案例分享

伴有淋巴结转移的食管鳞状细胞癌的特征代谢物

发布日期:2017.09.11 浏览次数(339)

众所周知,癌症是人类健康的三大杀手之一,每年有不计其数的患者死于癌症,其中食道癌的致死率在全球范围内在占第六位,在中国更是处于第四位。食道癌分为食管鳞状细胞癌和食管腺癌,在中国和东亚地区,90%以上的食管癌患者都是食管鳞状细胞癌。目前诊断食管鳞状细胞癌的手段是看淋巴结是否转移。但是淋巴结转移的患者在手术后的五年存活率仅为18-47%,而淋巴结转移前的食管癌患者通过手术后的存活率高达70-92%。

 

1519613885963219.jpg

文献解读

目前,对于患者的ESCC或mESCC诊断的几个主要临床病例因素如:年龄、肿瘤大小、肿瘤原发部位等。根据总结,这些手段对于一些早期的症状诊断十分不足。

因此对于临床诊断或治疗来说,提高诊断或预测的准确度,对于ESCC转移与否的监测建立灵敏的方法,以帮助外科医生选择适合的治疗方法是十分有必要的。本篇文献纳入了110名受试者,其中包括30名健康志愿者,40名淋巴结转移患者以及40名淋巴结未转移患者。对于来自不同组人群的临床血样进行基于气相色谱质谱联用平台(GC-MS)的非靶向代谢组学技术(nontarget metabolomics,谱领生物为本次代谢组学服务提供商)检测分析。

样本信息:

Clinical characteristics of patients and healthy subjects
 Lymph node non-metastatic ESCC patientsLymph node metastatic ESCC patientsHealthy controls
No. of subjects404030
Age(mean)60.4(44-73)63.0(45-81)5737(41-74)
Gender(male/female)29/1135/522/8
TNM Stage
ⅠA:20 
ⅡA:38ⅡB:9 
III0ⅢA:19;ⅢB:8;ⅢC:1 
03 

最初,研究团队对每组的血液样本进行处理并检测,得到数据并进行总体分析:

1.jpg

Fig 1. Scheduled multiple-reaction monitoring (S-MRM) chromatograph of five metabolites in positive ion mode. Full, dash and dot line stand for health control, ESCC patients and mESCC patients respectively.

从多维统计分析图中看出不同组样本间有明显的分离趋势。
然后将转移组和非转移组食管癌病人血清样本数据与正常控制组样本数据今天进行对比分析,找出差异物质,并结合数据库进行结构鉴定。

1519614300303298.jpg

1519614300117399.jpg

以上表格为淋巴结未转移组和淋巴结转移组病人分别于健康控制组对比得到的差异化合物,总体可用如下图表示:

8.jpg

疾病组(淋巴结转移和非转移组)与健康组之间对比有很多代谢物发生了变化,其中actic acid和fatty acids,升高,glucose、glutamine和TCA循环中的代谢物含量降低,这在癌症中是一个很常见的现象。另外支链氨基酸的代谢产物如:2-ketoisovaleric acid, 2-ketoisocap- 260 roic acid, 和3-methyl-2-oxovaleric acid的含量显著降低。另外,cysteine, methylcysteine, pyrogallol, tocopherol等和氧化应激有关的代谢物的含量明显降低。此外tryptophan, indolelactic acid, uric acid, p-cresol, phosphoethanolamine, 和 cholesterol等物质在疾病组含量明显降低,而α-hydroxybutyric acid, aspartic acid, β-alanine, 和 maltose的含量则显著升高,这些现象不仅可以看出代谢的变化同时也反映一些潜在的病理机制。
接着,研究团队又对疾病组(ESCC&mESCC)组进行对比分析:

9.jpg

Figure 2. Discriminating plots of non-metastatic and metastatic ESCC patients: (A) scores plot of the PLS-DA model, (B) plot of the permutation test (200 times) of the PLS-DA model, (C) scores plot of the OPLS-DA model, and (D) scores plot of the prediction analysis of the OPLS-DA model.

