The figure shows, on the top, the aberrations (deletions in red, duplications in green) found in the 11q25 region in controls and DLBCL cases after CNV analysis using a genotyping array.

<p>On the bottom, the figure shows the locations of the 9 PCR primers designed to cover the LOC283177 gene. qPCR confirmed the partial duplication of LOC283177 (P = 0.004), and the region of breakpoint was determined to be located between primers P4 and P5. Coordinates are shown with respect to the NCBI36/hg18 assembly.</p

Copy Number Variation Analysis on a Non-Hodgkin Lymphoma Case-Control Study Identifies an 11q25 Duplication Associated with Diffuse Large B-Cell Lymphoma

<div><p>Recent GWAS have identified several susceptibility loci for NHL. Despite these successes, much of the heritable variation in NHL risk remains to be explained. Common copy-number variants are important genomic sources of variability, and hence a potential source to explain part of this missing heritability. In this study, we carried out a CNV analysis using GWAS data from 681 NHL cases and 749 controls to explore the relationship between common structural variation and lymphoma susceptibility. Here we found a novel association with diffuse large B-cell lymphoma (DLBCL) risk involving a partial duplication of the C-terminus region of the <i>LOC283177</i> long non-coding RNA that was further confirmed by quantitative PCR. For chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), known somatic deletions were identified on chromosomes 13q14, 11q22-23, 14q32 and 22q11.22. Our study shows that GWAS data can be used to identify germline CNVs associated with disease risk for DLBCL and somatic CNVs for CLL/SLL.</p></div

Summary of the copy number variation (CNV) analysis by lymphoma subtype.

<p>Summary of the copy number variation (CNV) analysis by lymphoma subtype.</p

Additional file 1: Table S1. of A Fucus vesiculosus extract inhibits estrogen receptor activation and induces cell death in female cancer cell lines

248 genes analyzed for expression profiling (Nanostring™ nCounter®) and 6 housekeeping reference genes. Figure S1. Assays of toxicity. (A) FVE effects on membrane permeability and mitochondrial ATP. (B) Digitonin used as positive control for primary necrosis. (C) CCCP used as positive control for mitochondrial toxicity. Figure S2. Morphological alterations. (A) FVE-untreated and (B) -treated cells with 1.0 % FVE, 48 hr. Figure S3. Heatmap of differential mRNA expression following FVE treatment at 0.25 % and 1.0 % (4 hr) in MCF-7, T47D, MDA-MB-231, HEC-1-B, RL95-2 and OVCAR-3 cell lines; significance level, p <0.05. Figure S4. Treatment of MCF-7, MDA-MB-231, HEC-1-B, MES-SA, AN3-CA, OVCAR-3 and Caov-3 cells with apoptosis (VAD) and autophagy (3MA) inhibitors; *indicates significant difference with FVE without inhibitor (p <0.05). Figure S5. FVE-induced apoptosis via caspase3/7-mediated PARP cleavage in MDA-MB-231 cells; *p <0.05, **p <0.01 compared to controls. Figure S6. FVE down-regulates PI3K/Akt/mTOR signaling in MCF-7 cells. (A) FVE reduced Akt phosphorylation at Ser473 and Thr308, (B) decreased PI3K, 4-EB-P1 and p70S6K phosphorylation, and (C) promoted accumulation of phospho-Beclin-1 and LC3B II. Data are from >3 independent Western blots normalized by β-actin levels; *p <0.05, **p <0.01 compared to controls. Figure S7. FVE down-regulates PI3K/Akt/mTOR signaling in MDA-MB-231 cells. (A) FVE reduced Akt phosphorylation at Ser473 and Thr308, (B) decreased PI3K, 4-EB-P1 and p70S6K phosphorylation, and (C) promoted phospho-Beclin-1 and LC3B II accumulation. Data are from >3 independent Western blots normalized by β-actin levels; *p <0.05, **p < 0.01 compared to controls. Figure S8. Fucoidan up-regulates phosphor-Akt. (A) Fucoidan increased Akt phosphorylation at Ser473 in MCF-7 cells in a concentration-dependent manner; no change in Akt phosphorylation at Thr308. (B) Fucoidan increased Akt phosphorylation at Ser473 in MDA-MB-231 cells in a concentration- and time-dependent manner; no changes observed in Akt phosphorylation at Thr308. (PDF 11350 kb

Proteomic and Transcriptomic Analyses of Fecundity in the Brown Planthopper <i>Nilaparvata lugens</i> (Stål)

As an <i>r</i>-strategy insect species, the brown planthopper (BPH) <i>Nilaparvata lugens</i> (Stål) is a serious pest of rice crops in the temperate and tropical regions of Asia and Australia, which may be due to its robust fecundity. Here we combined 2-DE comparative proteomic and RNA-seq transcriptomic analyses to identify fecundity-related proteins and genes. Using high- and low-fecundity populations as sample groups, a total of 54 and 75 proteins were significantly altered in the third and sixth day brachypterous female stages, respectively, and 39 and 54 of these proteins were identified by MALDI-TOF/TOF MS. In addition, 71 966 unigenes were quantified by Illumina sequencing. On the basis of the transcriptomic analysis, 7408 and 1639 unigenes demonstrated higher expression levels in the high-fecundity population in the second day brachypterous female adults and the second day fifth instar nymphs, respectively, and 411 unigenes were up-regulated in both groups. Of these dozens of proteins and thousands of unigenes, five were differentially expressed at both the protein and mRNA levels at all four time points, suggesting that these genes may regulate fecundity. Glutamine synthetase (GS) was chosen for further functional studies. RNAi knockdown of the GS gene reduced the fecundity of <i>N. lugens</i> by 64.6%, disrupted ovary development, and inhibited vitellogenin (Vg) expression. Our results show that a combination of proteomic and transcriptomic analyses provided five candidate proteins and genes for further study. The knowledge gained from this study may lead to a more fundamental understanding of the fecundity of this important agricultural insect pest

