CR1(+) Tumor-Associated Macrophages Orchestrate an Immunosuppressive Niche in Hepatocellular Carcinoma: A Genetic and Multi-omics Dissection

CR1(+) Tumor-Associated Macrophages Orchestrate an Immunosuppressive Niche in Hepatocellular Carcinoma: A Genetic and Multi-omics Dissection | 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 CR1(+) Tumor-Associated Macrophages Orchestrate an Immunosuppressive Niche in Hepatocellular Carcinoma: A Genetic and Multi-omics Dissection Zhengjian Wang, Zhe Wang, Xuda Ji, Liping Zhao, Kai Zheng, Wen Yu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8712240/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Hepatocellular carcinoma (HCC) is characterized by a profoundly immunosuppressive tumor microenvironment (TME), which severely limits therapeutic efficacy. By integrating a multi-omics strategy, we identified complement receptor 1 (CR1) as a central regulator of this immunosuppressive milieu. Methods We performed Mendelian randomization (MR) analyses to infer the causal relationship between genetically predicted circulating CR1 levels and HCC risk, followed by metabolite mediation analyses. Bulk, single-cell, and spatial transcriptomic datasets from public cohorts and clinical samples were systematically analyzed to characterize CR1 expression patterns and cellular localization. Tumor microbiome profiling was conducted to explore potential microbe–immune interactions. Functional validation was performed using THP-1–derived macrophages, including gain- and loss-of-function experiments, phagocytosis assays, and macrophage–CD8⁺ T-cell co-culture systems. Results MR analysis identified a causal link between genetically predicted circulating CR1 levels and increased HCC risk (IVW OR = 0.907, P = 0.02), with specific blood metabolites potentially mediating this effect. Multi-omics profiling revealed that CR1 was overexpressed specifically in tumor tissues and predominantly enriched in tumor-associated macrophages (TAMs), where its expression strongly correlated with M2 polarization signatures. Elevated CR1 expression correlated with reduced CD8⁺ T cell infiltration, increased T cell exhaustion, and poorer patient survival. Spatial transcriptomics further confirmed significant co-localization of CR1 with the M2 marker CD206. Functionally, CR1 overexpression reprogrammed macrophages into an M2-like immunosuppressive phenotype, characterized by upregulation of CD206 and IL-10 and enhanced phagocytic activity, while CR1 knockdown promoted an M1-like state. Crucially, in co-culture systems, CR1-high macrophages markedly inhibited CD8⁺ T cell proliferation and effector functions—including IFN-γ production and granzyme B expression—concomitant with increased PD-L1 expression. Tumor microbiome analysis extended our findings, suggesting potential crosstalk between intratumoral bacteria and the CR1-driven immunosuppressive axis. Conclusions Our study identifies CR1 as an environmentally responsive master regulator that reshapes the immunological landscape of HCC by reprogramming TAMs, thereby positioning CR1 as a highly promising therapeutic target for restoring antitumor immunity. Severe acute pancreatitis–associated acute lung injury (SAP-ALI) Lung microbiome Gut–lung axis Microbial dysbiosis Immunometabolism Metabolic reprogramming Therapeutic targeting Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide and is closely associated with chronic liver diseases [ 1 – 3 ]. Despite substantial advances in early diagnosis and locoregional therapies, systemic treatment options for patients with advanced HCC remain limited, and clinical outcomes are still poor [ 2 , 4 ]. In recent years, immune checkpoint inhibitors have reshaped the therapeutic landscape of advanced HCC; however, overall response rates remain unsatisfactory, and both primary and acquired resistance are common [ 5 , 6 ]. The fundamental basis of this clinical challenge lies in the profoundly immunosuppressive tumor microenvironment (TME) of HCC—a complex and dynamic ecosystem composed of tumor cells, immune cells, stromal components, and non-cellular elements [ 7 , 8 ]. Therefore, elucidating the core mechanisms governing immunosuppressive networks within the TME has become a critical prerequisite for improving the efficacy of immunotherapy in HCC. Within the HCC TME, tumor-associated macrophages (TAMs) and CD8⁺ T cells represent two pivotal cellular populations that shape immune phenotypes and determine clinical outcomes [ 9 ]. Infiltrating macrophages are typically polarized toward an immunosuppressive M2-like phenotype, characterized by the secretion of interleukin-10 (IL-10) and transforming growth factor-β (TGF-β), high expression of co-inhibitory molecules such as programmed death-ligand 1 (PD-L1), and recruitment of regulatory T cells (Tregs), collectively fostering an immune-privileged niche that ultimately drives functional exhaustion of CD8⁺ T cells [ 10 , 11 ]. Although the protumorigenic roles of TAMs are well recognized [ 10 , 12 ], the upstream genetic signals and intrinsic molecular mechanisms that drive their M2 polarization—particularly how these processes are coupled to host genetic background—remain incompletely understood. Meanwhile, accumulating genetic evidence indicates that host genetic variants influence cancer susceptibility through regulation of gene expression [ 13 , 14 ]. However, how inherited risk is translated into specific functional immune phenotypes within the TME, including the involvement of intermediary processes such as metabolic reprogramming, remains largely unclear [ 15 , 16 ]. Complement receptor 1 (CR1) is a multifunctional receptor of the complement system, classically recognized for its roles in immune complex clearance and regulation of complement activation [ 17 , 18 ]. Emerging evidence suggests that CR1 is aberrantly expressed in several types of cancer and may be associated with tumor progression [ 17 , 19 ]. However, the specific functions of CR1 in innate immune cells—particularly macrophages—and its role within the TME remain largely unexplored [ 20 ]. Our preliminary bioinformatic analyses identified CR1 as a candidate gene potentially causally associated with HCC risk. Notably, CR1 expression exhibited pronounced cellular specificity, being predominantly enriched in macrophages, and marked spatial heterogeneity within tumor tissues, where it was closely linked to immunosuppressive signaling programs [ 21 ]. Collectively, these observations point to an untested hypothesis that CR1 may serve as a central molecular hub linking host genetic background, immunometabolic regulation, macrophage polarization, and T-cell dysfunction. Accordingly, this study aimed to systematically delineate the role of CR1 in immune evasion in HCC by implementing an integrative research strategy spanning from genetic causal inference to molecular and functional validation. First, we applied Mendelian randomization (MR) and mediation analyses to construct a genetic causal axis from CR1 to specific circulating metabolites and ultimately to HCC risk. We then integrated multi-omics datasets to comprehensively characterize the associations between CR1 expression, immunosuppressive TME features, and clinical outcomes. Finally, through a series of functional experiments—including tissue-based validation, cellular perturbation assays, and co-culture systems—we sought to demonstrate: (i) the enrichment of CR1⁺ TAMs in advanced HCC tissues and their spatial association with CD8⁺ T-cell exhaustion; (ii) the role of CR1 in reprogramming macrophage M2 polarization and associated functions, including phagocytic activity; and (iii) the suppressive effects of CR1-induced macrophages on CD8⁺ T-cell proliferation and effector functions. This study provides the first systematic evidence identifying CR1 as a central regulator of the immunosuppressive microenvironment in HCC, offering a novel “genetic–metabolic–immune” mechanistic framework for understanding immune escape and highlighting CR1 as a potential therapeutic target to overcome resistance to immunotherapy. 2. Materials and Methods 2.1. Data sources and preprocessing 2.1.1. Exposure, mediator, and outcome datasets All genetic exposure, mediator, and outcome datasets used in this study were obtained from publicly available resources. Protein quantitative trait loci (pQTL) data were derived from the FinnGen study (Release 10; https://www.finngen.fi/en ), based on a SomaScan v4 plasma proteomics genome-wide association study (GWAS) of approximately 830 individuals of Finnish ancestry, covering 7,156 proteins [ 22 ]. Expression quantitative trait loci (eQTL) data were obtained from the eQTLGen Consortium ( https://www.eqtlgen.org ), using the Phase I cross-tissue dataset derived from a large-scale meta-analysis of blood transcriptomes from 31,684 individuals [ 23 ]. Summary-level GWAS data for circulating metabolites were retrieved from the Canadian Longitudinal Study on Aging, which included 1,091 metabolites and 309 metabolite ratios measured in 8,299 participants [ 24 ]. GWAS summary statistics for HCC were obtained from FinnGen Release 12 under the phenotype code C3_HEPATOCELLU_CARC_EXALLC, including 947 clinically validated HCC cases and 378,749 controls, all of European ancestry. 2.1.2. Transcriptomic datasets Transcriptomic datasets used for functional characterization were obtained from three sources. Bulk RNA sequencing data were downloaded from The Cancer Genome Atlas (TCGA) data portal ( https://portal.gdc.cancer.gov/ ) for HCC. Level 3 HTSeq–Fragments Per Kilobase of transcript per Million mapped reads (FPKM)–normalized expression profiles and corresponding clinical information were retrieved, comprising 374 tumor samples and 50 adjacent non-tumorous liver tissues. Single-cell RNA sequencing (scRNA-seq) data were obtained from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ) under accession number GSE149614, which includes 21 samples (13 tumor tissues and 8 normal liver tissues). Spatial transcriptomics (ST) data were also downloaded from the GEO database under accession number GSE245908, consisting of two spatially profiled HCC tissue sections. 2.2. MR and mediation analyses 2.2.1. Instrumental variable selection and causal estimation To infer potential causal relationships between exposures and HCC, a two-sample MR framework was applied [ 25 ]. For each exposure (pQTLs, eQTLs, and circulating metabolites), single-nucleotide polymorphisms (SNPs) associated at a genome-wide significance threshold of P < 1 × 10⁻⁵ were selected as the initial candidates instrumental variables (IVs). Linkage disequilibrium (LD) clumping was then performed using PLINK software with an r² threshold of 0.001 and a 10,000-kb window to ensure the independence of IVs. The F-statistic was calculated for each IV, and only strong instruments with F > 10 were retained for subsequent analyses. The inverse-variance weighted (IVW) method was used as the primary approach to estimate causal effects, complemented by MR-Egger regression, the weighted median method, and the weighted mode method as sensitivity analyses [ 26 ]. When an exposure was instrumented by a single SNP, causal estimates were derived using the Wald ratio method. 2.2.2. Sensitivity analyses and mediation analyses To evaluate the robustness of the MR findings, multiple sensitivity analyses were conducted for all significant causal associations. Heterogeneity among IVs was assessed using Cochran’s Q test. Horizontal pleiotropy was assessed using the MR-Egger intercept test and the MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) global test [ 27 ]. Leave-one-out analyses were further performed to determine whether the overall causal estimates were disproportionately driven by any single SNP. For exposure–outcome pairs with significant causal effects identified in the primary MR analyses, a two-step mediation analysis was subsequently performed to investigate the potential mediating role of circulating metabolites. The indirect (mediated) effect was calculated as the product of the path coefficients β₁ (exposure → mediator) and β₂ (mediator → outcome), and the proportion mediated was defined as (β₁ × β₂)/β₃, where β₃ represents the total effect of the exposure on the outcome. A metabolite was considered a reliable mediator only if all of the following criteria were satisfied: (i) both the exposure–mediator and mediator–outcome associations reached statistical significance using the IVW method ( P 0.95) [ 28 ]; and (iii) reverse MR analyses provided no evidence supporting a causal effect of the metabolite on the exposure or of the outcome on the metabolite. 2.3. Multi-omics data analyses 2.3.1. Drug sensitivity analysis To evaluate the potential association between key gene expression and chemotherapeutic drug sensitivity, drug response data were obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) database ( https://www.cancerrxgene.org/ ). Predictive models were constructed using the R package pRRophetic , which applies elastic net regression trained on GDSC cancer cell line transcriptomic profiles and corresponding drug response data. The trained models were then used to predict the half-maximal inhibitory concentration (IC50) values of chemotherapeutic agents in the TCGA HCC cohort. Model training was performed using 10-fold cross-validation to ensure robustness and generalizability, and batch effects were corrected using the ComBat algorithm. Differences in predicted IC50 values between high- and low-expression groups of key genes were compared to assess the impact of gene expression levels on chemotherapeutic sensitivity. 2.3.2. Molecular docking Three-dimensional protein structures of the key genes were retrieved from the AlphaFold database ( https://alphafold.com/ ) [ 29 ]. The chemical structures of candidate compounds were obtained from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ). Molecular docking was performed using AutoDock Vina (v1.2.3), with each docking task repeated nine times [ 30 ]. The binding conformation with the lowest predicted binding energy was selected as the optimal model. Docking results were visualized using PyMOL software. 2.3.3. Intratumoral microbiome analysis Intratumoral microbiome profiling was based on previously published microbial abundance data processed using the Kraken algorithm [ 31 ]. Three categories of association analyses were systematically performed. First, Spearman correlation analyses were conducted between genus-level microbial abundances and immune cell infiltration scores estimated using the algorithm. Second, correlations between the expression levels of key genes (CR1, YME1L1, and MSI2) and microbial abundances were assessed. Third, expression profiles of previously reported cytotoxic T lymphocyte (CTL)–related gene sets and immunoregulatory molecules were integrated and correlated with microbial abundances to infer the potential impact of microbial composition on antitumor immune activity [ 32 , 33 ]. 2.3.4. Single-cell and spatial transcriptomic analyses scRNA-seq data were processed in the R environment using the Seurat package (v4.3.0) [ 34 ]. Rigorous quality control was performed by retaining cells with 200–2,500 detected genes (nFeature_RNA), mitochondrial gene expression < 10%, and total unique molecular identifier (UMI) counts within ± 3 median absolute deviations of the median. Potential doublets were identified and removed using DoubletFinder (v2.0.4) [ 35 ]. Gene expression values were normalized using the LogNormalize method with a scale factor of 10,000, and highly variable genes were identified. After regressing out mitochondrial gene content, ribosomal gene content, and cell cycle effects, batch effects across samples were corrected using the Harmony algorithm [ 36 ]. Principal component analysis (PCA) was subsequently performed, and the top 30 principal components were used for Uniform Manifold Approximation and Projection (UMAP) visualization and Louvain clustering. Cell type annotation was performed by integrating information from the CellMarker and PanglaoDB databases, published literature, and automated annotation using SingleR, and was further confirmed by examining the expression of canonical markers (e.g., EPCAM for epithelial cells, CD68 for macrophages, and CD3D for T cells). ST data were processed using Seurat (v4.3.0). Raw UMI count matrices were normalized and variance-stabilized using the SCTransform function. PCA and UMAP-based clustering were performed based on the top 3,000 highly variable genes. To deconvolute the cellular composition of each spatial spot, the RCTD (Robust Cell Type Decomposition) algorithm was applied using the annotated scRNA-seq dataset as the reference, enabling estimation of the relative proportions of distinct cell types within each spot [ 37 ]. 2.4. Experimental validation 2.4.1. Clinical sample collection and processing This study was conducted in strict accordance with ethical guidelines. A total of 30 paired HCC tumor tissues and matched adjacent non-tumorous liver tissues (located > 2 cm from the tumor margin) were collected from patients undergoing surgical resection. Written informed consent was obtained from all participants prior to surgery. The study protocol was approved by the Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University (approval no. SWYX:NO.2025 − 701). Immediately after resection, tissue specimens were divided into three portions according to experimental requirements: (i) fresh tissues were processed immediately for flow cytometric analysis; (ii) a portion of the tissues was snap-frozen in liquid nitrogen and stored at − 80°C for subsequent RNA and protein extraction; and (iii) the remaining tissues were fixed in 4% paraformaldehyde, paraffin-embedded, and sectioned into consecutive 4-µm slices for immunohistochemistry (IHC) and immunofluorescence (IF) analyses. 2.4.2. Immunohistochemistry and immunofluorescence Paraffin-embedded sections were baked at 60°C for 2 h, followed by standard deparaffinization and rehydration. Antigen retrieval was performed by heat induction in sodium citrate buffer (pH 6.0; Servicebio, G1202) using a microwave oven. Endogenous peroxidase activity was blocked by incubation with 3% hydrogen peroxide in methanol for 25 min at room temperature in the dark. Sections were then blocked with 5% bovine serum albumin (BSA; Servicebio, G5001) for 30 min. Slides were incubated overnight at 4°C in a humidified chamber with the following primary antibodies: rabbit anti-human CR1 monoclonal antibody (Abcam, ab235882; 1:200), mouse anti-human CD68 monoclonal antibody (Dako, M0814; 1:100), rabbit anti-human CD206 monoclonal antibody (Abcam, ab64693; 1:200), and mouse anti-human CD8A monoclonal antibody (Dako, M7103; 1:100). Phosphate-buffered saline (PBS) was used instead of the primary antibody as a negative control. After washing with PBS, sections were incubated with the corresponding horseradish peroxidase (HRP)–conjugated secondary antibodies (goat anti-rabbit/mouse IgG; Servicebio, GB23303/GB23301) for 50 min at room temperature. Signals were developed using a 3,3′-diaminobenzidine (DAB) kit (Servicebio, G1211), followed by hematoxylin counterstaining (Servicebio, G1004), differentiation with acid alcohol, and bluing with ammonia water. Sections were dehydrated through graded ethanol, cleared in xylene, and mounted with neutral balsam (Servicebio, G1401). For double immunofluorescence staining, a similar procedure was followed, except that fluorophore-conjugated secondary antibodies were used, and nuclei were counterstained with 4′,6-diamidino-2-phenylindole (DAPI). All stained slides were independently evaluated by two experienced pathologists blinded to clinical and pathological information. Expression levels of CR1, CD68, and CD206 were semi-quantitatively assessed using the H-score system: $$\:\text{H}-\text{s}\text{c}\text{o}\text{r}\text{e}=\sum\:({P}_{i}\times\:i)$$ where P i represents the percentage of cells stained at each intensity level (i = 0, negative; 1, weak; 2, moderate; 3, strong). Immunofluorescence results were quantified as the proportion of positive signal (positive area or positive cell percentage) [ 38 ]. 