Serum amyloid A1 promote progression of breast cancer and is associated with epithelial-mesenchymal transition

Introduction

Breast cancer remains the most commonly diagnosed malignancy among women worldwide, with approximately 2.3 million new cases reported globally, a figure projected to exceed 3.5 million by 2050 (1). Although advances in early detection and therapeutic strategies have led to a slight decline in mortality rates, tumor heterogeneity continues to pose significant clinical challenges, manifesting as high rates of recurrence and therapeutic resistance.

Tumor development is driven not only by genetic mutations within cancer cells but also by the dynamic remodeling of the tumor microenvironment (TME) (2). As a major stromal component of the TME, cancer-associated fibroblasts (CAFs) play a pivotal role in this process. CAFs exhibit considerable heterogeneity, originating from diverse cellular sources such as quiescent stellate cells, normal fibroblasts, bone marrow-derived fibroblasts, mesenchymal stem cells, endothelial cells, epithelial cells [via epithelial-to-mesenchymal transition, epithelial-mesenchymal transition (EMT)], as well as pericytes, smooth muscle cells, and adipocytes (3-5). Activated by stimuli including transforming growth factor beta (TGF-β), pro-inflammatory cytokines, and Notch signaling, CAFs acquire distinct functional subtypes. These activated CAFs secrete a spectrum of cytokines, growth factors, and matrix metalloproteinases that not only promote cancer cell proliferation, invasion, and angiogenesis but also contribute to drug resistance by remodeling the extracellular matrix into a dense physical barrier that impedes drug penetration (6,7).

A key mechanism through which CAFs and the TME exert their pro-tumorigenic effects is by inducing EMT in cancer cells (8). EMT is a fundamental biological program that confers invasive and metastatic capabilities. Its initiation is governed by the intricate interplay of multiple signaling cascades, including TGF-β/Smad, Wnt/β-catenin, Notch, and NF-κB, which converge to regulate core transcription factors such as Snail, Twist, and ZEB1 (9-11). Beyond the classical binary model, accumulating evidence highlights that EMT often manifests as a partial or hybrid epithelial/mesenchymal state, where cancer cells retain some epithelial traits while acquiring mesenchymal features (12-14). This plasticity is critical for circulating tumor cell generation, immune evasion, and metastatic colonization. Importantly, CAFs can actively induce EMT in adjacent cancer cells through paracrine signaling involving interleukin (IL)-6 and TGF-β, thereby establishing a positive feedback loop that exacerbates malignant progression (15).

This intricate interplay between the TME and cancer cell plasticity is further complicated by inflammatory signals. Inflammation is a well-established enabler of carcinogenesis, as sustained inflammatory activation continuously triggers cytokine and chemokine signaling, facilitating the adaptive transformation of cancer cells and allowing them to evade immune surveillance (16,17). Serum amyloid protein A (SAA) is a hallmark systemic inflammatory marker and a central mediator of inflammatory responses, regulating multiple cytokine signaling pathways (18,19). Previous studies have shown that serum amyloid A1 (SAA1) promotes angiogenesis, tumor invasion, and immune evasion by binding to pattern recognition receptors on immune cells (20-22). Notably, recent research in gastric cancer demonstrated that knockdown of SAA1 in CAFs hinders cancer cell migration (23). However, the specific role and functional significance of SAA1 in CAFs within the breast cancer TME remains largely unexplored. Therefore, this study aims to investigate the expression of SAA1 in breast cancer CAFs and elucidate its impact on the malignant progression of cancer cells. Our findings may provide a theoretical basis for a deeper understanding of the inflammatory mechanisms driving breast cancer and offer insights for the development of novel targeted intervention strategies. We present this article in accordance with the MDAR and ARRIVE reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2718/rc).

Methods

Data sources

The bulk RNA sequencing (bulk RNA-seq) data included 148 breast cancer tissues and adjacent normal tissues from GSE70947. The Single-cell RNA sequencing (scRNA-seq) data contained 17 breast cancer tissues from GSE161529. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

scRNA-seq data analysis

The scRNA-seq data were analyzed using the SCTransform package, which performs normalization, variance stabilization, and feature selection on a UMI-based gene expression matrix (24). The dimensions and resolution were set as 1:25 and 0.01 for initial analysis. Furthermore, the detailed cell annotations were based on the protocols established by Chen et al. (25).

Pathway enrichment analysis

The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses was performed by DAVID database (https://davidbioinformatics.nih.gov/). Additionally, gene set enrichment analysis (GSEA, https://www.gsea-msigdb.org/gsea/index.jsp) explored the potential functions of differentially expressed SAA1 by analyzing their correlation with all transcripts, revealing functional differences between groups.

Analysis the correlation between SAA1 and breast cancer TME

The correlation and survival between SAA1 and CAFs infiltrates in breast cancer was calculated by TIMER3.0 database (https://compbio.cn/timer3/).

Immunohistochemistry (IHC) and immunofluorescence (IF)

The SAA1 expression was verified by IHC experiment in seven patient samples and each sample performed in at least three slides (Table S1). After high-pressure repair using EDTA (Solarbio; E1170-100), the tissues were blocked with goat serum (ZSGB-BIO; SP-9000) for 10 min. Subsequently, the slides were incubated with anti-SAA1 (rabbit anti-human, 1:500, Bioss, bs-19359R) antibodies at 4 °C overnight. Then incubated with secondary antibody (ZSGB-BIO; SP-9001) and with tertiary antibody (ZSGB-BIO; SP-9001) both for 10 min.

