A pan-cancer landscape of LILRB4 identifies it as a context-dependent marker of the myeloid and antigen-presentation axis

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By integrating public multi-omics evidence, we delineate its panorama across expression, genetic alterations, DNA methylation, phosphorylation, and immune infiltration, and relate these to patient outcomes. LILRB4 is enriched in immune tissues and in monocytes, macrophages, and dendritic cells, and is upregulated in multiple cancers, with occasional discordance between transcript and protein levels. Its association with survival is cancer-type dependent—protective in cervical cancer, skin cutaneous melanoma, and uterine corpus endometrial carcinoma, but indicating higher risk in lower-grade glioma and in recurrence-related metrics of prostate adenocarcinoma. Genetic alterations are dominated by amplification and missense mutations and cluster within immunoglobulin domains, including a P184 hotspot, but carriers of alterations do not exhibit consistent survival differences. LILRB4 expression positively correlates with tumor mutational burden, microsatellite instability, and homologous recombination deficiency in several cancers. In lower-grade glioma, hypermethylation at a key promoter-proximal CpG site associates with longer survival, and multiple cancers display site- and cancer-specific phosphorylation remodeling. LILRB4 correlates strongly with macrophage infiltration, and co-expression and interaction networks are enriched for antigen processing and presentation, pointing to HLA-DRA. Collectively, these data position LILRB4 as a context-dependent marker of the myeloid and antigen-presentation axis in the tumor microenvironment and provide a basis for stratification and immunoregulatory strategies. LILRB4 pan-cancer macrophage infiltration antigen presentation phosphorylation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Inhibitory receptors play key roles in myeloid pathways within the tumor microenvironment, shaping antigen recognition/presentation and immune evasion 1–3 . LILRB4, also known as ILT3, is an inhibitory receptor bearing ITIM motifs 4 , predominantly expressed in monocytes 5 , macrophages 6–8 , and dendritic cells 9,10 . It signals via SHP-1/2 11,12 to dampen activation 13,14 and modulate antigen presentation 14,15 and T-cell activation thresholds 16,17 . Although its role in innate immune regulation has drawn attention, its cross-tumor landscape and clinical significance have not been systematically defined. Prior studies have largely focused on individual cancer types or single-omics layers, lacking panoramic comparisons that span transcripts, proteins, genetic alterations, epigenetics, and immune infiltration 18–20 . The relationship between LILRB4 and genomic instability metrics remains unclear; the functional implications of site-specific phosphorylation and promoter methylation across cancers also lack a unified framework; and potential discrepancies between transcript and protein levels remain to be resolved. To address these gaps, we integrated multi-omics resources to construct a pan-cancer landscape of LILRB4, covering expression and protein abundance, alteration spectra and mutation distribution, promoter methylation, and site-specific phosphorylation. Together with multi-algorithm immune deconvolution and co-expression/interaction network analysis, we systematically evaluated its links to macrophage infiltration and antigen-presentation pathways, and examined associations with genomic instability in a cross-cancer context. This study shows heterogeneous upregulation of LILRB4 across cancers with cancer-type-dependent prognostic associations; its epigenetic and phosphorylation remodeling converge with myeloid infiltration and antigen presentation, supporting its biological positioning as a context-dependent marker in the tumor microenvironment. Methods Study design and overall workflow We conducted a retrospective pan-cancer analysis based on public multi-omics resources, building a cross-layer comparison framework around LILRB4 (tissue/cell expression, cross-cancer differences, clinical outcomes, genetic alterations, promoter methylation, site-specific phosphorylation, and immune infiltration). Co-expression and protein-interaction data were integrated for functional clues and enrichment analysis. Analyses and figure order mirror the Results section. Portals, versions, and access dates are listed in Supplementary Table S1 (Resources and portals). Gene and phylogeny information Nucleotide and protein sequences of LILRB4 were retrieved from NCBI. Ensembl GeneTree/homology modules were used for phylogeny and paralog identification to contextualize family attributes and evolutionary background. Tissue and cell-type expression Using the Human Protein Atlas (HPA) Consensus dataset, we profiled LILRB4 expression in normal tissues; HPA single-cell modules were used to assess enrichment in key immune cells (monocytes, macrophages, dendritic cells). Expression units follow platform-provided normalization. Cross-cancer expression with supplemented normal controls We compared tumors versus paired/unpaired normals in TCGA via TIMER2.0. For cancer types lacking adequate TCGA normals, GEPIA2’s TCGA+GTEx pipeline supplemented controls. Within-cancer normalization and statistics followed platform defaults. Stage-wise expression comparisons were conducted using GEPIA2 staging modules with default settings. Protein and phosphoprotein expression Tumor–normal differences in total protein and site-level phosphorylation were assessed using CPTAC resources. Total protein differences served as reference to transcript-level trends. Site-level phosphorylation comparisons across cancers focused on S318, S319, S377, S417, T386, reporting direction and significance with emphasis on site- and cancer-specific remodeling. Survival analysis Overall survival (OS) and disease-free survival (DFS) were evaluated with GEPIA/GEPIA2 Kaplan–Meier and log-rank modules, using default expression stratification. Concordant or opposite trends between OS and DFS were reported to highlight cancer-type dependence. Genetic alteration analysis We used cBioPortal (TCGA) to summarize LILRB4 alteration frequencies, mutation types, and site distribution; lollipop/domain maps located mutations, and copy-number gains/losses were tallied. “Alterations” refer to somatic mutations and copy-number changes, excluding germline variants. Survival differences by alteration status used cBioPortal survival modules. Genomic instability associations TMB, MSI, and HRD metrics were obtained through Sangerbox per its standard