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Background New evidence from clinical and fundamental researches suggests that SNHG7 is involved in the occurrence and development of carcinomas. And the increased levels of SNHG7 are associated with poor prognosis in various kinds of tumors. However, the small sample size was the limitation for the prognostic value of SNHG7 in clinical application. The aim of the present meta-analysis was to conduct a qualitative analysis to explore the prognostic value of SNHG7 in various cancers. Methods Articles related to the SNHG7 as a prognostic biomarker for cancer patients, were comprehensive searched in several electronic databases. The enrolled articles were qualified via the preferred reporting items for systematic reviews and meta-analysis of observational studies in epidemiology checklists. Additionally, an online database based on The Cancer Genome Atlas (TCGA) was further used to validate our results. Results We analyzed 2418 cancer patients that met the specified criteria. The present research indicated that an elevated SNHG7 expression level was significantly associated with unfavorable overall survival (OS) (HR = 2.45, 95% CI: 2.12–2.85, p p p p p p Conclusions The present analysis suggests that elevated SNHG7 is significantly associated with unfavorable OS, tumor progression, LNM and DM in various carcinomas, and may be served as a promising biomarker to guide therapy for cancer patients.

Background Bone morphogenetic protein 9 (BMP9) has been identified as a crucial inducer of osteoblastic differentiation in mesenchymal stem cells (MSCs). Although microRNAs (miRNAs) are known to play a role in MSC osteogenesis, the mechanisms of action of miRNAs in BMP9-induced osteoblastic differentiation remain poorly understood. Methods In this study, we investigate the possible role of the miR17-92 cluster in the BMP9-induced osteogenic differentiation of MSCs by using both in vitro and in vivo bone formation assays. Results The results show that miR-17, a member of the miR17-92 cluster, significantly impairs BMP9-induced osteogenic differentiation. This impairment is effectively rescued by a miR-17 sponge, an antagomiR sequence against miR-17. Using TargetScan and the 3′-untranslated region luciferase reporter assays, we show that the direct target of miR-17 is the retinoblastoma gene (RB1), a gene that is pivotal to osteoblastic differentiation. We also confirm that RB1 is essential for the miR-17 effects on osteogenesis. Conclusion Our results indicate that miR-17 expression impairs normal osteogenesis by downregulating RB1 expression and significantly inhibiting the function of BMP9.

The present meta-analysis aimed to analyze available data to identify the prognostic role of NEAT1 in multiple carcinomas. A systematic search was performed by using several computerized databases from inception to June 7, 2017. The quantity of the publications was assessed according to MOOSE checklist. Pooled HRs with 95% CI was calculated to summarize the effect. A total of 12 studies with 3,262 cancer patients were pooled in the analysis to evaluate the prognostic value of NEAT1 in multiple tumors. High expression levels of NEAT1 were demonstrated to be associated with poor OS (HR = 1.71, 95%CI: 1.37–2.14, P P P = 0.002) and distant metastasis (HR: 2.80, 95%CI: 1.60–4.91, P = 0.0003) respectively. The results indicate that NEAT1 expression level is a prognostic biomarker for OS and metastasis in general tumors.

Electrooculogram (EOG) is one of the major artifacts in the design of electroencephalogram (EEG)-based brain computer interfaces (BCIs). That removing EOG artifacts automatically while retaining more neural data will benefit for further feature extraction and classification. In order to remove EOG artifacts automatically as well as reserve more useful information from raw EEG, this paper proposes a novel blind source separation method called CCA-EEMD (canonical correlation analysis, ensemble empirical mode decomposition). Technically, the major steps of CCA-EEMD are as follows: Firstly, the multiple-channel original EEG signals are separated into several uncorrelated components using CCA. Then, the EOG component can be identified automatically by its kurtosis value. Next, the identified EOG component is decomposed into several intrinsic mode functions (IMFs) by EEMD. The IMFs uncorrelated to the EOG component are recognized and retained, and a new component will be constructed by the retained IMFs. Finally, the clean EEG signals are reconstructed. Keep in mind that the novelty of this paper is that the identified EOG component is not removed directly but used to extract neural EEG data, which would keep more effective information. Our tests with the data of seven subjects demonstrate that the proposed method has distinct advantages over other two commonly used methods in terms of average root mean square error [37.71 ± 0.14 (CCA-EEMD), 44.72 ± 0.13 (CCA), 49.59 ± 0.16 (ICA)], signal-to-noise ratio [3.59 ± 0.24 (CCA-EEMD), −6.53 ± 0.18(CCA), −8.43 ± 0.26 (ICA)], and classification accuracy [0.88 ± 0.002 (CCA-EEMD), 0.79 ± 0.001 (CCA), 0.73 ± 0.002 (ICA)]. The proposed method can not only remove EOG artifacts automatically but also keep the integrity of EEG data to the maximum extent.

