OBJECTIVE: The prognosis of glioblastoma (GBM) is poor. The therapeutic outcome of conventional surgical and adjuvant treatments remains unsatisfactory, and therefore individualized adjuvant chemotherapy has aroused more attention. Microarrays have been applied to study mechanism of GBM development and progression but it has difficulty in determining responsible genes from the plethora of genes on microarrays unrelated to outcome. The present study was attempted to use bioinformatics method to investigate candidate genes that may influence chemosensitivity of GBM to Semustine (Me-CCNU). METHODS: Clinical data of 4 GBM patients in Affymetrix microarray were perfected through long-term follow-up study. Differential expression genes between the long- and short-survival groups were picked out, GO-analysis and pathway-analysis of the differential expression genes were performed. Me-CCNU-related signal transduction networks were constructed. The methods combined three steps before were used to screen core genes that influenced Me-CCNU chemosensitivity in GBM. RESULTS: In Affymetrix microarray there were altogether 2018 differential expression genes that influenced survival duration of GBM. Of them, 934 genes were up-regulated and 1084 down-regulated. They mainly participated in 94 pathways. Me-CCNU-related signal transduction networks were constructed. The total number of genes in the networks was 466, of which 66 were also found in survival duration-related differential expression genes. Studied key genes through GO-analysis, pathway-analysis and in the Me-CCNU-related signal transduction networks, 25 core genes that influenced chemosensitivity of GBM to Me-CCNU were obtained, including TP53, MAP2K2, EP300, PRKCA, TNF, CCND1, AKT2, RBL1, CDC2, ID2, RAF1, CDKN2C, FGFR1, SP1, CDK6, IGFBP3, MDM4, PDGFD, SOCS2, CCNG2, CDK2, SDC2, STMN1, TCF7L1, TUBB. CONCLUSION: Bioinformatics may help excavate and analyze large amounts of data in microarrays by means of rigorous experimental planning, scientific statistical analysis and collection of complete data about survival of GBM patients. In the present study, a novel differential gene expression pattern was constructed and advanced study will provide new targets for chemosensitivity of GBM.
OBJECTIVE: The prognosis of glioblastoma (GBM) is poor. The therapeutic outcome of conventional surgical and adjuvant treatments remains unsatisfactory, and therefore individualized adjuvant chemotherapy has aroused more attention. Microarrays have been applied to study mechanism of GBM development and progression but it has difficulty in determining responsible genes from the plethora of genes on microarrays unrelated to outcome. The present study was attempted to use bioinformatics method to investigate candidate genes that may influence chemosensitivity of GBM to Semustine (Me-CCNU). METHODS: Clinical data of 4 GBM patients in Affymetrix microarray were perfected through long-term follow-up study. Differential expression genes between the long- and short-survival groups were picked out, GO-analysis and pathway-analysis of the differential expression genes were performed. Me-CCNU-related signal transduction networks were constructed. The methods combined three steps before were used to screen core genes that influenced Me-CCNU chemosensitivity in GBM. RESULTS: In Affymetrix microarray there were altogether 2018 differential expression genes that influenced survival duration of GBM. Of them, 934 genes were up-regulated and 1084 down-regulated. They mainly participated in 94 pathways. Me-CCNU-related signal transduction networks were constructed. The total number of genes in the networks was 466, of which 66 were also found in survival duration-related differential expression genes. Studied key genes through GO-analysis, pathway-analysis and in the Me-CCNU-related signal transduction networks, 25 core genes that influenced chemosensitivity of GBM to Me-CCNU were obtained, including TP53, MAP2K2, EP300, PRKCA, TNF, CCND1, AKT2, RBL1, CDC2, ID2, RAF1, CDKN2C, FGFR1, SP1, CDK6, IGFBP3, MDM4, PDGFD, SOCS2, CCNG2, CDK2, SDC2, STMN1, TCF7L1, TUBB. CONCLUSION: Bioinformatics may help excavate and analyze large amounts of data in microarrays by means of rigorous experimental planning, scientific statistical analysis and collection of complete data about survival of GBM patients. In the present study, a novel differential gene expression pattern was constructed and advanced study will provide new targets for chemosensitivity of GBM.