Introduction

Triple-negative breast cancer (TNBC) is characterized by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression, differentiating it from other breast cancer subtypes1. TNBC is typically more aggressive, with a higher likelihood of recurrence and metastasis, and is more prevalent among younger patients and those of African descent. The prognosis for TNBC is generally worse compared to other breast cancer subtypes, marked by an increased risk of early recurrence and distant metastasis2. Treatment options are currently limited, mainly consisting of chemotherapy, with fewer targeted therapies available due to the lack of ER, PR, and HER2 expression3. Identifying novel prognostic genes is essential for elucidating the mechanisms underlying TNBC, improving patient management, and optimizing personalized treatment strategies. Such research is also critical for the development of targeted therapies, which could lead to more effective and less toxic treatment options for patients with TNBC.

Immune-related genes (IRGs) encode proteins essential for the immune response, playing pivotal roles in pathogen defense and immune homeostasis4. The relationship between IRGs and cancer is notable, as these genes are frequently activated in various cancers, serving as protective cellular pathways and contributing to tumorigenesis5. They can influence the tumor microenvironment and modulate interactions between cancer cells and the immune system, potentially promoting or inhibiting tumor growth and metastasis6. Abnormal DNA methylation of IRGs has been linked to an increased risk of breast cancer7 indicating that alterations in the methylation patterns of immune response-associated genes may influence the development or progression of the disease. Additionally, the study of IRGs has proven valuable in prognosticating gastric cancer. A prognostic model based on IRGs has emerged as an effective tool for patient stratification8 offering critical insights for the development of personalized treatment strategies tailored to individual genetic profiles and disease characteristics.

Abnormal DNA methylation of IRGs is linked to the risk of breast cancer7. Studies on IRGs have also contributed to the prognostic prediction of gastric cancer, with models based on IRGs proving effective for prognosis stratification and offering crucial insights for personalized cancer treatment8. Despite the recognized significance of IRGs in the progression of various cancers, their specific roles and mechanisms in TNBC remain inadequately understood. Investigating IRGs in TNBC could uncover novel therapeutic targets, offering potential pathways for new treatment strategies and optimizing personalized medicine and targeted therapies for patients with TNBC.

Single-cell RNA sequencing (scRNA-seq) is an advanced technique that enables gene expression profiling at the individual cell level, providing an in-depth understanding of cellular heterogeneity and function9. In addition, scRNA-seq facilitates the exploration of cell-to-cell communication and interactions by analyzing genes involved in signaling pathways, offering insights into the complex networks driving cancer progression10,11. Through this approach, it becomes possible to better decipher intricate gene expression patterns, uncover heterogeneity within cell populations, and gain valuable insights into cancer mechanisms, therapeutic targets, and the development of personalized treatment strategies.

This study utilized public RNA sequencing and scRNA-seq data to identify IRGs associated with prognosis in TNBC. Employing bioinformatics tools such as Least Absolute Shrinkage and Selection Operator (LASSO) and univariate Cox regression, a novel prognostic risk model was constructed to predict the survival of patients with TNBC. Additionally, potential biological functions and molecular mechanisms between high- and low-risk groups were explored, aiming to identify key cell types involved in TNBC. By examining the cellular dynamics within TNBC, this study provides valuable insights into the role of IRGs in TNBC. This comprehensive approach not only advances the understanding of IRGs’ mechanisms but also supports clinical prognosis prediction for TNBC and the development of personalized immunotherapies. The findings are expected to enhance therapeutic precision, improving patient outcomes by tailoring treatments to individual genetic profiles and risk levels.

Materials and methods

Data acquisition

In this study, RNA-seq data from 121 TNBC tissue samples and 112 control tissue samples, including complete survival information from individuals, were retrieved from The Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov/) database (as of September 27, 2024). The data, comprising transcripts per million (TPM), mutation spectrum, copy number variation (CNV), and clinical features (age, TNM stage), were processed using the TCGABiolinks package (v 2.30.4)12 and designated as the training set for subsequent analyses. In this study, after excluding patients with incomplete survival information from the sample data, screening and classification were performed based on the expression status of ER, PR, and HER2, with the criterion that all three receptors were negative. During extraction of clinical characteristics, sample type (tumor tissue “T” or normal tissue “N”) was distinguished by the 14th-15th digits of the sample ID (1–9 for T, 10–29 for N), and normal tissues were further confirmed as “Solid Tissue Normal” via clinical phenotype data. Finally, eligible TNBC and normal tissue samples were selected, with indicators like age, TNM stage, T stage, and N stage extracted for subsequent analysis (Table S1).

The GSE65194 (GPL570 platform; 41 TNBC tissue samples with complete survival data; as of September 30, 2024) and GSE148673 (GPL24676 platform; five TNBC tissue samples with complete survival data; as of October 14, 2024) datasets were sourced from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and were used as the testing and scRNA-seq sets, respectively.

A total of 1,811 IRGs were identified from the ImmPort database (https://www.immport.org/home)13 (Table S2).

Identification and function analyses of candidate genes and construction of protein-protein interaction (PPI) network

In the training set, the DESeq2 package (v 1.34.0)14 was utilized to identify differentially expressed genes (DEGs) between TNBC and control samples (|log2 fold change (FC)| > 1, p < 0.05). The results were visualized using heatmaps and volcano plots generated with the ComplexHeatmap (v 2.18.0)15 and ggplot2 (v 3.3.6)16 packages, with the top ten upregulated and downregulated DEGs (with the highest |log2FC|) labeled. The ggvenn package (v 0.1.10)17 was then applied to intersect DEGs with IRGs, generating a Venn diagram to display the results.

Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes18,19,20 (KEGG) pathway analyses were performed using ClusterProfiler (v 4.7.1.3)21 (p.adj < 0.05) to identify biological functions and signaling pathways involving the candidate genes. The top five most significant GO terms across three categories (biological process [BP], cellular component [CC], and molecular function [MF]) and KEGG entries were visualized using the enrichplot package (v 1.18.0)22.

To explore the PPIs of candidate genes, all identified candidate genes were submitted to the STRING database (http://string-db.org) to construct a PPI network (interaction score > 0.9). The PPI network was visualized using Cytoscape software (v 3.9.0)23.

Construction of random survival forest (RSF) model (risk model)

To identify feature genes from the candidate genes, univariate Cox regression analysis was conducted in the training set using the survival package (v 3.3.1)24 with a hazard ratio (HR) ≠ 1 and p < 0.05 as selection criteria. The proportional hazards (PH) assumption test was then performed using the cox.zph function of the survival package (v 3.3.1) (p > 0.05). The results were displayed in a forest plot using the forestplot package (v 3.1.3)25.

Subsequently, the LASSO algorithm was applied to identify prognostic genes from the feature genes using the glmnet package (v 4.1.8)26. The LASSO model used a feature coefficient of 0 as the cutoff and the optimal lambda (log(lambda.min)) as the criterion. In the training set, a random survival forest (RSF) model for prognostic genes was developed using the randomForestSRC package (v 3.2.3)27 (ntree = 200, mtry = 6). The model used the following formula, where hi(t|s) represents the prediction of the i-th prognostic gene for individual x at time t. Risk scores for all patients were then calculated.

\(h(t/x) = \frac{1}{B}\sum\limits_{{i - 1}}^{B} {h_{i} (t/x)}\)

Evaluation of RSF model

Based on the optimal cutoff of risk scores, all TNBC samples from the training and testing sets were classified into high-risk (HRG) and low-risk groups (LRG), respectively. Kaplan-Meier (K-M) curves were then used to evaluate the survival differences between the HRG and LRG in both sets. Additionally, the distributions of risk scores and survival times were visualized using the log-rank test from the survminer package (v 0.4.9)28 (p < 0.05). The expression of prognostic genes was displayed in heatmaps generated using the pheatmap package (v 1.0.12)29. Moreover, receiver operating characteristic (ROC) curves for 3-, 5-, and 7-year survival were plotted using the timeROC package (v 0.4)30 where areas under the curve (AUCs) greater than 0.6 indicated strong predictive performance of the RSF model.

Construction and evaluation of the nomogram model

In the training set, the rms package (v 6.5.0)31 and the survival package (v 3.3.1) were used to construct a nomogram model, which evaluated the prognostic value of the prognostic genes in TNBC by predicting the mortality rates (3-, 5-, and 7-year) based on total points. Subsequently, calibration and ROC curves were plotted using the rms package (v 6.5.0) and timeROC package (v 0.4) to assess the accuracy of the nomogram model. AUC > 0.6 indicated satisfactory prediction performance. Decision curve analysis (DCA) was also performed to evaluate the model’s accuracy using the ggDCA package (v 1.2; https://www.rdocumentation.org/packages/ggDCA/versions/1.1), where a slope approaching 1 indicated higher accuracy.

Co-expression network analysis of prognostic genes

The Spearman rank correlation coefficient was used to calculate the expression consistency among prognostic genes and a correlation coefficient matrix reflecting the intensity of gene co-expression was obtained. Subsequently, the prognostic genes and their correlation coefficients were imported into the Cytoscape visualization platform to construct a co expression network.

Difference analysis of risk scores in clinical features

In the training set, differences in risk scores across clinical features were identified using the Wilcoxon rank-sum test (p < 0.05). Furthermore, the expression of prognostic genes relative to risk scores and clinical features was visualized in a heatmap generated by the ComplexHeatmap package (v 2.18.0).

Gene-set enrichment analysis (GSEA) and gene-set variation analysis (GSVA)

To explore the functions implicated in TNBC development, GSEA was performed. In the training set, the DESeq2 package (v 1.34.0) was initially used to conduct differential expression analysis to obtain DEGs between HRG and LRG, ranked by log2FC in descending order. The GSEA function from the ClusterProfiler package (v 4.7.1.3) was then employed using “c2.cp.kegg.v7.5.1.symbols.gmt” from the Molecular Signatures Database (MSigDB) (https://www.gsea-msigdb.org/gsea/msigdb) (p < 0.05). The top five most significantly enriched pathways were displayed in a ridge plot created using the enrichplot package (v 1.18.0).

Additionally, to identify functional differences between HRG and LRG, GSVA was conducted. The ssGSEA function from the GSVA package (v 1.42.0)32 was first used to calculate GSVA scores for gene sets from HRG and LRG based on “c2.cp.kegg.v7.5.1.symbols.gmt.” The limma package (v 3.52.4)33 was then utilized to perform differential analysis of GSVA scores (|t| > 2, p < 0.05). The top ten most significantly upregulated and downregulated pathways were visualized using ggplot2 (v 3.3.6).