// Jia Liu 1, * , Jieru Lin 2, * , Yingqi Li 4 , Yunyuan Zhang 3 and Xian Chen 3 1 Department of Pharmacy, Guiyang Maternal and Child Health Care Hospital, Guiyang 550003, China 2 Department of Respiratory and Critical Care Medicine, Guizhou Provincial People's Hospital, Guiyang 550002, China 3 Department of Clinical Laboratory, The Affiliated Hospital of Qingdao University, Qingdao 266003, China 4 Department of Ophthalmology, The Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China * These authors contributed equally to this work Correspondence to: Xian Chen, email: cxkakicoco2014@163.com Keywords: cancer, prognosis, long non-coding RNA, taurine upregulated gene 1 Received: February 25, 2017 Accepted: April 29, 2017 Published: May 13, 2017 ABSTRACT LncRNA TUG1 has been demonstrated to be aberrantly expressed in several types of cancer and maybe serve as a prognostic marker for cancer patients. However, most individual studies have been limited by small sample sizes and controversial results. Therefore, this meta analysis was conducted to analyze available data to delineate the potential clinical application of lncRNA TUG1 on cancer prognosis, lymph node metastasis and tumor progression. Up to February 20, 2017, literature collections were conducted by comprehensive searching electronic databases, including Cochrane Library, PubMed, Embase, BioMed Central, Springer, ScienceDirect, ISI Web of Knowledge, together with three Chinese databases. The hazard ratios (HR) with 95% confidence interval (95% CI) were calculated to assess the strength of the association. Eight studies with a total of 840 cancer patients were included in the present meta analysis. The results indicated that elevated lncRNA TUG1 significantly predicted unfavorable overall survival (OS) (HR = 2.06, 95% CI: 1.23–3.45, P = 0.006), but failed to show incline to lymph node metastasis (HR: 1.16, 95% CI: 0.82–1.62, P = 0.40) and disease progression (III/IV vs. I/II: HR 1.16, 95% CI: 0.74–1.81, P = 0.52). In stratified analyses, a significantly unfavorable OS associated with elevated lncRNA TUG1 was observed in both bladder cancer (HR = 2.98, 95% CI: 1.84–4.83, P < 0.0001) and other system cancer (HR = 2.63, 95% CI: 1.42–4.87, P = 0.002), but not respiratory system cancer (HR = 0.93, 95% CI: 0.30–2.82, P = 0.895). The results indicated that increased lncRNA TUG1 was an independent prognostic biomarker for unfavorable OS but may not susceptible to lymph node metastasis and tumor progression in cancer patients.

Aims: Growing evidence from recent studies has shown that lncRNA HULC plays a role in the development of multiple carcinomas. This meta-analysis aimed to analyze available data to identify the prognostic value of HULC in multiple tumors. Methods: A systematic search was performed by using PubMed (medline), Embase, ISI Web of Knowledge, Springer, the Cochrane Library, Scopus, BioMed Central, ScienceDirect, Wanfang, Weipu, and China National Knowledge Internet (CNKI) computerized databases from inception to Nov 30, 2016. The quality of the publications was assessed according to the critical review checklist of the Dutch Cochrane Centre proposed by MOOSE and PRISMA. Pooled hazard ratios (HR) with 95% confidence interval (95% CI) were calculated to summarize the effect. Results: A total of ten studies with 1077 cancer patients were pooled in the present meta-analysis to evaluate the prognostic value of HULC in multiple tumors. High expression levels of HULC were demonstrated to be associated with poor overall survival (OS) (HR=2.44, 95%CI: 1.96-3.03, P=0.000). Subgroup analysis showed that cancer type (digestive or non-digestive disease), residence region (China), sample size (more or less than 100) and follow-up months (more or less than 60) did not alter the predictive value of HULC on OS in various cancers. Additionally, increased HULC expression was found to be moderately associated with tumor stage and progression (III/IV vs. I/II: HR=1.59, 95% CI: 1.31-1.92, P

