LINC02163 regulates growth and epithelial-to-mesenchymal transition phenotype via miR-593-3p/FOXK1 axis in gastric cancer cells

<p>Recently, long non-coding RNAs (lncRNAs) were involved in promoting gastric cancer (GC) initiation and progression. In the current study, we revealed that the expression level of LINC02163 was elevated in GC cell lines and tissues. Knockdown of LINC02163 inhibited GC cells growth and invasion both <i>in vitro</i> and <i>in vivo</i>. Mechanismly, LINC02163 exerted as a ceRNA and negatively regulated miR-593-3p expression. In addition, FOXK1 was identified as a down-stream target of miR-593-3p. The miR-593-3p/FOXK1 axis mediated LINC02163’s effect on GC. To the best of our knowledge, our findings provided the first evidence that LINC02163 functioned as an oncogene in GC. LINC02163 may be a candidate prognostic biomarker and a target for new therapies in GC patients.</p

Performance of Real-Time Elastography for the Staging of Hepatic Fibrosis: A Meta-Analysis

<div><p>Background</p><p>With the rapid development of real-time elastography (RTE), a variety of measuring methods have been developed for the assessment of hepatic fibrosis. We evaluated the overall performance of four methods based on RTE by performing meta-analysis of published literature.</p><p>Methods</p><p>Online journal databases and a manual search from April 2000 to April 2014 were used. Studies from different databases that meet inclusion criteria were enrolled. The statistical analysis was performed using a random-effects model and fixed-effects model for the overall effectiveness of RTE. The area under the receiver operating characteristic curve (AUROC) was calculated for various means. Fagan plot analysis was used to estimate the clinical utility of RTE, and the heterogeneity of the studies was explored with meta-regression analysis.</p><p>Results</p><p>Thirteen studies from published articles were enrolled and analyzed. The combined AUROC of the liver fibrosis index (LFI) for the evaluation of significant fibrosis (F≥2), advanced fibrosis (F≥3), and cirrhosis (F = 4) were 0.79, 0.94, and 0.85, respectively. The AUROC of the elasticity index (EI) ranged from 0.75 to 0.92 for F≥2 and 0.66 to 0.85 for F = 4. The overall AUROC of the elastic ratio of the liver for the intrahepatic venous vessels were 0.94, 0.93, and 0.96, respectively. The AUROC of the elastic ratio of the liver for the intercostal muscle in diagnosing advanced fibrosis and cirrhosis were 0.96 and 0.92, respectively. There was significant heterogeneity in the diagnostic odds ratio (DOR) for F≥2 of LFI mainly due to etiology (<i>p</i><0.01).</p><p>Conclusion</p><p>The elastic ratio of the liver for the intrahepatic vein has excellent precision in differentiating each stage of hepatic fibrosis and is recommend to be applied to the clinic.</p></div

Forest plot from meta-analysis of DOR value using a random-effect or fixed-effect model for significant fibrosis.

<p>(A) Forest plot of LFI and (B) Forest plot of ER1. DOR: diagnostic odds ratio; LFI: liver fibrosis index; ER1: the elastic ratio of the liver for the intrahepatic vein; Ochi (a): the training set of the subjects in the study by Ochi et al; Ochi (b): the validating set of the subjects in the study by Ochi et al.</p

Forest plot from meta-analysis of DOR value using a random-effect or fixed-effect model for significant fibrosis.

<p>(A) Forest plot of LFI and (B) Forest plot of ER1 and (C) Forest plot of ER2. DOR: diagnostic odds ratio; LFI: liver fibrosis index; ER1: the elastic ratio of the liver for the intrahepatic vein; ER2: the elastic ratio of the liver for the intercostal muscle; Ochi (a): the training set of the subjects in the study by Ochi et al; Ochi (b): the validating set of the subjects in the study by Ochi et al.</p

Forest plot from meta-analysis of DOR value using a random-effect or fixed-effect model for significant fibrosis.

