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miRNome of inflammatory breast cancer.

Maltseva DV, Galatenko VV, Samatov TR, Zhikrivetskaya SO, Khaustova NA, Nechaev IN, Shkurnikov MU, Lebedev AE, Mityakina IA, Kaprin AD, Schumacher U, Tonevitsky AG - BMC Res Notes (2014)

Bottom Line: Conclusive prognostic IBC molecular biomarkers which are also providing the perspectives for targeted therapy are lacking so far.Bioinformatic analysis was used to reveal IBC-specific miRNAs, deregulated pathways and potential miRNA targets. 31 differentially expressed miRNAs characterize IBC and mRNAs regulated by them and their associated pathways can functionally be attributed to IBC progression.We found that the mRNAs and pathways likely regulated by these miRNAs are highly relevant to cancer progression.

View Article: PubMed Central - PubMed

Affiliation: SRC Bioclinicum, Ugreshskaya str 2/85, 115088 Moscow, Russia. t.samatov@bioclinicum.com.

ABSTRACT

Background: Inflammatory breast cancer (IBC) is an extremely malignant form of breast cancer which can be easily misdiagnosed. Conclusive prognostic IBC molecular biomarkers which are also providing the perspectives for targeted therapy are lacking so far. The aim of this study was to reveal the IBC-specific miRNA expression profile and to evaluate its association with clinicopathological parameters.

Methods: miRNA expression profiles of 13 IBC and 17 non-IBC patients were characterized using comprehensive Affymetrix GeneChip miRNA 3.0 microarray platform. Bioinformatic analysis was used to reveal IBC-specific miRNAs, deregulated pathways and potential miRNA targets.

Results: 31 differentially expressed miRNAs characterize IBC and mRNAs regulated by them and their associated pathways can functionally be attributed to IBC progression. In addition, a minimal predictive set of 4 miRNAs characteristic for the IBC phenotype and associated with the TP53 mutational status in breast cancer patients was identified.

Conclusions: We have characterized the complete miRNome of inflammatory breast cancer and found differentially expressed miRNAs which reliably classify the patients to IBC and non-IBC groups. We found that the mRNAs and pathways likely regulated by these miRNAs are highly relevant to cancer progression. Furthermore a minimal IBC-related predictive set of 4 miRNAs associated with the TP53 mutational status and survival for breast cancer patients was identified.

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Related in: MedlinePlus

Cluster analysis heatmap for 13 IBC and 17 non-IBC samples based on the expression profile of the 31 differentially expressed miRNAs. The expression data are represented in a 2D format, with rows indicating miRNAs and columns indicating samples. High expression values are coded with red color and low expression values are coded with green.
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Fig1: Cluster analysis heatmap for 13 IBC and 17 non-IBC samples based on the expression profile of the 31 differentially expressed miRNAs. The expression data are represented in a 2D format, with rows indicating miRNAs and columns indicating samples. High expression values are coded with red color and low expression values are coded with green.

Mentions: Hierarchical clusterization for the heatmap (FigureĀ 1) was constructed based on the normalized log-scaled expression values (i.e., log-scaled expression values decreased by the mean value and divided by standard deviation) using Euclidean distance and average cluster method. The construction was performed by the Heatmap online service [22] that utilizes the heatmap tool of R package gplots [23]. For the hierarchical sample clusterization the p-value indicating the association of two resulting clusters with IBC status was obtained using one-sided binomial test. This p-value is the probability of observing the same or better classification accuracy for a classifier with no information rate, i.e., a classifier that attributes a sample to a class with the probability equal to the class percentage in the data.Figure 1


miRNome of inflammatory breast cancer.

Maltseva DV, Galatenko VV, Samatov TR, Zhikrivetskaya SO, Khaustova NA, Nechaev IN, Shkurnikov MU, Lebedev AE, Mityakina IA, Kaprin AD, Schumacher U, Tonevitsky AG - BMC Res Notes (2014)

Cluster analysis heatmap for 13 IBC and 17 non-IBC samples based on the expression profile of the 31 differentially expressed miRNAs. The expression data are represented in a 2D format, with rows indicating miRNAs and columns indicating samples. High expression values are coded with red color and low expression values are coded with green.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4289319&req=5

Fig1: Cluster analysis heatmap for 13 IBC and 17 non-IBC samples based on the expression profile of the 31 differentially expressed miRNAs. The expression data are represented in a 2D format, with rows indicating miRNAs and columns indicating samples. High expression values are coded with red color and low expression values are coded with green.
Mentions: Hierarchical clusterization for the heatmap (FigureĀ 1) was constructed based on the normalized log-scaled expression values (i.e., log-scaled expression values decreased by the mean value and divided by standard deviation) using Euclidean distance and average cluster method. The construction was performed by the Heatmap online service [22] that utilizes the heatmap tool of R package gplots [23]. For the hierarchical sample clusterization the p-value indicating the association of two resulting clusters with IBC status was obtained using one-sided binomial test. This p-value is the probability of observing the same or better classification accuracy for a classifier with no information rate, i.e., a classifier that attributes a sample to a class with the probability equal to the class percentage in the data.Figure 1

Bottom Line: Conclusive prognostic IBC molecular biomarkers which are also providing the perspectives for targeted therapy are lacking so far.Bioinformatic analysis was used to reveal IBC-specific miRNAs, deregulated pathways and potential miRNA targets. 31 differentially expressed miRNAs characterize IBC and mRNAs regulated by them and their associated pathways can functionally be attributed to IBC progression.We found that the mRNAs and pathways likely regulated by these miRNAs are highly relevant to cancer progression.

View Article: PubMed Central - PubMed

Affiliation: SRC Bioclinicum, Ugreshskaya str 2/85, 115088 Moscow, Russia. t.samatov@bioclinicum.com.

ABSTRACT

Background: Inflammatory breast cancer (IBC) is an extremely malignant form of breast cancer which can be easily misdiagnosed. Conclusive prognostic IBC molecular biomarkers which are also providing the perspectives for targeted therapy are lacking so far. The aim of this study was to reveal the IBC-specific miRNA expression profile and to evaluate its association with clinicopathological parameters.

Methods: miRNA expression profiles of 13 IBC and 17 non-IBC patients were characterized using comprehensive Affymetrix GeneChip miRNA 3.0 microarray platform. Bioinformatic analysis was used to reveal IBC-specific miRNAs, deregulated pathways and potential miRNA targets.

Results: 31 differentially expressed miRNAs characterize IBC and mRNAs regulated by them and their associated pathways can functionally be attributed to IBC progression. In addition, a minimal predictive set of 4 miRNAs characteristic for the IBC phenotype and associated with the TP53 mutational status in breast cancer patients was identified.

Conclusions: We have characterized the complete miRNome of inflammatory breast cancer and found differentially expressed miRNAs which reliably classify the patients to IBC and non-IBC groups. We found that the mRNAs and pathways likely regulated by these miRNAs are highly relevant to cancer progression. Furthermore a minimal IBC-related predictive set of 4 miRNAs associated with the TP53 mutational status and survival for breast cancer patients was identified.

Show MeSH
Related in: MedlinePlus