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

Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery

Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.

Alterations of Ceramides, Acylcarnitines, GlyceroLPLs, and Amines in NSCLC Tissues

Abnormal lipid metabolism plays an important role in cancer development. In this study, nontargeted lipidomic study on 230 tissue specimens from 79 nonsmall cell lung cancer (NSCLC) patients was conducted using ultraperformance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). Downregulation of sphingosine and medium-long-chain ceramides and short-medium-chain acylcarnitine, upregulation of long-chain acylcarnitine C20:0, and enhanced histamine methylation were revealed in NSCLC tissues. Compared with paired noncancerous tissues, adenocarcinoma (AC) tissues had significantly decreased levels of sphingosine, medium-long-chain ceramides (Cer d18:1/12:0 and Cer d16:1/14:0, Cer d18:0/16:0, Cer d18:1/16:0, Cer d18:2/16:0, Cer d18:2/18:0), short-medium-chain (C2-C16) acylcarnitines, LPC 20:0 and LPC 22:1, and significantly increased levels of the long-chain acylcarnitine C20:0, LPC 16:0, LPC P-16:0, LPC 20:1, LPC 20:2, glyceroPC, LPE 16:0, and LPE 18:2. In squamous cell carcinoma (SCC) tissues, sphingosine, Cer d18:2/16:0 and Cer d18:2/18:0, and short-medium-chain acylcarnitines had significantly lower levels, while long-chain acylcarnitines (C20:0, and C22:0 or C22:0 M), LPC 20:1, LPC 20:2, and N1,N12-diacetylspermine had significantly higher levels compared to controls. In AC and SCC tissues, the levels of LPG 18:0, LPG 18:1, and LPS 18:1 were significantly decreased, while the levels of ceramide-1-phosphate (C1P) d18:0/3:0 or LPE P-16:0, N1-acetylspermidine, and 1-methylhistamine were significantly increased than controls. Furthermore, an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model based on a 4-lipid panel was established, showing good discrimination ability between cancerous and noncancerous tissues.

Serum metabolic changes link metal mixture exposures to vascular endothelial inflammation in residents living surrounding rivers near abandoned lead-zinc mines

Metal exposure is associated with vascular endothelial inflammation, an early pathological phenotype of atherosclerotic cardiovascular events. However, the underlying mechanism linking exposure, metabolic changes, and outcomes remains unclear. We aimed to investigate the metabolic changes underlying the associations of chronic exposure to metal mixtures with vascular endothelial inflammation. We recruited 960 adults aged 20-75 years from residential areas surrounding rivers near abandoned lead-zinc mine and classified them into river area and non-river area exposure groups. Urine levels of 25 metals, Framingham risk score (FRS), and serum concentrations of intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1), as biomarkers of vascular endothelial inflammation, were assessed. A "meet-in-the-middle" approach was applied to identify causal intermediate metabolites and metabolic pathways linking metal exposure to vascular endothelial inflammation in representative metabolic samples from 64 participants. Compared to the non-river area exposure group, the river area exposure group had significantly greater urine concentrations of chromium, copper, cadmium, and lead; lower urine concentrations of selenium; elevated FRS; and increased concentrations of ICAM-1 and VCAM-1. In total, 38 differentially abundant metabolites were identified between the river area and non-river area exposure groups. Among them, 25 metabolites were significantly associated with FRS, 8 metabolites with ICAM-1 expression, and 10 metabolites with VCAM-1 expression. Furthermore, fructose, ornithine, alpha-ketoglutaric acid, urea, and cytidine monophosphate, are potential mediators of the relationship between metal exposure and vascular endothelial inflammation. Additionally, the metabolic changes underlying these effects included changes in arginine and proline metabolism, pyrimidine metabolism, starch and sucrose metabolism, galactose metabolism, arginine biosynthesis, and alanine, aspartate, and glutamate metabolism, suggesting the disturbance of amino acid metabolism, the tricarboxylic acid cycle, nucleotide metabolism, and glycolysis. Overall, our results reveal biomechanisms that may link chronic exposure to multiple metals with vascular endothelial inflammation and elevated cardiovascular risk.

