Multidimensional analysis to elucidate the possible mechanism of bone metastasis in breast cancer

doi:10.21203/rs.3.rs-3311889/v1

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Research Article

Multidimensional analysis to elucidate the possible mechanism of bone metastasis in breast cancer

https://doi.org/10.21203/rs.3.rs-3311889/v1

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Journal Publication

published 08 Dec, 2023

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Background

Breast cancer (BC) patients tend to suffer from distant metastasis, especially bone metastasis.

Methods

All the analysis based on open-accessed data was performed in R software, dependent on multiple algorithms and packages. The RNA levels of specific genes were detected using quantitative Real-time PCR as a method of detecting the RNA levels. In order to assess the ability of BC cells to proliferate, we utilized the CCK8 test, colony formation and the 5-Ethynyl-20-deoxyuridine assay. BC cells were evaluated for invasion and migration by using Transwell assays and wound healing assays.

Results

In our study, we identified the molecules involved in BC bone metastasis based on the data from multiple BC cohorts. Then, we comprehensively investigated the effect pattern and underlying biological role of these molecules. We found that in the identified molecules, the EMP1, ACKR3, ITGA10, MMP13, COL11A1, and THY1 were significantly correlated with patient prognosis and mainly expressed in CAFs. Therefore, we explored the CAFs in BC microenvironment. Results showed that CAFs could activate multiple carcinogenic pathways and most of these pathways play important role in cancer metastasis. Meanwhile, we noticed the interaction between CAFs and malignant, endothelial and M2 macrophage cells. Moreover, we found that CAFs could induce the remodeling of BC microenvironment and promote the malignant behavior of BC cells. Then, we identified MMP13 for further analysis. It was found that MMP13 can enhance the malignant phenotype of BC cells. Meanwhile, biological enrichment and immune infiltration analysis were conducted to present the effect pattern of MMP13 in BC.

Conclusions

Our result can improve the understanding of researchers on the underlying mechanisms of BC bone metastasis.

breast cancer

bone metastasis

prognosis

CAFs

MMP13

It is estimated that the mortality-to-incidence ratio for breast cancer (BC) is 15%, making it the most aggressive cancer among females (1). In 2018, there were 268,670 new cases worldwide, and BC occurs in 12% of all American women over their lifetime (2, 3). Molecular and histological evidence indicates that breast cancer can be divided into 3 subtypes: BC expressing human epidermal receptor 2 (HER2+), hormone receptor positive (ER+) or progesterone receptor positive (PR+) BC and triple-negative BC (ER-, PR-, HER2-) (4, 5). Molecularly, the therapeutic strategies for each type of BC differ, for example, hormone therapy, anti-HER2 monoclonal antibodies, and chemotherapy (6–8). Early diagnosis and treatment have contributed to a 38% decline in BC mortality, but high-quality prevention and effective targeted treatment can accelerate the decrease (9, 10). Genetic predisposition or family history is often associated with BC, so genomic alteration analysis could provide the most significant prognostic biomarker.

There are about 75% of stage IV BC patients who have developed skeletal metastases as the incidence of BC increases, representing about 60%-70% of all metastatic BC cases (11–13). Patients with bone metastases had a 40-month median OS in a study involving 7064 BC patients, and another study found a median survival of 50 months for BC patients with bone metastases (14, 15). In addition to affecting survival time, bone metastasis has also negatively affected the quality of life because of fatigue, pain, and skeletal-related events (SREs) (16–18). Treatments such as bone-modifying agents are available, but their reliability and effects on survival status limit their use (19). To understand the underlying mechanism, we need to develop a deep understanding of the crosstalk among BC cells, fibrocytes, endothelial cells, and muscle tissue in the tumor microenvironment and bone microenvironment (20–22). A good prognosis for BC patients with bone metastases requires suitable bone-targeting agents as well as identifying the responses of these patients through molecular mechanisms.

