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. 2026 Feb 16;26:434. doi: 10.1186/s12885-026-15716-w

IL-8 positive cancer-associated fibroblasts drive breast cancer progression and immune evasion: insights from GWAS and single-cell transcriptomics

Huifeng Liao 1,#, Huayan Li 2,#, Yanyu Qu 3,#, Liling Tang 4, Peixian Chen 1, Tiancheng He 1, Kun Zhang 1, Wei Li 1, Shuqing Yang 1, Jiawei Lin 1, Jin Lv 1, Xiaoyu Gu 5,✉, Xiangwei Liu 1,✉
PMCID: PMC13040909  PMID: 41692739

Abstract

Background

Interleukin-8 (IL-8) is a key inflammatory mediator in the tumor microenvironment (TME), where cancer-associated fibroblasts (CAFs) drive breast cancer progression by secreting various cytokines. This study investigates the role of IL-8 positive CAFs in breast cancer pathogenesis and explores their underlying molecular mechanisms.

Methods

We performed Mendelian randomization (MR) analysis to evaluate the potential causal relationship between IL-8 levels and breast cancer risk using genome-wide association study (GWAS) data. Subpopulations of IL-8 + CAFs were identified through single-cell RNA sequencing from the GSE180286 dataset. The scPagwas algorithm was used to integrate single-cell RNA sequencing data with GWAS data to investigate the relationship between IL-8 + CAFs and breast cancer. Using the BayesPrism package, we performed Bayesian deconvolution to classify patients into high and low IL-8 + CAFs groups for prognostic and therapeutic sensitivity analyses. Cell-cell interactions were examined using the CellChat package. Additionally, in vitro experiments were performed to confirm the pro-tumor effects of IL-8 + CAFs.

Results

The MR analysis revealed a causal effect between IL-8 exposure and increased breast cancer risk (OR = 1.251). Compared to IL-8-CAFs, IL-8 + CAFs exhibited a significant association with breast cancer. Survival analysis revealed that patients with high abundance of IL-8 + CAFs experienced a poorer prognosis. Moreover, patients in the high IL-8 + CAFs group were sensitive to most chemotherapy drugs, but demonstrated significant resistance to immunotherapy and immune evasion. Notably, significant interactions were observed between IL-8 + CAFs and various cells within the TME. Patients with high abundance of IL-8 + CAFs presented with increased stromal, immune, and ESTIMATE scores, along with reduced tumor purity scores. In vitro experiments confirmed that IL-8 significantly promoted tumor cell proliferation, colony formation, migration, and invasion.

Conclusion

IL-8 + CAFs represent a pivotal cellular subpopulation driving breast cancer progression and immune evasion, highlighting their potential as targets for personalized therapeutic strategies.

Keywords: IL-8, Cancer-associated fibroblasts, Mendelian randomization analysis, ScPagwas, BayesPrism

Introduction

Breast cancer (BRCA) remains one of the most common malignancies among women globally, with rising incidence and mortality rates [1, 2]. Despite significant advances in early diagnosis and therapeutic approaches that have improved patient survival, a subset of patients exhibits poor responses, resulting in disease progression and recurrence [3]. Therefore, elucidating the molecular mechanisms underlying BRCA is indispensable for the innovation of personalized therapeutic approaches.

Within the TME, CAFs emerge as pivotal constituents of the tumor stroma, exercising a decisive influence on tumor biology [4–7]. Beyond promoting tumor cell growth and metastasis through releasing various cytokines, chemokines, and extracellular matrix components, CAFs also critically regulate [8–10] responses within the TME. Recent studies have brought attention to IL-8 due to its significant role in promoting tumor progression [11–14]. Our preliminary research revealed that IL-8 is markedly overexpressed in CAFs compared to tumor cells [15]. Elevated IL-8 levels correlate strongly with tumor metastasis, recurrence, and poor prognosis across multiple cancer types [16–18]. Functionally, IL-8 acts as a pleiotropic cytokine that modulates inflammatory responses and immune-related processes [19]. Mechanistically, IL-8 enhances tumor cell survival and metastasis through activating PI3K/AKT signaling and fostering an immunosuppressive TME [20–22].

