Dataset Information

RNA m5C Methylation in breast cancer using MeRIP-Seq

ABSTRACT: RNA m5C methylation profile of MCF10A and MDA486 by using MeRIP-Seq protocol Immunoprecipitation of Methylated mRNA at Cytosine (m5C) residues: Affinity purified of anti-methyl cytosine (m5C) polyclonal antibody 7ug (Zymo Research, Catalog#A3001-50) was conjugated with protein-A magnetic beads for 2 h at 4°C in end to end rotator. After that, conjugated beads were extensively washed with RNA immunoprecipitation (RIP) wash buffer to remove unbound antibody. Fragmented 25 ug polyA RNA (mRNA) was incubated with m5C conjugated beads for overnight at 4°C in in the rotating platform in RIP buffer. RIP was done using Megna RNA Immunoprecipitation kit (Millipore, Catalog#17-700). m5C mRNA-immune bead complex was treated with proteinase K buffer to release m5C mRNA from the conjugated antibody. To isolate m5C, mRNA was treated with phenol:chloroform:isoamyl and mixed with 400 ul of chloroform, which was centrifuged at 14000 rpm for 10 minutes to separate aqueous phase. The aqueous phase was ethanol precipitated at -80°C for overnight, to get m5C mRNA. This precipitated m5C mRNA pellet was washed twice with 70% ethanol and air dried. Finally, m5C mRNA pellet was dissolved in nuclease free Water. The m5C mRNA integrity and conentration was quantified by bioanalyzer (Agilent) and Qubit 2.0 flurometer (Invitrogen). The fragmented mRNA was used by following TruSeq RNA Sample Preparation Guide to develop RNA-Seq library for sequencing.

ORGANISM(S): Homo sapiens

SUBMITTER: Cui Xiaodong

PROVIDER: S-ECPF-GEOD-53370 | biostudies-other |

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:BackgroundBreast invasive carcinoma (BRCA) is not a single disease as each subtype has a distinct morphology structure. Although several computational methods have been proposed to conduct breast cancer subtype identification, the specific interaction mechanisms of genes involved in the subtypes are still incomplete. To identify and explore the corresponding interaction mechanisms of genes for each subtype of breast cancer can impose an important impact on the personalized treatment for different patients.MethodsWe integrate the biological importance of genes from the gene regulatory networks to the differential expression analysis and then obtain the weighted differentially expressed genes (weighted DEGs). A gene with a high weight means it regulates more target genes and thus holds more biological importance. Besides, we constructed gene coexpression networks for control and experiment groups, and the significantly differentially interacting structures encouraged us to design the corresponding Gene Ontology (GO) enrichment based on gene coexpression networks (GOEGCN). The GOEGCN considers the two-side distinction analysis between gene coexpression networks for control and experiment groups. The method allows us to study how the modulated coexpressed gene couples impact biological functions at a GO level.ResultsWe modeled the binary classification with weighted DEGs for each subtype. The binary classifier could make a good prediction for an unseen sample, and the experimental results validated the effectiveness of our proposed approaches. The novel enriched GO terms based on GOEGCN for control and experiment groups of each subtype explain the specific biological function changes according to the two-side distinction of coexpression network structures to some extent.ConclusionThe weighted DEGs contain biological importance derived from the gene regulatory network. Based on the weighted DEGs, five binary classifiers were learned and showed good performance concerning the "Sensitivity," "Specificity," "Accuracy," "F1," and "AUC" metrics. The GOEGCN with weighted DEGs for control and experiment groups presented a novel GO enrichment analysis results and the novel enriched GO terms would further unveil the changes of specific biological functions among all the BRCA subtypes to some extent. The R code in this research is available at https://github.com/yxchspring/GOEGCN_BRCA_Subtypes.

Dataset Information

RNA m5C Methylation in breast cancer using MeRIP-Seq

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets