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Identification of phenotype deterministic genes using systemic analysis of transcriptional response.

Lee J, Park J, Choi C - Sci Rep (2014)

Bottom Line: We applied this method to identify causative genes associated with chemo-sensitivity to tamoxifen and epirubicin.Finally, we discovered two genes responsible for tamoxifen sensitivity and three genes associated with epirubicin sensitivity.The method we propose here can be widely applied to identify deterministic genes for different phenotypes with only minor differences in gene expression levels.

View Article: PubMed Central - PubMed

Affiliation: Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, Republic of Korea.

ABSTRACT
Systemic identification of deterministic genes for different phenotypes is a primary application of high-throughput expression profiles. However, gene expression differences cannot be used when the differences between groups are not significant. Therefore, novel methods incorporating features other than expression differences are required. We developed a promising method using transcriptional response as an operational feature, which is quantified as the correlation between expression levels of pathway genes and target genes of the pathway. We applied this method to identify causative genes associated with chemo-sensitivity to tamoxifen and epirubicin. Genes whose transcriptional response was dysregulated only in the drug-resistant patient group were chosen for in vitro validation in human breast cancer cells. Finally, we discovered two genes responsible for tamoxifen sensitivity and three genes associated with epirubicin sensitivity. The method we propose here can be widely applied to identify deterministic genes for different phenotypes with only minor differences in gene expression levels.

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Related in: MedlinePlus

Tamoxifen sensitivity is more strongly associated with SS than DEGs.(A) The number of differentially expressed genes (DEGs) between tamoxifen-sensitive and tamoxifen-resistant patient groups over eight datasets. There were no DEGs in four datasets with FDR < 0.05. (B) SR in tamoxifen-sensitive and tamoxifen-resistant patients. All datasets have different SR distributions with P < 0.0001. The 95% confidence intervals of SR are (0.073, 0.075) and (0.144, 0.149) in tamoxifen-resistant and tamoxifen-sensitive groups, respectively. Distribution of SR (C) and SS (D) for tamoxifen sensitivity were averaged over all datasets.
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f2: Tamoxifen sensitivity is more strongly associated with SS than DEGs.(A) The number of differentially expressed genes (DEGs) between tamoxifen-sensitive and tamoxifen-resistant patient groups over eight datasets. There were no DEGs in four datasets with FDR < 0.05. (B) SR in tamoxifen-sensitive and tamoxifen-resistant patients. All datasets have different SR distributions with P < 0.0001. The 95% confidence intervals of SR are (0.073, 0.075) and (0.144, 0.149) in tamoxifen-resistant and tamoxifen-sensitive groups, respectively. Distribution of SR (C) and SS (D) for tamoxifen sensitivity were averaged over all datasets.

Mentions: Differentially expressed genes (DEGs) between tamoxifen-resistant and tamoxifen-sensitive patients in the datasets were identified. Among the eight datasets, there were no DEGs in the four datasets using a false discovery rate (FDR) < 0.05. GSE6532B had the largest number of DEGs, which accounted for only 5% of the tested genes (Figure 2A). On the other hand, SR was significantly increased in tamoxifen-sensitive patients compared to tamoxifen-resistant patients in all datasets (Figure 2B, all datasets had a paired t-test P-value < 0.0001). The SR distribution of all pathway genes confirmed this result (Figure 2C, the distributions were not normal in Kolmogorov-smirnove, D'agostino & Pearson omnibus, and Shapiro-Wilk normality tests). Based on SR, we calculated SS for all pathway genes (Figure 2D). The number of target genes of pathway genes was not associated with SS (based on the P-value of the Spearman's rank order correlation coefficient of target gene number and SS). We visualized the difference in SR between the two groups (SR of tamoxifen-sensitive patients - SR of tamoxifen-resistant patients) for all pathway genes over several datasets (Figure 3A). Of the top-ranked genes, we selected five that had no differences in expression level between the two groups (Figure 3B), and performed in vitro assays to examine the association between these genes and tamoxifen sensitivity.


