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Background: Our systematic review summarizes the evidence concerning the accuracy of serum diagnostic and prognostic tests for colorectal cancer (CRC). Methods: The databases MEDLINE and EMBASE were searched iteratively to identify the relevant literature for serum markers of CRC published from 1950 to August 2012. The articles that provided adequate information to meet the requirements of the meta-analysis of diagnostic and prognostic markers were included. A 2-by-2 table of each diagnostic marker and its hazard ratio (HR) and the confidence interval (CI) of each prognostic marker was directly or indirectly extracted from the included papers, and the pooled sensitivity and specificity of the diagnostic marker and the pooled HR and the CI of the prognostic marker were subsequently calculated using the extracted data. Results: In total, 104 papers related to the diagnostic markers and 49 papers related to the prognostic serum markers of CRC were collected, and only 19 of 92 diagnostic markers were investigated in more than two studies, whereas 21 out of 44 prognostic markers were included in two or more studies. All of the pooled sensitivities of the diagnostic markers with > = 3 repetitions were less than 50%, and the meta-analyses of the prognostic markers with more than 3 studies were performed, VEGF with highest (2.245, CI: 1.347–3.744) and MMP-7 with lowest (1.099, CI: 1.018–1.187)) pooled HRs are presented. Conclusions: The quality of studies addressing the diagnostic and prognostic accuracy of the tests was poor, and the results were highly heterogeneous. The poor characteristics indicate that these tests are of little value for clinical practice. [ABSTRACT FROM AUTHOR]

A closer association has been found between the microRNA-146a rs2910164 polymorphism and the risk of head and neck carcinoma in some molecular epidemiological studies. Recently two meta-analyses were performed to explore the relationship between miRNA-146a polymorphisms and the susceptibility of squamous cell carcinoma of the head and neck (SCCHN); however, they yielded conflicting results in susceptibility regarding ethnic variations. Hence, the present study was performed to explain the relationship between the miRNA-146a rs2910164 polymorphism and the risk of SCCHN development of Chinese patients. We retrieved databases and screened eligible papers up to March 10, 2017 and then we extracted the essential data. The subgroup analyses were also performed based on the tumor site, region, and genotyping means. Crude odds ratios (OR) at 95% confidence intervals (CI) were chosen to describe the strength of the association. As a result, 6 publications were included in our study which involved 8 independent case-control studies. A significant association was found between miR-146a rs2910164 polymorphisms and the risk of SCCHN in Chinese patients according to the overall data [CC+CG vs. GG: OR = 1.13; 95%CI = 1.00–1.29; CC vs. GG: OR = 1.19; 95%CI = 1.03–1.38]. According to the subgroup analysis based on tumor site, the risk of cancer was significantly increased among laryngeal cancer (dominant model: OR = 1.76, 95%CI = 1.26~2.46, P = 0.001; homozygote model: OR = 1.83, 95%CI = 1.25~2.67, P = 0.002) and nasopharyngeal carcinoma (homozygote model: OR = 1.41, 95%CI = 1.05~1.90, P = 0.022). In summary, variant alleles of miR-146a rs2910164 alleles may have an association with the increased risk of SCCHN in Chinese patients, and these associations differed based on tumor site. Further studies including a larger sample size will be necessary to clarify these results. [ABSTRACT FROM AUTHOR]

Purpose: Many studies have reported that carbohydrate antigen 153 (CA153) in breast secretions (BS) can discriminate breast cancer (BC) patients from healthy individuals, indicating CA153 in BS as a potential index for BC. This meta-analysis aimed to evaluate the actual diagnostic value of CA153 in BS. Methods: Related papers were obtained from Pubmed, Embase, Scopus, Ovid, Sciverse, the Cochrane library, Chinese Biomedical literature Database (CBM), Technology of Chongqing (VIP), Wan Fang Data, and Chinese National Knowledge Infrastructure (CNKI). Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of CA153 in BS for BC diagnosis were analyzed with the random effect model. SROC and the area under the curve (AUC) were applied to assess overall diagnostic efficiency. Results: This meta-analysis included five studies with a total of 329 BC patients and 381 healthy subjects. For CA153 in BS, the summary sensitivity, specificity, and DOR to diagnose BC were 0.63 (95% confidence interval (CI): 0.57∼0.68), 0.82 (95% CI: 0.78∼0.86), and 9.18 (95% CI: 4.22∼19.95), respectively. Furthermore, the AUC of BS CA153 in the diagnosis of BC was 0.8614. Conclusions: CA153 in BS is a valuable molecular marker in diagnosing BC and should be applied in standard clinical practices of BC screening upon confirmation of our findings in a larger prospective study. [ABSTRACT FROM AUTHOR]

How can we predict the occurrence of acute kidney injury (AKI) in cancer patients based on machine learning with serum creatinine data? Given irregular and heterogeneous clinical data, how can we make the most of it for accurate AKI prediction? AKI is a common and significant complication in cancer patients, and correlates with substantial morbidity and mortality. Since no effective treatment for AKI still exists, it is important to take timely preventive measures. While several approaches have been proposed for predicting AKI, their scope and applicability are limited as they either assume regular data measured over a short hospital stay, or do not fully utilize heterogeneous data. In this paper, we provide an AKI prediction model with a greater applicability, which relaxes the constraints of existing approaches, and fully utilizes irregular and heterogeneous data for learning the model. In a cohort of 21,022 cancer patients who were registered into Korea Central Cancer Registry (KCCR) in Seoul National University Hospital between January 1, 2004 and December 31, 2013, our method achieves 0.7892 precision, 0.7506 recall, and 0.7576 F-measure in predicting whether a patient will develop AKI during the next 14 days. [ABSTRACT FROM AUTHOR]

Background: Published data on the relationship between matrix metalloproteinases (MMPs) polymorphisms and ovarian cancer risk have implicated inconclusive results. To evaluate the role of MMPs polymorphisms in ovarian cancer risk, a meta-analysis and systematic review were performed. Methods: MMPs polymorphisms which could be quantitatively synthesized were involved in meta-analysis. Five comparison models (homozygote model, heterozygote model, dominant model, recessive model, additive model) were carried out, a subgroup analysis was performed to clarify heterogeneity source. The remaining polymorphisms which could not be quantitatively synthesized were involved in systematic review. Results: 10 articles with 20 studies were included in this paper. Among those studies, 8 studies involving MMP1 rs1799750 and MMP3 rs34093618 could be meta-analyzed and 12 studies involving 12 polymorphisms could not. Meta-analysis showed that no associations were found between MMP1 rs1799750 (homozygote model: OR = 0.93, 95%CI = 0.70–1.23, POR = 0.60; heterozygote model: OR = 1.09, 95%CI = 0.78–1.54, POR = 0.61; dominant model: OR = 1.02, 95%CI = 0.83–1.25, POR = 0.84; recessive model: OR = 0.95, 95%CI = 0.75–1.21, POR = 0.67; additive model: OR = 1.00, 95%CI = 0.85–1.17, POR = 0.99), MMP3 rs34093618 (homozygote model: OR = 1.25, 95%CI = 0.70–2.24, POR = 0.46; heterozygote model: OR = 1.08, 95%CI = 0.51–2.31, POR = 0.84; dominant model: OR = 0.97, 95%CI = 0.68–1.38, POR = 0.85; recessive model: OR = 1.12, 95%CI = 0.69–1.80, POR = 0.65; additive model: OR = 1.01, 95%CI = 0.79–1.31, POR = 0.91) and ovarian cancer. Furthermore, similar results were detected in subgroup analysis. The systematic review on 12 polymorphisms suggested that MMP2 C-735T, MMP7 A-181G, MMP8 rs11225395, MMP9 rs6094237, MMP12 rs2276109, MMP20 rs2292730, MMP20 rs12278250, MMP20 rs9787933 might have a potential effect on ovarian cancer risk. Conclusions: In summary, polymorphisms of MMPs might not be associated with ovarian cancer risk. However, it is necessary to conduct more larger-scale, multicenter, and high-quality studies in the future. [ABSTRACT FROM AUTHOR]

