Open Access

Metabolic biomarkers in lung cancer screening and early diagnosis (Review)

  • Authors:
    • Yongjie Xu
    • Xuesi Dong
    • Chao Qin
    • Fei Wang
    • Wei Cao
    • Jiang Li
    • Yiwen Yu
    • Liang Zhao
    • Fengwei Tan
    • Wanqing Chen
    • Ni Li
    • Jie He
  • View Affiliations

  • Published online on: May 3, 2023     https://doi.org/10.3892/ol.2023.13851
  • Article Number: 265
  • Copyright: © Xu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Late diagnosis is one of the major contributing factors to the high mortality rate of lung cancer, which is now the leading cause of cancer‑associated mortality worldwide. At present, low‑dose CT (LDCT) screening in the high‑risk population, in which lung cancer incidence is higher than that of the low‑risk population is the predominant diagnostic strategy. Although this has efficiently reduced lung cancer mortality in large randomized trials, LDCT screening has high false‑positive rates, resulting in excessive subsequent follow‑up procedures and radiation exposure. Complementation of LDCT examination with biofluid‑based biomarkers has been documented to increase efficacy, and this type of preliminary screening can potentially reduce potential radioactive damage to low‑risk populations and the burden of hospital resources. Several molecular signatures based on components of the biofluid metabolome that can possibly discriminate patients with lung cancer from healthy individuals have been proposed over the past two decades. In the present review, advancements in currently available technologies in metabolomics were reviewed, with particular focus on their possible application in lung cancer screening and early detection.

Introduction

Lung cancer is one of the most common malignancies and is the leading cause of cancer-associated mortality worldwide (1). The poor prognosis and high mortality rate of lung cancer are mainly due to late diagnosis (2). At present, only ~15% of newly diagnosed lung cancer cases are diagnosed in the early stages (stages I–II), which contributes to >60% probability of 5-year survival when effective treatment is available (35). However, >60% patients with lung cancer are first diagnosed already in the advanced stages (stage IV) or already with metastatic tumors, who typically only have 5-year survival rates of <5% (6).

In addition to the reduction in exposure to tobacco smoke, screening for the early detection of lung cancer has been considered to be a major strategy for decreasing the rate of lung cancer mortality (7). At present, low-dose CT (LDCT) screening in the high-risk population is the predominant tool used for detecting lung cancer in the early stages (2). The results of the US National Lung Screening Trial (ClinicalTrials.gov number, NCT00047385) found that compared with chest X-ray examination, LDCT screening was associated with a 20% reduction in lung cancer-specific mortality in a high-risk group of participants defined by their smoking status (8). In addition, other previous studies have also confirmed the validity of LDCT screening for the early detection of lung cancer to reduce mortality rate (9,10). However, potentially healthy individuals are also at risk of being subjected to expensive and potentially harmful diagnostic procedures, such as positron emission tomography, transthoracic/bronchoscopic biopsy or even surgery, due to the considerably high false-positive rate of LDCT (nearly 96.4%) (8). Therefore, the combination of LDCT with additional biomarker-based tests has been proposed to be a more favorable strategy for improving the effectiveness of lung cancer screening programs whilst reducing the cost and harmfulness to otherwise healthy individuals (11). Such tests can either pre-select individuals from a high-risk population for LDCT examination or discriminate between benign and malignant pulmonary nodules detected by LDCT screening (12).

Several different components of blood, including specific serum/plasma proteins, autoantibodies, microRNA, cell-free DNA and circulating tumor cells, have all been proposed to be potential lung cancer biomarkers (1315). However, they typically have low specificities and few were found for wider beneficial application in clinical practice, especially for the early detection of lung cancer. Instead, monitoring cancer-related metabolites is an emerging and promising approach for the detection and diagnosis of a number of malignant tumors, including colorectal, gastric, gynecological and lung cancer (1620).

Method

A search for articles published in English on PubMed (https://pubmed.ncbi.nlm.nih.gov/) was conducted regarding the use of metabolic biomarkers in lung cancer screening and early diagnosis that were published between Jan 1, 2002 and Aug 1, 2022. The search terms used were ‘lung cancer’, ‘metabolites’, and ‘early detection’. A total of 127 unique articles were identified and examined. Of these 127 articles, 25 were excluded due to being inappropriate article types, such as chapters in books, comments or review articles. Among the remaining 102 articles, 59 articles were removed due to being on unrelated topics, such as those not on lung cancer, not on cancer detection, not on metabolomics or not on biofluids (blood, urine and exhaled breath). Following evaluation of the full text of the remaining 43 articles, 12 were rejected, as these studies did not encompass early-stage lung cancer cases or it was unclear if early-stage lung cancer cases were included in the studies. Finally, the present review included 31 articles for analysis, as shown in the flow chart in Fig. 1.

Metabolomics: A new source of cancer biomarkers

Metabolomics, also known as metabolic profiling, uses quantitative and qualitative analyses to determine key metabolism-associated molecules of different molecular masses (21,22). It reveals information into specific states of cancer that are otherwise not apparent (22). Previously, the assessment of metabolic changes is limited to measuring the levels of individual hormones and metabolites using imaging modalities and standard clinical laboratory tests (23). By contrast, metabolomics involves the measurement of vast numbers of metabolites systematically, including carbohydrates, nucleotides, carboxylic acids, amino acids and lipids in blood, urine, or other body fluids (24). Metabolomics has emerged to be a potentially powerful approach for identifying cancer biomarkers and drivers of tumorigenesis (25). In addition, compared with other ‘omes’, such as genome, transcriptome, and the proteome, the metabolome reflects the real-time status of a particular phenotype to reveal what exactly has happened in the organisms exactly, providing bona fide biomarkers for disease surveillance (Fig. 2).

Metabolomics techniques and technologies

The main methodologies involved in metabolomics have been based on nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) techniques, coupled with either gas chromatography (GS) or liquid chromatography (LC), each with their specific advantages and limitations (Table I). NMR is highly selective and non-destructive, rendering it recognized to be the gold standard for elucidating the structure of metabolites. However, the sensitivity of NMR is relatively low, only being able to detect metabolites with concentrations >10−5 M (26). By contrast, MS has higher sensitivity and selectivity (26). Modern MS provides highly specific chemical information, such as accurate molecular mass, isotope distribution patterns for element formulation determination, and characteristic fragment-ion information directly related to the chemical structure of metabolites (27). In addition, the high sensitivity of MS allows for the detection and measurement of a large number of primary metabolites (the initial end products created by a live organism as a result of growth) and secondary metabolites (aid in the performance of various biological tasks that are not engaged in the growth and maintenance of cellular activity) at picomolar to femtomolar levels. As one of the major tools for the collection of ‘omic’ information, MS techniques use big data for processing and interpretation by machine learning (28,29). These unique advantages make MS an essential tool for metabolomics analysis (30). Over the past decade, various comprehensive reviews have already discussed how NMR and MS work, and how each can be used for metabolomics (3133). Despite their own advantages and disadvantages, several studies have shown how they can be used to complement each other (3135). Indeed, the use of multiple technologies greatly broadens the level of metabolite coverage and the types of samples that can be studied (36).

