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Single-cell transcriptomics unveils multifaceted immune heterogeneity in early-onset versus late-onset cervical cancer

World Journal of Surgical Oncology volume 23, Article number: 12 (2025) Cite this article

Abstract

Early-onset (EOCC) and late-onset cervical cancers (LOCC) represent two clinically distinct subtypes, each defined by unique clinical manifestations and therapeutic responses. However, their immunological profiles remain poorly explored. Herein, we analyzed single-cell transcriptomic data from 4 EOCC and 4 LOCC samples to compare their immune architectures. Epithelial cells in EOCC exhibited a notable dual immunological phenotype, characterized by immune-suppressive properties driven by elevated CXCL production, alongside immune-stimulatory features linked to heightened HLA molecule expression. CD4 + and CD8 + T cells in LOCC demonstrated a heightened activation state, while NK cells exhibited diminished cytotoxicity. Macrophages in LOCC displayed enhanced polarization towards both M1 and M2 phenotypes, along with dendritic cells showing augmented antigen-presenting capacity. Regarding cancer-associated fibroblasts (CAFs), EOCC was enriched with inflammatory CAFs, whereas LOCC harbored a higher proportion of antigen-presenting CAFs. These findings reveal the multifaceted immune heterogeneity between EOCC and LOCC, underscoring the imperative for age-tailored immunotherapeutic strategies.

Introduction

Cervical cancer remains a prevalent malignancy among women globally, with an estimated 660,000 new cases and 350,000 deaths reported annually [1]. Despite widespread screening initiatives and the availability of prophylactic vaccines targeting high-risk human papillomavirus (HPV), cervical cancer persists as a significant public health challenge [2]. Precisely stratifying patients and customizing therapeutic approaches based on their distinct characteristics are pivotal for enhancing survival outcomes in cervical cancer [3, 4]. Among these characteristics, age has been recognized as a key factor influencing both clinical presentation and therapeutic response [5]. Younger cervical cancer patients often exhibit more aggressive histological subtypes, such as adenocarcinoma, and typically have a poorer prognosis due to rapid disease progression and resistance to conventional therapies [6, 7]. Conversely, older patients generally present with more indolent tumor behavior, although diagnosis often occurs at a more advanced stage, attributed to the subtle and insidious onset of symptoms [8, 9]. Classifying patients into early-onset and late-onset groups has become a widely accepted practice in various malignancies, such as breast and gastrointestinal cancers, providing critical insights that inform personalized treatment strategies [10,11,12,13,14]. Similarly, cervical cancer can be classified into early-onset (EOCC) and late-onset (LOCC) subtypes based on the age at diagnosis, a distinction that holds significant implications for therapeutic strategy formulation.

The tumor immune microenvironment (TIME), orchestrated by a sophisticated and intricate interplay among cancer cells, stromal components, and immune elements, serves as a critical factor in steering tumor biology and influencing therapeutic responses [15]. A recent study by Zhao et al., which analyzed bulk RNA sequencing (RNA-seq) data from cervical cancer samples in TCGA, revealed distinct differences in the TIME between EOCC and LOCC, as evidenced by divergent gene expression patterns in immune-related processes [16]. However, traditional sequencing methodologies, including bulk RNA-seq, are insufficiently sensitive to fully elucidate the intricate and dynamic features of the TIME. Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology that enables high-resolution gene expression profiling at the individual cell level, offering unparalleled precision in dissecting tumor and immune cell subpopulations to reveal distinct cellular phenotypes and interactions [17]. Comparative analyses leveraging scRNA-seq across diverse pathological subtypes and clinical stages have significantly deepened our understanding of the cervical cancer ecosystem, offering critical insights into the mechanisms of tumor initiation, progression, and therapeutic response [18,19,20,21]. However, a detailed comparative analysis at the single-cell level between EOCC and LOCC remains unexplored.

In this study, we stratified cervical cancer patients into EOCC and LOCC using a cutoff age of 50 [11, 22], followed by an exhaustive single-cell transcriptomic analysis encompassing 47,966 individual cells derived from 4 EOCC and 4 LOCC samples sourced from public repositories. Through meticulous examination of the transcriptomic landscapes of epithelial cells, NK/T cells, myeloid cells, and fibroblasts, we unveiled intricate heterogeneity in cellular composition, functional states, and intercellular communications between EOCC and LOCC. These findings offer a comprehensive delineation of the immune architecture in EOCC and LOCC patients at single-cell resolution. Importantly, the results underscore the imperative for age-specific immunotherapeutic strategies, which could be pivotal in optimizing clinical outcomes by addressing the distinct immune profiles of these two patient subgroups.

Methods

Acquisition of scRNA-seq data

ScRNA-seq data from eight primary cervical cancer specimens were obtained from publicly accessible repositories, with three samples sourced from the Gene Expression Omnibus (GEO) under accession number GSE197461 (http://www.ncbi.nlm.nih.gov/geo/) [23] and five from European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI) under accession number S-BSST1035 (https://www.ebi.ac.uk/) [19]. Patients were categorized into two cohorts based on their age at diagnosis: EOCC (< 50 years) and LOCC (≥ 50 years), with four patients in each cohort. Comprehensive clinical data, encompassing age at diagnosis, pathological classification, clinical stage, and HPV infection status, are detailed in Supplementary Table 1.

