A metabolomic approach to lung cancer

Metabolomics-based search for lung cancer markers among patients with different smoking status

Tobacco smoking is the main etiological factor of lung cancer (LC), which can also cause metabolome disruption. This study aimed to investigate whether the observed metabolic shift in LC patients was also associated with their smoking status. Untargeted metabolomics profiling was applied for the initial screening of changes in serum metabolic profile between LC and chronic obstructive pulmonary disease (COPD) patients, selected as a non-cancer group. Differences in metabolite profiles between current and former smokers were also tested. Then, targeted metabolomics methods were applied to verify and validate the proposed LC biomarkers. For untargeted metabolomics, a single extraction-dual separation workflow was applied. The samples were analyzed using a liquid chromatograph-high resolution quadrupole time-of-flight mass spectrometer. Next, the selected metabolites were quantified using liquid chromatography-triple-quadrupole mass spectrometry. The acquired data confirmed that patients' stratification based on smoking status impacted the discriminating ability of the identified LC marker candidates. Analyzing a validation set of samples enabled us to determine if the putative LC markers were truly robust. It demonstrated significant differences in the case of four metabolites: allantoin, glutamic acid, succinic acid, and sphingosine-1-phosphate. Our research showed that studying the influence of strong environmental factors, such as tobacco smoking, should be considered in cancer marker research since it reduces the risk of false positives and improves understanding of the metabolite shifts in cancer patients.

Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study

BACKGROUND

Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge.

OBJECTIVES

To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management.

METHODS

In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM.

RESULTS

We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample.

CONCLUSIONS

These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.

Causal role of metabolites in non-small cell lung cancer: Mendelian randomization (MR) study

On a global scale, lung cancer(LC) is the most commonly occurring form of cancer. Nonetheless, the process of screening and detecting it in its early stages presents significant challenges. Earlier research endeavors have recognized metabolites as potentially reliable biomarkers for LC. However, the majority of these studies have been limited in scope, featuring inconsistencies in terms of the relationships and levels of association observed.Moreover, there has been a lack of consistency in the types of biological samples utilized in previous studies. Therefore, the main objective of our research was to explore the correlation between metabolites and Non-small cell lung cancer (NSCLC).Thorough two-sample Mendelian randomization (TSMR) analysis, we investigated potential cause-and-effect relationships between 1400 metabolites and the risk of NSCLC.The analysis of TSMR revealed a significant causal impact of 61 metabolites on NSCLC.To ensure the reliability and validity of our findings, we perform FDR correction for P-values by Benjaminiand Hochberg(BH) method, Our results indicate that Oleate/vaccenate (18:1) levels and Caffeine to paraxanthine ratio may be causally associated with an increased risk of NSCLC [Oleate/vaccenate(18:1)levels: OR = 1.171,95%CI: 1.085-1.265, FDR = 0.036; Caffeine to paraxanthine ratio: OR = 1.386, 95%CI:1.191-1.612,FDR = 0.032].

A metabolomics study on carcinogenesis of ground-glass nodules

Objective

This study aimed to identify differential metabolites and key metabolic pathways between lung adenocarcinoma (LUAD) tissues and normal lung (NL) tissues using metabolomics techniques, to discover potential biomarkers for the early diagnosis of lung cancer.

Material and Methods

Forty-five patients with primary ground-glass nodules (GGN) identified on computed tomography imaging and who were willing to undergo surgery at Shanghai General Hospital from December 2021 to December 2022 were recruited to the study. All participants underwent video thoracoscopy surgery with segmental or wedge resection of the lung. Tissue samples for pathological examination were collected from the site of ground-glass nodules (GGN) lesion and 3 cm away from the lesion (NL). The pathology results were 35 lung adenocarcinoma (LUAD) cases (13 invasive adenocarcinoma, 14 minimally invasive adenocarcinoma, and eight adenocarcinoma in situ), 10 benign samples, and 45 NL tissues. For the untargeted metabolomics technique, 25 LUAD samples were assigned as the case group and 30 NL tissues as the control group. For the targeted metabolomics technique, ten LUAD samples were assigned as the case group and 15 NL tissues as the control group. Samples were analyzed by untargeted and targeted metabolomics, with liquid chromatography-tandem mass spectrometry detection used as part of the experimental procedure.

Results

Untargeted metabolomics revealed 164 differential metabolites between the case and control groups, comprising 110 up regulations and 54 down regulations. The main metabolic differences found by the untargeted method were organic acids and their derivatives. Targeted metabolomics revealed 77 differential metabolites between the case and control groups, comprising 69 up regulations and eight down regulations. The main metabolic changes found by the targeted method were fatty acids, amino acids, and organic acids. The levels of organic acids such as lactic acid, fumaric acid, and malic acid were significantly increased in LUAD tissue compared to NL. Specifically, an increased level of L-lactic acid was found by both untargeted (variable importance in projection [VIP] = 1.332, fold-change [FC] = 1.678, q = 0.000) and targeted metabolomics (VIP = 1.240, FC = 1.451, q = 0.043). Targeted metabolomics also revealed increased levels of fumaric acid (VIP = 1.481, FC = 1.764, q = 0.106) and L-malic acid (VIP = 1.376, FC = 1.562, q = 0.012). Most of the 20 differential fatty acids identified were downregulated, including dodecanoic acid (VIP = 1.416, FC = 0.378, q = 0.043) and tridecane acid (VIP = 0.880, FC = 0.780, q = 0.106). Furthermore, increased levels of differential amino acids were found in LUAD samples.

Conclusion

Lung cancer is a complex and heterogeneous disease with diverse genetic alterations. The study of metabolic profiles is a promising research field in this cancer type. Targeted and untargeted metabolomics revealed significant differences in metabolites between LUAD and NL tissues, including elevated levels of organic acids, decreased levels of fatty acids, and increased levels of amino acids. These metabolic features provide valuable insights into LUAD pathogenesis and can potentially serve as biomarkers for prognosis and therapy response.

Metabolomics and lipidomics in non-small cell lung cancer

Due to its insidious nature, lung cancer remains a leading cause of cancer-related deaths worldwide. Therefore, there is an urgent need to identify sensitive/specific biomarkers for early diagnosis and monitoring. The current study was designed to provide a current metabolic profile of non-small cell lung cancer (NSCLC) by systematically reviewing and summarizing various metabolomic/ lipidomic studies based on NSCLC blood samples, attempting to find biomarkers in human blood that can predict or diagnose NSCLC, and investigating the involvement of key metabolites in the pathogenesis of NSCLC. We searched all articles on lung cancer published in Elsevier, PubMed, Web of Science and the Cochrane Library between January 2012 and December 2022. After critical selection, a total of 31 studies (including 2768 NSCLC patients and 9873 healthy individuals) met the inclusion criteria, and 22 were classified as "high quality". Forty-six metabolites related to NSCLC were repeatedly identified, involving glucose metabolism, amino acid metabolism, lipid metabolism and nucleotide metabolism. Pyruvic acid, carnitine, phenylalanine, isoleucine, kynurenine and 3-hydroxybutyrate showed upward trends in all studies, citric acid, glycine, threonine, cystine, alanine, histidine, inosine, betaine and arachidic acid showed downward trends in all studies. This review summarizes the existing metabolomic/lipidomic studies related to the identification of blood biomarkers in NSCLC, examines the role of key metabolites in the pathogenesis of NSCLC, and provides an important reference for the clinical diagnosis and treatment of NSCLC. Due to the limited size and design heterogeneity of the existing studies, there is an urgent need for standardization of future studies, while validating existing findings with more studies.

