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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]

Background: Recent advances in targeted therapies have raised expectations that the clinical application of biomarkers would improve patient’s health outcomes and potentially save costs. However, the cost-effectiveness of biomarkers remains unclear irrespective of the cost-effectiveness of corresponding therapies. It is thus important to determine whether biomarkers for targeted therapies provide good value for money. This study systematically reviews economic evaluations of biomarkers for targeted therapies in metastatic colorectal cancer (mCRC) and assesses the cost-effectiveness of predictive biomarkers in mCRC. Methods: A literature search was performed using Medline, Embase, EconLit, NHSEED. Papers published from 2000 until June 2018 were searched. All economic evaluations assessing biomarker-guided therapies with companion diagnostics in mCRC were searched. To make studies more comparable, cost-effectiveness results were synthesized as per biomarker tests and corresponding therapies. Methodological quality was assessed using the Quality of Health Economic Studies (QHES) instrument. Results: Forty-six studies were included in this review. Of these, 17 studies evaluated the intrinsic value of cancer biomarkers, whereas the remaining studies focused on assessing the cost-effectiveness of corresponding drugs. Most studies indicated favourable cost-effectiveness of biomarkers for targeted therapies in mCRC. Some studies reported that biomarkers were cost-effective, while their corresponding therapies were not cost-effective. A considerable number of economic evaluations were conducted in pre-defined genetic populations and thus, often failed to fully capture the biomarker’s clinical and economic values. The average QHES score was 73.6. Conclusion: Cancer biomarkers for targeted therapies in mCRC were mostly found to be cost-effective; otherwise, they at least improved the cost-effectiveness of targeted therapies by saving some costs. However, this did not necessarily make their corresponding therapies cost-effective. While companion biomarkers reduced therapy costs, the savings were not sufficient to make the corresponding agents cost-effective. Evaluation of biomarkers was often restricted to the cost of tests and did not consider their clinical values or biomarker prevalence. [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]

Emerging evidence has shown microRNAs (miRNAs) play an important role in human disease research. Identifying potential association among them is significant for the development of pathology, diagnose and therapy. However, only a tiny portion of all miRNA-disease pairs in the current datasets are experimentally validated. This prompts the development of high-precision computational methods to predict real interaction pairs. In this paper, we propose a new model of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) by fusing multi-source information including miRNA sequences, miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In particular, we introduce miRNA sequence information and extract its features using natural language processing technique for the first time in the miRNA-disease prediction model. In the cross-validation experiment, LMTRDA obtained 90.51% prediction accuracy with 92.55% sensitivity at the AUC of 90.54% on the HMDD V3.0 dataset. To further evaluate the performance of LMTRDA, we compared it with different classifier and feature descriptor models. In addition, we also validate the predictive ability of LMTRDA in human diseases including Breast Neoplasms, Breast Neoplasms and Lymphoma. As a result, 28, 27 and 26 out of the top 30 miRNAs associated with these diseases were verified by experiments in different kinds of case studies. These experimental results demonstrate that LMTRDA is a reliable model for predicting the association among miRNAs and diseases. [ABSTRACT FROM AUTHOR]

For studying cancer and genetic diseases, the issue of identifying high correlation genes from high-dimensional data is an important problem. It is a great challenge to select relevant biomarkers from gene expression data that contains some important correlation structures, and some of the genes can be divided into different groups with a common biological function, chromosomal location or regulation. In this paper, we propose a penalized accelerated failure time model CHR-DE using a non-convex regularization (local search) with differential evolution (global search) in a wrapper-embedded memetic framework. The complex harmonic regularization (CHR) can approximate to the combination and ℓq (1 ≤ q < 2) for selecting biomarkers in group. And differential evolution (DE) is utilized to globally optimize the CHR’s hyperparameters, which make CHR-DE achieve strong capability of selecting groups of genes in high-dimensional biological data. We also developed an efficient path seeking algorithm to optimize this penalized model. The proposed method is evaluated on synthetic and three gene expression datasets: breast cancer, hepatocellular carcinoma and colorectal cancer. The experimental results demonstrate that CHR-DE is a more effective tool for feature selection and learning prediction. [ABSTRACT FROM AUTHOR]

Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome. [ABSTRACT FROM AUTHOR]

Several modern genomic technologies, such as DNA-Methylation arrays, measure spatially registered probes that number in the hundreds of thousands across multiple chromosomes. The measured probes are by themselves less interesting scientifically; instead scientists seek to discover biologically interpretable genomic regions comprised of contiguous groups of probes which may act as biomarkers of disease or serve as a dimension-reducing pre-processing step for downstream analyses. In this paper, we introduce an unsupervised feature learning technique which maps technological units (probes) to biological units (genomic regions) that are common across all subjects. We use ideas from fusion penalties and convex clustering to introduce a method for Spatial Convex Clustering, or SpaCC. Our method is specifically tailored to detecting multi-subject regions of methylation, but we also test our approach on the well-studied problem of detecting segments of copy number variation. We formulate our method as a convex optimization problem, develop a massively parallelizable algorithm to find its solution, and introduce automated approaches for handling missing values and determining tuning parameters. Through simulation studies based on real methylation and copy number variation data, we show that SpaCC exhibits significant performance gains relative to existing methods. Finally, we illustrate SpaCC’s advantages as a pre-processing technique that reduces large-scale genomics data into a smaller number of genomic regions through several cancer epigenetics case studies on subtype discovery, network estimation, and epigenetic-wide association. [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]

