Differences in Prediagnostic Serum Metabolomic and Lipidomic Profiles Between Cirrhosis Patients with and without Incident Hepatocellular Carcinoma

Hannah Powell,1,* Cristian Coarfa,2– 4,* Elisa Ruiz-Echartea,2– 4,* Sandra L Grimm,2– 4 Omar Najjar,1 Bing Yu,5 Luis Olivares,6 Michael E Scheurer,6 Christie Ballantyne,1 Abeer Alsarraj,1 Emad Mohamed Salem,1 Aaron P Thrift,1,2,7 Hashem B El Serag,1,2 Salma Kaochar1,2,4 1Department of Medicine, Baylor College of Medicine, Houston, TX, USA; 2Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA; 3Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, USA; 4Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; 5Department of Epidemiology, The University of Texas Health Science Center at Houston, Houston, TX, USA; 6Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; 7Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA*These authors contributed equally to this workCorrespondence: Salma Kaochar; Hashem B El Serag, Email [email protected]; [email protected]: Early detection of hepatocellular carcinoma (HCC) is crucial for improving patient outcomes, but we lack robust clinical biomarkers. This study aimed to identify a metabolite and/or lipid panel for early HCC detection.Methods: We developed a high-resolution liquid chromatography mass spectrometry (LC-MS)-based profiling platform and evaluated differences in the global metabolome and lipidome between 28 pre-diagnostic serum samples from patients with cirrhosis who subsequently developed HCC (cases) and 30 samples from patients with cirrhosis and no HCC (controls). We linked differentially expressed metabolites and lipids to their associated genes, proteins, and transcriptomic signatures in publicly available datasets. We used machine learning models to identify a minimal panel to distinguish between cases and controls.Results: Among cases compared with controls, 124 metabolites and 246 lipids were upregulated, while 208 metabolites and 73 lipids were downregulated. The top upregulated metabolites were glycoursodeoxycholic acid, 5-methyltetrahydrofolic acid, octanoyl-coenzyme A, and glycocholic acid. Elevated lipids comprised glycerol lipids, cardiolipin, and phosphatidylethanolamine, whereas suppressed lipids included oxidized phosphatidylcholine and lysophospholipids. There was an overlap between differentially expressed metabolites and lipids and previously published transcriptomic signatures, illustrating an association with liver disease severity. A panel of 12 metabolites that distinguished between cases and controls with an area under the receiver operating curve of 0.98 for the support vector machine (interquartile range, 0.9– 1).Conclusion: Using prediagnostic serum samples, we identified a promising metabolites panel that accurately identifies patients with cirrhosis who progressed to HCC. Further validation of this panel is required.Keywords: lipid dysregulation, biomarker, fatty acids, machine learning