基底样乳腺癌预后标志物的基因表达谱分析
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摘要
目的分析基底样乳腺癌(BLBC)术后组织样本的基因表达谱数据,筛选与预后相关的基因。方法从国际基因表达谱数据库GEO和EGA中检索并获取乳腺癌基因表达谱数据,按照PAM50分型得到BLBC样本,保留记录有生存信息且BLBC样本数量>20个的数据集,最终保留了4个数据集,共447个BLBC样本。采用Lipták's weighted meta-z检验分析基因与生存时间的关系,并对预后相关基因进行功能注释。结果通过分析,所得238个基因与BLBC预后好有关,62个基因与BLBC预后差有关(|meta Z score|<3.09,P<0.01)。基因功能注释发现,预后好相关基因显著富集在免疫应答功能,而预后差相关基因没有显著富集的基因功能。结论分析得到的238个基因和62个基因分别与BLBC的预后好和预后差有关,可能成为BLBC新的预后标志物。
Objective To analyze gene expression profiles of resected tumor tissues of basal-like breast cancer(BLBC),and to screen the genes associated with prognosis. Methods We retrieved and obtained gene expression datasets from gene expression databases GEO and EGA. BLBC samples were identified by the PAM50 subtyping model. Only the datasets which had more than 20 samples of BLBC with overall survivals were maintained for subsequent analysis. We finally obtained four datasets with 447 BLBC samples. Lipták' s weighted meta-z test was used to analyze associations of gene expressions and overall survivals. GO function annotation was used to annotate the prognostic genes. Results Among the genes,238 genes were associated with good prognosis of BLBC,and 62 genes were associated with poor prognosis(| meta Z score | <3. 09,P < 0. 01). GO function annotation showed that genes associated with good prognosis were enriched with immune response functions,whereas those related to poor prognosis had no enriched functions. Conclusion The 238 genes associated with good prognosis and the 62 genes related to poor prognosis are candidate prognostic biomarkers of BLBC.
Objective To analyze gene expression profiles of resected tumor tissues of basal-like breast cancer(BLBC),and to screen the genes associated with prognosis. Methods We retrieved and obtained gene expression datasets from gene expression databases GEO and EGA. BLBC samples were identified by the PAM50 subtyping model. Only the datasets which had more than 20 samples of BLBC with overall survivals were maintained for subsequent analysis. We finally obtained four datasets with 447 BLBC samples. Lipták' s weighted meta-z test was used to analyze associations of gene expressions and overall survivals. GO function annotation was used to annotate the prognostic genes. Results Among the genes,238 genes were associated with good prognosis of BLBC,and 62 genes were associated with poor prognosis(| meta Z score | <3. 09,P < 0. 01). GO function annotation showed that genes associated with good prognosis were enriched with immune response functions,whereas those related to poor prognosis had no enriched functions. Conclusion The 238 genes associated with good prognosis and the 62 genes related to poor prognosis are candidate prognostic biomarkers of BLBC.
引文
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[2]Parker JS,Mullins M,Cheang MCU,et al.Supervised risk predictor of breast cancer based on intrinsic subtypes[J].J Clin Oncol,2009,27(8):1160-1167.
[3]Kovac B,MkelTP,Vallenius T.Increasedα-actinin-1 destabilizes E-cadherin-based adhesions and associates with poor prognosis in basal-like breast cancer[J].PLo S One,2018,13(5):e0196986.
[4]Zhu X,Shan L,Wang F,et al.Hypermethylation of BRCA1 gene:implication for prognostic biomarker and therapeutic target in sporadic primary triple-negative breast cancer[J].Breast Cancer Res Treat,2015,150(3):479-486.
[5]Ray PS,Wang J,Qu Y,et al.FOXC1 is a potential prognostic biomarker with functional significance in basal-like breast cancer[J].Cancer Res,2010,70(10):3870-3876.
[6]Barrett T,Wilhite SE,Ledoux P,et al.NCBI GEO:archive for functional genomics data sets-update[J].Nucl Acids Res,2013,41(D1):D991-D995.
[7]Lappalainen I,Almeida-King J,Kumanduri V,et al.The European Genome-phenome Archive of human data consented for biomedical research[J].Nat Genet,2015,47(7):692-695.
[8]Gautier L,Cope L,Bolstad BM,et al.affy--analysis of Affymetrix Gene Chip data at the probe level[J].Bioinformatics,2004,20(3):307-315.
[9]Wilson CL,Miller CJ.Simpleaffy:a Bio Conductor package for Affymetrix Quality Control and data analysis[J].Bioinformatics,2005,21(18):3683-3685.
[10]Curtis C,Shah SP,Chin SF,et al.The genomic and transcriptomic architecture of 2000 breast tumours reveals novel subgroups[J].Nature,2012,486(7403):346-352.
[11]Huang DW,Sherman BT,Lempicki RA.Bioinformatics enrichment tools:paths toward the comprehensive functional analysis of large gene lists[J].Nucl Acids Res,2009,37(1):1-13.
[12]Huang DW,Sherman BT,Lempicki RA.Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[J].Nat Protoc,2009,4(1):44-57.
[13]Pawitan Y,Bj9hle J,Amler L,et al.Gene expression profiling spares early breast cancer patients from adjuvant therapy:derived and validated in two population-based cohorts[J].Breast Cancer Res,2005,7(6):R953-R964.
[14]Desmedt C,Piette F,Loi S,et al.Strong time dependence of the76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series[J].Clin Cancer Res,2007,13(11):3207-3214.
[15]Kao KJ,Chang KM,Hsu HC,et al.Correlation of microarraybased breast cancer molecular subtypes and clinical outcomes:implications for treatment optimization[J].BMC Cancer,2011,11:143.
[16]Budczies J,Klauschen F,Sinn BV,et al.Cutoff Finder:a comprehensive and straight forward Web application enabling rapid biomarker Cutoff optimization[J].PLo S One,2012,7(12):e51862.
[17]Nolan E,Savas P,Policheni AN,et al.Combined immune checkpoint blockade as a therapeutic strategy for BRCA1-mutated breast cancer[J].Sci Transl Med,2017,9(393):eaal4922.