Processing of scRNA-seq data

The scRNA-seq data were processed using the “Seurat” R package (version 4.4.0). Cells were excluded from further analyses if they met any of the following criteria: (1) detection of fewer than 200 or more than 5,000 genes; (2) hemoglobin gene content exceeding 3%; or (3) mitochondrial gene content exceeding 20%. Genes expressed in fewer than three cells were filtered out from downstream analyses. Following quality control, gene expression matrices were normalized using the NormalizeData function with default settings, and 2,000 highly variable features were identified using the FindVariableFeatures function. The data were subsequently scaled via the ScaleData function, and dimensionality reduction was performed through principal component analysis using the RunPCA function. The “Harmony” R package (version 1.2.0) was used to integrate multiple samples and mitigate batch effects, with the effectiveness of the integration demonstrated in Supplementary Fig. 2A.

Cell clustering and cell type identification

Cell clustering was performed using the FindClusters function in Seurat, employing the first 25 principal components with a resolution set at 0.2, which led to the identification of 17 distinct cell clusters. To identify cluster-specific marker genes, differential expression analysis was conducted between each cluster and all others using the FindAllMarkers function, applying the Wilcoxon rank-sum test. Significant DEGs were identified based on a |log2(fold change) | greater than 0.25 and an adjusted p-value of less than 0.05. These cell clusters were subsequently annotated into eight major cell types based on canonical marker genes, including epithelial cells, endothelial cells, fibroblasts, NK/T cells, myeloid cells, plasma cells, B cells, and mast cells. To gain more refined insights into these major cell types, epithelial cells, NK/T cells, myeloid cells, and fibroblasts were subjected to re-integration, re-clustering, and re-annotation following the same analytical pipeline.

Pathway enrichment and functional scoring analysis

GSVA scores were computed using the GSVA package (version 1.52.3) with gene sets sourced from the MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/) and CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/) databases. To explore and visualize the functional profiles associated with DEGs between the EOCC and LOCC groups, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using the clusterProfiler package (version 4.12.0). The functional status of immune cells, including CD4 + T cells, CD8 + T cells, NK cells, and macrophages, was assessed by calculating functional scores using the AddModuleScore function, with gene sets obtained from established studies [19, 20]. Additionally, the expression levels of immune-related molecules, encompassing chemokines, interleukins, immune checkpoints, and tumor necrosis factors (listed in Supplementary Table 2), were compared between the EOCC and LOCC groups.

Transcription factor (TF) activity analysis

The decoupleR package (version 2.10.0) was utilized to estimate the activity of transcriptional regulons sourced from the Collection of Transcriptional Regulatory Interactions (CollecTRI), a comprehensive resource that integrates signed TF-target gene interactions compiled from 12 distinct databases (https://github.com/saezlab/CollecTRI/). To quantify the activity levels of all transcription factors, a univariate linear model (run_ulm) was employed, with a threshold of at least 5 target genes per TF. Activity scores, expressed as t-values, were computed, where positive scores denote TF activation and negative scores indicate TF inhibition.

Copy number variation (CNV) analysis

The inferCNV package (version 1.20.0) was utilized to detect somatic large-scale chromosomal CNVs, including gains or losses of entire chromosomes or large chromosomal segments (https://github.com/broadinstitute/infercnv). Non-malignant cells, including endothelial cells and fibroblasts, were utilized as reference cells. Approximately 20% of epithelial cells were randomly selected to infer the CNVs across eight patient samples. The CNV score for each cell was determined by calculating the quadratic sum of CNV regions [24].

Pseudotime trajectory analysis

The Monocle package (version 2.32.0) was employed to perform single-cell pseudotime trajectory analysis. The count matrix derived from the specified cell type was utilized as the input for generating a CellDataSet object using the newCellDataSet function. Subsequent analyses, including the identification of DEGs, dimensionality reduction, and the construction of cell lineage trajectories, were conducted using default parameters. Pseudotime-dependent genes were identified through the differentialGeneTest function and subsequently visualized using the plot_genes_in_pseudotime function, which illustrated the dynamic changes in gene expression along the pseudotime axis. Additionally, the CytoTRACE package (version 0.3.3) was applied to assess the directionality of cell differentiation.

Cell-cell communication analysis

To unravel the intercellular communication networks among the eight major cell types, a comprehensive cell-cell communication analysis was performed using the CellChat package (version 2.1.2), incorporating all ligand-receptor pairs involved in secreted signaling, ECM-receptor interactions, and direct cell-cell contact. The mergeCellChat function was employed to construct a merged dataset from EOCC and LOCC samples, which was then leveraged to compare inter-group cell-cell interactions through the compareInteractions function.

Analysis of clinical implications

To evaluate survival differences between EOCC and LOCC, clinical data from 307 cervical cancer patients in The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) cohort and 5,341 cervical cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed. Survival analysis was performed using the Kaplan-Meier method, with statistical significance assessed via the log-rank test. Furthermore, multivariate Cox regression analysis was employed to evaluate the independent impact of age on survival outcomes, adjusting for clinical variables such as race, pathology, tumor differentiation, and clinical stage.

To investigate the clinical implications of immune-related genes and gene sets, transcriptomic data from 304 cervical cancer patients in TCGA-CESC cohort and 300 cervical cancer patients in the GSE44001 dataset were obtained, along with corresponding clinical information, from the TCGA Data Portal (https://portal.gdc.cancer.gov/) and the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/), respectively. Patients were stratified into high- and low-expression groups based on the median or optimal cutoff values determined using the survivalROC package. Data on tumor mutational burden (TMB), neoantigen load, and somatic copy-number alterations (SCNAs) for TCGA-CESC samples were sourced from previously published studies [25, 26]. The ESTIMATE algorithm was utilized to evaluate immune cell infiltration within tumor tissues based on gene expression data. In addition, the Tumor Immune Dysfunction and Exclusion (TIDE) signature was employed to predict patient responses to immune checkpoint blockers (ICBs), where a lower TIDE score suggests a better response.