In our study, we identified the molecules involved in BC bone metastasis based on the data from multiple BC cohorts. Then, we comprehensively investigated the effect pattern and underlying biological role of these molecules. We found that in the identified molecules, the EMP1, ACKR3, ITGA10, MMP13, COL11A1, and THY1 were significantly correlated with patient prognosis and mainly expressed in CAFs. Therefore, we explored the CAFs in BC microenvironment. Results showed that CAFs could activate multiple carcinogenic pathways and most of these pathways play important role in cancer metastasis. Meanwhile, we noticed the interaction between CAFs and malignant, endothelial and M2 macrophage cells. Moreover, we found that CAFs could induce the remodeling of BC microenvironment and promote the malignant behavior of BC cells. Then, we identified MMP13 for further analysis. BC cells were shown to exhibit an enhanced malignant phenotype following treatment with MMP13. Meanwhile, MMP13 in BC was analyzed based on biological enrichment and immune infiltration.

Acquisition of open-accessed data of BC patients

Gene Expression Omnibus (GEO) and Cancer Genome Atlas Program (TCGA) databases were used for the expression profile and clinical information. Detailed, the initial expression profile file was the “STAR-Counts” form and was sorted out using the R software. Clinical information was in the bcr-xml form and sorted out using the Perl code. For the GEO database, the projects GSE14017, GSE14018 and GSE137842 were finally selected, which contain the transcriptional profiling data BC bone metastasis. The GSE16554 and GSE175692 were removed from the study because of concerns about the quality of their data. Before analysis, all the data were pro-precessed, including probe annotation, data normalization and missing value completion. With the set threshold, the limma package was used to analyze differentially expressed genes (DEGs).

Protein-protein interaction (PPI) network

Based on input genes, we identified the PPI network. The Organism was “Homo”. All the edges and nodes were based on “evidence”. The export file generated from the STRING database was visualized using the Cytoscape software. Cytoscape's clueGO plug-in was used to visualize enrichment analysis. The terms with P < 0.001 were identified. The hub nodes were identified using the Cytohubba plug-in.

Prognosis analysis

As part of the prognostic analysis, univariate Cox regressions, LASSO regressions, and multivariate Cox regressions were examined.

Single-cell analysis

With the help of the TISCH project, the single-cell analysis was performed (http://tisch.comp-genomics.org/home/).

Tumor microenvironment quantification

The quantification of the BC tumor microenvironment was conducted using the XCell algorithm (23). Gene set enrichment analyses (ssGSEA) were used to calculate enrichment scores for specific immune functions (24).

Biological enrichment analysis

The investigation on biological enrichment was performed using the GSEA algorithm based on specific pathway file (25). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted using the clusterprofiler package (26).

Immunohistochemistry (IHC)

The representative image of IHC was get from the Human Protein Atlas (HPA) database (27).

Pan-cancer analysis

The pan-cancer analysis was performed using the online website Sangerbox (http://vip.sangerbox.com/).

Cell culture and quantitative Real-time PCR (qRT-PCR)

The human breast cancer cell lines (T47D, MCF-7, MDA-MB-231, MDA-MB-469) and normal cell lines (MCF-10A) were routinely stored in the laboratory. All the cells were incubated in conventional culture conditions. An RNA simple Total RNA Kit (Tiangen, China) was used for total RNA, which was then reverse transcribed to cDNA. The primers used for qRT-PCR were as follows: MMP13, forward primer, 5’-ACTGAGAGGCTCCGAGAAATG-3’; reverse primer, 5’-GAACCCCGCATCTTGGCTT-3’. GAPDH, forward primer, 5’-GGAGCGAGATCCCTCCAAAAT-3’, reverse primer, 5’-GGCTGTTGTCATACTTCTCATGG-3’.

Cell transfection

Cell transfection was carried out based on the lipofectamine 2000 regent following the protocol. The control and sh-MMP13 plasmids were purchased from Shanghai GenePharma (28).

Cell proliferation assay

The evaluation of cell proliferation ability was performed using the CCK8 and colony formation assay following the standard process (28).

Transwell

Following the standard protocol, we performed transwell assays with sh-MMP13 and control cells (28).

Wound-healing

Wound-healing assay was conducted based on the sh-MMP13 and control cells following the standard process (28).