pipeline. Within cancers, correlations between LILRB4 expression and each metric used built-in correlation modules; directions and consistent patterns are reported. Promoter methylation analysis From TCGA methylation arrays, we pinpointed representative CpG probes (cg02421734, cg04680738, cg05329879, cg05922591, cg12161905), focusing on cg05329879 near the 5′UTR–first-exon junction with high variability. We compared distributions (e.g., in LGG) and conducted survival and stratified analyses via MethSurv defaults. Immune infiltration estimation Macrophage M0/M1/M2 infiltration scores were obtained from TIMER2.0’s multi-algorithm deconvolution (CIBERSORT, CIBERSORT-ABS, MCPCOUNTER, EPIC, xCell, TIMER, quanTIseq, TIDE) with default parameters. Cross-cancer heatmaps and representative cancer details used TIMER2.0 correlation outputs (Spearman). Co-expression, protein interactions, and functional enrichment Top LILRB4 co-expressed genes were taken from GEPIA2; experimentally supported interactors were taken from STRING (evidence channel: “experiments”). The intersection identified core genes (HLA-DRA). KEGG and GO:MF enrichment on combined sets highlighted immune-related pathways. Statistics and reproducibility All statistics (differential tests, correlations, survival, enrichment) were reported directly from platform defaults (GEPIA2, TIMER2.0, UALCAN/CPTAC, cBioPortal, MethSurv, STRING). We did not reimplement algorithms locally. Significance thresholds and corrections follow platform outputs/figure legends. Figures were assembled in R using tidyverse, readr, reshape2, ggplot2, ggforce, RColorBrewer, ComplexHeatmap, circlize, VennDiagram, tidygraph/ggraph, grid, gridExtra, cowplot, and gtable. Results LILRB4 is highly expressed in the immune system and heterogeneously upregulated across cancers LILRB4 shows high expression across normal immune systems. It resides within the immune-regulatory-rich region 19p13.1 (Fig. S1a). Phylogeny places it within a primate-expanded immune-receptor family with paralogs including NCR1, FCAR, and GP6, suggesting origin from an amniote-stage duplication (Fig. S1b). In the HPA Consensus dataset, it is high in spleen, lung, lymph nodes, and spinal cord (Fig. S1c); single-cell resolution further indicates enrichment in monocytes, macrophages, and dendritic cells (Fig. S1d). Across tumors, LILRB4 is heterogeneously upregulated. TCGA shows significant increases in BRCA, ESCA, GBM, HNSC, KICH, KIRC, LIHC, PRAD, STAD, THCA, and UCEC; in SKCM, expression is closely related to metastatic status (Fig. 1a). After integrating GTEx to supplement normals, significant upregulation remains in DLBC, LAML, LGG, OV, SKCM, and TGCT, with downtrends in ACC and THYM (Fig. 1b). At the protein level (CPTAC), LILRB4 is higher in BRCA, KIRC, UCEC, LUSC, LUAD, HNSC, and GBM, but lower in LIHC (Fig. 1c). Stage analysis shows significant differences in KICH (F = 4.67, P = 0.00525), SKCM (F = 8.44, P = 1.52×10 -6 ), OV (F = 3.30, P = 0.0377), BLCA (F = 4.91, P = 0.00779), STAD (F = 2.84, P = 0.0376), THCA (F = 4.31, P = 0.00512), and ESCA (F = 2.95, P = 0.0341), with no clear stage dependence elsewhere (Fig. 1d; Fig. S2). Overall, LILRB4 exhibits immune-tissue-preferential expression and heterogeneous, cross-platform upregulation across cancers. LILRB4 expression associates with patient outcome in a cancer-type-specific manner In TCGA, LILRB4 shows cancer-dependent survival associations. GEPIA Kaplan-Meier analyses (Fig. 2a-b) show high expression linked to longer OS in CESC (HR = 0.55, P = 0.014), SKCM (HR = 0.58, P = 7.2e-05), and UCS (HR = 0.49, P = 0.037), but the opposite in LGG (HR = 1.7, P = 0.0038). DFS similarly differs: LGG (HR = 1.7, P = 0.0011) and PRAD (HR = 2.2, P = 0.00059) indicate higher recurrence risk with high expression, whereas UCS (HR = 0.46, P = 0.035) indicates longer DFS. Notably, UCS is protective in both OS and DFS, while LGG indicates high risk in both, underscoring cancer-type specificity. LILRB4 alterations are dominated by amplification and missense, cluster in Ig domains, and associate with TMB/MSI/HRD Alterations are mainly amplifications and missense (Fig. 3a), with higher frequencies (>2%) in SKCM, UCEC, GBM, and BLCA (Fig. 3a). Mutations cluster in the second and third Ig domains, with a recurrent P184 hotspot (P184H/L) in LUSC, STAD, SKCM, and UCEC (Fig. 3b). Survival by alteration status reveals no significant differences in OS, DSS, DFS, or PFS (all log-rank P > 0.1; Fig. 3c-f). LILRB4 expression shows positive, accompaniment-type correlations with genomic instability metrics, varying by cancer. TMB correlates in COAD, READ, and PRAD (r > 0.2, P < 0.05; Fig. 3g); MSI trends positive in STES and COAD (Fig. 3h); HRD correlates in SARC, PRAD, and KICH (Fig. 3i). These do not align with the null survival impact of P184H/L carriers, consistent with expression tracking instability background rather than being driven by a single functional mutation (Fig. 3g-i). Promoter methylation of LILRB4 in LGG is tightly linked to prognosis Promoter-region methylation is concentrated at five CpGs: cg02421734, cg04680738, cg05329879, cg05922591, and cg12161905, with cg05329879 near the 5′UTR–first-exon junction in an Open Sea (Fig. 4a). In LGG, multiple sites show generally higher methylation, with cg05329879 displaying the greatest inter-patient variability (Fig. 4b). Hypermethylation at cg05329879 associates with longer OS in LGG (HR = 0.309, P = 2.2e-09; Fig. 4c). Density indicates overall higher β values peaking around 0.8 (Fig. 4d). Stratification shows no evident age- or sex-based differences; whites show higher levels with broader spread in Black and Asian patients; no clear difference by ethnicity (Fig. 4e-h). cg05329879 is higher in G3 than G2 (Fig. 4i). LILRB4 shows site-specific phosphorylation remodeling across cancers CPTAC analyses reveal pronounced site- and cancer-specific remodeling: in BRCA, S319 is up, while S377 decreases across protein isoforms (Fig. 5a); in COAD, S377 is lower (Fig. 5b); in ccRCC, S318, S319, S377, S417, and T386 are broadly up (Fig. 5c); in LUAD, S319 is up while S377 is down (Fig. 5d); in LUSC, S318/S319 are up whereas S377 and S417 are down (Fig. 5e); in PAAD, S377 and S417 rise (Fig. 5f); in GBM, S318/S319 and S377 increase with marked group differences (Fig. 5g); in LIHC, S318/S319 increase while S377 and S407 decrease (Fig. 5h); in HNSC, S318/S319 are clearly higher in tumors (Fig. 5i). LILRB4 expression correlates strongly with tumor-associated macrophage infiltration The cross-cancer heatmap shows broad positive correlations between LILRB4 and macrophage subtype infiltration across multiple