Abstract
Although coiled-coil domain containing 69 (CCDC69) has been implicated in tumor biology, its role in triple-negative breast cancer (TNBC) and immune regulation remains unclear. Bulk transcriptomic data, single-cell RNA sequencing datasets, and spatial transcriptomics were used to investigate the role of CCDC69 in TNBC. Germinal center B-cell (GCB) signatures were quantified using single-sample gene set enrichment analysis (ssGSEA), and their associations with CCDC69 expression were assessed using correlation, ROC, and logistic regression analyses. An independent external TNBC cohort (GSE58812) was used to validate the association between CCDC69 expression, GCB infiltration, and survival outcomes. CCDC69 expression was significantly downregulated in breast cancer tissues but was associated with favorable survival outcomes in TNBC patients. CCDC69 expression positively correlated with multiple immune cell populations, particularly B cells and plasma cells. Single-cell RNA sequencing demonstrated that CCDC69 expression was enriched in B-lineage cells, especially GCB. Spatial transcriptomic analyses further showed that CCDC69-high regions were spatially co-localized with B-cell-rich and germinal center-like immune niches. In the TCGA TNBC cohort, CCDC69 expression was strongly associated with GCB infiltration and effectively distinguished GCB-high tumors. Logistic regression analysis confirmed that CCDC69 was independently associated with GCB enrichment. Findings were validated in the GSE58812 cohort. CCDC69 expression correlated with GCB-related signatures, showed good discriminative performance in identifying GCB-high tumors, and was associated with improved patient outcomes when combined with GCB enrichment. CCDC69 is closely associated with B-cell-mediated immune responses and germinal center niches in TNBC. Our findings suggest that CCDC69 may serve as a robust biomarker of immune activation and favorable prognosis.
Key words: CCDC69, Triple-negative breast cancer, Germinal center B cells, Tumor immune microenvironment, Spatial transcriptomics
Introduction
Triple-negative breast cancer (TNBC) is an aggressive molecular subtype of breast cancer characterized by the absence of estrogen receptor, progesterone receptor, and HER2 expression (1). Compared with other breast cancer subtypes, TNBC exhibits higher rates of recurrence and metastasis and lacks effective targeted therapies, resulting in poor clinical outcomes. In recent years, increasing evidence has highlighted the crucial role of the tumor immune microenvironment in TNBC progression and therapeutic response (2). In particular, immune-rich TNBC tumors are often associated with improved prognosis and better responses to immunotherapy (3- 5).
Among immune components within the tumor microenvironment, B cells have emerged as important regulators of antitumor immunity (6). Tumor-infiltrating B cells contribute to immune responses through antigen presentation, antibody production, and interactions with T cells (6,7). Notably, B cells organized in tertiary lymphoid structures (TLS) often form germinal center-like regions that support B-cell maturation, affinity maturation, and differentiation into plasma cells (8,9). The presence of germinal center B cells and TLS has been associated with favorable prognosis and enhanced immunotherapy responses in multiple cancers, including breast cancer (10).
CCDC69 is a coiled-coil domain-containing protein that has been sporadically implicated in the regulation of cell division and tumor-associated biological processes, yet its role in breast cancer remains largely unclear. Recent bioinformatic analyses indicate that CCDC69 is downregulated in breast cancer tissues and that reduced expression (11) is associated with unfavorable clinical outcomes (12). Moreover, immune-related analyses suggest that CCDC69 expression is positively correlated with the infiltration of antitumor immune cells (13,14), implicating a potential role in the tumor immune microenvironment and immunotherapeutic responsiveness.
In this study, we performed an integrated multi-omics analysis to investigate the role of CCDC69 in TNBC. We first examined the expression pattern and prognostic value of CCDC69 using pan-cancer and TNBC transcriptomic datasets. We then characterized the cellular sources of CCDC69 using single-cell RNA sequencing data and explored its spatial distribution within the tumor microenvironment using spatial transcriptomics. Finally, we evaluated the relationship between CCDC69 expression, germinal center B-cell infiltration, and patient prognosis. Our findings revealed that CCDC69 is closely associated with B-cell and germinal center immune niches in TNBC and may serve as a potential biomarker of immune activation and favorable clinical outcomes.
Material and Methods
Data collection
Transcriptomic data and corresponding clinical information for breast cancer were obtained from publicly available datasets. TNBC samples were identified based on molecular subtype classification using the PAM50 gene expression signature (15), in which basal-like tumors were considered representative of triple-negative breast cancer. Single-cell RNA sequencing (scRNA-seq) datasets were obtained from the Gene Expression Omnibus (GEO), including the TNBC tumor microenvironment dataset GSE263995 and the immune-cell-specific dataset GSE169246. Spatial transcriptomic data from breast cancer tissues were obtained from the publicly available dataset GSE210616.
Bulk transcriptomic analyses
Differential expression analysis was performed to evaluate the expression level of CCDC69 between tumor and normal tissues across cancer types. For TNBC samples, patients were stratified into CCDC69 high-expression and low-expression groups according to the median expression level.
To investigate the relationship between CCDC69 expression and the tumor immune microenvironment, immune cell infiltration levels were estimated using CIBERSORT (16) and single-sample gene set enrichment analysis (ssGSEA). CIBERSORT was applied to estimate the relative proportions of 22 immune cell types using the LM22 signature matrix, while ssGSEA was used to calculate enrichment scores for immune-related signatures. Spearman correlation analysis was performed to evaluate associations between CCDC69 expression and immune infiltration levels.
The association between somatic copy number alterations (SCNA) of CCDC69 and immune cell infiltration was also evaluated. SCNA categories included deep deletion, arm-level deletion, diploid/normal, arm-level gain, and high amplification, and immune infiltration levels were compared across these genomic alteration groups.
Single-cell transcriptomic analyses
Single-cell RNA sequencing data from GSE263995 were analyzed using the Seurat R package (17). Quality control filtering was applied to remove low-quality cells based on the number of detected genes, total counts, and mitochondrial gene expression levels. After normalization and scaling, principal component analysis (PCA) was performed for dimensionality reduction. To correct for potential batch effects across samples, the Harmony algorithm (18) was applied for batch correction. Cell clusters were then identified using a graph-based clustering algorithm and visualized using Uniform Manifold Approximation and Projection (UMAP). Cell-type annotation was performed based on canonical marker genes, and feature plots were generated to examine the distribution of CCDC69 expression across cell populations.
To further characterize immune cell subtypes associated with CCDC69, the immune-cell-specific dataset GSE169246 was analyzed using a similar pipeline. Germinal center B-cell clusters were annotated based on marker genes including AICDA, SERPINA9, MEF2B, and RGS13. The correlation between CCDC69 expression and the germinal center transcription factor BCL6 was evaluated using Spearman correlation analysis.
Spatial transcriptomic analysis
Spatial transcriptomic data from GSE210616 were analyzed using the Seurat R package. Raw spatial gene expression matrices generated from the 10x Genomics Visium platform (19) were loaded using the Load10X_Spatial function. Multiple spatial samples were integrated by merging individual Seurat objects to generate a combined spatial dataset.
Quality control filtering was performed to remove low-quality spatial spots. Spots with fewer than 200 detected genes, more than 7,000 detected genes, or mitochondrial gene content greater than 20% were excluded. Mitochondrial gene percentages were calculated using the PercentageFeatureSet function. After filtering, gene expression data were normalized using the LogNormalize method with a scale factor of 10,000. Highly variable genes were identified using the FindVariableFeatures function (vst method, top 2,000 genes), followed by data scaling using the ScaleData function. To quantify pathway and immune-related activities across spatial spots, module scores were calculated using the AddModuleScore function. Module scores were generated for CCDC69, immune-related signatures, and selected signaling pathways. A germinal center (GC) gene signature, including genes such as BCL6, AICDA, MEF2B, RGS13, and LMO2, was used to calculate GC module scores.
Spatial proximity analysis was performed to evaluate the spatial relationship between CCDC69-enriched regions and immune-related regions. Spatial coordinates of each spot were extracted using the GetTissueCoordinates function. Spots with high CCDC69 module scores were defined as those within the top quartile (75th percentile) of the score distribution. Similarly, immune-enriched or GC-enriched spots were defined based on the top quartile of their respective module scores. The nearest-neighbor distances between CCDC69-high spots and immune- or GC-high spots were calculated using the k-nearest neighbors algorithm in the FNN R package.
To characterize spatial immune niches within tumor tissues, spatial spots were clustered based on immune-related and pathway module scores using k-means clustering. This analysis classified spatial regions into distinct microenvironmental niches, such as immune-rich and immune-desert areas. Differences in CCDC69 module scores across spatial niches were evaluated using the Wilcoxon rank-sum test. Finally, the relationship between CCDC69 expression and GC-related signatures was assessed using Spearman correlation analysis across spatial spots.
