Development and validation of a novel bone metastasis-associated prognostic model for breast cancer

Development and validation of a novel bone metastasis-associated prognostic model for breast cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and validation of a novel bone metastasis-associated prognostic model for breast cancer Shilei Huang, Xiaobin Cao, Jingjing Liu, Xiang Ji, Yu Han, Xuejian Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3981365/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Bone is the main location where breast cancer spreads, which can greatly impact the outlook and well-being of those affected by the disease. Novel targets are crucial in clinical practice as traditional treatment for breast cancer patients with bone metastases is mostly futile. Current therapy for bone metastases in breast cancer patients is hindered by a lack of understanding of the root causes. Methods We created a prognostic bone metastasis signature called PABMS. The Prognostic Analysis of Bone metastases Score (PABMS) utilised genes associated with bone metastases to forecast the overall prognosis of breast cancer patients. The clinical significance and immunological environments of the signature were exhaustively examined. Results Differentially expressed genes (DEGs) associated with bone metastasis were identified by utilising data from the GSE14666 database in order to distinguish disseminated tumour cells (DTCs) from metastatic tumour cells (MTCs). The bone metastasis-related signature (PABMS), comprised of four genes (ZER, SBK1, L1CAM, and CXCL1), was identified. This signature exhibited a robust correlation with the overall survival of patients diagnosed with breast cancer. This was ascertained through the analysis of transcriptome data from a specific cohort of patients. The efficacy of the four-gene signature in distinguishing bone metastases was validated through the examination of protein levels in an independent cohort. There was a significant correlation between a high-risk score and an extremely adverse patient outcome, according to the findings. Stratification analysis and multivariate Cox regression demonstrated that the PABMS functioned independently as a prognostic factor. Risk score was found to positively correlate with the antigen-presenting process, inflammatory response, and expression levels of many immunosuppressive checkpoint molecules (PD-1, B7-H4, galectin-9, and FGL-1). The findings suggested that patients at high risk exhibited inhibition of the immune response. Conclusion This work created a practical signature using differentially expressed genes associated with bone metastases. This attribute has the potential to function as a highly effective predictive instrument and contribute to the enhancement of the cancer therapy methodology. Biological sciences/Cancer Biological sciences/Cancer/Breast cancer immunological landscape breast cancer and bone metastasis-related Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Breast cancer (BC) is a serious health problem since it is the leading cause of cancer-related deaths among women worldwide [ 1 , 2 ]. In 80% of cases, the main reason for death from breast cancer is the spread of the disease to other vital organs such the brain, liver, bones, and lungs [ 3 , 4 ]. Metastasis that occurs at a distance most frequently occurs in the bone [ 1 , 2 ]. Bone deterioration frequently results in bone-related issues such as pain, fractures, spinal cord compression, severe hypercalcemia, etc., which have a detrimental effect on the quality of life of patients with breast cancer [ 1 , 2 ]. As a result of treatment resistance, conventional breast cancer treatments like radiation, chemotherapy, and adjuvant chemotherapy, in addition to the newly authorised bone-specific medications, only provide partial relief for skeletal-related disorders[ 5 , 6 ]. Identifying new biomarkers is essential for diagnosing and treating breast cancer bone metastases due to their intricate nature. Breast cancer cells undergo a complicated metastatic process that begins in the blood and lymphatic system and continues through the bone environment adaptation and cell multiplication [ 7 ]. Over the past few decades, an increasing amount of evidence has shown that breast cancer cells that have metastasized to bones have unique molecular characteristics [ 8 ]. Bone metastasis of breast cancer is influenced by primary tumour stroma and specific stromal components in distant organs. This includes interactions between tumour cells and blood arteries, which affect the preference for bone tissue. [ 9 ]. Osteoclasts breakdown bone continuously as osteoblasts produce new bone, engaging in the process of remodelling bone tissue. Cancer frequently metastasizes to bones [ 10 ]. The unique bone environment and the biological composition of the bone marrow niche—which includes hematopoietic stem cells, osteoblasts, and osteoclasts—are exploited by metastatic breast cancer. These cells are crucial in the bone formation, remodelling, and healing processes [ 11 ]. A number of substances, including IL-6, lysyl oxidase, intercellular adhesion molecule 1, and vascular cell adhesion molecule 1 are released by breast cancer cells and can increase the activity of osteoclasts [ 11 ]. A study has demonstrated that miR-21, an oncomicroRNA with great conservation, triggers the formation of the pre-metastatic environment for bone metastasis in breast cancer. [ 6 ]. The findings have confirmed the significance of identifying new potential biomarkers for breast cancer patients at risk of bone metastases, while the molecular underpinnings are still unknown. By utilising vast publicly available gene expression datasets, researchers have the ability to accelerate and enhance the precision of biomarker identification for tumour surveillance. A metabolic signature associated with recurrence has been developed in order to evaluate the predictive value for patients diagnosed with stage I lung adenocarcinoma [ 12 ]. We investigated if bone metastasis-related genes may create a predictive gene signature in BC. This study identified differentially expressed genes (DEGs) between bone marrow aspirates (DTCs) and Computed Tomography (MTCs) using gene expression profiles from the GS14776 dataset. After identifying important differences between DTCs and MTCs, we used 1070 breast cancer samples from the TCGA dataset to develop a predictive bone metastasis signature (PABMS). We verified the predictive power of the signature by analysing the expression of certain proteins in tumour primary and metastatic specimens from patients with BC. Immunotherapies, which stimulate the immune system to combat tumours, have been highly successful in recent years [ 13 ]. We extensively analysed the clinical significance, immune checkpoint features, and immune cell infiltration of the novel PABMS in BC patients, considering the promising potential for immunotherapy. The analytical results may enhance the strategy for treating cancer, especially in the area of immunotherapy, and lower the risk of cancers metastasizing to the bone. Materials and methods Publicly available gene expression datasets Gene expression data from 1094 breast cancer tissues and 114 paracancer tissues were obtained from the TCGA database at https://tcga-data.nci.nih.gov/tcga . The transcriptome data were converted by a factor of two and quantile normalisation was used before the patients were standardised. The public had access to all relevant data on enrolled patients and their clinical outcomes. Supplementary Table S1 presents the patient data related to the TCGA cohort. GEO data download and preprocessing We obtained data on disseminated tumour cells (DTCs) from bone marrow aspirates and metastatic tumour cells (MTCs) from Computed Tomography scans from the Gene Expression Omnibus (GEO) datasets, specifically 8 samples of DTCs and 6 cases from GSE14776. The data preparation steps were as outlined: If the dataset was not log2 transformed, it was changed to log2. If the data was not quantile-normalized, the limma software was used for normalisation. The