Mutation patterns from whole genome sequencing of 94 conventional renal carcinomas from four different countries showing a notable excess in the proportion of A>T mutations in cases from Romania.

<p>See Scelo et al. [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005522#pgen.1005522.ref021" target="_blank">21</a>].</p

Causal pathway indicating how ALDH2 is an unconfounded marker (or instrument) of alcohol consumption in the association between alcohol and blood pressure.

<p>Causal pathway indicating how ALDH2 is an unconfounded marker (or instrument) of alcohol consumption in the association between alcohol and blood pressure.</p

Evaluation of the performance of the model including age at recruitment, sex and smoking status (red) and a classifier including 24-miRNA panel, age at recruitment, sex and smoking status (blue) assessed using the area under Receiver Operating Characteristics (ROC) curves (AUCs) in the IARC case-control study (2006–2012).

<p>Evaluation of the performance of the model including age at recruitment, sex and smoking status (red) and a classifier including 24-miRNA panel, age at recruitment, sex and smoking status (blue) assessed using the area under Receiver Operating Characteristics (ROC) curves (AUCs) in the IARC case-control study (2006–2012).</p

Select baseline characteristics of the study population (lung cancer cases and controls) recruited in the IARC case-control study (Moscow, Russia, 2006–2012).

<p>^a p value calculated using χ² test for categorical variables and Student’s t-test for continuous variables.</p><p>Select baseline characteristics of the study population (lung cancer cases and controls) recruited in the IARC case-control study (Moscow, Russia, 2006–2012).</p

Logistic regression prediction model with the 24-microRNA panel for patients with lung cancer vs controls in the IARC case-control study (Moscow, Russia, 2006–2012).

<p>^a Model containing the 24-miRNA panel (continuous, normalized Ct values).</p><p>^b Model containing sex (Male, Female), age at interview (continuous), smoking status (Never, Former, Current) and the 24-miRNA panel (continuous, normalized Ct values).</p><p>Abbreviations: OR, odds ratio; CI, confidence interval.</p><p>Logistic regression prediction model with the 24-microRNA panel for patients with lung cancer vs controls in the IARC case-control study (Moscow, Russia, 2006–2012).</p

Evaluation of the performance of the 24-miRNA classifier (left) and a classifier including the 24-miRNA panel, age at recruitment, sex and smoking status (right) assessed using area under Receiver Operating Characteristics (ROC) curves (AUCs) in the IARC case-control study (2006–2012).

<p>Evaluation of the performance of the 24-miRNA classifier (left) and a classifier including the 24-miRNA panel, age at recruitment, sex and smoking status (right) assessed using area under Receiver Operating Characteristics (ROC) curves (AUCs) in the IARC case-control study (2006–2012).</p

Complete list of significant differentially expressed miRNA (p-value < 0.05) in lung cancer patients as compared with controls in the IARC case-control study (Moscow, Russia, 2006–2012)—global miRNA expression profiling using TaqMan Human MicroRNA Array A + B Card Set (v3.0).

<p>^a Limma analysis non-adjusted p-value.</p><p>^b p-value corrected for multiple testing (Benjamini-Holm method).</p><p>FC: fold change (> 1 increased; < 1 decreased expression in lung cancer patients vs. non-cancer controls).</p><p>Complete list of significant differentially expressed miRNA (p-value < 0.05) in lung cancer patients as compared with controls in the IARC case-control study (Moscow, Russia, 2006–2012)—global miRNA expression profiling using TaqMan Human MicroRNA Array A + B Card Set (v3.0).</p

MiRNA detected in plasma samples of 100 patients with lung cancer vs 100 controls on TaqMan microRNA CARD A and CARD B, respectively in the IARC case-control study (2006–2012).

<p>MiRNA detected in plasma samples of 100 patients with lung cancer vs 100 controls on TaqMan microRNA CARD A and CARD B, respectively in the IARC case-control study (2006–2012).</p

Proteomics-Based Strategies To Identify Proteins Relevant to Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL), a malignant B-cell disorder, is characterized by a heterogeneous clinical course. Two-dimensional nano liquid chromatography (2D-nano–LC) coupled with matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (MALDI-TOF/TOF MS) (LC–MALDI) was used to perform qualitative and quantitative analysis on cellular extracts from 12 primary CLL samples. We identified 728 proteins and quantified 655 proteins using isobaric tag-labeled extracts. Four strategies were used to identify disease-related proteins. First, we integrated our CLL proteome with published gene expression data of normal B-cells and CLL cells to highlight proteins with preferential expression in the transcriptome of CLL. Second, as CLL’s outcome is heterogeneous, our quantitative proteomic data were used to indicate heterogeneously expressed proteins. Third, we used the quantitative data to identify proteins with differential abundance in poor prognosis CLL samples. Fourth, hierarchical cluster analysis was applied to identify hidden patterns of protein expression. These strategies identified 63 proteins, and 4 were investigated in a CLL cohort (39 patients). Thyroid hormone receptor-associated protein 3, T-cell leukemia/lymphoma protein 1A, and S100A8 were associated with high-risk CLL. Myosin-9 exhibited reduced expression in CLL samples from high-risk patients. This study shows the usefulness of proteomic approaches, combined with transcriptomics, to identify disease-related proteins

Performance of three different models for predicting UGI malignancy in dyspeptic patients.

*<p>Model1: age only; Model2: age plus four alarm symptoms (weigh loss, persistent vomiting, GI bleeding, dysphagia); Model3: risk-prediction model.</p>**<p>Brier<i>_scaled</i> = 1−Brier/Brier<i>_max</i>, where Brier<i>_max</i> = mean(p)<i>×</i>(1−mean(p)); and mean(p) is mean probability of outcome prediction based on model.</p