Dataset Information

Diagnostic and prognostic significance of serum apolipoprotein C-I in triple-negative breast cancer based on mass spectrometry.

ABSTRACT: Women with triple-negative breast cancer (TNBC) have poor prognosis because of the aggressive nature of the tumor, delayed diagnosis and non-specific symptoms in the early stages. Identification of novel specific TNBC serum biomarkers for screening and therapeutic purposes therefore remains an urgent clinical requirement.We obtained serum samples from a total of 380 recruited individuals split into mining and testing sets, with the aim of screening for reliable protein biomarkers from TNBC and non-TNBC (NTNBC) sera. Samples were assessed using mass spectrometry, followed by receiver operating characteristic (ROC), survival and hazard function curve as well as multivariate Cox regression analyses to ascertain the potential of the protein constituents as diagnostic and prognostic biomarkers for TNBC.We identified upregulated apolipoprotein C-I (apoC-I) with a validated positive effect on TNBC tumorigenesis, with confirmation in an independent test set and minimization of systematic bias by pre-analytical parameters. The apoC-I protein had superior diagnostic ability in distinguishing between TNBC and NTNBC cases. Moreover, the protein presented a more robust potential prognostic factor for TNBC than NTNBC. The apoC-I protein identified in this study presents an effective novel diagnostic and prognostic biomarker for TNBC, indicating that measurement of the peak intensity at 7785 Da in serum samples could facilitate improved early detection and estimation of postoperative survival prognosis for TNBC.

SUBMITTER: Song D

PROVIDER: S-EPMC4990397 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Diagnostic and prognostic significance of serum apolipoprotein C-I in triple-negative breast cancer based on mass spectrometry.

Song Dongjian D Yue Lifang L Zhang Junjie J Ma Shanshan S Zhao Wei W Guo Fei F Fan Yingzhong Y Yang Heying H Liu Qiuliang Q Zhang Da D Xia Ziqiang Z Qin Pan P Jia Jia J Yue Ming M Yu Jiekai J Zheng Shu S Yang Fuquan F Wang Jiaxiang J

Cancer biology & therapy 20160603 6

Women with triple-negative breast cancer (TNBC) have poor prognosis because of the aggressive nature of the tumor, delayed diagnosis and non-specific symptoms in the early stages. Identification of novel specific TNBC serum biomarkers for screening and therapeutic purposes therefore remains an urgent clinical requirement.We obtained serum samples from a total of 380 recruited individuals split into mining and testing sets, with the aim of screening for reliable protein biomarkers from TNBC and n ...[more]

PMID: 27260686

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Project description:BackgroundTriple-negative breast cancer (TNBC) is a heterogeneous disease with a worse prognosis. However, current therapies have rarely improved the outcome of patients with TNBC. Here we sought to identify novel biomarkers or targets for TNBC.Materials and methodsPatients GSE76275 clinic traits and their corresponding mRNA profiles for 198 TNBC and 67 non-TNBC were obtained from the GEO database. Weighted gene co-expression network analysis (WGCNA) of the GSE76275 keyed out hub genes, and the differentially expressed genes (DEGs) were identified with the cut-off of adjusted P (adj. P) <0.01 and |log2 fold-change (FC)| > 1.5. The hub - DEGs overlapping genes, as key genes, were considered for further study using Kaplan-Meier plotter online analysis. Subsequently, Breast Cancer Gene-Expression Miner v4.0 and tissue microarray analysis were applied to determine the transcriptional and translational levels of every key gene. Following plasmid transfection for overexpression, the proliferation of TNBC cells was determined by CCK8 and colony formation assay. Moreover, xenograft tumor models were canvassed to investigate their effect upon in vivo tumor growth.ResultsFour genes (SIDT1, ANKRD30A, GPR160, and CA12) were found to be associated with relapse-free survival (RFS) in TNBC through WGCNA and DEGs integrated analysis. Patients with a higher level of SIDT1 had significantly better RFS compared to those with lower levels. The transcriptional and translational levels of SIDT1 were validated as downregulated in patients with triple-negative status, negative estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Furthermore, SIDT1 inhibited proliferation of breast cancer cells (MDA-MB-231 and MDA-MB-468) and xenograft studies demonstrated that SIDT1 can suppress tumor growth in vivo.ConclusionThis study suggests that SIDT1 may play a crucial role in TNBC progression and has the potential as a prognostic biomarker of TNBC.

Project description:BackgroundTriple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, showing aggressive clinical behaviors and poor outcomes. It urgently needs new therapeutic strategies to improve the prognosis of TNBC. Bioinformatics analyses have been widely used to identify potential biomarkers for facilitating TNBC diagnosis and management.MethodsWe identified potential biomarkers and analyzed their diagnostic and prognostic values using bioinformatics approaches. Including differential expression gene (DEG) analysis, Receiver Operating Characteristic (ROC) curve analysis, functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, survival analysis, multivariate Cox regression analysis, and Non-negative Matrix Factorization (NMF).ResultsA total of 105 DEGs were identified between TNBC and other breast cancer subtypes, which were regarded as heterogeneous-related genes. Subsequently, the KEGG enrichment analysis showed that these genes were significantly enriched in 'cell cycle' and 'oocyte meiosis' related pathways. Four (FAM83B, KITLG, CFD and RBM24) of 105 genes were identified as prognostic signatures in the disease-free interval (DFI) of TNBC patients, as for progression-free interval (PFI), five genes (FAM83B, EXO1, S100B, TYMS and CFD) were obtained. Time-dependent ROC analysis indicated that the multivariate Cox regression models, which were constructed based on these genes, had great predictive performances. Finally, the survival analysis of TNBC subtypes (mesenchymal stem-like [MSL] and mesenchymal [MES]) suggested that FAM83B significantly affected the prognosis of patients.ConclusionsThe multivariate Cox regression models constructed from four heterogeneous-related genes (FAM83B, KITLG, RBM24 and S100B) showed great prediction performance for TNBC patients' prognostic. Moreover, FAM83B was an important prognostic feature in several TNBC subtypes (MSL and MES). Our findings provided new biomarkers to facilitate the targeted therapies of TNBC and TNBC subtypes.

Dataset Information

Diagnostic and prognostic significance of serum apolipoprotein C-I in triple-negative breast cancer based on mass spectrometry.

Publications

Diagnostic and prognostic significance of serum apolipoprotein C-I in triple-negative breast cancer based on mass spectrometry.

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