2.4.3. Cell culture and induction of macrophage differentiation The human monocytic cell line THP-1 (ATCC TIB-202) was cultured in RPMI-1640 medium (Gibco, 11875093) supplemented with 10% fetal bovine serum (FBS; Gibco, 10270106) and 1% penicillin–streptomycin (Gibco, 15140122) at 37°C in a humidified incubator with 5% CO₂. To induce macrophage differentiation, cells were seeded at a density of 5 × 10⁵ cells/mL and treated with 100 ng/mL phorbol 12-myristate 13-acetate (PMA; Sigma-Aldrich, P8139) for 48 h. The medium was then replaced with fresh complete medium and cells were cultured for an additional 24 h. Adherent cells exhibiting macrophage-like morphology were defined as M0 macrophages and used for subsequent experiments [ 39 ]. 2.4.4. Gene knockdown and overexpression CR1 expression was manipulated using RNA interference and plasmid-mediated overexpression approaches. THP-1-derived macrophages were transfected with small interfering RNAs (siRNAs) targeting human CR1 or negative control siRNA (si-NC) at a final concentration of 50 nM using Lipofectamine RNAiMAX (Invitrogen, 13778150), according to the manufacturer’s instructions. For overexpression experiments, the full-length human CR1 coding sequence was cloned into the pcDNA3.1 expression vector, and cells were transfected using Lipofectamine 3000 (Invitrogen, L3000015), with empty vector serving as a control. All siRNAs were synthesized by RiboBio (Guangzhou, China). Cells were harvested 48 h after transfection, and knockdown or overexpression efficiency was confirmed by quantitative real-time PCR (qRT-PCR) and Western blotting. The sequences of all siRNAs used in this study are listed below(Table 1 ). Table 1 Sequences of siRNAs targeting CR1 siRNA Forward (5′–3′) Reverse (5′–3′) si-CR1#1 GCAACAUCAUUGAGCUCAATT UUGAGCUCAUGAUGUUGCTT si-CR1#2 CCUGAAGAUGGAGCAGUUUTT AAACUGCUCCAUCUUCAGGTT si-NC UUCUCCGAACGUGUCACGUTT ACGUGACACGUUCGGAGAATT 2.4.5. RNA extraction and qRT-PCR Total RNA was extracted from cultured cells using TRIzol reagent (Invitrogen, 15596026) according to the manufacturer’s instructions. RNA concentration and purity were determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific) by measuring the optical density at 260/280 nm. For complementary DNA (cDNA) synthesis, 1 µg of total RNA was treated with genomic DNA eraser and reverse-transcribed using the PrimeScript RT reagent Kit with gDNA Eraser (Takara, RR047A). QRT-PCR was performed using TB Green Premix Ex Taq II (Tli RNaseH Plus) (Takara, RR820A) on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems). Each 20-µL reaction mixture contained 10 µL of 2× TB Green Premix Ex Taq II, 0.8 µL of forward primer (10 µM), 0.8 µL of reverse primer (10 µM), 2 µL of cDNA template, and 6.4 µL of RNase-free water. The amplification conditions were as follows: initial denaturation at 95°C for 30 s; 40 cycles of 95°C for 5 s and 60°C for 30 s; followed by a melt-curve analysis from 60°C to 95°C to verify product specificity. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the internal reference gene. Relative gene expression levels were calculated using the 2^−ΔΔCt method. The primer sequences used in this study are listed below(Table 2 ). Table 2 Sequences of primers used for qRT-PCR Gene Forward (5′–3′) Reverse (5′–3′) CR1 CACCATGGCCTCTGTGTCTA GGCAGGTAGGTGTTGTCAGG GAPDH GGAGCGAGATCCCTCCAAAAT GGCTGTTGTCATACTTCTCATGG 2.4.6. Western blot analysis Cells were lysed on ice for 30 min using radioimmunoprecipitation assay (RIPA) buffer (Beyotime, P0013B) supplemented with 1 mM phenylmethylsulfonyl fluoride (PMSF; Beyotime, ST506) and a protease inhibitor cocktail (Roche, 4693132001). Lysates were centrifuged at 12,000 rpm for 15 min at 4°C, and the supernatants were collected. Protein concentrations were determined using a bicinchoninic acid (BCA) protein assay kit (Beyotime, P0010). Equal amounts of protein (30 µg) were mixed with 6× loading buffer and denatured by boiling at 100°C for 10 min. Proteins were separated by 8% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) and subsequently transferred onto polyvinylidene difluoride (PVDF) membranes (Millipore, IPVH00010) using a wet transfer system at a constant current of 250 mA for 90 min. Membranes were blocked with Tris-buffered saline containing 0.1% Tween-20 (TBST) supplemented with 5% non-fat milk for 1 h at room temperature. The membranes were incubated overnight at 4°C with the following primary antibodies: rabbit anti-CR1 antibody (Abcam, ab235882; 1:1000) and mouse anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody (Proteintech, 60004-1-Ig; 1:5000). After three washes with TBST (10 min each), membranes were incubated with the corresponding HRP–conjugated goat anti-rabbit or anti-mouse secondary antibodies (Abcam, ab6721/ab6789; 1:5000) for 1 h at room temperature. Following extensive washing with TBST, protein bands were visualized using an enhanced chemiluminescence (ECL) detection kit (NCM Biotech, P10300) and imaged using a ChemiDoc MP Imaging System (Bio-Rad). Band intensities were quantified using ImageJ software (v1.53). 2.4.7. Macrophage polarization, phagocytosis and co-culture assays To evaluate the effects of CR1 on macrophage polarization, genetically manipulated THP-1–derived macrophages were stimulated with either 100 ng/mL lipopolysaccharide (LPS; Sigma, L4391) combined with 20 ng/mL interferon-γ (IFN-γ; PeproTech, 300-02) to induce M1 polarization, or with 20 ng/mL interleukin-4 (IL-4; PeproTech, 200-04) combined with 20 ng/mL interleukin-13 (IL-13; PeproTech, 200 − 13) to induce M2 polarization. After 24 h of stimulation, macrophage polarization status was assessed by flow cytometric analysis of surface markers (CD86 for M1 and CD206 for M2) or by qRT-PCR analysis of polarization-related genes (tumor necrosis factor-α [TNF-α] for M1 and IL-10 for M2). Macrophage phagocytic capacity was evaluated using fluorescein isothiocyanate (FITC)–labeled phagocytic particles incubated with macrophages for 2 h. The proportion of FITC-positive cells was quantified by flow cytometry as an indicator of phagocytic activity [ 40 ]. To investigate the effects of CR1-modulated macrophages on T-cell function, an in vitro co-culture system was established. CD8⁺ T cells were isolated from peripheral blood mononuclear cells (PBMCs) obtained from healthy donors using CD8 MicroBeads (Miltenyi Biotec, 130-045-201) and activated with anti-CD3/CD28 antibodies (1 µg/mL; Miltenyi Biotec, 130-093-387). Macrophages were directly co-cultured with activated CD8⁺ T cells at a ratio of 1:2 (macrophages:CD8⁺ T cells). After 72 h of co-culture, T cells were harvested and stained with carboxyfluorescein succinimidyl ester (CFSE; Invitrogen, C34554) to assess cell proliferation. Intracellular expression of effector molecules, including IFN-γ and granzyme B, was subsequently analyzed by flow cytometry [ 41 ]. 2.5. Statistical analysis For MR analyses, statistical significance was defined as P < 0.05 using the IVW method, and multiple testing was corrected using the false discovery rate (FDR) approach with the Benjamini–Hochberg procedure. All in vitro experimental data were obtained from at least three independent biological replicates and are presented as mean ± standard error of the mean (SEM). For normally distributed data, comparisons between two groups were performed using a two-tailed Student’s t-test, while comparisons among multiple groups were conducted using one-way analysis of variance (ANOVA), followed by Tukey’s post hoc test when appropriate. Non-normally distributed data were analyzed using the Mann–Whitney U test (two groups) or the Kruskal–Wallis test (multiple groups). Correlation analyses were performed using Pearson’s correlation or Spearman’s rank correlation, depending on data distribution and variance homogeneity. All statistical analyses were conducted using R software (v4.2.0) or GraphPad Prism 9. A two-sided P < 0.05 was considered statistically significant. 3. Results 3.1. MR analysis of pQTLs and HCC Based on the HCC GWAS summary statistics (outcome ID: C3_HEPATOCELLU_CARC_EXALLC), a total of 545 pQTL–outcome associations were extracted using the extract_instruments and extract_outcome_data functions. MR analyses were subsequently performed using the IVW method, identifying 316 pQTL-associated genes that showed significant causal associations with HCC risk (Fig. 1 A, IVW p < 0.05). The complete forest plots are provided in Supplementary Figure S1 . Among these, 138 genes (e.g., BST2, DCN, and PGLYRP1) were associated with an increased risk of HCC, whereas 178 genes (e.g., CLIC4, DMC1, and NEIL1) were associated with a decreased risk of HCC. Sensitivity analyses demonstrated that the overall causal estimates remained stable after sequential removal of individual SNPs, indicating that the results were not driven by any single instrument and were therefore robust. 3.2. MR analysis of eQTLs and HCC To further identify key candidate genes, eQTL analyses were performed for the 316 pQTL-associated genes, resulting in the extraction of 10 eQTLs–outcome associations. Subsequent MR analyses identified eight genes that were significantly associated with HCC risk (Fig. 1 B, IVW P < 0.05). Specifically, higher genetically predicted expression levels of KREMEN1, CR1, WBP2, YME1L1, and MSI2 were significantly associated with an increased risk of HCC (odds ratio [OR] = 1.309–2.407). In contrast, increased expression of CCDC24, PCSK7, and CTSH was associated with a reduced risk of HCC (OR = 0.737–0.819). Leave-one-out sensitivity analyses showed that the causal estimates remained consistent after the exclusion of each individual SNP in turn, confirming the robustness of these associations (Supplementary Fig. S2 A–H). 3.3. Mediation analysis: establishing a gene–metabolite–HCC causal axis 3.3.1. MR analysis of metabolites and HCC To identify potential causal metabolites associated with HCC at the metabolic level, HCC GWAS summary statistics (outcome ID: C3_HEPATOCELLU_CARC_EXALLC) were used to extract metabolite–outcome pairs through the extract_instruments and extract_outcome_data functions. A total of 118 metabolite–outcome pairs were obtained. Two-sample Mendelian randomization (MR) analyses were subsequently performed to systematically evaluate the causal relationships between circulating metabolites and HCC risk. This analysis identified 64 metabolite–HCC pairs showing significant causal associations (IVW P < 0.05) (Supplementary Fig. S3 ). Among these, several metabolites were negatively associated with HCC risk, including the 3-methyl-2-oxovalerate to 4-methyl-2-oxopentanoate ratio, serine to alpha-ketobutyrate ratio, 1-linoleoylglycerol (18:2), valine levels, and phosphoethanolamine levels. In contrast, other metabolites—such as arachidonoylcarnitine (C20:4), 2-hydroxybutyrate/2-hydroxyisobutyrate, cystathionine levels, and the aspartate to asparagine ratio—were positively associated with increased HCC risk. Leave-one-out sensitivity analyses showed that sequential removal of any single SNP did not materially alter the overall effect estimates or confidence intervals, indicating that the 64 identified metabolite–HCC causal associations were robust and reliable. 3.3.2. MR analysis of eQTLs and metabolites To further elucidate how genetic regulation may influence HCC development through metabolic pathways, the eight significant eQTL-associated genes identified above were paired as exposures with the 64 HCC-related metabolites as outcomes. A total of 25 eQTL–metabolite pairs were extracted. Two-sample MR analyses identified eight significant causal associations (IVW P < 0.05) (Fig. 1 C). These causal links primarily involved five key genes (YME1L1, MSI2, PCSK7, KREMEN1, and CR1) and eight metabolites, including arachidonoylcarnitine (C20:4), 2-hydroxybutyrate/2-hydroxyisobutyrate, carotene diol (1), the threonine to alpha-ketobutyrate ratio, the citrate to 4-hydroxyphenylpyruvate ratio, 4-methoxyphenol sulfate, the aspartate to asparagine ratio, and the glutarate (C5-DC) to salicylate ratio. Leave-one-out sensitivity analyses further demonstrated that the overall effect directions and confidence intervals remained stable after sequential exclusion of individual SNPs, with no substantial deviations observed, supporting the robustness of the identified eQTL–metabolite causal relationships (Supplementary Fig. S4 A–H). 3.3.3. Metabolite-mediated effects and reverse causality analyses After establishing the causal relationships between eQTLs and metabolites, as well as between metabolites and HCC, we further investigated the mediating roles of metabolites in the gene–HCC axis. The results indicated that 2-hydroxybutyrate/2-hydroxyisobutyrate levels, the C5-DC to salicylate ratio, and the aspartate to asparagine ratio may serve as key mediators linking CR1, YME1L1, and MSI2 to HCC risk. Colocalization analyses performed using coloc demonstrated strong evidence of shared causal variants at the eQTL–disease level for these three genes (SNP.PP.H4 > 0.95) (Fig. 1 D–F), indicating that the same genetic variants jointly influence gene expression and disease susceptibility, thereby supporting a common genetic architecture. To further validate the directionality and robustness of the inferred causal relationships, reverse Mendelian randomization analyses were conducted for the three genes (Fig. 1 G). No significant reverse causal effects were observed for CR1, MSI2, or YME1L1 ( P = 0.962, 0.149, and 0.083, respectively), providing additional support for a unidirectional causal pathway from genetically regulated gene expression to metabolic alterations and ultimately to HCC development. 3.4. Clinical relevance and drug response analyses of key genes 3.4.1. Associations between key gene expression and chemotherapeutic drug sensitivity Based on the GDSC database, drug response in tumor samples was predicted using the pRRophetic R package, and correlations between the expression levels of the key genes (CR1, YME1L1, and MSI2) and IC50 were analyzed. The results showed that the expression levels of the key genes were significantly associated with the predicted IC50 values of multiple anticancer agents (Supplementary Fig. S5 A–F). Specifically, high expression of CR1, YME1L1, and MSI2 was significantly associated with increased sensitivity (lower IC50 values) to bleomycin, bosutinib, and doxorubicin (Supplementary Fig. S5 A, C, F). In contrast, for bortezomib, bryostatin-1, and dasatinib, the relationships between gene expression and IC50 values exhibited drug- and gene-specific patterns (Supplementary Fig. S5 B, D, E). Collectively, these findings suggest that the expression status of these key genes may serve as potential predictors of chemotherapeutic response in HCC. 3.4.2. Associations between key gene expression and clinicopathological features To evaluate the clinical relevance of the key genes, clinicopathological characteristics from the TCGA-HCC cohort—including age, sex, tumor stage, T stage, N stage, and M stage—were systematically analyzed. Using boxplot visualization and appropriate statistical tests (Student’s t-test or Kruskal–Wallis test), we found that the expression levels of CR1, YME1L1, and MSI2 were all significantly associated with tumor stage and T stage ( P < 0.05) (Supplementary Fig. S6 A–X). Notably, CR1 expression showed strong positive correlations with tumor stage ( P = 4.5 × 10⁻³), tumor grade ( P < 1.0 × 10⁻⁴), T stage ( P = 9.8 × 10⁻³), and N stage ( P = 2.72 × 10⁻²). In advanced-stage (III–IV) or highly invasive (T3–T4) tumors, the expression levels of these three genes were consistently elevated, indicating that they may play important roles in HCC progression and invasiveness. 3.4.3. Molecular docking analyses validating the potential interactions of CR1, YME1L1, and MSI2 with dasatinib Given that drug sensitivity analyses indicated that dasatinib, a multi-target tyrosine kinase inhibitor, exhibited significant IC50 differences between high- and low-expression groups of CR1, YME1L1, and MSI2 (Supplementary Fig. S6 E), we further explored the potential binding modes between dasatinib and these proteins at the structural level. Protein–compound pairs were defined as follows: CR1 (P17927)–dasatinib, YME1L1 (Q96TA2)–dasatinib, and MSI2 (Q96DH6)–dasatinib. Docking binding energies are summarized in Supplementary Table S1 , and representative docking conformations are shown in Supplementary Fig. S7 A–C. The predicted binding energies were − 7.0 kcal/mol for CR1–dasatinib, − 7.3 kcal/mol for YME1L1–dasatinib, and − 6.3 kcal/mol for MSI2–dasatinib. Dasatinib was predicted to stably occupy the binding pockets of CR1, YME1L1, and MSI2, forming multiple hydrogen bonds and hydrophobic interactions. These results suggest potential direct interactions between dasatinib and these proteins, providing structural evidence supporting their candidacy as therapeutic targets. 3.5. Key gene expression reshapes the association patterns between the intratumoral microbiome and the immune microenvironment To investigate the roles of the key genes (CR1, YME1L1, and MSI2) within the tumor microenvironment, we analyzed the relationships between their expression levels and intratumoral microbial features. The results showed that the expression levels of the key genes did not significantly alter overall microbial α-diversity (Fig. 2 A–C), but markedly influenced the interactions between the microbiome and the immune system. Specifically, the abundances of certain intratumoral bacterial genera were broadly correlated with immune cell infiltration levels (Fig. 2 D). For example, Prochlorococcus and Succinimonas were positively correlated with subsets of innate immune cells, whereas Campylobacter and Desulfotalea were negatively correlated with adaptive immune populations, including γδ T cells and Tregs. Further network analyses demonstrated that CTL immune escape–related genes were primarily embedded within interaction networks involving specific microbial clusters (Fig. 2 E), whereas immunoregulatory genes exhibited more complex and widespread microbial interaction patterns (Fig. 2 F). These findings indicate that functionally distinct key genes may participate in tumor microenvironment regulation through differential “microbiome–immune” coupling pathways. 3.6. Single-cell and spatial transcriptomic analyses reveal the cellular specificity and spatial heterogeneity of key genes To systematically characterize the expression patterns of the key genes (CR1, YME1L1, and MSI2) in the HCC microenvironment at both the cellular and tissue levels, we integrated scRNA-seq and spatial transcriptomic datasets. 