quantile normalisation was performed using the Between Arrays technique in R. A blank probe was removed, the probe was matched with the gene, and many probes were linked to the same gene. We calculated the average value of gene expression. A difference analysis was performed using the Wilcox rank-sum test, with a P-value threshold of less than 0.05 being the filtering criterion. Candidate gene collection and data expression The study examined the differentially expressed genes (DEGs) in DTCs and MTCs samples from the GSE14776 dataset, as well as the DEGs in 1093 tissues of breast cancer patients and 114 tissues of paracancer retrieved from the TCGA database. We selected differentially expressed genes (DEGs) that were either up-regulated or down-regulated in both cohorts. Enrichment Analysis of bone metastasis-associated Genes Gene set enrichment analysis (GSEA), a commonly employed method for enrichment analysis, was performed on samples of BC with high and low risk in order to acquire a more comprehensive understanding of overall changes [ 14 ]. Development and Validation of the Bone Metastasis Signature Associated with Prognosis We selected the overall survival (OS) data for the PABMS block from the TCGA’s Pan-Cancer Atlas [ 15 ]. The TCGA dataset consisted of 1070 patients in the training set. We performed univariate Cox proportional hazards regression analysis to evaluate their predictive importance for overall survival in the training dataset [ 16 ]. To determine which genes have the greatest predictive values, we employed the Cox proportional hazards regression model with the least absolute shrinkage and selection operator (LASSO) [ 17 ]. The purpose of this was to identify the genes with the highest predictive values. Both of these models were applied utilizing fundamental standards. We employed a multivariate version of the Cox proportional hazards regression model to determine the target genes that make up a Prognostic and Predictive Biomarker Signature (PABMS). We created a method to calculate the PABMS value for individual patients by assigning weights to the normalised expression values of certain genes based on their relevant variables. The formula normalised the expression values of the target genes by setting the mean to 0 and the standard deviation to 1 [ 18 ]. This was done to provide a consistent cutoff value for categorising patients into low-risk or high-risk groups. The revolutionary PABMS was evaluated for its accuracy in predicting overall survival (OS) in two distinct groups using the receiver operating characteristic curve (ROC) and Kaplan-Meier survival analyses. We conducted univariate and multivariate Cox regression analysis to determine if PABMS was an independent prognostic risk factor. Immune cell infiltration analysis Transcriptome data was analysed using the deconvolution method CIBERSORT to determine the relative abundances of 22 immune cells in tumour samples. The algorithm may be accessed at https://cibersort.stanford.edu/ . The outcome was additionally refined utilising a significance level of P < 0.05 [ 19 , 20 ]. To reduce the impact of any confounding variables, the quantile normalisation approach was applied to standardise the gene expression profile data across all samples. Statistical analysis The statistical studies were carried out using GraphPad Prism version 8.0 and R version 4.0.5. The Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) curves by employing log-rank testing. To facilitate comparisons among the groups, the Mann-Whitney U and chi-square tests were applied. Every single P-value that was presented had a two-tailed tail. With the exception of those explicitly indicated otherwise, all analyses were deemed statistically significant if the P-value was below 0.05. Results Screening of DEGs between DTCs and MTCs in BC We analysed gene expression profiles in 8 DTCs and 6 MTCs from GSE14776 to compare the unique biological processes in these two types of cancer. Thus, 3373 up-regulated DEGs and 4677 down-regulated DEGs were found between DTCs samples and MTCs(Fig. 1 A). We also taking a P -value of < 0.05 as the threshold, and obtained 4221 DEGs between tumor and paracancer of BC samples in TCGA database, including 1412 up-regulated genes and 2809 down-regulated genes(Fig. 1 B). Venn analysis were performed to discover both up-regulated or down regulated DEGs in bone metastasis and BC samples. As shown in Fig. 1 C-D, there were 334 up-regulated and 276 down-regulated DEGs. Development of PABMS for BC in training cohort Significant differences were identified between breast cancer patients with and without bone metastases at the beginning of the study. We aimed to create a Prognostic Algorithm for Breast Cancer (PABMS) to enhance the accuracy of predicting outcomes in breast cancer. This was done by analysing 1070 breast cancer samples from the TCGA database. A univariate Cox proportional hazards regression analysis was conducted to discover genes associated with bone metastases that are correlated with overall survival. Among 610 bone metastasis-related genes, 80 were shown to be predictive for OS (P-value < 0.05). We employed LASSO Cox proportional hazards regression modelling to identify genes with the highest predictive capabilities. The minimum criteria were used to identify forty-four genes (Figs. 2 A and 2 B). Following multivariate Cox regression analysis, a unique prognostic signature consisting of four genes (ZER, SBK1, L1CAM, and CXCL1) was created in order to further develop a PABMS for prognosis (Fig. 2 C). The risk score model for each patient was created using the formula: risk score = 0.192 × normalised expression value of EZR-0.243 × normalised expression value of SBK1 + 0.359 × normalised expression value of L1CAM-0.384 × normalised expression value of CXCL1. Patients were categorised into low-risk and high-risk groups using the appropriate cutoff value of 0.488 (Fig. 2 D). We computed the area under the curve (AUC) values of the ROC and carried out Kaplan-Meier survival analysis in order to assess the predictive potential of the new PABMS. The AUC value at one year OS was 0.773, according to the data (Fig. 3 A). According to Fig. 3 B, patients in the high-risk group had OS that was considerably poorer than those in the low-risk group (P < 0.001). Next, we ran univariate and multivariate Cox regression analysis on the training set to see if risk score was an independent risk factor for OS of patients with BC. The Cox regression analysis showed that both PABMS and stage were significant predictor factors (age: P < 0.001; M staging: P < 0.001; T staging: P < 0.001; N staging: P < 0.001; stage: P < 0.001; PABMS: P < 0.001; Fig. 3 C). After correcting for clinicopathological characteristics such as age, stage, T staging, N staging, and M staging, the results showed that PABMS was a significant independent predictor of OS (P 60) M (M0 /M1) N (N0/ N1-3) Stage (stageⅠ-Ⅱ/ stageⅢ-Ⅳ) T (T1-T2/T3-T4) EZR 4.346(< 0.001) 0.652(0.524) -0.406(0.685) 0.28(0.780) 137.572(0.287) SBK1 -2.718(0.007) -0.665(0.516) -1.26(0.208) 1.177(0.240) 115.003(0.806) L1CAM -2.844(0.005) -1.079(0.297) -1.05(0.294) -0.425(0.671) 136.076(0.318) CXCL1 -6.439(< 0.001) 2.144(0.047) 3.335(< 0.001) 3.246(0.001) 135.707(0.326) riskScore 5.581(< 0.001) -1.584(0.133) -2.712(0.007) -2.969(0.003) 131.362(0.426) Note: t: t value of student's t test; P: P-value of student's t test. Association between PABMS and immune response in BC In order to ascertain the biological pathways associated with PABMS, we categorised BC samples into two groups based on the cut-off value: high-risk (n = 163) and low-risk (n = 907).Next, in order to identify the unique biological process characteristics between the two groups, we ran a GSEA. The results indicated that there was a significant association between high-risk patients and the positive regulation of several immune-related pathways, including chemokine signalling (NES = -1.52, P = 0.043), naïve versus Th17 enriched CD4 T cells (NES = -1.79, P = 0.007), and TNFA signalling via NF-KB (NES = -1.58, P = 0.038) (Fig. 4 A). In order to investigate the relationship