Single-cell transcriptomic analysis reveals the evolution of the immunosuppressive landscape from primary tumors to brain metastasis in LUAD

Single-cell transcriptomic analysis reveals the evolution of the immunosuppressive landscape from primary tumors to brain metastasis in LUAD | 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 Single-cell transcriptomic analysis reveals the evolution of the immunosuppressive landscape from primary tumors to brain metastasis in LUAD JunMing Jia, Huichao lin, Zeren Chen, Ke He, Hongqian Cao, Ziyan Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7605804/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Brain metastasis (BM) is a major cause of mortality in lung adenocarcinoma, yet the cellular and molecular basis of its immune microenvironment remodeling remains unclear. Here, we systematically analyzed primary lung adenocarcinoma (LUAD) and BM samples using single-cell RNA sequencing (scRNA-seq). CD74 High tumor-associated macrophages (TAMs) emerged as central receptor hubs, particularly within the APP–CD74 and MIF–CD74 axes. In the TCGA cohort, high CD74 expression correlated with suppressed phagocytosis-related gene sets and poor prognosis. Comparative analysis revealed strong transcriptional similarity between BM_Cluster_03 and LUAD_TAM_Cluster_15, both serving as dominant APP–CD74 receptor populations. Stratification by CD74 expression showed that CD74 High TAMs in LUAD were enriched in antigen presentation, phagocytosis, and adaptive immune pathways, whereas CD74 High TAMs in BM shifted toward metabolic adaptation and stress responses with reduced immune effector programs. This functional reprogramming was consistently observed across analyses, indicating that BM CD74 High TAMs transition from an immune-activated to a metabolically stressed state, thereby facilitating immunosuppressive remodeling and tumor colonization in the brain. Collectively, CD74 High TAMs represent key drivers of immune remodeling in lung adenocarcinoma brain metastasis, and the APP–CD74/MIF–CD74 axes may serve as potential therapeutic targets. lung adenocarcinoma (LUAD) brain metastasis of lung adenocarcinoma (LUAD-BM) tumor-associated macrophages (TAMs) CD74 APP CD74 axis MIF CD74 axis immune microenvironment remodeling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Lung cancer remains the leading cause of cancer incidence and mortality worldwide[ 1 ].LUAD, the most common histological subtype, accounts for approximately 40% of all lung cancer cases and exhibits a strong propensity for BM. Even with treatment, patients with BM continue to face poor outcomes [ 2 – 4 ]. Increasing evidence shows that the tumor microenvironment (TME) of BM is characterized by pronounced immunosuppression, including marked depletion of tumor-infiltrating lymphocytes, elevated neutrophil infiltration, and loss of concordance between PD-L1 expression and T cell–inflamed gene signatures[ 5 ].These alterations not only reflect impaired immune surveillance but also highlight the adaptive remodeling of the local microenvironment during metastatic dissemination. Thus, LUAD brain metastasis is accompanied by systemic reprogramming of the TME, which simultaneously weakens antitumor immune activity and establishes an immunosuppressive niche conducive to metastatic colonization and expansion. CD74, also known as the MHC class II invariant chain (Ii), was initially identified as a chaperone for MHC-II complex assembly. Subsequent studies revealed that CD74 can also act as a signaling receptor, most prominently for macrophage migration inhibitory factor (MIF), thereby shaping tumor immune responses[ 6 ]. CD74 is involved in antigen processing and presentation, while its interaction with MIF promotes immunosuppression and angiogenesis [ 7 , 8 ] In the context of immunotherapy, elevated CD74 expression has been proposed as a potential biomarker, positively associated with an inflamed TME and with clinical benefit from PD-1/CTLA-4 bispecific antibodies (AK104, cadonilimab)[ 8 , 9 ]. More recently, amyloid precursor protein (APP) was shown to suppress macrophage phagocytosis through the CD74/CXCR4 complex, suggesting that the APP–CD74 axis represents a promising target for therapeutic intervention [ 10 ]. TAMs are among the most abundant and functionally versatile immune cells within the TME, playing key roles in immune evasion and metastatic progression[ 11 ]。 Prior to overt dissemination, TAMs promote tumor invasion and metastasis by suppressing immune responses and inducing angiogenesis, thereby supporting tumor cell survival and growth at secondary sites[ 12 ]。 TAMs can secrete cytokines and growth factors such as VEGF and EGF to drive angiogenesis, thereby providing essential nutrients and vascular support for metastatic tumors, while immunosuppressive mediators including IL-10 and TGF-β dampen T cell antitumor activity, facilitating immune escape in lung cancer [ 13 ]。Thus, TAMs not only actively participate in TME remodeling during LUAD brain metastasis but also represent a central cellular subset driving immunosuppression and angiogenesis. Against this background, we employed single-cell sequencing to comprehensively characterize the cellular landscape and ligand–receptor interaction networks in LUAD and BM. We identified CD74 High TAMs as receptor hubs consistently occupying central positions across organs. Specifically, CD74 High TAMs in LUAD were enriched for antigen presentation and immune activation programs, whereas their counterparts in BM relied more on metabolic adaptation and stress responses, indicating functional divergence. These findings are consistent with prior evidence linking CD74 expression to immunotherapy responsiveness and with experimental studies showing enhanced efficacy upon MIF–CD74 blockade. Together, they underscore the pivotal role of CD74-centered signaling not only in immunosuppression and regulation of phagocytosis but also as a potential therapeutic target in BM. Result Both LUAD and BM communicate with TAMs through the APP–CD74 axis We reanalyzed the scRNA-seq dataset GSE131907[ 14 ], which included 11 primary LUAD tumor samples and 10 BM samples obtained from LUAD patients who underwent surgical resection without prior treatment. After quality control, normalization, and PCA, cells from LUAD and BM samples were subjected to dimensionality reduction and independent clustering using the UMAP algorithm. Subsequent manual annotation revealed that in LUAD samples, T cells were predominant (33.8%), followed by B cells (20.5%) and TAMs (17.9%), with neutrophils (6.0%), oligodendrocytes (4.2%), fibroblasts (1.0%), endothelial cells (1.1%), and pericytes (0.6%) present at lower proportions. In contrast, BM samples showed a markedly higher proportion of T cells (49.2%), followed by B cells (21.5%) and TAMs (13.0%), while neutrophils (9.6%), oligodendrocytes (3.4%), endothelial cells (3.7%), fibroblasts (4.5%), and pericytes (1.5%) were distributed at descending frequencies (Fig. 1 A,B, Figure S1). After cell annotation, we applied the inferCNV algorithm to infer copy number variation (CNV) across cellular subpopulations [ 16 ]. CNV scores were calculated for each cell based on major cell types and visualized as boxplots to compare CNV distributions among cell populations (Fig. 1 C, D). These results not only confirmed that epithelial cells originated from LUAD tumor cells but also indicated that tumor cells maintained marked genomic instability and CNV characteristics after brain metastasis. Notably, mild CNV signals were also observed in TAM subpopulations within LUAD samples, suggesting potential tumor-associated phenotypes or complex regulatory interactions within the tumor–immune microenvironment. To systematically compare cell–cell communication networks between primary LUAD and BM, we performed further analyses using CellChat. Both LUAD and BM exhibited distinct ligand–receptor pairs, while a subset of highly conserved and significant signaling axes was shared between the two contexts. Notably, CD74-centered interactions, including MIF–(CD74 + CXCR4), MIF–(CD74 + CD44), and APP–CD74, showed strong intercellular communication activity in both LUAD and BM (Fig. 1 E, F). Among these shared pathways, the APP–CD74 and MIF–CD74 axes displayed particularly high communication strength. Receptor localization analysis revealed that the receiving cells of these pathways were predominantly macrophages. Given that CD74 is an essential receptor in these axes, only macrophages with high CD74 expression can effectively engage with ligands such as APP and MIF (in coordination with co-receptors including CD44 and CXCR4) to trigger downstream signaling. Based on this, we designated CD74 High TAMs as the core recipient population of interest. Comparative analysis of LUAD- and BM-specific communication pathways further revealed striking differences in signaling directionality and biological function (Fig. 1 G). In BM, ligands such as ANGPT2, VEGFA, and SPP1 disrupted the blood–brain barrier (BBB) and suppressed T cell function, thereby promoting the establishment of an immunosuppressive microenvironment that facilitates tumor cell penetration of the BBB and colonization in the brain [ 22 – 24 ]. In contrast, LUAD-specific pathways were enriched in CCL2, CSF1, and CXCL12, which primarily recruit and polarize macrophages, driving immunosuppression and transendothelial migration, consistent with the more active immune surveillance and barrier defense observed in the LUAD[ 25 – 27 ]. The marked divergence of these signaling axes underscores the adaptive evolution of tumor cells within distinct organ microenvironments and provides a molecular basis for elucidating metastatic mechanisms and developing precise therapeutic strategies. Functions and prognostic significance of CD74 TAMs Next, GO enrichment analysis of the TCGA-LUAD cohort (2022) revealed that patients with high CD74 expression exhibited significantly reduced enrichment of phagocytosis-related gene sets, including phagocytosis and regulation of phagocytosis (Fig. 2 A) [ 18 ]. This finding suggests that activation of the APP–CD74 axis is closely associated with the suppression of TAM phagocytic function. Accordingly, elevated CD74 expression may negatively regulate TAM-mediated phagocytosis, thereby impairing their ability to eliminate tumor cells and contributing to immune evasion and tumor progression. Upon detailed subclustering of TAMs in LUAD, we identified 17 distinct TAM subclusters(Fig. 2 B). CellChat analysis of ligand–receptor interactions between these subclusters and other cell types in the microenvironment (Figure S2) revealed that LUAD_TAM_Cluster_15 exhibited the most prominent interactions, particularly along signaling axes such as APP–CD74, with endothelial and immune cells (Fig. 2 C), suggesting a pivotal role in regulating the tumor microenvironment. To further evaluate its biological and clinical significance, we constructed a signature gene set comprising LUAD_TAM_Cluster_15 marker genes in combination with CD74. Using this signature, single-sample gene set enrichment analysis (ssGSEA) was applied to the TCGA-LUAD cohort to stratify patients into high- and low-expression groups. Kaplan–Meier survival analysis demonstrated that patients with high LUAD_TAM_Cluster_15 signature expression had significantly shorter overall survival than those in the low-expression group (log-rank p = 0.016), indicating that enrichment of this TAM subpopulation is strongly associated with poor prognosis in LUAD (Fig. 2 D). Distribution and spatial features of CD74High TAMs in brain metastases To systematically investigate the distribution and function of CD74 High TAMs in BM, we first performed single-cell subclustering of TAMs and identified eight distinct subclusters (Fig. 3 A). To delineate the spatial distribution and phenotype of CD74 High TAMs, we visualized the expression patterns of CD74 and the macrophage marker CD68 based on scRNA-seq data. Both genes exhibited similar spatial localization, suggesting potential co-expression within the same cell population (Fig. 3 B). This observation was validated by multiplex immunofluorescence (mIHC), which confirmed high levels of co-expression and spatial enrichment of CD74 in TAMs from BM(Fig. 3 C). Cell–cell communication analysis revealed that BM_Cluster_03 functioned as the predominant recipient of the APP–CD74 signaling axis, forming extensive interaction networks with multiple signal-secreting cells in the tumor microenvironment, including tumor cells and endothelial cells (Fig. 3 D). Jaccard similarity analysis further demonstrated that BM_Cluster_03 exhibited the highest molecular resemblance to LUAD_TAM_Cluster_15 from primary tumors (Jaccard index = 0.28; Fig. 4 A). Both clusters represented CD74 High TAMs populations that served as central receptor hubs of the APP–CD74 pathway, acting as key executors of this signaling axis and playing pivotal roles in macrophage–tumor cell communication and microenvironmental regulation. Integrated CellChat analysis across all cell types confirmed that BM_Cluster_03 not only displayed the strongest receptor activity within the APP–CD74 axis but also functioned as a major hub for multiple immune-related signaling pathways, including MIF–(CD74 + CXCR4) (Fig. 4 B). BM_Cluster_03 established broad and dense communication networks with diverse immune and stromal cell types, including tumor cells, endothelial cells, T cells, and fibroblasts. Importantly, intersection and differential analyses of marker genes between BM_Cluster_03 and LUAD_TAM_Cluster_15 revealed that, despite their high similarity, BM_Cluster_03 retained a set of unique functional genes(Fig. 4 C). GO biological process enrichment indicated significant involvement of these genes in immune effector regulation, lymphocyte differentiation, leukocyte activation, and inflammatory responses. KEGG pathway analysis further highlighted roles in FcγR-mediated phagocytosis, complement and adhesion molecules, NOD-like receptor signaling, and inflammatory responses, suggesting that BM_Cluster_03 plays an important role in immune adaptation and microenvironmental remodeling in brain metastases. Taken together, these multilayered analyses demonstrate that BM_Cluster_03, although highly similar to LUAD_TAM_Cluster_15, occupies a central position in signal integration and immune regulation within the BM microenvironment. BM_Cluster_03 and LUAD_TAM_Cluster_15 represent CD74 High TAMs subset in BM and LUAD, respectively, both acting as receptor hubs in their corresponding tumor microenvironments. These findings identify CD74 High TAMs as key cellular populations that bridge primary tumor evolution and metastatic microenvironmental remodeling. CD74 expression level–associated differences in antigen presentation and metabolic adaptation of TAMs Following integration of TAMs from primary LUAD and BM, we stratified cells into four subgroups based on CD74 expression and tissue origin (Fig. 5 A–B): BM_CD74_High, BM_CD74_Low, LUAD_CD74_High, and LUAD_CD74_Low. Differential expression analysis across the four groups revealed both conserved features and clear functional divergence. Across organs and groups, we first observed a consistent feature: in both LUAD and BM (Fig. 5 C–D), CD74 High TAMs displayed significant upregulation of a series of MHC-II antigen presentation molecules, including HLA-DMA, HLA-DMB, HLA-DRB5, and HLA-DQA2. This pattern indicates that CD74 High TAMs broadly maintain antigen-processing and antigen-presenting capacity, with conserved reliance on lysosomal functions across distinct microenvironments[ 28 ]. These findings highlight CD74 High TAMs as a stable subcluster with antigen processing as a core program across organs. Beyond these shared features, subgroup-specific signatures revealed distinct functional divergence between LUAD and BM. In BM (Fig. 5 C), upregulation of ITM2B and complement molecules (C1QA, C1QC) suggested residual activity of immune recognition and complement pathways[ 29 – 32 ]. Conversely, downregulation of FTL and CSTB indicated altered regulation of iron homeostasis and proteolysis, which may affect TAM polarization and angiogenesis, thereby contributing to adaptation within the brain metastatic niche[ 33 – 37 ]. In LUAD (Fig. 5 D), CD74 High TAMs retained upregulation of MHC-II molecules, CST3, and CSTB, but were uniquely enriched for GSN and CD1C, implicating cytoskeletal remodeling and enhanced antigen presentation capacity[ 38 , 39 ]. Meanwhile, S100A8 and S100A9 were markedly downregulated, indicating reduced activity of inflammatory chemotaxis and myeloid recruitment pathways[ 40 ]. Collectively, LUAD CD74 High TAMs maintained strong antigen-processing activity but showed weaker inflammatory amplification compared with their BM counterparts. Cross-organ comparisons of CD74 High TAMs (Fig. 5 E) further demonstrated functional polarization. LUAD CD74 High TAMs were enriched for MARCO, LYZ, and FABP4, along with RPS4Y1 and HLA-DRA, highlighting their roles in phagocytosis, inflammation, and metabolic reprogramming[ 41 – 46 ]. In contrast, BM CD74 High TAMs upregulated TFF3, SPP1, and XIST. TFF3 has been linked to angiogenesis and metastatic potential; SPP1 is associated with immunosuppression, angiogenesis, and invasion; and XIST contributes to macrophage polarization, suggesting a shift toward immunosuppressive and vascular remodeling programs in BM[ 47 – 52 ]. In CD74 Low TAMs (Fig. 5 F), LUAD cells expressed higher levels of HLA-DRB1, HLA-DQA1, and HLA-DRB5, retaining partial antigen-presenting function. Conversely, BM CD74 Low TAMs upregulated RNASE1, HMOX1, and TFF3, indicating more pronounced metabolic stress and immunosuppressive programs[ 47 , 48 , 53 – 55 ]. Taken together, four-group comparisons revealed the conserved core function of CD74 High TAMs—antigen presentation—while also highlighting divergent cross-organ adaptations: LUAD CD74 High TAMs were biased toward phagocytosis and immune activation, whereas BM CD74 High TAMs shifted toward immunosuppression and vascular/metabolic adaptation. Differences in CD74 Low TAMs further reinforced this pattern, emphasizing the remodeling effect of the BM microenvironment. This transcriptional transition from immune activation to immunosuppression and metabolic adaptation not only weakens immune surveillance but also establishes permissive conditions for tumor colonization and expansion in the brain. Functional shift of CD74High TAMs from immune activation to metabolic stress in BM To further delineate CD74-associated functional differences, we performed GO enrichment and GSEA analyses of differentially expressed genes (Fig. 6 A–D). The results revealed that in both LUAD and BM, CD74 High TAMs consistently exhibited immune-related features (Fig. 6 A–B). Compared with CD74 Low TAMs, they were broadly enriched in pathways associated with antigen processing and presentation, adaptive immune response, and T cell activation (e.g., ADAPTIVE_IMMUNE_RESPONSE, ANTIGEN_PROCESSING_AND_PRESENTATION, T_CELL_ACTIVATION). In addition, multiple pathways related to cell adhesion and lymphocyte activation were preferentially enriched in CD74 High TAMs. These findings indicate that CD74 High TAMs retain a relatively stable “immune core” across contexts, whereas CD74 Low TAMs show markedly reduced activity in these processes. Despite these shared features, organ-specific differences were evident. In LUAD, CD74 High TAMs displayed stronger immune activity, with broader enrichment in antigen presentation, lymphocyte activation, and adhesion-related pathways, suggesting a greater role in immune surveillance and antitumor responses (Fig. 6 B). In BM, although CD74 High TAMs retained partial antigen-presenting capacity, their transcriptional programs were skewed toward adhesion and regulatory signaling, with progressive attenuation of immune effector features (Fig. 6 A). This shift suggests that TAMs in BM progressively disengage from immune activation and adapt to the demands of the local microenvironment. Cross-organ comparisons further highlighted these distinctions. In CD74 High TAMs (Fig. 6 C), enrichment extended beyond antigen presentation to include cell adhesion and its regulation, implying that differences between LUAD and BM primarily lie in immune cell interactions and adhesion-dependent processes. In CD74 Low TAMs (Fig. 6 D), enrichment also involved immune response, antigen presentation, and adhesion-related pathways, though the overall activity of these programs diverged between LUAD and BM. AUCell scoring further corroborated these observations. At the GO level, MHC complex assembly and antigen presentation terms exhibited higher activity in CD74 High TAMs. At the KEGG level, immune pathways such as phagosome, complement and coagulation cascades, Toll-like receptor signaling, and Th1/Th2/Th17 differentiation showed significant differences (Fig. 6 E). Together, these results demonstrate that while CD74 High TAMs consistently maintain core features of antigen presentation and lymphocyte activation across tissues, cross-organ comparisons revealed clear divergence in the magnitude and directionality of immune pathway activity. In summary, CD74 High TAMs universally preserve immune core features, such as antigen presentation and lymphocyte activation, and exhibit stronger immune functionality compared with CD74 Low TAMs. However, their functional trajectories diverge between LUAD and BM. CD74 High TAMs in LUAD favored antigen processing, phagocytosis, and immune effector programs, maintaining relatively active immune surveillance, whereas those in BM showed attenuated immune activity and increasing reliance on adhesion, regulatory, and metabolic pathways. Combined with single-cell pathway activity scoring, these results highlight a shift from immune activation in LUAD to immunosuppression and metabolic adaptation in BM. This transition not only compromises macrophage defense but also creates favorable conditions for tumor cells to breach the blood–brain barrier and establish colonization in the brain. Discussion This study began with a single-cell–level dissection of cell–cell communication networks in the TME of primary LUAD and BM, and subsequently focused on a receptor hub consistently occupying central positions across both organ contexts—CD74 High TAMs. Our single-cell and ligand–receptor analyses demonstrated that the communication frameworks of LUAD and BM are not simple replications but evolve in parallel along two tracks: “shared axes” and “organ-specific signals.” Cross-organ conserved pathways, such as MIF–CD74 and APP–CD74, remained highly active in both TME, underscoring the role of CD74 High TAMs as receptor hubs integrating upstream signals and driving downstream responses. Meanwhile, BM-enriched pathways including ANGPT2, VEGFA, and SPP1 favored blood–brain barrier (BBB) remodeling and immunosuppression[ 22 , 51 , 56 – 58 ], whereas LUAD was characterized by chemokine-driven recruitment and polarization networks (e.g., CCL2, CSF1, CXCL12), which not only facilitated myeloid cell trafficking and polarization but also perturbed BBB integrity to precondition a permissive niche for metastasis[ 25 , 58 – 61 ]. Consistent with prior studies, the BM microenvironment exhibited a “vascular-dependent and immunosuppressive” ecosystem, while LUAD maintained relatively active immune surveillance and may remotely prime BBB vulnerability, thereby fostering premetastatic niche formation[ 54 , 62 , 63 ]. Centering on metastasis, our single-cell resolution analyses further underscored the pivotal role of CD74 High TAMs. LUAD_TAM_Cluster_15 and BM_Cluster_03, identified in LUAD and BM respectively, exhibited high transcriptional similarity (Jaccard = 0.28) and both served as major recipient nodes for the APP–CD74 and MIF–(CD74 + CXCR4) axes, positioned at the “terminal hubs” of communication networks and functioning as central integrators of signaling between tumor cells, vasculature/stroma, and immune populations. Importantly, BM_Cluster_03 displayed stronger receptor activity within its interaction networks with multiple cell types—including tumor cells, endothelial cells, T cells, and fibroblasts—and was enriched in key pathways such as FcγR-mediated phagocytosis, complement and adhesion molecules, NOD-like receptor signaling, and inflammatory responses. These findings suggest a functional trajectory of BM_Cluster_03 toward “immunosuppression–vascular/adhesion–inflammatory adaptation,” consistent with the rapid ecological adaptation of metastatic cells within the brain. From a clinical perspective, the gene signature constructed from LUAD_TAM_Cluster_15 markers combined with CD74 was significantly associated with worse overall survival in the TCGA-LUAD cohort, demonstrating that “CD74 High TAMs enrichment” is not merely a descriptive transcriptomic feature but also a prognostic indicator of poor outcomes. Independent evidence from immunotherapy cohorts further supports this axis, showing positive correlations between CD74 expression, inflamed TMEs, and responses to PD-1/CTLA-4 combination immunotherapy[ 64 , 65 ]. At the functional level, four-way stratification (BM_CD74_High、BM_CD74_Low、LUAD_CD74_High、LUAD_CD74_Low) and AUCell scoring clarified both the “shared core” and “organ-specific divergence” of CD74 High TAMs. On one hand, CD74 High TAMs universally upregulated MHC-II antigen-presentation molecules, maintaining baseline antigen-processing capacity regardless of organ context. On the other hand, CD74 High TAMs in BM exhibited systematic downregulation of pathways such as phagosome, FcγR-mediated phagocytosis, and adaptive immunity, manifesting a “functionally suppressed” phenotype, whereas those in LUAD retained active phagocytic and immune effector programs. This functional transition—from immune activation toward immunosuppression and adhesion/metabolic adaptation—not only diminishes macrophage tumor-clearing capacity but also establishes cooperative niches for metastatic colonization and expansion under conditions of BBB disruption and vascular remodeling. These mechanisms are consistent with prior observations that APP–CD74 signaling suppresses phagocytosis and that blockade of MIF–CD74 synergizes with radiotherapy[ 8 , 10 , 66 ], thereby linking CD74-centered hubs to the interconnected processes of metastasis, immunosuppression, and vascular remodeling. Integrating these findings, we propose a direct model: in LUAD, chemokine and polarization networks are preferentially established, sustaining CD74 High TAMs in an immune-active state characterized by antigen presentation and immune collaboration. In BM, persistent angiogenic signaling and increased BBB permeability expose CD74 High TAMs to a distinct molecular milieu, driving a transition toward metabolic enhancement and stress adaptation. This functional reprogramming likely compromises their tumor-clearing ability and promotes metastatic outgrowth within an immunosuppressive environment. Therapeutically, restoring phagocytosis and antigen-processing functions in BM CD74 High TAMs or blocking MIF–CD74 and APP–CD74–mediated inhibitory effects could mitigate immune escape. Moreover, the interplay between vascular/barrier states and CD74 High TAMs metabolic stress may represent a critical link underlying BBB disruption and immunosuppressive niche formation. With imaging modalities already available to quantify BBB/tumor–brain barrier permeability, integration with circulating biomarker detection may allow dynamic monitoring of these processes, enabling early identification and intervention for BM [ 67 ]. This study was based on publicly available datasets (GEO, GSEA). Despite stringent quality control, residual batch effects and clinical heterogeneity cannot be excluded. In addition, ligand–receptor inference, CNV analysis, and pathway scoring represent model-based predictions that require direct protein-level and functional validation. Although our data delineate the ecological role and functional rewiring of CD74 High TAMs in LUAD–BM progression, the precise regulatory circuits, ligand sources, and signaling cascades remain to be elucidated. Moving forward, we plan two complementary approaches: (i) in vitro and ex vivo systems—including macrophage–tumor/endothelial coculture, organoids, and microfluidic BBB models—combined with genetic and pharmacologic perturbations to directly measure phagocytosis, antigen processing, and metabolic states, validated by spatial multiplex immunostaining; and (ii) in vivo models to systematically assess the effects of MIF–CD74 or APP–CD74 blockade in combination with radiotherapy, immunotherapy, or anti-angiogenic treatments, with particular attention to drug delivery, immunosuppressive dynamics, and lesion control. Through mechanistic validation, combinatorial interventions, and biomarker development, CD74 High TAMs may be advanced from a single-cell observation to a measurable, targetable, and predictive hub, forming an intervention–evaluation continuum bridging LUAD to BM and informing precision management of lung adenocarcinoma brain metastasis. Materials and methods Data sources and ethics In this study, we performed secondary analysis of the publicly available single-cell transcriptomic dataset GSE131907, which comprises 11 primary LUAD and 10 BM samples [ 14 ]. For external validation, bulk RNA sequencing data of LUAD were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ) data portal, and corresponding clinical follow-up information was retrieved from the UCSC Xena platform( https://xenabrowser.net/ ). Formalin-fixed, paraffin-embedded (FFPE) pathological sections were provided by the Second Affiliated Hospital of Harbin Medical University for histological validation. The study protocol was approved by the institutional ethics committee of the Second Affiliated Hospital of Harbin Medical University (approval number: KY2025-221). All ethics procedures conformed with the principles of the 1964 Declaration of Helsinki and its latest 2008 amendments. All participants in the study were informed and agreed to participate in the study. Single-cell data preprocessing and cell annotation Single-cell data analysis was performed in the R environment (version 4.4.3). Raw expression matrices were processed using Seurat (v5.2.1) [ 15 ] for quality control, normalization, and selection of highly variable genes. Dimensionality reduction was conducted by principal component analysis (PCA), followed by clustering based on a shared nearest-neighbor graph, and visualization was carried out using UMAP. Cell type annotation was performed according to known marker genes, and TAMs were extracted for downstream analyses. CNV analysis To identify potential malignant cell populations, CNV analysis of single-cell transcriptomic data was performed using inferCNV (v1.22.0) [ 16 ]. The input matrix was generated from raw count expression values of each cell, and gene position annotations were obtained and ordered according to the human genome coordinates using AnnoProbe (v0.1.7). Pericytes were selected as the reference population to establish baseline signals when constructing the inferCNV object. The analysis was run with the following parameters: cutoff = 0.1 (for 10x data), hclust_method = "ward.D2", denoise = TRUE, and HMM = FALSE. During the CNV scoring stage, TAMs, fibroblasts, and pericytes were further combined as reference populations to obtain more robust neutral intervals by estimating the mean and standard deviation. Based on these reference values, CNV signals were categorized as copy number loss, neutral, or copy number gain, and subsequently converted into scores to calculate the total CNV score for each single cell. Boxplots were then used to visualize CNV distributions across different cell types, enabling the distinction between malignant epithelial cells and non-malignant populations. Cell–cell communication analysis Cell–cell communication analysis was performed using CellChat (v2.1.2) to construct ligand–receptor interaction networks, estimate intercellular communication probabilities, and aggregate them into signaling pathways for comparison of differential communication axes between LUAD and BM samples [ 17 ]. Visualization was carried out with the built-in bubble plot function in CellChat to illustrate alterations in communication patterns among different cell populations. External validation with TCGA-LUAD and survival analysis In the TCGA-LUAD cohort, expression matrices were normalized and transformed using log2(TPM + 1), then merged with corresponding clinical follow-up data[ 18 ]. Patients were stratified according to CD74 expression levels or scores derived from CD74 High TAM signatures. Survival analyses were performed using the Kaplan–Meier method, with significance assessed by the log-rank test, and Cox proportional hazards models were applied. All survival analyses were conducted with the survival (v3.8-3) and survminer (v0.5.0) R packages. CD74-based stratification and differential expression analysis In TAMs from LUAD and BM, cells were stratified into CD74 High and CD74 low groups according to the median single-cell expression level of CD74, and four combined labels (BM_CD74_High, BM_CD74_Low, LUAD_CD74_High, and LUAD_CD74_Low) were generated. Differential expression analyses were performed across four comparisons (BM_CD74_High vs. BM_CD74_Low, LUAD_CD74_High vs. LUAD_CD74_Low, LUAD_CD74_High vs. BM_CD74_High, LUAD_CD74_Low vs. BM_CD74_Low) using the FindMarkers function in Seurat [ 15 ] 的 with the Wilcoxon rank-sum test (min.pct = 0.1, logfc.threshold = 0). Functional enrichment and gene set enrichment analysis We performed functional enrichment analysis of differentially expressed genes (DEGs). Specifically, annotation was conducted at the levels of Gene Ontology (GO, http://geneontology.org/ ) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.genome.jp/kegg/ ) pathways using clusterProfiler (v4.14.6) to identify key biological functions and signaling pathways associated with DEGs[ 19 – 21 ]. Gene ID conversion was performed with org.Hs.eg.db (v3.20.0), and gene sets from the Molecular Signatures Database (MSigDB, https://www.gseamsigdb.org/gsea/msigdb ) were retrieved using msigdbr (v25.1.1). Furthermore, gene set enrichment analysis (GSEA) was carried out using the full ranked gene list ordered by log2FoldChange, and enrichment results were visualized with enrichplot (v1.26.6). Single-cell pathway activity scoring To evaluate pathway activity at the single-cell level, AUCell (v1.28.0) was applied to construct gene sets based on differentially expressed or enriched terms and to calculate area under the curve (AUC) scores for each cell. Differences in pathway activity between groups were assessed using the Wilcoxon rank-sum test. Results were visualized as heatmaps with pheatmap (v1.0.12) and as boxplots with ggpubr (v0.6.1). Multiplex immunohistochemistry (mIHC) validation Formalin-fixed paraffin-embedded (FFPE) sections were subjected to deparaffinization, rehydration, and antigen retrieval. After blocking, sections were incubated with ABclonal monoclonal antibodies CD74 Rabbit mAb (clone A24027, Cat# A24027-50 µL) and CD68 Rabbit mAb (clone A23205, Cat# A23205-50 µL). HRP-conjugated secondary antibodies and a chromogenic detection system were subsequently applied, and nuclei were counterstained with DAPI. Slides were mounted directly after staining and imaged under a fluorescence microscope. Statistical analysis All statistical analyses were performed in the R environment. Differential expression analysis of single-cell data was conducted using the FindMarkers function in Seurat [ 15 ] with the Wilcoxon rank-sum test (min.pct = 0.1, logfc.threshold = 0). P values were adjusted using the Benjamini–Hochberg method, with significance defined as false discovery rate (FDR) < 0.05, and in some analyses further constrained by |log2FoldChange| ≥ 0.25. Functional enrichment and gene set enrichment analyses considered adjusted P values (p.adjust < 0.05) as the threshold for significance. Comparisons of pathway activity scores were assessed by the Wilcoxon rank-sum test, and P values with corresponding significance levels were reported directly. Survival analyses were performed using the Kaplan–Meier method and log-rank test, with Cox proportional hazards models fitted when appropriate. Declarations Funding: Not applicable. Clinical trial number: not applicable. Consent to Participate declaration: not applicable. Consent to Participate declaration: not applicable. Author Contribution Conceptualization, Junming Jia, Yang Li, Mingzhu Yin; data curation, Junming Jia,; formal analysis, Huichao Lin, Zeren Chen; investigation, Ke He, Hongren Cao, software, Ziyan li, Jiaxin Cao, supervision, Yang Li, Mingzhu Yin; writing-original draft, Junming Jia; writing-review&editing, Yang Li, Mingzhu Yin. Data Availability The scRNA-seq dataset was acquired from the GEO database, specifically under accession number GSE131907. The TCGALUAD cohort dataset was downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) portal, and corresponding clinical follow-up information was retrieved from the UCSC Xena platform(https://xenabrowser.net/). References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–63. https://doi.org/10.3322/caac.21834 . 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12:03:57","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181121,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7605804/v1/52ae62b4e454631faab8b747.html"},{"id":93134426,"identity":"c3302e95-8340-46c1-bc61-6b073a0172a7","added_by":"auto","created_at":"2025-10-09 12:03:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":144613,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated analysis of single-cell transcriptomic data\u003c/p\u003e\n\u003cp\u003e(A) UMAP showing the distribution of major cell populations in primary LUAD samples.\u003c/p\u003e\n\u003cp\u003e(B) UMAP showing the distribution of major cell populations in BM samples.\u003c/p\u003e\n\u003cp\u003e(C) CNV scores of cellular subclusters in primary LUAD.\u003c/p\u003e\n\u003cp\u003e(D) CNV scores of cellular subclusters in BM.\u003c/p\u003e\n\u003cp\u003e(E) Cell–cell communication among subclusters in primary LUAD.\u003c/p\u003e\n\u003cp\u003e(F) Cell–cell communication among subclusters in BM.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7605804/v1/de926e47c25827cb1eb5124f.png"},{"id":93134427,"identity":"71bc95fe-b065-41e0-9da5-77611a733149","added_by":"auto","created_at":"2025-10-09 12:03:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116681,"visible":true,"origin":"","legend":"\u003cp\u003eCD74\u003csup\u003eHigh\u003c/sup\u003e TAMs suppress phagocytic function through the APP–CD74 axis and predict poor prognosis\u003c/p\u003e\n\u003cp\u003e(A) GO enrichment analysis showing significantly reduced enrichment of phagocytosis-related gene sets in CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs.\u003c/p\u003e\n\u003cp\u003e(B) UMAP showing the distribution of macrophage subclusters in LUAD.\u003c/p\u003e\n\u003cp\u003e(C) CellChat analysis revealing APP ligand–mediated communication networks between endothelial cells and TAMs in LUAD.\u003c/p\u003e\n\u003cp\u003e(D) Kaplan–Meier survival analysis showing the prognostic impact of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs–associated gene signatures in LUAD patients.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7605804/v1/450f59d64c3172f6c92f1868.png"},{"id":93134433,"identity":"3b7fda2e-b36f-46ab-b470-4941f2ba5be2","added_by":"auto","created_at":"2025-10-09 12:03:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":394668,"visible":true,"origin":"","legend":"\u003cp\u003eEcological niche of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs in LUAD brain metastasis\u003c/p\u003e\n\u003cp\u003e(A) UMAP showing the distribution of TAM subclusters in BM.\u003c/p\u003e\n\u003cp\u003e(B) FeaturePlot showing the expression patterns of CD74 in TAMs (CD68: macrophage marker; DAPI: nuclear marker).\u003c/p\u003e\n\u003cp\u003e(C) Multiplex immunofluorescence (mIHC) demonstrating spatial colocalization of CD74 and CD68.\u003c/p\u003e\n\u003cp\u003e(D) CellChat analysis revealing CD74-related ligand–receptor interaction pathways in BM.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7605804/v1/d960b6a51e94937257fb17e1.png"},{"id":93134431,"identity":"db0e69eb-b56a-4d96-b478-7d6da882ede1","added_by":"auto","created_at":"2025-10-09 12:03:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84385,"visible":true,"origin":"","legend":"\u003cp\u003eCross-organ similarity and communication features of TAM subclusters\u003c/p\u003e\n\u003cp\u003e(A) Similarity score between BM TAM subclusters and LUAD_TAM_Cluster_15.\u003c/p\u003e\n\u003cp\u003e(B) CellChat analysis showing intercellular interactions between BM_TAM_Cluster_03 and other cell populations in BM.\u003c/p\u003e\n\u003cp\u003e(C) GO and KEGG enrichment analyses of BM-specific marker genes displayed as bar plots.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7605804/v1/35e5e8ed9dc13587b39d3a5a.png"},{"id":93134430,"identity":"d87d7b0e-9c97-4957-92e3-2cf7101ee965","added_by":"auto","created_at":"2025-10-09 12:03:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":118209,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential transcriptional features of TAMs stratified by CD74 expression across organ sites\u003c/p\u003e\n\u003cp\u003e(A) UMAP showing the distribution of TAM subclusters across LUAD and BM.\u003c/p\u003e\n\u003cp\u003e(B) UMAP showing four TAM groups (BM_CD74_High, BM_CD74_Low, LUAD_CD74_High, LUAD_CD74_Low) stratified by the median CD74 expression and tissue origin.\u003c/p\u003e\n\u003cp\u003e(C–F) Differential expression analyses of TAMs according to CD74 expression levels.\u003c/p\u003e\n\u003cp\u003e(C) Differentially expressed genes between CD74\u003csup\u003eHigh\u003c/sup\u003e and CD74\u003csup\u003eLow\u003c/sup\u003e TAMs in LUAD.\u003c/p\u003e\n\u003cp\u003e(D) Differentially expressed genes between CD74\u003csup\u003eHigh\u003c/sup\u003e and CD74\u003csup\u003eLow\u003c/sup\u003e TAMs in BM.\u003c/p\u003e\n\u003cp\u003e(E) Cross-organ differential expression features of CD74\u003csup\u003eLow\u003c/sup\u003e TAMs between LUAD and BM.\u003c/p\u003e\n\u003cp\u003e(F) Cross-organ differential expression features of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs between LUAD and BM.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7605804/v1/61461c029c979f52649dd88c.png"},{"id":93134436,"identity":"e075e74c-1983-45c1-9f14-e7a43eca1640","added_by":"auto","created_at":"2025-10-09 12:03:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":140857,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment differences of TAMs stratified by CD74 expression\u003c/p\u003e\n\u003cp\u003e(A–D) GSEA analyses showing functional enrichment differences across TAM groups.\u003c/p\u003e\n\u003cp\u003e(A) Gene set enrichment analysis of BM_CD74_High versus BM_CD74_Low TAMs.\u003c/p\u003e\n\u003cp\u003e(B) Gene set enrichment analysis of LUAD_CD74_High versus LUAD_CD74_Low TAMs.\u003c/p\u003e\n\u003cp\u003e(C) Cross-organ enrichment analysis of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs between LUAD and BM.\u003c/p\u003e\n\u003cp\u003e(D) Cross-organ enrichment analysis of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs between LUAD and BM.\u003c/p\u003e\n\u003cp\u003e(E) AUCell analysis showing cross-organ pathway enrichment differences of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs between LUAD and BM.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7605804/v1/1ac0a0fb67426ba12c5fb4c1.png"},{"id":93766232,"identity":"89a6f878-900c-4d17-bb32-3d8b3ef4317b","added_by":"auto","created_at":"2025-10-17 10:38:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1811397,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7605804/v1/caf4efba-df37-4a39-8c30-8bf3846a1bd4.pdf"},{"id":93134428,"identity":"1251727c-2810-4d4b-9098-71d3f8d336b6","added_by":"auto","created_at":"2025-10-09 12:03:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":836646,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1andS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7605804/v1/cfb7f0ad924cef77104187af.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell transcriptomic analysis reveals the evolution of the immunosuppressive landscape from primary tumors to brain metastasis in LUAD","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer remains the leading cause of cancer incidence and mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].LUAD, the most common histological subtype, accounts for approximately 40% of all lung cancer cases and exhibits a strong propensity for BM. Even with treatment, patients with BM continue to face poor outcomes [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Increasing evidence shows that the tumor microenvironment (TME) of BM is characterized by pronounced immunosuppression, including marked depletion of tumor-infiltrating lymphocytes, elevated neutrophil infiltration, and loss of concordance between PD-L1 expression and T cell\u0026ndash;inflamed gene signatures[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].These alterations not only reflect impaired immune surveillance but also highlight the adaptive remodeling of the local microenvironment during metastatic dissemination. Thus, LUAD brain metastasis is accompanied by systemic reprogramming of the TME, which simultaneously weakens antitumor immune activity and establishes an immunosuppressive niche conducive to metastatic colonization and expansion.