Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank

被引:2
作者
Borgmaestars, Emmy [1 ]
Jacobson, Sara [1 ]
Simm, Maja [1 ,2 ]
Johansson, Mattias [3 ]
Billing, Ola [1 ]
Lundin, Christina [1 ]
Nystroem, Hanna [1 ,4 ]
Oehlund, Daniel [4 ,5 ]
Lubovac-Pilav, Zelmina [6 ]
Jonsson, Paer [7 ]
Franklin, Oskar [1 ,8 ]
Sund, Malin [1 ,9 ,10 ]
机构
[1] Umea Univ, Dept Surg & Perioperat Sci Surg, Norrlands Univ Sjukhus 6M,M31, S-90185 Umea, Sweden
[2] Umea Univ, Dept Clin Sci Obstet & Gynecol, Umea, Sweden
[3] Int Agcy Res Canc, Genom Epidemiol Branch, Lyon, France
[4] Umea Univ, Wallenberg Ctr Mol Med, Umea, Sweden
[5] Umea Univ, Dept Radiat Sci Oncol, Umea, Sweden
[6] Univ Skovde, Dept Biol & Bioinformat, Skovde, Sweden
[7] Umea Univ, Dept Chem, S-ME3 Umea, Sweden
[8] Univ Colorado, Sch Med, Dept Surg, Div Surg Oncol, Aurora, CO USA
[9] Univ Helsinki, Dept Surg, Helsinki, Finland
[10] Helsinki Univ Hosp, Helsinki, Finland
基金
瑞典研究理事会;
关键词
Pancreatic neoplasms; biomarkers; risk; hyperglycemia; survival; BODY-MASS INDEX; DUCTAL ADENOCARCINOMA; BIOMARKER SIGNATURE; METABOLITES;
D O I
10.21037/jgo-23-930
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background: Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers. Methods: We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score. Results: Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714-0.854] compared to 0.681 (95% CI: 0.597-0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1]. Conclusions: Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.
引用
收藏
页码:755 / +
页数:27
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