We propose a role for FGF-10/KGF-2, I-TAC/CXCL11, OSM, osteoactivin/glycoprotein nonmetastatic melanoma protein B, and SCF as novel diagnostic biomarkers. CD30 ligand/TNFSF8, chordin-like 2, FGF-10/KGF-2, growth/differentiation factor 15, I-TAC/CXCL11, OSM, and SCF might represent as predictive biomarkers for gemcitabine and erlotinib response of patients with pancreatic cancer.

The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

No abstract

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.

The cancer community understands the value of blood profiling measurements in assessing and monitoring cancer. We describe an effort among academic, government, biotechnology, diagnostic, and pharmaceutical companies called the Blood Profiling Atlas in Cancer (BloodPAC) Project. BloodPAC will aggregate, make freely available, and harmonize for further analyses, raw datasets, relevant associated clinical data (e.g., clinical diagnosis, treatment history, and outcomes), and sample preparation and handling protocols to accelerate the development of blood profiling assays.

The overall survival of patients with pancreatic ductal adenocarcinoma is extremely low. Although gemcitabine is the standard used chemotherapy for this disease, clinical outcomes do not reflect significant improvements, not even when combined with adjuvant treatments. There is an urgent need for prognosis markers to be found. The aim of this study was to analyze the potential value of serum cytokines to find a profile that can predict the clinical outcome in patients with pancreatic cancer and to establish a practical prognosis index that significantly predicts patients' outcomes. We have conducted an extensive analysis of serum prognosis biomarkers using an antibody array comprising 507 human cytokines. Overall survival was estimated using the Kaplan-Meier method. Univariate and multivariate Cox's proportional hazard models were used to analyze prognosis factors. To determine the extent that survival could be predicted based on this index, we used the leave-one-out cross-validation model. The multivariate model showed a better performance and it could represent a novel panel of serum cytokines that correlates to poor prognosis in pancreatic cancer. B7-1/CD80, EG-VEGF/PK1, IL-29, NRG1-beta1/HRG1-beta1, and PD-ECGF expressions portend a poor prognosis for patients with pancreatic cancer and these cytokines could represent novel therapeutic targets for this disease.

BackgroundA popular query from scientists reading a biomedical abstract is to search for topic-related documents in bibliographic databases. Such a query is challenging because the amount of information attached to a single abstract is little, whereas classification-based retrieval algorithms are optimally trained with large sets of relevant documents. As a solution to this problem, we propose a query expansion method that extends the information related to a manuscript using its cited references.ResultsData on cited references and text sections in 249,108 full-text biomedical articles was extracted from the Open Access subset of the PubMed Central® database (PMC-OA). Of the five standard sections of a scientific article, the Introduction and Discussion sections contained most of the citations (mean = 10.2 and 9.9 citations, respectively). A large proportion of articles (98.4%) and their cited references (79.5%) were indexed in the PubMed® database.Using the MedlineRanker abstract classification tool, cited references allowed accurate retrieval of the citing document in a test set of 10,000 documents and also of documents related to six biomedical topics defined by particular MeSH® terms from the entire PMC-OA (p-value<0.01).Classification performance was sensitive to the topic and also to the text sections from which the references were selected. Classifiers trained on the baseline (i.e., only text from the query document and not from the references) were outperformed in almost all the cases. Best performance was often obtained when using all cited references, though using the references from Introduction and Discussion sections led to similarly good results. This query expansion method performed significantly better than pseudo relevance feedback in 4 out of 6 topics.ConclusionsThe retrieval of documents related to a single document can be significantly improved by using the references cited by this document (p-value<0.01). Using references from Introduction and Discussion performs almost as well as using all references, which might be useful for methods that require reduced datasets due to computational limitations. Cited references from particular sections might not be appropriate for all topics. Our method could be a better alternative to pseudo relevance feedback though it is limited by full text availability.

Most of the research studies developed applying microarray technology to the characterization of different pathological states of any disease may fail in reaching statistically significant results. This is largely due to the small repertoire of analysed samples, and to the limitation in the number of states or pathologies usually addressed. Moreover, the influence of potential deviations on the gene expression quantification is usually disregarded. In spite of the continuous changes in omic sciences, reflected for instance in the emergence of new Next-Generation Sequencing-related technologies, the existing availability of a vast amount of gene expression microarray datasets should be properly exploited. Therefore, this work proposes a novel methodological approach involving the integration of several heterogeneous skin cancer series, and a later multiclass classifier design. This approach is thus a way to provide the clinicians with an intelligent diagnosis support tool based on the use of a robust set of selected biomarkers, which simultaneously distinguishes among different cancer-related skin states. To achieve this, a multi-platform combination of microarray datasets from Affymetrix and Illumina manufacturers was carried out. This integration is expected to strengthen the statistical robustness of the study as well as the finding of highly-reliable skin cancer biomarkers. Specifically, the designed operation pipeline has allowed the identification of a small subset of 17 differentially expressed genes (DEGs) from which to distinguish among 7 involved skin states. These genes were obtained from the assessment of a number of potential batch effects on the gene expression data. The biological interpretation of these genes was inspected in the specific literature to understand their underlying information in relation to skin cancer. Finally, in order to assess their possible effectiveness in cancer diagnosis, a cross-validation Support Vector Machines (SVM)-based classification including feature ranking was performed. The accuracy attained exceeded the 92% in overall recognition of the 7 different cancer-related skin states. The proposed integration scheme is expected to allow the co-integration with other state-of-the-art technologies such as RNA-seq.