Discovering cooperative biomarkers for heterogeneous complex disease diagnoses

Abstract

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity.

Identifying the critical state of complex biological systems by the directed-network rank score method

“…The time-specific directed network at a time point is constructed based on an information-theoretic scheme ( Sun et al , 2019 ), which provides a direction determination index (as defined in Eq. 3 ) to evaluate the combined effect of gene combinations over a single gene from the perspective of mutual information (MI).…”

Identification and Validation of TRIM25 as a Glucose Metabolism Regulator in Prostate Cancer

“…Given the difficulty in finding genes that regulate the reprogramming of glucose metabolism from a large number of genes, multi-omics integration analysis helps to define key genes that lead to metabolic reprogramming in PCa. To identify the important biomolecules, several computational methods based on network analysis have been proposed [ 6 , 7 , 8 ]. The differential network (DN) analysis method aims at identifying the disease-related important biomolecules or modules [ 9 ].…”

“…to identify relationships between biomolecules, reduce false associations, and guide biological network construction. Combining gene expression data and protein–protein interaction (PPI) networks to define key nodes of biological development (MarkRank) has shown good predictive accuracy and high specificity associated with the disease [ 8 ]. K-DN can also cover experimental data gaps to explore the key factors affecting disease.…”

Knowledge-Guided “Community Network” Analysis Reveals the Functional Modules and Candidate Targets in Non-Small-Cell Lung Cancer

“…This new paradigm reflects the fact that human diseases are not caused by single molecular defects but are driven by complex interactions among a variety of molecular mediators [15]. The network-based methods have developed a plethora of topological parameters for discovering biomarkers [16], disease-associated genes [17], and drug targets [18,19]. Hub genes with higher node degree in PPI networks have been predicted to be diagnostic biomarkers for NSCLC and some have been experimentally validated, such as NCAPH [4].…”