5-Ethynyl-20-deoxyuridine (EdU) assay

EdU assay was performed using the sh-MMP13 and control cells following the standard process (29).

Statistical analysis

The analysis based on open-accessed data was conducted using the R software. The GraphPad Prism 8 and SPSS software.

Identification of the molecules involved in the bone metastasis process

Based on the limma package and set threshold, we performed DEGs analysis in GSE14017, GSE14018 and GSE137842. For GSE14017, between BC samples with bone and other organ metastases, 73 genes were downregulated and 374 were upregulated (Fig. 1A). For GSE14018, in BC samples with metastases of bone and other organs, 106 genes were upregulated and 25 downregulated (Fig. 1B). For GSE137842, a total of 3189 were downregulated and 2772 upregulated genes were identified between the BC samples with metastasis of bone and other organs (Fig. 1C). The intersection of these three databases identified 82 common genes involved in the bone metastasis process (Fig. 1D). In Fig. 1E, 82 genes in the BC sample are shown to be expressed. Based on the STRING database, the PPI network was shown in Fig. 1F. The cytohubba plug-in identified that in the PPI network, the COL1A1, COL3A1, COL11A1, COL1A2, POSTN and DCN were the hub nodes, which might exert a wide regulatory effect in the BC bone metastasis (Fig. 1G). ClueGO analysis indicated that these genes were mainly enriched in the regulation of the BMP signaling pathway, odontogenesis, extracellular matrix disassembly, tendon development, glycosaminoglycan catabolic process, skeletal system development, skeletal system morphogenesis, embryonic skeletal system development (Fig. 1E).

The bone metastasis-related genes EMP1, ACKR3, ITGA10, MMP13, COL11A1, THY1 were significanly correlated with prognosis

Univariate Cox regression analysis indicated that the EMP1, ACKR3, ITGA10, MMP13, COL11A1, and THY1 were significantly correlated with patient survival, in which the EMP1, ACKR3, MMP13, COL11A1, THY1 were risk factors, while ITGA10 was protective factor (Fig. 2A). Meanwhile, we found that all these prognosis-related genes were not differentially expressed in the primary tumor with or without bone metastasis, indicating that these genes exert their role inside the metastatic foci (Fig. 2B). Also, no remarkable difference in the level of prognosis-related genes was found in patients with < = 60 and > 60 old (Fig. 2C). Moreover, we noticed that all these genes were upregulated in the bone metastasis foci (Fig. 2D). For BC patients with stage III-IV, we noticed a higher expression level of ACKR3 and THY1 compared to those with stage I-II (Fig. 2E); For BC patients with N1-3, we noticed a higher expression level of ACKR3 and THY1 compared to those with N0 (Fig. 2F); For BC patients with T3-4, we noticed a lower expression level of MMP13 and COL11A1 compared to those with T1-2 (Fig. 2G); No significant difference was observed in the M stage (Fig. 2H). Then, we evaluated the expression pattern of these genes at the single-cell level through the TISCH website. Results indicated that these genes were primarily expressed in fibroblasts based on the BRCA_Alex, BRCA_EMTAB_8107, BRCA_GSE114727_inDrop, BRCA_GSE148673 and BRCA_GSE161529 projects (Fig. 2I).

Cell enrichment and interaction of fibroblasts

Subsequently, the microenvironment of BC tumors is influenced by cancer-associated fibroblasts (CAFs). In the EMTAB8107 project, KEGG analysis indicated that the CAFs could activate the activity of complement and coagulation cascades and ECM receptor interaction signaling while hampering the activity of allograft rejection, antigen processing and presentation, autoimmune thyroid disease, cell adhesion molecules cams, cytosolic DNA signaling pathway, graft versus host disease, hematopoietic cell lineage, natural killer cell-mediated cytotoxicity, primary immunodeficiency, and ribosome (Fig. 3A-B). For Hallmark analysis, we noticed that CAFs could activate the activity of angiogenesis, coagulation, epithelial-mesenchymal transition (EMT), myogenesis, UV response DN, while suppressing the activity of inflammatory response, interferon alpha response, interferon-gamma response, KRAS signaling, MYC target, oxidative phosphorylation (Fig. 3C-D). Another project, GSE114727_inDrop, also showed similar conclusions (Fig. 3E-H). In the EMTAB8107 project, cell interaction results indicated that the CAFs in BC microenvironment mainly interacted with the malignant and mono/macrophage cells (Fig. 3I). In the GSE114727_inDrop project, results indicated that the CAFs primarily interacted with mono/macrophage cells (Fig. 3J).