Validation analyses in the TCGA cohort
Tumor purity estimates were obtained using computational approaches. Spearman correlation and partial correlation analyses were conducted to evaluate the relationships among CCDC69 expression, AICDA expression, and tumor purity.
To further explore the relationship between CCDC69 expression and germinal center B-cell activity, a GC gene signature was constructed based on established GC-associated genes, including BCL6, AICDA, CD38, CD19, CD79A, CD83, and MEF2B. GC module scores and germinal center B-cell (GCB) infiltration scores were calculated using the ssGSEA method. Samples were stratified into GCB-high and GCB-low groups according to the median score. The association between CCDC69 expression and GC-related signatures was evaluated using Spearman correlation analysis.
Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performance of CCDC69 expression in distinguishing GCB-high and GCB-low tumors, and the area under the curve (AUC) was calculated.
Logistic regression analysis was performed to evaluate the association between CCDC69 expression and GCB enrichment. Variables including CCDC69 expression and clinicopathological characteristics were included in the univariate model, and variables with potential significance were further included in the multivariate logistic regression analysis. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated.
Survival analysis
Kaplan-Meier survival analyses were performed using the Kaplan-Meier Plotter database, an online tool integrating multiple breast cancer transcriptomic datasets. Patients were stratified into high- and low-expression groups according to the median expression level of CCDC69. Overall survival (OS), recurrence-free survival (RFS), and distant metastasis-free survival (DMFS) were evaluated, and statistical significance was assessed using the log-rank test.
External validation using the GSE58812 cohort
Gene expression data and corresponding clinical information for the validation cohort were obtained from the GEO database (GSE58812). A total of 107 TNBC samples with available survival data were included. Expression data were normalized according to the original platform annotation, and probes were mapped to gene symbols; when multiple probes corresponded to the same gene, the average expression value was used. Patients were stratified into high- and low-CCDC69 expression groups based on the median expression level. The GCB infiltration score for each sample was calculated using ssGSEA based on a predefined GCB-related gene signature, and samples were further classified into GCB-high and GCB-low groups using the median score as the cutoff. Differences in GCB infiltration between groups were assessed using the Wilcoxon rank-sum test, and the correlation between CCDC69 expression and GCB score was evaluated using Spearman's rank correlation coefficient. ROC curve analysis was performed to assess the ability of CCDC69 expression to distinguish GCB-high from GCB-low tumors, and the AUC was calculated. Kaplan-Meier survival analysis was conducted to evaluate OS and metastasis-free survival (MFS), with statistical significance assessed using the log-rank test.
Statistical analysis
All statistical analyses were performed using R software (version 4.2.0, Austria). Continuous variables were compared using the Wilcoxon rank-sum test, and correlation analyses were conducted using Spearman correlation coefficients. A two-sided P value <0.05 was considered statistically significant.
Results
CCDC69 expression was associated with prognosis and immune infiltration in TNBC
To investigate the clinical relevance of CCDC69 in cancer, we first examined its expression across multiple tumor types using data from The Cancer Genome Atlas (TCGA) and GTEx databases. Pan-cancer analysis revealed that CCDC69 expression was significantly reduced in tumor tissues compared with normal tissues in several cancer types, including breast cancer (Figure 1A).
Figure 1. Expression pattern, prognostic value, and immune infiltration of CCDC69 in triple-negative breast cancer (TNBC). A, Pan-cancer expression analysis of CCDC69. B, Kaplan-Meier analysis of overall survival (OS) in TNBC; C, recurrence-free survival (RFS); and D, distant metastasis-free survival (DMFS). E, Correlation between CCDC69 expression and immune cell infiltration estimated by CIBERSORT and ssGSEA. Spearman correlation analysis. F and G, Comparison of B-cell and plasma-cell infiltration scores between CCDC69 high- and low-expression groups. H, Association between somatic copy number alterations (SCNA) of CCDC69 and immune cell infiltration levels in basal-like breast cancer. B-D: Survival curves were generated using the Kaplan-Meier method and compared using the log-rank test. A and F-H: Data are reported as median and interquartile range. *P<0.05, **P<0.01, ***P<0.001; Wilcoxon rank-sum test.
