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

Gut microbiota metabolites impact immunologic responses to antiretroviral therapy in HIV-infected men who have sex with men

BACKGROUND

The association between gut microbial metabolites and immunologic non-response among people living with HIV (PLHIV) receiving antiretroviral therapy (ART) has not been well established. We aimed to characterize gut microbial metabolites among HIV-infected men who have sex with men (MSM) with different immunologic responses.

METHODS

We recruited HIV-infected MSM from Guangzhou Eighth People's Hospital and HIV-uninfected MSM (healthy controls, HC) from a local MSM community-based organization in Guangzhou between June and October 2021. HIV-infected MSM were grouped into good immunological responders (GIR) (CD4 + T cell count ≥ 350 cells/μl) and poor immunological responders (PIR) (CD4 + T cell count < 350 cells/μl) after 24 months of ART treatment. Online questionnaires and stool samples were collected. Microbial metabolites in stool were obtained through ultra-performance liquid chromatography coupled to a tandem mass spectrometry (UPLC-MS/MS) system. Differential metabolites were identified and analyzed using the Kruskal-Wallis test, followed by pairwise comparisons with the Wilcoxon rank-sum test. The least absolute selection and shrinkage operator was used to select potential metabolites biomarkers.

RESULTS

A total of 51 HC, 56 GIR, and 42 PIR were included. No statistically significant differences were observed in the median time since HIV diagnosis and ART duration between GIR and PIR. Among the 174 quantified metabolites, 81 significantly differed among HC, GIR, and PIR (P < 0.05). Among differential metabolites, indole-3-propionic acid significantly decreased from HC (11.39 nmol/g) and GIR (8.16 nmol/g) to PIR (6.50 nmol/g). The pathway analysis showed that tryptophan metabolism differed significantly between GIR and PIR (P < 0.05). Four potential metabolites biomarkers (dimethylglycine, cinnamic acid, 3-hydroxyisovaleric acid, and propionic acid) that distinguish GIR and PIR were identified, and the corresponding area under the curve based on potential biomarkers was 0.773 (95% CI: 0.675-0.871).

CONCLUSIONS

This study identified significant differences in gut microbial metabolites among HIV-infected MSM with different immunologic responses. These results indicate the potential of gut microbial metabolites as novel disease progression markers and therapeutic targets.

The prowess of metabolomics in cancer research: current trends, challenges and future perspectives

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Effect of End-Tethered Methoxy-PEO Chain Density on Uremic Toxin Adsorption

In 2023, around 850 million people globally were affected by chronic kidney disease, which leads to the retention of uremic toxins and excess fluid in the blood. This study examines the adsorption of these toxins to poly(ethylene oxide) (PEO) films, known for their low-fouling properties. The gold surfaces were treated with 5 mM end-thiolated methoxy-terminated PEO (m-PEO) and analyzed using dynamic contact angle measurements, X-ray photoelectron spectroscopy, and spectroscopic ellipsometry to confirm the PEO film's presence and determine chain density. The adsorption of 25 different uremic toxins to m-PEO films was evaluated by using liquid chromatography-mass spectrometry (LC/MS), focusing on their binding affinity and adsorption dynamics. Results showed the effective modification of surfaces with m-PEO, with a notable change in contact angles and chain density (∼0.5 and 0.8 chains/nm2). Interestingly, pyruvic acid showed significant adsorption, whereas other toxins, such as hippuric acid, creatinine, and xanthosine had minimal interactions with the film. This indicates that the adsorption of these toxins is not primarily concentration driven and is rather dependent on the chemical structure of each toxin. These findings provide important insights for designing low-fouling coatings for biomedical devices.

Adsorption Dynamics of Uremic Toxins to Cyclodextrin-Coated Magnetic Nano-Adsorbents

Chronic kidney disease entails a progressive decline in kidney function, hindering the kidneys' ability to excrete fluid, electrolytes, and metabolites. This dysfunction leads to metabolite accumulation in the bloodstream, which can reach toxic concentrations. Hemodialysis is an effective means of treating patients with kidney failure, but it does not clear all toxins effectively. Engineered nano-adsorbents can potentially improve the removal of retained toxins, particularly protein-bound types. Magnetic nanoparticles coated with α-, β-, and γ-cyclodextrin were synthesized, and physicochemical properties were characterized using thermogravimetric analysis, transmission electron microscopy, dynamic light scattering, and ζ-potential for their physiochemical properties. The effect of surface chemistry and incubation time on toxin adsorption was investigated using quantitative mass spectrometry techniques. All particle types demonstrated toxin adsorption to some level. Overall, the adsorption process was independent of metabolite concentration, suggesting a dynamic interplay between surface properties and solution composition. This insight will contribute to developing innovative adsorbent films designed to remove uremic toxins effectively.

A liquid crystal-decorated aptasensing gadget for rapid monitoring of A549 cells: Future portable test kit for lung cancer diagnosis

BACKGROUND

Presented here is a straightforward detection system designed to track non-small cell lung cancer (specifically A549 cells) using a combination of liquid crystals (LCs) and aptamer sequences, marking a pioneering approach in this field. A change in the alignment of LCs from perpendicular to random status by the aptamer-cell complex altered the murky polarized background of the aptasensor to multicolored.

RESULTS

The LC-designed aptasensor could determine A549 cancerous cells in the range of 2.0E+01-7.0E+07 cell mL-1 with a limit of detection (LOD) as low as 10 cell mL-1. Through precise quantification of A549 cells in human serum samples diluted 20 times, with recovery rates ranging from 97.59 % to 101.31 %, the suggested aptasensor proves to be a dependable method for cancer screening. Furthermore, the LC aptasensor was identified as a fast sensing array due to a 10-min incubation period for the aptamer-cell complexation.

SIGNIFICANCE

The LC aptasensor is label-free, operator-independent, low-cost, sensitive, and user-friendly, making it potent as a miniaturized portable sensing chip for efficient healthcare monitoring.