Statistical analysis

Statistical analyses were performed using RStudio (version 2024.04.2 + 764) in conjunction with R software (version 4.4.1). The specific R packages utilized at each stage of the analysis are detailed in the preceding sections. The unpaired two-sided Wilcoxon rank-sum test was applied to compare the gene set scores, TMB, neoantigen load, and CNV scores between the EOCC and LOCC groups.

Results

Single-cell transcriptome atlas of EOCC and LOCC

The EOCC and LOCC cohorts demonstrated significant differences in survival outcomes in both the TCGA and SEER datasets (Supplementary Fig. 1A and 1 C), which was further supported by multivariate analyses (Supplementary Fig. 1B and 1D). To elucidate the tumor ecosystem of EOCC and LOCC, we conducted a comprehensive analysis of scRNA-seq data derived from eight cervical cancer patients, comprising four samples per group. The clinical profiles of the enrolled patients are detailed in Supplementary Table 1. Following standard data processing and quality control, we retained 47,966 cells, comprising 21,179 cells from EOCC samples and 26,787 cells from LOCC samples. These cells were subsequently categorized into 17 distinct clusters (Fig. 1A) and further annotated into eight major cell types (Fig. 1B and Supplementary Fig. 2B and 2D). Cell type identification was based on the expression of canonical markers as follows (Fig. 1D-E): epithelial cells (KRT18, EPCAM, CDKN2A), endothelial cells (PECAM1, VWF, ENG), fibroblasts (DCN, COL1A1, COL3A1), NK/T cells (CD3D, CD3E, NKG7), myeloid cells (CD14, CD163, CSF1R), plasma cells (MZB1, IGHG1, TNFRSF17), B cells (MS4A1, CD19, CD79A), and mast cells (TPSAB1, MS4A2, CPA3). Among these, epithelial cells, NK/T cells, myeloid cells, and fibroblasts were the most predominant across the entire cell population, with fibroblasts being more prominently represented in EOCC samples, while epithelial cells, NK/T cells, and myeloid cells were more abundantly present in LOCC samples (Fig. 1C and F). Notably, the proportion of each cell type exhibited considerable variability across patients, highlighting pronounced inter-tumoral heterogeneity (Fig. 1F), a phenomenon extensively documented in scRNA-seq studies of cervical cancer and likely attributable to the large tumor volume and intricate intratumoral heterogeneity characteristic of cervical cancer [19, 20, 23].

Fig. 1
figure 1

scRNA-seq profiling of EOCC and LOCC samples. (A-C) t-SNE plots of 47,966 cells, grouped into 17 distinct clusters and 8 major cell types, color-coded by cluster (A), cell type (B), and group (C). (D) Bubble plots illustrating the expression levels of marker genes across the major cell types. (E) t-SNE plots displaying the expression of canonical markers for each cell type.(F) The proportion of the 8 major cell types within each group (left) and per sample (right). (G) Bubble plots showing significantly enriched KEGG pathways of DEGs between EOCC and LOCC. (H) Bar plots comparing cell-cell interaction numbers and strengths between EOCC and LOCC. (I) Circle plots depicting the differential interactions among each cell type. The line width denotes the degree of difference, with red and blue lines indicating higher levels in LOCC and EOCC, respectively

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A distinctly divergent gene expression profile was evident between EOCC and LOCC, with significant enrichment observed in pivotal oncogenic pathways, including PI3K-Akt, p53, Hedgehog, and MYC signaling (Fig. 1G and Supplementary Fig. 2 C and 2E). Furthermore, the DEGs were markedly enriched in immune-related signaling pathways such as TNF, IL17, NF-KB, and NOD-like receptor signaling, alongside immune processes including antigen processing and presentation, leukocyte transendothelial migration, Th17 cell differentiation, and FcγR-mediated phagocytosis (Fig. 1G). Additionally, pronounced disparities were identified in the frequency and intensity of cell-cell interactions among immune, epithelial, and mesenchymal cells, underscoring the significant immune heterogeneity between the EOCC and LOCC cohorts (Fig. 1H-I). Given that epithelial cells, NK/T cells, myeloid cells, and fibroblasts are integral components of the TIME and constitute the predominant cell populations, our subsequent analyses were concentrated on these four cell types to delve deeper into their heterogeneity.

Immune disparities in epithelial cells between EOCC and LOCC

Epithelial cells, the origin of tumorigenesis in cervical cancer, dominated the cellular landscape in both EOCC and LOCC samples (Fig. 1F). We identified a total of 27,282 epithelial cells, comprising 11,458 from EOCC and 15,824 from LOCC. These cells were subsequently stratified into nine distinct clusters (Epi C0–C8), each characterized by unique gene expression profiles and functional states (Fig. 2A and Supplementary Fig. 3A-B). EOCC and LOCC patients exhibited markedly different compositional patterns within these epithelial clusters. Specifically, EOCC samples displayed a higher prevalence of the Epi C1, C2, and C5 clusters, whereas LOCC samples were predominantly composed of the Epi C0, C3, and C4 clusters (Fig. 2B-C). Functional analysis through GSVA unveiled a significant divergence in the immune characteristics of these clusters (Supplementary Fig. 3A-B). Epi C2 and C5 were notably more involved in inflammatory responses and TNFα signaling pathways, while Epi C0 and C3 were primarily linked to IFNα/IFNγ responses and TGFβ signaling pathways. Moreover, the DEGs between LOCC- and EOCC-derived epithelial cells were significantly enriched in immune-related pathways, such as the NOD-like receptor, TNF, and IL17 signaling pathways (Fig. 2D).