Identification of miRNA-disease association is a fundamental challenge in human health clinic. However, the known miRNA-disease associations are rare and experimental verification methods are expensive and time-consuming. Therefore, there is a strong incentive to develop computational methods. In this paper, we calculate the similarity score for each miRNAs pair by integrating miRNA functional similarity and miRNA family information. We use the disease phenotype similarity data to construct the disease similarity network. Then we introduce a new miRNA-disease association prediction method (NETwork Group Similarity, NetGS) to explore the global network similarity, capturing the relationship between the disease and other diseases, the similarity between the potential disease-related miRNA and other miRNAs. Finally based on the consistency of diffusion profiles we get the miRNA-disease association scores. NetGS is tested by the leave-one-out cross validation and achieves an AUC value of 0.8450, which improves the prediction accuracy. NetGS can also be applied to solve the new miRNA-disease association and obtain reliable accuracy. Moreover, we use NetGS to predict new causing miRNAs of three cancers including breast cancer, lung cancer and Hepatocellular cancer. And the top predictions have been confirmed in the online databases. The encouraging results indicate that NetGS might play an essential role for future scientific research. [ABSTRACT FROM AUTHOR]

Background and Objectives: Current studies give us inconsistent results regarding the association of neoplasms and zinc(II) serum and tissues concentrations. The results of to-date studies using meta-analysis are summarized in this paper. Methods: Web of Science (Science citation index expanded), PubMed (Medline), Embase and CENTRAL were searched. Articles were reviewed by two evaluators; quality was assessed by Newcastle-Ottawa scale; meta-analysis was performed including meta-regression and publication bias analysis. Results: Analysis was performed on 114 case control, cohort and cross-sectional studies of 22737 participants. Decreased serum zinc level was found in patients with lung (effect size = −1.04), head and neck (effect size = −1.43), breast (effect size = −0.93), liver (effect size = −2.29), stomach (effect size = −1.59), and prostate (effect size = −1.36) cancers; elevation was not proven in any tumor. More specific zinc patterns are evident at tissue level, showing increase in breast cancer tissue (effect size = 1.80) and decrease in prostatic (effect size = −3.90), liver (effect size = −8.26), lung (effect size = −3.12), and thyroid cancer (effect size = −2.84). The rest of the included tumors brought ambiguous results, both in serum and tissue zinc levels across the studies. The association between zinc level and stage or grade of tumor has not been revealed by meta-regression. Conclusion: This study provides evidence on cancer-specific tissue zinc level alteration. Although serum zinc decrease was associated with most tumors mentioned herein, further – prospective - studies are needed. [ABSTRACT FROM AUTHOR]

Background: LncRNA CCAT1 is significantly overexpressed in various types of cancers, suggesting that it might be associated with prognosis and clinicopathological features in patients with cancer. Methods: A comprehensive search was performed in Pubmed, Web of Science, OVID and CNKI databases. We also retrieved articles from other sources, such as retrieving from the reference lists of relevant articles. Eligible studies were included based on defined exclusion and inclusion criteria to perform a meta-analysis. STATA 14.0 was used to estimate pooled hazard ratios (HRs) with 95% confidence interval (95% CI), the heterogeneity among studies and publication bias to judge the prognostic value. Results: A total of 1587 patients from 11 eligible studies were included in the meta-analysis. The results showed that high expression level of CCAT1 was significantly associated with shorter overall survival in cancer patients (HR 2.335, 95% CI:1.551–3.517); in the subgroup analysis, region (China or UK), sample size (more or less than 100), type of cancer (digestive or non-digestive disease) and paper quality (score more or less than 7) did not alter the association between CCAT1 expression and cancer prognosis but preoperative treatment did. And CCAT1 expression was an independent prognostic marker for overall survival in patients with cancer (pooled HR 2.195, 95%CI:1.316–3.664) using Cox multivariate analyses. The clinicopathological parameters analysis further showed that increased expression level of CCAT1 was correlated with tumor size, lymph node metastasis, TNM stage, distant metastasis, microvascular invasion and capsular formation in relevant cancers. Conclusions: The meta-analysis results from present study suggested that increased expression level of CCAT1 was associated with poor prognosis and can serve as an independent biomarker. And the expression level of CCAT1 was associated with clinicopathological features in relevant cancers. [ABSTRACT FROM AUTHOR]

miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3′-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets. [ABSTRACT FROM AUTHOR]

A consensus has not been reached regarding the association of MTHFR gene polymorphism and susceptibility to oral squamous cell carcinoma (OSCC). We performed a meta-analysis to better evaluate the association between MTHFR C677T, A1298C polymorphism and OSCC risk. The studies regarding the association of MTHFR C677T, A1298C polymorphisms and OSCC were identified in PubMed and EMBASE and Google Scholar. The pooled odd rates (ORs) with 95%CIs were estimated using a fixed-effect or random-effect model. The associations between MTHFR polymorphisms and OSCC risk were assessed under the dominant, recessive and additive models. A collective total of 1539 OSCC patients and 2131 normal controls were included across 13 studies. The minor T allele of MTHFR C677T was significantly associated with the increased risk of OSCC development(OR = 1.35, 95%CI 1.04–1.76). Individuals carrying the ‘‘T” allele (TT+CT) had a nearly 43% increased risk for OSCC development when compared with CC (OR = 1.43, 95%CI 1.02–1.99). Under additive model, the results also showed that individuals with CT or TT genotype were more susceptible to OSCC than CC (OR = 1.45, 95%CI 1.02–2.08; OR = 1.79, 95%CI 1.28–2.50; respectively). The subgroup analysis by ethnicity revealed that significant difference in C677T allele distribution could be observed in European (OR = 1.33, 95%CI 1.02–1.75) rather than Asian (OR = 1.59, 95%CI 0.91–2.78). No significant association of MTHFR A1298C polymorphism and OSCC risk could be observed. The present study revealed that T allele and TT genotype of MTHFR C677T polymorphism were significantly associated with the increased risk of OSCC development. [ABSTRACT FROM AUTHOR]

MicroRNAs are small non-coding RNAs that influence gene expression by binding to the 3’ UTR of target mRNAs in order to repress protein synthesis. Soon after discovery, microRNA dysregulation has been associated to several pathologies. In particular, they have often been reported as differentially expressed in healthy and tumor samples. This fact suggested that microRNAs are likely to be good candidate biomarkers for cancer diagnosis and personalized medicine. With the advent of Next-Generation Sequencing (NGS), measuring the expression level of the whole miRNAome at once is now routine. Yet, the collaborative effort of sharing data opens to the possibility of population analyses. This context motivated us to perform an in-silico study to distill cancer-specific panels of microRNAs that can serve as biomarkers. We observed that the problem of finding biomarkers can be modeled as a two-class classification task where, given the miRNAomes of a population of healthy and cancerous samples, we want to find the subset of microRNAs that leads to the highest classification accuracy. We fulfill this task leveraging on a sensible combination of data mining tools. In particular, we used: differential evolution for candidate selection, component analysis to preserve the relationships among miRNAs, and SVM for sample classification. We identified 10 cancer-specific panels whose classification accuracy is always higher than 92%. These panels have a very little overlap suggesting that miRNAs are not only predictive of the onset of cancer, but can be used for classification purposes as well. We experimentally validated the contribution of each of the employed tools to the selection of discriminating miRNAs. Moreover, we tested the significance of each panel for the corresponding cancer type. In particular, enrichment analysis showed that the selected miRNAs are involved in oncogenesis pathways, while survival analysis proved that miRNAs can be used to evaluate cancer severity. Summarizing: results demonstrated that our method is able to produce cancer-specific panels that are promising candidates for a subsequent in vitro validation. [ABSTRACT FROM AUTHOR]

