MicroRNAs (miRNAs) are defined as small non-coding RNAs ~22 nt in length. They regulate gene expression at a post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and therefore blocking translation. In the last decade, the dysfunction of miRNAs has been related to the development and progression of many diseases. Currently, researchers need a method to identify precisely the miRNA targets, prior to applying experimental approaches that allow a better functional characterization of miRNAs in biological processes and can thus predict their effects. Computational prediction tools provide a rapid method to identify putative miRNA targets. However, since a large number of tools for the prediction of miRNA:mRNA interactions have been developed, all with different algorithms, the biological researcher sometimes does not know which is the best choice for his study and many times does not understand the bioinformatic basis of these tools. This review describes the biological fundamentals of these prediction tools, characterizes the main sequence-based algorithms, and offers some insights into their uses by biologists.

The Epstein-Barr virus (EBV) infects more than 90% of the human population, playing a key role in the origin and progression of malignant and non-malignant diseases. Many attempts have been made to classify EBV according to clinical or epidemiological information; however, these classifications show frequent incongruences. For instance, they use a small subset of genes for sorting strains but fail to consider the enormous genomic variability and abundant recombinant regions present in the EBV genome. These could lead to diversity overestimation, alter the tree topology and misinterpret viral types when classified, therefore, a reliable EBV phylogenetic classification is needed to minimize recombination signals. Recombination events occur 2.5-times more often than mutation events, suggesting that recombination has a much stronger impact than mutation in EBV genomic diversity, detected within common ancestral node positions. The Hierarchical Bayesian Analysis of Population Structure (hierBAPS) resulted in the differentiation of 12 EBV populations showed seven monophyletic and five paraphyletic. The populations identified were related to geographic location, of which three populations (EBV-p1/Asia/GC, EBV-p2/Asia II/Tumors and EBV-p4/China/NPC) were related to tumor development. Therefore, we proposed a new consistent and non-simplistic EBV classification, beneficial in minimizing the recombination signal in the phylogeny reconstruction, investigating geography relationship and even infer associations to human diseases. These EBV classifications could also be useful in developing diagnostic applications or defining which strains need epidemiological surveillance.

Gallbladder cancer (GBC) is an aggressive neoplasm that in an early stage is generally asymptomatic and, in most cases, is diagnosed in advanced stages with a very low life expectancy because there is no curative treatment. Therefore, understanding the early carcinogenic mechanisms of this pathology is crucial to proposing preventive strategies for this cancer. The main risk factor is the presence of gallstones, which are associated with some environmental factors such as a sedentary lifestyle and a high-fat diet. Other risk factors such as autoimmune disorders and bacterial, parasitic and fungal infections have also been described. All these factors can generate a long-term inflammatory state characterized by the persistent activation of the immune system, the frequent release of pro-inflammatory cytokines, and the constant production of reactive oxygen species that result in a chronic damage/repair cycle, subsequently inducing the loss of the normal architecture of the gallbladder mucosa that leads to the development of GBC. This review addresses how the different risk factors could promote a chronic inflammatory state essential to the development of gallbladder carcinogenesis, which will make it possible to define some strategies such as anti-inflammatory drugs or public health proposals in the prevention of GBC.

Fucoidans are sulphated polysaccharides that can be obtained from brown seaweed and marine invertebrates. They have anti-cancer properties, through their targeting of several signaling pathways and molecular mechanisms within malignant cells. This review describes the chemical structure diversity of fucoidans and their similarity with other molecules such as glycosaminoglycan, which enable them to participation in diverse biological processes. Furthermore, this review summarizes their influence on the development of metastasis and drug resistance, which are the main obstacles to cure cancer. Finally, this article discusses how fucoidans have been used in clinical trials to evaluate their potential synergy with other anti-cancer therapies.

Colorectal cancer (CRC) comprises the third most common cancer worldwide and the second regarding number of deaths. In order to make a correct and early diagnosis to predict metastasis formation, biomarkers are an important tool. Although there are multiple signaling pathways associated with cancer progression, the most recognized are the MAPK pathway, p53 pathway, and TGF-β pathway. These pathways regulate many important functions in the cell, such as cell cycle regulation, proliferation, differentiation, and metastasis formation, among others. Changes in expression in genes belonging to these pathways are drivers of carcinogenesis. Often these expression changes are caused by mutations; however, epigenetic changes, such as DNA methylation, are increasingly acknowledged to play a role in the deregulation of oncogenic genes. This makes DNA methylation changes an interesting biomarkers in cancer. Among the newly identified biomarkers for CRC metastasis INHBB, SMOC2, BDNF, and TBRG4 are included, all of which are highly deregulated by methylation and closely associated with metastasis. The identification of such biomarkers in metastasis of CRC may allow a better treatment and early identification of cancer formation in order to perform better diagnostics and improve the life expectancy.