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Biological functions associated with the recurrent scores. (A-B) The recurrent score related biological process revealed by Gene ontology analysis in the CGGA 693 and CGGA 325 database . (C-D) The heatmap showed the recurrent score and the enrichment scores of apoptosis-related functions of each patient in the CGGA 693 and CGGA 325 database. The samples were arranged in ascending order of the recurrent score. The column graph and line graph on the right showed the R -value and P -value of the correlation analysis. (E) Flow chart for recurrent score correlation analysis. (F-G) Using Pearson correlation analysis, the top 18 apoptosis-related genes mostly correlated with recurrent score were selected in CGGA 693 and CGGA 325 database. (H-I) The relationship between recurrent score and 6 apoptosis-related genes in glioma. The correlation coefficients were demonstrated as the proportion of the pie charts. The bottom right showed the correlation coefficient. The red parts represented a positive correlation. The correlation was tested by Pearson correlation analysis. (J) Correlation between the expression of the 6 genes in CGGA 693 and CGGA 325 database. ( K ) Expression levels of the 6 genes in primary glioma and recurrent glioma in CGGA 325 database and CGGA 693 database. (L) Survival analyses of the 6 genes by Kaplan-Meier curves and log-rank tests based on CCGA 693 database and CGGA 325 database. ( M ) Protein levels of SH3GLB1, NEK6, CASP8 and <t>ITGB1</t> in normal tissues and GBM from The Human Protein Atlas database
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Biological functions associated with the recurrent scores. (A-B) The recurrent score related biological process revealed by Gene ontology analysis in the CGGA 693 and CGGA 325 database . (C-D) The heatmap showed the recurrent score and the enrichment scores of apoptosis-related functions of each patient in the CGGA 693 and CGGA 325 database. The samples were arranged in ascending order of the recurrent score. The column graph and line graph on the right showed the R -value and P -value of the correlation analysis. (E) Flow chart for recurrent score correlation analysis. (F-G) Using Pearson correlation analysis, the top 18 apoptosis-related genes mostly correlated with recurrent score were selected in CGGA 693 and CGGA 325 database. (H-I) The relationship between recurrent score and 6 apoptosis-related genes in glioma. The correlation coefficients were demonstrated as the proportion of the pie charts. The bottom right showed the correlation coefficient. The red parts represented a positive correlation. The correlation was tested by Pearson correlation analysis. (J) Correlation between the expression of the 6 genes in CGGA 693 and CGGA 325 database. ( K ) Expression levels of the 6 genes in primary glioma and recurrent glioma in CGGA 325 database and CGGA 693 database. (L) Survival analyses of the 6 genes by Kaplan-Meier curves and log-rank tests based on CCGA 693 database and CGGA 325 database. ( M ) Protein levels of SH3GLB1, NEK6, CASP8 and <t>ITGB1</t> in normal tissues and GBM from The Human Protein Atlas database
Bc Genexminer V4.7 Database, supplied by CH Instruments, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Biological functions associated with the recurrent scores. (A-B) The recurrent score related biological process revealed by Gene ontology analysis in the CGGA 693 and CGGA 325 database . (C-D) The heatmap showed the recurrent score and the enrichment scores of apoptosis-related functions of each patient in the CGGA 693 and CGGA 325 database. The samples were arranged in ascending order of the recurrent score. The column graph and line graph on the right showed the R -value and P -value of the correlation analysis. (E) Flow chart for recurrent score correlation analysis. (F-G) Using Pearson correlation analysis, the top 18 apoptosis-related genes mostly correlated with recurrent score were selected in CGGA 693 and CGGA 325 database. (H-I) The relationship between recurrent score and 6 apoptosis-related genes in glioma. The correlation coefficients were demonstrated as the proportion of the pie charts. The bottom right showed the correlation coefficient. The red parts represented a positive correlation. The correlation was tested by Pearson correlation analysis. (J) Correlation between the expression of the 6 genes in CGGA 693 and CGGA 325 database. ( K ) Expression levels of the 6 genes in primary glioma and recurrent glioma in CGGA 325 database and CGGA 693 database. (L) Survival analyses of the 6 genes by Kaplan-Meier curves and log-rank tests based on CCGA 693 database and CGGA 325 database. ( M ) Protein levels of SH3GLB1, NEK6, CASP8 and <t>ITGB1</t> in normal tissues and GBM from The Human Protein Atlas database
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CH Instruments pearson’s chi-squared test
Biological functions associated with the recurrent scores. (A-B) The recurrent score related biological process revealed by Gene ontology analysis in the CGGA 693 and CGGA 325 database . (C-D) The heatmap showed the recurrent score and the enrichment scores of apoptosis-related functions of each patient in the CGGA 693 and CGGA 325 database. The samples were arranged in ascending order of the recurrent score. The column graph and line graph on the right showed the R -value and P -value of the correlation analysis. (E) Flow chart for recurrent score correlation analysis. (F-G) Using Pearson correlation analysis, the top 18 apoptosis-related genes mostly correlated with recurrent score were selected in CGGA 693 and CGGA 325 database. (H-I) The relationship between recurrent score and 6 apoptosis-related genes in glioma. The correlation coefficients were demonstrated as the proportion of the pie charts. The bottom right showed the correlation coefficient. The red parts represented a positive correlation. The correlation was tested by Pearson correlation analysis. (J) Correlation between the expression of the 6 genes in CGGA 693 and CGGA 325 database. ( K ) Expression levels of the 6 genes in primary glioma and recurrent glioma in CGGA 325 database and CGGA 693 database. (L) Survival analyses of the 6 genes by Kaplan-Meier curves and log-rank tests based on CCGA 693 database and CGGA 325 database. ( M ) Protein levels of SH3GLB1, NEK6, CASP8 and <t>ITGB1</t> in normal tissues and GBM from The Human Protein Atlas database
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Biological functions associated with the recurrent scores. (A-B) The recurrent score related biological process revealed by Gene ontology analysis in the CGGA 693 and CGGA 325 database . (C-D) The heatmap showed the recurrent score and the enrichment scores of apoptosis-related functions of each patient in the CGGA 693 and CGGA 325 database. The samples were arranged in ascending order of the recurrent score. The column graph and line graph on the right showed the R -value and P -value of the correlation analysis. (E) Flow chart for recurrent score correlation analysis. (F-G) Using Pearson correlation analysis, the top 18 apoptosis-related genes mostly correlated with recurrent score were selected in CGGA 693 and CGGA 325 database. (H-I) The relationship between recurrent score and 6 apoptosis-related genes in glioma. The correlation coefficients were demonstrated as the proportion of the pie charts. The bottom right showed the correlation coefficient. The red parts represented a positive correlation. The correlation was tested by Pearson correlation analysis. (J) Correlation between the expression of the 6 genes in CGGA 693 and CGGA 325 database. ( K ) Expression levels of the 6 genes in primary glioma and recurrent glioma in CGGA 325 database and CGGA 693 database. (L) Survival analyses of the 6 genes by Kaplan-Meier curves and log-rank tests based on CCGA 693 database and CGGA 325 database. ( M ) Protein levels of SH3GLB1, NEK6, CASP8 and ITGB1 in normal tissues and GBM from The Human Protein Atlas database

