The relationships between the expression of SPP1 and IL33 during the dedifferentiation of breast cancer associated adipocytes and the prognosis of breast cancer

doi:10.21203/rs.3.rs-4371831/v1

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The relationships between the expression of SPP1 and IL33 during the dedifferentiation of breast cancer associated adipocytes and the prognosis of breast cancer

https://doi.org/10.21203/rs.3.rs-4371831/v1

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Purpose

We aimed to determine the biomarkers for breast cancer by studying the relationship between breast cancer and the dedifferentiation process of adipose tissue.

Methods

The public databases Gene Expression Omnibus (GEO) database, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) databases, the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database, the Kaplan-Meier Plotter database, the Human Protein Atlas (HPA) database and the TIMER 2.0 database were used to analyze. Immunohistochemical experiment was carried out to verify the expression of Secreted phosphoprotein 1 (SPP1) gene and the interleukin 33 (IL-33) gene in adipose tissue and breast cancer tissue respectively.

Results

Totally, 19 differentially expressed genes (DEGs) were identified and mainly enriched in the three signaling pathways. Compared with normal controls, SPP1 microRNA and IL33 microRNA were differently expressed (P < 0.05). And SPP1 and IL33 gene were related to the prognosis of estrogen receptor (ER) positive/human epidermal growth factor receptor 2 (HER-2) negative breast cancer patients (P < 0.05). Immunohistochemical results showed that the positive expression rate of SPP1 and IL33 in breast cancer were higher than that in normal tissues; both associated with macrophages in breast cancer.

Conclusion

SPP1 and IL33 affect the prognosis of breast cancer during the dedifferentiation of breast cancer related adipocytes. The SPP1 and IL33 perhaps are potential biomarkers for breast cancer.

SPP1

IL33

Cancer-associated adipocyte

de-differentiation

breast cancer

Recently, breast cancer has become the most concerned health problem for women because of its high mortality and incidence rate ^[1]. Now it is currently considered that the tumor microenvironment (TME), which includes adipose tissue, fibrous tissue, connective tissue, may be an important part of the mechanism of breast cancer progression and metastasis ^[2–3]. As is well known, breast cancer associated adipocytes have always been a research hotspot ^[4], however, its impact on breast cancer and its mechanism have not been clear.

Whereas the mechanism of how adipocyte dedifferentiation affects the progress and prognosis of breast cancer is still under investigation. It was confirmed that in vitro experiments, adipocytes can be co-cultured into dedifferentiated adipocytes to restore proliferation ^[5]. A co-culture experiment of adipocytes and breast cancer cells in vitro showed that compared to adipocytes cultured alone, adipocytes can observe significant morphological changes, adipocytes become slenderer, and show a degreased fibroblast-like phenotype, while cancer cells have no significant changes in morphology ^[6]. What’s more, it was demonstrated that, through single cell sequencing, breast cancer associated adipocytes can be dedifferentiated during tumor progression, which involves a variety of reactions including immune response, inflammation and extracellular matrix remodeling ^[7]. Therefore, we aimed to determine the biomarkers for breast cancer by studying the relationship between breast cancer and the dedifferentiation process of adipose tissue.

2.1 Data processing

Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) were used to download the relevant dataset GSE95827^[8] on the dedifferentiation process of cancer related adipocytes. We divided the dataset into three groups: the pre-adipocyte group co cultured with MCF-7 cells, the pre-adipocyte group co cultured with MDT-231 cells, and the control group with only pre-adipocyte. Conduct differential analysis between the experimental group and the control group, and R software limma package was used to screen differential genes under the conditions of | logFC |>1 and P < 0.05. Finally, we intersected the screened differentially expressed genes, and draw a heat map of their expression in each group.

2.2 Protein-Protein Interaction Network

In the STRING database (https://cn.string-db.org/), we performed protein-protein interaction (PPI) analysis and further visualize the PPI network with Cystoscape software.