\u003c/p\u003e\u003cp\u003eCD74, also known as the MHC class II invariant chain (Ii), was initially identified as a chaperone for MHC-II complex assembly. Subsequent studies revealed that CD74 can also act as a signaling receptor, most prominently for macrophage migration inhibitory factor (MIF), thereby shaping tumor immune responses[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CD74 is involved in antigen processing and presentation, while its interaction with MIF promotes immunosuppression and angiogenesis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] In the context of immunotherapy, elevated CD74 expression has been proposed as a potential biomarker, positively associated with an inflamed TME and with clinical benefit from PD-1/CTLA-4 bispecific antibodies (AK104, cadonilimab)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. More recently, amyloid precursor protein (APP) was shown to suppress macrophage phagocytosis through the CD74/CXCR4 complex, suggesting that the APP\u0026ndash;CD74 axis represents a promising target for therapeutic intervention [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTAMs are among the most abundant and functionally versatile immune cells within the TME, playing key roles in immune evasion and metastatic progression[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]。 Prior to overt dissemination, TAMs promote tumor invasion and metastasis by suppressing immune responses and inducing angiogenesis, thereby supporting tumor cell survival and growth at secondary sites[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]。 TAMs can secrete cytokines and growth factors such as VEGF and EGF to drive angiogenesis, thereby providing essential nutrients and vascular support for metastatic tumors, while immunosuppressive mediators including IL-10 and TGF-β dampen T cell antitumor activity, facilitating immune escape in lung cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]。Thus, TAMs not only actively participate in TME remodeling during LUAD brain metastasis but also represent a central cellular subset driving immunosuppression and angiogenesis.\u003c/p\u003e\u003cp\u003eAgainst this background, we employed single-cell sequencing to comprehensively characterize the cellular landscape and ligand\u0026ndash;receptor interaction networks in LUAD and BM. We identified CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs as receptor hubs consistently occupying central positions across organs. Specifically, CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs in LUAD were enriched for antigen presentation and immune activation programs, whereas their counterparts in BM relied more on metabolic adaptation and stress responses, indicating functional divergence. These findings are consistent with prior evidence linking CD74 expression to immunotherapy responsiveness and with experimental studies showing enhanced efficacy upon MIF\u0026ndash;CD74 blockade. Together, they underscore the pivotal role of CD74-centered signaling not only in immunosuppression and regulation of phagocytosis but also as a potential therapeutic target in BM.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eBoth LUAD and BM communicate with TAMs through the APP\u0026ndash;CD74 axis\u003c/h2\u003e\u003cp\u003eWe reanalyzed the scRNA-seq dataset GSE131907[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which included 11 primary LUAD tumor samples and 10 BM samples obtained from LUAD patients who underwent surgical resection without prior treatment. After quality control, normalization, and PCA, cells from LUAD and BM samples were subjected to dimensionality reduction and independent clustering using the UMAP algorithm. Subsequent manual annotation revealed that in LUAD samples, T cells were predominant (33.8%), followed by B cells (20.5%) and TAMs (17.9%), with neutrophils (6.0%), oligodendrocytes (4.2%), fibroblasts (1.0%), endothelial cells (1.1%), and pericytes (0.6%) present at lower proportions. In contrast, BM samples showed a markedly higher proportion of T cells (49.2%), followed by B cells (21.5%) and TAMs (13.0%), while neutrophils (9.6%), oligodendrocytes (3.4%), endothelial cells (3.7%), fibroblasts (4.5%), and pericytes (1.5%) were distributed at descending frequencies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,B, Figure S1). After cell annotation, we applied the inferCNV algorithm to infer copy number variation (CNV) across cellular subpopulations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. CNV scores were calculated for each cell based on major cell types and visualized as boxplots to compare CNV distributions among cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D). These results not only confirmed that epithelial cells originated from LUAD tumor cells but also indicated that tumor cells maintained marked genomic instability and CNV characteristics after brain metastasis. Notably, mild CNV signals were also observed in TAM subpopulations within LUAD samples, suggesting potential tumor-associated phenotypes or complex regulatory interactions within the tumor\u0026ndash;immune microenvironment.\u003c/p\u003e\u003cp\u003eTo systematically compare cell\u0026ndash;cell communication networks between primary LUAD and BM, we performed further analyses using CellChat. Both LUAD and BM exhibited distinct ligand\u0026ndash;receptor pairs, while a subset of highly conserved and significant signaling axes was shared between the two contexts. Notably, CD74-centered interactions, including MIF\u0026ndash;(CD74\u0026thinsp;+\u0026thinsp;CXCR4), MIF\u0026ndash;(CD74\u0026thinsp;+\u0026thinsp;CD44), and APP\u0026ndash;CD74, showed strong intercellular communication activity in both LUAD and BM (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F). Among these shared pathways, the APP\u0026ndash;CD74 and MIF\u0026ndash;CD74 axes displayed particularly high communication strength. Receptor localization analysis revealed that the receiving cells of these pathways were predominantly macrophages. Given that CD74 is an essential receptor in these axes, only macrophages with high CD74 expression can effectively engage with ligands such as APP and MIF (in coordination with co-receptors including CD44 and CXCR4) to trigger downstream signaling. Based on this, we designated CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs as the core recipient population of interest.\u003c/p\u003e\u003cp\u003eComparative analysis of LUAD- and BM-specific communication pathways further revealed striking differences in signaling directionality and biological function (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). In BM, ligands such as ANGPT2, VEGFA, and SPP1 disrupted the blood\u0026ndash;brain barrier (BBB) and suppressed T cell function, thereby promoting the establishment of an immunosuppressive microenvironment that facilitates tumor cell penetration of the BBB and colonization in the brain [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In contrast, LUAD-specific pathways were enriched in CCL2, CSF1, and CXCL12, which primarily recruit and polarize macrophages, driving immunosuppression and transendothelial migration, consistent with the more active immune surveillance and barrier defense observed in the LUAD[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The marked divergence of these signaling axes underscores the adaptive evolution of tumor cells within distinct organ microenvironments and provides a molecular basis for elucidating metastatic mechanisms and developing precise therapeutic strategies.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFunctions and prognostic significance of CD74 TAMs\u003c/h3\u003e\n\u003cp\u003eNext, GO enrichment analysis of the TCGA-LUAD cohort (2022) revealed that patients with high CD74 expression exhibited significantly reduced enrichment of phagocytosis-related gene sets, including \u003cem\u003ephagocytosis\u003c/em\u003e and \u003cem\u003eregulation of phagocytosis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This finding suggests that activation of the APP\u0026ndash;CD74 axis is closely associated with the suppression of TAM phagocytic function. Accordingly, elevated CD74 expression may negatively regulate TAM-mediated phagocytosis, thereby impairing their ability to eliminate tumor cells and contributing to immune evasion and tumor progression.\u003c/p\u003e\u003cp\u003eUpon detailed subclustering of TAMs in LUAD, we identified 17 distinct TAM subclusters(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). CellChat analysis of ligand\u0026ndash;receptor interactions between these subclusters and other cell types in the microenvironment (Figure S2) revealed that LUAD_TAM_Cluster_15 exhibited the most prominent interactions, particularly along signaling axes such as APP\u0026ndash;CD74, with endothelial and immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), suggesting a pivotal role in regulating the tumor microenvironment. To further evaluate its biological and clinical significance, we constructed a signature gene set comprising LUAD_TAM_Cluster_15 marker genes in combination with CD74. Using this signature, single-sample gene set enrichment analysis (ssGSEA) was applied to the TCGA-LUAD cohort to stratify patients into high- and low-expression groups. Kaplan\u0026ndash;Meier survival analysis demonstrated that patients with high LUAD_TAM_Cluster_15 signature expression had significantly shorter overall survival than those in the low-expression group (log-rank p\u0026thinsp;=\u0026thinsp;0.016), indicating that enrichment of this TAM subpopulation is strongly associated with poor prognosis in LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\n\u003ch3\u003eDistribution and spatial features of CD74High TAMs in brain metastases\u003c/h3\u003e\n\u003cp\u003eTo systematically investigate the distribution and function of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs in BM, we first performed single-cell subclustering of TAMs and identified eight distinct subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). To delineate the spatial distribution and phenotype of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs, we visualized the expression patterns of CD74 and the macrophage marker CD68 based on scRNA-seq data. Both genes exhibited similar spatial localization, suggesting potential co-expression within the same cell population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). This observation was validated by multiplex immunofluorescence (mIHC), which confirmed high levels of co-expression and spatial enrichment of CD74 in TAMs from BM(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eCell\u0026ndash;cell communication analysis revealed that BM_Cluster_03 functioned as the predominant recipient of the APP\u0026ndash;CD74 signaling axis, forming extensive interaction networks with multiple signal-secreting cells in the tumor microenvironment, including tumor cells and endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Jaccard similarity analysis further demonstrated that BM_Cluster_03 exhibited the highest molecular resemblance to LUAD_TAM_Cluster_15 from primary tumors (Jaccard index\u0026thinsp;=\u0026thinsp;0.28; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Both clusters represented \u003cb\u003eCD74\u003c/b\u003e\u003csup\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eTAMs\u003c/b\u003e populations that served as central receptor hubs of the APP\u0026ndash;CD74 pathway, acting as key executors of this signaling axis and playing pivotal roles in macrophage\u0026ndash;tumor cell communication and microenvironmental regulation.