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm. [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]

With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis. [ABSTRACT FROM AUTHOR]

System biology provides the basis to understand the behavioral properties of complex biological organisms at different levels of abstraction. Traditionally, analysing systems biology based models of various diseases have been carried out by paper-and-pencil based proofs and simulations. However, these methods cannot provide an accurate analysis, which is a serious drawback for the safety-critical domain of human medicine. In order to overcome these limitations, we propose a framework to formally analyze biological networks and pathways. In particular, we formalize the notion of reaction kinetics in higher-order logic and formally verify some of the commonly used reaction based models of biological networks using the HOL Light theorem prover. Furthermore, we have ported our earlier formalization of Zsyntax, i.e., a deductive language for reasoning about biological networks and pathways, from HOL4 to the HOL Light theorem prover to make it compatible with the above-mentioned formalization of reaction kinetics. To illustrate the usefulness of the proposed framework, we present the formal analysis of three case studies, i.e., the pathway leading to TP53 Phosphorylation, the pathway leading to the death of cancer stem cells and the tumor growth based on cancer stem cells, which is used for the prognosis and future drug designs to treat cancer patients. [ABSTRACT FROM AUTHOR]

Blood oxygenation mapping by the means of optical oximetry is of significant importance in clinical trials. This paper uses hyperspectral imaging technology to obtain in vivo images for blood oxygenation detection. The experiment involves dorsal skin fold window chamber preparation which was built on adult (8–10 weeks of age) female BALB/c nu/nu mice and in vivo image acquisition which was performed by hyperspectral imaging system. To get the accurate spatial and spectral information of targets, an automatic registration scheme is proposed. An adaptive feature detection method which combines the local threshold method and the level-set filter is presented to extract target vessels. A reliable feature matching algorithm with the correlative information inherent in hyperspectral images is used to kick out the outliers. Then, the registration images are used for blood oxygenation mapping. Registration evaluation results show that most of the false matches are removed and the smooth and concentrated spectra are obtained. This intensity invariant feature detection with outliers-removing feature matching proves to be effective in hyperspectral vessel image registration. Therefore, in vivo hyperspectral imaging system by the assistance of the proposed registration scheme provides a technique for blood oxygenation research. [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]

Identification of disease-causing genes is a fundamental challenge for human health studies. The phenotypic similarity among diseases may reflect the interactions at the molecular level, and phenotype comparison can be used to predict disease candidate genes. Online Mendelian Inheritance in Man (OMIM) is a database of human genetic diseases and related genes that has become an authoritative source of disease phenotypes. However, disease phenotypes have been described by free text; thus, standardization of phenotypic descriptions is needed before diseases can be compared. Several disease phenotype networks have been established in OMIM using different standardization methods. Two of these networks are important for phenotypic similarity analysis: the first and most commonly used network (mimMiner) is standardized by medical subject heading, and the other network (resnikHPO) is the first to be standardized by human phenotype ontology. This paper comprehensively evaluates for the first time the accuracy of these two networks in gene prioritization based on protein–protein interactions using large-scale, leave-one-out cross-validation experiments. The results show that both networks can effectively prioritize disease-causing genes, and the approach that relates two diseases using a logistic function improves prioritization performance. Tanimoto, one of four methods for normalizing resnikHPO, generates a symmetric network and it performs similarly to mimMiner. Furthermore, an integration of these two networks outperforms either network alone in gene prioritization, indicating that these two disease networks are complementary. [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]

Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation of lncRNA-disease associations, only a few studies had studied the roles of these associations in pathogenesis. In this paper, we investigated lncRNA-disease associations from a network view to understand the contribution of these lncRNAs to complex diseases. Specifically, we studied both the properties of the diseases in which the lncRNAs were implicated, and that of the lncRNAs associated with complex diseases. Regarding the fact that protein coding genes and lncRNAs are involved in human diseases, we constructed a coding-non-coding gene-disease bipartite network based on known associations between diseases and disease-causing genes. We then applied a propagation algorithm to uncover the hidden lncRNA-disease associations in this network. The algorithm was evaluated by leave-one-out cross validation on 103 diseases in which at least two genes were known to be involved, and achieved an AUC of 0.7881. Our algorithm successfully predicted 768 potential lncRNA-disease associations between 66 lncRNAs and 193 diseases. Furthermore, our results for Alzheimer's disease, pancreatic cancer, and gastric cancer were verified by other independent studies. [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]

Background: In this research, an meta-analysis was performed for assessment of the associations between O6-methyguanine-DNA methyltransferase (MGMT) promoter hypermethylation possessing low-grade intraepithelial lesion (LSIL), high-grade intraepithelial lesion (HSIL), cervical cancer (CC), and clinicopathological characters of CC. Methods: Literature selection were conducted through searching PubMed, Web of science, EMBASE, China National Knowledge Infrastructure and Wanfang databases (up to November 2018). An assessment of associations between MGMT methylation and LSIL, HSIL, CC risk and clinicopathological characteristics was performed through pooled odds ratios (ORs) with relevant 95% confidence intervals (CIs). Subgroup analyses, meta-regressions and Galbraith plots were conducted to conduct an exploration on the possible sources of heterogeneity. The genome-wide DNA methylation array studies were extracted from Gene Expression Omnibus (GEO) databases for validation of these outcomes. Results: In this meta-analysis of 25 published articles, MGMT hypermethylation gradually elevated the rates among control group (12.16%), LSIL (20.92%), HSIL (36.33%) and CC (41.50%) specimens. MGMT promoter methylation was significant associated with the increased risk of LSIL by 1.74-fold (P<0.001), HSIL by 3.71-fold (P<0.001) and CC by 7.08-fold (P<0.001) compared with control. A significant association between MGMT promoter methylation with FIGO stage was also found (OR = 2.81, 95% CI: 1.79–4.41, p<0.001). The results of GEO datasets showed that 5 CpG sites in MGMT with a great diagnostic value for the screening of cervical cancer. Conclusion: The meta-analysis indicated the association between MGMT promoter hypermethylation and squamous intraepithelial lesion and cervical cancer. MGMT methylation detection might have a potential value to be an epigenetic marker for the clinical diagnosis of cervical cancer. [ABSTRACT FROM AUTHOR]

