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Identification of key tumor stroma-associated transcriptional signatures correlated with survival prognosis and tumor progression in breast cancer

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Abstract

Background

The aberrant expression of stromal gene signatures in breast cancer has been widely studied. However, the association of stromal gene signatures with tumor immunity, progression, and clinical outcomes remains lacking.

Methods

Based on eight breast tumor stroma (BTS) transcriptomics datasets, we identified differentially expressed genes (DEGs) between BTS and normal breast stroma. Based on the DEGs, we identified dysregulated pathways and prognostic hub genes, hub oncogenes, hub protein kinases, and other key marker genes associated with breast cancer. Moreover, we compared the enrichment levels of stromal and immune signatures between breast cancer patients with bad and good clinical outcomes. We also investigated the association between tumor stroma-related genes and breast cancer progression.

Results

The DEGs included 782 upregulated and 276 downregulated genes in BTS versus normal breast stroma. The pathways significantly associated with the DEGs included cytokine–cytokine receptor interaction, chemokine signaling, T cell receptor signaling, cell adhesion molecules, focal adhesion, and extracellular matrix–receptor interaction. Protein–protein interaction network analysis identified the stromal hub genes with prognostic value in breast cancer, including two oncogenes (COL1A1 and IL21R), two protein kinases encoding genes (PRKACA and CSK), and a growth factor encoding gene (PLAU). Moreover, we observed that the patients with bad clinical outcomes were less enriched in stromal and antitumor immune signatures (CD8 + T cells and tumor-infiltrating lymphocytes) but more enriched in tumor cells and immunosuppressive signatures (MDSCs and CD4 + regulatory T cells) compared with the patients with good clinical outcomes. The ratios of CD8 + /CD4 + regulatory T cells were lower in the patients with bad clinical outcomes. Furthermore, we identified the tumor stroma-related genes, including MCM4, SPECC1, IMPA2, and AGO2, which were gradually upregulated through grade I, II, and III breast cancers. In contrast, COL14A1, ESR1, SLIT2, IGF1, CH25H, PRR5L, ABCA6, CEP126, IGDCC4, LHFP, MFAP3, PCSK5, RAB37, RBMS3, SETBP1, and TSPAN11 were gradually downregulated through grade I, II, and III breast cancers. It suggests that the expression of these stromal genes has an association with the progression of breast cancers. These progression-associated genes also displayed an expression association with recurrence-free survival in breast cancer patients.

Conclusions

This study identified tumor stroma-associated biomarkers correlated with deregulated pathways, tumor immunity, tumor progression, and clinical outcomes in breast cancer. Our findings provide new insights into the pathogenesis of breast cancer.

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Data availability

The datasets (GSE9014 [9], GSE83591 (n = 53)[11], GSE31192 (n = 17) [12], GSE26910 (n = 12) [13], GSE10797 (n = 33) [14], GSE8977 (n = 22) [15], GSE33692 (n = 22) [16], and GSE14548 (n = 34) [17]) were downloaded from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/), the TCGA breast cancer dataset was downloaded from the website https://gdc-portal.nci.nih.gov/.

References

  1. Guo S, Deng C-X. Effect of stromal cells in tumor microenvironment on metastasis initiation. Int J Biol Sci. 2018;14:2083–93. https://doi.org/10.7150/ijbs.25720.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Bussard KM, Mutkus L, Stumpf K, et al. Tumor-associated stromal cells as key contributors to the tumor microenvironment. Breast Cancer Res. 2016;18:84. https://doi.org/10.1186/s13058-016-0740-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Valkenburg KC, de Groot AE, Pienta KJ. Targeting the tumour stroma to improve cancer therapy. Nat Rev Clin Oncol. 2018;15:366–81. https://doi.org/10.1038/s41571-018-0007-1.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Hanahan D, Coussens LM. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell. 2012;21:309–22. https://doi.org/10.1016/j.ccr.2012.02.022.

