Institute of Pharmacology, Zhejiang University of Technology, Hangzhou 310014, China; Key Laboratory for Molecular Medicine and Chinese Medicine Preparations, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310002, China.
Key Laboratory for Molecular Medicine and Chinese Medicine Preparations, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310002, China.
J Pharm Biomed Anal. 2024 Jan 20;238:115806. doi: 10.1016/j.jpba.2023.115806. Epub 2023 Oct 18.
Cholelithiasis is a gastrointestinal disease that is associated with the highest socioeconomic cost. A diagnosis of cholelithiasis based on clinical features is significantly limited, and direct molecular insights into cholelithiasis and the relationship between cholelithiasis and clinical biochemical parameters are unclear.
Uncovering direct molecular insights into cholelithiasis and the relationship between cholelithiasis and clinical biochemical parameters. Identifying sensitive and specific biomarkers for this disease.
Parallel metabolomic and proteomic analyses of plasma from cholelithiasis patients (CPs) and healthy control individuals (HCs) without cholelithiasis were performed using ultrahigh-performance liquid chromatography-tandem mass spectrometry. A multimodule coexpression network analysis and integrated machine learning methods, including least absolute shrinkage and selection operator, random forest, and support vector machine, were used for bioinformatic analyses. An independent cohort and the cross-validation of the combination of two cohorts were used to evaluate the diagnostic performance of the panel.
Arachidonic acid metabolism was significantly different between the CP and HC groups. Glyceraldehyde-3-phosphate dehydrogenase, actin beta, phosphoglycerate mutase 1, Enolase 1, Myeloperoxidase, and actin alpha 1 were identified as potential proteins related to cholelithiasis. The correlation between the merged modules and clinical biochemical tests was calculated. A diagnostic panel composed of four candidate biomarkers, including 3-oxotetradecanoic acid, 12-hydroxydodecanoic acid, hemoglobin subunit delta (HBD), and fibrinogen beta chain (FGB), was proposed based on three modules that were significantly associated with cholelithiasis. The classification according to the diagnostic panel detected CPs with an area under the curve (AUC) of 0.955. External validation in an independent cohort resulted in similar accuracy (AUC=0.995).
This study provided some direct molecular insights into cholelithiasis by showing the differences in plasma metabolic and protein profiles between CPs and HCs and presented a potential biomarker panel with two metabolites (3-oxotetradecanoic acid, 12-hydroxydodecanoic acid) and two proteins (HBD, FGB) for predicting cholelithiasis. We also explored the potential correlation of clinical biochemical parameters with combined modules. These findings may provide some reference for the diagnosis of cholelithiasis in clinical practice.
胆石病是一种与最高社会经济成本相关的胃肠道疾病。基于临床特征诊断胆石病的方法具有明显的局限性，并且对胆石病的直接分子见解以及胆石病与临床生化参数之间的关系尚不清楚。
揭示胆石病的直接分子见解以及胆石病与临床生化参数之间的关系。鉴定该疾病的敏感和特异性生物标志物。
使用超高效液相色谱-串联质谱法对胆石病患者（CPs）和无胆石病的健康对照个体（HCs）的血浆进行平行代谢组学和蛋白质组学分析。使用多模块共表达网络分析和集成机器学习方法，包括最小绝对收缩和选择算子、随机森林和支持向量机，进行生物信息学分析。使用独立队列和两个队列的组合交叉验证来评估该组合的诊断性能。
CP 和 HC 组之间的花生四烯酸代谢明显不同。甘油醛-3-磷酸脱氢酶、β肌动蛋白、磷酸甘油酸变位酶 1、烯醇酶 1、髓过氧化物酶和α肌动蛋白 1被鉴定为与胆石病相关的潜在蛋白质。计算了合并模块与临床生化测试之间的相关性。基于与胆石病显著相关的三个模块，提出了由四个候选生物标志物（包括 3-氧代十四烷酸、12-羟基十二烷酸、血红蛋白亚单位 δ（HBD）和纤维蛋白原β链（FGB）组成的诊断面板。根据诊断面板进行的分类检测到 CPs 的曲线下面积（AUC）为 0.955。在独立队列中的外部验证得到了相似的准确性（AUC=0.995）。
本研究通过显示 CPs 和 HCs 之间血浆代谢和蛋白质谱的差异，为胆石病提供了一些直接的分子见解，并提出了一个潜在的生物标志物组合，其中包含两个代谢物（3-氧代十四烷酸、12-羟基十二烷酸）和两个蛋白质（HBD、FGB），用于预测胆石病。我们还探讨了临床生化参数与合并模块的潜在相关性。这些发现可能为临床实践中胆石病的诊断提供一些参考。