between PABMS and immune response, we compared the expression levels of seven well-established clusters of inflammatory mediators (HCK, IgG, interferon, LCK, MHC-I, MHC-II, and STAT1) between high-risk and low-risk groups (Fig. 4 B) [21] . To compare the two groups, gene set variation analysis was used using metagene cluster expression data. The study found a statistically significant association (P < 0.001) between the risk score and the interferon response, hemopoietic cell kinase, and STAT1 signal transduction pathways [22] . Figure 4 C displays the results of expression levels for four substantially differently expressed metagene clusters across all samples from both groups. We utilised a cross-correlogram to illustrate the correlations among the variables in order to validate the findings and enhance the comprehension of the linkages. The results showed that the risk score was positively associated with the immune response pathway (Fig. 4 D). Correlation between PABMS and immune cell infiltration or immunological checkpoint characteristics in breast cancer We utilised the CIBERSORT method to examine the makeup of intra-tumoral immune cells in the TCGA database, comparing high-risk and low-risk groups due to their tight relationship with immune response. The results indicated that samples from high-risk patients were characterized by Activated natural killer cells s(P = 0.001) and M2 Macrophages enrichment (P = 0.046) and a lack of CD8 + T cells(P = 0.029), T follicular helper cells (P = 0.005), monocytes (P = 0.03) and Neutrophils(P = 0.001) (Fig. 5 A). To further verify the association between risk score and immune cell infiltration, the cross-correlogram was used to present associations among these variables. The results consistently showed a favourable correlation between risk score and M2 Macrophages l (r = 0.14,P < 0.001), Resting mast cells l (r = 0.17,P < 0.001), and Neutrophils infiltration level (r = 0.12,P < 0.001) but was negatively correlated with CD8 + T cells (r = -0.13,P < 0.001), Activated memory CD4 + T cells (r = -0.12,P < 0.001), and T follicular helper cells level (r = -0.19, P < 0.001) (Fig. 5 B). For the purpose of investigating the connections between RAMS and immune checkpoint features, we participated in the study with sixteen immune checkpoint molecules. The B7-CD28 family, which includes CD28, CD80, CD86, B7-H3, PD-1, PD-L1, PD-L2, B7-H4, HHLA2, ICOSLG, ICOS, and TMIGD2, as well as other immune checkpoint members, such as Tim-3, galectin-9, LAG-3, and FGL-1, were incorporated into the study (Fig. 5 C)[ 23 – 26 ]. As shown in Fig. 5 C, the results indicated that the high-risk group had variable levels of PD-1, B7-H4, galectin-9, and FGL-1 expression. A cross-correlogram was employed to examine the correlation between a risk score and certain immune checkpoint molecules. In Fig. 5 D. Discussion Among numerous malignancies, metastatic breast cancer is the second leading cause of woman death [ 27 ]. Clinically, breast cancer can metastasize to various sites such as brain, liver, lung, and bone, and bone is the most common metastasis site [ 28 ]. Therefore, maintaining bone health is an important clinical challenge in breast cancer patients. In the past few years, the cross-talk between tumor cells and the bone microenvironment has been found important for bone metastasis in patients with BC. In this study, we found DEGs between DTCs and MTCs on gene expression profiles from the GSE14776 database, and identified 3373 up-regulated DEGs and 4677 down-regulated DEGs. In order to further screen for effective targets related to bone metastasis, DEGs between BC tissue samples and paracancer tissue samples were also been found. Both up-regulated or down-regulated DEGs were defined as BMRGs. We then identified a novel bone metastasis-associated signature significantly related to the OS of patients with BC. We examined the transcriptome of a training group from the TCGA database. In this study, four genes (ZER, SBK1, L1CAM and CXCL1) were identified and included in the bone metastasis -associated prognostic model. ZER (zyg-11 related cell cycle regulator), a substrate specificity factor for a CUL2 ubiquitin ligase complex, has been reported up-regulated at the Ta-T1 stage, but down-regulated at the T1-T2 stage in bladder cancer [ 29 ]. SBK1(SH3-binding domain kinase 1), was expressed in most of the cells and tissues examined, and the protein is highly up-regulated in ovarian serous adenocarcinoma while down-regulated in esophagus squamous cell carcinoma and stomach adenocarcinoma [ 30 ]. SBK1 overexpression promotes retinoblastoma cell proliferation, migration and invasion [ 31 ]. L1CAM(L1 cell adhesion molecule) plays a major role in the development of the nervous system and expressed in many human cancers and is often associated with bad prognosis [ 32 , 33 ]. Ganesh et al. [ 34 ] reported that L1CAM identifies metastasis-initiating cells in colorectal carcinoma exhibiting stem-like cell features, increased tumorigenic potential and enhanced chemoresistance. L1CAM also been reported that highly specific for recurrence in a subset of BC patients and may be associated with more aggressive behavior, particularly in luminal B breast cancers with higher Ki-67 expression [ 35 ]. Furthermore, L1CAM expressed in brain metastatic cells for spreading along brain capillaries and for metastatic outgrowth in BC patients [ 36 ]. CXCL1(C-X-C motif chemokine ligand 1) has been reported promote cancer growth and development, and may also point to a specific histogenetic pathway in BC [ 37 ]. They were associated with favorable outcomes and have been found to participate in tumor progression. The study found that high ZER expression is correlated with low CXCL1 and SBK1 expression, leading to a poor prognosis in breast cancer patients. Analysis of stratification and multivariate Cox analysis showed that PABMS was an independent prognostic factor for overall survival in breast cancer patients. We evaluated the robustness of PABMS with TNM staging and determined that PABMS is the most suitable indicator for predicting the prognosis of patients with BC. This result enhances our confidence that in the future, PABMS will be a valuable predictive tool due to its significance in cancer-associated deaths in patients with BC caused by bone metastases. Additional prospective studies are required to validate the data and conclusions. We used Gene Set Enrichment Analysis (GSEA) to identify the specific biological processes that differentiate high-risk and low-risk patients in order to explore the probable mechanism of PABMS. We discovered that high-risk was closely linked to the positive regulation of many immune-related pathways. The varying immune responses between the two groups likely account for the differences in overall prognosis and the development of cancer bone metastases. We analysed immunological changes linked with PABMS in both groups. To learn more about the immune traits associated with these two groups, analyses of seven clusters of inflammatory metagenes, immune cell infiltration, and immune checkpoint profiles were carried out. The study revealed a strong correlation between risk score and inflammatory response (STAT1 and interferon), as well as the expression levels of several immunosuppressive checkpoint molecules such as PD-1, galectin-9, B7-H4, and FGL-1. The results indicated that high-risk individuals were in an immunosuppressive condition. Low-risk individuals had high levels of CD8 + T cells and T follicular helper cells in their immune systems, together with low levels of M2 macrophage cells, suggesting an aggressive anti-tumor immune response. We conducted a thorough analysis of the function and signalling pathways of differentially expressed genes (DEGs) linked with bone metastases in breast cancer, and examined their impact on prognosis. BMRGs might aid in discovering new biomarkers, selecting appropriate patients for immunotherapy, and establishing a new PABMS model to forecast the effectiveness of tailored treatment strategies in the future. Additional study conducted in vitro or in vivo on BMRGs might enhance our comprehension of the milieu, immune evasion strategies, and new immunotherapeutic objectives in breast cancer. Declarations Ethics approval and consent to participate Not applicable Consent for publication The manuscript is approved by all authors for publication. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Funding None Acknowledgment We are grateful to all the participants who have made this research possible. Competing Interest The authors declare that they have no competing interests. References C.E. DeSantis, J. Ma, M.M. Gaudet, L.A. Newman, K.D. Miller, A. Goding Sauer, A. Jemal, R.L. Siegel, Breast cancer statistics, 2019, CA Cancer J Clin, 69 (2019) 438–451. F. Bray, J. Ferlay, I. Soerjomataram, R.L. Siegel, L.A. Torre, A. Jemal, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J Clin, 68 (2018) 394–424. N. Graham, B.Z. 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Ye, Gene expression profile and enrichment pathways in different stages of bladder cancer, Genet Mol Res, 12 (2013) 1479–1489. P. Wang, J. Guo, F. Wang, T. Shi, D. Ma, Human SBK1 is dysregulated in multiple cancers and promotes survival of ovary cancer SK-OV-3 cells, Mol Biol Rep, 38 (2011) 3551–3559. W. Feng, R. Zhu, J. Ma, H. Song, LncRNA ELFN1-AS1 Promotes Retinoblastoma Growth and Invasion via Regulating miR-4270/SBK1 Axis, Cancer Manag Res, 13 (2021) 1067–1073. H. Kiefel, S. Bondong, J. Hazin, J. Ridinger, U. Schirmer, S. Riedle, P. Altevogt, L1CAM: a major driver for tumor cell invasion and motility, Cell Adh Migr, 6 (2012) 374–384. P. Altevogt, K. Doberstein, M. Fogel, L1CAM in human cancer, Int J Cancer, 138 (2016) 1565–1576. P. Altevogt, A. Ben-Ze'ev, N. Gavert, U. Schumacher, H. Schäfer, S. Sebens, Recent insights into the role of L1CAM in cancer initiation and progression, Int J Cancer, 147 (2020) 3292–3296. I. Moisini, H. Zhang, M. D'Aguiar, D.G. Hicks, B.M. Turner, L1CAM Expression in Recurrent Estrogen Positive/HER2 Negative Breast Cancer: A Novel Biomarker Worth Considering, Appl Immunohistochem Mol Morphol, 29 (2021) 287–292. M. Valiente, A.C. Obenauf, X. Jin, Q. Chen, X.H. Zhang, D.J. Lee, J.E. Chaft, M.G. Kris, J.T. Huse, E. Brogi, J. Massagué, Serpins promote cancer cell survival and vascular co-option in brain metastasis, Cell, 156 (2014) 1002–1016. K. Ma, L. Yang, R. Shen, B. Kong, W. Chen, J. Liang, G. Tang, B. Zhang, Th17 cells regulate the production of CXCL1 in breast cancer, Int Immunopharmacol, 56 (2018) 320–329. Additional Declarations No competing interests reported. Supplementary Files supplementmaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3981365","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":280834110,"identity":"caf8e9a2-73f6-4f2b-8700-f6bdcdd3057a","order_by":0,"name":"Shilei Huang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shilei","middleName":"","lastName":"Huang","suffix":""},{"id":280834111,"identity":"21f018bd-c376-4348-8d53-b0aa6b67dde0","order_by":1,"name":"Xiaobin Cao","email":"","orcid":"","institution":"Zhengzhou University, BGI College, Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Cao","suffix":""},{"id":280834112,"identity":"404cb0f3-7ac1-4a04-98e5-c1919ea8eb12","order_by":2,"name":"Jingjing Liu","email":"","orcid":"","institution":"Zhengzhou University, BGI College, Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Liu","suffix":""},{"id":280834113,"identity":"1cf8e4cb-d963-44a7-bdf2-58e3ba5e2722","order_by":3,"name":"Xiang Ji","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Ji","suffix":""},{"id":280834114,"identity":"4f2d5168-0d4e-4fc1-bd7a-599959c80120","order_by":4,"name":"Yu Han","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Han","suffix":""},{"id":280834117,"identity":"a763b50c-08d8-4ea4-97a2-6c789e63f226","order_by":5,"name":"Xuejian Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYHACAxDBzM/MfPgBaVok29nSDEjSwmBwnkdBgij18v2Ht0nzth1mNz7MA9RcYxNNUAtjw7FiY962NGazw7wHHjAcS8ttIKSFmbHH8HFumw1QC1+CAWPDYcJa2Jh5DA7ntkkwGzfzGEgQpYWHjQdiiwEzsVokeNiKjf+cS2OWOAwM5ARi/AIKMckZZYeT+fsPH37wocaGsBYYSAaTCcQqBwE7UhSPglEwCkbBCAMAjSY1ZnIzznUAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Xuejian","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-02-23 09:45:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3981365/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3981365/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53111924,"identity":"e4146139-a5a7-4dd6-8205-d2e3da7cfc9f","added_by":"auto","created_at":"2024-03-20 17:47:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54281,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between bone metastasis and BC samples. (A) Volcano plot of DEGs between DTCs and MTCs. (B) Volcano plot of DEGs between BC tumor samples and paracancer samples. (C) Venn analysis of both up-regulated DEGs in DTC and BC samples (D) Venn analysis of both dowm-regulated DEGs in DTC and BC samples\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3981365/v1/c96a41349fccdf95a2a1cd2c.png"},{"id":53112389,"identity":"fa2df6e2-38c7-4b20-954d-e8e877402f83","added_by":"auto","created_at":"2024-03-20 17:55:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56111,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment of PABMS for BC inTCGAcohort. (A and B) LASSO Cox proportional hazards regression model identified 43 genes most related to OS. (C) Prognostic values of four selected genes with multivariate Cox proportional hazards regression analysis. (D)Heatmap of two-gene expression profiles, risk score distributions, and recurrence status of each patient in the high- and low-risk groups.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3981365/v1/c5c9b279138055aaaba138ba.png"},{"id":53111925,"identity":"c5fa7a0d-eaf8-4a0c-986d-ee3482a45796","added_by":"auto","created_at":"2024-03-20 17:47:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38123,"visible":true,"origin":"","legend":"\u003cp\u003eClinical related analysis for PABMS in TCGA cohort. (A) ROC curve analysis of the PABMS for OS. (B) Kaplan-Meier survival curves of OS for patients with BC based on the PABMS. (C and D) Univariate (C) and multivariate (D) Cox regression analyses of the associations between PABMS and clinical variables for predictive value of OS.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3981365/v1/9da26215753b408538d94809.png"},{"id":53111927,"identity":"8ada0270-403e-47bc-acab-cdfa4c182e2e","added_by":"auto","created_at":"2024-03-20 17:47:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":88656,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between PABMS and immune response in BC samples. (A) Significant enrichment of immune pathways between high-risk group and low-risk group. NES: normalized enrichment score. (B) Volcano plot of four clusters of inflammatory metagenes differentially enriched in the high- and low-risk groups. (C) Heatmap of four clusters of inflammatory metagenes differentially enriched in the high- and low-risk groups. (D) Cross-correlogram derived based on Pearson’s correlation coefficient values between risk scores and seven clusters of inflammatory metagenes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3981365/v1/6e6273ca5c3d2e380998c078.png"},{"id":53111928,"identity":"eaee1532-cf75-4305-b015-2fe7c4d3cd5f","added_by":"auto","created_at":"2024-03-20 17:47:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":76254,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between PABMS and immune cell infiltration or immune checkpoint profiles in BC samples. (A) Differences in immune cell infiltration abundances between high-risk group and low-risk group. (B) Cross-correlogram derived based on Pearson’s correlation coefficient values between risk scores and 22 tumor-infiltrated immune cells. (C) Heatmap of immune checkpoint profiles in the high- and low-risk groups. (D) Cross-correlogram was derived based on Pearson’s correlation coefficient values between risk scores and expression of immune checkpoint molecules. *: P\u0026lt;0.05, **: P\u0026lt;0.01, ***: P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3981365/v1/f190f58b4c2e5ce7a8f637ef.png"},{"id":58998431,"identity":"61cafe8c-95fd-487b-8e36-41318617960f","added_by":"auto","created_at":"2024-06-25 06:47:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":947826,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3981365/v1/f20ff45e-6abb-48ba-a3c5-0094d687655f.pdf"},{"id":53111922,"identity":"f7e278e2-5e16-4f38-bc57-2fd698523c53","added_by":"auto","created_at":"2024-03-20 17:47:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20753,"visible":true,"origin":"","legend":"","description":"","filename":"supplementmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3981365/v1/973293ae32ec016d0ff95dc4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a novel bone metastasis-associated prognostic model for breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer (BC) is a serious health problem since it is the leading cause of cancer-related deaths among women worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 80% of cases, the main reason for death from breast cancer is the spread of the disease to other vital organs such the brain, liver, bones, and lungs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Metastasis that occurs at a distance most frequently occurs in the bone [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Bone deterioration frequently results in bone-related issues such as pain, fractures, spinal cord compression, severe hypercalcemia, etc., which have a detrimental effect on the quality of life of patients with breast cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As a result of treatment resistance, conventional breast cancer treatments like radiation, chemotherapy, and adjuvant chemotherapy, in addition to the newly authorised bone-specific medications, only provide partial relief for skeletal-related disorders[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Identifying new biomarkers is essential for diagnosing and treating breast cancer bone metastases due to their intricate nature.\u003c/p\u003e \u003cp\u003eBreast cancer cells undergo a complicated metastatic process that begins in the blood and lymphatic system and continues through the bone environment adaptation and cell multiplication [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Over the past few decades, an increasing amount of evidence has shown that breast cancer cells that have metastasized to bones have unique molecular characteristics [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Bone metastasis of breast cancer is influenced by primary tumour stroma and specific stromal components in distant organs. This includes interactions between tumour cells and blood arteries, which affect the preference for bone tissue. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Osteoclasts breakdown bone continuously as osteoblasts produce new bone, engaging in the process of remodelling bone tissue. Cancer frequently metastasizes to bones [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The unique bone environment and the biological composition of the bone marrow niche\u0026mdash;which includes hematopoietic stem cells, osteoblasts, and osteoclasts\u0026mdash;are exploited by metastatic breast cancer. These cells are crucial in the bone formation, remodelling, and healing processes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A number of substances, including IL-6, lysyl oxidase, intercellular adhesion molecule 1, and vascular cell adhesion molecule 1 are released by breast cancer cells and can increase the activity of osteoclasts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A study has demonstrated that miR-21, an oncomicroRNA with great conservation, triggers the formation of the pre-metastatic environment for bone metastasis in breast cancer. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings have confirmed the significance of identifying new potential biomarkers for breast cancer patients at risk of bone metastases, while the molecular underpinnings are still unknown. By utilising vast publicly available gene expression datasets, researchers have the ability to accelerate and enhance the precision of biomarker identification for tumour surveillance. A metabolic signature associated with recurrence has been developed in order to evaluate the predictive value for patients diagnosed with stage I lung adenocarcinoma [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We investigated if bone metastasis-related genes may create a predictive gene signature in BC.\u003c/p\u003e \u003cp\u003eThis study identified differentially expressed genes (DEGs) between bone marrow aspirates (DTCs) and Computed Tomography (MTCs) using gene expression profiles from the GS14776 dataset. After identifying important differences between DTCs and MTCs, we used 1070 breast cancer samples from the TCGA dataset to develop a predictive bone metastasis signature (PABMS). We verified the predictive power of the signature by analysing the expression of certain proteins in tumour primary and metastatic specimens from patients with BC. Immunotherapies, which stimulate the immune system to combat tumours, have been highly successful in recent years [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We extensively analysed the clinical significance, immune checkpoint features, and immune cell infiltration of the novel PABMS in BC patients, considering the promising potential for immunotherapy. The analytical results may enhance the strategy for treating cancer, especially in the area of immunotherapy, and lower the risk of cancers metastasizing to the bone.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePublicly available gene expression datasets\u003c/h2\u003e \u003cp\u003eGene expression data from 1094 breast cancer tissues and 114 paracancer tissues were obtained from the TCGA database at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-data.nci.nih.gov/tcga\u003c/span\u003e\u003cspan address=\"https://tcga-data.nci.nih.gov/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The transcriptome data were converted by a factor of two and quantile normalisation was used before the patients were standardised. The public had access to all relevant data on enrolled patients and their clinical outcomes. Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e presents the patient data related to the TCGA cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGEO data download and preprocessing\u003c/h2\u003e \u003cp\u003eWe obtained data on disseminated tumour cells (DTCs) from bone marrow aspirates and metastatic tumour cells (MTCs) from Computed Tomography scans from the Gene Expression Omnibus (GEO) datasets, specifically 8 samples of DTCs and 6 cases from GSE14776.\u003c/p\u003e \u003cp\u003eThe data preparation steps were as outlined: If the dataset was not log2 transformed, it was changed to log2. If the data was not quantile-normalized, the limma software was used for normalisation. The quantile normalisation was performed using the Between Arrays technique in R. A blank probe was removed, the probe was matched with the gene, and many probes were linked to the same gene. We calculated the average value of gene expression. A difference analysis was performed using the Wilcox rank-sum test, with a P-value threshold of less than 0.05 being the filtering criterion.