3.6.1. Single-cell level: key genes exhibit cell type–specific expression patterns After rigorous quality control of high-quality single-cell data comprising 53,474 cells, the distributions and correlations of nCount_RNA, nFeature_RNA, percent.mt, and percent.ribo across samples were visualized (Supplementary Fig. S8 A, B). A total of 2,000 highly variable genes were identified (Supplementary Fig. S8 D), and the data were subsequently normalized and subjected to dimensionality reduction. PCA and Elbow plots were used to determine the major principal components (Supplementary Fig. S8 C, E), followed by batch-effect correction using the Harmony algorithm (Supplementary Fig. S8 F). UMAP analysis identified 12 distinct cellular clusters (Fig. 3 A), which were annotated into eight major cell types, including natural killer(NK)/T cells, macrophages, monocytes, endothelial cells, fibroblasts, B cells, epithelial cells, and plasma cells (Fig. 3 B). The expression patterns of canonical marker genes for each cell type are shown in Fig. 3 C, and their proportional distributions are summarized in Fig. 3 D, with fibroblasts exhibiting the most pronounced inter-sample variability. The key genes displayed markedly distinct expression preferences across cell populations (Fig. 4 A, B). CR1 exhibited highly cell-specific expression, being predominantly enriched in macrophages and monocytes. MSI2 showed a broader expression pattern, with relatively high expression in epithelial cells and subsets of stromal and myeloid cells, whereas YME1L1 demonstrated the most ubiquitous expression, being detected across nearly all identified cell types. Pathway activity analyses revealed that, compared with MSI2 and YME1L1, high CR1 expression was significantly enriched in multiple immune- and metabolism-related pathways, including complement activation, interferon-γ response, inflammatory response, JAK–STAT3 signaling, TNF-α signaling, KRAS signaling, and apoptosis, underscoring a central role for CR1 in remodeling the tumor immune microenvironment (Fig. 4 C). Furthermore, immune infiltration analysis using CIBERSORT demonstrated that CR1 expression was strongly positively correlated with M2 macrophage infiltration, while being significantly negatively correlated with CD8⁺ T cells, NK cells, and M1 macrophages (Fig. 4 D). In contrast, MSI2 and YME1L1 were more closely associated with NK cells, monocytes, and subsets of adaptive immune cell populations. Collectively, these findings highlight CR1 as a pivotal regulatory factor that may promote the formation of an immunosuppressive microenvironment by coordinating innate and adaptive immune responses. 3.6.2. Spatial transcriptomic level: key genes display distinct spatial heterogeneity Spatial transcriptomic analyses revealed pronounced spatial heterogeneity and cellular architectural organization within hepatocellular carcinoma tissues. After data normalization and dimensionality reduction, six distinct spatial clusters were identified through unsupervised clustering (Fig. 5 A, B). To resolve the cellular composition of each spatial spot, RCTD-based deconvolution was applied, enabling inference of cell-type proportions at each spatial location and construction of high-resolution spatial maps of cellular distribution (Fig. 5 C, D). Expression patterns of canonical marker genes across spatial domains further validated the reliability of the deconvolution results (Fig. 5 E). The key genes CR1, YME1L1, and MSI2 exhibited markedly distinct spatial expression patterns (Fig. 5 F–H). YME1L1 showed the most widespread and highest-intensity expression, MSI2 displayed relatively broad but moderate expression, whereas CR1 demonstrated a highly restricted and spatially localized enrichment pattern. These findings provide direct evidence that CR1 is specifically localized to discrete spatial niches within the tumor microenvironment, suggesting that CR1 may exert unique regulatory functions by acting on specific cellular assemblies or microanatomical domains within local tumor ecosystems. 3.7. Clinical validation: CR1 is highly expressed in tumor tissues and associated with an immunosuppressive microenvironment 3.7.1. CR1 is specifically overexpressed in HCC tumor tissues To validate the expression profile of CR1, paired HCC tumor tissues and adjacent non-tumorous liver tissues were analyzed by IHC, IF, and qRT-PCR. IHC staining demonstrated that CR1 expression intensity was markedly higher in tumor tissues than in adjacent tissues (Fig. 6 A). Quantitative analysis further confirmed a significant increase in both the positive staining area and staining intensity of CR1 in tumor samples (Fig. 6 B). Consistently, IF analysis revealed a significantly elevated CR1 fluorescence signal in tumor tissues (Fig. 6 C), and qRT-PCR showed that CR1 mRNA expression levels were significantly higher in tumor tissues than in paired adjacent tissues (Fig. 6 D). Collectively, these results demonstrate that CR1 is specifically overexpressed within the HCC tumor microenvironment. 3.7.2. High CR1 expression is associated with M2 macrophage enrichment and reduced CD8⁺ T-cell infiltration IF co-staining analyses revealed the spatial relationships between CR1 expression and macrophage subsets. In tumor tissues, CR1 signals exhibited significant co-localization with the M2 macrophage marker CD206, whereas an inverse and mutually exclusive spatial distribution was observed with the M1 macrophage marker CD86 (Fig. 6 E, G). Quantitative analyses demonstrated that the proportion of CD206⁺ M2 macrophages was significantly higher in tumor tissues than in adjacent non-tumorous tissues, while the proportion of CD86⁺ M1 macrophages was reduced (Fig. 6 F, H). Furthermore, based on 30 surgically resected HCC specimens, patients were stratified into CR1-high (n = 15) and CR1-low (n = 15) groups according to the median IHC H-score of CR1 in tumor tissues. Compared with the CR1-low group, patients with high CR1 expression exhibited significantly higher proportions of advanced-stage disease (Stage III–IV) and vascular invasion (Fig. 6 M, N). These findings indicate that high CR1 expression is not only closely associated with an immunosuppressive microenvironment but also significantly correlated with more aggressive tumor phenotypes. To further validate these observations at the single-cell level, flow cytometric analyses were performed. The results showed that the proportion of CD206⁺ M2-like macrophages was significantly increased in tumor tissues compared with adjacent tissues (Fig. 6 I, K), and the percentage of CR1⁺ cells within the macrophage population was also markedly elevated (Fig. 6 J, L), consistent with the spatial immunofluorescence findings. In parallel, the proportion of CD8⁺ T cells among CD45⁺ leukocytes was significantly decreased in tumor tissues (Fig. 7 A, B), indicating insufficient infiltration of cytotoxic T lymphocytes. Collectively, these clinical sample–based data demonstrate that high CR1 expression is tightly associated with M2 macrophage enrichment and reduced CD8⁺ T-cell infiltration, jointly defining an immunosuppressive tumor microenvironment. 3.8. CR1 drives macrophage M2 polarization and suppresses CD8⁺ T-cell function 3.8.1. Validation of CR1 overexpression and knockdown efficiency To verify the efficiency of CR1 genetic manipulation, THP-1–derived M0 macrophages were used to establish three experimental groups: negative control (NC), CR1 overexpression (OE), and CR1 knockdown (KD). QRT-PCR analysis showed that CR1 mRNA expression was significantly increased in the OE group and markedly reduced in the KD group compared with the NC group (Fig. 7 C). Consistently, Western blot analysis demonstrated a pronounced upregulation of CR1 protein in the OE group and a significant downregulation in the KD group, in accordance with the mRNA expression changes (Fig. 7 D). Densitometric quantification further confirmed that these differences were statistically significant (Fig. 7 E). Collectively, these results confirm the successful establishment of CR1 overexpression and knockdown macrophage models at both the transcriptional and protein levels. 3.8.2. CR1 promotes macrophage M2 polarization and enhances phagocytic function Following successful establishment of the CR1-manipulated macrophage models, we next evaluated the effects of CR1 on macrophage polarization phenotypes and functional properties. QRT-PCR analysis showed that, compared with the NC group, mRNA expression of the M2-associated marker IL-10 was significantly upregulated in the CR1 OE group and markedly reduced in the CR1 KD group. In contrast, the M1-associated marker TNF-α was downregulated in the OE group and upregulated in the KD group (Fig. 7 F, G), indicating that CR1 expression drives macrophage polarization toward an M2 phenotype. Flow cytometric analysis of surface markers further demonstrated that CR1 overexpression significantly increased the proportion of CD206⁺ M2 macrophages while reducing the proportion of CD86⁺ M1 macrophages, whereas CR1 knockdown produced the opposite effects (Fig. 7 H, I). Functional assays revealed that CR1 overexpression markedly enhanced macrophage phagocytic capacity, whereas CR1 knockdown significantly impaired this function (Fig. 7 J), indicating that CR1 not only regulates macrophage polarization status but also directly modulates macrophage effector functions. 3.8.3. CR1⁺ macrophages suppress CD8⁺ T-cell proliferation and cytotoxic function To clarify the regulatory effects of CR1⁺ macrophages on CD8⁺ T-cell function, an in vitro co-culture system was established, and T-cell proliferation and effector molecule expression were assessed. CFSE dilution assays showed that, compared with the NC group, CR1-overexpressing macrophages significantly suppressed CD8⁺ T-cell proliferation, whereas CR1 knockdown markedly enhanced T-cell proliferation (Fig. 8 A). With respect to effector function, the proportion of IFN-γ⁺ CD8⁺ T cells was significantly reduced in the CR1 overexpression group and increased in the CR1 knockdown group. Similarly, the percentage of granzyme B⁺ CD8⁺ T cells was decreased in the OE group and elevated in the KD group (Fig. 8 B, C). In the co-culture system, PD-L1 expression in macrophages was significantly upregulated in the CR1 overexpression group and downregulated in the CR1 knockdown group (Fig. 8 D). Collectively, these results indicate that CR1 mediates macrophage-induced suppression of CD8⁺ T-cell function, at least in part, through upregulation of PD-L1. 4. Discussion This study integrates multi-omics analyses with systematic experimental validation to identify CR1 as a key molecular link connecting genetic susceptibility, metabolic dysregulation, and the immunosuppressive tumor microenvironment in HCC. Our findings establish a coherent gene-to-function evidence framework: Mendelian randomization analyses demonstrate a causal effect of CR1 expression on HCC risk; mediation analyses implicate specific circulating metabolites as potential intermediates along this pathway; and multi-omics profiling together with wet-lab experiments collectively reveal that CR1 is highly expressed in TAMs, where it promotes M2 polarization and suppresses CTL function, thereby shaping a tumor microenvironment permissive for immune escape. First, our genetic evidence provides strong causal support for a pathogenic role of CR1 in HCC. By leveraging large-scale population-based eQTL and GWAS datasets, Mendelian randomization analyses effectively minimized confounding bias inherent to conventional observational studies and established genetically predicted high CR1 transcription levels in blood as an independent risk factor for HCC. This finding extends current understanding of the role of the complement system in cancer. The complement cascade is widely regarded as a “double-edged sword,” with previous studies primarily focusing on its direct cytolytic effects mediated by the membrane attack complex or its pro-inflammatory functions [ 18 , 42 ]. In contrast, our results suggest that, as a critical complement regulatory protein and receptor, CR1 may act as an “accomplice” in chronic inflammation–driven hepatocarcinogenesis through more refined immunomodulatory mechanisms. The observed mediation links between CR1 and specific circulating metabolites further imply that genetically determined CR1 levels may indirectly facilitate hepatic tumorigenesis by perturbing systemic immunometabolic homeostasis. More importantly, we delineated the specific cellular basis and molecular mechanisms underlying the pro-tumorigenic functions of CR1 at both the tissue and cellular levels. Single-cell and spatial transcriptomic analyses precisely localized CR1 expression to TAMs, particularly the M2 macrophage subset. Subsequent clinical validation demonstrated that high CR1 expression was significantly associated with more advanced pathological stage, increased infiltration of M2 macrophages, and reduced CTL infiltration in patients with HCC, providing a plausible immunological explanation for the unfavorable clinical outcomes. In vitro functional assays directly confirmed the regulatory role of CR1 in macrophage phenotypic programming: CR1 overexpression drove macrophages toward an M2 phenotype characterized by elevated CD206 and IL-10 expression, whereas CR1 knockdown promoted a shift toward a pro-inflammatory M1 phenotype. These findings elevate CR1 from a conventional complement clearance receptor to an intrinsic regulator of macrophage polarization. Mechanistically, this effect may involve CR1-mediated internalization of complement fragments (such as C3b) and the subsequent activation of downstream signaling pathways, which may intersect with canonical M2-polarizing pathways, including the IL-4/STAT6 axis [ 43 , 44 ], and warrants further investigation. Building upon these observations, we further elucidated the functional consequences of CR1 on tumor immune surveillance. Macrophage–CD8⁺ T-cell co-culture experiments demonstrated that CR1-overexpressing M2 macrophages markedly suppressed CTL proliferative capacity and effector molecule production, including IFN-γ and granzyme B. These findings were fully consistent with the bioinformatic observations that high CR1 expression was negatively correlated with CTL-related gene signatures, thereby establishing a coherent logical continuum from gene expression and cellular phenotype to immune function. This suppressive effect may be attributable to downregulation of co-stimulatory molecules (such as CD86) and upregulation of immune checkpoint molecules (such as PD-L1) on CR1-high macrophages, providing a theoretical rationale for therapeutic strategies that combine targeting of the complement pathway with immune checkpoint blockade [ 8 , 18 , 45 ]. In addition, this study provides a pioneering exploration of the potential links between the intratumoral microbiome and the CR1–immune axis. Our analyses showed that certain bacterial genera positively correlated with CR1 expression were also positively associated with immunosuppressive markers and negatively associated with CTL activity. These findings suggest that specific intratumoral microbial communities may, through currently undefined mechanisms—such as providing persistent antigenic stimulation or secreting bioactive metabolites—sustain or amplify CR1-dependent immunosuppressive programs in macrophages [ 46 ]. Although this association remains preliminary, it offers a novel perspective on “microbiome–immune” interactions in HCC, proposing that microbial communities may reshape the tumor microenvironment by modulating the function of host innate immune receptors such as CR1 [ 47 , 48 ]. Naturally, several limitations of this study should be acknowledged. First, the Mendelian randomization analyses were primarily based on datasets from populations of European ancestry, and the generalizability of these conclusions to other ethnic groups requires further validation. Second, the precise downstream signaling pathways through which CR1 regulates macrophage polarization remain incompletely defined. Finally, the functional and causal relationships between the intratumoral microbiome and CR1 expression need to be validated using more sophisticated in vivo and in vitro models, including advanced co-culture systems and germ-free or gnotobiotic animal models. 5. Conclusion By integrating Mendelian randomization, multi-omics analyses, clinical cohort validation, and in vitro functional experiments, this study systematically elucidates the pivotal role of CR1 in shaping the immunosuppressive tumor microenvironment of HCC. Genetic analyses demonstrated that genetically predicted circulating CR1 levels were significantly associated with HCC risk (IVW OR = 0.907, P = 0.02). Within tumor tissues, CR1 was specifically expressed in tumor-associated macrophages and co-localized with the M2 macrophage marker CD206, and was significantly correlated with CD8⁺ T-cell exhaustion and unfavorable clinical outcomes. Functionally, CR1 not only drives macrophage polarization toward an M2-like phenotype and enhances phagocytic capacity, but also suppresses CD8⁺ T-cell proliferation and effector function through upregulation of PD-L1. Collectively, this study establishes CR1 as a central regulatory factor of the HCC immune microenvironment across the “genetic–expression–functional–clinical” spectrum, providing a strong theoretical rationale for its development as both a prognostic biomarker and a potential therapeutic target in immunotherapy. Abbreviations ANOVA one-way analysis of variance cDNA complementary DNA CFSE carboxyfluorescein succinimidyl ester CR1 complement receptor 1 CTL cytotoxic T lymphocyte eQTLs expression quantitative trait loci FDR false discovery rate FPKM fragments per kilobase of transcript per million mapped reads GDSC Genomics of Drug Sensitivity in Cancer GEO Gene Expression Omnibus GWAS genome-wide association study HCC hepatocellular carcinoma IC50 half-maximal inhibitory concentration IF immunofluorescence IFN-γ interferon-gamma IHC immunohistochemistry IL-4 interleukin-4 IL-10 interleukin-10 IL-13 interleukin-13 IVs instrumental variables IVW inverse-variance weighted LD linkage disequilibrium MR Mendelian randomization MR-PRESSO Mendelian Randomization Pleiotropy RESidual Sum and Outlier PBMCs peripheral blood mononuclear cells PCA principal component analysis PD-L1 programmed death-ligand 1 pQTLs protein quantitative trait loci qRT-PCR quantitative real-time polymerase chain reaction RCTD robust cell type decomposition scRNA-seq single-cell RNA sequencing SEM standard error of the mean SNP single-nucleotide polymorphism ST spatial transcriptomics TAMs tumor-associated macrophages TCGA The Cancer Genome Atlas TGF-β transforming growth factor-beta TME tumor microenvironment TNF-α tumor necrosis factor-alpha Tregs regulatory T cells UMAP Uniform Manifold Approximation and Projection UMI unique molecular identifier. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and relevant ethical guidelines. The study protocol was reviewed and approved by the Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University (approval no. SWYX:NO.2025-701). This study exclusively used retrospectively archived human tissue samples and related clinical data, all of which were de-identified prior to analysis. According to the approval of the ethics committee, the requirement for written informed consent was waived. Consent for publication Not applicable Availability of data and materials The publicly available datasets analyzed in this study are available from the following repositories: FinnGen (https://www.finngen.fi/en), eQTLGen (https://www.eqtlgen.org), The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE149614 and GSE245908. Metabolomics GWAS data were obtained from the Canadian Longitudinal Study on Aging. Drug response data were retrieved from the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/). Protein structures were obtained from the AlphaFold database (https://alphafold.com/), and compound structures were retrieved from PubChem (https://pubchem.ncbi.nlm.nih.gov/). The clinical tissue samples and experimental data generated in this study are not publicly available due to ethical and privacy restrictions, but are available from the corresponding author upon reasonable request and with permission of the Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University. Competing interests The authors declare that they have no competing interests Funding This work was supported by Natural Science Foundation of Shandong Province (Youth Program) (ZR2025QC880). Authors’ contributions Zhengjian Wang and Zhe Wang conceived and designed the study. Zhengjian Wang performed the Mendelian randomization analyses, multi-omics bioinformatics analyses, and statistical analyses. XJ and LZ contributed to data curation, literature review, and interpretation of the results. KZ and WY performed the in vitro experiments, including cell culture, gene manipulation, flow cytometry, qRT-PCR, and Western blot analyses. HZ contributed to immunohistochemistry, immunofluorescence staining, and clinical sample processing. HC participated in data interpretation, figure preparation, and critical revision of the manuscript. FL supervised the entire project and critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. Consent for publication All authors agree to be published. References 1. Mauro E, de Castro T, Zeitlhoefler M, Sung MW, Villanueva A, Mazzaferro V, et al. Hepatocellular carcinoma: Epidemiology, diagnosis and treatment. JHEP Rep. 2025;7(12):101571. 