Bankscott5315

Multivariate Cox regression analysis indicated that methylated signature was independent of other clinical parameters. The Kaplan-Meier analysis results showed that CG site-based risk scores could significantly help distinguish between high- and low-risk patients in both the training (P = 0.000296) and test groups (P = 0.022). The area under the receiver operating characteristic curve in the training and test groups were estimated to be 0.771 and 0.724, respectively, for prognosis prediction. Finally, stratified analysis results suggested the remarkable prognostic value of CG site-based risk scores in CRC subtypes. We identified five methylated CG sites that could be used as an efficient overall survival (OS)-related biomarker for stage II/III CRC patients.This study aimed to characterize proteins and exosomal microRNAs (miRNAs) in the uterine flushings (UF) of cows associated with Day 7 (D7) pregnancy using the embryo donor cows of the embryo transfer program. Superovulated cows either were inseminated (AI cows) or remained non-inseminated (Ctrl cows). UF was collected on D7 in the presence of multiple embryos (AI cows) or without embryos (Ctrl cows) and subjected to isobaric tags for relative and absolute quantification protein analysis. A total of 336 proteins were identified, of which 260 proteins were more than 2-fold higher in AI cows than Ctrl cows. Gene ontology analysis revealed that many differentially expressed proteins were involved in "neutrophil-related" and "extracellular vesicular exosome-related" terms. In silico analysis of proteins with higher concentrations in the UF of AI identified 18 uniquely expressed proteins. Exosomes were isolated from the UF, from which RNA was subjected to miRNA-seq, identifying 37 miRNAs. Of these, three miRNAs were lower, and six miRNAs were higher in the UF of AI cows than those of Ctrl ones. The principal component analysis displayed a close association in miRNA and protein between bta-miR-29a, bta-miR-199b, SUGT1, and PPID. In addition, the receiver operating characteristic curve analysis showed that SUGT1 was the best predictor for the presence of embryos in the uterus. These findings suggest that the presence of multiple D7 embryos in the uterus can lead to significant changes in the protein composition and exosomal miRNA contents of UF, which could mediate innate immunological interactions between the pre-hatching embryo and the uterus in cows.Pancreatic cancer (PaCa) is the seventh most fatal malignancy, with more than 90% mortality rate within the first year of diagnosis. Its treatment can be improved the identification of specific therapeutic targets and their relevant pathways. click here Therefore, the objective of this study is to identify cancer specific biomarkers, therapeutic targets, and their associated pathways involved in the PaCa progression. RNA-seq and microarray datasets were obtained from public repositories such as the European Bioinformatics Institute (EBI) and Gene Expression Omnibus (GEO) databases. Differential gene expression (DE) analysis of data was performed to identify significant differentially expressed genes (DEGs) in PaCa cells in comparison to the normal cells. Gene co-expression network analysis was performed to identify the modules co-expressed genes, which are strongly associated with PaCa and as well as the identification of hub genes in the modules. The key underlaying pathways were obtained from the enrichment analysis oive therapies for PaCa.Background Aging is a well-studied concept, but no studies have comprehensively analyzed the association between aging-related genes (AGs) and hepatocellular carcinoma (HCC) prognosis. Methods Gene candidates were selected from differentially expressed genes and prognostic genes in The Cancer Genome Atlas (TCGA) database. A gene risk score for overall survival prediction was established using the least absolute shrinkage and selection operator (LASSO) regression analysis, and this was validated using data from the International Cancer Genome Consortium (ICGC) database. Functional analysis was conducted using gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis, and immune microenvironment and tumor stemness analyses. Results Initially, 72 AGs from the TCGA database were screened as differentially expressed between normal and tumor tissues and as genes associated with HCC prognosis. Then, seven AGs (POLA1, CDK1, SOCS2, HDAC1, MAPT, RAE1, and EEF1E1) were identified using the LASSO regression analysis. The seven AGs were used to develop a risk score in the training set, and the risk was validated to have a significant prognostic value in the ICGC set (p less then 0.05). Patients with high risk scores had lower tumor differentiation, higher stage, and worse prognosis (all p less then 0.05). Multivariate Cox regression analyses also confirmed that the risk score was an independent prognostic factor for HCC in both the TCGA and ICGC sets (all p less then 0.05). Further analysis showed that a high risk score was correlated with the downregulation of metabolism and tumor immunity. Conclusion The risk score predicts HCC prognosis and could thus be used as a biomarker not only for predicting HCC prognosis but also for deciding on treatment.In addition to their common usages to study gene expression, RNA-seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA-seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study cis-regulated genes by analyzing allele-specific expression (ASE) in the tissue of interest. However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA-seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA-seq and DNA-seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precisioii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA-seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.

