kaplan–meier plotter tool Search Results


kaplan–meier plotter  (GraphPad Software Inc)
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GraphPad Software Inc kaplan–meier plotter
Identification of differentially expressed genes (DEGs) in <t>PDAC</t> using RNA-seq and microarray data. ( A–C ) Scatter plots of FPKM values for gene expression of PDAC versus PDAC ( A ), non-tumor pancreas versus non-tumor pancreas ( B ), and PDAC versus matching non-tumor pancreas ( C ). Pearson correlation coefficients are shown above the corresponding scatter plots. ( D ) Heatmap of 1371 DEGs identified from the comparison between six PDAC and six non-tumor control specimens ( p < 0.05 and |log2FC| > 0.5). ( E, F ) Venn diagram of DEGs identified from previous published microarray studies of PDAC versus non-tumor controls using two types of Affymetrix microarray platforms. Overlapping upregulated DEGs ( n = 795) ( E ) and downregulated DEGs ( n = 252) ( F ) are shown. ( G ) Heatmaps and hierarchical clustering of all PDAC specimens versus non-tumor controls from three independent groups clearly show that these 1047 DEGs can distinguish PDAC from non-tumor pancreas specimens FC, fold change; FPKM, fragments per kilo-base per million mapped reads; PDAC, pancreatic ductal adenocarcinoma; RNA-seq, RNA sequencing.
Kaplan–Meier Plotter, supplied by GraphPad Software Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/kaplan%E2%80%93meier+plotter+tool/pmc08600871-62-16-20?v=GraphPad+Software+Inc
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kaplan–meier plotter - by Bioz Stars, 2026-07
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prism software 7.0  (GraphPad Software Inc)
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GraphPad Software Inc prism software 7.0
Identification of differentially expressed genes (DEGs) in <t>PDAC</t> using RNA-seq and microarray data. ( A–C ) Scatter plots of FPKM values for gene expression of PDAC versus PDAC ( A ), non-tumor pancreas versus non-tumor pancreas ( B ), and PDAC versus matching non-tumor pancreas ( C ). Pearson correlation coefficients are shown above the corresponding scatter plots. ( D ) Heatmap of 1371 DEGs identified from the comparison between six PDAC and six non-tumor control specimens ( p < 0.05 and |log2FC| > 0.5). ( E, F ) Venn diagram of DEGs identified from previous published microarray studies of PDAC versus non-tumor controls using two types of Affymetrix microarray platforms. Overlapping upregulated DEGs ( n = 795) ( E ) and downregulated DEGs ( n = 252) ( F ) are shown. ( G ) Heatmaps and hierarchical clustering of all PDAC specimens versus non-tumor controls from three independent groups clearly show that these 1047 DEGs can distinguish PDAC from non-tumor pancreas specimens FC, fold change; FPKM, fragments per kilo-base per million mapped reads; PDAC, pancreatic ductal adenocarcinoma; RNA-seq, RNA sequencing.
Prism Software 7.0, supplied by GraphPad Software Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/kaplan%E2%80%93meier+plotter+tool/pm35811688-222-12-15?v=GraphPad+Software+Inc
Average 90 stars, based on 1 article reviews
prism software 7.0 - by Bioz Stars, 2026-07
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prism version 9.0  (GraphPad Software Inc)
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GraphPad Software Inc prism version 9.0
Identification of differentially expressed genes (DEGs) in <t>PDAC</t> using RNA-seq and microarray data. ( A–C ) Scatter plots of FPKM values for gene expression of PDAC versus PDAC ( A ), non-tumor pancreas versus non-tumor pancreas ( B ), and PDAC versus matching non-tumor pancreas ( C ). Pearson correlation coefficients are shown above the corresponding scatter plots. ( D ) Heatmap of 1371 DEGs identified from the comparison between six PDAC and six non-tumor control specimens ( p < 0.05 and |log2FC| > 0.5). ( E, F ) Venn diagram of DEGs identified from previous published microarray studies of PDAC versus non-tumor controls using two types of Affymetrix microarray platforms. Overlapping upregulated DEGs ( n = 795) ( E ) and downregulated DEGs ( n = 252) ( F ) are shown. ( G ) Heatmaps and hierarchical clustering of all PDAC specimens versus non-tumor controls from three independent groups clearly show that these 1047 DEGs can distinguish PDAC from non-tumor pancreas specimens FC, fold change; FPKM, fragments per kilo-base per million mapped reads; PDAC, pancreatic ductal adenocarcinoma; RNA-seq, RNA sequencing.
Prism Version 9.0, supplied by GraphPad Software Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/kaplan%E2%80%93meier+plotter+tool/ppr0689645-40-8-7?v=GraphPad+Software+Inc
Average 90 stars, based on 1 article reviews
prism version 9.0 - by Bioz Stars, 2026-07
90/100 stars
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Image Search Results


