Integrated Single-Cell and Bulk RNA Sequencing Identifies SERPING1 as a Biomarker of Immune Infiltration and Prognosis in Triple-Negative Breast Cancer

Abstract

Background:

Triple-negative breast cancer (TNBC) is an aggressive malignancy that lacks effective treatment. Immune infiltration plays an important role in anti-tumor responses. Serpin family G1 (SERPING1), a biomarker associated with immune infiltration, has been implicated in multiple cancers, but its role in TNBC remains unclear.

Methods:

RNA sequencing and clinical data for TNBC were obtained from the Gene Expression Omnibus, the Cancer Genome Atlas, and the Molecular Taxonomy of Breast Cancer International Consortium databases. First, the expression, prognostic value, and biological functions of SERPING1 were analyzed. Then, the tumor microenvironment (TME) was comprehensively characterized, and the relationship between SERPING1 expression and immunotherapy response was assessed. Immunohistochemical staining was performed to confirm SERPING1 expression and the abundance of CD4+ T cells and CD8+ T cells in clinical specimens. Finally, single-cell analysis was conducted to investigate the role of SERPING1 in immune cell activation.

Results:

SERPING1 was downregulated in TNBC and was an independent predictor of survival. Functionally, SERPING1 activated the immune response in TNBC patients. Mechanistically, elevated SERPING1 levels lead to increased immune cell infiltration, particularly of CD4+ and CD8+ T cells, in the TME. Moreover, SERPING1 was primarily localized in cancer-associated fibroblasts (CAFs), with SERPING1+ apCAFs exhibiting increased communications with anti-tumor immune cells at the single-cell level.

Conclusions:

SERPING1 contributes to enhanced immune cell infiltration, desirable immunotherapy response, and improved prognosis. It thus can be utilized as a promising biomarker for immune infiltration and prognosis. These findings provide novel insights into TME-related immune regulation and may inform strategies to enhance immunotherapy efficacy in TNBC.

Graphical abstract

Keywords

triple negative breast neoplasms / tumor microenvironment / immunomodulation / prognosis / biomarker / immunotherapy

1. Introduction

Breast cancer (BC) is the most common malignancy among women globally, with triple-negative breast cancer (TNBC) accounting for approximately 15–20% of cases. TNBC is associated with a particularly poor prognosis, characterized by its high histological grade and increased rates of metastasis [1]. Due to the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression, TNBC is unresponsive to endocrine and HER2-targeted therapies [2]. As a result, surgery and chemotherapy remained the standard treatments for TNBC patients over the past decades [3].

In recent years, immune checkpoint inhibitors (ICI) have achieved some success across multiple tumor types [4, 5]. As the most immunogenic subtype of BC, TNBC has demonstrated clinical benefit from immunotherapy. The programmed cell death ligand 1 (PD-L1, also known as CD274) blocking antibodies, atezolizumab and pembrolizumab, combined with chemotherapy, have been approved as first-line treatment for PD-L1-positive metastatic TNBC [6, 7, 8]. However, due to limited expression of PD-L1 and substantial heterogeneity of TNBC tumor microenvironment (TME), not all patients benefit from ICI treatment, and approximately 60%–85% of them develop primary resistance to ICI monotherapy [8, 9]. Therefore, there is an urgent need to explore novel therapeutic targets and enhance the efficacy of immunotherapy in TNBC.

Currently, research on solid tumors has shifted from focusing solely on tumor cells to encompassing the TME [10]. Tumor-infiltrating lymphocytes (TILs) play a critical role in treatment response and prognosis, serving as key determinants of immunotherapy efficacy [11]. However, TME comprises not only lymphocytes but also stromal cells, immunosuppressive cells, extracellular matrix (ECM), and other components [12]. Increasing evidence indicates that cancer cells do not act alone but interact closely with different cell populations and ECM [13]. The ECM, as a key structural and supportive component of the TME, constitutes more than 90% of breast tumors. Among its constituents, cancer-associated fibroblasts (CAFs) are the most abundant cell type, characterized by marked heterogeneity and plasticity [14, 15]. CAFs profoundly influence tumor growth, invasion, and immune responses through ECM remodeling, metabolic regulation, and signaling interactions with both tumor and immune cells [16]. With the deepening of research on TME, novel targets correlated with immune infiltration have been identified [17]. These discoveries offer new opportunities to improve cancer treatment, particularly by enhancing immunotherapy and strengthening host anti-tumor immune responses.

