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Gene expression profiling leads to discovery of correlation of matrix metalloproteinase 11 and heparanase 2 in breast cancer progression.

Fu J, Khaybullin R, Zhang Y, Xia A, Qi X - BMC Cancer (2015)

Bottom Line: A group of 28 promising genes were identified with ≥ 10-fold changes of expression level and p-values < 0.05.In particular, MMP11 and HPSE2 were closely examined due to the important roles they play in cancer cell growth and migration.Real-time RT-qPCR analyses of both training and testing sets validated the gene expression profiles of MMP11 and HPSE2.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicinal Chemistry, College of Pharmacy, University of Florida, 1600 SW Archer Rd, Health Science Center P5-31, Gainesville, FL, 32610, USA. jfu@ufl.edu.

ABSTRACT

Background: In order to identify biomarkers involved in breast cancer, gene expression profiling was conducted using human breast cancer tissues.

Methods: Total RNAs were extracted from 150 clinical patient tissues covering three breast cancer subtypes (Luminal A, Luminal B, and Triple negative) as well as normal tissues. The expression profiles of a total of 50,739 genes were established from a training set of 32 samples using the Agilent Sure Print G3 Human Gene Expression Microarray technology. Data were analyzed using Agilent Gene Spring GX 12.6 software. The expression of several genes was validated using real-time RT-qPCR.

Results: Data analysis with Agilent GeneSpring GX 12.6 software showed distinct expression patterns between cancer and normal tissue samples. A group of 28 promising genes were identified with ≥ 10-fold changes of expression level and p-values < 0.05. In particular, MMP11 and HPSE2 were closely examined due to the important roles they play in cancer cell growth and migration. Real-time RT-qPCR analyses of both training and testing sets validated the gene expression profiles of MMP11 and HPSE2.

Conclusions: Our findings identified these 2 genes as a novel breast cancer biomarker gene set, which may facilitate the diagnosis and treatment in breast cancer clinical therapies.

No MeSH data available.


Related in: MedlinePlus

Heat map showing gene expression patterns of MMP1, MMP9, MMP11, HPSE, and HPSE2. The heat map indicates up-regulation (red), down-regulation (green), and mean gene expression (black). The columns represent individual tissue samples covering three breast cancer subtypes: Luminal A (red), Luminal B (yellow), and Triple negative (purple) as well as normal samples (blue). The rows are labeled with individual gene symbols
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Fig8: Heat map showing gene expression patterns of MMP1, MMP9, MMP11, HPSE, and HPSE2. The heat map indicates up-regulation (red), down-regulation (green), and mean gene expression (black). The columns represent individual tissue samples covering three breast cancer subtypes: Luminal A (red), Luminal B (yellow), and Triple negative (purple) as well as normal samples (blue). The rows are labeled with individual gene symbols

Mentions: As shown in Table 2, both MMP11 and HPSE2 were found on the top 28 genes list with fold changes ≥ 10. Consistent with the above notion, our gene expression profiling results showed that while MMP11 was up-regulated by 12.45 to 50.45 folds in breast cancer tissue samples compared with normal controls, HPSE2 was down-regulated by −15.26 to −29.29 folds (Table 2). The Box-and-Whisker plots for the normalized intensity (NI) values of these 2 genes are shown in Fig. 7, which highlights the important features and shows the variations of the gene expression in each subgroup. In addition, another 2 well-studied genes in the MMPs family, MMP1 and MMP9, were also found to be up-regulated (2.22–21.18 and 3.56–21.41 folds, respectively) from our gene expression microarray data, although they were not in the top 28 genes list. With regards to HPSE, it was slightly up-regulated in Luminal A and Triple negative samples (2.40 and 2.48 folds, respectively), but down-regulated (−1.33 folds) in Luminal B subtype, suggesting that HPSE was not a suitable biomarker. The heat map for these genes is shown in Fig. 8.Fig. 7


Gene expression profiling leads to discovery of correlation of matrix metalloproteinase 11 and heparanase 2 in breast cancer progression.