The protocol of IF experiment was as same as IHC until incubation with primary antibody. Then ACTA2 (mouse anti-rabbit/rat/human, 1:200, Abcam; ab7817) and SAA1 (rabbit anti-human, 1:500, Bioss, bs-19359R) antibodies were mixed and incubated at 4 °C overnight. Then incubated with secondary antibodies Alexa Fluor 488 and Alexa Fluor 594 at 37 °C for an hour. Finally, staining with DAPI (Abcam, ab104139) for 10 min.

Cell differentiation trajectories analysis

Pseudotemporal ordering of SAA1⁺ cells was using Monocle2. In brief, an aggregated gene-expression matrix was constructed by Seurat metadata. Data dimensionality was reduced using “DDRTree”, and cells were ordered using the “orderCells”. Differentially expressed genes across different development conditions were identified with the differential GeneTest function.

Cell migration and Matrigel invasion assays

The MCF7 (Procell system, CL-1049), MDA-MB-231 (Procell system, CL-0150) and Human Mammary Cancer Fibroblast Cells (HMCAFs) (Procell system, CP-H172) were purchased from Procell. Overexpression of SAA1 (OE-SAA1) was verified by RT-qPCR. The details of primer pairs were showed in Table S2.

For migration assays, about 1.5×10⁵ tumor cells were suspended in 200 mL serum-free medium and seeded into the upper chambers of a transwell (8-mm, Costar). The cells were incubated for 24 h for the migration assay. After incubation, methanol and 0.1% crystal violet (Solarbio, G1063) were used to fix and stain cells. For invasion assays, the transwell membrane was coated with Matrigel (Corning, 356234). And performed as migration assays. The incubated time was extended to 48 hours.

Another migration and invasion assays were performed, MCF-7 and MDA-MB-231 cells were seeded into the upper chambers of a transwell (8-mm, Costar) and OE-SAA1-HMCAFs and HMCAFs were seeded into bottom well.

In vivo tumorigenesis assay

Female BALB/c nude mice (aged 4 weeks) were obtained from SPF (Beijing) Biotechnology.co.Ltd and randomly divided into two groups (6 mice per group). A total of 3×10⁶ MDA-MB-231 cells mixed (1:1) with CAF in 200 µL of PBS: Matrigel at a 3:1 ratio were subcutaneously injected into the flank of nude mice. The tumor growth curve was recorded every week. The tumor volume was calculated according to the following formula: volume =1/3× length × width². The condition for the end of mercy was as follows: (I) weight loss reaches 20–25% of the original weight; (II) mice completely lose their appetite for more than 24 hours; (III) unable to eat or drink; (IV) severe infection; (V) tumor growth exceeds 10% of the original weight or the average diameter exceeds 20mm; (VI) ulceration, necrosis or infection appears on the tumor surface. Experiments were performed under a project license (No. KY-KJT-2023-286) granted by committee ethics board of Guangxi Zhuang Autonomous Region People’s Hospital in compliance with national guidelines for the care and use of animals. A protocol was prepared before the study without registration.

Ligand-receptor interactions

The intercellular communication focusing on secretory signaling pathways were calculated using CellChat (V1.5.0). In brief, the ligand-receptor interaction between three different types of basal cells and eight types of CAFs, together with immune cells and endothelial cells, were calculated. Special attention was given to the interactions between SAA1⁺ basal cell and CAFs and visualized.

Statistical analyses

Statistical analysis was conducted by GraphPad Prism 9 and R (version 4.2.0) via t-test or one-way analysis of variance (ANOVA). P<0.05 was considered significant (*, P<0.05; **, 0.05<P<0.01; ***, P<0.001).

Results

The molecular characteristics of breast cancer were identified via bulk RNA-seq

Total 148 breast cancer samples and paired normal breast tissues from GSE70947 were analyzed, and total 1994 differential expressed genes were obtained (Figure 1A). Those differential expression genes (DEGs) were mainly involved in regulate cell proliferation and inflammatory responses, such as angiogenesis, MAPK signaling pathway, and chemotaxis of neutrophils and macrophages (Figure 1B). Since SAA1 was an important marker of the inflammatory response, further GSEA analysis were performed to investigate the KEGG pathway correlated with SAA1. Adipocytokine, insulin, VEGF, TGFβ, JAK_STAT signaling pathway and Steroid hormone biosynthesis were the most significant pathway associated with SAA1 expression (Figure 1C-1H). The correlation heatmap revealed that tissues with high SAA1 expression exhibited elevated expression levels of metabolism-associated molecular markers including those related to inflammation, estrogen receptor signaling, glycolysis, and fatty acid oxidation compared to tissues with low SAA1 expression (Figure 1I).

