Mouse Platelet Derived Growth Factor Receptor Like Protein ELISA Kit

Background: The most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations. Results: In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network. Conclusion: Using a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating coexpression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression.

Neurofibromatosis type-1 (NF1) is associated with the growth of benign and malignant tumors. Approximately 15% of NF1 patients develop malignant peripheral nerve sheath tumors (MPNSTs), underlining the need to identify specific diagnostic/prognostic biomarkers associated with MPNST development. The Affymetrix Genome-Wide Human single-nucleotide polymorphism (SNP) Array 6.0 was used to perform SNP genotyping and copy number alteration (CNA), loss-of-heterozygosity (LOH), and copy number neutralLOH (CNNLOH) analyses of DNA isolated from 15 MPNSTs, five benign plexiform neurofibromas (PNFs), and patient-matched lymphocyte DNAs. MPNSTs exhibited high-level CNN-LOH, with recurrent changes occurring in MPNSTs but not PNFs. CNNLOH was evident in MPNSTs but occurred less frequently than genomic deletions. CNAs involving the ITGB8, PDGFA, Ras-related C3 botulinum toxin substrate 1 (RAC1) (7p21-p22), PDGFRL (8p22-p21.3), and matrix metallopeptidase 12 (MMP12) (11q22.3) genes were specific to MPNSTs. Pathway analysis revealed the MPNST-specific amplification of seven RhoGTPase pathway genes and several cytoskeletal remodeling/cell adhesion genes. In knockdown experiments employing short-hairpin RAC1, ROCK2, PTK2, and LIMK1 RNAs to transfect both control and MPNST-derived cell lines, cell adhesion was significantly increased in the MPNST cell lines, whereas wound healing, cell migration, and invasiveness were reduced, consistent with a role for these RhoGTPase pathway genes in MPNST development and metastasis. These results suggest new targets for therapeutic intervention in relation to MPNSTs. Hum Mutat 33:763776, 2012. (c) 2012 Wiley Periodicals, Inc.