Genomic analyses identify marker molecules and processes in metastatic breast cancer tissues

doi:10.21203/rs.3.rs-1296564/v1

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Genomic analyses identify marker molecules and processes in metastatic breast cancer tissues

https://doi.org/10.21203/rs.3.rs-1296564/v1

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Breast cancer metastasis is the major reason for deaths from breast cancer. Identification of breast cancer metastasis is of great importance for the management and prediction of cancer progression. However, the key genes and signaling pathways remain unclear in metastatic breast cancers. Our objective is to find the key molecules and signaling pathways by analyzing the RNA-sequence data. The GSE189411 was constructed by the Illumina NovaSeq 6000 (Mus musculus). The KEGG and GO analyses showed the cytokine−cytokine receptor interaction and human papillomavirus infection are the two major processes during the liver metastasis of breast cancer cells. Moreover, we discovered ten key relevant molecules including ITGB2, FCGR3A, CD86, CD80, FOXP3, SYK, CCR5, VCAM1, RAC2, ICAM1. Our study may provide novel insights for the early diagnosis of breast cancer metastasis.

Bioinformatics

Breast cancer is the most malignancy and common death reason in women¹. Due to the early detection and systemic treatments, the mortality from breast cancer in the US has declined². However, breast cancer is still the most common reason for death in developing countries such as Africa and Asia³. Early breast cancer without metastases is a curable disease⁴. Primary surgery is not the optimal choice for all patients with breast cancer⁵. Though targeted therapies have increased the survival rate, the tumor relapses are caused by the drug resistance mechanisms⁶.

Breast cancer metastasis is characterized by local invasion and transferring cancer cells to other organs⁷. Evidence shows that metastasis can originate from genetic and epigenetic changes⁸. The genetic alterations occur in the DNA sites, but the epigenetic alterations are associated with DNA methylation and histone acetylation⁹.

In this study, we analyzed the metastatic tissues from breast cancer mouse models by using the RNA-seq data. We found a variety of DEGs and significant biological processes. We also performed the gene enrichment and constructed the protein-protein interaction (PPI) network and biological processes map to figure out the relationships among the DEGs. The DEGs and functional processes will help the early diagnosis of breast cancer metastasis.

Data resources

Gene dataset GSE189411 was downloaded from the GEO database. The data was produced by the Illumina NovaSeq 6000 (Mus musculus) (Guangxi medical university, Shuangyong Road, Nanning, China). The analyzed dataset includes 3 groups of controls and 3 groups of metastatic liver tissues.

Data acquisition and processing

The data were organized and analyzed by the R package as previously described^10-14. We used a classical t-test to identify DEGs with P< 0.05 and fold change ≥1.5 as being statistically significant.

The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO)

KEGG and GO analyses were conducted by the R package (ClusterProfiler) and Reactome. P<0.05 was considered statistically significant.

Protein-protein interaction (PPI) networks

The Molecular Complex Detection (MCODE) of Cytoscope software (US) was used to construct the PPI networks. The significant modules were produced from constructed PPI networks and String networks. The pathway analyses were performed by using Reactome (https://reactome.org/), and P<0.05 was considered significant.

Identification of DEGs in liver tissue after seeding the breast cancer cells

To identify the impacts of cancer cells' metastasis in the liver, we analyzed the RNA-seq data from the liver tissues with the transplantation of cancer cells in the axilla. A total of 925 genes were identified with the threshold of P < 0.05. The top up-and-down-regulated genes were shown by the heatmap and volcano plot (Figure 1). The top ten DEGs were selected in Table 1.

Enrichment analysis of DEGs in liver tissue after the transplantation of breast cancer cells

