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Identification of clinically relevant protein targets in prostate cancer with 2D-DIGE coupled mass spectrometry and systems biology network platform.

Ummanni R, Mundt F, Pospisil H, Venz S, Scharf C, Barett C, Fälth M, Köllermann J, Walther R, Schlomm T, Sauter G, Bokemeyer C, Sültmann H, Schuppert A, Brümmendorf TH, Balabanov S - PLoS ONE (2011)

Bottom Line: Prostate cancer (PCa) is the most common type of cancer found in men and among the leading causes of cancer death in the western world.Proteomic data revealed 118 protein spots to be differentially expressed in cancer (n = 24) compared to benign (n = 21) prostate tissue.Further functional validation of individual proteins is ongoing and might provide new insights in PCa progression potentially leading to the design of novel diagnostic and therapeutic strategies.

View Article: PubMed Central - PubMed

Affiliation: Department of Oncology, Haematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald-Tumor Zentrum, University Hospital Eppendorf, Hamburg, Germany.

ABSTRACT
Prostate cancer (PCa) is the most common type of cancer found in men and among the leading causes of cancer death in the western world. In the present study, we compared the individual protein expression patterns from histologically characterized PCa and the surrounding benign tissue obtained by manual micro dissection using highly sensitive two-dimensional differential gel electrophoresis (2D-DIGE) coupled with mass spectrometry. Proteomic data revealed 118 protein spots to be differentially expressed in cancer (n = 24) compared to benign (n = 21) prostate tissue. These spots were analysed by MALDI-TOF-MS/MS and 79 different proteins were identified. Using principal component analysis we could clearly separate tumor and normal tissue and two distinct tumor groups based on the protein expression pattern. By using a systems biology approach, we could map many of these proteins both into major pathways involved in PCa progression as well as into a group of potential diagnostic and/or prognostic markers. Due to complexity of the highly interconnected shortest pathway network, the functional sub networks revealed some of the potential candidate biomarker proteins for further validation. By using a systems biology approach, our study revealed novel proteins and molecular networks with altered expression in PCa. Further functional validation of individual proteins is ongoing and might provide new insights in PCa progression potentially leading to the design of novel diagnostic and therapeutic strategies.

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Related in: MedlinePlus

Protein subnetworks of differentially expressed proteins in PCa.(A–D) Protein-protein physical/functional interaction sub networks generated by Ingenuity Pathway Analysis tool. Grey filled boxes are the differentially expressed proteins. Only significant sub networks were shown in the figure.
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pone-0016833-g006: Protein subnetworks of differentially expressed proteins in PCa.(A–D) Protein-protein physical/functional interaction sub networks generated by Ingenuity Pathway Analysis tool. Grey filled boxes are the differentially expressed proteins. Only significant sub networks were shown in the figure.

Mentions: The next significant sub network involves many known proteins to be associated with PCa which may also provide new target proteins which need to be characterized further. This network is probably involved in apoptosis, protein metabolic processes and Ca2+ signalling pathways. The sub networks derived from the proteomic data using Ingenuity Pathway Analysis have many common proteins connected with the important hubs such as AR, c-Myc, ERS1, Akt/PKB and their role in PCa progression or potential as disease markers is not known yet (Figure 6A–D). Protein network analysis and clustering of differentially expressed proteins revealed new targets such as DDAH1, ARG2, EIF4A3, Par4, PPA2, Prdx3 and Prdx4, which need further validation to define their potential application in clinical relevance in prostate cancer.


Identification of clinically relevant protein targets in prostate cancer with 2D-DIGE coupled mass spectrometry and systems biology network platform.

Ummanni R, Mundt F, Pospisil H, Venz S, Scharf C, Barett C, Fälth M, Köllermann J, Walther R, Schlomm T, Sauter G, Bokemeyer C, Sültmann H, Schuppert A, Brümmendorf TH, Balabanov S - PLoS ONE (2011)

Protein subnetworks of differentially expressed proteins in PCa.(A–D) Protein-protein physical/functional interaction sub networks generated by Ingenuity Pathway Analysis tool. Grey filled boxes are the differentially expressed proteins. Only significant sub networks were shown in the figure.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3037937&req=5

pone-0016833-g006: Protein subnetworks of differentially expressed proteins in PCa.(A–D) Protein-protein physical/functional interaction sub networks generated by Ingenuity Pathway Analysis tool. Grey filled boxes are the differentially expressed proteins. Only significant sub networks were shown in the figure.
Mentions: The next significant sub network involves many known proteins to be associated with PCa which may also provide new target proteins which need to be characterized further. This network is probably involved in apoptosis, protein metabolic processes and Ca2+ signalling pathways. The sub networks derived from the proteomic data using Ingenuity Pathway Analysis have many common proteins connected with the important hubs such as AR, c-Myc, ERS1, Akt/PKB and their role in PCa progression or potential as disease markers is not known yet (Figure 6A–D). Protein network analysis and clustering of differentially expressed proteins revealed new targets such as DDAH1, ARG2, EIF4A3, Par4, PPA2, Prdx3 and Prdx4, which need further validation to define their potential application in clinical relevance in prostate cancer.

Bottom Line: Prostate cancer (PCa) is the most common type of cancer found in men and among the leading causes of cancer death in the western world.Proteomic data revealed 118 protein spots to be differentially expressed in cancer (n = 24) compared to benign (n = 21) prostate tissue.Further functional validation of individual proteins is ongoing and might provide new insights in PCa progression potentially leading to the design of novel diagnostic and therapeutic strategies.

View Article: PubMed Central - PubMed

Affiliation: Department of Oncology, Haematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald-Tumor Zentrum, University Hospital Eppendorf, Hamburg, Germany.

ABSTRACT
Prostate cancer (PCa) is the most common type of cancer found in men and among the leading causes of cancer death in the western world. In the present study, we compared the individual protein expression patterns from histologically characterized PCa and the surrounding benign tissue obtained by manual micro dissection using highly sensitive two-dimensional differential gel electrophoresis (2D-DIGE) coupled with mass spectrometry. Proteomic data revealed 118 protein spots to be differentially expressed in cancer (n = 24) compared to benign (n = 21) prostate tissue. These spots were analysed by MALDI-TOF-MS/MS and 79 different proteins were identified. Using principal component analysis we could clearly separate tumor and normal tissue and two distinct tumor groups based on the protein expression pattern. By using a systems biology approach, we could map many of these proteins both into major pathways involved in PCa progression as well as into a group of potential diagnostic and/or prognostic markers. Due to complexity of the highly interconnected shortest pathway network, the functional sub networks revealed some of the potential candidate biomarker proteins for further validation. By using a systems biology approach, our study revealed novel proteins and molecular networks with altered expression in PCa. Further functional validation of individual proteins is ongoing and might provide new insights in PCa progression potentially leading to the design of novel diagnostic and therapeutic strategies.

Show MeSH
Related in: MedlinePlus