Formation of an Immunosuppressive Spatial Network Between Tumor-Associated SPP1⁺ Macrophages and Fibroblasts in Cervical Cancer with Distant Metastasis

Formation of an Immunosuppressive Spatial Network Between Tumor-Associated SPP1⁺ Macrophages and Fibroblasts in Cervical Cancer with Distant Metastasis | 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 Formation of an Immunosuppressive Spatial Network Between Tumor-Associated SPP1⁺ Macrophages and Fibroblasts in Cervical Cancer with Distant Metastasis Nian Zhao, Wenjie Cai, Yinghua Guo, Wenbo Liu, Yingxiao Jiang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7748094/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cervical cancer (CC) is a common malignancy among women worldwide, and distant metastasis is the main cause of treatment failure and mortality. Immune cells, stromal cells, and their interactive networks within the tumor microenvironment (TME) play a key role in the development, colonization, and immune escape in metastasis. For example, tumor-associated macrophages (TAMs) are often polarized to the M2 phenotype, thereby promoting angiogenesis and creating an immunosuppressive microenvironment; cancer-associated fibroblasts enhance the invasiveness of tumor cells through matrix remodeling; at the same time, the exhaustion of immune cell function further weakens the anti-tumor immune response and promotes tumor immune escape. Methods In this study, tumor tissues and peripheral blood mononuclear cell (PBMC) samples were collected from three cervical cancer patients with distant metastasis and three patients without metastasis at the First Affiliated Hospital of Shandong Second Medical University in 2024, yielding a total of 11 specimens. Subsequently, single-cell RNA sequencing (scRNA-seq) technology was used for detection. Cell clustering and Uniform Manifold Approximation and Projection (UMAP) visualization were implemented by Seurat software. Cell type annotation was performed by SingleR. Differential gene identification was completed by the function of FindMarkers. Furthermore, clusterProfiler was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis; CellPhoneDB was used to predict intercellular ligand-receptor interactions; and Monocle was used to perform pseudotime trajectory analysis. Results In metastatic cervical cancer tissue, the proportion of myofibroblast-like cancer-associated fibroblasts (myCAFs) is significantly increased, with high expression of genes such as COL4A1, FN1, and SPP1. MyCAFs closely interact with SPP1⁺ macrophages through the collagen and FN1-mediated signaling pathways, thereby activating pro-metastatic pathways such as NF-κB and TNF, and suppressing CD8⁺ T cell function. In peripheral blood, the proportion of inflammatory monocytes and non-classical monocytes increased, and they highly expressed pro-metastatic genes such as CX3CR1 and STAT1; in contrast, the number of NK cells and T cells decreased and their function was impaired. Further pseudotieme trajectory analysis revealed that during the metastasis process, the expression of key genes in CAFs, macrophages and peripheral blood mononuclear cells (such as COL4A1, MMP9, CX3CR1) changed dynamically, suggesting that they play an important role in driving cellular functional remodeling and abnormal immune microenvironment. Conclusion This study systematically delineated the immune landscapes of tumor tissue and peripheral blood in metastatic cervical cancer, revealing that the myCAFs–SPP1⁺ macrophage axis and peripheral immune remodeling play central roles in metastasis. Dynamic changes in key genes and functional impairment of immune cells collectively drive tumor progression. These findings provide new insights into the mechanisms of cervical cancer metastasis and offer potential ideas for the development of precision treatment strategies targeting the tumor microenvironment. Cervical cancer Distant metastasis Cancer-associated fibroblasts (CAFs) SPP1⁺ tumor-associated macrophages Immunosuppression Tumor microenvironment (TME) Single-cell RNA sequencing (scRNA-seq) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cervical cancer (CC) is one of the most common gynecological malignancies in women worldwide, with high morbidity and mortality rates, especially in developing countries( 1 ). Although the overall incidence of cervical cancer has declined in recent years with the promotion of vaccination and screening methods, patients with advanced cervical cancer and those with distant metastasis still face clinical challenges such as poor prognosis and unsatisfactory treatment outcomes( 2 ). Distant metastases (such as those to the lungs, bones, liver, etc.) are one of the main causes of death for patients with cervical cancer. In recent years, more and more studies have shown that distant tumor metastasis is not only a manifestation of the malignant biological behavior of tumor cells themselves, but also a complex process driven by the dynamic interactions between immune cells and stromal cells in the tumor microenvironment (TME)( 3 , 4 ). In this process, cell-cell communication plays a central role. It reshapes the tumor ecosystem through ligand-receptor (L-R)-mediated signaling, regulates immune response, extracellular matrix (ECM) remodeling, angiogenesis and immune escape, thereby promoting the invasion, migration and distal colonization of tumor cells( 5 ). Although previous studies have revealed the connection between certain key signaling pathways and immune escape, there is still a lack of systematic single-cell level analysis on which cell types drive which signaling axes in distant metastasis of cervical cancer and how to collaboratively construct an immunosuppressive microenvironment through cell communication( 6 ). Therefore, in-depth exploration of the changing characteristics of cell communication networks in tumor tissues and the peripheral immune system during the metastasis of cervical cancer is of great significance for clarifying the molecular mechanism of distant metastasis, screening potential therapeutic targets, and promoting individualized immunotherapy strategies. Materials and methods Patient samples and follow-up A total of 11 samples were collected in this study, including tumor tissues and peripheral blood mononuclear cell samples from 3 patients with metastatic cervical cancer and 3 patients without metastatic cervical cancer who underwent surgical resection in the First Affiliated Hospital of Shandong Second Medical University during 2024. This study was approved by the Ethics Committee of the First Affiliated Hospital of Shandong Second Medical University, and informed consent was obtained from all subjects. Single-cell RNA sequencing In this study, transcriptome sequencing was performed using the DNBelab C series high-throughput single-cell RNA library construction kit (MGI, 940-001818-00). The cells were diluted to approximately 1,000 cells /mL and loaded into the reservoir of the microfluidic chip. Then, barcode magnetic beads and droplet generating oil were added in sequence. Droplets containing single cells were generated and collected through the DNBelab C4 / DNBelab TaiM4 system. The beads capturing mRNA were recovered for reverse transcription (RT). The obtained cDNA was amplified by PCR and purified and quantified using the Qubit dsDNA Assay Kit (Thermo Fisher Scientific). Subsequently, in accordance with the manufacturer's instructions, cDNA fragmentation, size screening, terminal repair, A-tail addition, linker connection, library indexing PCR and library cyclization were completed in sequence to construct the 3’-end transcript library. The final library was purified and its quality was detected using the Qubit ssDNA Assay Kit (Thermo Fisher Scientific) and Qsep100 (Bioptic). Sequencing was performed on the DNBSEQ-T7 platform (MGI) using the DNBelab C4/DNBelab TaiM4 single-cell library preparation kit. DNBs were loaded onto a patterned array chip for paired-end sequencing. The sequencing Read length is set as follows: Read 1 is 30 bp long (including 10-bp cell barcode 1, 10-bp cell barcode 2 and the 10-bp unique molecular identifier UMI), Read 2 is 100 bp long (transcript sequence), and the 10-bp sample index barcode. scRNA-seq data analysis The sequencing data were first normalized using the R software packages. Subsequently, the RunUMAP function of the Seurat software was utilized for visualization based on the UMAP algorithm, and the shared nearest neighbor (SNN) algorithm was adopted for clustering to optimize the division of cell subgroups. Cell cluster annotation was accomplished through SingleR (v1.4.1) and combined with manual correction to ensure the accuracy of the markers. Differentially expressed genes (DEGs) were identified using the FindMarkers function in Seurat (threshold set as p 1). All heatmaps were generated using the ScaleData function for Z-score normalization. Functional enrichment analysis was performed using clusterProfiler (v4.1) based on the hypergeometric distribution algorithm for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Cell differentiation trajectory analysis was conducted using Monocle3E for pseudotime inference to reconstruct the evolutionary process of cell subpopulations. The intercellular communication network identified the relevant ligand-receptor pairs from the scRNA-seq data through the CellChat software package. Statistics and reproducibility For single-cell RNA sequencing (scRNA-seq) analysis, the experimental samples were divided into two groups: tumor tissues and peripheral blood mononuclear cells (PBMCs) from patients with distant metastasis, and tumor tissues and PBMCs from patients without metastasis. The quality control of the original sequencing data was carried out through CellRanger and Seurat software, with 9,000 to 13,000 high-quality cells obtained for each sample, and the sequencing quality exceeded 90%. In total, a gene expression matrix comprising 53,248 cells was generated and subjected to subsequent bioinformatic analyses. All Statistical analyses were performed using R software (version 4.2.1), following the default methods specified in the corresponding packages. Results Single-cell RNA analysis of the distribution landscape of cervical cancer cells with distant metastasis behavior This study analyzed the tumor microenvironment (TME) of cervical cancer (CC) with distant metastasis using single-cell RNA sequencing (scRNA-seq). Eleven samples from six patients (tumor tissues and peripheral blood mononuclear cells (PBMCs) from patients with and without metastases) were included in the study, all of which were pathologically confirmed to be CC (Fig. 1 A). After quality control, a total of 53,248 cells were obtained for analysis. After data normalization and batch effect correction (Harmony)( 7 ), cells were clustered and visualized using principal component analysis( 8 ) and uniform manifold approximation and projection (UMAP) ( 9 ) (Materials and Methods). Combined with the classical marker gene expression patterns, a total of major cell populations such as epithelial cells, T cells, macrophages, B cells, plasma cells, cancer-associated fibroblasts (CAFs), mast cells, endothelial cells and plasmacytoid dendritic cells (pDCs) were identified (Fig. 1 B, Supplementary Fig. 1A-B) ( 10 , 11 ). Different cell types exhibited specific transcriptional signatures (Fig. 1 C, Supplementary Figure S1 C–D). For example, epithelial cells highly express SERPINB4, SERPINB3, and KRT17; T cells highly express CD3D, GZMA, and GNLY; macrophages highly express CD163 and FCGR1A; B cells highly express MS4A1 and CD19; and CAFs highly express SFRP2, COL1A2, and DCN. Representative marker genes are also found in plasma cells, mast cells, endothelial cells, and pDCs. These differential expressions may be closely associated with the development of distant metastasis in cervical cancer.Further functional enrichment analysis revealed that the differentially expressed genes in the tissues with metastasis were mainly enriched in RNA splicing, viral response and virus-related processes in the biological process (BP) category annotated by gene ontology (GO)( 12 , 13 ) [12,13], and were significantly associated with multiple immune-related signaling pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis (Fig. 1 D). In addition, GO analysis indicated that polygenes are involved in functions such as protein secretion, signal transduction, and nucleoprotein localization, suggesting that intercellular communication plays a significant role in the transfer process. Cell interaction analysis further confirmed that CAFs exhibited significant self-communication and cross-cell type interactions, suggesting that they may play a key driving