CIP2A, cancerous inhibitor of protein phosphatase 2A, was initially recognized as an oncoprotein. Recently several studies revealed that CIP2A could function as a prognosis biomarker, however, the result remained not comprehensive, partly due to small number of patients included individually. Here we carried out a meta-analysis of published studies to assess the prognostic significance of CIP2A in solid tumors. All eligible studies were identified through searching PubMed, Embase and Web of Science database. In this meta-analysis, 22 studies involving 4,579 participants were included, and we verified that CIP2A over-expression was significantly related with poor overall survival (pooled HR = 1.844, 95% CI = 1.528–2.225, P<0.001) and short disease free survival (pooled HR = 1.808, 95% CI = 1.591–2.055, P<0.001) in solid tumors. Additionally, subgroup analysis suggested that the trend of a poor overall survival with an increased CIP2A expression was present in East-Asian and European patients, as well as in lung cancer and colorectal cancer. To sum up, CIP2A over-expression was associated with poor survival in human solid tumors and might be a predictive factor of poor prognosis. [ABSTRACT FROM AUTHOR]

Introduction: Bladder cancer (BC) is one of the most common urologic malignancies and it is urgently needed to identify novel potential prognostic biomarkers for predicting prognosis and progression of patients with BC in clinical practice. Previous research has revealed that long noncoding RNAs (lncRNAs) played critical roles in BC, and may serve as novel potential prognostic biomarkers in patients with BC. Therefore, we conducted this meta-analysis to clarify the prognostic potential of lncRNAs in BC patients. Methods: A comprehensive search was performed in PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI). According to the predefined exclusion and inclusion criteria, a total of 9 recently published articles comprising 13 lncRNAs and 666 BC patients were included into this meta-analysis. We analyzed the hazard ratios (HRs) and 95% confidence intervals (CIs) to determine the relationship between lncRNAs expression and survival outcomes. We also analyzed the odds ratio (ORs) and 95% confidence intervals (CIs) to assess the association between lncRNAs expression and clinicopathological characteristics, including histological grade, gender, multifocality, tumor size, and tumor stage. Results: Our results revealed that high lncRNAs expression was associated with shorter overall survival in Asian BC patients (pooled HR = 2.32, 95% CI: 1.35–4.00, P = 0.002, random-effect). High lncRNAs expression levels were significantly associated with histological grade (G2-G3 vs. G1: OR = 3.857, 95%CI: 1.293–11.502, P = 0.015, random-effect). Conclusions: In summary, this meta-analysis has demonstrated that lncRNAs could be used as potential prognostic markers for BC and high lncRNAs expression could predict poor prognosis among Asian BC patients. [ABSTRACT FROM AUTHOR]

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses (‘gained’ genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease. [ABSTRACT FROM AUTHOR]

Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset. [ABSTRACT FROM AUTHOR]

Glioma is the most common malignant intracranial tumour. Recently, several publications have suggested that miRNAs can be used as potential diagnostic biomarkers of glioma. Here we performed a meta-analysis to identify the diagnostic accuracy of differentially expressed circulating miRNAs in gliomas. Using PubMed, Medline and Cochrane databases, we searched for studies which evaluated a single or panel of miRNAs from circulating blood as potential biomarkers of glioma. Sixteen publications involving 23 studies of miRNAs from serum or plasma met our criteria and were included in this meta-analysis. The pooled diagnostic parameters were calculated by random effect models and overall diagnostic performance of altered miRNAs was illustrated by the summary receiver operator characteristic (SROC) curves. The pooled sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR) from each study were calculated. The pooled PLR, NLR and Diagnostic Odds Ratio were 6.39 (95% CI, 4.61–8.87), 0.15 (95% CI, 0.11–0.21) and 41.91 (95% CI, 23.15–75.88), respectively. The pooled sensitivity, specificity and area under the curve (AUC) were 0.87 (95% CI, 0.82–0.91), 0.86 (95% CI, 0.82–0.90) and 0.93 (95% CI, 0.91–0.95), respectively. This meta-analysis demonstrated that circulating miRNAs are capable of distinguishing glioma from healthy controls. Circulating miRNAs are promising diagnostic biomarkers for glioma and can potentially be used as a non-invasive early detection. [ABSTRACT FROM AUTHOR]

Background: Recent studies demonstrate that interstitial inorganic phosphate is significantly elevated in the breast cancer microenvironment as compared to normal tissue. In addition it has been shown that breast cancer cells express high levels of the NaPi-IIb carrier (SLC34A2), suggesting that this carrier may play a role in breast cancer progression. However, the biochemical behavior of inorganic phosphate (Pi) transporter in this cancer type remains elusive. Methods: In this work, we characterize the kinetic parameters of Pi transport in the aggressive human breast cancer cell line, MDA-MB-231, and correlated Pi transport with cell migration and adhesion. Results: We determined the influence of sodium concentration, pH, metabolic inhibitors, as well as the affinity for inorganic phosphate in Pi transport. We observed that the inorganic phosphate is dependent on sodium transport (K0,5 value = 21.98 mM for NaCl). Furthermore, the transport is modulated by different pH values and increasing concentrations of Pi, following the Michaelis-Menten kinetics (K0,5 = 0.08 mM Pi). PFA, monensin, furosemide and ouabain inhibited Pi transport, cell migration and adhesion. Conclusions: Taken together, these results showed that the uptake of Pi in MDA-MB-231 cells is modulated by sodium and by regulatory mechanisms of intracellular sodium gradient. General Significance: Pi transport might be regarded as a potential target for therapy against tumor progression. [ABSTRACT FROM AUTHOR]

HER-2 belongs to the human epidermal growth factor receptor (HER) family. Via different signal transduction pathways, HER-2 regulates normal cell proliferation, survival, and differentiation. Recently, it was reported that MCF10A, BT474, and MDA-MB-231 cells bearing the HER2 K753E mutation were resistant to lapatinib. Present study revealed that HER-2 mutant K753E showed some contrasting behaviour as compared to wild, L768S and V773L HER-2 in complex with lapatinib while similar to previously known lapatinib resistant L755S HER-2 mutant. Lapatinib showed stable but reverse orientation in binding site of K753E and the highest binding energy among studied HER2-lapatinib complexes but slightly lesser than L755S mutant. Results indicate that K753E has similar profile as L755S mutant for lapatinib. The interacting residues were also found different from other three studied forms as revealed by free energy decomposition and ligplot analysis. [ABSTRACT FROM AUTHOR]

Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs’ potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases’ statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a L1-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model’s superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction. [ABSTRACT FROM AUTHOR]

Introduction: The low incidence of oesophageal adenocarcinoma (EAC) in Barrett's oesophagus (BE) patients reinforces the need for risk stratification tools to make BE surveillance more effective. Therefore, we have undertaken a systematic review and meta-analysis of published studies on immunohistochemical (IHC) biomarkers in BE to determine the value of IHC biomarkers as neoplastic predictors in BE surveillance. Materials and methods: We searched MEDLINE, EMBASE, Web of Science, CENTRAL, Pubmed publisher, and Google scholar. All studies on IHC biomarkers in BE surveillance were included. ORs were extracted and meta-analyses performed with a random effects model. Results: 16 different IHC biomarkers were studied in 36 studies. These studies included 425 cases and 1835 controls. A meta- analysis was performed for p53, aspergillus oryzae lectin (AOL), Cyclin A, Cyclin D and alpha-methylacyl-CoA racemase. Aberrant p53 expression was significantly associated with an increased risk of neoplastic progression with an OR of 3.18 (95% CI 1.68 to 6.03). This association was confirmed for both non-dysplastic BE and BE with low-grade dysplasia (LGD). Another promising biomarker to predict neoplastic progression was AOL, with an OR of 3.04 (95% CI 2.05 to 4.49). Discussion: Use of p53 IHC staining may improve risk stratification in BE surveillance. Aberrant p53 expression in BE patients appeared to be associated with a significantly increased risk of neoplastic progression for both non-dysplastic and LGD BE patients. [ABSTRACT FROM AUTHOR]

