Serum Metabolic Disturbances in Lung Cancer Investigated through an Elaborative NMR-Based Serum Metabolomics Approach

Abstract

Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using high-field 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89–1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with p-value < 0.05) and diagnostic potential using the ROC curve analysis. The analysis revealed relevant metabolic disturbances associated with LC. Among various circulatory metabolites, six metabolites, including histidine, glutamine, glycine, threonine, alanine, and valine, were found to be of apposite diagnostic potential for clinical implications. These metabolic alterations indicated altered glucose metabolism, aberrant fatty acid synthesis, and augmented utilization of various amino acids including active glutaminolysis in LC.

A Systematic Role of Metabolomics, Metabolic Pathways, and Chemical Metabolism in Lung Cancer

“…According to serum-based metabolomics studies conducted in recent years, serum metabolites can be used to differentiate LC types and predict severity. Thus, the approach has extraordinary potential to help diagnose LC early and manage it clinically [ 46 , 47 , 48 , 49 ]. Most of the metabolomics studies were conducted on Chinese, European and Korean populations whereas only a few studies were conducted on Indian populations despite the increasing prevalence of LC in the country [ 50 ].…”

Serum Metabolomics of Retinoblastoma: Assessing the Differential Serum Metabolic Signatures of Unilateral and Bilateral Patients

“…To perform multivariate analysis, peaks in the CPMG spectra were assigned to specific metabolites by utilizing the 500 MHz metabolite spectral database library contained in the Chenomx NMR software package and human metabolome database. A total of 72 metabolites annotated were categorized broadly into specific groups including amino acids and their derivatives (29), nucleic acids (2), carbohydrates (9), organic compounds (16), and others (16) (Table S2). To obtain an overview of the initial grouping trend and class separation, the normalized concentration data sets were analyzed using the unsupervised principal component analysis (PCA) method.…”

“…Several research groups across the globe conducted metabolomic studies for retinoblastoma with the aim to understand and identify differential tumor staging for appropriate clinical decisions. − These studies employed distinct sample types, including tissue, tear, vitreous humor, and aqueous humor. Although serum being an ideal and informative fluid that has been utilized for discerning the key metabolic alterations associated with several cancer types, − it has not been scanned for retinoblastoma to date. Serum metabolomic profiling is an influential technique to investigate biomarkers for disease diagnostics/prognostics and also aided in proposing a suitable treatment strategy for different cancers including breast, lung, brain, gastric, and so on. − Thus, the serum metabolic disparity among the Rb patients has been explored, which allows us to scan their metabolic aberrations as well as provides an opportunity to acquire mechanistic insights underlying Rb along with probing the potential diagnostic and prognostic markers for Rb.…”

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

“…Different specific software for NMR, such as TopSpin (Bruker), MNova (Mestrelab Research), Spectrus Processor (ACD/Labs), Delta (JEOL) and Chenomx NMR Suite (Chenomx Inc.) can be used for this goal. Among them, JEOL Delta is the only one completely free of charge, while Chenomx NMR Suite seems to show the best performance because it is based on a sophisticated targeted profiling technology and on reference libraries containing hundreds of metabolite spectral data, allowing a user-friendly deconvolution of complex NMR spectra [151]. The spectral analysis and deconvolution can also be performed with non-specific software, such as Matlab (The MathWorks, Inc.) or R (The R Foundation).…”