To further understand the potential mechanisms of breast cancer cell transplantation in the liver, we performed the KEGG and GO analyses (Figure 2). We identified the top ten KEGG signaling pathways including “Cytokine−cytokine receptor interaction”, “Human papillomavirus infection”, “Chemokine signaling pathway”, “Axon guidance”, “Lysosome”, “Rheumatoid arthritis”, “Osteoclast differentiation”, “Viral protein interaction with cytokine and cytokine receptor”, “Arrhythmogenic right ventricular cardiomyopathy”, and “Glycosphingolipid biosynthesis − lacto and neolacto series”. We identified the top ten biological processes of GO including “positive regulation of cell adhesion”, “leukocyte migration”, “cell−substrate adhesion”, “cell chemotaxis”, “anatomical structure homeostasis”, “tissue homeostasis”, “myeloid leukocyte migration”, “granulocyte migration”, “neutrophil migration”, and “neutrophil chemotaxis”. We identified the top ten cellular components of GO including “receptor complex”, “collagen−containing extracellular matrix”, “membrane raft”, “membrane microdomain”, “basal part of cell”, “basal plasma membrane”, “early endosome”, “basolateral plasma membrane”, “9+0 non−motile cilium”, and “photoreceptor cell cilium”. We also identified the top ten molecular functions of GO including “actin binding”, “cell adhesion molecule binding”, “G protein−coupled receptor binding”, “actin filament binding”, “immune receptor activity”, “cargo receptor activity”, “scavenger receptor activity”, “G protein−coupled chemoattractant receptor activity”, “chemokine receptor activity”, and “chemokine binding”.

PPI network analysis

To determine the relationships of the DEGs, we constructed the PPI network by using the 811 nodes and 2055 edges with Cytoscope software (combine score > 0.4). Table 2 showed the top ten interactive genes with the highest degree scores. The top two modules were indicated in Figure 3. We further analyzed the PPI and DEG network by Reactome map (Figure 4) and identified the top ten functional processes including "Chemokine receptors bind chemokines", "WNT ligand biogenesis and trafficking", "Interleukin-10 signaling", "YAP1- and WWTR1 (TAZ)-stimulated gene expression", "Rhesus blood group biosynthesis", "Common Pathway of Fibrin Clot Formation", "RND1 GTPase cycle", "Neutrophil degranulation", "Nef mediated downregulation of MHC class I complex cell surface expression", and "BH3-only proteins associate with and inactivate anti-apoptotic BCL-2 members" (Supplemental Table S1).

The knowledge of the molecular mechanisms can improve the clinical treatment of breast cancers. Recent studies showed primary breast tumors that initiated metastases can be distinguished by their gene-expression profiles from those that remained localized¹⁵. Therefore, our study is to find out the potential marker genes in the metastasis tissues to improve the diagnosis of breast cancer in the early stage.

We figured out the “cytokine−cytokine receptor interaction” and “human papillomavirus infection” are the two major processes during the liver metastasis of breast cancer cells. Marcela Esquivel-Velázquez et al found a number of inflammatory cytokines such as IL6, IL17, and TNF are important for breast cancer initiation, promotion, angiogenesis, and metastasis¹⁶. Weilong Chen et al also found cytokines can change tumor cell behavior or reprogram tumor niche through several signaling pathways, thereby mediating the progress of drug resistance¹⁷. Niloofar Khodabandehlou et al found HPV DNA was detected in 48.6% of breast samples and HPV type 18 was the most prevalent virus genotype. Moreover, HPV was related to the upregulated inflammatory cytokines including IL1, IL6, and TNFα.

We also identified ten significant relevant molecules to the metastasis of breast cancer. ITGB2 is a prognostic marker gene for patients with breast cancer^{18, 19}. Patrick G Gavin et al found that the nucleotide polymorphisms in FCGR3A are strongly associated with breast cancer²⁰. Jun Fang et al found CD80 and CD86 are the prognostic markers of breast cancer²¹. Jiafeng Shou et al found the higher tumor infiltrating FOXP3+ Tregs is closely related to the worse outcome in breast cancer²². Circadian gene clocks and their target molecules regulate a variety of cell functions including cell metabolism, apoptosis, unfolded protein response, immune response, and aging^23-34. Interestingly, FOXP3 was found to be controlled by the circadian clock in the T cell, which may further affect the microenvironment in cancers³⁵. SYK was found to possess the promotor and repressor activities of breast cancers. David J Lamb et al found the SYK inhibitor BI1002494 showed no increased proliferation of breast cancer cells³⁶. Xuanmao Jiao et al found CCR5 is considered as a new therapeutic target for metastatic breast cancer³⁷. G protein-coupled receptor (GPCR) related signaling pathways involve different physiological and pathophysiological processes including metabolism, immune, and cancers^38-49. Strikingly, CCR5 is a critical GPCR protein, which regulates several immune cells such as T-lymphocytes, macrophages, and dendritic cells⁵⁰. Xiaoqin Huang et al found patients with breast cancer and diabetes can promote cancer adhesion to vascular endothelium via ICAM1 and VCAM1⁵¹. Yi Zhang et al reported that RAC2 is the negative coefficient that was correlated with a better prognosis⁵².

