human tissues Search Results


86
Human Protein Atlas casp8
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, <t>CASP8</t> and ITGB1 in normal tissues and GBM from The Human Protein Atlas database
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Miltenyi Biotec anti tf antibody
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, <t>CASP8</t> and ITGB1 in normal tissues and GBM from The Human Protein Atlas database
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Elabscience Biotechnology timp 2
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, <t>CASP8</t> and ITGB1 in normal tissues and GBM from The Human Protein Atlas database
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Elabscience Biotechnology sandwich elisa
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, <t>CASP8</t> and ITGB1 in normal tissues and GBM from The Human Protein Atlas database
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R&D Systems erythropoietin
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, <t>CASP8</t> and ITGB1 in normal tissues and GBM from The Human Protein Atlas database
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Boster Bio ecl chemiluminescent reagents
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, <t>CASP8</t> and ITGB1 in normal tissues and GBM from The Human Protein Atlas database
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Novus Biologicals human brain lysates
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, <t>CASP8</t> and ITGB1 in normal tissues and GBM from The Human Protein Atlas database
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Novus Biologicals adult normal tissue ffpe tma
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, <t>CASP8</t> and ITGB1 in normal tissues and GBM from The Human Protein Atlas database
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Novus Biologicals heart brain lysate cross reactivity varying amounts
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, <t>CASP8</t> and ITGB1 in normal tissues and GBM from The Human Protein Atlas database
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R&D Systems human erythropoietin
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, <t>CASP8</t> and ITGB1 in normal tissues and GBM from The Human Protein Atlas database
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Novus Biologicals human brain tissue lysates
PD-linked CHCHD2 mutants showed reduced binding to CHCHD10. ( A ) Co-immunoprecipitation of endogenous CHCHD2 by antibody against CHCHD2 in SK-N-SH cells. WCL: whole cell lysate, 5% total protein used in co-IP experiment. ( B ) Co-immunoprecipitation of endogenous CHCHD10 by antibody against CHCHD10 in SK-N-SH cells. WCL: whole cell lysate, 5% total protein used in co-IP experiment. ( C ) Co-immunoprecipitation of endogenous CHCHD2 by antibody against CHCHD2 in <t>human</t> <t>brain</t> <t>tissue</t> <t>lysates.</t> ( D ) Co-immunoprecipitation of CHCHD2 by a CHCHD2 antibody against middle region of CHCHD2 (labeled as CHCHD2-TF) in SK-N-SH cells transfected with non-tagged CHCHD2 WT/T61I/R145Q/Q126X. WCL: whole cell lysate, 5% total protein used in co-IP experiment. Lower arrow pointed to CHCHD2 Q126X and higher arrow pointed to full length CHCHD2. ( E ) Co-immunoprecipitation of CHCHD2 by a CHCHD2 antibody against middle region of CHCHD2 (labeled as CHCHD2-TF) in hESCs of H9 and isogenic lines harboring homozygous R145Q (−/−).WCL: whole cell lysate, 5% total protein used in co-IP experiment. Arrow pointed to CHCHD10. Representative results from R10 and R17 (R145Q−/−) were shown. ( F ) Quantification of protein abundance of CHCHD10 normalized with CHCHD2 on the co-immunoprecipitation complex from isogenic hESC lysates.
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R&D Systems human coagulation factor iii tissue factor quantikine elisa kit
PD-linked CHCHD2 mutants showed reduced binding to CHCHD10. ( A ) Co-immunoprecipitation of endogenous CHCHD2 by antibody against CHCHD2 in SK-N-SH cells. WCL: whole cell lysate, 5% total protein used in co-IP experiment. ( B ) Co-immunoprecipitation of endogenous CHCHD10 by antibody against CHCHD10 in SK-N-SH cells. WCL: whole cell lysate, 5% total protein used in co-IP experiment. ( C ) Co-immunoprecipitation of endogenous CHCHD2 by antibody against CHCHD2 in <t>human</t> <t>brain</t> <t>tissue</t> <t>lysates.</t> ( D ) Co-immunoprecipitation of CHCHD2 by a CHCHD2 antibody against middle region of CHCHD2 (labeled as CHCHD2-TF) in SK-N-SH cells transfected with non-tagged CHCHD2 WT/T61I/R145Q/Q126X. WCL: whole cell lysate, 5% total protein used in co-IP experiment. Lower arrow pointed to CHCHD2 Q126X and higher arrow pointed to full length CHCHD2. ( E ) Co-immunoprecipitation of CHCHD2 by a CHCHD2 antibody against middle region of CHCHD2 (labeled as CHCHD2-TF) in hESCs of H9 and isogenic lines harboring homozygous R145Q (−/−).WCL: whole cell lysate, 5% total protein used in co-IP experiment. Arrow pointed to CHCHD10. Representative results from R10 and R17 (R145Q−/−) were shown. ( F ) Quantification of protein abundance of CHCHD10 normalized with CHCHD2 on the co-immunoprecipitation complex from isogenic hESC lysates.
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Image Search Results