2.3 Gene ontology (GO) enrichment and pathway analysis

The STRING database was applied for the Conduct biological process, cell composition, molecular function, and pathway analysis on the selected differentially expressed genes.

2.4 Differential gene analysis

Through the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database (http://gepia2.cancer-pku.cn/), we analyzed and verified the difference of differential gene expression between in breast cancer and normal tissues in GEO database. SPP1 gene and interleukin 33 (IL33) gene were selected as differentially expressed genes during dedifferentiation of breast cancer associated adipocytes in breast cancer. The expression of SPP1 gene and IL33 gene in each stage of breast cancer was analyzed. We analyzed the prognostic correlation of SPP1 and IL33 genes in breast cancer through the Kaplan Meier Plotter database (https://kmplot.com/).

2.5 Immunohistochemical experiment

The paraffin sections of clinical tissue samples used in this experiment were from the First Affiliated Hospital of Chongqing Medical University, including paraffin sections of adipose tissue adjacent to breast cancer patients and paraffin sections of adipose tissue of non-breast cancer patients. Before the experiment, we marked the type of tissue and the type of detected protein on each paraffin section with a pencil. After sealing, add IL33 and SPP1 antibodies diluted at 1:500 according to the labeling on the slice. Re-stain with hematoxylin dye, seal the sections, observe, and take photos. The positive result should be the cytoplasm is brownish yellow, and the nucleus is blue purple; The negative result should be the cytoplasm is extremely light or colorless, and the nucleus is blue purple.

The Human Protein Atlas (HPA) database (https://www.proteinatlas.org/) was used to verify the correlation of SPP1 gene and IL33 gene with inflammatory cells in adipose tissue. The TIMER 2.0 database (http://timer.cistrome.org/) was used to verify the correlation between the two genes and inflammatory cells in breast cancer.

3.1 Genes for dedifferentiation of breast cancer associated adipocytes

We obtained 118 and 40 differentially expressed genes (DEGs)through grouping analysis from the GSE95827 dataset, which was related to the dedifferentiation process of cancer-related adipocytes in the GEO database (Fig. 1a, Fig. 1b). Then, 19 DEGs were obtained from the intersection (Fig. 1c). They were PRL2C5, INHBA, PRG4, A130040M12RIK, IL33, SLPI, TIMP1, SPP1, IL13RA1, OSMR, SAA3, BCL3, SBNO2, IL4RA, SLC39A14, PION, RUNX1, B4GALT5 and UGCG. The expression of DEGs in each group was plotted as a heat map (Fig. 1d), and it was significantly found that the differential genes were highly expressed in the experimental group and low expressed in the control group.

To clarify the relationship between these genes, we conducted PPI network analysis of differential genes in STRING (Fig. 2a), and then further visualized the PPI network through Cytoscape (Fig. 2b).

In order to further analyze the functional role of differential genes in breast cancer, STRING database was used to input differential genes and perform functional enrichment analysis and pathway analysis. In GO analysis, it was discovered that the differential genes were mainly involved in biological functions such as cytokine activity, immune receptor activity, and cytokine receptor binding. These genes were mainly enriched in the MAPK signaling pathway, PI3K/Akt signaling pathway, and TNF signaling pathway (Fig. 3).

The GEPIA database retrieves 1085 breast cancer tumor tissues and 291 normal adjacent tumor tissues and can analyze and verify the differences in the expression of all genes in the co expression network in tumor tissues and normal tissues. The results showed that the expression of SPP1 mRNA, IL33 mRNA, and metalloproteinases 9(MMP-9) mRNA in tumor tissue were significantly higher than that in normal tissue (P < 0.05, Fig. 4a, Fig. 4b). Because MMP-9 gene was not a differential gene in the dedifferentiation process of breast cancer related adipocytes, it was eliminated. SPP1 gene and IL33 gene were selected as differentially expressed genes during dedifferentiation of breast cancer associated adipocytes in breast cancer. At the same time, the relationship between the expression of SPP1 gene and IL33 gene and the stage of breast cancer was analyzed in GEPIA database. The results showed that there was no significant difference in the expression of SPP1 mRNA in each stage (P > 0.05) (Fig. 4c); paradoxically, there were significant differences in the expression of IL33 mRNA in each stage (P < 0.05), besides the expression of IL33 mRNA in stage IV breast cancer was significantly reduced (Fig. 4d).