In summary, our study identified the potential gene markers for the metastasis of breast cancer. The cytokine−cytokine receptor interaction and human papillomavirus infection are the mainly affected processes during the metastasis of breast cancer. This study may provide knowledge in the early diagnosis of metastatic breast cancer.

Author Contributions

Xiuying Wang, Mengyao Wang: Methodology and Writing. Hanming Gu, James Liu: Conceptualization, Writing- Reviewing and Editing.

Funding

This work was not supported by any funding.

Declarations of interest

There is no conflict of interest to declare.

[1] Torre LA, Islami F, Siegel RL, Ward EM, Jemal A: Global Cancer in Women: Burden and Trends. Cancer Epidemiol Biomarkers Prev 2017, 26:444-57.

[2] Jatoi I, Miller AB: Why is breast-cancer mortality declining? Lancet Oncol 2003, 4:251-4.

[3] Francies FZ, Hull R, Khanyile R, Dlamini Z: Breast cancer in low-middle income countries: abnormality in splicing and lack of targeted treatment options. Am J Cancer Res 2020, 10:1568-91.

[4] Sledge GW, Jr.: Curing Metastatic Breast Cancer. J Oncol Pract 2016, 12:6-10.

[5] Riis M: Modern surgical treatment of breast cancer. Ann Med Surg (Lond) 2020, 56:95-107.

[6] Mansoori B, Mohammadi A, Davudian S, Shirjang S, Baradaran B: The Different Mechanisms of Cancer Drug Resistance: A Brief Review. Adv Pharm Bull 2017, 7:339-48.

[7] van Zijl F, Krupitza G, Mikulits W: Initial steps of metastasis: cell invasion and endothelial transmigration. Mutat Res 2011, 728:23-34.

[8] Patel SA, Vanharanta S: Epigenetic determinants of metastasis. Mol Oncol 2017, 11:79-96.

[9] Handy DE, Castro R, Loscalzo J: Epigenetic modifications: basic mechanisms and role in cardiovascular disease. Circulation 2011, 123:2145-56.

[10] Yu G, Wang LG, Han Y, He QY: clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012, 16:284-7.

[11] Jing W, Hanming G: Identification of biological processes and potential inhibitors for aging skin. Research Square 2021.

[12] Hanming G: nuotrophils arthritis. Research Square 2021.

[13] Gu H, Wang W, Yuan G: Identification of biomarkers and candidate inhibitors for multiple myeloma. bioRxiv 2021:2021.02.25.432847.

[14] Zhang M, Wang J, Gu H: Identification of biological processes and signaling pathways in the stretched nucleus pulposus cells. bioRxiv 2021:2021.11.23.469730.

[15] Eccles SA, Welch DR: Metastasis: recent discoveries and novel treatment strategies. Lancet 2007, 369:1742-57.

[16] Esquivel-Velazquez M, Ostoa-Saloma P, Palacios-Arreola MI, Nava-Castro KE, Castro JI, Morales-Montor J: The role of cytokines in breast cancer development and progression. J Interferon Cytokine Res 2015, 35:1-16.

[17] Chen W, Qin Y, Liu S: Cytokines, breast cancer stem cells (BCSCs) and chemoresistance. Clin Transl Med 2018, 7:27.

[18] Wei J, Huang XJ, Huang Y, Xiong MY, Yao XY, Huang ZN, Li SN, Zhou WJ, Fang DL, Deng DH, Cheng P: Key immune-related gene ITGB2 as a prognostic signature for acute myeloid leukemia. Ann Transl Med 2021, 9:1386.

[19] Rojas K, Baliu-Pique M, Manzano A, Saiz-Ladera C, Garcia-Barberan V, Cimas FJ, Perez-Segura P, Pandiella A, Gyorffy B, Ocana A: In silico transcriptomic mapping of integrins and immune activation in Basal-like and HER2+ breast cancer. Cell Oncol (Dordr) 2021, 44:569-80.