algorithms, with strong inter-algorithm consistency (Fig. 6a). In COAD, READ, KIRP, SARC, and UCEC, different macrophage subtypes (M0, M1, M2) are consistently correlated with LILRB4 across methods, with correlation coefficients exceeding 0.89, indicating a highly coherent expression–infiltration coupling. COAD ranks highest and is most stable across algorithms; thus it was chosen for representative visualization (Fig. 6b). In COAD, LILRB4 positively correlates with M0 (CIBERSORT: rho = 0.17, P = 5.49×10 -3 ). M1 is also positive (xCell: rho = 0.89, P = 2.47×10 -94 ; CIBERSORT: rho = 0.45, P = 3.72×10 -15 ). Most M2 estimates are positive (quanTIseq: rho = 0.53, P = 2.31×10 -21 ; CIBERSORT-ABS: rho = 0.85, P = 1.38×10 -76 ), with the exception of TIDE M2, which is negative (rho = −0.695, P = 6.54×10 -60 ). For total macrophage metrics, MCPCOUNTER “Macrophage/Monocyte” shows rho = 0.92 (P = 1.48×10 -113 ), EPIC total macrophages rho = 0.95 (P = 1.65×10 -137 ), xCell total macrophages rho = 0.86 (P = 3.91×10 -83 ), and TIMER rho = 0.52 (P = 1.85×10 -20 ). LILRB4 co-expression and interaction networks implicate antigen-presentation pathways STRING identified 50 experimentally supported interactors forming a network (Fig. 7a). GEPIA2 co-expression yielded the top 100 genes, with C1QC, CD86, SLAMF8, and CD4 highlighted (Fig. 7b). TIMER confirmed consistent positive correlations across cancers (Fig. 7c). The intersection between co-expression and interaction sets yielded HLA-DRA as the core gene (Fig. 7d). KEGG enrichment of the combined gene set was dominated by immune pathways, with “Antigen processing and presentation” being most relevant (Fig. 7e); GO:MF enriched “MHC class II receptor activity,” “antigen peptide binding,” and “immune receptor activity,” forming a highly modular network (Fig. 7f). Discussion LILRB4 is highly enriched in normal immune tissues 21,22 and shows heterogeneous, cross-platform upregulation in many cancers, with striking cancer-type-dependent associations with outcome-protective in some, but indicating risk in LGG and recurrence-related metrics in PRAD. Discordance between protein and transcript levels (e.g., in LIHC) suggests combined influences of cellular composition and post-transcriptional/translational regulation 23 . Convergent immunologic evidence positions LILRB4 as a context-dependent marker along the myeloid/antigen-presentation axis 14,24 : its expression correlates consistently with macrophage infiltration across cancers and algorithms, and co-expression/interaction networks emphasize antigen processing/presentation, with HLA-DRA 25 emerging as a core intersection. At the genomic level, alterations are dominated by amplification and missense clustering in Ig domains with a hotspot, yet carriers do not show consistent survival differences; by contrast, expression tracks with instability metrics (TMB/MSI/HRD) in several cancers, consistent with a background-accompaniment model rather than a single functional mutation driver. Epigenetic and phosphorylation remodeling provide testable biology for cancer-type dependence: in LGG, promoter-proximal hypermethylation associates with longer survival and varies with grade; phosphorylation shows site- and cancer-specific remodeling, with opposing directions between S319 and S377 in some cancers, indicating distinct signaling states and functional outputs. Limitations include potential overestimation of single-gene/immune-score correlations due to deconvolution gene-set composition, purity/tissue differences influencing expression contrasts, batch/site effects in cross-platform integration, and the observational nature precluding causal inference. Follow-up should include single-cell/spatial validation under purity control, functional perturbation of the HLA-DRA axis and site-directed mutagenesis to dissect antigen-presentation roles, and systematic investigation of transcript-protein discordance. Conclusion LILRB4 is highly enriched in the immune system and heterogeneously upregulated across cancers, with cancer-dependent prognostic associations — protective in CESC, SKCM, and UCS, but indicating higher risk in LGG and recurrence-related metrics of PRAD. Genetically, amplifications and missense mutations dominate, cluster within Ig domains, and include the P184H/L hotspot, without consistent survival impact; expression correlates with TMB/MSI/HRD in selected cancers. Epigenetically and at the protein-modification level, hypermethylation at promoter-proximal cg05329879 in LGG associates with longer survival, and multiple cancers exhibit site- and cancer-specific phosphorylation remodeling. In the microenvironment, LILRB4 strongly correlates with macrophage infiltration; co-expression and interaction networks converge on antigen processing and presentation and highlight HLA-DRA. Altogether, the evidence supports LILRB4 as a context-dependent marker of the myeloid and antigen-presentation axis rather than a universally prognostic gene driven by a single alteration. Declarations Ethics approval This study used only publicly available databases and bioinformatic analyses and did not involve human participants, animals, or clinical specimens; therefore, ethical approval was not required. Consent to participate Not applicable, as no human participants were involved. Consent for publish Not applicable, as this article contains no individual person’s data. Availability of data and materials All datasets analyzed in this study are publicly available. TCGA data were obtained from the Genomic Data Commons (https://portal.gdc.cancer.gov/). GTEx data were accessed from the GTEx Portal (https://gtexportal.org/home/). CPTAC proteomic and phosphoproteomic data were retrieved from the CPTAC Data Portal (https://ualcan.path.uab.edu/analysis-prot.html). Expression and immune infiltration analyses were conducted using the Human Protein Atlas (HPA, https://www.proteinatlas.org/), GEPIA2 (http://gepia2.cancer-pku.cn/), and TIMER2.0 (http://timer.comp-genomics.org/). Genetic alteration data were collected from cBioPortal (https://www.cbioportal.org/). Protein interaction data were obtained from STRING (https://string-db.org/). DNA methylation analyses were performed via MethSurv (https://biit.cs.ut.ee/methsurv/). Genomic instability metrics (TMB, MSI, HRD) were obtained from Sangerbox (http://sangerbox.com/). Details of versions and access dates are provided in Supplementary Table S1. Competing interests The authors declare no competing interests. Funding This study received no funding. Authors’ contributions Chaoshun Zheng and Waiming Cheng conceived the study and coordinated the project. Xuhui He, Yueyue Guo, and Longsheng Zhang performed the data analyses. Jiabin Li drafted the manuscript. 