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在胰腺导管腺癌的KrasG12D小鼠模型中,与恶性进展相关的独特血清代谢组学特征。

Distinct serum metabolomics profiles associated with malignant progression in the KrasG12D mouse model of pancreatic ductal adenocarcinoma.

作者信息

LaConti Joseph J, Laiakis Evagelia C, Mays Anne Deslattes, Peran Ivana, Kim Sung Eun, Shay Jerry W, Riegel Anna T, Fornace Albert J, Wellstein Anton

出版信息

BMC Genomics. 2015;16 Suppl 1(Suppl 1):S1. doi: 10.1186/1471-2164-16-S1-S1. Epub 2015 Jan 15.

Abstract

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer deaths worldwide with less than a 6% 5-year survival rate. PDAC is associated with poor prognosis based on the late stage diagnosis of the disease. Current diagnostic tests lack the sensitivity and specificity to identify markers of early staging. Metabolomics has provided biomarkers for various diseases, stressors, and environmental exposures. In this study we utilized the p48-Cre/LSL-KrasG12D mouse model with age-matched wild type mice. This model shows malignant progression to PDAC analogous to the human disease stages via early and late pancreatic intra-epithelial neoplasia (PanIN) lesions.

RESULTS

Serum was collected from mice with early PanIN lesions (at 3-5 months) and with late PanIN or invasive PDAC lesions (13-16 months), as determined by histopathology. Metabolomics analysis of the serum samples was conducted through UPLC-TOFMS (Ultra Performance Liquid Chromatography coupled to Time-of-flight Mass Spectrometry). Multivariate data analysis revealed distinct metabolic patterns in serum samples collected during malignant progression towards invasive PDAC. Animals with early or late stage lesions were distinguished from their respective controls with 82.1% and 81.5% accuracy, respectively. This also held up for randomly selected subgroups in the late stage lesion group that showed less variability between animals. One of the metabolites, citrate, was validated through tandem mass spectrometry and showed increased levels in serum with disease progression. Furthermore, serum metabolite signatures from animals with early stage lesions identified controls and animals with late stage lesions with 81.5% accuracy (p<0.01) and vice-versa with 73.2% accuracy (p<0.01).

CONCLUSIONS

We conclude that metabolomics analysis of serum samples can identify the presence of early and late stage pancreatic cancer.

摘要

背景

胰腺导管腺癌(PDAC)是全球癌症死亡的第四大主要原因,5年生存率低于6%。由于该疾病诊断较晚,PDAC的预后较差。目前的诊断测试缺乏识别早期分期标志物的敏感性和特异性。代谢组学已为各种疾病、应激源和环境暴露提供了生物标志物。在本研究中,我们使用了p48-Cre/LSL-KrasG12D小鼠模型以及年龄匹配的野生型小鼠。该模型通过早期和晚期胰腺上皮内瘤变(PanIN)病变显示出向PDAC的恶性进展,类似于人类疾病阶段。

结果

通过组织病理学确定,从患有早期PanIN病变(3 - 5个月)以及晚期PanIN或浸润性PDAC病变(13 - 16个月)的小鼠中采集血清。通过超高效液相色谱 - 飞行时间质谱(UPLC - TOFMS)对血清样本进行代谢组学分析。多变量数据分析揭示了在向浸润性PDAC恶性进展过程中采集的血清样本中不同的代谢模式。患有早期或晚期病变的动物与各自的对照组区分开来,准确率分别为82.1%和81.5%。这在晚期病变组中随机选择的亚组中也成立,该亚组动物之间的变异性较小。其中一种代谢物柠檬酸盐通过串联质谱法得到验证,并且随着疾病进展血清中水平升高。此外,来自患有早期病变动物的血清代谢物特征以81.5%的准确率(p<0.01)识别出对照组和患有晚期病变的动物,反之亦然,准确率为73.2%(p<0.01)。

结论

我们得出结论,血清样本的代谢组学分析可以识别早期和晚期胰腺癌的存在。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fe31/4315147/041671d1c64e/1471-2164-16-S1-S1-1.jpg

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