Recently, there are more and more evidences from studies have revealed the association between microRNA-9 (miR-9) expression and outcome in multiple cancers, but inconsistent results have also been reported. It is necessary to rationalize a meta analysis of all available data to clarify the prognostic role of miR-9. Eligible studies were selected through multiple search strategies and the quality was assessed by MOOSE. Data was extracted from studies according to the key statistics index. All analyses were performed using STATA software. Twenty studies were selected in the meta-analysis to evaluate the prognostic role of miR-9 in multiple tumors. MiR-9 expression level was an independent prognostic biomarker for OS in tumor patients using multivariate and univariate analyses. High expression levels of miR-9 was demonstrated to associated with poor overall survival (OS) (HR = 2.23, 95 % CI: 1.56–3.17, P

A subject-based feature extraction method is discussed.The feature extraction method combines wavelet packet decomposition, common spatial patterns and fisher distance.The selection of sub-bands based on fisher distance tells apart those useless sub-bands and remain best sub-bands for each subject.The proposed method can extract suitable features for specific subject and achieve a higher classification accuracy than non-subject-based method. Background and objectiveFeature extraction of electroencephalogram (EEG) plays a vital role in brain-computer interfaces (BCIs). In recent years, common spatial pattern (CSP) has been proven to be an effective feature extraction method. However, the traditional CSP has disadvantages of requiring a lot of input channels and the lack of frequency information. In order to remedy the defects of CSP, wavelet packet decomposition (WPD) and CSP are combined to extract effective features. But WPD-CSP method considers less about extracting specific features that are fitted for the specific subject. So a subject-based feature extraction method using fisher WPD-CSP is proposed in this paper. MethodsThe idea of proposed method is to adapt fisher WPD-CSP to each subject separately. It mainly includes the following six steps: (1) original EEG signals from all channels are decomposed into a series of sub-bands using WPD; (2) average power values of obtained sub-bands are computed; (3) the specified sub-bands with larger values of fisher distance according to average power are selected for that particular subject; (4) each selected sub-band is reconstructed to be regarded as a new EEG channel; (5) all new EEG channels are used as input of the CSP and a six-dimensional feature vector is obtained by the CSP. The subject-based feature extraction model is so formed; (6) the probabilistic neural network (PNN) is used as the classifier and the classification accuracy is obtained. ResultsData from six subjects are processed by the subject-based fisher WPD-CSP, the non-subject-based fisher WPD-CSP and WPD-CSP, respectively. Compared with non-subject-based fisher WPD-CSP and WPD-CSP, the results show that the proposed method yields better performance (sensitivity: 88.7?0.9%, and specificity: 91?1%) and the classification accuracy from subject-based fisher WPD-CSP is increased by 6-12% and 14%, respectively. ConclusionsThe proposed subject-based fisher WPD-CSP method can not only remedy disadvantages of CSP by WPD but also discriminate helpless sub-bands for each subject and make remaining fewer sub-bands keep better separability by fisher distance, which leads to a higher classification accuracy than WPD-CSP method.