CAFs can remodel the tumor microenvironment of BC

XCell algorithm was utilized to quantify the tumor microenvironment of BC patients (Fig. 4A). Results showed that most of the immune or stromal cells quantified by the XCell algorithm were differentially expressed in different CAFs infiltration patients (Fig. 4B). For the immune function quantified by the ssGSEA algorithm, we noticed that in the patients with high CAFs infiltration, the APC_co_stimulation, CCR, cytolytic activity, inflammation-promoting, MHC_class_I, parainflammation and Type_II_IFN_response were activated (Fig. 4C). Also, there is a positive correlation between the two of CAFs, endothelial cells and M2 macrophages (Fig. 4D-F, CAFs-endothelial cells: cor = 0.353, CAFs-M2 macrophages: cor = 0.156, Endothelial-M2 macrophages: cor = 0.271). The correlation of CAFs, endothelial cells and M2 macrophages with EMP1, ACKR3, ITGA10, MMP13, COL11A1, THY1 was shown in Fig. 4G. Then, we extracted the gene expression pattern of four kinds of immune molecules, including chemokine, immunostimulator, MHC and receptors (Fig. 4H). Most of these immune-related molecules were differentially expressed in patients with high and low CAFs infiltration (Fig. 4I). Almost all immune pathways were negatively correlated with CAFs, according to pathway enrichment analysis (Fig. 4J).

BC proliferation, invasion, migration, and migration are promoted by CAFs

By adding the culture supernatant of CAFs to BC cell lines, we tried to observe whether CAFs would affect the biological behavior of BC. Based on the evidence from the transwell and CCK8 assay, the results of the transwell assay indicated that CAFs could promote the invasion, migration and proliferation ability (Fig. 5A-C). Results of GO-BP indicated that CAFs significantly affect the activity of cell-cell adhesion via plasma-membrane adhesion molecules, homophilic cell adhesion via plasma membrane adhesion molecules, extracellular matrix organization, regulation of membrane potential (Fig. 5D). Results of GO-CC showed that CAFs was remarkably correlated with the collagen-containing extracellular matrix, transmembrane transported complex, transporter complex, synaptic membrane (Fig. 5E). Results of GO-MF indicated that the CAFs was mainly enriched in the term of extracellular matrix structural constituent (Fig. 5F). Results of KEGG were also showed in Fig. 5G. The top three enriched Hallmark pathways of CAFs were EMT, apical junction and myogenesis (Fig. 5H-J).

Further exploration of MMP13 in BC

As a next step, we performed LASSO regression analysis for reducing the number of dimensions in the data (Fig. 6A-B). Then we identified two genes, including ITGA10 and MMP13 based on the molecules screened (Fig. 6C). The MMP13 was then selected for further analysis. There is a poor prognosis for patients with high MMP13 expression, whether it is overall survival, disease-free survival or progression-free survival (Fig. 6D-F). Hallmark analysis showed that in patients with high MMP13 expression, the top five upregulated pathways were EMT, UV response DN, coagulation, TGF-β signaling and angiogenesis (Fig. 6G). The top downregulated pathways were pancreas beta cells, allograft rejection, oxidative phosphorylation, MYC target and E2F targets (Fig. 6H). Based on the results of the immune infiltration analysis, MMP13 corresponds negatively to macrophages, neutrophils, Th1 cells, TgD and iDC, while corresponds negatively to pDC, NK CD56 bright cells and CD8 + T cells (Fig. 6I).