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: Characterizing the differential expression patterns of CASP8 and FADD in gliomas and normal tissues will play a crucial role in the further development of targeted therapeutic strategies for gliomas Fig. 9 RNA and protein levels of CASP8 and FADD in normal tissues and tumors. (A-B) RNA expression of CASP8 and FADD in normal tissues from the NCBI database (https://www.ncbi.nlm.nih.gov/). (C-D) RNA expression of CASP8 and FADD in normal tissues from the Human Protein Atlas database (https://www.proteinatlas.org/). (E) Protein levels of CASP8 and FADD in normal brain tissues from The Human Protein Atlas database. (F) Protein levels of CASP8 and FADD in normal tissues from The Human Protein Atlas database. (G) Protein levels of CASP8 and FADD in tumors from The Human Protein Atlas database

Techniques: Expressing

The association between recurrent score and classical apoptotic genes. (A) The relationship between the 6 genes and recurrent score in CGGA and TCGA database . (B) PPI network of CASP3, CASP9, FADD, CASP7, CASP8, BCL2,and the 9-gene signature from the STRING. (C-D) The expression levels of the 6 apoptotic genes in low- and high-risk levels . (E-J) Correlation between recurrent score and expression levels of apoptotic genes. *P<0.05; ***P<0.001; ns, not significant

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: The association between recurrent score and classical apoptotic genes. (A) The relationship between the 6 genes and recurrent score in CGGA and TCGA database . (B) PPI network of CASP3, CASP9, FADD, CASP7, CASP8, BCL2,and the 9-gene signature from the STRING. (C-D) The expression levels of the 6 apoptotic genes in low- and high-risk levels . (E-J) Correlation between recurrent score and expression levels of apoptotic genes. *P<0.05; ***P<0.001; ns, not significant

Article Snippet: Characterizing the differential expression patterns of CASP8 and FADD in gliomas and normal tissues will play a crucial role in the further development of targeted therapeutic strategies for gliomas Fig. 9 RNA and protein levels of CASP8 and FADD in normal tissues and tumors. (A-B) RNA expression of CASP8 and FADD in normal tissues from the NCBI database (https://www.ncbi.nlm.nih.gov/). (C-D) RNA expression of CASP8 and FADD in normal tissues from the Human Protein Atlas database (https://www.proteinatlas.org/). (E) Protein levels of CASP8 and FADD in normal brain tissues from The Human Protein Atlas database. (F) Protein levels of CASP8 and FADD in normal tissues from The Human Protein Atlas database. (G) Protein levels of CASP8 and FADD in tumors from The Human Protein Atlas database

Techniques: Expressing

RNA and protein levels of CASP8 and FADD in normal tissues and tumors. (A-B) RNA expression of CASP8 and FADD in normal tissues from the NCBI database (https://www.ncbi.nlm.nih.gov/). (C-D) RNA expression of CASP8 and FADD in normal tissues from the Human Protein Atlas database (https://www.proteinatlas.org/). (E) Protein levels of CASP8 and FADD in normal brain tissues from The Human Protein Atlas database. (F) Protein levels of CASP8 and FADD in normal tissues from The Human Protein Atlas database. (G) Protein levels of CASP8 and FADD in tumors 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: RNA and protein levels of CASP8 and FADD in normal tissues and tumors. (A-B) RNA expression of CASP8 and FADD in normal tissues from the NCBI database (https://www.ncbi.nlm.nih.gov/). (C-D) RNA expression of CASP8 and FADD in normal tissues from the Human Protein Atlas database (https://www.proteinatlas.org/). (E) Protein levels of CASP8 and FADD in normal brain tissues from The Human Protein Atlas database. (F) Protein levels of CASP8 and FADD in normal tissues from The Human Protein Atlas database. (G) Protein levels of CASP8 and FADD in tumors from The Human Protein Atlas database

