Search

Background: The aim of the present systematic review was to evaluate the hypothesis of an association between periodontitis and the development of cancer. Methods: Two reviewers independently screened electronic and manual sources for pertinent articles. Primary outcome measures were the occurrence of neoplasm diagnosis in exposed and non-exposed groups, reported to evaluate association between cancer and periodontitis. Results: Of the 490 initially retrieved papers 10 were included in the qualitative synthesis and eight in the quantitative synthesis; the eight papers covered six studies. Considering hazard ratios, a statistically significant association was found for all cancers studied (1.14; CI 95%: 1.04, 1.24), digestive tract cancer (1.34; CI 95%: 1.05, 1.72), pancreatic cancer (1.74; CI 95%: 1.21, 2.52), prostate cancer (1.25; CI 95%: 1.04, 1.51), breast cancer (1.11; CI 95%: 1.00, 1.23), corpus uteri cancer (2.20; CI 95%: 1.16, 4.18), lung cancer (1.24; CI 95%: 1.06, 1.45), hematological cancer (1.30; CI 95%: 1.11, 1.53), esophagus / oropharyngeal cancer pooled together (2.25; CI 95%: 1.30, 3.90) and Non-Hodgkin lymphoma (1.30; CI 95%: 1.11, 1.52). Conclusions: Despite the sparse scientific evidence and considering the low statistical power of the results, this systematic review revealed a substantial lack of studies with standardized and comparable methods to speculate about the association between periodontitis and cancer; more studies are need in order to explore further the scientific evidence of such correlation. [ABSTRACT FROM AUTHOR]

The ensemble method random forests has become a popular classification tool in bioinformatics and related fields. The out-of-bag error is an error estimation technique often used to evaluate the accuracy of a random forest and to select appropriate values for tuning parameters, such as the number of candidate predictors that are randomly drawn for a split, referred to as mtry. However, for binary classification problems with metric predictors it has been shown that the out-of-bag error can overestimate the true prediction error depending on the choices of random forests parameters. Based on simulated and real data this paper aims to identify settings for which this overestimation is likely. It is, moreover, questionable whether the out-of-bag error can be used in classification tasks for selecting tuning parameters like mtry, because the overestimation is seen to depend on the parameter mtry. The simulation-based and real-data based studies with metric predictor variables performed in this paper show that the overestimation is largest in balanced settings and in settings with few observations, a large number of predictor variables, small correlations between predictors and weak effects. There was hardly any impact of the overestimation on tuning parameter selection. However, although the prediction performance of random forests was not substantially affected when using the out-of-bag error for tuning parameter selection in the present studies, one cannot be sure that this applies to all future data. For settings with metric predictor variables it is therefore strongly recommended to use stratified subsampling with sampling fractions that are proportional to the class sizes for both tuning parameter selection and error estimation in random forests. This yielded less biased estimates of the true prediction error. In unbalanced settings, in which there is a strong interest in predicting observations from the smaller classes well, sampling the same number of observations from each class is a promising alternative. [ABSTRACT FROM AUTHOR]

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]

Objective: This study aimed to elucidate the effects of cholecystectomy on the risk of colorectal cancer (CRC) by conducting a meta-analysis of 10 cohort studies. Methods: The eligible cohort studies were selected by searching the PubMed and EMBASE databases from their origination to June 30, 2016, as well as by consulting the reference lists of the selected articles. Two authors individually collected the data from the 10 papers. When the data showed marked heterogeneity, we used a random-effects model to estimate the overall pooled risk; otherwise, a fixed effects model was employed. Results: The final analysis included ten cohort studies. According to the Newcastle-Ottawa Scale (NOS), nine papers were considered high quality. After the data of these 9 studies were combined, an increased risk of CRC was found among the individuals who had undergone cholecystectomy (risk ratio (RR) 1.22; 95% confidence interval (CI) 1.08–1.38). In addition, we also found a promising increased risk for colon cancer (CC) (RR 1.30, 95% CI 1.07–1.58), but no relationship between cholecystectomy and rectum cancer (RC) (RR 1.09; 95% CI 0.89–1.34) was observed. Additionally, in the sub-group analysis of the tumor location in the colon, a positive risk for ascending colon cancer (ACC) was found (RR 1.18, 95% CI 1.11–1.26). After combining the ACC, transverse colon cancer (TCC), sigmoid colon cancer (SCC) and descending colon cancer (DCC) patients, we found a positive relationship with cholecystectomy (RR 1.18, 95% CI 1.11–1.26). Furthermore, after combining the ACC and DCC patients, we also found a positive relationship with cholecystectomy (RR 1.28; 95% CI 1.11–1.26) in the sub-group analysis. In an additional sub-group analysis of patients from Western countries, there was a positive relationship between cholecystectomy and the risk of CRC (RR 1.20; 95% CI 1.05–1.36). Furthermore, a positive relationship between female gender and CRC was also found (RR 1.17; 95% CI 1.03–1.34). However, there was no relationship between gender and CC or RC. Furthermore, no publication bias was observed, and the sensitivity analysis indicated stable results. Conclusions: This meta-analysis of 10 cohort studies revealed that cholecystectomy is associated with an increased risk for CRC, CC and ACC, particularly in Western countries. No relationship between cholecystectomy and RC was observed. There was no relationship between gender and either CC or RC, but a positive relationship between female gender and CRC was observed. [ABSTRACT FROM AUTHOR]

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers. [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]