Biochemical effects of MMP13 on BC cells

We noticed a higher level of MMP13 in paired or non-paired BC tissue compared to the control normal tissue (Fig. 7A-B). Also, the IHC result obtained from the HPA database and we found the protein level of MMP13 is higher in BC samples (Fig. 7C-D). Cell lines expressing MMP13 were found to be highly expressed (Fig. 7E). qRT-PCR was used to validate knockdown efficiency and shRNA#2 was selected for further analysis. (Fig. 7F-G). As shown by CCK8 and colony formation assays, the inhibition of MMP13 significantly impeded the proliferation of BC cells. (Fig. 7H-J). This effect was also validated by the EdU assay (Fig. 7K). BC cell invasion and migration were significantly inhibited by suppressing MMP13 in transwell and wound-healing assays. (Fig. 8A-B).

Pan-cancer analysis of MMP13

We next evaluated the effect pattern of MMP13 in pan-cancer. A significant correlation was found between MMP13 and immune-related molecules in pan-cancer, including chemokine, immunostimulator, MHC and receptor (Fig. 9A). Moreover, MMP13 was correlated with many immune checkpoints in pan-cancer (Fig. 9B). The prognosis effect and expression pattern of MMP13 in pan-cancer was shown in Fig. 9C-D.

The BC morbidity rate continues to increase steadily and has become a major public health concern, accounting for 25% of cancer incidence and 15% of cancer-related deaths in women(1). Although mammographic screenings are still helping diagnose breast cancer, the current prevention and treatment strategies are ineffective (30). A genetic risk factor, including pathogenic mutations in genes such as checkpoint kinase 2, considerably impacts BC development; inherited mutations in BRCA1 and BRCA2 account for the majority of cases (31). There are no underlying mechanisms that are capable of preventing or treating breast cancer effectively. In light of mutations of genes, the microenvironment of tumors has been altered, and we can enhance the immune system's role in the fight against cancer to benefit patients. Melanoma, renal cell carcinoma, and non-small cell lung cancer are successfully combated by activating T cells and blocking CTLA-4 and PD-1/PD-L1 axes with monoclonal antibodies (32, 33). It is also possible to study some critically important inhibitors to enhance an immune-activated tumor microenvironment and speed up breast cancer elimination by investigating potential BC biomarkers.

BC patients with bone metastases have low survival rates, with less than 30% surviving over 5 years (34). The majority of BC metastases occur in the bones, except for the liver, brain, and lung. It is defined as the dissemination of cancer cells from primary cancer to secondary sites, which is the evolution of BC metastasis to bone (35). When cancer cells enter the bone marrow, they destroy bone tissue by interacting with osteoblasts, osteoclasts and bone stromal cells, and the cancer-associated stromal fibroblasts release various growth factors stored in bone tissue, causing cancer cells to proliferate continuously to form metastases (36). The function of vascular endothelial cells, hematopoietic stem cells, osteoblasts, osteoclasts, fibroblasts and immune cells is critical during the process of metastasis and the control of tumor dormancy at secondary sites (37–39). It may be beneficial to target these cells and the bone environment to develop novel approaches to prevent and treat bone metastatic BC in female patients.

Bioinformatics can exert a useful tool to understand diseases (40, 41). In our study, we identified the molecules involved in BC bone metastasis based on the data from multiple BC cohorts. Then, we comprehensively investigated the effect pattern and underlying biological role of these molecules. We found that in the identified molecules, the EMP1, ACKR3, ITGA10, MMP13, COL11A1, and THY1 were significantly correlated with patient prognosis and mainly expressed in CAFs. Therefore, we explored the CAFs in BC microenvironment. Results showed that CAFs could activate multiple carcinogenic pathways and most of these pathways play important role in cancer metastasis. Meanwhile, we noticed the interaction between CAFs and malignant, endothelial and M2 macrophage cells. Moreover, we found that CAFs could induce the remodeling of BC microenvironment and promote the malignant behavior of BC cells. Then, we identified MMP13 for further analysis. The result showed that MMP13 could enhance the malignant phenotype of BC cells. Meanwhile, biological enrichment and immune infiltration analysis were conducted to present the effect pattern of MMP13 in BC.

