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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Immune-related Gene-based Prognostic Signature for the Risk Stratification Analysis of Breast Cancer

Author(s): Dongqing Su, Qianzi Lu, Yi Pan, Yao Yu, Shiyuan Wang, Yongchun Zuo* and Lei Yang*

Volume 17, Issue 2, 2022

Published on: 14 December, 2021

Page: [196 - 205] Pages: 10

DOI: 10.2174/1574893616666211005110732

Price: $65

Abstract

Background: Breast cancer has plagued women for many years and caused many deaths around the world.

Methods: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis, and nomogram were also conducted in this study.

Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts.

Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method and may improve the treatment stratification of breast cancer patients in the future work.

Keywords: Breast cancer, risk score model, immune-related gene, prognosis, enrichment analysis, malignant.

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[1]
Roberts EW, Broz ML, Binnewies M, et al. Critical role for CD103+/CD141+ dendritic cells bearing CCR7 for tumor antigen trafficking and priming of T cell immunity in melanoma. Cancer Cell 2016; 30(2): 324-36.
[http://dx.doi.org/10.1016/j.ccell.2016.06.003] [PMID: 27424807]
[2]
Spranger S, Bao R, Gajewski TF. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature 2015; 523(7559): 231-5.
[http://dx.doi.org/10.1038/nature14404] [PMID: 25970248]
[3]
DeSantis CE, Ma J, Gaudet MM, et al. Breast cancer statistics, 2019. CA Cancer J Clin 2019; 69(6): 438-51.
[http://dx.doi.org/10.3322/caac.21583] [PMID: 31577379]
[4]
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019; 69(1): 7-34.
[http://dx.doi.org/10.3322/caac.21551] [PMID: 30620402]
[5]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71(3): 209-49.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[6]
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin 2020; 70(1): 7-30.
[http://dx.doi.org/10.3322/caac.21590] [PMID: 31912902]
[7]
Prat A, Parker JS, Fan C, Perou CM. PAM50 assay and the three-gene model for identifying the major and clinically relevant molecular subtypes of breast cancer. Breast Cancer Res Treat 2012; 135(1): 301-6.
[http://dx.doi.org/10.1007/s10549-012-2143-0] [PMID: 22752290]
[8]
Haibe-Kains B, Desmedt C, Loi S, et al. A three-gene model to robustly identify breast cancer molecular subtypes. J Natl Cancer Inst 2012; 104(4): 311-25.
[http://dx.doi.org/10.1093/jnci/djr545] [PMID: 22262870]
[9]
Ciriello G, Gatza ML, Beck AH, et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell 2015; 163(2): 506-19.
[http://dx.doi.org/10.1016/j.cell.2015.09.033] [PMID: 26451490]
[10]
Yang L, Wang S, Zhang Q, et al. Clinical significance of the immune microenvironment in ovarian cancer patients. Mol Omics 2018; 14(5): 341-51.
[http://dx.doi.org/10.1039/C8MO00128F] [PMID: 30129640]
[11]
Zeng D, Li M, Zhou R, et al. Tumor microenvironment characterization in gastric cancer identifies prognostic and immunotherapeutically relevant gene signatures. Cancer Immunol Res 2019; 7(5): 737-50.
[http://dx.doi.org/10.1158/2326-6066.CIR-18-0436] [PMID: 30842092]
[12]
Shen S, Wang G, Zhang R, et al. Development and validation of an immune gene-set based Prognostic signature in ovarian cancer. EBioMedicine 2019; 40: 318-26.
[http://dx.doi.org/10.1016/j.ebiom.2018.12.054] [PMID: 30594555]
[13]
Li J, Liu C, Chen Y, et al. Tumor characterization in breast cancer identifies immune-relevant gene signatures associated with prognosis. Front Genet 2019; 10: 1119.
[http://dx.doi.org/10.3389/fgene.2019.01119] [PMID: 31781173]
[14]
Dao FY, Lv H, Zhang D, Zhang ZM, Liu L, Lin H. DeepYY1: A deep learning approach to identify YY1-mediated chromatin loops. Brief Bioinform 2021; 22(4): 22.
[http://dx.doi.org/10.1093/bib/bbaa356] [PMID: 33279983]
[15]
Zhang ZM, Tan JX, Wang F, Dao FY, Zhang ZY, Lin H. Early diagnosis of hepatocellular carcinoma using machine learning method. Front Bioeng Biotechnol 2020; 8: 254.
[http://dx.doi.org/10.3389/fbioe.2020.00254] [PMID: 32292778]
[16]
Zhou R, Zeng D, Zhang J, et al. A robust panel based on tumour microenvironment genes for prognostic prediction and tailoring therapies in stage I-III colon cancer. EBioMedicine 2019; 42: 420-30.
[http://dx.doi.org/10.1016/j.ebiom.2019.03.043] [PMID: 30917936]
[17]
Lv Z, Ao C, Zou Q. Protein function prediction: From traditional classifier to deep learning. Proteomics 2019; 19(14): e1900119.
