Prognostic prediction by liver tissue proteomic profiling in patients with colorectal liver metastases - Reyes, Adalgiza; Marti, Josep; Marfà, Santiago; Jiménez, Wladimiro; Reichenbach, Vedrana; Pelegrina, Amalia; Fondevila, Constantino; Garcia Valdecasas, Juan Carlos; Fuster, Josep (Future oncology (London, England), 2017)
To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients. 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). The protein peak 7371 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 protein peaks (100% sensitivity, 90% specificity). 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.