可以看出在模型较好的前提下,疾病组之间也有很大的差异。接着对这两组数据进行对比分析,并对筛选出的差异物进行结构鉴定:

10.jpg

可以看出在淋巴结转移组中氨基酸及其衍生物如glutamine, cystine, tryptophan,γ-amino-butyric acid,valine,leucine,和pyrrole-2-carboxylic acid等含量明显降低,而glutamic acid则在淋巴结转移组含量升高。
以上物质大多已经发现和癌细胞的增殖、调亡、免疫逃跑、转移以及氧化压力等有关,可总结为如下图:

11.jpg

Figure 3. Heatmap and function classification of 15 differential metabolites between non-metastatic and metastatic ESCC patients. Heatmap (left) was produced by average normalized peak areas with z- score scaling of healthy controls (C), non-metastatic ESCC patients (E), and metastatic ESCC patients (M). These metabolites showed progressive elevation or decline with the progression of ESCC (from C to E to M). In total, 13 metabolites were assigned to five function groups (right), except for iminodiacetic acid and glycolic acid. The green background indicates that the function is improved in metastatic ESCC patients because of the metabolites with changed levels (left) compared to that in non-metastatic ESCC patients, whereas the functions classified with a red background represent the opposite of this.

为了在这些两组之间的差异代谢物之间找出用于诊断淋巴结是否转移的生物标志物,研究团队对这些代谢物运用相关算法进行计算,通过training set和test set筛选出特异性和灵敏度最高的诊断物质。

12.jpg

Figure 4. Box plots of serum valine, γ-aminobutyric acid, and pyrrole-2-carboxylic acid, ROC curves based on the binary logistic regression model by the combination of three serum metabolites, and their prediction plots based on the optimal cutoff value from ROC curves. (A) Values in the box plots are shown as the normalized peak areas of the metabolites in healthy subjects (healthy, green), non-metastatic patients (ESCC, blue), and metastatic patients (mESCC, red). (B) The ESCC samples from the training set were applied to construct a binary logistic regression model based on the combination of serum valine, γ-aminobutyric acid, and pyrrole-2-carboxylic acid, and the ROC curves of the training set (B, left) and test set (B, right) were obtained from the above established prediction model. (C) The optimal cutoff value with the highest sensitivity and specificity in the ROC curves of the training set was obtained (0.558) and applied to evaluate the prediction capacity (85 and 93.3% for test set and training set, respectively) of the current model, where 0 and 1 on the x axis represent ESCC and mESCC patients, respectively, and red diamonds represent samples.

最后筛选出三个物质作为鉴别淋巴结是否转移的“指示器”,分别为Valine, GABA和Pyrrole-2-carboxylic acid。

 

文献内容

Title:Serum Metabolomic Signatures of Lymph Node Metastasis of 2 Esophageal Squamous Cell Carcinoma.

Author:Hai Jin, Fan Qiao, Ling Chen, Chengjun Lu, Li Xu, Xianfu Gao .

Journal: Journal of proteome reseaech.

Keywords: Esophageal squamous cell carcinoma, metabolomics, serum, lymph node metastasis, gas chromatography/mass spectrometry.

Abstract:

Lymph node metastasis was recently proven to be the single most important prognosticfactor for esophageal cancer, an important malignant tumor with poor prognosis. A global metabolomics approach was applied to study lymph node metastasis of esophageal squamous cell carcinoma(ESCC). Metabolomics analyses were performed using gas chromatography/mass spectrometry together with univariate and multivariate statistical analyses. There were clear metabolic distinctions between ESCC patients and healthy subjects. ESCC patients could be well-classified according to lymph node metastasis. We further identified a series of differential serum metabolites for ESCC and lymph node metastatic ESCC patients, suggesting metabolic dysfunction in proliferation (aerobic glycolysis, glutaminolysis, fatty acid metabolism, and branched-chain amino acid consumption), apoptosis, migration, immune escape, and oxidative stress of cancer cells in metastatic ESCC patients. In total, three serum metabolites (valine, γ- aminobutyric acid, and pyrrole-2-carboxylic acid) were selected by binary logistic regression analysis, and their combined use resulted in high diagnostic capacity for ESCC metastasis by receiver operating characteristic analysis. The present metabolomics study staged ESCC patients by lymph node metastasis, and the results suggest promising applications of this approach in prognostic prediction, tailored therapeutics, and understanding the pathological mechanisms of poor prognosis of ESCC patients. .

点击打开文章下载链接

微信公众号或添加客服号