Clinical significance of simultaneous determination of serum tryptophan and tyrosine in patients with lung cancer

Normative Data on Serum and Plasma Tryptophan and Kynurenine Concentrations from 8089 Individuals Across 120 Studies: A Systematic Review and Meta-Analysis

In this systematic review and meta-analysis, a normative dataset is generated from the published literature on the kynurenine pathway in control participants extracted from case-control and methodological validation studies. Study characteristics were mapped, and studies were evaluated in terms of analytical rigour and methodological validation. Meta-analyses of variance between types of instruments, sample matrices and metabolites were conducted. Regression analyses were applied to determine the relationship between metabolite, sample matrix, biological sex, participant age and study age. The grand mean concentrations of tryptophan in the serum and plasma were 60.52 ± 15.38 μM and 51.45 ± 10.47 μM, respectively. The grand mean concentrations of kynurenine in the serum and plasma were 1.96 ± 0.51 μM and 1.82 ± 0.54 μM, respectively. Regional differences in metabolite concentrations were observed across America, Asia, Australia, Europe and the Middle East. Of the total variance within the data, mode of detection (MOD) accounted for up to 2.96%, sample matrix up to 3.23%, and their interaction explained up to 1.53%; the latter of which was determined to be negligible. This review was intended to inform future empirical research and method development studies and successfully synthesised pilot data. The pilot data reported in this study will inform future precision medicine initiatives aimed at targeting the kynurenine pathway by improving the availability and quality of normative data.

Development and internal validation of nomograms based on plasma metabolites to predict non-small cell lung cancer risk in smoking and nonsmoking populations

BACKGROUND

Lung cancer has significantly higher incidence and mortality rates worldwide. In this study, we analyzed the metabolic profiles of non-small cell lung cancer (NSCLC) patients and constructed prediction models for smokers and nonsmokers with internal validation.

METHODS

Plasma was collected from all patients enrolled for metabolic profiling by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The total population was divided into two groups according to smoking or not. Statistical analysis of metabolites was performed separately for each group and prediction models were constructed.

RESULTS

A total of 1723 patients (1109 NSCLC patients and 614 healthy controls) were enrolled from the affiliated hospital during 2018 to 2021. After grouping by smoking history, each group was statistically analyzed and prediction models were constructed, which resulted in eight indicators (propionylcarnitine, arginine, citrulline, etc.) significantly associated with lung cancer risk for smokers and eight indicators (dodecanoylcarnitine, hydroxybutyrylcarnitine, asparagine, etc.) for nonsmokers (p < 0.05). The smoker model indicated an AUC of 0.860 in the training set and 0.850 in the validation set. The nonsmoker model showed an AUC of 0.783 in the training set and 0.762 in the validation set. Further calibration tests for both models indicated excellent goodness-of-fit results.

CONCLUSIONS

In this study, we found a series of metabolites significantly associated with lung cancer incidence and constructed respectively prediction models for NSCLC risk in smokers and nonsmokers, with internal validation to confirm the efficiency to discriminate lung cancer risk in both smoking and nonsmoking states.

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

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

Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data

INTRODUCTION

While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value.

OBJECTIVES

Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies.

METHODS

Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility.

RESULTS

Overall survival (OS) is predicted in external validation cohort with AUROC_TEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROC_TEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased.

CONCLUSION

This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.

Plasma Free Amino Acid Profile in HIV-Positive Cases

Background:

Increasing the sensitivity and availability of LC-MS / MS devices may provide advantages in terms of revealing the changes in metabolic pathways in HIV-positive patients and elucidating the physiopathology.

Introduction:

The aim of this study was to determine the difference in amino acid level between HIV-positive patients and healthy individuals by using LC-MS / MS, and to investigate its relationship with HIV infection.

Material and Methods:

Concentrations of 36 different amino acids and their derivatives were measured and compared in venous plasma samples from 24 HIV-positive patients and 24 healthy individuals by using the LC-MS/MS method (Shimadzu North America, Columbia, MD, USA).

Results:

HIV-positive subjects had significantly lower alanine, 1-methyl-L-histidine, valine, aspartate, cysteine, cystine, methionine, lysine, glutamine, imino acid, tyrosine, tryptophan, threonine, sarcosine, and argininosuccinic acid and significantly higher 3-methyl-L -histidine, asparagine glutamate, and carnosine levels as compared to healthy controls. No significant differences were detected in other amino acids.

Conclusion:

The significant differences in amino acid profile between HIV-positive and healthy subjects may represent an auxiliary biomarker of cellular damage in asymptomatic HIV-positive patients that may be examined in more detail in further studies. It may also provide guidance for symptomatic cases in terms of the association between symptoms, clinical manifestations and deficiency or excess of certain amino acids in the context of the complete metabolomics record of HIV-positive patients.

Tryptophan and Its Metabolites in Lung Cancer: Basic Functions and Clinical Significance

Lung cancer is the most lethal malignancy worldwide. Recently, it has been recognized that metabolic reprogramming is a complex and multifaceted factor, contributing to the process of lung cancer. Tryptophan (Try) is an essential amino acid, and Try and its metabolites can regulate the progression of lung cancer. Here, we review the pleiotropic functions of the Try metabolic pathway, its metabolites, and key enzymes in the pathogenic process of lung cancer, including modulating the tumor environment, promoting immune suppression, and drug resistance. We summarize the recent advance in therapeutic drugs targeting the Try metabolism and kynurenine pathway and their clinical trials.