Advances in single-cell sequencing technology have uncovered substantial heterogeneity among CAFs [23, 24]. However, the precise characterization of IL-8 + CAFs and their functional significance in the TME remains incompletely understood. This study integrates transcriptomic profiling, MR analysis, and computational algorithms to systematically investigate IL-8 + CAFs in BRCA. Our findings not only advance the understanding of BRCA progression but also provide a theoretical foundation for translating these insights into clinical applications.

Methods

Data sources and processing

The genome-wide association study (GWAS) data for BRCA were downloaded from https://gwas.mrcieu.ac.uk/ (GWAS ID: ieu-a-1168), comprising 15,748 cases and 18,084 controls, for a total of 33,832 samples and 13,011,123 SNPs in the European population [25]. The pQTL data were sourced from https://www.decode.com/summarydata/, which provides genetic association statistics for plasma proteins in 4907 Icelanders [26]. No overlap between the two data sets has been reported publicly. IL-8 pQTL SNPs were used as instrumental variables, downloaded from pQTL datasets without explicit classification into cis- or trans-acting variants, with a significance threshold of P < 5.0 × 10− 8 and only SNPs with an F-statistic > 10 retained. The filtered instruments were subjected to LD clumping with parameters: kb = 10,000 and r2 = 0.1. The transcriptomic sequencing data of BRCA patients were acquired from The Cancer Genome Atlas (TCGA).

Single-cell data processing and analysis

The GSE180286 dataset was downloaded from Gene Expression Omnibus and 9 patients diagnosed with HR+/HER2- breast cancer were recruited [27]. Quality control (QC) and data preprocessing were performed using Seurat v4.3.0. Specifically, cells meeting any of the following criteria were excluded: fewer than 200 detected genes (nFeature_RNA < 200), more than 10,000 detected genes (nFeature_RNA > 10,000), total RNA counts less than 1000 (nCount_RNA < 1000), mitochondrial gene percentage exceeding 20% (percent.mt > 20%), ribosomal gene percentage exceeding 20% (percent.rb > 20%). 39,071 high-quality cells and 29,043 genes were retained for downstream analysis.

ScPagwas analysis

We employed the scPagwas algorithm v1.3.1 to integrate single-cell RNA sequencing (scRNA-seq) data with GWAS data [28]. This algorithm first maps genetic loci to adjacent genes based on GWAS summary statistics and associates them with relevant biological pathways. Subsequently, it calculates pathway activity scores for each cell by analyzing the relationship between SNP effect sizes and normalized gene expression through a regression model. Building on this, the algorithm ultimately generates a Trait-Related Scores (TRS) for each cell. The TRS enables identification of characteristics of specific cell subpopulations and their associations with phenotypes, thereby advancing biological insights and precision medicine.

Bayesian deconvolution

Bayesian deconvolution is a probability model-based technique that uses a predefined characteristic expression profile of IL-8 + CAFs as a reference template [29]. This profile was compared to the overall gene expression profile of tumor tissues to infer the relative abundance of each cell subpopulation in the samples. The obtained IL-8 + CAFs abundance data was subsequently analyzed for correlation with clinical prognostic indicators in BRCA patients to assess its potential role in disease progression.

Cell culture and transfection

Primary CAFs were isolated from tumor tissues of three BRCA patients through enzymatic digestion (0.01 mg/ml type II collagenase) and cultured in DMEM/F12 medium supplemented with 15% FBS. After culturing primary CAFs in serum-free medium for 24 h, the supernatant was collected and centrifuged to prepare conditioned medium (CM) for subsequent experiments. IL-8 overexpression was achieved using lentiviral transfection according to the manufacturer’s instructions, with Polymerase Chain Reaction verification of transfection efficiency. MCF-7 and MDA-MB-231 cells were cultured in DMEM and L15 medium, respectively, each supplemented with 10% FBS.