Fig. 2
figure 2

Characteristics of epithelial cells in EOCC and LOCC. (A-B) t-SNE plots of all epithelial cells, color-coded by cluster (A) and by group (B). (C) Bar plot illustrating the proportions of the nine epithelial clusters within each group. (D) Bubble plot showing significantly enriched KEGG pathways of DEGs in epithelial cells between EOCC and LOCC. (E) Violin plots depicting differentially expressed chemokines between EOCC and LOCC. (F) Kaplan-Meier curves illustrating the association between chemokine expression and cancer-specific survival (CSS). (G) Violin plots displaying differentially expressed HLA molecules between EOCC and LOCC. (H) Kaplan-Meier curves demonstrating the association between HLA expression and CSS. (I) Violin plots showing differentially expressed immune checkpoints between EOCC and LOCC. (J) Heatmap comparing the activity of immune-related TF regulons between EOCC and LOCC. (K) Heatmap visualizing CNVs across 22 chromosomes in the eight analyzed samples

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Notably, EOCC-derived epithelial cells exhibited dual immunological roles. On one hand, the EOCC group displayed elevated expression levels of immune-inhibitory chemokines such as CCL28, CXCL1, CXCL2, CXCL3, and CXCL8 (Fig. 2E), which were inversely correlated with immune cell infiltration and survival outcomes in TCGA-CESC patients (Fig. 2F and Supplementary Fig. 3 C-D). Conversely, EOCC samples also demonstrated significantly higher expression of HLA molecules, including HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DMB, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-F, and HLA-G (Fig. 2G). Notably, most of these HLA molecules were positively correlated with improved survival and better responses to immunotherapy in cervical cancer (Fig. 2H and Supplementary Fig. 3E). EOCC samples also exhibited heightened expression of stimulatory immune checkpoints such as B2M and ICAM1, alongside reduced expression of inhibitory immune checkpoints IDO1 and VSIR (Fig. 2I), potentially contributing to a more immunostimulatory TIME.

To elucidate the regulatory patterns underlying gene expression, we assessed the activity of TFs linked to immune-related genes, uncovering significant transcriptional regulon heterogeneity between EOCC and LOCC (Fig. 2J). Consistent with the elevated expression of CXCLs and HLA molecules, their corresponding TFs, including NFKB, IRF1, and CIITA, exhibited markedly higher activity in EOCC (Supplementary Fig. 4A-C). Additionally, epithelial cells derived from EOCC and LOCC exhibited distinct differentiation states (Supplementary Fig. 4D-F), and HLA expression displayed a dynamic shift along the pseudotime axis (Supplementary Fig. 4G), suggesting that the differential HLA expression patterns between EOCC and LOCC are linked to their respective differentiation states. Recognizing the critical role of CNVs in modulating gene expression, we analyzed CNVs across the eight enrolled samples, revealing widespread CNV accumulation in all epithelial cells, with considerable heterogeneity between different groups and samples (Fig. 2K). Specifically, the EOCC group primarily exhibited deletions on chromosomes 8 and 11, with amplifications on chromosome 6. In contrast, the LOCC group predominantly displayed deletions on chromosomes 5 and 13, alongside amplifications on chromosome 7. Besides, in line with the aging-associated increase in CNVs [26], LOCC-derived epithelial cells showed higher CNV scores (Supplementary Fig. 4H), which were further corroborated by genomic analysis of TCGA-CESC samples (Supplementary Fig. 4I).

Distinct immune profiles of NK/T cells in EOCC and LOCC

A total of 10,657 NK/T cells were identified, with 3,636 from EOCC and 7,021 from LOCC, which were further classified into 11 distinct clusters (Fig. 3A). Through analyzing the expression of canonical markers within each cluster, three primary cell types were identified: CD8 + T cells (marked by CD8A), CD4 + T cells (marked by CD4), and NK cells (marked by KLRC1) (Supplementary Fig. 5A-B). CD8 + T cells were further subdivided into cytotoxic CD8 + T cells (CD8 + T_cyto), proliferative CD8 + T cells (CD8 + T_pro), exhausted CD8 + T cells (CD8 + T_ex), and central-memory CD8 + T cells (CD8 + T_cm), based on the expression of canonical markers and DEGs (Fig. 3B and D, Supplementary Fig. 5 C-D). Similarly, CD4 + T cells were categorized into regulatory CD4 + T cells (CD4 + T_reg) and naive CD4 + T cells (CD4 + T_naive), while NK cells were distinguished into CD16- NK cells (NK_CD16-) and CD16 + NK cells (NK_CD16+) (Fig. 3B and D, Supplementary Fig. 5 C-D). Notably, EOCC and LOCC samples exhibited an overall comparable composition pattern of these cell types, with predominant populations—including CD4 + T_naive cells, CD4 + T_reg cells, CD8 + T_cyto cells, CD8 + T_ex cells, and NK_CD16- cells—each constituting over 5% of the total cellular milieu (Fig. 3C and E).

