Biomedical Literature Mining

Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma

Objective

Cancer stemness and M2 macrophages are intimately linked to the prognosis of lung adenocarcinoma (LUAD). For this reason, this investigation sought to identify the key genes relevant to cancer stemness and M2 macrophages, explore the relationship between these genes and clinical characteristics, and determine the potential mechanism.

Methods

LUAD transcriptomic data was analyzed from The Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus databases. Differential expression analysis was performed to discern abnormally expressed genes between LUAD and control samples in TCGA cohort. The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was applied to determine varyingly infiltrated immune cells in LUAD compared with the control samples in TCGA cohort. Weighted correlation network analysis (WGCNA) was performed to identify genes associated with mRNA expression-based stemness index (mRNAsi) and M2 macrophages. Least absolute shrinkage and selection operator (LASSO), RandomForest (RF) and support vector machine-recursive feature elimination (SVM-RFE) machine learning methods were conducted to detect gene signatures. Global survival evaluation (Kaplan-Meier curve) was applied to investigate the relationship between gene signatures and the survival time of LUAD patients. Receiver operating characteristic (ROC) curves were produced to define biomarkers relevant to diagnosis. Gene Set Enrichment Analysis (GSEA) was performed to probe the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diagnostic biomarkers. The public single-cell dataset of LUAD (GSE123902) was used to investigate the expression differences of diagnostic biomarkers in various cell types in LUAD. Real-time quantitative PCR (qRT-PCR) was performed to confirm key genes in lung adenocarcinoma cells.

Results

A total of 5,410 differentialy expressed genes (DEGs) as well as 15 differentially infiltrated immune cells were identified between LUAD and control sepcimens in TCGA cohort. Thirty-seven DEGs were associated with both M2 macrophages and mRNAsi according to the WGCNA analysis. Sixteen common gene signatures were obtained using three diverse algorithms. CBFA2T3, DENND3 and FCAMR were correlated to overall and disease-free survival of LUAD patients. ROC curves revealed that CBFA2T3 and DENND3 expression accurately classified LUAD and control samples. The results of single cell related analysis showed that two diagnostic biomarkers expressions were differed between the different tissue sources in M2-like macrophages. QRT-PCR demonstrated the mRNA expressions of CBFA2T3 and DENND3 were upregulated in lung adenocarcinoma cells A549 and H2122.

Conclusion

Our study identified CBFA2T3 and DENND3 as key genes associated with mRNAsi and M2 macrophages in LUAD and investigated the potential molecular mechanisms underlying this relationship.

Increased abundance of ADAM9 transcripts in the blood is associated with tissue damage

Background: Members of the ADAM (a disintegrin and metalloprotease domain) family have emerged as critical regulators of cell-cell signaling during development and homeostasis. ADAM9 is consistently overexpressed in various human cancers, and has been shown to play an important role in tumorigenesis. However, little is known about the involvement of ADAM9 during immune-mediated processes. Results: Mining of an extensive compendium of transcriptomic datasets identified important gaps in knowledge regarding the possible role of ADAM9 in immunological homeostasis and inflammation: 1) The abundance of ADAM9 transcripts in the blood was increased in patients with acute infection but, 2) changed very little after in vitro exposure to a wide range of pathogen-associated molecular patterns (PAMPs). 3) Furthermore it was found to increase significantly in subjects as a result of tissue injury or tissue remodeling, in absence of infectious processes. Conclusions: Our findings indicate that ADAM9 may constitute a valuable biomarker for the assessment of tissue damage, especially in clinical situations where other inflammatory markers are confounded by infectious processes.

Increased abundance of ADAM9 transcripts in the blood is associated with tissue damage

Background: Members of the ADAM (a disintegrin and metalloprotease domain) family have emerged as critical regulators of cell-cell signaling during development and homeostasis. ADAM9 is consistently overexpressed in various human cancers, and has been shown to play an important role in tumorigenesis. However, little is known about the involvement of ADAM9 during immune-mediated processes.