2. Hwang SY, Danpanichkul P, Agopian V, Mehta N, Parikh ND, Abou-Alfa GK, et al. Hepatocellular carcinoma: updates on epidemiology, surveillance, diagnosis and treatment. Clin Mol Hepatol. 2025;31(Suppl):S228-s54. 3. Chan SL, Sun HC, Xu Y, Zeng H, El-Serag HB, Lee JM, et al. 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Novel quantitative immunohistochemistry method using histone H3, family 3B as the internal reference standard for measuring human epidermal growth factor receptor 2 expression in breast cancer. Cancer. 2024;130(S8):1424-34. 39. Liu T, Huang T, Li J, Li A, Li C, Huang X, et al. Optimization of differentiation and transcriptomic profile of THP-1 cells into macrophage by PMA. PLoS One. 2023;18(7):e0286056. 40. Ko JH, Kim HJ, Jeong HJ, Lee HJ, Oh JY. Mesenchymal Stem and Stromal Cells Harness Macrophage-Derived Amphiregulin to Maintain Tissue Homeostasis. Cell Rep. 2020;30(11):3806-20.e6. 41. Zhang F, Jiang Q, Cai J, Meng F, Tang W, Liu Z, et al. Activation of NOD1 on tumor-associated macrophages augments CD8(+) T cell-mediated antitumor immunity in hepatocellular carcinoma. Sci Adv. 2024;10(40):eadp8266. 42. Roumenina LT, Daugan MV, Petitprez F, Sautès-Fridman C, Fridman WH. Context-dependent roles of complement in cancer. Nat Rev Cancer. 2019;19(12):698-715. 43. Chen S, Saeed A, Liu Q, Jiang Q, Xu H, Xiao GG, et al. Macrophages in immunoregulation and therapeutics. Signal Transduct Target Ther. 2023;8(1):207. 44. Yan L, Wang J, Cai X, Liou YC, Shen HM, Hao J, et al. Macrophage plasticity: signaling pathways, tissue repair, and regeneration. MedComm (2020). 2024;5(8):e658. 45. Zheng H, Peng X, Yang S, Li X, Huang M, Wei S, et al. Targeting tumor-associated macrophages in hepatocellular carcinoma: biology, strategy, and immunotherapy. Cell Death Discov. 2023;9(1):65. 46. Feng Y, Han MZ, Zhou YH, Wang YW, Wang Y, Sun T, et al. The multifaceted role of microbiota in liver cancer: pathogenesis, therapy, prognosis, and immunotherapy. Front Immunol. 2025;16:1575963. 47. Sun L, Ke X, Guan A, Jin B, Qu J, Wang Y, et al. Intratumoural microbiome can predict the prognosis of hepatocellular carcinoma after surgery. Clin Transl Med. 2023;13(7):e1331. 48. Jiang F, Dang Y, Zhang Z, Yan Y, Wang Y, Chen Y, et al. Association of intratumoral microbiome diversity with hepatocellular carcinoma prognosis. mSystems. 2025;10(1):e0076524. Supplementary Files SupplementaryFigureS1.pdf SupplementaryFigureS2.pdf SupplementaryFigureS3.pdf SupplementaryFigureS4.pdf SupplementaryFigureS5.pdf SupplementaryFigureS6.pdf SupplementaryFigureS7.pdf SupplementaryFigureS8.pdf FigureAbstract.pdf SupplementaryTableS1.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 25 Feb, 2026 Reviewers invited by journal 25 Feb, 2026 Editor assigned by journal 28 Jan, 2026 First submitted to journal 27 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8712240","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596668563,"identity":"5dd2a322-c250-495d-8f20-fcdf845ed962","order_by":0,"name":"Zhengjian Wang","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhengjian","middleName":"","lastName":"Wang","suffix":""},{"id":596668564,"identity":"b7fc8ac3-4b21-459e-a016-3fa790f27cd3","order_by":1,"name":"Zhe Wang","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Wang","suffix":""},{"id":596668565,"identity":"ef64a999-ddca-422c-ada6-f9d98e6b3a6b","order_by":2,"name":"Xuda Ji","email":"","orcid":"","institution":"Shandong University School of Medicine: Shandong University Cheeloo College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xuda","middleName":"","lastName":"Ji","suffix":""},{"id":596668566,"identity":"d1575f7c-6a1c-4f79-a150-dc458da4bd2c","order_by":3,"name":"Liping Zhao","email":"","orcid":"","institution":"Yunnan Province Third People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liping","middleName":"","lastName":"Zhao","suffix":""},{"id":596668567,"identity":"c771aa4e-870c-41ec-ac5d-8647a27f1b58","order_by":4,"name":"Kai Zheng","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zheng","suffix":""},{"id":596668568,"identity":"dbf0fd50-8a6a-4631-b092-535e82090dc2","order_by":5,"name":"Wen Yu","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Yu","suffix":""},{"id":596668569,"identity":"25d70070-b10f-4d80-8c8b-0a4587cab0cd","order_by":6,"name":"Hanzhe Zhang","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hanzhe","middleName":"","lastName":"Zhang","suffix":""},{"id":596668570,"identity":"b1b4851d-e830-491f-aaec-7419b4d0affa","order_by":7,"name":"Hong Chang","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Chang","suffix":""},{"id":596668571,"identity":"c2abd7a9-ba04-42e8-9434-dcbe9149675c","order_by":8,"name":"Fangfeng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBACPmYGhg9Amh+IGR8wsMmBRSXwaWFjZmCcAaQlGxgYmA0Y2IyJ0MKA0MImQZwWdh7Dhp87aiX4pduvVf4oM4g2OMB88DYPg10ebofxGDb2njkuITnnTNltnnMGuRsOsCVb8zAkF+PRYv6At+1YncGNnLTbjG1/gFp4zKR5GA4kNuCz5W/bMQl7oJbCn20gW/i/EdTSzNtWI2EgkX6MgReshYeNgBa2wmbZtgMSEjdymKVBfpl5mM3Yco5BMk4t/PyHNza+bauT4J+R/vAjMMRy+443P7zxpsIOpxYoOAzEPAYQNjOIMMCvHgjqgJj9AUFlo2AUjIJRMDIBAChxU/3/KgpsAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0008-1533-5694","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Fangfeng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-01-27 15:33:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8712240/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8712240/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103840809,"identity":"255b8c12-6b0d-46c4-828c-fb5c39872291","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2393207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMendelian randomization and colocalization analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMR, Mendelian randomization; pQTL, protein quantitative trait loci; eQTL, expression quantitative trait loci; HCC, hepatocellular carcinoma; GWAS, genome-wide association study; OR, odds ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eDistribution of ORs and \u003cem\u003eP\u003c/em\u003e values for 316 causal associations identified by pQTL-based MR analysis. \u003cstrong\u003e(B)\u003c/strong\u003e Distribution of ORs and \u003cem\u003eP\u003c/em\u003e values for eight causal associations identified by eQTL-based MR analysis. \u003cstrong\u003e(C)\u003c/strong\u003eDistribution of ORs and P values for eight causal associations between eQTLs and metabolites identified by MR analysis. \u003cstrong\u003e(D–F)\u003c/strong\u003e Coloc analyses of CR1 \u003cstrong\u003e(D)\u003c/strong\u003e, YME1L1 \u003cstrong\u003e(E)\u003c/strong\u003e, and MSI2 \u003cstrong\u003e(F)\u003c/strong\u003e, showing the regional association patterns between eQTL signals and HCC GWAS signals to evaluate whether they share common causal variants (SNP.PP.H4). \u003cstrong\u003e(G)\u003c/strong\u003e Reverse MR analysis using HCC as the exposure and CR1, MSI2, and YME1L1 expression as the outcomes, showing the distributions of ORs and P values for the reverse causal effects.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/d7f08381f88d54d3d8e5442e.png"},{"id":103840811,"identity":"2f7adc17-e6d4-4c29-b11c-d75cf0fc22bd","added_by":"auto","created_at":"2026-03-03 14:42:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5920522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between the intratumoral microbiome and the immune microenvironment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCTL, cytotoxic T lymphocyte; Tregs, regulatory T cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A–C)\u003c/strong\u003eComparison of microbial α-diversity indices (Richness, Shannon, Simpson, and Pielou) between high- and low-expression groups of key genes (CR1, MSI2, and YME1L1). P values were calculated using the Wilcoxon test and are indicated in the plots. \u003cstrong\u003e(D)\u003c/strong\u003e Heatmap showing Pearson correlation analysis between microbial genus abundance and immune cell infiltration levels. Red indicates positive correlations and blue indicates negative correlations. Neutrophils, eosinophils, and M0 macrophages were positively correlated with genera including Prochlorococcus, Succinimonas, and Acidibacillus, whereas γδ T cells, Tregs, and activated NK cells were negatively correlated with genera including Campylobacter, Desulfotalea, Lawsonia, Coprobacillus, Microcystis, and Sulfolobus. \u003cstrong\u003e(E)\u003c/strong\u003e Network showing the associations between CTL evasion–related genes (mainly MSI2 and YME1L1) and specific microbial taxa. \u003cstrong\u003e(F)\u003c/strong\u003e Network illustrating the broader and more complex interaction landscape between immunomodulatory genes (CR1, YME1L1, and MSI2) and tumor-resident microbes.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/075b566ad2e6da175df7700b.png"},{"id":103840822,"identity":"c77cf88a-f60d-4ddb-b6a0-d9baf6d91501","added_by":"auto","created_at":"2026-03-03 14:42:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3653949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell annotation of major cell populations in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHCC, hepatocellular carcinoma; PCA, principal component analysis; UMAP, Uniform Manifold Approximation and Projection; NK, natural killer.\u003c/p\u003e\n\u003cp\u003e(A) UMAP visualization of single cells based on PCA, identifying 12 distinct clusters. (B) Cell type annotation of the 12 clusters, which were classified into eight major cell populations: NK/T cells, macrophages, monocytes, endothelial cells, fibroblasts, B cells, epithelial cells, and plasma cells. (C) Dot plot showing the expression patterns of canonical marker genes across the eight annotated cell types. (D) Proportional distribution of the eight major cell populations across the two sample groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/c07e2fc9541bac155f9d814b.png"},{"id":103840804,"identity":"c375fbe7-2998-4ba7-80b0-b25821066f33","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2964681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell expression patterns of key genes and associated immunometabolic pathway activities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKey genes include CR1, YME1L1, and MSI2. PCA, principal component analysis; UMAP, Uniform Manifold Approximation and Projection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eScatter plot showing the overall expression patterns of key genes at the single-cell level. \u003cstrong\u003e(B)\u003c/strong\u003e Bubble plot depicting the expression of key genes across different cell types; bubble size represents the proportion of expressing cells, and color indicates the average expression level (red, high expression; blue, low expression). \u003cstrong\u003e(C)\u003c/strong\u003e Heatmap showing differences in immune- and metabolism-related pathway activities associated with key genes, with red indicating higher pathway activity and blue indicating lower activity. \u003cstrong\u003e(D)\u003c/strong\u003e Correlation analysis between key gene expression levels and immune cell infiltration; bubble color represents the direction and strength of correlation (Pearson’s correlation coefficient; red, positive; blue, negative), and bubble size reflects statistical significance (\u003cem\u003eP\u003c/em\u003e value). Immune cell infiltration was estimated using CIBERSORT.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/2c6cd80933bd705255f929f0.png"},{"id":103840798,"identity":"965050ad-95f7-4ca4-9747-b43fbbe65710","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7101769,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial transcriptomic profiling reveals cellular architecture in hepatocellular carcinoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eST, spatial transcriptomics; UMAP, Uniform Manifold Approximation and Projection. log2FC, log2 fold change.\u003c/p\u003e\n\u003cp\u003e(A) Spatial distribution of nCount_Spatial (unique molecular identifier [UMI] counts) across two spatial transcriptomic sections; regions with higher UMI counts generally correspond to epithelial-enriched areas. \u003cstrong\u003e(B)\u003c/strong\u003e UMAP visualization showing six distinct cellular clusters identified by unsupervised Louvain clustering. \u003cstrong\u003e(C)\u003c/strong\u003eDeconvolution results based on the spacexr package, illustrating the proportional composition of different cell types within each spatial spot. \u003cstrong\u003e(D)\u003c/strong\u003eSpatial cell-type map assigning each spot according to its dominant cell type. \u003cstrong\u003e(E)\u003c/strong\u003eDistribution of differentially expressed marker genes across major cell types, including log2FC and differences in cell-type proportions, supporting the reliability of cell-type annotation. \u003cstrong\u003e(F)\u003c/strong\u003e Scatter plots showing the spatial expression patterns of key genes. \u003cstrong\u003e(G)\u003c/strong\u003e Bubble plots showing the spatial expression abundance of key genes; bubble size represents the proportion of expressing spots, and color indicates average expression level (blue, high expression; red, low expression). \u003cstrong\u003e(H)\u003c/strong\u003e Spatial expression distributions of CR1, YME1L1, and MSI2 across two hepatocellular carcinoma sections.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/e00f08b8866f215cad5541b3.png"},{"id":103840803,"identity":"96d2d349-6da5-4728-9b6a-5830d9cb5b44","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8578706,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCR1 is upregulated in tumor tissues and associated with M2-like macrophage enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCR1, complement receptor 1; IHC, immunohistochemistry; IF, immunofluorescence; qRT-PCR, quantitative real-time polymerase chain reaction; HCC, hepatocellular carcinoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eRepresentative IHC images of CR1 expression in HCC tissues and paired adjacent non-tumorous tissues. \u003cstrong\u003e(B)\u003c/strong\u003e Quantification of CR1-positive staining area in tumor and adjacent tissues (****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). \u003cstrong\u003e(C)\u003c/strong\u003eQuantitative analysis of CR1-positive immunofluorescence signals in tumor and adjacent tissues (***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). \u003cstrong\u003e(D)\u003c/strong\u003e Relative mRNA expression levels of CR1 in tumor and adjacent tissues validated by qRT-PCR (****\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001). \u003cstrong\u003e(E)\u003c/strong\u003e Representative double immunofluorescence staining images showing colocalization of CR1 with CD206 (an M2 macrophage marker).\u003cstrong\u003e (F)\u003c/strong\u003eQuantitative analysis of CD206⁺ M2-like macrophage immunofluorescence signals (**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). \u003cstrong\u003e(G)\u003c/strong\u003e Representative double immunofluorescence staining images showing CR1 and CD86 (an M1 macrophage marker). \u003cstrong\u003e(H)\u003c/strong\u003eQuantitative analysis of CD86⁺ M1-like macrophage immunofluorescence signals (**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). \u003cstrong\u003e(I)\u003c/strong\u003e Representative flow cytometry plots showing CD206⁺ M2-like and CD86⁺ M1-like macrophages in tumor and adjacent tissues. \u003cstrong\u003e(J)\u003c/strong\u003eRepresentative flow cytometry plots showing CR1⁺ cells among CD68⁺ macrophages in tumor and adjacent tissues. \u003cstrong\u003e(K)\u003c/strong\u003e Statistical analysis of the proportions of M1 (CD86⁺) and M2 (CD206⁺) macrophages in tumor and adjacent tissues (**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). \u003cstrong\u003e(L)\u003c/strong\u003e Statistical analysis of the proportion of CR1⁺ cells among CD68⁺ macrophages in tumor and adjacent tissues (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). \u003cstrong\u003e(M)\u003c/strong\u003e Distribution of tumor stages (Stage I–II vs. Stage III–IV) between patients with high (n = 15) and low (n = 15) CR1 expression (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). \u003cstrong\u003e(N)\u003c/strong\u003e Distribution of vascular invasion between patients with high (n = 15) and low (n = 15) CR1 expression (*\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/3821d5a28285b0f626757fa1.png"},{"id":103840800,"identity":"347588d2-d903-46bb-b82c-969d43a7e903","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3511260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCR1 manipulation drives macrophage polarization and suppresses CD8⁺ T-cell function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCR1, complement receptor 1; qRT-PCR, quantitative real-time polymerase chain reaction; OE, overexpression; KD, knockdown; NC, negative control.