Jean-Pascal Gimeno
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Jean-Pascal Gimeno has a total of 23 co-authors. Here are the top co-authors by number of shared publications:
- Maxence Wisztorski
From the ‡Inserm U-1192, Laboratoire de Protéomique, Réponse Inflammatoire, Spectrométrie de Masse (PRISM), Université Lille 1, Cité Scientifique, 59655 Villeneuve D'Ascq, France
8 shared publications - Anne-Frédérique Dessein
UF Métabolisme général et maladies rares, Centre de biologie pathologie génétique, CHU Lille, Lille, France
7 shared publications - Audrey Vincent
Inserm UMR-S 1172, Centre de Recherche Jean-Pierre AUBERT Neurosciences et Cancer (JPArc), Team “Mucins, Epithelial Differentiation and Carcinogenesis”; University Lille; CHU Lille, 59045, Lille CEDEX, France
7 shared publications - Christian Gespach
INSERM U938, Centre de recherche Saint-Antoine and Universit e Pierre-et-Marie-Curie (UPMC), Paris, France; and
7 shared publications - Didier Monté
CNRS UMR 9031 Biologie Structurale Intégrative, Univ. Lille, Institut Pasteur de Lille, F-59000 Lille, France
7 shared publications Guillemette Huet
Universit e Lille, CNRS, Inserm, CHU Lille, UMR9020-UMR1277 -CANTHER -Cancer Heterogeneity, Plasticity and Resistance to Therapies, Lille, France
7 shared publications
Latest Publications
- Open Access
- 3 April 2023
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S3 from Cross-talk between YAP and RAR-RXR Drives Expression of Stemness Genes to Promote 5-FU Resistance and Self-Renewal in Colorectal Cancer Cells
Supplementary Figure S3 shows the validation of the YAP/RAR-RXR crosstalk in control and YAPdc-RKO colorectal cancer cells.
- 3 April 2023
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S3 from Cross-talk between YAP and RAR-RXR Drives Expression of Stemness Genes to Promote 5-FU Resistance and Self-Renewal in Colorectal Cancer Cells
Supplementary Figure S3 shows the validation of the YAP/RAR-RXR crosstalk in control and YAPdc-RKO colorectal cancer cells.
- Open Access
- 3 April 2023
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S1 from Cross-talk between YAP and RAR-RXR Drives Expression of Stemness Genes to Promote 5-FU Resistance and Self-Renewal in Colorectal Cancer Cells
Supplementary Figure S1 identifies YAP/RXRalpha and YAP/RARgamma molecular complexes in HT29-YAPdc cells expressing activated nuclear YAP
- 3 April 2023
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S1 from Cross-talk between YAP and RAR-RXR Drives Expression of Stemness Genes to Promote 5-FU Resistance and Self-Renewal in Colorectal Cancer Cells
Supplementary Figure S1 identifies YAP/RXRalpha and YAP/RARgamma molecular complexes in HT29-YAPdc cells expressing activated nuclear YAP
- Open Access
- 3 April 2023
- Published by American Association for Cancer Research (AACR)
Supplementary Table S2 from Cross-talk between YAP and RAR-RXR Drives Expression of Stemness Genes to Promote 5-FU Resistance and Self-Renewal in Colorectal Cancer Cells
Supplementary Table S2 presents the detailed proteomics of nuclear YAP-interactome identified in HT29-YAPdc cells
- 3 April 2023
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table S2 from Cross-talk between YAP and RAR-RXR Drives Expression of Stemness Genes to Promote 5-FU Resistance and Self-Renewal in Colorectal Cancer Cells
Supplementary Table S2 presents the detailed proteomics of nuclear YAP-interactome identified in HT29-YAPdc cells
- Open Access
Overall patient’s survival of glioblastoma associated to molecular markers: a pan-proteomic prospective study
SUMMARY: Molecular heterogeneities are a key feature of glioblastoma (GBM) pathology impeding patient’s stratification and leading to high discrepancies between patients mean survivals. Here, we established a molecular classification of GBM tumors using a pan-proteomic analysis. Then, we identified, from our proteomic data, 2 clusters of biomarkers associated with good or bad patient survival from 46 IDH wild-type GBMs. Three molecular groups have been identified and associated with systemic biology analyses. Group A tumors exhibit neurogenesis characteristics and tumorigenesis. Group B shows a strong immune cell signature and express poor prognosis markers while group C tumors are characterized by an anti-viral signature and tumor growth proteins. 124 proteins were found statistically different based on patient’s survival times, of which 10 are issued from alternative AltORF or non-coding RNA. After statistical analysis, a panel of markers associated to higher survival (PPP1R12A, RPS14, HSPD1 and LASP1) and another panel associated to lower survival (ALCAM, ANXA11, MAOB, IP_652563 and IGHM) has been validated by immunofluorescence. Taken together, our data will guide GBM prognosis and help to improve the current GBM classification by stratifying the patients and may open new opportunities for therapeutic development. Significance: Glioblastoma are very heterogeneous tumors with median survivals usually inferior to 20 months. We conducted a pan-proteomics analysis of glioblastoma (GBM) in order to stratify GBM based on the molecular contained. Forty-six GBM cases were classified into three groups where proteins are involved in specific pathways i.e. the first group has a neurogenesis signature and is associated with a better prognosis while the second group of patients has an immune profile with a bad prognosis. The third group is more associated to tumorigenesis. We correlated these results with the TCGA data. Finally, we have identified 28 new prognostic markers of GBM and from these 28, a panel of 4 higher and 5 lower survival markers were validated. With these 9 markers in hand, now pathologist can stratify GBM patients and can guide the therapeutic decision. Highlights: A novel stratification of glioblastoma based on mass spectrometry was established. Three groups with different molecular features and survival were identified. This new classification could improve prognostication and may help therapeutic options. 8 prognosis markers for oncologist therapeutic decision have been validated.
Open AccessOverall patient’s survival of glioblastoma associated to molecular markers: a pan-proteomic prospective study
SUMMARY: Molecular heterogeneities are a key feature of glioblastoma (GBM) pathology impeding patient’s stratification and leading to high discrepancies between patients mean survivals. Here, we established a molecular classification of GBM tumors using a pan-proteomic analysis. Then, we identified, from our proteomic data, 2 clusters of biomarkers associated with good or bad patient survival from 46 IDH wild-type GBMs. Three molecular groups have been identified and associated with systemic biology analyses. Group A tumors exhibit neurogenesis characteristics and tumorigenesis. Group B shows a strong immune cell signature and express poor prognosis markers while group C tumors are characterized by an anti-viral signature and tumor growth proteins. 124 proteins were found statistically different based on patient’s survival times, of which 10 are issued from alternative AltORF or non-coding RNA. After statistical analysis, a panel of markers associated to higher survival (PPP1R12A, RPS14, HSPD1 and LASP1) and another panel associated to lower survival (ALCAM, ANXA11, MAOB, IP_652563 and IGHM) has been validated by immunofluorescence. Taken together, our data will guide GBM prognosis and help to improve the current GBM classification by stratifying the patients and may open new opportunities for therapeutic development. Significance: Glioblastoma are very heterogeneous tumors with median survivals usually inferior to 20 months. We conducted a pan-proteomics analysis of glioblastoma (GBM) in order to stratify GBM based on the molecular contained. Forty-six GBM cases were classified into three groups where proteins are involved in specific pathways i.e. the first group has a neurogenesis signature and is associated with a better prognosis while the second group of patients has an immune profile with a bad prognosis. The third group is more associated to tumorigenesis. We correlated these results with the TCGA data. Finally, we have identified 28 new prognostic markers of GBM and from these 28, a panel of 4 higher and 5 lower survival markers were validated. With these 9 markers in hand, now pathologist can stratify GBM patients and can guide the therapeutic decision. Highlights: A novel stratification of glioblastoma based on mass spectrometry was established. Three groups with different molecular features and survival were identified. This new classification could improve prognostication and may help therapeutic options. 8 prognosis markers for oncologist therapeutic decision have been validated.