    Article  CAS  PubMed  Google Scholar 

  5. Choi H, Sheng J, Gao D, et al. Transcriptome analysis of individual stromal cell populations identifies stroma-tumor crosstalk in mouse lung cancer model. Cell Rep. 2015;10:1187–201. https://doi.org/10.1016/j.celrep.2015.01.040.

    Article  CAS  PubMed  Google Scholar 

  6. Pienta KJ, McGregor N, Axelrod R, Axelrod DE. Ecological therapy for cancer: defining tumors using an ecosystem paradigm suggests new opportunities for novel cancer treatments. Transl Oncol. 2008;1:158–64. https://doi.org/10.1593/tlo.08178.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Mao Y, Keller ET, Garfield DH, et al. Stromal cells in tumor microenvironment and breast cancer. Cancer Metastasis Rev. 2013;32:303–15. https://doi.org/10.1007/s10555-012-9415-3.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Conklin MW, Keely PJ. Why the stroma matters in breast cancer: insights into breast cancer patient outcomes through the examination of stromal biomarkers. Cell Adh Migr. 2012;6:249–60. https://doi.org/10.4161/cam.20567.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Finak G, Bertos N, Pepin F, et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat Med. 2008;14:518–27. https://doi.org/10.1038/nm1764.

    Article  CAS  PubMed  Google Scholar 

  10. Winslow S, Leandersson K, Edsjö A, Larsson C. Prognostic stromal gene signatures in breast cancer. Breast Cancer Res. 2015;17:23. https://doi.org/10.1186/s13058-015-0530-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Liu H, Dowdle JA, Khurshid S, et al. Discovery of stromal regulatory networks that suppress ras-sensitized epithelial cell proliferation. Dev Cell. 2017;41:392-407.e6. https://doi.org/10.1016/j.devcel.2017.04.024.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Harvell DME, Kim J, O’Brien J, et al. Genomic signatures of pregnancy-associated breast cancer epithelia and stroma and their regulation by estrogens and progesterone. Horm Cancer. 2013;4:140–53. https://doi.org/10.1007/s12672-013-0136-z.

    Article  CAS  PubMed  Google Scholar 

  13. Planche A, Bacac M, Provero P, et al. Identification of prognostic molecular features in the reactive stroma of human breast and prostate cancer. PLoS ONE. 2011;6: e18640. https://doi.org/10.1371/journal.pone.0018640.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Casey T, Bond J, Tighe S, et al. Molecular signatures suggest a major role for stromal cells in development of invasive breast cancer. Breast Cancer Res Treat. 2009;114:47–62. https://doi.org/10.1007/s10549-008-9982-8.

    Article  CAS  PubMed  Google Scholar 

  15. Karnoub AE, Dash AB, Vo AP, et al. Mesenchymal stem cells within tumour stroma promote breast cancer metastasis. Nature. 2007;449:557–63. https://doi.org/10.1038/nature06188.

    Article  CAS  PubMed  Google Scholar 

  16. Knudsen ES, Ertel A, Davicioni E, et al. Progression of ductal carcinoma in situ to invasive breast cancer is associated with gene expression programs of EMT and myoepithelia. Breast Cancer Res Treat. 2012;133:1009–24. https://doi.org/10.1007/s10549-011-1894-3.

    Article  CAS  PubMed  Google Scholar 

  17. Ma X-J, Dahiya S, Richardson E, et al. Gene expression profiling of the tumor microenvironment during breast cancer progression. Breast Cancer Res. 2009;11:R7. https://doi.org/10.1186/bcr2222.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Xia J, Gill EE, Hancock REW. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 2015;10:823–44. https://doi.org/10.1038/nprot.2015.052.

    Article  CAS  PubMed  Google Scholar 

  19. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27. https://doi.org/10.1093/biostatistics/kxj037.

    Article  PubMed  Google Scholar 

  20. Koboldt DC, Fulton RS, McLellan MD, et al. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490:61–70. https://doi.org/10.1038/nature11412.