Amino acid catabolite markers for early prognostication of pneumonia in patients with COVID-19

Effective early-stage markers for predicting which patients are at risk of developing SARS-CoV-2 infection have not been fully investigated. Here, we performed comprehensive serum metabolome analysis of a total of 83 patients from two cohorts to determine that the acceleration of amino acid catabolism within 5 days from disease onset correlated with future disease severity. Increased levels of de-aminated amino acid catabolites involved in the de novo nucleotide synthesis pathway were identified as early prognostic markers that correlated with the initial viral load. We further employed mice models of SARS-CoV2-MA10 and influenza infection to demonstrate that such de-amination of amino acids and de novo synthesis of nucleotides were associated with the abnormal proliferation of airway and vascular tissue cells in the lungs during the early stages of infection. Consequently, it can be concluded that lung parenchymal tissue remodeling in the early stages of respiratory viral infections induces systemic metabolic remodeling and that the associated key amino acid catabolites are valid predictors for excessive inflammatory response in later disease stages.

Untargeted Metabolomic Study of Lung Cancer Patients after Surgery with Curative Intent

Lung cancer (LC) is a leading cause of mortality, claiming more than 1.8 million deaths per year worldwide. Surgery is one of the most effective treatments when the disease is in its early stages. The study of metabolic alterations after surgical intervention with curative intent could be used to assess the response to treatment or the detection of cancer recurrence. In this study, we have evaluated the metabolomic profile of serum samples (n = 110) from preoperative (PRE) and postoperative (POST) LC patients collected at two different time points (1 month, A; 3-6 months, B) with respect to healthy people. An untargeted metabolomic platform based on reversed phase (RP) and hydrophilic interaction chromatography (HILIC), using ultra-high performance liquid chromatography (UHPLC) and mass spectrometry (MS), was applied (MassIVE ID MSV000092213). Twenty-two altered metabolites were annotated by comparing all the different studied groups. DG(14,0/22:1), stearamide, proline, and E,e-carotene-3,3'-dione were found altered in PRE, and their levels returned to those of a baseline control group 3-6 months after surgery. Furthermore, 3-galactosyllactose levels remained altered after intervention in some patients. This study provides unique insights into the metabolic profiles of LC patients after surgery at two different time points by combining complementary analytical methods.

Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review

Raman spectroscopy is a widely developing approach for noninvasive analysis that can provide information on chemical composition and molecular structure. High chemical specificity calls for developing different medical diagnostic applications based on Raman spectroscopy. This review focuses on the Raman-based techniques used in medical diagnostics and provides an overview of such techniques, possible areas of their application, and current limitations. We have reviewed recent studies proposing conventional Raman spectroscopy and surface-enhanced Raman spectroscopy for rapid measuring of specific biomarkers of such diseases as cardiovascular disease, cancer, neurogenerative disease, and coronavirus disease (COVID-19). As a result, we have discovered several most promising Raman-based applications to identify affected persons by detecting some significant spectral features. We have analyzed these approaches in terms of their potentially diagnostic power and highlighted the remaining challenges and limitations preventing their translation into clinical settings.

Intracellular metabolic profiling of drug resistant cells by surface enhanced Raman scattering

BACKGROUND

Intracellular metabolic profiling reveals real-time metabolic information useful for the study of underlying mechanisms of cells in particular conditions such as drug resistance. However, mass spectrometry (MS), one of the leading metabolomics technologies, usually requires a large number of cells and complex pretreatments. Surface enhanced Raman scattering (SERS) has an ultrahigh detection sensitivity and specificity, favorable for metabolomics analysis. However, some targeted SERS methods focus on very limited metabolite without global bioprofiling, and some label-free approaches try to fingerprint the metabolic response based on whole SERS spectral classification, but comprehensive interpretation of biological mechanisms was lacking. (95) RESULTS: We proposed a label-free SERS technique for intracellular metabolic profiling in complex cellular lysates within 3 min. We first compared three kinds of cellular lysis methods and sonication lysis shows the highest extraction efficiency of metabolites. To obtain comprehensive metabolic information, we collected a spectral set for each sample and further qualified them by the Pearson correlation coefficient (PCC) to calculate how many spectra should be acquired at least to gain the adequate information from a statistical and global view. In addition, according to our measurements with 10 pure metabolites, we can understand the spectra acquired from complex cellular lysates of different cell lines more precisely. Finally, we further disclosed the variations of 22 SERS bands in enzalutamide-resistant prostate cancer cells and some are associated with the androgen receptor signaling activity and the methionine salvage pathway in the drug resistance process, which shows the same metabolic trends as MS. (149) SIGNIFICANCE: Our technique has the capability to capture the intracellular metabolic fingerprinting with the optimized lysis approach and spectral set collection, showing high potential in rapid, sensitive and global metabolic profiling in complex biosamples and clinical liquid biopsy. This gives a new perspective to the study of SERS in insightful understanding of relevant biological mechanisms. (54).

Recent Analytical Advances for Decoding Metabolic Reprogramming in Lung Cancer

Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.

Biofluid Metabolic Profiling for Lung Cancer Screening via Reactive Matrix-Assisted Laser Desorption Ionization Mass Spectrometry

Lung cancer (LC) has the highest mortality rate among various cancer diseases. Developing an early screening method for LC with high classification accuracy is essential. Herein, 2-hydrazinoquinoline (2-HQ) is utilized as a dual-mode reactive matrix for metabolic fingerprint analysis and LC screening via matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). Metabolites in both positive mode and negative mode can be detected using 2-HQ as the matrix, and derivative analysis of aldehyde and ketone compounds can be achieved simultaneously. Hundreds of serum and urine samples from LC patients and healthy volunteers were analyzed. Combined with machine learning, LC patients and healthy volunteers were successfully distinguished with a high area under the curve value (0.996 for blind serum samples and 0.938 for urine). The MS signal was identified for metabolic profiling, and dysregulated metabolites of the LC group were analyzed. The above results showed that this method has great potential for rapid screening of LC.

How do prolonged anchorage-free lifetimes strengthen non-small-cell lung cancer cells to evade anoikis? - A link with altered cellular metabolomics

BACKGROUND

Malignant cells adopt anoikis resistance to survive anchorage-free stresses and initiate cancer metastasis. It is still unknown how varying periods of anchorage loss contribute to anoikis resistance, cell migration, and metabolic reprogramming of cancerous cells.

