Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2010 Jan 19;102(2):428-35.
doi: 10.1038/sj.bjc.6605450.

Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene

Affiliations
Meta-Analysis

Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene

F M Buffa et al. Br J Cancer. .

Erratum in

  • Br J Cancer. 2010 Sep 7;103(6):929

Abstract

Background: There is a need to develop robust and clinically applicable gene expression signatures. Hypoxia is a key factor promoting solid tumour progression and resistance to therapy; a hypoxia signature has the potential to be not only prognostic but also to predict benefit from particular interventions.

Methods: An approach for deriving signatures that combine knowledge of gene function and analysis of in vivo co-expression patterns was used to define a common hypoxia signature from three head and neck and five breast cancer studies. Previously validated hypoxia-regulated genes (seeds) were used to generate hypoxia co-expression cancer networks.

Results: A common hypoxia signature, or metagene, was derived by selecting genes that were consistently co-expressed with the hypoxia seeds in multiple cancers. This was highly enriched for hypoxia-regulated pathways, and prognostic in multivariate analyses. Genes with the highest connectivity were also the most prognostic, and a reduced metagene consisting of a small number of top-ranked genes, including VEGFA, SLC2A1 and PGAM1, outperformed both a larger signature and reported signatures in independent data sets of head and neck, breast and lung cancers.

Conclusion: Combined knowledge of multiple genes' function from in vitro experiments together with meta-analysis of multiple cancers can deliver compact and robust signatures suitable for clinical application.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Hypoxia gene-expression network in HNSCC (Vice 125 data set). Seeds (yellow) and learnt genes (blue) are shown; circle size is proportional to C score. Genes with top 20% C scores are shown. Solid edges connect cluster members with seeds; length is proportional to membership, colour represents Spearman correlation (blue, −1; red, +1). Green dotted edges connect seeds; their length is proportional to the shared neighbourhood, S. This figure appears in colour in the HTML version.
Figure 2
Figure 2
Hypoxia network mapped onto Reactome pathways (A) coloured by increasing C score from dark blue to bright red; and validation of up-regulated HNSCC (B) and BC (C) signatures by comparison with the literature. The proportion of literature-validated genes is shown as function of the number of top-ranked (by C score) genes considered; standard errors estimated by bootstrap. This figure appears in colour in the HTML version.
Figure 3
Figure 3
Common hypoxia signature of 51 genes. (A) Hypoxia/normoxia expression ratio in endothelial, smooth muscle, human mammalian epithelial, renal proximal tubule epithelial cells (EC, SMC, HMEC, RPTEC); and in (B) HIF1a/HIF2a siRNA experiment. (C, D) Connectivity-ranked forest plots: metastases- and recurrence-free survival (MFS, RFS) hazard ratio (HR) (red) with 95% confidence intervals, and HRs if permuted list (black). Control: random sampling of N=51 genes ( × 100 resampling).

Comment in

Similar articles

Cited by

References

    1. Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, Lin L, Chen G, Gharib TG, Thomas DG, Lizyness ML, Kuick R, Hayasaka S, Taylor JM, Iannettoni MD, Orringer MB, Hanash S (2002) Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 8: 816–824 - PubMed
    1. Butte AJ, Kohane IS (2003) Relevance networks: a first step towards finding genetic regulatory networks within microarray data. In The Analysis of Gene Expression Data Parmigiani G, Gar-rett ES, Irizarry RA, Zeger S (eds). Springer-Verlag: New York
    1. Carroll JS, Meyer CA, Song J, Li W, Geistlinger TR, Eeckhoute J, Brodsky AS, Keeton EK, Fertuck KC, Hall GF, Wang Q, Bekiranov S, Sementchenko V, Fox EA, Silver PA, Gingeras TR, Liu XS, Brown M (2006) Genome-wide analysis of estrogen receptor binding sites. Nat Genet 38: 1289–1297 - PubMed
    1. Chi JT, Wang Z, Nuyten DS, Rodriguez EH, Schaner ME, Salim A, Wang Y, Kristensen GB, Helland A, Børresen-Dale AL, Giaccia A, Longaker MT, Hastie T, Yang GP, van de Vijver MJ, Brown PO (2006) Gene expression programs in response to hypoxia: cell type specificity and prognostic significance in human cancers. PLoS Med 3: e47. - PMC - PubMed
    1. Choi P, Chen C (2005) Genetic expression profiles and biologic pathway alterations in head and neck squamous cell carcinoma. Cancer 104: 1113–1128 - PubMed

Publication types