Identification of differentially expressed genes (DEGs) in PDAC using RNA-seq and microarray data. ( A–C ) Scatter plots of FPKM values for gene expression of PDAC versus PDAC ( A ), non-tumor pancreas versus non-tumor pancreas ( B ), and PDAC versus matching non-tumor pancreas ( C ). Pearson correlation coefficients are shown above the corresponding scatter plots. ( D ) Heatmap of 1371 DEGs identified from the comparison between six PDAC and six non-tumor control specimens ( p < 0.05 and |log2FC| > 0.5). ( E, F ) Venn diagram of DEGs identified from previous published microarray studies of PDAC versus non-tumor controls using two types of Affymetrix microarray platforms. Overlapping upregulated DEGs ( n = 795) ( E ) and downregulated DEGs ( n = 252) ( F ) are shown. ( G ) Heatmaps and hierarchical clustering of all PDAC specimens versus non-tumor controls from three independent groups clearly show that these 1047 DEGs can distinguish PDAC from non-tumor pancreas specimens FC, fold change; FPKM, fragments per kilo-base per million mapped reads; PDAC, pancreatic ductal adenocarcinoma; RNA-seq, RNA sequencing.

Journal: BMC Genomics

Article Title: Identification and functional analysis of novel oncogene DDX60L in pancreatic ductal adenocarcinoma

doi: 10.1186/s12864-021-08137-5

Figure Lengend Snippet: Identification of differentially expressed genes (DEGs) in PDAC using RNA-seq and microarray data. ( A–C ) Scatter plots of FPKM values for gene expression of PDAC versus PDAC ( A ), non-tumor pancreas versus non-tumor pancreas ( B ), and PDAC versus matching non-tumor pancreas ( C ). Pearson correlation coefficients are shown above the corresponding scatter plots. ( D ) Heatmap of 1371 DEGs identified from the comparison between six PDAC and six non-tumor control specimens ( p < 0.05 and |log2FC| > 0.5). ( E, F ) Venn diagram of DEGs identified from previous published microarray studies of PDAC versus non-tumor controls using two types of Affymetrix microarray platforms. Overlapping upregulated DEGs ( n = 795) ( E ) and downregulated DEGs ( n = 252) ( F ) are shown. ( G ) Heatmaps and hierarchical clustering of all PDAC specimens versus non-tumor controls from three independent groups clearly show that these 1047 DEGs can distinguish PDAC from non-tumor pancreas specimens FC, fold change; FPKM, fragments per kilo-base per million mapped reads; PDAC, pancreatic ductal adenocarcinoma; RNA-seq, RNA sequencing.

Article Snippet: In accordance with the instructions, the prognostic value of two genes were also analyzed with the Kaplan–Meier Plotter (PDAC), and GraphPad Prism 8.3.0 was used to further display the data.