Immune analysis

Initially, the distribution of 22 immune cell types in HRG and LRG was determined using the CIBERSORT algorithm34,35. The Wilcoxon rank-sum test was then conducted to identify differentially distributed immune cells (p < 0.05). Following this, the psych package (v 2.2.9)36 was utilized for Spearman correlation analysis between prognostic genes and differential immune cells (|cor| > 0.3, p < 0.05). Additionally, the activity scores for seven anticancer immune procedures in HRG and LRG were obtained using the Tracking Tumor Immune Phenotype (TIP) platform (http://biocc.hrbmu.edu.cn/TIP/index.jsp). Wilcoxon rank-sum tests were performed to identify significant differences in immune procedure activity between HRG and LRG (p < 0.05). To elucidate the molecular mechanisms underlying TNBC, differences in the expression of five immune checkpoints (PD1, PD-L1, CTLA4, LAG3, and TIGIT) were assessed using the Wilcoxon rank-sum test (p < 0.05)37. Furthermore, the expression of 33 immunomodulators (including PDCD1, CTLA4, LAG3, TIGIT, CD27, CD28, CD40, CD80, CD86, ICOS, IL2, IL7, IL10, IL15, IL21, IFNG, TNF, CSF1R, CSF2, CCL5, CXCL9, CXCL10, CXCL11, CXCL12, CXCR3, CXCR4, CCR5, CCR7, CCR8, IDO1, TDO2, ARG1, and NOS2) was analyzed for correlation with risk scores in HRG and LRG using Spearman’s correlation (|cor| > 0.3, p < 0.05), using the Spearman function of the psych package (v 2.2.9)38.

To investigate the regulatory association between prognostic genes and immune checkpoints (CTLA4 and LAG3), pairwise correlation analysis was first conducted based on transcriptome expression data. Spearman rank correlation coefficient was used to quantify the expression consistency of various IRGs with CTLA4 and LAG3, and a significance p-value matrix was generated synchronously. Subsequently, a correlation heatmap visualization system was constructed using the pheatmap R package (1.0.12)39.

Microsatellite instability (MSI) and somatic mutation analyses

MSI plays a significant role in immunotherapy responses. In this study, MSI analysis was performed to assess differences in MSI status between HRG and LRG using the cbioportal website (https://www.cbioportal.org/). MSI was categorized into three statuses: MSI-high (MSI-H, score > 10), MSI-low (MSI-L, 4 < score < 10), and microsatellite stability (MSS, score < 4).

Somatic mutation data were retrieved using the TCGAmutations package (v 1.15.2) (https://github.com/PoisonAlien/TCGAmutations/releases) to analyze mutation levels in HRG (53 samples with mutation data) and LRG (64 samples with mutation data). The top 20 genes with the highest mutation frequencies were identified in both HRG and LRG. Additionally, GISTIC 2.040 was used to analyze CNVs in TNBC, identifying differences in chromosomal amplifications and deletions between HRG and LRG.

Drug sensitivity analysis

To identify potential therapeutic drugs for TNBC, the half-maximal inhibitory concentration (IC50) values of 138 drugs were calculated, followed by a Wilcoxon rank-sum test to screen for drugs showing significant differences in efficacy between HRG and LRG (p < 0.05)41.

Quality control for scRNA-seq set

In the scRNA-seq dataset, the Seurat package (v 5.0.1)42 was used to filter eligible cells based on the following criteria: cells with gene detection between 300 and 7,500 genes, genes detected in more than 200 but fewer than 75,000 cells, and a mitochondrial gene percentage of less than 20%. The NormalizeData function of the Seurat package was then applied to normalize the data of these selected cells. The top 2,000 most variable genes were identified for further analysis, and the top 10 genes were labeled using the FindVariableFeatures function. Afterward, the NormalizeData function was applied again to normalize all data. Subsequently, the ElbowPlot and Jackstraw functions of Seurat (v 5.0.1) were used to identify the principal components (PCs) with the highest contributions to the variable data, which were then utilized for subsequent analyses.

Next, the FindClusters and FindNeighbors functions of Seurat (v 5.0.1) were employed for cluster analysis, establishing cell types (resolution = 0.4) based on the identified PCs. The results were visualized in uniform manifold approximation and projection (UMAP) plots. Markers for all cell types were selected from published literature43 and cell types were annotated based on the expression of these markers across the various cell types.

Pseudotime analysis and cell communication analysis of crucial cells

Based on the expression of prognostic genes in the cell types, the study selected cells with high expression of prognostic genes for further analysis. These results were visualized using UMAP and violin plots. Subsequently, the FindClusters and FindNeighbors functions of Seurat (v 5.0.1) were used to perform clustering and sub-clustering analysis for the crucial cells to identify subtypes. Pseudotime analysis was then conducted on all subtypes using the Monocle package (v 2.26.0)44 to explore the developmental trajectories and states of crucial cells, along with the expression patterns of genes in each state. The expression of prognostic genes in each cell state was also analyzed. Additionally, communication types between annotated cell types were assessed and visualized using the CellChat package (v 1.6.1), with significance set at p < 0.05.

Statistical analysis

All bioinformatic analyses were performed using R language (v 4.3.1). The Wilcoxon rank-sum test was employed throughout the study to assess differences between groups, with a significance threshold of p < 0.05.

Results

PPI network and functions of 572 candidate genes

A total of 5,872 DEGs were initially identified, with 3,430 up-regulated and 2,442 down-regulated in the TNBC group, referred to as the DEGs1 set (Fig. 1a,b, Table S3). From the intersection of DEGs1 and IRGs, 572 candidate genes were identified (Fig. 1c, Table S4). These genes were enriched in 2,023 GO terms (1,831 BF terms, 54 CC terms, and 138 MF terms) and 94 KEGG pathways (Tables S5, 6). Notable GO terms included “chemotaxis,” “external side of plasma membrane,” and “cytokine activity,” while significant KEGG terms included “cytokine-cytokine receptor interaction,” “viral protein interaction with cytokine and cytokine receptor,” and “JAK-STAT signaling pathway” (Fig. 1d,e). Additionally, the PPI network revealed 431 candidate genes with interactions, such as ACVR1C interacting with INHBA, INHBB, BMP4, and TGFBR2, and CCL5 interacting with 31 genes, including CX3CL1 and CSF3 (Fig. S1).