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eRepresentative flow cytometry plots of CD45⁺CD8⁺ T cells in hepatocellular carcinoma and adjacent non-tumorous tissues. \u003cstrong\u003e(B)\u003c/strong\u003e Statistical analysis of the proportion of CD8⁺ T cells among CD45⁺ leukocytes in tumor and adjacent tissues (***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). \u003cstrong\u003e(C)\u003c/strong\u003e qRT-PCR analysis of CR1 mRNA expression in THP-1–derived M0 macrophages under different conditions (NC, CR1 overexpression [CR1-OE], and CR1 knockdown [CR1-KD]) (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ***\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001). \u003cstrong\u003e(D)\u003c/strong\u003e Representative Western blot images showing CR1 protein expression in different treatment groups. \u003cstrong\u003e(E)\u003c/strong\u003e Densitometric quantification of CR1 protein expression normalized to β-actin (**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). \u003cstrong\u003e(F)\u003c/strong\u003e qRT-PCR analysis of TNF-α mRNA expression (**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). \u003cstrong\u003e(G)\u003c/strong\u003e qRT-PCR analysis of IL-10 mRNA expression (**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). \u003cstrong\u003e(H)\u003c/strong\u003e Representative flow cytometry histograms and quantitative analysis of CD86⁺ M1-like macrophages (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). \u003cstrong\u003e(I)\u003c/strong\u003e Representative flow cytometry histograms and quantitative analysis of CD206⁺ M2-like macrophages (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). \u003cstrong\u003e(J)\u003c/strong\u003e Representative flow cytometry plots and quantitative analysis of macrophage phagocytic activity (**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/fcfac1d0f4054cf04eaa1d7a.png"},{"id":103840801,"identity":"ee249735-7cb1-483c-aaaf-1a4e02767380","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3048013,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCR1⁺ macrophages suppress CD8⁺ T-cell proliferation and cytotoxic function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCR1, complement receptor 1; CFSE, carboxyfluorescein succinimidyl ester; IFN-γ, interferon-γ; PD-L1, programmed death-ligand 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eRepresentative flow cytometry histograms and quantitative analysis of CD8⁺ T-cell proliferation assessed by CFSE dilution in the co-culture system (**\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). \u003cstrong\u003e(B)\u003c/strong\u003e Representative flow cytometry plots and quantitative analysis of Granzyme B⁺ CD8⁺ T cells (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). \u003cstrong\u003e(C)\u003c/strong\u003e Representative flow cytometry plots and quantitative analysis of IFN-γ⁺ CD8⁺ T cells (**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). \u003cstrong\u003e(D)\u003c/strong\u003eRepresentative flow cytometry plots and quantitative analysis of PD-L1 expression on macrophages in the co-culture system (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/a764ed0ee710a0ca554d265b.png"},{"id":104408079,"identity":"e9b272a0-ffed-4b8a-b8c1-3a350975e028","added_by":"auto","created_at":"2026-03-11 12:41:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":37373506,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/4429e54f-af8e-4c38-86b3-07f316319be8.pdf"},{"id":103840797,"identity":"7c6e100e-d8d0-4d69-8dc7-a434c822dd94","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23829,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/874ba58249cddf11d6d128b3.pdf"},{"id":104401103,"identity":"bb78c73c-8a15-4551-9e7c-b27dfd3f6724","added_by":"auto","created_at":"2026-03-11 12:11:52","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":501269,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/9d8fd14e61f09af212998660.pdf"},{"id":103840796,"identity":"7d2a3de1-0974-4ca5-ba19-cc8922981bc5","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":459439,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/cf829f84fc1274377053c5d7.pdf"},{"id":103840799,"identity":"29866d3e-aa26-4988-a0f8-a24a7976d2ed","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":435085,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/0e47f7805740266d064895e1.pdf"},{"id":103840806,"identity":"4edfc937-c503-4434-97ae-ef7fab3d9331","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2751291,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/c210a5d091defd115e9c4d60.pdf"},{"id":103840810,"identity":"2fc98c8a-599f-4d44-bfb2-a12286ba3456","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14608845,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/dd8a80d1b8bf45fee96fc443.pdf"},{"id":103840805,"identity":"d76fb064-2c0b-45e1-8399-42c5439c3b36","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":4949324,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/6a170e11028d84a0f8b94595.pdf"},{"id":103840812,"identity":"0dd7401f-f8f5-40f7-8268-a4a450d462cb","added_by":"auto","created_at":"2026-03-03 14:42:01","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":5894076,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/d6bf6e29ba4539621ca64d59.pdf"},{"id":103840808,"identity":"5913761f-729d-42d1-a245-618e5c3e8067","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":18496288,"visible":true,"origin":"","legend":"","description":"","filename":"FigureAbstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/dd90c53f96319582463f7033.pdf"},{"id":103840807,"identity":"c2889933-66c3-4516-a4d2-32f41c6e341c","added_by":"auto","created_at":"2026-03-03 14:42:00","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":12188,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8712240/v1/fe81c620d6fd5ee9eb9173c5.xlsx"}],"financialInterests":"","formattedTitle":"CR1(+) Tumor-Associated Macrophages Orchestrate an Immunosuppressive Niche in Hepatocellular Carcinoma: A Genetic and Multi-omics Dissection","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide and is closely associated with chronic liver diseases [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite substantial advances in early diagnosis and locoregional therapies, systemic treatment options for patients with advanced HCC remain limited, and clinical outcomes are still poor [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In recent years, immune checkpoint inhibitors have reshaped the therapeutic landscape of advanced HCC; however, overall response rates remain unsatisfactory, and both primary and acquired resistance are common [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The fundamental basis of this clinical challenge lies in the profoundly immunosuppressive tumor microenvironment (TME) of HCC\u0026mdash;a complex and dynamic ecosystem composed of tumor cells, immune cells, stromal components, and non-cellular elements [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, elucidating the core mechanisms governing immunosuppressive networks within the TME has become a critical prerequisite for improving the efficacy of immunotherapy in HCC.\u003c/p\u003e \u003cp\u003eWithin the HCC TME, tumor-associated macrophages (TAMs) and CD8⁺ T cells represent two pivotal cellular populations that shape immune phenotypes and determine clinical outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Infiltrating macrophages are typically polarized toward an immunosuppressive M2-like phenotype, characterized by the secretion of interleukin-10 (IL-10) and transforming growth factor-β (TGF-β), high expression of co-inhibitory molecules such as programmed death-ligand 1 (PD-L1), and recruitment of regulatory T cells (Tregs), collectively fostering an immune-privileged niche that ultimately drives functional exhaustion of CD8⁺ T cells [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although the protumorigenic roles of TAMs are well recognized [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the upstream genetic signals and intrinsic molecular mechanisms that drive their M2 polarization\u0026mdash;particularly how these processes are coupled to host genetic background\u0026mdash;remain incompletely understood. Meanwhile, accumulating genetic evidence indicates that host genetic variants influence cancer susceptibility through regulation of gene expression [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, how inherited risk is translated into specific functional immune phenotypes within the TME, including the involvement of intermediary processes such as metabolic reprogramming, remains largely unclear [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eComplement receptor 1 (CR1) is a multifunctional receptor of the complement system, classically recognized for its roles in immune complex clearance and regulation of complement activation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Emerging evidence suggests that CR1 is aberrantly expressed in several types of cancer and may be associated with tumor progression [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the specific functions of CR1 in innate immune cells\u0026mdash;particularly macrophages\u0026mdash;and its role within the TME remain largely unexplored [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our preliminary bioinformatic analyses identified CR1 as a candidate gene potentially causally associated with HCC risk. Notably, CR1 expression exhibited pronounced cellular specificity, being predominantly enriched in macrophages, and marked spatial heterogeneity within tumor tissues, where it was closely linked to immunosuppressive signaling programs [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Collectively, these observations point to an untested hypothesis that CR1 may serve as a central molecular hub linking host genetic background, immunometabolic regulation, macrophage polarization, and T-cell dysfunction.\u003c/p\u003e \u003cp\u003eAccordingly, this study aimed to systematically delineate the role of CR1 in immune evasion in HCC by implementing an integrative research strategy spanning from genetic causal inference to molecular and functional validation. First, we applied Mendelian randomization (MR) and mediation analyses to construct a genetic causal axis from CR1 to specific circulating metabolites and ultimately to HCC risk. We then integrated multi-omics datasets to comprehensively characterize the associations between CR1 expression, immunosuppressive TME features, and clinical outcomes. Finally, through a series of functional experiments\u0026mdash;including tissue-based validation, cellular perturbation assays, and co-culture systems\u0026mdash;we sought to demonstrate: (i) the enrichment of CR1⁺ TAMs in advanced HCC tissues and their spatial association with CD8⁺ T-cell exhaustion; (ii) the role of CR1 in reprogramming macrophage M2 polarization and associated functions, including phagocytic activity; and (iii) the suppressive effects of CR1-induced macrophages on CD8⁺ T-cell proliferation and effector functions. This study provides the first systematic evidence identifying CR1 as a central regulator of the immunosuppressive microenvironment in HCC, offering a novel \u0026ldquo;genetic\u0026ndash;metabolic\u0026ndash;immune\u0026rdquo; mechanistic framework for understanding immune escape and highlighting CR1 as a potential therapeutic target to overcome resistance to immunotherapy.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data sources and preprocessing\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Exposure, mediator, and outcome datasets\u003c/h2\u003e \u003cp\u003eAll genetic exposure, mediator, and outcome datasets used in this study were obtained from publicly available resources. Protein quantitative trait loci (pQTL) data were derived from the FinnGen study (Release 10; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), based on a SomaScan v4 plasma proteomics genome-wide association study (GWAS) of approximately 830 individuals of Finnish ancestry, covering 7,156 proteins [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Expression quantitative trait loci (eQTL) data were obtained from the eQTLGen Consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eqtlgen.org\u003c/span\u003e\u003cspan address=\"https://www.eqtlgen.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), using the Phase I cross-tissue dataset derived from a large-scale meta-analysis of blood transcriptomes from 31,684 individuals [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Summary-level GWAS data for circulating metabolites were retrieved from the Canadian Longitudinal Study on Aging, which included 1,091 metabolites and 309 metabolite ratios measured in 8,299 participants [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e GWAS summary statistics for HCC were obtained from FinnGen Release 12 under the phenotype code C3_HEPATOCELLU_CARC_EXALLC, including 947 clinically validated HCC cases and 378,749 controls, all of European ancestry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Transcriptomic datasets\u003c/h2\u003e \u003cp\u003eTranscriptomic datasets used for functional characterization were obtained from three sources. Bulk RNA sequencing data were downloaded from The Cancer Genome Atlas (TCGA) data portal (\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) for HCC. Level 3 HTSeq\u0026ndash;Fragments Per Kilobase of transcript per Million mapped reads (FPKM)\u0026ndash;normalized expression profiles and corresponding clinical information were retrieved, comprising 374 tumor samples and 50 adjacent non-tumorous liver tissues. Single-cell RNA sequencing (scRNA-seq) data were obtained from the Gene Expression Omnibus (GEO) database (\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) under accession number GSE149614, which includes 21 samples (13 tumor tissues and 8 normal liver tissues). Spatial transcriptomics (ST) data were also downloaded from the GEO database under accession number GSE245908, consisting of two spatially profiled HCC tissue sections.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. MR and mediation analyses\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Instrumental variable selection and causal estimation\u003c/h2\u003e \u003cp\u003eTo infer potential causal relationships between exposures and HCC, a two-sample MR framework was applied [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For each exposure (pQTLs, eQTLs, and circulating metabolites), single-nucleotide polymorphisms (SNPs) associated at a genome-wide significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10⁻⁵ were selected as the initial candidates instrumental variables (IVs). Linkage disequilibrium (LD) clumping was then performed using PLINK software with an r\u0026sup2; threshold of 0.001 and a 10,000-kb window to ensure the independence of IVs. The F-statistic was calculated for each IV, and only strong instruments with F\u0026thinsp;\u0026gt;\u0026thinsp;10 were retained for subsequent analyses.\u003c/p\u003e \u003cp\u003eThe inverse-variance weighted (IVW) method was used as the primary approach to estimate causal effects, complemented by MR-Egger regression, the weighted median method, and the weighted mode method as sensitivity analyses [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. When an exposure was instrumented by a single SNP, causal estimates were derived using the Wald ratio method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Sensitivity analyses and mediation analyses\u003c/h2\u003e \u003cp\u003eTo evaluate the robustness of the MR findings, multiple sensitivity analyses were conducted for all significant causal associations. Heterogeneity among IVs was assessed using Cochran\u0026rsquo;s Q test. Horizontal pleiotropy was assessed using the MR-Egger intercept test and the MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) global test [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Leave-one-out analyses were further performed to determine whether the overall causal estimates were disproportionately driven by any single SNP.\u003c/p\u003e \u003cp\u003eFor exposure\u0026ndash;outcome pairs with significant causal effects identified in the primary MR analyses, a two-step mediation analysis was subsequently performed to investigate the potential mediating role of circulating metabolites. The indirect (mediated) effect was calculated as the product of the path coefficients β₁ (exposure \u0026rarr; mediator) and β₂ (mediator \u0026rarr; outcome), and the proportion mediated was defined as (β₁ \u0026times; β₂)/β₃, where β₃ represents the total effect of the exposure on the outcome. A metabolite was considered a reliable mediator only if all of the following criteria were satisfied: (i) both the exposure\u0026ndash;mediator and mediator\u0026ndash;outcome associations reached statistical significance using the IVW method (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); (ii) colocalization analysis performed with the R package coloc demonstrated a high posterior probability of a shared causal variant (PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.95) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; and (iii) reverse MR analyses provided no evidence supporting a causal effect of the metabolite on the exposure or of the outcome on the metabolite.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Multi-omics data analyses\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the potential association between key gene expression and chemotherapeutic drug sensitivity, drug response data were obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Predictive models were constructed using the R package \u003cem\u003epRRophetic\u003c/em\u003e, which applies elastic net regression trained on GDSC cancer cell line transcriptomic profiles and corresponding drug response data. The trained models were then used to predict the half-maximal inhibitory concentration (IC50) values of chemotherapeutic agents in the TCGA HCC cohort.\u003c/p\u003e \u003cp\u003eModel training was performed using 10-fold cross-validation to ensure robustness and generalizability, and batch effects were corrected using the ComBat algorithm. Differences in predicted IC50 values between high- and low-expression groups of key genes were compared to assess the impact of gene expression levels on chemotherapeutic sensitivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Molecular docking\u003c/h2\u003e \u003cp\u003eThree-dimensional protein structures of the key genes were retrieved from the AlphaFold database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alphafold.com/\u003c/span\u003e\u003cspan address=\"https://alphafold.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The chemical structures of candidate compounds were obtained from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Molecular docking was performed using AutoDock Vina (v1.2.3), with each docking task repeated nine times [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The binding conformation with the lowest predicted binding energy was selected as the optimal model. Docking results were visualized using PyMOL software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Intratumoral microbiome analysis\u003c/h2\u003e \u003cp\u003eIntratumoral microbiome profiling was based on previously published microbial abundance data processed using the Kraken algorithm [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Three categories of association analyses were systematically performed. First, Spearman correlation analyses were conducted between genus-level microbial abundances and immune cell infiltration scores estimated using the algorithm. Second, correlations between the expression levels of key genes (CR1, YME1L1, and MSI2) and microbial abundances were assessed. Third, expression profiles of previously reported cytotoxic T lymphocyte (CTL)\u0026ndash;related gene sets and immunoregulatory molecules were integrated and correlated with microbial abundances to infer the potential impact of microbial composition on antitumor immune activity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Single-cell and spatial transcriptomic analyses\u003c/h2\u003e \u003cp\u003escRNA-seq data were processed in the R environment using the Seurat package (v4.3.0) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Rigorous quality control was performed by retaining cells with 200\u0026ndash;2,500 detected genes (nFeature_RNA), mitochondrial gene expression\u0026thinsp;\u0026lt;\u0026thinsp;10%, and total unique molecular identifier (UMI) counts within \u0026plusmn;\u0026thinsp;3 median absolute deviations of the median. Potential doublets were identified and removed using DoubletFinder (v2.0.4) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGene expression values were normalized using the LogNormalize method with a scale factor of 10,000, and highly variable genes were identified. After regressing out mitochondrial gene content, ribosomal gene content, and cell cycle effects, batch effects across samples were corrected using the Harmony algorithm [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Principal component analysis (PCA) was subsequently performed, and the top 30 principal components were used for Uniform Manifold Approximation and Projection (UMAP) visualization and Louvain clustering. Cell type annotation was performed by integrating information from the CellMarker and PanglaoDB databases, published literature, and automated annotation using SingleR, and was further confirmed by examining the expression of canonical markers (e.g., EPCAM for epithelial cells, CD68 for macrophages, and CD3D for T cells).\u003c/p\u003e \u003cp\u003eST data were processed using Seurat (v4.3.0). Raw UMI count matrices were normalized and variance-stabilized using the SCTransform function. PCA and UMAP-based clustering were performed based on the top 3,000 highly variable genes. To deconvolute the cellular composition of each spatial spot, the RCTD (Robust Cell Type Decomposition) algorithm was applied using the annotated scRNA-seq dataset as the reference, enabling estimation of the relative proportions of distinct cell types within each spot [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Experimental validation\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Clinical sample collection and processing\u003c/h2\u003e \u003cp\u003e This study was conducted in strict accordance with ethical guidelines. A total of 30 paired HCC tumor tissues and matched adjacent non-tumorous liver tissues (located\u0026thinsp;\u0026gt;\u0026thinsp;2 cm from the tumor margin) were collected from patients undergoing surgical resection. Written informed consent was obtained from all participants prior to surgery. The study protocol was approved by the Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University (approval no. SWYX:NO.2025\u0026thinsp;\u0026minus;\u0026thinsp;701).\u003c/p\u003e \u003cp\u003eImmediately after resection, tissue specimens were divided into three portions according to experimental requirements: (i) fresh tissues were processed immediately for flow cytometric analysis; (ii) a portion of the tissues was snap-frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent RNA and protein extraction; and (iii) the remaining tissues were fixed in 4% paraformaldehyde, paraffin-embedded, and sectioned into consecutive 4-\u0026micro;m slices for immunohistochemistry (IHC) and immunofluorescence (IF) analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Immunohistochemistry and immunofluorescence\u003c/h2\u003e \u003cp\u003eParaffin-embedded sections were baked at 60\u0026deg;C for 2 h, followed by standard deparaffinization and rehydration. Antigen retrieval was performed by heat induction in sodium citrate buffer (pH 6.0; Servicebio, G1202) using a microwave oven. Endogenous peroxidase activity was blocked by incubation with 3% hydrogen peroxide in methanol for 25 min at room temperature in the dark. Sections were then blocked with 5% bovine serum albumin (BSA; Servicebio, G5001) for 30 min.\u003c/p\u003e \u003cp\u003eSlides were incubated overnight at 4\u0026deg;C in a humidified chamber with the following primary antibodies: rabbit anti-human CR1 monoclonal antibody (Abcam, ab235882; 1:200), mouse anti-human CD68 monoclonal antibody (Dako, M0814; 1:100), rabbit anti-human CD206 monoclonal antibody (Abcam, ab64693; 1:200), and mouse anti-human CD8A monoclonal antibody (Dako, M7103; 1:100). Phosphate-buffered saline (PBS) was used instead of the primary antibody as a negative control.\u003c/p\u003e \u003cp\u003eAfter washing with PBS, sections were incubated with the corresponding horseradish peroxidase (HRP)\u0026ndash;conjugated secondary antibodies (goat anti-rabbit/mouse IgG; Servicebio, GB23303/GB23301) for 50 min at room temperature. Signals were developed using a 3,3\u0026prime;-diaminobenzidine (DAB) kit (Servicebio, G1211), followed by hematoxylin counterstaining (Servicebio, G1004), differentiation with acid alcohol, and bluing with ammonia water. Sections were dehydrated through graded ethanol, cleared in xylene, and mounted with neutral balsam (Servicebio, G1401).\u003c/p\u003e \u003cp\u003eFor double immunofluorescence staining, a similar procedure was followed, except that fluorophore-conjugated secondary antibodies were used, and nuclei were counterstained with 4\u0026prime;,6-diamidino-2-phenylindole (DAPI).\u003c/p\u003e \u003cp\u003eAll stained slides were independently evaluated by two experienced pathologists blinded to clinical and pathological information. Expression levels of CR1, CD68, and CD206 were semi-quantitatively assessed using the H-score system:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{H}-\\text{s}\\text{c}\\text{o}\\text{r}\\text{e}=\\sum\\:({P}_{i}\\times\\:i)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere P\u003csub\u003ei\u003c/sub\u003e represents the percentage of cells stained at each intensity level (i\u0026thinsp;=\u0026thinsp;0, negative; 1, weak; 2, moderate; 3, strong). Immunofluorescence results were quantified as the proportion of positive signal (positive area or positive cell percentage) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Cell culture and induction of macrophage differentiation\u003c/h2\u003e \u003cp\u003eThe human monocytic cell line THP-1 (ATCC TIB-202) was cultured in RPMI-1640 medium (Gibco, 11875093) supplemented with 10% fetal bovine serum (FBS; Gibco, 10270106) and 1% penicillin\u0026ndash;streptomycin (Gibco, 15140122) at 37\u0026deg;C in a humidified incubator with 5% CO₂.\u003c/p\u003e \u003cp\u003eTo induce macrophage differentiation, cells were seeded at a density of 5 \u0026times; 10⁵ cells/mL and treated with 100 ng/mL phorbol 12-myristate 13-acetate (PMA; Sigma-Aldrich, P8139) for 48 h. The medium was then replaced with fresh complete medium and cells were cultured for an additional 24 h. Adherent cells exhibiting macrophage-like morphology were defined as M0 macrophages and used for subsequent experiments [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Gene knockdown and overexpression\u003c/h2\u003e \u003cp\u003eCR1 expression was manipulated using RNA interference and plasmid-mediated overexpression approaches. THP-1-derived macrophages were transfected with small interfering RNAs (siRNAs) targeting human CR1 or negative control siRNA (si-NC) at a final concentration of 50 nM using Lipofectamine RNAiMAX (Invitrogen, 13778150), according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003cp\u003eFor overexpression experiments, the full-length human CR1 coding sequence was cloned into the pcDNA3.1 expression vector, and cells were transfected using Lipofectamine 3000 (Invitrogen, L3000015), with empty vector serving as a control. All siRNAs were synthesized by RiboBio (Guangzhou, China).\u003c/p\u003e \u003cp\u003eCells were harvested 48 h after transfection, and knockdown or overexpression efficiency was confirmed by quantitative real-time PCR (qRT-PCR) and Western blotting. The sequences of all siRNAs used in this study are listed below(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequences of siRNAs targeting CR1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003esiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward (5\u0026prime;\u0026ndash;3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse (5\u0026prime;\u0026ndash;3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esi-CR1#1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCAACAUCAUUGAGCUCAATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUUGAGCUCAUGAUGUUGCTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esi-CR1#2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCUGAAGAUGGAGCAGUUUTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAACUGCUCCAUCUUCAGGTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esi-NC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUUCUCCGAACGUGUCACGUTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACGUGACACGUUCGGAGAATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5. RNA extraction and qRT-PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from cultured cells using TRIzol reagent (Invitrogen, 15596026) according to the manufacturer\u0026rsquo;s instructions. RNA concentration and purity were determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific) by measuring the optical density at 260/280 nm. For complementary DNA (cDNA) synthesis, 1 \u0026micro;g of total RNA was treated with genomic DNA eraser and reverse-transcribed using the PrimeScript RT reagent Kit with gDNA Eraser (Takara, RR047A).\u003c/p\u003e \u003cp\u003eQRT-PCR was performed using TB Green Premix Ex Taq II (Tli RNaseH Plus) (Takara, RR820A) on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems). Each 20-\u0026micro;L reaction mixture contained 10 \u0026micro;L of 2\u0026times; TB Green Premix Ex Taq II, 0.8 \u0026micro;L of forward primer (10 \u0026micro;M), 0.8 \u0026micro;L of reverse primer (10 \u0026micro;M), 2 \u0026micro;L of cDNA template, and 6.4 \u0026micro;L of RNase-free water.\u003c/p\u003e \u003cp\u003eThe amplification conditions were as follows: initial denaturation at 95\u0026deg;C for 30 s; 40 cycles of 95\u0026deg;C for 5 s and 60\u0026deg;C for 30 s; followed by a melt-curve analysis from 60\u0026deg;C to 95\u0026deg;C to verify product specificity. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the internal reference gene. Relative gene expression levels were calculated using the 2^\u0026minus;ΔΔCt method. The primer sequences used in this study are listed below(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequences of primers used for qRT-PCR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward (5\u0026prime;\u0026ndash;3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse (5\u0026prime;\u0026ndash;3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACCATGGCCTCTGTGTCTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGCAGGTAGGTGTTGTCAGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGAGCGAGATCCCTCCAAAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGCTGTTGTCATACTTCTCATGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.4.6. Western blot analysis\u003c/h2\u003e \u003cp\u003eCells were lysed on ice for 30 min using radioimmunoprecipitation assay (RIPA) buffer (Beyotime, P0013B) supplemented with 1 mM phenylmethylsulfonyl fluoride (PMSF; Beyotime, ST506) and a protease inhibitor cocktail (Roche, 4693132001). Lysates were centrifuged at 12,000 rpm for 15 min at 4\u0026deg;C, and the supernatants were collected. Protein concentrations were determined using a bicinchoninic acid (BCA) protein assay kit (Beyotime, P0010).\u003c/p\u003e \u003cp\u003eEqual amounts of protein (30 \u0026micro;g) were mixed with 6\u0026times; loading buffer and denatured by boiling at 100\u0026deg;C for 10 min. Proteins were separated by 8% sodium dodecyl sulfate\u0026ndash;polyacrylamide gel electrophoresis (SDS\u0026ndash;PAGE) and subsequently transferred onto polyvinylidene difluoride (PVDF) membranes (Millipore, IPVH00010) using a wet transfer system at a constant current of 250 mA for 90 min. Membranes were blocked with Tris-buffered saline containing 0.1% Tween-20 (TBST) supplemented with 5% non-fat milk for 1 h at room temperature.\u003c/p\u003e \u003cp\u003eThe membranes were incubated overnight at 4\u0026deg;C with the following primary antibodies: rabbit anti-CR1 antibody (Abcam, ab235882; 1:1000) and mouse anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody (Proteintech, 60004-1-Ig; 1:5000). After three washes with TBST (10 min each), membranes were incubated with the corresponding HRP\u0026ndash;conjugated goat anti-rabbit or anti-mouse secondary antibodies (Abcam, ab6721/ab6789; 1:5000) for 1 h at room temperature. Following extensive washing with TBST, protein bands were visualized using an enhanced chemiluminescence (ECL) detection kit (NCM Biotech, P10300) and imaged using a ChemiDoc MP Imaging System (Bio-Rad). Band intensities were quantified using ImageJ software (v1.53).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e2.4.7. Macrophage polarization, phagocytosis and co-culture assays\u003c/h2\u003e \u003cp\u003eTo evaluate the effects of CR1 on macrophage polarization, genetically manipulated THP-1\u0026ndash;derived macrophages were stimulated with either 100 ng/mL lipopolysaccharide (LPS; Sigma, L4391) combined with 20 ng/mL interferon-γ (IFN-γ; PeproTech, 300-02) to induce M1 polarization, or with 20 ng/mL interleukin-4 (IL-4; PeproTech, 200-04) combined with 20 ng/mL interleukin-13 (IL-13; PeproTech, 200\u0026thinsp;\u0026minus;\u0026thinsp;13) to induce M2 polarization. After 24 h of stimulation, macrophage polarization status was assessed by flow cytometric analysis of surface markers (CD86 for M1 and CD206 for M2) or by qRT-PCR analysis of polarization-related genes (tumor necrosis factor-α [TNF-α] for M1 and IL-10 for M2).\u003c/p\u003e \u003cp\u003eMacrophage phagocytic capacity was evaluated using fluorescein isothiocyanate (FITC)\u0026ndash;labeled phagocytic particles incubated with macrophages for 2 h. The proportion of FITC-positive cells was quantified by flow cytometry as an indicator of phagocytic activity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo investigate the effects of CR1-modulated macrophages on T-cell function, an in vitro co-culture system was established. CD8⁺ T cells were isolated from peripheral blood mononuclear cells (PBMCs) obtained from healthy donors using CD8 MicroBeads (Miltenyi Biotec, 130-045-201) and activated with anti-CD3/CD28 antibodies (1 \u0026micro;g/mL; Miltenyi Biotec, 130-093-387). Macrophages were directly co-cultured with activated CD8⁺ T cells at a ratio of 1:2 (macrophages:CD8⁺ T cells). After 72 h of co-culture, T cells were harvested and stained with carboxyfluorescein succinimidyl ester (CFSE; Invitrogen, C34554) to assess cell proliferation. Intracellular expression of effector molecules, including IFN-γ and granzyme B, was subsequently analyzed by flow cytometry [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eFor MR analyses, statistical significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using the IVW method, and multiple testing was corrected using the false discovery rate (FDR) approach with the Benjamini\u0026ndash;Hochberg procedure. All in vitro experimental data were obtained from at least three independent biological replicates and are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM).\u003c/p\u003e \u003cp\u003eFor normally distributed data, comparisons between two groups were performed using a two-tailed Student\u0026rsquo;s t-test, while comparisons among multiple groups were conducted using one-way analysis of variance (ANOVA), followed by Tukey\u0026rsquo;s post hoc test when appropriate. Non-normally distributed data were analyzed using the Mann\u0026ndash;Whitney U test (two groups) or the Kruskal\u0026ndash;Wallis test (multiple groups). Correlation analyses were performed using Pearson\u0026rsquo;s correlation or Spearman\u0026rsquo;s rank correlation, depending on data distribution and variance homogeneity. All statistical analyses were conducted using R software (v4.2.0) or GraphPad Prism 9. A two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.1. MR analysis of pQTLs and HCC\u003c/h2\u003e \u003cp\u003eBased on the HCC GWAS summary statistics (outcome ID: C3_HEPATOCELLU_CARC_EXALLC), a total of 545 pQTL\u0026ndash;outcome associations were extracted using the extract_instruments and extract_outcome_data functions. MR analyses were subsequently performed using the IVW method, identifying 316 pQTL-associated genes that showed significant causal associations with HCC risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, IVW p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The complete forest plots are provided in Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Among these, 138 genes (e.g., BST2, DCN, and PGLYRP1) were associated with an increased risk of HCC, whereas 178 genes (e.g., CLIC4, DMC1, and NEIL1) were associated with a decreased risk of HCC. Sensitivity analyses demonstrated that the overall causal estimates remained stable after sequential removal of individual SNPs, indicating that the results were not driven by any single instrument and were therefore robust.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.2. MR analysis of eQTLs and HCC\u003c/h2\u003e \u003cp\u003eTo further identify key candidate genes, eQTL analyses were performed for the 316 pQTL-associated genes, resulting in the extraction of 10 eQTLs\u0026ndash;outcome associations. Subsequent MR analyses identified eight genes that were significantly associated with HCC risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, IVW \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, higher genetically predicted expression levels of KREMEN1, CR1, WBP2, YME1L1, and MSI2 were significantly associated with an increased risk of HCC (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.309\u0026ndash;2.407). In contrast, increased expression of CCDC24, PCSK7, and CTSH was associated with a reduced risk of HCC (OR\u0026thinsp;=\u0026thinsp;0.737\u0026ndash;0.819). Leave-one-out sensitivity analyses showed that the causal estimates remained consistent after the exclusion of each individual SNP in turn, confirming the robustness of these associations (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA\u0026ndash;H).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Mediation analysis: establishing a gene\u0026ndash;metabolite\u0026ndash;HCC causal axis\u003c/h2\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. MR analysis of metabolites and HCC\u003c/h2\u003e \u003cp\u003eTo identify potential causal metabolites associated with HCC at the metabolic level, HCC GWAS summary statistics (outcome ID: C3_HEPATOCELLU_CARC_EXALLC) were used to extract metabolite\u0026ndash;outcome pairs through the extract_instruments and extract_outcome_data functions. A total of 118 metabolite\u0026ndash;outcome pairs were obtained. Two-sample Mendelian randomization (MR) analyses were subsequently performed to systematically evaluate the causal relationships between circulating metabolites and HCC risk. This analysis identified 64 metabolite\u0026ndash;HCC pairs showing significant causal associations (IVW \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Among these, several metabolites were negatively associated with HCC risk, including the 3-methyl-2-oxovalerate to 4-methyl-2-oxopentanoate ratio, serine to alpha-ketobutyrate ratio, 1-linoleoylglycerol (18:2), valine levels, and phosphoethanolamine levels. In contrast, other metabolites\u0026mdash;such as arachidonoylcarnitine (C20:4), 2-hydroxybutyrate/2-hydroxyisobutyrate, cystathionine levels, and the aspartate to asparagine ratio\u0026mdash;were positively associated with increased HCC risk.\u003c/p\u003e \u003cp\u003eLeave-one-out sensitivity analyses showed that sequential removal of any single SNP did not materially alter the overall effect estimates or confidence intervals, indicating that the 64 identified metabolite\u0026ndash;HCC causal associations were robust and reliable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. MR analysis of eQTLs and metabolites\u003c/h2\u003e \u003cp\u003eTo further elucidate how genetic regulation may influence HCC development through metabolic pathways, the eight significant eQTL-associated genes identified above were paired as exposures with the 64 HCC-related metabolites as outcomes. A total of 25 eQTL\u0026ndash;metabolite pairs were extracted. Two-sample MR analyses identified eight significant causal associations (IVW \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These causal links primarily involved five key genes (YME1L1, MSI2, PCSK7, KREMEN1, and CR1) and eight metabolites, including arachidonoylcarnitine (C20:4), 2-hydroxybutyrate/2-hydroxyisobutyrate, carotene diol (1), the threonine to alpha-ketobutyrate ratio, the citrate to 4-hydroxyphenylpyruvate ratio, 4-methoxyphenol sulfate, the aspartate to asparagine ratio, and the glutarate (C5-DC) to salicylate ratio.