Magnetic Resonance Spectroscopy-based Metabolomic Biomarkers for Typing, Staging, and Survival Estimation of Early-Stage Human Lung Cancer

Guo

et al. 2021

Preprint

[Objectives] By studying the plasma metabolomics of patients with different pulmonary nodules and healthy people, we can find the difference in plasma low-molecular metabolites among them. [Methods] Patients with pulmonary nodules admitted to our department were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Meanwhile healthy people were enrolled as healthy population group (HPG). PLC and HPG were equally divided into the Discovery Set and Validation set. [Results] Five significant low-molecular metabolites were found by comparison of four groups as a whole. Four to six metabolites were selected by comparison of the three pulmonary nodule groups with healthy people respectively. The AUC of ROC of these metabolites were all＞0.93. Each pairwise comparison within the three pulmonary nodule groups all found three metabolites, whose AUC of ROC were all＞0.83. From the comparison of PLC and HPG in the discovery set, six metabolites were selected. Their AUC of ROC were all greater than 0.95 in the validation set, indicating that they had a strong ability to differentiate between primary lung cancer and healthy people. [Conclusions] We can find the significant changes of some low-molecular metabolites among three pulmonary nodules and healthy people. These metabolites had the potential to be biomarkers for screening and differential diagnosis of lung cancer.

Section: Discussioncontrasting

confidence: 59%

Plasma Metabolomics Study in Lung Cancer Screening and Differential Diagnosis

Guo

et al. 2021

Preprint

“…So far in only one study, both tissue and plasma from the same population were studied together. Berker et al examined paired tissue and serum samples from 93 patients showing a panel of metabolites indicating the survival time of patients in the early stage of NSCLC [27].…”

Section: Discussionmentioning

confidence: 99%

The Ability of Metabolomics to Discriminate Non-Small-Cell Lung Cancer Subtypes Depends on the Stage of the Disease and the Type of Material Studied

Kowalczyk

Kiśluk

Pietrowska

et al. 2021

Cancers

Identification of the NSCLC subtype at an early stage is still quite sophisticated. Metabolomics analysis of tissue and plasma of NSCLC patients may indicate new, and yet unknown, metabolic pathways active in the NSCLC. Our research characterized the metabolomics profile of tissue and plasma of patients with early and advanced NSCLC stage. Samples were subjected to thorough metabolomics analyses using liquid chromatography-mass spectrometry (LC-MS) technique. Tissue and/or plasma samples from 137 NSCLC patients were analyzed. Based on the early stage tissue analysis, more than 200 metabolites differentiating adenocarcinoma (ADC) and squamous cell lung carcinoma (SCC) subtypes as well as normal tissue, were identified. Most of the identified metabolites were amino acids, fatty acids, carnitines, lysoglycerophospholipids, sphingomyelins, plasmalogens and glycerophospholipids. Moreover, metabolites related to N-acyl ethanolamine (NAE) biosynthesis, namely glycerophospho (N-acyl) ethanolamines (GP-NAE), which discriminated early-stage SCC from ADC, have also been identified. On the other hand, the analysis of plasma of chronic obstructive pulmonary disease (COPD) and NSCLC patients allowed exclusion of the metabolites related to the inflammatory state in lungs and the identification of compounds (lysoglycerophospholipids, glycerophospholipids and sphingomyelins) truly characteristic to cancer. Our results, among already known, showed novel, thus far not described, metabolites discriminating NSCLC subtypes, especially in the early stage of cancer. Moreover, the presented results also indicated the activity of new metabolic pathways in NSCLC. Further investigations on the role of NAE biosynthesis pathways in the early stage of NSCLC may reveal new prognostic and diagnostic targets.

“…PLS-DA analyzes were able also effectively distinguish the metabolic profile of serum in nonsmall-cell lung carcinoma patients with or without microwave ablation treatment from that of healthy controls [10]. Recently, Berker et al published extended study where NMR spectra of measured matched tissue and serum samples were used also for typing, staging and survival estimation of early stages of lung cancer [11]. Despite the rapid growth of metabolomic studies, only a little part of them uses receiver operator characteristics (ROC) curve analysis as the standard method for describing and accessing the performance of medical diagnostic tools [12].…”

mentioning

confidence: 99%

Metabolomic profiling of blood plasma of patients with lung cancer and malignant tumors with metastasis in the lungs showed similar features and promising statistical discrimination against controls

Šarlinová

Baranovičová

Skaličanová

et al. 2021

neo

Targeting metabolomic pathways is a promising strategy for cancer treatment. Alterations in the metabolomic state have also an epigenetic impact making the metabolomic studies even more interesting. We explored metabolomic changes in blood plasma of patients with primary and secondary lung cancer and tried to explore their origin. We also applied a discrimination algorithm on the data. In the study, blood samples from 132 patients with primary lung cancer, 47 with secondary lung cancer, and 77 subjectively healthy subjects without any cancer history were used.The samples were measured by NMR spectroscopy. PCA and PLSDA analyzes did not distinguish between patients with primary and secondary lung tumors. Accordingly, no significantly changed levels of plasmatic metabolites were found between these groups. When comparing with healthy controls, significantly increased glucose, citrate, acetate, 3-hydroxybutyrate, and creatinine balanced with decreased pyruvate, lactate, alanine, tyrosine, and tryptophan were found as a common feature of both groups. Metabolomic analysis of blood plasma showed considerable proximity of patients with primary and secondary lung cancer. The changes observed can be partially explained as cancer-derived and also as changes showing ischemic nature. Random Forrest discrimination based on the relative concentration of metabolites in blood plasma performed very promising with AUC of 0.95 against controls; however noticeable parts of differencing metabolites are overlapping with those observed after ischemic injury in other studies.