Figure 1 The gene expression characteristics of breast cancer. (A) Volcano plot showed DEGs of breast tumor tissues with normal breast tissues by GSE70947 datasets. (B) The top 8 biological functions of DEGs. (C-H) The KEGG pathway correlated with SAA1 via GSEA analysis. (I) The alteration of gene expression associated with metabolism in different SAA1 expression samples. DEG, differential expression gene; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; SAA1, serum amyloid A1.

scRNA-seq revealed the TME of breast cancer

Under stringent quality controls, 22,140 high-quality cells were further analyzed from GSE161529. As shown in Figure 2, according to the marker genes of each cell type, 10 distinct cell types were identified (Figure 2A,2C). We found that Luminal cells (55%) were the most numerous, followed by T cells (16%, Figure 2B), in breast cancer. Feature plot indicated that SAA1 were mainly expressed on stroma cells and basal cells (Figure 2D). The IHC staining confirmed our finding, the SAA1 was expressed on breast cancer site but not in normal breast tissues (Figure 2E).

Figure 2 SAA1 expression in breast tumor microenvironment. (A) t-SNE plot representation of 22,140 high-quality cells of breast cancer. (B) Pie chart showed the proportion of each different cell types. (C) Dot plot showed marker genes of each cluster. (D) The characteristics of SAA1 expression in tumor microenvironment. (E) IHC-P verified the SAA1 protein expression in human breast cancer tissues (up) and normal breast tissues (down). Scale bars, 100 µm. IHC, immunohistochemistry; SAA1, serum amyloid A1; t-SNE, t-distributed stochastic neighbor embedding.

scRNA-seq revealed the SAA1 expression in TME

To investigate the role of SAA1 in TME, SAA1⁺ clusters were screened out for further analysis. Total 14 cell clusters were identified and defined as CAFs, tumor associated macrophage, basal cells and endothelial cells (Figure 3A,3B). Interestingly, SAA1 were only highly expressed in an independent basal cell cluster and an independent myofibroblastic CAFs (myCAFs) cluster, others were not (Figure 3C). The degree of infiltration of CAFs was positively correlated with the expression of SAA1 (Rho =0.417, P<0.05) (Figure 3D). Highly SAA1 expression with highly CAFs infiltration indicated poor overall survival for breast cancer patients (Figure 3E). The SAA1 expression had been confirmed on tumor cells in IHC (Figure 2E). To further verify the presence of these SAA1⁺ myCAFs in breast cancer tissues, we selected SAA1 and ACTA2 for IF experiment. Immunostaining for SAA1, which was identified by scRNA-seq, and ACTA2, which is the specific marker of CAFs, showed the two proteins can be merged in one cell (Figure 3F).

Figure 3 Cell heterogenous in SAA1⁺ cells. (A) t-SNE plot showing the 14 clusters of cells identified by SAA1⁺ expression in tumor microenvironment. (B) Violin plot showed marker genes of each cluster. (C) The SAA1 expression of different subclusters. (D) The correlation between SAA1 expression with CAF infiltration. (E) The cumulative survival analysis of breast cancer patients with SAA1 expression and CAFs infiltration. (F) IF analysis of the expression of SAA1 (red), in combination with the CAFs marker ACTA2 (green) and DNA staining by DAPI (blue) within the tissue paraffin sections from breast cancer samples. Scale bars, 20 mm. ACTA2, actin alpha 2; CAF, cancer-associated fibroblast; DAPI, DAPI staining; IF, immunofluorescence; SAA1, serum amyloid A1; t-SNE, t-distributed stochastic neighbor embedding.

scRNA-seq revealed the biological functions of SAA1⁺ cells in the TME

GO and KEGG enrichment analyses were conducted on the SAA1⁺ basal_1 cluster and SAA1⁺ myCAFs_2 cluster. GO analysis revealed that SAA1⁺ basal cells were significantly enriched in biological processes related to cell proliferation regulation, epithelial cell differentiation, mammary gland alveolus development, and cellular responses to growth factor stimulation (Figure 4A). KEGG analysis indicated potential associations with the estrogen signaling pathway and IL-17 signaling pathway (Figure 4B). Interestingly, SAA1⁺ CAFs exhibited functional parallels to SAA1⁺ basal cells, primarily influencing fibroblast proliferation, migration, apoptotic processes, and responses to growth factor stimuli (Figure 4C). KEGG analysis further confirmed that the enriched pathways in SAA1⁺ CAFs overlapped with those identified in SAA1⁺ basal cells (Figure 4D). These results demonstrate the pivotal role of SAA1 in the breast cancer TME, particularly in modulating inflammatory responses, tumor-stroma interactions, and hormone signaling pathways.

Figure 4 Reconstructing the developmental trajectory of SAA1⁺ cells can find some specific function of SAA1. (A) GO annotations showed the main functions of SAA1⁺ tumor cells. (B) KEGG annotations showed the main functions of SAA1⁺ tumor cells. (C) GO annotations showed the main functions of SAA1⁺ CAFs. (D) KEGG annotations showed the main functions of SAA1⁺ CAFs. (E) Monocle2 generated pseudotemporal trajectory of three basal cell types imported from Seurat data, colored by cell-name designation. (F) Pseudotime was colored in a gradient from dark to light blue, and the start of pseudotime was indicated of basal cells. (G) Monocle2 generated pseudotemporal trajectory of eight CAFs types imported from Seurat data, colored by cell-name designation. (H) Pseudotime was colored in a gradient from dark to light blue, and the start of pseudotime was indicated of CAFs. (I) Expression characteristics of EMT related genes in three basal cell types. (J) qPCR verified the COL1A1, CDH1 and VIM expressions in breast cancer tumor cells when over expressed SAA1. BRCA, breast cancer; CAF, cancer-associated fibroblast; EMT, epithelial mesenchymal transition; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; myCAF, myofibroblastic CAF; qPCR, quantitative polymerase chain reaction; SAA1, serum amyloid A1.

