Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network.

Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA).

Combining morphological and mitochondrial DNA data to describe a new species of Austroniscus Vanhöffen, 1914 (Isopoda, Janiroidea, Nannoniscidae) linking abyssal and hadal depths of the Puerto Rico Trench.

Hadal trenches are perceived as a unique deep-sea ecosystem with fundamentally different communities compared to the nearby abyss. So far, however, scarce information exists about how populations are genetically linked within a trench and about mechanisms for species divergence. The present study presents the morphological and molecular-genetic characterization and description of a new nannoniscid species within the genus Austroniscus Vanhöffen, 1914 obtained from abyssal and hadal depths of the Puerto Rico Trench, NW Atlantic.

A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients.

Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures.

An integrated approach to identify bimodal genes associated with prognosis in câncer.

Bimodal gene expression (where a gene expression distribution has two maxima) is associated with phenotypic diversity in different biological systems. A critical issue, thus, is the integration of expression and phenotype data to identify genuine associations. Here, we developed tools that allow both: i) the identification of genes with bimodal gene expression and ii) their association with prognosis in cancer patients from The Cancer Genome Atlas (TCGA).

A small interfering RNA (siRNA) database for SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) rapidly transformed into a global pandemic, for which a demand for developing antivirals capable of targeting the SARS-CoV-2 RNA genome and blocking the activity of its genes has emerged. In this work, we presented a database of SARS-CoV-2 targets for small interference RNA (siRNA) based approaches, aiming to speed the design process by providing a broad set of possible targets and siRNA sequences. The siRNAs sequences are characterized and evaluated by more than 170 features, including thermodynamic information, base context, target genes and alignment information of sequences against the human genome, and diverse SARS-CoV-2 strains, to assess possible bindings to off-target sequences.