\u003c/p\u003e\u003cp\u003eIntegrated CellChat analysis across all cell types confirmed that BM_Cluster_03 not only displayed the strongest receptor activity within the APP\u0026ndash;CD74 axis but also functioned as a major hub for multiple immune-related signaling pathways, including MIF\u0026ndash;(CD74\u0026thinsp;+\u0026thinsp;CXCR4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). BM_Cluster_03 established broad and dense communication networks with diverse immune and stromal cell types, including tumor cells, endothelial cells, T cells, and fibroblasts.\u003c/p\u003e\u003cp\u003eImportantly, intersection and differential analyses of marker genes between BM_Cluster_03 and LUAD_TAM_Cluster_15 revealed that, despite their high similarity, BM_Cluster_03 retained a set of unique functional genes(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). GO biological process enrichment indicated significant involvement of these genes in immune effector regulation, lymphocyte differentiation, leukocyte activation, and inflammatory responses. KEGG pathway analysis further highlighted roles in FcγR-mediated phagocytosis, complement and adhesion molecules, NOD-like receptor signaling, and inflammatory responses, suggesting that BM_Cluster_03 plays an important role in immune adaptation and microenvironmental remodeling in brain metastases.\u003c/p\u003e\u003cp\u003eTaken together, these multilayered analyses demonstrate that BM_Cluster_03, although highly similar to LUAD_TAM_Cluster_15, occupies a central position in signal integration and immune regulation within the BM microenvironment. BM_Cluster_03 and LUAD_TAM_Cluster_15 represent \u003cb\u003eCD74\u003c/b\u003e\u003csup\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eTAMs\u003c/b\u003e subset in BM and LUAD, respectively, both acting as receptor hubs in their corresponding tumor microenvironments. These findings identify \u003cb\u003eCD74\u003c/b\u003e\u003csup\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eTAMs\u003c/b\u003e as key cellular populations that bridge primary tumor evolution and metastatic microenvironmental remodeling.\u003c/p\u003e\n\u003ch3\u003eCD74 expression level–associated differences in antigen presentation and metabolic adaptation of TAMs\u003c/h3\u003e\n\u003cp\u003eFollowing integration of TAMs from primary LUAD and BM, we stratified cells into four subgroups based on CD74 expression and tissue origin (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;B): BM_CD74_High, BM_CD74_Low, LUAD_CD74_High, and LUAD_CD74_Low. Differential expression analysis across the four groups revealed both conserved features and clear functional divergence.\u003c/p\u003e\u003cp\u003eAcross organs and groups, we first observed a consistent feature: in both LUAD and BM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u0026ndash;D), CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs displayed significant upregulation of a series of MHC-II antigen presentation molecules, including HLA-DMA, HLA-DMB, HLA-DRB5, and HLA-DQA2. This pattern indicates that CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs broadly maintain antigen-processing and antigen-presenting capacity, with conserved reliance on lysosomal functions across distinct microenvironments[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These findings highlight CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs as a stable subcluster with antigen processing as a core program across organs.\u003c/p\u003e\u003cp\u003eBeyond these shared features, subgroup-specific signatures revealed distinct functional divergence between LUAD and BM. In BM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), upregulation of ITM2B and complement molecules (C1QA, C1QC) suggested residual activity of immune recognition and complement pathways[\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Conversely, downregulation of FTL and CSTB indicated altered regulation of iron homeostasis and proteolysis, which may affect TAM polarization and angiogenesis, thereby contributing to adaptation within the brain metastatic niche[\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs retained upregulation of MHC-II molecules, CST3, and CSTB, but were uniquely enriched for GSN and CD1C, implicating cytoskeletal remodeling and enhanced antigen presentation capacity[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Meanwhile, S100A8 and S100A9 were markedly downregulated, indicating reduced activity of inflammatory chemotaxis and myeloid recruitment pathways[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Collectively, LUAD CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs maintained strong antigen-processing activity but showed weaker inflammatory amplification compared with their BM counterparts.\u003c/p\u003e\u003cp\u003eCross-organ comparisons of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE) further demonstrated functional polarization. LUAD CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs were enriched for MARCO, LYZ, and FABP4, along with RPS4Y1 and HLA-DRA, highlighting their roles in phagocytosis, inflammation, and metabolic reprogramming[\u003cspan additionalcitationids=\"CR42 CR43 CR44 CR45\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In contrast, BM CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs upregulated TFF3, SPP1, and XIST. TFF3 has been linked to angiogenesis and metastatic potential; SPP1 is associated with immunosuppression, angiogenesis, and invasion; and XIST contributes to macrophage polarization, suggesting a shift toward immunosuppressive and vascular remodeling programs in BM[\u003cspan additionalcitationids=\"CR48 CR49 CR50 CR51\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn CD74\u003csup\u003eLow\u003c/sup\u003e TAMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF), LUAD cells expressed higher levels of HLA-DRB1, HLA-DQA1, and HLA-DRB5, retaining partial antigen-presenting function. Conversely, BM CD74\u003csup\u003eLow\u003c/sup\u003e TAMs upregulated RNASE1, HMOX1, and TFF3, indicating more pronounced metabolic stress and immunosuppressive programs[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTaken together, four-group comparisons revealed the conserved core function of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs\u0026mdash;antigen presentation\u0026mdash;while also highlighting divergent cross-organ adaptations: LUAD CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs were biased toward phagocytosis and immune activation, whereas BM CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs shifted toward immunosuppression and vascular/metabolic adaptation. Differences in CD74\u003csup\u003eLow\u003c/sup\u003e TAMs further reinforced this pattern, emphasizing the remodeling effect of the BM microenvironment. This transcriptional transition from immune activation to immunosuppression and metabolic adaptation not only weakens immune surveillance but also establishes permissive conditions for tumor colonization and expansion in the brain.\u003c/p\u003e\n\u003ch3\u003eFunctional shift of CD74High TAMs from immune activation to metabolic stress in BM\u003c/h3\u003e\n\u003cp\u003eTo further delineate CD74-associated functional differences, we performed GO enrichment and GSEA analyses of differentially expressed genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;D). The results revealed that in both LUAD and BM, CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs consistently exhibited immune-related features (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;B). Compared with CD74\u003csup\u003eLow\u003c/sup\u003e TAMs, they were broadly enriched in pathways associated with antigen processing and presentation, adaptive immune response, and T cell activation (e.g., ADAPTIVE_IMMUNE_RESPONSE, ANTIGEN_PROCESSING_AND_PRESENTATION, T_CELL_ACTIVATION). In addition, multiple pathways related to cell adhesion and lymphocyte activation were preferentially enriched in CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs. These findings indicate that CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs retain a relatively stable \u0026ldquo;immune core\u0026rdquo; across contexts, whereas CD74\u003csup\u003eLow\u003c/sup\u003e TAMs show markedly reduced activity in these processes.\u003c/p\u003e\u003cp\u003eDespite these shared features, organ-specific differences were evident. In LUAD, CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs displayed stronger immune activity, with broader enrichment in antigen presentation, lymphocyte activation, and adhesion-related pathways, suggesting a greater role in immune surveillance and antitumor responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In BM, although CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs retained partial antigen-presenting capacity, their transcriptional programs were skewed toward adhesion and regulatory signaling, with progressive attenuation of immune effector features (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). This shift suggests that TAMs in BM progressively disengage from immune activation and adapt to the demands of the local microenvironment.\u003c/p\u003e\u003cp\u003eCross-organ comparisons further highlighted these distinctions. In CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), enrichment extended beyond antigen presentation to include cell adhesion and its regulation, implying that differences between LUAD and BM primarily lie in immune cell interactions and adhesion-dependent processes. In CD74\u003csup\u003eLow\u003c/sup\u003e TAMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), enrichment also involved immune response, antigen presentation, and adhesion-related pathways, though the overall activity of these programs diverged between LUAD and BM.\u003c/p\u003e\u003cp\u003eAUCell scoring further corroborated these observations. At the GO level, MHC complex assembly and antigen presentation terms exhibited higher activity in CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs. At the KEGG level, immune pathways such as phagosome, complement and coagulation cascades, Toll-like receptor signaling, and Th1/Th2/Th17 differentiation showed significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Together, these results demonstrate that while CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs consistently maintain core features of antigen presentation and lymphocyte activation across tissues, cross-organ comparisons revealed clear divergence in the magnitude and directionality of immune pathway activity.