SERPING1, located on chromosome 11, encodes the C1-inhibitor, a highly glycosylated plasma protein that regulates the complement cascade, as well as fibrinolytic, clotting, and kinin pathways. It plays a crucial role in controlling vascular permeability and innate immunity [18]. Recently, SERPING1 has gained attention for its involvement in tumor biology and the TME [19]. Evidence indicated that SERPING1 was downregulated in liver cancer cells, with low expression linked to an immunosuppressive TME and poor prognosis [20]. Similarly, the anti-cancer role of SERPING1 was also demonstrated in gastric and prostate cancers [21, 22]. However, limited knowledge exists regarding the distinct roles of SERPING1 in TNBC, particularly in relation to TME and immune status, highlighting the need for further investigation.

Herein, we conducted a comprehensive investigation focusing on the function of SERPING1 in TNBC. We noticed that SERPING1 expression was significantly downregulated in TNBC compared with normal tissue, with low SERPING1 levels associated with poor prognosis and diminished responses to immunotherapy. Mechanistically, SERPING1 regulated the infiltration of various immune cells, especially T lymphocytes. Overall, our findings provided a thorough understanding of the complex roles of SERPING1 in TNBC and highlighted its potential as a predictive biomarker in clinical practice.

2. Material and Methods

2.1 Datasets

Gene expression profiles and clinicopathological information were downloaded from the Cancer Genome Atlas (TCGA) data portal. Single-cell RNA sequencing (scRNA-seq) data (GSE176078) were obtained from the Gene Expression Omnibus (GEO) database. External validation was performed using data from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), Kaplan-Meier plotter (KM plotter: https://kmplot.com/analysis/) and GEO datasets (GSE45827, GSE103091). Proteomic expression of SERPING1 was analyzed via UALCAN [23]. The immunotherapy response data were derived from GSE91061 dataset and IMvigor210 cohort.

2.2 Identification of Differentially Expressed Genes

RNA-seq data from 118 TNBC and 99 normal samples from TCGA were selected for analysis. Using the “DESeq2” package (version 1.40.2), 5239 differentially expressed genes (DEGs) were identified with |log2(fold change)| >1 and p 0.05, including 2906 upregulated and 2333 downregulated genes. Multiple testing was corrected using the Benjamini-Hochberg false discovery rate (FDR) method. Further comparison between low and high SERPING1 expression groups revealed 200 upregulated and 595 downregulated genes.

2.3 Immune Infiltration Analysis

ESTIMATE algorithm was used to calculate the tumor purity and immune scores of TNBC samples in TCGA cohort [24]. xCell, quanTIseq, MCPcounter, CIBERSORT Absolute, and EPIC algorithms were implemented using the “IOBR” R package (version 0.99.8). The relationship between SERPING1 expression and the tumor infiltration status of 29 immune gene sets was assessed using the ssGSEA algorithm [25]. Moreover, the TIMER algorithm(http://cistrome.org/TIMER/) was utilized to investigate the correlations between SERPING1 expression and CD4+ and CD8+ T cell infiltration in basal BC [26].

2.4 Immune Checkpoints and Immunotherapy Response Estimation

Major histocompatibility complex (MHC) molecules and immune checkpoints were compared between the high and low SERPING1 expression groups [27]. Immunophenoscore (IPS) scores from The Cancer Immunome Atlas (TCIA) (https://tcia.at/home) and tumor immune exclusion score from TIDE algorithm (http://tide.dfci.harvard.edu/) were assessed to evaluate tumor immunogenicity [28, 29].