[20] Gavin PG, Song N, Kim SR, Lipchik C, Johnson NL, Bandos H, Finnigan M, Rastogi P, Fehrenbacher L, Mamounas EP, Swain SM, Wickerham DL, Geyer CE, Jr., Jeong JH, Costantino JP, Wolmark N, Paik S, Pogue-Geile KL: Association of Polymorphisms in FCGR2A and FCGR3A With Degree of Trastuzumab Benefit in the Adjuvant Treatment of ERBB2/HER2-Positive Breast Cancer: Analysis of the NSABP B-31 Trial. JAMA Oncol 2017, 3:335-41.

[21] Fang J, Chen F, Liu D, Gu F, Chen Z, Wang Y: Prognostic value of immune checkpoint molecules in breast cancer. Biosci Rep 2020, 40.

[22] Shou J, Zhang Z, Lai Y, Chen Z, Huang J: Worse outcome in breast cancer with higher tumor-infiltrating FOXP3+ Tregs : a systematic review and meta-analysis. BMC Cancer 2016, 16:687.

[23] Reppert SM, Weaver DR: Coordination of circadian timing in mammals. Nature 2002, 418:935-41.

[24] Yuan G, Hua B, Cai T, Xu L, Li E, Huang Y, Sun N, Yan Z, Lu C, Qian R: Clock mediates liver senescence by controlling ER stress. Aging 2017, 9:2647-65.

[25] Zhu Z, Hua B, Xu L, Yuan G, Li E, Li X, Sun N, Yan Z, Lu C, Qian R: CLOCK promotes 3T3-L1 cell proliferation via Wnt signaling. IUBMB Life 2016, 68:557-68.

[26] Xu L, Cheng Q, Hua B, Cai T, Lin J, Yuan G, Yan Z, Li X, Sun N, Lu C, Qian R: Circadian gene Clock regulates mitochondrial morphology and functions by posttranscriptional way. bioRxiv 2018:365452.

[27] Yuan G, Xu L, Cai T, Hua B, Sun N, Yan Z, Lu C, Qian R: Clock mutant promotes osteoarthritis by inhibiting the acetylation of NFkappaB. Osteoarthritis Cartilage 2019, 27:922-31.

[28] Zhu Z, Hua B, Shang Z, Yuan G, Xu L, Li E, Li X, Sun N, Yan Z, Qian R, Lu C: Altered Clock and Lipid Metabolism-Related Genes in Atherosclerotic Mice Kept with Abnormal Lighting Condition. Biomed Res Int 2016, 2016:5438589.

[29] Mao SZ, Fan XF, Xue F, Chen R, Chen XY, Yuan GS, Hu LG, Liu SF, Gong YS: Intermedin modulates hypoxic pulmonary vascular remodeling by inhibiting pulmonary artery smooth muscle cell proliferation. Pulm Pharmacol Ther 2014, 27:1-9.

[30] Yuan G, Hua B, Yang Y, Xu L, Cai T, Sun N, Yan Z, Lu C, Qian R: The Circadian Gene Clock Regulates Bone Formation Via PDIA3. J Bone Miner Res 2017, 32:861-71.

[31] Zhu Z, Xu L, Cai T, Yuan G, Sun N, Lu C, Qian R: Clock represses preadipocytes adipogenesis via GILZ. J Cell Physiol 2018, 233:6028-40.

[32] Fan XF, Wang XR, Yuan GS, Wu DH, Hu LG, Xue F, Gong YS: [Effect of safflower injection on endoplasmic reticulum stress-induced apoptosts in rats with hypoxic pulmonary hypertension]. Zhongguo Ying Yong Sheng Li Xue Za Zhi 2012, 28:561-7.

[33] Cai T, Hua B, Luo D, Xu L, Cheng Q, Yuan G, Yan Z, Sun N, Hua L, Lu C: The circadian protein CLOCK regulates cell metabolism via the mitochondrial carrier SLC25A10. Biochim Biophys Acta Mol Cell Res 2019, 1866:1310-21.

[34] Li J, Wang W, Gu H: Identification of biological processes and signaling pathways for the knockout of REV-ERB in mouse brain. bioRxiv 2021:2021.11.22.469579.