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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-7595770","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530783664,"identity":"f9ebfd78-9fc9-4730-92bb-dc0ff35c7b9e","order_by":0,"name":"Jiabin Li","email":"","orcid":"","institution":"Radiotherapy Department, Jieyang People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiabin","middleName":"","lastName":"Li","suffix":""},{"id":530783665,"identity":"0a434d70-cb29-46d6-b0c1-4eadf9b738bc","order_by":1,"name":"Xuhui He","email":"","orcid":"","institution":"Department of Orthopedics, Jieyang People's 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15:47:08","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62941,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7595770/v1/825c1ecb44087e1a9197d6bd.html"},{"id":93795098,"identity":"eb3e9bc0-8ab2-4b84-93db-48651e4b3b18","added_by":"auto","created_at":"2025-10-17 15:47:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":686387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePan-cancer expression analysis of LILRB4.\u003c/strong\u003e a) Differential expression between tumor and normal tissues in the TCGA cohorts. b) Pan-cancer expression analysis using combined TCGA and GTEx data. c) Protein level comparison between tumor and normal tissues in the CPTAC proteomic datasets. d) Expression distribution across pathological stages in TCGA cohorts.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7595770/v1/95ed70a48064534998f93f04.png"},{"id":93795103,"identity":"5a6a6a93-33d0-48a4-bd0f-26ef4a451e9d","added_by":"auto","created_at":"2025-10-17 15:47:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":271689,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival analysis of LILRB4 expression.\u003c/strong\u003e a) Kaplan–Meier curves of overall survival (OS) in TCGA cohorts stratified by LILRB4 expression using the GEPIA platform (log-rank test). b) Kaplan–Meier curves of disease-free survival (DFS) in TCGA cohorts stratified by LILRB4 expression using the GEPIA platform (log-rank test).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7595770/v1/76f6ce5969a424061d18cebf.png"},{"id":93795100,"identity":"3347a551-745c-41e1-ba7f-4694530a304d","added_by":"auto","created_at":"2025-10-17 15:47:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":477100,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLILRB4 genetic alterations and their associations with genomic instability metrics.\u003c/strong\u003ea) Frequency of LILRB4 alterations across TCGA cohorts, analyzed using cBioPortal. b) Lollipop plot of mutation distribution within LILRB4 protein domains, highlighting missense mutations and hotspot sites. c–f) Kaplan–Meier survival analyses of overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), and progression-free survival (PFS) comparing patients with or without LILRB4 alterations, based on the cBioPortal survival module. g–i) Correlation analyses between LILRB4 expression and tumor mutational burden (TMB), microsatellite instability (MSI), and homologous recombination deficiency (HRD), obtained from the Sangerbox platform.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7595770/v1/058c648e8239d5557cfe06a7.png"},{"id":93795099,"identity":"978f1efc-47d8-4e66-91b6-dd938181fe20","added_by":"auto","created_at":"2025-10-17 15:47:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":223541,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePromoter methylation of LILRB4 and its association with prognosis in LGG. \u003c/strong\u003ea)Five representative CpG probes of LILRB4 identified from TCGA methylation arrays. b) Distribution of LILRB4 promoter methylation levels in the LGG cohort. c) Kaplan–Meier curve of overall survival (OS) according to cg05329879 methylation status, analyzed using the MethSurv platform. d) Density distribution of β values at the cg05329879 probe. e–h) Comparisons of cg05329879 methylation across subgroups stratified by age, sex, race, and ethnicity. i) Comparison of cg05329879 methylation levels between grade 2 and grade 3 tumors.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7595770/v1/fd6aec4ef89484212e8ef915.png"},{"id":93797000,"identity":"f61a724d-47a7-4216-aad3-9cd6639c4a53","added_by":"auto","created_at":"2025-10-17 15:55:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":509442,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSite-specific differences in LILRB4 phosphorylation across cancer types.\u003c/strong\u003ea) Breast cancer (BRCA); b) Colon adenocarcinoma (COAD); c) Kidney renal clear cell carcinoma (KIRC); d) Lung adenocarcinoma (LUAD); e) Lung squamous cell carcinoma (LUSC); f) Pancreatic adenocarcinoma (PAAD); g) Glioblastoma multiforme (GBM); h) Liver hepatocellular carcinoma (LIHC); i) Head and neck squamous cell carcinoma (HNSC). Data are based on CPTAC phosphoproteomic analyses.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7595770/v1/86403e5ab20823dc85d6b519.png"},{"id":93795112,"identity":"367c39e1-d56d-434d-bf00-8b7a603a63b5","added_by":"auto","created_at":"2025-10-17 15:47:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":781280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of LILRB4 expression with macrophage infiltration.\u003c/strong\u003e a) Pan-cancer heatmap showing correlations between LILRB4 expression and macrophage infiltration levels estimated by multiple algorithms. b) Correlations between LILRB4 expression and different macrophage subtypes (M0, M1, M2) in colorectal cancer (COAD), analyzed using the TIMER2.0 platform.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7595770/v1/ce15af91d98e1340e211d654.png"},{"id":93797734,"identity":"221cabc8-26cf-48c7-91f3-5d434f87d967","added_by":"auto","created_at":"2025-10-17 16:03:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":274943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-expression and interaction networks of LILRB4 with functional enrichment analyses. \u003c/strong\u003ea) Protein–protein interaction network of LILRB4 obtained from the STRING database. b) Top four genes most strongly correlated with LILRB4 from GEPIA2 co-expression analysis. c) Pan-cancer correlation heatmap generated using the TIMER platform. d) Intersection of interacting proteins and co-expressed genes identifying HLA-DRA as a core gene. e) KEGG pathway enrichment analysis of the combined gene set. f) GO molecular function enrichment analysis.