Pseudotime trajectory analysis elucidated the mechanistic basis for this functional similarity. In basal cells, SAA1 expression increased during differentiation and maturation, positioning SAA1⁺ basal cells at the terminal differentiation stage. The acquisition of functions in differentiation and proliferation regulation coincided with this progression (Figure 4E,4F). However, in CAFs, SAA1 expression decreased during differentiation, placing SAA1⁺ CAFs at an early differentiation stage. The functional concordance between SAA1⁺ CAFs and terminally differentiated SAA1⁺ basal cells suggests a potential EMT mechanism (Figure 4G,4H). CDH1 was down-regulated in basal_1, but vimentin (VIM) and collagen type I alpha 1 (COL1A1) were up-regulated (Figure 4I), which represent the markers of EMT and CAFs, respectively. RT-qPCR indicated that when over expressed SAA1 in MCF7 and MDA-MB-231 cells, the expression of COL1A1 and VIM were up-regulated, the CDH1expression was down-regulated (Figure 4J).

Cell interaction character of SAA1+ cells in TME

Pleiotrophin (PTN) pathway was specifically active in both SAA1⁺ basal cells (basal_1) and SAA1⁺ myCAFs (myCAF_2), but not in other cells (Figure 5A). SAA1⁺ cells were involved in all the key steps of the PTN signaling pathway, including sender, receiver, mediator and influencer. However, SAA1-cells only act as receiver and influencer (Figure 5B). The most frequent ligand-receptor pairs were PTN-NCL, PTN-SDC1, PTN-SDC4 and PTN-SDC2 (Figure 5C). The interaction of the PTN ligand-receptor pathway was the strongest in SAA1⁺ cells and inflammatory CAFs, and the expression of this ligand-receptor pathway was not detected in dCAF_2 (Figure 5D,5E). Consistent with the results of GO and KEGG analyses (Figure 4A-4D), the potential roles played by SAA1⁺ CAFs and SAA1⁺ basal cells in cell-to-cell communications were also highly consistent, specifically reflected in the significant consistency of the intensities of the two ligand receptor pathways, PTN and MDK. Similarly, as two different cell types, their respective roles in the TME also had differences in cell characteristics. THBS1, LAMB3, and COL6A1, as characteristic ligands of SAA1⁺ CAFs, had certain influences on the microenvironment. While CXCL2, as a characteristic ligand of SAA1⁺ basal cells, played a role in the TME (Figure 5F,5G).

Figure 5 Intercellular communication networks of SAA1⁺ cells in breast cancer TME. (A) Heatmap of signaling patterns and the relative importance of each cell group based on the computed network centrality measures of signaling networks. (B) Heatmap shows the relative importance of each cell group based on the computed four network centrality measures of PTN signaling network. (C) Relative contribution of each ligand-receptor pair to the overall communication network of PTN signaling pathway, which is the ratio of the total communication probability of the inferred network of each ligand-receptor pair to that of PTN signaling pathway. (D) Intercellular communication networks for spatially colocalized breast cancer populations. The circle plot showed the inferred intercellular communication network for PTN signaling pathways. The thicker the line represented, the more the number of interactions, and the stronger the interaction weights/strength between the two cell types. (E) The heatmap of PTN signaling pathways. The darker the color, the greater the communication probability between the two cell types. (F) Comparison of the significant ligand-receptor pairs of myCAFs_2 (SAA1⁺ CAFs) in tumor microenvironment. (G) Comparison of the significant ligand-receptor pairs of basal_1 (SAA1⁺ basal cell) in tumor microenvironment. CAF, cancer-associated fibroblast; PTN, pleiotrophin; SAA1, serum amyloid A1; TME, tumor microenvironment.

SAA1 enhanced the malignant capabilities of tumor cells

SAA1-overexpressing cell lines were established for further analysis, including OE-SAA1-MCF7, OE-SAA1-MDA-MB-231, and OE-SAA1-HMCAFs (Figure 6A). qPCR results demonstrated that SAA1 overexpression upregulated PTN expression in both MCF7 and MDA-MB-231 cell lines (Figure 6B). Moreover, the transwell migration and invasion assays were performed using OE-SAA1-MCF7 and OE-SAA1-MDA-MB-231 cells. Compared with control and empty vector groups, SAA1 overexpression significantly enhanced tumor cell migration and invasion capabilities (Figures 6C-6F). When co-cultured with SAA1-overexpressing CAFs, both MDA-MB-231 and MCF-7 cells exhibited markedly increased migratory and invasive capacities. Specifically, CAFs overexpressing SAA1 could amplify their pro-tumorigenic effects within the TME, further exacerbating the malignant behavior of breast cancer cells (Figures 6G-6J). These experimental results indicate that SAA1 in breast cancer CAFs likely enhances tumor aggressiveness by promoting cell migration and invasion, potentially through modulating intercellular interactions in the TME, thereby offering a potential therapeutic target for breast cancer treatment. A subcutaneous xenograft model was constructed by injected a mixture of SAA1-overexpressing CAFs and tumor cells into the lateral thigh of mice. Compared with the control group (non-SAA1-overexpressing CAFs), the tumor volume showed significant enlargement by the fourth week (Figure 6K,6L). These findings provided additional evidence that SAA1 in the breast cancer microenvironment facilitates malignant progression through its regulatory effects on CAFs.