3.2 The relationship between the expression of SPP1, IL33 genes and the prognosis of breast cancer

As we all know, breast cancer can be divided into ER positive/HER-2 negative, ER negative/HER-2 positive, ER positive/HER-2 positive and TNBC, by the expression of hormone receptor ER and HER-2 gene. A total of 2976 cases of breast cancer were retrieved from the Kaplan Meier Plotter database, and the expression of SPP1 gene and IL33 gene in the overall breast cancer and each breast cancer subtype was analyzed. It was found that there was statistical difference of overall survival (OS) between IL33 gene in the overall breast cancer, ER positive/HER-2 negative, ER negative/HER-2 positive, ER positive/HER-2 positive (P < 0.05), and SPP1 gene in the overall breast cancer, ER positive/HER-2 negative, ER positive/HER-2 positive There was a statistical difference (P < 0.05) in ER negative/HER-2 positive. This indicates that the overall survival period of breast cancer patients was shortened when SPP1 and IL33 genes are overexpressed, and the expression of SPP1 and IL33 genes was related to the poor prognosis of breast cancer patients. Among them, SPP1 gene only shortens the overall survival time in ER positive/HER-2 negative breast cancer patients, indicating that SPP1 gene related to dedifferentiation of breast cancer related adipocytes was related to the prognosis of ER positive/HER-2 negative breast cancer patients (Fig. 5).

3.3 SPP1, IL33 genes and adipose tissue and breast cancer tissue

To further verify the relationship between the two genes and adipose tissue and breast cancer tissue, we obtained the immunohistochemistry analysis of SPP1 gene (Fig. 6a, Fig. 6b, Fig. 6c, Fig. 6d) and IL33 gene (Fig. 7a, Fig. 7b, Fig. 7c, Fig. 7d) in adipose tissue adjacent to breast cancer and breast cancer tissue respectively. And we found that SPP1 was weakly expressed in non-breast cancer breast adipose tissue (Fig. 6a, Fig. 6b) and highly expressed in breast cancer adjacent adipose tissue (Fig. 6c, Fig. 6d). While we found that IL33 was also weakly expressed in non-breast cancer breast adipose tissue (Fig. 7a, Fig. 7b) and highly expressed in breast cancer adjacent adipose tissue Fig. 7c, Fig. 7d).

3.4 SPP1 and IL33 genes and inflammatory cells

The HPA database was used to verify that SPP1 gene was to be strongly associated with macrophages in adipose tissue (Fig. 8). Furthermore, we verified the correlation between the two genes and inflammatory cells in cancer through the TIMER 2.0 database. It was SPP1 that was strongly associated with macrophages in breast cancer except HER-2 over expressed found pression type (Fig S1), while IL33 was associated with CD8 + T cells, CD4 + T cells and M1 phenotype macrophages in breast cancer (Fig S2).

It is well known that cancer associated adipocytes are an important part of the tumor microenvironment ^[9–10]. This presumably due to the production of inflammatory and adipose factors by adipose tissue, which are important sources of postmenopausal sex hormone production. These factors are associated with an increased risk of cancer ^[11]. Although adipocytes, the primary stromal cells of the breast, have been determined to be plastic in physiology and cancer, the tumor-derived molecular mediators of tumor-adipocyte crosstalk have not been identified yet ^[12]. In the current study, we identified the two molecules, SAA1 and IL33, as the molecular crux of the crosstalk between breast cancer cells and human adipocytes.

