Serum phosphatidylethanolamine levels distinguish benign from malignant solitary pulmonary nodules and represent a potential diagnostic biomarker for lung cancer

Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules

Lung cancer is the leading cause of cancer-related deaths. Early detection and diagnosis are critical, as survival decreases with advanced stages. Approximately 1.6 million nodules are incidentally detected every year on chest CT scan images in the United States. This number of nodules identified is likely much larger after accounting for screening-detected nodules. Most of these nodules, whether incidentally or screening detected, are benign. Despite this, many patients undergo unnecessary invasive procedures to rule out cancer because our current stratification approaches are suboptimal, particularly for intermediate probability nodules. Thus, noninvasive strategies are urgently needed. Biomarkers have been developed to assist through the continuum of lung cancer care and include blood protein-based biomarkers, liquid biopsies, quantitative imaging analysis (radiomics), exhaled volatile organic compounds, and bronchial or nasal epithelium genomic classifiers, among others. Although many biomarkers have been developed, few have been integrated into clinical practice as they lack clinical utility studies showing improved patient-centered outcomes. Rapid technologic advances and large network collaborative efforts will continue to drive the discovery and validation of many novel biomarkers. Ultimately, however, randomized clinical utility studies showing improved patient outcomes will be required to bring biomarkers into clinical practice.

Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera

Differential diagnosis of pulmonary nodules detected by computed tomography (CT) remains a challenge in clinical practice. Here, we characterize the global metabolomes of 480 serum samples including healthy controls, benign pulmonary nodules, and stage I lung adenocarcinoma. The adenocarcinoma demonstrates a distinct metabolomic signature, whereas benign nodules and healthy controls share major similarities in metabolomic profiles. A panel of 27 metabolites is identified in the discovery cohort (n = 306) to distinguish between benign and malignant nodules. The discriminant model achieves an AUC of 0.915 and 0.945 in the internal validation (n = 104) and external validation cohort (n = 111), respectively. Pathway analysis reveals elevation in glycolytic metabolites associated with decreased tryptophan in serum of lung adenocarcinoma vs benign nodules and healthy controls, and demonstrates that uptake of tryptophan promotes glycolysis in lung cancer cells. Our study highlights the value of the serum metabolite biomarkers in risk assessment of pulmonary nodules detected by CT screening.

The Landscape of Lipid Metabolism in Lung Cancer: The Role of Structural Profiling

The aim of this study was to explore the relationship between lipids with different structural features and lung cancer (LC) risk and identify prospective biomarkers of LC. Univariate and multivariate analysis methods were used to screen for differential lipids, and two machine learning methods were used to define combined lipid biomarkers. A lipid score (LS) based on lipid biomarkers was calculated, and a mediation analysis was performed. A total of 605 lipid species spanning 20 individual lipid classes were identified in the plasma lipidome. Higher carbon atoms with dihydroceramide (DCER), phosphatidylethanolamine (PE), and phosphoinositols (PI) presented a significant negative correlation with LC. Point estimates revealed the inverse associated with LC for the n-3 PUFA score. Ten lipids were identified as markers with an area under the curve (AUC) value of 0.947 (95%, CI: 0.879-0.989). In this study, we summarized the potential relationship between lipid molecules with different structural features and LC risk, identified a panel of LC biomarkers, and demonstrated that the n-3 PUFA of the acyl chain of lipids was a protective factor for LC.

Plasm Metabolomics Study in Pulmonary Metastatic Carcinoma

Background

The lung is one of the most common metastatic sites of malignant tumors. Early detection of pulmonary metastatic carcinoma can effectively reduce relative cancer mortality. Human metabolomics is a qualitative and quantitative study of low-molecular metabolites in the body. By studying the plasm metabolomics of patients with pulmonary metastatic carcinoma or other lung diseases, we can find the difference in plasm levels of low-molecular metabolites among them. These metabolites have the potential to become biomarkers of lung metastases.

