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.

Human Protein Reference Database (HPRD) is an object database that integrates a wealth of information relevant to the function of human proteins in health and disease. Data pertaining to thousands of protein-protein interactions, posttranslational modifications, enzyme/substrate relationships, disease associations, tissue expression, and subcellular localization were extracted from the literature for a nonredundant set of 2750 human proteins. Almost all the information was obtained manually by biologists who read and interpreted >300,000 published articles during the annotation process. This database, which has an intuitive query interface allowing easy access to all the features of proteins, was built by using open source technologies and will be freely available at http://www.hprd.org to the academic community. This unified bioinformatics platform will be useful in cataloging and mining the large number of proteomic interactions and alterations that will be discovered in the postgenomic era.

Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative metaprofiling, which identifies and assesses the intersection of multiple gene expression signatures from a diverse collection of microarray data sets. We collected and analyzed 40 published cancer microarray data sets, comprising 38 million gene expression measurements from >3,700 cancer samples. From this, we characterized a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation. In addition, we characterized a transcriptional profile that is commonly activated in various types of undifferentiated cancer, suggesting common molecular mechanisms by which cancer cells progress and avoid differentiation. Finally, we validated these transcriptional profiles on independent data sets.T o identify genes potentially important in cancer, scientists have compared the global gene expression profiles of cancer tissue and corresponding normal tissue (1-11). Such analyses usually generate hundreds of genes differentially expressed in cancer relative to normal tissue, making it difficult to distinguish the genes that play a critical role in the neoplastic phenotype from those that represent epiphenomena or are spuriously differentially expressed. Another common experimental design is to compare cancer samples based on their degree of progression, as determined by histological grade, invasiveness, or metastatic potential (2,(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22). For example, it is known that high-grade undifferentiatedappearing cancers tend to behave more aggressively than their low-grade counterparts, often leading to poorer patient outcomes. To understand the mechanisms by which this progression occurs, many studies have compared the global gene expression profiles of undifferentiated and well differentiated cancers of the same origin. But again, like the ''cancer vs. normal'' studies, these analyses can also yield hundreds of differentially expressed genes. Thus, it remains a critical problem to elucidate the essential transcriptional features of neoplastic transformation and progression both to direct future research and to define candidate therapeutic targets.A logical approach for identifying the essential features of a process, given a large set of possibilities observed in a variety of independent systems, is to search for the intersection of observed possibilities across the set of systems, because it is expected that the essential features will be overrepresented and the system-specific, epiphenomenal, and spurious features will be underrepresented. Given the multitude of studies that have attempted to capture the cancer type-specific gene expression programs of neoplastic transformation and progressi...