Comprehensive analysis of the metabolomics and transcriptomics uncovers the dysregulated network and potential biomarkers of Triple Negative Breast Cancer

被引:1
作者
Gong, Sisi [1 ]
Huang, Rongfu [1 ]
Wang, Meie [1 ]
Lian, Fen [1 ]
Wang, Qingshui [2 ]
Liao, Zhijun [3 ]
Fan, Chunmei [1 ]
机构
[1] Fujian Med Univ, Affiliated Hosp 2, Clin Lab Ctr, Quanzhou, Peoples R China
[2] Fujian Univ Tradit Chinese Med, Coll Integrat Med, Fuzhou, Peoples R China
[3] Fujian Med Univ, Sch Basic Med Sci, Dept Biochem & Mol Biol, Fuzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Triple-negative breast cancer; Metabolomics; Transcriptomics; Biomarker; Pathways; MONOAMINE-OXIDASE; GLUCONEOGENESIS; EXPRESSION; TYROSINE; REVEALS;
D O I
10.1186/s12967-024-05843-y
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
Triple-negative breast cancer (TNBC) is known for its aggressive nature, lack of effective diagnostic tools and treatments, and generally poor prognosis. The objective of this study was to investigate metabolic changes in TNBC using metabolomics approaches and explore the underlying mechanisms through integrated analysis with transcriptomics. In this study, serum untargeted metabolic profiles were first examined between 18 TNBC patients and 21 healthy control (HC) subjects using liquid chromatography-mass spectrometry (LC-MS), identifying a total of 22 significantly differential metabolites (DMs). Subsequently, receiver operating characteristic analysis revealed that 7-methylguanine could serve as a potential biomarker for TNBC in both the discovery and validation sets. Additionally, transcriptomic datasets were retrieved from the GEO database to identify differentially expressed genes (DEGs) between TNBC and normal tissues. An integrative analysis of the DMs and DEGs was conducted, uncovering potential molecular mechanisms underlying TNBC. Notably, three pathways-tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis-were enriched, providing insight into the energy metabolism disorders in TNBC. Within these pathways, two DMs (4-hydroxyphenylacetaldehyde and oxaloacetic acid) and six DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) were identified as key components. In summary, this study highlights metabolic biomarkers that could potentially be used for the diagnosis and screening of TNBC. The comprehensive analysis of metabolomics and transcriptomics data offers a validated and in-depth understanding of TNBC metabolism.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] Metabolomics-transcriptomics joint analysis: unveiling the dysregulated cell death network and developing a diagnostic model for high-grade neuroblastoma
    Zhang, Wancun
    Zhang, Mengxin
    Sun, Meng
    Hu, Minghui
    Yu, Muchun
    Sun, Jushan
    Zhang, Xianwei
    Du, Bang
    [J]. FRONTIERS IN IMMUNOLOGY, 2024, 14
  • [32] Transcriptomic Analysis of Subtype-Specific Tyrosine Kinases as Triple Negative Breast Cancer Biomarkers
    Limsakul, Praopim
    Choochuen, Pongsakorn
    Charupanit, Gorn
    Charupanit, Krit
    [J]. CANCERS, 2023, 15 (02)
  • [33] Androgen receptor in triple negative breast cancer: A potential target for the targetless subtype
    Gerratana, L.
    Basile, D.
    Buono, G.
    De Placido, S.
    Giuliano, M.
    Minichillo, S.
    Coinu, A.
    Martorana, F.
    De Santo, I.
    Del Mastro, L.
    De Laurentiis, M.
    Puglisi, F.
    Arpino, G.
    [J]. CANCER TREATMENT REVIEWS, 2018, 68 : 102 - 110
  • [34] Clinical Identification of Dysregulated Circulating microRNAs and Their Implication in Drug Response in Triple Negative Breast Cancer (TNBC) by Target Gene Network and Meta-Analysis
    Qattan, Amal
    Al-Tweigeri, Taher
    Alkhayal, Wafa
    Suleman, Kausar
    Tulbah, Asma
    Amer, Suad
    [J]. GENES, 2021, 12 (04)
  • [35] Screening and Bioinformatics Analysis of MicroRNA Biomarkers in Triple-Negative Breast Cancer
    Fan, Jingjing
    Dong, Chao
    Ma, Binlin
    [J]. CRITICAL REVIEWS IN EUKARYOTIC GENE EXPRESSION, 2023, 33 (05): : 29 - 37
  • [36] Potential clinically useful prognostic biomarkers in triple-negative breast cancer: preliminary results of a retrospective analysis
    Ilie, Silvia Mihaela
    Bacinschi, Xenia Elena
    Botnariuc, Inga
    Anghel, Rodica Maricela
    [J]. BREAST CANCER-TARGETS AND THERAPY, 2018, 10 : 177 - 194
  • [37] Analysis of Breast Cancer Based on the Dysregulated Network
    Huo, Yanhao
    Li, Xianbin
    Xu, Peng
    Bao, Zhenshen
    Liu, Wenbin
    [J]. FRONTIERS IN GENETICS, 2022, 13
  • [38] Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis
    Huy-Hoang Dang
    Hoang Dang Khoa Ta
    Nguyen, Truc T. T.
    Anuraga, Gangga
    Wang, Chih-Yang
    Lee, Kuen-Haur
    Nguyen Quoc Khanh Le
    [J]. BIOMEDICINES, 2021, 9 (09)
  • [39] Identification of potential core genes in triple negative breast cancer using bioinformatics analysis
    Li, Man-Xiu
    Jin, Li-Ting
    Wang, Tie-Jun
    Feng, Yao-Jun
    Pan, Cui-Ping
    Zhao, Dei-Mian
    Shao, Jun
    [J]. ONCOTARGETS AND THERAPY, 2018, 11 : 4105 - 4112
  • [40] Identification by Comprehensive Bioinformatics Analysis of KIF15 as a Candidate Risk Gene for Triple-Negative Breast Cancer
    Sheng, Jiayu
    Li, Chunyang
    Dong, Mengting
    Jiang, Ke
    [J]. CANCER MANAGEMENT AND RESEARCH, 2020, 12 : 12337 - 12348