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Transcriptional profiling of peripheral blood mononuclear cells in pancreatic cancer patients identifies novel genes with potential diagnostic utility.

Baine MJ, Chakraborty S, Smith LM, Mallya K, Sasson AR, Brand RE, Batra SK - PLoS ONE (2011)

Bottom Line: Three hundred eighty-three genes were found to be significantly different between PC and healthy controls, with 65 having at least a 1.5 fold change in expression.Pathway analysis revealed that many of these genes fell into pathways responsible for hematopoietic differentiation, cytokine signaling, and natural killer (NK) cell and CD8+ T-cell cytotoxic response.We have also identified a gene predictor set that can potentially be developed further for use in diagnostic algorithms in PC.

View Article: PubMed Central - PubMed

Affiliation: Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center Omaha, Omaha, Nebraska, United States of America.

ABSTRACT

Background: It is well known that many malignancies, including pancreatic cancer (PC), possess the ability to evade the immune system by indirectly downregulating the mononuclear cell machinery necessary to launch an effective immune response. This knowledge, in conjunction with the fact that the trancriptome of peripheral blood mononuclear cells has been shown to be altered in the context of many diseases, including renal cell carcinoma, lead us to study if any such alteration in gene expression exists in PC as it may have diagnostic utility.

Methods and findings: PBMC samples from 26 PC patients and 33 matched healthy controls were analyzed by whole genome cDNA microarray. Three hundred eighty-three genes were found to be significantly different between PC and healthy controls, with 65 having at least a 1.5 fold change in expression. Pathway analysis revealed that many of these genes fell into pathways responsible for hematopoietic differentiation, cytokine signaling, and natural killer (NK) cell and CD8+ T-cell cytotoxic response. Unsupervised hierarchical clustering analysis identified an eight-gene predictor set, consisting of SSBP2, Ube2b-rs1, CA5B, F5, TBC1D8, ANXA3, ARG1, and ADAMTS20, that could distinguish PC patients from healthy controls with an accuracy of 79% in a blinded subset of samples from treatment naïve patients, giving a sensitivity of 83% and a specificity of 75%.

Conclusions: In summary, we report the first in-depth comparison of global gene expression profiles of PBMCs between PC patients and healthy controls. We have also identified a gene predictor set that can potentially be developed further for use in diagnostic algorithms in PC. Future directions of this research should include analysis of PBMC expression profiles in patients with chronic pancreatitis as well as increasing the number of early-stage patients to assess the utility of PBMCs in the early diagnosis of PC.

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Related in: MedlinePlus

Dendrogram of sample relatedness.A dendrogram of sample relatedness from the cluster analysis shown in Figure 1 using the statistically significant differentially expressed genes. Samples clustered into main groups, aligning well with classification of PC or HC. PC PBMC samples are indicated by grey bars while healthy PBMC samples are denoted by yellow bars.
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pone-0017014-g002: Dendrogram of sample relatedness.A dendrogram of sample relatedness from the cluster analysis shown in Figure 1 using the statistically significant differentially expressed genes. Samples clustered into main groups, aligning well with classification of PC or HC. PC PBMC samples are indicated by grey bars while healthy PBMC samples are denoted by yellow bars.

Mentions: A hierarchical clustering of the microarray data identified two clusters of samples, shown in Figure 1 and in dendrogram form in Figure 2, a PC group and a healthy control group. Two PC samples however clustered with the healthy controls, while one healthy control fell into the cluster containing the majority (32/35) of the PC samples. Additionally, the gene expression profile of one PC sample did not cluster with either the healthy controls or the other PC samples.


Transcriptional profiling of peripheral blood mononuclear cells in pancreatic cancer patients identifies novel genes with potential diagnostic utility.

Baine MJ, Chakraborty S, Smith LM, Mallya K, Sasson AR, Brand RE, Batra SK - PLoS ONE (2011)

Dendrogram of sample relatedness.A dendrogram of sample relatedness from the cluster analysis shown in Figure 1 using the statistically significant differentially expressed genes. Samples clustered into main groups, aligning well with classification of PC or HC. PC PBMC samples are indicated by grey bars while healthy PBMC samples are denoted by yellow bars.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3037404&req=5

pone-0017014-g002: Dendrogram of sample relatedness.A dendrogram of sample relatedness from the cluster analysis shown in Figure 1 using the statistically significant differentially expressed genes. Samples clustered into main groups, aligning well with classification of PC or HC. PC PBMC samples are indicated by grey bars while healthy PBMC samples are denoted by yellow bars.
Mentions: A hierarchical clustering of the microarray data identified two clusters of samples, shown in Figure 1 and in dendrogram form in Figure 2, a PC group and a healthy control group. Two PC samples however clustered with the healthy controls, while one healthy control fell into the cluster containing the majority (32/35) of the PC samples. Additionally, the gene expression profile of one PC sample did not cluster with either the healthy controls or the other PC samples.

Bottom Line: Three hundred eighty-three genes were found to be significantly different between PC and healthy controls, with 65 having at least a 1.5 fold change in expression.Pathway analysis revealed that many of these genes fell into pathways responsible for hematopoietic differentiation, cytokine signaling, and natural killer (NK) cell and CD8+ T-cell cytotoxic response.We have also identified a gene predictor set that can potentially be developed further for use in diagnostic algorithms in PC.

View Article: PubMed Central - PubMed

Affiliation: Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center Omaha, Omaha, Nebraska, United States of America.

ABSTRACT

Background: It is well known that many malignancies, including pancreatic cancer (PC), possess the ability to evade the immune system by indirectly downregulating the mononuclear cell machinery necessary to launch an effective immune response. This knowledge, in conjunction with the fact that the trancriptome of peripheral blood mononuclear cells has been shown to be altered in the context of many diseases, including renal cell carcinoma, lead us to study if any such alteration in gene expression exists in PC as it may have diagnostic utility.

Methods and findings: PBMC samples from 26 PC patients and 33 matched healthy controls were analyzed by whole genome cDNA microarray. Three hundred eighty-three genes were found to be significantly different between PC and healthy controls, with 65 having at least a 1.5 fold change in expression. Pathway analysis revealed that many of these genes fell into pathways responsible for hematopoietic differentiation, cytokine signaling, and natural killer (NK) cell and CD8+ T-cell cytotoxic response. Unsupervised hierarchical clustering analysis identified an eight-gene predictor set, consisting of SSBP2, Ube2b-rs1, CA5B, F5, TBC1D8, ANXA3, ARG1, and ADAMTS20, that could distinguish PC patients from healthy controls with an accuracy of 79% in a blinded subset of samples from treatment naïve patients, giving a sensitivity of 83% and a specificity of 75%.

Conclusions: In summary, we report the first in-depth comparison of global gene expression profiles of PBMCs between PC patients and healthy controls. We have also identified a gene predictor set that can potentially be developed further for use in diagnostic algorithms in PC. Future directions of this research should include analysis of PBMC expression profiles in patients with chronic pancreatitis as well as increasing the number of early-stage patients to assess the utility of PBMCs in the early diagnosis of PC.

Show MeSH
Related in: MedlinePlus