Fontana Andrea, Copetti Massimiliano, Di Gangi Iole Maria, Mazza Tommaso, Tavano Francesca, Gioffreda Domenica, Mattivi Fulvio, Andriulli Angelo, Vrhovsek Urska, Pazienza Valerio
Unit of Biostatistics I.R.C.C.S. "Casa Sollievo della Sofferenza" Hospital, San Giovanni Rotondo (FG), Italy.
Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all'Adige, Italy.
Oncotarget. 2016 Feb 23;7(8):8968-78. doi: 10.18632/oncotarget.7108.
Survival among patients with adenocarcinoma pancreatic cancer (PDCA) is highly variable, which ranges from 0% to 20% at 5 years. Such a wide range is due to tumor size and stage, as well other patients' characteristics. We analyzed alterations in the metabolomic profile, of PDCA patients, which are potentially predictive of patient's one-year mortality.
A targeted metabolomic assay was conducted on serum samples of patients diagnosed with pancreatic cancer. Statistical analyses were performed only for those 27 patients with information on vital status at follow-up and baseline clinical features. Random Forest analysis was performed to identify all metabolites and clinical variables with the best capability to predict patient's mortality risk at one year. Regression coefficients were estimated from multivariable Weibull survival model, which included the most associated metabolites. Such coefficients were used as weights to build a metabolite risk score (MRS) which ranged from 0 (lowest mortality risk) to 1 (highest mortality risk). The stability of these weights were evaluated performing 10,000 bootstrap resamplings.
MRS was built as a weighted linear combination of the following five metabolites: Valine (HR = 0.62, 95%CI: 0.11-1.71 for each standard deviation (SD) of 98.57), Sphingomyeline C24:1 (HR = 2.66, 95%CI: 1.30-21.09, for each SD of 20.67), Lysine (HR = 0.36, 95%CI: 0.03-0.77, for each SD of 51.73), Tripentadecanoate TG15 (HR = 0.25, 95%CI: 0.01-0.82, for each SD of 2.88) and Symmetric dimethylarginine (HR = 2.24, 95%CI: 1.28-103.08, for each SD of 0.62), achieving a very high discrimination ability (survival c-statistic of 0.855, 95%CI: 0.816-0.894). Such association was still present even after adjusting for the most associated clinical variables (confounders).
The mass spectrometry-based metabolomic profiling of serum represents a valid tool for discovering novel candidate biomarkers with prognostic ability to predict one-year mortality risk in patients with pancreatic adenocarcinoma.
胰腺腺癌（PDCA）患者的生存率差异很大，5年生存率在0%至20%之间。如此大的差异归因于肿瘤大小、分期以及其他患者特征。我们分析了PDCA患者代谢组学特征的变化，这些变化可能预测患者的一年死亡率。
对诊断为胰腺癌的患者血清样本进行靶向代谢组学检测。仅对27例有随访时生命状态信息和基线临床特征的患者进行统计分析。进行随机森林分析以识别所有具有最佳预测患者一年死亡风险能力的代谢物和临床变量。从多变量威布尔生存模型估计回归系数，该模型包括最相关的代谢物。这些系数用作权重来构建代谢物风险评分（MRS），范围从0（最低死亡风险）到1（最高死亡风险）。通过进行10000次自助重采样评估这些权重的稳定性。
MRS构建为以下五种代谢物的加权线性组合：缬氨酸（每标准差98.57时，HR = 0.62，95%CI：0.11 - 1.71）、鞘磷脂C24:1（每标准差20.67时，HR = 2.66，95%CI：1.30 - 21.09）、赖氨酸（每标准差51.73时，HR = 0.36，95%CI：0.03 - 0.77）、十五烷三酸甘油酯TG15（每标准差2.88时，HR = 0.25，95%CI：0.01 - 0.82）和对称二甲基精氨酸（每标准差0.62时，HR = 2.24，95%CI：1.28 - 103.08），具有非常高的辨别能力（生存c统计量为0.855，95%CI：0.816 - 0.894）。即使在调整了最相关的临床变量（混杂因素）后，这种关联仍然存在。
基于质谱的血清代谢组学分析是发现具有预测胰腺腺癌患者一年死亡风险预后能力的新型候选生物标志物的有效工具。
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