Methods

Patients with pulmonary nodules admitted to our department from February 1, 2019, to May 31, 2019, were collected. According to the postoperative pathological results, they were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Moreover, healthy people who underwent physical examination were enrolled as the healthy population group (HPG) during the same period. On the one hand, to study lung metastases screening in healthy people, PMC was compared with HPG. The multivariate statistical analysis method was used to find the significant low-molecular metabolites between the two groups, and their discriminating ability was verified by the ROC curve. On the other hand, from the perspective of differential diagnosis of lung metastases, three groups with different pulmonary lesions (PMC, BPN, and PLC) were compared as a whole, and then the other two groups were compared with PMC, respectively. The main low-molecular metabolites were selected, and their discriminating ability was verified.

Results

In terms of lung metastases screening for healthy people, four significant low-molecular metabolites were found by comparison of PMC and HPG. They were O-arachidonoyl ethanolamine, adrenoyl ethanolamide, tricin 7-diglucuronoside, and p-coumaroyl vitisin A. In terms of the differential diagnosis of pulmonary nodules, the significant low-molecular metabolites selected by the comparison of the three groups as a whole were anabasine, octanoylcarnitine, 2-methoxyestrone, retinol, decanoylcarnitine, calcitroic acid, glycogen, and austalide L. For the comparison of PMC and BPN, L-tyrosine, indoleacrylic acid, and lysoPC (16 : 0) were selected, while L-octanoylcarnitine, retinol, and decanoylcarnitine were selected for the comparison of PMC and PLC. Their AUCs of ROC are all greater than 0.80. It indicates that these substances have a strong ability to differentiate between pulmonary metastatic carcinoma and other pulmonary nodule lesions.

Conclusion

Through the research of plasm metabolomics, it is possible to effectively detect the changes in some low-molecular metabolites among primary lung cancer, pulmonary metastatic carcinoma, and benign pulmonary nodule patients and healthy people. These significant metabolites have the potential to be biomarkers for screening and differential diagnosis of lung metastases.

Metabolic linkages between zinc exposure and lung cancer risk: A nested case-control study

Epidemiologic studies have suggested that elevated concentrations of zinc are associated with a decreased risk of lung cancer, but the underlying mechanisms remain to be investigated. The metabolites are highly sensitive to environmental stress, which will help to reveal the linkages between zinc exposure and lung cancer risk. We designed a nested case-control study including 101 incident lung cancer cases and 1:2 age- and sex-frequency-matched 202 healthy controls from the Dongfeng-Tongji (DFTJ) cohort. Their plasma level of zinc was determined by using inductively coupled plasma-mass spectrometry (ICP-MS) and plasma profiles of metabolites were detected by using an untargeted metabolomics approach. The generalized linear models (GLM) were applied to assess the associations of plasma zinc with metabolites, and the mediation effects of zinc-related metabolites on zinc-lung cancer association were further testified. The concentrations of 55 metabolites had linear dose-response relationships with plasma zinc at a false discovery rate (FDR) < 0.05, among which L-proline, phosphatidylcholine (PC, 34:2), phosphatidylethanolamine (PE, O-36:5), L-altrose, and sphingomyelin (SM, 40:3) showed different levels between lung cancer cases and healthy controls (fold change = 0.92, 0.95, 1.07, 0.90, and 1.08, respectively, and all P < 0.05). The plasma concentration of SM(40:3) was negatively associated with incident risk of lung cancer [OR(95%CI) = 0.71(0.55, 0.91), P = 0.007] and could mediate 41.7% of the association between zinc and lung cancer risk (P = 0.004). Moreover, compared to the traditional factors, addition of SM(40:3) exerted improved prediction performance for incident risk of lung cancer [AUC(95%CIs) = 0.714(0.654, 0.775) vs. 0.663(0.600, 0.727), P = 0.030]. Our findings revealed metabolic profiles with zinc exposure and provide new insight into the alternations of metabolites underpinning the links between zinc exposure and lung cancer development.

Comprehensive plasma metabolomics and lipidomics of benign and malignant solitary pulmonary nodules

INTRODUCTION

Solitary pulmonary nodules (SPNs) are commonly found in imaging technologies, but are plagued by high false-positive rates.

OBJECTIVE

We aimed to identify metabolic alterations in SPN etiology and diagnosis using less invasive plasma metabolomics and lipidomics.

METHODS

In total, 1160 plasma samples were obtained from healthy volunteers (n = 280), benign SPNs (n = 157) and malignant SPNs (stage I, n = 723) patients enrolled from 5 independent centers. Gas chromatography-triple quadrupole mass spectrometry (GC‒MS) and liquid chromatography-Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometry (LC‒MS) were used to analyze the samples for untargeted metabolomics and lipidomics.

RESULTS AND CONCLUSION

GC‒MS-based metabolomics revealed 1336 metabolic features, while LC‒MS-based lipidomics revealed 6088 and 2542 lipid features in the positive and negative ion modes, respectively. The metabolic and lipidic characteristics of healthy vs. benign or malignant SPNs exhibited substantial pattern differences. Of note, benign and malignant SPNs had no significant variations in circulating metabolic and lipidic markers and were validated in four other centers. This study demonstrates evidence of early metabolic alterations that can possibly distinguish SPNs from healthy controls, but not between benign and malignant SPNs.

Imputation of Missing Values for Multi-Biospecimen Metabolomics Studies: Bias and Effects on Statistical Validity

The analysis of high-throughput metabolomics mass spectrometry data across multiple biological sample types (biospecimens) poses challenges due to missing data. During differential abundance analysis, dropping samples with missing values can lead to severe loss of data as well as biased results in group comparisons and effect size estimates. However, the imputation of missing data (the process of replacing missing data with estimated values such as a mean) may compromise the inherent intra-subject correlation of a metabolite across multiple biospecimens from the same subject, which in turn may compromise the efficacy of the statistical analysis of differential metabolites in biomarker discovery. We investigated imputation strategies when considering multiple biospecimens from the same subject. We compared a novel, but simple, approach that consists of combining the two biospecimen data matrices (rows and columns of subjects and metabolites) and imputes the two biospecimen data matrices together to an approach that imputes each biospecimen data matrix separately. We then compared the bias in the estimation of the intra-subject multi-specimen correlation and its effects on the validity of statistical significance tests between two approaches. The combined approach to multi-biospecimen studies has not been evaluated previously even though it is intuitive and easy to implement. We examine these two approaches for five imputation methods: random forest, k nearest neighbor, expectation-maximization with bootstrap, quantile regression, and half the minimum observed value. Combining the biospecimen data matrices for imputation did not greatly increase efficacy in conserving the correlation structure or improving accuracy in the statistical conclusions for most of the methods examined. Random forest tended to outperform the other methods in all performance metrics, except specificity.

Plasma characteristic metabolites of pediatric community-acquired pneumonia in traditional Chinese medicine syndrome differentiation

Community-acquired pneumonia (CAP) is the leading cause of lower respiratory tract infections in children. Heat syndrome (HS) and cold syndrome (CS) are two main syndrome types of pediatric CAP in traditional Chinese medicine (TCM). This study aimed to identify plasma metabolic profiles in pediatric CAP and to further select potential biomarkers to distinguish between HS and CS. An ultra-performance liquid chromatography coupled with linear ion trap quadrupole-orbitrap mass spectrometry method was applied to plasma samples of 296 patients and 55 healthy controls (HC). The samples were divided into the discovery group (n = 213, HS = 160, CS = 23, HC = 30) and the validation group (n = 138, HS = 93, CS = 20, HC = 25). The orthogonal partial least-squares discriminant analysis, the value of fold change, and Kruskal-Wallis test with false discovery rate correction (q-value <0.05) were applied to identify differential plasma metabolites. The area under the ROC curve (AUC) was used to evaluate the diagnostic performance of the screened metabolites. The results showed that the plasma levels of aspartic acid, phenylalanine, arginine, lysoPC20:1, lysoPE16:0, lysoPE18:0, and PE (16:0_22:6) were increased in CS compared with HC. The plasma levels of PC (18:1_18:1), PC (20:4_20:4), PE (16:0_18:2), lysoPE20:4, lysoPE18:2, and lysoPE22:6 were decreased, whereas, the plasma level of ceramide (d18:1_24:1) was increased in HS compared with HC. There were 13 differential metabolites in CS (AUC = 0.995) and 15 differential metabolites in HS (AUC = 0.954), compared with HC. A panel of seven biomarkers, including LysoPC20:1, lysoPE16:0, lysoPE18:2, lysoPE20:4, lysoPE22:6, PC (18:1_18:1), and PC (20:4_20:4) showed good discrimination between HS and CS with an AUC of 0.982. Altered plasma amino acids and lipids may provide an objective basis for TCM syndrome differentiation in pediatric CAP.