\u003c/p\u003e\u003cp\u003eIn summary, CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs universally preserve immune core features, such as antigen presentation and lymphocyte activation, and exhibit stronger immune functionality compared with CD74\u003csup\u003eLow\u003c/sup\u003e TAMs. However, their functional trajectories diverge between LUAD and BM. CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs in LUAD favored antigen processing, phagocytosis, and immune effector programs, maintaining relatively active immune surveillance, whereas those in BM showed attenuated immune activity and increasing reliance on adhesion, regulatory, and metabolic pathways. Combined with single-cell pathway activity scoring, these results highlight a shift from immune activation in LUAD to immunosuppression and metabolic adaptation in BM. This transition not only compromises macrophage defense but also creates favorable conditions for tumor cells to breach the blood\u0026ndash;brain barrier and establish colonization in the brain.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study began with a single-cell\u0026ndash;level dissection of cell\u0026ndash;cell communication networks in the TME of primary LUAD and BM, and subsequently focused on a receptor hub consistently occupying central positions across both organ contexts\u0026mdash;CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs.\u003c/p\u003e\u003cp\u003eOur single-cell and ligand\u0026ndash;receptor analyses demonstrated that the communication frameworks of LUAD and BM are not simple replications but evolve in parallel along two tracks: \u0026ldquo;shared axes\u0026rdquo; and \u0026ldquo;organ-specific signals.\u0026rdquo; Cross-organ conserved pathways, such as MIF\u0026ndash;CD74 and APP\u0026ndash;CD74, remained highly active in both TME, underscoring the role of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs as receptor hubs integrating upstream signals and driving downstream responses. Meanwhile, BM-enriched pathways including ANGPT2, VEGFA, and SPP1 favored blood\u0026ndash;brain barrier (BBB) remodeling and immunosuppression[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], whereas LUAD was characterized by chemokine-driven recruitment and polarization networks (e.g., CCL2, CSF1, CXCL12), which not only facilitated myeloid cell trafficking and polarization but also perturbed BBB integrity to precondition a permissive niche for metastasis[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR59 CR60\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Consistent with prior studies, the BM microenvironment exhibited a \u0026ldquo;vascular-dependent and immunosuppressive\u0026rdquo; ecosystem, while LUAD maintained relatively active immune surveillance and may remotely prime BBB vulnerability, thereby fostering premetastatic niche formation[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCentering on metastasis, our single-cell resolution analyses further underscored the pivotal role of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs. LUAD_TAM_Cluster_15 and BM_Cluster_03, identified in LUAD and BM respectively, exhibited high transcriptional similarity (Jaccard\u0026thinsp;=\u0026thinsp;0.28) and both served as major recipient nodes for the APP\u0026ndash;CD74 and MIF\u0026ndash;(CD74\u0026thinsp;+\u0026thinsp;CXCR4) axes, positioned at the \u0026ldquo;terminal hubs\u0026rdquo; of communication networks and functioning as central integrators of signaling between tumor cells, vasculature/stroma, and immune populations. Importantly, BM_Cluster_03 displayed stronger receptor activity within its interaction networks with multiple cell types\u0026mdash;including tumor cells, endothelial cells, T cells, and fibroblasts\u0026mdash;and was enriched in key pathways such as FcγR-mediated phagocytosis, complement and adhesion molecules, NOD-like receptor signaling, and inflammatory responses. These findings suggest a functional trajectory of BM_Cluster_03 toward \u0026ldquo;immunosuppression\u0026ndash;vascular/adhesion\u0026ndash;inflammatory adaptation,\u0026rdquo; consistent with the rapid ecological adaptation of metastatic cells within the brain.\u003c/p\u003e\u003cp\u003eFrom a clinical perspective, the gene signature constructed from LUAD_TAM_Cluster_15 markers combined with CD74 was significantly associated with worse overall survival in the TCGA-LUAD cohort, demonstrating that \u0026ldquo;CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs enrichment\u0026rdquo; is not merely a descriptive transcriptomic feature but also a prognostic indicator of poor outcomes. Independent evidence from immunotherapy cohorts further supports this axis, showing positive correlations between CD74 expression, inflamed TMEs, and responses to PD-1/CTLA-4 combination immunotherapy[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt the functional level, four-way stratification (BM_CD74_High、BM_CD74_Low、LUAD_CD74_High、LUAD_CD74_Low) and AUCell scoring clarified both the \u0026ldquo;shared core\u0026rdquo; and \u0026ldquo;organ-specific divergence\u0026rdquo; of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs. On one hand, CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs universally upregulated MHC-II antigen-presentation molecules, maintaining baseline antigen-processing capacity regardless of organ context. On the other hand, CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs in BM exhibited systematic downregulation of pathways such as phagosome, FcγR-mediated phagocytosis, and adaptive immunity, manifesting a \u0026ldquo;functionally suppressed\u0026rdquo; phenotype, whereas those in LUAD retained active phagocytic and immune effector programs. This functional transition\u0026mdash;from immune activation toward immunosuppression and adhesion/metabolic adaptation\u0026mdash;not only diminishes macrophage tumor-clearing capacity but also establishes cooperative niches for metastatic colonization and expansion under conditions of BBB disruption and vascular remodeling. These mechanisms are consistent with prior observations that APP\u0026ndash;CD74 signaling suppresses phagocytosis and that blockade of MIF\u0026ndash;CD74 synergizes with radiotherapy[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], thereby linking CD74-centered hubs to the interconnected processes of metastasis, immunosuppression, and vascular remodeling.\u003c/p\u003e\u003cp\u003eIntegrating these findings, we propose a direct model: in LUAD, chemokine and polarization networks are preferentially established, sustaining CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs in an immune-active state characterized by antigen presentation and immune collaboration. In BM, persistent angiogenic signaling and increased BBB permeability expose CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs to a distinct molecular milieu, driving a transition toward metabolic enhancement and stress adaptation. This functional reprogramming likely compromises their tumor-clearing ability and promotes metastatic outgrowth within an immunosuppressive environment. Therapeutically, restoring phagocytosis and antigen-processing functions in BM CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs or blocking MIF\u0026ndash;CD74 and APP\u0026ndash;CD74\u0026ndash;mediated inhibitory effects could mitigate immune escape. Moreover, the interplay between vascular/barrier states and CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs metabolic stress may represent a critical link underlying BBB disruption and immunosuppressive niche formation. With imaging modalities already available to quantify BBB/tumor\u0026ndash;brain barrier permeability, integration with circulating biomarker detection may allow dynamic monitoring of these processes, enabling early identification and intervention for BM [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study was based on publicly available datasets (GEO, GSEA). Despite stringent quality control, residual batch effects and clinical heterogeneity cannot be excluded. In addition, ligand\u0026ndash;receptor inference, CNV analysis, and pathway scoring represent model-based predictions that require direct protein-level and functional validation. Although our data delineate the ecological role and functional rewiring of CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs in LUAD\u0026ndash;BM progression, the precise regulatory circuits, ligand sources, and signaling cascades remain to be elucidated. Moving forward, we plan two complementary approaches: (i) in vitro and ex vivo systems\u0026mdash;including macrophage\u0026ndash;tumor/endothelial coculture, organoids, and microfluidic BBB models\u0026mdash;combined with genetic and pharmacologic perturbations to directly measure phagocytosis, antigen processing, and metabolic states, validated by spatial multiplex immunostaining; and (ii) in vivo models to systematically assess the effects of MIF\u0026ndash;CD74 or APP\u0026ndash;CD74 blockade in combination with radiotherapy, immunotherapy, or anti-angiogenic treatments, with particular attention to drug delivery, immunosuppressive dynamics, and lesion control. Through mechanistic validation, combinatorial interventions, and biomarker development, CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs may be advanced from a single-cell observation to a measurable, targetable, and predictive hub, forming an intervention\u0026ndash;evaluation continuum bridging LUAD to BM and informing precision management of lung adenocarcinoma brain metastasis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eData sources and ethics\u003c/h2\u003e\u003cp\u003eIn this study, we performed secondary analysis of the publicly available single-cell transcriptomic dataset GSE131907, which comprises 11 primary LUAD and 10 BM samples [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For external validation, bulk RNA sequencing data of LUAD were obtained from The Cancer Genome Atlas (TCGA, \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) data portal, and corresponding clinical follow-up information was retrieved from the UCSC Xena platform(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Formalin-fixed, paraffin-embedded (FFPE) pathological sections were provided by the Second Affiliated Hospital of Harbin Medical University for histological validation. The study protocol was approved by the institutional ethics committee of the Second Affiliated Hospital of Harbin Medical University (approval number: KY2025-221). All ethics procedures conformed with the principles of the 1964 Declaration of Helsinki and its latest 2008 amendments. All participants in the study were informed and agreed to participate in the study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSingle-cell data preprocessing and cell annotation\u003c/h2\u003e\u003cp\u003eSingle-cell data analysis was performed in the R environment (version 4.4.3). Raw expression matrices were processed using Seurat (v5.2.1) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] for quality control, normalization, and selection of highly variable genes. Dimensionality reduction was conducted by principal component analysis (PCA), followed by clustering based on a shared nearest-neighbor graph, and visualization was carried out using UMAP. Cell type annotation was performed according to known marker genes, and TAMs were extracted for downstream analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCNV analysis\u003c/h2\u003e\u003cp\u003eTo identify potential malignant cell populations, CNV analysis of single-cell transcriptomic data was performed using inferCNV (v1.22.0) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The input matrix was generated from raw count expression values of each cell, and gene position annotations were obtained and ordered according to the human genome coordinates using AnnoProbe (v0.1.7). Pericytes were selected as the reference population to establish baseline signals when constructing the inferCNV object. The analysis was run with the following parameters: cutoff\u0026thinsp;=\u0026thinsp;0.1 (for 10x data), hclust_method = \"ward.D2\", denoise\u0026thinsp;=\u0026thinsp;TRUE, and HMM\u0026thinsp;=\u0026thinsp;FALSE.