Nontargeted and targeted metabolomics approaches reveal the key amino acid alterations involved in multiple myeloma

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Mechanisms of the role of proto-oncogene activation in promoting malignant transformation of mature B cells

Malignant tumors continue to pose a significant threat to human life and safety and their development is primarily due to the activation of proto-oncogenes and the inactivation of suppressor genes. Among these, the activation of proto-oncogenes possesses greater potential to drive the malignant transformation of cells. Targeting oncogenes involved in the malignant transformation of tumor cells has provided a novel approach for the development of current antitumor drugs. Several preclinical and clinical studies have revealed that the development pathway of B cells, and the malignant transformation of mature B cells into tumors have been regulated by oncogenes and their metabolites. Therefore, summarizing the key oncogenes involved in the process of malignant transformation of mature B cells and elucidating the mechanisms of action in tumor development hold significant importance for the clinical treatment of malignant tumors.

Simultaneous quantitation of multiple myeloma related dietary metabolites in serum using HILIC-LC-MS/MS

Background

Recent studies from targeted and untargeted metabolomics have consistently revealed that diet-related metabolites, including carnitine (C0), several species of acylcarnitines (AcyCNs), amino acids, ceramides, and lysophosphatidylcholines (LPCs) may serve as potential multiple myeloma (MM) biomarkers. However, most of these approaches had some intrinsic limitations, namely low reproducibility and compromising the accuracy of the results.

Objective

This study developed and validated a precise, efficient, and reliable liquid chromatography tandem mass spectrometric (LC-MS/MS) method for measuring these 28 metabolic risk factors in human serum.

Design

This method employed isopropanol to extract the metabolites from serum, gradient elution on a hydrophilic interaction liquid chromatographic column (HILIC) for chromatographic separation, and multiple reaction monitor (MRM) mode with positive electrospray ionization (ESI) for mass spectrometric detection.

Results

The correlation coefficients of linear response for this method were more than 0.9984. Analytical recoveries ranged from 91.3 to 106.3%, averaging 99.5%. The intra-run and total coefficients of variation were 1.1-5.9% and 2.0-9.6%, respectively. We have simultaneously determined the serological levels of C0, several subclasses of AcyCNs, amino acids, ceramides, and LPCs within 15 min for the first time.

Conclusion

The established LC-MS/MS method was accurate, sensitive, efficient, and could be valuable in providing insights into the association between diet patterns and MM disease and added value in further clinical research.

Clinical diagnostic value of amino acids in laryngeal squamous cell carcinomas

Background

Early diagnosis and treatment can improve the survival rates of patients with laryngeal squamous cell carcinoma (LSCC). Therefore, it is necessary to discover new biomarkers for laryngeal cancer screening and early diagnosis.

Methods

We collected fasting plasma from LSCC patients and healthy volunteers, as well as cancer and para-carcinoma tissues from LSCC patients, and performed quantitative detection of amino acid levels using liquid chromatography-mass spectrometry. We used overall analysis and multivariate statistical analysis to screen out the statistically significant differential amino acids in the plasma and tissue samples, conducted receiver operating characteristic (ROC) analysis of the differential amino acids to evaluate the sensitivity and specificity of the differential amino acids, and finally determined the diagnostic value of amino acids for laryngeal cancer. Additionally, we identified amino acids in the plasma and tissue samples that are valuable for the early diagnosis of laryngeal cancer classified according to the tumor-node-metastasis (TNM) classification.

Results

Asparagine (Asp) and homocysteine (Hcy) were two amino acids of common significance in plasma and tissue samples, and their specificity and sensitivity analysis showed that they may be new biomarkers for the diagnosis and treatment of LSCC. According to the TNM staging system, phenylalanine (Phe) and isoleucine (Ile) were screened out in the plasma of LSCC patients at early (I and II) and advanced (III and IV) stages; ornithine hydrochloride (Orn), glutamic acid (Glu), and Glycine (Gly) were selected in the tissue. These dysregulated amino acids found in LSCC patients may be useful as clinical biomarkers for the early diagnosis and screening of LSCC.

The Interaction between Gut Microbiota and Host Amino Acids Metabolism in Multiple Myeloma

Although novel therapies have dramatically improved outcomes for multiple myeloma (MM) patients, relapse is inevitable and overall outcomes are heterogeneous. The gut microbiota is becoming increasingly recognized for its influence on host metabolism. To date, evidence has suggested that the gut microbiota contributes to MM, not only via the progressive activities of specific bacteria but also through the influence of the microbiota on host metabolism. Importantly, the abnormal amino acid metabolism, as well as the altered microbiome in MM, is becoming increasingly apparent, as is the influence on MM progression and the therapeutic response. Moreover, the gut-microbiota-host-amino-acid metabolism interaction in the progression of MM has been highlighted. Modulation of the gut microbiota (such as fecal microbiota transplantation, FMT) can be modified, representing a new angle in MM treatment that can improve outcomes. In this review, the relationship between gut microbiota, metabolism, and MM, together with strategies to modulate the microbiota, will be discussed, and some unanswered questions for ongoing and future research will be presented.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Serum metabolomics reveals the effects of accompanying treatment on fatigue in patients with multiple myeloma

PURPOSE

The renewal and iteration of chemotherapy drugs have resulted in more frequent long-term remissions for patients with multiple myeloma (MM). MM has transformed into a chronic illness for many patients, but the cancer-related fatigue (CRF) of many MM convalescent patients experience is frequently overlooked. We investigated whether the accompanying treatment of family members would affect MM patients' CRF and explore their serum metabolomics, so as to provide clinicians with new ideas for identifying and treating CRF of MM patients.

METHODS

This was a single-center study, and a total of 30 MM patients were included in the study. Patients were divided into two groups based on whether they have close family members accompanying them through the whole hospitalization treatment. These patients received regular chemotherapy by hematology specialists, and long-term follow-up was done by general practitioners. Patients' CRF assessment for several factors used the Chinese version of the Brief Fatigue Inventory (BFI-C). Face-to-face questionnaires were administered at a time jointly determined by the patient and the investigator. All questionnaires were conducted by a general practitioner. The LC-MS-based metabolomics analysis determined whether the patients' serum metabolites were related to their fatigue severity. A correlation analysis investigated the relationship between serum metabolites and clinical laboratory indicators.

RESULTS

The fatigue severity of MM patients whose family members participated in the treatment process (group A) was significantly lower than patients whose family members did not participate in the treatment process (group B). There was a statistically significant difference (fatigue severity composite score: t =  - 2.729, p = 0.011; fatigue interference composite score: t =  - 3.595, p = 0.001). There were no differences between the two groups of patients' gender, age, regarding clinical staging, tumor burden, blood routine, biochemical, or coagulation indexes. There were 11 metabolites, including guanidine acetic acid (GAA), 1-(Methylthio)-1-hexanethiol, isoeucyl-asparagine, L-agaritine, tryptophyl-tyrosine, and betaine, which significantly distinguished the two groups of MM patients. GAA had the strongest correlation with patient fatigue, and the difference was statistically significant (fatigue severity composite score: r = 0.505, p = 0.0044; fatigue interference composite score: r = 0.576, p = 0.0009). The results showed that GAA negatively correlated with albumin (r =  - 0.4151, p = 0.0226) and GGT (r =  - 0.3766, p = 0.0402). Meanwhile, GAA positively correlated with PT (r = 0.385, p = 0.0473), and the difference was statistically significant.

CONCLUSION

The study is the first to report that family presence throughout the whole hospitalization may alleviate CRF in MM patients. Moreover, the study evaluated serum metabolites linked to CRF in MM patients and found that CRF has a significant positive correlation with GAA. GAA may be a more sensitive biomarker than liver enzymes, PT, and serum albumin in predicting patient fatigue. While our sample may not represent all MM patients, it proposes a new entry point to help clinicians better identify and treat CRF in MM patients.