Secreted phosphoprotein 1(SPP1) is located on chromosome 4q22.1 and encodes 314 amino acid residues. It is a hydrophilic, secretory, and phosphorylated glycoprotein ^[13–14]. This protein is mainly located outside the cell membrane, with multiple phosphorylation sites, indicating that it is an active protein, which is mainly involved in extracellular matrix binding, ossification, osteoblast differentiation ^[15], cell adhesion, PI3K/Akt signal pathway ^[16–17], adhesion spot, extracellular matrix (ECM) receptor interaction Molecular functions and signaling pathways such as Toll like receptor signaling pathways ^[18]. The DEGs in this study were mainly enriched in the MAPK signaling pathway, PI3K/Akt signaling pathway, and TNF signaling pathway. Existing studies have shown that SPP1 in the cancer microenvironment plays a role in the progression of various cancers such as head and neck squamous cell carcinomas ^[19], lung cancer ^[20], pancreatic cancer ^[21], colorectal cancer ^[22], etc., and deteriorates their prognosis. In addition, chronic low-grade inflammation of adipose tissue can stimulate cancer progression, leading to the accumulation of adipose tissue macrophages, especially an increase in pro-inflammatory M1 phenotype macrophages ^[23–24]. This result is consistent with the results of this study, in which SPP1 gene was to be strongly associated with macrophages in adipose tissue (Fig. 10.). Besides in vitro studies it was proposed that SPP1 downregulates thyroid hormone receptor beta (TRβ), the expression of thyroid-stimulating hormone can aggravate lipid deposition in liver cells, thereby promoting the increase of M1 macrophages ^[25]. What’s more, the glycoprotein phosphoprotein osteopontin (OPN) encoded by SPP1 gene had previously been turned out to be associated with poor prognosis of breast cancer. Compared with OPN protein in tumor tissue, SPP1 mRNA was a stronger prognostic marker for phenotype selection ^[26]. Furthermore, our results showed that patients with positive expression of SPP1 had a longer overall survival period compared to patients with negative expression of SPP1, with a statistically significant difference(P < 0.05). It implicated that the expression of SPP1 in breast cancer is related to prognosis. Preclinically, a study revealed that blocking SPP1 or macrophage-specific deletion of SPP1 in mice could destroy the tumor immune barrier structure and sensitize hepatocellular carcinoma cells to immunotherapy ^[27]. The result provided the first key steps towards finding more effective therapies for hepatocellular carcinoma and have implications for us in the field of breast cancer.

The interleukin 33 (IL-33) is a member of the IL1 cytokine family, which is encoded by IL-33 gene ^[28]. IL33 belongs to a group of warning hormone molecules, which are released from cells during cell damage and trigger inflammatory tissue damage reactions. During inflammation or other types of stress, IL-33 expression is upregulated and released from necrotic or damaged cells. IL-33 is an inducer of type 2 immune response, involving various pathological diseases, including allergic, fibrotic, infectious, and chronic inflammatory diseases ^[29]. There were significant differences in the expression of IL33 mRNA in each stage (P < 0.05), besides the expression of IL33 mRNA in stage IV breast cancer was significantly reduced (Fig. 4D.). It has been found that matrix derived IL-33 drives type 2 inflammation and the recruitment of multiple immune cell types to the lung microenvironment, thereby promoting lung metastasis of breast cancer ^[20]. What’s more, we found that there was statistical difference of overall survival (OS) between IL33 gene in the overall breast cancer, ER positive/HER-2 negative, ER negative/HER-2 positive, ER positive/HER-2 positive (P < 0.05). IL-33 induces endocrine resistance in cancer by promoting cancer stem cell properties. These discoveries provide new mechanisms to link IL-33 with the pathogenesis of cancer and point out that IL-33 is a promising target for optimizing hormone therapy in clinical practice. Furthermore, the combined blockade of the IL33/ stimulation expressed gene 2 (ST2) and programmed death-ligand 1 (PD-L1) / programmed death receptor 1(PD-1) axes is more effective in hindering tumor progression than a single blockade of either axis, thus providing a potential new method for tumor immunotherapy ^[30].

