Dataset Information

Meta-analysis of clinical data using human meiotic genes identifies a novel cohort of highly restricted cancer-specific marker genes.

ABSTRACT: Identifying cancer-specific biomarkers represents an ongoing challenge to the development of novel cancer diagnostic, prognostic and therapeutic strategies. Cancer/testis (CT) genes are an important gene family with expression tightly restricted to the testis in normal individuals but which can also be activated in cancers. Here we develop a pipeline to identify new CT genes. We analysed and validated expression profiles of human meiotic genes in normal and cancerous tissue followed by meta-analyses of clinical data sets from a range of tumour types resulting in the identification of a large cohort of highly specific cancer biomarker genes, including the recombination hot spot activator PRDM9 and the meiotic cohesin genes SMC1beta and RAD21L. These genes not only provide excellent cancer biomarkers for diagnostics and prognostics, but may serve as oncogenes and have excellent drug targeting potential.

SUBMITTER: Feichtinger J

PROVIDER: S-EPMC3478461 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Meta-analysis of clinical data using human meiotic genes identifies a novel cohort of highly restricted cancer-specific marker genes.

Feichtinger Julia J Aldeailej Ibrahim I Anderson Rebecca R Almutairi Mikhlid M Almatrafi Ahmed A Alsiwiehri Naif N Griffiths Keith K Stuart Nicholas N Wakeman Jane A JA Larcombe Lee L McFarlane Ramsay J RJ

Oncotarget 20120801 8

Identifying cancer-specific biomarkers represents an ongoing challenge to the development of novel cancer diagnostic, prognostic and therapeutic strategies. Cancer/testis (CT) genes are an important gene family with expression tightly restricted to the testis in normal individuals but which can also be activated in cancers. Here we develop a pipeline to identify new CT genes. We analysed and validated expression profiles of human meiotic genes in normal and cancerous tissue followed by meta-anal ...[more]

PMID: 22918178

Similar Datasets

Project description:BACKGROUND: Mammalian germ cells undergo meiosis to produce sperm or eggs, haploid cells that are primed to meet and propagate life. Meiosis is initiated by retinoic acid and meiotic prophase is the first and most complex stage of meiosis when homologous chromosomes pair to exchange genetic information. Errors in meiosis can lead to infertility and birth defects. However, despite the importance of this process, germ cell-specific gene expression patterns during meiosis remain undefined due to difficulty in obtaining pure germ cell samples, especially in females, where prophase occurs in the embryonic ovary. Indeed, mixed signals from both germ cells and somatic cells complicate gonadal transcriptome studies. RESULTS: We developed a machine-learning method for identifying germ cell-specific patterns of gene expression in microarray data from mammalian gonads, specifically during meiotic initiation and prophase. At 10% recall, the method detected spermatocyte genes and oocyte genes with 90% and 94% precision, respectively. Our method outperformed gonadal expression levels and gonadal expression correlations in predicting germ cell-specific expression. Top-predicted spermatocyte and oocyte genes were both preferentially localized to the X chromosome and significantly enriched for essential genes. Also identified were transcription factors and microRNAs that might regulate germ cell-specific expression. Finally, we experimentally validated Rps6ka3, a top-predicted X-linked spermatocyte gene. Protein localization studies in the mouse testis revealed germ cell-specific expression of RPS6KA3, mainly detected in the cytoplasm of spermatogonia and prophase spermatocytes. CONCLUSIONS: We have demonstrated that, through the use of machine-learning methods, it is possible to detect germ cell-specific expression from gonadal microarray data. Results from this study improve our understanding of the transition from germ cells to meiocytes in the mammalian gonad. Further, this approach is applicable to other tissues for which isolating cell populations remains difficult.

Project description:Recently, ProFAT and BATLAS studies identified brown and white adipocytes marker genes based on analysis of large databases. They offered scores to determine the thermogenic status of adipocytes using the gene-expression data of these markers. In this work, we investigated the functional context of these genes. Gene Set Enrichment Analyses (KEGG, Reactome) of the BATLAS and ProFAT marker-genes identified pathways deterministic in the formation of brown and white adipocytes. The collection of the annotated proteins of the defined pathways resulted in expanded white and brown characteristic protein-sets, which theoretically contain all functional proteins that could be involved in the formation of adipocytes. Based on our previously obtained RNA-seq data, we visualized the expression profile of these proteins coding genes and found patterns consistent with the two adipocyte phenotypes. The trajectory of the regulatory processes could be outlined by the transcriptional profile of progenitor and differentiated adipocytes, highlighting the importance of suppression processes in browning. Protein interaction network-based functional genomics by STRING, Cytoscape and R-Igraph platforms revealed that different biological processes shape the brown and white adipocytes and highlighted key regulatory elements and modules including GAPDH-CS, DECR1, SOD2, IL6, HRAS, MTOR, INS-AKT, ERBB2 and 4-NFKB, and SLIT-ROBO-MAPK. To assess the potential role of a particular protein in shaping adipocytes, we assigned interaction network location-based scores (betweenness centrality, number of bridges) to them and created a freely accessible platform, the AdipoNET ( https//adiponet.com ), to conveniently use these data. The Eukaryote Promoter Database predicted the response elements in the UCP1 promoter for the identified, potentially important transcription factors (HIF1A, MYC, REL, PPARG, TP53, AR, RUNX, and FoxO1). Our integrative approach-based results allowed us to investigate potential regulatory elements of thermogenesis in adipose tissue. The analyses revealed that some unique biological processes form the brown and white adipocyte phenotypes, which presumes the existence of the transitional states. The data also suggests that the two phenotypes are not mutually exclusive, and differentiation of thermogenic adipocyte requires induction of browning as well as repressions of whitening. The recognition of these simultaneous actions and the identified regulatory modules can open new direction in obesity research.

Dataset Information

Meta-analysis of clinical data using human meiotic genes identifies a novel cohort of highly restricted cancer-specific marker genes.

Publications

Meta-analysis of clinical data using human meiotic genes identifies a novel cohort of highly restricted cancer-specific marker genes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets