Identification of novel biomarkers with potential for diagnosis and prognosis of gastric cancer: a bioinformatics approach

INTRODUCTION: Gastric cancer (GC) is the fifth most diagnosed neoplasia and the third leading cause of cancer-related deaths. A substantial number of patients exhibit an advanced GC stage once diagnosed. Therefore, the search for biomarkers contributes to the improvement and development of therapies. OBJECTIVE: This study aimed to identify potential GC biomarkers making use of in silico tools. METHODS: Gastric tissue microarray data available in Gene Expression Omnibus and The Cancer Genome Atlas Program was extracted. We applied statistical tests in the search for differentially expressed genes between tumoral and non-tumoral adjacent tissue samples. The selected genes were submitted to an in-house tool for analyses of functional enrichment, survival rate, histological and molecular classifications, and clinical follow-up data. A decision tree analysis was performed to evaluate the predictive power of the potential biomarkers. RESULTS: In total, 39 differentially expressed genes were found, mostly involved in extracellular structure organization, extracellular matrix organization, and angiogenesis. The genes SLC7A8, LY6E, and SIDT2 showed potential as diagnostic biomarkers considering the differential expression results coupled with the high predictive power of the decision tree models. Moreover, GC samples showed lower SLC7A8 and SIDT2 expression, whereas LY6E was higher. SIDT2 demonstrated a potential prognostic role for the diffuse type of GC, given the higher patient survival rate for lower gene expression. CONCLUSION: Our study outlines novel biomarkers for GC that may have a key role in tumor progression. Nevertheless, complementary in vitro analyses are still needed to further support their potential.

A Survey of Biological Data in a Big Data Perspective.

The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.

Analysis of carbohydrate-active enzymes and sugar transporters in Penicillium echinulatum: A genome-wide comparative study of the fungal lignocellulolytic system.

Penicillium echinulatum 2HH is an ascomycete well known for its production of cellulolytic enzymes. Understanding lignocellulolytic and sugar uptake systems is essential to obtain efficient fungi strains for the production of bioethanol. In this study we performed a genome-wide functional annotation of carbohydrate-active enzymes and sugar transporters involved in the lignocellulolytic system of P. echinulatum 2HH and S1M29 strains (wildtype and mutant, respectively) and eleven related fungi. Additionally, signal peptide and orthology prediction were carried out. We encountered a diverse assortment of cellulolytic enzymes in P. echinulatum, especially in terms of ß-glucosidases and endoglucanases. Other enzymes required for the breakdown of cellulosic biomass were also found, including cellobiohydrolases, lytic cellulose monooxygenases and cellobiose dehydrogenases. The S1M29 mutant, which is known to produce an increased cellulase activity, and the 2HH wild type strain of P. echinulatum did not show significant differences between their enzymatic repertoire. Nevertheless, we unveiled an amino acid substitution for a predicted intracellular ß-glucosidase of the mutant, which might contribute to hyperexpression of cellulases through a cellodextrin induction pathway. Most of the P. echinulatum enzymes presented orthologs in P. oxalicum 114-2, supporting the presence of highly similar cellulolytic mechanisms and a close phylogenetic relationship between these fungi. A phylogenetic analysis of intracellular ß-glucosidases and sugar transporters allowed us to identify several proteins potentially involved in the accumulation of intracellular cellodextrins. These may prove valuable targets in the genetic engineering of P. echinulatum focused on industrial cellulases production. Our study marks an important step in characterizing and understanding the molecular mechanisms employed by P. echinulatum in the enzymatic hydrolysis of lignocellulosic biomass.