure \\u003cspan class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.3 Subclassification and Prognostic Evaluation of TAMs\\u003c/h2\\u003e\\n \\u003cp\\u003eTo precisely delineate the subsets of tumor-associated macrophages (TAMs), we performed re-clustering of macrophages from HCC samples, yielding five distinct macrophage subpopulations (Clusters 0\\u0026ndash;4) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). Subsequently, based on the expression patterns of SPP1 or TREM2 in macrophages, four TAM subsets were identified: Cluster 0 (TAM_0), Cluster 1 (TAM_1), Cluster 3 (TAM_2), and Cluster 4 (TAM_3). Cluster 2 was excluded from further analysis due to the absence of both SPP1 and TREM2 expression (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB). Using the single-cell dataset, we applied the BayesPrism algorithm for deconvolution analysis (Figures \\u003cspan class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e A-D). Kaplan-Meier (KM) curves generated from the TCGA deconvolution results were used to evaluate the prognostic significance of TAM subsets, revealing that TAM_0 and TAM_3 were significantly associated with prognosis (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). We further validated these findings in two GEO datasets (GSE14520 and GSE116174), which confirmed the significance of only TAM_0 (Figures \\u003cspan class=\\\"InternalRef\\\"\\u003eS3\\u003c/span\\u003e E-H). This subset was therefore defined as the prognostic TAM subpopulation. Based on the median relative abundance of TAM_0 in the TCGA cohort, we stratified the tumor samples into two groups: TAM_0 Low and TAM_0 High. The association between TAM_0 abundance and clinicopathological features was assessed using the chi-square test. The results indicated that TAM_0 enrichment levels were correlated with pathological T stage, pathological stage, and OS events in HCC patients (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Furthermore, TIDE data for the TCGA cohort were obtained from the TIDE web portal, and a comparison between the TAM_0 Low and TAM_0 High groups revealed that high TAM_0 abundance was associated with elevated TIDE scores (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD). This suggests that enrichment of TAM_0 may enhance tumor immune evasion capability, which is linked to the prognosis of HCC patients.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eRelative Abundance of TAM_0 and clinical features of patients with hepatocellular carcinoma\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eRelative Abundance of TAM_0\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eP value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eStatistic\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eMethod\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLow (n\\u0026thinsp;=\\u0026thinsp;116)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHigh (n\\u0026thinsp;=\\u0026thinsp;117)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOS event, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.039\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e4.236\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eChisq test\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAlive\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e86 (36.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e72 (30.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDead\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30 (12.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e45 (19.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathologic T stage, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.004\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e13.473\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eChisq test\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e68 (29.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e49 (21.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18 (7.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e32 (13.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e29 (12.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e27 (11.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (0.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9(3.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathologic N stage, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.317\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eChisq test\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e115 (49.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e114 (48.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eN1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (0.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (1.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathologic M stage, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.566\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e0.329\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eChisq test\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e115 (49.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e115 (49.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eM1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (0.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (0.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathologic stage, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.029\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e9.033\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eChisq test\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStage Ⅰ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e68 (29.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e47 (20.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStage Ⅱ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18 (7.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e31 (13.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStage Ⅲ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e29(12.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e36 (15.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStage Ⅳ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1 (0.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3 (1.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHistologic grade, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.246\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e4.149\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eChisq test\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eG1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19 (8.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10 (4.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eG2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e49 (21%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e52 (22.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eG3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e42 (18%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e51(21.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eG4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (2.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4 (1.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTabular Note\\u003c/strong\\u003e: OS (Overall Survival), Chisq test (Chi-Square Test)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.4 Investigating the Developmental Trajectory and Functional Heterogeneity of TAM Subpopulations\\u003c/h2\\u003e\\n \\u003cp\\u003ePseudotime analysis revealed the developmental trajectory of TAMs (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). Cell state clustering based on gene expression profiles further indicated that TAMs transitioned from State 1 to State 2, ultimately differentiating at a branch point into State 3 and State 4, while State 5 potentially represents a rare transitional state (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB). When the predefined TAM subtypes (TAM_0, TAM_1, TAM_2, TAM_3) were overlaid onto the trajectory, TAM_0 was found to be widely distributed across the entire path, suggesting multipotent differentiation potential (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC, D). TAM_1 was concentrated in the upper branch after the bifurcation, implying it is a specialized subtype of this lineage. TAM_2 was primarily confined to the initial segment, potentially representing non-activated macrophages. Subsequently, we employed Gene Set Variation Analysis (GSVA) to further investigate the functional heterogeneity among different TAM subpopulations. A heatmap depicting functional similarity showed that TAM_3 exhibited relatively high similarity with the other three subpopulations. In striking contrast, TAM_0 showed markedly lower similarity to the other three subpopulations, with a particularly significant difference observed between TAM_0 and TAM_1 (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE). Furthermore, an enrichment heatmap illustrated the regulation of Hallmark gene sets by each TAM subpopulation. Overall, TAM_0 displayed a uniquely high activity signature in multiple pathways closely associated with tumor progression, such as angiogenesis and glycolysis, suggesting its potential involvement in tumor vascularization and metabolic reprogramming. TAM_1 was primarily enriched in pathways related to adipogenesis, while TAM_2 and TAM_3 showed high activity in E2F targets and cholesterol homeostasis, respectively (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eF). In summary, GSVA analysis elucidated the distinct biological functional heterogeneity among TAM subpopulations, which likely underlies their divergent roles in tumor biology.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.5 TAM_0 Shapes an Immunosuppressive Tumor Microenvironment by Remodeling Immune Infiltration and Upregulating Immune Checkpoint Genes\\u003c/h2\\u003e\\n \\u003cp\\u003eTo elucidate the role of the tumor-associated macrophage subtype TAM_0 in regulating the tumor immune microenvironment (TIME), we analyzed the TCGA dataset using both CIBERSORT and ssGSEA algorithms to compare immune cell infiltration differences between the TAM_0 Low and TAM_0 High groups. Among classical immune subtypes, the infiltration of unpolarized macrophages (Macrophages M0) was significantly higher in the TAM_0 High group compared to the Low group (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), suggesting TAM_0 may be involved in macrophage recruitment or differentiation. Naive B cells were more significantly infiltrated in the Low group (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), whereas no significant differences were observed between the two groups for pro-inflammatory M1 macrophages, pro-tumoral M2 macrophages, cytotoxic CD8\\u0026thinsp;+\\u0026thinsp;T cells, or regulatory T cells (Tregs) (P\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). At a more granular level of immune subpopulations, the TAM_0 High group showed significantly elevated infiltration of activated CD4\\u0026thinsp;+\\u0026thinsp;T cells, activated CD8\\u0026thinsp;+\\u0026thinsp;T cells, and highly immunosuppressive myeloid-derived suppressor cells (MDSC) (all \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Infiltration of other subsets, such as memory B cells and activated dendritic cells, also showed similar increasing trends (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB). Subsequently, we further analyzed the differential expression of immune checkpoint genes between the TAM_0 High and TAM_0 Low groups based on the GSE131793 dataset. We found that key immune checkpoint genes, including CD274 (encoding PD-L1, the primary ligand for PD-1), CTLA4 (encoding CTLA-4, an early T-cell immune checkpoint), and PDCD1 (encoding PD-1, an inhibitory receptor on T cells), were significantly upregulated in samples with high TAM_0 abundance (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC). In summary, these findings collectively suggest that TAM_0 shapes an immunosuppressive TIME by reprogramming the immune cell infiltration landscape and upregulating the expression of immune checkpoint genes, thereby promoting tumor immune escape.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.6 Identification of TAM_0 Prognostic Genes and Construction of a Prognostic Model\\u003c/h2\\u003e\\n \\u003cp\\u003eUsing all cell types (NK cells, T cells, B cells, endothelial cells, fibroblasts, macrophages, hepatocytes, TAM_0, TAM_1, TAM_2, TAM_3), TAM_0-specific differentially expressed genes were identified via the FindAllMarkers algorithm (p_val\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 and |avg_log2FC| \\u0026gt;1). After excluding genes not expressed in the GSE14520 and GSE116174 datasets, a total of 518 genes were retained. Genes with independent prognostic value were subsequently screened through univariable Cox regression analysis using the TCGA dataset and the two aforementioned GEO datasets (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA, Figures \\u003cspan class=\\\"InternalRef\\\"\\u003eS4\\u003c/span\\u003eA-B). This process identified 9 core genes common to all three datasets (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB). Subsequently, we applied LASSO regression analysis to the TCGA data for further refinement, narrowing the gene set down to 6 genes used to calculate a risk score (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC, D). The risk score was calculated using the following formula:\\u003c/p\\u003e\\n \\u003cp\\u003eRiskScore\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;3.239\\u0026thinsp;+\\u0026thinsp;SPP1\\u0026lowast;0.069\\u0026thinsp;+\\u0026thinsp;SLC11A1\\u0026lowast;0.092\\u0026thinsp;+\\u0026thinsp;HK2\\u0026lowast;0.0639\\u0026thinsp;+\\u0026thinsp;BCAT1\\u0026lowast;0.0424\\u0026thinsp;+\\u0026thinsp;PHLDA2\\u0026lowast;0.219\\u0026thinsp;+\\u0026thinsp;ANP32E\\u0026lowast;0.573\\u003c/p\\u003e\\n \\u003cp\\u003eTo enhance clinical applicability, we integrated the risk score with these 6 genes to construct a multivariable Cox model and generated a prognostic nomogram (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eE). The calibration curve demonstrated that the model had good predictive accuracy for patient OS (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eF). Furthermore, the Kaplan-Meier survival curve and time-dependent ROC curves, stratified based on the nomogram risk groups, showed that patients classified into the high-risk group exhibited significantly poorer survival outcomes, with a markedly reduced median survival time (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eG). The area under the curve (AUC) values for predicting 1-, 3-, and 5-year survival were 0.768, 0.731, and 0.740, respectively, indicating favorable diagnostic accuracy (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eH). Consistent conclusions were subsequently validated in the GSE14520 and GSE116174 datasets (Figures \\u003cspan class=\\\"InternalRef\\\"\\u003eS4\\u003c/span\\u003e C-F), suggesting that the model possesses robust stability and external reproducibility, holding potential clinical utility.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.7 GO/KEGG Enrichment Analysis of Model Genes and Spatial Transcriptomics Validation\\u003c/h2\\u003e\\n \\u003cp\\u003eWe performed GO and KEGG enrichment analysis on the 6 genes from the multivariable Cox model. The results for Biological Process (BP) and KEGG pathways are displayed via bubble plots. The model genes were significantly enriched in metabolism-related BP terms, such as \\u0026quot;organic cyclic compound catabolic process,\\u0026quot; \\u0026quot;generation of precursor metabolites and energy,\\u0026quot; and \\u0026quot;glucose metabolic process,\\u0026quot; suggesting a potential core role for these genes in metabolic regulation (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA, B). Subsequently, we analyzed spatial transcriptomics data (HRA000437) from three samples of patient HCC-1: HCC-1N (normal tissue), HCC-1L (adjacent leading-edge tissue), and HCC-1T (tumor tissue) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eC). As the KEGG pathways involved only a single model gene, the study utilized BP terms for spatial data visualization. The spatial expression patterns of all six model genes were simultaneously displayed, and the enrichment scores of five BP pathways were calculated and visualized on the spatial slices.The results revealed that in HCC-1T, the expression signals (color intensity) of most genes (e.g., HK2, SPP1) were significantly higher than those in HCC-1N and HCC-1L. The gene expression levels in HCC-1L were intermediate between normal and tumor tissues, reflecting a molecular phenotypic gradient from \\u0026quot;normal to tumor.