Background: The recent application of gene-sequencing technology has identified many new somatic mutations in patients with myelodysplastic syndromes (MDS). Among them, serine and arginine rich splicing factor 2 (SRSF2) mutations belonging to the RNA splicing pathway were of interest. Many studies have already reported the potential prognostic value of SRSF2 mutations in MDS patients, with controversial results. Therefore, a meta-analysis was performed to investigate their prognostic impact on MDS. Methods: Databases, including PubMed, Embase and the Cochrane Library, were searched for relevant studies published up to 14 October 2016. Overall survival (OS) was selected as the primary endpoint, and acute myeloid leukemia (AML) transformation was the secondary endpoint. We extracted the corresponding hazard ratios (HRs) and their 95% confidence intervals (CIs) for OS and AML transformation from multivariate Cox proportional hazards models. The combined HRs with their 95% CIs were calculated using fixed or random effect models. Results: A total of 10 cohort studies, covering 1864 patients with de novo MDS and 294 patients with SRSF2 mutations, were included in the final meta-analysis. Our results indicated that SRSF2 mutations had an adverse prognostic impact on OS (p<0.0001) and AML transformation (p = 0.0005) in the total population. Among the MDS patients with low or intermediate-1 risk defined according to the International Prognostic Scoring System (IPSS), SRSF2 mutations predicted a shorter OS (p = 0.009) and were more likely to transform to AML (p = 0.007). Conclusions: This meta-analysis indicates an independent, adverse prognostic impact of SRSF2 mutations on OS and AML transformation in patients with de novo MDS. This also applies to the subgroup of low- or intermediate-1-IPSS risk MDS. The identification of mutations in SRSF2 can improve current risk stratification and help make treatment decisions. [ABSTRACT FROM AUTHOR]

Promoter methylation in a number of tumor-suppressor genes (TSGs) can play crucial roles in the development of thyroid carcinogenesis. The focus of the current meta-analysis was to determine the impact of promoter methylation of eight selected candidate TSGs on thyroid cancer and to identify the most important molecules in this carcinogenesis pathway. A comprehensive search was performed using Pub Med, Scopus, and ISI Web of Knowledge databases, and eligible studies were included. The methodological quality of the included studies was evaluated according to the Newcastle Ottawa scale table and pooled odds ratios (ORs); 95% confidence intervals (CIs) were used to estimate the strength of the associations with Stata 12.0 software. Egger’s and Begg’s tests were applied to detect publication bias, in addition to the “Metatrim” method. A total of 55 articles were selected, and 135 genes with altered promoter methylation were found. Finally, we included eight TSGs that were found in more than four studies (RASSF1, TSHR, PTEN, SLC5A, DAPK, P16, RARβ2, and CDH1). The order of the pooled ORs for these eight TSGs from more to less significant was CDH1 (OR = 6.73), SLC5 (OR = 6.15), RASSF1 (OR = 4.16), PTEN (OR = 3.61), DAPK (OR = 3.51), P16 (OR = 3.31), TSHR (OR = 2.93), and RARβ2 (OR = 1.50). Analyses of publication bias and sensitivity confirmed that there was very little bias. Thus, our findings showed that CDH1 and SCL5A8 genes were associated with the risk of thyroid tumor genesis. [ABSTRACT FROM AUTHOR]

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is considered as the gold standard for accurate, sensitive, and fast measurement of gene expression. Prior to downstream statistical analysis, RT-qPCR fluorescence amplification curves are summarized into one single value, the quantification cycle (Cq). When RT-qPCR does not reach the limit of detection, the Cq is labeled as “undetermined”. Current state of the art qPCR data analysis pipelines acknowledge the importance of normalization for removing non-biological sample to sample variation in the Cq values. However, their strategies for handling undetermined Cq values are very ad hoc. We show that popular methods for handling undetermined values can have a severe impact on the downstream differential expression analysis. They introduce a considerable bias and suffer from a lower precision. We propose a novel method that unites preprocessing and differential expression analysis in a single statistical model that provides a rigorous way for handling undetermined Cq values. We compare our method with existing approaches in a simulation study and on published microRNA and mRNA gene expression datasets. We show that our method outperforms traditional RT-qPCR differential expression analysis pipelines in the presence of undetermined values, both in terms of accuracy and precision. [ABSTRACT FROM AUTHOR]

Midkine (MK) has been reported as the potential novel diagnostic biomarker for cancer in several studies, but their results were controversial. Therefore, we performed a diagnostic meta-analysis to assess the diagnostic value of serum MK in cancer patients. A systematic electronic and manual search was performed for relevant literatures through several databases up to June 1, 2017. The quality of the studies included in the meta-analysis was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. All analyses were conducted using stata12.0 software. Ten studies collectively included 1119 cancer patients and 1441 controls met the eligible criteria. The summary estimates were: sensitivity 0.78 (95% CI = 0.68–0.85), specificity 0.83 (95% CI = 0.72–0.90), positive likelihood ratio 4,54 (95% CI = 2.64–7.80), negative likelihood 0.27 (95% CI = 0.18–0.40), diagnostic odds ratio 16.79 (95% CI = 7.17–39.33), and area under the curve 0.87 (95% CI = 0.84–0.89). Publication bias was suggested by Deeks’ funnel plot asymmetry test (P = 0.92). According to our results, serum MK has greater diagnostic value in diagnosing cancer, however, more reliable studies in larger cohort should be conducted to evaluate the diagnostic accuracy of serum MK. [ABSTRACT FROM AUTHOR]

The phosphatase and tensin homolog (PTEN) gene is suggested to be a dormant tumor suppressor. However, the prognostic value of the loss of PTEN expression in renal cell carcinoma (RCC) remains controversial. Therefore, we conducted a meta-analysis to evaluate the association of PTEN expression with the clinicopathological presentations and outcomes of patients with RCC through immunohistochemistry staining analysis. We systematically searched for relevant studies in PubMed, Web of Science, and Embase until March 2016. Data regarding clinical stage, pathological type, Fuhrman grade, overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) was analyzed in the present study. In total, there were 12 studies with 2,368 patients included in this meta-analysis. The low PTEN expression in RCC was significantly associated with unfavorable DSS (HR = 1.568, 95% CI 1.015–2.242) in a random-effects model but not with OS (HR = 1.046, 95% CI 0.93–1.176) and PFS (HR = 1.244, 95% CI 0.907–1.704). Other results indicated that PTEN expression was not correlated with clinical stage, pathological type, and Fuhrman grade. This meta-analysis suggests that PTEN expression is of limited value in predicting the prognosis of patients with RCC for OS and PFS via immunohistochemistry staining analysis; and that for DSS, low PTEN expression is significantly associated with an unfavorable outcome. [ABSTRACT FROM AUTHOR]

Background: In our clinical practice we have observed that despite a high hepatic metastatic tumor burden, serum alkaline phosphatase (AP) levels are frequently normal in cases of metastatic neuroendocrine tumor (NET). Patients and methods: We retrospectively reviewed the records of patients with grade 1 and 2 NETs with liver metastases but without bone metastases seen at our institution in 2013. In total, 49 patients were included (22 female), with a median age of 60 years (range: 28 to 84 years). The primary tumors were located in the duodenum/pancreas (n = 29), small bowel (n = 17) or colon/rectum (n = 3); 10 cases were grade 1 and 39 grade 2. Hepatic involvement was bulky, with more than 10 lesions in 23 patients and a tumor burden above 10% of the liver volume in 26 patients. Results: Serum AP levels were elevated (≥ upper limit of normal (ULN)) in 16 patients. In multiparametric analysis, elevated serum AP levels were not associated with the primary site, grade, or number or volume of metastases. In multiparametric analysis, progression-free survival was only correlated with grade (p = 0.010) and AP level (p = 0.017). Conclusions: Serum AP levels are frequently normal in liver metastases from NET, even in the event of a major tumor burden, and the serum AP level can be of prognostic value. [ABSTRACT FROM AUTHOR]