\u003c/p\u003e\u003cp\u003eDuring the CNV scoring stage, TAMs, fibroblasts, and pericytes were further combined as reference populations to obtain more robust neutral intervals by estimating the mean and standard deviation. Based on these reference values, CNV signals were categorized as copy number loss, neutral, or copy number gain, and subsequently converted into scores to calculate the total CNV score for each single cell. Boxplots were then used to visualize CNV distributions across different cell types, enabling the distinction between malignant epithelial cells and non-malignant populations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCell\u0026ndash;cell communication analysis\u003c/h2\u003e\u003cp\u003eCell\u0026ndash;cell communication analysis was performed using CellChat (v2.1.2) to construct ligand\u0026ndash;receptor interaction networks, estimate intercellular communication probabilities, and aggregate them into signaling pathways for comparison of differential communication axes between LUAD and BM samples [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Visualization was carried out with the built-in bubble plot function in CellChat to illustrate alterations in communication patterns among different cell populations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eExternal validation with TCGA-LUAD and survival analysis\u003c/h2\u003e\u003cp\u003eIn the TCGA-LUAD cohort, expression matrices were normalized and transformed using log2(TPM\u0026thinsp;+\u0026thinsp;1), then merged with corresponding clinical follow-up data[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Patients were stratified according to CD74 expression levels or scores derived from CD74\u003csup\u003eHigh\u003c/sup\u003e TAM signatures. Survival analyses were performed using the Kaplan\u0026ndash;Meier method, with significance assessed by the log-rank test, and Cox proportional hazards models were applied. All survival analyses were conducted with the survival (v3.8-3) and survminer (v0.5.0) R packages.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eCD74-based stratification and differential expression analysis\u003c/h2\u003e\u003cp\u003eIn TAMs from LUAD and BM, cells were stratified into CD74\u003csup\u003eHigh\u003c/sup\u003e and CD74\u003csup\u003elow\u003c/sup\u003e groups according to the median single-cell expression level of CD74, and four combined labels (BM_CD74_High, BM_CD74_Low, LUAD_CD74_High, and LUAD_CD74_Low) were generated. Differential expression analyses were performed across four comparisons (BM_CD74_High vs. BM_CD74_Low, LUAD_CD74_High vs. LUAD_CD74_Low, LUAD_CD74_High vs. BM_CD74_High, LUAD_CD74_Low vs. BM_CD74_Low) using the FindMarkers function in Seurat [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] 的 with the Wilcoxon rank-sum test (min.pct\u0026thinsp;=\u0026thinsp;0.1, logfc.threshold\u0026thinsp;=\u0026thinsp;0).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eFunctional enrichment and gene set enrichment analysis\u003c/h2\u003e\u003cp\u003eWe performed functional enrichment analysis of differentially expressed genes (DEGs). Specifically, annotation was conducted at the levels of Gene Ontology (GO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geneontology.org/\u003c/span\u003e\u003cspan address=\"http://geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) pathways using clusterProfiler (v4.14.6) to identify key biological functions and signaling pathways associated with DEGs[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Gene ID conversion was performed with org.Hs.eg.db (v3.20.0), and gene sets from the Molecular Signatures Database (MSigDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gseamsigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gseamsigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were retrieved using msigdbr (v25.1.1). Furthermore, gene set enrichment analysis (GSEA) was carried out using the full ranked gene list ordered by log2FoldChange, and enrichment results were visualized with enrichplot (v1.26.6).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSingle-cell pathway activity scoring\u003c/h2\u003e\u003cp\u003eTo evaluate pathway activity at the single-cell level, AUCell (v1.28.0) was applied to construct gene sets based on differentially expressed or enriched terms and to calculate area under the curve (AUC) scores for each cell. Differences in pathway activity between groups were assessed using the Wilcoxon rank-sum test. Results were visualized as heatmaps with pheatmap (v1.0.12) and as boxplots with ggpubr (v0.6.1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eMultiplex immunohistochemistry (mIHC) validation\u003c/h2\u003e\u003cp\u003eFormalin-fixed paraffin-embedded (FFPE) sections were subjected to deparaffinization, rehydration, and antigen retrieval. After blocking, sections were incubated with ABclonal monoclonal antibodies CD74 Rabbit mAb (clone A24027, Cat# A24027-50 \u0026micro;L) and CD68 Rabbit mAb (clone A23205, Cat# A23205-50 \u0026micro;L). HRP-conjugated secondary antibodies and a chromogenic detection system were subsequently applied, and nuclei were counterstained with DAPI. Slides were mounted directly after staining and imaged under a fluorescence microscope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed in the R environment. Differential expression analysis of single-cell data was conducted using the FindMarkers function in Seurat [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] with the Wilcoxon rank-sum test (min.pct\u0026thinsp;=\u0026thinsp;0.1, logfc.threshold\u0026thinsp;=\u0026thinsp;0). P values were adjusted using the Benjamini\u0026ndash;Hochberg method, with significance defined as false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and in some analyses further constrained by |log2FoldChange| \u0026ge; 0.25. Functional enrichment and gene set enrichment analyses considered adjusted P values (p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as the threshold for significance. Comparisons of pathway activity scores were assessed by the Wilcoxon rank-sum test, and P values with corresponding significance levels were reported directly. Survival analyses were performed using the Kaplan\u0026ndash;Meier method and log-rank test, with Cox proportional hazards models fitted when appropriate.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\u003cp\u003eConsent to Participate declaration: not applicable.\u003c/p\u003e\u003cp\u003eConsent to Participate declaration: not applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Junming Jia, Yang Li, Mingzhu Yin; data curation, Junming Jia,; formal analysis, Huichao Lin, Zeren Chen; investigation, Ke He, Hongren Cao, software, Ziyan li, Jiaxin Cao, supervision, Yang Li, Mingzhu Yin; writing-original draft, Junming Jia; writing-review\u0026amp;editing, Yang Li, Mingzhu Yin.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe scRNA-seq dataset was acquired from the GEO database, specifically under accession number GSE131907. 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J Neurooncol. 2025;174:207\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11060-025-05054-5\u003c/span\u003e\u003cspan address=\"10.1007/s11060-025-05054-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"lung adenocarcinoma (LUAD), brain metastasis of lung adenocarcinoma (LUAD-BM), tumor-associated macrophages (TAMs), CD74, APP, CD74 axis, MIF, CD74 axis, immune microenvironment remodeling","lastPublishedDoi":"10.21203/rs.3.rs-7605804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7605804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrain metastasis (BM) is a major cause of mortality in lung adenocarcinoma, yet the cellular and molecular basis of its immune microenvironment remodeling remains unclear. Here, we systematically analyzed primary lung adenocarcinoma (LUAD) and BM samples using single-cell RNA sequencing (scRNA-seq). CD74\u003csup\u003eHigh\u003c/sup\u003e tumor-associated macrophages (TAMs) emerged as central receptor hubs, particularly within the APP\u0026ndash;CD74 and MIF\u0026ndash;CD74 axes. In the TCGA cohort, high CD74 expression correlated with suppressed phagocytosis-related gene sets and poor prognosis. Comparative analysis revealed strong transcriptional similarity between BM_Cluster_03 and LUAD_TAM_Cluster_15, both serving as dominant APP\u0026ndash;CD74 receptor populations. Stratification by CD74 expression showed that CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs in LUAD were enriched in antigen presentation, phagocytosis, and adaptive immune pathways, whereas CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs in BM shifted toward metabolic adaptation and stress responses with reduced immune effector programs. This functional reprogramming was consistently observed across analyses, indicating that BM CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs transition from an immune-activated to a metabolically stressed state, thereby facilitating immunosuppressive remodeling and tumor colonization in the brain. Collectively, CD74\u003csup\u003eHigh\u003c/sup\u003e TAMs represent key drivers of immune remodeling in lung adenocarcinoma brain metastasis, and the APP\u0026ndash;CD74/MIF\u0026ndash;CD74 axes may serve as potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Single-cell transcriptomic analysis reveals the evolution of the immunosuppressive landscape from primary tumors to brain metastasis in LUAD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-09 12:03:51","doi":"10.21203/rs.3.rs-7605804/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":"8dea0d34-5e9d-4c54-bc52-925b772fee5a","owner":[],"postedDate":"October 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T10:38:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-09 12:03:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7605804","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7605804","identity":"rs-7605804","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}