Results: Mining of an extensive compendium of transcriptomic datasets identified important gaps in knowledge regarding the possible role of ADAM9 in immunological homeostasis and inflammation: 1) The abundance of ADAM9 transcripts in the blood was increased in patients with acute infection but, 2) changed very little after in vitro exposure to a wide range of pathogen-associated molecular patterns (PAMPs). 3) Furthermore it was found to increase significantly in subjects as a result of tissue injury or tissue remodeling, in absence of infectious processes.

Conclusions: Our findings indicate that ADAM9 may constitute a valuable biomarker for the assessment of tissue damage, especially in clinical situations where other inflammatory markers are confounded by infectious processes.

Information technologies for vaccine research

Vaccinology is a combinatorial science which studies the diversity of pathogens and the human immune system, and formulations that can modulate immune responses and prevent or cure disease. Huge amounts of data are produced by genomics and proteomics projects and large-scale screening of pathogen-host and antigen-host interactions. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Future development will also include systemic models of vaccine responses. Immunomics, the large-scale screening of immune processes which includes powerful immunoinformatic tools, offers great promise for future translation of basic immunology research advances into successful vaccines.

Automatic extraction of nanoparticle properties using natural language processing: NanoSifter an application to acquire PAMAM dendrimer properties

In this study, we demonstrate the use of natural language processing methods to extract, from nanomedicine literature, numeric values of biomedical property terms of poly(amidoamine) dendrimers. We have developed a method for extracting these values for properties taken from the NanoParticle Ontology, using the General Architecture for Text Engineering and a Nearly-New Information Extraction System. We also created a method for associating the identified numeric values with their corresponding dendrimer properties, called NanoSifter.

We demonstrate that our system can correctly extract numeric values of dendrimer properties reported in the cancer treatment literature with high recall, precision, and f-measure. The micro-averaged recall was 0.99, precision was 0.84, and f-measure was 0.91. Similarly, the macro-averaged recall was 0.99, precision was 0.87, and f-measure was 0.92. To our knowledge, these results are the first application of text mining to extract and associate dendrimer property terms and their corresponding numeric values.

A PubMed-wide associational study of infectious diseases

BACKGROUND

Computational discovery is playing an ever-greater role in supporting the processes of knowledge synthesis. A significant proportion of the more than 18 million manuscripts indexed in the PubMed database describe infectious disease syndromes and various infectious agents. This study is the first attempt to integrate online repositories of text-based publications and microbial genome databases in order to explore the dynamics of relationships between pathogens and infectious diseases.

METHODOLOGY/PRINCIPAL FINDINGS

Herein we demonstrate how the knowledge space of infectious diseases can be computationally represented and quantified, and tracked over time. The knowledge space is explored by mapping of the infectious disease literature, looking at dynamics of literature deposition, zooming in from pathogen to genome level and searching for new associations. Syndromic signatures for different pathogens can be created to enable a new and clinically focussed reclassification of the microbial world. Examples of syndrome and pathogen networks illustrate how multilevel network representations of the relationships between infectious syndromes, pathogens and pathogen genomes can illuminate unexpected biological similarities in disease pathogenesis and epidemiology.

CONCLUSIONS/SIGNIFICANCE

This new approach based on text and data mining can support the discovery of previously hidden associations between diseases and microbial pathogens, clinically relevant reclassification of pathogenic microorganisms and accelerate the translational research enterprise.

Discovering drug targets through the web

Traditionally, drug-target discovery is a "wet-bench" experimental process, depending on carefully designed genetic screens, biochemical tests and cellular assays to identify proteins and genes that are associated with a particular disease or condition. However, recent advances in DNA sequencing, transcript profiling, protein identification and protein quantification are leading to a flood of genomic and proteomic data that is, or potentially could be, linked to disease data. The quantity of data generated by these high throughput methods is forcing scientists to re-think the way they do traditional drug-target discovery. In particular it is leading them more and more towards identifying potential drug targets using computers. In fact, drug-target identification is now being done as much on the desk-top as on the bench-top. This review focuses on describing how drug-target discovery can be done in silico (i.e. via computer) using a variety of bioinformatic resources that are freely available on the web. Specifically, it highlights a number of web-accessible sequence databases, automated genome annotation tools, text mining tools; and integrated drug/sequence databases that can be used to identify drug targets for both endogenous (genetic and epigenetic) diseases as well as exogenous (infectious) diseases.

Molecular basis of cell-biomaterial interaction: insights gained from transcriptomic and proteomic studies

With the growing interest in clinical interventions that involve medical devices, the role for new biomaterials in modern medicine is currently expanding at a phenomenal rate. Failure of most implant materials stems from an inability to predict and control biological phenomena, such as protein adsorption and cell interaction, resulting in an inappropriate host response to the materials. Contemporary advances in biological investigation are starting to shift focus in the biomaterials field, in particular with the advent of high-throughput methodologies for gene and protein expression profiling. Here, we examine the role that emerging transcriptomic and proteomic technologies could play in relation to biomaterial development and usage. Moreover, a number of studies are highlighted which have utilized such approaches in order to try to create a deeper understanding of cell-biomaterial interactions and, hence, improve our ability to predict and control the biocompatibility of new materials.

A combined approach to data mining of textual and structured data to identify cancer-related targets

Background

We present an effective, rapid, systematic data mining approach for identifying genes or proteins related to a particular interest. A selected combination of programs exploring PubMed abstracts, universal gene/protein databases (UniProt, InterPro, NCBI Entrez), and state-of-the-art pathway knowledge bases (LSGraph and Ingenuity Pathway Analysis) was assembled to distinguish enzymes with hydrolytic activities that are expressed in the extracellular space of cancer cells. Proteins were identified with respect to six types of cancer occurring in the prostate, breast, lung, colon, ovary, and pancreas.

Results

The data mining method identified previously undetected targets. Our combined strategy applied to each cancer type identified a minimum of 375 proteins expressed within the extracellular space and/or attached to the plasma membrane. The method led to the recognition of human cancer-related hydrolases (on average, ~35 per cancer type), among which were prostatic acid phosphatase, prostate-specific antigen, and sulfatase 1.

Conclusion

The combined data mining of several databases overcame many of the limitations of querying a single database and enabled the facile identification of gene products. In the case of cancer-related targets, it produced a list of putative extracellular, hydrolytic enzymes that merit additional study as candidates for cancer radioimaging and radiotherapy. The proposed data mining strategy is of a general nature and can be applied to other biological databases for understanding biological functions and diseases.

Biological networks and analysis of experimental data in drug discovery

Cellular life can be represented and studied as the 'interactome'--a dynamic network of biochemical reactions and signaling interactions between active proteins. Systemic networks analysis can be used for the integration and functional interpretation of high-throughput experimental data, which are abundant in drug discovery but currently poorly utilized. The composition and topology of complex networks are closely associated with vital cellular functions, which have important implications for life science research. Here we outline recent advances in the field, available tools and applications of network analysis in drug discovery.

Harnessing the power of gene microarrays for the study of brain aging and Alzheimer's disease: statistical reliability and functional correlation

