Untargeted plasma metabolomics for risk prediction of hepatocellular carcinoma: A prospective study in two Chinese cohorts

Abstract

Characterization of metabolic perturbation prior to hepatocellular carcinoma (HCC) may deepen the understanding of causal pathways and identify novel biomarkers for early prevention. We conducted two 1:1 matched nested case‐control studies (108 and 55 pairs) to examine the association of plasma metabolome (profiled using LC‐MS) with the risk of HCC based on two prospective cohorts in China. Differential metabolites were identified by paired t tests and orthogonal partial least‐squares discriminant analysis (OPLS‐DA). Weighted gene coexpression network analysis (WGCNA) was performed to classify metabolites into modules for identifying biological pathways involved in hepatocarcinogenesis. We assessed the risk predictivity of metabolites using multivariable logistic regression models. Among 612 named metabolites, 44 differential metabolites were identified between cases and controls, including 12 androgenic/progestin steroid hormones, 8 bile acids, 10 amino acids, 6 phospholipids, and 8 others. These metabolites were associated with HCC in the multivariable logistic regression analyses, with odds ratios ranging from 0.19 (95% confidence interval [CI]: 0.11‐0.35) to 5.09 (95% CI: 2.73‐9.50). WGCNA including 612 metabolites showed 8 significant modules related to HCC risk, including those representing metabolic pathways of androgen and progestin, primary and secondary bile acids, and amino acids. A combination of 18 metabolites of independent effects showed the potential to predict HCC risk, with an AUC of 0.87 (95% CI: 0.82‐0.92) and 0.86 (95% CI: 0.80‐0.93) in the training and validation sets, respectively. In conclusion, we identified a panel of plasma metabolites that could be implicated in hepatocellular carcinogenesis and have the potential to predict HCC risk.

Identification of pre‐diagnostic lipid sets associated with liver cancer risk using untargeted lipidomics and chemical set analysis: A nested case‐control study within the ATBC cohort

“…Improved understanding of the hepatic metabolic reprogramming that may predispose individuals to liver cancer initiation can enable novel prevention strategies 29,30 . In our analysis, we found that several lipid classes were strongly and significantly associated with the development of liver cancer, suggesting that a pro‐cancerous modification in lipid biochemistry may increase the initiation of hepatocarcinogenesis.…”

“…In comparison to prior epidemiology studies, 14,[29][30][31][32][33][34][35][36][37][38][39][40] the majority of which have utilized generic and broad metabolic extraction and data collection protocols, this study, to the best of our knowledge, is the first liver cancer study to characterize the pre-diagnostic circulating lipidome using an untargeted lipidomics method. We tested over 400 annotated lipids from 28 lipid classes using chemical set enrichment analysis methods to identify structural and functional lipid sets that were associated with liver cancer.…”

A Metabolomic Signature of Obesity and Risk of Colorectal Cancer: Two Nested Case–Control Studies

“…To validate results from the PLCO, we designed a 1:1 matched, nested case–control study for CRC based on a prospective cohort in Jiangsu Province, China. Details of the study design have been described elsewhere [ 16 ]. Briefly, a total of 44,962 adults completed an interviewer-administered electronic questionnaire and underwent physical examinations at baseline.…”

“…In the Jiangsu cohort, untargeted metabolites in the plasma were measured using UHPLC-MS/MS at Metabolon, as described in detail elsewhere [ 16 , 18 ]. Briefly, based on ACQUITY UPLC (Waters, Milford, MA, USA) and Q Exactive HF hybrid Quadrupole-Orbitrap (Thermo Fisher Scientific, San Jose, CA, USA), four independent UHPLC-MS/MS methods were applied: two separate reverse-phase (RP)/UHPLC-MS/MS methods with positive-ion mode electrospray ionization (ESI), RP/UHPLC-MS/MS with negative-ion mode ESI, and hydrophilic interaction liquid chromatography (HILIC)/UHPLC-MS/MS with negative-ion mode ESI.…”

Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine

“…UPLC-MS-based platforms have discovered 44 dysregulated metabolites between HCC patients and controls. After least absolute shrinkage and selection operator (LASSO) and ROC curve analysis, 18 metabolites (including quinolinate, glycocholic acid, and citraconate) were selected with the potential to predict HCC risk in both training and validation sets (82). To distinguish early and advanced HCC patients, an NMR-based studies used serum samples from 64 HCC patients in early (28 populations) and advanced (36 populations) disease stages for biomarker Metabolomics-based biomarkers discovery for tailored therapeutics for HCC patients.…”