Cell proliferation, colony formation, migration, and invasion

We used the cell counting kit-8 (CCK-8) to assess the proliferative capacity of tumor cells. Tumor cells were seeded into 96-well plates at a suitable density. CCK-8 reagent was added to each well. The absorbance of the samples was then measured at a 450 nm wavelength with a microplate reader. Colony formation, migration and invasion experiments were conducted as previously described [15]. Three independent biological replicates were performed for each assay.

Statistical analysis

Statistical analysis was performed using SPSS, and one-way ANOVA was used to compare the quantitative data between the control and experimental cohorts. *: P < 0.05, **: P < 0.01, ***: P < 0.001.

Results

Causal Relationship Between IL-8 pQTL and BRCA

We explored the causal relationship between IL-8 pQTL and BRCA through MR analysis. Both the inverse variance weighted (P = 0.020) and weighted median (P = 0.027) methods indicated a significant causal association between IL-8 pQTL and BRCA (Fig. 1A-B). The inverse variance weighted analysis showed an odds ratio (OR) of 1.251, indicating that a genetically predicted one standard deviation increase in circulating IL-8 level was associated with a 25.1% higher odds of BRCA. Sensitivity analysis with the leave-one-out method demonstrated that the results remained stable after sequentially removing each each IL-8 pQTL SNP (Fig. 1C). Furthermore, there was no heterogeneity or pleiotropy (P > 0.05) in the MR results (Table 1; Fig. 1D). Additionally, MR-PRESSO indicated no horizontal pleiotropy in this MR analysis (P = 0.954).

Fig. 1.

Fig. 1

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MR analysis of IL-8 pQTL and BRCA. A The causal effect of IL-8 pQTL on BRCA. B Scatter plot depicting the causal relationship between IL-8 pQTL and BRCA using various MR methods. C Leave-one-out analysis of IL-8 pQTL in BRCA. D Funnel plot of MR analysis

Table 1.

The heterogeneity and Pleiotropy of individual SNPs

Heterogeneity Pleiotropy MR‑PRESSO
MR Egger IVW MR Egger
Exposure Outcome P P P P
IL-8 Breast cancer 0.869 0.949 0.940 0.954
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Significant association of IL-8 + CAFs with BRCA

We conducted single-cell analysis of the GSE180286 dataset and identified six distinct subpopulations of CAFs (Fig. 2A-C). Based on IL-8 expression levels, Fibroblast 5 was designated as IL-8 + CAFs, with the remaining subpopulations classified as IL-8-CAFs (Fig. 2D). The CellChat analysis, a novel algorithm, quantifies the association between distinct cell populations and specific genetic characteristics by calculating trait-related scores (TRS), helping to identify subpopulations central to genetic traits [28]. We applied scPagwas analysis to project the genetic data from GWAS onto scRNA-seq data, obtaining TRS for different cell subpopulations in BRCA. We found that the TRS of IL-8 + CAFs were significantly higher than those of IL-8-CAFs, indicating that IL-8 + CAFs were more closely associated with the genetic risk of BRCA than IL-8-CAFs at the genetic level (Fig. 2E).

Fig. 2.