Figure 6 SAA1 enhance the proliferation, invasion and migration abilities of tumor cells. (A) qPCR verified the SAA1 expression after constructed over expressed SAA1 cell models. (B) PTN expression of over expressed SAA1 tumor cells was verified by qPCR. (C) Micrograph of changes in the migration ability of tumor cells treated with over expressed SAA1 for 12 h (up, 231 cells; down, MCF7 cells). (D) Micrograph of changes in the invasive ability of tumor cells treated with over expressed SAA1 for 24 h (up, 231 cells; down, MCF7 cells). (E) Bar graph represents the number of migration tumor cells contained differential expression of SAA1. (F) Bar graph represents the number of invasion tumor cells contained differential expression of SAA1. (G) Micrograph of changes in the migration ability of tumor cells treated with co-cultured by over expressed SAA1 HMCAFs for 12 h (up, 231 cells; down, MCF7 cells). (H) Micrograph of changes in the invasive ability of tumor cells treated with co-cultured by over expressed SAA1 HMCAFs for 24 h (up, 231 cells; down, MCF7 cells). (I) Bar graph represents the number of migration tumor cells treated with co-cultured by over expressed SAA1 HMCAFs. (J) Bar graph represents the number of invasion tumor cells treated co-cultured by over expressed SAA1 HMCAFs. (K,L) SAA1⁺ HMCAFs noteworthily enhanced the subcutaneous tumor growth. Cells were fixed with methanol and stained with 0.1% crystal violet; scale bar: 100 μm. HMCAF, human mammary cancer fibroblast cell; MCF7, Michigan Cancer Foundation-7; MOCK, mock-transfected control; NC, negative control; OE-SAA1, overexpression of saa1; PTN, pleiotrophin; qPCR, quantitative polymerase chain reaction; SAA1, serum amyloid A1.

Discussion

This study not only confirmed the expression of SAA1 in breast cancer tumor cells but also revealed its high-level expression in CAFs for the first time. Notably, elevated SAA1 levels and increased CAFs infiltration both predict a significant decrease in overall survival rates among breast cancer patients. Functional experiments demonstrated that SAA1 promotes EMT in breast tumor cells by downregulating CDH1 and upregulating VIM and COL1A1 expression in epithelial cells. Furthermore, SAA1⁺ cells accelerate tumor progression by activating the PTN signaling pathway, presenting a novel potential therapeutic target for breast cancer intervention.

Current epidemiological and Mendelian randomization studies have shown that elevated levels of SAA in the blood are significantly associated with the risk of breast cancer, and their concentrations increase with the progression of the disease (26,27). It is notable that high expression of SAA1 is closely related to the poor prognosis of patients with mesenchymal-like triple-negative breast cancer which is consistent with its potential role in the inflammatory TME (28). Moreover, SAA1 has also been confirmed to affect the occurrence and development of breast cancer by regulating autophagy (29). SAA1 can bind to TLRs on the surface of breast cancer cells, stimulate the production of pro-inflammatory factors such as IL-1β and NLRP3 inflammasome, and create a pro-inflammatory environment to support tumor growth (19,28). Previous studies have shown that SAA1 can enhance apoptosis resistance and contribute to paclitaxel resistance in breast cancer by activating the PI3K/NF- κB and p38 MAPK signaling pathways via the TLR/MYD88 axis (20). Our study revealed the presence of SAA1 overexpressing tumor cells and CAFs within the breast cancer TME. Interestingly, SAA1⁺ basaloid tumor cells and CAFs exhibit highly consistent biological functions, primarily involving cell differentiation, proliferation, migration, and apoptosis. Both groups function through estrogen signaling pathways and IL-17-mediated inflammatory response pathways. Notably, while these two cell types share remarkable molecular expression profiles and functional similarities, their developmental trajectories diverge significantly. Particularly in SAA1⁺ tumor cells, the expression levels of genes associated with EMT show marked elevation as SAA1 expression intensifies. These results suggest that SAA1 may promote the malignant progression of breast cancer by altering the EMT of the TME.

