Cochran Darcy, NourEldein Mai, Bezdekova Dominika, Schram Aaron, Howard Réka, Powers Robert
Department of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588-0304, USA.
Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588-0304, USA.
Trends Analyt Chem. 2024 Nov;180. doi: 10.1016/j.trac.2024.117918. Epub 2024 Aug 19.
Cancer is a leading cause of world-wide death and a major subject of clinical studies focused on the identification of new diagnostic tools. An in-depth meta-analysis of 244 clinical metabolomics studies of human serum samples highlights a reproducibility crisis. A total of 2,206 unique metabolites were reported as statistically significant across the 244 studies, but 72% (1,582) of these metabolites were identified by only one study. Further analysis shows a random disparate disagreement in reported directions of metabolite concentration changes when detected by multiple studies. Statistical models revealed that 1,867 of the 2,206 metabolites (85%) are simply statistical noise. Only 3 to 12% of these metabolites reach the threshold of statistical significance for a specific cancer type. Our findings demonstrate the absence of a detectable metabolic response to cancer and provide evidence of a serious need by the metabolomics community to establish widely accepted best practices to improve future outcomes.
癌症是全球主要死因,也是专注于识别新诊断工具的临床研究的主要课题。对244项人类血清样本临床代谢组学研究的深入荟萃分析凸显了可重复性危机。在这244项研究中,总共报告了2206种独特的具有统计学意义的代谢物,但其中72%(1582种)仅在一项研究中被鉴定出来。进一步分析表明,当多项研究检测时,报告的代谢物浓度变化方向存在随机的明显差异。统计模型显示,2206种代谢物中有1867种(85%)只是统计噪声。这些代谢物中只有3%至12%达到特定癌症类型的统计学显著性阈值。我们的研究结果表明不存在可检测到的对癌症的代谢反应,并提供了证据,证明代谢组学领域迫切需要建立广泛接受的最佳实践以改善未来结果。