role in the distal metastasis of cervical cancer (Fig. 1 E). CAFs display significant intercellular interactions in the metastatic TME of cervical cancer During the process of tumor metastasis, Collagen, as the main component of extracellular matrix (ECM), interacts with receptors on the surface of tumor cells and stromal cells, activating multiple cellular communication signaling pathways, thereby regulating key steps such as adhesion, invasion, migration and angiogenesis of tumor cells ( 14 , 15 ). COL4A, as a key component of the extracellular matrix (ECM), can bind to CD44, providing adhesion sites and thereby influencing cellular adhesion capacity( 16 ). In tumor cells, this interaction facilitates adhesion to the basement membrane and promotes invasive behavior; for instance, gastric cancer cells have been reported to infiltrate surrounding tissues via COL4A–CD44 interactions( 17 ). In this study, CAFs demonstrated robust intercellular communication through the COLLAGEN signaling pathway (Fig. 2 A). Figure B lists the ligand-receptor pairs in the COLLAGEN signaling pathway and ranks them according to their contribution to the total signaling pathway. In particular, COL4A1-CD44 and COL4A1-ITGA1 + ITGB1 show the highest contribution, indicating the key role of ECM components derived from CAFs in regulating cellular interactions. Among them, COL4A1 is an important component of type IV collagen in the extracellular matrix( 18 ). CD44 is involved in cell adhesion, migration, tumor invasion, immune regulation, etc. ( 19 ) ITGA1 + ITGB1 is a member of the integrin family and can form heterodimer receptors with α1 and β1 subunits( 15 ) .This receptor can specifically recognize specific amino acid sequences in COL4A1, such as the proline-rich hydroxylated G-F-P-G-E-R sequence ( 20 ), thereby mediating the adhesion of cells to COL4A1. During the process of tumor metastasis, ITGA1 + ITGB1 on the surface of tumor cells bind to COL4A1 in the tumor microenvironment, which can promote the adhesion of tumor cells on the basement membrane, thereby helping tumor cells penetrate the basement membrane and infiltrate the surrounding tissues (Fig. 2 B). The communication signal transduction of COLLAGEN cells in tumor metastasis is most intense among CAFs-epithelial, CAFs-CAFs, CAFs-endothelial and CAFs-Macrophages (Fig. 2 C-D), and its main function is to penetrate the basement membrane and promote tumor cell metastasis. In addition to the COLLAGEN signaling pathway, FN1-related signals also exhibit abnormal high-intensity signal transmission in cervical cancer with metastasis. CAFs, as high-intensity signal sources, engage in cellular dialogue with multiple cell populations (Fig. 2 E). During this process, FN1 secreted by CAFs binds to CD44 produced by communicating cells, FN1 binds to (ITGA4/5 + ITGB1) and FN1 binds to (ITGAV + ITGB1) (Fig. 2 F). FN1 (fibronectin 1) secreted by CAFs binds to the CD44 receptor on the surface of tumor cells or immune cells and is a key link in the cell-matrix interaction in the tumor microenvironment (TME) ( 21 ). This combination significantly affects tumor progression and treatment response by regulating processes such as cell adhesion, migration, signal transduction and immune escape. Tumor metastasis FN1 cell communication signaling was most intense between CAFs-epithelial, CAFs-CAFs, CAFs-endothelial, and CAFs-macrophages (Fig. 2 G-H). Beyond collagen and FN1, additional signaling pathways contributed to the metastatic ecosystem. THBS signaling axes played important roles among tumor-associated cell populations (Fig. 2 I), while pathways such as CXCL, EGF, TGF-β, and VEGF were also implicated in distant metastasis of cervical cancer (Figure S2–S4). Collectively, these results highlight CAFs as central hubs of intercellular communication, driving the establishment of a pro-metastatic microenvironment. Cellular population characteristics of CAFs in metastatic cervical cancer tissues Cancer-associated fibroblasts (CAFs) are a type of cell present in the tumor microenvironment (TEM) and play an important regulatory and supportive role( 22 ). Recent studies have shown that CAFs are not a homogeneous cell population, but rather consist of multiple subpopulations, each of which may have different functions and phenotypes ( 23 ). In our analysis, CAFs in metastatic samples showed strong intercellular communication with other cell populations. To determine which CAF subsets are functionally important, We performed a subpopulation analysis of CAFs and divided them into eight subpopulations based on their different gene expression characteristics (Fig. 3 A, S5D). And the proportions of CAFs subgroups in patients with metastasis and those without metastasis were analyzed (Fig. 3 B). Among patients with metastasis, the vast majority of CAFs belong to myCAFs myofibroblasts (myCAFs). myCAFs are mainly characterized by ECM remodeling, contractile function and promotion of angiogenesis, and are commonly found in matrix-enriched regions of solid tumors ( 24 ).Among these, IGFBP7 was markedly overexpressed in myCAFs and identified as a marker gene. IGFBP7 is significantly overexpressed in myCAFs and serves as a marker gene. The function of IGFBP7 is cancer cell-specific and microenvironment-dependent, and its underlying mechanisms require further investigation. Previous studies have reported that IGFBP7 expression is associated with prognosis in several cancers, including hepatocellular carcinoma and gastric cancer, where differential expression may indicate distinct clinical outcomes ( 25 ). As a critical factor in the TME, IGFBP7 may represent a potential therapeutic target for anti-fibrotic or anti-tumor strategies (Fig. 3 C, S5A–C). IGFBP7 is primarily secreted by CAFs, endothelial cells, or tumor cells, and acts on adjacent or distant cells through body fluid diffusion to promote intercellular communication and regulate multiple signaling pathways ( 26 ). In addition to the differences in subpopulations, metastatic CAFs showed a large number of gene expression differences as a whole. A large number of cells highly expressed genes such as COL4A1, MCAM, ITGA1, and SPP1, all of which are related to cell–cell communication( 27 ) (Fig. 3 D) (Fig. 2 , S6-S7). Gene set enrichment analysis (GSEA) revealed that these genes were predominantly involved in tumor progression, ECM signaling, and regulation of cancer cell stemness (Fig. 3 E). In addition, GO analysis showed that these differentially expressed genes were primarily involved in cell-cell binding and protein binding, which are closely related to cell communication. This suggests that distant metastasis of cervical cancer is closely related to cell communication between CAFs in the in situ tumor (Fig. 3 F). Cellular communication characteristics between CAFs and macrophages in cervical cancer with distant metastasis As shown in Fig. 2 C–D, CAFs acted as strong signaling sources to immune cells, particularly through the COLLAGEN and FN1 signaling pathways. We analyzed the proportions of CAF subpopulations in tumor samples with and without metastasis to identify the CAF subpopulations that are primarily involved in cell communication. We analyzed the proportions of cell subsets involved in intercellular communication, including CAFs and macrophages (Fig. 4 A-B). CAFs are the main components of the tumor matrix and promote tumor growth, metastasis and treatment resistance by secreting various extracellular matrix (ECM) components and cytokines ( 28 ). COL4A1, a component of type IV collagen, can be secreted by CAFs ( 29 ) We analyzed these signal subgroups emitted by CAFs. In myCAFs, Collagen genes such as COL4A1 and COL4A2, as well as their subgenes COL4A1 1.1 and COL4A2 2.2, were highly expressed (Fig. 4 C, Fig. 3 D), with extremely significant differential expression. In a subpopulation analysis of CAFs, the proportion of myCAFs and pCAFs in cervical cancers with metastasis was much higher than that in cervical cancers without metastasis. Therefore, we analyzed the expression of key collagen signaling genes not only in myCAFs but also in pCAFs, and the two results showed consistency. Moreover, myCAFs are the most numerous CAFs cells in metastatic cervical cancer. Therefore, the signal source genes of the collagen signaling pathway in metastatic cervical cancer are mainly derived from myCFAs. Subgroup analysis of macrophages was performed and macrophages were divided into 14 subgroups (S7 B,S8 A-B) based on their different gene expression profiles. Pathway analysis of differentially expressed genes in macrophages (Fig. 4 D) revealed that in metastatic cervical cancer, strongly expressed signaling-related genes were predominantly enriched in the NF-κB signaling pathway ( 30 ), TNF signaling pathway( 31 ), and transcriptional misregulation in cancer( 32 ). these findings suggest that the tumor microenvironment is characterized by immune evasion, inflammatory responses, and transcriptional dysregulation, which collectively drive cervical cancer metastasis, progression, and immune escape. In macrophages of cervical cancer with metastasis, CAF-derived signals promoted M2 macrophages to secrete CCL2 and CXCL8, and promote the high expression of MMP9, MMP14, and SPP1. MMP9 and MMP14 promote macrophages to destroy the collagen layer (ECM) of tumor tissues, facilitating the metastasis of tumor cells. SPP1 is related to the polarization of macrophages, causing them to polarize towards M2 macrophages. In addition, a large number of immune-related regulatory genes such as CCL2, TNF, and CXCL8 regulate the immune microenvironment of tumor tissues in cervical cancer with metastasis (Fig. 4 E-F). Notably, the signaling pathways activated in metastatic macrophages were highly consistent with those triggered by CAF-derived signals (Fig. 4 G, S6A–B), suggesting functional continuity or synergy between CAFs and macrophages in metastasis-associated communication networks. Single-cell transcriptomic features of peripheral blood in cervical cancer patients with distant metastasis Peripheral blood samples were collected from cervical cancer patients with metastasis and compared with those from patients without metastasis. The overall cell types included monocytes (Mono), NK cells, T cells, B cells, plasma cells (PC), plasmacytoid dendritic cells (pDC). Overall, no significant differences in cell composition were observed (Fig. 5 A). All cells from the six samples were annotated, confirming the above cell types, and the top five marker genes of each type were displayed (Fig. 5 B). Further dimensionality reduction clustering analysis divided the cells from the six samples into 17 clusters (0–16), and the gene expression of each cluster was plotted (Fig. 5 C). In monocytes, MS4A7, LILRB2, FCN1, and CD68 genes related to immunosuppression and tumor-associated inflammation are significantly up-regulated( 33 ), suggesting polarization toward an immunosuppressive M2 phenotype, potentially promoting tumor metastasis. In NK cells, TRDC and KLRF1 were upregulated ( 34 ), indicating functional impairment. In T cells, genes such as IL7R and TCF7 were highly expressed( 35 ), indicating that the survival and memory functions of T cells were impaired, making it difficult to maintain long-term anti-tumor effects. In B cells, TCL1A was highly expressed( 36 ), indicating abnormal antibody secretion and class switching, which may lead to the production of cancer-promoting antibodies or the decreased ability to clear tumor cells. In PC cells, IGHA2 and IGHG2 were upregulated( 37 ), suggesting altered antibody secretion profiles that may facilitate immune evasion. In pDCs, LILRA4 and CLEC4C were highly expressed ( 38 ), indicating impaired antiviral and immune activation functions, hindering effective T cell priming and thereby promoting metastasis (Fig. 5 D). The cell proportion statistics of patients with and without metastasis showed that the proportion of monocytes in patients with metastasis was significantly increased, while the number of NK cells, T cells and B cells with anti-tumor activity was significantly reduced (Fig. 5 E). It is suggested that the recruitment of monocytes and the decrease of immune cells may be closely related to the distant metastasis of cervical cancer. Further analysis of intercellular communication revealed strong signal communication between monocytes and NK cells, T cells, and pDCs, while NK cells and T cells had weakened communication with other cells. Since NK cells and T cells are key tumor killer cells, this decreased communication may impair their recognition and elimination of tumor cells, thereby facilitating distant metastasis (Fig. 5 F). GO and KEGG functional analysis of genes mainly involved in signal transduction