CAFs are a heterogeneous stromal cell population and the most important component of the tumor microenvironment. Our result showed that bone metastasis-related genes were mainly expressed in CAFs. Also, in BC microenvironment, the patients with high CAFs infiltration might have a higher activity of multiple pathways involved in cancer metastasis, including EMT, angiogenesis and coagulation. Meanwhile, CAFS can inhibit the inflammatory response. In colon cancer, Hu et al. found that exosomes secreted by CAFs could enhance chemotherapy resistance and metastasis ability through regulating EMT pathways and tumor stemness (42). Kashima et al. showed that in esophageal cancer, CAFs could promote lymph node metastasis (43). In BC, Wen et al. noticed that the IL32 generated by CAFs could enhance the invasion and metastasis ability through integrin β3-p38 MAPK signaling (44). Zheng et al. found that the biglycan secreted by CAFs in BC can exert a cancer-promoting factor by inducing the immunosuppressive microenvironment (45). Our results indicated that the CAFs might contribute to the bone metastasis process of BC, making it a potential target for clinical applications. Moreover, we found that CAFs could significantly promote the invasion, migration and proliferation ability of BC cells.

MMP13 is a member of matrix metalloprotease. It is reported that MMP is an interesting gene associated with cancer progression, angiogenesis promotion, metastasis and immune surveillance avoidance (46). Zhang et al. indicated that in ovarian cancer, HIF-1α could promote cancer invasion and migration by targeting MMP13 and inducing the hypoxia microenvironment (47). Kumamoto et al. noticed that ING2 can facilitate colon cancer progression through increasing the expression level of MMP13 (48). Also, Ma et al. found that MMP3 and MMP13 are the hub genes of anaplastic thyroid cancer based on transcriptome sequencing (49).

Although we have carried out a lot of analysis to elaborate on the possible mechanism of bone metastasis of BC and obtained some trustworthy results, some shortcomings still need to be mentioned. Firstly, underlying race bias is inevitable. Detailed, the enrolled sample were mostly Western populations. However, there are still differences in genomics among different races, which may reduce the reliability of our conclusions. Secondly, follow-up biological research is still needed to validate our conclusions.

Ethics approval and consent to participate

Not applicable.

Availability of Data and Materials statement

Breast cancer sample were gathered from openly available databases including TCGA database (https://tcga-data.nci.nih.gov/tcga/) and GEO database (https://www.ncbi.nlm.nih/geo/query/). Another data that support the findings of this study are available on request from the corresponding author.

Authors' contributions

KY, ZXJ, and ZTX designed the study and performed all analysis. LSY, JLC, ZW, LYL and TJL wrote the manuscript. ZJ, BQ and LJ revised the manuscript. All the authors participated in the data analysis and approved the final version of the manuscript.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgements

We acknowledge the help in plot from online tool of home-for-researchers (https://www.home-for-researchers.com/).

Funding

Not Applicable.

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No competing interests reported.

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published 08 Dec, 2023

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Editorial decision: Major revision
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Multidimensional analysis to elucidate the possible mechanism of bone metastasis in breast cancer

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Acquisition of open-accessed data of BC patients

Protein-protein interaction (PPI) network

Prognosis analysis

Single-cell analysis

Tumor microenvironment quantification

Biological enrichment analysis

Immunohistochemistry (IHC)

Pan-cancer analysis

Cell culture and quantitative Real-time PCR (qRT-PCR)

Cell transfection

Cell proliferation assay

Transwell

Wound-healing

5-Ethynyl-20-deoxyuridine (EdU) assay

Statistical analysis

Results

Identification of the molecules involved in the bone metastasis process

The bone metastasis-related genes EMP1, ACKR3, ITGA10, MMP13, COL11A1, THY1 were significanly correlated with prognosis

Cell enrichment and interaction of fibroblasts

CAFs can remodel the tumor microenvironment of BC

BC proliferation, invasion, migration, and migration are promoted by CAFs

Further exploration of MMP13 in BC

Biochemical effects of MMP13 on BC cells

Pan-cancer analysis of MMP13

Discussion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1