A functional network construction method to interpret the pathological process of colorectal cancer. (22nd June 2020)

• Record Type: Journal Article
• Authors: Chen, Bolin; Yang, Manting; Gao, Li; Jiang, Tao; Shang, Xuequn
• Is Part Of: International journal of data mining and bioinformatics. Volume 23:Number 3(2020)
• Page Start: 251
• Page End: 264
• Publication Date: 2020-06-22
• Subjects: CRC -- colorectal cancer -- enrichment analysis -- functional interaction network -- functional evolution network; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

The prognosis of cancers remains a challenge due to the limited understanding of their pathogenic mechanisms and cancerous processes. To overcome this, many studies employ a set of Differentially Expressed Genes (DEGs) to analyse the significant dysfunctions alone with the pathological stages. However, those studies often ignore the fact that DEGs detected from cancer patients tend to be highly heterogeneous with each other, which could easily mislead the enriched dysfunctions toward to those less relevant functions. Hence, in this study, we propose a novel method to generate the functional evolution network to describe the transferring processes of dysfunctions of the cancer. Results interpret that the proposed network construction method has a powerful capacity in detecting the most relevant cellular functions compared with existing methods, which could be employed to explore the evolution processes of cancers and may provide a new method for therapeutic intervention.