We next evaluated the prognostic significance of CCDC69 in triple-negative breast cancer. TNBC patients with high CCDC69 expression exhibited significantly better overall survival than those with low expression (Figure 1B). Similarly, higher CCDC69 expression was associated with longer RFS and DMFS (Figure 1C and D).
Given the strong clinical association, we further explored whether CCDC69 expression is linked to the tumor immune microenvironment. CCDC69 expression was positively correlated with multiple immune cell populations (Figure 1E). In particular, B-cell and plasma-cell infiltration scores were significantly higher in tumors with high CCDC69 expression (Figure 1F and G).
In addition, we examined the relationship between SCNA of CCDC69 and immune infiltration in basal-like breast cancer. Arm-level deletions of CCDC69 were associated with reduced infiltration of B cells, CD8+ T cells, neutrophils, and dendritic cells, whereas arm-level gains were associated with increased infiltration of these immune populations. Deep deletions were also associated with macrophage infiltration (Figure 1H). These findings indicated a close relationship between CCDC69 expression and the immune landscape of TNBC.
Single-cell transcriptomic analysis revealed enrichment of CCDC69 in B lineage and germinal center B cells
We analyzed the single-cell RNA-sequencing dataset GSE263995. Unsupervised clustering identified multiple cellular populations within the tumor microenvironment (Figure 2A and B). Feature plot analysis showed that CCDC69 expression was predominantly enriched in clusters 4 and 10 (Figure 2C).
Figure 2. Single-cell transcriptomic analysis identified CCDC69 enrichment in B lineage cells. A and B, UMAP visualization and clustering of the TNBC single-cell dataset GSE263995. C, Feature plot showing CCDC69 expression across cell clusters. D and E, Expression of canonical B-cell markers (MS4A1, CD79A, CD19, CD79B) and plasma-cell markers (JCHAIN, DERL3).
Cell-type annotation based on canonical markers revealed that cluster 4 expressed B-cell markers (MS4A1, CD79A, CD19, CD79B), whereas cluster 10 expressed plasma-cell markers (JCHAIN and DERL3) (Figure 2D and E), indicating that CCDC69 expression is primarily localized within B-lineage immune cells.
To further characterize the B-cell subtype associated with CCDC69 expression, we analyzed an independent immune-cell-specific single-cell dataset (GSE169246). UMAP clustering identified multiple immune populations (Figure 3A and B), among which cluster 16 showed the highest CCDC69 expression (Figure 3C). This cluster displayed strong expression of germinal center B-cell markers, including AICDA, SERPINA9, MEF2B, and RGS13 (Figure 3D). Consistently, correlation analysis demonstrated a positive association between CCDC69 and the germinal center transcription factor BCL6 (r=0.25, P<0.01; Figure 3E). These results suggest that CCDC69 expression is preferentially enriched in germinal center B cells within the TNBC immune microenvironment.
Figure 3. CCDC69 expression is enriched in germinal center B cells in immune-specific single-cell data. A and B, Uniform Manifold Approximation and Projection (UMAP) visualization of immune cell populations in dataset GSE169246. C, Feature plot showing CCDC69 expression across immune cell clusters. D, Expression of germinal center B-cell markers (AICDA, SERPINA9, MEF2B, RGS13). E, Correlation analysis between CCDC69 and BCL6. Spearman correlation analysis.
Spatial transcriptomics revealed localization of CCDC69 in B-cell and germinal center niches
To further determine whether CCDC69 is spatially associated with B-cell immune niches, we analyzed spatial transcriptomic data from breast cancer tissues. After quality control of spatial spots (Figure 4A and B), spatial proximity analysis showed that CCDC69-high spots were located closer to B-cell-enriched regions, indicating a spatial co-localization pattern within the tissue (Figure 4C and D).
Figure 4. Spatial association of CCDC69 with B-cell and germinal center (GC) niches. A and B, Quality control metrics of spatial transcriptomic spots. C, Nearest-neighbor distance analysis between CCDC69-high spots and B-cell-enriched regions. D, Spatial co-localization patterns of CCDC69 and B-cell signatures. E, Comparison of CCDC69 expression between immune-rich and immune-desert niches. F and G, Spatial proximity and distribution of CCDC69-high spots and GC-high regions. H, Comparison of CCDC69 expression in GC-rich and GC-poor areas. I, Correlation between CCDC69 expression and GC module score. J, Correlation between CCDC69 and AICDA expression in the triple-negative breast cancer cohort. K, Partial correlation analysis among CCDC69, AICDA, and tumor purity. Data are reported as median and interquartile range. **P<0.01, ****P<0.0001; Wilcoxon rank-sum test. Correlations were assessed using Spearman correlation analysis.
