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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

Volcano plots. The distribution of the gene expression fold changes and corrected p-values in each subgroup a Luminal A, b Luminal B, and c Triple negative compared with normal controls were shown. A total number of 4569 genes with p-value < 0.05 were used for the analysis. Genes with absolute fold change ≥ 10 and p-value < 0.05 are indicated in red. Plots are generated using Gene Spring 12.6 with moderated t-test and Benjamini-Hochberg testing correction
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Fig2: Volcano plots. The distribution of the gene expression fold changes and corrected p-values in each subgroup a Luminal A, b Luminal B, and c Triple negative compared with normal controls were shown. A total number of 4569 genes with p-value < 0.05 were used for the analysis. Genes with absolute fold change ≥ 10 and p-value < 0.05 are indicated in red. Plots are generated using Gene Spring 12.6 with moderated t-test and Benjamini-Hochberg testing correction

Mentions: Next, the gene expression fold changes were further constrained to be ≥ 10 while still keeping the corrected p-value < 0.05. The distributions of the fold changes and p-values of genes in each subgroup were shown in Fig. 2 as volcano plots. Moreover, 28 genes were identified to show fold changes ≥ 10 in all three breast cancer subtypes. Figure 3 shows the heat map [31] representing the gene expression profiling of these 28 genes. Cancer samples are shown on the left grouped by breast cancer subtypes, while normal controls are displayed on the right. The detailed fold change values of these 28 genes are listed in Table 2. The gene regulation patterns of all 28 genes were consistent among the three breast cancer subtypes. Interestingly, most of these genes (25 genes) were down-regulated, and only 3 genes (COL10A1, MMP11, and TUBB3) were up-regulated in cancer tissues.Fig. 2


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)

Volcano plots. The distribution of the gene expression fold changes and corrected p-values in each subgroup a Luminal A, b Luminal B, and c Triple negative compared with normal controls were shown. A total number of 4569 genes with p-value < 0.05 were used for the analysis. Genes with absolute fold change ≥ 10 and p-value < 0.05 are indicated in red. Plots are generated using Gene Spring 12.6 with moderated t-test and Benjamini-Hochberg testing correction
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Volcano plots. The distribution of the gene expression fold changes and corrected p-values in each subgroup a Luminal A, b Luminal B, and c Triple negative compared with normal controls were shown. A total number of 4569 genes with p-value < 0.05 were used for the analysis. Genes with absolute fold change ≥ 10 and p-value < 0.05 are indicated in red. Plots are generated using Gene Spring 12.6 with moderated t-test and Benjamini-Hochberg testing correction
Mentions: Next, the gene expression fold changes were further constrained to be ≥ 10 while still keeping the corrected p-value < 0.05. The distributions of the fold changes and p-values of genes in each subgroup were shown in Fig. 2 as volcano plots. Moreover, 28 genes were identified to show fold changes ≥ 10 in all three breast cancer subtypes. Figure 3 shows the heat map [31] representing the gene expression profiling of these 28 genes. Cancer samples are shown on the left grouped by breast cancer subtypes, while normal controls are displayed on the right. The detailed fold change values of these 28 genes are listed in Table 2. The gene regulation patterns of all 28 genes were consistent among the three breast cancer subtypes. Interestingly, most of these genes (25 genes) were down-regulated, and only 3 genes (COL10A1, MMP11, and TUBB3) were up-regulated in cancer tissues.Fig. 2

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