ALBERT

Description: Squamous cell lung carcinoma (SQCLC), a common and fatal subtype of lung cancer, caused lots of mortalities and showed different outcomes in prognosis. This study was to screen key genes and to figure a prognostic signature to cluster the patients with SQCLC. RNA-Seq data from 550 patients with SQCLC were downloaded from The Cancer Genome Atlas (TCGA). Genetically changed genes were identified and analyzed in univariate survival analysis. Genes significantly influencing prognosis were selected with frequency higher than 100 in lasso regression. Meanwhile, area under the curve (AUC) values and hazard ratios (HR) for seed genes were obtained with R Language. Functional enrichment analysis was performed and clustering effectiveness of the selected common gene set was analyzed with Kaplan-meier. Finally, the stability and validity of the optimal clustering model were verified. A total of 7222 genetically changed genes were screened, including 1045 ones with p 〈 0.05, 1746, p 〈 0.1 and 2758, p 〈 0.2. The common gene sets with more than 100 frequencies were 14-Genes, 10-Genes and 6-Genes. Genes with p 〈 0.05 participated in positive regulation of ERK1 and ERK2 cascade, angiogenesis, platelet degranulation, cell - matrix adhesion, extracellular matrix organization, macrophage activation and so on. A four-gene clustering model in 14-Genes ( DPPA, TTTY16, TRIM58, HKDC1, ZNF589, ALDH7A1, LINC01426, IL19, LOC101928358, TMEM92, HRASLS, JPH1, LOC100288778, GCGR ) was verified as the optimal. The discovery of four-gene clustering model in 14-Genes can cluster the patient samples effectively. This model would help predict the outcomes of patients with SQCLC then improve the treatment strategies. This article is protected by copyright. All rights reserved

Description: Intramuscular fat (IMF) is the most important evaluating indicator of chicken meat quality, the content of which is positively correlated with tenderness, flavor, and succulence of the meat. Chicken IMF deposition process is regulated by many factors, including genetic, nutrition, and environment. Although large number of omics’ studies focused on the IMF deposition process, the molecular mechanism of chicken IMF deposition is still poorly understood. In order to study the role of miRNAs in chicken intramuscular adipogenesis, the intramuscular adipocyte differentiation model (IMF-preadipocytes and IMF-adipocytes) was established and subject to miRNA-Seq. A total of 117 differentially expressed miRNAs between two groups were obtained. Target genes prediction and functional enrichment analysis revealed that eight pathways involved in lipid metabolism related processes, such as fatty acid metabolism and fatty acid elongation. Meanwhile a putative miRNA, gga-miR-18b-3p, was identified be served a function in the intramuscular adipocyte differentiation. Luciferase assay suggested that the gga-miR-18b-3p targeted to the 3′UTR of ACOT13. Subsequent functional experiments demonstrated that gga-miR-18b-3p acted as an inhibitor of intramuscular adipocyte differentiation by targeting ACOT13. Our findings laid a new theoretical foundation for the study of lipid metabolism, and also provided a potential target to improve the meat quality in the poultry industry.

Description: Many life activities and key functions in organisms are maintained by different types of protein–protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein–protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a “bio-word” segmentation system and a word representation model used for learning the distributed representation for each “bio-word”. The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of “bio language processing technology,” which could cause a technological revolution and could be applied to improve the quality of predictions in other problems.