Fig. 3
figure 3

Characteristics of NK/T cells in EOCC and LOCC. (A-C) t-SNE plots of NK/T cells, with color-coded by cluster (A), cell type (B), and group (C). (D) Bubble plots depicting the expression levels of marker genes across various NK/T-cell subpopulations. (E) Bar plot illustrating the relative proportions of the eight identified NK/T-cell subpopulations within each group. (F) Bubble plots displaying significantly enriched KEGG pathways of DEGs between EOCC and LOCC derived NK/T cells. (G-H) Violin plots comparing immune-related gene set scores between EOCC and LOCC in CD4 + T cells (G) and CD8 + T cells (H). (I-J) Violin plots demonstrating differential expression of immune-related molecules between EOCC and LOCC in CD4 + T cells (I) and CD8 + T cells (J). Significance levels: ***, p-value < 0.001; ****, p-value < 0.0001

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We subsequently conducted a DEG analysis to explore transcriptional disparities between NK/T cells derived from LOCC and EOCC. KEGG pathway analysis revealed a pronounced enrichment of DEGs in immune-related processes, including antiviral responses, T helper cell differentiation, and NK cell-mediated cytotoxicity, as well as within critical immune pathways such as IL17, TNF, and T cell receptor signaling (Fig. 3F). The LOCC group demonstrated significantly elevated functional scores for CD4 effector memory, CD4 naïve memory, CD4 transitional memory, and CD4 recent activation, indicative of a heightened activation state in CD4 + cells (Fig. 3G, Supplementary Fig. 5G). Accordingly, CD4 + T cells derived from LOCC samples demonstrated a downregulation of inhibitory checkpoints, such as CTLA4 and PDCD1, coupled with an upregulation of immune-stimulatory molecules, including CD48 and IL2RG (Fig. 3I, Supplementary Fig. 5H). A similarly activated phenotype was observed in CD8 + T cells from LOCC samples, characterized by elevated functional scores for CD8 cytotoxicity and CD8 effector memory, alongside reduced pre-exhaustion and terminal exhaustion scores (Fig. 3H, Supplementary Fig. 5G). Moreover, LOCC-derived CD8 + T cells displayed decreased expression of inhibitory immune checkpoints CTLA4, PDCD1, HAVCR2, and LAG3, accompanied by increased levels of immune-stimulatory chemokines CCL3, CCL4, CCL5, and XCL2 (Fig. 3J, Supplementary Fig. 5I and 5 K). However, NK cells derived from LOCC demonstrated a diminished cytotoxic capacity, characterized by lower cytotoxic scores, upregulated expression of the inhibitory checkpoint TIGIT, and reduced levels of cytotoxic genes GZMA and GZMK (Supplementary Fig. 5E-F and 5 J).

Diverse immune characteristics of myeloid cells in EOCC and LOCC

A total of 3,067 myeloid cells were identified, comprising 812 from EOCC and 2,255 from LOCC, which were subsequently classified into nine distinct clusters (Fig. 4A). Through a comprehensive analysis of canonical marker gene expression profiles within each cluster, three primary myeloid cell types were delineated: macrophages (CD14+), dendritic cells (CD1C+), and neutrophils (CSF3R+) (Fig. 4D, Supplementary Fig. 6A). As macrophages represented the predominant cell type in both EOCC and LOCC (Supplementary Fig. 6C), they were further subdivided into seven distinct clusters (Macro C0–C6), each characterized by specific marker genes and functional states (Fig. 4B, Supplementary Fig. 6B and 6D). Macro C0 and Macro C3, distinguished by elevated expression of CCL18 and CCL5, respectively (Supplementary Fig. 6D), were more prominent in LOCC (Fig. 4C and E). In contrast, Macro C1 and Macro C2, associated with inflammatory responses and cell proliferation (Supplementary Fig. 6B and 6D), were more frequently observed in EOCC (Fig. 4C and E).

Fig. 4
figure 4

Characteristics of myeloid cells in EOCC and LOCC. (A-C) t-SNE plots of all myeloid cells, color-coded by cluster (A), cell type (B), and group (C). (D) Bubble plots illustrating the expression levels of marker genes across different myeloid subpopulations. (E) Pie charts showing the proportions of myeloid subpopulations within each group. (F) Bubble plots displaying significantly enriched KEGG pathways of DEGs between EOCC and LOCC derived myeloid cells. (G) Violin plots comparing immune-related gene set scores in macrophages between EOCC and LOCC. (H) Violin plots demonstrating differential expression of immune-related molecules in macrophages between EOCC and LOCC. (I) Violin plots showing differentially expressed HLA in DC cells between EOCC and LOCC. Significance levels: *, p-value < 0.05; **, p-value < 0.01; ***, p-value < 0.001; ****, p-value < 0.0001

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Differential expression analysis of myeloid cells derived from LOCC and EOCC revealed a significant enrichment in pathways associated with antigen processing and presentation, neutrophil extracellular trap formation, chemokine signaling, and C-type lectin receptor signaling (Fig. 4F), highlighting distinct immune functional states. Furthermore, LOCC samples exhibited a pronounced pro-inflammatory phenotype, characterized by elevated pro-inflammatory and diminished anti-inflammatory functional scores (Fig. 4G). Intriguingly, LOCC samples demonstrated enhanced polarization towards both M1 and M2 macrophage phenotypes, accompanied by increased scores in immune surveillance and immune escape, suggesting a dual-enhanced M1 and M2 polarization profile (Fig. 4G). Consistent with this, LOCC samples showed elevated expression of both M1 markers (TNF) and M2 markers (MRC1 and HLA-DRA), along with heightened expression of immune-stimulatory molecules (CCL3, CCL4, CCL5, CD40, IL32, and IL2RG) as well as immune-inhibitory markers (CXCL8, CXCR4, and CCL20) (Fig. 4H, Supplementary Fig. 6E-F). Additionally, dendritic cells in LOCC exhibited a heightened antigen-presenting capability, as evidenced by the upregulated expression of HLA molecules, including HLA-B, HLA-DQA2, HLA-DQB1, and HLA-DQB2 (Fig. 4I).