Lung Cancer Subtyping: A Short Review

As of 2022, lung cancer is the most commonly diagnosed cancer worldwide, with the highest mortality rate. There are three main histological types of lung cancer, and it is more important than ever to accurately identify the subtypes since the development of personalized, type-specific targeted therapies that have improved mortality rates. Traditionally, the gold standard for the confirmation of histological subtyping is tissue biopsy and histopathology. This, however, comes with its own challenges, which call for newer sampling techniques and adjunctive tools to assist in and improve upon the existing diagnostic workflow. This review aims to list and describe studies from the last decade (n = 47) that investigate three such potential omics techniques-namely (1) transcriptomics, (2) proteomics, and (3) metabolomics, as well as immunohistochemistry, a tool that has already been adopted as a diagnostic adjunct. The novelty of this review compared to similar comprehensive studies lies with its detailed description of each adjunctive technique exclusively in the context of lung cancer subtyping. Similarities between studies evaluating individual techniques and markers are drawn, and any discrepancies are addressed. The findings of this study indicate that there is promising evidence that supports the successful use of omics methods as adjuncts to the subtyping of lung cancer, thereby directing clinician practice in an economical and less invasive manner.

Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study

BACKGROUND

Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening.

METHODS

Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls.

RESULTS

The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly.

CONCLUSION

Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.

Changes of Facial Lipidomics by Intense Pulsed Light Treatment Based on LC-MS

BACKGROUND

Intense pulsed light (IPL) has been widely used to improve cutaneous photoaging in recent years. Several studies began to explore the changes of skin barrier function after treatment, but the changes of skin surface lipids (SSL), especially specific lipid content and types are still unclear.

METHODS

A total of 25 female volunteers were included in our study, and each of them received three full-face treatments with one month apart. Before the first treatment and 1 month after the last treatment, we collected clinical photos and skin stratum corneum samples from individuals. A 5-level scale was used to evaluate the efficacy of IPL treatment, liquid chromatography-mass spectrometry (LC-MS), and Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) were used to analyze the changes of SSL.

RESULTS

Two patients got no improvement after treatment, 6 patients had poor improvement and mild improvement was achieved in 9 patients, 5 and 3 patients reported moderate and significant improvement. The overall "effective" rate was 68 % and the "significant effective" rate was 32 %. The results showed 18 lipid subclasses and 487 lipid molecules were identified. The change of total lipid volume was not statistically significant (P = 0.088>0.05), but lipid subclass analysis showed the amount of Triglyceride (TG), Phosphatidic Acid (PA), Phosphatidylglycerol (PG) and Lysophosphatidylglycerol (LPG) were significantly increased (P < 0.05). There were 55 kinds of lipid molecules with significant difference after treatment (P < 0.05), and 51 of them belong to TG. The analysis of chain saturation of TG showed that the quantity of TG with 0, 1 and 2 unsaturated bonds increased significantly (P < 0.05).

CONCLUSIONS

IPL treatment does not have a significant effect on the overall amount of lipids while the amount of TG, PA, PG, LPG were significantly increased. These lipid changes may potentially improve the skin barrier function, but more high-quality and comprehensive studies are still needed.

BULLET POINT

Lipidomics analysis based on LC-MS; Changes of skin surface lipid after IPL treatment; the relationships between skin surface lipid and skin barrier functions.

LEVEL OF EVIDENCE IV

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Energy metabolism as the hub of advanced non-small cell lung cancer management: a comprehensive view in the framework of predictive, preventive, and personalized medicine