Table I.

Comparison between NMR and MS.

Table I.

Comparison between NMR and MS.

PropertiesNMRMS
SensitivityLowHigh, at picomolar and femtomolar levels
SelectivityGenerally used for non-selective analysisGenerally used for selective and non-selective (targeted and untargeted) analysis
Sample measurementAll metabolites with NMR concentration levels can be detected in one assayDifferent chromatographic techniques are required for different types of metabolites
Sample recoveryi) Non-destructive; andi) Destructive; and ii) samples can't be
ii) samples can be recovered and stored for a longrecovered
period; iii) samples can be analyzed multiple times
ReproducibilityHighModerate
Sample preparationSmall sample volumesi) High quality of sample preparation; and
ii) different columns and optimized ionization conditions are required
Number of metabolites detected40-200, depending on spectral resolution≥500

[i] NMR, nuclear magnetic resonance; MS, mass spectrometry.

Metabolomics for the early detection of lung cancer

Over the past two decades, a number of metabolomics studies have been performed based on NMR and/or MS techniques to generate metabolite profiles that can discriminate patients with lung cancer from healthy individuals using different types of biological samples. The present review summarizes the studies that include early-stage lung cancer cases to evaluate the viability of using metabolomics for the early detection of lung cancer.

Blood metabolomics

Blood samples, including plasma and serum, are the most widely studied biological fluids for lung cancer research. It can be used for characterizing metabolic markers, using both targeted (measuring a specific set or family of compounds only) and untargeted (global profiling) approaches (Table II) (3754).

Table II.

List of metabolic studies of blood and urine applied to lung cancer.

Table II.

List of metabolic studies of blood and urine applied to lung cancer.

A, Serum and plasma

First author, yearResearch subjectsAnalytical techniquesMajor findings(Refs.)
Rocha et al, 201185 LC (69 eLC) and 78 CTRNMRCompared with CTR samples, LC samples had significantly: i) Higher levels of LDL, VLDL, lactic acid and pyruvic acid; and ii) lower levels of HDL, glucose, citrate, formate, acetate, amino acids (such as alanine, glutamine and histidine) and methanol(37)
Deja et al, 201477 LC (35 eLC) and 22 COPDNMRCompared with COPD samples, LC samples had significantly: i) Higher levels of N-acetylated glycoproteins, leucine, lysine, mannose, choline and lipids; and ii) lower levels of acetate, citrate and methanol(38)
Klupczynska et al, 201690 LC (70 eLC) and 63 CTRLiquid chromatography-MSA new set of six amino acids (aspartic acid, β-alanine, histidine, asparagine, phenylalanine and serine) ensured higher accuracy to distinguish LC from CTR (from 90.3 to 77.1% depending on the histological type)(44)
Puchades-Carrasco et al, 2016142 LC (72 eLC) and 87 CTRNMRCompared with CTR samples, LC samples had significantly: i) Higher levels of leucine/isoleucine, acetate, N-acetyl-cysteine, glutamate, methanol, glycerol, creatine and lactic acid; and ii) lower levels of HDL, LDL, VLDL, adipic acid, lipids, glutamine, Choline–N(CH3)3, threonine and histidine(39)
Louis et al, 2016331 LC (93 eLC) and 315 CTRNMRCompared with CTR samples, LC samples had significantly: i) Higher levels of glucose, N-acetylated glycoproteins, b-hydroxybutyrate, leucine, lysine, tyrosine, threonine, glutamine, valine and aspartate; and ii) lower levels of alanine, lactate, sphingomyelin and phosphatidylcholine (and other cholinated phospholipids), citrate and other phospholipids(45)
Klupczynska et al, 201750 eLC and 25 CTR UHPLC-Q-Orbitrap-HRMSIn total, 36 metabolites were significantly altered between LC and CTR samples, including carnitine, acyl-carnitines, malic acid, pyroglutamic acid, histidine and histamine. A signature consisting of 12 of these identified metabolites allowed the building of a cancer classifier characterized by a receiver operating characteristic AUC value of 0.836(41)
Ros-Mazurczyk et al, 201731 LC (25 eLC) and 92 CTRGC-MSCompared with CTR samples, LC samples had significantly: i) Higher levels of benzaldehyde; and ii) lower levels of 17 metabolites, including amino acids, carboxylic acids and alcohols(40)
Chen et al, 201530 LC (22 eLC) and 30 CTRGC-MS and liquid chromatography-MSCompared with CTR samples, LC samples had significantly: i) Higher levels of phosphorylcholine, glycerophospho-N-arachidonoyl, ethanolamine, γ-linolenic acid, α-hydroxyisobutyric acid and 9,12-octadecadienoic acid; and ii) lower levels of prasterone sulphate, sphingosine, serine and 2,3,4-trihydroxybutyric acids(42)
Mazzone et al, 201694 LC (57 eLC) and 190 CTRGC-MS and liquid chromatography-MS79 metabolites significantly increased and 70 metabolites decreased in the LC group. In total, the ratios of 9,723 metabolites differed significantly between the LC and CTR groups(46)
Yu et al, 2017199 eLC and 147 CTRESI-MSLC samples had significantly increased levels of LPE(18:1) and ePE(40:4) but decreased levels of C(18:2)CE and SM(22:0) compared with those in CTR samples(47)
Ros-Mazurczyk et al, 2017100 eLC and 300 CTRMALDI-MS and liquid chromatography-MSPC, diacylophospholipids and SMs were frequently upregulated, whilst LPC 18:2, LPC 18:1 and LPC 18:0 were significantly downregulated in LC samples(43)
Xiang et al, 201899 LC (33 eLC) and 112 CTRLiquid chromatography-MSIn total, 28 endogenous metabolites were present at significantly different levels in patients with LC compared with those in CTR group. Cortisol, cortisone and 4-methoxyphenylacetic acid had high sensitivity and specificity values (AUC=0.955) as biomarkers for discriminating between LC and CTR(48)
Klupczynska et al, 201920 eLC and 20 CTRLiquid chromatography-MSCompared with CTR samples, LC samples had significantly: i) Higher levels of PC (PC 42:4, 42:1, 44:3 and 40:2) and LPC (LPC 26:0 and 26:1); and ii) lower levels of PC 34:4. A biomarker panel of the seven aforementioned metabolites was able to distinguish eLC from CTR with a sensitivity of 70–90% and a specificity of 90–93%(49)
Zhang et al, 2020156 LC (130 eLC) and 60 CTRLiquid chromatography-MSβ-hydroxybutyric acid, LPC 20:3, PC acyl ester C40:6, citric acid and fumaric acid were significantly different between CTR and eLC samples(50)
Huang et al, 2020200 eLC and 200 CTRLDI-MSCompared with CTR samples, LC samples had significantly: i) Higher levels of cystenie, histamine, fatty acid, uracil and uric acid; and ii) Lower levels of hydroxypicolinic acid and indoleacrylic acid. A biomarker panel of these seven metabolites was able to distinguish eLC from CTR with a sensitivity of 70–90% and a specificity of 90–93%(51)
Derveaux et al, 2021114 LC (48 eLC) and 118 CTRNMRIn total, 62 metabolites, including lactate, valine, alanine, maleic acid and phenylalanine, were significantly different between LC and CTR samples(52)
Qi et al, 202198 LC (52 eLC) and 75 CTRLiquid chromatography-MSIn total, five metabolites (palmitic acid, heptadecanoic acid, 4-oxoproline, tridecanoic acid and ornithine) had the potential for early lung cancer screening. The discrimination accuracy and AUC score reached as high as 0.829 and 0.869(53)
Wang et al, 2022171 eLC and 140 CTRLiquid chromatography-MSIn total, nine lipids (LPC 16:0, 18:0 and 20:4; PC 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2; and 16:0-22:6 and triglycerides 16:0-18:1) were identified as the features most important for early-stage cancer detection(54)