Journal: BMC Immunology

Article Title: Establishment and validation of a recurrent prediction model for glioma: extrinsic apoptotic molecules FADD and CASP8 are closely associated with glioma recurrence

doi: 10.1186/s12865-025-00746-z

Figure Lengend Snippet: Biological functions associated with the recurrent scores. (A-B) The recurrent score related biological process revealed by Gene ontology analysis in the CGGA 693 and CGGA 325 database . (C-D) The heatmap showed the recurrent score and the enrichment scores of apoptosis-related functions of each patient in the CGGA 693 and CGGA 325 database. The samples were arranged in ascending order of the recurrent score. The column graph and line graph on the right showed the R -value and P -value of the correlation analysis. (E) Flow chart for recurrent score correlation analysis. (F-G) Using Pearson correlation analysis, the top 18 apoptosis-related genes mostly correlated with recurrent score were selected in CGGA 693 and CGGA 325 database. (H-I) The relationship between recurrent score and 6 apoptosis-related genes in glioma. The correlation coefficients were demonstrated as the proportion of the pie charts. The bottom right showed the correlation coefficient. The red parts represented a positive correlation. The correlation was tested by Pearson correlation analysis. (J) Correlation between the expression of the 6 genes in CGGA 693 and CGGA 325 database. ( K ) Expression levels of the 6 genes in primary glioma and recurrent glioma in CGGA 325 database and CGGA 693 database. (L) Survival analyses of the 6 genes by Kaplan-Meier curves and log-rank tests based on CCGA 693 database and CGGA 325 database. ( M ) Protein levels of SH3GLB1, NEK6, CASP8 and ITGB1 in normal tissues and GBM from The Human Protein Atlas database

Article Snippet: The correlation was tested by Pearson correlation analysis. (J) Correlation between the expression of the 6 genes in CGGA 693 and CGGA 325 database. ( K ) Expression levels of the 6 genes in primary glioma and recurrent glioma in CGGA 325 database and CGGA 693 database. (L) Survival analyses of the 6 genes by Kaplan-Meier curves and log-rank tests based on CCGA 693 database and CGGA 325 database. ( M ) Protein levels of SH3GLB1, NEK6, CASP8 and ITGB1 in normal tissues and GBM from The Human Protein Atlas database

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