    Article  CAS  Google Scholar 

  21. Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47–e47. https://doi.org/10.1093/nar/gkv007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Cochran WG. The combination of estimates from different experiments. Biometrics. 1954;10:101–29. https://doi.org/10.2307/3001666.

    Article  Google Scholar 

  23. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc: Ser B (Methodol). 1995;57:289–300.

    Google Scholar 

  24. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50. https://doi.org/10.1073/pnas.0506580102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kanehisa M, Furumichi M, Tanabe M, et al. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45:D353–61. https://doi.org/10.1093/nar/gkw1092.

    Article  CAS  PubMed  Google Scholar 

  26. Clarke DJB, Kuleshov MV, Schilder BM, et al. eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks. Nucleic Acids Res. 2018;46:W171–9. https://doi.org/10.1093/nar/gky458.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Janky R, Verfaillie A, Imrichová H, et al. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol. 2014;10: e1003731. https://doi.org/10.1371/journal.pcbi.1003731.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13. https://doi.org/10.1093/nar/gky1131.

    Article  CAS  PubMed  Google Scholar 

  29. Chin C-H, Chen S-H, Wu H-H, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8:S11. https://doi.org/10.1186/1752-0509-8-S4-S11.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504. https://doi.org/10.1101/gr.1239303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Yoshihara K, Shahmoradgoli M, Martínez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612. https://doi.org/10.1038/ncomms3612.

    Article  CAS  PubMed  Google Scholar 

  32. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. https://doi.org/10.1186/1471-2105-14-7.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Terry Therneau. A package for survival analysis in R, August 23, 2021. https://cran.rproject.org/web/packages/survival/index.html.

  34. Multiple Comparisons Using R. In: Routledge & CRC Press. https://www.routledge.com/Multiple-Comparisons-Using-R/Bretz-Hothorn-Westfall/p/book/9781584885740. Accessed 14 Feb 2021

  35. Luo X, Xiong X, Shao Q, et al. The tumor suppressor interferon regulatory factor 8 inhibits β-catenin signaling in breast cancers, but is frequently silenced by promoter methylation. Oncotarget. 2017;8:48875–88. https://doi.org/10.18632/oncotarget.16511.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Wolf B, Goebel G, Hackl H, Fiegl H. Reduced mRNA expression levels of NFE2L2 are associated with poor outcome in breast cancer patients. BMC Cancer. 2016. https://doi.org/10.1186/s12885-016-2840-x.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Wang X, Wang G, Shi Y, et al. PPAR-delta promotes survival of breast cancer cells in harsh metabolic conditions. Oncogenesis. 2016;5:e232–e232. https://doi.org/10.1038/oncsis.2016.41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ferrari N, Mohammed ZMA, Nixon C, et al. Expression of RUNX1 correlates with poor patient prognosis in triple negative breast cancer. PLoS ONE. 2014. https://doi.org/10.1371/journal.pone.0100759.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ferralli J, Chiquet-Ehrismann R, Degen M. KLF4α stimulates breast cancer cell proliferation by acting as a KLF4 antagonist. Oncotarget. 2016;7:45608–21. https://doi.org/10.18632/oncotarget.10058.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Kren BT, Unger GM, Abedin MJ, et al. Preclinical evaluation of cyclin dependent kinase 11 and casein kinase 2 survival kinases as RNA interference targets for triple negative breast cancer therapy. Breast Cancer Res. 2015;17:19. https://doi.org/10.1186/s13058-015-0524-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Feng Y, Spezia M, Huang S, et al. Breast cancer development and progression: risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis. Genes Dis. 2018;5:77–106. https://doi.org/10.1016/j.gendis.2018.05.001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Liu W, Ye H, Liu Y-F, et al. Transcriptome-derived stromal and immune scores infer clinical outcomes of patients with cancer. Oncol Lett. 2018;15:4351–7. https://doi.org/10.3892/ol.2018.7855.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zhang S-C, Hu Z-Q, Long J-H, et al. clinical implications of tumor-infiltrating immune cells in breast cancer. J Cancer. 2019;10:6175–84. https://doi.org/10.7150/jca.35901.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Jin YW, Hu P. Tumor-infiltrating CD8 T cells predict clinical breast cancer outcomes in young women. Cancers (Basel). 2020;12:E1076. https://doi.org/10.3390/cancers12051076.

    Article  CAS  Google Scholar 

  45. Ai L, Mu S, Wang Y, et al. Prognostic role of myeloid-derived suppressor cells in cancers: a systematic review and meta-analysis. BMC Cancer. 2018;18:1220. https://doi.org/10.1186/s12885-018-5086-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Huang Y, Ma C, Zhang Q, et al. CD4+ and CD8+ T cells have opposing roles in breast cancer progression and outcome. Oncotarget. 2015;6:17462–78. https://doi.org/10.18632/oncotarget.3958.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Jia D, Liu Z, Deng N, et al. A COL11A1-correlated pan-cancer gene signature of activated fibroblasts for the prioritization of therapeutic targets. Cancer Lett. 2016;382:203–14. https://doi.org/10.1016/j.canlet.2016.09.001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bret C, Moreaux J, Schved J-F, et al. SULFs in human neoplasia: implication as progression and prognosis factors. J Transl Med. 2011;9:72. https://doi.org/10.1186/1479-5876-9-72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Grigoriadis A, Mackay A, Reis-Filho JS, et al. Establishment of the epithelial-specific transcriptome of normal and malignant human breast cells based on MPSS and array expression data. Breast Cancer Res. 2006;8:R56. https://doi.org/10.1186/bcr1604.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Xiang Y-J, Fu Q-Y, Ma Z-B, et al. Screening for candidate genes related to breast cancer with cDNA microarray analysis. Chronic Dis Transl Med. 2015;1:65–72. https://doi.org/10.1016/j.cdtm.2015.02.001.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Norton N, Advani PP, Serie DJ, et al. Assessment of tumor heterogeneity, as evidenced by gene expression profiles, pathway activation, and gene copy number, in patients with multifocal invasive lobular breast tumors. PLoS ONE. 2016;11: e0153411. https://doi.org/10.1371/journal.pone.0153411.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Winslow S, Lindquist KE, Edsjö A, Larsson C. The expression pattern of matrix-producing tumor stroma is of prognostic importance in breast cancer. BMC Cancer. 2016;16:841. https://doi.org/10.1186/s12885-016-2864-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Makoukji J, Makhoul NJ, Khalil M, et al. Gene expression profiling of breast cancer in Lebanese women. Sci Rep. 2016;6:36639. https://doi.org/10.1038/srep36639.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Dong M, How T, Kirkbride KC, et al. The type III TGF-beta receptor suppresses breast cancer progression. J Clin Invest. 2007;117:206–17. https://doi.org/10.1172/JCI29293.

