Prognostic prediction by liver tissue proteomic profiling in patients with colorectal liver metastases.

Aim: To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients.Materials& Methods: Prognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tre...

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Publicado en:Future Oncology Vol. 13; no. 8; pp. 875 - 883
Autores principales: Reyes, Adalgiza, Marti, Josep, Marfà, Santiago, Jiménez, Wladimiro, Reichenbach, Vedrana, Pelegrina, Amalia, Fondevila, Constantino, Garcia Valdecasas, Juan Carlos, Fuster, Josep
Formato: Journal Article
Publicado: Taylor & Francis Ltd Apr2017
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Apr2017
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      pub: Taylor & Francis Ltd
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        atl: Prognostic prediction by liver tissue proteomic profiling in patients with colorectal liver metastases.
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        au:
          Reyes, Adalgiza
          Marti, Josep
          Marfà, Santiago
          Jiménez, Wladimiro
          Reichenbach, Vedrana
          Pelegrina, Amalia
          Fondevila, Constantino
          Garcia Valdecasas, Juan Carlos
          Fuster, Josep
        affil: Liver Surgery & Transplantation Unit, Department of Surgery, ICMDM, Hospital Clinic, IDIBAPS, CIBERehd, Villarroel, 170, 08036, Barcelona, Spain
      sug:
        subj:
          Liver Neoplasms
          Colorectal Neoplasms Pathology
          Proteomics
          Liver Neoplasms Metabolism
          Proteomics Methods
          Male
          Liver Neoplasms Mortality
          Female
          Case Control Studies
          Prognosis
          Aged, 80 and Over
          Aged
          Adult
          Colorectal Neoplasms Mortality
          Middle Age
          Aged, 80 & over
          Aged: 65+ years
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Male
          Female
      ab: Aim: To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients.Materials& Methods: Prognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tree algorithm of surface-enhanced laser desorption/ionization TOF-MS proteomic profiles from cryopreserved CRLM (patients) and normal liver tissue (controls).Results: The protein peak 7371 m/z showed the clearest differences between CRLM and control groups (94.1% sensitivity, 100% specificity, p < 0.001). The algorithm that best differentiated favorable and unfavorable groups combined 2970 and 2871 m/z protein peaks (100% sensitivity, 90% specificity).Conclusion: Proteomic profiling in liver samples using classification and regression tree algorithms is a promising technique to differentiate healthy subjects from CRLM patients and to classify the severity of CRLM patients.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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