Fig. 1
figure 1

Candidate gene screening and analysis. (a) Density heat map of TheDEGs; (b) Volcano diagram of DEGs; (c) Screening of candidate genes; (d) GO enrichment ring plot of candidate genes; (e) KEGG enrichment bubble map of candidate genes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway map was obtained from KEGG (https://www.kegg.jp/). KEGG is a publicly available resource under the terms of the academic uselicense18,19,20.

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RSF model showed excellent prediction performance for prognostic genes in TNBC

Nineteen feature genes were identified through univariate Cox regression (HR ≠ 1, p < 0.05) and PH assumption tests (p > 0.05). Of these, only GPHA2 (p = 0.0236, HR = 6.39), FAM3B (p = 0.0143, HR = 1.61), KIR3DL3 (p = 0.0348, HR = 7.93), MASP1 (p = 0.043, HR = 3.51), and GREM2 (p = 0.0422, HR = 1.61) were validated as risk factors for TNBC (Fig. 2a; Table 1). The LASSO algorithm reduced the set to BPI, GPHA2, KIR3DL3, MASP1, RASGRP1, and SEMA4A with log(lambda.min) = -2.556085571649, where BPI (p = 0.0068, HR = 0.362), RASGRP1 (p = 0.0017, HR = 0.399), and SEMA4A (p = 0.0081, HR = 0.362) were identified as protective factors for TNBC (Fig. 2d). The RSF model was then constructed based on these six prognostic genes. Using the optimal cutoffs (3.322212 in the training set and 4.701808 in the testing set), the two sets were divided into HRG and LRG. In the training set, the HRG contained 55 samples, and in the testing set, it contained 14 samples, while the LRG had 66 and 27 samples, respectively (Figs. 2c,d, S2a,b). K-M curves revealed that the HRG exhibited lower survival probabilities in both sets (Figs. 2e, S2c), and high-risk scores significantly impacted patient survival time in both sets (Figs. 2f, S2d). Additionally, heatmaps showed higher expression of RASGRP1, BPI, GPHA2, and SEMA4A in the LRG in both the training and testing sets. The AUC values for 3-, 5-, and 7-year survival in both sets were all greater than 0.6, indicating strong predictive performance of the RSF model (Figs. 2g, S2e). There were complex co expression associations among the prognostic genes, such as the thick lines connecting SEMA4A with genes such as BPI and RASGRP1, indicating strong correlation. This suggests that these genes may have a synergistic expression pattern in TNBC and jointly participate in specific biological processes (Fig. S3).

Fig. 2
figure 2

Prognostic risk model construction. (a) Univariate COX regression analysis; (b) LASSO regression coefficient path plot and LASSO regression cross-validation curve; (c) Training set (Survival distribution of patients in the high- and low-risk groups of the TNBC sample); (d) Training set (Risk score curves of prognostic genes in TNBC samples from high-risk and low-risk groups); (e) Training set (K-M curves of patients in the high-risk and low-risk groups of TNBC samples); (f) Training set (Heat map of the expression of prognostic genes in the high-risk group and low-risk group of TNBC samples); (g) Training set (ROC curve analysis of risk models).

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Table 1 Single-factor COX regression PH hypothesis test.
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Nomogram showed excellent diagnostic performance for prognostic genes in TNBC

The nomogram showed that BPI had the best prediction performance among the six prognostic genes for TNBC, while MAP1 exhibited the weakest prediction performance (Fig. 3A). The calibration curves showed slopes approaching 1, and the AUC values for 3-, 5-, and 7-year survival were all greater than 0.7, further supporting the excellent predictive accuracy of the nomogram (Fig. 3B,C). Moreover, the net benefit analysis demonstrated that the nomogram provided a strong predictive effect for TNBC outcomes (Fig. 3D).

Fig. 3
figure 3

Prognostic risk model relevant analysis - Construction of prognostic gene nomogram. (a) Array diagram construction; (b) Calibration curve; (c) ROC analysis at 3, 5, 7 years; (d) DCA curves for 3, 5, and 7 years .

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Differences in clinical features and function pathways in TNBC

TNM stages III and IV were associated with higher risk scores compared to stages I and II (Fig. 4A). Similarly, N-stage 2 and 3 also showed significantly higher risk scores than N-stage 1 and 0 (Fig. 4B). However, no significant differences were observed in age and T-stage (Fig. 4C,D). Based on the expression of prognostic genes, RASGRP1, BPI, and SEMA4A exhibited higher expression in LRG (Fig. 4E). GSEA revealed key biological functions and pathways involved in the development of TNBC, including the “chemokine signaling pathway,” “T cell receptor signaling pathway,” and “B cell receptor signaling pathway” (Fig. 4F). Additionally, GSVA identified 37 pathways with significant differences between HRG and LRG (p < 0.05). Pathways such as “primary immunodeficiency” and “allograft rejection” were significantly down-regulated in HRG, while “renin angiotensin system” and “pentose and glucuronate interconversions” were up-regulated in HRG (Fig. 4G, Table S7).

Fig. 4
figure 4

Prognostic risk model relevant analysis - Analysis in relation to clinicopathological features. (a) Differences in risk scores at different stages; (b) Differences in risk scores for different N stages; (c) Differences in risk scores at different ages; (d) Differences in risk scores for different T stages; (e) Expression heat map of prognostic genes with different risk scores and different pathological features; (f) GSEA analysis between high-risk and low-risk groups; (g) Analysis of GSVA differences between high-risk and low-risk groups.

