1H-NMR based metabonomic profiling of human esophageal cancer tissue

Abstract

BackgroundThe biomarker identification of human esophageal cancer is critical for its early diagnosis and therapeutic approaches that will significantly improve patient survival. Specially, those that involves in progression of disease would be helpful to mechanism research.MethodsIn the present study, we investigated the distinguishing metabolites in human esophageal cancer tissues (n = 89) and normal esophageal mucosae (n = 26) using a 1H nuclear magnetic resonance (1H-NMR) based assay, which is a highly sensitive and non-destructive method for biomarker identification in biological systems. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least-squares-discriminant anlaysis (OPLS-DA) were applied to analyse 1H-NMR profiling data to identify potential biomarkers.ResultsThe constructed OPLS-DA model achieved an excellent separation of the esophageal cancer tissues and normal mucosae. Excellent separation was obtained between the different stages of esophageal cancer tissues (stage II = 28; stage III = 45 and stage IV = 16) and normal mucosae. A total of 45 metabolites were identified, and 12 of them were closely correlated with the stage of esophageal cancer. The downregulation of glucose, AMP and NAD, upregulation of formate indicated the large energy requirement due to accelerated cell proliferation in esophageal cancer. The increases in acetate, short-chain fatty acid and GABA in esophageal cancer tissue revealed the activation of fatty acids metabolism, which could satisfy the need for cellular membrane formation. Other modified metabolites were involved in choline metabolic pathway, including creatinine, creatine, DMG, DMA and TMA. These 12 metabolites, which are involved in energy, fatty acids and choline metabolism, may be associated with the progression of human esophageal cancer.ConclusionOur findings firstly identify the distinguishing metabolites in different stages of esophageal cancer tissues, indicating the attribution of metabolites disturbance to the progression of esophageal cancer. The potential biomarkers provide a promising molecular diagnostic approach for clinical diagnosis of human esophageal cancer and a new direction for the mechanism study.

Identification of a RNA-Seq Based 8-Long Non-Coding RNA Signature Predicting Survival in Esophageal Cancer

“…Higher gastric acid secretion has been observed in gastroesophageal junction adenocarcinoma, including Barrett esophageal cancer compared to normal subjects [35,36]. Amino acid and lipids metabolism disorders, such as increasing leucine level [37] and highly activated fatty acids metabolism [38], were also found to be responsible for the development of esophageal cancer. Therefore, it is plausible to infer that the eight prognostic lncRNAs participate in the progression of esophageal cancer through interacting with PCGs in these esophageal cancer-related biological pathways.…”

Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease

“…Metabolomics potential can be used to evaluate cancer staging [41][42][43]. To differentiate the metabolomic fingerprints of early stage (I and II) patients from advanced stage (III and IV) lung cancer patients, the OPLS-DA model was built (Fig.…”

Serum metabolomics for early diagnosis of esophageal squamous cell carcinoma by UHPLC-QTOF/MS

“…Metabolomics, the global semi-quantitative assessment of endogenous small molecule metabolites within a biological system, has been successfully utilized in cancer biomarker discovery (Spratlin et al 2009;Zhang et al 2013, b;Ke et al 2015). Up to now, a few ESCC metabolomics studies has been reported using specimen of serum (Djukovic et al 2010;Zhang et al 2011;Ikeda et al 2012;Zhang et al 2012aZhang et al , b, 2013Jin et al 2014;Mir et al 2015), plasma (Hasim et al 2012;Liu et al 2013;Xu et al 2013;Ma et al 2014), urine (Davis et al 2012;Hasim et al 2012), tissue (Wu et al 2009;Yakoub et al 2010;Wang et al 2013;Yang et al 2013;Lynam-Lennon et al 2014) or gastric content (Kumar et al 2012), and conducted on mass spectrometry (MS) and nuclear magnetic resonance (NMR) platforms. These studies revealed significantly perturbed metabolic expression in ESCC patients compared with healthy controls.…”