B, Urine

First author, yearResearch subjectsAnalytical techniquesMajor findings(Refs.)

Hanai et al, 201220 LC (11 eLC) and 20 CTRGC-MSTetrahydrofuran, 2-chloroethanol, 2-pentanone, 2-methylpyrazine, cyclohexanone, 2-ethyl-1-hexanol, 2-phenyl-2-propanol, isophorone and 2,6-diisopropylphenol were significantly upregulated in LC samples(62)
Mathe et al, 2014469 LC (211 eLC) and 536 CTR UPLC-ESI-QTOF-MSLevels of creatine riboside, nacetylneuraminic acid, cortisol sulfate and an unidentified glucuronidated compound referred to as ‘561+’ were significantly elevated in the LC group(63)
Haznadar et al, 2016178 LC (28 eLC) and 351 CTRLiquid chromatography-MSCreatine riboside was associated with lung cancer risk in the overall case-control set, whilst creatine riboside and nacetylneuraminic acid were associated with lung cancer risk in a European-American cohort(64)
Funai et al, 202046 LC (23 eLC) and 185 CTRLiquid chromatography-MS and NMRO-aminohippuric acid was significantly upregulated in LC samples compared with that in CTR samples(65)

[i] LC, lung cancer; eLC, early lung cancer; CTR, control; COPD, chronic obstructive pulmonary disease; NMR, nuclear magnetic resonance; MS, mass spectrometry; GC-MS, gas chromatography-MS; UHPLC-Q-Orbitrap-HRMS, ultra-high-performance liquid chromatography-quadrupole-Orbitrap-high-resolution MS; ESI, electrospray ionization; MALDI, Matrix-Assisted Laser Desorption Ionization; LDI, laser desorption/ionization; UPLC-ESI-QTOF, ultraperformance liquid chromatography-electrospray-ionization-quadrupole time-of-flight; AUC, area under the curve; LDL, low density lipoprotein; VLDL, very low-density lipoprotein; HDL, high-density lipoprotein; PC, phosphatidylcholines; LPC, lysophosphatidylcholine; SM, sphingomyelin.

Previous studies have described alterations in the levels of amino acids, especially in alanine, glutamine, histidine, leucine, isoleucine, lysine and serine, in the serum or plasma of patients with lung cancer (3742,44). These alterations may be associated with the increase in amino acid demand caused by the proliferation of the tumor cells, highlighting the important role of amino acid metabolic pathways in lung cancer progression.

Lactic acid is another commonly altered metabolite in patients with lung cancer (37,39). Previous studies on lactate metabolism in patients with cancer have suggested that changes in the level of this metabolite are due to the increased glucose uptake and lactate production by the tumor in the absence of oxygen (55,56). In support of this, in various tumor cells such as lung carcinoma, renal carcinoma and gastric carcinoma cells, glucose is preferentially catabolized through fermentation into lactate even when oxygen is not limiting, in a phenomenon known as the Warburg effect (55,57).

Phospholipids are important constituents of the cell membrane (58). Phospholipid metabolic pathways are regularly found to be dysregulated in lung cancer, yielding distinct signatures (58). Several studies have revealed perturbed levels of phospholipids in the blood of patients with lung cancer (42,43,46,47,54). Yu et al (47) previously performed a study based on electrospray ionization-MS and found that lung cancer samples had significantly increased levels of lysophosphatidylethanolamine(18:1) and phosphatidylethanolamine(40:4), and decreased levels of cholesterol ester(18:2) and sphingomyelin(22:0) compared with those in healthy controls. The cancer classifier built using these four phospholipids was characterized by Area under the ROC Curve (AUC) values to be 82.3 and 80.8% in the training and validation set, respectively (47). Another previous study based on LC-MS encompassed 100 patients with early lung cancer and a matched group of 300 healthy individuals regarding sex, age and smoking exposure. Upregulation of phosphatidylcholines (PC), diacylophospholipids and SMs coupled with the downregulation of lysophosphatidylcholines (LPCs), such as LPC 18:2, LPC 18:1 and LPC 18:0 distinguished lung cancer cases from controls. An effective cancer classifier composed of seven components was built with an AUC of 0.88 (43). Recently, Wang et al (54) performed single-cell RNA sequencing of different early-stage lung cancers and found that global aberration of lipid metabolism in various cell types, including T cells, B cells, fibroblasts, endothelial cells and epithelial cells, with glycerophospholipid metabolism as the most altered (with the smallest P-value) lipid metabolism-related pathway. Untargeted lipidomics was performed in an exploratory cohort of 311 participants with nine phospholipids (LPC 16:0, 18:0 and 20:4; PC 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2 and 16:0-22:6; and triglycerides 16:0-18:1-18:1) being identified to be the features most beneficial for early lung cancer detection. Using these nine features, a LC-MS-based targeted assay achieved 100.0% specificity for early lung cancer detection on an independent validation cohort (54). In a hospital-based lung cancer screening cohort of 1,036 participants examined by LDCT and a prospective clinical cohort containing 109 participants, the assay reached ≥90.0% sensitivity and 92.0% specificity for the discrimination of patients with lung cancer compared with healthy controls (54).

Urine metabolomics

Compared with other biospecimens, urine can be obtained from larger cohorts non-invasively and is therefore accepted more easily by the public. Similar to blood samples, urine also contains useful metabolic information for detecting the occurrence of lung cancer (59). The composition of urine is naturally less complex in comparison with that of plasma or serum, making it more popular for metabolomics analysis (59). In addition, once the blood is filtered by the glomerulus, certain components like metabolites can be concentrated in the urine, making their detection easier compared with that of other types of biological fluids. The first study to measure potential urinary metabolomic cancer biomarkers based on NMR and MS was published in 2006 and the concentration of nucleosides was found to be significantly increased in patients with breast cancer when compared with the normal controls (60). Carrola et al (61) first showed the valuable potential of using NMR-based metabolomics for finding putative biomarkers of lung cancer in the urine in 2011. Previous studies have since analyzed urinary metabolites employing either NMR or MS for early detection of lung cancer (Table II) (6265).