in blood showed that these genes were mainly involved in immune and inflammatory regulation (e.g., viral response, defense response to symbionts, viral defense response), intercellular activation (e.g., cyst cavity, cytoplasmic cyst cavity, secretory granule cavity), and apoptosis-related signaling pathways (e.g., peptidase regulatory activity) (Fig. 5 G). These findings suggest that peripheral blood intercellular signaling has an important effect on the immune activity of monocytes and may play a key role in the process of distant metastasis of cervical cancer. Immune characteristics of monocytes in metastatic cervical cancer patients The number of monocytes was significantly increased in patients with metastatic cervical cancer (Fig. 5 E). Based on the expression characteristics of the key marker genes, monocytes were further subdivided into 10 subtypes, including classical mono, nonclassical mono, and transitional mono (Fig. 6 A). Among them, the proportions of inflammatory mono and nonclassical mono were significantly increased, while the proportions of tissue resident mono were not significantly different between the metastatic and non-metastatic groups (Fig. 6 B). The results of differential gene analysis showed that CX3CR1, RHOC, CST1, STAT1 and GBP1 were highly expressed in the metastasis group as a whole (Fig. 6 C)( 39 – 43 ). Specifically, CX3CR1 and RHOC suggested that the migration ability of monocytes was enhanced, which may promote their recruitmen into tumor tissues( 39 , 40 ). The up-regulation of CTSL indicated stronger matrix degradation ability within the tumor microenvironment, favoring tumor cell invasion and metastasis ( 41 ). The high expression of STAT1 and GBP1 reflected the activation of inflammatory response, but this chronic inflammation may also induce the formation of immunosuppression( 42 , 43 ) .In contrast, genes related to antigen presentation and immune activation such as HLA-DQA2 and CD14 were significantly downregulated in the transfer group, suggesting weakened antigen processing and presentation capabilities of monocytes, thereby limiting the effective initiation of adaptive immune responses( 44 ); Moreover, the down-regulation of immunomodulatory genes such as KLF10 and SIGLEC10 may disrupt the immune surveillance function and promote immune evasion( 45 ), while the down-regulation of CEBPD can relieve the inhibition of M2 macrophage polarization, driving the transformation of TAMs to the immunosuppressive phenotype ( 45 ) (Fig. 6 D). Functional enrichment analysis further showed that up-regulated genes were mainly involved in biological processes such as signal transduction, antigen processing and presentation, viral infection and cytoskeleton regulation, while down-regulated genes concentrated in immune response pathways such as antigen processing and presentation (Fig. 6 E). These alterations are closely associated with the progression and distant metastasis of cervical cancer. Figure F shows the heat map of gene expression in the mono cell subpopulation (Fig. 6 F). Further analysis of the expression pattern showed (Fig. 6 G) that the expression regions of CX3CR1, RHOC, CTSL, STAT1 and GBP1 were basically the same before and after metastasis, but the expression levels were significantly increased after metastasis. The distribution of CD14, HLA-DQA2, TNFAIP3, CXCR4, CEBPD and KLF10 genes maintained comparable distribution patterns but were markedly downregulated in metastatic patients. Remodeling of intercellular communication networks between blood and tissue cells in cervical cancer patients We performed a systematic analysis of the intercellular communication patterns between blood and tumor tissues in cervical cancer patients with and without metastasis, and and found significant abnormalities across multiple signaling pathways. In the ADGRE5 signaling pathway, non-metastatic patients exhibited dense interactions between blood-derived T cells, NK cells, and monocytes with CAFs and epithelial cells, whereas in metastatic group, the overall communication intensity of this network was markedly reduced, particularly among immune cells; Furthermore, extensive and strong communication between monocytes, macrophages, CAFs and most other cells was found in the non-metastatic group, while these interactions were significantly reduced or disappeared in the metastatic group, indicating that ADGRE5-mediated immune–stromal communication was significantly restricted after metastasis (Fig. 7 A-B). In the CCL signaling pathway, the signals in the non-metastasis group were mainly mediated by T cells, endothelial cells and macrophages, while T cells, macrophages and NK cells in the blood in the metastasis group transmitted numerous signals to other cells, suggesting a close association with tumor progression. Specifically, the communication network dominated by blood pDCs, macrophages and endothelial cells was more active in the non-metastatic group, involving epithelial cells, endothelial cells and CAFs, while in the metastatic group, most of these communications were weakened or lost, but interactions between T cells, macrophages, and other cells were significantly enhanced, showing a shift of dominant signaling cells from pDCs to T cells, suggesting that CCL-mediated interactions were restructured during metastasis (Fig. 7 C-D). In the COLLAGEN signaling pathway, abundant communication was observed among CAFs, B cells, epithelial cells, and endothelial cells in non-metastatic group, while the communication among macrophages, T cells and B cells was significantly reduced in the metastasis group. Overall, communication in the non-metastasis group was more extensive. Tissue cells such as epithelial cells and CAFs were the main signaling sources, transmitting signals to tumor tissues and blood cells. while in the metastatic group, signaling sources were restricted to a limited number of cells such as CAFs, endothelial cells, and epithelial cells, with markedly reduced interactions with immune cells (Fig. 7 E-F). In the SPP1 signaling pathway, macrophages were the main signaling source in the non-metastatic group, accompanied by T cells, B cells and Mast cells; however, in the metastasis group, except for macrophages, the signals of other immune cells almost completely disappeared, suggesting that the weakened communication among immune cells may promote tumor immune evasion and drug resistance and thereby affect the prognosis. At the same time, the communication between macrophages and CAFs was significantly enhanced, indicating that the spatial network inhibitory environment formed by these two cell types promoted tumor distant metastasis (Fig. 7 G-H). In the FN1 signaling pathway, macrophages were the main signaling source in the non-metastasis group, but their signaling disappeared in the metastasis group, while the signaling strength of other cells showed little change, suggesting that the loss of macrophages as a key signaling source may weaken its immune surveillance effect on tumors, and make tumors easier to escape immunosuppression (Fig. 7 I-J). Collectively, the communication network between blood and tumor cells in cervical cancer patients is significantly reformed after metastasis, which is mainly manifested as the significant weakening of immune cell-related signals and the transformation of dominant cells in some pathways. The abnormal cellular interaction network may weaken the immune surveillance function and promote tumor metastasis and immune evasion. Gene-driven reprogramming of cell differentiation trajectories promotes distant metastasis in cervical cancer In the preceding analysis, we have revealed that multiple cell types are involved in intercellular communication within tumor tissues and peripheral blood, among which cancer-associated fibroblasts (CAFs), macrophages, and peripheral blood monocytes (Mono) represent the core populations mediating signal transduction. To further explore the dynamics of these cells during tumor metastasis, we performed pseudotemporal analysis of their differentiation trajectories, which revealed that several key signaling pathway genes played critical roles in driving cell differentiation (Fig. 8 A-C). In CAFs, COL4A1, FN1, and SPP1 were identified as major genes driving differentiation. In metastatic patients, CAFs exhibited a gradual increase in COL4A1 expression, suggesting that it may promote tumor cell migration during metastasis by altering extracellular matrix (ECM) structure(27). FN1 expression also increased along pseudotime, strengthening tumor cell adhesion and dependence on the ECM, thereby accelerating metastatic progression( 46 ). SPP1 was significantly increased in the late pseudo time period, which may accelerate tumor invasion and metastasis by regulating the interaction between tumor cells and matrix components and affecting the function of immune cells ( 6 ) (Fig. 8 D). In macrophages, MMP9 and MMP14 act as matrix metalloproteinases, and their decreased expression may imply a transition from a pro-invasive phenotype to an immunosuppressive or other functional state of macrophages ( 47 , 48 ). At the same time, the reduction of SPP1 expression in the late pseudo time period suggested that it may impair the recruitment and activation ability of immune cells in the late stage of tumor metastasis, thereby facilitating immune evasion ( 49 ) (Fig. 8 E).In peripheral blood mononuclear cells (PBMCs), CX3CR1, CTSL, and GBP1 showed pseudo-time-dependent expression changes. The increase of CX3CR1 may promote the infiltration of CX3CR1⁺TAMs and the secretion of cytokines (such as TGF-β and IL-27), thereby inducing epithelial-mesenchymal transition (EMT) and promoting angiogenesis to facilitate tumor invasion ( 50 ). The high expression of CTSL destroyed the integrity of basement membrane by degrading extracellular matrix (ECM) components ( 51 ) Increased GBP1 expression may promote tumor progression in cervical cancer by binding to the interacting protein HNRNPK and regulating the alternative splicing of CD44 ( 52 ). Notably, the expression of these genes declined after the establishment of distant metastases, suggesting that new molecular mechanisms may take over their roles in later stages of tumor progression (Fig. 8 F). Further analysis of the differentiation trajectories of blood immune cells found that monocytes had a more complex differentiation path in non-metastatic samples, but showed a rapid and simplified differentiation process in metastatic patients (Fig. 8 G). NK cells showed complex dynamics in non-metastatic samples, while their differentiation tended to be simplified in metastatic samples (Fig. 8 H). T cells showed diverse directions of differentiation in the nonmetastatic patients, whereas in the metastatic patients, there was marked " cluster isolation" phenomenon, suggesting compression or remodeling of their functional heterogeneity (Fig. 8 I). Collectively, key genes driving cellular differentiation reshape the tumor immune microenvironment and remodel the trajectories of CAFs, macrophages, and peripheral immune cells, thereby promoting distant metastasis of cervical cancer. This finding provides novel insights into the mechanisms of cervical cancer metastasis and highlight potential molecular targets for therapeutic intervention. Conclusions In this study, the immune profiles of tumor tissues and peripheral blood of cervical cancer patients with distant metastasis were systematically delineated by single-cell transcriptomics, and the key mechanisms of metastasis were revealed. We identified that myofibroblast-like cancer-associated fibroblasts (myCAFs) and SPP1⁺ macrophages form an immunosuppressive spatial communication network, which activates pro-inflammatory and immunosuppressive pathways such as NF-κB and TNF through COLLAGEN and FN1 signaling pathways, thereby promoting extracellular matrix remodeling and immune evasion. Concurrently, the expansion of CX3CR1⁺ inflammatory monocytes in the peripheral blood along with the dysfunction and simplified differentiation trajectories of NK cells and T cells jointly facilitated the occurrence of tumor metastasis. Pseudotime analysis further revealed dynamic expression changes of key genes, including COL4A1, FN1, SPP1, CX3CR1, and GBP1, highlighting their roles in tumor–immune co-evolution. These findings not only advance our mechanistic understanding of cervical cancer metastasis but also provide novel perspectives and theoretical foundations for developing precision therapies targeting the myCAFs-SPP1⁺ macrophage axis and peripheral immune remodeling. Abbreviations CC Cervical cancer TME the tumor microenvironment TAMs tumor-associated macrophages PBMC peripheral blood mononuclear cell scRNA-seq single-cell