Meta-Analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymes

被引：31

作者：

Barfeld S.J. ^{[2
]}

East P. ^{[1
]}

Zuber V. ^{[2
]}

Mills I.G. ^{[2
,3
]}

机构：

[1] Cancer Research UK London Research Institute, Bioinformatics and Biostatistics, 44 Lincoln's Inn Fields, London

[2] Prostate Cancer Research Group, Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership University of Oslo, Oslo University Hospital, Oslo

[3] Institute of Cancer Research and Oslo University Hospital, Department of Cancer Prevention and Urology, Oslo

来源：

BMC Medical Genomics | / 7卷 / 1期

关键词：

Prostate cancer; Signature; Transcription;

D O I：

10.1186/s12920-014-0074-9

中图分类号：

学科分类号：

摘要：

Background: Tumorigenesis is characterised by changes in transcriptional control. Extensive transcript expression data have been acquired over the last decade and used to classify prostate cancers. Prostate cancer is, however, a heterogeneous multifocal cancer and this poses challenges in identifying robust transcript biomarkers. Methods: In this study, we have undertaken a meta-Analysis of publicly available transcriptomic data spanning datasets and technologies from the last decade and encompassing laser capture microdissected and macrodissected sample sets. Results: We identified a 33 gene signature that can discriminate between benign tissue controls and localised prostate cancers irrespective of detection platform or dissection status. These genes were significantly overexpressed in localised prostate cancer versus benign tissue in at least three datasets within the Oncomine Compendium of Expression Array Data. In addition, they were also overexpressed in a recent exon-Array dataset as well a prostate cancer RNA-seq dataset generated as part of the The Cancer Genomics Atlas (TCGA) initiative. Biologically, glycosylation was the single enriched process associated with this 33 gene signature, encompassing four glycosylating enzymes. We went on to evaluate the performance of this signature against three individual markers of prostate cancer, v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) expression, prostate specific antigen (PSA) expression and androgen receptor (AR) expression in an additional independent dataset. Our signature had greater discriminatory power than these markers both for localised cancer and metastatic disease relative to benign tissue, or in the case of metastasis, also localised prostate cancer. Conclusion: In conclusion, robust transcript biomarkers are present within datasets assembled over many years and cohorts and our study provides both examples and a strategy for refining and comparing datasets to obtain additional markers as more data are generated. © 2014 Barfeld et al.

引用

共 46 条

[1]

Rhodes D.R., Yu J., Shanker K., Deshpande N., Varambally R., Ghosh D., Barrette T., Pandey A., Chinnaiyan A.M., Large-scale meta-Analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression, Proc Natl Acad Sci U S A, 101, pp. 9309-9314, (2004)

[2]

Segal E., Friedman N., Koller D., Regev A., A module map showing conditional activity of expression modules in cancer, Nat Genet, 36, pp. 1090-1098, (2004)

[3]

Horvath S., Zhang B., Carlson M., Lu K.V., Zhu S., Felciano R.M., Laurance M.F., Zhao W., Qi S., Chen Z., Lee Y., Scheck A.C., Liau L.M., Wu H., Geschwind D.H., Febbo P.G., Kornblum H.I., Cloughesy T.F., Nelson S.F., Mischel P.S., Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target, Proc Natl Acad Sci U S A, 103, pp. 17402-17407, (2006)

[4]

Stuart R.O., Wachsman W., Berry C.C., Wang-Rodriguez J., Wasserman L., Klacansky I., Masys D., Arden K., Goodison S., McClelland M., Wang Y., Sawyers A., Kalcheva I., Tarin D., Mercola D., In silico dissection of cell-Type-Associated patterns of gene expression in prostate cancer, Proc Natl Acad Sci USA, 101, pp. 615-620, (2004)

[5]

Tomlins S.A., Mehra R., Rhodes D.R., Cao X., Wang L., Dhanasekaran S.M., Kalyana-Sundaram S., Wei J.T., Rubin M.A., Pienta K.J., Shah R.B., Chinnaiyan A.M., Integrative molecular concept modeling of prostate cancer progression, Nat Genet, 39, pp. 41-51, (2007)

[6]

Ramaswamy S., Ross K.N., Lander E.S., Golub T.R., A molecular signature of metastasis in primary solid tumors, Nat Genet, 33, pp. 49-54, (2003)

[7]

Hoerl A.E., Kennard R.W., Ridge regression: Biased estimation for nonorthogonal problems, Technometrics, 12, pp. 55-67, (1970)

[8]

Varambally S., Yu J., Laxman B., Rhodes D.R., Mehra R., Tomlins S.A., Shah R.B., Chandran U., Monzon F.A., Becich M.J., Wei J.T., Pienta K.J., Ghosh D., Rubin M.A., Chinnaiyan A.M., Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression, Cancer Cell, 8, pp. 393-406, (2005)

[9]

Ward J.H., Hierarchical grouping to optimize an objective function, J Am Stat Assoc, 58, pp. 236-244, (1963)

[10]

McQuitty L.L., Similarity analysis by reciprocal pairs for discrete and continuous data, Educ Psychol Measure, 26, pp. 825-831, (1966)

← 1 2 3 4 5 →