A Comprehensive LC-MS Metabolomics Assay for Quantitative Analysis of Serum and Plasma

Background/Objectives: Targeted metabolomics is often criticized for the limited metabolite coverage that it offers. Indeed, most targeted assays developed or used by researchers measure fewer than 200 metabolites. In an effort to both expand the coverage and improve the accuracy of metabolite quantification in targeted metabolomics, we decided to develop a comprehensive liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay that could quantitatively measure more than 700 metabolites in serum or plasma. Methods: The developed assay makes use of chemical derivatization followed by reverse phase LC-MS/MS and/or direct flow injection MS (DFI-MS) in both positive and negative ionization modes to separate metabolites. Multiple reaction monitoring (MRM), in combination with isotopic standards and multi-point calibration curves, is used to detect and absolutely quantify the targeted metabolites. The assay has been adapted to a 96-well plate format to enable automated, high-throughput sample analysis. Results: The assay (called MEGA) is able to detect and quantify 721 metabolites in serum/plasma, covering 20 metabolite classes and many commonly used clinical biomarkers. The limits of detection were determined to range from 1.4 nM to 10 mM, recovery rates were from 80% to 120%, and quantitative precision was within 20%. LC-MS/MS metabolite concentrations of the NIST® SRM®1950 plasma standard were found to be within 15% of NMR quantified levels. The MEGA assay was further validated in a large dietary intervention study. Conclusions: The MEGA assay should make comprehensive quantitative metabolomics much more affordable, accessible, automatable, and applicable to large-scale clinical studies.

Identification of a Novel Biomarker Panel for Breast Cancer Screening

Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management.

Plant Metabolomics: The Future of Anticancer Drug Discovery

Drug development from medicinal plants constitutes an important strategy for finding natural anticancer therapies. While several plant secondary metabolites with potential antitumor activities have been identified, well-defined mechanisms of action remained uncovered. In fact, studies of medicinal plants have often focused on the genome, transcriptome, and proteome, dismissing the relevance of the metabolome for discovering effective plant-based drugs. Metabolomics has gained huge interest in cancer research as it facilitates the identification of potential anticancer metabolites and uncovers the metabolomic alterations that occur in cancer cells in response to treatment. This holds great promise for investigating the mode of action of target metabolites. Although metabolomics has made significant contributions to drug discovery, research in this area is still ongoing. In this review, we emphasize the significance of plant metabolomics in anticancer research, which continues to be a potential technique for the development of anticancer drugs in spite of all the challenges encountered. As well, we provide insights into the essential elements required for performing effective metabolomics analyses.

Metabolomic Profiling of Pulmonary Neuroendocrine Neoplasms

BACKGROUND/OBJECTIVES

Pulmonary neuroendocrine neoplasms (NENs) account for 20% of malignant lung tumors. Their management is challenging due to their diverse clinical features and aggressive nature. Currently, metabolomics offers a range of potential cancer biomarkers for diagnosis, monitoring tumor progression, and assessing therapeutic response. However, a specific metabolomic profile for early diagnosis of lung NENs has yet to be identified. This study aims to identify specific metabolomic profiles that can serve as biomarkers for early diagnosis of lung NENs.

METHODS

We measured 153 metabolites using liquid chromatography combined with mass spectrometry (LC-MS) in the plasma of 120 NEN patients and compared them with those of 71 healthy individuals. Additionally, we compared these profiles with those of 466 patients with non-small-cell lung cancers (NSCLCs) to ensure clinical relevance.

RESULTS

We identified 21 metabolites with consistently altered plasma concentrations in NENs. Compared to healthy controls, 18 metabolites were specific to carcinoid tumors, 5 to small-cell lung carcinomas (SCLCs), and 10 to large-cell neuroendocrine carcinomas (LCNECs). These findings revealed alterations in various metabolic pathways, such as fatty acid biosynthesis and beta-oxidation, the Warburg effect, and the citric acid cycle.

CONCLUSIONS

Our study identified biomarker metabolites in the plasma of patients with each subtype of lung NENs and demonstrated significant alterations in several metabolic pathways. These metabolomic profiles could potentially serve as biomarkers for early diagnosis and better management of lung NENs.

Combining Metabolomics and Machine Learning to Identify Diagnostic and Prognostic Biomarkers in Patients with Non-Small Cell Lung Cancer Pre- and Post-Radiation Therapy

Lung cancer is the leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) accounting for over 85% of cases and poor prognosis in advanced stages. This study explored shifts in circulating metabolite levels in NSCLC patients versus healthy controls and examined the effects of conventionally fractionated radiation therapy (CFRT) and stereotactic body radiation therapy (SBRT). We enrolled 91 NSCLC patients (38 CFRT and 53 SBRT) and 40 healthy controls. Plasma metabolite levels were assessed using semi-targeted metabolomics, revealing 32 elevated and 18 reduced metabolites in patients. Key discriminatory metabolites included ethylmalonic acid, maltose, 3-phosphoglyceric acid, taurine, glutamic acid, glycocolic acid, and d-arabinose, with a combined Receiver Operating Characteristics curve indicating perfect discrimination between patients and controls. CFRT and SBRT affected different metabolites, but both changes suggested a partial normalization of energy and amino acid metabolism pathways. In conclusion, metabolomics identified distinct metabolic signatures in NSCLC patients with potential as diagnostic biomarkers. The differing metabolic responses to CFRT and SBRT reflect their unique therapeutic impacts, underscoring the utility of this technique in enhancing NSCLC diagnosis and treatment monitoring.

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

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

Metabolomics for searching validated biomarkers in cancer studies: a decade in review

INTRODUCTION

In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways.

AREAS COVERED

This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types.

EXPERT OPINION

Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

Lung cancer metabolomics: a pooled analysis in the Cancer Prevention Studies

BACKGROUND

A better understanding of lung cancer etiology and the development of screening biomarkers have important implications for lung cancer prevention.

