Primer sequences, annealing temperatures and amplicon sizes for the qRT-PCR analysis of the studied genes.

<p>Primer sequences, annealing temperatures and amplicon sizes for the qRT-PCR analysis of the studied genes.</p

Clinical characteristics of the study groups.

<p>^a Student’s T test</p><p>^b Mann-Whitney U test</p><p>^c Chi-square test</p><p>^d Fisher’s exact test</p><p>All tests were 2-tailed.</p><p>Clinical characteristics of the study groups.</p

Notch signaling pathway pair-wise mRNA co-expression analysis in preeclamptic placentas.

<p><b>CC</b>: Correlation coefficient.</p><p>Notch signaling pathway pair-wise mRNA co-expression analysis in preeclamptic placentas.</p

Notch signaling pathway pair-wise mRNA co-expression analysis in normal placentas.

<p><b>CC</b>: Correlation coefficient.</p><p>Notch signaling pathway pair-wise mRNA co-expression analysis in normal placentas.</p

Expression analysis of Notch signaling pathway between normal and PE placentas.

<p><b>P-value (c):</b> crude p-value; <b>P-value (a):</b> P-value adjusted for gestation period, mode of delivery and smoking during pregnancy. All values are presented as mean ± SEM.</p><p>Expression analysis of Notch signaling pathway between normal and PE placentas.</p

Schematic representation of Notch pathway molecules mRNA expression profile in our series of preeclampsia-complicated late preterm and term placentas.

<p>Red: Downregulation; White: Normal expression; Sky Blue: Overexpression.</p

Schematic depiction of the studied Notch signaling pathway molecules in late preterm and term placentas.

<p>Schematic depiction of the studied Notch signaling pathway molecules in late preterm and term placentas.</p

Box and whisker plots depicting statistically significant associations in preeclampsia-complicated late preterm and term placentas.

<p>(A) <i>NOTCH3</i> was not expressed in preeclamptic (PE) women that smoked during their pregnancy versus women with PE that did not smoke (0.00±0.00 vs. 0.29±0.09, p = 0.029). (B) <i>DLL3</i> mRNA expression was higher in babies born from PE pregnancies with Birth Weight Centile (BWC) <5 compared with babies born from PE pregnancies with BWC >5 (0.91±0.30 vs. 0.42±0.19, p = 0.041). (C) <i>HEY2</i> transcript levels were increased in women with PE who were on their fist parity versus women with PE that gave birth to their 2^nd or 3^rd child (0.44±0.13 vs. 0.05±0.02, p = 0.034). (D) <i>HEY2</i> mRNA expression was higher in women with PE who gave birth with a Caesarian Section compared with women with PE who gave birth naturally (0.63±0.17 vs. 0.15±0.05, p = 0.028). (E) NOTCH3 intracellular domain (NICD3) protein levels were higher in babies born from women with pregnancies complicated by PE with BWC <5 versus babies born from women with pregnancies complicated by PE with BWC >5 (1.15±0.24 vs. 0.16±0.07, p = 0.028). The thick line near the center of each rectangular box represents the median value, the bottom and top edges of the box indicate the 1^st (Q₁) and 3^rd (Q₃) quartiles, and the ends of the whiskers depict the 10^th (P₁₀) and 90^th (P₉₀) percentiles.</p

Chromosome correlation maps of gene expression signatures could provide useful information on gene regulatory mechanisms in urinary bladder cancer

<p>Chromosome correlation maps display correlations between gene expression patterns on the same chromosome and are considered of major importance in the understanding of how gene expression is regulated. It has not yet been elucidated based on chromosome correlation, whether gene expression among same chromosomes from different tumor samples is governed by similar patterns; and if it exists we do not know whether it is of linear nature or not. In the present study we used urinary bladder carcinoma as the model of our hypothesis. Following microarray experimentation in combination with raw microarray data extraction from the GEO, we collected a data cohort of 129 bladder cancer and 17 normal samples and performed network analysis for the co-deregulated genes using Ingenuity Pathway Analysis (IPA). Chromosome mapping, mathematical modeling and data simulations were performed using the WebGestalt and Matlab software. The top deregulated molecules among all bladder cancer samples were implicated in the PI3K/AKT signaling, cell cycle, Myc-mediated apoptosis signaling and ERK5 signaling pathways. Their most prominent molecular and cellular functions were related to cell cycle, cell death, gene expression, molecular transport and cellular growth and proliferation. Chromosome correlation maps allowed us to detect significantly co-expressed genes along the chromosomes. We identified strong correlations among tumors of Tα-grade 1, as well as for those of Tα-grade 2, in chromosomes 1, 2, 3, 7, 12 and 19. Chromosomal domains of gene co-expression were revealed for the normal tissues, as well. The expression data were further simulated, exhibiting an excellent fit (0.7</p

Linear correlations in chromosomal-based gene expression in urinary bladder cancer

<p>Introduction & Objectives: Gene expression is a very tidy and well coordinated procedure. Consecutive genes are often similarly expressed. We hypothesized that correlations might exist between genes of the same chromosome, yet belonging to different urinary bladder cancer (BC) samples, in order to indicate a common regulation for genes following this pattern.</p> <p>Materials & Methods: We analyzed BC gene expression profiles, with emphasis in linear correlations of gene expression based on their chromosomal locations. Samples from 10 human BCs and 5 normal tissues were analyzed by whole genome microarrays, along with a computational approach, for their expression profiles. After raw data normalization and classification, differentially expressed genes (DE) were sorted according to their chromosome distributions and were further investigated for linear correlations among them. Chromosomal activity in terms of gene expression was measured by calculating the average expression of all DE genes for each chromosome, both for tumour and control samples.</p> <p>Results: Chromosome-based expression analysis predicted that among the most active chromosomes were chromosomes 9 and X. Similarly, control samples also manifested high expression activity on the X chromosome. The genes that exhibited significant linear correlations (p<0.05) among tumor samples on chromosomes 4, 8, 13, 21 and 22, were as follows: TACR3, RNF150, ANXA10, CENTD1, EXOC1, GRSF1 for chromosome 4; ANXA13, DENND3, FGF20, EFHA2, DNAJC5B, MRPS28, FABP5 for chromosome 8; ITGBL1, RXFP2, KL, MYCBP2, FARP1 for chromosome 13; KRTAP19-1, IFNAR1, SON for chromosome 21; MORC2, PLA2G6, ACO2, ARHGAP8 for chromosome 22; SERPINA7, TMEM164, ARHGAP6, APLN, FHL1, PNMA6A, UBL4A, PRDX4, POLA1, MXRA5 for chromosome X.</p> <p>Conclusions: Despite the fact that linear correlations occurred among distinct patients, the expression of the genes appeared to be correlated among them, in a similar manner. We have previously reported that there are hints of common mechanisms between BCs of different stage/grade, employing microarray analysis. Chromosomal correlation analysis comes to support our previous findings, since it revealed genes bearing common regulation among samples of different histology. Gene expression correlations can further assist us to understand more in-depth the mechanisms underlying tumour progression and biology.</p