Exploration of gene functions for esophageal squamous cell carcinoma using network-based guilt by association principle

Abstract

Gene networks have been broadly used to predict gene functions based on guilt by association (GBA) principle. Thus, in order to better understand the molecular mechanisms of esophageal squamous cell carcinoma (ESCC), our study was designed to use a network-based GBA method to identify the optimal gene functions for ESCC. To identify genomic bio-signatures for ESCC, microarray data of GSE20347 were first downloaded from a public functional genomics data repository of Gene Expression Omnibus database. Then, differentially expressed genes (DEGs) between ESCC patients and controls were identified using the LIMMA method. Afterwards, construction of differential co-expression network (DCN) was performed relying on DEGs, followed by gene ontology (GO) enrichment analysis based on a known confirmed database and DEGs. Eventually, the optimal gene functions were predicted using GBA algorithm based on the area under the curve (AUC) for each GO term. Overall, 43 DEGs and 67 GO terms were gained for subsequent analysis. GBA predictions demonstrated that 13 GO functions with AUC>0.7 had a good classification ability. Significantly, 6 out of 13 GO terms yielded AUC>0.8, which were determined as the optimal gene functions. Interestingly, there were two GO categories with AUC>0.9, which included cell cycle checkpoint (AUC=0.91648), and mitotic sister chromatid segregation (AUC=0.91597). Our findings highlight the clinical implications of cell cycle checkpoint and mitotic sister chromatid segregation in ESCC progression and provide the molecular foundation for developing therapeutic targets.

Figure 1.. A, Box plot of gene expressions in esophageal squamous cell carcinoma (ESCC) and the matched normal adjacent samples before normalization. B, Box plot of gene expressions in ESCC and matched normal adjacent samples after normalization. The X-axis indicates samples and the Y-axis is expression level of genes. The black line in the center is the median expression value; the consistent distribution represented a good standardization.

Figure 2.. Differentially co-expressed network construction for esophageal squamous cell carcinoma based on differentially expressed genes.

Figure 3.. Pie chart showing the weight distribution of interactions in the differential co-expression network. The weight values were classified into the following groups: >0.9–1.0, >0.8–0.9, >0.7–0.8, >0.6–0.7, >0.5–0.6 and 0.4–0.5.

Figure 4.. Gene function prediction by means of guilt by association method based on area under the curve (AUC) values. The histogram shows AUCs across all gene oncology categories, which were identified relying on a single list constructed from the count of co-expression members.