The potential prognostic value of GATA binding protein 3 (GATA3) in breast cancer has recently increased, although the evidence is inconclusive. This meta-analysis of 10 articles involving 5,080 breast cancer patients explored the prognostic and clinicopathological value of GATA3 in breast cancer. Time to tumor progression (TTP) and overall survival (OS) were primary endpoints. Pooled hazard ratio (HR), pooled risk ratio (RR), and 95% confidence interval (CI) were calculated to evaluate the association between GATA3, prognosis, and clinicopathological parameters. High GATA3 expression predicts breast cancer, with a HR (HR = 0.671; 95% CI = 0.475–0.947; P = 0.023) of TTP, but is not associated with OS (HR = 0.889; 95% CI = 0.789–1.001; P = 0.052). GATA3 overexpression is associated with positive ER (RR = 3.155; 95% CI = 1.680–5.923; P = 0.000), positive PR (RR = 3.949; 95% CI = 1.567–9.954, P = 0.004), lower nuclear grade (RR = 0.435; 95% CI = 0.369–0.514; P = 0.000), and smaller tumor size (RR = 0.816; 95% CI = 0.709–0.940; P = 0.005). High GATA3 expression may predict TTP in breast cancer, and such patients may show better clinicopathological features. [ABSTRACT FROM AUTHOR]

In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, ath-ased iRNA-isease ssociation (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations. [ABSTRACT FROM AUTHOR]

Objective: To investigate the replication validity of biomedical association studies covered by newspapers. Methods: We used a database of 4723 primary studies included in 306 meta-analysis articles. These studies associated a risk factor with a disease in three biomedical domains, psychiatry, neurology and four somatic diseases. They were classified into a lifestyle category (e.g. smoking) and a non-lifestyle category (e.g. genetic risk). Using the database Dow Jones Factiva, we investigated the newspaper coverage of each study. Their replication validity was assessed using a comparison with their corresponding meta-analyses. Results: Among the 5029 articles of our database, 156 primary studies (of which 63 were lifestyle studies) and 5 meta-analysis articles were reported in 1561 newspaper articles. The percentage of covered studies and the number of newspaper articles per study strongly increased with the impact factor of the journal that published each scientific study. Newspapers almost equally covered initial (5/39 12.8%) and subsequent (58/600 9.7%) lifestyle studies. In contrast, initial non-lifestyle studies were covered more often (48/366 13.1%) than subsequent ones (45/3718 1.2%). Newspapers never covered initial studies reporting null findings and rarely reported subsequent null observations. Only 48.7% of the 156 studies reported by newspapers were confirmed by the corresponding meta-analyses. Initial non-lifestyle studies were less often confirmed (16/48) than subsequent ones (29/45) and than lifestyle studies (31/63). Psychiatric studies covered by newspapers were less often confirmed (10/38) than the neurological (26/41) or somatic (40/77) ones. This is correlated to an even larger coverage of initial studies in psychiatry. Whereas 234 newspaper articles covered the 35 initial studies that were later disconfirmed, only four press articles covered a subsequent null finding and mentioned the refutation of an initial claim. Conclusion: Journalists preferentially cover initial findings although they are often contradicted by meta-analyses and rarely inform the public when they are disconfirmed. [ABSTRACT FROM AUTHOR]

The application of circulating tumor DNA(ctDNA) represents a non-invasive method for tumor detection. Its prognostic significance in patients with colorectal cancer is controversial. We performed a systematic review of data from published studies to assess the prognostic values of ctDNA in patients with colorectal cancer. We searched Medline, Embase, Web of Science, the Cochrane Library, and Scopus databases to identify eligible studies reporting disease-free survival (DFS) and overall survival (OS) stratified by ctDNA prior to December 6, 2016. We evaluated the quality and design of these studies. A total of 22 studies were eligible for systematic review. Among them, 11 studies investigated the prognostic value of ctDNA on disease-free survival (DFS). Seven of 11 studies showed that ctDNA was an independent variable to estimate the probability of DFS by multivariate analyses. Thirteen studies assessed the relationship between ctDNA and overall survival (OS). Eight of 13 studies showed that ctDNA was an independent predictor of worse OS through the use of multivariate analyses. This analysis provides evidence that ctDNA may be a prognostic biomarker, negatively correlated with the survival of patients with colorectal cancer. [ABSTRACT FROM AUTHOR]

Purpose: High expression of highly upregulated in iver cancer (HULC) has been found associated with increased metastasis and poor prognosis with cancer. This meta-analysis aimed to determine the pooled effect of HULC on metastasis and prognosis of cancers. Method: The studies were accessed using multiple databases. RevMan5.3 and STATA14.0 were used to estimate pooled effects, the heterogeneity among studies, and publication bias for the association of HULC and overall survival (OS). Results: A total of 9 studies of 966 cancer patients were included. Risk of lymph node metastasis was increased with high versus low HULC expression (pooled odds ratio [OR] = 4.83, 95% confidence interval [CI] 1.59–14.63) as was distant metastasis (pooled OR = 5.44, 95% CI 2.33–12.74). Furthermore, OS time was shortened with high HULC expression (pooled hazard ratio [HR] = 1.48, 95% CI 1.03–2.12), especially in Chinese patients (pooled HR = 2.04, 95% CI 1.55–2.68), and risk of recurrence was increased (pooled OR = 6.68, 95% CI 2.77–16.13). Conclusion: HULC might be a potential biomarker for therapy and prognosis surveillance in cancers. [ABSTRACT FROM AUTHOR]

Because the prognosis of melanoma is challenging and inaccurate when using current clinical approaches, clinicians are seeking more accurate molecular markers to improve risk models. Accordingly, we performed a survival analysis on 404 samples from The Cancer Genome Atlas (TCGA) cohort of skin cutaneous melanoma. Using our recently developed gene network model, we identified biological signatures that confidently predict the prognosis of melanoma (p-value < 10−5). Our model predicted 38 cases as low–risk and 54 cases as high–risk. The probability of surviving at least 5 years was 64% for low–risk and 14% for high–risk cases. In particular, we found that the overexpression of specific genes in the mitotic cell cycle pathway and the underexpression of specific genes in the interferon pathway are both associated with poor prognosis. We show that our predictive model assesses the risk more accurately than the traditional Clark staging method. Therefore, our model can help clinicians design treatment strategies more effectively. Furthermore, our findings shed light on the biology of melanoma and its prognosis. This is the first in vivo study that demonstrates the association between the interferon pathway and the prognosis of melanoma. [ABSTRACT FROM AUTHOR]

Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use. Here, we present a method called iterative Contextual Transcriptional Activity Inference of Regulators (icTAIR) to resolve these issues. icTAIR takes a regulator’s previously-identified target gene list and combines it with gene expression data from a context, quantifying that regulator’s activity for that context. It then calculates the correlation between each listed target gene’s expression and the quantitative score of regulatory activity, removes the uncorrelated genes from the list, and iterates the process until it derives a stable list of refined target genes. To validate and demonstrate icTAIR’s power, we use it to refine the MSigDB c3 database of TF, miRNA and unclassified motif target gene lists for breast cancer. We then use its output for survival analysis with clinicopathological multivariable adjustment in 7 independent breast cancer datasets covering 3,430 patients. We uncover many novel prognostic regulators that were obscured prior to refinement, in particular NFY, and offer a detailed look at the composition and relationships among the breast cancer prognostic regulome. We anticipate icTAIR will be of general use in contextually refining regulator target genes for discoveries across many contexts. The icTAIR algorithm can be downloaded from . [ABSTRACT FROM AUTHOR]