Techniques: RNA Sequencing, Microarray, Gene Expression, Comparison, Control

Identification of overlapped genes responsible for the progression of PDAC. ( A-B ) Venn diagram of DEGs identified in the RNA-seq analysis of this study and in previous microarray studies of PDAC versus non-tumor controls. Overlapping upregulated ( A ) and downregulated ( B ) DEGs across multiple platforms are shown. ( C ) Venn diagram of the identified DEGs and the Top 100 overall survival-related genes in PDAC. Five most significant potential oncogenes were identified: MYEOV , KCNN4 , FAM83A , S100A16 , and DDX60L . ( D ) Expression of five significant potential oncogenes in PDAC tissues of the GEPIA database. One-way ANOVA is used to analyze significant differences in the gene expression levels between PDAC normal tissues and cancer tissues. *represents significant differences in RNA expression, p < 0.01. DDX60L , DExD/H-box 60 like; DEGs, differentially-expressed genes; FAM83A , family with sequence similarity 83 member A; GEPIA, Gene Expression Profiling Interactive Analysis; KCNN4 , potassium calcium-activated channel subfamily N member 4; MYEOV , myeloma overexpressed; PDAC, pancreatic ductal adenocarcinoma; RNA-seq, RNA sequencing; S100A16 , S100 calcium binding protein A16

Journal: BMC Genomics

Article Title: Identification and functional analysis of novel oncogene DDX60L in pancreatic ductal adenocarcinoma

doi: 10.1186/s12864-021-08137-5

Figure Lengend Snippet: Identification of overlapped genes responsible for the progression of PDAC. ( A-B ) Venn diagram of DEGs identified in the RNA-seq analysis of this study and in previous microarray studies of PDAC versus non-tumor controls. Overlapping upregulated ( A ) and downregulated ( B ) DEGs across multiple platforms are shown. ( C ) Venn diagram of the identified DEGs and the Top 100 overall survival-related genes in PDAC. Five most significant potential oncogenes were identified: MYEOV , KCNN4 , FAM83A , S100A16 , and DDX60L . ( D ) Expression of five significant potential oncogenes in PDAC tissues of the GEPIA database. One-way ANOVA is used to analyze significant differences in the gene expression levels between PDAC normal tissues and cancer tissues. *represents significant differences in RNA expression, p < 0.01. DDX60L , DExD/H-box 60 like; DEGs, differentially-expressed genes; FAM83A , family with sequence similarity 83 member A; GEPIA, Gene Expression Profiling Interactive Analysis; KCNN4 , potassium calcium-activated channel subfamily N member 4; MYEOV , myeloma overexpressed; PDAC, pancreatic ductal adenocarcinoma; RNA-seq, RNA sequencing; S100A16 , S100 calcium binding protein A16

Article Snippet: In accordance with the instructions, the prognostic value of two genes were also analyzed with the Kaplan–Meier Plotter (PDAC), and GraphPad Prism 8.3.0 was used to further display the data.

Techniques: RNA Sequencing, Microarray, Expressing, Gene Expression, RNA Expression, Sequencing, Binding Assay

Knockdown of DDX60L inhibited the migration of PDAC cells, but not their proliferation. ( A, B ) Viability of siRNA-transfected PDAC cells. CCK-8 assay analyzed the siControl-, siMYEOV-, and siDDX60L-transfected PANC-1 ( A ) and MiaPaca2 ( B ) cells. ( C, D ) The invasive ability of siRNA-transfected PDAC cells. Transwell assay analyzed the siControl-, siMYEOV-, and siDDX60L-transfected PANC-1 ( C ) and MiaPaca2 ( D ) cells. One-way ANOVA is used to analyze significant differences. * p < 0.05; ** p < 0.01; *** p < 0.001. CCK-8, Cell Counting Kit-8; DDX60L , DExD/H-box 60 like; MYEOV, myeloma overexpressed; PDAC, pancreatic ductal adenocarcinoma

Journal: BMC Genomics

Article Title: Identification and functional analysis of novel oncogene DDX60L in pancreatic ductal adenocarcinoma

doi: 10.1186/s12864-021-08137-5

Figure Lengend Snippet: Knockdown of DDX60L inhibited the migration of PDAC cells, but not their proliferation. ( A, B ) Viability of siRNA-transfected PDAC cells. CCK-8 assay analyzed the siControl-, siMYEOV-, and siDDX60L-transfected PANC-1 ( A ) and MiaPaca2 ( B ) cells. ( C, D ) The invasive ability of siRNA-transfected PDAC cells. Transwell assay analyzed the siControl-, siMYEOV-, and siDDX60L-transfected PANC-1 ( C ) and MiaPaca2 ( D ) cells. One-way ANOVA is used to analyze significant differences. * p < 0.05; ** p < 0.01; *** p < 0.001. CCK-8, Cell Counting Kit-8; DDX60L , DExD/H-box 60 like; MYEOV, myeloma overexpressed; PDAC, pancreatic ductal adenocarcinoma

Article Snippet: In accordance with the instructions, the prognostic value of two genes were also analyzed with the Kaplan–Meier Plotter (PDAC), and GraphPad Prism 8.3.0 was used to further display the data.

