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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/109383
Metastatic Tissue Proteomic Profiling Predicts 5-Year Outcomes in Patients with Colorectal Liver Metastases
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Colorectal cancer (CRC) is one of the most common cancers in the developed countries, and nearly 70% of patients with CRC develop colorectal liver metastases (CRLMs). During the last decades, several scores have been proposed to predict recurrence after CRLM resection. However, these risk scoring systems do not accurately reflect the prognosis of these patients. Therefore, this investigation was designed to identify a proteomic profile in human hepatic tumor samples to classify patients with CRLM as 'mild' or 'severe' based on the 5-year survival. The study was performed on 85 CRLM tumor samples. Firstly, to evaluate any distinct tumor proteomic signatures between mild and severe CRLM patients, a training group of 57 CRLM tumor samples was characterized by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, and a classification and regression tree (CART) analysis was subsequently performed. Finally, 28 CRLM tumor samples were used to confirm and validate the results obtained. Based on all the protein peaks detected in the training group, the CART analysis was generated, and four peaks were considered to be the most relevant to construct a diagnostic algorithm. Indeed, the multivariate model yielded a sensitivity of 85.7% and a specificity of 86.1%, respectively. In addition, the receiver operating characteristic (ROC) curve showed an excellent diagnostic accuracy to discriminate mild from severe CRLM patients (area under the ROC: 0.903). Finally, the validation process yielded a sensitivity and specificity of 68.8% and 83.3%, respectively. We identified a proteomic profile potentially useful to determine the prognosis of CRLM patients based on the 5-year survival.
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MARFÀ BRUIX, Santiago, et al. Metastatic Tissue Proteomic Profiling Predicts 5-Year Outcomes in Patients with Colorectal Liver Metastases. Translational Oncology. 2016. Vol. 9, num. 5, pags. 445-452. ISSN 1944-7124. [consulted: 7 of June of 2026]. Available at: https://hdl.handle.net/2445/109383