BACKGROUND Mounting evidence from recent studies has revealed the association of lncRNA PANDAR expression levels with outcomes in several types of cancer. However, inconsistent results have also been reported, which rationalized a meta-analysis of available data to analyze the prognostic value of lncRNA PANDAR. METHODS From inception to May 26, 2018, electronic literature databases including PubMed (medline), the Cochrane Library, ScienceDirect, Springer, ISI Web of Knowledge, Wiley Online library, BioMed Central, and Embase were searched for literature collections. The hazard ratios (HR) with 95% confidence interval (95% CI) were utilized to calculate pooled effect size. RESULTS A total of 1,132 cancer patients were enrolled in the present meta-analysis to assess the prognostic value of PANDAR in various carcinomas. Promoted PANDAR expression was demonstrated to significantly predict unfavorable OS (HR = 1.77, 95% CI: 1.12 - 2.80, p = 0.014) by the random effects model. According to the stratified analyses and meta-regression results, the heterogeneity of present analysis may be attributed to the differences of cancer resources. Furthermore, over-expression of PANDAR was revealed to be effectively predictive of cancer progression (HR = 1.70, 95% CI: 1.41 - 2.05, p < 0.00001) and LNM (HR = 1.71, 95% CI: 1.39 - 2.10, p < 0.00001). CONCLUSIONS The present findings indicate that increased PANDAR is associated with poor OS in patients with general carcinomas and may serve as a useful clinical prognostic biomarker.

Aim. There is increasing evidence that high expression levels of the gastric carcinoma highly expressed transcript 1 (GHET1), a long noncoding RNA (lncRNA), are associated with cancer prognosis and may be used as a valuable biomarker for cancer patients. The purpose of this meta-analysis was to analyze existing data to reveal potential clinical applications of GHET1 for cancer prognosis and tumor progression. All of these studies included in this meta-analysis were collected through a variety of retrieval strategies; and the enrolled articles were qualified via the meta-analysis of enrolled studies in epidemiology (MOOSE) and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) checklists. Materials and Methods. The literature collection was performed by a comprehensive search through electronic databases for studies published on or before March 10, 2019. These included the Cochrane library, PubMed, Embase, Web of Science, Springer, Science Direct, and three Chinese databases: CNKI, Weipu, and Wanfang. Seven studies that met the specified criteria were analyzed in the present research. Results. The combined results indicate that an elevated GHET1 expression level is significantly associated with poor overall survival (OS) (HR=2.40, 95% CI: 1.87–3.08, p<0.001) and tumor progression (III/IV vs. I/II: HR=1.80, 95% CI: 1.48–2.18, p<0.001) in multiple cancers. The elevated GHET1 expression was also associated with lymph node metastasis (LNM) (HR=2.44, 95% CI: 1.86–3.20, p<0.001) in Chinese cancer patients. Conclusions. The present findings indicate that an increased GHET1 expression level is associated with poor OS, tumor progression, and LNM in patients with multiple tumors and may serve as a useful prognostic biomarker in Chinese cancer patients.

Background Increasing evidence revealed that high expression level of lncRNA SNHG1 was associated with the unfavorable prognosis of cancer and maybe used as a valuable biomarker for cancer patients. The present meta->analysis is to analyze existing data to reveal potential clinical application of SNHG1 on cancer prognosis and tumor progression. All of the included studies were collected through a variety of retrieval strategies. And the articles were qualified by MOOSE and PRISMA checklists. Methods Up to Mar 20, 2018, literature collection was performed by comprehensive search through electronic databases, including the Cochrane library, PubMed, Embase, Web of science, Springer, Science direct, and three Chinese databases: CNKI, Weipu, and Wanfang. We analyzed 14 studies that met the criteria, and concluded that the increased SHNG1 level was correlated with poor OS and tumor progression. Results The combined results indicated that elevated SNHG1 expression level was significantly associated with poor OS (HR = 2.06, 95% CI: 1.69–2.52, P