2.5 Screening for the Hub Genes Associated With Immune Infiltration

Weighted Gene Co-expression Network Analysis (WGCNA) was conducted using the “WGCNA” R package on TNBC samples from the TCGA database [30]. Hierarchical clustering was performed based on gene expression, identifying genes with high similarity within modules using the dynamic cut tree method. The Module Eigengene (ME) value for each module and its correlation coefficients with ESTIMATE scores were calculated. A soft thresholding power of 3 was selected. The minimum module size was set at 30. Then, least absolute shrinkage and selection operator (LASSO) COX regression analysis was used to determine the final hub genes associated with immune infiltration.

2.6 Functional Enrichment Analysis

To annotate potential biological processes associated with SERPING1, we performed enrichment analysis using Gene Ontology (GO) (http://geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.kegg.jp/), with the “ClusterProfiler” R package (version 4.8.2). Multiple testing was corrected using the Benjamini-Hochberg FDR method. GO analysis was conducted based on 3 categories, including biological process (BP), molecular function (MF), and cellular component (CC). Additionally, Gene Set Enrichment Analysis (GSEA) was performed as a secondary step, where it evaluates sets of biologically relevant genes, such as those in specific pathways, and categorizes gene groups based on their enrichment scores.

2.7 Patients and Tissue Specimens

A total of 18 TNBC samples and 28 normal breast tissue samples were collected from the Second Affiliated Hospital of Harbin Medical University (HMU). Informed consent was obtained from all participants, and the study was approved by the Ethics Committee of HMU. All samples were diagnosed by a minimum of 3 pathologists, and the tissue microarray was conducted following standard procedures. All research was performed following the Declaration of Helsinki.

2.8 Immunohistochemical Staining

The tissue microarrays were immunostained as described previously [31]. The anti-SERPING1 (1:50, 12259-1-AP, Rabbit, Proteintech, Wuhan, China), anti-CD8 (1:200; HPA037756; Merck KGaA, Darmstadt, Germany) and anti-CD4 (1:50; ab231460; Abcam, Shanghai, China) were used as primary antibodies. Staining intensity was scored from 0 to 3, with 0 indicating no staining, 1 indicating light brown, 2 indicating brown, and 3 indicating tan. The extent of staining was rated from 0 to 4 based on the percentage of positive cells: 0–25%, 25–50%, 50–75%, and 75–100%. The immunohistochemistry (IHC) score was calculated as the product of staining intensity and extent scores, yielding a range from 0 to 12. Scores 0–4 were assigned as negative, and scores 5–12 were assigned as positive [32]. The percentages of CD4+ and CD8+ T cells in each TNBC sample were determined by analyzing at least five 40\times field-of-view regions. The sections were independently reviewed by two pathologists.

2.9 ScRNA-seq Analysis

GSE176078 was conducted using the R package “Seurat” according to standard procedures. We used the harmony dimensionality reduction algorithm to de-batch the 9 TNBC samples. We visualized the dimensionality reduction via the “UMAP” function, and all cells were annotated based on reference datasets from the CellMarker 2.0 (http://bio-bigdata.hrbmu.edu.cn/CellMarker/). Intercellular communications were computed by the R package “CellChat”, which leverages a receptor-ligand interaction database.

2.10 Statistical Analysis

Statistical analyses were conducted using R software (version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria). Categorical variables were assessed using the \chi² test. A paired t test was used for paired analysis of TCGA samples. Quantitative data were tested for normal distribution and variance homogeneity using the Kolmogorov-Smirnov method. Differences between groups were analyzed using the Wilcox test. Survival data were analyzed using Kaplan-Meier (KM) curves, the log-rank test, and both univariate and multivariate Cox regression analyses. Pearson’s test was carried out for correlation analysis. A p-value of less than 0.05 was considered statistically significant. All plots were generated using R software.

