Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer

Abstract

Gene coexpression network analysis is a powerful “data-driven” approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise “meta-analysis” framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types. The analysis was conducted using an elaborate weighted gene coexpression network (WGCNA) methodology and identified over 3,000 robust gene coexpression modules. The modules covered a range of known tumor features, such as proliferation, extracellular matrix remodeling, hypoxia, inflammation, angiogenesis, tumor differentiation programs, specific signaling pathways, genomic alterations, and biomarkers of individual tumor subtypes. To prioritize genes with respect to those tumor features, we ranked genes within each module by connectivity, leading to identification of module-specific functionally prominent hub genes. To showcase the utility of this network information, we positioned known cancer drug targets within the coexpression networks and predicted that Anakinra, an anti-rheumatoid therapeutic agent, may be promising for development in colorectal cancer. We offer a comprehensive, normalized and well documented collection of >3000 gene coexpression modules in a variety of cancers as a rich data resource to facilitate further progress in cancer research.

Identification of long non-coding RNA competing interactions and biological pathways associated with prognosis in pediatric and adolescent cytogenetically normal acute myeloid leukemia

“…Genes were considered as independent individuals, which may result in undermining potential relationship between genes [ 13 ]. To overcome these challenges and to reduce the difficulty in biological interpretation, we constructed a weighted correlation network [ 14 ]. Then, we analyzed the relationships among the modules, OS, FAB category and the molecular mutations associated with prognosis (FLT3-ITD, NPM1, CEBPA).…”

“…The genes in a prognostic gene signature were considered as independent individuals, which may result in ignoring potential relationships between the genes [ 13 ]. To overcome these challenges and to reduce the difficulty in biological interpretation, we constructed a weighted correlation network and the significant modules associated with prognosis were selected [ 14 ]. Based on these modules information, we identified 4 biological pathways associated with overall survival (OS).…”

The development trends of antineoplastics targeting PD-1/PD-L1 based on scientometrics and patentometrics

“…The new drugs discovery through new uses of existing drugs has significantly reduced the cost of research and development, expanded the indications and clinical treatment scope of the original drugs, and the clinical application has tended to be safe, reasonable and effective, with reducing the risk of discovering new drugs in the traditional way at the same time [54] . Drug repositioning strategy is one of the best risk-benefit ratio strategies in the currently known drug discovery strategies, and it is also one of the effective methods to solve the dilemma of high investment and low success rate in new drug discovery [55][56][57] . The rapid development of tumor immunotherapy and the promising clinical More and more research institutions and enterprises have accelerated the protection of biomarker products in the field of intellectual property.…”

Identification and functional analysis of spermatogenesis‐associated gene modules in azoospermia by weighted gene coexpression network analysis

“…Details regarding the gene coexpression network construction are summarized in Section 2. 21 Using WGCNA, we identified six coexpressed modules in our data set ( Figure 2C and 2D; Supporting Information Table 4) based on a soft threshold of 26 (Figure 2A and 2B). If the scale-free topology fit index of the reference data set is greater than 0.8 for low powers (<30) as defined in the previous study, the topology of the network is scalefree, and no batch effects exist.…”