Colorectal cancer (CRC) is one of the most deadly cancers worldwide. Significant progress has been made in understanding the molecular pathogenesis of CRC, which has led to successful early diagnosis, surgical intervention and combination chemotherapy. However, limited therapeutic options are available for metastatic and/or drug-resistant CRC. While the aberrantly activated Wnt/β-catenin pathway plays a critical initiating role in CRC development, disruption of the bone morphogenetic protein (BMP) pathway causes juvenile polyposis syndrome, suggesting that BMP signaling may play a role in CRC development. However, conflicting results have been reported concerning the possible roles of BMP signaling in sporadic colon cancer. Here, we investigated the effect of BMP2 on the proliferation, migration, invasiveness and tumor growth capability of human CRC cells. Using an adenovirus vector that overexpresses BMP2 and the piggyBac transposon-mediated stable BMP2 overexpression CRC line, we found that exogenous BMP2 effectively inhibited HCT116 cell proliferation and colony formation. BMP2 was shown to suppress colon cancer cell migration and invasiveness. Under a low serum culture condition, forced expression of BMP2 induced a significantly increased level of apoptosis in HCT116 cells. Using a xenograft tumor model, we found that forced expression of BMP2 in HCT116 cells suppressed tumor growth, accompanied by decreased cell proliferation activity. Taken together, our results strongly suggest that BMP2 plays an important inhibitory role in governing the proliferation and aggressive features of human CRC cells.

SOX2OT has been demonstrated to be aberrantly expressed in several types of cancer and maybe serve as a prognostic marker for cancer patients. However, most individual studies have been limited by small sample sizes and controversial results. Therefore, the present meta-analysis was conducted to analyze available data to reveal the potential clinical application of SOX2OT on cancer prognosis, tumor progression, distance metastasis and lymph node metastasis. Up to February 20, 2017, literature collections were conducted by comprehensive searching electronic databases, including Cochrane Library, PubMed, Embase, BioMed Central, Springer, ScienceDirect, ISI Web of Knowledge, together with three Chinese databases: China National Knowledge Internet (CNKI), Weipu and Wanfang. The hazard ratios (HR) with 95% confidence interval (95% CI) were calculated to assess the strength of the association. Five studies with a total of 481 cancer patients were included in the present meta-analysis. The results indicated that elevated SOX2OT significantly predicted unfavorable overall survival (OS) (HR = 2.44, 95% CI: 1.75-3.39, P

Numerous individual studies evaluating the relationship between CD44V6 over-expression and prognostic impact in patients with osteosarcoma (OS) have yielded in conclusive results. This meta-analysis aimed to determine the value of cell adhesion molecule CD44V6 in prognosis of OS by conducting a systematic review and meta-analysis. A comprehensive search was conducted using PubMed (medline), Embase, ISI Web of Knowledge, Springer, the Cochrane Library, Scopus, BioMed Central, ScienceDirect, Wanfang, Weipu, and China National Knowledge Internet (CNKI) databases from inception through May 26, 2015. All available articles written in English or Chinese that investigated the expression of CD44V6 and the prognosis of OS were included. The quantity of the studies was evaluated according to the critical review checklist of the Dutch Cochrane Centre proposed by MOOSE. Finally, a total of eight studies with 486 OS patients were involved and the results indicated that the positive expression of CD44V6 predicts neoplasm metastasis (RR = 1.76, 95 % CI 1.38–2.25, p

In order to remove artifacts automatically and effectively from the Electroencephalography (EEG) in Brain Computer Interfaces (BCIs), a new preprocessing algorithm called EMD-ICA (Empirical Mode Decomposition, Independent Component Analysis) is explored. The EMD-ICA method includes the following steps: Firstly, EEG signals from single or multiple channels are decomposed into a series of intrinsic mode functions (IMFs) using EMD. Each IMF can be approximately used as an input channel of the ICA, and these IMFs constitute the input matrix of the ICA. Then, the input matrix is separated into a set of statistics independent components (ICs) by ICA. Furthermore, each of statistics ICs is analyzed by using the method of sample entropy to automatically determine whether it is artifact signal. Finally, the ICs determined as artifacts are eliminated and the remaining ICs are reconstructed. The reconstructed EEG is used in the following feature extraction and classification. To evaluate the effect of the proposed method, common spatial patterns (CSP) and support vector machine (SVM) algorithm are used to extract and classify the EEG data from two datasets. The experimental results show that the proposed method can remove various kinds of artifacts effectively, and improve the recognition accuracy greatly.