Background: Pleural fluid homocysteine (HCY) can be useful for diagnosis of malignant pleural effusion (MPE). There are no published studies comparing the diagnostic accuracy of HCY with other tumour markers in pleural fluid for diagnosis of MPE. The aim was to compare the accuracy of HCY with that of carcinoembryonic antigen (CEA), cancer antigen (CA) 15.3, CA19.9 and CA125 in pleural fluid and to develop a probabilistic model using these biomarkers to differentiate benign (BPE) from MPE. Methods: Patients with pleural effusion were randomly included. HCY, CEA, CA15.3, CEA19.9 and CA125 were quantified in pleural fluid. Patients were classified into two groups: MPE or BPE. By applying logistic regression analysis, a multivariate probabilistic model was developed using pleural fluid biomarkers. The diagnostic accuracy was determined by receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Results: Population of study comprised 133 patients (72 males and 61 females) aged between 1 and 96 years (median = 70 years), 81 BPE and 52 MPE. The logistic regression analysis included HCY (p<0.0001) and CEA (p = 0.0022) in the probabilistic model and excluded the other tumour markers. The probabilistic model was: HCY+CEA = Probability(%) = 100×(1+e-z)-1, where Z = 0.5471×[HCY]+0.3846×[CEA]–8.2671. The AUCs were 0.606, 0.703, 0.778, 0.800, 0.846 and 0.948 for CA125, CA19.9, CEA, CA15.3, HCY and HCY+CEA, respectively. Conclusions: Pleural fluid HCY has higher accuracy for diagnosis of MPE than CEA, CA15.3, CA19.9 and CA125. The combination of HCY and CEA concentrations in pleural fluid significantly improves the diagnostic accuracy of the test. [ABSTRACT FROM AUTHOR]

In transcriptomics, micro RNAs (miRNAs) has gained much interest especially as potential disease indicators. However, apart from holding a great promise related to their clinical application, a lot of inconsistent results have been published. Our aim was to compare the miRNA expression levels in ovarian cancer and healthy subjects using the Bayesian multilevel model and to assess their potential usefulness in diagnosis. We have analyzed a case-control observational data on expression profiling of 49 preselected miRNA-based ovarian cancer indicators in 119 controls and 59 patients. A Bayesian multilevel model was used to characterize the effect of disease on miRNA levels controlling for differences in age and body weight. The difference between the miRNA level and health status of the patient on the scale of the data variability were discussed in the context of their potential usefulness in diagnosis. Additionally, the cross-validated area under the ROC curve (AUC) was used to assess the expected out-of-sample discrimination index of a different sets of miRNAs. The proposed model allowed us to describe the set of miRNA levels in patients and controls. Three highly correlated miRNAs: miR-101-3p, miR-142-5p, miR-148a-3p rank the highest with almost identical effect sizes that ranges from 0.45 to 1.0. For those miRNAs the credible interval for AUC ranged from 0.63 to 0.67 indicating their limited discrimination potential. A little benefit in adding information from other miRNAs was observed. There were several miRNAs in the dataset (miR-604, hsa-miR-221-5p) for which inferences were uncertain. For those miRNAs more experimental effort is needed to fully assess their effect in the context of new hits discovery and usefulness as disease indicators. The proposed multilevel Bayesian model can be used to characterize the panel of miRNA profile and to assess the difference in expression levels between healthy and cancer individuals. [ABSTRACT FROM AUTHOR]

The thermoanalytical technique differential scanning calorimetry (DSC) has been applied to characterize protein denaturation patterns (thermograms) in blood plasma samples and relate these to a subject's health status. The analysis and classification of thermograms is challenging because of the high-dimensionality of the dataset. There are various methods for group classification using high-dimensional data sets; however, the impact of using high-dimensional data sets for cancer classification has been poorly understood. In the present article, we proposed a statistical approach for data reduction and a parametric method (PM) for modeling of high-dimensional data sets for two- and three- group classification using DSC and demographic data. We compared the PM to the non-parametric classification method K-nearest neighbors (KNN) and the semi-parametric classification method KNN with dynamic time warping (DTW). We evaluated the performance of these methods for multiple two-group classifications: (i) normal versus cervical cancer, (ii) normal versus lung cancer, (iii) normal versus cancer (cervical + lung), (iv) lung cancer versus cervical cancer as well as for three-group classification: normal versus cervical cancer versus lung cancer. In general, performance for two-group classification was high whereas three-group classification was more challenging, with all three methods predicting normal samples more accurately than cancer samples. Moreover, specificity of the PM method was mostly higher or the same as KNN and DTW-KNN with lower sensitivity. The performance of KNN and DTW-KNN decreased with the inclusion of demographic data, whereas similar performance was observed for the PM which could be explained by the fact that the PM uses fewer parameters as compared to KNN and DTW-KNN methods and is thus less susceptible to the risk of overfitting. More importantly the accuracy of the PM can be increased by using a greater number of quantile data points and by the inclusion of additional demographic and clinical data, providing a substantial advantage over KNN and DTW-KNN methods. [ABSTRACT FROM AUTHOR]