RESULTS

Our study demonstrated that prolonging the anchorage-free lifetime of non-small-cell lung cancer NCI-H460 cells for 7 days strengthened anoikis resistance, as shown by higher half-life and capability to survive and grow without anchorage, compared to wild-type cells or those losing anchorage for 3 days. While the prolonged anchorage-free lifetime was responsible for the increased aggressive feature of survival cells to perform rapid 3-dimensional migration during the first 3 h of a transwell assay, no significant influence was observed with 2-dimensional surface migration detected at 12 and 24 h by a wound-healing method. Metabolomics analysis revealed significant alteration in the intracellular levels of six (oxalic acid, cholesterol, 1-ethylpyrrolidine, 1-(3-methylbutyl)-2,3,4,6-tetramethylbenzene, β-alanine, and putrescine) among all 37 identified metabolites during 7 days without anchorage. Based on significance values, enrichment ratios, and impact scores of all metabolites and their associated pathways, three principal metabolic activities (non-standard amino acid metabolism, cell membrane biosynthesis, and oxidative stress response) offered potential links with anoikis resistance.

CONCLUSIONS

These findings further our insights into the evolution of anoikis resistance in lung cancer cells and identify promising biomarkers for early lung cancer diagnosis.

Liquid Biopsy in Lung Cancer: Biomarkers for the Management of Recurrence and Metastasis

Liquid biopsies have emerged as a promising tool for the detection of metastases as well as local and regional recurrence in lung cancer. Liquid biopsy tests involve analyzing a patient's blood, urine, or other body fluids for the detection of biomarkers, including circulating tumor cells or tumor-derived DNA/RNA that have been shed into the bloodstream. Studies have shown that liquid biopsies can detect lung cancer metastases with high accuracy and sensitivity, even before they are visible on imaging scans. Such tests are valuable for early intervention and personalized treatment, aiming to improve patient outcomes. Liquid biopsies are also minimally invasive compared to traditional tissue biopsies, which require the removal of a sample of the tumor for further analysis. This makes liquid biopsies a more convenient and less risky option for patients, particularly those who are not good candidates for invasive procedures due to other medical conditions. While liquid biopsies for lung cancer metastases and relapse are still being developed and validated, they hold great promise for improving the detection and treatment of this deadly disease. Herein, we summarize available and novel approaches to liquid biopsy tests for lung cancer metastases and recurrence detection and describe their applications in clinical practice.

Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling

The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems. To address this need, this study employs a multiscale modeling approach to evaluate first-line chemotherapy response of individual patient tumors based on metabolomic analysis of tumor core biopsies obtained during routine clinical evaluation. Model parameters were calculated for a patient cohort as a function of these metabolomic profiles, previously obtained from high-resolution 2DLC-MS/MS analysis. Evaluation metrics were defined to classify patients as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) following first-line chemotherapy. Response was simulated for each patient and compared to actual response. The results show that patient classifications were significantly separated from each other, and also when grouped as DC vs. PD and as CR/PR vs. SD/PD, by fraction of initial tumor radius metric at 6 days post simulated bolus drug injection. This study shows that patient first-line chemotherapy response can in principle be evaluated from multiscale modeling integrated with tumor tissue metabolomic data, offering a first step towards individualized lung cancer treatment prognosis.

Serum Metabolomics Profiling Reveals Metabolic Alterations Prior to a Diagnosis with Non-Small Cell Lung Cancer among Chinese Community Residents: A Prospective Nested Case-Control Study

The present high mortality of lung cancer in China stems mainly from the lack of feasible, non-invasive and early disease detection biomarkers. Serum metabolomics profiling to reveal metabolic alterations could expedite the disease detection process and suggest those patients who are harboring disease. Using a nested case-control design, we applied ultra-high-performance liquid chromatography/mass spectrometry (LC-MS)-based serum metabolomics to reveal the metabolomic alterations and to indicate the presence of non-small cell lung cancer (NSCLC) using serum samples collected prior to disease diagnoses. The studied serum samples were collected from 41 patients before a NSCLC diagnosis (within 3.0 y) and 38 matched the cancer-free controls from the prospective Shanghai Suburban Adult Cohort. The NSCLC patients markedly presented cellular metabolism alterations in serum samples collected prior to their disease diagnoses compared with the cancer-free controls. In total, we identified 18 significantly expressed metabolites whose relative abundance showed either an upward or a downward trend, with most of them being lipid and lipid-like molecules, organic acids, and nitrogen compounds. Choline metabolism in cancer, sphingolipid, and glycerophospholipid metabolism emerged as the significant metabolic disturbance of NSCLC. The metabolites involved in these biological processes may be the distinctive features associated with NSCLC prior to a diagnosis.

Electronic cigarette liquids impair metabolic cooperation and alter proteomic profiles in V79 cells

Background

Although still considered a safer alternative to classical cigarettes, growing body of work points to harmful effects of electronic cigarettes (e-cigarettes) affecting a range of cellular processes. The biological effect of e-cigarettes needs to be investigated in more detail considering their widespread use.

Methods

In this study, we treated V79 lung fibroblasts with sub-cytotoxic concentration of e-cigarette liquids, with and without nicotine. Mutagenicity was evaluated by HPRT assay, genotoxicity by comet assay and the effect on cellular communication by metabolic cooperation assay. Additionally, comprehensive proteome analysis was performed via high resolution, parallel accumulation serial fragmentation-PASEF mass spectrometry.

Results

E-cigarette liquid concentration used in this study showed no mutagenic or genotoxic effect, however it negatively impacted metabolic cooperation between V79 cells. Both e-cigarette liquids induced significant depletion in total number of proteins and impairment of mitochondrial function in treated cells. The focal adhesion proteins were upregulated, which is in accordance with the results of metabolic cooperation assay. Increased presence of posttranslational modifications (PTMs), including carbonylation and direct oxidative modifications, was observed. Data are available via ProteomeXchange with identifier PXD032071.

Conclusions

Our study revealed impairment of metabolic cooperation as well as significant proteome and PTMs alterations in V79 cells treated with e-cigarette liquid warranting future studies on e-cigarettes health impact.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02102-w.

Integrative metabolomics and transcriptomics analysis reveals novel therapeutic vulnerabilities in lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) comprises the majority (~85%) of all lung tumors, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being the most frequently diagnosed histological subtypes. Multi-modal omics profiling has been carried out in NSCLC, but no studies have yet reported a unique metabolite-related gene signature and altered metabolic pathways associated with LUAD and LUSC.

METHODS

We integrated transcriptomics and metabolomics to analyze 30 human lung tumors and adjacent noncancerous tissues. Differential co-expression was used to identify modules of metabolites that were altered between normal and tumor.

RESULTS

We identified unique metabolite-related gene signatures specific for LUAD and LUSC and key pathways aberrantly regulated at both transcriptional and metabolic levels. Differential co-expression analysis revealed that loss of coherence between metabolites in tumors is a major characteristic in both LUAD and LUSC. We identified one metabolic onco-module gained in LUAD, characterized by nine metabolites and 57 metabolic genes. Multi-omics integrative analysis revealed a 28 metabolic gene signature associated with poor survival in LUAD, with six metabolite-related genes as individual prognostic markers.

