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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

Schematic representation of the breast cancer gene expression profiling study. A total number of 150 tissue samples were examined. After RNA extraction and purification, 32 RNA samples were selected as the training set for microarray gene expression profiling. The remaining 118 samples were used as a testing set to validate the gene expression results by real-time RT-qPCR
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Fig1: Schematic representation of the breast cancer gene expression profiling study. A total number of 150 tissue samples were examined. After RNA extraction and purification, 32 RNA samples were selected as the training set for microarray gene expression profiling. The remaining 118 samples were used as a testing set to validate the gene expression results by real-time RT-qPCR

Mentions: The 150 tissue samples in the study included all three subtypes of breast cancer (Luminal A, Lumina B, Triple negative) as well as normal tissue samples. After RNA extraction and purification, 32 RNA samples (19 from Luminal A, 3 from Luminal B, 3 from Triple negative, and 7 normal samples) were selected as the training set for microarray gene expression profiling. The remaining 118 samples were used as a testing set to validate the gene expression results from the training set (Fig. 1). In the training set, 2 samples C501 (Luminal A) and N513 (normal) were from the same patient, others are unmatched samples.Fig. 1


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)

Schematic representation of the breast cancer gene expression profiling study. A total number of 150 tissue samples were examined. After RNA extraction and purification, 32 RNA samples were selected as the training set for microarray gene expression profiling. The remaining 118 samples were used as a testing set to validate the gene expression results by real-time RT-qPCR
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig1: Schematic representation of the breast cancer gene expression profiling study. A total number of 150 tissue samples were examined. After RNA extraction and purification, 32 RNA samples were selected as the training set for microarray gene expression profiling. The remaining 118 samples were used as a testing set to validate the gene expression results by real-time RT-qPCR
Mentions: The 150 tissue samples in the study included all three subtypes of breast cancer (Luminal A, Lumina B, Triple negative) as well as normal tissue samples. After RNA extraction and purification, 32 RNA samples (19 from Luminal A, 3 from Luminal B, 3 from Triple negative, and 7 normal samples) were selected as the training set for microarray gene expression profiling. The remaining 118 samples were used as a testing set to validate the gene expression results from the training set (Fig. 1). In the training set, 2 samples C501 (Luminal A) and N513 (normal) were from the same patient, others are unmatched samples.Fig. 1

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