Genome-wide screening of copy number alterations and LOH events in renal cell carcinomas and integration with gene expression profile-0

<p><b>Copyright information:</b></p><p>Taken from "Genome-wide screening of copy number alterations and LOH events in renal cell carcinomas and integration with gene expression profile"</p><p>http://www.molecular-cancer.com/content/7/1/6</p><p>Molecular Cancer 2008;7():6-6.</p><p>Published online 14 Jan 2008</p><p>PMCID:PMC2253555.</p><p></p>d blue blocks). Each tumor sample was compared to its matched normal blood sample, and regions of DNA copy number gain (red lines) and copy number loss (green lines) were plotted along each chromosome. Datasets from only GeneChip50K Hind arrays were used

MOESM2 of Tumor size, stage and grade alterations of urinary peptidome in RCC

Additional file 2: Table S2.   A) Peptides with a statistically significant (p < 0.05) urinary abundance (m/z area) according to the pT. B) Peptides with a statistical significant (p < 0.05) urinary abundance (m/z area) compared to CTRLs and at different pT (1 = pT1a; 2 = pT1b; 3 = pT2a and 4 = pT > 2b). Up/down refer to the over/under representation of the peptide in ccRCC patients compared to control subject

MOESM5 of Tumor size, stage and grade alterations of urinary peptidome in RCC

Additional file 5: Table S5.   Identification of MALDI signals varied according to tumour size, pT or grade. LM = linear mode, RM = reflector mode, PTM = possible hypothetical modification predicted by Mascot

Iron Stores, Hepcidin, and Aortic Stiffness in Individuals with Hypertension

<div><p>Background & Aims</p><p>Iron accumulation within the arterial wall has been hypothesized to promote atherosclerosis progression. Aim of this study was to evaluate whether the hormone hepcidin and iron stores are associated with arterial stiffness in subjects with essential hypertension.</p><p>Methods</p><p>Circulating hepcidin, ferritin, and mutations in the hemochromatosis gene were compared between subjects included in the first vs. third tertile (n=284 each) of carotid-femoral pulse wave velocity (PWV) in an unselected cohort of patients with arterial hypertension.</p><p>Results</p><p>At univariate logistic regression analysis, high PWV was associated with higher ferritin levels (p=0.010), but lower hepcidin (p=0.045), and hepcidin ferritin/ratio (p<0.001). Hemochromatosis mutations predisposing to iron overload were associated with high PWV (p=0.025). At multivariate logistic regression analysis, high aortic stiffness was associated with older age, male sex, lower BMI, higher systolic blood pressure and heart rate, hyperferritinemia (OR 2.05, 95% c.i. 1.11-3.17 per log ng/ml; p=0.022), and lower circulating hepcidin concentration (OR 0.29, 95% c.i. 0.16-0.51 per log ng/ml; p<0.001). In subgroup analyses, high PWV was associated with indices of target organ damage, including micro-albuminuria (n=125, p=0.038), lower ejection fraction (n=175, p=0.031), cardiac diastolic dysfunction (p=0.004), and lower S wave peak systolic velocity (p<0.001). Ferritin was associated with cardiac diastolic dysfunction, independently of confounders (p=0.006).</p><p>Conclusions</p><p>In conclusion, hyperferritinemia is associated with high aortic stiffness and cardiac diastolic dysfunction, while low circulating hepcidin with high aortic stiffness.</p></div

Clinical features of 568 Italian patients with essential hypertension stratified according to common carotid arteries stiffness (third vs. first tertile).

<p>Data are shown as means±SD, prevalence (% value), median {interquartile range}, as required. CCA: common carotid arteries; PWV: pulse wave veIocity; IMT: intima-media thickness, HDL: high-density lipoprotein cholesterol; SBP: systolic blood pressure; DBP: diastolic blood pressure; bpm: beats per minute; IL18: interleukin-18, SAA: serum amyloid A protein, hs-CRP: high sensitivity C reactive protein. Comparisons were made by fitting data to logistic regression models. OR: odds ratio for high vs. low CCA stiffness; CI: confidence intervals.</p><p>*Adjusted for age and sex.</p><p>^Available in 175 individuals.</p><p>° per 1 log increase.</p><p>Clinical features of 568 Italian patients with essential hypertension stratified according to common carotid arteries stiffness (third vs. first tertile).</p

Predictors of circulating hepcidin in 568 patients with arterial hypertension.