Fu J, Khaybullin R, Zhang Y, Xia A, Qi X - BMC Cancer (2015)

Heat map showing gene expression patterns of MMP1, MMP9, MMP11, HPSE, and HPSE2. The heat map indicates up-regulation (red), down-regulation (green), and mean gene expression (black). The columns represent individual tissue samples covering three breast cancer subtypes: Luminal A (red), Luminal B (yellow), and Triple negative (purple) as well as normal samples (blue). The rows are labeled with individual gene symbols
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4477316&req=5

Fig8: Heat map showing gene expression patterns of MMP1, MMP9, MMP11, HPSE, and HPSE2. The heat map indicates up-regulation (red), down-regulation (green), and mean gene expression (black). The columns represent individual tissue samples covering three breast cancer subtypes: Luminal A (red), Luminal B (yellow), and Triple negative (purple) as well as normal samples (blue). The rows are labeled with individual gene symbols
Mentions: As shown in Table 2, both MMP11 and HPSE2 were found on the top 28 genes list with fold changes ≥ 10. Consistent with the above notion, our gene expression profiling results showed that while MMP11 was up-regulated by 12.45 to 50.45 folds in breast cancer tissue samples compared with normal controls, HPSE2 was down-regulated by −15.26 to −29.29 folds (Table 2). The Box-and-Whisker plots for the normalized intensity (NI) values of these 2 genes are shown in Fig. 7, which highlights the important features and shows the variations of the gene expression in each subgroup. In addition, another 2 well-studied genes in the MMPs family, MMP1 and MMP9, were also found to be up-regulated (2.22–21.18 and 3.56–21.41 folds, respectively) from our gene expression microarray data, although they were not in the top 28 genes list. With regards to HPSE, it was slightly up-regulated in Luminal A and Triple negative samples (2.40 and 2.48 folds, respectively), but down-regulated (−1.33 folds) in Luminal B subtype, suggesting that HPSE was not a suitable biomarker. The heat map for these genes is shown in Fig. 8.Fig. 7

Bottom Line: A group of 28 promising genes were identified with ≥ 10-fold changes of expression level and p-values < 0.05.In particular, MMP11 and HPSE2 were closely examined due to the important roles they play in cancer cell growth and migration.Real-time RT-qPCR analyses of both training and testing sets validated the gene expression profiles of MMP11 and HPSE2.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicinal Chemistry, College of Pharmacy, University of Florida, 1600 SW Archer Rd, Health Science Center P5-31, Gainesville, FL, 32610, USA. jfu@ufl.edu.

ABSTRACT

Background: In order to identify biomarkers involved in breast cancer, gene expression profiling was conducted using human breast cancer tissues.

Methods: Total RNAs were extracted from 150 clinical patient tissues covering three breast cancer subtypes (Luminal A, Luminal B, and Triple negative) as well as normal tissues. The expression profiles of a total of 50,739 genes were established from a training set of 32 samples using the Agilent Sure Print G3 Human Gene Expression Microarray technology. Data were analyzed using Agilent Gene Spring GX 12.6 software. The expression of several genes was validated using real-time RT-qPCR.

Results: Data analysis with Agilent GeneSpring GX 12.6 software showed distinct expression patterns between cancer and normal tissue samples. A group of 28 promising genes were identified with ≥ 10-fold changes of expression level and p-values < 0.05. In particular, MMP11 and HPSE2 were closely examined due to the important roles they play in cancer cell growth and migration. Real-time RT-qPCR analyses of both training and testing sets validated the gene expression profiles of MMP11 and HPSE2.

Conclusions: Our findings identified these 2 genes as a novel breast cancer biomarker gene set, which may facilitate the diagnosis and treatment in breast cancer clinical therapies.

No MeSH data available.


Related in: MedlinePlus