Network based analyses of gene expression profile of LCN2 overexpression in esophageal squamous cell carcinoma

Abstract

LCN2 (lipocalin 2) is a member of the lipocalin family of proteins that transport small, hydrophobic ligands. LCN2 is elevated in various cancers including esophageal squamous cell carcinoma (ESCC). In this study, LCN2 was overexpressed in the EC109 ESCC cell line and we applied integrated analyses of the gene expression data to identify protein-protein interactions (PPI) network to enhance our understanding of the role of LCN2 in ESCC. Through further mining of PPI sub-networks, hundreds of differentially expressed genes (DEGs) were identified to interact with thousands of other proteins. Subcellular localization analyses found the DEGs and their directly or indirectly interacting proteins distributed in multiple layers, which was applied to analyze the possible paths between two DEGs. Gene Ontology annotation generated a functional annotation map and found hundreds of significant terms, especially those associated with the known and potential roles of LCN2 protein. The algorithm of Random Walk with Restart was applied to prioritize the DEGs and identified several cancer-related DEGs ranked closest to LCN2 protein. These analyses based on PPI network have greatly expanded our understanding of the mRNA expression profile of LCN2 overexpression for future examination of the roles and mechanisms of LCN2.

Figure 1. PPI sub-network generation by mapping DEGs to the HPRD&BioGRID parental PPI network.

(A) PPI sub-networks of total DEGs. (B) LCN2-central PPI sub-network. (C) Internal interactions of DEGs. Different colors of nodes indicate the types of proteins represented. Green and red nodes represent proteins encoded by down- and up-regulated genes, respectively. Blue nodes represent interacting proteins which were not significantly differentially expressed. The arrangement of nodes was applied to the “Spring Embedded” layout in Cytoscape.

Figure 2. Power law distribution of node degree.

(A) Degree distribution of the downregulated DEG PPI sub-network. (B) Degree distribution of the upregulated DEG PPI sub-network. (C) Degree distribution of the total DEG PPI sub-network. The graph displays a decreasing trend of degree distribution with an increase in number of links displaying scale-free topology.

Figure 3. Subcellular layers illustrating the PPI sub-network.

(A) The total DEG PPI network. (B) LCN2-central PPI sub-network. (C) 28 possible paths from LCN2 to FOXP1.

Figure 4. Functional map of the total DEG PPI sub-network.

Functionally grouped network with terms as nodes linked based on their kappa score level (≥0.3). Functionally related groups partially overlap. The similar GO terms were labeled in the same color. The interested GO term group related or potentially related to LCN2 function was indicated by a Roman numeral.

Figure 5. Priorization analyses of DEGs in the total DEG PPI sub-network.

(A) Random Walk with Restart algorithm was used to score all proteins in the PPI network for their network proximity to the seed node of LCN2. The node size in the PPI sub-network is designed in a gradient according to their scores. (B) The DEGs were extracted from (A) to better show their size. (C) The DEGs were re-arranged according to their closeness to LCN2 protein. The more negative the log10-transformed score, the further the node from LCN2. DEGs were classified into seven layers (from A to G, the Y axis) according to their range of scores as described in the Result section.