Background: Evidence is growing that low-dose aspirin used as an adjuvant treatment of cancer is associated with an increased survival and a reduction in metastatic spread. We therefore extended up to August 2017 an earlier systematic search and meta-analyses of published studies of low-dose aspirin taken by patients with a diagnosis of cancer. Methods: Searches were completed in Medline and Embase to August 2017 using a pre-defined search strategy to identify reports of relevant studies. References in all the selected papers were scanned. Two reviewers independently applied pre-determined eligibility criteria and extracted data on cause-specific cancer deaths, overall mortality and the occurrence of metastatic spread. Meta-analyses were then conducted for different cancers and heterogeneity and publication bias assessed. Sensitivity analyses and attempts to reduce heterogeneity were conducted. Results: Analyses of 29 studies reported since an earlier review up to April 2015 are presented in this report, and these are then pooled with the 42 studies in our earlier publication. Overall meta-analyses of the 71 studies are presented, based on a total of over 120 thousand patients taking aspirin. Ten of the studies also give evidence on the incidence of metastatic cancer spread. There are now twenty-nine observational studies describing colorectal cancer (CRC) and post-diagnostic aspirin. Pooling the estimates of reduction by aspirin which are reported as hazard ratios (HR), gives an overall HR for aspirin and CRC mortality 0.72 (95% CI 0.64–0.80). Fourteen observational studies have reported on aspirin and breast cancer mortality and pooling those that report the association with aspirin as a hazard ratio gives HR 0.69 (0.53–0.90). Sixteen studies report on aspirin and prostate cancer mortality and a pooled estimate yields an HR of 0.87 (95% CI 0.73–1.05). Data from 12 reports relating to other cancers are also listed. Ten studies give evidence of a reduction in metastatic spread; four give a pooled HR 0.31 (95% CI 0.18, 0.54) and five studies which reported odds ratio of metastatic spread give OR 0.79 (0.66 to 0.95). Conclusion: Being almost entirely from observational studies, the evidence of benefit from aspirin is limited. There is heterogeneity between studies and the results are subject to important biases, only some of which can be identified. Nevertheless, the evidence would seem to merit wide discussion regarding whether or not it is adequate to justify the recommendation of low-dose therapeutic aspirin, and if it is, for which cancers? [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]

High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses. [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]

Background: The association between height and lung cancer risk has been investigated by epidemiological studies but the results are inconsistent. This meta-analysis was to evaluate whether the height is associated with lung cancer. Methods: We identified relevant articles by searching the MEDLINE and EMBASE databases, and reviewed the reference lists of selected papers. A random effect model was used to calculate summary odds ratios (OR) and relative risk (RR) with 95% confidence intervals (95% CI). Publication bias was estimated using Egger’s regression asymmetry test. Results: We included a total 16 studies (15 prospective studies and one case–control study) on adult height and lung cancer risk in the meta-analysis. Overall, per 10-cm height increases were associated with increased risk of lung cancer (RR 1.06; 95% CI 1.03–1.09, I2 = 43.6%). Conclusions: In this meta-analysis, high adult height is related to increased lung cancer risk. Well-designed, large prospective studies are required to obtain a better indication of the relationship. [ABSTRACT FROM AUTHOR]

Renal cell carcinoma (RCC) comprises a heterogenous group of tumors. Traditionally, papillary RCC (pRCC) is associated with a favorable outcome compared to clear cell RCC (ccRCC), while other series report equivalent or worse prognosis. In this paper we comparatively evaluate outcome of pRCC versus ccRCC in two large multi-institutional databases (cohort study), including distribution of pRCC subtypes 1 and 2. Retrospective data of 1,943 surgically treated pRCC patients from 17 European/ North American centers between 1984–2015 were compared to 5,600 ccRCC patients from a database comprising 11 European/ North American centers (1984–2011). Median follow-up was 64.6 months. Differences between pRCC, subtypes, and ccRCC were compared with t-tests, Chi^2-tests, and exact Fisher tests. Cancer-specific mortality was analyzed with cumulative incidence curves and Cox cause-specific hazard models. The robustness of our results was examined with sensitivity analyses. We present that cancer-specific mortality rates and variables as stage, lymph node, and distant metastasis differ significantly between groups. Furthermore, we demonstrate that patients with non-metastatic pRCC had a significantly better cancer-specific mortality (HR 0.76, p = 0.007), when compared to ccRCC. Additionally, pRCC type 2 versus ccRCC exhibited no difference in cancer-specific mortality (HR 0.9, p = 0.722), whereas pRCC type 1 versus ccRCC displayed a risk of death reduced by 69% (p = 0.044). Taken together, outcome of pRCC versus ccRCC varies significantly in non-metastatic disease. Furthermore, pRCC type 2 exhibited no difference in cancer-specific mortality, whereas pRCC type 1 displayed a significantly reduced risk of death. Consequently, there is urgent need to respect histopathological entities and their subtypes, when assigning follow-up or targeted therapy to RCC patients. [ABSTRACT FROM AUTHOR]

In the scientific literature, spin refers to reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light. The presence of spin in biomedical research can negatively impact the development of further studies, clinical practice, and health policies. This systematic review aims to explore the nature and prevalence of spin in the biomedical literature. We searched MEDLINE, PreMEDLINE, Embase, Scopus, and hand searched reference lists for all reports that included the measurement of spin in the biomedical literature for at least 1 outcome. Two independent coders extracted data on the characteristics of reports and their included studies and all spin-related outcomes. Results were grouped inductively into themes by spin-related outcome and are presented as a narrative synthesis. We used meta-analyses to analyse the association of spin with industry sponsorship of research. We included 35 reports, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses. The nature of spin varied according to study design. The highest (but also greatest) variability in the prevalence of spin was present in trials. Some of the common practices used to spin results included detracting from statistically nonsignificant results and inappropriately using causal language. Source of funding was hypothesised by a few authors to be a factor associated with spin; however, results were inconclusive, possibly due to the heterogeneity of the included papers. Further research is needed to assess the impact of spin on readers’ decision-making. Editors and peer reviewers should be familiar with the prevalence and manifestations of spin in their area of research in order to ensure accurate interpretation and dissemination of research. [ABSTRACT FROM AUTHOR]