Background: Recent literature has suggested that tumor sidedness in colorectal cancer (CRC) is an independent prognostic and potentially predictive marker of survival. The aims of the study were to determine the prognostic significance of tumor sidedness in colorectal cancer patients undergoing primary tumor resection and to assess associated tumor biology. Methods: A total of 3281 consecutive patients who underwent surgical resection of their primary CRC from January 1998 to December 2012 were analyzed for association with tumor biologic factors and with overall survival. Metastatic patients were excluded from analysis. Results: Left sided CRCs were associated with a number of additional key prognostic markers including BRAFV600E wildtype status (P<0.001), mismatch repair proficiency (p<0.001), absence of peritumoral lymphocytic response (p = 0.001), high lymphocyte-to-monocyte ratio (p<0.001) and low neutrophil-to-lymphocyte ratio (p<0.001). In primary analysis with 3067 patients, there was no statistical difference in sidedness in the univariate analysis (p = 0.291). Three further subgroup analyses were performed. In the first subgroup, only stage III patients were analyzed. In the second, patients with mismatch repair deficiency were removed. In the third, additional clinicopathologic variables known to be independently prognostic were added into analysis. In all three subgroup analyses tumor sidedness was not an independent prognostic marker. Conclusions: Tumor sidedness was not an independent prognostic marker of CRC. However, right sided CRCs were associated with several key independent prognostic markers supporting a hypothesis that tumor sidedness is a surrogate for other biomarkers. [ABSTRACT FROM AUTHOR]

Drug screening studies typically involve assaying the sensitivity of a range of cancer cell lines across an array of anti-cancer therapeutics. Alongside these sensitivity measurements high dimensional molecular characterizations of the cell lines are typically available, including gene expression, copy number variation and genomic mutations. We propose a sparse multitask regression model which learns discriminative latent characteristics that predict drug sensitivity and are associated with specific molecular features. We use ideas from Bayesian nonparametrics to automatically infer the appropriate number of these latent characteristics. The resulting analysis couples high predictive performance with interpretability since each latent characteristic involves a typically small set of drugs, cell lines and genomic features. Our model uncovers a number of drug-gene sensitivity associations missed by single gene analyses. We functionally validate one such novel association: that increased expression of the cell-cycle regulator C/EBPδ decreases sensitivity to the histone deacetylase (HDAC) inhibitor panobinostat. [ABSTRACT FROM AUTHOR]

Background: This meta-analysis presents evidence regarding the diagnostic accuracy of mammaglobin detected using the RT-PCR technique, related to the presence of sentinel node metastasis in breast cancer patients. Methods: The following databases were consulted: Cochrane, Lilacs, Scielo, Hinary, PubMed, Elsevier, Embase, ProQuest, the Universidad del Rosario´s Centro de Recursos Para el Aprendizaje y la Investigación (CRAI-UR) [Resource Center for Learning and Research], and the Google Scholar search engine. The quality of the studies was assessed using the QUADAS-2 and CASpe tools. The selected studies presented the necessary data to calculate diagnostic validity index of mammaglobin detection using RT-PCR, compared with the reference standard test. Global values for the sensitivity, specificity, positive predictive value, negative predictive value, probability ratios, diagnostic ORs, and summary ROC curves of this meta-analysis were obtained using the Meta-DiSc 1.4 program. Results: Initially, 731 articles were obtained; but only 25 were included in the meta-analysis. Sensitivity was 84% (95% CI: 83% - 86%), and specificity was 92% (95% CI: 91% - 93%). Positive and negative predictive values were 9.26 (95% CI: 6.47–13.26) and 0.17 (95% CI: 0.13–0.23), respectively. The diagnostic OR was 66.34 (95% CI: 42.52–103.52). The predictive area under the sROC curve was 94.78 (Q = 0.8876). Conclusions: The evaluated diagnostic index showed that the expression of the mammaglobin biomarker has diagnostic prediction for detecting lymph node metastasis in breast cancer patients, when analyzed using RT-PCR, although more than 50% heterogeneity was found. [ABSTRACT FROM AUTHOR]

MiRNAs and proteins play important roles in different stages of breast tumor development and serve as biomarkers for the early diagnosis of breast cancer. A new algorithm that combines machine learning algorithms and multilayer complex network analysis is hereby proposed to explore the potential diagnostic values of miRNAs and proteins. XGBoost and random forest algorithms were employed to screen the most important miRNAs and proteins. Maximal information coefficient was applied to assess intralayer and interlayer connection. A multilayer complex network was constructed to identify miRNAs and proteins that could serve as biomarkers for breast cancer. Proteins and miRNAs that are nodes in the network were subsequently categorized into two network layers considering their distinct functions. The betweenness centrality was used as the first measurement of the importance of the nodes within each single layer. The degree of the nodes was chosen as the second measurement to map their signalling pathways. By combining these two measurements into one score and comparing the difference of the same candidate between normal tissue and cancer tissue, this novel multilayer network analysis could be applied to successfully identify molecules associated with breast cancer. [ABSTRACT FROM AUTHOR]

Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities to understand disease etiology and pathogenesis. Although there have been numerous computational methods proposed to predict reliable miRNA-disease associations, they suffer from various limitations that affect the prediction accuracy and their applicability. In this study, we develop a novel method to discover disease-related candidate miRNAs based on Adaptive Multi-View Multi-Label learning(AMVML). Specifically, considering the inherent noise existed in the current dataset, we propose to learn a new affinity graph adaptively for both diseases and miRNAs from multiple similarity profiles. We then simultaneously update the miRNA-disease association predicted from both spaces based on multi-label learning. In particular, we prove the convergence of AMVML theoretically and the corresponding analysis indicates that it has a fast convergence rate. To comprehensively illustrate the prediction performance of our method, we compared AMVML with four state-of-the-art methods under different validation frameworks. As a result, our method achieves comparable performance under various evaluation metrics, which suggests that our method is capable of discovering greater number of true miRNA-disease associations. The case study conducted on thyroid neoplasms further identified a potential diagnostic biomarker. Together, the experimental results confirms the utility of our method and we anticipate that our method could serve as a reliable and efficient tool for uncovering novel disease-related miRNAs. [ABSTRACT FROM AUTHOR]

Since their inception, several tools have been developed for cluster analysis and heatmap construction. The application of such tools to the number and types of genome-wide data available from next generation sequencing (NGS) technologies requires the adaptation of statistical concepts, such as in defining a most variable gene set, and more intricate cluster analyses method to address multiple omic data types. Additionally, the growing number of publicly available datasets has created the desire to estimate the statistical significance of a gene signature derived from one dataset to similarly group samples based on another dataset. The currently available number of tools and their combined use for generating heatmaps, along with the several adaptations of statistical concepts for addressing the higher dimensionality of genome-wide NGS-derived data, has created a further challenge in the ability to replicate heatmap results. We introduce NOJAH (NOt Just Another Heatmap), an interactive tool that defines and implements a workflow for genome-wide cluster analysis and heatmap construction by creating and combining several tools into a single user interface. NOJAH includes several newly developed scripts for techniques that though frequently applied are not sufficiently documented to allow for replicability of results. These techniques include: defining a most variable gene set (a.k.a., ‘core genes’), estimating the statistical significance of a gene signature to separate samples into clusters, and performing a result merging integrated cluster analysis. With only a user uploaded dataset, NOJAH provides as output, among other things, the minimum documentation required for replicating heatmap results. Additionally, NOJAH contains five different existing R packages that are connected in the interface by their functionality as part of a defined workflow for genome-wide cluster analysis. The NOJAH application tool is available at with corresponding source code available at . [ABSTRACT FROM AUTHOR]

Current methods to quantify T-cell clonal expansion only account for variance due to random sampling from a highly diverse repertoire space. We propose a beta-binomial model to incorporate time-dependent variance into the assessment of differentially abundant T-cell clones, identified by unique T Cell Receptor (TCR) β-chain rearrangements, and show that this model improves specificity for detecting clinically relevant clonal expansion. Using blood samples from ten healthy donors, we modeled the variance of T-cell clones within each subject over time and calibrated the dispersion parameters of the beta distribution to fit this variance. As a validation, we compared pre- versus post-treatment blood samples from urothelial cancer patients treated with atezolizumab, where clonal expansion (quantified by the earlier binomial model) was previously reported to correlate with benefit. The beta-binomial model significantly reduced the false-positive rate for detecting differentially abundant clones over time compared to the earlier binomial method. In the urothelial cancer cohort, the beta-binomial model enriched for tumor infiltrating lymphocytes among the clones detected as expanding in the peripheral blood in response to therapy compared to the binomial model and improved the overall correlation with clinical benefit. Incorporating time-dependent variance into the statistical framework for measuring differentially abundant T-cell clones improves the model's specificity for T-cells that correlate more strongly with the disease and treatment setting of-interest. Reducing background-level clonal expansion, therefore, improves the quality of clonal expansion as a biomarker for assessing the T cell immune response and correlations with clinical measures. [ABSTRACT FROM AUTHOR]

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases’ substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing to a comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations. [ABSTRACT FROM AUTHOR]

Tumour heterogeneity plays a large role in the response of tumour tissues to radiation therapy. Inherent biological, physical, and even dose deposition heterogeneity all play a role in the resultant observed response. We here implement the use of Haralick textural analysis to quantify the observed glycogen production response, as observed via Raman spectroscopic mapping, of tumours irradiated within a murine model. While an array of over 20 Haralick features have been proposed, we here concentrate on five of the most prominent features: homogeneity, local homogeneity, contrast, entropy, and correlation. We show that these Haralick features can be used to quantify the inherent heterogeneity of the Raman spectroscopic maps of tumour response to radiation. Furthermore, our results indicate that Haralick-calculated textural features show a statistically significant dose dependent variation in response heterogeneity, specifically, in glycogen production in tumours irradiated with clinically relevant doses of ionizing radiation. These results indicate that Haralick textural analysis provides a quantitative methodology for understanding the response of murine tumours to radiation therapy. Future work in this area can, for example, utilize the Haralick textural features for understanding the heterogeneity of radiation response as measured by biopsied patient tumour samples, which remains the standard of patient tumour investigation. [ABSTRACT FROM AUTHOR]