\\u0026quot; This indicates tumor region-specific spatial expression of the model genes (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eD). Furthermore, the activity of the various metabolic pathways was significantly higher in HCC-1T compared to HCC-1N and HCC-1L (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eE), and the areas of high activity showed strong spatial overlap with the tumor tissue regions. This heterogeneity in spatial gene and pathway expression likely reflects intra-tumoral metabolic reprogramming in HCC and further confirms the reliability of the model genes.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eBy integrating multi-omics data, this study systematically delineated the heterogeneity landscape of tumor-associated macrophages (TAMs) in hepatocellular carcinoma (HCC) and intensively investigated the biological functions and clinical significance of a key subset\\u0026mdash;TAM_0. We identified several pivotal findings: (1) The intercellular interaction network within the HCC tumor microenvironment (TME) is remodeled, characterized by a general attenuation of communication activity among macrophages in tumor tissues; (2) We successfully identified five TAM subpopulations, among which the TAM_0 subset was validated as an independent prognostic risk factor across multiple independent cohorts; (3) Based on the signature genes of TAM_0, we constructed a risk score system comprising six genes, which demonstrated robust performance in predicting overall survival for HCC patients; (4) Spatial transcriptomics analysis visually confirmed the tumor region-specific expression patterns of these model genes and their association with metabolic reprogramming.\\u003c/p\\u003e\\u003cp\\u003eTAMs, as the most abundant innate immune cell population within the TME with central roles in functional regulatory networks, have seen their intrinsic heterogeneity become a core research frontier in tumor immunology \\u003csup\\u003e[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]\\u003c/sup\\u003e. In this study, by re-clustering macrophages from the scRNA-seq dataset and filtering based on the TAM-specific markers SPP1 and TREM2 \\u003csup\\u003e[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]\\u003c/sup\\u003e, we identified four functional TAM subpopulations (excluding the cluster negative for both markers). Among these, the TAM_0 subset (SPP1+/TREM2+) exhibited the most significant prognostic predictive value. In the TCGA-LIHC dataset, a higher abundance of TAM_0 was significantly associated with poorer overall survival (OS), more advanced Pathologic T stage, and worse overall pathologic stage. This association was further validated in two independent GEO datasets (GSE14520 and GSE116174). These findings resonate with prior reports \\u003csup\\u003e[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]\\u003c/sup\\u003e, gradually establishing SPP1+/TREM2\\u0026thinsp;+\\u0026thinsp;TAMs as a crucial pro-tumoral population in HCC. Furthermore, pseudotime trajectory analysis revealed that TAM_0 was widely distributed across the developmental path, suggesting diverse differentiation potential and rich biological functionality, which may underpin its ability to adapt to TME signals and execute functions relevant to tumor growth.\\u003c/p\\u003e\\u003cp\\u003eAnother major finding of this study is the elucidation of the critical role played by TAM_0 in shaping an immunosuppressive TME. We noted that high TAM_0 abundance correlated with a higher Tumor Immune Dysfunction and Exclusion (TIDE) score, providing a direct clue to the mechanism underlying its association with poor prognosis \\u003csup\\u003e[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]\\u003c/sup\\u003e. Further analysis revealed increased infiltration of unpolarized macrophages (M0) and myeloid-derived suppressor cells (MDSCs) in the TAM_0 High group. Both cell types can suppress effector T cell function and contribute to tumor progression \\u003csup\\u003e[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]\\u003c/sup\\u003e. Concurrently, we observed significant upregulation of key immune checkpoint genes, including CD274, CTLA-4, and PDCD1, in the TAM_0 High group. These results collectively depict a scenario where TAM_0 potentially fosters an immunosuppressive microenvironment conducive to tumor immune escape by recruiting immature myeloid cells, engaging and \\\"exhausting\\\" activated T cells, and upregulating immune checkpoint molecules. This strongly implies that HCC patients with high TAM_0 expression might be potential beneficiaries of immune checkpoint inhibitor therapy, a hypothesis warranting validation in future prospective studies.