The expression of Programmed cell Death Ligand 1 (PD-L1) is observed in many malignant tumors and is associated with poor prognosis including Gastric Cancer (GC). The relationship between PD-L1 expression and prognosis, however, is controversial in GC. This paper purports to use a meta-analysis to investigate the relationship between PD-L1 expression and prognosis in GC. For this study, the following databases were searched for articles published from June 2003 until February 2017: PubMed, EBSCO, Web of Science and Cochrane Library. The baseline information extracted were: authors, year of publication, country where the study was performed, study design, sample size, follow-up time, baseline characteristics of the study population, pathologic data, overall survival (OS). A total of 15 eligible studies covering 3291 patients were selected for a meta-analysis based on specified inclusion and exclusion criteria. The analysis showed that the expression level of PD-L1 was associated with the overall survival in GC (Hazard Ratio, HR = 1.46, 95%CI = 1.08–1.98, P = 0.01, random-effect). In addition to the above, subgroup analysis showed that GC patients with deeper tumor infiltration, positive lymph-node metastasis, positive venous invasion, Epstein-Barr virus infection positive (EBV+), Microsatellite Instability (MSI) are more likely to expression PD-L1. The results of this meta-analysis suggest that GC patients, specifically EBV+ and MSI, may be prime candidates for PD-1 directed therapy. These findings support anti-PD-L1/PD-1 antibodies as a kind of immunotherapy which is promising for GC. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) with bile duct tumor thrombus (BDTT) in the clinic is rare, and surgical treatment is currently considered the most effective treatment. However, the influence of BDTT on the prognosis of HCC patients who underwent surgery remains controversial in previous studies. Therefore, this paper uses meta-analysis method to elucidate this controversy. Methods: In this study, we conducted a literature search on databases PubMed, Embase and Web of Science from inception until September 2016. Each study was evaluated with Newcastle-Ottawa Scale (NOS). The pooled effect was calculated, and the association between BDTT and overall survival (OS) or disease-free survival (DFS) was reevaluated using meta-analysis for hazard ratio (HR) and 95% confidence interval (CI). Results: A total of 11 studies was included containing 5295 patients. The (HR) for OS and DFS was 3.21 and 1.81, 95%CI was 2.34–4.39 and 1.17–2.78 respectively. Conclusions: The results showed that HCC patients with BDTT had a worse prognosis than those without BDTT after hepatic resection or liver transplantation (LT). [ABSTRACT FROM AUTHOR]

Background: Previous studies have noted significant gender difference in the risk of liver cancer among hepatitis B chronic infection patients. Some indicated that it might be due to lifestyle-related differences. This paper tests whether or not such a gender discrepancy among the chronic hepatitis B population is confounded by lifestyle and environment related exposures. Methods: We retrieved a sample of 1863 participants from a prospective cohort in Haimen City, China in 2003. Liver disease severity was categorized as “normal”, “mild”, “moderate”, and “severe” based on a clinical diagnosis. Lifestyle and environmental exposures were measured by questionnaires. We used factor analysis and individual variables to represent lifestyle and environmental exposures. We applied the cumulative logit models to estimate the effect of gender on liver disease severity and how it was impacted by lifestyle and environmental exposures. Results: Gender and HBeAg positivity were independent risk factors for more severe liver disease. Compared to females, males were 2.08 times as likely to develop more severe liver disease (95% CI: 1.66–2.61). Participants who were HBeAg positivite were 2.19 times (95% CI: 1.61–2.96) as likely to develop more severe liver disease compared to those who were negative. Controlling for lifestyle and environmental exposures did not change these estimations. Conclusions: Males in the HBV infected population have an increased risk of severe liver disease. This gender effect is independent of the lifestyle and environmental exposures addressed in this study. Our findings support the hypothesis that gender discrepancies in HCC risk are attributable to intrinsic differences between males and females. [ABSTRACT FROM AUTHOR]

One of the most accepted findings across psychology is that people are unrealistically optimistic in their judgments of comparative risk concerning future life events—they judge negative events as less likely to happen to themselves than to the average person. Harris and Hahn (2011), however, demonstrated how unbiased (non-optimistic) responses can result in data patterns commonly interpreted as indicative of optimism due to statistical artifacts. In the current paper, we report the results of 5 studies that control for these statistical confounds and observe no evidence for residual unrealistic optimism, even observing a ‘severity effect’ whereby severe outcomes were overestimated relative to neutral ones (Studies 3 & 4). We conclude that there is no evidence supporting an optimism interpretation of previous results using the prevalent comparison method. [ABSTRACT FROM AUTHOR]