The majority of urine metabolomic studies into patients with lung cancer found alterations in creatine and creatinine, making creatine and creatinine potentially valuable biomarkers for early lung cancer detection (61,63). To further assess whether creatine was elevated in the urine samples of subjects prior to lung cancer diagnosis, a detailed prospective study based on LC-MS was previously conducted (64). Urine samples from 178 patients with lung cancer and 351 volunteers were collected and examined, where it was revealed that elevated creatine levels were associated with lung cancer risk in both European- and African-Americans (64). Consistently, creatine and creatinine have been shown to be upregulated in other biofluids, such as serum and saliva, in patients with lung cancer (39,66). In the human body, creatine is synthesized from methionine, glycine, and arginine, which is then converted into creatinine (67). Therefore, the increase in creatine and creatinine levels may be associated with upregulated amino acid metabolism. Nevertheless, the promising results of urinary creatine and creatinine in early lung cancer detection remain to be validated by independent studies based on material collected real-time during lung cancer screening.

Exhaled breath metabolomics

Normal metabolism produces a variety of volatile organic compounds (VOCs), which can be expelled through respiration (68). Therefore, exhaled breath has also been explored as a potential source of cancer biomarkers. It was first shown that VOCs in the exhaled breath could be used to differentiate patients with lung cancer from healthy individuals in 1999 (69). Since then, accumulating evidence has been supporting the utility of VOC detections in the exhaled breath for the early detection of lung cancer, most yielding high degrees of sensitivity and specificity (Table III) (7078).

Table III.

Metabolic studies of exhaled breath applied to LC.

Table III.

Metabolic studies of exhaled breath applied to LC.

First author, yearResearch subjectsAnalytical techniquesMajor FindingsSensitivity, %Specificity, %(Refs.)
Phillips et al, 199987 LC (16 eLC) and 91 CTRGC-MSA predictive model employing nine VOCs (butane, tridecane, tridecane, octane, hexane, heptane, hexane, pentane and decane) exhibited sufficient sensitivity and specificity to be considered as a screen for LC in a high-risk population85.180.5(70)
Poli et al, 200536 eLC, 25 COPD and 50 CTRGC-MSThe combination of the 13 VOCs allowed the correct classification of the cases into groups. Together with conventional diagnostic approaches, VOC analysis could be used as a complementary test for the early diagnosis of LC72.293.6(71)
Phillips et al, 2007193 LC (120 eLC) and 211 CTRGC-MSMean typicality scores employing a 16-VOC model were significantly higher in patients with LC compared with those in the control group. The predictive model achieved near-maximal performance with six breath VOC84.680.(72)
Fu et al, 201497 LC (50 eLC), 32 BPN and 88 CTRFT-ICR-MSThe concentrations of 2-butanone, 2-hydroxyacetaldehyde, 3-hydroxy-2-butanone and 4-hydroxyhexenal in the exhaled breath of patients with LC were significantly higher compared with that in the BPN and CTR samples89.881.3(73)
Li et al, 201585 LC (44 eLC), 34 BPN and 85 CTRFT-ICR-MSA model based on elevated levels of the six carbonyl VOCs (2-butanone, 2-hydroxyacetaldehyde, 3-hydroxy-2-butanone, 4-hydroxyhexenal, acrolein and malondialdehyde) effectively discriminates patients with LC from healthy controls in addition to patients with BPN9684(74)
Sakumura et al, 2017107 LC (70 eLC) and 29 CTRGC-MSIn total, 68 VOCs were detected as LC markers and a combination of five VOCs (CHN, methanol, CH3CN, isoprene and 1-propanol) was sufficient for 89.0% screening accuracy9589(75)
Rudnicka et al, 2019108 LC (17 eLC) and 121 CTRGC-MSIn total, 88 VOCs were identified in the exhaled breath. The statistical analysis revealed seven analytes (acetone, methyl acetate, isoprene, methyl vinyl ketone, cyclohexane, 2-methylheptane and cyclohexanone) to have the highest discriminatory powerValidation group, 80; test group, 86.4Validation group, 91.2; test group, 86.4(76)
Chen et al, 2021160 LC (74 eLC), 70 BPN and 122 CTRGC-MSIn total, 20 VOCs discriminated LC from CTR (AUC=0.987); 19 VOCs discriminated LC from BPN (AUC=0.809)NRNR(77)
Tsou et al, 2021148 LC (8 eLC) and 168 CTRSIFT-MSIn total, 116 VOCs were analyzed in the exhaled breath samples and a quantitative VOCs databank integrated with the application of an XGBoost classifier (a machine learning method) provided a persuasive platform for lung cancer prediction9688(78)

[i] LC, lung cancer; eLC, early lung cancer; CTR, control; COPD, chronic obstructive pulmonary disease; BPN, benign pulmonary nodules; GC-MS, gas chromatography-mass spectrometry; NR, not reported; FT-ICR-MS, Fourier transform-ion cyclotron resonance mass spectroscopy; SIFT-MS, selected ion flow tube mass spectrometry; VOC, volatile organic compound; AUC, area under the curve.

Lung cancer causes oxidative stress and induces oxidase enzymes in tumor tissues, which in turn produce higher concentrations of specific VOCs, especially carbonyl VOCs in the exhaled breath. Carbonyl VOCs are produced by biochemical pathways, such as the respiratory chain and oxidative phosphorylation pathway, as intermediates, some of which can yield unique information into specific pathways, such as lipid oxidation induced by free radicals (79,80).

Consequently, several studies have focused on the identification of carbonyl VOCs as markers of lung cancer in the exhaled breath (73,74,81). Fu et al (73) previously found that the concentrations of 2-butanone, 2-hydroxyacetaldehyde, 3-hydroxy-2-butanone and 4-hydroxyhexenal (4-HHE) in the exhaled breath of patients with lung cancer were significantly higher compared to those in the exhaled breath of healthy controls and patients with benign pulmonary nodules (BPN). This was found using Fourier transform-ion cyclotron resonance MS (73). Bousamra et al (81) then showed that the sensitivity and specificity of breath analysis was associated with the number of the elevated VOCs. Among patients with lung cancer, three or four elevated cancer markers (2-butanone, 2-hydroxyacetaldehyde, 3-hydroxy-2-butanone and 4-HHE) produced a specificity of 95% to discriminate patients with lung cancer from healthy controls. Furthermore, an enhanced model based on the elevated levels of the six carbonyl VOCs, including the four markers identified in Fu's work (73), plus acrolein and malondialdehyde, was found to effectively discriminate patients with lung cancer from healthy individuals in addition to patients with BPN (73). The sensitivity in each case was ≥96%, with specificity ranging from 64% for BPN to 86% for smokers and 100% for non-smokers and for the three groups combined 84% (74).