Identification of phenotype deterministic genes using systemic analysis of transcriptional response.

Lee J, Park J, Choi C - Sci Rep (2014)

Tamoxifen sensitivity is more strongly associated with SS than DEGs.(A) The number of differentially expressed genes (DEGs) between tamoxifen-sensitive and tamoxifen-resistant patient groups over eight datasets. There were no DEGs in four datasets with FDR < 0.05. (B) SR in tamoxifen-sensitive and tamoxifen-resistant patients. All datasets have different SR distributions with P < 0.0001. The 95% confidence intervals of SR are (0.073, 0.075) and (0.144, 0.149) in tamoxifen-resistant and tamoxifen-sensitive groups, respectively. Distribution of SR (C) and SS (D) for tamoxifen sensitivity were averaged over all datasets.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958917&req=5

f2: Tamoxifen sensitivity is more strongly associated with SS than DEGs.(A) The number of differentially expressed genes (DEGs) between tamoxifen-sensitive and tamoxifen-resistant patient groups over eight datasets. There were no DEGs in four datasets with FDR < 0.05. (B) SR in tamoxifen-sensitive and tamoxifen-resistant patients. All datasets have different SR distributions with P < 0.0001. The 95% confidence intervals of SR are (0.073, 0.075) and (0.144, 0.149) in tamoxifen-resistant and tamoxifen-sensitive groups, respectively. Distribution of SR (C) and SS (D) for tamoxifen sensitivity were averaged over all datasets.
Mentions: Differentially expressed genes (DEGs) between tamoxifen-resistant and tamoxifen-sensitive patients in the datasets were identified. Among the eight datasets, there were no DEGs in the four datasets using a false discovery rate (FDR) < 0.05. GSE6532B had the largest number of DEGs, which accounted for only 5% of the tested genes (Figure 2A). On the other hand, SR was significantly increased in tamoxifen-sensitive patients compared to tamoxifen-resistant patients in all datasets (Figure 2B, all datasets had a paired t-test P-value < 0.0001). The SR distribution of all pathway genes confirmed this result (Figure 2C, the distributions were not normal in Kolmogorov-smirnove, D'agostino & Pearson omnibus, and Shapiro-Wilk normality tests). Based on SR, we calculated SS for all pathway genes (Figure 2D). The number of target genes of pathway genes was not associated with SS (based on the P-value of the Spearman's rank order correlation coefficient of target gene number and SS). We visualized the difference in SR between the two groups (SR of tamoxifen-sensitive patients - SR of tamoxifen-resistant patients) for all pathway genes over several datasets (Figure 3A). Of the top-ranked genes, we selected five that had no differences in expression level between the two groups (Figure 3B), and performed in vitro assays to examine the association between these genes and tamoxifen sensitivity.

Bottom Line: We applied this method to identify causative genes associated with chemo-sensitivity to tamoxifen and epirubicin.Finally, we discovered two genes responsible for tamoxifen sensitivity and three genes associated with epirubicin sensitivity.The method we propose here can be widely applied to identify deterministic genes for different phenotypes with only minor differences in gene expression levels.

View Article: PubMed Central - PubMed

Affiliation: Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, Republic of Korea.

ABSTRACT
Systemic identification of deterministic genes for different phenotypes is a primary application of high-throughput expression profiles. However, gene expression differences cannot be used when the differences between groups are not significant. Therefore, novel methods incorporating features other than expression differences are required. We developed a promising method using transcriptional response as an operational feature, which is quantified as the correlation between expression levels of pathway genes and target genes of the pathway. We applied this method to identify causative genes associated with chemo-sensitivity to tamoxifen and epirubicin. Genes whose transcriptional response was dysregulated only in the drug-resistant patient group were chosen for in vitro validation in human breast cancer cells. Finally, we discovered two genes responsible for tamoxifen sensitivity and three genes associated with epirubicin sensitivity. The method we propose here can be widely applied to identify deterministic genes for different phenotypes with only minor differences in gene expression levels.

Show MeSH
Related in: MedlinePlus