In more recent years, a significant increase in the number of available biological experiments has taken place due to the widespread use of massive sequencing data. Furthermore, the continuous developments in the machine learning and in the high performance computing areas, are allowing a faster and more efficient analysis and processing of this type of data. However, biological information about a certain disease is normally widespread due to the use of different sequencing technologies and different manufacturers, in different experiments along the years around the world. Thus, nowadays it is of paramount importance to attain a correct integration of biologically-related data in order to achieve genuine benefits from them. For this purpose, this work presents an integration of multiple Microarray and RNA-seq platforms, which has led to the design of a multiclass study by collecting samples from the main four types of leukemia, quantified at gene expression. Subsequently, in order to find a set of differentially expressed genes with the highest discernment capability among different types of leukemia, an innovative parameter referred to as coverage is presented here. This parameter allows assessing the number of different pathologies that a certain gen is able to discern. It has been evaluated together with other widely known parameters under assessment of an ANOVA statistical test which corroborated its filtering power when the identified genes are subjected to a machine learning process at multiclass level. The optimal tuning of gene extraction evaluated parameters by means of this statistical test led to the selection of 42 highly relevant expressed genes. By the use of minimum-Redundancy Maximum-Relevance (mRMR) feature selection algorithm, these genes were reordered and assessed under the operation of four different classification techniques. Outstanding results were achieved by taking exclusively the first ten genes of the ranking into consideration. Finally, specific literature was consulted on this last subset of genes, revealing the occurrence of practically all of them with biological processes related to leukemia. At sight of these results, this study underlines the relevance of considering a new parameter which facilitates the identification of highly valid expressed genes for simultaneously discerning multiple types of leukemia. [ABSTRACT FROM AUTHOR]

Background: Nasopharyngeal cancer (NPC), despite being one of the most malignant head and neck carcinomas (HNC), lacks comprehensive prognostic biomarkers that predict patient survival. Therefore, this systematic review and meta-analysis is aimed to evaluate the potential prognostic value of miRNAs as prognostic biomarkers in NPC. Methods: PRISMA guidelines were used to conduct this systematic review and meta-analysis study. Permutations of multiple “search key-words” were used for the search strategy, which was limited to articles published between January 2012 and March 2018. The retrieved articles were meticulously searched with multi-level screening by two reviewers and confirmed by other reviewers. Meta-analysis was performed using Hazard Ratios (HR) and associated 95% Confidence Interval (CI) of survival obtained from previously published studies. Publication bias was assessed by Egger’s bias indicator test and funnel plot symmetry. Results: A total of 5069 patients across 21 studies were considered eligible for inclusion in the systematic review, with 65 miRNAs being evaluated in the subsequent meta-analysis. Most articles included in this study originated from China and one study from North Africa. The forest plot was generated using cumulated survival data, resulting in a pooled HR value of 1.196 (95% CI: 0.893–1.601) indicating that the upregulated miRNAs increased the likelihood of death of NPC patients by 19%. Conclusion: To our knowledge, this is the first meta-analysis that examines the prognostic effectiveness of miRNAs as biomarkers in NPC patients. We noted that the combined effect estimate of HR across multiple studies indicated that increased miRNA expression in NPC potentially leads to poor overall survival. However, further large-scale prospective studies on the clinical significance of the miRNAs, with sizable cohorts are necessary in order to obtain conclusive results. [ABSTRACT FROM AUTHOR]

We aimed to characterize the clinical significance of epigenetic loss of death-associated protein kinase (DAPK) gene function through promoter methylation in the development and prognosis of lymphoma. PubMed, Web of Science and ProQuest databases were searched for relevant studies. Twelve studies involving 709 patients with lymphoma were identified. The prognostic value of DAPK methylation was expressed as risk ratio (RR) and its corresponding 95% confidence interval (CI), while the associations between DAPK methylation and the clinical characteristics of patients with lymphoma were expressed as odd ratios (ORs) and their corresponding 95% CIs. Meta-analysis showed that the 5-year survival rate was significantly lower in lymphoma patients with hypermethylated DAPK (RR = 0.85, 95% CI (0.73, 0.98), P = 0.025). Sensitivity analysis demonstrated consistent result. However, no associations were found between DAPK methylation and clinicopathological features of lymphoma, in relation to gender (OR = 1.07, 95% CI (0.72, 1.59), P = 0.751), age (OR = 1.01, 95% CI (0.66, 1.55), P = 0.974), international prognostic index (OR = 1.20, 95% CI (0.63, 2.27), P = 0.575), B symptoms (OR = 0.76, 95% CI (0.38, 1.51), P = 0.452), serum lactate dehydrogenase (OR = 1.13, 95% CI (0.62, 2.05), P = 0.683), and BCL-2 expression (OR = 1.55, 95% CI (0.91, 2.66), P = 0.106). Lymphoma patients with hypermethylated DAPK are at risk for poorer 5-year survival rate. DAPK methylation may serve as a negative prognostic biomarker among lymphoma patients, although it may not be associated with the progression of lymphoma. [ABSTRACT FROM AUTHOR]

