Serum and urine metabolomics analyses reveal metabolic pathways and biomarkers in relation to nasopharyngeal carcinoma

Identifying biomarkers for nasopharyngeal carcinoma diagnosis and chemoradiotherapy response using serum endogenous small molecules profiling

Changes in metabolic characteristics are important features of tumor progression and prognosis, including nasopharyngeal carcinoma (NPC). Identifying serum metabolites as potential diagnostic and chemoradiotherapy response biomarkers for NPC is therefore crucial. In this study, ultra-performance liquid chromatography coupled with linear ion trap quadrupole orbitrap high-resolution mass spectrometry (UPLC-LTQ-Orbitrap MS) was used to analyze metabolic variations among controls, NPC patients, and NPC patients undergoing chemoradiotherapy (CRT). Univariate and multivariate analyses revealed seven differential metabolites between the control and NPC groups and eleven metabolites between the CRT and NPC groups. Five common metabolites, gluconic acid, palmitic acid, LysoPC (15:0/0:0), stearic acid, and LysoPC (20:2(11Z,14Z)/0:0), were consistently altered across groups. Notably, the first four metabolites were adjusted closer to normal after chemoradiotherapy, while this change is not reflected at LysoPC (20:2(11Z,14Z)/0:0). These common metabolites were enriched in five pathways. These findings underscore the importance of serum metabolite profiling in NPC diagnosis and treatment response assessment and offer a promising foundation for further clinical research.

Regulatory role of miRNAs in nasopharyngeal cancer involving PTEN/PI3K/AKT, TGFβ/SMAD, RAS/MAPK, Wnt/β-catenin and pRB-E2F signaling pathways: A review

MicroRNAs (miRNA) are small and conserved noncoding RNA molecules that regulate gene expression at the posttranscriptional level. These groups of RNAs are crucial in various cellular processes, especially in mediating disease pathogenesis, particularly cancer. The dysregulation of miRNAs was reported in many cancer types, including nasopharyngeal cancer (NPC), which is a malignant tumor of the nasopharynx. In this review, miRNAs involvement in crucial signaling pathways associated with NPC such as PTEN/PI3K/AKT, TGFβ/SMAD, RAS/MAPK, Wnt/β-catenin and pRB-E2F was investigated. miRNAs could function as tumor suppressor-miR or onco-miR in NPC profoundly influenced cell cycle, apoptosis, proliferation, migration, and metastasis. This comprehensive review of current literature provided a thorough profile of miRNAs and their interplay with the aforementioned signaling pathways in NPC. Understanding these molecular interactions could remarkably impact the diagnosis, prognosis, and therapeutic strategies for NPC.

Metabolomics of head and neck cancer in biofluids: an integrative systematic review

INTRODUCTION

Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC.

OBJECTIVES

To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine.

METHODS

A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine).

RESULTS

Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented.

CONCLUSION

The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

The combined analysis of urine and blood metabolomics profiles provides an accurate prediction of the training and competitive status of Chinese professional swimmers

Objective: The purpose of this study was to employ metabolomics for the analysis of urine metabolites in swimmers, with the aim of establishing models for assessing their athletic status and competitive potential. Furthermore, the study sought to compare the identification efficacy of multi-component (urine and blood) model versus single-component (urine or blood) models, in order to determine the optimal approach for evaluating training and competitive status. Methods: A total of 187 Chinese professional swimmers, comprising 103 elite and 84 sub-elite level athletes, were selected as subjects for this study. Urine samples were obtained from each participant and subjected to nuclear magnetic resonance (NMR) metabolomics analysis. Significant urine metabolites were screened through multivariable logistic regression analysis, and an identification model was established. Based on the previously established model of blood metabolites, this study compared the discriminative and predictive performance of three models: either urine or blood metabolites model and urine + blood metabolites model. Results: Among 39 urine metabolites, 10 were found to be significantly associated with the athletic status of swimmers (p < 0.05). Of these, levels of 2-KC, cis-aconitate, formate, and LAC were higher in elite swimmers compared to sub-elite athletes, while levels of 3-HIV, creatinine, 3-HIB, hippurate, pseudouridine, and trigonelline were lower in elite swimmers. Notably, 2-KC and 3-HIB exhibited the most substantial differences. An identification model was developed to estimate physical performance and athletic level of swimmers while adjusting for different covariates and including 2-KC and 3-HIB. The urine metabolites model showed an area under the curve (AUC) of 0.852 (95% CI: 0.793-0.912) for discrimination. Among the three identification models tested, the combination of urine and blood metabolites showed the highest performance than either urine or blood metabolites, with an AUC of 0.925 (95% CI: 0.888-0.963). Conclusion: The two urine metabolites, 2-KC and 3-HIV, can serve as significant urine metabolic markers to establish a discrimination model for identifying the athletic status and competitive potential of Chinese elite swimmers. Combining two screened urine metabolites with four metabolites reported exhibiting significant differences in blood resulted in improved predictive performance compared to using urine metabolites alone. These findings indicate that combining blood and urine metabolites has a greater potential for identifying and predicting the athletic status and competitive potential of Chinese professional swimmers.

An Individualized Prognostic Model in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma Based on Serum Metabolomic Profiling

PURPOSE

This study aims to evaluate the value of a serum metabolomics-based metabolic signature for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients, thereby assisting clinical decisions.

METHODS

In this retrospective study, a total of 320 LA-NPC patients were randomly divided into a training set (ca. 70%; n = 224) and a validation set (ca. 30%; n = 96). Serum samples were analyzed using widely targeted metabolomics. Univariate and multivariate Cox regression analyses were used to identify candidate metabolites related to progression-free survival (PFS). Patients were categorized into high-risk and low-risk groups based on the median metabolic risk score (Met score), and the PFS difference between the two groups was compared using Kaplan-Meier curves. The predictive performance of the metabolic signature was evaluated using the concordance index (C-index) and the time-dependent receiver operating characteristic (ROC), and a comprehensive nomogram was constructed using the Met score and other clinical factors.

RESULTS

Nine metabolites were screened to build the metabolic signature and generate the Met score, which effectively separated patients into low- and high-risk groups. The C-index in the training and validation sets was 0.71 and 0.73, respectively. The 5-year PFS was 53.7% (95% CI, 45.12-63.86) in the high-risk group and 83.0% (95%CI, 76.31-90.26) in the low-risk group. During the construction of the nomogram, Met score, clinical stage, pre-treatment EBV DNA level, and gender were identified as independent prognostic factors for PFS. The predictive performance of the comprehensive model was better than that of the traditional model.

CONCLUSION

The metabolic signature developed through serum metabolomics is a reliable prognostic indicator of PFS in LA-NPC patients and has important clinical significance.