Abstract
Pancreatic cancer (PC) is a highly fatal disease, yet its causes remain unclear. Comprehensive analysis of different types of PC genetic data plays a crucial role in understanding its pathogenic mechanisms. Currently, non-negative matrix factorization (NMF)-based methods are widely used for genetic data analysis. Nevertheless, it is a challenge for them to integrate and decompose different types of genetic data simultaneously. In this paper, a non-NMF network analysis method, NMFNA, is proposed, which introduces a graph-regularized constraint to the NMF, for identifying modules and characteristic genes from two-type PC data of methylation (ME) and copy number variation (CNV). Firstly, three PC networks, i.e., ME network, CNV network, and ME–CNV network, are constructed using the Pearson correlation coefficient (PCC). Then, modules are detected from these three PC networks effectively due to the introduced graph-regularized constraint, which is the highlight of the NMFNA. Finally, both gene ontology (GO) and pathway enrichment analyses are performed, and characteristic genes are detected by the multimeasure score, to deeply understand biological functions of PC core modules. Experimental results demonstrated that the NMFNA facilitates the integration and decomposition of two types of PC data simultaneously and can further serve as an alternative method for detecting modules and characteristic genes from multiple genetic data of complex diseases.
Highlights
Pancreatic cancer (PC) is a highly fatal disease of the digestive system and it is becoming an increasingly common cause of cancer mortality, yet its pathogenic mechanisms remain unclear (Mizrahi et al, 2020)
1,147 819 to PC and are more likely to reveal the pathogenic mechanisms of PC
It is seen that the NMFNA hits eight genes and is the winner of all compared methods
Summary
Pancreatic cancer (PC) is a highly fatal disease of the digestive system and it is becoming an increasingly common cause of cancer mortality, yet its pathogenic mechanisms remain unclear (Mizrahi et al, 2020). Gong et al (2014) integrated pathway information into PC survival analysis and applied the doubly regularized Cox regression model to microarray data to identify both PC-related genes and pathways. Li et al (2018) identified two hub genes of PC from the integrated microarray data and validated them in RNA-sequencing data by k-nearest neighbor and random forest algorithms These studies provided several underlying biomarkers and can help cancer researchers design new strategies for the early detection and diagnosis of PC (Gong et al, 2014)
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