METHODS

We included 623 matched case-control pairs from the Cancer Prevention Study (CPS) cohorts. Pre-diagnosis blood samples were collected between 1998 and 2001 in the CPS-II Nutrition cohort and 2006 and 2013 in the CPS-3 cohort and were sent for metabolomics profiling simultaneously. Cancer-free controls at the time of case diagnosis were 1:1 matched to cases on date of birth, blood draw date, sex, and race/ethnicity. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression, controlling for confounders. The Benjamini-Hochberg method was used to correct for multiple comparisons.

RESULTS

Sphingomyelin (d18:0/22:0) (OR: 1.32; 95% CI: 1.15, 1.53, FDR = 0.15) and taurodeoxycholic acid 3-sulfate (OR: 1.33; 95% CI: 1.14, 1.55, FDR = 0.15) were positively associated with lung cancer risk. Participants diagnosed within 3 years of blood draw had a 55% and 48% higher risk of lung cancer per standard deviation increase in natural log-transformed sphingomyelin (d18:0/22:0) and taurodeoxycholic acid 3-sulfate level, while 26% and 28% higher risk for those diagnosed beyond 3 years, compared to matched controls. Lipid and amino acid metabolism accounted for 47% to 80% of lung cancer-associated metabolites at P < 0.05 across all participants and subgroups. Notably, ever-smokers exhibited a higher proportion of lung cancer-associated metabolites (P < 0.05) in xenobiotic- and lipid-associated pathways, whereas never-smokers showed a more pronounced involvement of amino acid- and lipid-associated metabolic pathways.

CONCLUSIONS

This is the largest prospective study examining untargeted metabolic profiles regarding lung cancer risk. Sphingomyelin (d18:0/22:0), a sphingolipid, and taurodeoxycholic acid 3-sulfate, a bile salt, may be risk factors and potential screening biomarkers for lung cancer. Lipid and amino acid metabolism may contribute significantly to lung cancer etiology which varied by smoking status.

Exploring the astonishing beneficial effects of round gourd (Praecitrullus fistulosus) and plant lectins towards cancer: A comprehensive review

Praecitrullus fistulosus, commonly known as round gourd or tinda, is a remarkable source of bioactive substances like polyphenols, antioxidants, carotene, magnesium, and vitamin C. It is considered one of the Cucurbitaceae family due to its medicinal features. Plant lectins are carbohydrate-binding proteins that can bind and identify the carbohydrate moieties upon cancerous cells demonstrated some anticancer potentials. Several plant lectins are helpful as cancer biomarkers because they can find cancer cells and contribute to cell death initiation via apoptosis and autophagy, suggesting the possible role of cancer-inhibiting pathways. Therefore, round gourd and lectins might be useful in the controlling of cancer. This study compiled the most recent scientific literature regarding the round gourd and numerous plant lectins, and the clinical trials of lectins exploring their effects on cancer were examined. Research according to the literature, round gourd, and lectins demonstrated pharmacological alterations not only in cancer but in many other disorders as well. Thus, clinical investigations proved the beneficial impacts of round gourd and lectins on cancer due to their antioxidants, anti-inflammatory, and anticarcinogenic properties. Further studies are required to fully comprehend the potential applications of these plant-derived compounds against cancer, as well as to identify the round gourd components and clarify their mode of action.

Metabolomics of Ramadan fasting and associated risk of chronic diseases

BACKGROUND

The dramatic change in lifestyle associated with Ramadan fasting raises questions about its effect on metabolism and health. Metabolites, as the end product of metabolism, are excellent candidates to be studied in this regard.

OBJECTIVE

This study aims to investigate the effect of Ramadan fasting on the metabolic profile and risk of chronic diseases.

METHODS

The London Ramadan study (LORANS) is an observational study in which 2 blood samples were collected from 72 participants a few days before and after the fasting month of Ramadan. We conducted metabolomic profiling using nuclear magnetic resonance spectroscopy to assess the change in individual metabolites from before to after Ramadan. Also, we generated metabolic scores (scaled from 0 to 100) for 7 chronic diseases in the UK Biobank and assessed the association of Ramadan fasting with these scores in LORANS.

RESULTS

Of the 72 participants, 35 were male (48.6%); the mean (± standard deviation) age was 45.7 (±16) y. Ramadan fasting was associated with changes in 14 metabolites (1 inflammation marker, 1 amino acid, 2 glycolysis-related metabolites, 2 ketone bodies, 2 triglyceride, and 6 lipoprotein subclasses), independent of changes in body composition. Using data from 117,981 participants in the UK Biobank, we generated metabolic scores for diabetes, hypertension, coronary artery disease, renal failure, colorectal cancer, breast cancer, and lung cancer. The metabolic scores for lung cancer, colorectal cancer, and breast cancer were lower after Ramadan in LORANS (-4.74, 9.6%, 95% confidence interval -6.56, -2.91, P < 0.001), (-1.09, -2.4%, -1.69, -0. 50, P < 0.001), and (-0.48, -1.1%, -0. 81, -0.15, P = 0.006), respectively.

CONCLUSIONS

Ramadan fasting is associated with short-term favorable changes in the metabolic profile concerning risk of some chronic diseases. These findings should be further investigated in future, larger studies of longer follow-up with clinical outcomes.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Metabolomics and lipidomics in non-small cell lung cancer

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

[Research Progress on Tumor Metabolic Biomarkers in Liquid Biopsy of Lung Cancer]

Liquid biopsy is gradually being applied in the clinical diagnosis and treatment of lung cancer. At present, with the development of metabolomics, more and more metabolic biomarkers are considered as potential sensitive markers reflecting the occurrence and development of tumors. This article summarizes the changes in the main metabolic pathways of lung cancer, including glucose metabolism, amino acid metabolism, lipid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and purine metabolism. Meanwhile, this article reviews the role of metabolic biomarkers in the early diagnosis of lung cancer, predicting disease progression, and evaluating the efficacy of chemotherapy and immunotherapy, aiming to provide effective biomarkers for tumor diagnosis and treatment.
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Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Adsorption Dynamics of Uremic Toxins to Novel Modified Magnetic Nanoparticles