\\u003c/p\\u003e\\u003cp\\u003eBased on the specific marker genes of the prognostic TAM subset, we constructed a multi-gene prognostic model comprising six genes (SPP1, SLC11A1, HK2, BCAT1, PHLDA2, ANP32E) via LASSO and Cox regression. This model demonstrated favorable predictive accuracy and stability in both the internal training set (TCGA) and two external validation sets (GSE14520, GSE116174). Notably, the functions of these genes align well with our GSVA results. For instance, SPP1 is a well-known oncogenic factor playing key roles in cell migration, macrophage recruitment, and immune regulation \\u003csup\\u003e[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]\\u003c/sup\\u003e. HK2 is a key rate-limiting enzyme in glycolysis, and its upregulation, consistent with the high glycolytic pathway activity in TAM_0 revealed in this study, suggests this subset's role in regulating tumor cell metabolic reprogramming within the TME \\u003csup\\u003e[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]\\u003c/sup\\u003e. BCAT1 is a key enzyme in branched-chain amino acid metabolism, and its high expression corresponds to the activation of amino acid metabolic pathways in TAM_0. Previous studies have indicated that BCAT1 can promote HCC progression by activating the AKT signaling pathway and epithelial-mesenchymal transition (EMT) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]\\u003c/sup\\u003e. Furthermore, using spatial transcriptomics, we visually demonstrated, for the first time in situ, the specific enrichment of these genes within the core tumor regions of HCC and found a high spatial overlap between the activity of related metabolic pathways and the tumor areas, providing strong evidence for the reliability of our model.\\u003c/p\\u003e\\u003cp\\u003eHowever, this study has several limitations. First, our findings are primarily based on retrospective bioinformatic analyses of public databases. The relatively limited sample size and lack of multi-center cohort support might not fully capture the TAM heterogeneity across HCC patients from different regions or with varying etiologies (e.g., HBV, HCV, NASH). Future prospective clinical cohorts and experimental studies are needed for confirmation. Second, although we inferred TAM_0 abundance in HCC bulk data using deconvolution algorithms, the most precise quantification still relies on immunohistochemistry or flow cytometry performed on tissue or fresh samples. Finally, the constructed model has not yet been validated in prospective clinical cohorts, and its prognostic predictive efficacy for future patients requires further confirmation.\\u003c/p\\u003e\\u003cp\\u003eIn conclusion, this study systematically reveals the heterogeneity of TAMs in HCC and identifies a key immunosuppressive subset, TAM_0. We elucidate the potential mechanisms by which TAM_0 promotes HCC progression through remodeling the immune microenvironment and upregulating immune checkpoints. Based on these findings, we developed a prognostic model with promising clinical applicability. These discoveries not only deepen the understanding of the mechanisms by which TAMs regulate HCC progression but also provide novel perspectives and a basis for HCC prognosis assessment and targeted therapy.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study employed multi-omics technologies to systematically characterize the heterogeneity of tumor-associated macrophages (TAMs) in hepatocellular carcinoma (HCC), building upon prior research to refine the understanding of TAM subtype classification, biological functions, and clinical prognostic value. We elucidated the significant role of the TAM_0 (SPP1+/TREM2+) subset within the HCC immune microenvironment. Based on TAM_0 gene signatures, we constructed a robust prognostic signature comprising six genes (SPP1, SLC11A1, HK2, BCAT1, PHLDA2, ANP32E), which effectively predicts patient outcomes. Furthermore, subsequent spatial transcriptomics analysis provided in situ confirmation of the tumor region-specific expression patterns of these model genes. Future efforts should focus on refining this signature and validating its clinical utility through prospective studies. Our work not only deepens the comprehension of the HCC immune microenvironment but also provides novel biomarkers and a theoretical foundation for patient prognosis prediction and potential targeted immunotherapy strategies.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe original ideas of this manuscript were conceived and designed by Qin Yang and Hongjie Qiu. Hongjie Qiu and Bingbing Lin collected and analyzed the data. The figures and tables were prepared by Ruoqin Zhao, and Diya Xie. Qiang Zhu and Yuekai You assisted in data interpretation. Qin Yang drafted the initial manuscript, \\u0026nbsp;Kun Zhang \\u0026nbsp; reviewed and edited the initial manuscript. All authors have read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by Fuzhou First General Hospital Health Key Research and Development Project(2023-YJ-zd3), Natural Science Foundation of Xiamen Municipality (3502Z20227286), Natural Science Foundation of Fujian Province (2023J01015), Key Clinical Specialty Discipline Construction Program of Fuzhou,Fujian, P.R.C (Grant no.20220301).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets utilized in this study were obtained from publicly available databases. The single-cell RNA sequencing data (GSE149614) and the bulk RNA-seq validation datasets (GSE14520 and GSE116174) were sourced from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The primary transcriptome cohort (TCGA-LIHC) was obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). The spatial transcriptomics data are available at the National Center for Bioinformation under accession number HRA000437 (https://www.cncb.ac.cn/).