Other potential metabolic biomarkers that can be used for the early detection of lung cancer were also obtained in previous studies. Typical examples are isoprene, methanol and acetone (71,75,76). Despite these promising advances, the lack of normalization and standardization has led to significant variations in the VOC profiles and/or concentrations among the different studies and no commercial products have been used in clinical practice due to the lack of uniform sampling standards and sample storage methods.

Conclusions

The metabolome is the most representative of the molecular phenotype of an organism, where the concentrations of metabolites directly reflect the current biochemical activity of the organism. Therefore, metabolomics is considered a suitable approach for increasing the efficacy of detecting early-stage lung cancer. However, such applications require a deeper understanding into how these measurements relate are associated with human physiology and cancer pathology. It remains to be elucidated which metabolites can be measured in biofluids which are readily accessible to accurately reflect cancer status. Although progress has been made, it remains unclear to what extent the metabolites in biofluids can reveal about the metabolic activity of the tumor. Additional metabolomics experiments in fluids associating these measurements to the physiology of cancer would be a promising future direction.

In addition, global metabolic alterations in biofluids do not allow for the differentiation of cancer from other diseases with systemic metabolic alterations such as hypercholesteremia and phenylketonuria. The issue of such confounding effects in metabolomic analysis will be minimized if the tumor tissues are tested appropriately using NMR and MS. Therefore, it would be of benefit to identify differentiating metabolites in tissues through untargeted metabolomics before testing them in the biofluids through targeted metabolomics. Several studies have recently highlighted the role of extracellular vesicles (EVs) and their cargo (protein and RNAs) in lung cancer diagnosis (8285), proposing EVs to be another potential source of cancer biomarkers. Therefore, combined metabolomics approaches for EVs phenotyping would provide vital insights into the characteristics of EVs in cancer and potentially identify novel strategies for the early detection of lung cancer.

One of the challenges with metabolomics is the vast number and chemical complexity of metabolites that exist, such that no current metabolomics approach can cover these complexities comprehensively. This leads to inaccuracy in the early detection of lung cancer. The present review proposes that currently, the optimal metabolomics method for research would be combination with other ‘omics’ approaches to comprehensively elucidate the changes in metabolites in lung cancer whilst also to improve the accuracy of lung cancer screening.

Acknowledgements

Not applicable.

Funding

The present study was funded by grants from the National Key Research and Development Program of China (grant no. 2018YFC1315000) and the National Natural Science Foundation of China (grant no. 82273722).

Availability of data and materials

Not applicable.

Authors' contributions

YJX and XSD were involved in conceptualization, writing, and reviewing. CQ, FW and JL were involved in writing, and reviewing. YWY, LZ, FWT, WQC and WC were involved in reviewing and editing. JH and NL were involved in study concept and design, draft manuscript preparation and analysis and interpretation. All authors reviewed the paper and approved the final version of the manuscript. Data authentication is not applicable.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A and Bray F: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 71:209–249. 2021. View Article : Google Scholar : PubMed/NCBI

2 

International Early Lung Cancer Action Program Investigators, . Henschke CI, Yankelevitz DF, Libby DM, Pasmantier MW, Smith JP and Miettinen OS: Survival of patients with stage I lung cancer detected on CT screening. N Engl J Med. 355:1763–1771. 2006. View Article : Google Scholar : PubMed/NCBI

3 

Blandin Knight S, Crosbie PA, Balata H, Chudziak J, Hussell T and Dive C: Progress and prospects of early detection in lung cancer. Open Biol. 7:1700702017. View Article : Google Scholar : PubMed/NCBI

4 

Eggert JA, Palavanzadeh M and Blanton A: Screening and early detection of lung cancer. Semin Oncol Nurs. 33:129–140. 2017. View Article : Google Scholar : PubMed/NCBI

5 

Shlomi D, Abud M, Liran O, Bar J, Gai-Mor N, Ilouze M, Onn A, Ben-Nun A, Haick H and Peled N: Detection of lung cancer and EGFR mutation by electronic nose system. J Thorac Oncol. 12:1544–1551. 2017. View Article : Google Scholar : PubMed/NCBI

6 

Arbour KC and Riely GJ: Systemic therapy for locally advanced and metastatic non-small cell lung cancer: A Review. JAMA. 322:764–774. 2019. View Article : Google Scholar : PubMed/NCBI

7 

Hoffman PC, Mauer AM and Vokes EE: Lung cancer. Lancet. 355:479–485. 2000. View Article : Google Scholar : PubMed/NCBI

8 

National Lung Screening Trial Research Team, . Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM and Sicks JD: Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 365:395–409. 2011. View Article : Google Scholar : PubMed/NCBI

9 

Pastorino U, Silva M, Sestini S, Sabia F, Boeri M, Cantarutti A, Sverzellati N, Sozzi G, Corrao G and Marchiano A: Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: New confirmation of lung cancer screening efficacy. Ann Oncol. 30:16722019. View Article : Google Scholar : PubMed/NCBI

10 

de Koning HJ, van der Aalst CM, de Jong PA, Scholten ET, Nackaerts K, Heuvelmans MA, Lammers JJ, Weenink C, Yousaf-Khan U, Horeweg N, et al: Reduced lung-cancer mortality with volume CT Screening in a randomized trial. N Engl J Med. 382:503–513. 2020. View Article : Google Scholar : PubMed/NCBI

11 

Chu GCW, Lazare K and Sullivan F: Serum and blood based biomarkers for lung cancer screening: A systematic review. BMC Cancer. 18:1812018. View Article : Google Scholar : PubMed/NCBI

12 

Priola AM, Priola SM, Giaj-Levra M, Basso E, Veltri A, Fava C and Cardinale L: Clinical implications and added costs of incidental findings in an early detection study of lung cancer by using low-dose spiral computed tomography. Clin Lung Cancer. 14:139–148. 2013. View Article : Google Scholar : PubMed/NCBI

13 

Hassanein M, Rahman JS, Chaurand P and Massion PP: Advances in proteomic strategies toward the early detection of lung cancer. Proc Am Thorac Soc. 8:183–188. 2011. View Article : Google Scholar : PubMed/NCBI

14 

Hassanein M, Callison JC, Callaway-Lane C, Aldrich MC, Grogan EL and Massion PP: The state of molecular biomarkers for the early detection of lung cancer. Cancer Prev Res (Phila). 5:992–1006. 2012. View Article : Google Scholar : PubMed/NCBI

15 

Sozzi G and Boeri M: Potential biomarkers for lung cancer screening. Transl Lung Cancer Res. 3:139–148. 2014.PubMed/NCBI

16 

Spratlin JL, Serkova NJ and Eckhardt SG: Clinical applications of metabolomics in oncology: A review. Clin Cancer Res. 15:431–440. 2009. View Article : Google Scholar : PubMed/NCBI

17 

Li X, Kulkarni AS, Liu X, Gao WQ, Huang L, Hu Z and Qian K: Metal-Organic framework hybrids aid metabolic profiling for colorectal cancer. Small Methods. 5:e20010012021. View Article : Google Scholar : PubMed/NCBI

