Measurement of structural change in co-expression networks from cancer gene expression data. (12th March 2021)

• Record Type: Journal Article
• Authors: Li, Qianran; Ghersi, Dario; Thapa, Ishwor; Zhang, Ling; Ali, Hesham; Cooper, Kate
• Is Part Of: International journal of data mining and bioinformatics. Volume 24:Number 4(2020)
• Page Start: 291
• Page End: 305
• Publication Date: 2021-03-12
• Subjects: gene expression -- correlation network -- Jaccard similarity -- the cancer genome atlas -- mutation rate; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Profiling progression and development of cancer is an important step for informing clinical decisions in the diagnosis, prognosis and treatment of cancer. The hallmarks of cancer progression are well established; for example, many studies have concluded that surveillance of gene expression relationships during cancer progression would inform diagnostic and treatment decisions. Differential network analysis offers a systems-level insight into cancer progression from a high-level point of view, and once further understood, could become a transformative approach for measuring cancer progression. In this work, we investigate an approach to measure pairwise change in network topology between cancer stages for four cancers, including thyroid carcinoma, colon and rectum adenocarcinoma, stomach adenocarcinoma, and kidney renal papillary cell carcinoma. We use a network-based approach to describe systems-level views of how a network model changes over the course of a four-stage disease progression in these cancers and examine how mutation rate corresponds to network structure. Lastly, we present a case study in comparing primary versus metastatic tumour network structure. The results of this study demonstrate the applicability of such an approach and provide insights into next steps that are needed for differential network comparison in cancer.