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Immune mechanism and potential drugs of TNBC

To further understand TNBC’s immune regulatory mechanisms, its immune profile was examined. M2 macrophages, resting dendritic cells (DCs), and activated memory CD4(+) T cells showed differential distribution between HRG and LRG (p < 0.05), marking them as differential immune cells (Fig. 5A,B). Among these, activated memory CD4(+) T cells exhibited a positive correlation with RASGRP1 (Fig. 5C). In the anticancer immunity cycle, only Step 4, involving recruitment of CD4(+) T cells, CD8(+) T cells, T helper type 1 (Th1) cells, and natural killer (NK) cells, showed significant differences between HRG and LRG (Fig. 5D). Further analysis of IRGs revealed significant differences in the expression of two immune checkpoints, CTLA4 and LAG3, between HRG and LRG, with ten immunomodulators (including CTLA4, TIGIT, CD28, CD80, CD86, ICOS, IL2, CSF1R, CCR7, and NOS2) showing significantly negative correlations with risk scores in LRG (p < 0.05) (Fig. 5E,F). Additionally, the top 20 most frequently mutated genes were identified, with TP53 exhibiting the highest mutation frequencies, found in 77% of HRG and 86% of LRG samples (Fig. S4a). Both amplifications and deletions were more prevalent in HRG compared to LRG (Fig. S4a). MSI analysis indicated a higher distribution of MSI-H and MSI-L patients in HRG and LRG (Fig. S4c), suggesting that DNA mutations might contribute to decreased MSI scores and disrupt cell regulation, facilitating tumorigenesis. There was a correlation between the expression of IRGs and immune checkpoint CTLA4 and LAG3. KIR3DL3, RASGRP1, and SEMA4A were significantly positively correlated with CTLA4, indicating that these IRGs might be involved in the TNBC immune regulatory network, affecting the tumor immune microenvironment and immune escape process by interacting with immune checkpoints (Fig. S5).

Fig. 5
figure 5

Comprehensive analysis of immune-related indicators and prognostic characteristics in high- risk and Low - risk Groups. (a) Proportion of immune cells in high-risk group and low-risk group; (b) Screening of differential immune cells between high-risk and low-risk groups; (c) Correlation analysis of prognostic genes and differential immune cells (e) Expression level of immune checkpoint genes; (f) Distribution of immunomodulators in the high-risk and low-risk groups of the training set.

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Furthermore, the IC50 values of 138 drugs were calculated, identifying 38 drugs with significant differences between HRG and LRG. Among them, 28 drugs showed higher sensitivity in LRG, while 10 were more effective in HRG (Fig. S4d, Table S8). Notably, NU.7441 (p < 0.001) and PLX4720 (p < 0.05) were more sensitive in LRG, while MG.132 (p < 0.05) and X17.AAG (p < 0.05) exhibited greater sensitivity in HRG.

Epithelial cells, fibroblasts, and T cells as crucial cells

After quality control, a total of 6,957 eligible cells were retained from 8,944 cells, and 27,186 genes were selected for further analysis (Fig. S6a,b). The top 2,000 most variable genes were identified, with the top ten genes including IGHGP, IGHG1, IGHG3, IGLC2, IGLC3, IGKC, IGKV1-16, IGKV3-11, TFF1, and HBB. Of these, all but TFF1 and HBB played key roles in the synthesis of immunoglobulins and the production of antibody diversity (Fig. S6c). Using the top 15 PCs, which accounted for significant contributions to gene variation, all cells were clustered into 15 distinct cell clusters, with cluster 0 containing more cells than the others (Fig. S6d,e, Fig. 6a). These 15 clusters were subsequently annotated as six cell types: endothelial cells, fibroblasts, T cells, B cells, epithelial cells, and macrophages (Fig. 6b,c). In these clusters, CD8A, CD3D, and CD3E were predominantly expressed in T cells, while CD79A, MS4A1, CD79B, and CD19 were primarily expressed in B cells (Table 2). The expression of prognostic genes in these six cell types was also examined. For example, the five prognostic genes showed expression in endothelial cells, with BPI, RASGRP1, and SEMA4A also expressed in T cells, while MASP1 was mainly expressed in fibroblasts. Endothelial cells, T cells, and fibroblasts were selected as the crucial cell types for further analysis (Figs. 6d, S7).

Fig. 6
figure 6

Single-cell analysis: Data quality control and preliminary clustering results. (a) Seurat clustering tree with resolution ranging from 0.1 to 0.9; (b) UMAP of cell clusters; (c) cell subtype annotated for all cells; (d) Marker Genes expression bubble map in different cell populations.

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Table 2 Cell marker gene.
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Trajectory profiling revealed the branched progression of crucial cells

Pseudotime analysis revealed that epithelial cells comprised six subtypes, with subtype 1 mainly representing early differentiation and subtype 2 being predominantly associated with late differentiation (Fig. 7a,b). BPI, RASGRP1, and SEMA4A were all expressed during late differentiation of epithelial cells, with SEMA4A showing the highest expression in the metaphase stage (Fig. 7c). In fibroblasts, there were four subtypes, with subtype 1 corresponding to early differentiation, and subtypes 2 and 3 being linked to late differentiation (Fig. 7d,e). Unlike epithelial cells, MASP1 was expressed only during the early differentiation of fibroblasts, while RASGRP1 was expressed in late differentiation, specifically in subtype 2. SEMA4A exhibited a generally downward expression trend throughout fibroblast differentiation (Fig. 7f). For T cells, four subtypes were identified, with subtype 3 being predominantly associated with late differentiation, and RASGRP1 expressed early, with a general downward trend in expression (Fig. 8a–c).