CONCLUSIONS

We demonstrated the clinical utility of this integrated metabolic gene signature in LUAD by using it to guide repurposing of AZD-6482, a PI3Kβ inhibitor which significantly inhibited three genes from the 28-gene signature. Overall, we have integrated metabolomics and transcriptomics analyses to show that LUAD and LUSC have distinct profiles, inferred gene signatures with prognostic value for patient survival, and identified therapeutic targets and repurposed drugs for potential use in NSCLC treatment.

Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival

INTRODUCTION

Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive.

OBJECTIVES

This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS.

METHODS

Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events.

RESULTS

Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates).

CONCLUSION

Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.

Combined metabolomics with transcriptomics reveals potential plasma biomarkers correlated with non-small-cell lung cancer proliferation through the At pathway

BACKGROUND

Non-small-cell lung cancer (NSCLC) is one of the main types of lung cancer. Due to lack of effective biomarkers for early detection of NSCLC, the therapeutic effect is not ideal. This study aims to reveal potential biomarkers for clinical diagnosis.

METHODS

The plasma metabolic profiles of the patients were characterized by liquid chromatography-mass spectrometry (LC-MS). Differential metabolites were screened by p < 0.05 and VIP > 1. Multivariate statistical analysis was used to search for potential biomarkers. Receiver operating characteristic (ROC) curve was used to evaluate the predictors of potential biomarkers. Pathway enrichment analysis was performed on metabolomics data by Ingenuity Pathway Analysis (IPA) and transcriptomics data from GEO were used for validation.

RESULTS

A plasma metabolite biomarker panel including 13(S)-hydroxyoctadecadienoic acid (13(S)-HODE) and arachidonic acid was chose. The area under the ROC curve were 0.917, 0.900 and 0.867 for the panel in the different algorithm like Partial Least Squares Discrimination Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF). The candidate biomarkers were associated with the Akt pathway. Genes involved in the biological pathway had significant changes in the expression levels.

CONCLUSION

13(S)-HODE and arachidonic acid may be potential biomarkers of NSCLC. The Akt pathway was associated with this biomarker panel in NSCLC. Further studies are needed to clarify the mechanisms of disruption in this pathway.

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

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.

Identifying metabolic alterations in newly diagnosed small cell lung cancer patients

BACKGROUND

Small cell lung cancer (SCLC) is a malignant disease with poor prognosis. At the time of diagnosis most patients are already in a metastatic stage. Current diagnosis is based on imaging, histopathology, and immunohistochemistry, but no blood-based biomarkers have yet proven to be clinically successful for diagnosis and screening. The precise mechanisms of SCLC are not fully understood, however, several genetic mutations, protein and metabolic aberrations have been described. We aim at identifying metabolite alterations related to SCLC and to expand our knowledge relating to this aggressive cancer.

METHODS

A total of 30 serum samples of patients with SCLC, collected at the time of diagnosis, and 25 samples of healthy controls were included in this study. The samples were analyzed with nuclear magnetic resonance spectroscopy. Multivariate, univariate and pathways analyses were performed.

RESULTS

Several metabolites were identified to be altered in the pre-treatment serum samples of small-cell lung cancer patients compared to healthy individuals. Metabolites involved in tricarboxylic acid cycle (succinate: fold change (FC) = 2.4, p = 0.068), lipid metabolism (LDL triglyceride: FC = 1.3, p = 0.001; LDL-1 triglyceride: FC = 1.3, p = 0.012; LDL-2 triglyceride: FC = 1.4, p = 0.009; LDL-6 triglyceride: FC = 1.5, p < 0.001; LDL-4 cholesterol: FC = 0.5, p = 0.007; HDL-3 free cholesterol: FC = 0.7, p = 0.002; HDL-4 cholesterol FC = 0.8, p < 0.001; HDL-4 apolipoprotein-A1: FC = 0.8, p = 0.005; HDL-4 apolipoprotein-A2: FC ≥ 0.7, p ≤ 0.001), amino acids (glutamic acid: FC = 1.7, p < 0.001; glutamine: FC = 0.9, p = 0.007, leucine: FC = 0.8, p < 0.001; isoleucine: FC = 0.8, p = 0.016; valine: FC = 0.9, p = 0.032; lysine: FC = 0.8, p = 0.004; methionine: FC = 0.8, p < 0.001; tyrosine: FC = 0.7, p = 0.002; creatine: FC = 0.9, p = 0.030), and ketone body metabolism (3-hydroxybutyric acid FC = 2.5, p < 0.001; acetone FC = 1.6, p < 0.001), among other, were found deranged in SCLC.

CONCLUSIONS

This study provides novel insight into the metabolic disturbances in pre-treatment SCLC patients, expanding our molecular understanding of this malignant disease.

Detection and verification of coexisting diagnostic markers in plasma and serum of patients with non-small-cell lung cancer

Aim: To screen and identify the potential biomarkers co-existing in plasma and serum of patients with non-small-cell lung cancer (NSCLC), and establish appropriate diagnostic models. Methods: A cohort of 195 plasma samples and 180 serum samples were obtained from healthy controls (HCs), adenocarcinoma (AdC) and squamous cell carcinoma (SqCC) patients enrolled from the First Affiliated Hospital of Nanjing Medical University. Metabolites in plasma and serum were analyzed by GC-MS. Results: Hypoxanthine was found to have good performance in the differential diagnosis of NSCLC (including AdC and SqCC) and HC (area under the receiver operating characteristic [AUROC] ≥0.85). Combinations of metabolites could be used for differential diagnosis of NSCLC and HC (AUROC >0.93), AdC and HC (AUROC >0.91), SqCC and HC (AUROC >0.95), AdC and SqCC (AUROC >0.72). Conclusions: Metabolomics based on GC-MS can screen and identify the differential metabolites coexisting in plasma and serum of patients with NSCLC, and prediction models established by this method can be used for the differential diagnosis of patients with NSCLC.

Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor

Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (¹H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel ¹H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the ¹H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).

