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  • Original Article
  • Open Access

Serum proteomic pattern for predicting recurrence of undifferentiated nasopharyngeal carcinoma after radiotherapy

A potential surrogate marker
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  • 1Email author
Clinical Proteomics1:13104365


    Although most patients with early-stage nasopharyngeal carcinoma (NPC) can be cured by radiotherapy, there is a high recurrence rate in patients with advanced NPC. We attempted to identify proteomic patterns in sera for predicting tumor recurrence. Pretreatment sera were collected from 64 NPC patients with complete remission after radiotherapy. Serum proteins were profiled by SELDI ProteinChip technology, and correlated with local/distant recurrence.

    Forty proteomic features were significantly different between the patient groups with and without tumor recurrence. Univariate analyses showed that 32 of them were significantly associated with time to first recurrence. Multivariate Cox-regression analyses identified International Union Against Cancer (UICC) stage and two proteomic features with mass/charge (m/z) values of 8808 and 6626 as independent prognostic indicators for tumor recurrence. The hazard ratios were 2.0 (95% confidence interval, CI 1.3–3.2) and 0.79 (95% CI 0.64–0.96) for a double of peak intensity of proteomic feature m/z 8808 and m/z 6626, respectively. These two proteomic features were also independent prognosticators for overall survival. A decision tree was constructed to predict the tumor recurrence by using UICC stage, proteomic feature m/z 8808, and proteomic feature m/z 6626, and evaluated by Leave-One-Out crossvalidation. Kaplan-Meier analysis confirmed that the decision tree could predict both recurrencefree survival and overall survival. The positive and negative predictive values for tumor recurrence within 4 yr were 74 and 89%, respectively.

    A serum proteomic pattern comprising features m/z 8808 and m/z 6626 is a potential surrogate marker of disease recurrence after radiotherapy in NPC.

    Key Words

    • ProteinChip array
    • prognosis
    • decision tree
    • survival