Fig. 2

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scRNA-seq and scPagwas analyses of IL-8. A-B Dimensionality reduction and visualization of GSE180286 using UMAP (A) and t-SNE (B). C The dotplot showing average expression levels of key marker genes across cell subtypes. D Expression levels of IL-8 in six clusters of CAFs. E The left UMAP visualization illustrates the spatial arrangement of distinct cell populations, while the central plot demonstrates cellular distribution according to TRS scoring. On the right, violin charts present the variation in TRS values among different cell subtypes

IL-8 + CAFs associated with poor prognosis

To explore the impact of the relative abundance of different CAFs subpopulations in the TME on patient prognosis, we applied Bayesian deconvolution to the CAFs subpopulations using the BayesPrism v2.2.2 scPagwas package. The analysis demonstrated a high correlation between protein-coding genes, long non-coding RNAs (lncRNAs) and pseudogenes in BRCA samples and scRNA-seq data, suggesting consistency in protein-coding gene expression levels between scRNA-seq and bulk RNA-seq (Fig. 3A). We categorized BRCA patients into high and low IL-8 + CAFs groups based on the deconvolution score. Survival analysis revealed that the high IL-8 + CAFs group was associated with worse survival (Fig. 3B). Overall, these insights suggest that IL-8 + CAFs may play a crucial role in the development of BRCA.

Fig. 3.

Fig. 3

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Prognostic analysis, immunotherapy sensitivity analysis, and chemotherapy sensitivity analysis of IL-8 + CAFs. A Consistency of gene expression across different types of genes between scRNA-seq and bulk RNA-seq data. B Prognostic evaluation comparing breast cancer patients with high and low IL-8 + CAFs expression (P = 0.04). C-F There were significant differences in TIDE (C), Dysfunction (D), Exclusion (E), and CAF (F) scores between the high and low IL-8 + CAFs groups. G Drug sensitivity analysis of cisplatin, cytarabine, cyclophosphamide, dactinomycin, dasatinib, epirubicin, fludarabine, gemcitabine, olaparib, and vorinostat between high (red) and low (blue) IL-8 + CAFs groups. *: P < 0.05, **: P < 0.01, ***: P < 0.001, ****: P < 0.0001

Response to immunotherapy and drug sensitivity analysis of IL-8 + CAFs

Compared to the low IL-8 + CAFs group, the high IL-8 + CAFs group exhibited higher computational scores in TIDE, Dysfunction, Exclusion, and CAF analyses. Based on the predictive algorithms, patients in the high IL-8 + CAFs group may have poorer predicted responses to immunotherapy (Fig. 3C–F). We employed the R packages pRRophetic v1.2, which leverage ridge regression models pre-trained on the Genomics of Drug Sensitivity in Cancer (GDSC) cell line half-maximal inhibitory concentration (IC50) values to estimate cytotoxic sensitivity. Interestingly, the high IL-8 + CAFs group was predicted to have lower IC50 values for most chemotherapy agents commonly used in BRCA, including cisplatin, cytarabine, cyclophosphamide, dactinomycin, dasatinib, epirubicin, epirubicin, gemcitabine, olaparib, and vorinostat (Fig. 3G). The computational findings indicate that patients in the high IL-8 + CAFs group may derive greater benefit from chemotherapy. Collectively, these findings provide a strategy for personalized treatment of BRCA.

Tumor microenvironment analysis of IL-8 + CAFs

Patients with high IL-8 + CAF infiltration exhibited elevated Stromal, Immune, and ESTIMATE scores, while those with low infiltration demonstrated greater tumor purity (Fig. 4A). Using the CellChat v1.6.1 package, we performed cell-cell communication analysis. Figures 4B-C showed the communication intensity and quantity between IL-8 + CAFs and different cell types. Additionally, IL-8 + CAFs showed significant interactions with various cell types (Fig. 4D), particularly in the MDK-NCL, PTN-NCL, and CXCL12-CXCR4 pathways (Fig. 4E). Moreover, we found that the high IL-8 + CAFs group exhibited significantly upregulated gene expression in multiple immune and inflammation-related pathways, particularly in TNF family members, TNF family member receptors, TGF-β family members, TGF-β family members receptors, interleukins, and interleukin receptors (Fig. 5A-F). These results emphasize the critical function of IL-8 + CAFs in modulating TME, offering valuable insights into the molecular pathways through which IL-8 + CAFs contribute to tumor progression and immune evasion.