Purpose: miRNAs are stable and can be extracted from tissues, blood and other body fluid without degradation. miRNAs are abnormally expressed in the presence of a pathological status, including cancer. Therefore, miRNAs are ideal biomarkers for cancer diagnosis and prognosis. Patients with triple negative breast cancer (TNBC) suffer the worst prognosis, although great efforts have been made. Many studies have investigated the role of miRNAs in predicting the outcomes of TNBC patients for better adjustment of treatment. However, results were inconsistent. Thus, we performed a meta-analysis to summarize the published studies for conclusive results. Methods: Eligible studies from different database were retrieved from the online databases, and we used STSTA 12.0 to analysis the prognostic role of miRNAs in triple negative breast cancer. Results: Overall high miRNA expression indicated a worse survival with HR value of 1.78 (95% CI: 0.97–3.25). However, subtotal HRs of oncogenic miRNAs and tumor suppressive miRNAs were 2.73 (95% CI: 2.08–3.57; P<0.001) and 0.44 (95% CI: 0.21–0.90; P = 0.024), respectively, and no heterogeneity was observed within the subgroups. Conclusions: The miRNAs showed a slightly stronger prognostic value for disease-free survival, relapse-free survival and distant metastasis-free survival compared to the overall survival of TNBC patients. Circulating miRNAs could serve as potential biomarkers for the prognosis of TNBC patients and need further investigation. [ABSTRACT FROM AUTHOR]

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner’s-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner’s curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25–50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner’s curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10−5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure. [ABSTRACT FROM AUTHOR]

Transcript levels do not faithfully predict protein levels, due to post-transcriptional regulation of gene expression mediated by RNA binding proteins (RBPs) and non-coding RNAs. We developed a multivariate linear regression model integrating RBP levels and predicted RBP-mRNA regulatory interactions from matched transcript and protein datasets. RBPs significantly improved the accuracy in predicting protein abundance of a portion of the total modeled mRNAs in three panels of tissues and cells and for different methods employed in the detection of mRNA and protein. The presence of upstream translation initiation sites (uTISs) at the mRNA 5’ untranslated regions was strongly associated with improvement in predictive accuracy. On the basis of these observations, we propose that the recently discovered widespread uTISs in the human genome can be a previously unappreciated substrate of translational control mediated by RBPs. [ABSTRACT FROM AUTHOR]

Background: Methylation of tumor suppressor gene promoter leads to transcription inactivation and is involved in tumorigenesis. Several studies demonstrate a potential association between the Death-Associated Protein Kinase (DAPK) gene promoter methylation and bladder cancer risk, tumor stage and histological grade. Due to inconsistent results of these studies, we performed this meta-analysis to ascertain the association. Methods: Studies were retrieved from the PubMed, Embase, Web of Science and the Cochrane Library databases. Study selection and data extraction were executed by two reviewers independently. Meta-analysis was performed using Stata 13.0 and Review Manager 5.3 software. Results: A total of 21 articles involving 15 case control and 8 case series studies were included in this meta-analysis. DAPK promoter methylation was associated with bladder cancer risk (OR: 5.81; 95%CI = 3.83–8.82, P<0.00001). The frequency of DAPK promoter methylation was equal in bladder cancer tissue and paired adjacent normal tissue (OR: 0.87; 95%CI = 0.31–2.48, P = 0.794). Furthermore, DAPK promoter methylation was associated with higher histological grade (OR: 1.52; 95%CI = 1.10–2.09, P = 0.011) but not associated with tumor stage (OR: 1.12; 95%CI = 0.67–1.87, P = 0.668). Conclusions: The result suggests that DAPK promoter methylation is significantly increased in bladder cancer patients compared to normal controls. DAPK promoter methylation could serve as a biomarker for bladder cancer detection and management. [ABSTRACT FROM AUTHOR]

The silencing of the tumor suppressor gene O-6-methylguanine-DNA methyltransferase (MGMT) by promoter methylation commonly occurs in human cancers. The relationship between MGMT promoter methylation and gastric cancer (GC) remains inconsistent. This study aimed to evaluate the potential value of MGMT promoter methylation in GC patients. Electronic databases were searched to identify eligible studies. The pooled odds ratio (OR) and the corresponding 95% confidence interval (95% CI) were used to evaluate the effects of MGMT methylation on GC risk and clinicopathological characteristics. In total, 31 eligible studies including 2988 GC patients and 2189 nonmalignant controls were involved in meta-analysis. In the pooled analysis, MGMT promoter methylation was significantly associated with GC risk (OR = 3.34, P < 0.001) and substantial heterogeneity (P < 0.001). Meta-regression and subgroup analyses based on the testing method, sample material and ethnicity failed to explain the sources of heterogeneity. Interestingly, MGMT methylation showed a trend associated with gender, and methylation is lower in males compared with females (OR = 0.76, 95% CI = 0.56–1.03). We did not find a significant association in relation to tumor types, clinical stage, age status or H. pylori status in cancer (all P > 0.1). MGMT promoter methylation may be correlated with the prognosis of GCs in disease free survival (DFS) or overall survival (OS) for univariate analysis. MGMT promoter methylation may play a crucial role in the carcinogenesis and prognosis of GC. MGMT methylation was not correlated with tumor types, clinical stage, age status, H. pylori status. However, the result of the association of MGMT methylation and gender should be considered with caution. [ABSTRACT FROM AUTHOR]

Objective: Recently, numerous studies have reported that hexokinase-2 (HK2) is aberrantly expressed in cancer, indicating that HK2 plays a pivotal role in the development and progression of cancer. However, its prognostic significance in solid tumor remains unclear. Accordingly, we performed a meta-analysis to assess the prognostic value of HK2 in solid tumor. Methods: Eligible studies were identified using PubMed, Embase, and Web of Science databases. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS) or progression-free survival (PFS)/disease-free survival (DFS)/relapse-free survival (RFS) were estimated with random effects or fixed effects models, respectively. Subgroup analysis was also performed according to patients’ ethnicities, tumor types, detection methods, and analysis types. Results: Data from 21 included studies with 2532 patients were summarized. HK2 overexpression was significantly associated with worse OS (pooled HR = 1.90, 95% CI = 1.51–2.38, p < 0.001) and PFS (pooled HR = 2.91, 95% CI = 2.02–4.22, p < 0.001) in solid tumor. As to a specific form of cancer, the negative effect of HK2 on OS was observed in hepatocellular carcinoma (pooled HR = 2.06, 95% CI = 1.67–2.54, p < 0.001), gastric cancer (pooled HR = 1.72, 95% CI = 1.09–2.71, p = 0.020), colorectal cancer (pooled HR = 2.89, 95% CI = 1.62–5.16, p < 0.001), but not in pancreatic cancer (pooled HR = 1.13, 95% CI = 0.28–4.66, p = 0.864). No publication bias was found in the included studies for OS (Begg’s test, p = 0.325; Egger’s test, p = 0.441). Conclusion: In this meta-analysis, we identified that elevated HK2 expression was significantly associated with shorter OS and PFS in patients with solid tumor, but the association varies according to cancer type. [ABSTRACT FROM AUTHOR]