Techniques: Knockdown, Migration, Transfection, CCK-8 Assay, Transwell Assay, Cell Counting

Knockdown of DDX60L induced apoptosis of PDAC cells, but not cell cycle arrest. ( A, B ) Apoptosis of siRNA-transfected PDAC cells. FACS analyzed the apoptosis of the siControl-, siMYEOV-, and siDDX60L-transfected PANC-1 ( A ) and MiaPaca2 ( B ) cells. ( C, D ) Cell cycle of siRNA-transfected PDAC cells. FACS analyzed the cell cycle of the siControl-, siMYEOV-, and siDDX60L-transfected PANC-1 ( C ) and MiaPaca2 ( D ) cells. One-way ANOVA is used to analyze significant differences. Ns, no significant differences, * p < 0.05; ** p < 0.01; *** p < 0.001. DDX60L , DExD/H-box 60 like; FACS, fluorescence-activated cell sorting; MYEOV, myeloma overexpressed; PDAC, pancreatic ductal adenocarcinoma

Journal: BMC Genomics

Article Title: Identification and functional analysis of novel oncogene DDX60L in pancreatic ductal adenocarcinoma

doi: 10.1186/s12864-021-08137-5

Figure Lengend Snippet: Knockdown of DDX60L induced apoptosis of PDAC cells, but not cell cycle arrest. ( A, B ) Apoptosis of siRNA-transfected PDAC cells. FACS analyzed the apoptosis of the siControl-, siMYEOV-, and siDDX60L-transfected PANC-1 ( A ) and MiaPaca2 ( B ) cells. ( C, D ) Cell cycle of siRNA-transfected PDAC cells. FACS analyzed the cell cycle of the siControl-, siMYEOV-, and siDDX60L-transfected PANC-1 ( C ) and MiaPaca2 ( D ) cells. One-way ANOVA is used to analyze significant differences. Ns, no significant differences, * p < 0.05; ** p < 0.01; *** p < 0.001. DDX60L , DExD/H-box 60 like; FACS, fluorescence-activated cell sorting; MYEOV, myeloma overexpressed; PDAC, pancreatic ductal adenocarcinoma

Article Snippet: In accordance with the instructions, the prognostic value of two genes were also analyzed with the Kaplan–Meier Plotter (PDAC), and GraphPad Prism 8.3.0 was used to further display the data.

Techniques: Knockdown, Transfection, Fluorescence, FACS

Transcriptome analyses showed alterations of genes and signaling pathways in siMYEOV- and siDDX60L-transfected PDAC cells. ( A ) Heatmap and Venn diagram of DEGs identified in siMYEOV-transfected MiaPaca2 and PANC-1 cells. ( B ) DAVID-KEGG analysis of the DEGs in siMYEOV-transfected MiaPaca2 and PANC-1 cells ( p < 0.05). ( C ) Heatmap and Venn diagram of DEGs identified in siDDX60L-transfected MiaPaca2 and PANC-1 cells. ( D ) DAVID-KEGG analysis of the DEGs in siDDX60L-transfected MiaPaca2 and PANC-1 cells ( p < 0.05). DAVID-KEGG, Database for Annotation, Visualization and Integrated Discovery-Kyoto Encyclopedia of Genes and Genomes; DDX60L, DExD/H-box 60 like; DEGs, differentially-expressed genes; PDAC, pancreatic ductal adenocarcinoma; MYEOV, myeloma overexpressed

