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Identification of ApoA1, HPX and POTEE genes by omic analysis in breast cancer

Cine, Naci; Baykal, Ahiviet Tarik; Sunnetci, Deniz; Canturk, Zafer; Serhatli, Muge; Savli, Hakan


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        "affiliation": "Kocaeli Univ, Fac Med, Dept Med Genet, TR-41380 Kocaeli, Turkey", 
        "name": "Cine, Naci"
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      {
        "affiliation": "Istanbul Medipol Univ, Sch Med, Dept Med Biochem, Istanbul, Turkey", 
        "name": "Baykal, Ahiviet Tarik"
      }, 
      {
        "affiliation": "Kocaeli Univ, Fac Med, Dept Med Genet, TR-41380 Kocaeli, Turkey", 
        "name": "Sunnetci, Deniz"
      }, 
      {
        "affiliation": "Kocaeli Univ, Dept Gen Surg, Fac Med, Kocaeli, Turkey", 
        "name": "Canturk, Zafer"
      }, 
      {
        "affiliation": "TUBITAK, Marmara Res Ctr, Genet Engn & Biotechnol Inst, Kocaeli, Turkey", 
        "name": "Serhatli, Muge"
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      {
        "affiliation": "Kocaeli Univ, Fac Med, Dept Med Genet, TR-41380 Kocaeli, Turkey", 
        "name": "Savli, Hakan"
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    "description": "Breast cancer is the most common cancer among women and accounts for 23% of all female types of cancers. It is well recognized that breast cancer represents a heterogeneous group of tumors, and the molecular events involved in the progression to cancer remain undetermined. Moreover, available prognostic and predictive markers are not sufficient for the accurate determination of the risk for many breast cancer patients. Thus, it is necessary to discover new molecular markers for accurate prediction of clinical outcome and individualized therapy. In the present study, we performed omics-based whole-genome trancriptomic and whole proteomic profiling with network and pathway analyses of breast tumors to identify gene expression patterns related to clinical outcome. A total of 20 samples from tumors and 14 normal appearing breast tissues were analyzed using both gene expression microarrays and LC-MS/MS. We identified 585 downregulated and 413 upregulated genes by gene expression microarrays. Among these genes, HPX, POTEE and ApoAl were the most significant genes correlated with the proteomic profile. Our data revealed that these identified genes are closely related to breast cancer and may be involved in robust detection of disease progression.", 
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      "pages": "1078-1086", 
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