Zinc is an essential dietary element that has been implicated in the pathogenesis of prostate cancer, a cancer that disproportionately affects men of African descent. Studies assessing the association of zinc intake and prostate cancer have yielded inconsistent results. Furthermore, very little is known about the relationship between zinc intake and prostate cancer among African Americans. We examined the association between self-reported zinc intake and prostate cancer in a hospital-based case-control study of African Americans. We then compared our results with previous studies by performing a meta-analysis to summarize the evidence regarding the association between zinc and prostate cancer. Newly diagnosed African American men with histologically confirmed prostate cancer (n = 127) and controls (n = 81) were recruited from an urban academic urology clinic in Washington, DC. Controls had higher zinc intake, with a mean of 14 mg/day versus 11 mg/day for cases. We observed a non-significant, non-linear increase in prostate cancer when comparing tertiles of zinc intake (OR <6.5 vs 6.5–12.5mg/day 1.8, 95% CI: 0.6,5.6; OR <6.5 vs >12.5mg/day 1.3, 95% CI: 0.2,6.5). The pooled estimate from 17 studies (including 3 cohorts, 2 nested case-control, 11 case-control studies, and 1 randomized clinical trial, with a total of 111,199 participants and 11,689 cases of prostate cancer) was 1.07hi vs lo 95% CI: 0.98–1.16. Using a dose-response meta-analysis, we observed a non-linear trend in the relationship between zinc intake and prostate cancer (p for nonlinearity = 0.0022). This is the first study to examine the relationship between zinc intake in black men and risk of prostate cancer and systematically evaluate available epidemiologic evidence about the magnitude of the relationship between zinc intake and prostate cancer. Despite of the lower intake of zinc by prostate cancer patients, our meta-analysis indicated that there is no evidence for an association between zinc intake and prostate cancer. [ABSTRACT FROM AUTHOR]

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at . [ABSTRACT FROM AUTHOR]

Background: Vimentin is a member of the intermediate filament proteins and a canonical marker of the epithelial-mesenchymal transition (EMT), which is pivotal in tumorigenesis, metastasis and invasion in non-small cell lung cancer (NSCLC). The current meta-analysis aimed to investigate the associations between vimentin and prognosis and progression in NSCLC. Methods: Databases with literature published in English, including PubMed, Web of Science, Embase, Science Direct, Wiley Online Library, Ovid, Cochrane Central Register of Controlled Trials, LILACS and Google Scholar, and the CNKI, VIP, CBM and WanFang databases in Chinese were used for the literature search. The key terms included (1) ‘vimentin’ OR ‘vim’ OR ‘vmt’ OR ‘vm’ OR ‘hel113’ OR ‘ctrct30’ and (2) ‘pulmon*’ OR ‘lung’ OR ‘alveolar’ and (3) ‘cancer’ OR ‘carcinoma’ OR ‘tumor’ OR ‘adenocarcinoma’ OR ‘squamous’ OR ‘neoplas*’ OR ‘malignan*’. The data were combined by random effect model and the H value and I2 were used to assess the heterogeneity. All the meta-analysis was conducted using Stata 12.0. Results: Thirty-two qualified studies (4118 cases) were included in the current meta-analysis. Twelve studies with 1750 patients were included to assess the significance of vimentin in the overall survival (OS) of NSCLC; the pooled hazard ratio (HR) was 1.831 (confidence interval (CI): 1.315–2.550, P<0.001) in the univariate analysis and 1.266 (CI: 0.906–1.768, P = 0.167) in the multivariate analysis. Four studies with 988 cases were applicable to determine the significance of vimentin in the disease-free survival (DFS) of NSCLC; the pooled HR of the DFS was 1.224 (CI: 0.921–1.628, P = 0.164) in the univariate analysis and 1.254 (CI: 0.985–1.956, P = 0.067) in the multivariate analysis. Regarding the relationships between vimentin and clinicopathological factors, the pooled odds ratio (OR) with 3406 NSCLCs indicated that up-regulated vimentin was associated with smoking (OR = 1.359, CI: 1.098–1.683, P = 0.004), poor differentiation (OR = 2.133, CI: 1.664–2.735, P<0.001), an advanced TNM stage (OR = 3.275, CI: 1.987–5.397, P<0.001), vascular invasion (OR = 3.492, CI: 1.063–11.472, P = 0.039), lymph node metastasis (OR = 2.628, CI: 1.857–3.718, P<0.001), recurrence (OR = 1.631, CI: 1.052–2.528, P = 0.029) and pleural invasion (OR = 2.346, CI: 1.397–3.941, P = 0.001). There was no significant correlation between vimentin and age, gender, diameter, T stage, distant metastasis, or marginal invasion (P>0.05). Conclusion: An overexpression of vimentin may predict the progression and an unfavorable survival of NSCLC. Vimentin may represent a helpful biomarker and a potential target for the treatment strategies of NSCLC. Additional, prospective studies with large samples are necessary to confirm the significance of vimentin in NSCLC. [ABSTRACT FROM AUTHOR]

Desorption electrospray ionization—mass spectrometry (DESI-MS) imaging was used to analyze unmodified human brain tissue sections from 39 subjects sequentially in the positive and negative ionization modes. Acquisition of both MS polarities allowed more complete analysis of the human brain tumor lipidome as some phospholipids ionize preferentially in the positive and others in the negative ion mode. Normal brain parenchyma, comprised of grey matter and white matter, was differentiated from glioma using positive and negative ion mode DESI-MS lipid profiles with the aid of principal component analysis along with linear discriminant analysis. Principal component–linear discriminant analyses of the positive mode lipid profiles was able to distinguish grey matter, white matter, and glioma with an average sensitivity of 93.2% and specificity of 96.6%, while the negative mode lipid profiles had an average sensitivity of 94.1% and specificity of 97.4%. The positive and negative mode lipid profiles provided complementary information. Principal component–linear discriminant analysis of the combined positive and negative mode lipid profiles, via data fusion, resulted in approximately the same average sensitivity (94.7%) and specificity (97.6%) of the positive and negative modes when used individually. However, they complemented each other by improving the sensitivity and specificity of all classes (grey matter, white matter, and glioma) beyond 90% when used in combination. Further principal component analysis using the fused data resulted in the subgrouping of glioma into two groups associated with grey and white matter, respectively, a separation not apparent in the principal component analysis scores plots of the separate positive and negative mode data. The interrelationship of tumor cell percentage and the lipid profiles is discussed, and how such a measure could be used to measure residual tumor at surgical margins. [ABSTRACT FROM AUTHOR]

Background: O6-methylguanine-DNA methyl-transferase (MGMT) gene, a DNA repair gene, plays a critical role in the repair of alkylated DNA adducts that form following exposure to genotoxic agents. MGMT is generally expressed in various tumors, and its function is frequently lost because of hypermethylation in the promoter. The promoter methylation of MGMT has been extensively investigated in head and neck squamous cell carcinoma (HNSCC). However, the association between the promoter methylation of MGMT and HNSCC risk remains inconclusive and inconsistent. Therefore, we performed a meta-analysis to better clarify the association between the promoter methylation of MGMT and HNSCC risk. Methods: A systematical search was conducted in PubMed, Web of Science, EMBASE, and Ovid for studies on the association between MGMT promoter methylation and HNSCC. Odds ratio (ORs) and 95% confidence intervals (CI) were calculated to estimate association between MGMT promoter methylation and risk of HNSCC. The meta-regression and subgroup analysis were undertaken to explore the potential sources of heterogeneity. Results: Twenty studies with 1,030 cases and 775 controls were finally included in this study. The frequency of MGMT promoter methylation was 46.70% in HNSCC group and 23.23% in the control group. The frequency of MGMT promoter methylation in HNSCC group was significantly higher than the control group (OR = 2.83, 95%CI = 2.25–3.56). Conclusion: This meta-analysis indicates that aberrant methylation of MGMT promoter was significantly associated with the risk of HNSCC, and it may be a potential molecular marker for monitoring the disease and may provide new insights to the treatment of HNSCC. [ABSTRACT FROM AUTHOR]