18 

Su H, Li X, Huang L, Cao J, Zhang M, Vedarethinam V, Di W, Hu Z and Qian K: Plasmonic alloys reveal a distinct metabolic phenotype of early gastric cancer. Adv Mater. 33:e20079782021. View Article : Google Scholar : PubMed/NCBI

19 

Pei C, Liu C, Wang Y, Cheng D, Li R, Shu W, Zhang C, Hu W, Jin A, Yang Y and Wan J: simpleFeOOH@Metal-Organic framework core-satellite nanocomposites for the serum metabolic fingerprinting of gynecological cancers. Angew Chem Int Ed Engl. 59:10831–10835. 2020. View Article : Google Scholar : PubMed/NCBI

20 

Duarte IF, Rocha CM and Gil AM: Metabolic profiling of biofluids: Potential in lung cancer screening and diagnosis. Expert Rev Mol Diagn. 13:737–748. 2013. View Article : Google Scholar : PubMed/NCBI

21 

German JB, Hammock BD and Watkins SM: Metabolomics: Building on a century of biochemistry to guide human health. Metabolomics. 1:3–9. 2005. View Article : Google Scholar : PubMed/NCBI

22 

Clish CB: Metabolomics: An emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud. 1:a0005882015. View Article : Google Scholar : PubMed/NCBI

23 

Schmidt DR, Patel R, Kirsch DG, Lewis CA, Vander Heiden MG and Locasale JW: Metabolomics in cancer research and emerging applications in clinical oncology. CA Cancer J Clin. 71:333–358. 2021. View Article : Google Scholar : PubMed/NCBI

24 

Liu X and Locasale JW: Metabolomics: A primer. Trends Biochem Sci. 42:274–284. 2017. View Article : Google Scholar : PubMed/NCBI

25 

Rinschen MM, Ivanisevic J, Giera M and Siuzdak G: Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol. 20:353–367. 2019. View Article : Google Scholar : PubMed/NCBI

26 

Tsedilin AM, Fakhrutdinov AN, Eremin DB, Zalesskiy SS, Chizhov AO, Kolotyrkina NG and Ananikov VP: How sensitive and accurate are routine NMR and MS measurements? Mendeleev Commun. 25:454–456. 2015. View Article : Google Scholar

27 

Dervilly-Pinel G, Courant F, Chereau S, Royer AL, Boyard-Kieken F, Antignac JP, Monteau F and Le Bizec B: Metabolomics in food analysis: Application to the control of forbidden substances. Drug Test Anal. 4 (Suppl 1):S59–S69. 2012. View Article : Google Scholar : PubMed/NCBI

28 

Li XJ, Hayward C, Fong PY, Dominguez M, Hunsucker SW, Lee LW, McLean M, Law S, Butler H, Schirm M, et al: A blood-based proteomic classifier for the molecular characterization of pulmonary nodules. Sci Transl Med. 5:207ra1422013. View Article : Google Scholar : PubMed/NCBI

29 

Zhang J, Rector J, Lin JQ, Young JH, Sans M, Katta N, Giese N, Yu W, Nagi C, Suliburk J, et al: Nondestructive tissue analysis for ex vivo and in vivo cancer diagnosis using a handheld mass spectrometry system. Sci Transl Med. 9:eaan39682017. View Article : Google Scholar : PubMed/NCBI

30 

Lei Z, Huhman DV and Sumner LW: Mass spectrometry strategies in metabolomics. J Biol Chem. 286:25435–25442. 2011. View Article : Google Scholar : PubMed/NCBI

31 

Wishart DS: Advances in metabolite identification. Bioanalysis. 3:1769–1782. 2011. View Article : Google Scholar : PubMed/NCBI

32 

Zhang A, Sun H, Wang P, Han Y and Wang X: Modern analytical techniques in metabolomics analysis. Analyst. 137:293–300. 2012. View Article : Google Scholar : PubMed/NCBI

33 

Dunn WB, Bailey NJ and Johnson HE: Measuring the metabolome: Current analytical technologies. Analyst. 130:606–625. 2005. View Article : Google Scholar : PubMed/NCBI

34 

Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, et al: The human serum metabolome. PLoS One. 6:e169572011. View Article : Google Scholar : PubMed/NCBI

35 

Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, Bjorndahl TC, Krishnamurthy R, Saleem F, Liu P, et al: The human urine metabolome. PLoS One. 8:e730762013. View Article : Google Scholar : PubMed/NCBI

36 

Wishart DS: Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov. 15:473–484. 2016. View Article : Google Scholar : PubMed/NCBI

37 

Rocha CM, Carrola J, Barros AS, Gil AM, Goodfellow BJ, Carreira IM, Bernardo J, Gomes A, Sousa V, Carvalho L and Duarte IF: Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma. J Proteome Res. 10:4314–4324. 2011. View Article : Google Scholar : PubMed/NCBI

38 

Deja S, Porebska I, Kowal A, Zabek A, Barg W, Pawelczyk K, Stanimirova I, Daszykowski M, Korzeniewska A, Jankowska R and Mlynarz P: Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease. J Pharm Biomed Anal. 100:369–380. 2014. View Article : Google Scholar : PubMed/NCBI

39 

Puchades-Carrasco L, Jantus-Lewintre E, Perez-Rambla C, Garcia-Garcia F, Lucas R, Calabuig S, Blasco A, Dopazo J, Camps C and Pineda-Lucena A: Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer. Oncotarget. 7:12904–12916. 2016. View Article : Google Scholar : PubMed/NCBI

40 

Ros-Mazurczyk M, Wojakowska A, Marczak L, Polanski K, Pietrowska M, Polanska J, Dziadziuszko R, Jassem J, Rzyman W and Widlak P: Panel of serum metabolites discriminates cancer patients and healthy participants of lung cancer screening-a pilot study. Acta Biochim Pol. 64:513–518. 2017. View Article : Google Scholar : PubMed/NCBI

41 

Klupczynska A, Derezinski P, Garrett TJ, Rubio VY, Dyszkiewicz W, Kasprzyk M and Kokot ZJ: Study of early stage non-small-cell lung cancer using Orbitrap-based global serum metabolomics. J Cancer Res Clin Oncol. 143:649–659. 2017. View Article : Google Scholar : PubMed/NCBI

42 

Chen Y, Ma Z, Min L, Li H, Wang B, Zhong J and Dai L: Biomarker identification and pathway analysis by serum metabolomics of lung cancer. Biomed Res Int. 2015:1836242015.PubMed/NCBI

43 

Ros-Mazurczyk M, Jelonek K, Marczyk M, Binczyk F, Pietrowska M, Polanska J, Dziadziuszko R, Jassem J, Rzyman W and Widlak P: Serum lipid profile discriminates patients with early lung cancer from healthy controls. Lung Cancer. 112:69–74. 2017. View Article : Google Scholar : PubMed/NCBI

44 

Klupczynska A, Derezinski P, Dyszkiewicz W, Pawlak K, Kasprzyk M and Kokot ZJ: Evaluation of serum amino acid profiles' utility in non-small cell lung cancer detection in Polish population. Lung Cancer. 100:71–76. 2016. View Article : Google Scholar : PubMed/NCBI