EMT represents a pivotal mechanism in tumor progression, enhancing capabilities for migration, invasion, metastasis, and self-renewal (30,31). By activating the FPR2/Rac1/NF-κB pathway and regulating matrix metalloproteinases, SAA1 induces the EMT process of cancer cells, thereby promoting matrix remodeling and supporting cell migration (32). Similar to previous studies, our findings revealed that OE-SAA1 upregulated COL1A1 and VIM expression while downregulating CDH1. This suggests that SAA1 enhances the migration and invasiveness of breast cancer cells through EMT. E-cadherin (encoded by the CDH1 gene) is a major epithelial marker, a calcium-dependent transmembrane protein, and is mainly expressed in epithelial cells. The loss of E-cadherin leads to the destruction of cell-cell adhesion and increased cell motility, thus enhancing tumor invasiveness and metastasis (33,34). VIM protein is a type III intermediate filament that maintains cytoskeletal organization and adhesion stability. VIM enhances the migration activity of breast cancer cells by regulating the stability of Axl and Scrib (35). The COL1A1 gene encodes type I collagen and is a target protein for the TGF-β transcription factor. The overexpression of COL1A1 is closely related to the metastasis, stage and poor survival rate of colorectal cancer and breast cancer. It promotes the migration and invasion of tumor cells by remodeling the extracellular matrix, activating EMT-related signaling pathways (such as TGF-β, Wnt and Notch), and enhancing cell-matrix interactions (36,37). However, it remains unknown how SAA1 functions in the breast cancer TME after promoting EMT.

CAFs, as an extremely important cellular component in the TME, interact closely with tumor cells and participate in regulating the EMT process of tumor cells. Especially, CAFs can up-regulate the expression of EMT-related transcription factors such as SNAIL, SLUG, and TWIST1/2 (38,39). In addition, CAFs can also promote the metastasis of tumor cells by secreting factors to induce angiogenesis, extracellular matrix remodeling, and energy metabolism reprogramming (40). All these processes are closely related to EMT. Our study have shown that knocking down SAA1 significantly inhibits the proliferation, migration and invasion of MDA-MB-231 and MCF-7 cells, while OE-SAA1 enhances their malignant behavior (Figures S1,S2). Moreover, when SAA1-overexpressing CAFs are co-cultured with tumor cells, they also promote the malignant progression of tumor cells, suggesting that SAA1 may exert an interaction between CAFs and tumor cells through paracrine mechanisms. The results of subcutaneous tumor transplantation experiments also further confirmed.

Further studies found that the PTN signaling pathway was only activated in SAA1⁺ tumor cells and CAFs, and affected the TME through ligand-receptor interaction. PTN is a secreted growth factor highly expressed in approximately 60% of human breast tumors and is known to promote angiogenesis, remodel the TME, and induce EMT (41). PTN binds to its receptor PTPRZ1 to promote endothelial cell proliferation, migration, and tube formation, thereby enhancing the in situ growth and metastatic potential of breast cancer (42). Furthermore, PTN can form a positive feedback loop by upregulating PTPRZ1, activating the NF-κB pathway and contributing to chemoresistance (43). At the cellular level, PTN disrupts cytoskeletal protein complexes, weakens calcium-dependent homotypic cell adhesion, and promotes N-cadherin degradation, thereby inducing morphological EMT changes (44). Previous studies have also shown that PTN is specifically expressed in inflammatory breast cancer, and SAA1, as an acute-phase protein, may serve as a potential upstream regulator of PTN through its role in the inflammatory microenvironment (45). The correlation analysis showed that the expression of SAA1 was positively correlated with that of PTN (Figure S3A). Moreover, OE-SAA1 in vitro significantly upregulated the level of PTN, confirming the positive regulatory effect of SAA1 on PTN (Figure S3B). On this basis, we further discovered that the EMT-related genes COL1A1 and VIM were positively correlated with the expression of PTN, while CDH1 was negatively correlated with the expression of PTN (Figure S4). This trend is highly consistent with the EMT phenotype regulated by SAA1, suggesting that SAA1 may be involved in EMT regulation through the PTN signaling pathway in breast cancer. This hypothesis is further supported by existing evidence that PTN promotes EMT in gliomas, pancreatic cancer, and hepatocellular carcinoma. Although current evidence supports that SAA1⁺ cells can specifically activate the PTN pathway, the underlying regulatory mechanism requires further experimental validation. Future studies will focus on how SAA1 regulates the expression of PTN and its receptor, as well as the functional significance of this signaling axis in breast cancer progression, to more comprehensively elucidate its molecular mechanism.

Conclusions

In summary, we verified the expression of SAA1 in breast cancer TME at the scRNA level, and found that the high expression of SAA1 could promote the malignant progression of tumors and the occurrence of EMT transformation, which may be closely related to the activation of PTN signaling pathway. However, further studies are needed to explore the interactions and potential molecular mechanisms among SAA1, EMT and PTN.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR and ARRIVE reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2718/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2718/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2718/prf

