Application of Weighted Gene Coexpression Network Analysis in Biomedicine Highthroughput biological monitoring method can simultaneously detect thousands of parameters of the same sample, and its application in biomedicine is more and more widespread. However, how to systematically analyze and extract useful information from highthroughput data remains an important issue. The emergence of network biology makes people have a deeper understanding of complex biological systems, and the implementation of tissuecell functions has modular characteristics. At present, related networks are increasingly used in bioinformatics. Weighted gene coexpression network analysis, WGCNA is a systematic biological tool for describing gene expression patterns in samples. In this review, the progress in disease classification and prognosis, pathogenesis and other related fields is systematically reviewed. Firstly, the principle, analysis process, advantages and disadvantages are summarized. Secondly, it introduces how to annotate diseases, normal tissues, drugs, evolution and genome. Finally, the new application space is forecasted with the new high throughput technology. It is hoped that researchers will have a better understanding of the application of WGCNA.

Cancer Biomarker Discovery from Gene Coexpression Networks Using Community Detection Methods Finding the network biomarkers of cancers and the analysis of cancer driving genes that are involved in these biomarkers are essential for understanding the dynamics of cancer. Clusters of genes in coexpression networks are commonly known as functional units. This work is based on the hypothesis that the dense clusters or communities in the gene coexpression networks of cancer patients may represent functional units regarding cancer initiation and progression. In this study, RNAseq gene expression data of three cancers  Breast Invasive Carcinoma (BRCA), Colorectal Adenocarcinoma (COAD) and Glioblastoma Multiforme (GBM)  from The Cancer Genome Atlas (TCGA) are used to construct gene coexpression networks using Pearson Correlation. Six wellknown community detection algorithms are applied on these networks to identify communities with five or more genes. A permutation test is performed to further mine the communities that are conserved in other cancers, thus calling them conserved communities. Then survival analysis is performed on clinical data of three cancers using the conserved community genes as prognostic covariates. The communities that could distinguish the cancer patients between high and lowrisk groups are considered as cancer biomarkers. In the present study, 16 such network biomarkers are discovered.