High-Throughput Salivary Metabolite Profiling on an Ultralow Noise Tip-Enhanced Laser Desorption Ionization Mass Spectrometry Platform for Noninvasive Diagnosis of Early Lung Cancer

Lung cancer (LC) is a widespread cancer that is the cause of the highest mortality rate accounting for 25% of all cancer deaths. To date, most LC patients are diagnosed at the advanced stage owing to the lack of obvious symptoms in the early stage and the limitations of current clinical diagnostic techniques. Therefore, developing a high throughput technique for early screening is of great importance. In this work, we established an effective and rapid salivary metabolic analysis platform for early LC diagnosis and combined metabolomics and transcriptomics to reveal the metabolic fluctuations correlated to LC. Saliva samples were collected from a total of 150 volunteers including 89 patients with early LC, 11 patients with advanced LC, and 50 healthy controls. The metabolic profiling of noninvasive samples was investigated on an ultralow noise TELDI-MS platform. In addition, data normalization methods were screened and assessed to overcome the MS signal variation caused by individual difference for biomarker mining. For untargeted metabolic profiling of saliva samples, around 264 peaks could be reliably detected in each sample. After multivariate analysis, 23 metabolites were sorted out and verified to be related to the dysfunction of the amino acid and nucleotide metabolism in early LC. Notably, transcriptomic data from online TCGA repository were utilized to support findings from the salivary metabolomics experiment, including the disorder of amino acid biosynthesis and amino acid metabolism. Based on the verified differential metabolites, early LC patients could be clearly distinguished from healthy controls with a sensitivity of 97.2% and a specificity of 92%. The ultralow noise TELDI-MS platform displayed satisfactory ability to explore salivary metabolite information and discover potential biomarkers that may help develop a noninvasive screening tool for early LC.

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

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.

Metabolic Changes in Early-Stage Non-Small Cell Lung Cancer Patients after Surgical Resection

Simple Summary

Considerable progress in the treatment of non–small cell lung cancer (NSCLC) has been made possible by large-scale technologies that scan the gene expression in tumor cells. While gene expression is informative, it is the changes to cellular metabolism that directly affect the initiation and the progression of the disease. Altered metabolic processes in cancer include how the tumor cells handle fat, proteins, and sugar, produce energy, divide (grow), or migrate. We have used nuclear magnetic resonance and mass spectrometry to survey and document the metabolic changes in blood and urine samples collected from NSCLC patients before and after their lung tumors were surgically removed. We found several molecular compounds that changed in abundance in the blood or urine after surgery, many of which are related to cancer cell metabolism. Further documentation of these changes in large patient populations will lead to non-invasive ways to screen, diagnose, or monitor disease progression in lung cancer patients.

Abstract

Metabolic alterations in malignant cells play a vital role in tumor initiation, proliferation, and metastasis. Biofluids from patients with non–small cell lung cancer (NSCLC) harbor metabolic biomarkers with potential clinical applications. In this study, we assessed the changes in the metabolic profile of patients with early-stage NSCLC using mass spectrometry and nuclear magnetic resonance spectroscopy before and after surgical resection. A single cohort of 35 patients provided a total of 29 and 32 pairs of urine and serum samples, respectively, pre-and post-surgery. We identified a profile of 48 metabolites that were significantly different pre- and post-surgery: 17 in urine and 31 in serum. A higher proportion of metabolites were upregulated than downregulated post-surgery (p < 0.01); however, the median fold change (FC) was higher for downregulated than upregulated metabolites (p < 0.05). Purines/pyrimidines and proteins had a larger dysregulation than other classes of metabolites (p < 0.05 for each class). Several of the dysregulated metabolites have been previously associated with cancer, including leucyl proline, asymmetric dimethylarginine, isopentenyladenine, fumaric acid (all downregulated post-surgery), as well as N6-methyladenosine and several deoxycholic acid moieties, which were upregulated post-surgery. This study establishes metabolomic analysis of biofluids as a path to non-invasive diagnostics, screening, and monitoring in NSCLC.

Putative markers for the detection of early-stage bladder cancer selected by urine metabolomics

BACKGROUND

Early detection of bladder cancer remains challenging because patients with early-stage bladder cancer usually have no incentive to take cytology or cystoscopy tests if they are asymptomatic. Our goal is to find non-invasive marker candidates that may help us gain insight into the metabolism of early-stage bladder cancer and be examined in routine health checks.

RESULTS

We acquired urine samples from 124 patients diagnosed with early-stage bladder cancer or hernia (63 cancer patients and 61 controls). In which 100 samples were included in our marker discovery cohort, and the remaining 24 samples were included in our independent test cohort. We obtained metabolic profiles of 922 compounds of the samples by gas chromatography-mass spectrometry. Based on the metabolic profiles of the marker discovery cohort, we selected marker candidates using Wilcoxon rank-sum test with Bonferroni correction and leave-one-out cross-validation; we further excluded compounds detected in less than 60% of the bladder cancer samples. We finally selected eight putative markers. The abundance of all the eight markers in bladder cancer samples was high but extremely low in hernia samples. Moreover, the up-regulation of these markers might be in association with sugars and polyols metabolism.

CONCLUSIONS

In the present study, comparative urine metabolomics selected putative metabolite markers for the detection of early-stage bladder cancer. The suggested relations between early-stage bladder cancer and sugars and polyols metabolism may create opportunities for improving the detection of bladder cancer.

High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis

Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.

Serum Metabolite Profiles in Participants of Lung Cancer Screening Study; Comparison of Two Independent Cohorts

Serum metabolome is a promising source of molecular biomarkers that could support early detection of lung cancer in screening programs based on low-dose computed tomography. Several panels of metabolites that differentiate lung cancer patients and healthy individuals were reported, yet none of them were validated in the population at high-risk of developing cancer. Here we analyzed serum metabolome profiles in participants of two lung cancer screening studies: MOLTEST-BIS (Poland, n = 369) and SMAC-1 (Italy, n = 93). Three groups of screening participants were included: lung cancer patients, individuals with benign pulmonary nodules, and those without any lung alterations. Concentrations of about 400 metabolites (lipids, amino acids, and biogenic amines) were measured by a mass spectrometry-based approach. We observed a reduced level of lipids, in particular cholesteryl esters, in sera of cancer patients from both studies. Despite several specific compounds showing significant differences between cancer patients and healthy controls within each study, only a few cancer-related features were common when both cohorts were compared, which included a reduced concentration of lysophosphatidylcholine LPC (18:0). Moreover, serum metabolome profiles in both noncancer groups were similar, and differences between cancer patients and both groups of healthy participants were comparable. Large heterogeneity in levels of specific metabolites was observed, both within and between cohorts, which markedly impaired the accuracy of classification models: The overall AUC values of three-state classifiers were 0.60 and 0.51 for the test (MOLTEST) and validation (SMAC) cohorts, respectively. Therefore, a hypothetical metabolite-based biomarker for early detection of lung cancer would require adjustment to lifestyle-related confounding factors that putatively affect the composition of serum metabolome.

Comparative metabolomics studies of blood collected in streck and heparin tubes from lung cancer patients

Metabolomics analysis of blood from patients (n = 42) undergoing surgery for suspected lung cancer was performed in this study. Venous and arterial blood was collected in both Streck and Heparin tubes. A total of 96 metabolites were detected, affected by sex (n = 56), collection tube (n = 33), and blood location (n = 8). These metabolites belonged to a wide array of compound classes including lipids, acids, pharmaceutical agents, signalling molecules, vitamins, among others. Phospholipids and carboxylic acids accounted for 28% of all detected compounds. Out of the 33 compounds significantly affected by collection tube, 18 compounds were higher in the Streck tubes, including allantoin and ketoleucine, and 15 were higher in the Heparin tubes, including LysoPC(P-16:0), PS 40:6, and chenodeoxycholic acid glycine conjugate. Based on our results, it is recommended that replicate blood samples from each patient should be collected in different types of blood collection tubes for a broader range of the metabolome. Several metabolites were found at higher concentrations in cancer patients such as lactic acid in Squamous Cell Carcinoma, and lysoPCs in Adenocarcinoma and Acinar Cell Carcinoma, which may be used to detect early onset and/or to monitor the progress of the cancer patients.