Fig. 7
figure 7

Quasi - temporal analysis of key cell subtypes and their prognostic gene expression patterns. (a) UMAP plot of epithelial cell subtypes. Different colors represent different subtypes of epithelial cells; (b) Distribution of key epithelial cells on the differentiation trajectory. The lines signify the differentiation trajectory, and dots of different colors represent different subtypes of epithelial cells, indicating the position and distribution of each subtype during the differentiation process; (c) Plot of prognostic genes (such as BPI, RASGRP1, SEMA4A, etc.) in key epithelial cells along the pseudo - time. The abscissa is the pseudo - time, and the ordinate is the relative gene expression level. Curves of different colors represent different prognostic genes; (d) UMAP plot of fibroblast subtypes. Different colors distinguish different subtypes of fibroblasts, presenting the distribution characteristics of fibroblast subtypes; (e) Distribution of key fibroblasts on the differentiation trajectory. (f) Plot of prognostic genes (such as MASP1, RASGRP1, SEMA4A, etc.) in key fibroblasts along the pseudo - time. With the pseudo - time as the abscissa and the relative gene expression level as the ordinate, curves of different colors correspond to different prognostic genes. The abscissa is the pseudo - time, the ordinate is the relative gene expression level, and curves of different colors represent the corresponding prognostic genes.

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Fig. 8
figure 8

Quasi - temporal analysis of T - cell subtypes and prognostic gene expression patterns. (a) UMAP plot of T - cell subtypes; (b) Distribution of key T - cells on the differentiation trajectory; (c) Plot of prognostic genes in key T - cells along the pseudo - time. The abscissa is the pseudo - time, the ordinate is the relative gene expression level, and curves of different colors represent the corresponding prognostic genes.

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Characterization of cell-cell communication involved in TNBC

Regarding cell communication, fibroblasts were found to have the most communication types with epithelial cells, endothelial cells, and macrophages. In particular, fibroblasts exhibited strong communication with endothelial cells, macrophages, and T cells (Fig. S8a,b). Additionally, MIF-(CD74 + CD44) was identified as playing a significant role in the communication between macrophages and epithelial cells, as well as in macrophage-fibroblast interactions (Fig. S8c). These findings shed light on the communication networks of crucial cells in TNBC, offering valuable insights into the immune and cellular interactions that may contribute to TNBC development and progression.

Discussion

TNBC is characterized by the absence of ER, PR, and HER2 expression. This subtype is often more aggressive and associated with a poorer prognosis compared to other breast cancer types2. IRGs are critical in the immune response and have been implicated in various cancers, including TNBC5. This study utilized multiple bioinformatics techniques, such as LASSO and univariate Cox regression, to systematically assess the prognostic significance of IRGs in TNBC. Six IRGs (BPI, GPHA2, KIR3DL3, MASP1, RASGRP1, and SEMA4A) were identified as key prognostic factors in TNBC. A prognostic risk score model (RSF model) based on these genes was then developed, and its performance was evaluated using the dataset. Furthermore, a nomogram with strong clinical utility was constructed to facilitate risk stratification. The study also explores critical pathways, drug sensitivity variations, and the expression patterns of prognostic genes at the single-cell level between high and low-risk groups, offering insights into the molecular mechanisms of IRG-related prognostic genes in TNBC. The goal is to provide valuable information for targeted therapies and prognostic diagnostics in TNBC. By revealing the roles of IRGs in TNBC, this research offers a scientific foundation for further investigations, potentially advancing more personalized and effective treatment approaches.

Similarly, previous studies have also constructed prognostic models for TNBC. For instance, Yang et al. integrated hypoxia-related genes and employed the Weighted Gene Co-expression Network Analysis (WGCNA) method along with single-sample gene set enrichment analysis to build a prognostic model45. In contrast, Wang et al. developed a nomogram model based solely on immune-related gene expression46. The differences in predictive outcomes between our model and these previous studies may stem from variations in sample selection, as the included samples exhibited distinct characteristics (e.g., ethnicity, age, disease stage), which could lead to differences in gene expression profiles. Additionally, discrepancies in research methodologies (such as thresholds for gene screening and algorithms used) might result in the selection of different genes. In this study, we identified six IRGs (immune-related genes) closely associated with the immune microenvironment, which demonstrate significant unique value in predicting TNBC prognosis. Through in-depth analysis of these genes, we can better understand their roles in the immune microenvironment of TNBC, thereby providing new perspectives for prognostic prediction and therapeutic strategies for this disease.

Bactericidal/Permeability-Increasing Protein (BPI) is a cationic protein predominantly found in human neutrophils, a type of white blood cell essential for immune defense47. Its primary functions include bactericidal activity and enhancing the permeability of bacterial outer membranes, thereby facilitating the clearance of pathogens. Under normal physiological conditions, BPI plays a key role in protecting the body from bacterial infections48,49. A study isolated a peptide from fish BPI that exhibited antibacterial properties and inhibited the growth of colon cancer cells, highlighting its dual potential in combating both infections and cancer48. Furthermore, research has shown that BPI can inhibit angiogenesis, the formation of new blood vessels essential for tumor growth and metastasis, suggesting its potential use in cancer therapy to deprive tumors of critical nutrients49. In the context of TNBC, BPI may modulate tumor growth and metastasis by influencing immune cells in the tumor microenvironment, such as macrophages and neutrophils50. This positions BPI as a potentially influential factor in the immunological dynamics of TNBC, which could affect its progression and spread. Additionally, the results of our analysis, including the construction of a prognostic score model (RSF model) and a nomogram, suggest that BPI holds significant promise for enhancing the diagnosis and prognosis of TNBC. These models could improve predictive accuracy for patient outcomes and lead to more effective therapeutic strategies targeting the immune response in TNBC.

