Association of the Expression Level of miR-16 with Prognosis of Solid Cancer Patients: A Meta-Analysis and Bioinformatic Analysis

Objective. To assess the association between the expression level of miR-16 and prognosis of solid cancer patients by meta-analysis and bioinformatic analysis. Methods. PubMed, Web of Science, and Embase databases were searched until October 31, 2019, to identify eligible studies reporting the association of the miR-16 status with the prognosis of solid cancer patients. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled, and a heterogeneity test was conducted. Sensitivity analysis and a publication bias test were also carried out. Furthermore, the miRpower database was used to validate the association. Results. Thirteen articles with 2303 solid cancer patients were included in the meta-analysis. Solid cancer patients with low expression level of miR-16 had shorter survival time (I2=84.0%, HR=1.47, 95% CI: 1.13-1.91, P=0.004). In the subgroup analyses of cancer sites, low miR-16 expression level was associated with poor prognosis in the reproductive system cancers (I2=33.3%, HR=1.24, 95% CI: 1.06-1.45, P=0.008). Sensitivity analysis suggested that the pooled HR was stable and omitting a single study did not change the significance of the pooled HR. Begg’s test and Egger’s test revealed no publication bias in the meta-analysis. In bioinformatic analysis, the significant association between miR-16 level and prognosis of patients with reproductive system cancers was further confirmed (HR=1.21, 95% CI: 1.03-1.42, P=0.017). Conclusion. Low expression level of miR-16 is an indicator for poor prognosis of solid cancer patients, particularly in reproductive system cancers

The high-risk features and effect of postoperative radiotherapy on survival for patients with surgically treated stage IIIA-N2 non-small cell lung cancer

Abstract Objectives Although postoperative radiotherapy (PORT) could reduce the incidence of local recurrence in patients with IIIA-N2 non-small cell lung cancer (NSCLC), the role of PORT on survival in patients with surgically treated stage IIIA-N2 NSCLC remains controversial. Therefore, this study was designed to evaluate the effect of PORT on survival for patients with surgically treated stage IIIA-N2 NSCLC. Materials and methods This study population was chosen from the Surveillance, Epidemiology, and End Results database. The Cox proportional hazards regression analysis was used to determine significant contributors to overall survival (OS) and cancer special survival (CSS) outcomes. To balance baseline characteristics between the non-PORT group and PORT group, propensity score matching (PSM) with 1:1 propensity nearest-neighbor match by 0.001 matching tolerance was conducted by R software. Furthermore, a Kaplan–Meier curve was used to visualize the OS and CSS between the PORT group and non-PORT group survival probability. Results Of all evaluated cases, 4511 with IIIA-N2 NSCLC were eligible for inclusion, of which 1920 were enrolled into the PORT group. On univariate analysis and multivariate analysis, sex, age, year of diagnosis, race, histologic type, T stage, PORT, use of chemotherapy, and positive regional nodes were significantly associated with OS and CSS in IIIA-N2 NSCLC (P < 0.05). However, PORT was not significantly associated with OS (univariate HR = 0.92, 95%CI 0.85–0.99, P = 0.02; multivariate HR = 1.01, 95%CI 0.93–1.08, P = 0.91) and CSS (univariate HR = 0.92, 95%CI 0.85–1.01, P = 0.06; multivariate HR = 1.103 95%CI 0.94–1.12, P = 0.56) in IIIA-N2 NSCLC. Meanwhile, after PSM, neither OS nor CSS did differ significantly between the non-PORT group and PORT group (OS HR = 1.08, 95%CI 0.98–1.19, P = 0.12; CSS HR = 1.10, 95%CI 0.99–1.23, P = 0.07). Conclusion PORT did not contribute to a survival benefit in patients with surgically treated stage IIIA-N2 NSCLC

Association of CDKN2BAS Polymorphism rs4977574 with Coronary Heart Disease: A Case-Control Study and a Meta-Analysis

The goal of our study was to explore the significant association between a non-protein coding single nucleotide polymorphism (SNP) rs4977574 of CDKN2BAS gene and coronary heart disease (CHD). A total of 590 CHD cases and 482 non-CHD controls were involved in the present association study. A strong association of rs4977574 with CHD was observed in females (genotype: p = 0.002; allele: p = 0.002, odd ratio (OR) = 1.57, 95% confidential interval (CI) = 1.18–2.08). Moreover, rs4977574 was more likely to be a risk variant of CHD under the recessive model in females (χ2 = 10.29, p = 0.003, OR = 2.14, 95% CI = 1.31–2.77). A breakdown analysis by age had shown that there was an 87% increased risk of CHD for females younger than 65 years (genotype: χ2 = 14.64, degrees of freedom (df) = 2, p = 0.0002; allele: χ2 = 11.31, df = 1, p = 0.0008, OR = 1.87, 95% CI = 1.30–2.70). Similar observation was also found in males younger than 65 years (genotype: χ2 = 8.63, df = 2, p = 0.04; allele: χ2 = 7.55, df = 1, p = 0.006, OR = 1.45, 95% CI = 1.11–1.90). p values were adjusted by age, sex, smoking, high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C). Meta-analysis of 23 studies among 36,452 cases and 39,781 controls showed a strong association between rs4977574 and the risk of CHD (p &lt; 0.0001, OR = 1.27, 95% CI = 1.22–1.31)