Based on immune scores, spatial spots were further categorized into immune-rich and immune-desert niches. CCDC69 expression was significantly higher in immune-rich regions (Figure 4E), supporting its association with immune-active microenvironments.
Given the enrichment of CCDC69 in GCB at the single-cell level, we next examined its spatial relationship with GC niches. Using a GC gene signature, spatial proximity analysis showed that CCDC69-high spots were located closer to GC-high regions (Figure 4F), and spatial mapping further demonstrated a clear neighboring distribution between these regions (Figure 4G). In addition, CCDC69 expression was significantly elevated in GC-rich areas (Figure 4H). Spearman correlation analysis further revealed a positive correlation between CCDC69 expression and GC module scores (r=0.35, P<0.01; Figure 4I).
To validate these findings in an independent dataset, we analyzed the TCGA TNBC cohort. CCDC69 expression showed a strong positive correlation with AICDA, a representative marker of germinal center B cells (r=0.659; Figure 4J). Because tumor purity may influence immune-related gene expression, we further examined the relationships among CCDC69, AICDA, and tumor purity. Tumor purity was negatively correlated with both AICDA and CCDC69, and the positive correlation between CCDC69 and AICDA remained significant after adjusting for tumor purity (Figure 4K).
CCDC69 was strongly associated with GCB infiltration and favorable prognosis in TNBC
Patients in the TCGA TNBC cohort were divided into high- and low-CCDC69 expression groups according to the median expression level. The clinicopathological characteristics of the two groups are summarized in Table 1. No significant differences were observed in age, tumor stage, T stage, or N stage between the two groups.
Table 1. Clinicopathological characteristics of patients in the TCGA triple-negative breast cancer (TNBC) cohort stratified by CCDC69 expression level.
| Variable | High (n=110) | Low (n=111) | P value |
|---|---|---|---|
| Age, years (mean±SD) | 55.05±12.52 | 54.88±11.08 | 0.919 |
| Tumor stage, n (%) | 0.245 | ||
| I-II | 92 (83.6) | 92 (82.9) | |
| III-IV | 15 (13.6) | 22 (19.8) | |
| Unknown | 3 (2.7) | 1 (0.9) | |
| T stage, n (%) | 0.462 | ||
| T1 | 29 (26.4) | 16 (14.4) | |
| T2 | 66 (60.0) | 78 (70.3) | |
| T3-T4 | 15 (13.6) | 16 (14.4) | |
| Unknown | 0 (0.0) | 1 (0.9) | |
| N stage, n (%) | 0.358 | ||
| N0 | 64 (58.2) | 68 (61.3) | |
| N1 | 35 (31.8) | 27 (24.3) | |
| N2-N3 | 10 (9.1) | 15 (13.5) | |
| Unknown | 1 (0.9) | 0 (0.0) |
Data are reported as means and SD or number and percentage. Student’s t-test for continuous variables and chi-squared test for categorical variables.
To further quantify the association between CCDC69 expression and GCB infiltration, we analyzed the TCGA TNBC cohort. The GCB infiltration score was significantly higher in the CCDC69 high-expression group compared with the low-expression group (Figure 5A). Consistently, CCDC69 expression showed a strong positive correlation with the GCB score (r=0.79; Figure 5B).
Figure 5. Association of CCDC69 expression with germinal center B-cell (GCB) infiltration and prognosis in TNBC (triple-negative breast cancer). A, Comparison of GCB infiltration scores between CCDC69 high- and low-expression groups; Wilcoxon rank-sum test. B, Correlation between CCDC69 expression and GCB score. Spearman correlation analysis. C, ROC analysis evaluating the predictive performance of CCDC69 for GCB enrichment. D, Logistic regression analysis of CCDC69 and GCB infiltration. E and F, Kaplan-Meier analyses of recurrence-free survival (RFS) and overall survival (OS) in B-cell-enriched TNBC samples stratified by CCDC69 expression.
ROC analysis demonstrated that CCDC69 expression effectively discriminated GCB-high from GCB-low tumors, with an AUC of 0.905 (Figure 5C). Logistic regression analysis further confirmed that CCDC69 expression was significantly associated with GCB enrichment in both univariate and multivariate models (Figure 5D; Supplementary Tables S1 and S2).
Finally, to evaluate the clinical relevance of this association, survival analyses were performed in B-cell-enriched TNBC samples from the Kaplan-Meier Plotter database. Patients with high CCDC69 expression exhibited significantly longer RFS and OS than those with low expression (Figure 5E and F).
External validation confirmed the association between CCDC69 expression and GCB infiltration in TNBC
To validate our findings, we analyzed an independent TNBC cohort from GSE58812 (n=107). Consistent with the TCGA results, the GCB infiltration score was significantly higher in the CCDC69 high-expression group (Figure 6A). Moreover, CCDC69 expression remained strongly positively correlated with GCB infiltration (r=0.71; Figure 6B). ROC analysis further confirmed that CCDC69 expression could effectively discriminate GCB-high from GCB-low tumors (AUC=0.833; Figure 6C). Due to the limited sample size of the validation cohort, survival analyses did not reach statistical significance; however, a consistent trend toward improved OS and MFS was observed in patients with higher CCDC69 expression and GCB enrichment (Figure 6D and E).
Figure 6. External validation of the association between CCDC69 expression and germinal center B-cell (GCB) infiltration in the GSE58812 triple-negative breast cancer (TNBC) cohort. A, Comparison of GCB infiltration scores between CCDC69 high- and low-expression groups. Data are reported as median and interquartile range. Wilcoxon rank-sum test. B, Correlation between CCDC69 expression and GCB-related GSEA score. Spearman correlation analysis. C, ROC curve showing the performance of CCDC69 expression in distinguishing GCB-high and GCB-low tumors. D, Kaplan-Meier curve of overall survival (OS) stratified by CCDC69 expression and GCB enrichment. E, Kaplan-Meier curve of metastasis-free survival (MFS) stratified by CCDC69 expression and GCB enrichment.
Discussion
In this study, we performed an integrative multi-omics analysis to investigate the role of CCDC69 in the tumor immune microenvironment of TNBC. Our results demonstrated that CCDC69 expression was significantly associated with favorable clinical outcomes and increased immune infiltration in TNBC. Through single-cell transcriptomic analysis, we further identified that CCDC69 expression was predominantly enriched in B lineage immune cells, particularly GCB. Spatial transcriptomic analysis revealed that CCDC69-high regions spatially co-localize with B-cell-enriched and germinal center niches within tumor tissues. Finally, quantitative analyses in the TCGA TNBC cohort showed that CCDC69 expression was strongly correlated with GCB infiltration and can effectively distinguish GCB-enriched tumors. Collectively, these findings suggest that CCDC69 represents a potential marker of B-cell-driven immune activation and favorable prognosis in TNBC.
Accumulating evidence has highlighted the critical role of B cells in shaping antitumor immunity (20- 22). Beyond their classical role in antibody production, tumor-infiltrating B cells can function as antigen-presenting cells and secrete cytokines that regulate both innate and adaptive immune responses (23). In many tumors, B cells are spatially organized within TLS, where they frequently undergo germinal center-like reactions that facilitate affinity maturation and differentiation into antibody-secreting plasma cells (23). The formation of TLS and the presence of germinal center B cells have been increasingly recognized as hallmarks of immunologically active tumor microenvironments and have been associated with improved clinical outcomes across multiple cancer types (24). Moreover, recent studies have demonstrated that TLS-rich tumors exhibit enhanced responses to immune checkpoint blockade (25), suggesting that B-cell-mediated humoral immunity may play an important role in antitumor immune surveillance and therapeutic responsiveness. Consistent with these observations, our analyses revealed that CCDC69 expression was strongly correlated with B-cell and plasma-cell infiltration. These findings suggest that elevated CCDC69 expression may reflect the presence of TLS-like immune niches and germinal center-associated B-cell activity within the tumor microenvironment, thereby indicating a more immune-active tumor state characterized by enhanced humoral immune responses.
A key finding of our study is the preferential enrichment of CCDC69 in germinal center B cells. Single-cell RNA sequencing analysis revealed that CCDC69 expression is primarily localized to B-cell and plasma-cell populations, with particularly high expression in germinal center B-cell clusters characterized by canonical markers such as AICDA, SERPINA9, and MEF2B. Germinal centers represent specialized microanatomical structures where B cells undergo somatic hypermutation and clonal selection, ultimately generating high-affinity antibody responses (26). The positive correlation between CCDC69 and the germinal center transcription factor BCL6 further supports the association between CCDC69 expression and germinal center biology. These findings suggest that CCDC69 may be involved in germinal center-associated immune programs or may serve as a transcriptional marker of B cells undergoing germinal center differentiation.