Article Snippet: Characterizing the differential expression patterns of CASP8 and FADD in gliomas and normal tissues will play a crucial role in the further development of targeted therapeutic strategies for gliomas Fig. 9 RNA and protein levels of CASP8 and FADD in normal tissues and tumors. (A-B) RNA expression of CASP8 and FADD in normal tissues from the NCBI database (https://www.ncbi.nlm.nih.gov/). (C-D) RNA expression of CASP8 and FADD in normal tissues from the Human Protein Atlas database (https://www.proteinatlas.org/). (E) Protein levels of CASP8 and FADD in normal brain tissues from The Human Protein Atlas database. (F) Protein levels of CASP8 and FADD in normal tissues from The Human Protein Atlas database. (G) Protein levels of CASP8 and FADD in tumors from The Human Protein Atlas database

Techniques: RNA Expression

PD-linked CHCHD2 mutants showed reduced binding to CHCHD10. ( A ) Co-immunoprecipitation of endogenous CHCHD2 by antibody against CHCHD2 in SK-N-SH cells. WCL: whole cell lysate, 5% total protein used in co-IP experiment. ( B ) Co-immunoprecipitation of endogenous CHCHD10 by antibody against CHCHD10 in SK-N-SH cells. WCL: whole cell lysate, 5% total protein used in co-IP experiment. ( C ) Co-immunoprecipitation of endogenous CHCHD2 by antibody against CHCHD2 in human brain tissue lysates. ( D ) Co-immunoprecipitation of CHCHD2 by a CHCHD2 antibody against middle region of CHCHD2 (labeled as CHCHD2-TF) in SK-N-SH cells transfected with non-tagged CHCHD2 WT/T61I/R145Q/Q126X. WCL: whole cell lysate, 5% total protein used in co-IP experiment. Lower arrow pointed to CHCHD2 Q126X and higher arrow pointed to full length CHCHD2. ( E ) Co-immunoprecipitation of CHCHD2 by a CHCHD2 antibody against middle region of CHCHD2 (labeled as CHCHD2-TF) in hESCs of H9 and isogenic lines harboring homozygous R145Q (−/−).WCL: whole cell lysate, 5% total protein used in co-IP experiment. Arrow pointed to CHCHD10. Representative results from R10 and R17 (R145Q−/−) were shown. ( F ) Quantification of protein abundance of CHCHD10 normalized with CHCHD2 on the co-immunoprecipitation complex from isogenic hESC lysates.

Journal: Human Molecular Genetics

Article Title: PD-linked CHCHD2 mutations impair CHCHD10 and MICOS complex leading to mitochondria dysfunction

doi: 10.1093/hmg/ddy413

Figure Lengend Snippet: PD-linked CHCHD2 mutants showed reduced binding to CHCHD10. ( A ) Co-immunoprecipitation of endogenous CHCHD2 by antibody against CHCHD2 in SK-N-SH cells. WCL: whole cell lysate, 5% total protein used in co-IP experiment. ( B ) Co-immunoprecipitation of endogenous CHCHD10 by antibody against CHCHD10 in SK-N-SH cells. WCL: whole cell lysate, 5% total protein used in co-IP experiment. ( C ) Co-immunoprecipitation of endogenous CHCHD2 by antibody against CHCHD2 in human brain tissue lysates. ( D ) Co-immunoprecipitation of CHCHD2 by a CHCHD2 antibody against middle region of CHCHD2 (labeled as CHCHD2-TF) in SK-N-SH cells transfected with non-tagged CHCHD2 WT/T61I/R145Q/Q126X. WCL: whole cell lysate, 5% total protein used in co-IP experiment. Lower arrow pointed to CHCHD2 Q126X and higher arrow pointed to full length CHCHD2. ( E ) Co-immunoprecipitation of CHCHD2 by a CHCHD2 antibody against middle region of CHCHD2 (labeled as CHCHD2-TF) in hESCs of H9 and isogenic lines harboring homozygous R145Q (−/−).WCL: whole cell lysate, 5% total protein used in co-IP experiment. Arrow pointed to CHCHD10. Representative results from R10 and R17 (R145Q−/−) were shown. ( F ) Quantification of protein abundance of CHCHD10 normalized with CHCHD2 on the co-immunoprecipitation complex from isogenic hESC lysates.

Article Snippet: Human brain tissue lysates were from Novus Centennial, CO. Elamipretide was from MedChemExpress Monmouth Junction, NJ.

Techniques: Binding Assay, Immunoprecipitation, Co-Immunoprecipitation Assay, Labeling, Transfection, Quantitative Proteomics