1H NMR-based metabolomics of paired tissue, serum and urine samples reveals an optimized panel of biofluids metabolic biomarkers for esophageal cancer

The mechanism of action of Botrychium (Thunb.) Sw. for prevention of idiopathic pulmonary fibrosis based on 1H-NMR-based metabolomics

OBJECTIVES

This study aimed to reveal the anti-fibrotic effects of Botrychium ternatum (Thunb.) Sw. (BT) against idiopathic pulmonary fibrosis (IPF) and to preliminarily analyze its potential mechanism on bleomycin-induced IPF rats.

METHODS

The inhibition of fibrosis progression in vivo was assessed by histopathology combined with biochemical indicators. In addition, the metabolic regulatory mechanism was investigated using 1H-nuclear magnetic resonance-based metabolomics combined with multivariate statistical analysis.

KEY FINDINGS

Firstly, biochemical analysis revealed that BT notably suppressed the expression of hydroxyproline and transforming growth factor-β1 in the pulmonary tissue. Secondly, Masson's trichrome staining and hematoxylin and eosin showed that BT substantially improved the structure of the damaged lung and significantly inhibited the proliferation of collagen fibers and the deposition of extracellular matrix. Finally, serum metabolomic analysis suggested that BT may exert anti-fibrotic effects by synergistically regulating tyrosine metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; and synthesis and degradation of ketone bodies.

CONCLUSIONS

Our study not only clarifies the potential anti-fibrotic mechanism of BT against IPF at the metabolic level but also provides a theoretical basis for developing BT as an effective anti-fibrotic agent.

NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection

Metabolic changes precede malignant histology. However, it remains unclear whether detectable characteristic metabolome exists in esophageal squamous cell carcinoma (ESCC) tissues and biofluids for early diagnosis. Here, we conduct NMR- and MS-based metabolomics on 1,153 matched ESCC tissues, normal mucosae, pre- and one-week post-operative sera and urines from 560 participants across three hospitals, with machine learning and WGCNA. Aberrations in 'alanine, aspartate and glutamate metabolism' proved to be prevalent throughout the ESCC evolution, consistently identified by NMR and MS, and reflected in 16 serum and 10 urine metabolic signatures in both discovery and validation sets. NMR-based simplified panels of any five serum or urine metabolites outperform clinical serological tumor markers (AUC = 0.984 and 0.930, respectively), and are effective in distinguishing early-stage ESCC in test set (serum accuracy = 0.994, urine accuracy = 0.879). Collectively, NMR-based biofluid screening can reveal characteristic metabolic events of ESCC and be feasible for early detection (ChiCTR2300073613).

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.

Multiomic Approaches for Cancer Biomarker Discovery in Liquid Biopsies: Advances and Challenges

Cancer is a complex and heterogeneous disease that poses a significant threat to global health. Early diagnosis and treatment are critical for improving patient outcomes, and the use of liquid biopsies has emerged as a promising approach for cancer detection and monitoring. Traditionally, cancer diagnosis has relied on invasive tissue biopsies, the collection of which can prove challenging for patients and the results of which may not always provide accurate results due to tumor heterogeneity. Liquid biopsies have gained increasing attention as they provide a non-invasive and accessible source of cancer biomarkers, which can be used to diagnose cancer, monitor treatment response, and detect relapse. The integration of -omics technologies, such as proteomics, genomics, and metabolomics, has further enhanced the capabilities of liquid biopsies by introducing precision oncology and enabling the tailoring of treatment for individual patients based on their unique tumor biology. In this review, we will discuss the challenges and advances in the field of cancer liquid biopsies and the integration of -omics technologies for different types of liquid biopsies, including blood, tear, urine, sweat, saliva, and cerebrospinal fluid.