Importantly, spatial transcriptomic analysis provided additional evidence supporting the association between CCDC69 and germinal center immune niches. We observed that CCDC69-high regions were spatially closer to B-cell-enriched areas and germinal center-like regions within tumor tissues. Moreover, CCDC69 expression was significantly elevated in immune-rich spatial niches and positively correlated with germinal center gene signatures. These spatial observations reinforce the notion that CCDC69 expression is closely linked to local immune organization within the tumor microenvironment. Given that germinal center reactions within TLS are critical for sustaining antitumor immune responses (27,28), the spatial association between CCDC69 and germinal center niches may partly explain its strong prognostic significance in TNBC.
Another important observation was the strong quantitative relationship between CCDC69 expression and GCB infiltration. In the TCGA TNBC cohort, CCDC69 expression showed a robust correlation with the GCB score and demonstrated high diagnostic accuracy for identifying GCB-enriched tumors. Furthermore, higher CCDC69 expression was associated with significantly improved recurrence-free survival and overall survival in B-cell-enriched TNBC samples. These findings suggest that CCDC69 may serve as a surrogate biomarker for germinal center-associated immune activity and could potentially help identify patients with immune-active tumors and better clinical outcomes.
We identified a strong and consistent association between CCDC69 expression and GCB infiltration in TNBC across independent cohorts. Notably, this relationship was robustly reproduced in the external GSE58812 dataset, where CCDC69 expression remained significantly correlated with GCB-related signatures and effectively discriminated GCB-high from GCB-low tumors. These findings suggest that CCDC69 may serve as a reliable surrogate marker of GCB infiltration in TNBC. From a biological perspective, GCB infiltration has been increasingly recognized as an indicator of active humoral immune responses within the tumor microenvironment, which may contribute to improved anti-tumor immunity and favorable clinical outcomes. The strong correlation between CCDC69 expression and GCB signatures observed in our study implies a potential role of CCDC69 in shaping or reflecting B-cell-mediated immune responses in TNBC. Although survival analyses in the validation cohort did not reach statistical significance, a consistent trend toward improved overall survival and metastasis-free survival was observed in patients with higher CCDC69 expression and enhanced GCB infiltration. This may be attributed to the relatively limited sample size of the external cohort, which reduces statistical power. Larger, well-annotated cohorts are warranted to further validate the prognostic value of CCDC69.
Several limitations of this study should also be acknowledged. First, our findings are primarily based on computational analyses of publicly available datasets, and experimental validation will be necessary to confirm the functional role of CCDC69 in B cells and germinal center reactions. Second, although spatial transcriptomic analysis suggests a close association between CCDC69 and germinal center niches, the spatial resolution of current datasets does not fully capture the cellular interactions within TLS structures. Future studies using high-resolution spatial imaging and functional assays may provide deeper insights into the role of CCDC69 in TLS formation and immune regulation. In addition, prospective clinical studies will be required to determine whether CCDC69 expression can predict responses to immunotherapy in TNBC patients.
In summary, our study provided a comprehensive multi-omics characterization of CCDC69 in TNBC and revealed a strong association between CCDC69 expression and GCB-mediated immune responses. These findings highlight CCDC69 as a potential biomarker of immune-active tumor microenvironments and favorable prognosis in TNBC. Further mechanistic investigations may elucidate whether CCDC69 plays a functional role in B-cell immunity and whether it could serve as a target for enhancing antitumor immune responses.
Funding Statement
This study was supported by the Key In-Hospital Research Project of Wenshan Zhuang and Miao Autonomous Prefecture People's Hospital (No. ZDXM2023002) and the 2024 Scientific Research Development Special Fund Project of Wenshan Prefecture (No. WS-KYFZXM24001).
Footnotes
Funding: This study was supported by the Key In-Hospital Research Project of Wenshan Zhuang and Miao Autonomous Prefecture People's Hospital (No. ZDXM2023002) and the 2024 Scientific Research Development Special Fund Project of Wenshan Prefecture (No. WS-KYFZXM24001).
Supplementary Materials
Click to view [pdf].
Data Availability Statement
All datasets used in this study are publicly accessible and no new datasets were generated.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All datasets used in this study are publicly accessible and no new datasets were generated.