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7595770/v1/b63c7ced5429ac5d1145f7f1.png"},{"id":96364683,"identity":"bf371ee3-9b0a-4112-83c5-8422c0263595","added_by":"auto","created_at":"2025-11-20 10:09:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4252153,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7595770/v1/3a0289f1-7298-48a6-8b74-5bdc81be7dec.pdf"},{"id":93796999,"identity":"66e6a592-543c-4f07-b3c7-91e9272a6524","added_by":"auto","created_at":"2025-10-17 15:55:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":952820,"visible":true,"origin":"","legend":"","description":"","filename":"LILRB4Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7595770/v1/6b3144d51e0128d5426cbd07.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA pan-cancer landscape of LILRB4 identifies it as a context-dependent marker of the myeloid and antigen-presentation axis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInhibitory receptors play key roles in myeloid pathways within the tumor microenvironment, shaping antigen recognition/presentation and immune evasion\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. LILRB4, also known as ILT3, is an inhibitory receptor bearing ITIM motifs\u003csup\u003e4\u003c/sup\u003e, predominantly expressed in monocytes\u003csup\u003e5\u003c/sup\u003e, macrophages\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e, and dendritic cells\u003csup\u003e9,10\u003c/sup\u003e. It signals via SHP-1/2\u003csup\u003e11,12\u003c/sup\u003e to dampen activation\u003csup\u003e13,14\u003c/sup\u003e and modulate antigen presentation\u003csup\u003e14,15\u003c/sup\u003e and T-cell activation thresholds\u003csup\u003e16,17\u003c/sup\u003e. Although its role in innate immune regulation has drawn attention, its cross-tumor landscape and clinical significance have not been systematically defined.\u003c/p\u003e\u003cp\u003ePrior studies have largely focused on individual cancer types or single-omics layers, lacking panoramic comparisons that span transcripts, proteins, genetic alterations, epigenetics, and immune infiltration\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e. The relationship between LILRB4 and genomic instability metrics remains unclear; the functional implications of site-specific phosphorylation and promoter methylation across cancers also lack a unified framework; and potential discrepancies between transcript and protein levels remain to be resolved.\u003c/p\u003e\u003cp\u003eTo address these gaps, we integrated multi-omics resources to construct a pan-cancer landscape of LILRB4, covering expression and protein abundance, alteration spectra and mutation distribution, promoter methylation, and site-specific phosphorylation. Together with multi-algorithm immune deconvolution and co-expression/interaction network analysis, we systematically evaluated its links to macrophage infiltration and antigen-presentation pathways, and examined associations with genomic instability in a cross-cancer context.\u003c/p\u003e\u003cp\u003eThis study shows heterogeneous upregulation of LILRB4 across cancers with cancer-type-dependent prognostic associations; its epigenetic and phosphorylation remodeling converge with myeloid infiltration and antigen presentation, supporting its biological positioning as a context-dependent marker in the tumor microenvironment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and overall workflow\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a retrospective pan-cancer analysis based on public multi-omics resources, building a cross-layer comparison framework around LILRB4 (tissue/cell expression, cross-cancer differences, clinical outcomes, genetic alterations, promoter methylation, site-specific phosphorylation, and immune infiltration). Co-expression and protein-interaction data were integrated for functional clues and enrichment analysis. Analyses and figure order mirror the Results section. Portals, versions, and access dates are listed in Supplementary Table S1 (Resources and portals).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene and phylogeny information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNucleotide and protein sequences of LILRB4 were retrieved from NCBI. Ensembl GeneTree/homology modules were used for phylogeny and paralog identification to contextualize family attributes and evolutionary background.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue and cell-type expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the Human Protein Atlas (HPA) Consensus dataset, we profiled LILRB4 expression in normal tissues; HPA single-cell modules were used to assess enrichment in key immune cells (monocytes, macrophages, dendritic cells). Expression units follow platform-provided normalization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-cancer expression with supplemented normal controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe compared tumors versus paired/unpaired normals in TCGA via TIMER2.0. For cancer types lacking adequate TCGA normals, GEPIA2\u0026rsquo;s TCGA+GTEx pipeline supplemented controls. Within-cancer normalization and statistics followed platform defaults. Stage-wise expression comparisons were conducted using GEPIA2 staging modules with default settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein and phosphoprotein expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor\u0026ndash;normal differences in total protein and site-level phosphorylation were assessed using CPTAC resources. Total protein differences served as reference to transcript-level trends. Site-level phosphorylation comparisons across cancers focused on S318, S319, S377, S417, T386, reporting direction and significance with emphasis on site- and cancer-specific remodeling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall survival (OS) and disease-free survival (DFS) were evaluated with GEPIA/GEPIA2 Kaplan\u0026ndash;Meier and log-rank modules, using default expression stratification. Concordant or opposite trends between OS and DFS were reported to highlight cancer-type dependence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic alteration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used cBioPortal (TCGA) to summarize LILRB4 alteration frequencies, mutation types, and site distribution; lollipop/domain maps located mutations, and copy-number gains/losses were tallied. \u0026ldquo;Alterations\u0026rdquo; refer to somatic mutations and copy-number changes, excluding germline variants. Survival differences by alteration status used cBioPortal survival modules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic instability associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTMB, MSI, and HRD metrics were obtained through Sangerbox