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.

Combined Metabolomics with Transcriptomics Reveals Important Serum Biomarkers Correlated with Lung Cancer Proliferation through a Calcium Signaling Pathway

Lung cancer (LC) is one of the most malignant cancers in the world, but currently, it lacks effective noninvasive biomarkers to assist its early diagnosis. Our study aims to discover potential serum diagnostic biomarkers for LC. In our study, untargeted serum metabolomics of a discovery cohort and targeted analysis of a test cohort were performed based on gas chromatography-mass spectrometry. Both univariate and multivariate statistical analyses were employed to screen for differential metabolites between LC and healthy control (HC), followed by the selection of candidate biomarkers through multiple algorithms. The results showed that 15 metabolites were significantly dysregulated between LC and HC, and a panel, comprising cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid, was demonstrated to have excellent differentiating capability for LC based on multiple classification modelings. In addition, the molecular interaction analysis combined with transcriptomics revealed a close correlation between the candidate biomarkers and LC proliferation via a Ca²⁺ signaling pathway. Our study discovered that cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid in combination could be a promising diagnostic biomarker for LC, and most importantly, our results will shed some light on the pathophysiological mechanism underlying LC to understand it deeply. The data that support the findings of this study are openly available in MetaboLights at https://www.ebi.ac.uk/metabolights/, reference number MTBLS1517.

Ongoing challenges in implementation of lung cancer screening

Lung cancer is the leading cause of cancer deaths in Europe and around the world. Although available therapies have undergone considerable development in the past decades, the five-year survival rate for lung cancer remains low. This sobering outlook results mainly from the advanced stages of cancer most patients are diagnosed with. As the population at risk is relatively well defined and early stage disease is potentially curable, lung cancer outcomes may be improved by screening. Several studies already show that lung cancer screening (LCS) with low-dose computed tomography (LDCT) reduces lung cancer mortality. However, for a successful implementation of LCS programmes, several challenges have to be overcome: selection of high-risk individuals, standardization of nodule classification and measurement, specific training of radiologists, optimization of screening intervals and screening duration, handling of ancillary findings are some of the major points which should be addressed. Last but not least, the psychological impact of screening on screened individuals and the impact of potential false positive findings should not be neglected. The aim of this review is to discuss the different challenges of implementing LCS programmes and to give some hints on how to overcome them. Finally, we will also discuss the psychological impact of screening on quality of life and the importance of smoking cessation.

Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives

Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.

Mathermycin, an anti-cancer molecule that targets cell surface phospholipids

Mathermycin, a lantipeptide isolated from marine actinomycete Marinactinospora thermotolerans, is an antibiotic that has been shown to disrupt bacterial plasma membrane. We now provide evidences that mathermycin can also disrupt cancer, but not normal, cell plasma membranes through targeting phosphatidylethanolamine (PE), which is located only in the inner leaflet of the plasma membrane in normal cells but in both the inner and outer leaflets of the membrane in tumor cells. Our data shows that mathermycin inhibits the metabolic activity and induces mainly necrotic death of all cancer cell lines with EC₅₀ between 4.2 and 16.9 μM, while normal cell lines have EC₅₀ between 113 and 129 μM. The cytotoxicity of mathermycin could be inhibited by exogenous PE, but not phosphoserine and phosphocholine. The formation of mathermycin-PE complexes was confirmed by in silico analysis, HPLC and MS spectrometer. Furthermore, mathermycin exhibited similar cytotoxicity toward cancer and multidrug resistant cancer cells, which could be due to its ability to inhibit mitochondrial function, as shown by our data from the Seahorse™ metabolic analyzer. This study demonstrates that mathermycin is a potentially effective class of anti-tumor chemotherapeutics that do not easily develop resistance due to a mechanism of action targeting PE.

Predicting human health from biofluid-based metabolomics using machine learning

Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified—where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.