    Article  CAS  PubMed  Google Scholar 

  55. Castillo LF, Tascón R, Lago Huvelle MA, et al. Glypican-3 induces a mesenchymal to epithelial transition in human breast cancer cells. Oncotarget. 2016;7:60133–54. https://doi.org/10.18632/oncotarget.11107.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Johnson RW, Finger EC, Olcina MM, et al. Induction of LIFR confers a dormancy phenotype in breast cancer cells disseminated to the bone marrow. Nat Cell Biol. 2016;18:1078–89. https://doi.org/10.1038/ncb3408.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Fu J, Khaybullin R, Zhang Y, et al. Gene expression profiling leads to discovery of correlation of matrix metalloproteinase 11 and heparanase 2 in breast cancer progression. BMC Cancer. 2015. https://doi.org/10.1186/s12885-015-1410-y.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Ferreira MA, Gamazon ER, Al-Ejeh F, et al. Genome-wide association and transcriptome studies identify target genes and risk loci for breast cancer. Nat Commun. 2019;10:1741. https://doi.org/10.1038/s41467-018-08053-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Esquivel-Velázquez M, Ostoa-Saloma P, Palacios-Arreola MI, et al. The role of cytokines in breast cancer development and progression. J Interferon Cytokine Res. 2015;35:1–16. https://doi.org/10.1089/jir.2014.0026.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Chow MT, Luster AD. Chemokines in cancer. Cancer Immunol Res. 2014;2:1125–31. https://doi.org/10.1158/2326-6066.CIR-14-0160.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Stacker SA, Achen MG. The VEGF signaling pathway in cancer: the road ahead. Chin J Cancer. 2013;32:297–302. https://doi.org/10.5732/cjc.012.10319.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Bao Y, Wang L, Shi L, et al. Transcriptome profiling revealed multiple genes and ECM-receptor interaction pathways that may be associated with breast cancer. Cell Mol Biol Lett. 2019;24:38. https://doi.org/10.1186/s11658-019-0162-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Pavlova NN, Thompson CB. The emerging hallmarks of cancer metabolism. Cell Metab. 2016;23:27–47. https://doi.org/10.1016/j.cmet.2015.12.006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Catez F, Dalla Venezia N, Marcel V, et al. Ribosome biogenesis: an emerging druggable pathway for cancer therapeutics. Biochem Pharmacol. 2019;159:74–81. https://doi.org/10.1016/j.bcp.2018.11.014.

    Article  CAS  PubMed  Google Scholar 

  65. Stegh AH. Targeting the p53 signaling pathway in cancer therapy—the promises, challenges, and perils. Expert Opin Ther Targets. 2012;16:67–83. https://doi.org/10.1517/14728222.2011.643299.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Wu H-T, Liu J, Li G-W, et al. The transcriptional STAT3 is a potential target, whereas transcriptional STAT5A/5B/6 are new biomarkers for prognosis in human breast carcinoma. Oncotarget. 2017;8:36279–88. https://doi.org/10.18632/oncotarget.16748.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Mathot P, Grandin M, Devailly G, et al. DNA methylation signal has a major role in the response of human breast cancer cells to the microenvironment. Oncogenesis. 2017;6: e390. https://doi.org/10.1038/oncsis.2017.88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Tchou J, Kossenkov AV, Chang L, et al. Human breast cancer associated fibroblasts exhibit subtype specific gene expression profiles. BMC Med Genomics. 2012;5:39. https://doi.org/10.1186/1755-8794-5-39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Chen D, Yang H. Integrated analysis of differentially expressed genes in breast cancer pathogenesis. Oncol Lett. 2015;9:2560–6. https://doi.org/10.3892/ol.2015.3147.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Pawson T, Nash P. Protein–protein interactions define specificity in signal transduction. Genes Dev. 2000;14:1027–47. https://doi.org/10.1101/gad.14.9.1027.

    Article  CAS  PubMed  Google Scholar 

  71. He L, Wang D, Wei N, Guo Z. Identification of potential therapeutic targets using breast cancer stroma expression profiling. Transl Cancer Res. 2016. https://doi.org/10.21037/7804.

    Article  Google Scholar 

  72. Wang L-N, Cui Y-X, Ruge F, Jiang WG. Interleukin 21 and its receptor play a role in proliferation, migration and invasion of breast cancer cells. Cancer Genomics Proteomics. 2015;12:211–21.

    CAS  PubMed  Google Scholar 

  73. Liu J, Shen J-X, Wu H-T, et al. Collagen 1A1 (COL1A1) promotes metastasis of breast cancer and is a potential therapeutic target. Discov Med. 2018;25:211–23.

    CAS  PubMed  Google Scholar 

  74. Moody SE, Schinzel AC, Singh S, et al. PRKACA mediates resistance to HER2-targeted therapy in breast cancer cells and restores anti-apoptotic signaling. Oncogene. 2015;34:2061–71. https://doi.org/10.1038/onc.2014.153.