Kidney dysfunction leads to the retention of metabolites in the blood compartment, some of which reach toxic levels. Uremic toxins are associated with the progression of kidney disease and other symptoms of kidney failure (i.e., nausea, itchiness, and hypertension). Toxin removal ameliorates symptoms and reduces further organ damage, but membrane-based methods are inadequate for this purpose. Engineered adsorbents may facilitate enhanced removal of retained toxins, especially those bound strongly by proteins. Poly 2-(methacryloyloxy)ethyl phosphorylcholine-co-β-cyclodextrin (p(MPC-co-PMβCD)) coated magnetic nanoparticles are synthesized, characterized for their physicochemical properties (Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), thermogravimetric analysis(TGA), gel permeation chromatography (GPC), and transmission electron microscope (TEM), and evaluated toxin adsorption from a complex solution for the first time to quantify the effects of film chemistry and incubation time on the adsorbed toxinome (the collection of toxins). Uremic toxins are bound by even "low-fouling" polymer films themselves; providing further insight into how small molecule interactions with "low-fouling" films may affect protein-surface interactions. These results suggest a dynamic interaction between toxins and surfaces that is not driven by solution concentration alone. This knowledge will help advance the design of novel adsorbent films for clearing uremic toxins.

The microbiome in the pathogenesis of lung cancer: The role of microbiome in lung cancer pathogenesis

As one of the malignant tumors with high incidence rate and high mortality, lung cancer seriously threatens the life safety of patients. Research shows that microorganisms are closely related to lung cancer. The microbiome is symbiotic with the host and plays a vital role in the functions of the human body. Microbiota dysbiosis is correlated with development of lung cancer. However, the underlying mechanisms are poorly understood. This paper summarizes the composition characteristics of the gut-lung axis microbiome and intratumoral microbiome in patients with lung cancer. We then expound five potential carcinogenic mechanisms based on microorganisms, such as genotoxicity, metabolism, inflammation, immune response, and angiogenesis. Next, we list three high-throughput sequencing methods, and finally looks forward to the prospect of microorganisms as novel targets for early diagnosis and treatment of lung cancer.

Plasma Lipidomics Profiling to Identify the Biomarkers of Diagnosis and Radiotherapy Response for Advanced Non-Small-Cell Lung Cancer Patients

Background

Advanced lung cancer that contributes to a heavy burden on medical institutions is the leading cause of cancer-related death and is often accompanied by metabolic disorders. In this study, we aimed to explore the biomarkers of diagnosis and radiotherapy response in non-small-cell lung cancer (NSCLC) patients by plasma lipidomics analysis.

Method

Using triple-quadrupole mass spectrometer analysis, our research characterized the plasma lipid metabolomics profile of 25 healthy controls and 31 advanced NSCLC patients in each of three different radiotherapy phases.

Results

The results showed altered lipid elements and concentrations among NSCLC patients with different radiotherapy phases, NSCLC subtypes, and different radiotherapeutic responses. We found that compared to the healthy controls, myelin-associated glycoprotein (MAG), phosphatidylinositol (PI), and phosphatidylserine (PS) were mainly and significantly altered lipid elements (> twofold, and p < 0.05) among NSCLC patients with different radiotherapy phases. Through comparison of lipid elements between bad and good responses of NSCLC patients with radiotherapy, the obviously declined phosphatidylglycerol (PG 18 : 0/14 : 0, 18 : 1/18 : 3, and 18 : 0/20 : 1) or markedly elevated PI (20 : 0/22 : 5 and 18 : 2/22 : 4) and phosphatidic acid (PA 14 : 0/20 : 4, 14 : 0/20 : 3, and 18 : 2/22 : 4) could indicate poor therapeutic response for NSCLC patients. The results of ROC curve analysis suggested that PG (18 : 0/20 : 1 and 18 : 0/14 : 0) could clearly predict the radiotherapeutic response for NSCLC patients, and PS (18 : 0/20 : 0) and cholesterol were the first two lipid components with the most potential for the diagnosis of advanced NSCLC.

Conclusion

Our results indicated that plasma lipidomics profiling might have a vital value to uncover the heterogeneity of lipid metabolism in healthy people and advanced NSCLC patients with different radiotherapy phase, and further to screen out radiotherapeutic response-specific biomarkers.

Exploring Optimal Biomarker Sources: A Comparative Analysis of Exosomes and Whole Plasma in Fasting and Non-Fasting Conditions for Liquid Biopsy Applications

The study of liquid biopsy with plasma samples is being conducted to identify biomarkers for clinical use. Exosomes, containing nucleic acids and metabolites, have emerged as possible sources for biomarkers. To evaluate the effectiveness of exosomes over plasma, we analyzed the small non-coding RNAs (sncRNAs) and metabolites extracted from exosomes in comparison to those directly extracted from whole plasma under both fasting and non-fasting conditions. We found that sncRNA profiles were not affected by fasting in either exosome or plasma samples. Our results showed that exosomal sncRNAs were found to have more consistent profiles. The plasma miRNA profiles contained high concentrations of cell-derived miRNAs that were likely due to hemolysis. We determined that certain metabolites in whole plasma exhibited noteworthy concentration shifts in relation to fasting status, while others did not. Here, we propose that (1) fasting is not required for a liquid biopsy study that involves both sncRNA and metabolomic profiling, as long as metabolites that are not influenced by fasting status are selected, and (2) the utilization of exosomal RNAs promotes robust and consistent findings in plasma samples, mitigating the impact of batch effects derived from hemolysis. These findings advance the optimization of liquid biopsy methodologies for clinical applications.

Tumor-educated platelet blood tests for Non-Small Cell Lung Cancer detection and management

Liquid biopsy approaches offer a promising technology for early and minimally invasive cancer detection. Tumor-educated platelets (TEPs) have emerged as a promising liquid biopsy biosource for the detection of various cancer types. In this study, we processed and analyzed the TEPs collected from 466 Non-small Cell Lung Carcinoma (NSCLC) patients and 410 asymptomatic individuals (controls) using the previously established thromboSeq protocol. We developed a novel particle-swarm optimization machine learning algorithm which enabled the selection of an 881 RNA biomarker panel (AUC 0.88). Herein we propose and validate in an independent cohort of samples (n = 558) two approaches for blood samples testing: one with high sensitivity (95% NSCLC detected) and another with high specificity (94% controls detected). Our data explain how TEP-derived spliced RNAs may serve as a biomarker for minimally-invasive clinical blood tests, complement existing imaging tests, and assist the detection and management of lung cancer patients.

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

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.

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

Lung cancer (LC) is considered as one of the leading causes of cancer-associated mortalities. Cancer cells' reprogrammed metabolism results in changes in metabolite concentrations, which can be utilized to identify a distinct metabolic pattern or fingerprint for cancer detection or diagnosis. By detecting different metabolic variations in the expression levels of LC patients, this will help and enhance early diagnosis methods as well as new treatment strategies. The majority of patients are identified at advanced stages after undergoing a number of surgical procedures or diagnostic testing, including the invasive procedures. This could be overcome by understanding the mechanism and function of differently regulated metabolites. Significant variations in the metabolites present in the different samples can be analyzed and used as early biomarkers. They could also be used to analyze the specific progression and type as well as stages of cancer type making it easier for the treatment process. The main aim of this review article is to focus on rewired metabolic pathways and the associated metabolite alterations that can be used as diagnostic and therapeutic targets in lung cancer diagnosis as well as treatment strategies.

Combined analysis of gut microbiome and serum metabolomics reveals novel biomarkers in patients with early-stage non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the predominant form of lung cancer and is one of the most fatal cancers worldwide. Recently, the International Association for the Study of Lung Cancer (IASLC) proposed a novel grading system based on the predominant and high-grade histological patterns for invasive pulmonary adenocarcinoma (IPA). To improve outcomes for NSCLC patients, we combined serum metabolomics and fecal microbiology to screen biomarkers in patients with early-stage NSCLC and identified characteristic microbial profiles in patients with different grades of IPA. 26 genera and 123 metabolites were significantly altered in the early-stage NSCLC patients. Agathobacter, Blautia, Clostridium, and Muribaculacea were more abundant in the early-stage NSCLC patients compared with healthy controls. For the different grades of IPA, the characteristic microorganisms are as follows: Blautia and Marinobacter in IPA grade type 1; Dorea in IPA grade type 2; and Agathobacter in IPA grade type 3. In the metabolome results, the early-stage NSCLC group mainly included higher levels of sphingolipids (D-erythro-sphingosine 1-phosphate, palmitoyl sphingomyelin), fatty acyl (Avocadyne 1-acetate, 12(S)-HETE, 20-Carboxy-Leukotriene B4, Thromboxane B3, 6-Keto-prostaglandin f1alpha, Sebacic acid, Tetradecanedioic acid) and glycerophospholipids (LPC 20:2, LPC 18:0, LPC 18:4, LPE 20:2, LPC 20:1, LPC 16:1, LPC 20:0, LPA 18:2, LPC 17:1, LPC 17:2, LPC 19:0). Dysregulation of pathways, such as sphingolipid metabolism and sphingolipid signaling pathway may become an emerging therapeutic strategy for early-NSCLC. Correlation analysis showed that gut microbiota and serum metabolic profiles were closely related, while Muribaculacea and Clostridium were the core genera. These findings provide new biomarkers for the diagnosis of early-stage NSCLC and the precise grading assessment of prognostic-related IPAs, which are of clinical importance and warrant further investigation of the underlying molecular mechanisms.

Serum untargeted metabolomics reveal metabolic alteration of non-small cell lung cancer and refine disease detection

This study was performed to characterize the metabolic alteration of non-small-cell lung cancer (NSCLC) and discover blood-based metabolic biomarkers relevant to lung cancer detection. An untargeted metabolomics-based approach was applied in a case-control study with 193 NSCLC patients and 243 healthy controls. Serum metabolomics were determined by using an ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method. We screened differential metabolites based on univariate and multivariate analysis, followed by identification of the metabolites and related pathways. For NSCLC detection, machine learning was employed to develop and validate the model based on the altered serum metabolite features. The serum metabolic pattern of NSCLC was definitely different from the healthy condition. In total, 278 altered features were found in the serum of NSCLC patients comparing with healthy people. About one-fifth of the abundant differential features were identified successfully. The altered metabolites were enriched in metabolic pathways such as phenylalanine metabolism, linoleic acid metabolism, and biosynthesis of bile acids. We demonstrated a panel of 10 metabolic biomarkers which representing excellent discriminating capability for NSCLC discrimination, with a combined area under the curve (AUC) in the validation set of 0.95 (95% CI: 0.91-0.98). Moreover, this model showed a desirable performance for the detection of NSCLC at an early stage (AUC = 0.95, 95% CI: 0.92-0.97). Our study offers a perspective on NSCLC metabolic alteration. The finding of the biomarkers might shed light on the clinical detection of lung cancer, especially for those cancers in an early stage in Chinese population.

Multi-omics analysis of biomarkers and molecular mechanism of rheumatoid arthritis with bone destruction

OBJECTIVES

Our study aimed to elucidate the role of metabolites, bacteria, and fungi in rheumatoid arthritis (RA) patients with bone destruction (BD(+)) and identify some biomarkers to predicate bone progression of RA.

METHODS

Plasma metabolites of the 127 RA patients and 69 healthy controls were conducted by using nontargeted liquid chromatography-mass spectrometry (LC-MS). The gut bacteria and fungi were assessed by 16S rRNA and internal transcribed spacer (ITS).

RESULTS

Compared with RA patients without bone destruction (BD(-)), some metabolites, bacteria, and fungi were altered in BD(+). Seven metabolites were selected as key metabolites for classifying the BD(+) and BD(-) groups with moderate accuracy (AUC=0.71). Metabolites-groups, metabolites-metabolites, and metabolites-clinical factors had a certain correlation, and 7 metabolites were enriched in glycerophospholipid metabolism and L-arginine and proline metabolism pathways. The bacteria and fungi of the BD(+) group showed significant differences in composition and function compared with BD(-) group. The changed 4 bacteria and 12 fungi yielded accuracy (AUC=0.74 and AUC=0.87, respectively) for the two groups. Taking 7 metabolites, 4 bacteria, and 12 fungi as a panel for AUC analysis, an improved AUC of 0.99 significantly discriminated the two groups. The changed metabolites, gut bacteria, and fungi may affect the pathway related to L-arginine.