Despite canine B-cell Lymphoma (BCL) representing the most common haematological tumour, epigenetic events driving development and progression are scarcely known. Recently, canine Diffuse Large BCL (DLBCL) DNA methylome by genome-wide CpG microarray has identified genes and pathways associated to pathogenesis. To validate data previously obtained by array analysis, the CLBL-1 cell line was used and the HOXD10, FGFR2, ITIH5 and RASAL3 genes were selected. CLBL-1 cells were treated with two hypomethylating drugs (HDs; IC50, 50% inhibitory concentration), i.e. azacytidine and decitabine (DEC), either alone or in combination with three histone deacetylase inhibitors (HDACis; IC20), i.e. valproic acid, trichostatin and vorinostat. Following the incubation with both HDs, an overall decrease of promoter methylation was highlighted, thus confirming target genes hypermethylation. The highest mRNA restoration was observed following the exposure to HDs combined with HDACis, and mostly with valproic acid. Contrasting results were only obtained for RASAL3. An in vivo confirmation was finally attempted treating Nod-Scid mice engrafted with CLBL-1 cells with DEC. Although DEC did not arrest tumour growth, target genes promoter methylation was significantly reduced in DEC-treated mice vs controls. Overall, this work demonstrates that CLBL-1 cell line represents a reliable in vitro model to validate the methylation-dependent silencing of key genes for BCL; moreover, it may be useful for xenograft models in mice, despite its aggressive behaviour. In future, functional studies will be performed to deepen the role of selected genes on BCL pathogenesis and progression, and their methylation-dependent mechanism of regulation. [ABSTRACT FROM AUTHOR]

Accurate assessment of the association between continuous variables such as gene expression and survival is a critical aspect of precision medicine. In this report, we provide a review of some of the available survival analysis and validation tools by referencing published studies that have utilized these tools. We have identified pitfalls associated with the assumptions inherent in those applications that have the potential to impact scientific research through their potential bias. In order to overcome these pitfalls, we have developed a novel method that enables the logrank test method to handle continuous variables that comprehensively evaluates survival association with derived aggregate statistics. This is accomplished by exhaustively considering all the cutpoints across the full expression gradient. Direct side-by-side comparisons, global ROC analysis, and evaluation of the ability to capture relevant biological themes based on current understanding of RAS biology all demonstrated that the new method shows better consistency between multiple datasets of the same disease, better reproducibility and robustness, and better detection power to uncover biological relevance within the selected datasets over the available survival analysis methods on univariate gene expression and penalized linear model-based methods. [ABSTRACT FROM AUTHOR]

Sequencing of the T cell receptor repertoire is a powerful tool for deeper study of immune response, but the unique structure of this type of data makes its meaningful quantification challenging. We introduce a new method, the Gamma-GPD spliced threshold model, to address this difficulty. This biologically interpretable model captures the distribution of the TCR repertoire, demonstrates stability across varying sequencing depths, and permits comparative analysis across any number of sampled individuals. We apply our method to several datasets and obtain insights regarding the differentiating features in the T cell receptor repertoire among sampled individuals across conditions. We have implemented our method in the open-source R package powerTCR. [ABSTRACT FROM AUTHOR]

Background: Ovarian cancer is the leading cause of death by gynecologic cancers in the Western world. The aim of the study was to identify microRNAs (miRNAs) associated with prognosis and/or resistance to chemotherapy among patients with epithelial ovarian cancer. Methods: Using information from the Pelvic Mass Study we identified a cohort of women with epithelial ovarian cancer. Tumor tissues were then collected and analyzed by global miRNA microarrays. MiRNA profiling was then linked to survival and time to progression using Cox proportional-hazards regression models. Logistic regression models were used for the analysis of resistance to chemotherapy. Our results were validated using external datasets retrieved from the NCBI Gene Expression Omnibus database. Results: A total of 197 patients with epithelial ovarian cancer were included for miRNA microarray analysis. In multivariate analyses we identified a number of miRNAs significantly correlated with overall survival (miR-1183 (HR: 1.42, 95% CI:1.17–1.74, p = 0.0005), miR-126-3p (HR: 1.38, 95% CI:1.11–1.71, p = 0.0036), time to progression (miR-139-3p (HR: 1.48, 95% CI: 1.13–1.94, p = 0.0047), miR-802 (HR: 0.48, 95% CI: 0.29–0.78, p = 0.0035)), progression free survival (miR-23a-5p (HR:1.32, 95% CI:1.09–1.61, p = 0.004), miR-23a-3p (HR:1.70, 95% CI:1.15–2.51, p = 0.0074), miR-802 (HR: 0.48, 95% CI: 0.29–0.80, p = 0.0048)), and resistance to chemotherapy (miR-1234 (HR: 0.26, 95% CI: 0.11–0.64, p = 0.003)). A few miRNAs identified in our training cohort, were validated in external cohorts with similar results. Conclusion: Eight miRNAs were identified as significant predictors of overall survival, progression free survival, time to progression, and chemotherapy resistance. A number of these miRNAs were significantly validated using external datasets. Inter-platform and inter-laboratory variations may have influence on the ability to compare and reproduce miRNA results. The use of miRNAs as potential markers of relapse and survival in ovarian cancer warrants further investigation. [ABSTRACT FROM AUTHOR]

Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. Recent developments in subgroup analysis consider subgroups that are defined in terms of the predicted individual treatment effect, i.e. the difference between the predicted outcome under treatment and the predicted outcome under control for each individual, which in turn may depend on multiple biomarkers. In this work, we study the properties of different modelling strategies to estimate the predicted individual treatment effect. We explore linear models and compare different estimation methods, such as maximum likelihood and the Lasso with and without randomized response. For the latter, we implement confidence intervals based on the selective inference framework to account for the model selection stage. We illustrate the methods in a dataset of a treatment for Alzheimer disease (normal response) and in a dataset of a treatment for prostate cancer (survival outcome). We also evaluate via simulations the performance of using the predicted individual treatment effect to identify subgroups where a novel treatment leads to better outcomes compared to a control treatment. [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]