Energy metabolism is a hub of governing all processes at cellular and organismal levels such as, on one hand, reparable vs. irreparable cell damage, cell fate (proliferation, survival, apoptosis, malignant transformation etc.), and, on the other hand, carcinogenesis, tumor development, progression and metastazing versus anti-cancer protection and cure. The orchestrator is the mitochondria who produce, store and invest energy, conduct intracellular and systemically relevant signals decisive for internal and environmental stress adaptation, and coordinate corresponding processes at cellular and organismal levels. Consequently, the quality of mitochondrial health and homeostasis is a reliable target for health risk assessment at the stage of reversible damage to the health followed by cost-effective personalized protection against health-to-disease transition as well as for targeted protection against the disease progression (secondary care of cancer patients against growing primary tumors and metastatic disease). The energy reprogramming of non-small cell lung cancer (NSCLC) attracts particular attention as clinically relevant and instrumental for the paradigm change from reactive medical services to predictive, preventive and personalized medicine (3PM). This article provides a detailed overview towards mechanisms and biological pathways involving metabolic reprogramming (MR) with respect to inhibiting the synthesis of biomolecules and blocking common NSCLC metabolic pathways as anti-NSCLC therapeutic strategies. For instance, mitophagy recycles macromolecules to yield mitochondrial substrates for energy homeostasis and nucleotide synthesis. Histone modification and DNA methylation can predict the onset of diseases, and plasma C7 analysis is an efficient medical service potentially resulting in an optimized healthcare economy in corresponding areas. The MEMP scoring provides the guidance for immunotherapy, prognostic assessment, and anti-cancer drug development. Metabolite sensing mechanisms of nutrients and their derivatives are potential MR-related therapy in NSCLC. Moreover, miR-495-3p reprogramming of sphingolipid rheostat by targeting Sphk1, 22/FOXM1 axis regulation, and A2 receptor antagonist are highly promising therapy strategies. TFEB as a biomarker in predicting immune checkpoint blockade and redox-related lncRNA prognostic signature (redox-LPS) are considered reliable predictive approaches. Finally, exemplified in this article metabolic phenotyping is instrumental for innovative population screening, health risk assessment, predictive multi-level diagnostics, targeted prevention, and treatment algorithms tailored to personalized patient profiles-all are essential pillars in the paradigm change from reactive medical services to 3PM approach in overall management of lung cancers. This article highlights the 3PM relevant innovation focused on energy metabolism as the hub to advance NSCLC management benefiting vulnerable subpopulations, affected patients, and healthcare at large.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00357-5.

First insight about the ability of specific glycerophospholipids to discriminate non-small cell lung cancer subtypes

Introduction: Discrimination between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) subtypes in non-small cell lung cancer (NSCLC) patients is a significant challenge in oncology. Lipidomics analysis provides a promising approach for this differentiation. Methods: In an accompanying paper, we explored oxPCs levels in a cohort of 200 NSCLC patients. In this research, we utilized liquid chromatography coupled with mass spectrometry (LC-MS) to analyze the lipidomics profile of matching tissue and plasma samples from 25 NSCLC patients, comprising 11 ADC and 14 SCC cases. This study builds upon our previous findings, which highlighted the elevation of oxidised phosphatidylcholines (oxPCs) in NSCLC patients. Results: We identified eight lipid biomarkers that effectively differentiate between ADC and SCC subtypes using an untargeted approach. Notably, we observed a significant increase in plasma LPA 20:4, LPA 18:1, and LPA 18:2 levels in the ADC group compared to the SCC group. Conversely, tumour PC 16:0/18:2, PC 16:0/4:0; CHO, and plasma PC 16:0/18:2; OH, PC 18:0/20:4; OH, PC 16:0/20:4; OOH levels were significantly higher in the ADC group. Discussion: Our study is the first to report that plasma LPA levels can distinguish between ADC and SCC patients in NSCLC, suggesting a potential role for LPAs in NSCLC subtyping. This finding warrants further investigation into the mechanisms underlying these differences and their clinical implications.

A comparative study of plasma and dried blood spot metabolomics and its application to diabetes mellitus

Metabolomics has become a promising method for understanding pathological mechanisms. Plasma (PLS) is the most common sample type used for metabolomics studies, and dried blood spot (DBS) sampling has been regarded as a good strategy due to its unique characteristics. However, how results obtained from DBS can be correlated to results obtained from PLS remains unclear. To bridge the results and to investigate the feasibility of using DBS to study metabolomics, we performed a comparative study using 64 paired PLS and DBS samples. The number of features extracted from the two different sample types was investigated. The concentration correlations of the identified metabolites between the DBS and PLS were individually studied. Approximately 47 % showed a strong correlation, 19 % showed a moderate correlation, and 34 % showed a low or even negligible correlation. Finally, we applied both PLS- and DBS-based metabolomics to explore the dysregulated metabolites in diabetes mellitus (DM) patients. Thirty-two non-DM subjects and 32 DM patients were enrolled, and 2 significant metabolites were found in both PLS and DBS samples. In summary, detailed correlation information between PLS and DBS metabolites was first explored in this study, and it is anticipated that these results could facilitate future applications in DBS-based metabolomics.

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.

Electropotential-Inspired Star-Shaped Gold Nanoconfined Multiwalled Carbon Nanotubes: A Proof-of-Concept Electrosensoring Interface for Lung Metastasis Biomarkers

Herein, an innovative way of designing a star-shaped gold nanoconfined multiwalled carbon nanotube-engineered sensoring interface (AuNS@MWCNT//GCE) is demonstrated for quantification of methionine (MTH); a proof of concept for lung metastasis. The customization of the AuNS@MWCNT is assisted by surface electrochemistry and thoroughly discussed using state-of-the-art analytical advances. Micrograph analysis proves the protrusion of nanotips on the surface of potentiostatically synthesized AuNPs and validates the hypothesis of Turkevich seed (AuNP)-mediated formation of AuNSs. In addition, a facile synthesis of electropotential-assisted transformation of MWCNTs to luminescent nitrogen-doped graphene quantum dots (N_d-GQDs avg. ∼4.3 nm) is unveiled. The sensor elucidates two dynamic responses as a function of C_MTH ranging from 2 to 250 μM and from 250 to 3000 μM with a detection limit (DL) of ∼0.20 μM, and is robust to interferents except for tiny response of a similar -SH group bearing Cys (<9.00%). The high sensitivity (0.44 μA·μM^-1·cm^-2) and selectivity of the sensor can be attributed to the strong hybridization of the Au nanoparticle with the sp² C atom of the MWCNTs, which makes them a powerful electron acceptor for Au-SH-MTH interaction as evidenced by density functional theory (DFT) calculations. The validation of the acceptable recovery of MTH in real serum and pharma samples by standard McCarthy-Sullivan assay reveals the holding of great promise to provide valuable information for early diagnosis as well as assessing the therapeutic consequence of lung metastasis.

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.

Metabolomics insights into the prenatal exposure effects of polybrominated diphenyl ethers on neonatal birth outcomes

BACKGROUND

Effects of polybrominated diphenyl ethers (PBDEs) on neonatal birth outcomes vary across previous studies, and the related mechanism investigation remains poorly understood, especially at the metabolic level.

OBJECTIVES

To evaluate the associations between prenatal PBDEs exposure and neonatal birth outcomes including gestational age, neonatal weight, birth length, head circumference (HC), Apgar score at 1 min (Apgar1) and 5 min, and further reveal the underlying metabolic disorders in a population-based birth cohort study.

METHODS

Gas chromatography-triple quadrupole tandem mass spectrometry (GC-MS/MS) based targeted method and GC-MS based untargeted method were respectively conducted to obtain PBDE levels and metabolic profiles of 200 placental tissue samples from a typical e-waste recycling area (Guiyu) and reference area (Haojiang) in China. Spearman correlation and regression analyses were applied to assess the associations between the placental PBDE levels and birth outcomes. Metabolome-wide association studies and the meet-in-the-middle approach were employed to explore disruptions linking PBDE exposures and the corresponding adverse birth outcomes.

RESULTS

Eight out of 27 PBDE congeners were detected in placenta with more than 50% frequency in at least one district and significantly higher in Guiyu than those in Haojiang. The lower HC and Apgar1 had significant associations with PBDE exposures after adjustment for potential confounders. A total of 66, 16 and 14 metabolites were significantly correlated with PBDE exposures, HC and Apgar1, respectively. 4 and 12 PBDE-related metabolites were significantly associated with the risks of decreasing neonatal HC and Apgar1. The disrupted metabolites were mainly involved in the pentose phosphate pathway, ascorbate metabolism, threonine metabolism, butanoate metabolism, lipid metabolism, and arginine biosynthesis.

CONCLUSIONS

In this birth cohort, higher placental PBDE levels were significantly associated with the lower HC and Apgar1. The associations might be modified by multiple metabolic disturbances through increasing oxidative stress, mediating neurotoxicity, maternal gut microbiota dysbiosis and vasodilatation regulation.