RNA sequencing UMAP Uniform Manifold Approximation and Projection GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes CAFs cancer-associated fibroblasts myCAFs myofibroblast-like cancer-associated fibroblasts pDCs plasmacytoid dendritic cells ECM extracellular matrix Declarations Ethics approval and consent to participate This study was reviewed and approved by the Medical Research Ethics Committee of Weifang People’s Hospital (approval No. KYLL20231101-3). All six patients provided written informed consent. Consent for publication All authors have approved the submitted version. Availability of data and material The data presented in this study are available on request from the corresponding author. Competing interests All authors present no Conflicts of Interest. Funding This work was supported by National Natural Science Foundation of China International (Regional) Cooperation Project (No. 3221101608), Shandong Provincial Natural Science Foundation Youth Project (No. ZR2024QC085), Shandong Provincial Medical and Health Science and Technology Project (No. 202401030359), Shandong Provincial Medical and Health Science and Technology Project (No. 202309031407), Weifang Municipal Health Commission Scientific Research Project (No. WFWSJK-2025-092). Authors’ contributions N-Z, WJ-C and YH-G performed data analysis, and prepared the figures and the manuscript draft; WB-L and YX-J collected and processed CC samples; GF-Z, Y-L and MM-Z analyzed RNAseq data; HB-X and GL-C provided CC samples and patient’s profile; FR-H and NN-L conceptualized and supervised the study and designed the experiments, and revised the manuscript. All authors approved and contributed to the final version of the manuscript. Acknowledgements We acknowledge the financial support from the National Natural Science Foundation of China International (Regional) Cooperation Project, Shandong Provincial Natural Science Foundation Youth Project, Shandong Provincial Medical and Health Science and Technology Project, Shandong Provincial Medical and Health Science and Technology Project, Weifang Municipal Health Commission Scientific Research Project. References Francoeur AA, Monk BJ, Tewari KS. Treatment advances across the cervical cancer spectrum. 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02:00:23","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156162,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7748094/v1/c24ffb874b0542dab24b149d.html"},{"id":93638766,"identity":"21f0bf26-3a07-4f44-8028-8ee520e8c2df","added_by":"auto","created_at":"2025-10-16 02:00:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":795441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCellular composition and functional characteristics of the tumor microenvironment of cervical cancer.\u003c/strong\u003e \u003cstrong\u003eA. \u0026nbsp;\u003c/strong\u003eSchematic diagram of the research process. Single-cell RNA sequencing (scRNA-seq) analysis was performed on tumor tissues and peripheral blood mononuclear cells (PBMCs) of patients with cervical cancer (including metastatic and non-metastatic cases), and different cell types in the tumor microenvironment were identified. \u003cstrong\u003eB.\u003c/strong\u003e UMAP visualization showing the distribution of major cell types in tumor tissues from six patients (metastatic and non-metastatic). Each dot represents a single cell, and different colors correspond to different cell populations. \u003cstrong\u003eC. \u003c/strong\u003eThe dot plot shows the expression levels (log₂ multiple changes) and expression ratios of representative marker genes of each cell type. \u003cstrong\u003eD.\u003c/strong\u003e Functional enrichment analysis of differentially expressed genes in metastatic tumor tissues. The bar chart shows the significantly enriched terms of biological processes (BP), cellular components (CC), molecular functions (MF), and KEGG pathways, with the bar length indicating the number of genes. \u003cstrong\u003eE.\u003c/strong\u003e The heat map shows the intensity of interaction among different cell types in metastatic cervical cancer tissues. The depth of color represents the relative interaction level, with red indicating enhancement and blue indicating weakening.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7748094/v1/ac37767ee164de9e4cc35ee0.png"},{"id":93638767,"identity":"79122f25-351b-4a37-bb6e-b84fbcf1c806","added_by":"auto","created_at":"2025-10-16 02:00:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":951533,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntercellular communication network in metastatic cervical cancer tissue.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e. Intercellular communication network based on collagen (COLLAGEN) ligand-receptor pairs. Nodes represent cell types, lines represent intercellular communication, and line thickness reflects the strength of communication. \u003cstrong\u003eB.\u003c/strong\u003e Ranking of the contributions of different ligand-receptor pairs in the collagen signaling pathway. Ligand-receptor pairs are shown on the left, and a bar chart of contribution is shown on the right. \u003cstrong\u003eC.\u003c/strong\u003e The strength of input and output interactions between different cell types. In the bubble chart, bubble size represents the number (count), and color represents the cell type. \u003cstrong\u003eD\u003c/strong\u003e. Heat map of the relative contribution of each cell type in the collagen signaling pathway. Source cells are listed as target cells, and the color gradient (light blue to dark red) indicates low to high contribution. \u003cstrong\u003eE.\u003c/strong\u003e Intercellular communication network based on fibronectin (FN1) ligand-receptor pairs. Same legend as A. \u003cstrong\u003eF\u003c/strong\u003e. Ranking of the contributions of ligand-receptor pairs in the FN1 signaling pathway. Ligand-receptor pairs are shown on the left, and contribution bar charts are shown on the right. \u003cstrong\u003eG.\u003c/strong\u003e Heatmap of the relative contributions of different cell types to the FN1 signaling pathway, same legend as D. \u003cstrong\u003eH\u003c/strong\u003e. The strength of input and output interactions between different cell types in the FN1 signaling pathway. Bubble size indicates number, and color indicates cell type. \u003cstrong\u003eI.\u003c/strong\u003e Intercellular communication network based on the THBS ligand-receptor pair, same legend as A.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7748094/v1/bc7bc4f7e3b6823f4b264b19.png"},{"id":93638768,"identity":"327f6d98-16c5-4a38-b7f5-531b8de84487","added_by":"auto","created_at":"2025-10-16 02:00:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":578587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacteristics of CAF subpopulations in cervical cancer tissues. A.\u003c/strong\u003e UMAP visualization of CAFs in tissues from patients without metastasis (normal) and with metastasis (tumor). Different colors represent distinct CAF subpopulations, including myCAFs, iCAFs, mCAFs, CAFs_CXCL13⁺, invCAFs, and pCAFs. \u003cstrong\u003eB.\u003c/strong\u003e Stacked bar plot showing the proportions of CAF subpopulations across individual samples (normal1-3 and tumor1-3), with colors corresponding to the subgroups defined in A. \u003cstrong\u003eC. \u003c/strong\u003eHeatmap illustrating the expression levels of marker genes across CAF subpopulations. Rows represent marker genes, columns represent CAF clusters, and the color gradient (light blue to dark red) indicates relative expression levels.\u003cstrong\u003e D.\u003c/strong\u003eVolcano plot showing differentially expressed genes (DEGs) between non-metastatic and metastatic CAFs. The x-axis denotes log₂(fold change), and the y-axis denotes –log₁₀(p_val). Red dots represent upregulated genes, and blue dots represent downregulated genes. \u003cstrong\u003eE\u003c/strong\u003e. Gene set enrichment analysis (GSEA) plots of DEGs. Different colors represent distinct gene sets, with the x-axis indicating the gene ranking and the y-axis representing the running enrichment score. \u003cstrong\u003eF.\u003c/strong\u003e Bubble plot of functional enrichment analysis for DEGs. The x-axis shows the gene ratio, the y-axis displays enriched functional terms, bubble size reflects the number of genes, and bubble color indicates the adjusted p-value.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7748094/v1/48e5de90e0081019a04e0b97.png"},{"id":93638769,"identity":"9d0f2d59-b0e4-4dc4-958c-3587bee5fc84","added_by":"auto","created_at":"2025-10-16 02:00:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":664371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacteristics of CAFs and Macrophage Subpopulations in Metastatic Cervical Cancer Tissues. A. \u003c/strong\u003eAnalysis of the proportion of CAFs subtypes in patients with metastasis. The vertical axis represents the CAF subtype, and the horizontal axes respectively indicate the number of cells (left) and the proportion (right). \u003cstrong\u003eB. \u003c/strong\u003eAnalysis of the proportion of macrophage subsets in patients with metastasis. \u003cstrong\u003eC. \u003c/strong\u003eViolin plots show the differential gene expression of different CAF subtypes in patients with metastasis. \u003cstrong\u003eD. \u003c/strong\u003eBubble plot shows pathway enrichment analysis of highly expressed genes in macrophages in tissues with metastasis. The horizontal axis represents the gene ratio (GeneRatio), the vertical axis represents the pathway name, the bubble size indicates the number of genes (Count), and the color represents the corrected P-value (p.Just).\u003cstrong\u003e E.\u003c/strong\u003e Volcano plots show the differential genes of macrophage subsets in tissues with metastasis. Red dots indicate significantly up-regulated genes, while blue dots indicate significantly down-regulated genes. \u003cstrong\u003eF.\u003c/strong\u003e The spatial distribution map shows the expression of differential genes in macrophages between patients without metastasis and those with metastasis. Each dot represents a cell, and the depth of the color indicates the level of gene expression. \u003cstrong\u003eG.\u003c/strong\u003e The GSEA analysis results showed the pathway enrichment of macrophages in the tissues with metastasis. The horizontal axis represents gene sequencing, the vertical axis represents enrichment fraction (ES), and the colors represent different pathways.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7748094/v1/a591ecfada80ebee3c56c01e.png"},{"id":93638774,"identity":"45741876-eb98-4659-9139-49d641748ebc","added_by":"auto","created_at":"2025-10-16 02:00:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":695315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell Composition and Heterogeneity in the Peripheral Blood of Patients with Cervical Cancer. A. \u003c/strong\u003eSingle-cell UMAP dimensionality reduction visualization of peripheral blood of patients with (tumor) and without (normal) metastasis. Different colors represent different cell types (monocytes Mono, NK cells, T cells, B cells, plasma cells PC, pDC). \u003cstrong\u003eB.\u003c/strong\u003eexpression of the top five marker genes for each cell type.\u003cstrong\u003e C.\u003c/strong\u003e Gene expression heatmap of 17 cell clusters, showing the differences in molecular characteristics of each cluster. \u003cstrong\u003eD.\u003c/strong\u003e Gene expression heatmap of 17 cell clusters, showing the differences in molecular characteristics of each cluster. \u003cstrong\u003eE\u003c/strong\u003e. Differential heat map of intercellular communication, showing the difference in signal intensity between different cell types. \u003cstrong\u003eF. \u003c/strong\u003eBubble plot of marker gene expression for each cell type. \u003cstrong\u003eG.\u003c/strong\u003e GO and KEGG functional enrichment analysis of peripheral blood cell-related genes.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7748094/v1/522fe823df677cebd0072056.png"},{"id":93638790,"identity":"dc0fe769-d9e5-4584-a3d9-5234aa641daf","added_by":"auto","created_at":"2025-10-16 02:00:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":610773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of Peripheral-Blood Monocytes from Patients with Cervical Cancer. A.