Springer Nature
Browse

Gene-associated methylation status of ST14 as a predictor of survival and hormone receptor positivity in breast Cancer

Posted on 2021-08-22 - 03:19
Abstract Background Genomic profiles of specific gene sets have been established to guide personalized treatment and prognosis for patients with breast cancer (BC). However, epigenomic information has not yet been applied in a clinical setting. ST14 encodes matriptase, a proteinase that is widely expressed in BC with reported prognostic value. Methods In this present study, we evaluated the effect of ST14 DNA methylation (DNAm) on overall survival (OS) of patients with BC as a representative example to promote the use of the epigenome in clinical decisions. We analyzed publicly available genomic and epigenomic data from 1361 BC patients. Methylation was characterized by the β-value from CpG probes based on sequencing with the Illumina Human 450 K platform. Results A high mean DNAm (β > 0.6779) across 34 CpG probes for ST14, as the gene-associated methylation (GAM) pattern, was associated with a longer OS after adjusting age, stage, histology and molecular features in Cox model (p value < 0.001). A high GAM status was also associated with a higher XBP1 expression level and higher proportion of hormone-positive BC (p value < 0.001). Pathway analysis revealed that altered GAM was related to matrisome-associated pathway. Conclusions Here we show the potential role of ST14 DNAm in BC prognosis and warrant further study.

CITE THIS COLLECTION

DataCite
3 Biotech
3D Printing in Medicine
3D Research
3D-Printed Materials and Systems
4OR
AAPG Bulletin
AAPS Open
AAPS PharmSciTech
Abhandlungen aus dem Mathematischen Seminar der Universität Hamburg
ABI Technik (German)
Academic Medicine
Academic Pediatrics
Academic Psychiatry
Academic Questions
Academy of Management Discoveries
Academy of Management Journal
Academy of Management Learning and Education
Academy of Management Perspectives
Academy of Management Proceedings
Academy of Management Review
or
Select your citation style and then place your mouse over the citation text to select it.

SHARE

email

Usage metrics

BMC Cancer

AUTHORS (8)

Yang-Hong Dai
Ying-Fu Wang
Po-Chien Shen
Cheng-Hsiang Lo
Jen-Fu Yang
Chun-Shu Lin
Hsing-Lung Chao
Wen-Yen Huang

CATEGORIES

need help?