In this paper, a total variation (TV) minimization strategy is proposed to overcome the problem of sparse spatial resolution and large amounts of noise in low dose positron emission tomography (PET) imaging reconstruction. Two types of objective function were established based on two statistical models of measured PET data, least-square (LS) TV for the Gaussian distribution and Poisson-TV for the Poisson distribution. To efficiently obtain high quality reconstructed images, the alternating direction method (ADM) is used to solve these objective functions. As compared with the iterative shrinkage/thresholding (IST) based algorithms, the proposed ADM can make full use of the TV constraint and its convergence rate is faster. The performance of the proposed approach is validated through comparisons with the expectation-maximization (EM) method using synthetic and experimental biological data. In the comparisons, the results of both LS-TV and Poisson-TV are taken into consideration to find which models are more suitable for PET imaging, in particular low-dose PET. To evaluate the results quantitatively, we computed bias, variance, and the contrast recovery coefficient (CRC) and drew profiles of the reconstructed images produced by the different methods. The results show that both Poisson-TV and LS-TV can provide a high visual quality at a low dose level. The bias and variance of the proposed LS-TV and Poisson-TV methods are 20% to 74% less at all counting levels than those of the EM method. Poisson-TV gives the best performance in terms of high-accuracy reconstruction with the lowest bias and variance as compared to the ground truth (14.3% less bias and 21.9% less variance). In contrast, LS-TV gives the best performance in terms of the high contrast of the reconstruction with the highest CRC. [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]

This paper introduces an approach to classification of RNA-seq read counts using grey relational analysis (GRA) and Bayesian Gaussian process (GP) models. Read counts are transformed to microarray-like data to facilitate normal-based statistical methods. GRA is designed to select differentially expressed genes by integrating outcomes of five individual feature selection methods including two-sample t-test, entropy test, Bhattacharyya distance, Wilcoxon test and receiver operating characteristic curve. GRA performs as an aggregate filter method through combining advantages of the individual methods to produce significant feature subsets that are then fed into a nonparametric GP model for classification. The proposed approach is verified by using two benchmark real datasets and the five-fold cross-validation method. Experimental results show the performance dominance of the GRA-based feature selection method as well as GP classifier against their competing methods. Moreover, the results demonstrate that GRA-GP considerably dominates the sparse Poisson linear discriminant analysis classifiers, which were introduced specifically for read counts, on different number of features. The proposed approach therefore can be implemented effectively in real practice for read count data analysis, which is useful in many applications including understanding disease pathogenesis, diagnosis and treatment monitoring at the molecular level. [ABSTRACT FROM AUTHOR]

Existing techniques to reconstruct tree models of progression for accumulative processes, such as cancer, seek to estimate causation by combining correlation and a frequentist notion of temporal priority. In this paper, we define a novel theoretical framework called CAPRESE (CAncer PRogression Extraction with Single Edges) to reconstruct such models based on the notion of probabilistic causation defined by Suppes. We consider a general reconstruction setting complicated by the presence of noise in the data due to biological variation, as well as experimental or measurement errors. To improve tolerance to noise we define and use a shrinkage-like estimator. We prove the correctness of our algorithm by showing asymptotic convergence to the correct tree under mild constraints on the level of noise. Moreover, on synthetic data, we show that our approach outperforms the state-of-the-art, that it is efficient even with a relatively small number of samples and that its performance quickly converges to its asymptote as the number of samples increases. For real cancer datasets obtained with different technologies, we highlight biologically significant differences in the progressions inferred with respect to other competing techniques and we also show how to validate conjectured biological relations with progression models. [ABSTRACT FROM AUTHOR]

Long non-coding ribonucleic acids (lncRNAs) have been proposed as biomarkers in prostate cancer. This paper proposes a selection method which uses data from tiled microarrays to identify relatively long regions of moderate expression independent of the microarray platform and probe design. The method is used to search for candidate long non-coding ribonucleic acids (lncRNAs) at locus 8q24 and is run on three independent experiments which all use samples from prostate cancer patients. The robustness of the method is tested by utilizing repeated copies of tiled probes. The method shows high consistency between experiments that used the same samples, but different probe layout. There also is statistically significant consistency when comparing experiments with different samples. The method selected the long non-coding ribonucleic acid PCNCR1 in all three experiments. [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: Some authors have studied the relationship between the presence of polyps, adenomas and cancers of upper gastrointestinal tract (stomach and duodenum) and risk of colorectal polyps and neoplasms; however, the results are controversial, which may be due to study sample size, populations, design, clinical features, and so on. No meta-analysis, which can be generalized to a larger population and could provide a quantitative pooled risk estimate of the relationship, of this issue existed so far. Methods: We performed a meta-analysis to evaluate risk of colorectal polyps or neoplasms in patients with polyps, adenomas or cancers in upper gastrointestinal tract comparing with controls. A search was conducted through PubMed, EMBASE, reference lists of potentially relevant papers, and practice guidelines up to 27 November 2013 without languages restriction. Odd ratios (ORs) were pooled using random-effects models. Results: The search yielded 3 prospective and 21 retrospective case-control studies (n = 37152 participants). The principal findings included: (1) OR for colorectal polyps was 1.15 (95% CI, 1.04–1.26) in the gastric polyps group comparing with control groups; (2) Patients with gastric polyps and neoplasms have higher risk (OR, 1.31 [95% CI, 1.06–1.62], and 1.72 [95% CI, 1.42–2.09], respectively) of colorectal neoplasms comparing with their controls; and (3) Positive association was found between the presence of colorectal neoplasms and sporadic duodenal neoplasms (OR, 2.59; 95% CI, 1.64–4.11). Conclusions: Findings from present meta-analysis of 24 case-control studies suggest that the prevalence of colorectal polyps was higher in patients with gastric polyps than in those without gastric polyps, and the risk of colorectal neoplasms increases significantly in patients with gastric polyps, neoplasms, and duodenal neoplasms. Therefore, screening colonoscopy should be considered for patients with upper gastrointestinal polyps and neoplasms. [ABSTRACT FROM AUTHOR]

Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data. [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]