per its standard pipeline. Within cancers, correlations between LILRB4 expression and each metric used built-in correlation modules; directions and consistent patterns are reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePromoter methylation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom TCGA methylation arrays, we pinpointed representative CpG probes (cg02421734, cg04680738, cg05329879, cg05922591, cg12161905), focusing on cg05329879 near the 5\u0026prime;UTR\u0026ndash;first-exon junction with high variability. We compared distributions (e.g., in LGG) and conducted survival and stratified analyses via MethSurv defaults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune infiltration estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMacrophage M0/M1/M2 infiltration scores were obtained from TIMER2.0\u0026rsquo;s multi-algorithm deconvolution (CIBERSORT, CIBERSORT-ABS, MCPCOUNTER, EPIC, xCell, TIMER, quanTIseq, TIDE) with default parameters. Cross-cancer heatmaps and representative cancer details used TIMER2.0 correlation outputs (Spearman).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-expression, protein interactions, and functional enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTop LILRB4 co-expressed genes were taken from GEPIA2; experimentally supported interactors were taken from STRING (evidence channel: \u0026ldquo;experiments\u0026rdquo;). The intersection identified core genes (HLA-DRA). KEGG and GO:MF enrichment on combined sets highlighted immune-related pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistics and reproducibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistics (differential tests, correlations, survival, enrichment) were reported directly from platform defaults (GEPIA2, TIMER2.0, UALCAN/CPTAC, cBioPortal, MethSurv, STRING). We did not reimplement algorithms locally. Significance thresholds and corrections follow platform outputs/figure legends. Figures were assembled in R using tidyverse, readr, reshape2, ggplot2, ggforce, RColorBrewer, ComplexHeatmap, circlize, VennDiagram, tidygraph/ggraph, grid, gridExtra, cowplot, and gtable.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eLILRB4 is highly expressed in the immune system and heterogeneously upregulated across cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLILRB4 shows high expression across normal immune systems. It resides within the immune-regulatory-rich region 19p13.1 (Fig. S1a). Phylogeny places it within a primate-expanded immune-receptor family with paralogs including NCR1, FCAR, and GP6, suggesting origin from an amniote-stage duplication (Fig. S1b). In the HPA Consensus dataset, it is high in spleen, lung, lymph nodes, and spinal cord (Fig. S1c); single-cell resolution further indicates enrichment in monocytes, macrophages, and dendritic cells (Fig. S1d).\u003c/p\u003e\n\u003cp\u003eAcross tumors, LILRB4 is heterogeneously upregulated. TCGA shows significant increases in BRCA, ESCA, GBM, HNSC, KICH, KIRC, LIHC, PRAD, STAD, THCA, and UCEC; in SKCM, expression is closely related to metastatic status (Fig. 1a). After integrating GTEx to supplement normals, significant upregulation remains in DLBC, LAML, LGG, OV, SKCM, and TGCT, with downtrends in ACC and THYM (Fig. 1b). At the protein level (CPTAC), LILRB4 is higher in BRCA, KIRC, UCEC, LUSC, LUAD, HNSC, and GBM, but lower in LIHC (Fig. 1c). Stage analysis shows significant differences in KICH (F = 4.67, P = 0.00525), SKCM (F = 8.44, P = 1.52\u0026times;10\u003csup\u003e-6\u003c/sup\u003e), OV (F = 3.30, P = 0.0377), BLCA (F = 4.91, P = 0.00779), STAD (F = 2.84, P = 0.0376), THCA (F = 4.31, P = 0.00512), and ESCA (F = 2.95, P = 0.0341), with no clear stage dependence elsewhere (Fig. 1d; Fig. S2). Overall, LILRB4 exhibits immune-tissue-preferential expression and heterogeneous, cross-platform upregulation across cancers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLILRB4 expression associates with patient outcome in a cancer-type-specific manner\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn TCGA, LILRB4 shows cancer-dependent survival associations. GEPIA Kaplan-Meier analyses (Fig. 2a-b) show high expression linked to longer OS in CESC (HR = 0.55, P = 0.014), SKCM (HR = 0.58, P = 7.2e-05), and UCS (HR = 0.49, P = 0.037), but the opposite in LGG (HR = 1.7, P = 0.0038). DFS similarly differs: LGG (HR = 1.7, P = 0.0011) and PRAD (HR = 2.2, P = 0.00059) indicate higher recurrence risk with high expression, whereas UCS (HR = 0.46, P = 0.035) indicates longer DFS. Notably, UCS is protective in both OS and DFS, while LGG indicates high risk in both, underscoring cancer-type specificity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLILRB4 alterations are dominated by amplification and missense, cluster in Ig domains, and associate with TMB/MSI/HRD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlterations are mainly amplifications and missense (Fig. 3a), with higher frequencies (\u0026gt;2%) in SKCM, UCEC, GBM, and BLCA (Fig. 3a). Mutations cluster in the second and third Ig domains, with a recurrent P184 hotspot (P184H/L) in LUSC, STAD, SKCM, and UCEC (Fig. 3b). Survival by alteration status reveals no significant differences in OS, DSS, DFS, or PFS (all log-rank P \u0026gt; 0.1; Fig. 3c-f).\u003c/p\u003e\n\u003cp\u003eLILRB4 expression shows positive, accompaniment-type correlations with genomic instability metrics, varying by cancer. TMB correlates in COAD, READ, and PRAD (r \u0026gt; 0.2, P \u0026lt; 0.05; Fig. 3g); MSI trends positive in STES and COAD (Fig. 3h); HRD correlates in SARC, PRAD, and KICH (Fig. 3i). These do not align with the null survival impact of P184H/L carriers, consistent with expression tracking instability background rather than being driven by a single functional mutation (Fig. 3g-i).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePromoter methylation of LILRB4 in LGG is tightly linked to prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePromoter-region methylation is concentrated at five CpGs: cg02421734, cg04680738, cg05329879, cg05922591, and cg12161905, with cg05329879 near the 5\u0026prime;UTR\u0026ndash;first-exon junction in an Open Sea (Fig. 4a). In LGG, multiple sites show generally higher methylation, with cg05329879 displaying the greatest inter-patient variability (Fig. 4b).\u003c/p\u003e\n\u003cp\u003eHypermethylation at cg05329879 associates with longer OS in LGG (HR = 0.309, P = 2.2e-09; Fig. 4c). Density indicates overall higher \u0026beta; values peaking around 0.8 (Fig. 4d). Stratification shows no evident age- or sex-based differences; whites show higher levels with broader spread in Black and Asian patients; no clear difference by ethnicity (Fig. 4e-h). cg05329879 is higher in G3 than G2 (Fig. 4i).