Recently, a growing number of biological research and scientific experiments have demonstrated that microRNA (miRNA) affects the development of human complex diseases. Discovering miRNA-disease associations plays an increasingly vital role in devising diagnostic and therapeutic tools for diseases. However, since uncovering associations via experimental methods is expensive and time-consuming, novel and effective computational methods for association prediction are in demand. In this study, we developed a computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction (MDHGI) to discover new miRNA-disease associations by integrating the predicted association probability obtained from matrix decomposition through sparse learning method, the miRNA functional similarity, the disease semantic similarity, and the Gaussian interaction profile kernel similarity for diseases and miRNAs into a heterogeneous network. Compared with previous computational models based on heterogeneous networks, our model took full advantage of matrix decomposition before the construction of heterogeneous network, thereby improving the prediction accuracy. MDHGI obtained AUCs of 0.8945 and 0.8240 in the global and the local leave-one-out cross validation, respectively. Moreover, the AUC of 0.8794+/-0.0021 in 5-fold cross validation confirmed its stability of predictive performance. In addition, to further evaluate the model's accuracy, we applied MDHGI to four important human cancers in three different kinds of case studies. In the first type, 98% (Esophageal Neoplasms) and 98% (Lymphoma) of top 50 predicted miRNAs have been confirmed by at least one of the two databases (dbDEMC and miR2Disease) or at least one experimental literature in PubMed. In the second type of case study, what made a difference was that we removed all known associations between the miRNAs and Lung Neoplasms before implementing MDHGI on Lung Neoplasms. As a result, 100% (Lung Neoplasms) of top 50 related miRNAs have been indexed by at least one of the three databases (dbDEMC, miR2Disease and HMDD V2.0) or at least one experimental literature in PubMed. Furthermore, we also tested our prediction method on the HMDD V1.0 database to prove the applicability of MDHGI to different datasets. The results showed that 50 out of top 50 miRNAs related with the breast neoplasms were validated by at least one of the three databases (HMDD V2.0, dbDEMC, and miR2Disease) or at least one experimental literature. [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]

Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several studies have attempted to identify disease-gene associations, the number of possible disease-gene associations is very small. High-throughput technologies have been established experimentally to identify the association between genes and diseases. However, these techniques are still quite expensive, time consuming, and even difficult to perform. Thus, based on currently available data and knowledge, computational methods have served as alternatives to provide more possible associations to increase our understanding of disease mechanisms. Here, a new network-based algorithm, namely, Disease-Gene Association (DGA), was developed to calculate the association score of a query gene to a new possible set of diseases. First, a large-scale protein interaction network was constructed, and the relationship between two interacting proteins was calculated with regard to the disease relationship. Novel plausible disease-gene pairs were identified and statistically scored by our algorithm using neighboring protein information. The results yielded high performance for disease-gene prediction, with an F-measure of 0.78 and an AUC of 0.86. To identify promising candidates of disease-gene associations, the association coverage of genes and diseases were calculated and used with the association score to perform gene and disease selection. Based on gene selection, we identified promising pairs that exhibited evidence related to several important diseases, e.g., inflammation, lipid metabolism, inborn errors, xanthomatosis, cerebellar ataxia, cognitive deterioration, malignant neoplasms of the skin and malignant tumors of the cervix. Focusing on disease selection, we identified target genes that were important to blistering skin diseases and muscular dystrophy. In summary, our developed algorithm is simple, efficiently identifies disease–gene associations in the protein-protein interaction network and provides additional knowledge regarding disease-gene associations. This method can be generalized to other association studies to further advance biomedical science. [ABSTRACT FROM AUTHOR]

Understanding how gene alterations induce oncogenesis plays an important role in cancer research and may be instructive for cancer prevention and treatment. We conducted a parameter sensitivity analysis to the mitochondrial apoptosis model. Both a nonlinear bifurcation analysis of the deterministic dynamics and energy barrier analysis of the corresponding stochastic models were performed. We found that the parameter sensitivity ranking according to the change of the bifurcation-point locations in deterministic models and the change of the barrier heights from a living to death state of the cell in stochastic models are highly correlated. For the model we considered, in combination with previous knowledge that the parameters significantly affecting the system’s bifurcation point are strongly associated with frequently mutated oncogenic genes, we conclude that the energy barrier height can be used as indicator of oncogenesis as well as bifurcation point. We provide a possible mechanism that may help elucidate the logic of cancer initiation from the view of stochastic dynamics and energy landscape. And we show the equivalence of energy barrier height and bifurcation-point location in determining the parameter sensitivity spectrum for the first time. [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]

Numerous problems encountered in computational biology can be formulated as optimization problems. In this context, optimization of drug release characteristics or dosing schedules for anticancer agents has become a prominent area not only for the development of new drugs, but also for established drugs. However, in complex systems, optimization of drug exposure is not a trivial task and cannot be efficiently addressed through trial-error simulation exercises. Finding a solution to those problems is a challenging task which requires more advanced strategies like optimal control theory. In this work, we perform an optimal control analysis on a previously developed computational model for the testosterone effects of Triptorelin in prostate cancer patients with the goal of finding optimal drug-release characteristics. We demonstrate how numerical control optimization of non-linear models can be used to find better therapeutic approaches in order to improve the final outcome of the 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]