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Comparative Study
. 2004 Jan-Feb;6(1):1-6.
doi: 10.1016/s1476-5586(04)80047-2.

ONCOMINE: a cancer microarray database and integrated data-mining platform

Daniel R Rhodes  1 , Jianjun Yu, K Shanker, Nandan Deshpande, Radhika Varambally, Debashis Ghosh, Terrence Barrette, Akhilesh Pandey, Arul M Chinnaiyan
Affiliations
Comparative Study

ONCOMINE: a cancer microarray database and integrated data-mining platform

Daniel R Rhodes et al. Neoplasia. 2004 Jan-Feb.

Abstract

DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research community. Here, we present ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinical-based and pathology-based analyses are available for exploration. Data can be queried and visualized for a selected gene across all analyses or for multiple genes in a selected analysis. Furthermore, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets.

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Figures

Figure 1
Figure 1
ERBB2 (Her2/neu) gene centric expression analysis as revealed by ONCOMINE. (A) ERBB2 is overexpressed in a subset of breast cancers relative to normal breast tissue (P = .0567). (B) ERBB2 is significantly overexpressed in DLBCL relative to normal blood B-cells (P = 1.2e-6), in non small cell lung cancer relative to normal lung (P = 1.1e-5), and in ovarian carcinoma relative to normal ovary (P = 1.0e-5), but not in hepatocellular carcinoma or prostate cancer relative to their respective normal tissue. Y-axis units are normalized expression values (standard deviations above or below the median per array). The number of samples in each class is given in parentheses. Adenoca. indicates adenocarcinoma; Ca. indicates carcinoma; DLBCL indicates diffuse large B-cell lymphoma.
Figure 2
Figure 2
Genes encoding secreted proteins most significantly overexpressed in ovarian carcinoma relative to normal ovary samples as revealed by ONCOMINE. PRSS8, the sixth most significant gene, was previously shown to be an accurate serum biomarker for ovarian carcinoma [28]. Red signifies overexpressed relative to the mean normal value, black equally expressed, and green underexpressed. The number of samples in each class is given in parentheses.
Figure 3
Figure 3
Therapeutic targets overexpressed in cancer as revealed by ONCOMINE. (A) PTGS2 (COX-2) is significantly overexpressed in bladder cancer relative to normal bladder samples (Q = 3.1e-15), confirming previous work that COX-2 is a potential target for bladder cancer. (B) ABL1 is significantly overexpressed in pancreatic cancer relative to normal pancreas samples (Q = 0.0097), suggesting that the Abl tyrosine kinase inhibitor, Gleevec, should be investigated for use. The number of samples in each class is given in parentheses.
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