Covariate selection is a fundamental step when building sparse prediction models in order to avoid overfitting and to gain a better interpretation of the classifier without losing its predictive accuracy. In practice the LASSO regression of Tibshirani, which penalizes the likelihood of the model by the L1 norm of the regression coefficients, has become the gold-standard to reach these objectives. Recently Lee and Oh developed a novel random-effect covariate selection method called the modified unbounded penalty (MUB) regression, whose penalization function can equal minus infinity at 0 in order to produce very sparse models. We sought to compare the predictive accuracy and the number of covariates selected by these two methods in several high-dimensional datasets, consisting in genes expressions measured to predict response to chemotherapy in breast cancer patients. These comparisons were performed by building the Receiver Operating Characteristics (ROC) curves of the classifiers obtained with the selected genes and by comparing their area under the ROC curve (AUC) corrected for optimism using several variants of bootstrap internal validation and cross-validation. We found consistently in all datasets that the MUB penalization selected a remarkably smaller number of covariates than the LASSO while offering a similar—and encouraging—predictive accuracy. The models selected by the MUB were actually nested in the ones obtained with the LASSO. Similar findings were observed when comparing these results to those obtained in their first publication by other authors or when using the area under the Precision-Recall curve (AUCPR) as another measure of predictive performance. In conclusion, the MUB penalization seems therefore to be one of the best options when sparsity is required in high-dimension. Further investigation in other datasets is however required to validate these findings. [ABSTRACT FROM AUTHOR]

It is becoming increasingly clear that major malignancies such as breast, colorectal and gastric cancers are not single disease entities, but comprising multiple cancer subtypes of distinct molecular properties. Molecular subtyping has been widely used to dissect inter-tumor biological heterogeneity, in relation to clinical outcomes. A key step of this methodology is to perform unsupervised classification of gene expression profiles, which, however, often suffers challenges of high-dimensionality, feature redundancy as well as noise and irrelevant information. To overcome these limitations, we propose ELM-CC, which employs hidden observation features obtained from extreme learning machines (ELMs) for cancer classification. To demonstrate the effectiveness and usefulness, we applied ELM-CC for gastric and ovarian cancer subtyping. Comparing with the widely-used consensus clustering method, our approach demonstrated much better clustering performance and identified molecular subtypes that are much more clinically relevant. [ABSTRACT FROM AUTHOR]

Objective: Cohort studies in Europe, but not in North-America, showed an association between exposure to outdoor particulate matter with aerodynamic diameter ≤10 μm (PM10) and lung cancer risk. Only a case-control study on lung cancer and PM10 in South Korea has so far been performed. For the first time in Europe we analyzed quantitatively this association using a case-control study design in highly polluted areas in Italy. Methods: The Environment And Genetics in Lung cancer Etiology (EAGLE) study, a population-based case-control study performed in the period 2002–2005 in the Lombardy Region, north-west Italy, enrolled 2099 cases and 2120 controls frequency-matched for area of residence, gender, and age. For this study we selected subjects with complete active and passive smoking history living in the same municipality since 1980 until study enrollment. Fine resolution annual PM10 estimates obtained by applying land use regression modeling to satellite data calibrated with fixed site monitor measurements were used. We assigned each subject the PM10 average estimates for year 2000 based on enrollment address. We used logistic regression models to calculate odds ratios (OR) and 95% confidence intervals (CI) adjusted for matching variables, education, smoking, and dietary and occupational variables. Results: We included 3473 subjects, 1665 cases (1318 men, 347 women) and 1808 controls (1368 men, 440 women), with PM10 individual levels ranging from 2.3 to 53.8 μg/m3 (mean: 46.3). We found increasing lung cancer risk with increasing PM10 category (P-value for trend: 0.04). The OR per 10 μg/m3 was 1.28 (95% CI: 0.95–1.72). The association appeared stronger for squamous cell carcinoma (OR 1.44, 95% CI: 0.90–2.29). Conclusion: In a population living in highly polluted areas in Italy, our study added suggestive evidence of a positive association between PM10 exposure and lung cancer risk. This study emphasizes the need to strengthen policies to reduce airborne pollution. [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]

Background: There are many epidemiological pieces of evidence that show IPF patients have the highest risk of lung cancer. We conducted a systematic review of all published data to define the characteristics of lung cancer that develops in IPF by performing a meta-analysis. Method: This study was performed based on the PRISMA guideline. Documents gathered by searching through the Web of Sciences, Scopus, PubMed/Medline, OVID, and COCHRANE databases which published before 03/25/2018 that related to lung cancer in IPFs’ patients. Articles were searched using standard keywords as well as Mesh and Mesh Entry and all probabilistic combinations of words using Boolean operators. Data searching, extracting and quality appraising were done by two researchers, independently. At last, Random-effects size based on Cochrane test and I2 were used. The review protocol has been registered in PROSPERO with ID: . Results: Based on the meta-analysis conducted in 35 (0.18%) included studies, the total sample size of patients with IPF was estimated 131947 among whom 6384 had LC. The total rate of LC prevalence in IPF patients was estimated to be 13.54% (95% CI: 10.43–17.4) that was significantly 9 times higher in men vs. Women and smoker vs. non-smoker. Highest to lowest prevalence of cellular (histological) subtypes of lung cancer in IPF were SQCC (37.82%), ADC (30.79%), SmCC (20.48%), LCC (5.21%), and ADQC (4.81%), respectively. The highest and lowest stage of lung cancer in IPF patients was estimated at III and II, respectively. The highest involvement location of lung cancer in IPF patients was in the Peripheral. Also, the prevalence of the tumor region involved from the highest to the lowest was estimated to be in the RLL, LLL, RUL and LUL regions. Conclusions: Lung cancer in IPF, most commonly SQCC, presents in elderly heavy smokers with a male, locating in peripheral regions and the lower part of lung predominance. [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]

