A comparative analysis of gene expression profiles between early and advanced carotid atherosclerotic plaque was performed to identify key genes and pathways involved in the progression of carotid atherosclerotic plaque. Gene expression data set GSE28829 was downloaded from Gene Expression Omnibus, including 13 early and 16 advanced atherosclerotic plaque samples from human carotid. Differentially expressed genes (DEGs) were identified using the package limma of R. Principal component analysis was carried out for the DEGs with package rgl of R. A gene coexpression network was constructed with information from COXPRESdb and then visualized with Cytoscape. Functional enrichment analysis was performed with DAVID and pathway enrichment analysis was done with KEGG. A total of 319 DEGs were identified in the advanced atherosclerotic plaque samples compared with early atherosclerotic plaque samples, including 267 up-regulated genes and 52 down-regulated genes. In the gene coexpression network, TYRO protein tyrosine kinase binding protein was the hub gene with a degree of 23. Functional enrichment analysis and pathway enrichment analysis suggested that the immune response played a critical role in the progression of carotid atherosclerotic plaque. A number of key genes were revealed in carotid atherosclerotic plaque, and are potential biomarkers for diagnosis or treatment. These findings may also guide future research to better decipher the progression of atherosclerosis.

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Protein-protein interactions (PPIs) are highly important because of their main role in cellular processes and biochemical pathways; therefore, PPI can be very useful in the prediction of protein functions. Experimental techniques of PPI detection have certain drawbacks; hence computational methods can be used to complement wet lab techniques. Such methods can be applied to PPI prediction as well as validation of experimental results. Computational algorithms can lead to many false PPI predictions, which in turn result in non-adequate performance. We have developed a novel method based on combined analysis, entitled PPIccc. Three different descriptors for PPIccc included gene co-expression values, codon usage similarity and conservation of surface residues between protein products of a gene pair, which combined to predict PPI. Validation of results based on Human Protein Reference Database (HPRD) indicated improvement of performance in our proposed method. The results also revealed that conservation of surface residues between proteins in combination with codon usage similarity of their related genes increase the performance of PPI prediction. This means that codon usage similarity and surface residues between proteins (only sequence-based features) can predict PPIs as good as PPIccc.

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