High performance exhaled breath biomarkers for diagnosis of lung cancer and potential biomarkers for classification of lung cancer

Exhaled breath analysis has emerged as a promising non-invasive method for diagnosing lung cancer (LC), whereas reliable biomarkers are lacking. Herein, a standardized and systematic study was presented for LC diagnosis, classification and metabolism exploration. To improve the reliability of biomarkers, a validation group was included, and quality control for breath sampling and analysis, comprehensive pollutants analysis, and strict biomarker screening were performed. The performance of exhaled breath biomarkers was shown to be excellent in diagnosing LC even in early stages (stage I and II) with surpassing 0.930 area under the receiver operating characteristic (ROC) curve (AUC), 90% of sensitivity and 88% of specificity both in the discovery and validation analyses. Meanwhile, in these two groups, diagnosing subtypes of LC attained AUCs over 0.930 and reached 1.00 in the two subtypes of adenocarcinomas. It is demonstrated that the metabolism changes in LC are possibly related to lipid oxidation, gut microbial, cytochrome P450 and glutathione S-transferase, and glutathione pathways change in LC progression. Overall, the reliable biomarkers contribute to the clinical application of breath analysis in screening LC patients as well as those in early stages.

A case of personalized and precision medicine: Pharmacometabolomic applications to rare cancer, microbiological investigation, and therapy

RATIONALE

Advances in metabolomics, together with consolidated genetic approaches, have opened the way for investigating the health of patients using a large number of molecules simultaneously, thus providing firm scientific evidence for personalized medicine and consequent interventions. Metabolomics is an ideal approach for investigating specific biochemical alterations occurring in rare clinical situations, such as those caused by rare associations between comorbidities and immunosuppression.

METHODS

Metabolomic database matching enables clear identification of molecular factors associated with a metabolic disorder and can provide a rationale for elaborating personalized therapeutic protocols. Mass spectrometry (MS) forms the basis of metabolomics and uses mass-to-charge ratios for metabolite identification. Here, we used an MS-based approach to diagnose and develop treatment options in the clinical case of a patient afflicted with a rare disease further complicated by immunosuppression. The patient's data were analyzed using proprietary databases, and a personalized and efficient therapeutic protocol was consequently elaborated.

RESULTS

The patient exhibited significant alterations in homocysteine:methionine and homocysteine:thiodiglycol acid plasma concentration ratios, and these were associated with low immune system function. This led to cysteine concentration deficiency causing extreme oxidative stress. Plasmatic thioglycolic acid concentrations were initially altered and were used for therapeutic follow-up and to evaluate cysteine levels.

CONCLUSIONS

An MS-based pharmacometabolomics approach was used to define a personalized protocol in a clinical case of rare peritoneal carcinosis with confounding immunosuppression. This personalized protocol reduced both oxidative stress and resistance to antibiotics and antiviral drugs.

Comprehensive Dipeptide Analysis Revealed Cancer-Specific Profile in the Liver of Patients with Hepatocellular Carcinoma and Hepatitis

As the physical properties and functionality of dipeptides differ from those of amino acids, they have attracted attention in metabolomics; however, their functions in vivo have not been clarified in detail. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and its major cause is chronic hepatitis. This study was conducted to explore tumor-specific dipeptide characteristics by performing comprehensive dipeptide analysis in the tumor and surrounding nontumor tissue of patients with HCC. Dipeptides were analyzed by liquid chromatography tandem mass spectrometry and capillary electrophoresis tandem mass spectrometry. Principal component analysis using 236 detected dipeptides showed differences in the dipeptide profiles between nontumor and tumor tissues; however, no clear difference was observed in etiological comparison. In addition, the N- and C-terminal amino acid compositions of the detected dipeptides significantly differed, suggesting the substrate specificity of enzyme proteins, such as peptidase. Furthermore, hepatitis-derived HCC may show a characteristic dipeptide profile even before tumor formation. These results provide insight into HCC pathogenesis and may help identify novel biomarkers for diagnosis.

Liquid Chromatography-Mass Spectrometry-Based Tissue Metabolic Profiling Reveals Major Metabolic Pathway Alterations and Potential Biomarkers of Lung Cancer

Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography-mass spectrometry-based nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. A total of 339 metabolites were identified in metabolic profiling. Also, 241 differential metabolites were found between lung carcinoma tissues (LCTs) and paired distal noncancerous tissues; amino acids, purine metabolites, fatty acids, phospholipids, and most of lysophospholipids significantly increased in LCTs, while 3-phosphoglyceric acid, phosphoenolpyruvate, 6-phosphogluconate, and citrate decreased. Additionally, pathway enrichment analysis revealed that energy, purine, amino acid, lipid, and glutathione metabolism are markedly disturbed in lung cancer (LCa). Using binary logistic regression, we further defined candidate biomarkers for different subtypes of lung tumor. Xanthine and PC 35:2 were selected as combinational biomarkers for distinguishing benign from malignant lung tumors with a 0.886 area under curve (AUC) value, and creatine, myoinositol and LPE 16:0 were defined as combinational biomarkers for discriminating adenocarcinoma from squamous cell lung carcinoma with a 0.934 AUC value. Overall, metabolic characterization and pathway disturbance demonstrated apparent metabolic reprogramming in LCa. The defined candidate metabolite marker panels are useful for subtyping of lung tumors to assist clinical diagnosis.

Treatment With Hydrolyzed Diet Supplemented With Prebiotics and Glycosaminoglycans Alters Lipid Metabolism in Canine Inflammatory Bowel Disease

Canine inflammatory bowel disease (IBD) is a chronic, immunologically mediated intestinal disorder, resulting from the complex interaction of genetic, environmental and immune factors. Hydrolyzed diets are used in dogs with food-responsive diarrhea (FRD) to reduce adverse responses to immunostimulatory proteins. Prebiotics (PRBs) and glycosaminoglycans (GAGs) have previously been demonstrated to show anti-inflammatory activity in the intestinal mucosa. Notably, hydrolyzed diets combined with the administration of PRBs and GAGs offer a promising approach for the treatment of canine IBD. Our aim was to investigate the effects of hydrolyzed diet and GAG+PRB co-treatment on the serum metabolomic profile of IBD dogs. Dogs with IBD randomly received either hydrolyzed diet supplemented with GAGs and PRBs (treatment 1) or hydrolyzed diet alone (treatment 2) for 10 weeks. A targeted metabolomics approach using mass spectrometry was performed to quantify changes in the serum metabolome before and after treatment and between treatment 1 and 2. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), hierarchical cluster analysis (HCA) and univariate statistics were used to identify differences between the treatment groups. PCA, PLS-DA, and HCA showed a clear clustering of IBD dogs before and after hydrolyzed diet, indicating that the treatment impacted the serum metabolome. Univariate analysis revealed that most of the altered metabolites were involved in lipid metabolism. The most impacted lipid classes were components of cell membranes, including glycerophospholipids, sphingolipids, and di- and triglycerides. In addition, changes in serum metabolites after GAG+PRB co-treatment suggested a possible additional beneficial effect on the lipid metabolism in IBD dogs. In conclusion, the present study showed a significant increase in metabolites that protect gut cell membrane integrity in response to hydrolyzed diet alone or in combination with GAG+PRB co-treatment. Administration of such treatment over 70 days improved selected serum biomarkers of canine IBD, possibly indicating improved intestinal membrane integrity.

