Journal of clinical bioinformatics最新文献

Background: Copy number alterations (CNAs) represent an important component of genetic variations. Such alterations are related with certain type of cancer including those of the pancreas, colon, and breast, among others. CNAs have been used as biomarkers for cancer prognosis in multiple studies, but few works report on the relation of CNAs with the disease progression. Moreover, most studies do not consider the following two important issues. (I) The identification of CNAs in genes which are responsible for expression regulation is fundamental in order to define genetic events leading to malignant transformation and progression. (II) Most real domains are best described by structured data where instances of multiple types are related to each other in complex ways.

Results: Our main interest is to check whether the colorectal cancer (CRC) progression inference benefits when considering both (I) the expression levels of genes with CNAs, and (II) relationships (i.e. dissimilarities) between patients due to expression level differences of the altered genes. We first evaluate the accuracy performance of a state-of-the-art inference method (support vector machine) when subjects are represented only through sets of available attribute values (i.e. gene expression level). Then we check whether the inference accuracy improves, when explicitly exploiting the information mentioned above. Our results suggest that the CRC progression inference improves when the combined data (i.e. CNA and expression level) and the considered dissimilarity measures are applied.

Conclusions: Through our approach, classification is intuitively appealing and can be conveniently obtained in the resulting dissimilarity spaces. Different public datasets from Gene Expression Omnibus (GEO) were used to validate the results.

Background: The human body plays host to a vast array of bacteria, found in oral cavities, skin, gastrointestinal tract and the vagina. Some bacteria are harmful while others are beneficial to the host. Despite the availability of many methods to identify bacteria, most of them are only applicable to specific and cultivable bacteria and are also tedious. Based on high throughput sequencing technology, this work derives 16S rRNA sequences of bacteria and analyzes probiotics and pathogens species.

Results: We constructed a database that recorded the species of probiotics and pathogens from literature, along with a modified Smith-Waterman algorithm for assigning the taxonomy of the sequenced 16S rRNA sequences. We also constructed a bacteria disease risk model for seven diseases based on 98 samples. Applicability of the proposed platform is demonstrated by collecting the microbiome in human gut of 13 samples.

Conclusions: The proposed platform provides a relatively easy means of identifying a certain amount of bacteria and their species (including uncultivable pathogens) for clinical microbiology applications. That is, detecting how probiotics and pathogens inhabit humans and how affect their health can significantly contribute to develop a diagnosis and treatment method.

Background: EHR (Electronic Health Record) system has led to development of specialized form of clinical databases which enable storage of information in temporal prospective. It has been a big challenge for mining this form of clinical data considering varied temporal points. This study proposes a conjoined solution to analyze the clinical parameters akin to a disease. We have used "association rule mining algorithm" to discover association rules among clinical parameters that can be augmented with the disease. Furthermore, we have proposed a new algorithm, SN algorithm, to map clinical parameters along with a disease state at various temporal points.

Result: SN algorithm is based on Jacobian approach, which augurs the state of a disease 'Sn' at a given temporal point 'Tn' by mapping the derivatives with the temporal point 'T0', whose state of disease 'S0' is known. The predictive ability of the proposed algorithm is evaluated in a temporal clinical data set of brain tumor patients. We have obtained a very high prediction accuracy of ~97% for a brain tumor state 'Sn' for any temporal point 'Tn'.

Conclusion: The results indicate that the methodology followed may be of good value to the diagnostic procedure, especially for analyzing temporal form of clinical data.

In the context of translational and clinical oncology, mathematical models can provide novel insights into tumor-related processes and can support clinical oncologists in the design of the treatment regime, dosage, schedule, toxicity and drug-sensitivity. In this review we present an overview of mathematical models in this field beginning with carcinogenesis and proceeding to the different cancer treatments. By doing so we intended to highlight recent developments and emphasize the power of such theoretical work.We first highlight mathematical models for translational oncology comprising epidemiologic and statistical models, mechanistic models for carcinogenesis and tumor growth, as well as evolutionary dynamics models which can help to describe and overcome a major problem in the clinic: therapy resistance. Next we review models for clinical oncology with a special emphasis on therapy including chemotherapy, targeted therapy, radiotherapy, immunotherapy and interaction of cancer cells with the immune system.As evident from the published studies, mathematical modeling and computational simulation provided valuable insights into the molecular mechanisms of cancer, and can help to improve diagnosis and prognosis of the disease, and pinpoint novel therapeutic targets.

Background: Identification of prognostic mRNA biomarkers has been done for various cancer types. The data that are published from such studies are archived in public repositories. There are hundreds of such datasets available for multiple cancer types in public repositories. Wealth of such data can be utilized to study prognostic implications of mRNA in different cancers as well as in different populations or subtypes of same cancer.

Description: We have created a web application that can be used for studying prognostic implications of mRNA biomarkers in a variety of cancers. We have compiled data from public repositories such as GEO, EBI Array Express and The Cancer Genome Atlas for creating this tool. With 64 patient series from 18 cancer types in our database, this tool provides the most comprehensive resource available for survival analysis to date. The tool is called PROGgene and it is available at http://www.compbio.iupui.edu/proggene.

Conclusions: We present this tool as a hypothesis generation tool for researchers to identify potential prognostic mRNA biomarkers to follow up with further research. For this reason, we have kept the web application very simple and straightforward. We believe this tool will be useful in accelerating biomarker discovery in cancer and quickly providing results that may indicate disease-specific prognostic value of specific biomarkers.

Background: The ciliary body epithelia (CBE) of the eye produce the aqueous humor (AH). The equilibrium between the AH production by the CBE and the outflow through the trabecular meshwork ultimately determines the intraocular pressure (IOP). An increased IOP is a major risk factor for primary open angle glaucoma (POAG). This study aims to elucidate the molecular machinery of the most important function of the CBE: the AH production and composition, and aims to find possible new molecular clues for POAG and AH production-lowering drugs.

Methods: We performed a gene expression analysis of the non-pigmented (NPE) and pigmented epithelia (PE) of the human CBE of post mortem eyes. We used 44 k Agilent microarrays against a common reference design. Functional annotations were performed with the Ingenuity knowledge database.

Results: We built a molecular model of AH production by combining previously published physiological data with our current genomic expression data. Next, we investigated molecular CBE transport features which might influence AH composition. These features included caveolin- and clathrin vesicle-mediated transport of large biomolecules, as well as a range of substrate specific transporters. The presence of these transporters implies that, for example, immunoglobins, thyroid hormone, prostaglandins, cholesterol and vitamins can be secreted by the CBE along with the AH. In silico, we predicted some of the molecular apical interactions between the NPE and PE, the side where the two folded epithelia face each other. Finally, we found high expression of seven POAG disease genes in the plasma membrane of extracellular space of the CBE, namely APOE, CAV1, COL8A2, EDNRA, FBN1, RFTN1 and TLR4 and we found possible new targets for AH lowering drugs in the AH.

Conclusions: The CBE expresses many transporters, which account for AH production and/or composition. Some of these entries have also been associated with POAG. We hypothesize that the CBE may play a more prominent role than currently thought in the pathogenesis of POAG, for example by changing the composition of AH.

Background: The continuing discovery of new types and functions of small non-coding RNAs is suggesting the presence of regulatory mechanisms far more complex than the ones currently used to study and design Gene Regulatory Networks. Just focusing on the roles of micro RNAs (miRNAs), they have been found to be part of several intra-pathway regulatory motifs. However, inter-pathway regulatory mechanisms have been often neglected and require further investigation.

Results: In this paper we present the result of a systems biology study aimed at analyzing a high-level inter-pathway regulatory motif called Pathway Protection Loop, not previously described, in which miRNAs seem to play a crucial role in the successful behavior and activation of a pathway. Through the automatic analysis of a large set of public available databases, we found statistical evidence that this inter-pathway regulatory motif is very common in several classes of KEGG Homo Sapiens pathways and concurs in creating a complex regulatory network involving several pathways connected by this specific motif. The role of this motif seems also confirmed by a deeper review of other research activities on selected representative pathways.

Conclusions: Although previous studies suggested transcriptional regulation mechanism at the pathway level such as the Pathway Protection Loop, a high-level analysis like the one proposed in this paper is still missing. The understanding of higher-level regulatory motifs could, as instance, lead to new approaches in the identification of therapeutic targets because it could unveil new and "indirect" paths to activate or silence a target pathway. However, a lot of work still needs to be done to better uncover this high-level inter-pathway regulation including enlarging the analysis to other small non-coding RNA molecules.

Background: Large scale understanding of complex and dynamic alterations in cellular and subcellular levels during cancer in contrast to normal condition has facilitated the emergence of sophisticated systemic approaches like network biology in recent times. As most biological networks show modular properties, the analysis of differential modularity between normal and cancer protein interaction networks can be a good way to understand cancer more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard.

Methods: In the current study, the computational analysis of previously published protein interaction networks (PINs) has been conducted to identify the molecular complexes and crucial nodes of the networks. Protein molecules involved in ten major cancer signal transduction pathways were used to construct the networks based on expression data of five tissues e.g. bone, breast, colon, kidney and liver in both normal and cancer conditions. MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively.

Results: In case of all tissues, cancer PINs show higher level of clustering (formation of molecular complexes) than the normal ones. In contrast, lower level modular overlapping is found in cancer PINs than the normal ones. Thus a proposition can be made regarding the formation of some giant nodes in the cancer networks with very high degree and resulting in reduced overlapping among the network modules though the predicted molecular complex numbers are higher in cancer conditions.

Conclusion: The study predicts some major molecular complexes that might act as the important regulators in cancer progression. The crucial nodes identified in this study can be potential drug targets to combat cancer.

Background: Detection of brain lesions disseminated in space and time by magnetic resonance imaging remains a cornerstone for the diagnosis of clinically definite multiple sclerosis. We have sought to determine if gene expression biomarkers could contribute to the clinical diagnosis of multiple sclerosis.

Methods: We employed expression levels of 30 genes in blood from 199 subjects with multiple sclerosis, 203 subjects with other neurologic disorders, and 114 healthy control subjects to train ratioscore and support vector machine algorithms. Blood samples were obtained from 46 subjects coincident with clinically isolated syndrome who progressed to clinically definite multiple sclerosis determined by conventional methods. Gene expression levels from these subjects were inputted into ratioscore and support vector machine algorithms to determine if these methods also predicted that these subjects would develop multiple sclerosis. Standard calculations of sensitivity and specificity were employed to determine accuracy of these predictions.

Results: Our results demonstrate that ratioscore and support vector machine methods employing input gene transcript levels in blood can accurately identify subjects with clinically isolated syndrome that will progress to multiple sclerosis.

Conclusions: We conclude these approaches may be useful to predict progression from clinically isolated syndrome to multiple sclerosis.

Asthma is a prevalent disease in pediatric patients and most of the cases begin at very early years of life in children. Early identification of patients at high risk of developing the disease can alert us to provide them the best treatment to manage asthma symptoms. Often evaluating patients with high risk of developing asthma from huge data sets (e.g., electronic medical record) is challenging and very time consuming, and lack of complex analysis of data or proper clinical logic determination might produce invalid results and irrelevant treatments. In this article, we used data from the Pediatric Research Database (PRD) to develop an asthma prediction model from past All Patient Refined Diagnosis Related Groupings (APR-DRGs) coding assignments. The knowledge gleamed in this asthma prediction model, from both routinely use by physicians and experimental findings, will become fused into a knowledge-based database for dissemination to those involved with asthma patients. Success with this model may lead to expansion with other diseases.