Fig. 4.

Fig. 4

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The Microenvironment analysis of IL-8 + CAFs. A Differences in stromal, immune, estimate, and tumor purity scores between the high and low IL-8 + CAFs groups. B-C. The intensity (B) and quantity (C) of communication between different cell subtyes. D Cell-cell communication between IL-8 + CAFs and other cells. E Ligand-receptor pairs between IL-8 + CAFs and other cell subtypes

Fig. 5.

Fig. 5

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Differences in immune and inflammation-related pathways between high and low IL-8 + CAFs groups. (A-F) Comparative analysis key genes expression in TNF family members A, TNF family member receptors B, TGF-β family members C, TGF-β family members receptors D, interleukins E, and interleukin receptors F between high and low IL-8 + CAFs groups

The oncogenic effect of IL-8 + CAFs

We isolated and cultured primary CAFs from tumor tissues of BRCA patients. After overexpressing IL-8, we performed CCK-8, colony formation, wound healing, and Transwell assays. The results indicated that IL-8 significantly promoted tumor cell proliferation, colony formation, migration, and invasion (Fig. 6A-E).

Fig. 6.

Fig. 6

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The effect of overexpression of IL-8 in CAFs on tumor cells. A-E Overexpression of IL-8 promotes the proliferation (A-B), colony formation (C), wound healing (D), and invasive (F) ability of MCF-7 and MDA-MB-231. Three independent biological replicates were performed for each assay. *: P < 0.05, **: P < 0.01, ***: P < 0.001. One-way ANOVA was used to compare the quantitative data among the control and experimental cohorts

Discussion

Here, we systematically reveal the crucial role of IL-8 + CAFs in BRCA progression and immune evasion, emphasizing their complex functions within the TME. Our results demonstrate a significant causal relationship between IL-8 exposure and BRCA risk, suggesting that IL-8 might serve as a valuable biomarker for BRCA. Furthermore, the abundance of IL-8 + CAFs is significantly associated with patient prognosis and immune response, providing new perspectives for understanding the biological mechanism of BRCA and paving the way for personalized treatment.

As a critical inflammatory mediator, IL-8 has received increasing attention for its role in modulating the TME. Experimental validation shows that IL-8 significantly promotes the proliferation, migration, and invasion of tumor cells, further elucidating the link between IL-8 levels and poor prognosis in BRCA patients. IL-8 + CAFs may play a central role in BRCA progression through their pro-tumor functions.

Previous studies indicate that elevated IL-8 levels may suppress immune cell function, facilitating immune evasion [30–32]. Our research findings show that IL-8 + CAFs are associated with various immune suppression pathways, consistent with prior studies. This suggests that IL-8 + CAFs may drive a tumor-promoting microenvironment, thereby partly explaining the correlation between IL-8 + CAFs and resistance to immunotherapy. High Dysfunction scores typically indicate that tumor cells may inhibit T cell function, resulting in less effective immune responses [33]. Similarly, high Exclusion scores imply that immune cells, particularly T cells, fail to infiltrate effectively the tumor, severely diminishing the impact of immunotherapy. Our findings reveal a significant correlation between IL-8 + CAFs and elevated Dysfunction and Exclusion scores, further validating their role in immune evasion.