<p>Comparisons were made by fitting data to generalized linear models. OR: odds ratio; c.i.: confidence interval;</p><p>* Adjusted for serum ferritin.</p><p>We included variables available for all patients evaluated, when p≤0.10 for association with ferritin levels at univariate analysis.</p

Heart function parameters in 175 Italian patients with essential hypertension stratified according to common carotid arteries stiffness (third vs. first tertile).

<p>Data are shown as prevalence (% value), median {interquartile range}, as required. Comparisons were made by fitting data to logistic regression models. OR: odds ratio for high vs. low CCA stiffness; CI: confidence intervals.</p><p>* Adjusted for age and sex. LVMI: left ventricular mass; PWT: posterior wall thickness; IVS: inter-ventricular septum.</p><p>Heart function parameters in 175 Italian patients with essential hypertension stratified according to common carotid arteries stiffness (third vs. first tertile).</p

Iron status in 568 Italian patients with essential hypertension stratified according to common carotid arteries stiffness (third vs. first tertile).

<p>Data are shown as prevalence (% value), median {interquartile range}, as required. Comparisons were made by fitting data to logistic regression models. OR: odds ratio for high vs. low CCA stiffness; CI: confidence intervals.</p><p>* Adjusted for age and sex.</p><p>° per 1 log increase;</p><p>^ Defined as C282Y/C282Y, C282Y/H63D, or H63D/H63D vs. H63D/wild-type and wild-type/wild-type [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134635#pone.0134635.ref020" target="_blank">20</a>].</p><p>Iron status in 568 Italian patients with essential hypertension stratified according to common carotid arteries stiffness (third vs. first tertile).</p

Urinary Signatures of Renal Cell Carcinoma Investigated by Peptidomic Approaches

<div><p>Renal Cell Carcinoma (RCC) is typically asymptomatic and surgery usually increases patient's lifespan only for early stage tumours. Moreover, solid renal masses cannot be confidently differentiated from RCC. Therefore, markers to distinguish malignant kidney tumours and for their detection are needed. Two different peptide signatures were obtained by a MALDI-TOF profiling approach based on urine pre-purification by C8 magnetic beads. One cluster of 12 signals could differentiate malignant tumours (n = 137) from benign renal masses and controls (n = 153) with sensitivity of 76% and specificity of 87% in the validation set. A second cluster of 12 signals distinguished clear cell RCC (n = 118) from controls (n = 137) with sensitivity and specificity values of 84% and 91%, respectively. Most of the peptide signals used in the two models were observed at higher abundance in patient urines and could be identified as fragments of proteins involved in tumour pathogenesis and progression. Among them: the Meprin 1α with a pro-angiogenic activity, the Probable G-protein coupled receptor 162, belonging to the GPCRs family and known to be associated with several key functions in cancer, the Osteopontin that strongly correlates to tumour stages and invasiveness, the Phosphorylase b kinase regulatory subunit alpha and the SeCreted and TransMembrane protein 1.</p></div

Performances of the cluster of twelve signals to discriminate ccRCC patients from controls (False = Controls; True = ccRCC) with <i>k-fold</i> = 10 cross-validation (A) and of the model, originated in the training phase using about 60% of the data, in validation test using the other about 40% of the studied subjects (B).

<p>Spec.  =  Specificity; Sens.  =  Sensitivity; pred.  =  Prediction; Class precision: a  =  Negative Predictive Value and b  =  Positive Predictive Value. Precision  =  Relative number of correctly classified examples among all examples classified as positive i.e. precision  =  (Positives Correctly Classified)/(Total Predicted Positives). Note that the Total Predicted Positives is the sum of True Positives and False Positives. This is the same as the Negative Predictive Value.</p><p>True True  =  True positive; true False  =  True negative.</p><p>Performances of the cluster of twelve signals to discriminate ccRCC patients from controls (False  =  Controls; True  =  ccRCC) with <i>k-fold</i>  =  10 cross-validation (A) and of the model, originated in the training phase using about 60% of the data, in validation test using the other about 40% of the studied subjects (B).</p