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 (), and fibrinogen beta chain (), 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 (, ) 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.

中文翻译：

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血浆代谢组学和蛋白质组学揭示了胆石症的新分子见解和生物标志物组

胆石症是一种胃肠道疾病，其社会经济成本最高。基于临床特征的胆石症诊断非常有限，并且对胆石症的直接分子见解以及胆石症与临床生化参数之间的关系尚不清楚。揭示胆石症的直接分子见解以及胆石症与临床生化参数之间的关系。识别这种疾病的敏感和特异性生物标志物。使用超高效液相色谱-串联质谱法对胆石症患者 (CP) 和无胆石症的健康对照个体 (HC) 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组合的交叉验证来评估小组的诊断性能。 CP 组和 HC 组之间的花生四烯酸代谢存在显着差异。 3-磷酸​​甘油醛脱氢酶、肌动蛋白β、磷酸甘油酸变位酶1、烯醇化酶1、髓过氧化物酶和肌动蛋白α1被确定为与胆石症相关的潜在蛋白质。计算合并模块与临床生化检测之间的相关性。基于与胆石症显着相关的三个模块，提出了由四个候选生物标志物组成的诊断组，包括3-氧代十四烷酸、12-羟基十二烷酸、血红蛋白亚基δ()和纤维蛋白原β链()。根据诊断小组的分类检测到 CP 的曲线下面积 (AUC) 为 0.955。独立队列的外部验证得出了相似的准确性（AUC=0.995）。这项研究通过显示 CP 和 HC 之间血浆代谢和蛋白质谱的差异，提供了对胆石症的一些直接分子见解，并提出了一个潜在的生物标志物组，其中包含两种代谢物（3-氧代十四烷酸、12-羟基十二烷酸）和两种蛋白质 (, )预测胆石症。我们还探讨了临床生化参数与组合模块的潜在相关性。这些发现可为临床胆石症的诊断提供一定的参考。