Noninvasive biomarkers for lung cancer diagnosis, where do we stand?

The 2010's saw demonstration of the power of lung cancer screening to reduce mortality. However, with implementation of lung cancer screening comes the challenge of diagnosing millions of lung nodules every year. When compared to other cancers with widespread screening strategies (breast, colorectal, cervical, prostate, and skin), obtaining a lung nodule tissue biopsy to confirm a positive screening test remains associated with higher morbidity and cost. Therefore, non-invasive diagnostic biomarkers may have a unique opportunity in lung cancer to greatly improve the management of patients at risk. This review covers recent advances in the field of liquid biomarkers and computed tomographic imaging features, with special attention to new methods for combination of biomarkers as well as the use of artificial intelligence for the discrimination of benign from malignant nodules.

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.

Toward Complete Structure Elucidation of Glycerophospholipids in the Gas Phase through Charge Inversion Ion/Ion Chemistry

Shotgun lipidomics has recently gained popularity for lipid analysis. Conventionally, shotgun analysis of glycerophospholipids via direct electrospray ionization tandem mass spectrometry (ESI-MS/MS) provides glycerophospholipid (GPL) class (i.e., headgroup composition) and fatty acyl composition. Reliant on low-energy collision-induced dissociation (CID), traditional ESI-MS/MS fails to define fatty acyl regiochemistry along the glycerol backbone or carbon-carbon double bond position(s) in unsaturated fatty acyl substituents. Therefore, isomeric GPLs are often unresolved, representing a significant challenge for shotgun-MS approaches. We developed a top-down shotgun-MS method utilizing gas-phase ion/ion charge inversion chemistry that provides near-complete GPL structural identification. First, in negative ion mode, CID of mass-selected GPL anions generates fatty acyl carboxylate anions via fragmentation of ester bonds linking the fatty acyl substituents at the sn-1 and sn-2 positions of the glycerol backbone. Product anions, including fatty acyl carboxylate ions, were then derivatized in the mass spectrometer via an ion/ion charge inversion reaction with tris-phenanthroline magnesium dications. Subsequent CID of charge-inverted fatty acyl complex cations yielded isomer-specific product ion spectra that permit (i) unambiguous assignment of carbon-carbon double bond position(s) and (ii) relative quantitation of isomeric fatty acyl substituents. The outlined strategy was applied to the analysis of targeted GPLs extracted from human plasma, including several proposed plasma biomarkers. A single experiment thus facilitates assignment of the GPL headgroup, fatty acyl composition, carbon-carbon double bond position(s) in unsaturated fatty acyl chains, and, in some cases, fatty acyl sn-position and relative abundances for isomeric fatty acyl substituents. Ultimately, this MSn platform paired with ion/ion chemistry permitted identification of major, and some minor, isomeric contributors that are unresolved using conventional ESI-MS/MS.

Quantitative multi-omics analysis of the effects of mitochondrial dysfunction on lipid metabolism in Saccharomyces cerevisiae

In this study, combined genome, transcriptome, and metabolome analysis was performed for eight Saccharomyces cerevisiae mitochondrial respiration-deficient mutants. Each mutant exhibited a unique nuclear genome mutation pattern; the nuclear genome mutations, and thus potentially affected genes and metabolic pathways, showed a co-occurrence frequency of ≤ 3 among the eight mutants. For example, only a lipid metabolism-related pathway was likely to be affected by the nuclear genome mutations in one of the mutants. However, large deletions in the mitochondrial genome were the shared characteristic among the eight mutants. At the transcriptomic level, lipid metabolism was the most significantly enriched Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway for differentially expressed genes (DEGs) co-occurring in both ≥ 4 and ≥ 5 mutants. Any identified DEG enriched in lipid metabolism showed the same up-/down-regulated pattern among nearly all eight mutants. Further, 126 differentially expressed lipid species (DELS) were identified, which also showed the same up-/down-regulated pattern among nearly all investigated mutants. It was conservatively demonstrated that the similar change pattern of lipid metabolism in the entire investigated mutant population was attributed to mitochondrial dysfunction. The change spectrum of lipid species was presented, suggesting that the number and change degree of up-regulated lipid species were higher than those of down-regulated lipid species. Additionally, energy storage lipids increased in content and plasma-membrane phospholipid compositions varied in the relative proposition. The results for the genome, transcriptome, and lipidome were mutually validated, which provides quantitative data revealing the roles of mitochondria from a global cellular perspective.