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLILRB4 shows site-specific phosphorylation remodeling across cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCPTAC analyses reveal pronounced site- and cancer-specific remodeling: in BRCA, S319 is up, while S377 decreases across protein isoforms (Fig. 5a); in COAD, S377 is lower (Fig. 5b); in ccRCC, S318, S319, S377, S417, and T386 are broadly up (Fig. 5c); in LUAD, S319 is up while S377 is down (Fig. 5d); in LUSC, S318/S319 are up whereas S377 and S417 are down (Fig. 5e); in PAAD, S377 and S417 rise (Fig. 5f); in GBM, S318/S319 and S377 increase with marked group differences (Fig. 5g); in LIHC, S318/S319 increase while S377 and S407 decrease (Fig. 5h); in HNSC, S318/S319 are clearly higher in tumors (Fig. 5i).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLILRB4 expression correlates strongly with tumor-associated macrophage infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cross-cancer heatmap shows broad positive correlations between LILRB4 and macrophage subtype infiltration across multiple algorithms, with strong inter-algorithm consistency (Fig. 6a). In COAD, READ, KIRP, SARC, and UCEC, different macrophage subtypes (M0, M1, M2) are consistently correlated with LILRB4 across methods, with correlation coefficients exceeding 0.89, indicating a highly coherent expression\u0026ndash;infiltration coupling.\u003c/p\u003e\n\u003cp\u003eCOAD ranks highest and is most stable across algorithms; thus it was chosen for representative visualization (Fig. 6b). In COAD, LILRB4 positively correlates with M0 (CIBERSORT: rho = 0.17, P = 5.49\u0026times;10\u003csup\u003e-3\u003c/sup\u003e). M1 is also positive (xCell: rho = 0.89, P = 2.47\u0026times;10\u003csup\u003e-94\u003c/sup\u003e; CIBERSORT: rho = 0.45, P = 3.72\u0026times;10\u003csup\u003e-15\u003c/sup\u003e). Most M2 estimates are positive (quanTIseq: rho = 0.53, P = 2.31\u0026times;10\u003csup\u003e-21\u003c/sup\u003e; CIBERSORT-ABS: rho = 0.85, P = 1.38\u0026times;10\u003csup\u003e-76\u003c/sup\u003e), with the exception of TIDE M2, which is negative (rho = \u0026minus;0.695, P = 6.54\u0026times;10\u003csup\u003e-60\u003c/sup\u003e). For total macrophage metrics, MCPCOUNTER \u0026ldquo;Macrophage/Monocyte\u0026rdquo; shows rho = 0.92 (P = 1.48\u0026times;10\u003csup\u003e-113\u003c/sup\u003e), EPIC total macrophages rho = 0.95 (P = 1.65\u0026times;10\u003csup\u003e-137\u003c/sup\u003e), xCell total macrophages rho = 0.86 (P = 3.91\u0026times;10\u003csup\u003e-83\u003c/sup\u003e), and TIMER rho = 0.52 (P = 1.85\u0026times;10\u003csup\u003e-20\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLILRB4 co-expression and interaction networks implicate antigen-presentation pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSTRING identified 50 experimentally supported interactors forming a network (Fig. 7a). GEPIA2 co-expression yielded the top 100 genes, with C1QC, CD86, SLAMF8, and CD4 highlighted (Fig. 7b). TIMER confirmed consistent positive correlations across cancers (Fig. 7c). The intersection between co-expression and interaction sets yielded HLA-DRA as the core gene (Fig. 7d). KEGG enrichment of the combined gene set was dominated by immune pathways, with \u0026ldquo;Antigen processing and presentation\u0026rdquo; being most relevant (Fig. 7e); GO:MF enriched \u0026ldquo;MHC class II receptor activity,\u0026rdquo; \u0026ldquo;antigen peptide binding,\u0026rdquo; and \u0026ldquo;immune receptor activity,\u0026rdquo; forming a highly modular network (Fig. 7f).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLILRB4 is highly enriched in normal immune tissues\u003csup\u003e21,22\u003c/sup\u003e and shows heterogeneous, cross-platform upregulation in many cancers, with striking cancer-type-dependent associations with outcome-protective in some, but indicating risk in LGG and recurrence-related metrics in PRAD. Discordance between protein and transcript levels (e.g., in LIHC) suggests combined influences of cellular composition and post-transcriptional/translational regulation\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eConvergent immunologic evidence positions LILRB4 as a context-dependent marker along the myeloid/antigen-presentation axis\u003csup\u003e14,24\u003c/sup\u003e: its expression correlates consistently with macrophage infiltration across cancers and algorithms, and co-expression/interaction networks emphasize antigen processing/presentation, with HLA-DRA\u003csup\u003e25\u003c/sup\u003e emerging as a core intersection.\u003c/p\u003e\u003cp\u003eAt the genomic level, alterations are dominated by amplification and missense clustering in Ig domains with a hotspot, yet carriers do not show consistent survival differences; by contrast, expression tracks with instability metrics (TMB/MSI/HRD) in several cancers, consistent with a background-accompaniment model rather than a single functional mutation driver. Epigenetic and phosphorylation remodeling provide testable biology for cancer-type dependence: in LGG, promoter-proximal hypermethylation associates with longer survival and varies with grade; phosphorylation shows site- and cancer-specific remodeling, with opposing directions between S319 and S377 in some cancers, indicating distinct signaling states and functional outputs.\u003c/p\u003e\u003cp\u003eLimitations include potential overestimation of single-gene/immune-score correlations due to deconvolution gene-set composition, purity/tissue differences influencing expression contrasts, batch/site effects in cross-platform integration, and the observational nature precluding causal inference. Follow-up should include single-cell/spatial validation under purity control, functional perturbation of the HLA-DRA axis and site-directed mutagenesis to dissect antigen-presentation roles, and systematic investigation of transcript-protein discordance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eLILRB4 is highly enriched in the immune system and heterogeneously upregulated across cancers, with cancer-dependent prognostic associations \u0026mdash; protective in CESC, SKCM, and UCS, but indicating higher risk in LGG and recurrence-related metrics of PRAD. Genetically, amplifications and missense mutations dominate, cluster within Ig domains, and include the P184H/L hotspot, without consistent survival impact; expression correlates with TMB/MSI/HRD in selected cancers. Epigenetically and at the protein-modification level, hypermethylation at promoter-proximal cg05329879 in LGG associates with longer survival, and multiple cancers exhibit site- and cancer-specific phosphorylation remodeling. In the microenvironment, LILRB4 strongly correlates with macrophage infiltration; co-expression and interaction networks converge on antigen processing and presentation and highlight HLA-DRA. Altogether, the evidence supports LILRB4 as a context-dependent marker of the myeloid and antigen-presentation axis rather than a universally prognostic gene driven by a single alteration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used only publicly available databases and bioinformatic analyses and did not involve human participants, animals, or clinical specimens; therefore, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as no human participants were involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as this article contains no individual person\u0026rsquo;s data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets analyzed in this study are publicly available. TCGA data were obtained from the Genomic Data Commons (https://portal.gdc.cancer.gov/). GTEx data were accessed from the GTEx Portal (https://gtexportal.org/home/). CPTAC proteomic and phosphoproteomic data were retrieved from the CPTAC Data Portal (https://ualcan.path.uab.edu/analysis-prot.html). Expression and immune infiltration analyses were conducted using the Human Protein Atlas (HPA, https://www.proteinatlas.org/), GEPIA2 (http://gepia2.cancer-pku.cn/), and TIMER2.0 (http://timer.comp-genomics.org/). Genetic alteration data were collected from cBioPortal (https://www.cbioportal.org/). Protein interaction data were obtained from STRING (https://string-db.org/). DNA methylation analyses were performed via MethSurv (https://biit.cs.ut.ee/methsurv/). Genomic instability metrics (TMB, MSI, HRD) were obtained from Sangerbox (http://sangerbox.com/). Details of versions and access dates are provided in Supplementary Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChaoshun Zheng and Waiming Cheng conceived the study and coordinated the project. Xuhui He, Yueyue Guo, and Longsheng Zhang performed the data analyses. Jiabin Li drafted the manuscript. All authors revised the manuscript and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLogtenberg MEW, Scheeren FA, Schumacher TN. The CD47-SIRP\u0026alpha; Immune Checkpoint. Immunity. 2020;52(5):742\u0026ndash;752. doi:10.1016/j.immuni.2020.04.011\u003c/li\u003e\n\u003cli\u003eWolff F, Leisch M, Greil R, Risch A, Pleyer L. The double-edged sword of (re)expression of genes by hypomethylating agents: from viral mimicry to exploitation as priming agents for targeted immune checkpoint modulation. Cell Commun Signal. 2017;15(1):13. doi:10.1186/s12964-017-0168-z\u003c/li\u003e\n\u003cli\u003eZhu M, Li N, Fan L, Wu R, Cao L, Ren Y, Lu C, Zhang L, Cai Y, Shi Y, et al. 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Immunol. 2025;16. doi:10.3389/fimmu.2025.1559301\u003c/li\u003e\n\u003cli\u003eTakahashi N, Itoi S, Su M-T, Endo S, Takai T. Co-localization of Fibronectin Receptors LILRB4/gp49B and Integrin on Dendritic Cell Surface. Tohoku J Exp Med. 2022;257(3):171\u0026ndash;180. doi:10.1620/tjem.2022.J014\u003c/li\u003e\n\u003cli\u003eWang N, Tan S, Liu H, Nie Y, Wang M, Liu H, Han S, Wu Z, Ma J, Sha Z. SHP-1 negatively regulates LPS-induced M1 polarization, phagocytic activity, inflammation and oxidative stress in primary macrophages of Chinese tongue sole (Cynoglossussemilaevis). Fish Shellfish Immunol. 2025;163:110375. doi:10.1016/j.fsi.2025.110375\u003c/li\u003e\n\u003cli\u003eWang M, Tan S, Liu J, Chang M, Wang N, Liu H, Zhang W, Xia J, Yang Y, Huang W, et al. SHP-1 regulates T cell-mediated early adaptive immunity in Chinese tongue sole (Cynoglossus semilaevis) infected with Mycobacterium marinum. 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Int Immunopharmacol. 2025;144:113706. doi:10.1016/j.intimp.2024.113706\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"LILRB4, pan-cancer, macrophage infiltration, antigen presentation, phosphorylation","lastPublishedDoi":"10.21203/rs.3.rs-7595770/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7595770/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInhibitory receptors modulate antigen presentation and myeloid responses within the tumor microenvironment, yet the cross-cancer landscape and clinical significance of LILRB4 remain unclear. By integrating public multi-omics evidence, we delineate its panorama across expression, genetic alterations, DNA methylation, phosphorylation, and immune infiltration, and relate these to patient outcomes. LILRB4 is enriched in immune tissues and in monocytes, macrophages, and dendritic cells, and is upregulated in multiple cancers, with occasional discordance between transcript and protein levels. Its association with survival is cancer-type dependent\u0026mdash;protective in cervical cancer, skin cutaneous melanoma, and uterine corpus endometrial carcinoma, but indicating higher risk in lower-grade glioma and in recurrence-related metrics of prostate adenocarcinoma. Genetic alterations are dominated by amplification and missense mutations and cluster within immunoglobulin domains, including a P184 hotspot, but carriers of alterations do not exhibit consistent survival differences. LILRB4 expression positively correlates with tumor mutational burden, microsatellite instability, and homologous recombination deficiency in several cancers. In lower-grade glioma, hypermethylation at a key promoter-proximal CpG site associates with longer survival, and multiple cancers display site- and cancer-specific phosphorylation remodeling. LILRB4 correlates strongly with macrophage infiltration, and co-expression and interaction networks are enriched for antigen processing and presentation, pointing to HLA-DRA. Collectively, these data position LILRB4 as a context-dependent marker of the myeloid and antigen-presentation axis in the tumor microenvironment and provide a basis for stratification and immunoregulatory strategies.\u003c/p\u003e","manuscriptTitle":"A pan-cancer landscape of LILRB4 identifies it as a context-dependent marker of the myeloid and antigen-presentation axis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 15:47:02","doi":"10.21203/rs.3.rs-7595770/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03198216-46e0-4caf-94cd-bb9f6e0ba7f6","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T10:08:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 15:47:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7595770","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7595770","identity":"rs-7595770","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}