Funding: This study was funded by the Guangxi Natural Science Foundation (No. 2023GXNSFBA026014), Self-financed Science and Technology Projects in Guangxi (No. Z-A20230067), Guangxi Science and Technology Major Project (No. GuikeAA22096018), and Guangxi Key Research and Development Project (No. GuikeAB22080094).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2718/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Experiments were performed under a project license (No. KY-KJT-2023-286) granted by committee ethics board of Guangxi Zhuang Autonomous Region People’s Hospital in compliance with national guidelines for the care and use of animals. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Gu H, Wang R, Beeraka NM, et al. Global burden and trends of breast cancer: GLOBOCAN 2022 estimates of incidence and mortality in 185 countries. Chin Med J (Engl) 2026;139:404-14. [Crossref] [PubMed]
2. Sharma M, Kumar U, Singh S. Tumor microenvironment in breast cancer: cellular crosstalk, pathways, and therapeutic insights. Mol Biol Rep 2025;53:41. [Crossref] [PubMed]
3. Yang J, Xin B, Wang X, et al. Cancer-associated fibroblasts in breast cancer in the single-cell era: Opportunities and challenges. Biochim Biophys Acta Rev Cancer 2025;1880:189291. [Crossref] [PubMed]
4. Jia H, Chen X, Zhang L, et al. Cancer associated fibroblasts in cancer development and therapy. J Hematol Oncol 2025;18:36. [Crossref] [PubMed]
5. Wu Y, Li S, Yu H, et al. Integrative Single-Cell and Spatial Transcriptomics Analysis Reveals ECM-remodeling Cancer-associated Fibroblast-Derived POSTN as a Key Mediator in Pancreatic Ductal Adenocarcinoma Progression. Int J Biol Sci 2025;21:3573-96. [Crossref] [PubMed]
6. Ghazimoradi MH, Babashah S. The transcriptional regulators GATA6 and TET1 regulate the TGF-β pathway in cancer-associated fibroblasts to promote breast cancer progression. Cell Death Discov 2025;11:164. [Crossref] [PubMed]
7. Bullock E, Rozyczko A, Shabbir S, et al. Cancer-associated fibroblast driven paracrine IL-6/STAT3 signaling promotes migration and dissemination in invasive lobular carcinoma. Breast Cancer Res 2025;27:121. [Crossref] [PubMed]
8. Winkler J, Tan W, Diadhiou CM, et al. Single-cell analysis of breast cancer metastasis reveals epithelial-mesenchymal plasticity signatures associated with poor outcomes. J Clin Invest 2024;134:e164227. [Crossref] [PubMed]
9. Sutana P, Luetragoon T, Buakaew W, et al. 3-Hydroxy-β-ionone Suppresses Breast Cancer Progression by Inducing Apoptosis and Blocking EMT Through the TGF-β/Smad Signaling Pathway. Int J Mol Sci 2025;26:8771. [Crossref] [PubMed]
10. Liu X, Zhu Y, Huang W, et al. MYG1 interacts with HSP90 to promote breast cancer progression through Wnt/β-catenin and Notch signaling pathways. Exp Cell Res 2025;446:114448. [Crossref] [PubMed]
11. Chen L, Cen Y, Qian K, et al. MMP1-induced NF-κB activation promotes epithelial-mesenchymal transition and sacituzumab govitecan resistance in hormone receptor-positive breast cancer. Cell Death Dis 2025;16:346. [Crossref] [PubMed]
12. Topa J, Richert J, Stokowy T, et al. Characterizing epithelial-mesenchymal transition-linked heterogeneity in breast cancer circulating tumor cells at a single-cell level. Mol Oncol 2025;19:3685-705. [Crossref] [PubMed]
13. Huang X, Zhang M, Pearce AD, et al. Epithelial-Mesenchymal Transition Activates YAP to Drive Malignant Progression and Immune Evasion. Cancers (Basel) 2025;17:2767. [Crossref] [PubMed]
14. Perez-Moreno E, Ortega-Hernández V, Zavala VA, et al. Suppression of breast cancer metastatic behavior by microRNAs targeting EMT transcription factors. A relevant participation of miR-196a-5p and miR-22-3p in ZEB1 expression. Breast Cancer Res Treat 2025;212:277-90.
15. Fiori ME, Di Franco S, Villanova L, et al. Cancer-associated fibroblasts as abettors of tumor progression at the crossroads of EMT and therapy resistance. Mol Cancer 2019;18:70. [Crossref] [PubMed]
16. Mao H, Zhao X, Sun SC. NF-κB in inflammation and cancer. Cell Mol Immunol 2025;22:811-39. [Crossref] [PubMed]
17. Xie Y, Liu F, Wu Y, et al. Inflammation in cancer: therapeutic opportunities from new insights. Mol Cancer 2025;24:51. [Crossref] [PubMed]
18. Olivier DW, Eksteen C, Plessis MD, et al. Inflammation and Tumor Progression: The Differential Impact of SAA in Breast Cancer Models. Biology (Basel) 2024;13:654. [Crossref] [PubMed]
19. Fourie C, Shridas P, Davis T, et al. Serum amyloid A and inflammasome activation: A link to breast cancer progression? Cytokine Growth Factor Rev 2021;59:62-70. [Crossref] [PubMed]
20. Chen K, Zhao Y, Jin T, et al. Serum Amyloid A1 Mediates Paclitaxel Resistance via MD2-Dependent Pathways in Triple-Negative Breast Cancer. Drug Dev Res 2025;86:e70047. [Crossref] [PubMed]