Metabolites as Prognostic Markers for Metastatic Non-Small Cell Lung Cancer (NSCLC) Patients Treated with First-Line Platinum-Doublet Chemotherapy

The metabolic requirements of metastatic non-small cell lung (mNSCLC) tumors from patients receiving first-line platinum-doublet chemotherapy are hypothesized to imprint a blood signature suitable for survival prediction. Pre-treatment samples prospectively collected at baseline from a randomized phase III trial were assayed using nuclear magnetic resonance (NMR) spectroscopy (n = 341) and ultra-high performance liquid chromatography – mass spectrometry (UPLC-MS) (n = 297). Distributions of time to event outcomes were estimated by Kaplan-Meier analysis, and baseline characteristics adjusted Cox regression modeling was used to correlate markers’ levels to time to event outcomes. Sixteen polar metabolites were significantly correlated with overall survival (OS) by univariate analysis (p < 0.025). Formate, 2-hydroxybutyrate, glycine and myo-inositol were selected for a multivariate model. The median OS was 6.6 months in the high-risk group compared to 11.4 months in the low risk group HR (Hazard Ratio) = 1.99, 95% C.I. (Confidence Interval) 1.45–2.68; p < 0.0001). Modeling of lipids by class (sphingolipids, acylcarnitines and lysophosphatidylcholines) revealed a median OS = 5.7 months vs. 11. 9 months for the high vs. low risk group. (HR: 2.23, 95% C.I. 1.55–3.20; p < 0.0001). These results demonstrate that metabolic profiles from pre-treatment samples may be useful to stratify clinical outcomes for mNSCLC patients receiving chemotherapy. Genomic and longitudinal measurements pre- and post-treatment may yield addition information to personalize treatment decisions further.

Advances in lung cancer biomarkers: The role of (metal-) metabolites and selenoproteins

Lung cancer (LC) is the second most common cause of death in men after prostate cancer, and the third most recurrent type of tumor in women after breast and colon cancers. Unfortunately, when LC symptoms begin to appear, the disease is already in an advanced stage and the survival rate only reaches 2%. Thus, there is an urgent need for early diagnosis of LC using specific biomarkers, as well as effective therapies and strategies against LC. On the other hand, the influence of metals on more than 50% of proteins is responsible for their catalytic properties or structure, and their presence in molecules is determined in many cases by the genome. Research has shown that redox metal dysregulation could be the basis for the onset and progression of LC disease. Moreover, metals can interact between them through antagonistic, synergistic and competitive mechanisms, and for this reason metals ratios and correlations in LC should be explored. One of the most studied antagonists against the toxic action of metals is selenium, which plays key roles in medicine, especially related to selenoproteins. The study of potential biomarkers able to diagnose the disease in early stage is conditioned by the development of new analytical methodologies. In this sense, omic methodologies like metallomics, proteomics and metabolomics can greatly assist in the discovery of biomarkers for LC early diagnosis.

Large-scale targeted metabolomics method for metabolite profiling of human samples

Targeted metabolomics has significant advantages for quantification but suffers from reduced metabolite coverage. In this study, we developed a large-scale targeted metabolomics method and expanded its applicability to various human samples. This approach initially involved unbiased identification of metabolites in human cells, tissues and body fluids using ultra high-performance liquid chromatography (UHPLC) coupled to high-resolution Orbitrap mass spectrometry (MS). Targeted metabolomics method was established with utility of UHPLC-triple quadrupole MS, which enables targeted profiling of over 400 biologically important metabolites (e.g., amino acids, sugars, nucleotides, dipeptides, coenzymes, and fatty acids), covering 92 metabolic pathways (e.g., Krebs cycle, glycolysis, amino acids metabolism, ammonia recycling, and one-carbon metabolism). The present method displayed better sensitivity, repeatability and linearity than the Orbitrap MS-based untargeted metabolomics approach and demonstrated excellent performance in lung cancer biomarker discovery, in which 107 differential metabolites were able to discriminate between carcinoma and adjacent normal tissues, implicating the Warburg effect, alteration of redox state, and nucleotide metabolism of lung cancer. This new method is flexible and expandable and offers many advantages for metabolomics analysis, such as wide metabolite coverage, good repeatability and linearity and excellent capability in biomarker discovery, making it useful for both basic and clinical metabolic research.

A comprehensive analysis of metabolomics and transcriptomics in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer death globally. More accurate and reliable diagnostic methods/biomarkers are urgently needed. Joint application of metabolomics and transcriptomics technologies possesses the high efficiency of identifying key metabolic pathways and functional genes in lung cancer patients. In this study, we performed an untargeted metabolomics analysis of 142 NSCLC patients and 159 healthy controls; 35 identified metabolites were significantly different between NSCLC patients and healthy controls, of which 6 metabolites (hypoxanthine, inosine, L-tryptophan, indoleacrylic acid, acyl-carnitine C10:1, and lysoPC(18:2)) were chosen as combinational potential biomarkers for NSCLC. The area under the curve (AUC) value, sensitivity (SE), and specificity (SP) of these six biomarkers were 0.99, 0.98, and 0.99, respectively. Potential diagnostic implications of the metabolic characteristics in NSCLC was studied. The metabolomics results were further verified by transcriptomics analysis of 1027 NSCLC patients and 108 adjacent peritumoral tissues from TCGA database. This analysis identified 2202 genes with significantly different expressions in cancer cells compared to normal controls, which in turn defined pathways implicated in the metabolism of the compounds revealed by metabolomics analysis. We built a fully connected network of metabolites and genes, which shows a good correspondence between the transcriptome analysis and the metabolites selected for diagnosis. In conclusion, this work provides evidence that the metabolic biomarkers identified may be used for NSCLC diagnosis and screening. Comprehensive analysis of metabolomics and transcriptomics data offered a validated and comprehensive understanding of metabolism in NSCLC.