Background: Laryngeal squamous cell carcinoma (LSCC) is a head and neck cancer type. In this study, we introduced a novel inflammation- and nutrition-based prognostic system, referred to as COR-BMI (Combination of red blood cell distribution width and body mass index), for LSCC patients. Methods: A total of 807 LSCC patients (784 male and 23 female, 22–87 y of age) who underwent surgery were enrolled in this retrospective cohort study. The patients were stratified by COR-BMI into three groups: COR-BMI (0) (RDW ≤ 13.1 and BMI ≥ 25); COR-BMI (1) (RDW ≤ 13.1 and BMI < 18.5 or 18.5 ≤ BMI < 25; RDW > 13.1 and 18.5 ≤ BMI < 25 or BMI ≥ 25); or COR-BMI (2) (RDW > 13.1 and BMI < 18.5). Cox regression models were used to investigate the association between COR-BMI and cancer-specific survival (CSS) rate among LSCC patients. Results: The 5-y, 10-y, and 15-y CSS rates were 71.6%, 60.1%, and 55.4%, respectively. There were significant differences among the COR-BMI groups in age (< 60 versus ≥ 60 y; P = 0.005) and T stage (T1, T2, T3, or T4; P = 0.013). Based on the results, COR-BMI (1 versus 0: HR = 1.76; 95% CI = 0.98–3.15; 2 versus 0: HR = 2.91; 95% CI = 1.53–5.54, P = 0.001) was a significant independent predictor of CSS. Conclusion: COR-BMI is a novel inflammation- and nutrition-based prognostic system, which could predict long-term survival in LSCC patients who underwent surgery. [ABSTRACT FROM AUTHOR]

The integration of data and knowledge from heterogeneous sources can be a key success factor in drug design, drug repurposing and multi-target therapies. In this context, biological networks provide a useful instrument to highlight the relationships and to model the phenomena underlying therapeutic action in cancer. In our work, we applied network-based modeling within a novel bioinformatics pipeline to identify promising multi-target drugs. Given a certain tumor type/subtype, we derive a disease-specific Protein-Protein Interaction (PPI) network by combining different data-bases and knowledge repositories. Next, the application of suitable graph-based algorithms allows selecting a set of potentially interesting combinations of drug targets. A list of drug candidates is then extracted by applying a recent data fusion approach based on matrix tri-factorization. Available knowledge about selected drugs mechanisms of action is finally exploited to identify the most promising candidates for planning in vitro studies. We applied this approach to the case of Triple Negative Breast Cancer (TNBC), a subtype of breast cancer whose biology is poorly understood and that lacks of specific molecular targets. Our “in-silico” findings have been confirmed by a number of in vitro experiments, whose results demonstrated the ability of the method to select candidates for drug repurposing. [ABSTRACT FROM AUTHOR]

Background: Estimation of the prognosis of resectable pancreatic ductal adenocarcinoma (PDAC) currently relies on tumour-related factors such as resection margins and on lymph-node ratio (LNR) both inconveniently available only postoperatively. Our aim was to assess the accuracy of preoperative laboratory data in predicting PDAC prognosis. Methods: Collection of laboratory and clinical data was retrospective from 265 consecutive patients undergoing surgery for PDAC at Helsinki University Hospital. Cancer-specific survival assessment utilized Kaplan-Meier analysis, and independent associations between factors were by the Cox regression model. Results: During follow-up, 76% of the patients died of PDAC, with a median survival time of 19.6 months. In univariate analysis, CRP, albumin, CEA, and CA19-9 were significantly associated with postoperative cancer-specific survival. In multivariate analysis, taking into account age, gender, LNR, resection margins, tumour status, and adjuvant chemotherapy, the preoperative biomarkers independently associated with adverse prognosis were hypoalbuminemia (< 36 g/L, hazard ratio (HR) 1.56, 95% confidence interval (CI) 1.10–2.19, p = 0.011), elevated CRP (> 5 mg/L, HR 1.44, 95% CI 1.03–2.02, p = 0.036), CEA (> 5 μg/L, HR 1.60, 95% CI 1.07–2.53, p = 0.047), and CA19-9 (≥555 kU/L, HR 1.91, 95% CI 1.18–3.08, p = 0.008). Conclusion: For patients with resectable PDAC, preoperative CRP, along with albumin and tumour markers, is useful for predicting prognosis. [ABSTRACT FROM AUTHOR]

Previous studies have indicated that carcinoembryonic antigen (CEA) and cancer antigen 15–3 (CA15-3) levels are both independent prognostic factors in breast cancer. However, the utility of CEA and CA15-3 levels as conventional cancer biomarkers in patients with triple-negative breast cancer (TNBC) remains controversial. The current study was performed to explore the predictive value of pre-therapeutic serum CEA and CA15-3 levels, and nomograms were developed including these serum cancer biomarkers to improve the prognostic evaluation of TNBC patients. Pre-therapeutic CA15-3 and CEA concentrations were measured in 247 patients with stage I–IV TNBC. Kaplan-Meier analysis showed that TNBC patients with high levels of both CEA and CA15-3 had shorter overall survival (OS) and disease-free survival (DFS) rates than those in the low-level groups (p<0.05). Multivariate analysis suggested that pre-therapeutic CA15-3 and CEA levels are independent predictive elements for OS (p = 0.022 and p = 0.040, respectively) and DFS (p = 0.023 and p = 0.028, respectively). In addition, novel nomograms were established and validated to provide personal forecasts of OS and DFS for patients with TNBC. These novel nomograms may help physicians to select the optimal treatment plans to ensure the best outcomes for TNBC patients. [ABSTRACT FROM AUTHOR]

Secretory clusterin (sCLU) is a potential prognostic tumour biomarker, but results of different sCLU studies are inconsistent. We conducted this meta-analysis to evaluate the precise predictive value of sCLU. Qualified studies were identified by performing online searches in PubMed, EMBASE, and Web of Science. The selected articles were divided into three groups based on scoring method for clusterin detection. Pooled hazard ratios (HRs) with 95% confidence interval (CI) for patient survival and disease recurrence were calculated to determine the correlation between sCLU expression and cancer prognosis. Heterogeneity was assessed using I2 statistics, and specific heterogeneity in different groups was analysed. Elevated sCLU was significantly associated with recurrence-free survival in groups 1 and 3 (group 1: pooled HR = 1.35, 95% CI = 1.01 to 1.79; group 3: pooled HR = 1.80, 95% CI = 1.22 to 2.65). However, clusterin expression was not associated with overall survival in all three groups. Results showed that only the heterogeneity of group 2 was very strong (p = 0.013, I2 = 76.3%), in which the specimens were scored through sCLU staining intensity only. sCLU is a potential biomarker for tumour prognosis, and IHC methods can be more standardised if both intensity and staining proportion are considered. [ABSTRACT FROM AUTHOR]

Objective: There is a heated debate on whether the prognostic value of NME1 is favorable or unfavorable. Thus, we carried out a meta-analysis to evaluate the relationship between NME1 expression and the prognosis of patients with digestive system neoplasms. Methods: We searched PubMed, EMBASE and Web of Science for relevant articles. The pooled odd ratios (ORs) and corresponding 95%CI were calculated to evaluate the prognostic value of NME1 expression in patients with digestive system neoplasms, and the association between NME1 expression and clinicopathological factors. We also performed subgroup analyses to find out the source of heterogeneity. Results: 2904 patients were pooled from 28 available studies in total. Neither the incorporative OR combined by 17 studies with overall survival (OR = 0.65, 95%CI:0.41–1.03, P = 0.07) nor the pooled OR with disease-free survival (OR = 0.75, 95%CI:0.17–3.36, P = 0.71) in statistics showed any significance. Although we couldn’t find any significance in TNM stage (OR = 0.78, 95%CI:0.44–1.36, P = 0.38), elevated NME1 expression was related to well tumor differentiation (OR = 0.59, 95%CI:0.47–0.73, P<0.00001), negative N status (OR = 0.54, 95%CI:0.36–0.82, P = 0.003) and Dukes’ stage (OR = 0.43, 95%CI:0.24–0.77, P = 0.004). And in the subgroup analyses, we only find the “years” which might be the source of heterogeneity of overall survival in gastric cancer. Conclusions: The results showed that statistically significant association was found between NME1 expression and the tumor differentiation, N status and Dukes’ stage of patients with digestive system cancers, while no significance was found in overall survival, disease-free survival and TNM stage. More and further researches should be conducted to reveal the prognostic value of NME1. [ABSTRACT FROM AUTHOR]