Details

Autor(en) / Beteiligte

• Shu, Xiang
• Zheng, Wei
• Yu, Danxia
• Li, Hong‐Lan
• Lan, Qing
• Yang, Gong
• Cai, Hui
• Ma, Xiao
• Rothman, Nathaniel
• Gao, Yu‐Tang
• Jia, Wei
• Xiang, Yong‐Bing
• Shu, Xiao‐Ou

Titel

Prospective metabolomics study identifies potential novel blood metabolites associated with pancreatic cancer risk

Ist Teil von

• International journal of cancer, 2018-11, Vol.143 (9), p.2161-2167

Ort / Verlag

United States: Wiley Subscription Services, Inc

Erscheinungsjahr

2018

Quelle

Access via Wiley Online Library

Beschreibungen/Notizen

• Using a metabolomics approach, we systematically searched for circulating metabolite biomarkers for pancreatic cancer risk in a case‐control study nested within two prospective Shanghai cohorts. Included in our study were 226 incident pancreatic cancer cases and their individually‐matched controls. Untargeted mass spectrometry platforms were used to measure metabolites in blood samples collected prior to cancer diagnosis. Conditional logistic regression was performed to assess the associations of metabolites with pancreatic cancer risk. We identified 10 metabolites associated with pancreatic cancer, after accounting for multiple comparisons (the Benjamini‐Hochberg false discovery rate <0.05). The majority of the identified metabolites were glycerophospholipids (ORs per SD increase: 0.44–2.32; p values: 7.2 × 10−4 to 1.0 × 10−6), six of which were associated with decreased risk and one with increased risk. Additionally, levels of coumarin (OR = 1.96, p = 3.7 × 10−6) and picolinic acid (OR = 2.53, p = 5.0 × 10−5) were positively associated with pancreatic cancer risk, while tetracosanoic acid was inversely associated with risk (OR = 0.48, p = 7.16 × 10−7). Four metabolites remained statistically significant after mutual adjustment. Our study provides novel evidence that the dysregulation of glycerophospholipids may play an important role in pancreatic cancer development. What's new? Despite the lack of reliable biomarkers for risk assessment and early diagnosis, to date few metabolomics studies have been conducted to identify biomarkers for pancreatic cancer risk. In this nested case‐control study using pre‐diagnostic plasma samples, the authors identified ten metabolites associated with risk of pancreatic cancer after adjusting for multiple comparisons. Seven of these metabolites were glycerophospholipids, the majority of which were inversely associated with pancreatic cancer risk, providing novel evidence that glycerophospholipids dysregulation may be related to pancreatic cancer. The new metabolite biomarkers may be useful in identifying high‐risk individuals for screening and chemoprevention for this deadly malignancy.