Lipid metabolism analysis in esophageal cancer and associated drug discovery

Jiao, Ruidi; Jiang, Wei; Xu, Kunpeng; Luo, Qian; Wang, Luhua; Zhao, Chao

doi:10.1016/j.jpha.2023.08.019

Cited by 5 publications

(1 citation statement)

References 133 publications

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“…Several studies have shown that lipid metabolism may have an impact on tumor progression. Jiao et al ’s ( 2023 ) study revealed the influence of lipid metabolism-related enzyme LPCAT on the progression of ESCC. Therefore, the Kruskal–Wallis test and Wilcoxon test were performed to reveal the combination of differential lipids with clinical information of ESCC patients.…”

Section: Discussionmentioning

confidence: 99%

Plasma-based lipidomics reveals potential diagnostic biomarkers for esophageal squamous cell carcinoma: a retrospective study

Chen,

Gu,

Rong

et al. 2024

PeerJ

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Background Esophageal squamous cell carcinoma (ESCC) is highly prevalent and has a high mortality rate. Traditional diagnostic methods, such as imaging examinations and blood tumor marker tests, are not effective in accurately diagnosing ESCC due to their low sensitivity and specificity. Esophageal endoscopic biopsy, which is considered as the gold standard, is not suitable for screening due to its invasiveness and high cost. Therefore, this study aimed to develop a convenient and low-cost diagnostic method for ESCC using plasma-based lipidomics analysis combined with machine learning (ML) algorithms. Methods Plasma samples from a total of 40 ESCC patients and 31 healthy controls were used for lipidomics study. Untargeted lipidomics analysis was conducted through liquid chromatography-mass spectrometry (LC-MS) analysis. Differentially expressed lipid features were filtered based on multivariate and univariate analysis, and lipid annotation was performed using MS-DIAL software. Results A total of 99 differential lipids were identified, with 15 up-regulated lipids and 84 down-regulated lipids, suggesting their potential as diagnostic targets for ESCC. In the single-lipid plasma-based diagnostic model, nine specific lipids (FA 15:4, FA 27:1, FA 28:7, FA 28:0, FA 36:0, FA 39:0, FA 42:0, FA 44:0, and DG 37:7) exhibited excellent diagnostic performance, with an area under the curve (AUC) exceeding 0.99. Furthermore, multiple lipid-based ML models also demonstrated comparable diagnostic ability for ESCC. These findings indicate plasma lipids as a promising diagnostic approach for ESCC.

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Section: Discussionmentioning

confidence: 99%