21. Ma J, Ma Y, Zhou C, et al. SAA1 Induces TGF-β1 Secretion by Ovarian Cancer Cells, Leading to M2 Macrophage Polarization and Inhibition of NK Cell Activity. FASEB J 2025;39:e71239. [Crossref] [PubMed]
22. Lee J, Beatty GL. Serum Amyloid A Proteins and Their Impact on Metastasis and Immune Biology in Cancer. Cancers (Basel) 2021;13:3179. [Crossref] [PubMed]
23. Yasukawa Y, Hattori N, Iida N, et al. SAA1 is upregulated in gastric cancer-associated fibroblasts possibly by its enhancer activation. Carcinogenesis 2021;42:180-9. [Crossref] [PubMed]
24. Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol 2019;20:296. [Crossref] [PubMed]
25. Chen Y, Pal B, Lindeman GJ, et al. R code and downstream analysis objects for the scRNA-seq atlas of normal and tumorigenic human breast tissue. Sci Data 2022;9:96. [Crossref] [PubMed]
26. Yang X, Hu Q. Dissecting the role of inflammatory biomarkers in breast cancer: Insights from Mendelian randomization. Medicine (Baltimore) 2025;104:e43732. [Crossref] [PubMed]
27. Zhou J, Sheng J, Fan Y, et al. Association between serum amyloid A levels and cancers: a systematic review and meta-analysis. Postgrad Med J 2018;94:499-507. [Crossref] [PubMed]
28. Ignacio RMC, Gibbs CR, Kim S, et al. Serum amyloid A predisposes inflammatory tumor microenvironment in triple negative breast cancer. Oncotarget 2019;10:511-26. [Crossref] [PubMed]
29. du Plessis M, Davis TA, Olivier DW, et al. A functional role for Serum Amyloid A in the molecular regulation of autophagy in breast cancer. Front Oncol 2022;12:1000925. [Crossref] [PubMed]
30. Luo Y, Sheng R, Tan X, et al. Role of EMT in drug resistance of breast cancer: molecular mechanisms and therapeutic strategies. Front Oncol 2025;15:1680751. [Crossref] [PubMed]
31. Hashemi M, Arani HZ, Orouei S, et al. EMT mechanism in breast cancer metastasis and drug resistance: Revisiting molecular interactions and biological functions. Biomed Pharmacother 2022;155:113774. [Crossref] [PubMed]
32. Liu X, Song Y, Hu S, et al. Serum amyloid A contributes to radiation-induced lung injury by activating macrophages through FPR2/Rac1/NF-κB pathway. Int J Biol Sci 2024;20:4941-56. [Crossref] [PubMed]
33. Bullock E, Brunton VG. E-Cadherin-Mediated Cell-Cell Adhesion and Invasive Lobular Breast Cancer. Adv Exp Med Biol 2025;1464:259-75. [Crossref] [PubMed]
34. Padmanaban V, Krol I, Suhail Y, et al. E-cadherin is required for metastasis in multiple models of breast cancer. Nature 2019;573:439-44. [Crossref] [PubMed]
35. Wang Z, Zhang N, Zhang M, et al. GTP Cyclohydrolase Drives Breast Cancer Development and Promotes EMT in an Enzyme-Independent Manner. Cancer Res 2023;83:3400-13. [Crossref] [PubMed]
36. Ma B, Li F, Ma B. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer. Open Life Sci 2023;18:20220776. [Crossref] [PubMed]
37. Salimian N, Peymani M, Ghaedi K, et al. Collagen 1A1 (COL1A1) and Collagen11A1(COL11A1) as diagnostic biomarkers in Breast, colorectal and gastric cancers. Gene 2024;892:147867. [Crossref] [PubMed]
38. Peña R, Baulida J. Snail1 as a key prognostic biomarker of cancer-associated fibroblasts in breast tumors. Biochim Biophys Acta Rev Cancer 2025;1880:189316. [Crossref] [PubMed]
39. Fang Z, Meng Q, Xu J, et al. Signaling pathways in cancer-associated fibroblasts: recent advances and future perspectives. Cancer Commun (Lond) 2023;43:3-41. [Crossref] [PubMed]
40. Li P, Zhang H, Chen T, et al. Cancer-associated fibroblasts promote proliferation, angiogenesis, metastasis and immunosuppression in gastric cancer. Matrix Biol 2024;132:59-71. [Crossref] [PubMed]
41. Lynn KD, Roland CL, Brekken RA. VEGF and pleiotrophin modulate the immune profile of breast cancer. Cancers (Basel) 2010;2:970-88. [Crossref] [PubMed]
42. Yang M, Wang B, Yin Y, et al. PTN-PTPRZ1 signaling axis blocking mediates tumor microenvironment remodeling for enhanced glioblastoma treatment. J Control Release 2023;353:63-76. [Crossref] [PubMed]
43. Huang P, Ouyang DJ, Chang S, et al. Chemotherapy-driven increases in the CDKN1A/PTN/PTPRZ1 axis promote chemoresistance by activating the NF-κB pathway in breast cancer cells. Cell Commun Signal 2018;16:92. [Crossref] [PubMed]
44. Perez-Pinera P, Alcantara S, Dimitrov T, et al. Pleiotrophin disrupts calcium-dependent homophilic cell-cell adhesion and initiates an epithelial-mesenchymal transition. Proc Natl Acad Sci U S A 2006;103:17795-800. [Crossref] [PubMed]
45. Huang J, Zhu Y, Zeng W, et al. Single-cell transcriptome analysis reveals the malignant characteristics of tumour cells and the immunosuppressive landscape in HER2-positive inflammatory breast cancer. J Exp Clin Cancer Res 2025;44:196. [Crossref] [PubMed]