Multi-Omics reveals SPP1+ Malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma cancer

Multi-Omics reveals SPP1+ Malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-Omics reveals SPP1+ Malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma cancer Bei li Wang, Juan Lian, Jia ling Xu, Tao Hua, Jie Ding, Tuo Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7400400/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Background Lung adenocarcinoma cancer (LUAD), a common lung cancer subtype, is significantly influenced by the immune microenvironment. Immune checkpoint inhibitors have shown limited efficacy in Lung adenocarcinoma cancer due to the immunosuppressive tumor microenvironment (TME). Identifying predictive biomarkers for immunotherapy response remains an urgent clinical need. Methods Multi-Omics data of LUAD were analyzed to investigate the immune microenvironment in LUAD. Single cell RNA-seq was used for exploring the intercellular communication mechanisms in TME. Spatial transcriptomic analysis confirmed the spatial co-localization of SPP1 + Malignant and CXCR4 + TAM, while in vitro experiments validated the functions of biomarker. Results This study delineated the cellular heterogeneity and dynamic shifts within the LUAD tumor microenvironment, resolving the malignant transformation trajectory. Crucially, we identified SPP1⁺ malignant and CXCR4⁺ TAM crosstalk as a driver of exhaustion of CD8T, which induced poor immunotherapy response. Spatial transcriptomics confirmed co-localization of SPP1 + Malignant and CXCR4 + TAM, while in vitro experiments demonstrated that CXCR4 plays an important role in the functions of LUAD cells. Conclusions This study uncovers the SPP1⁺ malignant and CXCR4⁺ TAM crosstalk as a novel TME-driven resistance mechanism and provides a potential biomarker for stratifying LUAD patients likely to benefit from immunotherapy. Tumor-associated macrophages Prognosis Immune microenvironment Immunotherapy Biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Lung cancer is responsible for a disproportionately high percentage of cancer-related mortalities globally, presenting a significant threat to public health [ 1 ]. Among its various subtypes, lung adenocarcinoma (LUAD) has emerged as the most prevalent form, currently constituting around 50% of all lung cancer diagnoses [ 2 ]. Despite its high incidence, the prognosis for LUAD patients remains dismal, with a 5-year survival rate hovering at approximately 15% [ 3 , 4 ]. This grim reality underscores the urgent need for in-depth exploration of its pathogenesis, early diagnostic methods, and treatment strategies, which are crucial for enhancing our understanding of the disease, improving clinical outcomes, and ultimately saving patients' lives. Medical research has increasingly recognized the pivotal role of the immune microenvironment within tumor tissues in the occurrence, development, and treatment responses of tumors. Comprising immune cells, non-immune cells, and the extracellular matrix, the immune microenvironment exerts a profound influence on immune responses through cell-cell interactions and cytokine secretion [ 5 – 8 ]. There is a complex and dynamic interplay between immune cells and tumor cells [ 9 ]. On one hand, immune cells have the potential to recognize and eliminate tumor cells; on the other hand, under the influence of tumors, they can create an immunosuppressive environment that promotes tumor progression [ 10 ]. Notably, inflammation plays a pivotal role in the relationship between the immune microenvironment and LUAD [ 6 , 11 , 12 ]. Inflammatory response is a defense mechanism of the body against stimuli, but long-term chronic inflammation is closely associated with the occurrence and development of tumors [ 13 , 14 ]. In LUAD, the infiltration of inflammatory cells and the release of inflammatory mediators can alter the balance of the immune microenvironment, promoting the proliferation, survival, and metastasis of tumor cells [ 14 , 15 ]. For instance, cytokines secreted by inflammatory cells, such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), can activate oncogenic signaling pathways in tumor cells, enhancing their invasive and metastatic capabilities [ 16 – 18 ]. Tumor-associated macrophages (TAM), a major component of the tumor immune microenvironment, are intricately linked to inflammation [ 19 , 20 ]. TAM can be recruited by tumor cells and polarized into a phenotype with immunosuppressive functions. In the inflammatory tumor microenvironment, TAM promotes tumor growth, proliferation, invasion, and metastasis by secreting cytokines like vascular endothelial growth factor (VEGF) to stimulate angiogenesis and immunosuppressive factors such as interleukin-10 (IL-10) to dampen the body's anti-tumor immune response[ 20 ]. For example, TAM-derived VEGF promotes the formation of new blood vessels, providing nutrients and oxygen for tumor cells and facilitating their dissemination [ 21 ]. Understanding the detailed mechanism of TAM action, especially its regulatory role in the context of inflammation, holds great promise for developing novel cancer therapies [ 22 ]. The C-X-C motif chemokine receptor 4 (CXCR4) is widely expressed in various cells, including immune cells [ 23 ]. In immune cells, CXCR4 is mainly responsible for guiding cell migration, helping immune cells home to specific tissue regions, and participating in the regulation of immune responses [ 24 , 25 ]. In tumor-associated macrophages (TAM), CXCR4 also plays an important role [ 26 ]. It participates in regulating the migration of TAM, promoting the aggregation of TAM at the tumor site, and simultaneously affects the activation process and functional state of TAM, as well as the interaction between TAM and other cells, thereby influencing the balance of the immune microenvironment [ 27 , 28 ]. TAM surface CXCR4 can bind to its ligand CXCL12, activating downstream signaling pathways such as the PI3K-Akt pathway [ 28 ]. This activation not only promotes TAM migration but also modulates the secretion of cytokines and chemokines, influencing the overall balance of the immune microenvironment. However, the specific regulatory mechanism of CXCR4 + TAM in the immune microenvironment of LUAD, especially the mechanism mediated by inflammation, has not been fully elucidated and urgently requires in-depth study. Traditional research methods have many limitations in exploring the tumor immune microenvironment and are difficult to comprehensively analyze the interactions between cells and the dynamic changes over time [ 29 , 30 ]. The rise of bioinformatics has brought new opportunities to this field. Among them, survival prognosis prediction models can predict patients' survival based on a large amount of data, providing a reference for clinical decision-making [ 31 – 34 ]. More importantly, single-cell spatiotemporal analysis technology goes a step further on this basis. It can accurately analyze the state and function of each cell at the single-cell level, determine the specific location of cells in the spatial dimension, and dynamically track the changes of cells in the temporal dimension. It provides strong support for in-depth research on tumor-related mechanisms from multiple dimensions [ 35 – 37 ]. In this study, single-cell spatiotemporal analysis is employed to comprehensively analyze the transcriptomic profiles of LUAD and normal tissues. The objectives are to identify distinct cell types and their proportional changes in the tumor microenvironment, clarify the malignant transformation trajectory of epithelial cells, and analyze the functions of myeloid and T cells along with cell - cell communication. Through spatial transcriptomic analysis and in vitro experiments, the aim is to uncover the specific interactions of CXCR4 + TAM within the samples and validate the functions of key genes. The goal is to comprehensively understand the LUAD transcriptomic landscape, identify key cellular and molecular mechanisms in tumor progression, and confirm CXCR4 as a potential therapeutic target, thus providing a solid foundation for the development of more effective treatment strategies for LUAD patients. 2. Material and Methods 2.1 Single-cell Sequencing Data Processing and Analysis Single-cell RNA sequencing data of lung cancer were derived from the article "Single-cell RNA sequencing reveals distinct tumor microenvironment patterns in lung adenocarcinoma" published by Philip Bischoff and other researchers [ 38 ]. TCGA-LUAD cohort were deposited in The Cancer Genome Atlas Program (TCGA, https://www.cancer.gov/ccg/research/genome-sequencing/tcga ), while Imvigor210 cohort was downloaded in “IMvigor210CoreBiologies” R package. The original data samples were screened to meet the following criteria: 1) paired tumor and adjacent non-cancer samples; 2) the number of cells in each sample was not less than 3000 and not more than 8000. The Seurat package in R software (version 4.3.2) was used to process the raw data of each sample. 2.2 Immune Infiltration and drug susceptibility Analysis Immune cell composition was performed by CIBERSORT and the TIDE was predicted by Tumor Immune Dysfunction and Exclusion (TIDE, http://tide.dfci.harvard.edu/ ). Drug susceptibility analysis was performed by “oncoPredict” R package. 2.3 Pseudotime TrajectoryAnalysis Pseudotime analysis was performed by “Monocle2” R package. This algorithm reduces high-dimensional single-cell gene expression data into a low-dimensional space. Differential cells were order along the predicted trajectories based on their transcriptional states. 2.4 Cell lines and Culture LUAD cell lines, including A549 and Calu-3 were obtained from the Shanghai Institute of Cell Biology (China). LUAD cell lines were routinely maintained in RPMI-1640 and DMEM (Gibco, USA) medium containing 10% fetal bovine serum (Gibco, USA), 1% streptomycin, and penicillin. All cells were cultured at 37°C under a 5% CO 2 atmosphere in a cell incubator. 2.5 Real-Time Quantitative PCR (RT-qPCR) Total RNA was obtained employing the TRIzol reagent (Invitrogen). Reverse transcription was performed using the Prime Script TMRT kit (Takara, RR047A) and the Taq II Kit, and PCR amplification was conducted using SYBR Premix Ex according to the manufacturer’s instructions. CXCR4 primers utilized were as follows: forward, ACTACACCGAGGAAATGGGCT; reverse, CCCACAATGCCAGTTAAGAAGA. 