Paul Timpson
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View article: Supplementary Methods 1 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes
Supplementary Methods 1 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes Open
Supplementary Methods
View article: Supplementary Tables S1-S11 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes
Supplementary Tables S1-S11 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes Open
Supplementary Tables S1-S11
View article: Table 3 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes
Table 3 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes Open
Associations between gene expression and stage group and OS in MOC.
View article: Figure 2 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes
Figure 2 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes Open
Kaplan–Meier curves of OS in (A) main tumor groups (n = 582)—MBOT, MOC, LGI, and UGI; (B) patients with MOC by FIGO stage (n = 184); (C) patients with MOC by pattern of invasion in all stages (n = …
View article: Table 2 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes
Table 2 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes Open
Overall survival in MOC by pattern of invasion.
View article: Supplementary Figures S1-S13 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes
Supplementary Figures S1-S13 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes Open
Supplementary Figures S1-S13
View article: Figure 1 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes
Figure 1 from Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes Open
Schema of study numbers for each analysis to describe different cohort numbers due to pathology review and missing data. MOC, mucinous ovarian carcinoma; MBOT, mucinous borderline ovarian tumor; LGI, lower gastrointestinal; UGI, upper gast…
View article: Figure 2 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Figure 2 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
DAPs and dysregulated pathways in PDA tumors compared with adjacent normal tissue. A, DAPs (n = 395; P < 0.05) between PDA and adjacent normal tissue. B, Top enriched pathway proteins upregulated in PDA tissue. …
View article: Supplementary Figure 8 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 8 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 8 shows Kaplan-Meier curves displaying (A) the three-year survival for groups dichotomised by proteomic risk score in ProCan data. (B) The recurrence-free survival for groups dichotomized by proteomic risk score in Pro…
View article: Supplementary Table 1 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Table 1 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Table 1 shows additional clinical characteristics of the study cohort. Gln61Arg: Glutamine to Arginine at position 61, Gln61His: Glutamine to Histidine at position 61, Gly12Ala: Glycine to Alanine at position 12, Gly12Arg: Gl…
View article: Supplementary Table 2 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Table 2 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Table 2 shows the distribution of the clinical variables across the four proteomic-based clusters. Gln61Arg: Glutamine to Arginine at position 61, Gln61His: Glutamine to Histidine at position 61, Gly12Ala: Glycine to Alanine …
View article: Table 3 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Table 3 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Clinical variables across the proteomic-based risk groups.
View article: Supplementary Figure 2 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 2 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 2 shows figures related to the Consensus clustering analyses. (A) A delta area plot displays the relative change in the cumulative distribution function (CDF) curve comparing k and k-1 clusters from our cohort. (B) The…
View article: Supplementary Figure 10 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 10 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 10 shows the differential abundance and pathway enrichment analyses based on KRAS mutations. (A) Volcano plot displaying the differentially abundant proteins between KRAS mutant PDA and KRAS wild-type PDA. (B) Pathways…
View article: Supplementary Figure 9 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 9 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 9: Kaplan-Meier plot for patients dichotomized by a proteomic risk score that uses only the ten proteins detected in blood by mass spectrometry (PURB, GALM, SERPINA3, OAS3, KRT2, NUDT2, SERPINA4, CUTA, POSTN, CLEC11A).
View article: Supplementary Figure 3 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 3 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 3 shows differential abundance and pathway enrichment analyses of clusters. (A) Volcano plot displaying the differentially expressed proteins between PDA samples classified between groups 1 and 3 against groups 2 and 4…
View article: Figure 3 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Figure 3 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Proteomic subtypes of PDA. A, A clustered heatmap displaying the highly variable protein intensities across the four clusters detected within this cohort. B, Kaplan–Meier plots of the survival rates of the four clusters. C…
View article: Supplementary Figure 1 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 1 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 1 shows the main pathways associated with lists of proteins of interest. (A) Summary of Enrichment analysis in DisGeNET for the 20-protein panel, showing a highly significant association with pancreatic neoplasm. (B) S…
View article: Supplementary Figure 5 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 5 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 5 shows the proteomic information for the 18 risk-score proteins. (A) Peptide detection rate within the dataset. (B) Protein Missingness in our tumor data only. (C) Protein Missingness in the CPTAC Pancreatic Cancer Co…
View article: Supplementary Figure 16 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 16 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 16 shows a Forest plot presentation of the hazard ratios for each KRT protein detected in the tumor samples from the study cohort, derived from a univariate Cox regression analysis with overall survival as the outcome …
View article: Supplementary Figure 6 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 6 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 6 shows a forest plot detailing the hazard ratio of the proteomic risk score and clinically relevant variables for PDA within our study cohort using multivariable Cox regression modeling.
View article: Supplementary Table 3 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Table 3 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Table 3 shows the differentially abundant proteins, their associated pathways, and the potential drug targets identified across the four proteomic-based clusters.
View article: Supplementary Figure 13 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 13 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 13 shows the differential abundance and pathway enrichment analyses based on HRD status. (A) Volcano plot displaying the differentially abundant proteins between tumor samples with and without HRD. (B) Pathways enriche…
View article: Supplementary Figure 14 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 14 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 14 shows the differential abundance and pathway enrichment analyses based on COSMIC Signature-3 status. (A) Volcano plot displaying the differentially abundant proteins between tumors with and without somatic mutations…
View article: Supplementary Figure 4 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Figure 4 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Figure 4 shows the Kaplan-Meier survival curves for each of the 18 proteins within the risk score. Median cut-off was used to dichotomize patients into two groups.
View article: Supplementary Table 5 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Table 5 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Supplementary Table 5 shows the distribution of clinical variables in the study cohort across the two proteomic-based risk score groups. Gln61Arg: Glutamine to Arginine at position 61, Gln61His: Glutamine to Histidine at position 61, Gly12…
View article: Figure 4 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Figure 4 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
Proteomic risk score for mortality. A, Forest plot detailing the multivariable HRs for the overall survival of each of the proteins used to build the proteomic risk score. B, Kaplan–Meier curve displaying the overall survival…
View article: Supplementary Data 1 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Data 1 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
List of the different differentially abundant proteins for the different groups of interest
View article: Supplementary Data 2 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Supplementary Data 2 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
List of the enriched pathways among the different groups of interest
View article: Figure 1 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification
Figure 1 from Mapping the Proteomic Landscape of Pancreatic Cancer: Prognostic Insights and Subtype Stratification Open
A schematic representing (A) data collection from 30-μm sections of 115 PDA and 61 adjacent normal fresh-frozen tissues that were prepared (19) for (B) DIA-MS. C, Protein quantification from DIA-MS data files using DIA…