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eCandidate gene collection and data expression\u003c/h2\u003e \u003cp\u003eThe study examined the differentially expressed genes (DEGs) in DTCs and MTCs samples from the GSE14776 dataset, as well as the DEGs in 1093 tissues of breast cancer patients and 114 tissues of paracancer retrieved from the TCGA database. We selected differentially expressed genes (DEGs) that were either up-regulated or down-regulated in both cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment Analysis of bone metastasis-associated Genes\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eGene set enrichment analysis (GSEA), a commonly employed method for enrichment analysis, was performed on samples of BC with high and low risk in order to acquire a more comprehensive understanding of overall changes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and Validation of the Bone Metastasis Signature Associated with Prognosis\u003c/h2\u003e \u003cp\u003eWe selected the overall survival (OS) data for the PABMS block from the TCGA\u0026rsquo;s Pan-Cancer Atlas [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The TCGA dataset consisted of 1070 patients in the training set. We performed univariate Cox proportional hazards regression analysis to evaluate their predictive importance for overall survival in the training dataset [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. To determine which genes have the greatest predictive values, we employed the Cox proportional hazards regression model with the least absolute shrinkage and selection operator (LASSO) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The purpose of this was to identify the genes with the highest predictive values. Both of these models were applied utilizing fundamental standards. We employed a multivariate version of the Cox proportional hazards regression model to determine the target genes that make up a Prognostic and Predictive Biomarker Signature (PABMS). We created a method to calculate the PABMS value for individual patients by assigning weights to the normalised expression values of certain genes based on their relevant variables. The formula normalised the expression values of the target genes by setting the mean to 0 and the standard deviation to 1 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This was done to provide a consistent cutoff value for categorising patients into low-risk or high-risk groups. The revolutionary PABMS was evaluated for its accuracy in predicting overall survival (OS) in two distinct groups using the receiver operating characteristic curve (ROC) and Kaplan-Meier survival analyses. We conducted univariate and multivariate Cox regression analysis to determine if PABMS was an independent prognostic risk factor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmune cell infiltration analysis\u003c/h2\u003e \u003cp\u003eTranscriptome data was analysed using the deconvolution method CIBERSORT to determine the relative abundances of 22 immune cells in tumour samples. The algorithm may be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersort.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersort.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The outcome was additionally refined utilising a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To reduce the impact of any confounding variables, the quantile normalisation approach was applied to standardise the gene expression profile data across all samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical studies were carried out using GraphPad Prism version 8.0 and R version 4.0.5. The Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) curves by employing log-rank testing. To facilitate comparisons among the groups, the Mann-Whitney U and chi-square tests were applied. Every single P-value that was presented had a two-tailed tail. With the exception of those explicitly indicated otherwise, all analyses were deemed statistically significant if the P-value was below 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eScreening of DEGs between DTCs and MTCs in BC\u003c/h2\u003e \u003cp\u003eWe analysed gene expression profiles in 8 DTCs and 6 MTCs from GSE14776 to compare the unique biological processes in these two types of cancer. Thus, 3373 up-regulated DEGs and 4677 down-regulated DEGs were found between DTCs samples and MTCs(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). We also taking a \u003cem\u003eP\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 as the threshold, and obtained 4221 DEGs between tumor and paracancer of BC samples in TCGA database, including 1412 up-regulated genes and 2809 down-regulated genes(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Venn analysis were performed to discover both up-regulated or down regulated DEGs in bone metastasis and BC samples. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D, there were 334 up-regulated and 276 down-regulated DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of PABMS for BC in training cohort\u003c/h2\u003e \u003cp\u003eSignificant differences were identified between breast cancer patients with and without bone metastases at the beginning of the study. We aimed to create a Prognostic Algorithm for Breast Cancer (PABMS) to enhance the accuracy of predicting outcomes in breast cancer. This was done by analysing 1070 breast cancer samples from the TCGA database. A univariate Cox proportional hazards regression analysis was conducted to discover genes associated with bone metastases that are correlated with overall survival. Among 610 bone metastasis-related genes, 80 were shown to be predictive for OS (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We employed LASSO Cox proportional hazards regression modelling to identify genes with the highest predictive capabilities. The minimum criteria were used to identify forty-four genes (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Following multivariate Cox regression analysis, a unique prognostic signature consisting of four genes (ZER, SBK1, L1CAM, and CXCL1) was created in order to further develop a PABMS for prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The risk score model for each patient was created using the formula: risk score\u0026thinsp;=\u0026thinsp;0.192 \u0026times; normalised expression value of EZR-0.243 \u0026times; normalised expression value of SBK1\u0026thinsp;+\u0026thinsp;0.359 \u0026times; normalised expression value of L1CAM-0.384 \u0026times; normalised expression value of CXCL1. Patients were categorised into low-risk and high-risk groups using the appropriate cutoff value of 0.488 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eWe computed the area under the curve (AUC) values of the ROC and carried out Kaplan-Meier survival analysis in order to assess the predictive potential of the new PABMS. The AUC value at one year OS was 0.773, according to the data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). According to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, patients in the high-risk group had OS that was considerably poorer than those in the low-risk group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Next, we ran univariate and multivariate Cox regression analysis on the training set to see if risk score was an independent risk factor for OS of patients with BC. The Cox regression analysis showed that both PABMS and stage were significant predictor factors (age: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; M staging: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; T staging: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; N staging: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; stage: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; PABMS: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). After correcting for clinicopathological characteristics such as age, stage, T staging, N staging, and M staging, the results showed that PABMS was a significant independent predictor of OS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationships between the expressions of the genes in PABMS and riskScore and the clinicopathological factors in breast cancer: t(P)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003egene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003e(\u0026le;60/\u0026gt;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003cp\u003e(M0 /M1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003cp\u003e(N0/ N1-3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003cp\u003e(stageⅠ-Ⅱ/ stageⅢ-Ⅳ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT\u003c/p\u003e \u003cp\u003e(T1-T2/T3-T4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEZR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.346(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.652(0.524)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.406(0.685)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28(0.780)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e137.572(0.287)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.718(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.665(0.516)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.26(0.208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.177(0.240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e115.003(0.806)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL1CAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.844(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.079(0.297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.05(0.294)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.425(0.671)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e136.076(0.318)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.439(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.144(0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.335(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.246(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e135.707(0.326)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eriskScore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.581(\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.584(0.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.712(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.969(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e131.362(0.426)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: t: t value of student's t test; P: P-value of student's t test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between PABMS and immune response in BC\u003c/h2\u003e \u003cp\u003eIn order to ascertain the biological pathways associated with PABMS, we categorised BC samples into two groups based on the cut-off value: high-risk (n\u0026thinsp;=\u0026thinsp;163) and low-risk (n\u0026thinsp;=\u0026thinsp;907).Next, in order to identify the unique biological process characteristics between the two groups, we ran a GSEA. The results indicated that there was a significant association between high-risk patients and the positive regulation of several immune-related pathways, including chemokine signalling (NES = -1.52, P\u0026thinsp;=\u0026thinsp;0.043), na\u0026iuml;ve versus Th17 enriched CD4 T cells (NES = -1.79, P\u0026thinsp;=\u0026thinsp;0.007), and TNFA signalling via NF-KB (NES = -1.58, P\u0026thinsp;=\u0026thinsp;0.038) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eIn order to investigate the relationship between PABMS and immune response, we compared the expression levels of seven well-established clusters of inflammatory mediators (HCK, IgG, interferon, LCK, MHC-I, MHC-II, and STAT1) between high-risk and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB)\u003csup\u003e[21]\u003c/sup\u003e. To compare the two groups, gene set variation analysis was used using metagene cluster expression data. The study found a statistically significant association (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the risk score and the interferon response, hemopoietic cell kinase, and STAT1 signal transduction pathways \u003csup\u003e[22]\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC displays the results of expression levels for four substantially differently expressed metagene clusters across all samples from both groups. We utilised a cross-correlogram to illustrate the correlations among the variables in order to validate the findings and enhance the comprehension of the linkages. The results showed that the risk score was positively associated with the immune response pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between PABMS and immune cell infiltration or immunological checkpoint characteristics in breast cancer\u003c/h2\u003e \u003cp\u003eWe utilised the CIBERSORT method to examine the makeup of intra-tumoral immune cells in the TCGA database, comparing high-risk and low-risk groups due to their tight relationship with immune response. The results indicated that samples from high-risk patients were characterized by Activated natural killer cells s(P\u0026thinsp;=\u0026thinsp;0.001) and M2 Macrophages enrichment (P\u0026thinsp;=\u0026thinsp;0.046) and a lack of CD8\u0026thinsp;+\u0026thinsp;T cells(P\u0026thinsp;=\u0026thinsp;0.029), T follicular helper cells (P\u0026thinsp;=\u0026thinsp;0.005), monocytes (P\u0026thinsp;=\u0026thinsp;0.03) and Neutrophils(P\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). To further verify the association between risk score and immune cell infiltration, the cross-correlogram was used to present associations among these variables. The results consistently showed a favourable correlation between risk score and M2 Macrophages l (r\u0026thinsp;=\u0026thinsp;0.14,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Resting mast cells l (r\u0026thinsp;=\u0026thinsp;0.17,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Neutrophils infiltration level (r\u0026thinsp;=\u0026thinsp;0.12,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but was negatively correlated with CD8\u0026thinsp;+\u0026thinsp;T cells (r = -0.13,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Activated memory CD4\u0026thinsp;+\u0026thinsp;T cells (r = -0.12,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and T follicular helper cells level (r = -0.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFor the purpose of investigating the connections between RAMS and immune checkpoint features, we participated in the study with sixteen immune checkpoint molecules. The B7-CD28 family, which includes CD28, CD80, CD86, B7-H3, PD-1, PD-L1, PD-L2, B7-H4, HHLA2, ICOSLG, ICOS, and TMIGD2, as well as other immune checkpoint members, such as Tim-3, galectin-9, LAG-3, and FGL-1, were incorporated into the study (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC)[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, the results indicated that the high-risk group had variable levels of PD-1, B7-H4, galectin-9, and FGL-1 expression. A cross-correlogram was employed to examine the correlation between a risk score and certain immune checkpoint molecules. In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAmong numerous malignancies, metastatic breast cancer is the second leading cause of woman death [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Clinically, breast cancer can metastasize to various sites such as brain, liver, lung, and bone, and bone is the most common metastasis site [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, maintaining bone health is an important clinical challenge in breast cancer patients. In the past few years, the cross-talk between tumor cells and the bone microenvironment has been found important for bone metastasis in patients with BC.\u003c/p\u003e \u003cp\u003eIn this study, we found DEGs between DTCs and MTCs on gene expression profiles from the GSE14776 database, and identified 3373 up-regulated DEGs and 4677 down-regulated DEGs. In order to further screen for effective targets related to bone metastasis, DEGs between BC tissue samples and paracancer tissue samples were also been found. Both up-regulated or down-regulated DEGs were defined as BMRGs. We then identified a novel bone metastasis-associated signature significantly related to the OS of patients with BC. We examined the transcriptome of a training group from the TCGA database.