\u003c/strong\u003e UMAP dimensionality reduction visualization of monocyte subsets. The left panel shows cell clustering (clusters 0-10) and the right panel shows cell type annotation. \u003cstrong\u003eB. \u003c/strong\u003eThe proportion distribution of each monocyte subsets in the metastatic group and the non-metastatic group. \u003cstrong\u003eC\u003c/strong\u003e. Violin plot of differential gene expression for each monocyte subpopulation. \u003cstrong\u003eD\u003c/strong\u003e. Volcano plot of differential genes in monocytes subsets. \u003cstrong\u003eE\u003c/strong\u003e. KEGG pathway enrichment analysis of up-regulated genes. \u003cstrong\u003eF\u003c/strong\u003e. heatmap of monocyte subpopulation marker gene expression. \u003cstrong\u003eG\u003c/strong\u003e. Heat map showing the expression distribution of key genes in monocyte subsets before and after metastasis, left column normal tissue, right column tumor tissue, color represents gene expression level.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7748094/v1/785f9e161c12e4759d28a1f8.png"},{"id":93638792,"identity":"06f41afb-83de-4cfe-ad62-03c11377cd72","added_by":"auto","created_at":"2025-10-16 02:00:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":701754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of intercellular communication between tumor tissues and peripheral blood in cervical cancer. A.\u003c/strong\u003e The intercellular communication network of the ADGRE5 signaling pathway, where nodes represent cell types and connections represent ligand-receptor interactions. \u003cstrong\u003eB. \u003c/strong\u003eHeat maps of the intercellular communication intensity of the ADGRE5 signaling pathway in tumor tissues of patients without metastasis (left) and with metastasis (right). The horizontal axis represents the cell type, the vertical axis represents the ligand-receptor pair, and the color represents the communication intensity (Z-score standardized). \u003cstrong\u003eC-D\u003c/strong\u003e. Communication network and heatmap of the CCL signaling pathway. \u003cstrong\u003eE-F.\u003c/strong\u003e Communication network and heatmap of the COLLAGEN signaling pathway. \u003cstrong\u003eG-H.\u003c/strong\u003e Communication network and heatmap of the SPP1 signaling pathway. \u003cstrong\u003eI-J. \u003c/strong\u003eCommunication network and heatmap of the FN1 signaling pathway.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7748094/v1/0c89dc6fbeb0403c478896a2.png"},{"id":93638776,"identity":"22bac151-e3c1-4807-bcb6-470ceac2b34e","added_by":"auto","created_at":"2025-10-16 02:00:22","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":636122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCellular differentiation features and analysis workflow in metastatic cervical cancer. A-C. \u003c/strong\u003eDifferentiation trajectories analysis of cancer-associated fibroblasts (CAFs), macrophages, and peripheral blood monocytes. \u003cstrong\u003eD.\u003c/strong\u003e Pseudotime trajectory of CAF differentiation showing dynamic expression changes of COL4A1, FN1, and SPP1. \u003cstrong\u003eE.\u003c/strong\u003ePseudotime trajectory of macrophage differentiation displaying expression dynamics of MMP9, MMP14, and SPP1. \u003cstrong\u003eF. \u003c/strong\u003ePseudotime trajectory of peripheral blood monocyte differentiation revealing dynamic expression of CX3CR1, CTSL, and GBP1. \u003cstrong\u003eG-I. \u003c/strong\u003eDifferentiation trajectories of monocytes, NK cells, and T cells in peripheral blood mononuclear cells in the group with metastasis. \u003cstrong\u003eJ.\u003c/strong\u003e Workflow of cervical cancer metastasis analysis, including sample collection (tumor tissue and blood), sequencing analysis, cell type identification, and construction of intercellular communication networks to uncover the role of the tumor microenvironment in metastasis.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7748094/v1/a18923a40c897ba4fc33b4a9.png"},{"id":94239576,"identity":"c3d08304-1d20-4980-8abd-c6ea36827f07","added_by":"auto","created_at":"2025-10-24 03:16:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7003440,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7748094/v1/a113e602-eba3-4bb1-9c16-19cdd42b5ba6.pdf"},{"id":93639398,"identity":"1a2d7e49-52a0-48f9-a3d3-d8e43e09a4b4","added_by":"auto","created_at":"2025-10-16 02:08:22","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2231762,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation..docx","url":"https://assets-eu.researchsquare.com/files/rs-7748094/v1/93a98f2ffca7c0dc7d063dab.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFormation of an Immunosuppressive Spatial Network Between Tumor-Associated SPP1⁺ Macrophages and Fibroblasts in Cervical Cancer with Distant Metastasis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer (CC) is one of the most common gynecological malignancies in women worldwide, with high morbidity and mortality rates, especially in developing countries(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although the overall incidence of cervical cancer has declined in recent years with the promotion of vaccination and screening methods, patients with advanced cervical cancer and those with distant metastasis still face clinical challenges such as poor prognosis and unsatisfactory treatment outcomes(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Distant metastases (such as those to the lungs, bones, liver, etc.) are one of the main causes of death for patients with cervical cancer. In recent years, more and more studies have shown that distant tumor metastasis is not only a manifestation of the malignant biological behavior of tumor cells themselves, but also a complex process driven by the dynamic interactions between immune cells and stromal cells in the tumor microenvironment (TME)(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In this process, cell-cell communication plays a central role. It reshapes the tumor ecosystem through ligand-receptor (L-R)-mediated signaling, regulates immune response, extracellular matrix (ECM) remodeling, angiogenesis and immune escape, thereby promoting the invasion, migration and distal colonization of tumor cells(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Although previous studies have revealed the connection between certain key signaling pathways and immune escape, there is still a lack of systematic single-cell level analysis on which cell types drive which signaling axes in distant metastasis of cervical cancer and how to collaboratively construct an immunosuppressive microenvironment through cell communication(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, in-depth exploration of the changing characteristics of cell communication networks in tumor tissues and the peripheral immune system during the metastasis of cervical cancer is of great significance for clarifying the molecular mechanism of distant metastasis, screening potential therapeutic targets, and promoting individualized immunotherapy strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient samples and follow-up\u003c/h2\u003e\u003cp\u003eA total of 11 samples were collected in this study, including tumor tissues and peripheral blood mononuclear cell samples from 3 patients with metastatic cervical cancer and 3 patients without metastatic cervical cancer who underwent surgical resection in the First Affiliated Hospital of Shandong Second Medical University during 2024. This study was approved by the Ethics Committee of the First Affiliated Hospital of Shandong Second Medical University, and informed consent was obtained from all subjects.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSingle-cell RNA sequencing\u003c/h3\u003e\n\u003cp\u003eIn this study, transcriptome sequencing was performed using the DNBelab C series high-throughput single-cell RNA library construction kit (MGI, 940-001818-00). The cells were diluted to approximately 1,000 cells /mL and loaded into the reservoir of the microfluidic chip. Then, barcode magnetic beads and droplet generating oil were added in sequence. Droplets containing single cells were generated and collected through the DNBelab C4 / DNBelab TaiM4 system. The beads capturing mRNA were recovered for reverse transcription (RT). The obtained cDNA was amplified by PCR and purified and quantified using the Qubit dsDNA Assay Kit (Thermo Fisher Scientific). Subsequently, in accordance with the manufacturer's instructions, cDNA fragmentation, size screening, terminal repair, A-tail addition, linker connection, library indexing PCR and library cyclization were completed in sequence to construct the 3\u0026rsquo;-end transcript library. The final library was purified and its quality was detected using the Qubit ssDNA Assay Kit (Thermo Fisher Scientific) and Qsep100 (Bioptic). Sequencing was performed on the DNBSEQ-T7 platform (MGI) using the DNBelab C4/DNBelab TaiM4 single-cell library preparation kit. DNBs were loaded onto a patterned array chip for paired-end sequencing. The sequencing Read length is set as follows: Read 1 is 30 bp long (including 10-bp cell barcode 1, 10-bp cell barcode 2 and the 10-bp unique molecular identifier UMI), Read 2 is 100 bp long (transcript sequence), and the 10-bp sample index barcode.\u003c/p\u003e\n\u003ch3\u003escRNA-seq data analysis\u003c/h3\u003e\n\u003cp\u003eThe sequencing data were first normalized using the R software packages. Subsequently, the RunUMAP function of the Seurat software was utilized for visualization based on the UMAP algorithm, and the shared nearest neighbor (SNN) algorithm was adopted for clustering to optimize the division of cell subgroups. Cell cluster annotation was accomplished through SingleR (v1.4.1) and combined with manual correction to ensure the accuracy of the markers. Differentially expressed genes (DEGs) were identified using the FindMarkers function in Seurat (threshold set as p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, |log2FC| \u0026gt;1). All heatmaps were generated using the ScaleData function for Z-score normalization. Functional enrichment analysis was performed using clusterProfiler (v4.1) based on the hypergeometric distribution algorithm for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Cell differentiation trajectory analysis was conducted using Monocle3E for pseudotime inference to reconstruct the evolutionary process of cell subpopulations. The intercellular communication network identified the relevant ligand-receptor pairs from the scRNA-seq data through the CellChat software package.\u003c/p\u003e\n\u003ch3\u003eStatistics and reproducibility\u003c/h3\u003e\n\u003cp\u003eFor single-cell RNA sequencing (scRNA-seq) analysis, the experimental samples were divided into two groups: tumor tissues and peripheral blood mononuclear cells (PBMCs) from patients with distant metastasis, and tumor tissues and PBMCs from patients without metastasis. The quality control of the original sequencing data was carried out through CellRanger and Seurat software, with 9,000 to 13,000 high-quality cells obtained for each sample, and the sequencing quality exceeded 90%. In total, a gene expression matrix comprising 53,248 cells was generated and subjected to subsequent bioinformatic analyses. All Statistical analyses were performed using R software (version 4.2.1), following the default methods specified in the corresponding packages.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSingle-cell RNA analysis of the distribution landscape of cervical cancer cells with distant metastasis behavior\u003c/h2\u003e\u003cp\u003eThis study analyzed the tumor microenvironment (TME) of cervical cancer (CC) with distant metastasis using single-cell RNA sequencing (scRNA-seq). Eleven samples from six patients (tumor tissues and peripheral blood mononuclear cells (PBMCs) from patients with and without metastases) were included in the study, all of which were pathologically confirmed to be CC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). After quality control, a total of 53,248 cells were obtained for analysis. After data normalization and batch effect correction (Harmony)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), cells were clustered and visualized using principal component analysis(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and uniform manifold approximation and projection (UMAP) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) (Materials and Methods). Combined with the classical marker gene expression patterns, a total of major cell populations such as epithelial cells, T cells, macrophages, B cells, plasma cells, cancer-associated fibroblasts (CAFs), mast cells, endothelial cells and plasmacytoid dendritic cells (pDCs) were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Supplementary Fig.