Divergent immune properties of fibroblasts in EOCC and LOCC

A total of 4,923 cancer-associated fibroblasts (CAFs) were identified, with 4,442 originating from EOCC and 481 from LOCC, which were subsequently categorized into six distinct clusters (Fig. 5A). Through the analysis of canonical marker gene expression within each cluster, four principal CAF subtypes were delineated: antigen-presenting CAFs (apCAFs), distinguished by the expression of HLA-DRA, HLA-DRB1, HLA-DPA1, and HLA-DPB1; inflammatory CAFs (iCAFs), characterized by CCL2, IL6, and CXCL2; myofibroblastic CAFs (myCAFs), marked by INHBA and MMP11; and vascular CAFs (vCAFs), identified by MCAM, RGS5, and MYH11 (Fig. 5B and D-E). Notably, iCAFs were found to be more prevalent in EOCC, whereas apCAFs, myCAFs, and vCAFs were more commonly observed in LOCC (Fig. 5C and F).

Fig. 5
figure 5

Characteristics of fibroblasts in EOCC and LOCC. (A-C) t-SNE plots of fibroblasts, distinguished by cluster (A), cell type (B), and group (C). (D) t-SNE plots illustrating the expression of canonical markers across fibroblast subtypes. (E) Bubble plots depicting marker gene expression across fibroblast subtypes. (F) Pie charts displaying the distribution of fibroblast subtypes within EOCC and LOCC. (G) Bubble plots representing significantly enriched KEGG pathways of DEGs in fibroblasts between EOCC and LOCC. (H-I) Violin plots showing differentially expressed chemokines and HLA molecules in fibroblasts between EOCC and LOCC. (J-L) Pseudotime trajectory and cytoTRACE analysis of fibroblasts from EOCC and LOCC. (M) Box plot comparing cytoTRACE scores between EOCC and LOCC derived fibroblasts. (N) Dynamic expression changes in chemokines along the pseudotime axis

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DEG analysis of CAFs from LOCC and EOCC revealed a significant enrichment of pathways associated with antigen processing and presentation, leukocyte transendothelial migration, C-type lectin receptor signaling, and FcγR-mediated phagocytosis (Fig. 5G), suggesting a distinct immune-modulatory function of CAFs between the two groups. In alignment with the previously documented immunosuppressive role of iCAFs [27], the EOCC cohort exhibited elevated levels of immune-inhibitory chemokines, including CCL2, CXCL1, CXCL2, CXCL3, and CXCL12 (Fig. 5H, Supplementary Fig. 7A-E). Of these chemokines, CCL2 and CXCL12 were negatively correlated with responses to immunotherapy, as evidenced by their positive association with TIDE scores (Supplementary Fig. 7F-G). The LOCC group displayed an increased expression of HLA molecules such as HLA-A, HLA-B, HLA-C, HLA-DRA, and HLA-F, consistent with the higher prevalence of apCAFs in LOCC samples (Fig. 5I). Remarkably, EOCC- and LOCC-derived CAFs exhibited divergent distribution patterns along pseudotime trajectories (Fig. 5J-K). Further validation through CytoTRACE analysis indicated that EOCC-derived CAFs had significantly elevated CytoTRACE scores (Fig. 5L-M), reflecting a less differentiated cellular state. Moreover, the dynamic shifts in gene expression along the pseudotime axis identified CCL2, CXCL1, CXCL2, CXCL3, and CXCL12 as pseudotime-dependent genes (Fig. 5N), indicating that the functional divergence between CAFs from EOCC and LOCC is linked to their differentiation status.

Distinctive intercellular communication patterns between EOCC and LOCC

Intercellular communication among tumor cells, immune cells, and stromal cells is essential for modulating immune responses, reshaping the TIME, and ultimately influencing tumor progression and therapeutic efficacy [28]. As depicted in Fig. 6A, a pronounced disparity in signaling dynamics was observed between the EOCC and LOCC groups. The EOCC group demonstrated elevated signaling activity in pathways such as FGF, IFN-II, CTSG, GDF, CD70, KIT, 5alphaP, SEMA7, CD137, OCLN, CADM, and SELE. In contrast, the LOCC group exhibited increased activity in pathways including CD96, ncWNT, TNF, GRN, CD34, CSF, CNTN, BAG, Histamine, LIFR, DHT, CD6, SELPLG, WNT, CysLTs, PERIOSTIN, PVR, 12oxoLTB4, and SN. Moreover, substantial differences in the relative intensities of outgoing and incoming signals within immune-related pathways—such as MHC-I, MHC-II, CCL, CXCL, complement, TGFβ, and TNF—were evident across specific cell types between the EOCC and LOCC (Fig. 6B-C).

Fig. 6
figure 6

Characteristics of cell-cell interactions in EOCC and LOCC. (A) Bar plots comparing the relative information flow through signal pathways between the EOCC and LOCC groups. Red and blue text denote elevated levels in EOCC and LOCC, respectively. (B-C) Heatmap comparing outgoing (B) and incoming (C) signaling patterns of immune-related pathways in EOCC and LOCC. (D-E) Dot plots showing ligand-receptor interactions between epithelial cells and immune cells (NK/T and myeloid cells). (F) Dot plots showing ligand-receptor interactions between fibroblasts and immune cells (NK/T and myeloid cells). (G) Dot plots displaying ligand-receptor interactions within NK/T cells and myeloid cells