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe author(s) declare that there are no potential conflicts of interest in the research, writing, and/or publication of this article.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eYang JD, Hainaut P, Gores GJ, Amadou A, Plymoth A, Roberts LR. A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastroenterol Hepatol. 2019 Oct;16(10):589-604.\\u003c/li\\u003e\\n\\u003cli\\u003eBray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-263. \\u003c/li\\u003e\\n\\u003cli\\u003eWang Y, Deng B. Hepatocellular carcinoma: molecular mechanism, targeted therapy, and biomarkers. Cancer Metastasis Rev. 2023;42(3):629-652. \\u003c/li\\u003e\\n\\u003cli\\u003eYeung YP, Lo CM, Liu CL, Wong BC, Fan ST, Wong J. Natural history of untreated nonsurgical hepatocellular carcinoma. Am J Gastroenterol. 2005;100(9):1995-2004. \\u003c/li\\u003e\\n\\u003cli\\u003eBicer F, Kure C, Ozluk AA, El-Rayes BF, Akce M. Advances in Immunotherapy for Hepatocellular Carcinoma (HCC). Curr Oncol. 2023;30(11):9789-9812. Published 2023 Nov 7. \\u003c/li\\u003e\\n\\u003cli\\u003eAkabane M, Imaoka Y, Lee GR, Pawlik TM. 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Nat Biotechnol, 2014. 32(4): p. 381-386.\\u003c/li\\u003e\\n\\u003cli\\u003eLiberzon, A., et al., Molecular signatures database (MSigDB) 3.0. Bioinformatics, 2011. 27(12): p. 1739-40.\\u003c/li\\u003e\\n\\u003cli\\u003eColaprico, A., et al., TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res, 2016. 44(8): p. e71.\\u003c/li\\u003e\\n\\u003cli\\u003eBarrett, T., et al., NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res, 2013. 41(Database issue): p. D991-5.\\u003c/li\\u003e\\n\\u003cli\\u003eRoessler, S., et al., A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients. Cancer Res, 2010. 70(24): p. 10202-12.\\u003c/li\\u003e\\n\\u003cli\\u003eNing, J., et al., Macrophage-coated tumor cluster aggravates hepatoma invasion and immunotherapy resistance via generating local immune deprivation. 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Myeloid-Derived Suppressor Cells in Immune Microenvironment Promote Progression of Esophagogastric Junction Adenocarcinoma. Front Oncol. 2021;11:640080. Published 2021 Mar 29. \\u003c/li\\u003e\\n\\u003cli\\u003ePalma A. The Landscape of SPP1 + Macrophages Across Tissues and Diseases: A Comprehensive Review. Immunology. 2025;176(2):179-196.\\u003c/li\\u003e\\n\\u003cli\\u003eJin F, Wang Y, Zhu Y, et al. The miR-125a/HK2 axis regulates cancer cell energy metabolism reprogramming in hepatocellular carcinoma. Sci Rep. 2017;7(1):3089. Published 2017 Jun 8.\\u003c/li\\u003e\\n\\u003cli\\u003eDing Y, Wang X, Lu S, et al. BCAT1, as a prognostic factor for HCC, can promote the development of liver cancer through activation of the AKT signaling pathway and EMT. J Mol Histol. 2023;54(1):25-39.\\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\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Hepatocellular carcinoma, Tumor-associated macrophages, Single-cell RNA sequencing, Tumor microenvironment, Prognostic model, Immunotherapy\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7981845/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7981845/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eHepatocellular carcinoma (HCC) is a highly prevalent malignancy with a poor prognosis and limited response to immunotherapy, largely due to its heterogeneous tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are key regulators in the TME, though their subsets and clinical roles remain incompletely understood. To address this, we integrated multi-omics data and performed single-cell transcriptome clustering, identifying five distinct TAM subsets. Among these, the SPP1+/TREM2\\u0026thinsp;+\\u0026thinsp;subset (TAM_0) was highlighted as an independent prognostic risk factor, associated with advanced disease stage, immunosuppressive TME remodeling, and upregulation of immune checkpoint genes. Based on these findings, a robust six-gene prognostic model (including SPP1, SLC11A1, HK2, BCAT1, PHLDA2, and ANP32E) was constructed and validated across multiple cohorts, demonstrating high accuracy in predicting overall survival. Spatial transcriptomics further confirmed that these genes and related metabolic pathways were specifically enriched in tumor regions. This study systematically delineates TAM heterogeneity in HCC, identifies a key immunosuppressive TAM subset, and provides a clinically applicable prognostic model for risk stratification and personalized treatment.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Multi-Omics Deciphering of the Characteristics, Functional Mechanisms, and Prognostic Value of Tumor-Associated Macrophage Subsets in Hepatocellular Carcinoma\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-26 10:52:48\",\"doi\":\"10.21203/rs.3.rs-7981845/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"b8ec7cf4-8d2f-423d-bf41-4cb22e34c1f3\",\"owner\":[],\"postedDate\":\"November 26th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-12-24T12:24:52+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-26 10:52:48\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7981845\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7981845\",\"identity\":\"rs-7981845\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}