High-Performance Thin-Layer Chromatography Coupled with Single Quadrupole: Application the Identification and Differentiation of Rehmanniae Radix and Its Different Processing Products from Raw Materials to Commercial Products

The authentication of ingredients in formulas is crucial yet challenging, particularly for constituents with comparable compositions but vastly divergent efficacy. Rehmanniae Radix and its derivatives are extensively utilized in food supplements, which contain analogous compositions but very distinct effects. Rehmanniae Radix, also a difficult-to-detect herbal ingredient, was chosen as a case to explore a novel HPTLC-QDa MS technique for the identification of herbal ingredients in commercial products. Through systematic condition optimization, including thin layer and mass spectrometry, a stable and reproducible HPTLC-QDa MS method was established, which can simultaneously detect oligosaccharides and iridoids. Rehmannia Radix and its processed products were then analyzed to screen five markers that could distinguish between raw and prepared Rehmannia Radix. An HPTLC-QDa-SIM method was further established for formula detection by using the five markers and validated using homemade prescriptions and negative controls. Finally, this method was applied to detect raw and prepared Rehmannia Radix in 12 commercial functional products and supplements

DataSheet_2_Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning.csv

BackgroundWe attempted to develop a progression prediction model for local advanced rectal cancer(LARC) patients who received preoperative neoadjuvant chemoradiotherapy(NCRT) and operative treatment to identify high-risk patients in advance.MethodsData from 272 LARC patients who received NCRT and total mesorectal excision(TME) from 2011 to 2018 at the Fourth Hospital of Hebei Medical University were collected. Data from 161 patients with rectal cancer (each sample with one target variable (progression) and 145 characteristic variables) were included. One Hot Encoding was applied to numerically represent some characteristics. The K-Nearest Neighbor (KNN) filling method was used to determine the missing values, and SmoteTomek comprehensive sampling was used to solve the data imbalance. Eventually, data from 135 patients with 45 characteristic clinical variables were obtained. Random forest, decision tree, support vector machine (SVM), and XGBoost were used to predict whether patients with rectal cancer will exhibit progression. LASSO regression was used to further filter the variables and narrow down the list of variables using a Venn diagram. Eventually, the prediction model was constructed by multivariate logistic regression, and the performance of the model was confirmed in the validation set.ResultsEventually, data from 135 patients including 45 clinical characteristic variables were included in the study. Data were randomly divided in an 8:2 ratio into a data set and a validation set, respectively. Area Under Curve (AUC) values of 0.72 for the decision tree, 0.97 for the random forest, 0.89 for SVM, and 0.94 for XGBoost were obtained from the data set. Similar results were obtained from the validation set. Twenty-three variables were obtained from LASSO regression, and eight variables were obtained by considering the intersection of the variables obtained using the previous four machine learning methods. Furthermore, a multivariate logistic regression model was constructed using the data set; the ROC indicated its good performance. The ROC curve also verified the good predictive performance in the validation set.ConclusionsWe constructed a logistic regression model with good predictive performance, which allowed us to accurately predict whether patients who received NCRT and TME will exhibit disease progression.</p

DataSheet_1_Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning.csv

BackgroundWe attempted to develop a progression prediction model for local advanced rectal cancer(LARC) patients who received preoperative neoadjuvant chemoradiotherapy(NCRT) and operative treatment to identify high-risk patients in advance.MethodsData from 272 LARC patients who received NCRT and total mesorectal excision(TME) from 2011 to 2018 at the Fourth Hospital of Hebei Medical University were collected. Data from 161 patients with rectal cancer (each sample with one target variable (progression) and 145 characteristic variables) were included. One Hot Encoding was applied to numerically represent some characteristics. The K-Nearest Neighbor (KNN) filling method was used to determine the missing values, and SmoteTomek comprehensive sampling was used to solve the data imbalance. Eventually, data from 135 patients with 45 characteristic clinical variables were obtained. Random forest, decision tree, support vector machine (SVM), and XGBoost were used to predict whether patients with rectal cancer will exhibit progression. LASSO regression was used to further filter the variables and narrow down the list of variables using a Venn diagram. Eventually, the prediction model was constructed by multivariate logistic regression, and the performance of the model was confirmed in the validation set.ResultsEventually, data from 135 patients including 45 clinical characteristic variables were included in the study. Data were randomly divided in an 8:2 ratio into a data set and a validation set, respectively. Area Under Curve (AUC) values of 0.72 for the decision tree, 0.97 for the random forest, 0.89 for SVM, and 0.94 for XGBoost were obtained from the data set. Similar results were obtained from the validation set. Twenty-three variables were obtained from LASSO regression, and eight variables were obtained by considering the intersection of the variables obtained using the previous four machine learning methods. Furthermore, a multivariate logistic regression model was constructed using the data set; the ROC indicated its good performance. The ROC curve also verified the good predictive performance in the validation set.ConclusionsWe constructed a logistic regression model with good predictive performance, which allowed us to accurately predict whether patients who received NCRT and TME will exhibit disease progression.</p