\u003c/p\u003e \u003cp\u003eIn this study, four genes (ZER, SBK1, L1CAM and CXCL1) were identified and included in the bone metastasis -associated prognostic model. ZER (zyg-11 related cell cycle regulator), a substrate specificity factor for a CUL2 ubiquitin ligase complex, has been reported up-regulated at the Ta-T1 stage, but down-regulated at the T1-T2 stage in bladder cancer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. SBK1(SH3-binding domain kinase 1), was expressed in most of the cells and tissues examined, and the protein is highly up-regulated in ovarian serous adenocarcinoma while down-regulated in esophagus squamous cell carcinoma and stomach adenocarcinoma [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. SBK1 overexpression promotes retinoblastoma cell proliferation, migration and invasion [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. L1CAM(L1 cell adhesion molecule) plays a major role in the development of the nervous system and expressed in many human cancers and is often associated with bad prognosis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Ganesh et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] reported that L1CAM identifies metastasis-initiating cells in colorectal carcinoma exhibiting stem-like cell features, increased tumorigenic potential and enhanced chemoresistance. L1CAM also been reported that highly specific for recurrence in a subset of BC patients and may be associated with more aggressive behavior, particularly in luminal B breast cancers with higher Ki-67 expression [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, L1CAM expressed in brain metastatic cells for spreading along brain capillaries and for metastatic outgrowth in BC patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. CXCL1(C-X-C motif chemokine ligand 1) has been reported promote cancer growth and development, and may also point to a specific histogenetic pathway in BC [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. They were associated with favorable outcomes and have been found to participate in tumor progression. The study found that high ZER expression is correlated with low CXCL1 and SBK1 expression, leading to a poor prognosis in breast cancer patients.\u003c/p\u003e \u003cp\u003eAnalysis of stratification and multivariate Cox analysis showed that PABMS was an independent prognostic factor for overall survival in breast cancer patients. We evaluated the robustness of PABMS with TNM staging and determined that PABMS is the most suitable indicator for predicting the prognosis of patients with BC. This result enhances our confidence that in the future, PABMS will be a valuable predictive tool due to its significance in cancer-associated deaths in patients with BC caused by bone metastases. Additional prospective studies are required to validate the data and conclusions.\u003c/p\u003e \u003cp\u003eWe used Gene Set Enrichment Analysis (GSEA) to identify the specific biological processes that differentiate high-risk and low-risk patients in order to explore the probable mechanism of PABMS. We discovered that high-risk was closely linked to the positive regulation of many immune-related pathways. The varying immune responses between the two groups likely account for the differences in overall prognosis and the development of cancer bone metastases. We analysed immunological changes linked with PABMS in both groups. To learn more about the immune traits associated with these two groups, analyses of seven clusters of inflammatory metagenes, immune cell infiltration, and immune checkpoint profiles were carried out. The study revealed a strong correlation between risk score and inflammatory response (STAT1 and interferon), as well as the expression levels of several immunosuppressive checkpoint molecules such as PD-1, galectin-9, B7-H4, and FGL-1. The results indicated that high-risk individuals were in an immunosuppressive condition. Low-risk individuals had high levels of CD8\u0026thinsp;+\u0026thinsp;T cells and T follicular helper cells in their immune systems, together with low levels of M2 macrophage cells, suggesting an aggressive anti-tumor immune response.\u003c/p\u003e \u003cp\u003eWe conducted a thorough analysis of the function and signalling pathways of differentially expressed genes (DEGs) linked with bone metastases in breast cancer, and examined their impact on prognosis. BMRGs might aid in discovering new biomarkers, selecting appropriate patients for immunotherapy, and establishing a new PABMS model to forecast the effectiveness of tailored treatment strategies in the future. Additional study conducted in vitro or in vivo on BMRGs might enhance our comprehension of the milieu, immune evasion strategies, and new immunotherapeutic objectives in breast cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The manuscript is approved by all authors for publication.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;Availability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003eAcknowledgment\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the participants who have made this research possible.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eC.E. 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Zhang, Th17 cells regulate the production of CXCL1 in breast cancer, Int Immunopharmacol, 56 (2018) 320\u0026ndash;329.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"immunological landscape, breast cancer, and bone metastasis-related","lastPublishedDoi":"10.21203/rs.3.rs-3981365/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3981365/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBone is the main location where breast cancer spreads, which can greatly impact the outlook and well-being of those affected by the disease. Novel targets are crucial in clinical practice as traditional treatment for breast cancer patients with bone metastases is mostly futile. Current therapy for bone metastases in breast cancer patients is hindered by a lack of understanding of the root causes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe created a prognostic bone metastasis signature called PABMS. The Prognostic Analysis of Bone metastases Score (PABMS) utilised genes associated with bone metastases to forecast the overall prognosis of breast cancer patients. The clinical significance and immunological environments of the signature were exhaustively examined.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes (DEGs) associated with bone metastasis were identified by utilising data from the GSE14666 database in order to distinguish disseminated tumour cells (DTCs) from metastatic tumour cells (MTCs). The bone metastasis-related signature (PABMS), comprised of four genes (ZER, SBK1, L1CAM, and CXCL1), was identified. This signature exhibited a robust correlation with the overall survival of patients diagnosed with breast cancer. This was ascertained through the analysis of transcriptome data from a specific cohort of patients. The efficacy of the four-gene signature in distinguishing bone metastases was validated through the examination of protein levels in an independent cohort. There was a significant correlation between a high-risk score and an extremely adverse patient outcome, according to the findings. Stratification analysis and multivariate Cox regression demonstrated that the PABMS functioned independently as a prognostic factor. Risk score was found to positively correlate with the antigen-presenting process, inflammatory response, and expression levels of many immunosuppressive checkpoint molecules (PD-1, B7-H4, galectin-9, and FGL-1). The findings suggested that patients at high risk exhibited inhibition of the immune response.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis work created a practical signature using differentially expressed genes associated with bone metastases. This attribute has the potential to function as a highly effective predictive instrument and contribute to the enhancement of the cancer therapy methodology.\u003c/p\u003e","manuscriptTitle":"Development and validation of a novel bone metastasis-associated prognostic model for breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 17:47:37","doi":"10.21203/rs.3.rs-3981365/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5926c2c1-1e62-445a-8a15-a8afe6abc352","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29569500,"name":"Biological sciences/Cancer"},{"id":29569501,"name":"Biological sciences/Cancer/Breast cancer"}],"tags":[],"updatedAt":"2024-06-25T06:39:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-20 17:47:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3981365","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3981365","identity":"rs-3981365","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}