\u003c/p\u003e \u003cp\u003eLeave-one-out sensitivity analyses further demonstrated that the overall effect directions and confidence intervals remained stable after sequential exclusion of individual SNPs, with no substantial deviations observed, supporting the robustness of the identified eQTL\u0026ndash;metabolite causal relationships (Supplementary Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA\u0026ndash;H).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Metabolite-mediated effects and reverse causality analyses\u003c/h2\u003e \u003cp\u003eAfter establishing the causal relationships between eQTLs and metabolites, as well as between metabolites and HCC, we further investigated the mediating roles of metabolites in the gene\u0026ndash;HCC axis. The results indicated that 2-hydroxybutyrate/2-hydroxyisobutyrate levels, the C5-DC to salicylate ratio, and the aspartate to asparagine ratio may serve as key mediators linking CR1, YME1L1, and MSI2 to HCC risk. Colocalization analyses performed using coloc demonstrated strong evidence of shared causal variants at the eQTL\u0026ndash;disease level for these three genes (SNP.PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.95) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD\u0026ndash;F), indicating that the same genetic variants jointly influence gene expression and disease susceptibility, thereby supporting a common genetic architecture. To further validate the directionality and robustness of the inferred causal relationships, reverse Mendelian randomization analyses were conducted for the three genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). No significant reverse causal effects were observed for CR1, MSI2, or YME1L1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.962, 0.149, and 0.083, respectively), providing additional support for a unidirectional causal pathway from genetically regulated gene expression to metabolic alterations and ultimately to HCC development.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Clinical relevance and drug response analyses of key genes\u003c/h2\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. Associations between key gene expression and chemotherapeutic drug sensitivity\u003c/h2\u003e \u003cp\u003eBased on the GDSC database, drug response in tumor samples was predicted using the pRRophetic R package, and correlations between the expression levels of the key genes (CR1, YME1L1, and MSI2) and IC50 were analyzed. The results showed that the expression levels of the key genes were significantly associated with the predicted IC50 values of multiple anticancer agents (Supplementary Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eA\u0026ndash;F). Specifically, high expression of CR1, YME1L1, and MSI2 was significantly associated with increased sensitivity (lower IC50 values) to bleomycin, bosutinib, and doxorubicin (Supplementary Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eA, C, F). In contrast, for bortezomib, bryostatin-1, and dasatinib, the relationships between gene expression and IC50 values exhibited drug- and gene-specific patterns (Supplementary Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eB, D, E). Collectively, these findings suggest that the expression status of these key genes may serve as potential predictors of chemotherapeutic response in HCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. Associations between key gene expression and clinicopathological features\u003c/h2\u003e \u003cp\u003eTo evaluate the clinical relevance of the key genes, clinicopathological characteristics from the TCGA-HCC cohort\u0026mdash;including age, sex, tumor stage, T stage, N stage, and M stage\u0026mdash;were systematically analyzed. Using boxplot visualization and appropriate statistical tests (Student\u0026rsquo;s t-test or Kruskal\u0026ndash;Wallis test), we found that the expression levels of CR1, YME1L1, and MSI2 were all significantly associated with tumor stage and T stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA\u0026ndash;X). Notably, CR1 expression showed strong positive correlations with tumor stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.5 \u0026times; 10⁻\u0026sup3;), tumor grade (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.0 \u0026times; 10⁻⁴), T stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.8 \u0026times; 10⁻\u0026sup3;), and N stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.72 \u0026times; 10⁻\u0026sup2;). In advanced-stage (III\u0026ndash;IV) or highly invasive (T3\u0026ndash;T4) tumors, the expression levels of these three genes were consistently elevated, indicating that they may play important roles in HCC progression and invasiveness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3. Molecular docking analyses validating the potential interactions of CR1, YME1L1, and MSI2 with dasatinib\u003c/h2\u003e \u003cp\u003eGiven that drug sensitivity analyses indicated that dasatinib, a multi-target tyrosine kinase inhibitor, exhibited significant IC50 differences between high- and low-expression groups of CR1, YME1L1, and MSI2 (Supplementary Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eE), we further explored the potential binding modes between dasatinib and these proteins at the structural level. Protein\u0026ndash;compound pairs were defined as follows: CR1 (P17927)\u0026ndash;dasatinib, YME1L1 (Q96TA2)\u0026ndash;dasatinib, and MSI2 (Q96DH6)\u0026ndash;dasatinib. Docking binding energies are summarized in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, and representative docking conformations are shown in Supplementary Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eA\u0026ndash;C. The predicted binding energies were \u0026minus;\u0026thinsp;7.0 kcal/mol for CR1\u0026ndash;dasatinib, \u0026minus;\u0026thinsp;7.3 kcal/mol for YME1L1\u0026ndash;dasatinib, and \u0026minus;\u0026thinsp;6.3 kcal/mol for MSI2\u0026ndash;dasatinib. Dasatinib was predicted to stably occupy the binding pockets of CR1, YME1L1, and MSI2, forming multiple hydrogen bonds and hydrophobic interactions. These results suggest potential direct interactions between dasatinib and these proteins, providing structural evidence supporting their candidacy as therapeutic targets.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Key gene expression reshapes the association patterns between the intratumoral microbiome and the immune microenvironment\u003c/h2\u003e \u003cp\u003eTo investigate the roles of the key genes (CR1, YME1L1, and MSI2) within the tumor microenvironment, we analyzed the relationships between their expression levels and intratumoral microbial features. The results showed that the expression levels of the key genes did not significantly alter overall microbial α-diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;C), but markedly influenced the interactions between the microbiome and the immune system. Specifically, the abundances of certain intratumoral bacterial genera were broadly correlated with immune cell infiltration levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). For example, Prochlorococcus and Succinimonas were positively correlated with subsets of innate immune cells, whereas Campylobacter and Desulfotalea were negatively correlated with adaptive immune populations, including γδ T cells and Tregs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther network analyses demonstrated that CTL immune escape\u0026ndash;related genes were primarily embedded within interaction networks involving specific microbial clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), whereas immunoregulatory genes exhibited more complex and widespread microbial interaction patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). These findings indicate that functionally distinct key genes may participate in tumor microenvironment regulation through differential \u0026ldquo;microbiome\u0026ndash;immune\u0026rdquo; coupling pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Single-cell and spatial transcriptomic analyses reveal the cellular specificity and spatial heterogeneity of key genes\u003c/h2\u003e \u003cp\u003eTo systematically characterize the expression patterns of the key genes (CR1, YME1L1, and MSI2) in the HCC microenvironment at both the cellular and tissue levels, we integrated scRNA-seq and spatial transcriptomic datasets.\u003c/p\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1. Single-cell level: key genes exhibit cell type\u0026ndash;specific expression patterns\u003c/h2\u003e \u003cp\u003eAfter rigorous quality control of high-quality single-cell data comprising 53,474 cells, the distributions and correlations of nCount_RNA, nFeature_RNA, percent.mt, and percent.ribo across samples were visualized (Supplementary Fig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eA, B). A total of 2,000 highly variable genes were identified (Supplementary Fig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eD), and the data were subsequently normalized and subjected to dimensionality reduction. PCA and Elbow plots were used to determine the major principal components (Supplementary Fig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eC, E), followed by batch-effect correction using the Harmony algorithm (Supplementary Fig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eUMAP analysis identified 12 distinct cellular clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), which were annotated into eight major cell types, including natural killer(NK)/T cells, macrophages, monocytes, endothelial cells, fibroblasts, B cells, epithelial cells, and plasma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The expression patterns of canonical marker genes for each cell type are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, and their proportional distributions are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, with fibroblasts exhibiting the most pronounced inter-sample variability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe key genes displayed markedly distinct expression preferences across cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). CR1 exhibited highly cell-specific expression, being predominantly enriched in macrophages and monocytes. MSI2 showed a broader expression pattern, with relatively high expression in epithelial cells and subsets of stromal and myeloid cells, whereas YME1L1 demonstrated the most ubiquitous expression, being detected across nearly all identified cell types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePathway activity analyses revealed that, compared with MSI2 and YME1L1, high CR1 expression was significantly enriched in multiple immune- and metabolism-related pathways, including complement activation, interferon-γ response, inflammatory response, JAK\u0026ndash;STAT3 signaling, TNF-α signaling, KRAS signaling, and apoptosis, underscoring a central role for CR1 in remodeling the tumor immune microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eFurthermore, immune infiltration analysis using CIBERSORT demonstrated that CR1 expression was strongly positively correlated with M2 macrophage infiltration, while being significantly negatively correlated with CD8⁺ T cells, NK cells, and M1 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In contrast, MSI2 and YME1L1 were more closely associated with NK cells, monocytes, and subsets of adaptive immune cell populations. Collectively, these findings highlight CR1 as a pivotal regulatory factor that may promote the formation of an immunosuppressive microenvironment by coordinating innate and adaptive immune responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2. Spatial transcriptomic level: key genes display distinct spatial heterogeneity\u003c/h2\u003e \u003cp\u003eSpatial transcriptomic analyses revealed pronounced spatial heterogeneity and cellular architectural organization within hepatocellular carcinoma tissues. After data normalization and dimensionality reduction, six distinct spatial clusters were identified through unsupervised clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). To resolve the cellular composition of each spatial spot, RCTD-based deconvolution was applied, enabling inference of cell-type proportions at each spatial location and construction of high-resolution spatial maps of cellular distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D). Expression patterns of canonical marker genes across spatial domains further validated the reliability of the deconvolution results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). The key genes CR1, YME1L1, and MSI2 exhibited markedly distinct spatial expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF\u0026ndash;H). YME1L1 showed the most widespread and highest-intensity expression, MSI2 displayed relatively broad but moderate expression, whereas CR1 demonstrated a highly restricted and spatially localized enrichment pattern.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese findings provide direct evidence that CR1 is specifically localized to discrete spatial niches within the tumor microenvironment, suggesting that CR1 may exert unique regulatory functions by acting on specific cellular assemblies or microanatomical domains within local tumor ecosystems.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Clinical validation: CR1 is highly expressed in tumor tissues and associated with an immunosuppressive microenvironment\u003c/h2\u003e \u003cdiv id=\"Sec39\" class=\"Section3\"\u003e \u003ch2\u003e3.7.1. CR1 is specifically overexpressed in HCC tumor tissues\u003c/h2\u003e \u003cp\u003eTo validate the expression profile of CR1, paired HCC tumor tissues and adjacent non-tumorous liver tissues were analyzed by IHC, IF, and qRT-PCR. IHC staining demonstrated that CR1 expression intensity was markedly higher in tumor tissues than in adjacent tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Quantitative analysis further confirmed a significant increase in both the positive staining area and staining intensity of CR1 in tumor samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Consistently, IF analysis revealed a significantly elevated CR1 fluorescence signal in tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), and qRT-PCR showed that CR1 mRNA expression levels were significantly higher in tumor tissues than in paired adjacent tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Collectively, these results demonstrate that CR1 is specifically overexpressed within the HCC tumor microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003e3.7.2. High CR1 expression is associated with M2 macrophage enrichment and reduced CD8⁺ T-cell infiltration\u003c/h2\u003e \u003cp\u003eIF co-staining analyses revealed the spatial relationships between CR1 expression and macrophage subsets. In tumor tissues, CR1 signals exhibited significant co-localization with the M2 macrophage marker CD206, whereas an inverse and mutually exclusive spatial distribution was observed with the M1 macrophage marker CD86 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, G). Quantitative analyses demonstrated that the proportion of CD206⁺ M2 macrophages was significantly higher in tumor tissues than in adjacent non-tumorous tissues, while the proportion of CD86⁺ M1 macrophages was reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, H). Furthermore, based on 30 surgically resected HCC specimens, patients were stratified into CR1-high (n\u0026thinsp;=\u0026thinsp;15) and CR1-low (n\u0026thinsp;=\u0026thinsp;15) groups according to the median IHC H-score of CR1 in tumor tissues. Compared with the CR1-low group, patients with high CR1 expression exhibited significantly higher proportions of advanced-stage disease (Stage III\u0026ndash;IV) and vascular invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eM, N). These findings indicate that high CR1 expression is not only closely associated with an immunosuppressive microenvironment but also significantly correlated with more aggressive tumor phenotypes.\u003c/p\u003e \u003cp\u003eTo further validate these observations at the single-cell level, flow cytometric analyses were performed. The results showed that the proportion of CD206⁺ M2-like macrophages was significantly increased in tumor tissues compared with adjacent tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI, K), and the percentage of CR1⁺ cells within the macrophage population was also markedly elevated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ, L), consistent with the spatial immunofluorescence findings. In parallel, the proportion of CD8⁺ T cells among CD45⁺ leukocytes was significantly decreased in tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B), indicating insufficient infiltration of cytotoxic T lymphocytes. Collectively, these clinical sample\u0026ndash;based data demonstrate that high CR1 expression is tightly associated with M2 macrophage enrichment and reduced CD8⁺ T-cell infiltration, jointly defining an immunosuppressive tumor microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section2\"\u003e \u003ch2\u003e3.8. CR1 drives macrophage M2 polarization and suppresses CD8⁺ T-cell function\u003c/h2\u003e \u003cdiv id=\"Sec42\" class=\"Section3\"\u003e \u003ch2\u003e3.8.1. Validation of CR1 overexpression and knockdown efficiency\u003c/h2\u003e \u003cp\u003eTo verify the efficiency of CR1 genetic manipulation, THP-1\u0026ndash;derived M0 macrophages were used to establish three experimental groups: negative control (NC), CR1 overexpression (OE), and CR1 knockdown (KD). QRT-PCR analysis showed that CR1 mRNA expression was significantly increased in the OE group and markedly reduced in the KD group compared with the NC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Consistently, Western blot analysis demonstrated a pronounced upregulation of CR1 protein in the OE group and a significant downregulation in the KD group, in accordance with the mRNA expression changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Densitometric quantification further confirmed that these differences were statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Collectively, these results confirm the successful establishment of CR1 overexpression and knockdown macrophage models at both the transcriptional and protein levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec43\" class=\"Section3\"\u003e \u003ch2\u003e3.8.2. CR1 promotes macrophage M2 polarization and enhances phagocytic function\u003c/h2\u003e \u003cp\u003eFollowing successful establishment of the CR1-manipulated macrophage models, we next evaluated the effects of CR1 on macrophage polarization phenotypes and functional properties. QRT-PCR analysis showed that, compared with the NC group, mRNA expression of the M2-associated marker IL-10 was significantly upregulated in the CR1 OE group and markedly reduced in the CR1 KD group. In contrast, the M1-associated marker TNF-α was downregulated in the OE group and upregulated in the KD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF, G), indicating that CR1 expression drives macrophage polarization toward an M2 phenotype. Flow cytometric analysis of surface markers further demonstrated that CR1 overexpression significantly increased the proportion of CD206⁺ M2 macrophages while reducing the proportion of CD86⁺ M1 macrophages, whereas CR1 knockdown produced the opposite effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH, I). Functional assays revealed that CR1 overexpression markedly enhanced macrophage phagocytic capacity, whereas CR1 knockdown significantly impaired this function (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ), indicating that CR1 not only regulates macrophage polarization status but also directly modulates macrophage effector functions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec44\" class=\"Section3\"\u003e \u003ch2\u003e3.8.3. CR1⁺ macrophages suppress CD8⁺ T-cell proliferation and cytotoxic function\u003c/h2\u003e \u003cp\u003eTo clarify the regulatory effects of CR1⁺ macrophages on CD8⁺ T-cell function, an in vitro co-culture system was established, and T-cell proliferation and effector molecule expression were assessed. CFSE dilution assays showed that, compared with the NC group, CR1-overexpressing macrophages significantly suppressed CD8⁺ T-cell proliferation, whereas CR1 knockdown markedly enhanced T-cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith respect to effector function, the proportion of IFN-γ⁺ CD8⁺ T cells was significantly reduced in the CR1 overexpression group and increased in the CR1 knockdown group. Similarly, the percentage of granzyme B⁺ CD8⁺ T cells was decreased in the OE group and elevated in the KD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB, C). In the co-culture system, PD-L1 expression in macrophages was significantly upregulated in the CR1 overexpression group and downregulated in the CR1 knockdown group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Collectively, these results indicate that CR1 mediates macrophage-induced suppression of CD8⁺ T-cell function, at least in part, through upregulation of PD-L1.