The wild mouse bone marrow has a unique myeloid and lymphoid composition and phenotype.

The murine bone marrow has a central role in immune function and health as the primary source of leukocytes in adult mice. Laboratory mice provide a human-homologous, genetically manipulable and reproducible model that has enabled an immeasurable volume of high-quality immunological research. However, recent research has questioned the translatability of laboratory mouse research into humans and proposed that the exposure of mice to their wild and natural environment may hold the key to further immunological breakthroughs. To date, there have been no studies providing an in-depth cellular analysis of the wild mouse bone marrow. This study utilized wild mice from an isolated island population (Isle of May, Scotland, UK) and performed flow cytometric and histological analysis to characterize the myeloid, lymphoid, hematopoietic progenitor, and adipocyte compartments within the wild mouse bone marrow. We find that, compared to laboratory mouse bone marrow, the wild mouse bone marrow differs in every cell type assessed. Some of the major distinctions include; a smaller B cell compartment with an enriched presence of plasma cells, increased proportions of KLRG1+ CD8+ T cells, diminished CD11b expression in the myeloid lineage and a five-fold enlargement of the eosinophil compartment. We conclude that the wild mouse bone marrow is dramatically distinct from its laboratory counterparts, with multiple phenotypes that to our knowledge have never been observed in laboratory models. Further research into these unique features may uncover novel immunological mechanisms and grant a greater understanding of the role of the immune system in a natural setting.

Understanding the development and function of the gut microbiota in health and inflammation.

The gut microbiota is known to play an important role in maintaining gut health through a symbiotic relationship with the host. Altered gut microbiota is a common feature of several diseases of the gastrointestinal tract; however, the causal relationship between microbiota and disease pathogenesis is poorly understood. Necrotising enterocolitis (NEC) and inflammatory bowel disease (IBD) are both severe inflammatory diseases affecting the gastrointestinal tract. Although they affect very different patient populations, with NEC primarily being a disease of prematurity and IBD predominantly affecting adults although children can be affected, they both demonstrate common features of gut microbial dysbiosis and a dysregulated host immune response. By comparing and contrasting the changes in gut microbiota, host immune response and function, we aim to highlight common features in diseases that may seem clinically unrelated. Key areas of interest are the role of pattern recognition receptors in altered recognition and responses to the gut microbiota by the host immune system and the associated dysfunctional gut epithelial barrier. The challenge of identifying causal relationships between microbiota and disease is ever-present; however, considering a disease-agnostic approach may help to identify mechanistic pathways shared across several clinical diseases.

Educating the Healthcare Workforce to Support Digital Transformation.

Digital transformation of the healthcare workforce is a priority if we are to leverage the potential of digital technologies, artificial intelligence in clinical decision support and the potential of data captured within electronic health records. Educational programmes need to be diverse and support the digital novices through to the champions whom will be responsible for procuring and implementing digital solutions. In order to professionalise the workforce in this area, digital competencies need to be built into training from early on and be underpinned by frameworks that help to guide regulators and professional bodies and support educational providers to deliver them. Here we describe Manchester's involvement in the development of digital competency frameworks and our digital transformation education programmes that we have created, including a Massive Online Open Course and a professional development course for England's Topol Digital Fellows.

Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment.

There is much discussion concerning 'digital transformation' in healthcare and the potential of artificial intelligence (AI) in healthcare systems. Yet it remains rare to find AI solutions deployed in routine healthcare settings. This is in part due to the numerous challenges inherent in delivering an AI project in a clinical environment. In this article, several UK healthcare professionals and academics reflect on the challenges they have faced in building AI solutions using routinely collected healthcare data.These personal reflections are summarised as 10 practical tips. In our experience, these are essential considerations for an AI healthcare project to succeed. They are organised into four phases: conceptualisation, data management, AI application and clinical deployment. There is a focus on conceptualisation, reflecting our view that initial set-up is vital to success. We hope that our personal experiences will provide useful insights to others looking to improve patient care through optimal data use.

Differential Expression of Soluble Receptor for Advanced Glycation End-products in Mice Susceptible or Resistant to Chronic Colitis.

