The expression levels of JMJD8 in 113 BRCA samples, along with their adjacent normal tissues, were analyzed using mRNA sequencing data from The Cancer Genome Atlas (TCGA-BRCA) dataset. Clinical characteristics, including tumor status, histologic grade, pathologic stage, and vascular invasion, were assessed in relation to their respective clinical patterns. Differences in transcriptional expression were evaluated using Student’s T-test.

We further investigated JMJD8 protein levels in BRCA through immunohistochemistry (IHC) on a commercial tissue microarray (Bioaitech Co., Xi’an, China, cat # F601101) comprising 10 BRCA and 10 normal tissue samples. The IHC procedure involved dewaxing and hydrating the tissues, followed by antigen retrieval and blocking endogenous peroxidase activity. Slides were then incubated with the primary antibody overnight at 4°C. After secondary antibody incubation, staining was performed using diaminobenzidine (DAB).

Two independent pathologists evaluated and scored the staining results. JMJD8 protein expression was quantified based on the proportion of positively stained tumor cells and staining intensity. Specifically, the percentage of immunoreactive tumor cells was scored as follows: 1 (<10%), 2 (10–25%), 3 (26–49%), and 4 (≥50%). Staining intensity was visually graded as 1 (negative), 2 (light yellow), 3 (light brown), and 4 (dark brown). The final immunoreactivity score for each sample was calculated by multiplying the percentage score by the intensity score.

Based on the ssGSEA algorithm provided in R-packet-GSVA [1.46.0], the provided markers of 22 immune cells were used to calculate the immune infiltration of the corresponding data.

The Receiver Operating Characteristic (ROC) curve was applied to assess the specificity and sensitivity of gene prediction accuracy, using the area under the ROC curve (AUC) as a diagnostic value based on the “pROC” package in the statistical software (version 1.18.0) was used.

The Kaplan-Meier curve was performed to compare overall survival (OS) between the differential expression groups of JMJD8 including 1005 BRCA samples in the TCGA database and KM Plotter, respectively. The correlation between JMJD8 expression and survival was analyzed to discover significant prognostic factors.

Gene expression correlation analysis was performed for given sets of mRNA expression data in TCGA-BRCA were used for analysis. The correlation coefficient was determined by the Spearman method.

The breast cancer cell lines EMT6, 4T1, and the macrophage cell line RAW264.7 used in this study were obtained from the China Center for Type Culture Collection. The complete culture medium for cell growth consisted of Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. Cells were cultured in an incubator at 37°C with 5% CO₂, and those in the logarithmic growth phase were used for subsequent experiments.

The siRNAs used in this study were sourced from Genscript Co., Ltd. (Nanjing, China). For siRNA transfection into breast cancer cell lines, we used Lipofectamine 2000 (Invitrogen, Carlsbad, USA), following the manufacturer’s protocol.

The transfection process began by seeding breast cancer cells in culture plates and growing them in an appropriate medium until they reached approximately 50% confluence. At this point, the siRNAs were prepared for transfection. Each siRNA was diluted in Opti-MEM medium and then mixed with Lipofectamine 2000 reagent in a separate tube. This siRNA-Lipofectamine 2000 mixture was incubated at room temperature for 20 minutes to allow siRNA-lipid complexes to form.

After incubation, the siRNA-lipid complex was added to the cells in the culture plates at a final siRNA concentration of 100 nM. The cells were then incubated with the transfection mixture for 48 hours to ensure efficient uptake and effective gene silencing.

The siRNAs used in this study had the following sequences:

siJMJD8-1: 5’-GACTTGCCCTTCCAGGAGT-3’

siJMJD8-2: 5’-GGCAATGACACCCTGTACT-3’

cDNA was amplified using SYBR-Green PCR Master Mix (TAKARA, Tokyo, Japan) on a QuantStudio 5 system (ABI, Carlsbad, USA) under the following conditions: initial denaturation at 95°C for 10 minutes, followed by 40 cycles of denaturation at 95°C for 15 seconds and annealing/extension at 60°C for 1 minute. JMJD8 mRNA levels were quantified using the 2^−ΔΔCt method, with GAPDH serving as the internal control.

