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Results: 2

1.
Fig. 2.

Fig. 2. From: Differential Gene Expression in Granulosa Cells from Polycystic Ovary Syndrome Patients with and without Insulin Resistance: Identification of Susceptibility Gene Sets through Network Analysis.

Functional analysis of microarray data by IPA tools indicated differential expression (P ≤ 0.001) of diabetes mellitus, inflammatory response, and cardiovascular disease genes in the granulosa cells of PCOS-IR (n = 4) (A–C) and PCOS non-IR (n = 3) subjects (D and E) vs. matched controls (n = 3). A, Histogram analysis of diabetes mellitus genes in PCOS-IR with at least 1.8-fold change in expression. B, Histogram analysis of inflammation response genes in PCOS-IR with at least 1.58-fold change in expression. C, Histogram analysis of cardiovascular disease genes in PCOS-IR with at least 1.59-fold change in expression. D, Histogram analysis of inflammation response genes in PCOS non-IR with at least 3.11-fold change in expression. E, Histogram analysis of cardiovascular disease genes in PCOS non-IR with at least 1.98-fold change in expression.

Surleen Kaur, et al. J Clin Endocrinol Metab. 2012 October;97(10):E2016-E2021.
2.
Fig. 1.

Fig. 1. From: Differential Gene Expression in Granulosa Cells from Polycystic Ovary Syndrome Patients with and without Insulin Resistance: Identification of Susceptibility Gene Sets through Network Analysis.

A, Global gene expression profiles of the control and the two phenotypes of PCOS—PCOS non-IR and PCOS-IR granulosa cells. The 17,958 probe sets left after excluding all control probe sets and probe sets present in less than 80% of the samples were reduced to three dimensions using classical multidimensional scaling (MDS). The figure represents the gene expression data in three dimensions, with each plotted point representing one sample and with color used to represent the sample type: red, control (n = 3); blue, PCOS non-IR (n = 3); and green, PCOS-IR (n = 4). B, Venn diagram illustrating the number of probe sets identified as significantly differentially expressed (FC ≥ 1.5; P ≤ 0.001) in each pairwise comparison (control vs. PCOS non-IR, control vs. PCOS-IR, and PCOS non-IR vs. PCOS-IR) and common between them. A Venn diagram was constructed using limma Bioconductor package. C, Supervised hierarchical clustering for 217 unique probe sets significant (P ≤ 0.001) in at least one of the three pairwise comparisons was performed by Ward's method and 1-(Pearson's correlation) as the distance measure. Each row represents a single gene; each column represents a sample. Expression values were color coded: blue, transcript level below the median; green, equal to median; and yellow, greater than median. D, To compare the results of all PCOS comparison with each pairwise comparison (control vs. PCOS non-IR, control vs. PCOS-IR, and PCOS non-IR vs. PCOS-IR), the mean probe set expression values (on a log2 scale) for all probe sets were plotted. In each scatterplot, 222 differentially expressed probe sets (P ≤ 0.001) in all PCOS groups were overlapped and displayed in red. The distribution of red points showed the specific differences between the differentially expressed probe sets of all PCOS groups and the pairwise comparisons indicating the importance of studying the gene expression profiles of the two phenotypes of PCOS separately.

Surleen Kaur, et al. J Clin Endocrinol Metab. 2012 October;97(10):E2016-E2021.

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