In part I of the NextBio tutorials, we explain how and why NextBio helps explore data relevant to a gene query
A single gene can be studied in many ways- genome-wide analysis for associations with diseases, experimental studies of function in cell lines or animal models, or pharmaceutical research as a potential drug target. At NextBio, we aggregate these diverse kinds of data, normalize and integrate them on a common platform, coalescing diffuse “data clouds” to address focused research questions.
In the first of our NextBio Public tutorials, we explain how to use simple NextBio functions to find this aggregated data relevant to a gene, beginning with GWAS associations, to descriptions of tissues and cell lines and results from pharmacological experiments, all in a matter of minutes.
CSMD3 is part of the three-member CUB and Sushi multiple domain (CSMD) family of proteins identified in humans, mice and rats. All three members are transmembrane proteins, but not much else is known about their functions.. CSMD2 is expressed at high levels in brain tissue and in some head and neck cancer cell lines, while CSMD1 may act as a weak suppressor of oral and squamous cell carcinomas (some studies support this function, others disagree). Recently, CSMD3 mutations were identified as being significant in human ovarian adenocarcinomas.
Curious to see what was known about this gene, we found 8 published papers in PubMed that describe it. Three of these describe the gene in brain tissues. One of them, a 2008 paper by Floris et al. in the European Journal of Human Genetics describes the potential function of a CSMD3 mutation in autism-linked phenotypes. Though at least 6 cancer GWAS identify CSMD3 as a significantly altered gene in colorectal and prostate cancers, there are few reports on the importance of CSMD3 perturbations in cancer phenotypes. Mining NextBio’s curated databases, we found over one hundred studies identifying CSMD3 mutation, copy number variation, methylation and altered expression linked to different kinds of cancer.
Transcriptional regulators important for differentiation processes influence levels of CSMD3 in RNA expression. Epigenetic mechanisms like DNA methylation and histone deacetylation change CSMD3 expression in cancer cell lines, and in embryonic development in mice. Several compounds evaluated for their anti-cancer effects also perturb CSMD3 expression.
Integrating diverse kinds of data from multiple experiments reveals a broad amount of existing information about CSMD3, all in the public domain. Effectively mining such integrated information could help design innovative hypothesis and strengthen experimental findings, eventually making the transition from ‘statistically significant association’ to drug target, diagnostic biomarker or more much quicker.
Watch our tutorial, and drop us a line telling us what you think. We’re always interested in other correlations in NextBio’s databases and would love to hear about them!