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.

Update on liquid biopsy in clinical management of non-small cell lung cancer

Lung cancer, a leading cause of cancer-related mortality, has a low rate of early diagnosis and a poor prognosis for advanced stages. Recent advances in further mastery of the biology of tumors promote the diagnosis and therapy, especially for non-small cell lung cancer (NSCLC). However, tumor tissue-based information is often not available in most cases due to the invasive and high risk nature of the tumor biopsy procedures. Liquid biopsy, based on the multiple liquid samples including circulating tumor cells (CTC), circulating tumor DNA (ctDNA), and tumor-derived exosome obtained from blood or urine as well as other body fluids, can also provide valuable tumor-related information, playing an important role in management of NSCLC in clinical practice. It is widely believed that concordance of detection for tumor by liquid samples in comparison with tissue biopsy for both early and advanced stage NSCLC patients is optimistic. We herein review the current and future clinical application of liquid biopsy, including early diagnosis and management of precise personalized treatment in lung cancer. The future directions of development for liquid biopsy are also discussed in this review.

Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

The present review is an update of the research and development efforts regarding the use of molecular biomarkers in the lung cancer screening setting. The two main unmet clinical needs, namely, the refinement of risk to improve the selection of individuals undergoing screening and the characterization of undetermined nodules found during the computed tomography-based screening process are the object of the biomarkers described in the present review. We first propose some principles to optimize lung cancer biomarker discovery projects. Then, we summarize the discovery and developmental status of currently promising molecular candidates, such as autoantibodies, complement fragments, microRNAs, circulating tumor DNA, DNA methylation, blood protein profiling, or RNA airway or nasal signatures. We also mention other emerging biomarkers or new technologies to follow, such as exhaled breath biomarkers, metabolomics, sputum cell imaging, genetic predisposition studies, and the integration of next-generation sequencing into study of circulating DNA. We also underline the importance of integrating different molecular technologies together with imaging, radiomics, and artificial intelligence. We list a number of completed, ongoing, or planned trials to show the clinical utility of molecular biomarkers. Finally, we comment on future research challenges in the field of biomarkers in the context of lung cancer screening and propose a design of a trial to test the clinical utility of one or several candidate biomarkers.

Similarities in Blood Mononuclear Cell Membrane Phospholipid Profiles During Malignancy

Phospholipids (PLs), key elements of cellular membranes, are regulated reciprocally with membrane proteins and can act as sensors for alterations in physiological or pathological states of cells including initiation and development of cancer. On the other hand, peripheral blood mononuclear cells (MNCs) play an important role in antitumor immune response by reacting to cancerous modifications in distant organs. In the current study, we tested the hypothesis that tumor initiation and development are reflected in the alteration pattern of the MNC PL component. We analyzed MNC membrane PL fractions in samples from healthy individuals and from patients with diverse types of cancers to reveal possible alterations induced by malignancy. Compared to healthy controls, the cancer samples demonstrated shifts in several membrane PL profiles. In particular, when analyzing cancer data pooled together, there were significantly higher levels in lysophosphatidylcholine, phosphatidylcholine, and phosphatidylethanolamine fractions, and significantly lower quantities in phosphatidylinositol, phosphatidylserine, and phosphatidic acid fractions in cancer samples compared to controls. The levels of sphingomyelins and diphosphatidylglycerols were relatively unaffected. Most of the differences in PLs were sustained during the analysis of individual cancers such as breast cancer and chronic lymphocytic leukemia. Our findings suggest the presence of a common pattern of changes in MNC PLs during malignancy.