BACKGROUND: Identifying the factors that contribute to chronicity in inflamed colitic tissue is not trivial. However, in mouse models of colitis, we can investigate at preclinical timepoints. We sought to validate murine Trichuris muris infection as a model for identification of factors that promote development of chronic colitis. METHODS: We compared preclinical changes in mice with a resolving immune response to T. muris (resistant) vs mice that fail to expel the worms and develop chronic colitis (susceptible). Findings were then validated in healthy controls and patients with suspected or confirmed IBD. RESULTS: The receptor for advanced glycation end products (RAGE) was highly dysregulated between resistant and susceptible mice before the onset of any pathological signs. Increased soluble RAGE (sRAGE) in the serum and feces of resistant mice correlated with reduced colitis scores. Mouse model findings were validated in a preliminary clinical study: fecal sRAGE was differentially expressed in patients with active IBD compared with IBD in remission, patients with IBD excluded, or healthy controls. CONCLUSIONS: Preclinical changes in mouse models can identify early pathways in the development of chronic inflammation that human studies cannot. We identified the decoy receptor sRAGE as a potential mechanism for protection against chronic inflammation in colitis in mice and humans. We propose that the RAGE pathway is clinically relevant in the onset of chronic colitis and that further study of sRAGE in IBD may provide a novel diagnostic and therapeutic target.

Problem-based learning in clinical bioinformatics education: Does it help to create communities of practice?

We have now reached the genomics era within medicine; genomics is being used to personalise treatment, make diagnoses, prognoses, and predict adverse outcomes resulting from treatment with certain drugs. Genomic data is now abundant in healthcare, and the newly created profession of clinical bioinformaticians are responsible for its analysis. In the United Kingdom, clinical bioinformaticians are trained within a 3-year programme, integrating a work-based placement with a part-time Master's degree. As this profession is still developing, trainees can feel isolated from their peers whom are located in other hospitals and can find it difficult to gain the mentorship that they require to complete their training. Building strong networks or communities of practice (CoPs) and allowing sharing of knowledge and experiences is one solution to addressing this isolation. Within the Master's delivered at the University of Manchester, we have integrated group-centred problem-based learning (PBL) using real clinical case studies worked on during each course unit. This approach is combined with a flipped style of teaching providing access to online content in our Virtual Learning Environment before the course. The face-to-face teaching is used to focus on the application of the students' knowledge to clinical case studies. In this study, we conducted semistructured interviews with 8 students, spanning 3 cohorts of students. We evaluated the effectiveness of this style of teaching and whether it had contributed to the formation of CoPs between our students. Our findings demonstrated that this style of teaching was preferred by our students to a more traditional lecture-based format and that the problem-based learning approach enabled the formation of CoPs within these cohorts. These CoPs are valuable in the development of this new profession and assist with the production of new guidelines and policies that are helping to professionalise this new group of healthcare scientists.

The circadian regulator BMAL1 programmes responses to parasitic worm infection via a dendritic cell clock.

Resistance to the intestinal parasitic helminth Trichuris muris requires T-helper 2 (TH2) cellular and associated IgG1 responses, with expulsion typically taking up to 4 weeks in mice. Here, we show that the time-of-day of the initial infection affects efficiency of worm expulsion, with strong TH2 bias and early expulsion in morning-infected mice. Conversely, mice infected at the start of the night show delayed resistance to infection, and this is associated with feeding-driven metabolic cues, such that feeding restriction to the day-time in normally nocturnal-feeding mice disrupts parasitic expulsion kinetics. We deleted the circadian regulator BMAL1 in antigen-presenting dendritic cells (DCs) in vivo and found a loss of time-of-day dependency of helminth expulsion. RNAseq analyses revealed that IL-12 responses to worm antigen by circadian-synchronised DCs were dependent on BMAL1. Therefore, we find that circadian machinery in DCs contributes to the TH1/TH2 balance, and that environmental, or genetic perturbation of the DC clock results in altered parasite expulsion kinetics.

TIGERi: modeling and visualizing the responses to perturbation of a transcription factor network.