The TNF-signaling pathway plays an important role in tumor progression [34]. On the one hand, it promotes tumor cell survival by inhibiting apoptosis [35]. On the other hand, it potentially contributes to tumorigenesis through B cell-mediated carcinogenic effects. Studies demonstrate that TNF/TNF receptor family members, such as TNFSF13B, can suppress tumor immune responses, thereby accelerating tumor progression [36–38]. Moreover, the TGF-β signaling pathway enhances tumor cell metastasis and invasion by inducing epithelial-mesenchymal transition, while also promoting immune evasion through suppression of CD8 + T cells and NK cell functions [39–42]. Additionally, multiple interleukins and their receptors are closely associated with chronic inflammation, with IL-6/IL-6R driving tumor growth through pro-inflammatory signaling activation, and IL-4/IL-10 exacerbating the immunosuppressive microenvironment by inhibiting tumor immunity [43–45]. Furthermore, chemokine receptors CXCR1/CXCR2 play pivotal roles in the TME by facilitating angiogenesis [46]. Collectively, these mechanisms form a complex immunoregulatory network within the tumor microenvironment, likely representing key mechanisms through which IL-8 + CAFs mediate immune evasion. Our findings suggest that IL-8 + CAFs may foster tumor progression by amplifying inflammatory signals and synergizing with multiple pathways including TNF and TGF-β signaling pathways, ultimately establishing an immunosuppressive microenvironment conducive to tumor progression.

Despite emphasizing the importance of IL-8 + CAFs in BRCA, several limitations warrant consideration. Our research relies on existing single-cell RNA sequencing data and GWAS data, lacking direct experimental validation. Although we identified the IL-8 + CAFs subpopulation in the single-cell data and observed its correlation with the genetic risk of BRCA, MR analysis reflected systemic IL8 rather than IL8 + CAFs. To improve explanatory power, future research should further validate the actual role of IL8 + CAFs in the tumor microenvironment through clinical samples and animal experiments. Second, we were unable to delve deeply into the specific molecular mechanisms by which IL-8 + CAFs exert their effects in BRCA. The lack of in vivo experiments limits our comprehensive understanding of IL-8 + CAFs in physiological and pathological contexts. This study confirmed the tumor-promoting effects of IL-8 overexpression in vitro. However, in the absence of IL-8-blocking experiments, causal inference remains incomplete, limiting our understanding of IL-8’s role in related signaling pathways. Although we have predicted patient responses of immune and chemotherapy using algorithms, cell-line models are artificial systems grown in vitro and often fail to fully recapitulate the complex physiological conditions present in vivo. Patient populations exhibit substantial genetic and phenotypic heterogeneity, while classical cell lines are typically derived from limited sources and subjected to extensive passaging, which leads to a mismatch between their genetic backgrounds and the diversity of patient cohorts, thereby limiting predictive accuracy. Therefore, subsequent research should incorporate a broader range of experimental approaches, including in vivo models and clinical trials for validation.

In conclusion, we confirm the causal relationship between IL-8 and BRCA, providing significant insights for understanding tumor progression and immune evasion mechanisms. It also suggests new strategies for personalized treatment of BRCA. Further investigation into the molecular mechanisms by which IL-8 + CAFs promote tumor progression will provide essential theoretical support for improving patient outcomes and developing more effective treatment strategies.

Abbreviation

TME

Tumor microenvironment

CAFs

Cancer-associated fibroblasts

GWAS

Genome-Wide Association Study

scRNA-seq

Single-cell RNA sequencing

TRS

Trait-Related Scores

OD

Optical density

GDSC

Genomics of Drug Sensitivity in Cancer

IC50

Half-maximal inhibitory concentration

Authors’ contributions

HFL and HYL performed the research and wrote the manuscript; YYQ, LLT, PXC, TCH, KZ, WL, SQY, JWL, JL and XYG reviewed the article and analyzed the data; XWL designed the study. All authors read and approved the final manuscript.

Funding

This research was sponsored by The Science and Technology Bureau of Foshan.

Data availability

All remaining data and materials are available from the authors upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the First People’s Hospital of Foshan (2320001006552). Written informed consent was obtained from all participants. The patient was pathologically diagnosed as primary breast cancer, and the postoperative specimens were collected during 2023–2024.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Huifeng Liao, Huayan Li and Yanyu Qu contributed equally to this work.

Contributor Information

Xiaoyu Gu, Email: 404410500@qq.com.

Xiangwei Liu, Email: 18038861925@163.com.

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