CONCLUSIONS

Our nontargeted LC-MS, 16S rRNA, and ITS highlighted a novel link among the metabolites, bacteria, fungi, and pathology of BD(+), which could contribute to our understanding of the role of metabolites, bacteria, and fungi in BD(+) etiology and offered some novel biomarkers to predict the bone progression of RA.

Liquid biopsy and non-small cell lung cancer: are we looking at the tip of the iceberg?

The possibility to analyse the tumour genetic material shed in the blood is undoubtedly one of the main achievements of translational research in the latest years. In the modern clinical management of advanced non-small cell lung cancer, molecular characterisation plays an essential role. In parallel, immunotherapy is widely employed, but reliable predictive markers are not available yet. Liquid biopsy has the potential to face the two issues and to increase its role in advanced NSCLC in the next future. The aim of this review is to summarise the main clinical applications of liquid biopsy in advanced non-small cell lung cancer, underlining both its potential and limitations from a clinically driven perspective.

Changes in Metabolism as a Diagnostic Tool for Lung Cancer: Systematic Review

Lung cancer is the leading cause of cancer-related mortality worldwide, with five-year survival rates varying from 3–62%. Screening aims at early detection, but half of the patients are diagnosed in advanced stages, limiting therapeutic possibilities. Positron emission tomography-computed tomography (PET-CT) is an essential technique in lung cancer detection and staging, with a sensitivity reaching 96%. However, since elevated 18F-fluorodeoxyglucose (¹⁸F-FDG) uptake is not cancer-specific, PET-CT often fails to discriminate between malignant and non-malignant PET-positive hypermetabolic lesions, with a specificity of only 23%. Furthermore, discrimination between lung cancer types is still impossible without invasive procedures. High mortality and morbidity, low survival rates, and difficulties in early detection, staging, and typing of lung cancer motivate the search for biomarkers to improve the diagnostic process and life expectancy. Metabolomics has emerged as a valuable technique for these pitfalls. Over 150 metabolites have been associated with lung cancer, and several are consistent in their findings of alterations in specific metabolite concentrations. However, there is still more variability than consistency due to the lack of standardized patient cohorts and measurement protocols. This review summarizes the identified metabolic biomarkers for early diagnosis, staging, and typing and reinforces the need for biomarkers to predict disease progression and survival and to support treatment follow-up.

Age-Associated Gut Dysbiosis, Marked by Loss of Butyrogenic Potential, Correlates With Altered Plasma Tryptophan Metabolites in Older People Living With HIV

BACKGROUND

Imbalance in tryptophan (TRP) metabolism and its neuroactive metabolites, serotonin and kynurenine (KYN), is a known pathogenic mechanism underlying neurocognitive impairment. Gut microbiota plays an important role in TRP metabolism, and the production of these neuroactive molecules affects neurocognitive function. Although both HIV infection and normal aging independently induce gut dysbiosis and influence TRP metabolism, their interactive effects on compositional/functional changes in gut microbiota and consequent alterations in TRP metabolites remain largely undetermined.

METHODS

Older people living with HIV infection (PLWH, aged 50-70 years, n = 22) were enrolled in this cross-sectional pilot study. Metagenomic analysis of fecal microbiome using 16S Ribosomal ribonucleic acid gene sequencing and metabolomics analysis of plasma using mass spectrometry with a reverse-phase iquid chromatography tandem mass spectrometry were performed. Statistical analyses included the univariate linear regression and Spearman correlation analyses.

RESULTS

Age-associated changes in plasma levels of key neuroactive TRP metabolites, serotonin and KYN, were seen in PLWH. Specifically, we observed age-dependent decreases in serotonin and increases in KYN and KYN-to-TRP ratio, indicative of dysfunctional TRP metabolism. Furthermore, the gut dysbiosis seen in older PLWH is characterized by a reduction of Firmicutes/Bacteroidetes ratio and butyrate-producing microbial families Lachnospiraceae and Lactobacillaceae. Of importance, correspondent with gut dysbiosis, increasing age was significantly associated with decreased plasma butyrate levels, which in turn correlated positively with serotonin and negatively with KYN/TRP ratio.

CONCLUSIONS

Age-dependent gut microbial dysbiosis distinguished by a decrease in butyrogenic potential is a key pathogenic feature associated with the shift in TRP metabolism from serotonin to KYN in older PLWH.

Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome

The five-year survival rate of lung cancer patients is very low, mainly because most newly diagnosed patients present with locally advanced or metastatic disease. Therefore, early diagnosis is key to the successful treatment and management of lung cancer. Unfortunately, early detection methods of lung cancer are not ideal. In this brief review, we described early detection methods such as chest X-rays followed by bronchoscopy, sputum analysis followed by cytological analysis, and low-dose computed tomography (LDCT). In addition, we discussed the potential of metabolomic fingerprinting, compared to that of other biomarkers, including molecular targets, as a low-cost, high-throughput blood-based test that is both feasible and affordable for early-stage lung cancer screening of at-risk populations. Accordingly, we proposed a paradigm shift to metabolomics as an alternative to molecular and proteomic-based markers in lung cancer screening, which will enable blood-based routine testing and be accessible to those patients at the highest risk for lung cancer.

Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) fingerprinting enabled treatment monitoring of pulmonary carcinoma cells in real time

Lung cancer is one of the leading causes of cancer related deaths in the United States. A novel volatile analysis platform is needed to complement current diagnostic techniques and better elucidate chemical signatures of lung cancer and subsequent treatments. A systems biology bottom-up approach using cell culture volatilomics was employed to identify pathological volatile fingerprints of lung cancer in real time. An advanced secondary electrospray ionization (SESI) source, named SuperSESI was used in this study and directly attached to a Thermo Q-Exactive high-resolution mass spectrometer (HRMS). We performed a series of experiments to determine if our optimized SESI-HRMS platform can distinguish between cancer types by sampling their in vitro volatilome profiles. We detected 60 significant volatile organic compound (VOC) features in positive mode that were deemed of cancer cell origin. The cell derived features were used for subsequent analyses to distinguish between our two studied lung cancer types, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Partial least squares-discriminant analysis (PLS-DA) model revealed a good separation of the two cancer types, suggesting unique chemical composition of their headspace profiles. A receiver operating characteristic (ROC) curve using 10 prominent features was used to predict disease type, with an area under the curve (AUC) of 0.811. Cultures were also treated with cisplatin to determine the feasibility of classifying drug treatment from expelled gases. A PLS-DA model revealed independent clustering based on their headspace profiles. An ROC curve using the top features driving separation of PLS-DA model suggested good accuracy with an AUC of 1. It is thus possible to benefit from the advantages of this platform to distinguish the unique volatile fingerprints of cancers to uncover potential biomarkers for cancer type differentiation and treatment monitoring.

Identifying metabolic alterations in newly diagnosed small cell lung cancer patients

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Metabolomic Profiling in Lung Cancer: A Systematic Review

Lung cancer continues to be a significant burden worldwide and remains the leading cause of cancer-associated mortality. Two considerable challenges posed by this disease are the diagnosis of 61% of patients in advanced stages and the reduced five-year survival rate of around 4%. Noninvasively collected samples are gaining significant interest as new areas of knowledge are being sought and opened up. Metabolomics is one of these growing areas. In recent years, the use of metabolomics as a resource for the study of lung cancer has been growing. We conducted a systematic review of the literature from the past 10 years in order to identify some metabolites associated with lung cancer. More than 150 metabolites have been associated with lung cancer-altered metabolism. These were detected in different biological samples by different metabolomic analytical platforms. Some of the published results have been consistent, showing the presence/alteration of specific metabolites. However, there is a clear variability due to lack of a full clinical characterization of patients or standardized patients selection. In addition, few published studies have focused on the added value of the metabolomic profile as a means of predicting treatment response for lung cancer. This review reinforces the need for consistent and systematized studies, which will help make it possible to identify metabolic biomarkers and metabolic pathways responsible for the mechanisms that promote tumor progression, relapse and eventually resistance to therapy.

Specific Gut Microbiome and Serum Metabolome Changes in Lung Cancer Patients

Background

Lung cancer (LC) is one of the most aggressive, prevalent and fatal malignancies. Gut microbes and their associated metabolites are thought to cause and modulate LC development, albeit influenced by the host genetic make-up and environment. Herein, we identified and classified gut microbiota and serum metabolites associated with LC.

Methods

Stool samples were collected from 41 LC patients and 40 healthy volunteers. The gut microbiota was analyzed using 16S rRNA gene sequencing. Serum samples were collected from the same LC patients (n=30) and healthy volunteers (n=30) and serum metabolites were analyzed using liquid chromatography-mass spectrometry (LC-MS). Microbiome and metabolome data were analyzed separately and integrated for combined analysis using various bioinformatics methods.

Results

Serum metabolomics uncovered 870 metabolites regulated in 76 metabolic pathways in both groups. Microbial diversity analyses identified 15967 operational taxonomic units (OTUs) in groups. Of these, the abundance of 232 OTUs was significantly different between HC and LC groups. Also, serum levels of glycerophospholipids (LysoPE 18:3, LysoPC 14:0, LysoPC 18:3), Imidazopyrimidines (Hypoxanthine), AcylGlcADG 66:18; AcylGlcADG (22:6/22:6/22:6) and Acylcarnitine 11:0 were substantially different between HC and LC groups. Combined analysis correlated LC-associated microbes with metabolites, such as Erysipelotrichaceae_UCG_003, Clostridium and Synergistes with glycerophospholipids.

Conclusions

There is an intricate relationship between gut microbiome and levels of several metabolites such as glycerophospholipids and imidazopyrimidines. Microbial-associated metabolites are potential diagnostic biomarkers and therapeutic targets for LC.

Current and Future Development in Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related deaths in North America and other developed countries. One of the reasons lung cancer is at the top of the list is that it is often not diagnosed until the cancer is at an advanced stage. Thus, the earliest diagnosis of lung cancer is crucial, especially in screening high-risk populations, such as smokers, exposure to fumes, oil fields, toxic occupational places, etc. Based on the current knowledge, it looks that there is an urgent need to identify novel biomarkers. The current diagnosis of lung cancer includes different types of imaging complemented with pathological assessment of biopsies, but these techniques can still not detect early lung cancer developments. In this review, we described the advantages and disadvantages of current methods used in diagnosing lung cancer, and we provide an analysis of the potential use of body fluids as carriers of biomarkers as predictors of cancer development and progression.

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

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

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

Simple Summary

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

Abstract

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

HIV-Infected Individuals on ART With Impaired Immune Recovery Have Altered Plasma Metabolite Profiles

Background

Multiple host factors may influence immune reconstitution in HIV-infected people after the initiation of suppressive antiretroviral therapy (ART). Aberrant metabolic pathways have been reported in people with HIV (PWH) on ART. We hypothesized that alterations in plasma metabolites were associated with immune recovery following ART.

Methods

In this cross-sectional study, the plasma metabolomic profiles of PWH on ART were evaluated. PWH of slow and fast immune recovery were classified by increase in CD4 T cells following 2 years of ART. Targeted plasma metabolite profiling by liquid chromatography–mass spectrometry and gas chromatography–mass spectrometry to determine metabolite signatures for HIV recovery identified >200 metabolites.

Results

Notably, indole-3-propionic acid was downregulated during HIV, possibly reflecting impaired gastrointestinal epithelium homeostasis. The most important metabolite discriminating between the PWH with fast and slow immune recovery was cysteine. Upregulated cysteine and cysteine pathways may contribute to redox-balance maintenance and T-cell function in PWH with fast immune recovery. Additionally, serine and glycine metabolism and bile acid biosynthesis were the most perturbed metabolic pathways in PWH.