Serum Lipidomics Profiling to Identify Biomarkers for Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is the leading cause of cancer death worldwide, which ranks top in both incidence and mortality. To broaden our understanding of the lipid metabolic alterations in NSCLC and to identify potential biomarkers for early diagnosis, we performed nontargeted lipidomics analysis in serum from 66 early-stage NSCLC, 40 lung benign disease patients (LBD), and 40 healthy controls (HC) using Ultrahigh Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry (UHPLC-Q-TOF/MS). The identified biomarker candidates of phosphatidylcholines (PCs) and phosphatidylethanolamines (PEs) were further externally validated in a cohort including 30 early-stage NSCLC, 30 LBD, and 30 HC by a targeted lipidomic analysis. We observed a significantly altered lipid metabolic profile in early-stage NSCLC and identified panels of PCs and PEs to distinguish NSCLC patients and HC. The levels of PCs and PEs were found to be dysregulated in glycerophospholipid metabolism, which was the top altered pathway in early-stage NSCLC. Receiver operating characteristic (ROC) curve analysis revealed that panels of PCs and PEs exhibited good performance in differentiating early-stage NSCLC and HC. The levels of PE(16:0/16:1), PE(16:0/18:3), PE(16:0/18:2), PE(18:0/16:0), PE(17:0/18:2), PE(18:0/17:1), PE(17:0/18:1), PE(20:5/16:0), PE(18:0/18:1), PE(18:1/20:4), PE(18:0/20:3), PC(15:0/18:1), PC(16:1/20:5), and PC(18:0/20:1) in early-stage NSCLC were significantly increased compared with HC (p<0.05). Overall, our study has thus highlighted the power of using comprehensive lipidomic approaches to identify biomarkers and underlying mechanisms in NSCLC.

Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics

The pathogenesis of ME/CFS, a disease characterized by fatigue, cognitive dysfunction, sleep disturbances, orthostatic intolerance, fever, irritable bowel syndrome (IBS), and lymphadenopathy, is poorly understood. We report biomarker discovery and topological analysis of plasma metabolomic, fecal bacterial metagenomic, and clinical data from 50 ME/CFS patients and 50 healthy controls. We confirm reports of altered plasma levels of choline, carnitine and complex lipid metabolites and demonstrate that patients with ME/CFS and IBS have increased plasma levels of ceramide. Integration of fecal metagenomic and plasma metabolomic data resulted in a stronger predictive model of ME/CFS (cross-validated AUC = 0.836) than either metagenomic (cross-validated AUC = 0.745) or metabolomic (cross-validated AUC = 0.820) analysis alone. Our findings may provide insights into the pathogenesis of ME/CFS and its subtypes and suggest pathways for the development of diagnostic and therapeutic strategies.

Tumor targeting with docosahexaenoic acid-conjugated docetaxel for inhibiting lung cancer metastasis to bone

Docetaxel (DTX) is currently used as a first- or second-line drug treatment for patients with lung cancer, however, it is less effective for the treatment of patients with bone metastasis of lung cancer. This is primarily due to the fact that docetaxel is nonspecific. In the present study, docosahexaenoic acid (DHA) was selected as a tumor-targeting ligand, and DHA-conjugated DTX (DTX-DHA) was prepared for inhibiting lung cancer metastasis to bone. The anti-cancer activity assay revealed that DTX-DHA exhibited a similar antitumor efficacy to DTX in vitro. The maximum tolerated dose of DTX-DHA was increased compared with that of DTX. The present study results indicated that DTX-DHA exhibited an improved inhibition efficacy of lung cancer metastasis to bone in comparison with DTX in vivo. Encouragingly, the mean survival time of the DTX-DHA group (30.60 days) was increased compared with the DTX group (26.10 days; P<0.01). Furthermore, the results of cell migration and osteoclast-induced formation assays suggested that DTX-DHA inhibited lung cancer metastasis to bone primarily by affecting lung cancer cell migration. These results indicate that DTX-DHA may exhibit a potential therapeutic effect against lung cancer metastasis to bone.