The double faced role of xanthine oxidoreductase in cancer

Xanthine oxidoreductase (XOR) is a critical, rate-limiting enzyme that controls the last two steps of purine catabolism by converting hypoxanthine to xanthine and xanthine to uric acid. It also produces reactive oxygen species (ROS) during the catalytic process. The enzyme is generally recognized as a drug target for the therapy of gout and hyperuricemia. The catalytic products uric acid and ROS act as antioxidants or oxidants, respectively, and are involved in pro/anti-inflammatory actions, which are associated with various disease manifestations, including metabolic syndrome, ischemia reperfusion injury, cardiovascular disorders, and cancer. Recently, extensive efforts have been devoted to understanding the paradoxical roles of XOR in tumor promotion. Here, we summarize the expression of XOR in different types of cancer and decipher the dual roles of XOR in cancer by its enzymatic or nonenzymatic activity to provide an updated understanding of the mechanistic function of XOR in cancer. We also discuss the potential to modulate XOR in cancer therapy.

Therapeutic Potential of Glutamine Pathway in Lung Cancer

Cancer cells tend to obtain the substances needed for their development depending on altering metabolic characteristics. Among the reorganized metabolic pathways, Glutamine pathway, reprogrammed to be involved in the physiological process including energy supply, biosynthesis and redox homeostasis, occupies an irreplaceable role in tumor cells and has become a hot topic in recent years. Lung cancer currently maintains a high morbidity and mortality rate among all types of tumors and has been a health challenge that researchers have longed to overcome. Therefore, this study aimed to clarify the essential role of glutamine pathway played in the metabolism of lung cancer and its potential therapeutic value in the interventions of lung cancer.

Revealing the metabolic mechanism of dandelion extract against A549 cells using UPLC-QTOF MS

Dandelion extract shows potential anticancer activity and is expected to be a new type of natural anti-cancer drug. However, the effect mechanism of dandelion extract to lung cancer cells is still unclear. Here, untargeted metabolomics approach based on liquid chromatography-mass spectrograph (LC-MS) was used to characterize the metabolic responses of A549 cell to dandelion extract exposure, to provide new clues for the anti-tumor mechanism of dandelion extract from the perspective of metabolomics. A total of 16 differentially expressed and time-related metabolites were identified between dandelion extract exposure and control groups. The perturbed metabolic pathways of A549 cells after dandelion extract exposure mainly include the glycerophospholipid metabolism and purine metabolism. These results concluded that dandelion extract may exert anticancer activity by affecting the malignant proliferation, disturbing the stability of cell membrane structure, reducing the adhesion of tumor cells to extracellular matrix and fibronectin and finally inducing tumor cell death.

The phospholipid membrane compositions of bacterial cells, cancer cell lines and biological samples from cancer patients

While cancer now impacts the health and well-being of more of the human population than ever before, the exponential rise in antimicrobial resistant (AMR) bacterial infections means AMR is predicted to become one of the greatest future threats to human health. It is therefore vital that novel therapeutic strategies are developed that can be used in the treatment of both cancer and AMR infections. Whether the target of a therapeutic agent be inside the cell or in the cell membrane, it must either interact with or cross this phospholipid barrier to elicit the desired cellular effect. Here we summarise findings from published research into the phospholipid membrane composition of bacterial and cancer cell lines and biological samples from cancer patients. These data not only highlight key differences in the membrane composition of these biological samples, but also the methods used to elucidate and report the results of this analogous research between the microbial and cancer fields.

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

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

Metabolic Profiling Revealed Prediction Biomarkers for Infantile Hemangioma in Umbilical Cord Blood Sera: A Prospective Study

Infantile hemangioma (IH), the most common benign tumor in infancy, mostly arises and has rapid growth before 3 months of age. Because irreversible skin changes occur in the early proliferative stage, early medical treatment is essential to reduce the permanent sequelae caused by IH. Yet there are still no early screening biomarkers for IH before its visible emergence. This study aimed to explore prediction biomarkers using noninvasive umbilical cord blood (UCB). A prospective study of the metabolic profiling approach was performed on UCB sera from 28 infants with IH and 132 matched healthy controls from a UCB population comprising over 1500 infants (PeptideAtlas: PASS01675) using liquid chromatography-mass spectrometry. The metabolic profiling results exhibited the characteristic metabolic aberrance of IH. Machine learning suggested a panel of biomarkers to predict the occurrence of IH, with the area under curve (AUC) values in the receiver operating characteristic analysis all >0.943. Phenylacetic acid had potential to predict infants with large IH (diameter >2 cm) from those with small IH (diameter <2 cm), with an AUC of 0.756. The novel biomarkers in noninvasive UCB sera for predicting IH before its emergence might lead to a revolutionary clinical utility.