3. Results

3.1 SERPING1 was a Hub Gene Related to Immune Infiltration in TME

A total of 5239 DEGs were identified between TNBC and normal tissues in TCGA database, including 2906 upregulated and 2333 downregulated genes (Fig. 1A). ESTIMATE algorithm was applied to assess the level of infiltration of immune and stromal cells in each TNBC sample (Supplementary Table 1). DEGs and results of ESTIMATE were then incorporated into the WGCNA in search of immune infiltration-related biomarkers. 8 co-expression modules were recognized with a power of 3 as the optimal soft threshold (Fig. 1B,C). Based on Pearson’s correlation coefficient, a heat map illustrating module-trait relationships was drawn to assess the relationships between modules. The turquoise module exhibited the highest correlation with immune infiltration scores and was designated as an “immune infiltration-related DEGs (IRDEGs) module” (Fig. 1D,E). Univariate Cox regression analysis of the 1239 IRDEGs in turquoise module identified 46 genes significantly associated with overall survival (OS) (p 0.05; Supplementary Table 2). Subsequently, these significant genes were subjected to LASSO COX regression analysis and eleven crucial genes, namely, NUAK2, ANKDD1B, ACSS2, RASGRP1, SDS, SERPING1, ZBED6CL, HLA-DQB2, PRDM12, ZNF296 and PEG10 were generated (Fig. 1F,G and Supplementary Table 3). SERPING1, which has not been previously reported in TNBC, was selected as a candidate target for further investigation.

3.2 Downregulation of SERPING1 was Associated With Advanced Tumor Stage and Poor Prognosis in TNBC

Unpaired and paired analyses of TCGA cohort revealed that SERPING1 was significantly downregulated in cancer tissues compared to normal tissues (Fig. 2A). Consistent results were observed in the GSE45827 dataset (Fig. 2B). To validate the expression of SERPING1, we constructed a tissue array consisting of 18 TNBC samples and 28 normal samples from patients in our department and performed IHC staining. The IHC scores confirmed significantly lower expression of SERPING1 in TNBC compared to normal tissues (Fig. 2C). KM survival analysis from TCGA cohort demonstrated that low SERPING1 group showed inferior OS than high SERPING1 group (Fig. 2D). Both univariate and multivariate Cox regression analyses confirmed that SERPING1 served as an independent prognostic factor for OS in TNBC patients (Table 1). These findings were corroborated by analyses of the METABRIC and GSE103091 cohorts (Fig. 2E and Supplementary Fig. 1). Additionally, among different BC subtypes, the lowest expression of SERPING1 mRNA and protein was observed in TNBC (Fig. 2F and Supplementary Fig. 2). Further validation using KM plotter database indicated that higher SERPING1 expression was associated with improved survival only in basal BC, which includes the majority of TNBC (Fig. 2G–J). Moreover, low SERPING expression was significantly correlated with large tumor size and advanced tumor stage (Fig. 2K–N). These findings highlighted that SERPING1 may serve as a positive prognostic biomarker in TNBC.

3.3 Functional Enrichment Analysis of SERPING1 in TNBC

Limited evidence exists regarding the biological characteristics of SERPING1 in malignancies, particularly in TNBC. Therefore, GO and KEGG enrichment analyses using the DEGs between low and high SERPING1 groups were conducted to annotate their functions and investigate the role of SERPING1. The results highlighted the crucial role of SERPING1 in immune regulation. GO enrichment analysis indicated that SERPING1 was associated with essential BP such as leukocyte-mediated immunity, lymphocyte-mediated immunity and activation of immune response. Moreover, SERPING1 was linked to specific CC, such as the immunological synapse and MHC protein complex, both crucial for T cell activation. MF analysis revealed enrichment in receptor ligand activity, cytokine activity, immune receptor activity, and cytokine binding, underscoring its functional contribution to immune regulation (Fig. 3A). KEGG pathway analysis also revealed the involvement of SERPING1 in immune signaling, such as cytokine-cytokine receptor interaction pathway and T cell receptor signaling pathway (Fig. 3B). Consistently, GSEA demonstrated significant enrichment of immune response-related pathways in the high SERPING1 group (Fig. 3C).

