Abstract: Background and purpose: Gene chip is a nucleic acid sequence analysis method which is based on hybridization. It is a high-through put assay which can widely detect the level of gene expression in different tissues and cell types. This study aimed to compare and bioinformatically analyze differentially expressed genes between higher malignant degree of prostate cancer tissues and prostate inflammation tissues. Methods: The total RNAs were isolated from tissues of prostate cancer and prostate inflammation by TRIzol method and then purified, reversely transcribed to cDNA with incorporating biotin labeling probe, hybridized with Affymetrix Human U133 Plus 2.0 (covering 47 000 transcripts，representing 38 500 distinct genes). Picture signals of fluorescence in gene array were scanned and differential expression of gene in two tissues were compared by Command Console Software 4.0. These differential expressed genes were analyzed by bioinformatics methods finally. Results: According to the fold change ≥2, P<0.05, 1 819 differential expression genes including 1 025 up-regulated genes and 794 down-regulated genes were discovered. GO enrichment analysis displayed that these differentially expressed genes were mainly involved in cell cycle, cell metabolism, etc. KEGG pathway analysis found that these genes were mainly involved in some metabolism pathways including purine nucleotide metabolism. The interactions between the proteins encoded by these genes were analyzed by STING. Twenty key nodes genes including TPX2, ANLN, NUSAP1, MELK, DLGAP5, KIF11, TOP2A, RRM2 were discovered. Then this study revealed CEP55 and ANLN might be related to the occurrence and metastasis of prostate cancer by looking through literature. Conclusion: During the development of prostate cancer, the activation of genes related to cell cycle and cell migration, the abnormalities of genes related to metabolism and the inhibition of genes related to cell adhesion play critical roles in the development of prostate cancer. CEP55 and ANLN were related to the occurrence and prognosis of prostate cancer by systematic analysis which provided a valuable clue for the next experiment.

Key words: Prostate cancer, Gene chip, Differential genes, Bioinformatics