Tape stripping and lipidomics reveal skin surface lipid abnormity in female melasma

The skin barrier of melasma is involved in the pathogenesis of melasma. Previous studies have shown that there are differences in the expression of epidermal lipid genes in melasma, but little is known about the epidermis lipid composition of melasma. Compared with the non-lesional skin, the content of total lipids, phosphatidic acid, phosphatidylserine, and ceramide (Cer) increased significantly in the lesional skin. Multivariate data analysis indicated that 40 individual Cer lipid species were responsible for the discrimination. In terms of acyl chain length in Cer, the expressions of very long chain (VLC) (C20-C26) and ultra-long chain (ULC) (>C26) increased significantly in the lesional skin. However, Cer[AH] had negative correlations with the activation of melanocytes in the lesional skin. Some lipid species had lower expression in lesional skin with high activation of melanocytes, as well as the high darkness. The epidermal thickness of lesional skin was higher compared with the non-lesional skin. These results suggest that Cer increased significantly in the lesional skin of melasma, possibly as a compensatory mechanism to maintain skin barrier function. Between different groups of darkness and activation of melanocytes, the change of ceramides might have correlation with the pigmentation progress of melasma.

Mass spectrometry in the lipid study of cancer

Introduction: Cancer is a heterogeneous disease that exploits various metabolic pathways to meet the demand for increased energy and structural components. Lipids are biomolecules that play essential roles as high energy sources, mediators, and structural components of biological membranes. Accumulating evidence has established that altered lipid metabolism is a hallmark of cancer.Areas covered: Mass spectrometry (MS) is a label-free analytical tool that can simultaneously identify and quantify hundreds of analytes. To date, comprehensive lipid studies exclusively rely on this technique. Here, we reviewed the use of MS in the study of lipids in various cancers and discuss its instrumental limitations and challenges.Expert opinion: MS and MS imaging have significantly contributed to revealing altered lipid metabolism in a variety of cancers. Currently, a single MS approach cannot profile the entire lipidome because of its lack of sensitivity and specificity for all lipid classes. For the metabolic pathway investigation, lipid study requires the integration of MS with other molecular approaches. Future developments regarding the high spatial resolution, mass resolution, and sensitivity of MS instruments are warranted.

Roles of Lipid Profiles in Human Non-Small Cell Lung Cancer

Aims: This review aims to identify lipid biomarkers of non-small cell lung cancer (NSCLC) in human tissue samples and discuss the roles of lipids in tissue molecular identification, the discovery of potential biomarkers, and surgical margin assessment. Methods: A review of the literature focused on lipid-related research using mass spectrometry (MS) techniques in human NSCLC tissues from January 1, 2015, to November 20, 2020, was conducted. The quality of included studies was assessed using the QUADAS-2 tool. Results: Twelve studies met the inclusion criteria and were included in the review. The risk of bias was unclear in the majority of the studies. The contents of lipids including fatty acids, phosphatidyl choline, phosphatidyl ethanolamine, phosphatidyl inositol, cardiolipin, phosphatidyl serine, phosphatidyl glycerol, ceramide, lysophosphatidylethanolamine, lysophosphatidylcholine, and lysophosphatidylglycerol differed significantly between cancer and healthy tissues. The sensitivity or specificity of the discrimination model was reported in 8 studies, and the sensitivity and specificity varied among the reported methods. The lipid profiles differed between adenocarcinoma and squamous cell carcinoma NSCLC subtypes. Conclusion: In preclinical studies, MS analysis and multiple discrimination models can be combined to distinguish NSCLC tissues from healthy tissues based on lipid profiles, which provides a new opportunity to evaluate the surgical margin and cancer subtype intraoperatively. Future studies should provide guidance for selecting patients and discrimination models to develop an improved method for clinical application.