3.4 High Expression of SERPING1 was Associated With Inflamed TME

To better understand the impact of SERPING1 on immune cells within TME, immune cell infiltration analysis was conducted using the “Xcell” algorithm. High SERPING1 group presented lower tumor purity and higher percentage of anti-tumor immune cells, including CD8+ T cells, CD4+ memory T cells, B cells, macrophages M1 and activated dendritic cells (DC) (Fig. 4A). These findings were validated using other reliable algorithms (Fig. 4B and Supplementary Fig. 3A–C). Moreover, ssGSEA analysis of immune function scores revealed an activated immune status in the high SERPING1 group (Fig. 4C). Given that the enrichment analysis predominantly highlighted T cell-related pathways (regulation of T cell activation, T cell receptor signaling pathway, and Th1 and Th2 cell differentiation) and that SERPING1 demonstrates a positive correlation with CD4+ T cells and CD8+ T cells, we hypothesize that SERPING1 may inhibit tumorigenesis by promoting the infiltration of protective immune cells, particularly T cells (Fig. 4D). Tissue microarrays were prepared from the same batch in our department, ensuring that samples sharing the same ID number had similar background tissue characteristics. Two well-prepared tissue microarrays were subjected to IHC staining for CD4 and CD8. As shown in Fig. 4E, the high SERPING1 expression context exhibited a greater abundance of infiltrating CD4+ T cells and CD8+ T cells. These observations indicated that SERPING1 overexpression was associated with an inflamed TME in TNBC.

3.5 SERPING1 Expression Could Predict the Clinical Immunotherapy Efficacy of TNBC

We next investigated whether SERPING1 could predict patient response to ICI therapy. ICI response biomarkers, including MHC molecules and immune checkpoints, were all elevated in the high SERPING1 group (Fig. 5A). In addition, patients with high SERPING1 expression exhibited a significantly higher IPS score and a lower exclusion score, indicating a stronger likelihood of response to immunotherapy (Fig. 5B,C). To further validate the immune association of SERPING1, we analyzed data from a melanoma immunotherapy cohort (GSE91061), which revealed significantly higher SERPING1 expression in responders compared with non-responders (Fig. 5D). Consistently, analysis of the IMvigor210 cohort, a widely used benchmark dataset for immunotherapy studies, showed that high SERPING1 expression was associated with favorable prognosis and improved immunotherapy response (Fig. 5E,F).

Subsequently, we compared the predictive performance of SERPING1 with established biomarkers, including CD274 and TILs. In both the GSE91061 and IMvigor210 cohorts, SERPING1 achieved AUC values comparable to those of CD274 and TILs. Furthermore, incorporating SERPING1 into combined models with CD274 and/or TILs modestly improved predictive performance (Fig. 5G,H).

3.6 Single-cell RNA Sequencing Profiles Clarified the Contribution of SERPING1 to Immune Infiltration

To examine the regulatory role of SERPING1 on tumor immunity at single-cell resolution, we performed dimensionality reduction analysis on scRNA-seq data containing 9 TNBC samples from GSE176078. 15 cell clusters were annotated and assigned to 9 cell types, namely B cells, CD4+ T cells, CD8+ T cells, DC, endothelial cells, epithelial cells, CAFs, macrophages and natural killer (NK) cells (Fig. 6A and Supplementary Fig. 4A). SERPING1 was mainly expressed in CAFs, and it was less expressed in macrophages, endothelial cells and DC, which was consistent with the result of higher immune scores and higher proportions of these cells in high SERPING1 group (Fig. 6B).

CAFs were then isolated and re-clustered into 8 subpopulations (Supplementary Fig. 4B). Based on canonical marker genes, these subpopulations were annotated as antigen-presenting CAFs (apCAFs), inflammatory CAFs (iCAFs), myofibroblastic CAFs (myCAFs), vascular CAFs (vCAFs) and a distinct SERPING1-KRT8+ CAF population (Fig. 6C,D). This SERPING1-KRT8+ subset could not be assigned to any established CAF subtypes. Notably, SERPING1 was expressed in all other CAF subtypes but was absent in the SERPING1-KRT8+ subset (Fig. 6E). Cell communication analysis revealed that SERPING1+ apCAFs exhibited the most interactions with immune cells (Fig. 6F). Ligand-receptor interaction analysis indicated that these apCAFs engaged extensively with T cells via the MHC-I, MHC-Ⅱ, and CXCL signaling pathways, all of which have been previously implicated in immune activation. In contrast, the MIF signaling pathway, associated with immunosuppression, was significantly enriched in the crosstalk of SERPING1-KRT8+ CAFs (Fig. 6G,H). Furthermore, SERPING-KRT8+ CAFs displayed reduced MHC-I expression and significantly fewer interactions with CD8+ T cells compared to all SERPING1+ CAF subtypes (Fig. 6G,H and Supplementary Fig. 4C). These findings indicated that SERPING1 loss in CAFs may impair immune activation with TNBC TME.

