Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile

Modularity in Biological Networks

Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of methods for discovering the mechanistic and functional properties of biological systems. Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. Conversely, from the statistical physics and network science perspective, graph modularity has been theoretically studied and several methods of a very general nature have been developed. It is our perspective that in particular for the modularity detection problem, biology and theoretical physics/network science are less connected than they should. The central goal of this review is to provide the necessary background and present the most applicable and pertinent methods for community detection in a way that motivates their further usage in biological research.

NMFNA: A Non-negative Matrix Factorization Network Analysis Method for Identifying Modules and Characteristic Genes of Pancreatic Cancer

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.

Integrated analysis of tumor differentiation genes in pancreatic adenocarcinoma

BACKGROUND

Tumor differentiation is an important process in the development of cancer. It is valuable to identify key differentiation related genes in the prognosis and therapy of pancreatic adenocarcinoma.

METHODS

The mRNA expression data were downloaded from the Cancer Genome Atlas database. Then, differentially expressed tumor differentiation related genes were identified. Additionally, Gene Ontology functional categories and Kyoto Encyclopedia of Genes and Genomes biochemical pathway was used to explore the function. In addition, receiver operating characteristic and survival analysis were carried out to assess the diagnosis and prognosis value. Finally, the electronic validation of selected tumor differentiation related genes was performed.

RESULTS

A total of 932 genes were identified. Among which, 8 genes including JUB, ERLIN1, HMGA2, FAM110B, EGFR, MCM2, TCTA and SSTR1 were differentially expressed in all different tumor differentiation grades. Functional analysis revealed those genes between highly differentiated and other differentiation were remarkably enriched in pancreatic adenocarcinoma and cell cycle pathway. Finally, ERLIN1, HMGA2, FAM110B, EGFR, MCM2, BCL2L1, E2F1 and RAC1 were associated with the survival time of pancreatic adenocarcinoma patient. Among these genes, JUB, ERLIN1, FAM110B, MCM2 and BCL2L1 also had a diagnosis value for pancreatic adenocarcinoma. Additionally, the expression trend of JUB, HMGA2 and MCM2 was increased along with the tumor differentiation grades. And the expression trend of FAM110B was decreased along with the tumor differentiation grades. The electronic validation result was consistent with the bioinformatics analysis.

CONCLUSIONS

12 tumor differentiation related genes including JUB, ERLIN1, HMGA2, FAM110B, EGFR, MCM2, TCTA, SSTR1, BCL2L1, E2F1, RAC1 and STAT1 played crucial roles in the differentiation of pancreatic adenocarcinoma.

The evolution into personalized therapies in pancreatic ductal adenocarcinoma: challenges and opportunities

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer related mortality in the United States in 2030, with a 5-year overall survival of less than 10% despite decades of extensive research. Pancreatic cancer is marked by the accumulation of complex molecular changes, complex tumor-stroma interaction, and an immunosuppressive tumor microenvironment. PDAC has proven to be resistant to many cytotoxic, targeted and immunologic treatment approaches. Areas covered: In this paper, we review the major areas of research in PDAC, with highlights on the challenges and areas of opportunity for personalized treatment approaches. Expert commentary: The focus of research in pancreatic cancer has moved away from developing conventional cytotoxic combinations. The marked advances in understanding the molecular biology of this disease especially in the areas of the microenvironment, metabolism, and DNA repair have opened new opportunities for developing novel treatment strategies. Improved understanding of molecular abnormalities allows the development of personalized treatment approaches.