Conclusions

These results provide a starting point for developing biomarker candidates for immune recovery in PWH on ART and provide insight into the interplay of metabolism and immune response in HIV infection.

Circulating Tumor Cell and Metabolites as Novel Biomarkers for Early-Stage Lung Cancer Diagnosis

Background

Lung cancer is a malignant tumor that has the highest morbidity and mortality rate among all cancers. Early diagnosis of lung cancer is a key factor in reducing mortality and improving prognosis.

Methods

In this study, we performed CTC next-generation sequencing (NGS) in early-stage lung cancer patients to identify lung cancer-related gene mutations. Meanwhile, a serum liquid chromatography-tandem mass spectrometry (LC-MS) untargeted metabolomics analysis was performed in the CTC-positive patients. To screen potential diagnostic markers for early lung cancer.

Results

62.5% (30/48) of lung cancer patients had ≥1 CTC. By CTC NGS, we found that > 50% of patients had 4 commonly mutated genes, namely, NOTCH1, IGF2, EGFR, and PTCH1. 47.37% (9/19) patients had ARIDH1 mutations. Additionally, 30 CTC-positive patients and 30 healthy volunteers were subjected to LC-MS untargeted metabolomics analysis. We found 100 different metabolites, and 10 different metabolites were identified through analysis, which may have potential clinical application value in the diagnosis of CTC-positive early-stage lung cancer (AUC >0.9).

Conclusions

Our results indicate that NGS of CTC and metabolomics may provide new tumor markers for the early diagnosis of lung cancer.

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation

Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.4 million new occurrences. The detection of lung regions in images is a classic medical challenge. Studies show that computational methods contribute significantly to the medical diagnosis of lung pathologies by Computerized Tomography (CT), as well as through Internet of Things (IoT) methods based in the context on the health of things. The present work proposes a new model based on IoT for classification and segmentation of pulmonary CT images, applying the transfer learning technique in deep learning methods combined with Parzen’s probability density. The proposed model uses an Application Programming Interface (API) based on the Internet of Medical Things to classify lung images. The approach was very effective, with results above 98% accuracy for classification in pulmonary images. Then the model proceeds to the lung segmentation stage using the Mask R-CNN network to create a pulmonary map and use fine-tuning to find the pulmonary borders on the CT image. The experiment was a success, the proposed method performed better than other works in the literature, reaching high segmentation metrics values such as accuracy of 98.34%. Besides reaching 5.43 s in segmentation time and overcoming other transfer learning models, our methodology stands out among the others because it is fully automatic. The proposed approach has simplified the segmentation process using transfer learning. It has introduced a faster and more effective method for better-performing lung segmentation, making our model fully automatic and robust.

9G TestTM Cancer/Lung: A Desirable Companion to LDCT for Lung Cancer Screening

Simple Summary

Lung cancer is the most common cause of cancer-related deaths globally. Patients diagnosed at early-stage (0–I) have a higher survival rate than the metastasized stages (III–IV). Thus, there is great potential to reduce mortality by diagnosing lung cancer at stage 0~I through community screening. LDCT is a promising method, but it has a high false-positive rate. Therefore, a biomarker test that can be used in combination with LDCT for lung cancer screening to reduce false-positive rates is highly awaited. The present study evaluated the applicability of 9G test^TM Cancer/Lung test to detect stage 0~IV lung cancer. 9G test^TM Cancer/Lung test detects stage I, stage II, stage III, and stage IV cancers with the sensitivities of 77.5%, 78.1%, 67.4%, and 33.3%, respectively, at the specificity of 97.3%. These results indicate that the 9G test^TM Cancer/Lung can be used in conjunction with LDCT to screen lung cancer.

Abstract

A complimentary biomarker test that can be used in combination with LDCT for lung cancer screening is highly desirable to improve the diagnostic capacity of LDCT and reduce the false-positive rates. Most importantly, the stage I lung cancer detection rate can be dramatically increased by the simultaneous use of a biomarker test with LDCT. The present study was conducted to evaluate 9G test^TM Cancer/Lung’s sensitivity and specificity in detecting Stage 0~IV lung cancer. The obtained results indicate that the 9G test^TM Cancer/Lung can detect lung cancer with overall sensitivity and specificity of 75.0% (69.1~80.3) and 97.3% (95.0~98.8), respectively. The detection of stage I, stage II, stage III, and stage IV cancers with sensitivities of 77.5%, 78.1%, 67.4%, and 33.3%, respectively, at the specificity of 97.3% have never been reported before. The receiver operating characteristic curve analysis allowed us to determine the population-weighted AUC of 0.93 (95% CI, 0.91–0.95). These results indicate that the 9G test^TM Cancer/Lung can be used in conjunction with LDCT to screen lung cancer. Furthermore, obtained results indicate that the use of 9G test^TM Cancer/Lung with LDCT for lung cancer screening can increase stage I cancer detection, which is crucial to improve the currently low 5-year survival rates.

Resolving Metabolic Heterogeneity in Experimental Models of the Tumor Microenvironment from a Stable Isotope Resolved Metabolomics Perspective

The tumor microenvironment (TME) comprises complex interactions of multiple cell types that determines cell behavior and metabolism such as nutrient competition and immune suppression. We discuss the various types of heterogeneity that exist in solid tumors, and the complications this invokes for studies of TME. As human subjects and in vivo model systems are complex and difficult to manipulate, simpler 3D model systems that are compatible with flexible experimental control are necessary for studying metabolic regulation in TME. Stable Isotope Resolved Metabolomics (SIRM) is a valuable tool for tracing metabolic networks in complex systems, but at present does not directly address heterogeneous metabolism at the individual cell level. We compare the advantages and disadvantages of different model systems for SIRM experiments, with a focus on lung cancer cells, their interactions with macrophages and T cells, and their response to modulators in the immune microenvironment. We describe the experimental set up, illustrate results from 3D cultures and co-cultures of lung cancer cells with human macrophages, and outline strategies to address the heterogeneous TME.

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

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