4. Discussion

TNBC is the most aggressive subtype of BC, with limited treatment strategies and high immunogenicity. Recently, anti-PD-L1 antibodies have been approved for the treatment of both early- and late-TNBC, demonstrating significant benefits. Although the application of molecular targeted therapies and ICIs has extended survival in some patients with advanced TNBC, treatment resistance and relapse remain frequent due to the complexity of the TME. Within the TME, immune cells, remodeled ECM, soluble factors, epigenetic alterations, and reprogrammed fibroblasts interact to shape the anti-tumor immune response, driving tumor initiation, progression, and metastasis. Therefore, an in-depth investigation of the role of TME in TNBC is crucial for optimizing therapeutic strategies and enhancing immunotherapy efficacy.

In our study, by integrating clinical data, bioinformatics analyses and IHC validation, we identify SERPING1 as a potential biomarker associated with immune infiltration in TNBC. SERPING1 encodes a serine protease inhibitor that inactivates C1r and C1s proteases of the C1 complex in the classical complement pathway, thereby suppressing complement activation. Initially, SERPING1 was primarily studied in the context of hereditary angioedema and partial complement component deficiency. Recently, its role in various tumors has gained increasing attention [20, 33]. In BC, low SERPING1 expression was reported as an early diagnostic marker for bone metastasis and was significantly downregulated in metastatic lymph nodes of hormone receptor-positive BC [34, 35]. However, the specific function and mechanism of SERPING1 in TNBC remain unclear. Our study revealed that SERPING1 expression was decreased in TNBC, with low expression strongly associated with advanced stage and poor patient survival. These findings are consistent with studies in prostate and gastric cancers, indicating that SERPING1 loss may act as an adverse factor in the progression of multiple tumor types [21, 36, 37].

Research by Shen et al. [38] demonstrated that SERPING1 could inhibit lung cancer progression via the TSC2/mTOR signaling pathway and SERPING1 was linked to immunotherapy response. In this study, GO and KEGG functional enrichment analyses indicated that SERPING1-related genes were primarily involved in immune regulatory pathways, suggesting a potential role of SERPING1 in anti-tumor immune regulation in TNBC. In addition, we applied multiple robust bioinformatics approaches to analyze the immune microenvironment of TNBC samples from the TCGA cohort. Our results revealed that high SERPING1 expression was significantly associated with an immune-activated TME, characterized by elevated infiltration of anti-tumor immune cells, including CD8+ T cells, CD4+ T cells, M1 macrophages, and DC. Immunotherapy response analysis showed that the high SERPING1 group exhibited significantly increased levels of MHC molecules, immune checkpoint genes, and IPS, indicating enhanced immune responsiveness. Moreover, immunotherapy efficacy analysis in two independent external cohorts further supported its potential as a biomarker for immunotherapy prediction.

GSEA analysis further indicated that SERPING1-related genes are primarily enriched in T cell regulatory pathways, thereby enhancing anti-tumor immunity through activation of T cells within the TME. To validate this hypothesis, IHC staining was performed on tissue microarrays from our hospital. The results demonstrated that infiltration levels of CD4+ and CD8+ T cells were significantly higher in the SERPING1-positive group compared with the SERPING1-negative group. Subsequently, scRNA-seq analysis revealed that SERPING1 was predominantly expressed in CAFs. CAFs are the most abundant cellular component of the TME and constitute a highly heterogeneous population derived from multiple cell types. They exert complex functions in tumor progression by secreting soluble factors, releasing exosomes, providing nutrients, remodeling the ECM, and modulating immune cell activity [15]. Recent studies have clarified the functions of distinct CAF subpopulations in BC, which can be roughly classified into myCAFs, iCAFs and apCAFs [39]. MyCAFs are mainly located near tumor cells, highly express ECM-related proteins, and promote the formation of an immunosuppressive TME. In contrast, iCAFs are situated farther from tumor cells, exhibit low \alpha-SMA expression, and secrete inflammatory factors such as IL-6, IL-8, IL-11, and LIF, which can facilitate cytotoxic T cell recruitment [40, 41]. CAFs have also been implicated in modulating responses to immunotherapy in TNBC. Tian et al. [42] reported that apCAFs expressing MHC II molecules and CD74A are significantly correlated with enhanced immune infiltration in TNBC. Moreover, reprogramming CAFs has been shown to improve the efficacy of immunotherapy in TNBC [43, 44]. These findings indicate that different CAF subpopulations may play opposing roles in BC progression and immunomodulation, highlighting the potential of targeting specific CAF subsets as a therapeutic strategy.