2.6 Cell Proliferation Assay 5×10 3 cells were seeded into a single well of a 96-well plate containing 100 µL of complete culture medium. After 24 h, the culture medium was replaced with a full culture medium containing CCK8 reagent. The absorbance was measured at 450 nm after 1, 2, 3, and 4 days. 2.7 Colony Formation Assay LUAD cells were seeded at a low density of 600 ~ 1000 cells per well in 6-well plates and cultured for approximately 2 weeks. Following the incubation period, the cell colonies were washed with PBS, fixed in 4% paraformaldehyde for 20 min, stained with crystal violet, and documented and enumerated. 2.8 EdU (5-Ethynyl-2-deoxyuridine) Assay 2×10 4 cells were seeded into a single well of a 96-well plate containing 100 µL of complete culture medium. After 24 h, the cells were incubated with EdU reagent according to the manufacturer's instructions, and cell proliferation was measured using a fluorescence microscope (Thermo Fisher Scientific, America). 2.9 Invasion Assay The invasion assay was conducted by first trypsinizing the cells, which were then resuspended in a serum-free medium and quantified. A cell suspension was carefully added to the upper chamber of a Transwell insert pre-coated with Matrigel. Following an incubation period of 36 h to allow cell invasion through the Matrigel, the non-invading cells on the upper surface of the insert were gently removed. The invaded cells on the lower surface were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and subsequently rinsed with PBS. 2.10 Flow Cytometry Assay To assess apoptosis, cells (1×10 6 ) were stained with Annexin V-FITC and propidium iodide (PI) apoptosis detection kit (BD Biosciences) according to the manufacturer's instructions. After staining using BD FACS Melody flow cytometry, the percentage of early and late apoptotic cells was assessed. 2.11 Statistical Analysis Data are presented as the mean ± standard deviation (SD) of three independent experiments. All experiments were repeated at least three times. Intergroup differences were analyzed using Student’s t-test or one-way analysis of variance (ANOVA), and statistical analyses were performed with GraphPad Prism 9 software. Significance levels were denoted as follows: * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001. 3. Results 3.1 Quality Control and Cell Classification of LUAD scRNA-seq Data The workflow for this manuscript was summarized in Fig. 1 . The single-cell transcriptome dataset utilized in this study is distinguished by its high-quality data and the inclusion of diverse immune cell populations, rendering it an ideal resource for reanalyzing the tumor microenvironment [ 39 ]. To guarantee the reliability and accuracy of the data, a series of strict quality control criteria were meticulously implemented, a total of 69,867 high-quality cells were retained for subsequent annotation and analysis. Subsequently, leveraging a comprehensive set of well-established classical cell-type marker genes, these retained cells were systematically classified into eight distinct subgroups. These subgroups include Epithelials, Endothelials, Fibroblasts, Mast cells, Myeloid cells, T cells, B cells, and Plasma cells (Fig. 2 A). The marker genes for each cell type were further visualized and validated using dot-plots and feature-plots (Fig. 2 B-C). These visualizations not only illustrate the expression levels of marker genes across different cell types but also highlight the specificity of each marker in defining the respective cell populations. Notably, although all eight cell types were detected in both tumor and normal control samples (Fig. 2 A), the proportions of these cell types exhibited substantial differences between the two groups. In the tumor samples, certain cell types showed significant expansion or contraction compared to the normal controls, suggesting a profound remodeling of the cellular composition. This significant alteration in cell-type proportions strongly indicates a modified tumor microenvironment in lung adenocarcinoma, which likely plays a critical role in tumor progression, immune evasion, and response to therapy (Fig. 2 D-E). These findings set the stage for further in-depth analyses to explore the functional implications of these changes and their potential as therapeutic targets. 3.2 Characterization and Trajectory Analysis of Epithelial Cell Subpopulations in LUAD To comprehensively elucidate the progression mechanism of LUAD, we focused our investigation on epithelial cells, as they play a crucial role in the tumor microenvironment. By conducting a subset analysis of these cells, our objective was to uncover the underlying cellular processes that drive the development of LUAD. Through unsupervised clustering analysis of epithelial cells, we identified 27 distinct cell clusters (Fig. 3 A). Our findings revealed that clusters 5, 8, and 13 were predominantly enriched in normal tissues, suggesting their involvement in maintaining the physiological functions of healthy lung tissues. In stark contrast, clusters 0, 1, 2, 3, 4, 7, 9, 10, 14, 15, 16, 17, 18, 19, 20, and 22 were significantly more abundant in tumor samples (Fig. 3 A). This differential distribution indicates that these clusters may be closely associated with the malignant transformation and progression of LUAD. To precisely assess the malignancy of each cell cluster, we employed inferCNV analysis, which is a powerful tool for detecting copy number variations in single cells. By integrating the inferCNV results with sample source information (normal vs. tumor), we were able to accurately classify the cell clusters. Based on the characteristic marker genes of each cluster (Fig. 3 B), we identified several subtypes of malignant tumor cells, including CXCL14 + malignant cells, CXCR4 + malignant cells, HSPA1A + malignant cells, PLAT + malignant cells, SCGB3A1 + malignant cells, SPP1 + malignant cells, and TM4SF4 + malignant cells (Fig. 3 C). The specific marker genes for each cell subtype were visualized in the feature plot, providing a clear depiction of the molecular signatures of these cells (Fig. 3 D). To further explore the developmental trajectories of epithelial cells during LUAD progression, we performed pseudotime trajectory analysis. This analysis allowed us to reconstruct the dynamic changes in cell states and map the potential paths of cell differentiation. Our results clearly delineated two distinct trajectories, with normal and tumor tissues following separate routes (Fig. 3 E). According to the pseudotime analysis, all trajectories originated from normal epithelial cells, which then underwent a series of molecular and cellular changes, ultimately leading to the formation of SPP1 + malignant cells (Fig. 3 F). This finding suggests that SPP1 + malignant cells may represent the end stage of the malignant transformation process of epithelial cells in LUAD. Functional enrichment analysis indicated that oxidative phosphorylation and ATP synthesis were active in initial stage, while cytoplasmic translation and antigen processing were more active in branch 2 (Fig. 3 G). In conclusion, our study provides a comprehensive understanding of the malignant transformation trajectory of epithelial cells in the LUAD microenvironment. The identification of distinct cell clusters and their associated marker genes, as well as the mapping of developmental trajectories, offers valuable insights into the molecular mechanisms underlying LUAD progression. 3.3 CXCR4 + TAM is closely associated with poor prognosis in LUAD Given that myeloid cells constitute a substantial proportion of both tumor and normal samples in our dataset, we conducted an in-depth investigation into their functional characteristics. First, we isolated myeloid cells from the dataset and performed a refined clustering analysis, which resulted in the identification of ten distinct subgroups: CCL18 + macrophage, CCL9 + macrophage, conventional dendritic cell 1 (cDC1), conventional dendritic cell 2 (cDC2), CXCR4 + TAM, FABP4 + macrophage, IL1B + TAM, macrophage, monocyte, and plasmacytoid dendritic cell (pDC) (Fig. 4 A-B). The marker genes highly expressed in each myeloid subpopulation were visualized using dot plots (Fig. 4 C), providing a molecular signature for each cell type. As depicted in the cell proportion plot (Fig. 4 D), the relative abundances of CCL18 + macrophage, FABP4 + macrophage, monocyte, macrophage, and pDC were significantly lower in tumor samples compared to normal tissues. Conversely, CCL9 + macrophage, cDC1, cDC2, CXCR4 + TAM, and IL1B + TAM showed increased proportions in tumor samples, suggesting their potential involvement in tumor-promoting processes. To elucidate the developmental dynamics of myeloid cells, we performed a pseudotime trajectory analysis. This analysis revealed five distinct developmental trajectories, all of which originated from macrophages and converged towards CXCR4 + TAM ( Fig. S1 A-B ). This finding suggests a hierarchical differentiation pathway within the myeloid cell lineage, potentially indicating the role of CXCR4 + TAM as a terminal effector in the tumor microenvironment. We then conducted CIBERSORTx for immune deconvolution, using bulk microarray data from TCGA-LUAD. The results showed that elevated infiltration of CXCR4 + macrophages was significantly associated with poorer OS (Fig. 4 E). To further explore the functional states of myeloid subpopulations, we calculated the cytokine and inflammatory scores for each cell type. Notably, cDC2, CXCR4 + TAM, and IL1B + TAM exhibited the highest scores in both cytokine and inflammatory responses (Fig. 4 F-G), a conclusion further supported by UMAP visualization (Fig. 4 H). These results indicate that these myeloid subpopulations may play a central role in promoting chronic inflammation and tumor progression. In summary, we identified distinct myeloid cell clusters with differential abundance between tumor and normal tissues, delineated the developmental trajectories leading to CXCR4 + TAM, and revealed the functional significance of specific myeloid subpopulations in regulating the inflammatory tumor microenvironment. 