[35] Yang G, Zhang H, Liu Y, Feng Y, Luo XQ, Liu ZQ, Geng XR, Wang S, Zheng PY, Feng BS, Liu ZG, Yang PC, Li HB, Wu SD: Alternation of circadian clock modulates forkhead box protein-3 gene transcription in CD4(+) T cells in the intestine. J Allergy Clin Immunol 2016, 138:1446-9 e10.

[36] Lamb DJ, Rust A, Rudisch A, Gluxam T, Harrer N, Machat H, Christ I, Colbatzky F, Wernitznig A, Osswald A, Sommergruber W: Inhibition of SYK kinase does not confer a pro-proliferative or pro-invasive phenotype in breast epithelium or breast cancer cells. Oncotarget 2020, 11:1257-72.

[37] Jiao X, Nawab O, Patel T, Kossenkov AV, Halama N, Jaeger D, Pestell RG: Recent Advances Targeting CCR5 for Cancer and Its Role in Immuno-Oncology. Cancer Res 2019, 79:4801-7.

[38] Stewart A, Fisher RA: Introduction: G Protein-coupled Receptors and RGS Proteins. Prog Mol Biol Transl Sci 2015, 133:1-11.

[39] Yuan G, Yang S, Yang S: Macrophage RGS12 contributes to osteoarthritis pathogenesis through enhancing the ubiquitination. Genes & Diseases 2021.

[40] Yuan G, Yang S, Yang S, Ng A, Oursler MJ: RGS12 is a critical proinflammatory factor in the pathogenesis of inflammatory arthritis via acting in Cox2-RGS12-NF kappa B pathway activation loop. J Bone Miner Res: WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2019. pp. 147-.

[41] Appleton CT, James CG, Beier F: Regulator of G-protein signaling (RGS) proteins differentially control chondrocyte differentiation. J Cell Physiol 2006, 207:735-45.

[42] Yuan G, Yang S, Ng A, Fu C, Oursler MJ, Xing L, Yang S: RGS12 Is a Novel Critical NF-kappaB Activator in Inflammatory Arthritis. iScience 2020, 23:101172.

[43] Fu C, Yuan G, Yang ST, Zhang D, Yang S: RGS12 Represses Oral Cancer via the Phosphorylation and SUMOylation of PTEN. J Dent Res 2020:22034520972095.

[44] Xie K, Martemyanov KA: Control of striatal signaling by g protein regulators. Front Neuroanat 2011, 5:49.

[45] Yuan G, Fu C, Yang ST, Yuh DY, Hajishengallis G, Yang S: RGS12 Drives Macrophage Activation and Osteoclastogenesis in Periodontitis. J Dent Res 2021:220345211045303.

[46] Yuan G, Yang S, Gautam M, Luo W, Yang S: Macrophage regulator of G-protein signaling 12 contributes to inflammatory pain hypersensitivity. Ann Transl Med 2021, 9:448.

[47] Rosenbaum DM, Rasmussen SG, Kobilka BK: The structure and function of G-protein-coupled receptors. Nature 2009, 459:356-63.

[48] Yuan G, Yang S, Liu M, Yang S: RGS12 is required for the maintenance of mitochondrial function during skeletal development. Cell Discov 2020, 6:59.

[49] Yuan G, Huang Y, Yang ST, Ng A, Yang S: RGS12 inhibits the progression and metastasis of multiple myeloma by driving M1 macrophage polarization and activation in the bone marrow microenvironment. Cancer Commun (Lond) 2022, 42:60-4.

[50] Oppermann M: Chemokine receptor CCR5: insights into structure, function, and regulation. Cell Signal 2004, 16:1201-10.

[51] Huang X, He D, Ming J, He Y, Zhou C, Ren H, He X, Wang C, Jin J, Ji L, Willard B, Pan B, Zheng L: High-density lipoprotein of patients with breast cancer complicated with type 2 diabetes mellitus promotes cancer cells adhesion to vascular endothelium via ICAM-1 and VCAM-1 upregulation. Breast Cancer Res Treat 2016, 155:441-55.

[52] Zhang Y, Di X, Chen G, Liu J, Zhang B, Feng L, Cheng S, Wang Y: An immune-related signature that to improve prognosis prediction of breast cancer. Am J Cancer Res 2021, 11:1267-85.

Tables 1-2 are available in the Supplementary Files section.

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Genomic analyses identify marker molecules and processes in metastatic breast cancer tissues

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