BACKGROUND: Transcription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimental and theoretical methodologies. Less work has focused on using these models to examine how TF networks respond to changes in the cellular environment. METHODS: In this paper, we have developed a simple, pragmatic methodology, TIGERi (Transcription-factor-activity Illustrator for Global Explanation of Regulatory interaction), to model the response of an inferred TF network to changes in cellular environment. The methodology was tested using publicly available data comparing gene expression profiles of a mouse p38α (Mapk14) knock-out line to the original wild-type. RESULTS: Using the model, we have examined changes in the TF network resulting from the presence or absence of p38α. A part of this network was confirmed by experimental work in the original paper. Additional relationships were identified by our analysis, for example between p38α and HNF3, and between p38α and SOX9, and these are strongly supported by published evidence. FXR and MYC were also discovered in our analysis as two novel links of p38α. To provide a computational methodology to the biomedical communities that has more user-friendly interface, we also developed a standalone GUI (graphical user interface) software for TIGERi and it is freely available at https://github.com/namshik/tigeri/ . CONCLUSIONS: We therefore believe that our computational approach can identify new members of networks and new interactions between members that are supported by published data but have not been integrated into the existing network models. Moreover, ones who want to analyze their own data with TIGERi could use the software without any command line experience. This work could therefore accelerate researches in transcriptional gene regulation in higher eukaryotes.

A Survey of Bioinformatics Database and Software Usage through Mining the Literature.

Computer-based resources are central to much, if not most, biological and medical research. However, while there is an ever expanding choice of bioinformatics resources to use, described within the biomedical literature, little work to date has provided an evaluation of the full range of availability or levels of usage of database and software resources. Here we use text mining to process the PubMed Central full-text corpus, identifying mentions of databases or software within the scientific literature. We provide an audit of the resources contained within the biomedical literature, and a comparison of their relative usage, both over time and between the sub-disciplines of bioinformatics, biology and medicine. We find that trends in resource usage differs between these domains. The bioinformatics literature emphasises novel resource development, while database and software usage within biology and medicine is more stable and conservative. Many resources are only mentioned in the bioinformatics literature, with a relatively small number making it out into general biology, and fewer still into the medical literature. In addition, many resources are seeing a steady decline in their usage (e.g., BLAST, SWISS-PROT), though some are instead seeing rapid growth (e.g., the GO, R). We find a striking imbalance in resource usage with the top 5% of resource names (133 names) accounting for 47% of total usage, and over 70% of resources extracted being only mentioned once each. While these results highlight the dynamic and creative nature of bioinformatics research they raise questions about software reuse, choice and the sharing of bioinformatics practice. Is it acceptable that so many resources are apparently never reused? Finally, our work is a step towards automated extraction of scientific method from text. We make the dataset generated by our study available under the CC0 license here: http://dx.doi.org/10.6084/m9.figshare.1281371.

Bias in the reporting of sex and age in biomedical research on mouse models.

In animal-based biomedical research, both the sex and the age of the animals studied affect disease phenotypes by modifying their susceptibility, presentation and response to treatment. The accurate reporting of experimental methods and materials, including the sex and age of animals, is essential so that other researchers can build on the results of such studies. Here we use text mining to study 15,311 research papers in which mice were the focus of the study. We find that the percentage of papers reporting the sex and age of mice has increased over the past two decades: however, only about 50% of the papers published in 2014 reported these two variables. We also compared the quality of reporting in six preclinical research areas and found evidence for different levels of sex-bias in these areas: the strongest male-bias was observed in cardiovascular disease models and the strongest female-bias was found in infectious disease models. These results demonstrate the ability of text mining to contribute to the ongoing debate about the reproducibility of research, and confirm the need to continue efforts to improve the reporting of experimental methods and materials.

Quality of methods reporting in animal models of colitis.

BACKGROUND: Current understanding of the onset of inflammatory bowel diseases relies heavily on data derived from animal models of colitis. However, the omission of information concerning the method used makes the interpretation of studies difficult or impossible. We assessed the current quality of methods reporting in 4 animal models of colitis that are used to inform clinical research into inflammatory bowel disease: dextran sulfate sodium, interleukin-10, CD45RB T cell transfer, and 2,4,6-trinitrobenzene sulfonic acid (TNBS). METHODS: We performed a systematic review based on PRISMA guidelines, using a PubMed search (2000-2014) to obtain publications that used a microarray to describe gene expression in colitic tissue. Methods reporting quality was scored against a checklist of essential and desirable criteria. RESULTS: Fifty-eight articles were identified and included in this review (29 dextran sulfate sodium, 15 interleukin-10, 5 T cell transfer, and 16 TNBS; some articles use more than 1 colitis model). A mean of 81.7% (SD = ±7.038) of criteria were reported across all models. Only 1 of the 58 articles reported all essential criteria on our checklist. Animal age, gender, housing conditions, and mortality/morbidity were all poorly reported. CONCLUSIONS: Failure to include all essential criteria is a cause for concern; this failure can have large impact on the quality and replicability of published colitis experiments. We recommend adoption of our checklist as a requirement for publication to improve the quality, comparability, and standardization of colitis studies and will make interpretation and translation of data to human disease more reliable.