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To further elucidate intercellular signaling at the molecular level, we conducted an in-depth ligands-receptors (LRs) analysis. Within the interactions between epithelial and immune cells (NK/T and myeloid cells), signaling pathways related to antigen presentation (HLA-A/B/C-CD8A), complement activation (C3-C3AR1, C3-(ITGAX + ITGB2), C3-(ITGAM + ITGB2)), and IFN-II signaling (IFNG-(IFNGR1 + IFNGR2)) were markedly more pronounced in EOCC samples (Fig. 6D-E). In contrast, pathways such as TGFβ (TGFB1-(TGFBR1 + TGFBR2)), CD6 (CD6-ALCAM), CD96 (CD96-NECTIN1), and CD99 (CD99-CD99, CD99-PILRA) exhibited elevated activity in LOCC samples (Fig. 6D-E). Within the framework of interactions between CAFs and immune cells (NK/T and myeloid cells), the EOCC group exhibited enhanced involvement in CXCL signaling (CXCL12-CXCR4), whereas the LOCC group demonstrated a significantly greater engagement in collagen signaling (COL6A1-3/COL4A1-2/COL1A1-2-SDC4/CD44) (Fig. 6F). Moreover, distinct intercellular communications were observed within NK/T and myeloid cells, with the LOCC group showing greater activity in MHC-II (HLA-DRB1/DRA/DQA2-CD44), MIF (MIF-(CD74 + CXCR4)), and TNF (TNF-TNFRSF1A, TNF-TNFRSF1B) signaling, while the EOCC group exhibited enhanced activity in SPP1 (SPP1-CD44, SPP1-(ITGAV-ITGB1), SPP1-(ITGA4-ITGB1)) and complement (C3-(ITGAX + ITGB2)) signaling (Fig. 6G).

Discussion

Despite the pronounced clinical distinctions between EOCC and LOCC, their immunological landscapes remain inadequately characterized. In this study, we meticulously compared the immune profiles of four EOCC and four LOCC samples at single-cell resolution. Our analysis revealed significant immune heterogeneity between EOCC and LOCC across a broad spectrum of cell populations within the TIME, including epithelial cells, NK/T cells, myeloid cells, and fibroblasts (Fig. 7). EOCC was characterized by epithelial cells that exhibited dual immunological phenotypes, heightened NK cell cytotoxicity, and a predominance of inflammatory CAFs. In contrast, LOCC was distinguished by activated CD4 + and CD8 + T cells, macrophages polarized towards both M1 and M2 phenotypes, dendritic cells with enhanced antigen-presenting capabilities, and an increased abundance of antigen-presenting CAFs. These findings highlight the complex and multifaceted nature of immune heterogeneity between EOCC and LOCC, underscoring the need for age-specific immunotherapeutic strategies.

Fig. 7
figure 7

Schematic summary of key findings. Red and blue text indicate elevated levels or heightened activation states in EOCC and LOCC, respectively

Full size image

Epithelial cells constitute a critical component within the TIME of cervical cancer, as they are instrumental in generating and presenting antigens to immune cells, thereby initiating and orchestrating the immune response. A paradoxical pattern in antigen dynamics emerged between EOCC and LOCC. Specifically, LOCC samples exhibited a higher neoantigen burden, likely attributable to age-associated increases in TMB (Supplementary Fig. 4J-K) [29]. In contrast, EOCC samples demonstrated a markedly enhanced antigen presentation capacity, characterized by elevated expression of HLA molecules, which may be associated with their differentiation status (Supplementary Fig. 4G) and increased regulon activity of the corresponding transcription factors IRF1 and CIITA (Supplementary Fig. 4A-C) [30, 31]. A potential biological explanation for this paradox is that tumor cells may downregulate HLA expression to counterbalance the increased neoantigen load, thereby impairing their presentation to T cells and promoting immune evasion [32, 33]. Moreover, epithelial cells possess an inherent ability to regulate both the composition and functional state of immune cells within the TIME through the strategic expression of immune-related molecules [34]. EOCC-derived epithelial cells were distinguished by elevated levels of chemokines such as CCL28, CXCL1, CXCL2, CXCL3, and CXCL8, which have been implicated in promoting immune suppression by recruiting immune cells and modulating inflammatory processes [35,36,37]. Conversely, LOCC-derived epithelial cells demonstrated heightened expression of clinically targetable immune checkpoints, including IDO1 and VSIR, thus positioning them as more favorable candidates for corresponding immune checkpoint inhibitors (ICIs) [38, 39]. Thus, through single-cell analysis, our study elucidated a complex immune heterogeneity within epithelial cells between EOCC and LOCC.

NK/T cells play an indispensable role in the TIME for effective anti-tumor defense, with NK cells delivering rapid innate responses by directly eliminating tumor cells and modulating immune dynamics, while T cells are responsible for targeted adaptive immunity [40]. Our analysis uncovered an elevated activation state of CD4 + and CD8 + T cells in LOCC samples, as reflected by increased functional scores in activation and cytotoxicity, alongside reduced exhaustion scores. This finding is unanticipated, given that T cells in LOCC samples were expected to exhibit diminished functionality due to thymic atrophy in elderly patients [41]. A plausible explanation for this discrepancy is the significantly lower expression of immune checkpoints, including CTLA4, PDCD1, HAVCR2, and LAG3, in CD4 + and CD8 + T cells from LOCC samples, which are known to strongly inhibit T cell responses [42, 43]. Noteworthy, multiple ICIs have been developed to target these immune checkpoints, such as Ipilimumab, Nivolumab, Sabatolimab, and Relatlimab, positioning LOCC patients as prime candidates for these therapeutic interventions [44,45,46]. Contrasting the relative functional inferiority of T cells, EOCC samples displayed a notably enhanced activation state in NK cells, as reflected by increased cytotoxicity scores, a reduction in the expression of the inhibitory checkpoint TIGIT, and elevated levels of cytotoxic genes GZMA and GZMK. This observation is in concordance with the findings of Lee et al., who utilized deconvolution algorithms to estimate immune cell composition in TCGA-CESC samples and identified a predominance of NK cells in LOCC patients [22]. Thus, our single-cell analysis unveiled the diverse phenotypic and functional heterogeneity among NK/T cells derived from EOCC and LOCC.

