A Long Noncoding RNA Signature That Predicts Pathological Complete Remission Rate Sensitively in Neoadjuvant Treatment of Breast Cancer

Translational Oncology
Gen WangKunwei Shen

Abstract

Mounting evidence suggests that long noncoding RNAs (lncRNAs) are closely related to pathological complete response (pCR) in neoadjuvant treatment of breast cancer. Here, we construct lncRNA associated models to predict pCR rate. LncRNA expression profiles of breast cancer patients treated with neoadjuvant chemotherapy (NAC) were obtained from Gene Expression Omnibus by repurposing existing microarray data. The prediction model was firstly built by analyzing the correlation between pCR and lncRNA expression in the discovery dataset GSE 25066 (n=488). Another three independent datasets, GSE20194 (n=278), GSE20271 (n=178), and GSE22093 (n=97), were integrated as the validation cohort to assess the prediction efficiency. A novel lncRNA signature (LRS) consisting of 36 lncRNAs was identified. Based on this LRS, patients with NAC treatment were divided into two groups: LRS-high group and LRS-low group, with positive correlation of pCR rate in the discovery dataset. In the validation cohort, univariate and multivariate analyses both demonstrated that high LRS was associated with higher pCR rate. Subgroup analysis confirmed that this model performed well in luminal B [odds ratio (OR)=5.4; 95% confidence interval (CI)=2.7-10.8; P=1.47e...Continue Reading

Citations

Oct 24, 2019·Cancer Biomarkers : Section a of Disease Markers·Emre ÖzgürUgur Gezer
Jun 15, 2021·Frontiers in Oncology·Cecilia ZampedriJorge Melendez-Zajgla

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Datasets Mentioned

BETA
GSE22093

Software Mentioned

DAVID
R
SeqMap
Database for Annotation , Visualization , and Integrated Disco...
GENCODE
Cytoscape
Diagonal Linear Discriminant Analysis ( DLDA )
genefu
R package ROCR
ComBat

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