Cell communication analysis indicated that SERPING1+ apCAFs exhibit strong interactions with anti-tumor immune cells, including CD8+ T cells, CD4+ T cells, and NK cells. Consistent with our findings, previous studies have demonstrated that SERPING1+ CAFs are closely associated with T cell infiltration in nasopharyngeal carcinoma and melanoma [45, 46, 47]. In contrast, ligand-receptor interaction analysis of SERPING1-KRT8+ CAFs revealed enrichment of the MIF signaling pathway, which has been reported to promote immunosuppression by inhibiting p53 expression and activating multiple oncogenic signaling pathways [48, 49]. Compared with all SERPING1+ CAF subtypes, SERPING1-KRT8+ CAFs exhibited markedly fewer interactions with T cells and lacked enrichment of CXCL signaling pathways. Moreover, previous research reported that KRT8 upregulation in CAFs was associated with enhanced tumor growth and metastasis [50]. Differential expression between SERPING1+ apCAFs and SERPING1-KRT8+ CAFs revealed enrichment in cytokine secretion pathways (Supplementary Fig. 5). Collectively, our findings indicated that the reduced SERPING1 expression in CAFs may diminish cytokine and chemokine production, limit immune cell infiltration, and consequently promote an immunosuppressive microenvironment associated with poor prognosis in TNBC. However, the precise molecular mechanisms of SERPING1 in TNBC warrant further investigation.

To the best of our knowledge, this is the first study to systematically elucidate the role of SERPING1 in TNBC patients. However, there are still some limitations. First, the IHC validation cohort included only 18 TNBC samples due to limited tissue availability and strict inclusion criteria. Second, although our conclusions were supported by multiple public datasets and single-cell analyses, functional experiments are needed to verify the role of SERPING1 in CAFs. Additionally, the molecular mechanisms underlying SERPING1-mediated immune regulation in TNBC remained to be fully elucidated. Further studies with larger clinical cohorts and mechanistic experiments are warranted to validate and extend these findings.

5. Conclusion

In conclusion, our analysis indicated that SERPING1 is a favorable prognostic factor, with its expression positively correlated with immune activity and response to immunotherapy in TNBC. Mechanically, SERPING1+ apCAFs could promote immune activation through interactions with T cells in the TME. These findings highlighted SERPING1 as a potential biomarker for immune infiltration and prognostic prediction in TNBC. We expected to provide the foundation for further in-depth research on SERPING1 in TNBC.

Disclosure

The paper is listed as, “Integrated single-cell and bulk RNA sequencing identifies SERPING1 as an immune infiltration regulator and therapeutic target in Triple-negative breast cancer ” as a preprint on (SSRN) at: (https://www.researchsquare.com/article/rs-7064973/v1.pdf).

Availability of Data and Materials

The datasets generated and/or analyzed during the current study are available in the TCGA database on the Xena website (http://xena.ucsc.edu/), the GEO (https://www.ncbi.nlm.nih.gov/geo/), the METABRIC database (https://www.bccrc.ca/dept/mo/), the Kaplan-Meier plotter (KM plotter, https://kmplot.com/analysis/), and the UALCAN tool (https://ualcan.path.uab.edu/). The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Further inquiries can be directed to the corresponding authors.

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Funding

National Natural Science Foundation of China(81872135)