Title Analysis of intrinsic breast cancer subtypes: the clinical utility of epigenetic biomarkers and TP53 mutation status in triple-negative cases /
Authors Sadzevičienė, Ieva ; Šnipaitienė, Kristina ; Ščėsnaitė-Jerdiakova, Asta ; Daniūnaitė, Kristina ; Sabaliauskaitė, Rasa ; Laurinavičienė, Aida ; Drobnienė, Monika ; Ostapenko, Valerijus ; Jarmalaitė, Sonata
DOI 10.3390/ijms232315429
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Is Part of International journal of molecular sbciences.. Basel : MDPI. 2022, vol. 23, no. 23, art. no. 15429, p. [1-14].. eISSN 1422-0067
Keywords [eng] breast cancer ; triple-negative breast cancer ; DNA hypermethylation ; TP53 ; FILIP1L ; MT1E
Abstract [eng] This study aimed at analyzing the DNA methylation pattern and TP53 mutation status of intrinsic breast cancer (BC) subtypes for improved characterization and survival prediction. DNA methylation of 17 genes was tested by methylation-specific PCR in 116 non-familial BRCA mutation-negative BC and 29 control noncancerous cases. At least one gene methylation was detected in all BC specimens and a 10-gene panel statistically significantly separated tumors from noncancerous breast tissues. Methylation of FILIP1L and MT1E was predominant in triple-negative (TN) BC, while other BC subtypes were characterized by RASSF1, PRKCB, MT1G, APC, and RUNX3 hypermethylation. TP53 mutation (TP53-mut) was found in 38% of sequenced samples and mainly affected TN BC cases (87%). Cox analysis revealed that TN status, age at diagnosis, and RUNX3 methylation are independent prognostic factors for overall survival (OS) in BC. The combinations of methylated biomarkers, RUNX3 with MT1E or FILIP1L, were also predictive for shorter OS, whereas methylated FILIP1L was predictive of a poor outcome in the TP53-mut subgroup. Therefore, DNA methylation patterns of specific genes significantly separate BC from noncancerous breast tissues and distinguishes TN cases from non-TN BC, whereas the combination of two-to-three epigenetic biomarkers can be an informative tool for BC outcome predictions.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2022
CC license CC license description