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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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Principal component analysis can separate normal and tumor tissue.(A) Scatterplot of the first three principal components of PCA from the protein expression data. The blue stars represent the normal tissues, whereas the red stars show tumors. (B) Distribution of information with respect to differential expression between tumor and normal tissues. Each cross represents a protein. The p-values in two-sided t-test are represented by the x-axis, whereas the projections (red: projection onto S3, blue: projection onto complementary space) are represented by the values on the y-axis. Apparently for all proteins with significant differential expression in the original data (log10(p)<−2) the differential expression of the residual component (blue stars) is not significant (log10(p)<−1), whereas the p-values of the PCA-based components (red stars) are similar to the original p-values (x-axis). (C–D) Median accuracy and odds ratio of predictive tumor/normal classification. The blue curves show the increase of model quality by increased sample size used for biomarker model. The red stars show the qualities of the logit model based on the first three principal components. (E) The output of the regression model (y-axis) indicates the existence of two tumor classes differing significantly according to their separability. Normal tissues (blue and green boxes) using protein expression.
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pone-0016833-g003: Principal component analysis can separate normal and tumor tissue.(A) Scatterplot of the first three principal components of PCA from the protein expression data. The blue stars represent the normal tissues, whereas the red stars show tumors. (B) Distribution of information with respect to differential expression between tumor and normal tissues. Each cross represents a protein. The p-values in two-sided t-test are represented by the x-axis, whereas the projections (red: projection onto S3, blue: projection onto complementary space) are represented by the values on the y-axis. Apparently for all proteins with significant differential expression in the original data (log10(p)<−2) the differential expression of the residual component (blue stars) is not significant (log10(p)<−1), whereas the p-values of the PCA-based components (red stars) are similar to the original p-values (x-axis). (C–D) Median accuracy and odds ratio of predictive tumor/normal classification. The blue curves show the increase of model quality by increased sample size used for biomarker model. The red stars show the qualities of the logit model based on the first three principal components. (E) The output of the regression model (y-axis) indicates the existence of two tumor classes differing significantly according to their separability. Normal tissues (blue and green boxes) using protein expression.

Mentions: A 3-dimensional scatterplot of the first three principal components of the tissue samples shows a good separation between tumors and normal tissues (Figure 3A). Moreover, figure 3B, depicting the logarithmic p-values of the original data xi for each protein on the x-axis and the components xp,i and xr,i on the y-axis, shows that almost all differential expression can be reflected by the PCA-based component (red stars).


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)

Principal component analysis can separate normal and tumor tissue.(A) Scatterplot of the first three principal components of PCA from the protein expression data. The blue stars represent the normal tissues, whereas the red stars show tumors. (B) Distribution of information with respect to differential expression between tumor and normal tissues. Each cross represents a protein. The p-values in two-sided t-test are represented by the x-axis, whereas the projections (red: projection onto S3, blue: projection onto complementary space) are represented by the values on the y-axis. Apparently for all proteins with significant differential expression in the original data (log10(p)<−2) the differential expression of the residual component (blue stars) is not significant (log10(p)<−1), whereas the p-values of the PCA-based components (red stars) are similar to the original p-values (x-axis). (C–D) Median accuracy and odds ratio of predictive tumor/normal classification. The blue curves show the increase of model quality by increased sample size used for biomarker model. The red stars show the qualities of the logit model based on the first three principal components. (E) The output of the regression model (y-axis) indicates the existence of two tumor classes differing significantly according to their separability. Normal tissues (blue and green boxes) using protein expression.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0016833-g003: Principal component analysis can separate normal and tumor tissue.(A) Scatterplot of the first three principal components of PCA from the protein expression data. The blue stars represent the normal tissues, whereas the red stars show tumors. (B) Distribution of information with respect to differential expression between tumor and normal tissues. Each cross represents a protein. The p-values in two-sided t-test are represented by the x-axis, whereas the projections (red: projection onto S3, blue: projection onto complementary space) are represented by the values on the y-axis. Apparently for all proteins with significant differential expression in the original data (log10(p)<−2) the differential expression of the residual component (blue stars) is not significant (log10(p)<−1), whereas the p-values of the PCA-based components (red stars) are similar to the original p-values (x-axis). (C–D) Median accuracy and odds ratio of predictive tumor/normal classification. The blue curves show the increase of model quality by increased sample size used for biomarker model. The red stars show the qualities of the logit model based on the first three principal components. (E) The output of the regression model (y-axis) indicates the existence of two tumor classes differing significantly according to their separability. Normal tissues (blue and green boxes) using protein expression.
Mentions: A 3-dimensional scatterplot of the first three principal components of the tissue samples shows a good separation between tumors and normal tissues (Figure 3A). Moreover, figure 3B, depicting the logarithmic p-values of the original data xi for each protein on the x-axis and the components xp,i and xr,i on the y-axis, shows that almost all differential expression can be reflected by the PCA-based component (red stars).

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