\u0026nbsp;1A-B) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Different cell types exhibited specific transcriptional signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC\u0026ndash;D). For example, epithelial cells highly express SERPINB4, SERPINB3, and KRT17; T cells highly express CD3D, GZMA, and GNLY; macrophages highly express CD163 and FCGR1A; B cells highly express MS4A1 and CD19; and CAFs highly express SFRP2, COL1A2, and DCN. Representative marker genes are also found in plasma cells, mast cells, endothelial cells, and pDCs. These differential expressions may be closely associated with the development of distant metastasis in cervical cancer.Further functional enrichment analysis revealed that the differentially expressed genes in the tissues with metastasis were mainly enriched in RNA splicing, viral response and virus-related processes in the biological process (BP) category annotated by gene ontology (GO)(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) [12,13], and were significantly associated with multiple immune-related signaling pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In addition, GO analysis indicated that polygenes are involved in functions such as protein secretion, signal transduction, and nucleoprotein localization, suggesting that intercellular communication plays a significant role in the transfer process. Cell interaction analysis further confirmed that CAFs exhibited significant self-communication and cross-cell type interactions, suggesting that they may play a key driving role in the distal metastasis of cervical cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCAFs display significant intercellular interactions in the metastatic TME of cervical cancer\u003c/h3\u003e\n\u003cp\u003eDuring the process of tumor metastasis, Collagen, as the main component of extracellular matrix (ECM), interacts with receptors on the surface of tumor cells and stromal cells, activating multiple cellular communication signaling pathways, thereby regulating key steps such as adhesion, invasion, migration and angiogenesis of tumor cells (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). COL4A, as a key component of the extracellular matrix (ECM), can bind to CD44, providing adhesion sites and thereby influencing cellular adhesion capacity(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In tumor cells, this interaction facilitates adhesion to the basement membrane and promotes invasive behavior; for instance, gastric cancer cells have been reported to infiltrate surrounding tissues via COL4A\u0026ndash;CD44 interactions(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In this study, CAFs demonstrated robust intercellular communication through the COLLAGEN signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Figure B lists the ligand-receptor pairs in the COLLAGEN signaling pathway and ranks them according to their contribution to the total signaling pathway. In particular, COL4A1-CD44 and COL4A1-ITGA1\u0026thinsp;+\u0026thinsp;ITGB1 show the highest contribution, indicating the key role of ECM components derived from CAFs in regulating cellular interactions. Among them, COL4A1 is an important component of type IV collagen in the extracellular matrix(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). CD44 is involved in cell adhesion, migration, tumor invasion, immune regulation, etc. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) ITGA1\u0026thinsp;+\u0026thinsp;ITGB1 is a member of the integrin family and can form heterodimer receptors with α1 and β1 subunits(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) .This receptor can specifically recognize specific amino acid sequences in COL4A1, such as the proline-rich hydroxylated G-F-P-G-E-R sequence (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), thereby mediating the adhesion of cells to COL4A1. During the process of tumor metastasis, ITGA1\u0026thinsp;+\u0026thinsp;ITGB1 on the surface of tumor cells bind to COL4A1 in the tumor microenvironment, which can promote the adhesion of tumor cells on the basement membrane, thereby helping tumor cells penetrate the basement membrane and infiltrate the surrounding tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The communication signal transduction of COLLAGEN cells in tumor metastasis is most intense among CAFs-epithelial, CAFs-CAFs, CAFs-endothelial and CAFs-Macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D), and its main function is to penetrate the basement membrane and promote tumor cell metastasis. In addition to the COLLAGEN signaling pathway, FN1-related signals also exhibit abnormal high-intensity signal transmission in cervical cancer with metastasis. CAFs, as high-intensity signal sources, engage in cellular dialogue with multiple cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). During this process, FN1 secreted by CAFs binds to CD44 produced by communicating cells, FN1 binds to (ITGA4/5\u0026thinsp;+\u0026thinsp;ITGB1) and FN1 binds to (ITGAV\u0026thinsp;+\u0026thinsp;ITGB1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). FN1 (fibronectin 1) secreted by CAFs binds to the CD44 receptor on the surface of tumor cells or immune cells and is a key link in the cell-matrix interaction in the tumor microenvironment (TME) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This combination significantly affects tumor progression and treatment response by regulating processes such as cell adhesion, migration, signal transduction and immune escape. Tumor metastasis FN1 cell communication signaling was most intense between CAFs-epithelial, CAFs-CAFs, CAFs-endothelial, and CAFs-macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-H). Beyond collagen and FN1, additional signaling pathways contributed to the metastatic ecosystem. THBS signaling axes played important roles among tumor-associated cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI), while pathways such as CXCL, EGF, TGF-β, and VEGF were also implicated in distant metastasis of cervical cancer (Figure S2\u0026ndash;S4). Collectively, these results highlight CAFs as central hubs of intercellular communication, driving the establishment of a pro-metastatic microenvironment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCellular population characteristics of CAFs in metastatic cervical cancer tissues\u003c/h3\u003e\n\u003cp\u003eCancer-associated fibroblasts (CAFs) are a type of cell present in the tumor microenvironment (TEM) and play an important regulatory and supportive role(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Recent studies have shown that CAFs are not a homogeneous cell population, but rather consist of multiple subpopulations, each of which may have different functions and phenotypes (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In our analysis, CAFs in metastatic samples showed strong intercellular communication with other cell populations. To determine which CAF subsets are functionally important, We performed a subpopulation analysis of CAFs and divided them into eight subpopulations based on their different gene expression characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, S5D). And the proportions of CAFs subgroups in patients with metastasis and those without metastasis were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Among patients with metastasis, the vast majority of CAFs belong to myCAFs myofibroblasts (myCAFs). myCAFs are mainly characterized by ECM remodeling, contractile function and promotion of angiogenesis, and are commonly found in matrix-enriched regions of solid tumors (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).Among these, IGFBP7 was markedly overexpressed in myCAFs and identified as a marker gene. IGFBP7 is significantly overexpressed in myCAFs and serves as a marker gene. The function of IGFBP7 is cancer cell-specific and microenvironment-dependent, and its underlying mechanisms require further investigation. Previous studies have reported that IGFBP7 expression is associated with prognosis in several cancers, including hepatocellular carcinoma and gastric cancer, where differential expression may indicate distinct clinical outcomes (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). As a critical factor in the TME, IGFBP7 may represent a potential therapeutic target for anti-fibrotic or anti-tumor strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, S5A\u0026ndash;C). IGFBP7 is primarily secreted by CAFs, endothelial cells, or tumor cells, and acts on adjacent or distant cells through body fluid diffusion to promote intercellular communication and regulate multiple signaling pathways (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In addition to the differences in subpopulations, metastatic CAFs showed a large number of gene expression differences as a whole. A large number of cells highly expressed genes such as COL4A1, MCAM, ITGA1, and SPP1, all of which are related to cell\u0026ndash;cell communication(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S6-S7). \u003cb\u003eGene set enrichment analysis (GSEA)\u003c/b\u003e revealed that these genes were predominantly involved in tumor progression, ECM signaling, and regulation of cancer cell stemness (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). In addition, \u003cb\u003eGO analysis\u003c/b\u003e showed that these differentially expressed genes were primarily involved in cell-cell binding and protein binding, which are closely related to cell communication. This suggests that distant metastasis of cervical cancer is closely related to cell communication between CAFs in the in situ tumor (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCellular communication characteristics between CAFs and macrophages in cervical cancer with distant metastasis\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u0026ndash;D, CAFs acted as strong signaling sources to immune cells, particularly through the COLLAGEN and FN1 signaling pathways. We analyzed the proportions of CAF subpopulations in tumor samples with and without metastasis to identify the CAF subpopulations that are primarily involved in cell communication. We analyzed the proportions of cell subsets involved in intercellular communication, including CAFs and macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). CAFs are the main components of the tumor matrix and promote tumor growth, metastasis and treatment resistance by secreting various extracellular matrix (ECM) components and cytokines (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). COL4A1, a component of type IV collagen, can be secreted by CAFs (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) We analyzed these signal subgroups emitted by CAFs. In myCAFs, Collagen genes such as COL4A1 and COL4A2, as well as their subgenes COL4A1 1.1 and COL4A2 2.2, were highly expressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), with extremely significant differential expression. In a subpopulation analysis of CAFs, the proportion of myCAFs and pCAFs in cervical cancers with metastasis was much higher than that in cervical cancers without metastasis. Therefore, we analyzed the expression of key collagen signaling genes not only in myCAFs but also in pCAFs, and the two results showed consistency. Moreover, myCAFs are the most numerous CAFs cells in metastatic cervical cancer. Therefore, the signal source genes of the collagen signaling pathway in metastatic cervical cancer are mainly derived from myCFAs. Subgroup analysis of macrophages was performed and macrophages were divided into 14 subgroups (S7 B,S8 A-B) based on their different gene expression profiles. Pathway analysis of differentially expressed genes in macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) revealed that in metastatic cervical cancer, strongly expressed signaling-related genes were predominantly enriched in the NF-κB signaling pathway (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), TNF signaling pathway(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and transcriptional misregulation in cancer(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). these findings suggest that the tumor microenvironment is characterized by immune evasion, inflammatory responses, and transcriptional dysregulation, which collectively drive cervical cancer metastasis, progression, and immune escape. In macrophages of cervical cancer with metastasis, CAF-derived signals promoted M2 macrophages to secrete CCL2 and CXCL8, and promote the high expression of MMP9, MMP14, and SPP1. MMP9 and MMP14 promote macrophages to destroy the collagen layer (ECM) of tumor tissues, facilitating the metastasis of tumor cells. SPP1 is related to the polarization of macrophages, causing them to polarize towards M2 macrophages. In addition, a large number of immune-related regulatory genes such as CCL2, TNF, and CXCL8 regulate the immune microenvironment of tumor tissues in cervical cancer with metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F). Notably, the signaling pathways activated in metastatic macrophages were highly consistent with those triggered by CAF-derived signals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG, S6A\u0026ndash;B), suggesting functional continuity or synergy between CAFs and macrophages in metastasis-associated communication networks.