Consequently, it is considered that SPP1 may interact with IL33 in promoting breast cancer tissue growth during adipose tissue dedifferentiation. Previous studies have suggested an interaction between the SPP1 gene and IL33. On the one hand, it has been showed that performed immunohistochemistry analysis of high mobility group box 1 (HMGB1), OPN, IL-33-a pattern recognition receptor, with HMGB1 being its important ligand, in the mouse model of endometriosis, the lesioned staining levels of OPN, and IL-33 were both significantly higher than that of normal endometrium and increased progressively as lesions progressed ^[31]. On the other hand, IL33 can block the growth factor receptor (EGFR) signaling pathway through pharmacological effects, leading to reduced expression of SPP1, which encodes OPN in mouse and human eosinophils. the IL-33-amphiregulin-osteopontin axis directs fibrotic responses in eosinophilic airway inflammation ^[32]. In brief, we consider that SPP1 gene and IL33 in the dedifferentiation process of breast cancer related adipocytes may affect the prognosis of breast cancer by influencing macrophages with inflammation.

Consequently, the dedifferentiation process of breast cancer related adipocytes is related to breast cancer. SPP1 and IL33 affect the prognosis of breast cancer through PI3K/Akt pathway during the dedifferentiation of breast cancer related adipocytes, and perhaps are potential biomarkers for breast cancer.

Conflicts of interest: We declared that we have no conflicts of interest to this work.

Ethics approval: We confirm that all methods and experimental protocols of this study was approved by the ethics committee of the First Affiliated Hospital of Chongqing Medical University and certify that the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. We confirm that we have obtained informed consents from all subjects and/or their legal guardian(s).

Acknowledgements: Firstly, grateful acknowledgement is made for my supervisor Dr. Jinxiang Tan who gave me considerable help by me and of suggestion, comments and criticism. In addition, I deeply thank my research participants for helping me. Finally, I would like to thank my parents and friends for their encouragement and inspiring me every time.

Author contributions: Yuchan Jiang, Jinxiang Tan designed experiments. Yuchan Jiang and Shi han analyzed and interpreted data. Yuchan Jiang, Shi Han, Xing Feng, Junfeng Li, Huan Zhang, Yidan Gao, Xing Feng and Jinxiang Tan wrote the paper.

Data availability: All data discussed in this manuscript are included in the main manuscript text or supplementary materials.

Code availability: All code developed is standard and easily reproducible.

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No competing interests reported.

FigS1.tiff
Fig. S1: SPP1 that was strongly associated with macrophages in breast cancer except HER-2 over expressed found expression type.
FigS2.tiff
Fig. S2: IL33 was associated with CD8+T cells, CD4+T cells and M1 phenotype macrophages in breast cancer.

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The relationships between the expression of SPP1 and IL33 during the dedifferentiation of breast cancer associated adipocytes and the prognosis of breast cancer

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Abstract

Purpose

Methods

Results

Conclusion

Figures

1 INTRODCTION

2 METERIAL AND METHODS

2.1 Data processing

2.2 Protein-Protein Interaction Network

2.3 Gene ontology (GO) enrichment and pathway analysis

2.4 Differential gene analysis

2.5 Immunohistochemical experiment

3 RESULTS

3.1 Genes for dedifferentiation of breast cancer associated adipocytes

3.2 The relationship between the expression of SPP1, IL33 genes and the prognosis of breast cancer

3.3 SPP1, IL33 genes and adipose tissue and breast cancer tissue

3.4 SPP1 and IL33 genes and inflammatory cells

4 DISCUSSIONS

5 CONCLUSIONS

Declarations

References

Additional Declarations

Supplementary Files

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