The sequences of the specific primers used for amplification were as follows:

GAPDH forward, 5’-AGATCCCTCCAAAATCAAGTGG-3’;

GAPDH reverse, 5’- GGCAGAGATGATGACCCTTTT-3’;

JMJD8 forward, 5’-TCTTCGGGGACAACAACTTC-3’;

JMJD8 reverse, 5’-TCAGGTGGGTAAAGGAACCA-3’.

Following 72 hours of siRNA transfection, cells were collected and lysed in RIPA buffer. Equal amounts of protein from each group were separated via 12% SDS-PAGE and transferred onto PVDF membranes. The membranes were blocked with 5% BSA at room temperature for 1 hour, then incubated with anti-JMJD8 antibody (Abclonal, A10476) followed by HRP-conjugated secondary antibody (Abcam, ab288151). Signal detection was conducted using ECL chemiluminescence reagents, with results recorded on a chemiluminescent imaging system. Afterward, the membranes were stripped, re-blocked, and sequentially incubated with anti-actin antibody (Abcam, ab6276) and HRP-conjugated secondary antibody (Abcam, ab205719), with ECL detection and imaging performed once more.

The impact of JMJD8 knockdown on cGAS-STING pathway proteins was assessed via Western blotting. EMT6 or 4T1 cell lines, in different treatment groups, were seeded into 6-well plates at a density of 2 × 10^5 cells per well. After attachment, RAW264.7 cells were added at a density of 5 × 10^5 cells per well for co-culture over 36 hours. To analyze cGAS-STING pathway activation, total protein was extracted from the co-cultured cells using a non-denaturing tissue/cell lysis kit (Solarbio) with a broad-spectrum protease inhibitor mixture (EDTA-free, BOSTER) and a broad-spectrum phosphatase inhibitor mixture (EDTA-free, BOSTER). For Western blot analysis, 40 μg of protein was used, following the protocol described above. The antibodies included Anti-β-actin (CST, 4967),Anti-IRF3 (CST, 4302), Anti-Phospho-IRF3 (CST, 4947), Anti-TBK1 (CST, 3504), Anti-Phospho-TBK1 (CST, 5483) And Anti PD-L1 (CST, 64988).

After 72 hours of transfection, the cells were collected for tumor xenograft implantation. The Breast Cancer xenograft model was established by subcutaneously injecting different groups of Breast Cancer cells into the right armpits of six-week-old BALB/c nude mice (1×10^7 cells per mouse; three animals per group). Tumor size was recorded every other day, and tumor volume was calculated according to the formula: width^2 × length/2. Fourteen days after tumor cell implantation, all mice were euthanized, and tumor tissues were collected.

To analyze the overall immune landscape of breast cancer, tumor-draining lymph nodes (TDLNs) and tumor tissues were harvested 14 days post-treatment. Fresh tissues were carefully dissected into small fragments and digested using a mixture of enzymes, including neutral protease, collagenase, and hyaluronidase, to prepare a single-cell suspension. The isolated cells were then stained with Alexa Fluor 488 anti-mouse CD11c, Alexa Fluor 647 anti-mouse CD86, PE anti-mouse CD80 antibodies, APC anti-mouse CD8a Antibody, PE/Cy5 anti-mouse CD3 Antibody and PE/Cy7 anti-mouse CCR7 Antibody (BioLegend, USA) before being analyzed by flow cytometry using a BD Calibur system. Additionally, the secretion of interferon-gamma (IFN-β) within the tumors was quantified using an ELISA kit (Dakewe Biotech Co., Ltd.).

The Wilcoxon rank sum test was used to assess the significant differential expression levels of the JMJD8 in BRCA with the threshold of gene expression being selected as the median method. Correlation of gene expression was evaluated by Spearman’s correlation coefficient. Univariate Cox analysis was used to screen for potential risk factors, and multivariate Cox analysis was used to verify the independent variate of JMJD8 expression on overall survival. All statistical analyses were performed with the R statistical software (version 4.2.1). A P-value of less than 0.05 is considered statistically significant.