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSingle-cell transcriptomic features of peripheral blood in cervical cancer patients with distant metastasis\u003c/h2\u003e\u003cp\u003ePeripheral blood samples were collected from cervical cancer patients with metastasis and compared with those from patients without metastasis. The overall cell types included monocytes (Mono), NK cells, T cells, B cells, plasma cells (PC), plasmacytoid dendritic cells (pDC). Overall, no significant differences in cell composition were observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). All cells from the six samples were annotated, confirming the above cell types, and the top five marker genes of each type were displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Further dimensionality reduction clustering analysis divided the cells from the six samples into 17 clusters (0\u0026ndash;16), and the gene expression of each cluster was plotted (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In monocytes, MS4A7, LILRB2, FCN1, and CD68 genes related to immunosuppression and tumor-associated inflammation are significantly up-regulated(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), suggesting polarization toward an immunosuppressive M2 phenotype, potentially promoting tumor metastasis. In NK cells, TRDC and KLRF1 were upregulated (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), indicating functional impairment. In T cells, genes such as IL7R and TCF7 were highly expressed(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), indicating that the survival and memory functions of T cells were impaired, making it difficult to maintain long-term anti-tumor effects. In B cells, TCL1A was highly expressed(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), indicating abnormal antibody secretion and class switching, which may lead to the production of cancer-promoting antibodies or the decreased ability to clear tumor cells. In PC cells, IGHA2 and IGHG2 were upregulated(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), suggesting altered antibody secretion profiles that may facilitate immune evasion. In pDCs, LILRA4 and CLEC4C were highly expressed (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), indicating impaired antiviral and immune activation functions, hindering effective T cell priming and thereby promoting metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The cell proportion statistics of patients with and without metastasis showed that the proportion of monocytes in patients with metastasis was significantly increased, while the number of NK cells, T cells and B cells with anti-tumor activity was significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). It is suggested that the recruitment of monocytes and the decrease of immune cells may be closely related to the distant metastasis of cervical cancer. Further analysis of intercellular communication revealed strong signal communication between monocytes and NK cells, T cells, and pDCs, while NK cells and T cells had weakened communication with other cells. Since NK cells and T cells are key tumor killer cells, this decreased communication may impair their recognition and elimination of tumor cells, thereby facilitating distant metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). GO and KEGG functional analysis of genes mainly involved in signal transduction in blood showed that these genes were mainly involved in immune and inflammatory regulation (e.g., viral response, defense response to symbionts, viral defense response), intercellular activation (e.g., cyst cavity, cytoplasmic cyst cavity, secretory granule cavity), and apoptosis-related signaling pathways (e.g., peptidase regulatory activity) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). These findings suggest that peripheral blood intercellular signaling has an important effect on the immune activity of monocytes and may play a key role in the process of distant metastasis of cervical cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eImmune characteristics of monocytes in metastatic cervical cancer patients\u003c/h2\u003e\u003cp\u003eThe number of monocytes was significantly increased in patients with metastatic cervical cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Based on the expression characteristics of the key marker genes, monocytes were further subdivided into 10 subtypes, including classical mono, nonclassical mono, and transitional mono (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Among them, the proportions of inflammatory mono and nonclassical mono were significantly increased, while the proportions of tissue resident mono were not significantly different between the metastatic and non-metastatic groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The results of differential gene analysis showed that CX3CR1, RHOC, CST1, STAT1 and GBP1 were highly expressed in the metastasis group as a whole (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC)(\u003cspan additionalcitationids=\"CR40 CR41 CR42\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Specifically, CX3CR1 and RHOC suggested that the migration ability of monocytes was enhanced, which may promote their recruitmen into tumor tissues(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The up-regulation of CTSL indicated stronger matrix degradation ability within the tumor microenvironment, favoring tumor cell invasion and metastasis (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). The high expression of STAT1 and GBP1 reflected the activation of inflammatory response, but this chronic inflammation may also induce the formation of immunosuppression(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) .In contrast, genes related to antigen presentation and immune activation such as HLA-DQA2 and CD14 were significantly downregulated in the transfer group, suggesting weakened antigen processing and presentation capabilities of monocytes, thereby limiting the effective initiation of adaptive immune responses(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e); Moreover, the down-regulation of immunomodulatory genes such as KLF10 and SIGLEC10 may disrupt the immune surveillance function and promote immune evasion(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), while the down-regulation of CEBPD can relieve the inhibition of M2 macrophage polarization, driving the transformation of TAMs to the immunosuppressive phenotype (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Functional enrichment analysis further showed that up-regulated genes were mainly involved in biological processes such as signal transduction, antigen processing and presentation, viral infection and cytoskeleton regulation, while down-regulated genes concentrated in immune response pathways such as antigen processing and presentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). These alterations are closely associated with the progression and distant metastasis of cervical cancer. Figure F shows the heat map of gene expression in the mono cell subpopulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Further analysis of the expression pattern showed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG) that the expression regions of CX3CR1, RHOC, CTSL, STAT1 and GBP1 were basically the same before and after metastasis, but the expression levels were significantly increased after metastasis. The distribution of CD14, HLA-DQA2, TNFAIP3, CXCR4, CEBPD and KLF10 genes maintained comparable distribution patterns but were markedly downregulated in metastatic patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eRemodeling of intercellular communication networks between blood and tissue cells in cervical cancer patients\u003c/h2\u003e\u003cp\u003eWe performed a systematic analysis of the intercellular communication patterns between blood and tumor tissues in cervical cancer patients with and without metastasis, and and found significant abnormalities across multiple signaling pathways. In the ADGRE5 signaling pathway, non-metastatic patients exhibited dense interactions between blood-derived T cells, NK cells, and monocytes with CAFs and epithelial cells, whereas in metastatic group, the overall communication intensity of this network was markedly reduced, particularly among immune cells; Furthermore, extensive and strong communication between monocytes, macrophages, CAFs and most other cells was found in the non-metastatic group, while these interactions were significantly reduced or disappeared in the metastatic group, indicating that ADGRE5-mediated immune\u0026ndash;stromal communication was significantly restricted after metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). In the CCL signaling pathway, the signals in the non-metastasis group were mainly mediated by T cells, endothelial cells and macrophages, while T cells, macrophages and NK cells in the blood in the metastasis group transmitted numerous signals to other cells, suggesting a close association with tumor progression. Specifically, the communication network dominated by blood pDCs, macrophages and endothelial cells was more active in the non-metastatic group, involving epithelial cells, endothelial cells and CAFs, while in the metastatic group, most of these communications were weakened or lost, but interactions between T cells, macrophages, and other cells were significantly enhanced, showing a shift of dominant signaling cells from pDCs to T cells, suggesting that CCL-mediated interactions were restructured during metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D). In the COLLAGEN signaling pathway, abundant communication was observed among CAFs, B cells, epithelial cells, and endothelial cells in non-metastatic group, while the communication among macrophages, T cells and B cells was significantly reduced in the metastasis group. Overall, communication in the non-metastasis group was more extensive. Tissue cells such as epithelial cells and CAFs were the main signaling sources, transmitting signals to tumor tissues and blood cells. while in the metastatic group, signaling sources were restricted to a limited number of cells such as CAFs, endothelial cells, and epithelial cells, with markedly reduced interactions with immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-F). In the SPP1 signaling pathway, macrophages were the main signaling source in the non-metastatic group, accompanied by T cells, B cells and Mast cells; however, in the metastasis group, except for macrophages, the signals of other immune cells almost completely disappeared, suggesting that the weakened communication among immune cells may promote tumor immune evasion and drug resistance and thereby affect the prognosis. At the same time, the communication between macrophages and CAFs was significantly enhanced, indicating that the spatial network inhibitory environment formed by these two cell types promoted tumor distant metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG-H). In the FN1 signaling pathway, macrophages were the main signaling source in the non-metastasis group, but their signaling disappeared in the metastasis group, while the signaling strength of other cells showed little change, suggesting that the loss of macrophages as a key signaling source may weaken its immune surveillance effect on tumors, and make tumors easier to escape immunosuppression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI-J). Collectively, the communication network between blood and tumor cells in cervical cancer patients is significantly reformed after metastasis, which is mainly manifested as the significant weakening of immune cell-related signals and the transformation of dominant cells in some pathways. The abnormal cellular interaction network may weaken the immune surveillance function and promote tumor metastasis and immune evasion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eGene-driven reprogramming of cell differentiation trajectories promotes distant metastasis in cervical cancer\u003c/h2\u003e\u003cp\u003eIn the preceding analysis, we have revealed that multiple cell types are involved in intercellular communication within tumor tissues and peripheral blood, among which cancer-associated fibroblasts (CAFs), macrophages, and peripheral blood monocytes (Mono) represent the core populations mediating signal transduction. To