Activated macrophages are crucial during acute PM2.5 exposure-induced angiogenesis in lung cancer

The importance of ambient particulate matter (PM2.5) in lung cancer progression is well established; however, the precise mechanisms by which PM2.5 modulates lung cancer development have not yet been determined. The present study demonstrated increased mRNA and protein expression levels of vascular endothelial growth factor in PM2.5-induced macrophages. However, no significant changes to the expression levels of angiogenic cytokines (vascular endothelial growth factor A, matric metallopeptidase 9, basic fibroblast growth factor and platelet-derived growth factor) were observed in the Lewis lung carcinoma (LLC) cell line in response to acute PM2.5 exposure. PM2.5-induced activated macrophages were revealed to upregulate angiogenic cytokine expression following the acute exposure of LLC cells to PM2.5-induced macrophage supernatant. In vivo, the pro-angiogenic and macrophage accumulation functions of PM2.5 were supported by the establishment of a polyvinyl alcohol sponge implantation mouse model. Furthermore, PM2.5 was demonstrated to increase angiogenesis and macrophage recruitment in mice that were subcutaneously injected with LLCs. These results indicated that PM2.5 increases angiogenesis, and macrophages are crucial mediators of PM2.5-induced angiogenesis in lung cancer. These findings may provide novel insights for the development of lung cancer treatment strategies.

Targeting tryptophan availability to tumors: the answer to immune escape?

Tumoral immune escape is an obstacle to successful cancer therapy. Tryptophan (Trp) metabolites along the kynurenine pathway induce immunosuppression involving apoptosis of effector immune cells, which tumors use to escape an immune response. Production of these metabolites is initiated by indoleamine 2,3-dioxygenase (IDO1). IDO1 inhibitors, however, do not always overcome the immune escape and another enzyme expressed in tumors, Trp 2,3-dioxygenase (TDO2), has been suggested as the reason. However, without Trp, tumors cannot achieve an immune escape through either enzyme. Trp is therefore key to immune escape. In this perspective paper, Trp availability to tumors will be considered and strategies limiting it proposed. One major determinant of Trp availability is the large increase in plasma free (non-albumin-bound) Trp in cancer patients, caused by the low albumin and the high non-esterified fatty acid (NEFA) concentrations in plasma. Albumin infusions, antilipolytic therapy or both could be used, if indicated, as adjuncts to immunotherapy and other therapies. Inhibition of amino acid uptake by tumors is another strategy and α-methyl-DL-tryptophan or other potential inhibitors could fulfill this role. Glucocorticoid receptor antagonists may have a role in preventing glucocorticoid induction of TDO in host liver and tumors expressing it and in undermining the permissive effect of glucocorticoids on IDO1 induction by cytokines. Nicotinamide may be a promising TDO2 inhibitor lacking disadvantages of current inhibitors. Establishing the Trp disposition status of cancer patients and in various tumor types may provide the information necessary to formulate tailored therapeutic approaches to cancer immunotherapy that can also undermine tumoral immune escape.

Characterizing the network of drugs and their affected metabolic subpathways

A fundamental issue in biology and medicine is illustration of the overall drug impact which is always the consequence of changes in local regions of metabolic pathways (subpathways). To gain insights into the global relationship between drugs and their affected metabolic subpathways, we constructed a drug-metabolic subpathway network (DRSN). This network included 3925 significant drug-metabolic subpathway associations representing drug dual effects. Through analyses based on network biology, we found that if drugs were linked to the same subpathways in the DRSN, they tended to share the same indications and side effects. Furthermore, if drugs shared more subpathways, they tended to share more side effects. We then calculated the association score by integrating drug-affected subpathways and disease-related subpathways to quantify the extent of the associations between each drug class and disease class. The results showed some close drug-disease associations such as sex hormone drugs and cancer suggesting drug dual effects. Surprisingly, most drugs displayed close associations with their side effects rather than their indications. To further investigate the mechanism of drug dual effects, we classified all the subpathways in the DRSN into therapeutic and non-therapeutic subpathways representing drug therapeutic effects and side effects. Compared to drug side effects, the therapeutic effects tended to work through tissue-specific genes and these genes tend to be expressed in the adrenal gland, liver and kidney; while drug side effects always occurred in the liver, bone marrow and trachea. Taken together, the DRSN could provide great insights into understanding the global relationship between drugs and metabolic subpathways.

Simultaneous determination of tyrosine, tryptophan and 5-hydroxytryptamine in serum of MDD patients by high performance liquid chromatography with fluorescence detection

BACKGROUND

Tyrosine (Tyr), Tryptophan (Trp) and 5-hydroxytryptamine (5-HT) are important amino acids in vivo and have been hypothesized to be involved in many mental disorders. We developed a rapid and sensitive HPLC method for simultaneous measurement of serum Tyr, Trp and 5-HT and explored the clinical significances of Tyr, Trp and 5-HT and the 5-HT/Trp ratio for patients with major depressive disorder (MDD) disease.

METHODS

Serum samples were deproteinized by 5% perchloric acid and separated on an Atlantis C18 column (4.6 × 150 mm, 5 μm) with the mobile phase consisting of 0.1 mol/l KH(2)PO(4) and methanol (85:15, V/V).The eluates were monitored by the fluorescence detection with programmed wavelength.

RESULTS

Analysis was achieved in <12.0 min. The limits of quantification were 0.014, 0.005, and 0.024 μmol/l for Tyr, Trp and 5-HT, respectively. Reproducibility and recovery were satisfactory. Tyr, Trp and 5-HT and the 5-HT/Trp ratio were significantly decreased in patients with MDD.

CONCLUSIONS

In diseases, like MDD, Tyr, Trp and 5-HT play an important role. This method can potentially be applied as prognostic or diagnostic tool or even to follow the evolution of the illness or of the treatment.