Myeloid cells are pivotal modulators of tumor immunity, orchestrating both immune suppression and activation, thereby shaping the antitumor response [47]. Our analysis revealed that myeloid cells derived from LOCC exhibited a profile more conducive to antitumor activity, characterized by a pro-inflammatory macrophage phenotype and enhanced antigen-presenting functions in dendritic cells. Macrophages are typically polarized into pro-inflammatory M1 and anti-inflammatory M2 subtypes, each executing distinct and often opposing roles [48, 49]. Notably, LOCC-derived macrophages exhibited a pronounced dual polarization towards both M1 and M2 phenotypes, as reflected by functional scores and polarization markers, suggesting intricate compositional profiles of myeloid subpopulations within the TIME. Given the involvement of M2-like macrophages in tumor progression and drug resistance, strategies aimed at reprogramming M2 to M1 phenotypes are currently under intensive investigation in clinical trials [50, 51], offering potential therapeutic advantages, particularly for LOCC patients.

CAFs constitute another critical modulator within the TIME, significantly impacting cancer progression, immune evasion, and therapeutic resistance through the secretion of various immune factors and extracellular matrix remodeling [52]. Our analysis uncovered an enrichment of immune-inhibitory iCAFs in EOCC, whereas LOCC exhibited a higher proportion of antigen-presenting CAFs. Crucially, iCAFs serve as the predominant sources of CXCL12 within the TIME, a chemokine known to mediate immunosuppressive effects on immune cells via the CXCL12-CXCR4 axis [53]. In alignment with this, we identified heightened CXCL12-CXCR4 signaling between CAFs and NK/T cells in the EOCC group (Fig. 6F). Given the pivotal role of the CXCL12-CXCR4 axis in immune suppression (Supplementary Fig. 6F, 7E, and 7G), modulation of this signaling cascade has emerged as a promising strategy in cancer immunotherapy [54, 55]. Consequently, EOCC patients present as optimal candidates for these targeted therapeutic interventions.

In the absence of a standardized age classification for EOCC and LOCC, we adopted 50 years as the cutoff, given its widespread use in other malignancies [11, 13, 14, 22] and its alignment with the median age of onset for cervical cancer, which is approximately 50 years [56]. Survival analyses based on SEER and TCGA cervical cancer datasets further revealed significant prognostic differences between patients above and below 50 years (Supplementary Fig. 1). Future studies incorporating more refined age stratifications, such as quartiles or continuous age variables, could provide deeper insights into the impact of age on immune responses in EOCC and LOCC. In addition, while this study’s findings are grounded in scRNA-seq data analysis, further experimental validation is essential to confirm their biological relevance. For example, the development of organoid models derived from EOCC and LOCC patients could offer a robust platform for validating age-associated immune distinctions, elucidating their underlying mechanisms, and identifying potential therapeutic targets.

In summary, our study provided an in-depth analysis of the intricate cellular composition and functional states of tumor, immune, and stromal cells at the single-cell level, revealing a multifaceted immune heterogeneity between EOCC and LOCC. These insights underscore the imperative of developing age-specific immunotherapeutic strategies meticulously tailored to the distinct immunological landscapes.

Limitations of the study

Several limitations may have impacted the outcomes of our study. First, the utilization of publicly available scRNA-seq datasets introduces the potential for biases stemming from batch effects and uncontrolled clinical variables, which should be considered when interpreting the results. Second, the limited availability of age-related data in publicly accessible datasets resulted in the inclusion of only eight scRNA-seq samples, which may have constrained the statistical power and generalizability of the findings. Third, several key immune populations, including B cells, plasma cells, and endothelial cells, were not further investigated due to their limited abundance. Given the critical roles of these populations in the TIME [57, 58], future research incorporating larger sample sizes and advanced cell enrichment methodologies is essential to gain a more comprehensive understanding of their distinct contributions in EOCC and LOCC.

Data availability

Data is provided within the manuscript or supplementary information files.

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Acknowledgements

We are very grateful to GEO, EMBL-EBI, and TCGA database for providing the single-cell transcriptomics data, multi-omics data, and clinical information. We would like to thank Dr. Xiaoyun Lei and Song Mao for their technical support, which was crucial for the completion of this work.

Funding

The study was supported by the National Natural Science Foundation of China (Grant No. 82303256) and the FuQing Postdoc Program of Xiangya Hospital, Central South University.

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Authors and Affiliations

  1. Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China

    Qian Chen, Hong Zhu & Shan Li

  2. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China

    Qian Chen, Hong Zhu & Shan Li

  3. Department of Oncology, Hunan University of Medicine General Hospital, Huaihua, China

    Dongfeng Deng

  4. Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China

    Shan Li

Authors
  1. Qian Chen
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  2. Dongfeng Deng
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  3. Hong Zhu
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  4. Shan Li
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Contributions

S. L. conceived and supervised the project. S. L., and Q. C. designed the research. Q. C. and D. D. collected the data. Q. C., S. L., D. D., and H. Z. analyzed and interpreted the data. Q. C., S. L., D. D., and H. Z. prepared the manuscript and figures. All authors reviewed the manuscript.

Corresponding author

Correspondence to Shan Li.

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Ethical approval and consent to participate

Since this was a retrospective study utilizing publicly available data, ethical approval was not required. As all the analyzed data used in our research were obtained from public databases, informed consent to participate was not required for this study.

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The authors declare no competing interests.

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Chen, Q., Deng, D., Zhu, H. et al. Single-cell transcriptomics unveils multifaceted immune heterogeneity in early-onset versus late-onset cervical cancer. World J Surg Onc 23, 12 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12957-025-03654-z

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12957-025-03654-z

Keywords

World Journal of Surgical Oncology

ISSN: 1477-7819

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