further explore the dynamics of these cells during tumor metastasis, we performed pseudotemporal analysis of their differentiation trajectories, which revealed that several key signaling pathway genes played critical roles in driving cell differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-C). In CAFs, COL4A1, FN1, and SPP1 were identified as major genes driving differentiation. In metastatic patients, CAFs exhibited a gradual increase in COL4A1 expression, suggesting that it may promote tumor cell migration during metastasis by altering extracellular matrix (ECM) structure(27). FN1 expression also increased along pseudotime, strengthening tumor cell adhesion and dependence on the ECM, thereby accelerating metastatic progression(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). SPP1 was significantly increased in the late pseudo time period, which may accelerate tumor invasion and metastasis by regulating the interaction between tumor cells and matrix components and affecting the function of immune cells (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). In macrophages, MMP9 and MMP14 act as matrix metalloproteinases, and their decreased expression may imply a transition from a pro-invasive phenotype to an immunosuppressive or other functional state of macrophages (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). At the same time, the reduction of SPP1 expression in the late pseudo time period suggested that it may impair the recruitment and activation ability of immune cells in the late stage of tumor metastasis, thereby facilitating immune evasion (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE).In peripheral blood mononuclear cells (PBMCs), CX3CR1, CTSL, and GBP1 showed pseudo-time-dependent expression changes. The increase of CX3CR1 may promote the infiltration of CX3CR1⁺TAMs and the secretion of cytokines (such as TGF-β and IL-27), thereby inducing epithelial-mesenchymal transition (EMT) and promoting angiogenesis to facilitate tumor invasion (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The high expression of CTSL destroyed the integrity of basement membrane by degrading extracellular matrix (ECM) components (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) Increased GBP1 expression may promote tumor progression in cervical cancer by binding to the interacting protein HNRNPK and regulating the alternative splicing of CD44 (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Notably, the expression of these genes declined after the establishment of distant metastases, suggesting that new molecular mechanisms may take over their roles in later stages of tumor progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). Further analysis of the differentiation trajectories of blood immune cells found that monocytes had a more complex differentiation path in non-metastatic samples, but showed a rapid and simplified differentiation process in metastatic patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). NK cells showed complex dynamics in non-metastatic samples, while their differentiation tended to be simplified in metastatic samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH). T cells showed diverse directions of differentiation in the nonmetastatic patients, whereas in the metastatic patients, there was marked \" cluster isolation\" phenomenon, suggesting compression or remodeling of their functional heterogeneity (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eI). Collectively, key genes driving cellular differentiation reshape the tumor immune microenvironment and remodel the trajectories of CAFs, macrophages, and peripheral immune cells, thereby promoting distant metastasis of cervical cancer. This finding provides novel insights into the mechanisms of cervical cancer metastasis and highlight potential molecular targets for therapeutic intervention.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, the immune profiles of tumor tissues and peripheral blood of cervical cancer patients with distant metastasis were systematically delineated by single-cell transcriptomics, and the key mechanisms of metastasis were revealed. We identified that myofibroblast-like cancer-associated fibroblasts (myCAFs) and SPP1⁺ macrophages form an immunosuppressive spatial communication network, which activates pro-inflammatory and immunosuppressive pathways such as NF-κB and TNF through COLLAGEN and FN1 signaling pathways, thereby promoting extracellular matrix remodeling and immune evasion. Concurrently, the expansion of CX3CR1⁺ inflammatory monocytes in the peripheral blood along with the dysfunction and simplified differentiation trajectories of NK cells and T cells jointly facilitated the occurrence of tumor metastasis. Pseudotime analysis further revealed dynamic expression changes of key genes, including COL4A1, FN1, SPP1, CX3CR1, and GBP1, highlighting their roles in tumor\u0026ndash;immune co-evolution. These findings not only advance our mechanistic understanding of cervical cancer metastasis but also provide novel perspectives and theoretical foundations for developing precision therapies targeting the myCAFs-SPP1⁺ macrophage axis and peripheral immune remodeling.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCC \u0026nbsp; Cervical cancer\u003c/p\u003e\n\u003cp\u003eTME \u0026nbsp; the tumor microenvironment \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTAMs \u0026nbsp; tumor-associated macrophages\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePBMC \u0026nbsp; peripheral blood mononuclear cell\u003c/p\u003e\n\u003cp\u003escRNA-seq \u0026nbsp; single-cell RNA sequencing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUMAP \u0026nbsp; Uniform Manifold Approximation and Projection\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; Gene Ontology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; Kyoto Encyclopedia of Genes and Genomes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCAFs \u0026nbsp; cancer-associated fibroblasts\u003c/p\u003e\n\u003cp\u003emyCAFs \u0026nbsp; myofibroblast-like cancer-associated fibroblasts\u003c/p\u003e\n\u003cp\u003epDCs \u0026nbsp; plasmacytoid dendritic cells\u003c/p\u003e\n\u003cp\u003eECM \u0026nbsp; extracellular matrix\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Medical Research Ethics Committee of Weifang People\u0026rsquo;s Hospital (approval No. KYLL20231101-3). All six patients provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors present no Conflicts of Interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China International (Regional) Cooperation Project (No. 3221101608), Shandong Provincial Natural Science Foundation Youth Project (No. ZR2024QC085), Shandong Provincial Medical and Health Science and Technology Project (No. 202401030359), Shandong Provincial Medical and Health Science and Technology Project (No. 202309031407), Weifang Municipal Health Commission Scientific Research Project (No. WFWSJK-2025-092).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN-Z, WJ-C and YH-G performed data analysis, and prepared the figures and the manuscript draft; WB-L and YX-J collected and processed CC samples; GF-Z, Y-L and MM-Z analyzed RNAseq data; HB-X and GL-C provided CC samples and patient\u0026rsquo;s profile; FR-H and NN-L conceptualized and supervised the study and designed the experiments, and revised the manuscript. All authors approved and contributed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the financial support from the National Natural Science Foundation of China International (Regional) Cooperation Project, Shandong Provincial Natural Science Foundation Youth Project, Shandong Provincial Medical and Health Science and Technology Project, Shandong Provincial Medical and Health Science and Technology Project, Weifang Municipal Health Commission Scientific Research Project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFrancoeur AA, Monk BJ, Tewari KS. Treatment advances across the cervical cancer spectrum. Nature Reviews Clinical Oncology. 2025;22(3):182-99.\u003c/li\u003e\n\u003cli\u003eCohen AC, Roane BM, Leath CA, 3rd. Novel Therapeutics for Recurrent Cervical Cancer: Moving Towards Personalized Therapy. 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Journal of Translational Medicine. 2024;22(1).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cervical cancer, Distant metastasis, Cancer-associated fibroblasts (CAFs), SPP1⁺ tumor-associated macrophages, Immunosuppression, Tumor microenvironment (TME), Single-cell RNA sequencing (scRNA-seq)","lastPublishedDoi":"10.21203/rs.3.rs-7748094/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7748094/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCervical cancer (CC) is a common malignancy among women worldwide, and distant metastasis is the main cause of treatment failure and mortality. Immune cells, stromal cells, and their interactive networks within the tumor microenvironment (TME) play a key role in the development, colonization, and immune escape in metastasis. For example, tumor-associated macrophages (TAMs) are often polarized to the M2 phenotype, thereby promoting angiogenesis and creating an immunosuppressive microenvironment; cancer-associated fibroblasts enhance the invasiveness of tumor cells through matrix remodeling; at the same time, the exhaustion of immune cell function further weakens the anti-tumor immune response and promotes tumor immune escape.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this study, tumor tissues and peripheral blood mononuclear cell (PBMC) samples were collected from three cervical cancer patients with distant metastasis and three patients without metastasis at the First Affiliated Hospital of Shandong Second Medical University in 2024, yielding a total of 11 specimens. Subsequently, single-cell RNA sequencing (scRNA-seq) technology was used for detection. Cell clustering and Uniform Manifold Approximation and Projection (UMAP) visualization were implemented by Seurat software. Cell type annotation was performed by SingleR. Differential gene identification was completed by the function of FindMarkers. Furthermore, clusterProfiler was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis; CellPhoneDB was used to predict intercellular ligand-receptor interactions; and Monocle was used to perform pseudotime trajectory analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn metastatic cervical cancer tissue, the proportion of myofibroblast-like cancer-associated fibroblasts (myCAFs) is significantly increased, with high expression of genes such as COL4A1, FN1, and SPP1. MyCAFs closely interact with SPP1⁺ macrophages through the collagen and FN1-mediated signaling pathways, thereby activating pro-metastatic pathways such as NF-κB and TNF, and suppressing CD8⁺ T cell function. In peripheral blood, the proportion of inflammatory monocytes and non-classical monocytes increased, and they highly expressed pro-metastatic genes such as CX3CR1 and STAT1; in contrast, the number of NK cells and T cells decreased and their function was impaired. Further pseudotieme trajectory analysis revealed that during the metastasis process, the expression of key genes in CAFs, macrophages and peripheral blood mononuclear cells (such as COL4A1, MMP9, CX3CR1) changed dynamically, suggesting that they play an important role in driving cellular functional remodeling and abnormal immune microenvironment.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study systematically delineated the immune landscapes of tumor tissue and peripheral blood in metastatic cervical cancer, revealing that the myCAFs\u0026ndash;SPP1⁺ macrophage axis and peripheral immune remodeling play central roles in metastasis. Dynamic changes in key genes and functional impairment of immune cells collectively drive tumor progression. These findings provide new insights into the mechanisms of cervical cancer metastasis and offer potential ideas for the development of precision treatment strategies targeting the tumor microenvironment.\u003c/p\u003e","manuscriptTitle":"Formation of an Immunosuppressive Spatial Network Between Tumor-Associated SPP1⁺ Macrophages and Fibroblasts in Cervical Cancer with Distant Metastasis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 02:00:17","doi":"10.21203/rs.3.rs-7748094/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"83efc624-07c6-42b6-969d-afc30024a48d","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-24T03:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 02:00:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7748094","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7748094","identity":"rs-7748094","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}