45 

Louis E, Adriaensens P, Guedens W, Bigirumurame T, Baeten K, Vanhove K, Vandeurzen K, Darquennes K, Vansteenkiste J, Dooms C, et al: Detection of lung cancer through metabolic changes measured in blood plasma. J Thorac Oncol. 11:516–523. 2016. View Article : Google Scholar : PubMed/NCBI

46 

Mazzone PJ, Wang XF, Beukemann M, Zhang Q, Seeley M, Mohney R, Holt T and Pappan KL: Metabolite profiles of the serum of patients with non-small cell carcinoma. J Thorac Oncol. 11:72–78. 2016. View Article : Google Scholar : PubMed/NCBI

47 

Yu Z, Chen H, Ai J, Zhu Y, Li Y, Borgia JA, Yang JS, Zhang J, Jiang B, Gu W and Deng Y: Global lipidomics identified plasma lipids as novel biomarkers for early detection of lung cancer. Oncotarget. 8:107899–107906. 2017. View Article : Google Scholar : PubMed/NCBI

48 

Xiang C, Jin S, Zhang J, Chen M, Xia Y, Shu Y and Guo R: Cortisol, cortisone, and 4-methoxyphenylacetic acid as potential plasma biomarkers for early detection of non-small cell lung cancer. Int J Biol Markers. 33:314–320. 2018. View Article : Google Scholar : PubMed/NCBI

49 

Klupczynska A, Plewa S, Kasprzyk M, Dyszkiewicz W, Kokot ZJ and Matysiak J: Serum lipidome screening in patients with stage I non-small cell lung cancer. Clin Exp Med. 19:505–513. 2019. View Article : Google Scholar : PubMed/NCBI

50 

Zhang L, Zheng J, Ahmed R, Huang G, Reid J, Mandal R, Maksymuik A, Sitar DS, Tappia PS, Ramjiawan B, et al: A High-Performing plasma metabolite panel for early-stage lung cancer detection. Cancers (Basel). 12:6222020. View Article : Google Scholar : PubMed/NCBI

51 

Huang L, Wang L, Hu X, Chen S, Tao Y, Su H, Yang J, Xu W, Vedarethinam V, Wu S, et al: Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma. Nat Commun. 11:35562020. View Article : Google Scholar : PubMed/NCBI

52 

Derveaux E, Thomeer M, Mesotten L, Reekmans G and Adriaensens P: Detection of lung cancer via blood plasma and 1H-NMR metabolomics: Validation by a semi-targeted and quantitative approach using a protein-binding competitor. Metabolites. 11:5372021. View Article : Google Scholar : PubMed/NCBI

53 

Qi SA, Wu Q, Chen Z, Zhang W, Zhou Y, Mao K, Li J, Li Y, Chen J and Huang Y and Huang Y: High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis. Sci Rep. 11:118052021. View Article : Google Scholar : PubMed/NCBI

54 

Wang G, Qiu M, Xing X, Zhou J, Yao H, Li M, Yin R, Hou Y, Li Y, Pan S, et al: Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis. Sci Transl Med. 14:eabk27562022. View Article : Google Scholar : PubMed/NCBI

55 

Warburg O: On the origin of cancer cells. Science. 123:309–314. 1956. View Article : Google Scholar : PubMed/NCBI

56 

Mathupala SP, Ko YH and Pedersen PL: Hexokinase-2 bound to mitochondria: Cancer's stygian link to the ‘Warburg Effect’ and a pivotal target for effective therapy. Semin Cancer Biol. 19:17–24. 2009. View Article : Google Scholar : PubMed/NCBI

57 

Dang CV: Links between metabolism and cancer. Genes Dev. 26:877–890. 2012. View Article : Google Scholar : PubMed/NCBI

58 

Rocha CM, Barros AS, Gil AM, Goodfellow BJ, Humpfer E, Spraul M, Carreira IM, Melo JB, Bernardo J, Gomes A, et al: Metabolic profiling of human lung cancer tissue by 1H high resolution magic angle spinning (HRMAS) NMR spectroscopy. J Proteome Res. 9:319–332. 2010. View Article : Google Scholar : PubMed/NCBI

59 

Maksymiuk AW, Tappia PS, Sitar DS, Akhtar PS, Khatun N, Parveen R, Ahmed R, Ahmed RB, Cheng B, Huang G, et al: Use of amantadine as substrate for SSAT-1 activity as a reliable clinical diagnostic assay for breast and lung cancer. Future Sci OA. 5:FSO3652018. View Article : Google Scholar : PubMed/NCBI

60 

Cho SH, Jung BH, Lee SH, Lee WY, Kong G and Chung BC: Direct determination of nucleosides in the urine of patients with breast cancer using column-switching liquid chromatography-tandem mass spectrometry. Biomed Chromatogr. 20:1229–1236. 2006. View Article : Google Scholar : PubMed/NCBI

61 

Carrola J, Rocha CM, Barros AS, Gil AM, Goodfellow BJ, Carreira IM, Bernardo J, Gomes A, Sousa V, Carvalho L and Duarte IF: Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of urine. J Proteome Res. 10:221–230. 2011. View Article : Google Scholar : PubMed/NCBI

62 

Hanai Y, Shimono K, Matsumura K, Vachani A, Albelda S, Yamazaki K, Beauchamp GK and Oka H: Urinary volatile compounds as biomarkers for lung cancer. Biosci Biotechnol Biochem. 76:679–684. 2012. View Article : Google Scholar : PubMed/NCBI

63 

Mathe EA, Patterson AD, Haznadar M, Manna SK, Krausz KW, Bowman ED, Shields PG, Idle JR, Smith PB, Anami K, et al: Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res. 74:3259–3270. 2014. View Article : Google Scholar : PubMed/NCBI

64 

Haznadar M, Cai Q, Krausz KW, Bowman ED, Margono E, Noro R, Thompson MD, Mathe EA, Munro HM, Steinwandel MD, et al: Urinary metabolite risk biomarkers of lung cancer: A prospective cohort study. Cancer Epidemiol Biomarkers Prev. 25:978–986. 2016. View Article : Google Scholar : PubMed/NCBI

65 

Funai K, Honzawa K, Suzuki M, Momiki S, Asai K, Kasamatsu N, Kawase A, Shinke T, Okada H, Nishizawa S and Takamoto H: Urinary fluorescent metabolite O-aminohippuric acid is a useful biomarker for lung cancer detection. Metabolomics. 16:1012020. View Article : Google Scholar : PubMed/NCBI

66 

Jiang X, Chen X, Chen Z, Yu J, Lou H and Wu J: High-Throughput salivary metabolite profiling on an ultralow noise tip-enhanced laser desorption ionization mass spectrometry platform for noninvasive diagnosis of early lung cancer. J Proteome Res. 20:4346–4356. 2021. View Article : Google Scholar : PubMed/NCBI