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

Abstract Background The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery. Methods Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student’s t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs. Results DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03). Conclusion Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma

Distinguishing Lung Adenocarcinoma from Lung Squamous Cell Carcinoma by Two Hypomethylated and Three Hypermethylated Genes: A Meta-Analysis

<div><p>Significant differences in the aberrant methylation of genes exist among various histological types of non-small cell lung cancer (NSCLC), which includes adenocarcinoma (AC) and squamous cell carcinoma (SCC). Different chemotherapeutic regimens should be administered to the two NSCLC subtypes due to their unique genetic and epigenetic profiles. The purpose of this meta-analysis was to generate a list of differentially methylated genes between AC and SCC. Our meta-analysis encompassed 151 studies on 108 genes among 12946 AC and 10243 SCC patients. Our results showed two hypomethylated genes (<i>CDKN2A</i> and <i>MGMT</i>) and three hypermethylated genes (<i>CDH13</i>, <i>RUNX3</i> and <i>APC</i>) in ACs compared with SCCs. In addition, our results showed that the pooled specificity and sensitivity values of <i>CDH13</i> and <i>APC</i> were higher than those of <i>CDKN2A</i>, <i>MGMT</i> and <i>RUNX3</i>. Our findings might provide an alternative method to distinguish between the two NSCLC subtypes.</p></div

Flow diagram.

<p>The flow diagram of the stepwise selection from relevant studies.</p

Forest and funnel plots of <i>CDH13</i>, <i>RUNX3</i> and <i>APC</i>.

<p>The forest plots of <i>CDH13</i>, <i>RUNX3</i> and <i>APC</i> displayed the effect size and 95% CIs for the included studies. Our results showed that the total ORs of <i>CDH13</i>, <i>RUNX3</i> and <i>APC</i> demonstrated that the methylation of <i>CDH13</i>, <i>RUNX3</i> and <i>APC</i> in AC were significantly more frequent than in SCC. Funnel plots of meta-analyses of <i>CDH13</i>, <i>RUNX3</i> and <i>APC</i> demonstrated no publication biases in the included studies. The details of abbreviations (M-H, ORs, CIs, and SE) and weight were shown in the legends of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149088#pone.0149088.g002" target="_blank">Fig 2</a>.</p

Forest and funnel plots of <i>CDKN2A</i> and <i>MGMT</i>.

<p>The forest plots of <i>CDKN2A</i> and <i>MGMT</i> displayed the effect size and 95% CIs for the included studies. Funnel plots suggested no publication bias in the meta-analyses of <i>CDKN2A</i> and <i>MGMT</i> genes. Our results showed that the total ORs for <i>CDKN2A</i> and <i>MGMT</i> were less than1, which demonstrated the methylation of <i>CDKN2A</i> and <i>MGMT</i> in AC were relatively higher than in SCC. Funnel plots of meta-analyses of <i>CDH13</i>, <i>RUNX3</i> and <i>APC</i> demonstrated no publication biases in the included studies. In addition, M-H denotes Mantel-Haenszel statistical method to calculate the combined odds ratios (ORs) and the corresponding 95% confidence intervals (95% CIs). Weight denotes the weighted average of the intervention effect estimated in each study. SE denotes standard errors.</p

Deep Learning-Based Robust Visible Light Positioning for High-Speed Vehicles

Robustness is a key factor for real-time positioning and navigation, especially for high-speed vehicles. While visible light positioning (VLP) based on LED illumination and image sensors is widely studied, most of the VLP systems still suffer from the high positioning latency and the image blurs caused by high-speed movements. In this paper, a robust VLP system for high-speed vehicles is proposed based on a deep learning and data-driven approach. The proposed system can significantly increase the success rate of decoding VLP-LED user identifications (UID) from blurred images and reduce the computational latency for detecting and extracting VLP-LED stripe image regions from captured images. Experimental results show that the success rate of UID decoding using the proposed BN-CNN model could be higher than 98% when that of the traditional Zbar-based decoder falls to 0, while the computational time for positioning is decreased to 9.19 ms and the supported moving speed of our scheme can achieve 38.5 km/h. Therefore, the proposed VLP system can enhance the robustness against high-speed movement and guarantee the real-time response for positioning and navigation for high-speed vehicles