3.4 LUAD exhibit a significantly increased infiltration of exhausted CD8 + T cells Building upon our comprehensive characterization of myeloid cells in the LUAD microenvironment, we next turned our attention to T cells, which also constitute a major component of the tumor immune landscape. Leveraging the same dataset, we isolated T cells and performed a high-resolution re-clustering analysis. This approach led to the identification of twelve distinct T - cell subpopulations: cytotoxic cells, dysfunctional CD8T, effector CD4T, effector CD8T, exhausted CD8T, mucosal-associated invariant T (MAIT) cells, memory CD4T, memory CD8T, naïve CD4T, natural killer (NK) cells, proliferating CD8T, and regulatory T (Treg) cells (Fig. 5 A-B). Dot plots were utilized to visualize the marker genes highly expressed in each subpopulation (Fig. 5 C), providing a molecular fingerprint for each T cell type. The cell proportion plot (Fig. 5 D) illustrated significant differences in the relative abundances of these T cell subpopulations between tumor and healthy samples. Notably, the proportions of cytotoxic CD8T cells and Treg cells were substantially increased in tumor samples compared to normal tissues, suggesting their active participation in tumor-related immune responses. To decipher the developmental dynamics of T cells, we employed a pseudotime trajectory analysis. This analysis revealed three distinct developmental trajectories, all of which originated from NK cells and terminated at Treg cells (Fig. 5 E-F). This finding implies a potential developmental hierarchy within the T cell compartment. To further validate our observations, we are currently in the process of calculating cytokine, naïve, and exhausted scores for each T cell subpopulation (Fig. 5 G-I). These scores will provide additional insights into the functional states of T cells and help elucidate their roles in shaping the tumor immune microenvironment. Our ongoing analyses aim to comprehensively understand the complex interplay between different T cell subpopulations and their contributions to LUAD progression, which may guide the development of more effective immunotherapeutic strategies. 3.5 Intercellular Communication Networks Unveil Pathogenesis and Therapeutic Targets in LUAD Building on our detailed characterization of individual cell types and their developmental trajectories in LUAD, we next sought to explore the complex landscape of cell-cell communication within the tumor microenvironment. To achieve this, we employed CellChatDB, a meticulously curated database that compiles literature-supported ligand-receptor interactions. The overall cell-cell communication network was first visualized using a circular plot, which depicted the number of interactions and their corresponding weights/strengths among different cell populations (Fig. 6 A). Given the complexity of the communication network, we further dissected the signaling patterns originating from each cell group. This analysis revealed a particularly robust interaction network between epithelial cells and myeloid cells (Fig. 6 B), suggesting a significant crosstalk between these two cell types that likely influences tumor progression. To highlight key molecular interactions, we generated a bubble plot, which emphasized the importance of specific signaling pathways such as SCGB3A2-MARCO and APP-CD74 in mediating communication between epithelial and myeloid cells (Fig. 6 C). Guided by our previous pseudotime analysis, which implicated CXCR4 in tumorigenesis and development, we focused on identifying the cell types engaged in active CXCR-CXCL signaling. Consistent with our earlier findings, the CCL signaling pathway was found to be highly active among fibroblast cells, T cells, and myeloid cells (Fig. 6 D), underscoring the role of these signaling axes in coordinating cellular functions within the tumor microenvironment. To provide a holistic view of the interrelationships among all cell populations, we constructed a correlation heatmap. This analysis not only validated our previous observations but also revealed that CXCR4 + TAM exhibited significant correlations with tumor cells and other immune cell types (Fig. 6 E). Collectively, our analysis of intercellular communication networks provides critical insights into the molecular mechanisms underlying LUAD pathogenesis and may inform the development of novel therapeutic strategies targeting cell-cell interactions. 3.6 Spatial Organization of Cell Subpopulations Reveals Functional Interplay in LUAD Tissues Building on our understanding of cellular composition, developmental trajectories, and intercellular communication in LUAD, we further explored the spatial organization of cell type subpopulations within tumor tissues. The dataset (GSE189487) of Lung Adenocarcinoma, retrieved from the Gene Expression Omnibus (GEO) database, encompasses samples derived from patients diagnosed with lung adenocarcinoma and normal individuals [ 39 ]. Leveraging spatial transcriptomics, we analyzed tissue sections from both cancerous and normal vasculature regions of LUAD patients. By integrating hematoxylin and eosin (HE) staining results with tumor region annotations, we classified the spatial transcriptomic spots into seven distinct cell clusters: malignant cells, epithelial cells, myeloid cells, fibroblasts, CXCR4 + TAM, SPP1 + malignant cells, as well as T and B cells (Fig. 7 A). The spatial distribution patterns of these seven cell types were visualized in Fig. 7 B, providing a comprehensive overview of their localization within the tissue microenvironment. To validate the accuracy of the spatial classification, we examined the expression of specific marker genes for each cell type (Fig. 7 C). Notably, we identified spatial spots that co-expressed CXCR4, SPP1, and EPCAM, indicating the spatial co-localization of these key molecules (Fig. 7 D-F), with higher expression levels highlighted in red. Furthermore, the signature scores of CXCR4 + TAM and SPP1 + malignant cells exhibited a significant positive correlation (Fig. 7 G), suggesting a potential functional interplay between these cell populations in the tumor microenvironment. Additionally, we also visualized the expression of classic T cell markers (Fig. 7 H-I), contributing to a more complete understanding of the spatial immune cell landscape in LUAD. 3.7 The heterogeneity of immunotherapy and immune infiltration between SPP1 + Malignant high /CXCR4 + TAM high and SPP1 + Malignant low /CXCR4 + TAM low To further investigate the heterogeneity of SPP1 + Malignant high /CXCR4 + TAM high and SPP1 + Malignant low /CXCR4 + TAM low , 22 immune cell components were calculated for each patient using the “ssGSEA” methods. Among these immune cells, CD8T cells and Macrophage M0 were highly expressed in the SPP1 + Malignant low /CXCR4 + TAM low group (Fig. 8 A-B), while the low infiltration group were low expression. These results suggest that the reason for better prognosis in the low infiltration group may be related to the immune microenvironment and have a higher level of immune infiltration in LUAD. At the same time, we evaluated the effect of low and high infiltration on TIDE score, Dysfunction score, Exclusion score, and MSI score using the “TIDE” algorithm. The result showed that the TIDE score, Dysfunction score, Exclusion score in SPP1 + Malignant high /CXCR4 + TAM high were significantly increased, while the MSI score was relatively low. (Fig. 8 C-F). This indicated that the immune escape potential of patients in high infiltration increased, and the efficacy of immune checkpoint suppressive therapy might be poorer. To validate the results, we performed the high and low infiltration with the IMvigor210 dataset. SPP1 + Malignant high /CXCR4 + TAM high patients showed greater disease progression and the immune desert type accounted for a larger proportion (Fig. 8 G-H). Finally, Drug susceptibility prediction indicated that IC50 values for Cytarabine and Gemcitabine were significantly lower in high infiltration group, while Gefitinib had the opposite trend (Fig. 8 I-K). 3.8 Validation of CXCR4-Driven LUAD Progression In Vitro To translate our in-silico and spatial findings into functional insights, we conducted a series of in vitro experiments to validate the role of key genes in LUAD progression. Given the critical role of CXCR4 identified in our previous analyses, we first examined its expression levels in three lung cell lines. Quantitative analysis revealed that the A549 cell line exhibited an approximately 8-fold increase in CXCR4 expression compared to a reference control (BEAS-2B cell lines) (Fig. 9 A). Subsequently, we employed multiple functional assays to investigate the impact of CXCR4 on tumor cell behavior. The results of the CCK-8 assay indicated that knockdown of CXCR4 significantly enhanced cell viability ( Fig. 9 B), suggesting that lung cancer cells with low expression of CXCR4 might enhance their survival ability by evading the killing effect of CXCR4 + TAM. Consistent with this finding, colony formation assays demonstrated that CXCR4-knockdown cells formed a greater number of colonies with larger sizes (Fig. 9 C), indicating enhanced proliferative capacity. The EdU assay demonstrated that compared with the shNC group, the EdU positive cell rates of A549 and Calu-3 cell lines treated with shCXCR4 were significantly increased, which also indicated that knockdown of CXCR4 could promote the cell proliferation of these two cell lines (Fig. 9 D). To assess the role of CXCR4 in tumor cell invasion and migration, we performed Transwell assays. The results showed that the knockdown of CXCR4 significantly enhanced the invasive and migratory abilities of A549 and Calu-3 cells (Fig. 9 E), highlighting its importance in the metastatic potential of LUAD cells. Finally, flow cytometry analysis revealed that CXCR4 knockdown inhibited apoptosis in lung cancer cells (Fig. 9 F), further emphasizing the multifaceted role of CXCR4 in regulating tumor cell fate. These in vitro findings provide strong functional evidence supporting the significance of CXCR4 in LUAD development and progression, laying the foundation for future therapeutic interventions targeting this key molecule. 4. Discussion LUAD poses a significant global health challenge with dismal survival rates. Previous research has highlighted the importance of the immune microenvironment, inflammation, and specific cell types like TAM in LUAD progression [ 40 , 41 ]. However, traditional research methods often lack the ability to comprehensively analyze dynamic cell-cell interactions and temporal changes in the tumor microenvironment. Existing studies on the regulatory mechanism of CXCR4 + TAM, especially in the context of inflammation in LUAD, remain incomplete, hindering the development of targeted therapies. Our study innovatively applied single-cell spatiotemporal analysis to comprehensively explore the LUAD transcriptomic landscape. This approach enabled us to precisely identify cell types, map the malignant transformation trajectory of epithelial cells, and analyze the functions and communication of myeloid and T cells [ 42 ]. By integrating spatial transcriptomics and in vitro experiments, we provided a multi-dimensional understanding of LUAD, which is a significant advancement compared to previous single-level studies. We successfully characterized the cell composition of LUAD and normal tissues, revealing substantial differences in cell-type proportions. For epithelial cells, we identified specific malignant cell subtypes and demonstrated that SPP1 + malignant cells may be the end - stage product of epithelial cell transformation. In myeloid cells, we found that CXCR4 + TAM is the terminal effector in a hierarchical differentiation pathway and plays a crucial role in promoting inflammation. For T cells, we discovered a potential developmental hierarchy from NK cells to Treg cells. The intercellular communication analysis revealed strong interactions between epithelial and myeloid cells, with CXCR4 + TAM as a key regulator. In vitro experiments further validated that CXCR4 promotes LUAD progression, suggesting it as a potential therapeutic target. Despite these achievements, our study has limitations. The in vitro experiments only used a limited number of cell lines, which may not fully represent the complexity of LUAD in vivo. Additionally, the mechanisms by which CXCR4 + TAM regulates the immune microenvironment through inflammation are not fully explored. Future research could expand the scope of in vitro and in vivo models, investigate the detailed molecular mechanisms of CXCR4 - mediated inflammation, and explore the feasibility of developing CXCR4-targeted drugs in combination with other immunotherapies to improve LUAD treatment outcomes. In conclusion, our study has provided a comprehensive understanding of the single-cell spatiotemporal landscape of LUAD, identified key cellular and molecular mechanisms underlying tumor progression, and validated the potential of CXCR4 as a therapeutic target. These findings have significant implications for the development of novel treatment strategies for LUAD. Declarations Funding The authors received no financial support for the research, authorship, and publication of this article. Data availability The datasets analyzed during the current study are available in online repositories: https://www.ncbi.nlm.nih.gov/geo/; GSE189487and the UCSC/ TCGA-Hub repository, https://xenabrowser.net/datapages/; TCGA-LUAD. Statements Ethical approval and informed consent statements This article does not contain any studies with human or animal participants. Consent to publish Not applicable. Clinical Trial Number Not applicable. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Beili Wang and Juan Lian wrote the main manuscript text and prepared all figures and tables. Jialing Xu and Tao Hua performed the experiments. Jie Ding and Tuo Wang analyzed the data. 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displaying the average expression levels of known markers in specified cell clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(D-F)\u003c/strong\u003e Spatial feature plots illustrating the expression of CXCR4 \u003cstrong\u003e(D)\u003c/strong\u003e, SPP1\u003cstrong\u003e (E)\u003c/strong\u003e, and EPCAM \u003cstrong\u003e(F)\u003c/strong\u003e in tissue sections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(G) \u003c/strong\u003ePearson correlation analysis of the signature scores for SPP1\u003csup\u003e+\u003c/sup\u003e malignant cells (x-axis) and CXCR4\u003csup\u003e+\u003c/sup\u003e TAM (y-axis) within the SPP1\u003csup\u003e+\u003c/sup\u003e malignant cells/CXCR4\u003csup\u003e+\u003c/sup\u003e TAM cluster in patient samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(H) \u003c/strong\u003eSpatial feature plots representing the gene expression levels of CD3D, CD8A, CD4 and LYZ in tissue sections from patient samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(I) \u003c/strong\u003eSpatial feature plots representing the gene expression levels of MS4A1, GZMA, PRF1 and GNLY in tissue sections from patient samples.\u003c/p\u003e","description":"","filename":"figure7new.png","url":"https://assets-eu.researchsquare.com/files/rs-7400400/v1/23749fd1caee6ab7feab3853.png"},{"id":92529008,"identity":"abec90a7-c1e6-4648-a248-76953f1fdff1","added_by":"auto","created_at":"2025-09-30 16:28:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2207606,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe heterogeneity of immunotherapy and immune infiltration between SPP1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e Malignant\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ehigh\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e/CXCR4\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eTAM\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ehigh\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and SPP1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e Malignant\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003elow\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e/CXCR4\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eTAM\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003elow\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The proportion of immune cells in SPP1\u003csup\u003e+\u003c/sup\u003e malignant /CXCR4\u003csup\u003e+\u003c/sup\u003e TAM low and high infiltration samples in CIBERSORT algorithm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e The proportion of immune cells in SPP1\u003csup\u003e+\u003c/sup\u003e malignant /CXCR4\u003csup\u003e+\u003c/sup\u003e TAM low and high infiltration samples in QUANTISEQ algorithm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C-F)\u003c/strong\u003e The TIDE score (C) Dysfunctional score (D) Exclusion score (E) and MSI score (F) in two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(G-H)\u003c/strong\u003e Barplots show progression pf disease (G) and type of immune response in two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(I-K)\u003c/strong\u003e The heterogeneity of Cytarabine (I) Gefitinib (J) Gemcitabine (K)drug sensitivity in two groups.\u003c/p\u003e","description":"","filename":"figure8new.png","url":"https://assets-eu.researchsquare.com/files/rs-7400400/v1/7add2068cc3bc94be314e92d.png"},{"id":92529013,"identity":"9dbd3225-88ea-40e8-b08d-eab53910bd07","added_by":"auto","created_at":"2025-09-30 16:28:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":7886301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of CXCR4-Driven LUAD Progression \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIn Vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e RT-qPCR analysis of \u003cem\u003eCXCR4\u003c/em\u003e mRNA expression levels in four human LUAD cell lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) \u003c/strong\u003eCCK-8 assay to evaluate the effects of CXCR4 knockdown on cell viability in LUAD cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C) \u003c/strong\u003eColony formation assay to assess the impact of CXCR4 knockdown on cell proliferation, with quantification of results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(D)\u003c/strong\u003e EdU assay for detecting DNA synthesis activity in LUAD cells with CXCR4 knockdown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(E) \u003c/strong\u003eTranswell invasion assay demonstrating the effects of CXCR4 knockdown on cell invasion, with quantification of results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(F) \u003c/strong\u003eFlow cytometry analysis of apoptosis rates in LUAD cells with CXCR4 knockdown, including quantification of results.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7400400/v1/166453ac4d09247c4433c7db.png"},{"id":92533194,"identity":"1960a593-9c79-4c1d-93a1-1d76df0b3060","added_by":"auto","created_at":"2025-09-30 17:00:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":53632913,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7400400/v1/c7680cc6-d5a3-4f56-a3dd-882d57da6d8c.pdf"},{"id":92529000,"identity":"7eda9813-d255-49ca-ae92-f9bdc7113cca","added_by":"auto","created_at":"2025-09-30 16:28:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":576506,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure20250501.docx","url":"https://assets-eu.researchsquare.com/files/rs-7400400/v1/a5902b6e29dc5602db39a149.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Omics reveals SPP1+ Malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer is responsible for a disproportionately high percentage of cancer-related mortalities globally, presenting a significant threat to public health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among its various subtypes, lung adenocarcinoma (LUAD) has emerged as the most prevalent form, currently constituting around 50% of all lung cancer diagnoses [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite its high incidence, the prognosis for LUAD patients remains dismal, with a 5-year survival rate hovering at approximately 15% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This grim reality underscores the urgent need for in-depth exploration of its pathogenesis, early diagnostic methods, and treatment strategies, which are crucial for enhancing our understanding of the disease, improving clinical outcomes, and ultimately saving patients' lives.