Analysis of differential metabolites in lung cancer patients based on metabolomics and bioinformatics

Aim: Based on metabonomics, the metabolic markers of lung cancer patients were analyzed, combined with bioinformatics to explore the underlying disease mechanism. Materials & methods: Based on case-control design, using UPLC-Q-TOF/MS, urine metabolites were detected in discovery and validation set. Multivariate statistical analysis were performed to identify potential markers for lung cancer. A network analysis was constructed to integrate lung cancer disease targets with the above metabolic markers, and its possible mechanism and biological significance were explained. Results: A total of 35 potential markers were identified, 11 of which overlapped. Five key markers have a good linear correlation with serum biochemical indicators. Conclusion: The occurrence and development of lung cancer are closely related to disturbance of D-Glutamine and D-glutamate metabolism, amino acid imbalance. This test was registered on China clinical trial registration center (www.chictr.org.cn/index.aspx), registration number was ChiCTR1900025543.

Exploring potential biomarkers for lung adenocarcinoma using LC-MS/MS metabolomics

Background

The average 5-year survival rate of lung adenocarcinoma patients is only 15% to 17%, which is primarily due to late-stage diagnosis and a lack of specific prognostic evaluations that can recommend effective therapies. Additionally, there is no clinically recognized biomarker that is effective for early-stage diagnosis.

Methods

Tissue samples from 10 lung adenocarcinoma patients (both tumor and non-tumor tissues) and 10 benign lung tumor samples were collected. The significantly differentially represented metabolites from the three groups were analyzed by liquid chromatography and tandem mass spectrometry.

Results

Pathway analysis indicated that central carbon metabolism was the top altered pathway in lung adenocarcinoma, while protein digestion and absorption, and central carbon metabolism were the top altered pathways in benign lung tumors. Receiver operating characteristic curve analysis revealed that adenosine 3′-monophosphate, creatine, glycerol, and 14 other differential metabolites were potential sensitive and specific biomarkers for the diagnosis and prognosis of lung adenocarcinoma.

Conclusion

Our findings suggest that the metabolomics approach may be a useful method to detect potential biomarkers in lung adenocarcinoma patients.

A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection

The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC). The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified β-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required.

GLUT5-mediated fructose utilization drives lung cancer growth by stimulating fatty acid synthesis and AMPK/mTORC1 signaling

Lung cancer (LC) is a leading cause of cancer-related deaths worldwide. Its rapid growth requires hyperactive catabolism of principal metabolic fuels. It is unclear whether fructose, an abundant sugar in current diets, is essential for LC. We demonstrated that, under the condition of coexistence of metabolic fuels in the body, fructose was readily used by LC cells in vivo as a glucose alternative via upregulating GLUT5, a major fructose transporter encoded by solute carrier family 2 member 5 (SLC2A5). Metabolomic profiling coupled with isotope tracing demonstrated that incorporated fructose was catabolized to fuel fatty acid synthesis and palmitoleic acid generation in particular to expedite LC growth in vivo. Both in vitro and in vivo supplement of palmitoleic acid could restore impaired LC propagation caused by SLC2A5 deletion. Furthermore, molecular mechanism investigation revealed that GLUT5-mediated fructose utilization was required to suppress AMPK and consequently activate mTORC1 activity to promote LC growth. As such, pharmacological blockade of in vivo fructose utilization using a GLUT5 inhibitor remarkably curtailed LC growth. Together, this study underscores the importance of in vivo fructose utilization mediated by GLUT5 in governing LC growth and highlights a promising strategy to treat LC by targeting GLUT5 to eliminate those fructose-addicted neoplastic cells.

Absolute quantification of selenoproteins and selenometabolites in lung cancer human serum by column switching coupled to triple quadrupole inductively coupled plasma mass spectrometry

One of the most important causes of the high mortality rate and low life expectancy of lung cancer is the detection at advanced stages. Thus, there is an urgent need for early diagnosis and the search of new selective biomarkers. Selenium is an important constituent of selenoproteins and a powerful antioxidant able to protect against cancer. In this work, the absolute quantification of selenium in selenoproteins and the total content in selenometabolites has been performed for the first time in serum from lung cancer patients (LC) and healthy controls (HC). To this end, a method for the simultaneous speciation of selenoproteins using size exclusion chromatography (SEC) and affinity chromatography (AF) with detection by ICP-QQQ-MS, and quantification by isotopic dilution (IDA) (SEC-AF-HPLC-SUID-ICP-QQQ-MS) was developed to determine the selenium concentration in eGPx, SEPP1 and SeAlb, as well as total selenometabolites, to find alterations that may serve as biomarkers of this disease. In the same way, a method based on anion-exchange chromatography coupled to ICP-QQQ-MS was developed to quantify selenometabolites (SeCys2, SeMeSeCys, SeMet, selenite and selenate) in the same LC and HC serum samples. The results showed that the averaged concentrations of selenium in eGPx, SeAlb and selenite were significantly higher in LC patients (LC (eGPx: 21.24 ± 0.77 ng g^-1; SeAlb: 49.56 ± 3.16 ng g^-1 and Se(IV): 6.20 ± 1.22 ng g^-1) than in HC group (eGPx: 16.96 ± 0.53 ng g^-1; SeAlb: 38.33 ± 2.66 ng g^-1 and Se(IV): 3.56 ± 0.55 ng g^-1). In addition, the ratios between selenoproteins and selenometabolites have been calculated for the first to study their potential use as LC biomarkers. The rates eGPx/SEPP1, SEPP1/SeAlb, eGPx/Se(IV) and SEPP1/Se(IV) were significantly different between LC and HC groups.

Metabolic profiles of serum samples from ground glass opacity represent potential diagnostic biomarkers for lung cancer

Background

Lung cancer is a leading cause of cancer deaths worldwide. Low-dose computed tomography (LDCT) screening trials indicated that LDCT is effective for the early detection of lung cancer, but the findings were accompanied by high false positive rates. Therefore, the detection of lung cancer needs complementary blood biomarker tests to reduce false positive rates.

Methods

In order to evaluate the potential of metabolite biomarkers for diagnosing lung cancer and increasing the effectiveness of clinical interventions, serum samples from subjects participating in a low-dose CT-scan screening were analyzed by using untargeted liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). Samples were acquired from 34 lung patients with ground glass opacity diagnosed lung cancer and 39 healthy controls.

Results

In total, we identified 9 metabolites in electron spray ionization (ESI)(+) mode and 7 metabolites in ESI(-) mode. L-(+)-gulose, phosphatidylethanolamine (PE)(22:2(13Z,16Z)/15:0), cysteinyl-glutamine, S-japonin, threoninyl-glutamine, chlorate, 3-oxoadipic acid, dukunolide A, and malonic semialdehyde levels were observed to be elevated in serum samples of lung cancer cases when compared to those of healthy controls. By contrast, 1-(2-furanylmethyl)-1H-pyrrole, 2,4-dihydroxybenzoic acid, monoethyl carbonate, guanidinosuccinic acid, pseudouridine, DIMBOA-Glc, and 4-feruloyl-1,5-quinolactone levels were lower in serum samples of lung cancer cases compared with those of healthy controls.

Conclusions

This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant ground glass opacity from benign ground glass opacity. Further studies in larger pools of samples are warranted.