Glycoprotein hormone subunit alpha 2 (GPHA2) is a key component of the glycoprotein hormone, which plays a significant role in regulating glucose metabolism51. In TNBC, GPHA2 may be implicated in tumor cell proliferation. It likely contributes to cancer cell proliferation by activating intracellular signaling pathways that signal for cell division and growth52. For instance, GPHA2 may interact with cyclin-dependent kinase (CDK) complexes, which are essential for regulating cell cycle progression53. By modulating the activity of these complexes, GPHA2 could promote tumor cell proliferation. Given this potential, developing targeted therapies that inhibit GPHA2 function could disrupt the proliferation, migration, and invasion of TNBC cells—key processes in cancer progression. Targeting GPHA2 presents a novel approach to combatting the aggressive biological behavior of TNBC, offering promising new strategies for treatment. However, it is essential to note that further preclinical and clinical research is necessary to validate the safety and efficacy of such therapeutic interventions.

Killer cell immunoglobulin-like receptor, subunit 3 (KIR3DL3), is primarily expressed on the surface of NK cells and a subset of T cells. As a key immunomodulatory molecule, KIR3DL3 plays an essential role in the recognition of self and non-self cells, as well as in the regulation of immune cell activity54. In the tumor microenvironment of TNBC, NK cells are critical immune surveillance cells. The expression status of KIR3DL3 on NK cells influences their capacity to recognize and eliminate TNBC tumor cells55. This interaction is essential for the immune response against TNBC, as it directly impacts the effectiveness of NK cell-mediated tumor cell lysis54. Investigating the dynamics of KIR3DL3 expression and its impact on NK cell function could provide valuable insights into potential immunotherapeutic strategies targeting the immune synapse in TNBC.

MASP1 is involved in modulating immune responses and may play a dual role within the tumor microenvironment. On one hand, MASP1 can trigger inflammatory reactions that affect the tumor microenvironment, potentially influencing cancer cell survival and proliferation. On the other hand, by activating the complement system, MASP1 may exert direct cytotoxic effects on tumor cells, aiding in their elimination56. Understanding MASP1’s role in immune responses and its interaction with the complement system may offer valuable insights into developing therapeutic strategies that target these pathways in cancer treatment, particularly in TNBC.

Ras Guanyl-Nucleotide-Releasing Protein 1 (RASGRP1) is pivotal in cellular signal transduction pathways57. Dysregulated expression or activity of RASGRP1 can lead to aberrant activation of the Ras-MAPK (MAPK) signaling pathway, promoting TNBC cell proliferation. For instance, RASGRP1 overexpression accelerates cell cycle progression in TNBC cells, significantly increasing the proliferation rate58. This occurs because the activated Ras-MAPK pathway upregulates cell cycle proteins like cyclin D1, facilitating the transition from the G1 phase to the S phase, thus providing favorable conditions for cell proliferation59. Understanding RASGRP1’s role in cellular signaling pathways and its impact on TNBC proliferation may guide the development of therapeutic strategies targeting this pathway, potentially leading to more effective TNBC treatments.

Semaphorin 4 A (SEMA4A) is involved in several physiological processes, including neural development, immune regulation, and angiogenesis60. In TNBC, SEMA4A may directly contribute to cancer cell proliferation and migration. It likely activates intracellular signaling pathways that promote tumor growth61. Regarding cell migration, SEMA4A may regulate the expression of adhesion molecules and cytoskeletal proteins, facilitating cell detachment from the primary tumor and invasion into surrounding tissues62. Given these functions, SEMA4A represents an attractive therapeutic target for TNBC. Targeting SEMA4A could potentially inhibit tumor cell proliferation and metastatic spread, offering a promising avenue for treatment strategies that could improve patient outcomes.

This study identified three types of differential immune cells between the high-risk and low-risk groups: resting DCs, M2 macrophages, and activated memory CD4(+) T cells.

Resting DCs are crucial antigen-presenting cells in the immune system63. Within the tumor microenvironment of TNBC, these cells may play a role in immune surveillance against tumor cells. However, tumor-secreted transforming growth factor-beta (TGF-β) can suppress the expression of major histocompatibility complex (MHC) molecules and costimulatory molecules on DC surfaces64. This suppression impairs DCs’ ability to activate T cells, a critical step in initiating an immune response against the tumor. Thus, tumor cells may evade immune detection, contributing to TNBC progression and metastasis64. Understanding these interactions is essential for developing immunotherapeutic strategies to enhance the immune system’s capacity to recognize and combat TNBC.

M2 macrophages, which secrete TGF-β, promote the epithelial-mesenchymal transition (EMT) in tumor cells. EMT enables cancer cells to become more motile, facilitating their detachment from the primary tumor and subsequent metastasis to distant sites65. Additionally, M2 macrophages release interleukin-10 (IL-10), which suppresses the activity of T cells and NK cells, further aiding the tumor in evading immune surveillance66. The role of M2 macrophages in TNBC progression highlights their potential as therapeutic targets. Targeting M2 macrophages could disrupt the immunosuppressive tumor microenvironment, enhancing the immune response against TNBC and offering a novel strategy to improve patient outcomes. This approach could lead to more effective immunotherapeutic interventions that harness the immune system to combat this aggressive form of breast cancer.

Activated memory CD4(+) T cells, a subset of helper T cells, play a pivotal role in the immune response67. These cells are generated following initial antigenic stimulation, undergoing clonal expansion and differentiation into memory cells capable of rapidly responding to subsequent exposures to the same pathogen68. Upon activation, memory CD4(+) T cells enhance the anti-tumor immune response by secreting cytokines, such as interferon-gamma (IFN-γ)69. They also assist in the activation and proliferation of cytotoxic T lymphocytes (CTLs), which work together to exert anti-tumor effects70. Suppression or insufficient numbers of memory CD4(+) T cells can hinder effective immune surveillance, enabling tumor cells to evade detection and elimination. This impaired immune response often correlates with poorer patient prognosis, as the immune system becomes less capable of controlling and potentially eradicating cancer cells71. Modulating the activity of memory CD4(+) T cells may therefore be crucial for improving cancer treatment outcomes.

Based on the expression of prognostic genes in cells, three key cell types were identified: endothelial cells, T cells, and fibroblasts. Epithelial cells, the primary origin of TNBC, can undergo transformation due to mutations in critical genes like TP53. These mutations lead to an accumulation of DNA damage, impairing the regulation of cell cycle checkpoints and initiating abnormal proliferation that forms tumor cells72. During the EMT, epithelial cells lose their polarity and intercellular junctions, acquiring mesenchymal cell-like characteristics73. This transition enables tumor cells to migrate and invade, facilitating the spread of cancer within the breast and to distant sites, contributing to the aggressive nature of TNBC. Understanding EMT’s role in TNBC development could offer novel therapeutic strategies targeting the metastatic potential of these tumors.

Fibroblasts, when activated within the TNBC tumor microenvironment, undergo phenotypic changes and are termed cancer-associated fibroblasts (CAFs)74. CAFs secrete numerous extracellular matrix components, contributing to increased fibrosis in tumor tissue75. They also release growth factors and cytokines, such as platelet-derived growth factor (PDGF), fibroblast growth factor (FGF), and TGF-β76 which stimulate the proliferation and migration of TNBC cells, driving disease progression. By secreting these signaling molecules, CAFs create a tumor-promoting microenvironment that supports cancer cell survival, growth, and metastasis—key hallmarks of cancer progression77. Understanding the role of fibroblasts in TNBC could unveil new therapeutic targets, allowing for the development of strategies that inhibit fibroblast activity and reduce the supportive microenvironment that fosters TNBC tumor growth and spread.

T cells play a pivotal role in the anti-tumor immune response against TNBC78. CTLs are capable of identifying and eliminating TNBC cells, contributing to the immune system’s ability to clear cancerous cells (PMID: 30520029). Th1 cells, a subset of T helper cells, secrete IFN-γ, which activates macrophages to enhance their phagocytic and cytotoxic activities against TNBC cells79. Additionally, IFN-γ upregulates the expression of MHC molecules on the surface of TNBC cells, enhancing the presentation of tumor antigens and facilitating the recognition and destruction of tumor cells by CTLs80.

Immune checkpoint inhibitors function by blocking the interaction between immune checkpoint molecules, such as programmed death-1 (PD-1) on T cells and programmed death-ligand 1 (PD-L1) on tumor cells. This blockade releases the inhibitory signals on T cells, reactivating them to perform their anti-tumor functions3. Research has shown that Nu-7441, a DNA-PK inhibitor, targets mesenchymal-type cells in epithelial ovarian cancer (EOC), making them sensitive to DNA damage repair inhibitors such as Nu-744181. Furthermore, Nu-7441 has demonstrated significant therapeutic effects in renal cell carcinoma (RCC)82. Currently, the treatment of TNBC primarily relies on chemotherapy, immune checkpoint inhibitors (such as PD-1/PD-L1 inhibitors), and certain targeted therapies. Firstly, due to the lack of clear targeting molecules in TNBC, its therapeutic efficacy is relatively poor, and it is prone to recurrence and metastasis41. Secondly, chemotherapy, as the main treatment modality for TNBC, can control tumor growth in the short term but often induces severe side effects and frequently leads to drug resistance83. Finally, PD-1/PD-L1 inhibitors such as pembrolizumab have been utilized for advanced TNBC, but their efficacy is limited to specific patient populations and is often accompanied by immune-related adverse effects84. In contrast, the advantage of the NU-7441 drug lies in its ability to directly interfere with DNA repair and enhance cancer cell radiosensitivity to inhibit tumor growth. This approach may effectively reduce the side effects of chemotherapy and improve treatment durability85 suggesting its potential as a novel treatment strategy that could expand the clinical application of PARP inhibitors and related drugs. These inhibitors have shown62promise in TNBC treatment by potentially enhancing the body’s immune response against the cancer.

Many current studies focus on immune-related genes (IRGs), but previous research typically relied on large-scale transcriptomic data5. While these data provide overall gene expression profiles, they fail to address the complex interactions and heterogeneity among immune cell subsets in the tumor microenvironment (TME)—a limitation that our study overcomes. By integrating LASSO regression with random survival forest (RSF) models, we optimized prognostic gene screening and risk score calculation methods, analyzing multiple IRGs to construct a prognostic model. This model demonstrates higher predictive accuracy and stability compared to traditional Cox regression models86, identifying six prognostic genes associated with triple-negative breast cancer (TNBC): BPI, GPHA2, KIR3DL3, MASP1, RASGRP1, and SEMA4A. These findings offer novel insights into TNBC prognosis. However, our study has limitations, including the lack of mechanistic investigations and in vivo/in vitro experimental validation.

Future work will explore the functional mechanisms of these IRGs. For example, Western blotting or qPCR could assess how changes in key gene expression affect downstream signaling pathways, while gene editing techniques might reveal impacts on cellular proliferation, migration, and invasion. Validating the functional roles of these genes in TNBC cells will deepen our understanding of TNBC pathogenesis and guide the development of targeted therapies. This study highlights the prognostic relevance of these genes, laying groundwork for mechanistic exploration and therapeutic innovation.