Purpose: This systematic review with network meta-analysis compared the efficacy and safety of currently licensed second-line treatments in patients with late stage non-small cell lung cancer (NSCLC). Methods: Randomised controlled trials (RCTs) of participants with advanced/metastatic NSCLC receiving second/third line treatments were screened. We searched electronic databases (MEDLINE; EMBASE; Web of Science) from January, 2000 to July, 2017. Two reviewers screened bibliographic records, extracted data, and assessed risk of bias of included studies. The outcomes were overall survival (OS), progression-free survival (PFS), and drug-related grade 3–5 adverse-events (AEs). We pooled study-specific hazard ratios (HR; for OS and PFS) and risk ratios (RR; for AEs) using conventional and network-meta-analyses, and ranked interventions by the surface under the cumulative ranking curve. Findings: We included 11 RCTs (7,581 participants) comparing nine drugs. All drugs except for erlotinib significantly improved OS compared to docetaxel. Nivolumab was the highest ranking drug followed by atezolizumab and pembrolizumab. There was no significant difference in OS across these three drugs (HR = 0.98, 95% CI 0.79, 1.21 for nivolumab vs atezolizumab; HR = 0.98, 95% CI 0.77, 1.25 for nivolumab vs pembrolizumab). For PFS, ramucirumab + docetaxel and nivolumab were the drugs with the highest ranking. All interventions except ramucirumab + docetaxel had a reduced risk for severe drug-related AEs vs. docetaxel. Of the drugs with the highest ranking on AEs, nivolumab was significantly safer compared to atezolizumab (RR = 0.55, 95% CI 0.38, 0.79) or pembrolizumab (RR = 0.52, 95% CI 0.34, 0.81). Implications: Nivolumab, pembrolizumab and atezolizumab exhibited superior benefit/risk balance compared to other licensed drugs used late stage NSCLC. Our results indicate that the use of immunotherapies in people diagnosed with non-specific late stage NSCLC should be promoted. The use of docetaxel may now be judged irrelevant as a comparator intervention for approval of new drugs for second line treatment of NSCLC. Study registration number: PROSPERO . [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]

Background: We evaluated the associations of handgrip strength and cognitive function in cancer survivors ≥ 60 years old using data from the National Health and Nutrition Examination Survey (NHANES). Methods: Data in two waves of NHANES (2011–2014) were aggregated. Handgrip strength in kilogram (kg) was defined as the maximum value achieved using either hand. Two cognitive function tests were conducted among adults 60 years and older. The Animal Fluency Test (AFT) examines categorical verbal fluency (a component of executive function), and the Digital Symbol Substitution test (DSST) assesses processing speed, sustained attention, and working memory. Survey analysis procedures were used to account for the complex sampling design of the NHANES. Multiple linear regression models were used to estimate associations of handgrip strength with cognitive test scores, adjusting for confounders (age, gender, race/ethnicity, education, marital status, smoking status, depressive symptoms and leisure time physical activity). Results: Among 383 cancer survivors (58.5% women, mean age = 70.9 years, mean BMI = 29.3 kg/m2), prevalent cancer types were breast (22.9%), prostate (16.4%), colon (6.9%) and cervix (6.2%). In women, each increase in kg of handgrip strength was associated with 0.20 (95% CI: 0.08 to 0.33) higher score on AFT and 0.83 (95% CI: 0.30 to 1.35) higher score on DSST. In men, we observed an inverted U-shape association where cognitive function peaked at handgrip strength of 40–42 kg. Conclusions: Handgrip strength, a modifiable factor, appears to be associated with aspects of cognitive functions in cancer survivors. Prospective studies are needed to address their causal relationship. [ABSTRACT FROM AUTHOR]

In the traditional two-step procedure used in intensity-modulated radiation therapy, fluence map optimization (FMO) is performed first, followed by use of a leaf sequencing algorithm (LSA). By contrast, direct aperture optimization (DAO) directly optimizes aperture leaf positions and weights. However, dose calculation using the Monte Carlo (MC) method for DAO is often time-consuming. Therefore, a rapid DAO (RDAO) algorithm is proposed that uses a dose influence matrix based piecewise aperture dose model (DIM-PADM). In the proposed RDAO algorithm, dose calculation is based on the dose influence matrix instead of MC. The dose dependence of aperture leafs is modeled as a piecewise function using the DIM. The corresponding DIM-PADM-based DAO problem is solved using a simulated annealing algorithm.The proposed algorithm was validated through application to TG119, prostate, liver, and head and neck (H&N) cases from the common optimization for radiation therapy dataset. Compared with the two-step FMO–LSA procedure, the proposed algorithm resulted in more precise dose conformality in all four cases. Specifically, for the H&N dataset, the cost value for the planned target volume (PTV) was decreased by 32%, whereas the cost value for the two organs at risk (OARs) was decreased by 60% and 92%. Our study of the proposed novel DIM-PADM-based RDAO algorithm makes two main contributions: First, we validate the use of the proposed algorithm, in contrast to the FMO–LSA framework, for direct optimization of aperture leaf positions and show that this method results in more precise dose conformality. Second, we demonstrate that compared to MC, the DIM-PADM-based method significantly reduces the computational time required for DAO. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to develop a statistical model for cell death by irreversible electroporation (IRE) and to show that the statistic model is more accurate than the electric field threshold model in the literature using cervical cancer cells in vitro. Methods: HeLa cell line was cultured and treated with different IRE protocols in order to obtain data for modeling the statistical relationship between the cell death and pulse-setting parameters. In total, 340 in vitro experiments were performed with a commercial IRE pulse system, including a pulse generator and an electric cuvette. Trypan blue staining technique was used to evaluate cell death after 4 hours of incubation following IRE treatment. Peleg-Fermi model was used in the study to build the statistical relationship using the cell viability data obtained from the in vitro experiments. A finite element model of IRE for the electric field distribution was also built. Comparison of ablation zones between the statistical model and electric threshold model (drawn from the finite element model) was used to show the accuracy of the proposed statistical model in the description of the ablation zone and its applicability in different pulse-setting parameters. Results: The statistical models describing the relationships between HeLa cell death and pulse length and the number of pulses, respectively, were built. The values of the curve fitting parameters were obtained using the Peleg-Fermi model for the treatment of cervical cancer with IRE. The difference in the ablation zone between the statistical model and the electric threshold model was also illustrated to show the accuracy of the proposed statistical model in the representation of ablation zone in IRE. Conclusions: This study concluded that: (1) the proposed statistical model accurately described the ablation zone of IRE with cervical cancer cells, and was more accurate compared with the electric field model; (2) the proposed statistical model was able to estimate the value of electric field threshold for the computer simulation of IRE in the treatment of cervical cancer; and (3) the proposed statistical model was able to express the change in ablation zone with the change in pulse-setting parameters. [ABSTRACT FROM AUTHOR]

Background: mTOR-Is positively influence the occurrence and course of certain tumors after solid organ transplantation. The effect of mTOR-Is on the overall incidence of tumors irrespective of their origin is not entirely clear. Furthermore, conflicting data have been shown on mortality under mTOR-Is. Methods: The current literature was searched for prospective randomized controlled renal transplantation trials. There were 1415 trials screened of which 13 could be included (pts. = 5924). A minimum follow-up of 24 months was mandatory for inclusion. Incidence of malignancies and patient survival was assessed in meta-analyses. Results: The average follow-up of all trials was 40.6 months. Malignancy was significantly reduced under mTOR-Is compared to CNIs (RR 0.70, CI 0.49–0.99, p = 0.046). This effect remained stable when combined with CNIs (RR 0.58, CI 0.34–1.00, p = 0.05). When NMSCs were excluded the risk for malignancy remained significantly reduced under mTOR-I therapy (mono and combi) (RR 0.43, CI 0.24–0.77, p = 0.0046). Graft survival was minimally decreased under mTOR-Is (RR 0.99, CI 0.98–1.00, p = 0.054). This effect was abrogated when mTOR-Is were combined with CNIs (RR 0.99, CI 0.97–1.02, p = 0.50). Patient survival was not different (RR 1.00, CI 0.99–1.01, p = 0.54). Conclusions: Posttransplant patients have a lower incidence of malignancy when treated with an mTOR-I no matter if it is used in combination with CNIs or not. This beneficial effect remains significant even when NMSCs are excluded. With currently used mTOR-I-based regimen patient and graft survival is not different compared to CNI therapies. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at . [ABSTRACT FROM AUTHOR]

Background: There are two main types of strategies to identify target population for lung cancer screening: 1) strategies based on age and cumulative smoking criteria, 2) risk prediction models allowing the calculation of an individual risk. The objective of this study was to compare different strategies to identify the proportion of the Spanish population at high risk of developing lung cancer, susceptible to be included in a lung cancer screening programme. Methods: Cross-sectional study. We used the data of the Spanish National Interview Health Survey (ENSE) of 2011–2012 (21,006 individuals) to estimate the proportion of participants at high risk of developing lung cancer. This estimation was performed using the U.S. national lung screening trial (NLST) criteria and a 6-year prediction model (PLCOm2012), both independently and in combination. Results: The prevalence of individuals at high risk of developing lung cancer according to the NLST criteria was 4.9% (7.9% for men, 2.4% for women). Among the 1,034 subjects who met the NLST criteria, 533 (427 men and 106 women) had a 6-year lung cancer risk ≥2.0%. The combination of these two selection strategies showed that 2.5% of the Spanish population had a high risk of developing lung cancer. However, this selection process did not take into account different groups of subjects <75 years old having an individual risk of lung cancer ≥2%, such as heavy smokers <55 years old who were long-time former smokers, and ever-smokers having smoked <30 pack-years with other risk factors. Conclusions: Further research is needed to determine which selection strategy achieves a higher benefit/harm ratio and to assess other prevention strategies for individuals with elevated risk for lung cancer but who do not meet the screening eligibility criteria. [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]

Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel extension of the p-value—a second-generation p-value (pδ)–that formally accounts for scientific relevance and leverages this natural Type I Error control. The approach relies on a pre-specified interval null hypothesis that represents the collection of effect sizes that are scientifically uninteresting or are practically null. The second-generation p-value is the proportion of data-supported hypotheses that are also null hypotheses. As such, second-generation p-values indicate when the data are compatible with null hypotheses (pδ = 1), or with alternative hypotheses (pδ = 0), or when the data are inconclusive (0 < pδ < 1). Moreover, second-generation p-values provide a proper scientific adjustment for multiple comparisons and reduce false discovery rates. This is an advance for environments rich in data, where traditional p-value adjustments are needlessly punitive. Second-generation p-values promote transparency, rigor and reproducibility of scientific results by a priori specifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with alternative or null hypotheses. [ABSTRACT FROM AUTHOR]

Objectives: To compare clinical outcomes of concurrent chemoradiotherapy (CCRT) with those of radiotherapy alone for stage II nasopharyngeal carcinoma in the intensity-modulated radiotherapy (IMRT) era. Materials and methods: We comprehensively searched PubMed, Embase, and the Cochrane Library to identify eligible studies. Overall survival (OS), progression-free survival (PFS), distant metastasis-free survival (DMFS), locoregional recurrence-free survival (LRRFS) with hazard ratios (HRs), and toxicities with odd ratios (ORs) were analyzed. Results: A total of seven studies met the criteria, with 1302 patients who were treated with IMRT alone or IMRT plus concurrent chemotherapy. No significant survival benefit was shown by CCRT regardless of OS (HR = 1.17, 95% CI 0.73–1.89, P = 0.508), PFS (HR = 0.76, 95% CI 0.38–1.50, P = 0.430), DMFS (HR = 0.89, 95% CI 0.33–2.41, P = 0.816), or LRRFS (HR = 1.03, 95% CI 0.95–1.12, P = 0.498). Additionally, CCRT notably increased the risk of acute grade 3–4 leukopenia (OR = 4.432, 95% CI 2.195–8.952, P < 0.001), compared to IMRT alone. Conclusion: Adding concurrent chemotherapy to IMRT led to no survival benefit and increased acute toxicity reactions for stage II nasopharyngeal carcinoma. [ABSTRACT FROM AUTHOR]

Quality control, global biases, normalization, and analysis methods for RNA-Seq data are quite different than those for microarray-based studies. The assumption of normality is reasonable for microarray based gene expression data; however, RNA-Seq data tend to follow an over-dispersed Poisson or negative binomial distribution. Little research has been done to assess how data transformations impact Gaussian model-based clustering with respect to clustering performance and accuracy in estimating the correct number of clusters in RNA-Seq data. In this article, we investigate Gaussian model-based clustering performance and accuracy in estimating the correct number of clusters by applying four data transformations (i.e., naïve, logarithmic, Blom, and variance stabilizing transformation) to simulated RNA-Seq data. To do so, an extensive simulation study was carried out in which the scenarios varied in terms of: how genes were selected to be included in the clustering analyses, size of the clusters, and number of clusters. Following the application of the different transformations to the simulated data, Gaussian model-based clustering was carried out. To assess clustering performance for each of the data transformations, the adjusted rand index, clustering error rate, and concordance index were utilized. As expected, our results showed that clustering performance was gained in scenarios where data transformations were applied to make the data appear “more” Gaussian in distribution. [ABSTRACT FROM AUTHOR]

Hepatitis B virus (HBV) infection is a common problem in the world, especially in China. More than 60–80% of hepatocellular carcinoma (HCC) cases can be attributed to HBV infection in high HBV prevalent regions. Although traditional Sanger sequencing has been extensively used to investigate HBV sequences, NGS is becoming more commonly used. Further, it is unknown whether word pattern frequencies of HBV reads by Next Generation Sequencing (NGS) can be used to investigate HBV genotypes and predict HCC status. In this study, we used NGS to sequence the pre-S region of the HBV sequence of 94 HCC patients and 45 chronic HBV (CHB) infected individuals. Word pattern frequencies among the sequence data of all individuals were calculated and compared using the Manhattan distance. The individuals were grouped using principal coordinate analysis (PCoA) and hierarchical clustering. Word pattern frequencies were also used to build prediction models for HCC status using both K-nearest neighbors (KNN) and support vector machine (SVM). We showed the extremely high power of analyzing HBV sequences using word patterns. Our key findings include that the first principal coordinate of the PCoA analysis was highly associated with the fraction of genotype B (or C) sequences and the second principal coordinate was significantly associated with the probability of having HCC. Hierarchical clustering first groups the individuals according to their major genotypes followed by their HCC status. Using cross-validation, high area under the receiver operational characteristic curve (AUC) of around 0.88 for KNN and 0.92 for SVM were obtained. In the independent data set of 46 HCC patients and 31 CHB individuals, a good AUC score of 0.77 was obtained using SVM. It was further shown that 3000 reads for each individual can yield stable prediction results for SVM. Thus, another key finding is that word patterns can be used to predict HCC status with high accuracy. Therefore, our study shows clearly that word pattern frequencies of HBV sequences contain much information about the composition of different HBV genotypes and the HCC status of an individual. [ABSTRACT FROM AUTHOR]

One of the goals of cancer research is to identify a set of genes that cause or control disease progression. However, although multiple such gene sets were published, these are usually in very poor agreement with each other, and very few of the genes proved to be functional therapeutic targets. Furthermore, recent findings from a breast cancer gene-expression cohort showed that sets of genes selected randomly can be used to predict survival with a much higher probability than expected. These results imply that many of the genes identified in breast cancer gene expression analysis may not be causal of cancer progression, even though they can still be highly predictive of prognosis. We performed a similar analysis on all the cancer types available in the cancer genome atlas (TCGA), namely, estimating the predictive power of random gene sets for survival. Our work shows that most cancer types exhibit the property that random selections of genes are more predictive of survival than expected. In contrast to previous work, this property is not removed by using a proliferation signature, which implies that proliferation may not always be the confounder that drives this property. We suggest one possible solution in the form of data-driven sub-classification to reduce this property significantly. Our results suggest that the predictive power of random gene sets may be used to identify the existence of sub-classes in the data, and thus may allow better understanding of patient stratification. Furthermore, by reducing the observed bias this may allow more direct identification of biologically relevant, and potentially causal, genes. [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]