Metabolomics of Small Intestine Neuroendocrine Tumors and Related Hepatic Metastases

FOXA2-initiated transcriptional activation of INHBA induced by methylmalonic acid promotes pancreatic neuroendocrine neoplasm progression

Pancreatic neuroendocrine neoplasms (PanNENs) are a group of highly heterogeneous neoplasms originating from the endocrine islet cells of the pancreas with characteristic neuroendocrine differentiation, more than 60% of which represent metastases when diagnosis, causing major tumor-related death. Metabolic alterations have been recognized as one of the hallmarks of tumor metastasis, providing attractive therapeutic targets. However, little is known about the molecular mechanism of metabolic changes regulating PanNEN progression. In this study, we first identified methylmalonic acid (MMA) as an oncometabolite for PanNEN progression, based on serum metabolomics of metastatic PanNEN compared with non-metastatic PanNEN patients. One of the key findings was the potentially novel mechanism of epithelial-mesenchymal transition (EMT) triggered by MMA. Inhibin βA (INHBA) was characterized as a key regulator of MMA-induced PanNEN progression according to transcriptomic analysis, which has been validated in vitro and in vivo. Mechanistically, INHBA was activated by FOXA2, a neuroendocrine (NE) specific transcription factor, which was initiated during MMA-induced progression. In addition, MMA-induced INHBA upregulation activated downstream MITF to regulate EMT-related genes in PanNEN cells. Collectively, these data suggest that activation of INHBA via FOXA2 promotes MITF-mediated EMT during MMA inducing PanNEN progression, which puts forward a novel therapeutic target for PanNENs.

Metabolomic profile of neuroendocrine tumors identifies methionine, porphyrin, and tryptophan metabolisms as key dysregulated pathways associated with patient survival

OBJECTIVE

Metabolic profiling is a valuable tool to characterize tumor biology but remains largely unexplored in neuroendocrine tumors (NETs). Our aim was to comprehensively assess the metabolomic profile of NETs and identify novel prognostic biomarkers and dysregulated molecular pathways.

DESIGN AND METHODS

Multiplatform untargeted metabolomic profiling (GC-MS, CE-MS, and LC-MS) was performed in plasma from 77 patients with G1-2 extra-pancreatic NETs enrolled in the AXINET trial (NCT01744249) (study cohort) and from 68 non-cancer individuals (control). The prognostic value of each differential metabolite (n = 155) in NET patients (P < .05) was analyzed by univariate and multivariate analyses adjusted for multiple testing and other confounding factors. Related pathways were explored by Metabolite Set Enrichment Analysis (MSEA) and Metabolite Pathway Analysis (MPA).

RESULTS

Thirty-four metabolites were significantly associated with progression-free survival (PFS) (n = 16) and/or overall survival (OS) (n = 27). Thirteen metabolites remained significant independent prognostic factors in multivariate analysis, 3 of them with a significant impact on both PFS and OS. Unsupervised clustering of these 3 metabolites stratified patients in 3 distinct prognostic groups (1-year PFS of 71.1%, 47.7%, and 15.4% (P = .012); 5-year OS of 69.7%, 32.5%, and 27.7% (P = .003), respectively). The MSEA and MPA of the 13-metablolite signature identified methionine, porphyrin, and tryptophan metabolisms as the 3 most relevant dysregulated pathways associated with the prognosis of NETs.

CONCLUSIONS

We identified a metabolomic signature that improves prognostic stratification of NET patients beyond classical prognostic factors for clinical decisions. The enriched metabolic pathways identified reveal novel tumor vulnerabilities that may foster the development of new therapeutic strategies for these patients.

Metabolism of pancreatic neuroendocrine tumors: what can omics tell us?

Introduction

Reprogramming of cellular metabolism is now a hallmark of tumorigenesis. In recent years, research on pancreatic neuroendocrine tumors (pNETs) has focused on genetic and epigenetic modifications and related signaling pathways, but few studies have been devoted to characterizing the metabolic profile of these tumors. In this review, we thoroughly investigate the metabolic pathways in pNETs by analyzing the transcriptomic and metabolomic data available in the literature.

Methodology

We retrieved and downloaded gene expression profiles from all publicly available gene set enrichments (GSE43797, GSE73338, and GSE117851) to compare the differences in expressed genes based on both the stage and MEN1 mutational status. In addition, we conducted a systematic review of metabolomic data in NETs.

Results

By combining transcriptomic and metabolomic approaches, we have identified a distinctive metabolism in pNETs compared with controls without pNETs. Our analysis showed dysregulations in the one-carbon, glutathione, and polyamine metabolisms, fatty acid biosynthesis, and branched-chain amino acid catabolism, which supply the tricarboxylic acid cycle. These targets are implicated in pNET cell proliferation and metastasis and could also have a prognostic impact. When analyzing the profiles of patients with or without metastasis, or with or without MEN1 mutation, we observed only a few differences due to the scarcity of published clinical data in the existing research. Consequently, further studies are now necessary to validate our data and investigate these potential targets as biomarkers or therapeutic solutions, with a specific focus on pNETs.

Joint analysis of the metabolomics and transcriptomics uncovers the dysregulated network and develops the diagnostic model of high-risk neuroblastoma

High-risk neuroblastoma (HR-NB) has a significantly lower survival rate compared to low- and intermediate-risk NB (LIR-NB) due to the lack of risk classification diagnostic models and effective therapeutic targets. The present study aims to characterize the differences between neuroblastomas with different risks through transcriptomic and metabolomic, and establish an early diagnostic model for risk classification of neuroblastoma.Plasma samples from 58 HR-NB and 38 LIR-NB patients were used for metabolomics analysis. Meanwhile, NB tissue samples from 32 HR-NB and 23 LIR-NB patients were used for transcriptomics analysis. In particular, integrative metabolomics and transcriptomic analysis was performed between HR-NB and LIR-NB. A total of 44 metabolites (P < 0.05 and fold change > 1.5) were altered, including 12 that increased and 32 that decreased in HR-NB. A total of 1,408 mRNAs (P < 0.05 and |log2(fold change)|> 1) showed significantly altered in HR-NB, of which 1,116 were upregulated and 292 were downregulated. Joint analysis of both omic data identified 4 aberrant pathways (P < 0.05 and impact ≥ 0.5) consisting of glycerolipid metabolism, retinol metabolism, arginine biosynthesis and linoleic acid metabolism. Importantly, a HR-NB risk classification diagnostic model was developed using plasma circulating-free S100A9, CDK2, and UNC5D, with an area under receiver operating characteristic curve of 0.837 where the sensitivity and specificity in the validation set were both 80.0%. This study presents a novel pioneering study demonstrating the metabolomics and transcriptomics profiles of HR-NB. The glycerolipid metabolism, retinol metabolism, arginine biosynthesis and linoleic acid metabolism were altered in HR-NB. The risk classification diagnostic model based on S100A9, CDK2, and UNC5D can be clinically used for HR-NB risk classification.

High-resolution magic angle spinning NMR for intact biological specimen analysis: Initial discovery, recent developments, and future directions

High-resolution magic angle spinning (HRMAS) NMR, an approach for intact biological material analysis discovered more than 25 years ago, has been advanced by many technical developments and applied to many biomedical uses. This article provides a history of its discovery, first by explaining the key scientific advances that paved the way for HRMAS NMR's invention, and then by turning to recent developments that have profited from applying and advancing the technique during the last 5 years. Developments aimed at directly impacting healthcare include HRMAS NMR metabolomics applications within studies of human disease states such as cancers, brain diseases, metabolic diseases, transplantation medicine, and adiposity. Here, the discussion describes recent HRMAS NMR metabolomics studies of breast cancer and prostate cancer, as well as of matching tissues with biofluids, multimodality studies, and mechanistic investigations, all conducted to better understand disease metabolic characteristics for diagnosis, opportune windows for treatment, and prognostication. In addition, HRMAS NMR metabolomics studies of plants, foods, and cell structures, along with longitudinal cell studies, are reviewed and discussed. Finally, inspired by the technique's history of discoveries and recent successes, future biomedical arenas that stand to benefit from HRMAS NMR-initiated scientific investigations are presented.

The role of glycometabolic plasticity in cancer

Dysregulated glycometabolism represented by the Warburg effect is well recognized as a hallmark of cancer that can be driven by oncogenes (e.g., c-Myc, K-ras, and BRAF) and contribute to cellular malignant transformation. The Warburg effect reveals the different glycometabolic patterns of cancer cells, but this unique glycometabolic pattern has the characteristic of plasticity rather than changeless which can vary with different internal or external stimuli during evolution. Glycometabolic plasticity enables cancer cells to modulate glycometabolism to support progression, metastasis, treatment resistance and recurrence. In this review, we report the characteristics of glycometabolic plasticity during different stages of cancer evolution, providing insight into the molecular mechanisms of glycometabolic plasticity in cancer. In addition, we discussed the challenges and future research directions of glycometabolism research in cancer.

Application of metabolomics in clinical and laboratory gastrointestinal oncology

Metabolites are versatile bioactive molecules. They are not only the substrates and/or the products of enzymatic reactions but also act as the regulators in the systemic metabolism. Metabolomics is a high-throughput analytical strategy to qualify or quantify as many metabolites as possible in the metabolomes. It is an indispensable part of systems biology. The leading techniques in this field are mainly based on mass spectrometry and nuclear magnetic resonance spectroscopy. The metabolomic analysis has gained wide use in bioscience fields. In the tumor research arena, metabolomics can be employed to identify biomarkers for prediction, diagnosis, and prognosis. Chemotherapeutic effect evaluation and personalized medicine decision-making can also benefit from metabolomic analysis of patient biofluid or biopsy samples. Many cell-level studies can help in disease exploration. In this review, the basic features and principles of varied metabolomic analysis are introduced. The value of metabolomics in clinical and laboratory gastrointestinal cancer studies is discussed, especially for mass spectrometry applications. Besides, combined use of metabolomics and other tools to solve problems in cancer practice is briefly illustrated. In summary, metabolomics paves a new way to explore cancerous diseases in the light of small molecules.

Comprehensive Plasma Metabolomic Profile of Patients with Advanced Neuroendocrine Tumors (NETs). Diagnostic and Biological Relevance

Simple Summary

Metabolic flexibility is one of the key hallmarks of cancer and metabolites are the final products of this adaptation, reflecting the aberrant changes of tumors. However, the metabolic plasticity of each cancer type is still unknown, and specifically to date, there are no data on metabolic profile in neuroendocrine tumors. The aim of our retrospective study was to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. We provided, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use, selecting a reduced set of metabolites of high diagnostic accuracy. We have identified 32 novel enriched metabolic pathways in NETs related with the TCA cycle, and with arginine, pyruvate or glutathione metabolism, which have distinct implications in oncogenesis and may open innovative avenues of clinical research.

Abstract

Purpose: High-throughput “-omic” technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. Methods: Plasma samples from 77 NETs and 68 controls were profiled by GC−MS, CE−MS and LC−MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential. Results: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential. Conclusions: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research.