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

<p>Abstract</p> <p>Background</p> <p>Clear cell renal carcinoma (RCC) is the most common and invasive adult renal cancer. For the purpose of identifying RCC biomarkers, we investigated chromosomal regions and individual genes modulated in RCC pathology. We applied the dual strategy of assessing and integrating genomic and transcriptomic data, today considered the most effective approach for understanding genetic mechanisms of cancer and the most sensitive for identifying cancer-related genes.</p> <p>Results</p> <p>We performed the first integrated analysis of DNA and RNA profiles of RCC samples using Affymetrix technology. Using 100K SNP mapping arrays, we assembled a genome-wide map of DNA copy number alterations and LOH areas. We thus confirmed the typical genetic signature of RCC but also identified other amplified regions (e.g. on chr. 4, 11, 12), deleted regions (chr. 1, 9, 22) and LOH areas (chr. 1, 2, 9, 13). Simultaneously, using HG-U133 Plus 2.0 arrays, we identified differentially expressed genes (DEGs) in tumor vs. normal samples. Combining genomic and transcriptomic data, we identified 71 DEGs in aberrant chromosomal regions and observed, in amplified regions, a predominance of up-regulated genes (27 of 37 DEGs) and a trend to clustering. Functional annotation of these genes revealed some already implicated in RCC pathology and other cancers, as well as others that may be novel tumor biomarkers.</p> <p>Conclusion</p> <p>By combining genomic and transcriptomic profiles from a collection of RCC samples, we identified specific genomic regions with concordant alterations in DNA and RNA profiles and focused on regions with increased DNA copy number. Since the transcriptional modulation of up-regulated genes in amplified regions may be attributed to the genomic alterations characteristic of RCC, these genes may encode novel RCC biomarkers actively involved in tumor initiation and progression and useful in clinical applications.</p

Preliminary assessment of the new Sysmex XN parameter Iron-Def for identifying iron deficiency

Background: Although the clinical assessment of iron status is usually based on iron stores, a rapid and accurate diagnosis of iron deficiency is challenging since ferritin is often unavailable as an urgent test and its value is frequently increased in acute phase conditions. This study was therefore aimed at evaluating the diagnostic performance of the new Sysmex XN "Iron Deficiency?" (Iron-Def) parameter for identifying patients with iron deficiency. Materials and methods: The study population consisted of 688 consecutive patients (median age: 71 years; 341 women and 347 men), referred for routine diagnostics to the Laboratory of Clinical Pathology of Lecco Hospital, Italy. A complete clinical chemistry profile and haematological testing were performed for identifying iron deficiency anaemia. Results: A significant negative correlation was found between Sysmex XN Iron-Def and ferritin, serum iron, mean cell haemoglobin concentration, mean cell haemoglobin, mean corpuscular volume and age, while a positive correlation was noted with transferrin, percentage of microcytic red cell, red blood cell count and red blood cell distribution width. The diagnostic accuracy of Iron-Def for identifying patients with a percentage of saturation of transferrin &lt;15% (n=104) was 84%, with a sensitivity of 0.952 and specificity of 0.538. A sub-analysis of 71 patients with ferritin &lt;20 ng/dL yielded an even better diagnostic performance (86%, with a sensitivity of 0.935 and specificity of 0.620). Discussion: Although additional confirmatory investigations would be needed, the preliminary findings of our study attest that Iron-Def may be an easy, inexpensive, rapid and reliable parameter for screening iron deficiency anaemia

Evaluation of body fluid mode of Sysmex XN-9000 for white blood cell counts in cerebrospinal fluid

Background: This study was planned to evaluate the analytical performance of the novel and fully automated Sysmex XN-9000 analyzer for rapid analysis of cerebrospinal fluid (CSF) samples. Methods: Forty-four CSF samples were used for method comparison studies between Sysmex XN-9000 body fluid mode and conventional optical microscopy. The bias between data obtained with the two methods was estimated with Bland-Altman plot analysis. The analytical evaluation also included the assessment of imprecision, linearity and carry-over. Results: A good agreement was found between results obtained with Sysmex XN-9000 body fluid mode and optical microscopy. The mean bias was 1.6 7106 cells/L for total white blood cells (95% CI: 1221.8 7106 to 25.1 7106 cells/L), 1.3 7106 cells/L for polymorphonuclear cells (95% CI: 1213.9 7106 to 16.5 7106 cells/L) and 120.6 7106 cells/L for mononuclear cells (95% CI: 1221.5 7106 to 20.3 7106 cells/L). The carryover was found to be lower than 0.01% and the imprecision was lower than 5%. The XN-9000 body fluid mode was also characterized by excellent linearity in the range of values comprised between 85 7106\u20133,197 7106 cells/L, with correlation coefficients (r) always equal to 1.00 (P<0.001). Conclusions: The Sysmex XN-9000 body fluid mode displays excellent analytical performance in terms of imprecision, linearity, carry-over and comparability with conventional optical microscopy, so that it may be used as a first-line, screening technique for rapid analysis of CSF samples referred for both routine and, especially, for urgent testing

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