Constructing a gene co-expression network (GCN) is an effective way to characterize the correlation patterns among genes. Gene networks provide the potential to identify hundreds of genes that are associated with complex human diseases and that could serve as points for therapeutic interventions 3, 4, and this information is important for predicting the functions of new genes and finding genes that play key roles in complex human diseases. Studies have shown that each gene is estimated on average to interact with four to eight other genes 1 and to be involved in 10 biological functions 2. Instead, genes interact with each other and jointly affect human health. However, recent studies have shown that individual genes do not work alone. It is believed that whether a gene is expressed or not affects the synthesis of downstream proteins, which are the building blocks of the human body. Genes are functional units of genetic materials. Genetics plays an important role in the aetiologies of many complex human diseases. Thus, this work will be of great interest to readers of various scientific disciplines, in particular, genetic and genomic scientists as well as medical scientists working on cancer. The modified WGCNA pipeline identified two sets of novel miRNAs associated with OSCC, in addition to the existing miRNAs reported by Shiah et al. OSCC is the sixth most common cancer worldwide. (2014), which contains forty oral squamous cell carcinoma (OSCC) specimens and their matched non-tumourous epithelial counterparts. We illustrated the modified WGCNA pipeline by analysing the miRNA dataset provided by Shiah et al. In this paper, we modified the current WGCNA pipeline to analyse high-throughput genomic data from paired design. However, it is currently unclear how to appropriately apply WGCNA to genomic data from paired design. Paired design is a powerful tool that can reduce batch effects. However, genomic data usually contain substantial batch effects, which could mask true genomic signals. The network approach (e.g., weighted gene co-expression network analysis (WGCNA)) is a powerful tool. Investigating how genes jointly affect complex human diseases is important, yet challenging.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |