OBJECTIVE: The purpose of this study was to construct a pathway-based network using differentially expressed genes in prostate cancer (PCa) screened by cDNA microarray chips in domestic research to visualize the relations among the genes obtained from the microarray data, and identify the genes with significant influence on this network by statistical analysis. It also aimed to search for the genes that play key roles in the tumorigenesis of PCa, and probe into the molecular mechanism of PCa pathogenesis in Chinese men. METHODS: The relevant domestic literature of recent years were reviewed to sum up differentially expressed genes in PCa according to the screened microarray data. The OMIM database was used to analyze the relations among these genes and build a network of biological pathway. Furthermore, a statistical method, namely node contraction, was employed to compare the importance of the key genes. RESULTS: According to the gene expression profiling data, there were 113 differentially expressed genes, 51 up-regulated and 62 down-regulated. A pathway-based network including 68 inter-related genes was constructed using the OMIM database. The importance of every key node was calculated using the method of node contraction, and 12 key genes were identified, incuding c-MYC, VEGF, HSPCA, TGFbeta1, RANTES, EGR1, etc, which probably played important roles in the pathogenesis and progression of prostate cancer. CONCLUSION: We applied bioinformatics to the analysis of the gene expression profiling data in China, constructed a network of the differentially expressed genes using the OMIM database and method of node contraction, appraised the importance of the key genes, and established a method for the overall analysis of the gene chip data, which have paved a new ground for further researches on the pathogenesis of prostate cancer in Chinese men.
OBJECTIVE: The purpose of this study was to construct a pathway-based network using differentially expressed genes in prostate cancer (PCa) screened by cDNA microarray chips in domestic research to visualize the relations among the genes obtained from the microarray data, and identify the genes with significant influence on this network by statistical analysis. It also aimed to search for the genes that play key roles in the tumorigenesis of PCa, and probe into the molecular mechanism of PCa pathogenesis in Chinese men. METHODS: The relevant domestic literature of recent years were reviewed to sum up differentially expressed genes in PCa according to the screened microarray data. The OMIM database was used to analyze the relations among these genes and build a network of biological pathway. Furthermore, a statistical method, namely node contraction, was employed to compare the importance of the key genes. RESULTS: According to the gene expression profiling data, there were 113 differentially expressed genes, 51 up-regulated and 62 down-regulated. A pathway-based network including 68 inter-related genes was constructed using the OMIM database. The importance of every key node was calculated using the method of node contraction, and 12 key genes were identified, incuding c-MYC, VEGF, HSPCA, TGFbeta1, RANTES, EGR1, etc, which probably played important roles in the pathogenesis and progression of prostate cancer. CONCLUSION: We applied bioinformatics to the analysis of the gene expression profiling data in China, constructed a network of the differentially expressed genes using the OMIM database and method of node contraction, appraised the importance of the key genes, and established a method for the overall analysis of the gene chip data, which have paved a new ground for further researches on the pathogenesis of prostate cancer in Chinese men.