The number of diet induced obese population is increasing every year, and the incidence of type 2 diabetes is also on the rise. Histone methylation and acetylation have been shown to be associated with lipogenesis and obesity by manipulating gene expression via the formation of repression or activation domains on chromosomes.

In this study, we aimed to explore gene activation or repression and related biological processes by histone modification across the whole genome on a high-fat diet (HFD) condition. We also aimed to elucidate the correlation of these genes that modulated by histone modification with energy metabolism and inflammation under both short-term and long-term HFD conditions.

We performed ChIP-seq analysis of H3K9me2 and H3K9me3 in brown and white adipose tissues (WATs; subcutaneous adipose tissue) from mice fed with a standard chow diet (SCD) or HFD and a composite analysis of the histone modification of H3K9me2, H3K9me3, H3K4me1 and H3K27ac throughout the whole genome. We also employed ance of eating behavior on obesity and could assist the identification of putative therapeutic targets for the prevention and treatment of metabolic disorders in the future.

This study expanded our understanding of the influence of eating behavior on obesity and could assist the identification of putative therapeutic targets for the prevention and treatment of metabolic disorders in the future.Motivation Long non-coding RNAs (lncRNAs) play important roles in cancer development. Prediction of lncRNA-cancer association is necessary for efficiently discovering biomarkers and designing treatment for cancers. Currently, several methods have been developed to predict lncRNA-cancer associations. However, most of them do not consider the relationships between lncRNA with other molecules and with cancer prognosis, which has limited the accuracy of the prediction. Method Here, we constructed relationship matrices between 1,679 lncRNAs, 2,759 miRNAs, and 16,410 genes and cancer prognosis on three types of cancers (breast, lung, and colorectal cancers) to predict lncRNA-cancer associations. link2 The matrices were iteratively reconstructed by matrix factorization to optimize low-rank size. This method is called detecting lncRNA cancer association (DRACA). Results Application of this method in the prediction of lncRNAs-breast cancer, lncRNA-lung cancer, and lncRNA-colorectal cancer associations achieved an area under curve (AUC) of 0.810, 0.796, and 0.795, respectively, by 10-fold cross-validations. The performances of DRACA in predicting associations between lncRNAs with three kinds of cancers were at least 6.6, 7.2, and 6.9% better than other methods, respectively. To our knowledge, this is the first method employing cancer prognosis in the prediction of lncRNA-cancer associations. When removing the relationships between cancer prognosis and genes, the AUCs were decreased 7.2, 0.6, and 5% for breast, lung, and colorectal cancers, respectively. Moreover, the predicted lncRNAs were found with greater numbers of somatic mutations than the lncRNAs not predicted as cancer-associated for three types of cancers. DRACA predicted many novel lncRNAs, whose expressions were found to be related to survival rates of patients. The method is available at https//github.com/Yanh35/DRACA.Chronic lymphocytic leukemia (CLL) is the most common form of adult leukemia in the Western world with a highly variable clinical course. Its striking genetic heterogeneity is not yet fully understood. Although the CLL genetic landscape has been well-described, patient stratification based on mutation profiles remains elusive mainly due to the heterogeneity of data. Here we attempted to decrease the heterogeneity of somatic mutation data by mapping mutated genes in the respective biological processes. From the sequencing data gathered by the International Cancer Genome Consortium for 506 CLL patients, we generated pathway mutation scores, applied ensemble clustering on them, and extracted abnormal molecular pathways with a machine learning approach. We identified four clusters differing in pathway mutational profiles and time to first treatment. Interestingly, common CLL drivers such as ATM or TP53 were associated with particular subtypes, while others like NOTCH1 or SF3B1 were not. This study provides an important step in understanding mutational patterns in CLL.As multicellular organisms age, they undergo a reduction in tissue and organ function. Researchers have put forward a theory that stem cell aging is the main factor responsible for decreased tissue and organ function. link3 The adult stem cells guarantee the maintenance and repair of adult tissues and organs. Among adult stem cells, mesenchymal stem cells (MSCs) are emerging as hopeful candidates for cell-based therapy of numerous diseases. In recent years, high-throughput sequencing technologies have evolved to identify circular RNAs (circRNAs) associated with an increasing number of diseases, such as cancer and age-related diseases. It has been reported that circRNAs can compete with microRNAs (miRNAs) to affect the stability or translation of target RNAs and further regulate gene expression at the transcriptional level. However, the role of circRNAs expressed in MSCs in aging mechanisms has not yet been deciphered. The aim of this study was to explore and analyze the expression profiles of age-related circRNAs in MSCs.