Construction and validation of a prognostic risk model for breast cancer based on protein expression View Full Text


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Article Info

DATE

2022-07-04

AUTHORS

Bo Huang, Xujun Zhang, Qingyi Cao, Jianing Chen, Chenhong Lin, Tianxin Xiang, Ping Zeng

ABSTRACT

Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients. More... »

PAGES

148

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12920-022-01299-5

    DOI

    http://dx.doi.org/10.1186/s12920-022-01299-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1149199254

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35787690


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    75 model
    76 mortality
    77 new biomarkers
    78 new therapeutic direction
    79 overall survival
    80 pathway
    81 patients
    82 performance
    83 predictive performance
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    85 primary tumor tissues
    86 prognosis
    87 prognosis-related proteins
    88 prognostic analysis
    89 prognostic model
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    91 protein
    92 protein expression
    93 proteomic characterization
    94 proteomic data
    95 receiver
    96 regression analysis
    97 risk curves
    98 risk model
    99 stage
    100 study
    101 survival
    102 survival curves
    103 survival time
    104 therapeutic directions
    105 time
    106 tissue
    107 tumor tissue
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