We conducted a comprehensive analysis of JMJD8 mRNA expression levels in BRCA samples alongside adjacent normal tissue samples, utilizing RNA-sequencing datasets from TCGA. Our findings revealed that JMJD8 was significantly upregulated in breast cancer tissues when compared to normal adjacent tissues ( Figures 1a, b , Supplementary Table S1 ). Then, we examined JMJD8 protein levels in a tissue microarray consisting of 10 normal tissues and 10 BRCA tissues using IHC. The results showed that JMJD8 expression was significantly higher in BRCA tissues compared to normal tissues at the protein level ( Figures 1c, d ). This upregulation suggests a potential role for JMJD8 in the pathogenesis of breast cancer and highlights its importance as a candidate for further investigation in this context.

To further investigate the clinical implications of JMJD8 expression, we conducted a subgroup analysis of RNA-sequencing data from TCGA. Our results indicate that JMJD8 is significantly upregulated in breast cancer tissues characterized by advanced pathological stages and higher histological grades. Additionally, we found a notable increase in JMJD8 expression in patients with negative HER2 status and positive estrogen receptor (ER) status. This upregulation was also observed across different menopausal statuses, both pre- and post-menopause, and was particularly pronounced in the luminal A (lumA) subtype of breast cancer.

These findings suggest that elevated JMJD8 expression is intricately linked to more aggressive clinical characteristics, highlighting its potential role as a biomarker for tumor progression and aggressiveness. The association of JMJD8 with key clinical features underscores its relevance in understanding breast cancer pathology and could inform future therapeutic strategies aimed at targeting this molecule ( Figure 2 ).

We utilized ROC curves to evaluate the predictive performance of JMJD8 for BRCA outcomes. Our analysis indicated that JMJD8 demonstrates a notable predictive accuracy when distinguishing between normal and tumor tissue, with AUC of 0.697 (95% CI: 0.663–0.731), as illustrated in Figure 3a .

To further investigate the prognostic significance of JMJD8 mRNA expression levels in BRCA, we employed Kaplan-Meier survival analysis to correlate expression levels with patient survival and clinical follow-up duration. Log-rank tests were conducted to assess statistical significance. The results of our study revealed that elevated JMJD8 mRNA expression is significantly associated with poor prognosis (Log-rank p=0.043), as shown in Figure 3b . These findings underscore the potential of JMJD8 as a valuable biomarker for predicting clinical outcomes in breast cancer patients.

Univariate Cox analysis showed that high JMJD8 expression was significantly correlated with poor Disease Specific Survival (HR=0.55 95%CI=0.30 – 0.99 P=0.046). ( Table 1 ). Besides, Multivariate Cox analysis confirmed pathological stages (Stage III: HR=5.278, 95% CI=2.157 - 12.913, p<0.001; Stage IV: HR=30.386, 95% CI=11.130 - 82.960, p<0.001) were another independent risk factors in BRCA.

We have characterized the JMJD8-associated immune-infiltrating landscape within the tumor microenvironment of BRCA. Our analysis revealed a positive correlation between JMJD8 expression and the presence of M2 macrophages and monocytes, while demonstrating a negative correlation with CD8 T cells and activated CD4 memory T cells ( Figure 4a ).

These findings suggest that JMJD8 may be linked to immune evasion mechanisms in breast cancer. The positive association with M2 macrophages, which are often implicated in promoting tumor progression and immune suppression, along with the negative association with anti-tumor CD8 T cells, indicates that elevated JMJD8 levels could facilitate an environment conducive to immune escape ( Figures 4b–e ). This highlights the potential role of JMJD8 in modulating the immune landscape of breast cancer, warranting further investigation into its implications for immune response and therapeutic strategies.