\u003c/p\u003e\u003cp\u003eMedical research has increasingly recognized the pivotal role of the immune microenvironment within tumor tissues in the occurrence, development, and treatment responses of tumors. Comprising immune cells, non-immune cells, and the extracellular matrix, the immune microenvironment exerts a profound influence on immune responses through cell-cell interactions and cytokine secretion [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There is a complex and dynamic interplay between immune cells and tumor cells [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. On one hand, immune cells have the potential to recognize and eliminate tumor cells; on the other hand, under the influence of tumors, they can create an immunosuppressive environment that promotes tumor progression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, inflammation plays a pivotal role in the relationship between the immune microenvironment and LUAD [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Inflammatory response is a defense mechanism of the body against stimuli, but long-term chronic inflammation is closely associated with the occurrence and development of tumors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In LUAD, the infiltration of inflammatory cells and the release of inflammatory mediators can alter the balance of the immune microenvironment, promoting the proliferation, survival, and metastasis of tumor cells [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For instance, cytokines secreted by inflammatory cells, such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), can activate oncogenic signaling pathways in tumor cells, enhancing their invasive and metastatic capabilities [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTumor-associated macrophages (TAM), a major component of the tumor immune microenvironment, are intricately linked to inflammation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. TAM can be recruited by tumor cells and polarized into a phenotype with immunosuppressive functions. In the inflammatory tumor microenvironment, TAM promotes tumor growth, proliferation, invasion, and metastasis by secreting cytokines like vascular endothelial growth factor (VEGF) to stimulate angiogenesis and immunosuppressive factors such as interleukin-10 (IL-10) to dampen the body's anti-tumor immune response[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For example, TAM-derived VEGF promotes the formation of new blood vessels, providing nutrients and oxygen for tumor cells and facilitating their dissemination [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Understanding the detailed mechanism of TAM action, especially its regulatory role in the context of inflammation, holds great promise for developing novel cancer therapies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe C-X-C motif chemokine receptor 4 (CXCR4) is widely expressed in various cells, including immune cells [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In immune cells, CXCR4 is mainly responsible for guiding cell migration, helping immune cells home to specific tissue regions, and participating in the regulation of immune responses [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In tumor-associated macrophages (TAM), CXCR4 also plays an important role [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It participates in regulating the migration of TAM, promoting the aggregation of TAM at the tumor site, and simultaneously affects the activation process and functional state of TAM, as well as the interaction between TAM and other cells, thereby influencing the balance of the immune microenvironment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. TAM surface CXCR4 can bind to its ligand CXCL12, activating downstream signaling pathways such as the PI3K-Akt pathway [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This activation not only promotes TAM migration but also modulates the secretion of cytokines and chemokines, influencing the overall balance of the immune microenvironment. However, the specific regulatory mechanism of CXCR4\u0026thinsp;+\u0026thinsp;TAM in the immune microenvironment of LUAD, especially the mechanism mediated by inflammation, has not been fully elucidated and urgently requires in-depth study.\u003c/p\u003e\u003cp\u003eTraditional research methods have many limitations in exploring the tumor immune microenvironment and are difficult to comprehensively analyze the interactions between cells and the dynamic changes over time [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The rise of bioinformatics has brought new opportunities to this field. Among them, survival prognosis prediction models can predict patients' survival based on a large amount of data, providing a reference for clinical decision-making [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. More importantly, single-cell spatiotemporal analysis technology goes a step further on this basis. It can accurately analyze the state and function of each cell at the single-cell level, determine the specific location of cells in the spatial dimension, and dynamically track the changes of cells in the temporal dimension. It provides strong support for in-depth research on tumor-related mechanisms from multiple dimensions [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, single-cell spatiotemporal analysis is employed to comprehensively analyze the transcriptomic profiles of LUAD and normal tissues. The objectives are to identify distinct cell types and their proportional changes in the tumor microenvironment, clarify the malignant transformation trajectory of epithelial cells, and analyze the functions of myeloid and T cells along with cell - cell communication. Through spatial transcriptomic analysis and in vitro experiments, the aim is to uncover the specific interactions of CXCR4\u003csup\u003e+\u003c/sup\u003e TAM within the samples and validate the functions of key genes. The goal is to comprehensively understand the LUAD transcriptomic landscape, identify key cellular and molecular mechanisms in tumor progression, and confirm CXCR4 as a potential therapeutic target, thus providing a solid foundation for the development of more effective treatment strategies for LUAD patients.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Single-cell Sequencing Data Processing and Analysis\u003c/h2\u003e\u003cp\u003eSingle-cell RNA sequencing data of lung cancer were derived from the article \"Single-cell RNA sequencing reveals distinct tumor microenvironment patterns in lung adenocarcinoma\" published by Philip Bischoff and other researchers [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. TCGA-LUAD cohort were deposited in The Cancer Genome Atlas Program (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/ccg/research/genome-sequencing/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/ccg/research/genome-sequencing/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while Imvigor210 cohort was downloaded in \u0026ldquo;IMvigor210CoreBiologies\u0026rdquo; R package. The original data samples were screened to meet the following criteria: 1) paired tumor and adjacent non-cancer samples; 2) the number of cells in each sample was not less than 3000 and not more than 8000. The Seurat package in R software (version 4.3.2) was used to process the raw data of each sample.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Immune Infiltration and drug susceptibility Analysis\u003c/h2\u003e\u003cp\u003eImmune cell composition was performed by CIBERSORT and the TIDE was predicted by Tumor Immune Dysfunction and Exclusion (TIDE, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Drug susceptibility analysis was performed by \u0026ldquo;oncoPredict\u0026rdquo; R package.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Pseudotime TrajectoryAnalysis\u003c/h2\u003e\u003cp\u003ePseudotime analysis was performed by \u0026ldquo;Monocle2\u0026rdquo; R package. This algorithm reduces high-dimensional single-cell gene expression data into a low-dimensional space. Differential cells were order along the predicted trajectories based on their transcriptional states.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Cell lines and Culture\u003c/h2\u003e\u003cp\u003eLUAD cell lines, including A549 and Calu-3 were obtained from the Shanghai Institute of Cell Biology (China). LUAD cell lines were routinely maintained in RPMI-1640 and DMEM (Gibco, USA) medium containing 10% fetal bovine serum (Gibco, USA), 1% streptomycin, and penicillin. All cells were cultured at 37\u0026deg;C under a 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere in a cell incubator.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Real-Time Quantitative PCR (RT-qPCR)\u003c/h2\u003e\u003cp\u003eTotal RNA was obtained employing the TRIzol reagent (Invitrogen). Reverse transcription was performed using the Prime Script TMRT kit (Takara, RR047A) and the Taq II Kit, and PCR amplification was conducted using SYBR Premix Ex according to the manufacturer\u0026rsquo;s instructions. CXCR4 primers utilized were as follows: forward, ACTACACCGAGGAAATGGGCT; reverse, CCCACAATGCCAGTTAAGAAGA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Cell Proliferation Assay\u003c/h2\u003e\u003cp\u003e5\u0026times;10\u003csup\u003e3\u003c/sup\u003e cells were seeded into a single well of a 96-well plate containing 100 \u0026micro;L of complete culture medium. After 24 h, the culture medium was replaced with a full culture medium containing CCK8 reagent. The absorbance was measured at 450 nm after 1, 2, 3, and 4 days.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Colony Formation Assay\u003c/h2\u003e\u003cp\u003eLUAD cells were seeded at a low density of 600\u0026thinsp;~\u0026thinsp;1000 cells per well in 6-well plates and cultured for approximately 2 weeks. Following the incubation period, the cell colonies were washed with PBS, fixed in 4% paraformaldehyde for 20 min, stained with crystal violet, and documented and enumerated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 EdU (5-Ethynyl-2-deoxyuridine) Assay\u003c/h2\u003e\u003cp\u003e2\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells were seeded into a single well of a 96-well plate containing 100 \u0026micro;L of complete culture medium. After 24 h, the cells were incubated with EdU reagent according to the manufacturer's instructions, and cell proliferation was measured using a fluorescence microscope (Thermo Fisher Scientific, America).