Simultaneous quantification of serum monounsaturated and polyunsaturated phosphatidylcholines as potential biomarkers for diagnosing non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the most common malignancies worldwide. In this study, we investigated Ultrahigh Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry and Gas Chromatography Time-of-Flight/Mass Spectrometry-based non-targeted metabolomic profiles of serum samples obtained from early-stage NSCLC patients and healthy controls (HC). Metabolic pathways and the biological relevance of potential biomarkers were extensively studied to gain insights into dysregulated metabolism in NSCLC. The identified biomarker candidates were further externally validated via a targeted metabolomics analysis. The global metabolomics profiles could clearly distinguish NSCLC patients from HC. Phosphatidylcholine (PC) levels were found to be dysregulated in glycerophospholipid (GPL) metabolism, which was the top altered pathway in early-stage NSCLC. Compared with those in HC, significant increases in the levels of saturated and monounsaturated PCs such as PC (15:0/18:1), PC (18:0/16:0) and PC (18:0/20:1) were observed in NSCLC. Additionally, relative to those in HC, the levels of 9 polyunsaturated PCs, namely, PC (17:2/2:0), PC (18:4/3:0), and PC (15:0/18:2), and so on were significantly decreased in NSCLC patients. A panel of 12 altered PCs had good diagnostic performance in differentiating early-stage NSCLC patients from HC, and these PCs may thus be used as serum biomarkers for the early diagnosis of NSCLC.

Comprehensive targeted super-deep next generation sequencing enhances differential diagnosis of solitary pulmonary nodules

Background

A non-invasive method to predict the malignancy of surgery-candidate solitary pulmonary nodules (SPN) is urgently needed.

Methods

Super-depth next generation sequencing (NGS) of 35 paired tissues and plasma DNA was performed as an attempt to develop an early diagnosis approach.

Results

Only ~6% of malignant nodule patients had driver mutations in the circulating tumour DNA (ctDNA) with >10,000-fold sequencing depth, and the concordance of mutation between tDNA and ctDNA was 3.9%. The first innovative whole mutation scored model in this study predicted 33.3% of malignant SPN with 100% specificity.

Conclusions

These results showed that lung cancer gene-targeted deep capture sequencing is not efficient enough to achieve ideal sensitivity by simply increasing the sequencing depth of ctDNA from early candidates. The sequencing could not be evaluated hotspot mutations in the early tumour stage. Nevertheless, a larger cohort is required to optimize this model, and more techniques may be incorporated to benefit the SPN high-risk population.

A Lipidomics Approach to Identifying Key Lipid Species Involved in VEGF-Inhibitor Mediated Attenuation of Bleomycin-Induced Pulmonary Fibrosis

PURPOSE

Poor molecular characterization of idiopathic pulmonary fibrosis (IPF) has led to insufficient understanding of the pathogenesis of the disease, resulting in lack of effective therapies and poor prognosis. Particularly, the role of lipid imbalance due to impaired lipid metabolism in the pathogenesis of IPF has been poorly studied.

EXPERIMENTAL DESIGN

The authors have used shotgun lipidomics in a bleomycin (BLM) mouse model of pulmonary fibrosis with vascular endothelial growth factor (VEGF)-inhibitor CBO-P11 as a therapeutic measure, to identify a comprehensive set of lipids that contribute to the pathogenesis of pulmonary fibrosis.

RESULTS

The authors report that attenuation of BLM-induced fibrotic response with CBO-P11 cotreatment is accompanied by a decrease in total lipid content and specific downregulation of lipids, which are upregulated in response to BLM treatment.

CONCLUSION AND CLINICAL RELEVANCE

Dysregulated lipids identified in this study hold the potential of being future biomarkers for IPF.

Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review

Lung cancer is the leading cause of cancer-related death. Next-generation metabolomics is becoming a powerful emerging technology for studying the systems biology and chemistry of health and disease. This mini review summarized the main platforms of next-generation metabolomics and its main applications in lung cancer including early diagnosis, pathogenesis, classifications and precision medicine. The period covers between 2009 and August, 2017. The major issues and future directions of metabolomics in lung cancer research and clinical applications were also discussed.

Lipidomics: Prospects from a technological perspective

Over the last two decades, lipidomics has evolved into an 'omics' technology pari passu with benchmarking 'omics' technologies, such as genomics or proteomics. The driving force behind this development was a constant advance in mass spectrometry and related technologies. The aim of this opinion article is to give the interested reader a concise but still comprehensive overview about the technological state of the art in lipidomics, current challenges and perspectives for future development. As such, this article guides through the whole workflow of lipidomics, from sampling to data analysis. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.