67 

Slack A, Yeoman A and Wendon J: Renal dysfunction in chronic liver disease. Crit Care. 14:2142010. View Article : Google Scholar : PubMed/NCBI

68 

Farraia MV, Cavaleiro Rufo J, Paciencia I, Mendes F, Delgado L and Moreira A: The electronic nose technology in clinical diagnosis: A systematic review. Porto Biomed J. 4:e422019. View Article : Google Scholar : PubMed/NCBI

69 

Phillips M, Gleeson K, Hughes JM, Greenberg J, Cataneo RN, Baker L and McVay WP: Volatile organic compounds in breath as markers of lung cancer: A cross-sectional study. Lancet. 353:1930–1933. 1999. View Article : Google Scholar : PubMed/NCBI

70 

Phillips M, Cataneo RN, Cummin AR, Gagliardi AJ, Gleeson K, Greenberg J, Maxfield RA and Rom WN: Detection of lung cancer with volatile markers in the breath. Chest. 123:2115–2123. 2003. View Article : Google Scholar : PubMed/NCBI

71 

Poli D, Carbognani P, Corradi M, Goldoni M, Acampa O, Balbi B, Bianchi L, Rusca M and Mutti A: Exhaled volatile organic compounds in patients with non-small cell lung cancer: Cross sectional and nested short-term follow-up study. Respir Res. 6:712005. View Article : Google Scholar : PubMed/NCBI

72 

Phillips M, Altorki N, Austin JH, Cameron RB, Cataneo RN, Greenberg J, Kloss R, Maxfield RA, Munawar MI, Pass HI, et al: Prediction of lung cancer using volatile biomarkers in breath. Cancer Biomark. 3:95–109. 2007. View Article : Google Scholar : PubMed/NCBI

73 

Fu XA, Li M, Knipp RJ, Nantz MH and Bousamra M: Noninvasive detection of lung cancer using exhaled breath. Cancer Med. 3:174–181. 2014. View Article : Google Scholar : PubMed/NCBI

74 

Li M, Yang D, Brock G, Knipp RJ, Bousamra M, Nantz MH and Fu XA: Breath carbonyl compounds as biomarkers of lung cancer. Lung Cancer. 90:92–97. 2015. View Article : Google Scholar : PubMed/NCBI

75 

Sakumura Y, Koyama Y, Tokutake H, Hida T, Sato K, Itoh T, Akamatsu T and Shin W: Diagnosis by volatile organic compounds in exhaled breath from lung cancer patients using support vector machine algorithm. Sensors (Basel). 17:2872017. View Article : Google Scholar : PubMed/NCBI

76 

Rudnicka J, Kowalkowski T and Buszewski B: Searching for selected VOCs in human breath samples as potential markers of lung cancer. Lung Cancer. 135:123–129. 2019. View Article : Google Scholar : PubMed/NCBI

77 

Chen X, Muhammad KG, Madeeha C, Fu W, Xu L, Hu Y, Liu J, Ying K, Chen L and Yurievna GO: Calculated indices of volatile organic compounds (VOCs) in exhalation for lung cancer screening and early detection. Lung Cancer. 154:197–205. 2021. View Article : Google Scholar : PubMed/NCBI

78 

Tsou PH, Lin ZL, Pan YC, Yang HC, Chang CJ, Liang SK, Wen YF, Chang CH, Chang LY, Yu KL, et al: Exploring volatile organic compounds in breath for high-accuracy prediction of lung cancer. Cancers (Basel). 13:14312021. View Article : Google Scholar : PubMed/NCBI

79 

Hakim M, Broza YY, Barash O, Peled N, Phillips M, Amann A and Haick H: Volatile organic compounds of lung cancer and possible biochemical pathways. Chem Rev. 112:5949–5966. 2012. View Article : Google Scholar : PubMed/NCBI

80 

Fan TW, Lane AN, Higashi RM, Farag MA, Gao H, Bousamra M and Miller DM: Altered regulation of metabolic pathways in human lung cancer discerned by (13)C stable isotope-resolved metabolomics (SIRM). Mol Cancer. 8:412009. View Article : Google Scholar : PubMed/NCBI

81 

Bousamra M II, Schumer E, Li M, Knipp RJ, Nantz MH, van Berkel V and Fu XA: Quantitative analysis of exhaled carbonyl compounds distinguishes benign from malignant pulmonary disease. J Thorac Cardiovasc Surg. 148:1074–1080; discussion 1080-1. 2014. View Article : Google Scholar : PubMed/NCBI

82 

Paramanantham A, Asfiya R, Das S, McCully G and Srivastava A: Extracellular Vesicle (EVs) associated non-coding RNAs in lung cancer and therapeutics. Int J Mol Sci. 23:136372022. View Article : Google Scholar : PubMed/NCBI

83 

Hua Y, Dai C, He Q, Cai X and Li M: Autoantibody panel on small extracellular vesicles for the early detection of lung cancer. Clin Immunol. 245:1091752022. View Article : Google Scholar : PubMed/NCBI

84 

Cammarata G, de Miguel-Perez D, Russo A, Peleg A, Dolo V, Rolfo C and Taverna S: Emerging noncoding RNAs contained in extracellular vesicles: Rising stars as biomarkers in lung cancer liquid biopsy. Ther Adv Med Oncol. 14:175883592211312292022. View Article : Google Scholar : PubMed/NCBI

85 

Pedraz-Valdunciel C, Giannoukakos S, Gimenez-Capitan A, Fortunato D, Filipska M, Bertran-Alamillo J, Bracht JWP, Drozdowskyj A, Valarezo J, Zarovni N, et al: Multiplex Analysis of CircRNAs from plasma extracellular vesicle-enriched samples for the detection of early-stage non-small cell lung cancer. Pharmaceutics. 14:20342022. View Article : Google Scholar : PubMed/NCBI

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Spandidos Publications style
Xu Y, Dong X, Qin C, Wang F, Cao W, Li J, Yu Y, Zhao L, Tan F, Chen W, Chen W, et al: Metabolic biomarkers in lung cancer screening and early diagnosis (Review). Oncol Lett 25: 265, 2023
APA
Xu, Y., Dong, X., Qin, C., Wang, F., Cao, W., Li, J. ... He, J. (2023). Metabolic biomarkers in lung cancer screening and early diagnosis (Review). Oncology Letters, 25, 265. https://doi.org/10.3892/ol.2023.13851
MLA
Xu, Y., Dong, X., Qin, C., Wang, F., Cao, W., Li, J., Yu, Y., Zhao, L., Tan, F., Chen, W., Li, N., He, J."Metabolic biomarkers in lung cancer screening and early diagnosis (Review)". Oncology Letters 25.6 (2023): 265.
Chicago
Xu, Y., Dong, X., Qin, C., Wang, F., Cao, W., Li, J., Yu, Y., Zhao, L., Tan, F., Chen, W., Li, N., He, J."Metabolic biomarkers in lung cancer screening and early diagnosis (Review)". Oncology Letters 25, no. 6 (2023): 265. https://doi.org/10.3892/ol.2023.13851