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Invasion Assay\u003c/h2\u003e\u003cp\u003eThe invasion assay was conducted by first trypsinizing the cells, which were then resuspended in a serum-free medium and quantified. A cell suspension was carefully added to the upper chamber of a Transwell insert pre-coated with Matrigel. Following an incubation period of 36 h to allow cell invasion through the Matrigel, the non-invading cells on the upper surface of the insert were gently removed. The invaded cells on the lower surface were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and subsequently rinsed with PBS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Flow Cytometry Assay\u003c/h2\u003e\u003cp\u003eTo assess apoptosis, cells (1\u0026times;10\u003csup\u003e6\u003c/sup\u003e) were stained with Annexin V-FITC and propidium iodide (PI) apoptosis detection kit (BD Biosciences) according to the manufacturer's instructions. After staining using BD FACS Melody flow cytometry, the percentage of early and late apoptotic cells was assessed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Statistical Analysis\u003c/h2\u003e\u003cp\u003eData are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) of three independent experiments. All experiments were repeated at least three times. Intergroup differences were analyzed using Student\u0026rsquo;s t-test or one-way analysis of variance (ANOVA), and statistical analyses were performed with GraphPad Prism 9 software. Significance levels were denoted as follows: *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and ****\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Quality Control and Cell Classification of LUAD scRNA-seq Data\u003c/h2\u003e\u003cp\u003eThe workflow for this manuscript was summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The single-cell transcriptome dataset utilized in this study is distinguished by its high-quality data and the inclusion of diverse immune cell populations, rendering it an ideal resource for reanalyzing the tumor microenvironment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. To guarantee the reliability and accuracy of the data, a series of strict quality control criteria were meticulously implemented, a total of 69,867 high-quality cells were retained for subsequent annotation and analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, leveraging a comprehensive set of well-established classical cell-type marker genes, these retained cells were systematically classified into eight distinct subgroups. These subgroups include Epithelials, Endothelials, Fibroblasts, Mast cells, Myeloid cells, T cells, B cells, and Plasma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The marker genes for each cell type were further visualized and validated using dot-plots and feature-plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C). These visualizations not only illustrate the expression levels of marker genes across different cell types but also highlight the specificity of each marker in defining the respective cell populations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNotably, although all eight cell types were detected in both tumor and normal control samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), the proportions of these cell types exhibited substantial differences between the two groups. In the tumor samples, certain cell types showed significant expansion or contraction compared to the normal controls, suggesting a profound remodeling of the cellular composition. This significant alteration in cell-type proportions strongly indicates a modified tumor microenvironment in lung adenocarcinoma, which likely plays a critical role in tumor progression, immune evasion, and response to therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-E). These findings set the stage for further in-depth analyses to explore the functional implications of these changes and their potential as therapeutic targets.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Characterization and Trajectory Analysis of Epithelial Cell Subpopulations in LUAD\u003c/h2\u003e\u003cp\u003eTo comprehensively elucidate the progression mechanism of LUAD, we focused our investigation on epithelial cells, as they play a crucial role in the tumor microenvironment. By conducting a subset analysis of these cells, our objective was to uncover the underlying cellular processes that drive the development of LUAD.\u003c/p\u003e\u003cp\u003eThrough unsupervised clustering analysis of epithelial cells, we identified 27 distinct cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Our findings revealed that clusters 5, 8, and 13 were predominantly enriched in normal tissues, suggesting their involvement in maintaining the physiological functions of healthy lung tissues. In stark contrast, clusters 0, 1, 2, 3, 4, 7, 9, 10, 14, 15, 16, 17, 18, 19, 20, and 22 were significantly more abundant in tumor samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). This differential distribution indicates that these clusters may be closely associated with the malignant transformation and progression of LUAD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo precisely assess the malignancy of each cell cluster, we employed inferCNV analysis, which is a powerful tool for detecting copy number variations in single cells. By integrating the inferCNV results with sample source information (normal vs. tumor), we were able to accurately classify the cell clusters. Based on the characteristic marker genes of each cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), we identified several subtypes of malignant tumor cells, including CXCL14\u003csup\u003e+\u003c/sup\u003e malignant cells, CXCR4\u003csup\u003e+\u003c/sup\u003e malignant cells, HSPA1A\u003csup\u003e+\u003c/sup\u003e malignant cells, PLAT\u003csup\u003e+\u003c/sup\u003e malignant cells, SCGB3A1\u003csup\u003e+\u003c/sup\u003e malignant cells, SPP1\u003csup\u003e+\u003c/sup\u003e malignant cells, and TM4SF4\u003csup\u003e+\u003c/sup\u003e malignant cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The specific marker genes for each cell subtype were visualized in the feature plot, providing a clear depiction of the molecular signatures of these cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eTo further explore the developmental trajectories of epithelial cells during LUAD progression, we performed pseudotime trajectory analysis. This analysis allowed us to reconstruct the dynamic changes in cell states and map the potential paths of cell differentiation. Our results clearly delineated two distinct trajectories, with normal and tumor tissues following separate routes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). According to the pseudotime analysis, all trajectories originated from normal epithelial cells, which then underwent a series of molecular and cellular changes, ultimately leading to the formation of SPP1\u003csup\u003e+\u003c/sup\u003e malignant cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). This finding suggests that SPP1\u003csup\u003e+\u003c/sup\u003e malignant cells may represent the end stage of the malignant transformation process of epithelial cells in LUAD. Functional enrichment analysis indicated that oxidative phosphorylation and ATP synthesis were active in initial stage, while cytoplasmic translation and antigen processing were more active in branch 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eIn conclusion, our study provides a comprehensive understanding of the malignant transformation trajectory of epithelial cells in the LUAD microenvironment. The identification of distinct cell clusters and their associated marker genes, as well as the mapping of developmental trajectories, offers valuable insights into the molecular mechanisms underlying LUAD progression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3 CXCR4\u003csup\u003e+\u003c/sup\u003e TAM is closely associated with poor prognosis in LUAD\u003c/h2\u003e\u003cp\u003eGiven that myeloid cells constitute a substantial proportion of both tumor and normal samples in our dataset, we conducted an in-depth investigation into their functional characteristics. First, we isolated myeloid cells from the dataset and performed a refined clustering analysis, which resulted in the identification of ten distinct subgroups: CCL18\u003csup\u003e+\u003c/sup\u003e macrophage, CCL9\u003csup\u003e+\u003c/sup\u003e macrophage, conventional dendritic cell 1 (cDC1), conventional dendritic cell 2 (cDC2), CXCR4\u003csup\u003e+\u003c/sup\u003e TAM, FABP4\u003csup\u003e+\u003c/sup\u003e macrophage, IL1B\u003csup\u003e+\u003c/sup\u003e TAM, macrophage, monocyte, and plasmacytoid dendritic cell (pDC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). The marker genes highly expressed in each myeloid subpopulation were visualized using dot plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), providing a molecular signature for each cell type. As depicted in the cell proportion plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), the relative abundances of CCL18\u0026thinsp;+\u0026thinsp;macrophage, FABP4\u003csup\u003e+\u003c/sup\u003e macrophage, monocyte, macrophage, and pDC were significantly lower in tumor samples compared to normal tissues. Conversely, CCL9\u003csup\u003e+\u003c/sup\u003e macrophage, cDC1, cDC2, CXCR4\u003csup\u003e+\u003c/sup\u003e TAM, and IL1B\u003csup\u003e+\u003c/sup\u003e TAM showed increased proportions in tumor samples, suggesting their potential involvement in tumor-promoting processes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo elucidate the developmental dynamics of myeloid cells, we performed a pseudotime trajectory analysis. This analysis revealed five distinct developmental trajectories, all of which originated from macrophages and converged towards CXCR4\u003csup\u003e+\u003c/sup\u003e TAM (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B\u003c/b\u003e). This finding suggests a hierarchical differentiation pathway within the myeloid cell lineage, potentially indicating the role of CXCR4\u003csup\u003e+\u003c/sup\u003e TAM as a terminal effector in the tumor microenvironment.\u003c/p\u003e\u003cp\u003eWe then conducted CIBERSORTx for immune deconvolution, using bulk microarray data from TCGA-LUAD. The results showed that elevated infiltration of CXCR4\u003csup\u003e+\u003c/sup\u003e macrophages was significantly associated with poorer OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). To further explore the functional states of myeloid subpopulations, we calculated the cytokine and inflammatory scores for each cell type. Notably, cDC2, CXCR4\u003csup\u003e+\u003c/sup\u003e TAM, and IL1B\u003csup\u003e+\u003c/sup\u003e TAM exhibited the highest scores in both cytokine and inflammatory responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-G), a conclusion further supported by UMAP visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). These results indicate that these myeloid subpopulations may play a central role in promoting chronic inflammation and tumor progression.\u003c/p\u003e\u003cp\u003eIn summary, we identified distinct myeloid cell clusters with differential abundance between tumor and normal tissues, delineated the developmental trajectories leading to CXCR4\u003csup\u003e+\u003c/sup\u003e TAM, and revealed the functional significance of specific myeloid subpopulations in regulating the inflammatory tumor microenvironment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4 LUAD exhibit a significantly increased infiltration of exhausted CD8\u0026thinsp;+\u0026thinsp;T cells\u003c/h2\u003e\u003cp\u003eBuilding upon our comprehensive characterization of myeloid cells in the LUAD microenvironment, we next turned our attention to T cells, which also constitute a major component of the tumor immune landscape. Leveraging the same dataset, we isolated T cells and performed a high-resolution re-clustering analysis. This approach led to the identification of twelve distinct T - cell subpopulations: cytotoxic cells, dysfunctional CD8T, effector CD4T, effector CD8T, exhausted CD8T, mucosal-associated invariant T (MAIT) cells, memory CD4T, memory CD8T, na\u0026iuml;ve CD4T, natural killer (NK) cells, proliferating CD8T, and regulatory T (Treg) cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). Dot plots were utilized to visualize the marker genes highly expressed in each subpopulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), providing a molecular fingerprint for each T cell type.