Novel bio-marker discovery for stratification and prognosis of breast cancer patients
Transcriptomics Dataset
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General Information
AccessionGSE61304
TitleNovel bio-marker discovery for stratification and prognosis of breast cancer patients
DescriptionThe study entails novel bio-marker discovery of Tumor Aggressive Grade signature (TAGs) genes and their role in recurrence free survival of breast cancer (BC) patients. Current BC dataset was used for co-expression analysis of TAGs genes and their role in BC progression. Additionally, recent findings have suggested an importance of structural organization of sense-antisense gene pairs (SAGPs) for transcription, post-transcriptional and post-translational events and their associations with cancer and disease. We studied SAGPs in which both gene partners are protein encoding genes (coding-coding SAGPs), their role in human BC development and demonstrated their potential for BC stratification and prognosis. Based on gene expression and correlation analyses we identified the robust set of breast cancer-relevant SAGPs (BCR-SAGPs). We isolated and characterized the sense-antisense gene signature (SAGS) and evaluated its prognostic potential in various gene expression datasets comprising 1161 BC patients. The methods used included the Cox proportional survival analysis, statistical analysis of clinicopathologic parameters and differential gene expression. The SAGS was effective in identification of BC patients with the most aggressive disease. Independently, we validated the SAGS using 58 RNA samples of breast cancer tumors purchased from OriGene Technologies (Rockville, MD).
Landing Pagehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61304
RepositoryGene Expression Omnibus
Tools used to analyze the dataset
Keyword
Dataset Accession
Enrichr
Published May 2016
A comprehensive gene set enrichment analysis web server 2016 update
7759
65
Characteristic Direction
Published March 2014
A geometrical multivariate approach to identify differentially expressed genes
26829
GeneMANIA
Published June 2010
Biological network integration for gene prioritization and predicting gene function
7435
L1000CDS2
Published August 2016
An ultra-fast LINCS L1000 Characteristic Direction signature search engine
7756
PAEA
Published November 2015
Enrichment analysis tool implementing the principal angle method
3879
Canned Analyses generated with the dataset
Filters  
Dataset Accession
Tool Name
Direction
Geneset
Interaction network and enrichment analysis of genes downregulated in breast adenocarcinoma
GeneMANIA, Characteristic Direction
The analysis explores the gene interaction network and pathway enrichment of the top 50 mo...
Interaction network and enrichment analysis of genes upregulated in breast adenocarcinoma
GeneMANIA, Characteristic Direction
The analysis explores the gene interaction network and pathway enrichment of the top 50 mo...
Small molecules which mimic breast adenocarcinoma
L1000CDS2, Characteristic Direction
The L1000 database was queried in order to identify small molecule perturbations which mim...
Small molecules which reverse breast adenocarcinoma
L1000CDS2, Characteristic Direction
The L1000 database was queried in order to identify small molecule perturbations which rev...
Enrichment analysis of genes dysregulated in breast adenocarcinoma
PAEA, Characteristic Direction
An enrichment analysis was performed on the top most dyresgulated genes determined by appl...
Enrichment analysis of genes downregulated in breast adenocarcinoma
Enrichr, Characteristic Direction
An enrichment analysis was performed on the top 500 most downregulated genes identified by...
Enrichment analysis of genes upregulated in breast adenocarcinoma
Enrichr, Characteristic Direction
An enrichment analysis was performed on the top 500 most upregulated genes identified by a...
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