To further elucidate the biological role of JMJD8 in the development of BRCA, we employed siRNAs to effectively knock down JMJD8 expression in EMT6 and 4T1 cell lines. The efficiency of this knockdown was confirmed using qRT-PCR ( Figures 5a, b ) and WB analysis ( Figures 5c, d ). Based on this, we assessed the changes in the cGAS-STING pathway following JMJD8 knockdown. It was observed that the knockdown of JMJD8 effectively relieved the inhibition on the phosphorylation of the cGAS-STING pathway ( Figures 5e, f ). This suggests that JMJD8 plays a crucial role in the immune response and tumor progression in breast cancer.

Subsequently, we investigated the impact of JMJD8 knockdown on the proliferative capacity of BRCA cells in an in vivo animal model. Our results demonstrated that the depletion of JMJD8 significantly inhibited BRCA cell proliferation ( Figures 5g-j ). To explore the specific effects of JMJD8 on anti-tumor immunity, we collected tumor tissues for immune analysis. IFN-β is a hallmark cytokine that responds to STING activation and plays a critical role in the activation and maturation of antigen-presenting cells (15). ELISA assays indicated that JMJD8 knockout resulted in increased secretion of IFN-β ( Figures 5k, l ). Based on this, we analyzed the migration status of DCs (identified as CCR7⁺ within the CD11c⁺ cells) in the tumor microenvironment. The results showed that the proportion of DCs with migration tendency significantly increased after JMJD8 knockdown, which can be attributed to the enhanced antigen-presenting capacity of DCs mediated by IFN-β signaling ( Supplementary Figure S1 ). Additionally, flow cytometric analysis of mature dendritic cells (identified as CD80⁺/CD86⁺ within the CD11c⁺ cells) from tumor-draining lymph nodes revealed that these DCs effectively home to the tumor-draining lymph nodes and undergo maturation ( Figures 5m-p ). We subsequently analyzed the infiltration of CD8⁺ T cells in the tumor microenvironment. Building upon the migration and maturation of DCs, the proportion of CD8⁺ T cell infiltration significantly increased following JMJD8 knockdown ( Supplementary Figure S2 ). ELISA results showed that the levels of IL-12 and TNF-α in the tumor microenvironment were upregulated in response to JMJD8 knockdown ( Supplementary Figure S3 ). These findings elucidate the impact of JMJD8 on the tumor immunosuppressive microenvironment, demonstrating that its knockdown helps restore anti-tumor immunity in the host.

To further explore the relationship between JMJD8 and infiltrating immune cells, we conducted a correlation analysis using a coexpression heatmap to assess the associations between JMJD8 and various immune cell markers. These markers are known to characterize immunosuppressive cells, including monocytes and tumor-associated macrophages. The results revealed significant positive correlations between JMJD8 and several key markers: GATA3 (r = 0.517, p < 0.001), STAT6 (r = 0.495, p < 0.001), TNFRSF10C (r = 0.375, p < 0.001), TNFRSF12A (r = 0.175, p < 0.001), BTN2A1 (r = 0.144, p < 0.001), PDCD6 (r = 0.320, p < 0.001), and NECTIN2 (r = 0.529, p < 0.001). This pattern implies that JMJD8 fosters an immune-evasive landscape characterized by suppressive myeloid cells and dampened T cell function.

In contrast, we also examined the correlation of JMJD8 with T cell markers, including PDCD1 (r = -0.165, p < 0.001), CTLA4 (r = -0.268, p < 0.001), and TIGIT (r = -0.224, p < 0.001) ( Figure 6 ). Notably, these results suggest that JMJD8 is negatively correlated with immunosuppressive immune checkpoints. Critically, functional knockdown experiments demonstrate that reducing JMJD8 expression leads to a significant upregulation of PD-L1( Supplementary Figure S4 ). This compensatory PD-L1 induction reveals a vulnerability: Tumors with high JMJD8 expression may exhibit reduced sensitivity to immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis due to the dominance of its immunosuppressive myeloid programs. This suggests that combinatorial approaches involving JMJD8 inhibition (e.g., siRNA, small molecules) and anti-PD-1/PD-L1 immune checkpoint blockade could be particularly effective in overcoming JMJD8-mediated immunosuppression and enhancing ICI response rates.