Possibility of multivariate function composed of plasma amino acid profiles as a novel screening index for non-small cell lung cancer: a case control study

Abstract

BackgroundThe amino-acid balance in cancer patients often differs from that in healthy individuals, because of metabolic changes. This study investigated the use of plasma amino-acid profiles as a novel marker for screening non-small-cell lung cancer (NSCLC) patients.MethodsThe amino-acid concentrations in venous blood samples from pre-treatment NSCLC patients (n = 141), and age-matched, gender-matched, and smoking status-matched controls (n = 423), were measured using liquid chromatography and mass spectrometry. The resultant study data set was subjected to multiple logistic regression analysis to identify amino acids related with NSCLC and construct the criteria for discriminating NSCLC patients from controls. A test data set derived from 162 patients and 3,917 controls was used to validate the stability of the constructed criteria.ResultsThe plasma amino-acid profiles significantly differed between the NSCLC patients and the controls. The obtained model (including alanine, valine, isoleucine, histidine, tryptophan and ornithine concentrations) performed well, with an area under the curve of the receiver-operator characteristic curve (ROC_AUC) of >0.8, and allowed NSCLC patients and controls to be discriminated regardless of disease stage or histological type.ConclusionsThis study shows that plasma amino acid profiling will be a potential screening tool for NSCLC.

Clinical Implementation of Metabolomics

“…Unsupervised learning methods are especially useful for investigating the latent structure and decreasing the redundancy of data and therefore they are sometime performed in combination. The advantages of unsupervised methods are that they minimize the loss of information (Maeda et al, 2010). However, whether the results of unsupervised methods can provide the appropriate interpretation or not depends on the setting of parameters or the problem to be analyzed.…”

“…This reagent is applied to the modelling of a diagnostic index, "AminoIndex technology", from differences in PFAA profiles between non-cachectic colorectal/breast/lung cancer patients and healthy individuals. (Maeda et al, 2010;Okamoto et al, 2009). .…”

“…Recently, various data mining methodologies have been reported for identifying and prioritizing reliable metabolomic markers with high diagnostic capability (Caruana, 2006;Duda, 2001;Gu et al, 2011;Kim et al, 2010;Maeda et al, 2010;Montoliu et al, 2009). In cohort studies, the definite diagnoses of the patients are normally known beforehand.…”

“…Target traits can be discrete (e.g., disease vs. healthy, grade of disease) or continuous (e.g., measurement value). Supervised methods are also applicable to discover and predict which metabolites are responsible for the target traits (Maeda et al, 2010;Okamoto et al, 2009;Zhang et al, 2006). However, the generality of the model obtained from those methods can not be always guaranteed because of the potential overfitting or bias of data.…”

Metabolomics for tumor marker discovery and identification based on chromatography–mass spectrometry

“…Review MS-based metabolomic platforms are increasingly applied to the screening of tumor markers and have been used in recent years for colorectal cancer [39][40][41][42][43][44][45][46], lung cancer [47][48][49][50][51][52][53], papillary thyroid carcinoma [54], oral cancer [55,56] and pancreatic cancer [57]. Here, due to space limitations, the authors review the development of metabolomics in the discovery of tumor markers only for specific malignancies with high incidence, including primary liver cancer, gynecologic cancer and genitourinary cancer.…”