Table 2 Evidence on the circulating metabolites as diagnostic biomarkers in cancers
From: Cancer metabolites: promising biomarkers for cancer liquid biopsy
Application | Authors | Year | Cancer Type | Methodology | Findings(Ref) |
|---|---|---|---|---|---|
Dignosis | Yu, S., et al | 2022 | Papillary thyroid cancer | UHPLC-MS | A novel metabolic biomarker signature was identified to discriminate papillary thyroid cancer from the benign thyroid nodule [109] |
Wang M., et al | 2022 | Colorectal cancer | UPLC-TOF–MS | By screening the differential plasma metabolites and further quantitative analysis, we found the plasma biomarkers that can be used in the diagnosis of colorectal cancer. [110] | |
Ossoliński K., et al | 2022 | Bladder cancer | NMR, LDI-MS, ICP-OES | Three different analytical platforms demonstrate that the identified distinct serum metabolites have potential to be used for noninvasive detection, staging, and grading of BC [116] | |
Wang G., et al | 2022 | Lung cancer | UHPLC-MS | A machine learning model made of nine lipids, named Lung Cancer Artificial Intelligence Detector, effectively identifies patients in the early stages of lung cancer [43] | |
Wang G., et al | 2021 | Pancreatic ductal adenocarcinoma cancer | UHPLC-MS | They optimized 17 characteristic metabolites as detection features and developed a liquid chromatography-mass spectrometry-based targeted assay, proposeing that the machine learning-aided lipidomics approach be used for early detection of PDAC [42] | |
Casadei-Gardini A., et al | 2020 | Hepatocellular Carcinoma | NMR | This study analysis identified a set of metabolites with possible clinical and biological implication in HCC pathophysiology [111] | |
Prognosis | Triozzi, P. L., et al | 2022 | Melanoma | UPLC-MS | Blood metabolomics as predictive biomarkers reflect patient response to anti-PD-1 immune checkpoint therapy [112] |
Liu, L., et al | 2022 | Esophageal squamous cell carcinoma | GC-TOFMS | A panel of 12 esophageal squamous cell carcinoma tumor-associated serum metabolites has the potential for monitoring surgery efficacy and disease relapse [114] | |
Zhuang J., et al | 2022 | Bladder cancer | NMR, UPLC-MS | Serum metabolic profiles of neoadjuvant chemotherapy sensitivity are significantly different in bladder cancer patients. Glycine, hypoxanthine, taurine and glutamine may be the potential biomarkers for clinical treatment [113] | |
Luo X., et al | 2020 | Pancreatic cancer | UPLC-MS | Five new metabolite biomarkers in plasma were verified and can be used to diagnose pancreatic cancer. And Succinic acid and gluconic acid have strong ability to monitor the progression and metastasis of pancreatic cancer [115] | |
Combination | Huang Y., et al | 2022 | Breast cnacer | NPELDI-MS | It provide an efficient serum metabolic tool to characterize breast cancer and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases. [117] |