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study integrates multi-omics analyses with systematic experimental validation to identify CR1 as a key molecular link connecting genetic susceptibility, metabolic dysregulation, and the immunosuppressive tumor microenvironment in HCC. Our findings establish a coherent gene-to-function evidence framework: Mendelian randomization analyses demonstrate a causal effect of CR1 expression on HCC risk; mediation analyses implicate specific circulating metabolites as potential intermediates along this pathway; and multi-omics profiling together with wet-lab experiments collectively reveal that CR1 is highly expressed in TAMs, where it promotes M2 polarization and suppresses CTL function, thereby shaping a tumor microenvironment permissive for immune escape.\u003c/p\u003e \u003cp\u003eFirst, our genetic evidence provides strong causal support for a pathogenic role of CR1 in HCC. By leveraging large-scale population-based eQTL and GWAS datasets, Mendelian randomization analyses effectively minimized confounding bias inherent to conventional observational studies and established genetically predicted high CR1 transcription levels in blood as an independent risk factor for HCC. This finding extends current understanding of the role of the complement system in cancer. The complement cascade is widely regarded as a \u0026ldquo;double-edged sword,\u0026rdquo; with previous studies primarily focusing on its direct cytolytic effects mediated by the membrane attack complex or its pro-inflammatory functions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In contrast, our results suggest that, as a critical complement regulatory protein and receptor, CR1 may act as an \u0026ldquo;accomplice\u0026rdquo; in chronic inflammation\u0026ndash;driven hepatocarcinogenesis through more refined immunomodulatory mechanisms. The observed mediation links between CR1 and specific circulating metabolites further imply that genetically determined CR1 levels may indirectly facilitate hepatic tumorigenesis by perturbing systemic immunometabolic homeostasis.\u003c/p\u003e \u003cp\u003eMore importantly, we delineated the specific cellular basis and molecular mechanisms underlying the pro-tumorigenic functions of CR1 at both the tissue and cellular levels. Single-cell and spatial transcriptomic analyses precisely localized CR1 expression to TAMs, particularly the M2 macrophage subset. Subsequent clinical validation demonstrated that high CR1 expression was significantly associated with more advanced pathological stage, increased infiltration of M2 macrophages, and reduced CTL infiltration in patients with HCC, providing a plausible immunological explanation for the unfavorable clinical outcomes. In vitro functional assays directly confirmed the regulatory role of CR1 in macrophage phenotypic programming: CR1 overexpression drove macrophages toward an M2 phenotype characterized by elevated CD206 and IL-10 expression, whereas CR1 knockdown promoted a shift toward a pro-inflammatory M1 phenotype. These findings elevate CR1 from a conventional complement clearance receptor to an intrinsic regulator of macrophage polarization. Mechanistically, this effect may involve CR1-mediated internalization of complement fragments (such as C3b) and the subsequent activation of downstream signaling pathways, which may intersect with canonical M2-polarizing pathways, including the IL-4/STAT6 axis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and warrants further investigation.\u003c/p\u003e \u003cp\u003eBuilding upon these observations, we further elucidated the functional consequences of CR1 on tumor immune surveillance. Macrophage\u0026ndash;CD8⁺ T-cell co-culture experiments demonstrated that CR1-overexpressing M2 macrophages markedly suppressed CTL proliferative capacity and effector molecule production, including IFN-γ and granzyme B. These findings were fully consistent with the bioinformatic observations that high CR1 expression was negatively correlated with CTL-related gene signatures, thereby establishing a coherent logical continuum from gene expression and cellular phenotype to immune function. This suppressive effect may be attributable to downregulation of co-stimulatory molecules (such as CD86) and upregulation of immune checkpoint molecules (such as PD-L1) on CR1-high macrophages, providing a theoretical rationale for therapeutic strategies that combine targeting of the complement pathway with immune checkpoint blockade [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, this study provides a pioneering exploration of the potential links between the intratumoral microbiome and the CR1\u0026ndash;immune axis. Our analyses showed that certain bacterial genera positively correlated with CR1 expression were also positively associated with immunosuppressive markers and negatively associated with CTL activity. These findings suggest that specific intratumoral microbial communities may, through currently undefined mechanisms\u0026mdash;such as providing persistent antigenic stimulation or secreting bioactive metabolites\u0026mdash;sustain or amplify CR1-dependent immunosuppressive programs in macrophages [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Although this association remains preliminary, it offers a novel perspective on \u0026ldquo;microbiome\u0026ndash;immune\u0026rdquo; interactions in HCC, proposing that microbial communities may reshape the tumor microenvironment by modulating the function of host innate immune receptors such as CR1 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNaturally, several limitations of this study should be acknowledged. First, the Mendelian randomization analyses were primarily based on datasets from populations of European ancestry, and the generalizability of these conclusions to other ethnic groups requires further validation. Second, the precise downstream signaling pathways through which CR1 regulates macrophage polarization remain incompletely defined. Finally, the functional and causal relationships between the intratumoral microbiome and CR1 expression need to be validated using more sophisticated in vivo and in vitro models, including advanced co-culture systems and germ-free or gnotobiotic animal models.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eBy integrating Mendelian randomization, multi-omics analyses, clinical cohort validation, and in vitro functional experiments, this study systematically elucidates the pivotal role of CR1 in shaping the immunosuppressive tumor microenvironment of HCC. Genetic analyses demonstrated that genetically predicted circulating CR1 levels were significantly associated with HCC risk (IVW OR\u0026thinsp;=\u0026thinsp;0.907, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). Within tumor tissues, CR1 was specifically expressed in tumor-associated macrophages and co-localized with the M2 macrophage marker CD206, and was significantly correlated with CD8⁺ T-cell exhaustion and unfavorable clinical outcomes. Functionally, CR1 not only drives macrophage polarization toward an M2-like phenotype and enhances phagocytic capacity, but also suppresses CD8⁺ T-cell proliferation and effector function through upregulation of PD-L1. Collectively, this study establishes CR1 as a central regulatory factor of the HCC immune microenvironment across the \u0026ldquo;genetic\u0026ndash;expression\u0026ndash;functional\u0026ndash;clinical\u0026rdquo; spectrum, providing a strong theoretical rationale for its development as both a prognostic biomarker and a potential therapeutic target in immunotherapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eone-way analysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomplementary DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecarboxyfluorescein succinimidyl ester\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCR1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomplement receptor 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecytotoxic T lymphocyte\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeQTLs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eexpression quantitative trait loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFPKM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efragments per kilobase of transcript per million mapped reads\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenomics of Drug Sensitivity in Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egenome-wide association study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehepatocellular carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIC50\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehalf-maximal inhibitory concentration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmunofluorescence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIFN-γ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterferon-gamma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmunohistochemistry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin-4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin-10\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-13\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin-13\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einstrumental variables\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einverse-variance weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elinkage disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR-PRESSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian Randomization Pleiotropy RESidual Sum and Outlier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBMCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperipheral blood mononuclear cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD-L1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogrammed death-ligand 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epQTLs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein quantitative trait loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eqRT-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003equantitative real-time polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCTD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erobust cell type decomposition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003escRNA-seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle-cell RNA sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard error of the mean\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle-nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003espatial transcriptomics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAMs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor-associated macrophages\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTGF-β\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etransforming growth factor-beta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF-α\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor necrosis factor-alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTregs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eregulatory T cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniform Manifold Approximation and Projection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eunique molecular identifier.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and relevant ethical guidelines. The study protocol was reviewed and approved by the Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University (approval no. SWYX:NO.2025-701). This study exclusively used retrospectively archived human tissue samples and related clinical data, all of which were de-identified prior to analysis. According to the approval of the ethics committee, the requirement for written informed consent was waived.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe publicly available datasets analyzed in this study are available from the following repositories: FinnGen (https://www.finngen.fi/en), eQTLGen (https://www.eqtlgen.org), The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE149614 and GSE245908. Metabolomics GWAS data were obtained from the Canadian Longitudinal Study on Aging. Drug response data were retrieved from the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/). Protein structures were obtained from the AlphaFold database (https://alphafold.com/), and compound structures were retrieved from PubChem (https://pubchem.ncbi.nlm.nih.gov/).\u003c/p\u003e\n\u003cp\u003eThe clinical tissue samples and experimental data generated in this study are not publicly available due to ethical and privacy restrictions, but are available from the corresponding author upon reasonable request and with permission of the Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Foundation of Shandong Province (Youth Program) (ZR2025QC880).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhengjian Wang and Zhe Wang conceived and designed the study. Zhengjian Wang performed the Mendelian randomization analyses, multi-omics bioinformatics analyses, and statistical analyses. XJ and LZ contributed to data curation, literature review, and interpretation of the results. KZ and WY performed the in vitro experiments, including cell culture, gene manipulation, flow cytometry, qRT-PCR, and Western blot analyses. HZ contributed to immunohistochemistry, immunofluorescence staining, and clinical sample processing. HC participated in data interpretation, figure preparation, and critical revision of the manuscript. FL supervised the entire project and critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\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\u003eAll authors agree to be published.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e1. \u0026nbsp; Mauro E, de Castro T, Zeitlhoefler M, Sung MW, Villanueva A, Mazzaferro V, et al. Hepatocellular carcinoma: Epidemiology, diagnosis and treatment. JHEP Rep. 2025;7(12):101571.\u003c/li\u003e\n \u003cli\u003e2.\u0026nbsp; \u0026nbsp;Hwang SY, Danpanichkul P, Agopian V, Mehta N, Parikh ND, Abou-Alfa GK, et al. Hepatocellular carcinoma: updates on epidemiology, surveillance, diagnosis and treatment. 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Association of intratumoral microbiome diversity with hepatocellular carcinoma prognosis. mSystems. 2025;10(1):e0076524.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Severe acute pancreatitis–associated acute lung injury (SAP-ALI), Lung microbiome, Gut–lung axis, Microbial dysbiosis, Immunometabolism, Metabolic reprogramming, Therapeutic targeting","lastPublishedDoi":"10.21203/rs.3.rs-8712240/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8712240/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHepatocellular carcinoma (HCC) is characterized by a profoundly immunosuppressive tumor microenvironment (TME), which severely limits therapeutic efficacy. By integrating a multi-omics strategy, we identified complement receptor 1 (CR1) as a central regulator of this immunosuppressive milieu.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe performed Mendelian randomization (MR) analyses to infer the causal relationship between genetically predicted circulating CR1 levels and HCC risk, followed by metabolite mediation analyses. Bulk, single-cell, and spatial transcriptomic datasets from public cohorts and clinical samples were systematically analyzed to characterize CR1 expression patterns and cellular localization. Tumor microbiome profiling was conducted to explore potential microbe\u0026ndash;immune interactions. Functional validation was performed using THP-1\u0026ndash;derived macrophages, including gain- and loss-of-function experiments, phagocytosis assays, and macrophage\u0026ndash;CD8⁺ T-cell co-culture systems.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMR analysis identified a causal link between genetically predicted circulating CR1 levels and increased HCC risk (IVW OR\u0026thinsp;=\u0026thinsp;0.907, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), with specific blood metabolites potentially mediating this effect. Multi-omics profiling revealed that CR1 was overexpressed specifically in tumor tissues and predominantly enriched in tumor-associated macrophages (TAMs), where its expression strongly correlated with M2 polarization signatures. Elevated CR1 expression correlated with reduced CD8⁺ T cell infiltration, increased T cell exhaustion, and poorer patient survival. Spatial transcriptomics further confirmed significant co-localization of CR1 with the M2 marker CD206. Functionally, CR1 overexpression reprogrammed macrophages into an M2-like immunosuppressive phenotype, characterized by upregulation of CD206 and IL-10 and enhanced phagocytic activity, while CR1 knockdown promoted an M1-like state. Crucially, in co-culture systems, CR1-high macrophages markedly inhibited CD8⁺ T cell proliferation and effector functions\u0026mdash;including IFN-γ production and granzyme B expression\u0026mdash;concomitant with increased PD-L1 expression. Tumor microbiome analysis extended our findings, suggesting potential crosstalk between intratumoral bacteria and the CR1-driven immunosuppressive axis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study identifies CR1 as an environmentally responsive master regulator that reshapes the immunological landscape of HCC by reprogramming TAMs, thereby positioning CR1 as a highly promising therapeutic target for restoring antitumor immunity.\u003c/p\u003e","manuscriptTitle":"CR1(+) Tumor-Associated Macrophages Orchestrate an Immunosuppressive Niche in Hepatocellular Carcinoma: A Genetic and Multi-omics Dissection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 14:41:49","doi":"10.21203/rs.3.rs-8712240/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-02-25T06:53:44+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-25T05:20:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T02:11:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2026-01-27T10:32:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08c9f9fe-7a34-4004-9d6c-6c7179fa4f6e","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T12:20:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 14:41:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8712240","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8712240","identity":"rs-8712240","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}