Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy

PeerJ
Shanliang ZhongSiying Zhou

Abstract

We aimed to identify prognostic signature based on autophagy-related genes (ARGs) for breast cancer patients. The datasets of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to construct multiple-ARG risk signature. In total, 32 ARGs were identified as differentially expressed between tumors and adjacent normal tissues based on TCGA. Six ARGs (IFNG, TP63, PPP1R15A, PTK6, EIF4EBP1 and NKX2-3) with non-zero coefficient were selected from the 32 ARGs using LASSO regression. The 6-ARG signature divided patients into high-and low-risk group. Survival analysis indicated that low-risk group had longer survival time than high-risk group. We further validated the 6-ARG signature using dataset from GEO and found similar results. We analyzed the associations between ARGs and breast cancer survival in TCGA and nine GEO datasets, and obtained 170 ARGs with significant associations. EIF4EBP1, FOS and FAS were the top three ARGs with highest numbers of significant associations. EIF4EBP1 may be a key ARG which had a higher expression level in patients with more malignant molecular subtypes and higher grade b...Continue Reading

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Datasets Mentioned

BETA
GSE19783
GSE96058
GSE88770
GSE19615

Methods Mentioned

BETA
RNA-Seq

Software Mentioned

affy package
LASSO
R
glmnet
clusterProfiler
GOplot
ROCR
cytoscape
R package
DESeq2

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