Journal: BMC Genomics

Article Title: Identification and functional analysis of novel oncogene DDX60L in pancreatic ductal adenocarcinoma

doi: 10.1186/s12864-021-08137-5

Figure Lengend Snippet: Transcriptome analyses showed alterations of genes and signaling pathways in siMYEOV- and siDDX60L-transfected PDAC cells. ( A ) Heatmap and Venn diagram of DEGs identified in siMYEOV-transfected MiaPaca2 and PANC-1 cells. ( B ) DAVID-KEGG analysis of the DEGs in siMYEOV-transfected MiaPaca2 and PANC-1 cells ( p < 0.05). ( C ) Heatmap and Venn diagram of DEGs identified in siDDX60L-transfected MiaPaca2 and PANC-1 cells. ( D ) DAVID-KEGG analysis of the DEGs in siDDX60L-transfected MiaPaca2 and PANC-1 cells ( p < 0.05). DAVID-KEGG, Database for Annotation, Visualization and Integrated Discovery-Kyoto Encyclopedia of Genes and Genomes; DDX60L, DExD/H-box 60 like; DEGs, differentially-expressed genes; PDAC, pancreatic ductal adenocarcinoma; MYEOV, myeloma overexpressed

Article Snippet: In accordance with the instructions, the prognostic value of two genes were also analyzed with the Kaplan–Meier Plotter (PDAC), and GraphPad Prism 8.3.0 was used to further display the data.

Techniques: Protein-Protein interactions, Transfection

Knockdown of DDX60L and MYEOV inhibited the expression of CXCL2 and led to good prognosis in PDAC. ( A ) Expression of CXCL2 in siDDX60L- and siMYEOV-transfected MiaPaCa2 and PANC-1 cells. Two-way ANOVA is used to analyze significant differences in the gene expression levels between control cancer cells and siRNA treated cancer cells. **** p < 0.0001. ( B ) The Kaplan–Meier Plotter analysis showed that patients from the TCGA-PDAC dataset ( n = 177) who expressed high levels of CXCL2 had a poor prognosis. ( C-D ) The Kaplan–Meier Plotter analysis showed that PDAC patients with low CXCL2 and DDX60L expression in tumors ( C ) and those with low CXCL2 and MYEOV expression in tumors ( D ) had a significantly improved overall survival. The p -values are shown. CXCL2 , C-X-C motif chemokine ligand 1; DDX60L , DExD/H-box 60 like; MYEOV , myeloma overexpressed; OS, overall survival; PDAC, pancreatic ductal adenocarcinoma; TCGA, The Cancer Genome Atlas

Journal: BMC Genomics

Article Title: Identification and functional analysis of novel oncogene DDX60L in pancreatic ductal adenocarcinoma

doi: 10.1186/s12864-021-08137-5

Figure Lengend Snippet: Knockdown of DDX60L and MYEOV inhibited the expression of CXCL2 and led to good prognosis in PDAC. ( A ) Expression of CXCL2 in siDDX60L- and siMYEOV-transfected MiaPaCa2 and PANC-1 cells. Two-way ANOVA is used to analyze significant differences in the gene expression levels between control cancer cells and siRNA treated cancer cells. **** p < 0.0001. ( B ) The Kaplan–Meier Plotter analysis showed that patients from the TCGA-PDAC dataset ( n = 177) who expressed high levels of CXCL2 had a poor prognosis. ( C-D ) The Kaplan–Meier Plotter analysis showed that PDAC patients with low CXCL2 and DDX60L expression in tumors ( C ) and those with low CXCL2 and MYEOV expression in tumors ( D ) had a significantly improved overall survival. The p -values are shown. CXCL2 , C-X-C motif chemokine ligand 1; DDX60L , DExD/H-box 60 like; MYEOV , myeloma overexpressed; OS, overall survival; PDAC, pancreatic ductal adenocarcinoma; TCGA, The Cancer Genome Atlas

Article Snippet: In accordance with the instructions, the prognostic value of two genes were also analyzed with the Kaplan–Meier Plotter (PDAC), and GraphPad Prism 8.3.0 was used to further display the data.

Techniques: Knockdown, Expressing, Transfection, Gene Expression, Control