In:
Scientific Reports, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2019-03-14)
Abstract:
Gene co-expression network analysis (GCNA) can detect alterations in regulatory activities in case/control comparisons. We propose a framework to detect novel genes and networks for predicting breast cancer recurrence. Thirty-four prognosis candidate genes were selected based on a literature review. Four Gene Expression Omnibus Series (GSE) microarray datasets (n = 920) were used to create gene co-expression networks based on these candidates. We applied the framework to four comparison groups according to node (+/−) and recurrence (+/−). We identified a sub-network containing two candidate genes ( LST1 and IGHM ) and six novel genes ( IGHA1 , IGHD , IGHG1 , IGHG3 , IGLC2 , and IGLJ3 ) related to B cell-specific immunoglobulin. These novel genes were correlated with recurrence under the control of node status and were found to function as tumor suppressors; higher mRNA expression indicated a lower risk of recurrence (hazard ratio, HR = 0.87, p = 0.001). We created an immune index score by performing principle component analysis and divided the genes into low and high groups. This discrete index significantly predicted relapse-free survival (RFS) (high: HR = 0.77, p = 0.019; low: control). Public tool KM Plotter and TCGA-BRCA gene expression data were used to validate. We confirmed these genes are correlated with RFS and distal metastasis-free survival (DMFS) in triple-negative breast cancer (TNBC) and general breast cancer.
Type of Medium:
Online Resource
ISSN:
2045-2322
DOI:
10.1038/s41598-019-40826-w
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2019
detail.hit.zdb_id:
2615211-3