Extracting patterns of database and software usage from the bioinformatics literature.

MOTIVATION: As a natural consequence of being a computer-based discipline, bioinformatics has a strong focus on database and software development, but the volume and variety of resources are growing at unprecedented rates. An audit of database and software usage patterns could help provide an overview of developments in bioinformatics and community common practice, and comparing the links between resources through time could demonstrate both the persistence of existing software and the emergence of new tools. RESULTS: We study the connections between bioinformatics resources and construct networks of database and software usage patterns, based on resource co-occurrence, that correspond to snapshots of common practice in the bioinformatics community. We apply our approach to pairings of phylogenetics software reported in the literature and argue that these could provide a stepping stone into the identification of scientific best practice. AVAILABILITY AND IMPLEMENTATION: The extracted resource data, the scripts used for network generation and the resulting networks are available at http://bionerds.sourceforge.net/networks/.

The quality of methods reporting in parasitology experiments.

There is a growing concern both inside and outside the scientific community over the lack of reproducibility of experiments. The depth and detail of reported methods are critical to the reproducibility of findings, but also for making it possible to compare and integrate data from different studies. In this study, we evaluated in detail the methods reporting in a comprehensive set of trypanosomiasis experiments that should enable valid reproduction, integration and comparison of research findings. We evaluated a subset of other parasitic (Leishmania, Toxoplasma, Plasmodium, Trichuris and Schistosoma) and non-parasitic (Mycobacterium) experimental infections in order to compare the quality of method reporting more generally. A systematic review using PubMed (2000-2012) of all publications describing gene expression in cells and animals infected with Trypanosoma spp was undertaken based on PRISMA guidelines; 23 papers were identified and included. We defined a checklist of essential parameters that should be reported and have scored the number of those parameters that are reported for each publication. Bibliometric parameters (impact factor, citations and h-index) were used to look for association between Journal and Author status and the quality of method reporting. Trichuriasis experiments achieved the highest scores and included the only paper to score 100% in all criteria. The mean of scores achieved by Trypanosoma articles through the checklist was 65.5% (range 32-90%). Bibliometric parameters were not correlated with the quality of method reporting (Spearman's rank correlation coefficient <-0.5; p>0.05). Our results indicate that the quality of methods reporting in experimental parasitology is a cause for concern and it has not improved over time, despite there being evidence that most of the assessed parameters do influence the results. We propose that our set of parameters be used as guidelines to improve the quality of the reporting of experimental infection models as a pre-requisite for integrating and comparing sets of data.

The languages of health in general practice electronic patient records: a Zipf's law analysis.

BACKGROUND: Natural human languages show a power law behaviour in which word frequency (in any large enough corpus) is inversely proportional to word rank - Zipf's law. We have therefore asked whether similar power law behaviours could be seen in data from electronic patient records. RESULTS: In order to examine this question, anonymised data were obtained from all general practices in Salford covering a seven year period and captured in the form of Read codes. It was found that data for patient diagnoses and procedures followed Zipf's law. However, the medication data behaved very differently, looking much more like a referential index. We also observed differences in the statistical behaviour of the language used to describe patient diagnosis as a function of an anonymised GP practice identifier. CONCLUSIONS: This works demonstrate that data from electronic patient records does follow Zipf's law. We also found significant differences in Zipf's law behaviour in data from different GP practices. This suggests that computational linguistic techniques could become a useful additional tool to help understand and monitor the data quality of health records.

Evaluating the Emotion Ontology through use in the self-reporting of emotional responses at an academic conference.

BACKGROUND: We evaluate the application of the Emotion Ontology (EM) to the task of self-reporting of emotional experience in the context of audience response to academic presentations at the International Conference on Biomedical Ontology (ICBO). Ontology evaluation is regarded as a difficult task. Types of ontology evaluation range from gauging adherence to some philosophical principles, following some engineering method, to assessing fitness for purpose. The Emotion Ontology (EM) represents emotions and all related affective phenomena, and should enable self-reporting or articulation of emotional states and responses; how do we know if this is the case? Here we use the EM 'in the wild' in order to evaluate the EM's ability to capture people's self-reported emotional responses to a situation through use of the vocabulary provided by the EM. RESULTS: To achieve this evaluation we developed a tool, EmOntoTag, in which audience members were able to capture their self-reported emotional responses to scientific presentations using the vocabulary offered by the EM. We furthermore asked participants using the tool to rate the appropriateness of an EM vocabulary term for capturing their self-assessed emotional response. Participants were also able to suggest improvements to the EM using a free-text feedback facility. Here, we present the data captured and analyse the EM's fitness for purpose in reporting emotional responses to conference talks. CONCLUSIONS: Based on our analysis of this data set, our primary finding is that the audience are able to articulate their emotional response to a talk via the EM, and reporting via the EM ontology is able to draw distinctions between the audience's response to a speaker and between the speakers (or talks) themselves. Thus we can conclude that the vocabulary provided at the leaves of the EM are fit for purpose in this setting. We additionally obtained interesting observations from the experiment as a whole, such as that the majority of emotions captured had positive valence, and the free-form feedback supplied new terms for the EM. AVAILABILITY: EmOntoTag can be seen at http://www.bioontology.ch/emontotag; source code can be downloaded from http://emotion-ontology.googlecode.com/svn/trunk/apps/emontotag/and the ontology is available at http://purl.obolibrary.org/obo/MFOEM.owl.

Taming EHR data: using semantic similarity to reduce dimensionality.

Medical care data is a valuable resource that can be used for many purposes including managing and planning for future health needs as well as clinical research. However, the heterogeneity and complexity of medical data can be an obstacle in applying data mining techniques. Much of the potential value of this data therefore goes untapped. In this paper we have developed a methodology that reduces the dimensionality of primary care data, in order to make it more amenable to visualisation, mining and clustering. The methodology involves employing a combination of ontology-based semantic similarity and principal component analysis (PCA) to map the data into an appropriate and informative low dimensional space. Throughout the study, we had access to anonymised patient data from primary care in Salford, UK. The results of our application of this methodology show that diagnosis codes in primary care data can be used to map patients into an informative low dimensional space, which in turn provides the opportunity to support further data exploration and medical hypothesis formulation.

bioNerDS: exploring bioinformatics' database and software use through literature mining.

BACKGROUND: Biology-focused databases and software define bioinformatics and their use is central to computational biology. In such a complex and dynamic field, it is of interest to understand what resources are available, which are used, how much they are used, and for what they are used. While scholarly literature surveys can provide some insights, large-scale computer-based approaches to identify mentions of bioinformatics databases and software from primary literature would automate systematic cataloguing, facilitate the monitoring of usage, and provide the foundations for the recovery of computational methods for analysing biological data, with the long-term aim of identifying best/common practice in different areas of biology. RESULTS: We have developed bioNerDS, a named entity recogniser for the recovery of bioinformatics databases and software from primary literature. We identify such entities with an F-measure ranging from 63% to 91% at the mention level and 63-78% at the document level, depending on corpus. Not attaining a higher F-measure is mostly due to high ambiguity in resource naming, which is compounded by the on-going introduction of new resources. To demonstrate the software, we applied bioNerDS to full-text articles from BMC Bioinformatics and Genome Biology. General mention patterns reflect the remit of these journals, highlighting BMC Bioinformatics's emphasis on new tools and Genome Biology's greater emphasis on data analysis. The data also illustrates some shifts in resource usage: for example, the past decade has seen R and the Gene Ontology join BLAST and GenBank as the main components in bioinformatics processing. ABSTRACT: Conclusions We demonstrate the feasibility of automatically identifying resource names on a large-scale from the scientific literature and show that the generated data can be used for exploration of bioinformatics database and software usage. For example, our results help to investigate the rate of change in resource usage and corroborate the suspicion that a vast majority of resources are created, but rarely (if ever) used thereafter. bioNerDS is available at http://bionerds.sourceforge.net/.

Genetic analysis of the Trichuris muris-induced model of colitis reveals QTL overlap and a novel gene cluster for establishing colonic inflammation.