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe cell proportion plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) illustrated significant differences in the relative abundances of these T cell subpopulations between tumor and healthy samples. Notably, the proportions of cytotoxic CD8T cells and Treg cells were substantially increased in tumor samples compared to normal tissues, suggesting their active participation in tumor-related immune responses.\u003c/p\u003e\u003cp\u003eTo decipher the developmental dynamics of T cells, we employed a pseudotime trajectory analysis. This analysis revealed three distinct developmental trajectories, all of which originated from NK cells and terminated at Treg cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F). This finding implies a potential developmental hierarchy within the T cell compartment.\u003c/p\u003e\u003cp\u003eTo further validate our observations, we are currently in the process of calculating cytokine, na\u0026iuml;ve, and exhausted scores for each T cell subpopulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG-I). These scores will provide additional insights into the functional states of T cells and help elucidate their roles in shaping the tumor immune microenvironment. Our ongoing analyses aim to comprehensively understand the complex interplay between different T cell subpopulations and their contributions to LUAD progression, which may guide the development of more effective immunotherapeutic strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Intercellular Communication Networks Unveil Pathogenesis and Therapeutic Targets in LUAD\u003c/h2\u003e\u003cp\u003eBuilding on our detailed characterization of individual cell types and their developmental trajectories in LUAD, we next sought to explore the complex landscape of cell-cell communication within the tumor microenvironment. To achieve this, we employed CellChatDB, a meticulously curated database that compiles literature-supported ligand-receptor interactions.\u003c/p\u003e\u003cp\u003eThe overall cell-cell communication network was first visualized using a circular plot, which depicted the number of interactions and their corresponding weights/strengths among different cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Given the complexity of the communication network, we further dissected the signaling patterns originating from each cell group. This analysis revealed a particularly robust interaction network between epithelial cells and myeloid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), suggesting a significant crosstalk between these two cell types that likely influences tumor progression. To highlight key molecular interactions, we generated a bubble plot, which emphasized the importance of specific signaling pathways such as SCGB3A2-MARCO and APP-CD74 in mediating communication between epithelial and myeloid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGuided by our previous pseudotime analysis, which implicated CXCR4 in tumorigenesis and development, we focused on identifying the cell types engaged in active CXCR-CXCL signaling. Consistent with our earlier findings, the CCL signaling pathway was found to be highly active among fibroblast cells, T cells, and myeloid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), underscoring the role of these signaling axes in coordinating cellular functions within the tumor microenvironment.\u003c/p\u003e\u003cp\u003eTo provide a holistic view of the interrelationships among all cell populations, we constructed a correlation heatmap. This analysis not only validated our previous observations but also revealed that CXCR4\u003csup\u003e+\u003c/sup\u003e TAM exhibited significant correlations with tumor cells and other immune cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Collectively, our analysis of intercellular communication networks provides critical insights into the molecular mechanisms underlying LUAD pathogenesis and may inform the development of novel therapeutic strategies targeting cell-cell interactions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Spatial Organization of Cell Subpopulations Reveals Functional Interplay in LUAD Tissues\u003c/h2\u003e\u003cp\u003eBuilding on our understanding of cellular composition, developmental trajectories, and intercellular communication in LUAD, we further explored the spatial organization of cell type subpopulations within tumor tissues. The dataset (GSE189487) of Lung Adenocarcinoma, retrieved from the Gene Expression Omnibus (GEO) database, encompasses samples derived from patients diagnosed with lung adenocarcinoma and normal individuals [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Leveraging spatial transcriptomics, we analyzed tissue sections from both cancerous and normal vasculature regions of LUAD patients. By integrating hematoxylin and eosin (HE) staining results with tumor region annotations, we classified the spatial transcriptomic spots into seven distinct cell clusters: malignant cells, epithelial cells, myeloid cells, fibroblasts, CXCR4\u003csup\u003e+\u003c/sup\u003e TAM, SPP1\u003csup\u003e+\u003c/sup\u003e malignant cells, as well as T and B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The spatial distribution patterns of these seven cell types were visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, providing a comprehensive overview of their localization within the tissue microenvironment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo validate the accuracy of the spatial classification, we examined the expression of specific marker genes for each cell type (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Notably, we identified spatial spots that co-expressed CXCR4, SPP1, and EPCAM, indicating the spatial co-localization of these key molecules (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-F), with higher expression levels highlighted in red. Furthermore, the signature scores of CXCR4\u003csup\u003e+\u003c/sup\u003e TAM and SPP1\u003csup\u003e+\u003c/sup\u003e malignant cells exhibited a significant positive correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG), suggesting a potential functional interplay between these cell populations in the tumor microenvironment. Additionally, we also visualized the expression of classic T cell markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH-I), contributing to a more complete understanding of the spatial immune cell landscape in LUAD.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.7 The heterogeneity of immunotherapy and immune infiltration between SPP1\u003csup\u003e+\u003c/sup\u003e Malignant\u003csup\u003ehigh\u003c/sup\u003e/CXCR4\u003csup\u003e+\u003c/sup\u003eTAM\u003csup\u003ehigh\u003c/sup\u003e and SPP1\u003csup\u003e+\u003c/sup\u003e Malignant\u003csup\u003elow\u003c/sup\u003e/CXCR4\u003csup\u003e+\u003c/sup\u003eTAM\u003csup\u003elow\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eTo further investigate the heterogeneity of SPP1\u003csup\u003e+\u003c/sup\u003e Malignant\u003csup\u003ehigh\u003c/sup\u003e/CXCR4\u003csup\u003e+\u003c/sup\u003eTAM\u003csup\u003ehigh\u003c/sup\u003e and SPP1\u003csup\u003e+\u003c/sup\u003e Malignant\u003csup\u003elow\u003c/sup\u003e/CXCR4\u003csup\u003e+\u003c/sup\u003eTAM\u003csup\u003elow\u003c/sup\u003e, 22 immune cell components were calculated for each patient using the \u0026ldquo;ssGSEA\u0026rdquo; methods. Among these immune cells, CD8T cells and Macrophage M0 were highly expressed in the SPP1\u003csup\u003e+\u003c/sup\u003e Malignant\u003csup\u003elow\u003c/sup\u003e/CXCR4\u003csup\u003e+\u003c/sup\u003eTAM\u003csup\u003elow\u003c/sup\u003e group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B), while the low infiltration group were low expression. These results suggest that the reason for better prognosis in the low infiltration group may be related to the immune microenvironment and have a higher level of immune infiltration in LUAD. At the same time, we evaluated the effect of low and high infiltration on TIDE score, Dysfunction score, Exclusion score, and MSI score using the \u0026ldquo;TIDE\u0026rdquo; algorithm. The result showed that the TIDE score, Dysfunction score, Exclusion score in SPP1\u003csup\u003e+\u003c/sup\u003e Malignant\u003csup\u003ehigh\u003c/sup\u003e/CXCR4\u003csup\u003e+\u003c/sup\u003eTAM\u003csup\u003ehigh\u003c/sup\u003e were significantly increased, while the MSI score was relatively low. (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-F). This indicated that the immune escape potential of patients in high infiltration increased, and the efficacy of immune checkpoint suppressive therapy might be poorer. To validate the results, we performed the high and low infiltration with the IMvigor210 dataset. SPP1\u003csup\u003e+\u003c/sup\u003e Malignant\u003csup\u003ehigh\u003c/sup\u003e/CXCR4\u003csup\u003e+\u003c/sup\u003eTAM\u003csup\u003ehigh\u003c/sup\u003e patients showed greater disease progression and the immune desert type accounted for a larger proportion (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG-H). Finally, Drug susceptibility prediction indicated that IC50 values for Cytarabine and Gemcitabine were significantly lower in high infiltration group, while Gefitinib had the opposite trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eI-K).