<p>(A) Forest plot of LFI and (B) Forest plot of ER1 and (C) Forest plot of ER2. DOR: diagnostic odds ratio; LFI: liver fibrosis index; ER1: the elastic ratio of the liver for the intrahepatic vein; ER2: the elastic ratio of the liver for the intercostal muscle; Ochi (a): the training set of the subjects in the study by Ochi et al; Ochi (b): the validating set of the subjects in the study by Ochi et al.</p

Characteristics of studies evaluating the performance of real time elastography for staging liver fibrosis.

<p>RTE, real time elastography; LFI, liver fibrosis index; ER1, the elastic ratio of the liver for the intrahepatic venous; ER2, the elastic ratio of the liver for the intercostal muscle; EI, elastic ratio; CHB, chronic hepatitis B; CHC, chronic hepatitis C; ALD, alcoholic liver disease, NAFLD, nonalcoholic liver fatty disease; AIH, autoimmune hepatitis; PBC, primary biliary cirrhosis.</p><p>Characteristics of studies evaluating the performance of real time elastography for staging liver fibrosis.</p

Flow diagram of search results and study selection.

<p>Flow diagram of search results and study selection.</p

Additional file 1 of Reduced representative methylome profiling of cell-free DNA for breast cancer detection

Additional file 1: Fig. S1. In silico restriction digestion analysis. a The amount of remaining reads from AT-rich reads deletion of a public WGBS dataset for K562 cells. b The CG rate of remaining reads from AT-rich reads deletion of a public WGBS dataset for K562 cells, compared with a public RRBS dataset for K562 cells. c The percentage of remaining reads mapping to CpG island, CpG shore or CpG shelf, compared to the original WGBS dataset and a RRBS dataset. d The percentage of remaining reads mapping to promoter, compared to the original WGBS dataset and a RRBS dataset. Fig. S2. Genome plot for the GBGT1 gene locus compares read coverage between and WGBS, XRBS, RRBS, 1-cut RRMP and 4-cut RRMP. Boxes represent reads, and unmethylated (blue) and methylated (red) CpGs are indicated. CpG islands are indicated. Fig. S3. RRMP efficiently captures CpGs in CpG islands and promoters. a, b, Plots show the number of CpG islands (a) or promoters (b) with at least 100-fold combined coverage as a function of sequencing depth (x axis) for 4-cut RRMP(K562), XRBS (K562), WGBS (K562) and RRBS (SW1353). Enrichment for functional elements at a uniform sequencing depth of 10 billion base pairs is indicated. Vertical gray line indicates break in x-axis scale. c Plot compares CpG coverage as a function of sequencing depth (x-axis) for WGBS, XRBS, RRBS, 1-cut RRMP and 4-cut RRMP. d, e, Downsampling analysis plot as in panel c but restricted to CpGs within CpG islands (d) and gene promoters (e). f Heat map shows genome-wide DNA methylation in 100-kb windows for 1- cut RRMP, XRBS, WGBS from K562 cells. Fig. S4. RRMP detects tumor-related methylation differences. a Heat map shows Pearson correlation of RRMP methylation profiles of 100 kb windows generated from 9 tumor cell lines and 3 WBC samples from healthy donors. b Heat map depicts hypermethylated and hypomethylated regions in each type of tumor cells compared to WBC samples. Fig. S5. Cell-line-specific DNA hypomethylation from RRMP correlates with H3K27ac signal. Heat map depicts 8-kb regions centered on H3K27ac peaks identified in NCI-H460 and HT29 ChIP-seq datasets. Rows are ordered by DNA methylation difference between both cell lines. Peaks not specifically hypermethylated in either cell line (‘Others’) were downsampled for visualization. Fig. S6. RRMP efficiently captures CpGs in CpG islands and promoters using cfDNA. a Plot compares CpG coverage as a function of sequencing depth (x-axis) for 4-cut RRMP and EM-seq. b, c, Downsampling analysis plot as in panel c but restricted to CpGs within CpG islands (b) and gene promoters (c). d Plot shows coverage depth of CpGs in 4-cut RRMP and EM-seq at a uniform sequencing depth of 10 billion base pairs. e Length distribution of cfDNA fragments from breast cancer patients (BC, n = 29) and non-breast cancer individuals (NBC, n = 27)