BACKGROUND: Genetic susceptibility to colonic inflammation is poorly defined at the gene level. Although Genome Wide Association studies (GWAS) have identified loci in the human genome which confer susceptibility to Inflammatory Bowel Disease (Crohn's and Ulcerative Colitis), it is not clear if precise loci exist which confer susceptibility to inflammation at specific locations within the gut e.g. small versus large intestine. Susceptibility loci for colitis in particular have been defined in the mouse, although specific candidate genes have not been identified to date. We have previously shown that infection with Trichuris muris (T. muris) induces chronic colitis in susceptible mouse strains with clinical, histological, and immunological homology to human colonic Crohn's disease. We performed an integrative analysis of colitis susceptibility, using an F2 inter-cross of resistant (BALB/c) and susceptible (AKR) mice following T. muris infection. Quantitative Trait Loci (QTL), polymorphic and expression data were analysed alongside in silico workflow analyses to discover novel candidate genes central to the development and biology of chronic colitis. RESULTS: 7 autosomal QTL regions were associated with the establishment of chronic colitis following infection. 144 QTL genes had parental strain SNPs and significant gene expression changes in chronic colitis (expression fold-change ≥ +/-1.4). The T. muris QTL on chromosome 3 (Tm3) mapped to published QTL in 3 unrelated experimental models of colitis and contained 33 significantly transcribed polymorphic genes. Phenotypic pathway analysis, text mining and time-course qPCR replication highlighted several potential cis-QTL candidate genes in colitis susceptibility, including FcgR1, Ptpn22, RORc, and Vav3. CONCLUSION: Genetic susceptibility to induced colonic mucosal inflammation in the mouse is conserved at Tm3 and overlays Cdcs1.1. Genes central to the maintenance of intestinal homeostasis reside within this locus, implicating several candidates in susceptibility to colonic inflammation. Combined methodology incorporating genetic, transcriptional and pathway data allowed identification of biologically relevant candidate genes, with Vav3 newly implicated as a colitis susceptibility gene of functional relevance.

The language of gene ontology: a Zipf's law analysis.

BACKGROUND: Most major genome projects and sequence databases provide a GO annotation of their data, either automatically or through human annotators, creating a large corpus of data written in the language of GO. Texts written in natural language show a statistical power law behaviour, Zipf's law, the exponent of which can provide useful information on the nature of the language being used. We have therefore explored the hypothesis that collections of GO annotations will show similar statistical behaviours to natural language. RESULTS: Annotations from the Gene Ontology Annotation project were found to follow Zipf's law. Surprisingly, the measured power law exponents were consistently different between annotation captured using the three GO sub-ontologies in the corpora (function, process and component). On filtering the corpora using GO evidence codes we found that the value of the measured power law exponent responded in a predictable way as a function of the evidence codes used to support the annotation. CONCLUSIONS: Techniques from computational linguistics can provide new insights into the annotation process. GO annotations show similar statistical behaviours to those seen in natural language with measured exponents that provide a signal which correlates with the nature of the evidence codes used to support the annotations, suggesting that the measured exponent might provide a signal regarding the information content of the annotation.

Progressive lung cancer determined by expression profiling and transcriptional regulation.

Clinically, our ability to predict disease outcome for patients with early stage lung cancer is currently poor. To address this issue, tumour specimens were collected at surgery from non-small cell lung cancer (NSCLC) patients as part of the European Early Lung Cancer (EUELC) consortium. The patients were followed-up for three years post-surgery and patients who suffered progressive disease (PD, tumour recurrence, metastasis or a second primary) or remained disease-free (DF) during follow-up were identified. RNA from both tumour and adjacent-normal lung tissue was extracted from patients and subjected to microarray expression profiling. These samples included 36 adenocarcinomas and 23 squamous cell carcinomas from both PD and DF patients. The microarray data was subject to a series of systematic bioinformatics analyses at gene, network and transcription factor levels. The focus of these analyses was 2-fold: firstly to determine whether there were specific biomarkers capable of differentiating between PD and DF patients, and secondly, to identify molecular networks which may contribute to the progressive tumour phenotype. The experimental design and analyses performed permitted the clear differentiation between PD and DF patients using a set of biomarkers implicated in neuroendocrine signalling and allowed the inference of a set of transcription factors whose activity may differ according to disease outcome. Potential links between the biomarkers, the transcription factors and the genes p21/CDKN1A and Myc, which have previously been implicated in NSCLC development, were revealed by a combination of pathway analysis and microarray meta-analysis. These findings suggest that neuroendocrine-related genes, potentially driven through p21/CDKN1A and Myc, are closely linked to whether or not a NSCLC patient will have poor clinical outcome.