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Validation of CXCR4-Driven LUAD Progression \u003cem\u003eIn Vitro\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eTo translate our in-silico and spatial findings into functional insights, we conducted a series of \u003cem\u003ein vitro\u003c/em\u003e experiments to validate the role of key genes in LUAD progression. Given the critical role of CXCR4 identified in our previous analyses, we first examined its expression levels in three lung cell lines. Quantitative analysis revealed that the A549 cell line exhibited an approximately 8-fold increase in CXCR4 expression compared to a reference control (BEAS-2B cell lines) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, we employed multiple functional assays to investigate the impact of CXCR4 on tumor cell behavior. The results of the CCK-8 assay indicated that knockdown of CXCR4 significantly enhanced cell viability \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB), suggesting that lung cancer cells with low expression of CXCR4 might enhance their survival ability by evading the killing effect of CXCR4\u003csup\u003e+\u003c/sup\u003e TAM. Consistent with this finding, colony formation assays demonstrated that CXCR4-knockdown cells formed a greater number of colonies with larger sizes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC), indicating enhanced proliferative capacity. The EdU assay demonstrated that compared with the shNC group, the EdU positive cell rates of A549 and Calu-3 cell lines treated with shCXCR4 were significantly increased, which also indicated that knockdown of CXCR4 could promote the cell proliferation of these two cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eTo assess the role of CXCR4 in tumor cell invasion and migration, we performed Transwell assays. The results showed that the knockdown of CXCR4 significantly enhanced the invasive and migratory abilities of A549 and Calu-3 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE), highlighting its importance in the metastatic potential of LUAD cells. Finally, flow cytometry analysis revealed that CXCR4 knockdown inhibited apoptosis in lung cancer cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF), further emphasizing the multifaceted role of CXCR4 in regulating tumor cell fate. These \u003cem\u003ein vitro\u003c/em\u003e findings provide strong functional evidence supporting the significance of CXCR4 in LUAD development and progression, laying the foundation for future therapeutic interventions targeting this key molecule.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLUAD poses a significant global health challenge with dismal survival rates. Previous research has highlighted the importance of the immune microenvironment, inflammation, and specific cell types like TAM in LUAD progression [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, traditional research methods often lack the ability to comprehensively analyze dynamic cell-cell interactions and temporal changes in the tumor microenvironment. Existing studies on the regulatory mechanism of CXCR4\u003csup\u003e+\u003c/sup\u003e TAM, especially in the context of inflammation in LUAD, remain incomplete, hindering the development of targeted therapies.\u003c/p\u003e\u003cp\u003eOur study innovatively applied single-cell spatiotemporal analysis to comprehensively explore the LUAD transcriptomic landscape. This approach enabled us to precisely identify cell types, map the malignant transformation trajectory of epithelial cells, and analyze the functions and communication of myeloid and T cells [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. By integrating spatial transcriptomics and \u003cem\u003ein vitro\u003c/em\u003e experiments, we provided a multi-dimensional understanding of LUAD, which is a significant advancement compared to previous single-level studies.\u003c/p\u003e\u003cp\u003eWe successfully characterized the cell composition of LUAD and normal tissues, revealing substantial differences in cell-type proportions. For epithelial cells, we identified specific malignant cell subtypes and demonstrated that SPP1\u003csup\u003e+\u003c/sup\u003e malignant cells may be the end - stage product of epithelial cell transformation. In myeloid cells, we found that CXCR4\u003csup\u003e+\u003c/sup\u003e TAM is the terminal effector in a hierarchical differentiation pathway and plays a crucial role in promoting inflammation. For T cells, we discovered a potential developmental hierarchy from NK cells to Treg cells. The intercellular communication analysis revealed strong interactions between epithelial and myeloid cells, with CXCR4\u003csup\u003e+\u003c/sup\u003e TAM as a key regulator. \u003cem\u003eIn vitro\u003c/em\u003e experiments further validated that CXCR4 promotes LUAD progression, suggesting it as a potential therapeutic target.\u003c/p\u003e\u003cp\u003eDespite these achievements, our study has limitations. The in vitro experiments only used a limited number of cell lines, which may not fully represent the complexity of LUAD in vivo. Additionally, the mechanisms by which CXCR4\u003csup\u003e+\u003c/sup\u003e TAM regulates the immune microenvironment through inflammation are not fully explored. Future research could expand the scope of in vitro and in vivo models, investigate the detailed molecular mechanisms of CXCR4 - mediated inflammation, and explore the feasibility of developing CXCR4-targeted drugs in combination with other immunotherapies to improve LUAD treatment outcomes.\u003c/p\u003e\u003cp\u003eIn conclusion, our study has provided a comprehensive understanding of the single-cell spatiotemporal landscape of LUAD, identified key cellular and molecular mechanisms underlying tumor progression, and validated the potential of CXCR4 as a therapeutic target. These findings have significant implications for the development of novel treatment strategies for LUAD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in online repositories: https://www.ncbi.nlm.nih.gov/geo/; GSE189487and the UCSC/ TCGA-Hub repository, https://xenabrowser.net/datapages/; TCGA-LUAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and informed consent statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human or animal participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBeili Wang and Juan Lian wrote the main manuscript text and prepared all figures and tables. Jialing Xu and Tao Hua performed the experiments. Jie Ding and Tuo Wang analyzed the data. Cong Cao and Zejie Liu contributed to the study design and methodology. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. Lung cancer. Lancet. 2021;398(10299):535-54. Epub 20210721. doi: 10.1016/s0140-6736(21)00312-3. PubMed PMID: 34273294.\u003c/li\u003e\n\u003cli\u003eNicholson AG, Tsao MS, Beasley MB, Borczuk AC, Brambilla E, Cooper WA, et al. The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015. J Thorac Oncol. 2022;17(3):362-87. Epub 20211120. doi: 10.1016/j.jtho.2021.11.003. PubMed PMID: 34808341.\u003c/li\u003e\n\u003cli\u003eSuccony L, Rassl DM, Barker AP, McCaughan FM, Rintoul RC. Adenocarcinoma spectrum lesions of the lung: Detection, pathology and treatment strategies. Cancer Treat Rev. 2021;99:102237. 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PubMed PMID: 38782901; PubMed Central PMCID: PMC11116453.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tumor-associated macrophages, Prognosis, Immune microenvironment, Immunotherapy, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-7400400/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7400400/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLung adenocarcinoma cancer (LUAD), a common lung cancer subtype, is significantly influenced by the immune microenvironment. Immune checkpoint inhibitors have shown limited efficacy in Lung adenocarcinoma cancer due to the immunosuppressive tumor microenvironment (TME). Identifying predictive biomarkers for immunotherapy response remains an urgent clinical need.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eMulti-Omics data of LUAD were analyzed to investigate the immune microenvironment in LUAD. Single cell RNA-seq was used for exploring the intercellular communication mechanisms in TME. Spatial transcriptomic analysis confirmed the spatial co-localization of SPP1\u003csup\u003e+\u003c/sup\u003e Malignant and CXCR4\u003csup\u003e+\u003c/sup\u003e TAM, while \u003cem\u003ein vitro\u003c/em\u003e experiments validated the functions of biomarker.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThis study delineated the cellular heterogeneity and dynamic shifts within the LUAD tumor microenvironment, resolving the malignant transformation trajectory. Crucially, we identified SPP1⁺ malignant and CXCR4⁺ TAM crosstalk as a driver of exhaustion of CD8T, which induced poor immunotherapy response. Spatial transcriptomics confirmed co-localization of SPP1\u003csup\u003e+\u003c/sup\u003e Malignant and CXCR4\u003csup\u003e+\u003c/sup\u003e TAM, while \u003cem\u003ein vitro\u003c/em\u003e experiments demonstrated that CXCR4 plays an important role in the functions of LUAD cells.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study uncovers the SPP1⁺ malignant and CXCR4⁺ TAM crosstalk as a novel TME-driven resistance mechanism and provides a potential biomarker for stratifying LUAD patients likely to benefit from immunotherapy.\u003c/p\u003e","manuscriptTitle":"Multi-Omics reveals SPP1+ Malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 16:28:02","doi":"10.21203/rs.3.rs-7400400/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T05:52:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T03:17:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-15T08:23:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T04:31:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T05:52:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-27T16:18:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263229653722351283405489303379595733837","date":"2025-09-21T15:18:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146946905079407536764385377248458165685","date":"2025-09-20T17:22:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165667609108837781009881926343503018170","date":"2025-09-20T14:21:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114213590319435682642147589779395026163","date":"2025-09-19T02:46:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339831853537678403912238178602944303740","date":"2025-09-18T15:28:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-18T14:06:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-13T05:21:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-10T08:25:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-09T12:18:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-09-09T12:14:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0186ad63-d5c7-4349-a333-ee51047e4b28","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T04:09:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 16:28:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7400400","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7400400","identity":"rs-7400400","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}