    Article  CAS  PubMed  Google Scholar 

  75. Xiao T, Li W, Wang X, et al. Estrogen-regulated feedback loop limits the efficacy of estrogen receptor–targeted breast cancer therapy. Proc Natl Acad Sci USA. 2018;115:7869–78. https://doi.org/10.1073/pnas.1722617115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Banys-Paluchowski M, Witzel I, Aktas B, et al. The prognostic relevance of urokinase-type plasminogen activator (uPA) in the blood of patients with metastatic breast cancer. Sci Rep. 2019;9:2318. https://doi.org/10.1038/s41598-018-37259-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

This work was supported by China Pharmaceutical University (Grant numbers 3150120001 to XW).

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Authors and Affiliations

  1. Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China

    Md. Nazim Uddin & Xiaosheng Wang

  2. Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China

    Md. Nazim Uddin & Xiaosheng Wang

  3. Big Data Research Institute, China Pharmaceutical University, Nanjing, 211198, China

    Md. Nazim Uddin & Xiaosheng Wang

  4. Institute of Food Science and Technology, Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, 1205, Bangladesh

    Md. Nazim Uddin

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Contributions

MNU performed data analyses, conceived the research, and prepare the manuscript. XW conceived the research, designed analysis strategies, and wrote the manuscript. Both authors read and approved the final manuscript.

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Correspondence to Xiaosheng Wang.

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Supplementary Information

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Supplementary file1 (DOCX 1386 KB)

12282_2022_1332_MOESM2_ESM.xlsx

Supplementary file2: Table S1. Description of individual breast tumor stroma study included in the meta-analysis. GEO: Gene Expression Omnibu. (XLSX 11 KB)

Supplementary file3: Table S2. The marker genes of immune and stromal signatures. (XLSX 11 KB)

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Supplementary file4: Table S3. The patient's characteristics of GSE9014, including number of samples, tumor grade, hormone receptor status, lymph node status, and types of therapies. (XLSX 12 KB)

Supplementary file5: Table S4. List of upregulated genes in the breast tumor stroma. (XLSX 40 KB)

Supplementary file6: Table S5. List of downregulated genes in the breast tumor stroma. (XLSX 20 KB)

Supplementary file7: Table S6. Enriched KEGG pathways associated with stromal upregulated DEGs. (XLSX 12 KB)

Supplementary file8: Table S7. Enriched KEGG pathways associated with stromal downregulated DEGs. (XLSX 11 KB)

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Supplementary file9: Table S8. Putative enriched transcription factors through Transcription Factor Enrichment Analysis (TFEA). (XLSX 9 KB)

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Supplementary file10: Table S9. Ranked list of enriched kinases based on overlap between known kinase–substrate phosphorylation interactions and the proteins. (XLSX 13 KB)

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Supplementary file11. Table S10. List of master transcription factors associated with the regulation of stromal DEGs. (XLSX 10 KB)

Supplementary file12. Table S11. List of 233 hub genes (Degree>=25) identified by degree method. (XLSX 14 KB)

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Supplementary file13: Table S12. List of 1955 genes which are significantly dysregulated (F-test, P < 0.05) among three grades. (XLSX 95 KB)

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Supplementary file14: Table S13. List of 1471 genes which are significantly dysregulated (Student t-test, P < 0.05) between the bad clinical and good clinical outcome groups. (XLSX 84 KB)

12282_2022_1332_MOESM15_ESM.xlsx

Supplementary file15: Table S14. List of 124 genes which are are commonly found between the grades-basis (grade I, grade II, and grade III) and clinical outcome basis (bad versus good clinical outcome) analysis. (XLSX 14 KB)

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Uddin, M.N., Wang, X. Identification of key tumor stroma-associated transcriptional signatures correlated with survival prognosis and tumor progression in breast cancer. Breast Cancer 29, 541–561 (2022). https://doi.org/10.1007/s12282-022-01332-6

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  • DOI: https://doi.org/10.1007/s12282-022-01332-6

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