During normal brain aging, numerous alterations develop in the physiology, biochemistry and structure of neurons and glia. Aging changes occur in most brain regions and, in the hippocampus, have been linked to declining cognitive performance in both humans and animals. Age-related changes in hippocampal regions also may be harbingers of more severe decrements to come from neurodegenerative disorders such as Alzheimer's disease (AD). However, unraveling the mechanisms underlying brain aging, AD and impaired function has been difficult because of the complexity of the networks that drive these aging-related changes. Gene microarray technology allows massively parallel analysis of most genes expressed in a tissue, and therefore is an important new research tool that potentially can provide the investigative power needed to address the complexity of brain aging/neurodegenerative processes. However, along with this new analytic power, microarrays bring several major bioinformatics and resource problems that frequently hinder the optimal application of this technology. In particular, microarray analyses generate extremely large and unwieldy data sets and are subject to high false positive and false negative rates. Concerns also have been raised regarding their accuracy and uniformity. Furthermore, microarray analyses can result in long lists of altered genes, most of which may be difficult to evaluate for functional relevance. These and other problems have led to some skepticism regarding the reliability and functional usefulness of microarray data and to a general view that microarray data should be validated by an independent method. Given recent progress, however, we suggest that the major problem for current microarray research is no longer validity of expression measurements, but rather, the reliability of inferences from the data, an issue more appropriately redressed by statistical approaches than by validation with a separate method. If tested using statistically defined criteria for reliability/significance, microarray data do not appear a priori to require more independent validation than data obtained by any other method. In fact, because of added confidence from co-regulation, they may require less. In this article we also discuss our strategy of statistically correlating individual gene expression with biologically important endpoints designed to address the problem of evaluating functional relevance. We also review how work by ourselves and others with this powerful technology is leading to new insights into the complex processes of brain aging and AD, and to novel, more comprehensive models of aging-related brain change.

Importance of databases in experimental and clinical allergology

Information technology (IT) is leading us to reconsider some of the approaches we have been using in both basic research and clinical work in allergology. Resources mainly coming from the advent of the Internet are further amplified by the parallel development of other new tools, such as molecular biology and nanotechnology. These three powerful tools are now available and are cross-linked to a certain degree to express their power when applied to biomedical fields. Bioinformatics applied to allergy simplifies our way of handling an increasing wealth of knowledge. This review assesses the current status of allergen databases that are mainly dedicated to sequence homology collection for computational purposes. Whether or not they integrate features that are now typical of IT in other biomedical fields is analyzed as well. The results of these analyses are discussed with a view to the critical need of integrating biochemical data with clinical, epidemiological information and how this goal can be reached by the use of proteomic microarrays for IgE detection. Future directions for a more comprehensive use of allergen databases are proposed.

PubFinder: a tool for improving retrieval rate of relevant PubMed abstracts

Since it is becoming increasingly laborious to manually extract useful information embedded in the ever-growing volumes of literature, automated intelligent text analysis tools are becoming more and more essential to assist in this task. PubFinder (www.glycosciences.de/tools/PubFinder) is a publicly available web tool designed to improve the retrieval rate of scientific abstracts relevant for a specific scientific topic. Only the selection of a representative set of abstracts is required, which are central for a scientific topic. No special knowledge concerning the query-syntax is necessary. Based on the selected abstracts, a list of discriminating words is automatically calculated, which is subsequently used for scoring all defined PubMed abstracts for their probability of belonging to the defined scientific topic. This results in a hit-list of references in the descending order of their likelihood score. The algorithms and procedures implemented in PubFinder facilitate the perpetual task for every scientist of staying up-to-date with current publications dealing with a specific subject in biomedicine.

Biologic monitoring of exposure to environmental chemicals throughout the life stages: requirements and issues for consideration for the National Children's Study

Biomonitoring of exposure is a useful tool for assessing environmental exposures. The matrices available for analyses include blood, urine, breast milk, adipose tissue, and saliva, among others. The sampling can be staged to represent the particular time period of concern: preconceptionally from both parents, from a pregnant woman during each of the three trimesters, during and immediately after childbirth, from the mother postnatally, and from the child as it develops to 21 years of age. The appropriate sample for biomonitoring will depend upon matrix availability, the time period of concern for a particular exposure or health effect, and the different classes of environmental chemicals to be monitored. This article describes the matrices available for biomonitoring during the life stages being evaluated in the National Children's Study; the best biologic matrices for exposure assessment for each individual chemical class, including consideration of alternative matrices; the analytical methods used for analysis, including quality control procedures and less costly alternatives; the costs of analysis; optimal storage conditions; and chemical and matrix stability during long-term storage.