[http://dx.doi.org/10.1002/pmic.201900119] [PMID: 31187588]
[18]
Zhao J, Wang Y, Lao Z, et al. Prognostic immune-related gene models for breast cancer: A pooled analysis. OncoTargets Ther 2017; 10: 4423-33.
[http://dx.doi.org/10.2147/OTT.S144015] [PMID: 28979134]
[19]
de Melo Gagliato D, Cortes J, Curigliano G, et al. Tumor-infiltrating lymphocytes in breast cancer and implications for clinical practice. Biochim Biophys Acta Rev Cancer 2017; 1868(2): 527-37.
[http://dx.doi.org/10.1016/j.bbcan.2017.10.003] [PMID: 29061314]
[20]
Lee HJ, Kim JY, Park IA, et al. Prognostic significance of tumor-infiltrating lymphocytes and the tertiary lymphoid structures in HER2-positive breast cancer treated with adjuvant trastuzumab. Am J Clin Pathol 2015; 144(2): 278-88.
[http://dx.doi.org/10.1309/AJCPIXUYDVZ0RZ3G] [PMID: 26185313]
[21]
Denkert C, von Minckwitz G, Darb-Esfahani S, et al. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: A pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol 2018; 19(1): 40-50.
[http://dx.doi.org/10.1016/S1470-2045(17)30904-X] [PMID: 29233559]
[22]
Pruneri G, Vingiani A, Denkert C. Tumor infiltrating lymphocytes in early breast cancer. Breast 2018; 37: 207-14.
[http://dx.doi.org/10.1016/j.breast.2017.03.010] [PMID: 28363679]
[23]
Hsu JB, Lee GA, Chang TH, et al. Radiomic immunophenotyping of GSEA-assessed immunophenotypes of glioblastoma and its implications for prognosis: A feasibility study. Cancers (Basel) 2020; 12(10): 12.
[http://dx.doi.org/10.3390/cancers12103039] [PMID: 33086550]
[24]
Chen Y, Li ZY, Zhou GQ, Sun Y. An immune-related gene prognostic index for head and neck squamous cell carcinoma. Clin Cancer Res 2021; 27(1): 330-41.
[http://dx.doi.org/10.1158/1078-0432.CCR-20-2166] [PMID: 33097495]
[25]
Le VH, Kha QH, Hung TNK, Le NQK. Risk score generated from CT-based radiomics signatures for overall survival prediction in non-small cell lung cancer. Cancers (Basel) 2021; 13(14): 13.
[http://dx.doi.org/10.3390/cancers13143616] [PMID: 34298828]
[26]
Curtis C, Shah SP, Chin SF, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012; 486(7403): 346-52.
[http://dx.doi.org/10.1038/nature10983] [PMID: 22522925]
[27]
Bhattacharya S, Andorf S, Gomes L, et al. ImmPort: Disseminating data to the public for the future of immunology. Immunol Res 2014; 58(2-3): 234-9.
[http://dx.doi.org/10.1007/s12026-014-8516-1] [PMID: 24791905]
[28]
Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9: 559.
[29]
Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43: e47-7.
[30]
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(43): 15545-50.
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
[31]
Yu G, Wang LG, Han Y, He QY. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284-7.
[http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]
[32]
Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011; 27(12): 1739-40.
[http://dx.doi.org/10.1093/bioinformatics/btr260] [PMID: 21546393]
[33]
Hänzelmann S, Castelo R, Guinney J. GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013; 14: 7.
[http://dx.doi.org/10.1186/1471-2105-14-7] [PMID: 23323831]
[34]
Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 2016; 32(18): 2847-9.
[http://dx.doi.org/10.1093/bioinformatics/btw313] [PMID: 27207943]
[35]
Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 2008; 26(8): 1364-70.
[http://dx.doi.org/10.1200/JCO.2007.12.9791] [PMID: 18323559]
[36]
Goeman JJ. L1 penalized estimation in the Cox proportional hazards model. Biom J 2010; 52(1): 70-84.
[PMID: 19937997]
[37]
Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc B 1996; 58: 267-88.
[http://dx.doi.org/10.1111/j.2517-6161.1996.tb02080.x]
[38]
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.
[http://dx.doi.org/10.1038/ncomms3612] [PMID: 24113773]
[39]
Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 2015; 160(1-2): 48-61.
[http://dx.doi.org/10.1016/j.cell.2014.12.033] [PMID: 25594174]
[40]
Wang S, Zhang Q, Yu C, Cao Y, Zuo Y, Yang L. Immune cell infiltration-based signature for prognosis and immunogenomic analysis in breast cancer. Brief Bioinformatics 2021; 22(2): 2020-31.
[http://dx.doi.org/10.1093/bib/bbaa026]
[41]
Wang S, Xiong Y, Zhang Q, et al. Clinical significance and immunogenomic landscape analyses of the immune cell signature based prognostic model for patients with breast cancer. Brief Bioinform 2021; 22(4): 2020-31.
[http://dx.doi.org/10.1093/bib/bbaa026] [PMID: 33302293]

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