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TANC1 methylation as a novel biomarker for the diagnosis of patients with anti-tuberculosis drug-induced liver injury.

ABSTRACT: We aimed to elucidate the differences in genomic methylation patterns between ADLI and non-ADLI patients to identify DNA methylation-based biomarkers. Genome-wide DNA methylation patterns were obtained using Infinium MethylationEPIC (EPIC) BeadChip array to analyze 14 peripheral blood samples (7 ADLI cases, 7 non-ADLI controls). Changes in the mRNA and DNA methylation in the target genes of another 120 peripheral blood samples (60 ADLI cases, 60 non-ADLI controls) were analyzed by real-time polymerase chain reaction and pyrosequencing, respectively. A total of 308 hypermethylated CpG sites and 498 hypomethylated CpG sites were identified. Significantly, hypermethylated CpG sites cg06961147 and cg24666046 in TANC1 associated with ADLI was identified by genome-wide DNA methylation profiling. The mRNA expression of TANC1 was lower in the cases compared to the controls. Pyrosequencing validated these two differentially methylated loci, which was consistent with the results from the EPIC BeadChip array. Receiver operating characteristic analysis indicated that the area under the curve of TANC1 (cg06961147, cg24666046, and their combinations) was 0.812, 0.842, and 0.857, respectively. These results indicate that patients with ADLI have different genomic methylation patterns than patients without ADLI. The hypermethylated differentially methylated site cg06961147 combined with cg24666046 in TANC1 provides evidence for the diagnosis of ADLI.

SUBMITTER: Wu D

PROVIDER: S-EPMC8408132 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Anti-tuberculosis drug-induced liver injury (ATB-DILI) is an adverse reaction with a high incidence and the greatest impact on tuberculosis treatment. However, there is a lack of effective biomarkers for the early prediction of ATB-DILI. Herein, this study uses UPLC‒MS/MS to reveal the plasma metabolic profile and lipid profile of ATB-DILI patients before drug administration and screen new biomarkers for predicting ATB-DILI. Methods: A total of 60 TB patients were enrolled, and plasma was collected before antituberculosis drug administration. The untargeted metabolomics and lipidomics analyses were performed using UPLC‒MS/MS, and the high-resolution mass spectrometer Q Exactive was used for data acquisition in both positive and negative ion modes. The random forest package of R software was used for data screening and model building. Results: A total of 60 TB patients, including 30 ATB-DILI patients and 30 non-ATB-DILI subjects, were enrolled. There were no significant differences between the ATB-DILI and control groups in age, sex, smoking, drinking or body mass index (p > 0.05). Twenty-two differential metabolites were selected. According to KEGG pathway analysis, 9 significantly enriched metabolic pathways were found, and both drug metabolism-other enzymes and niacin and nicotinamide metabolic pathways were found in both positive and negative ion models. A total of 7 differential lipid molecules were identified between the two groups. Ferroptosis and biosynthesis of unsaturated fatty acids were involved in the occurrence of ATB-DILI. Random forest analysis showed that the model built with the top 30 important variables had an area under the ROC curve of 0.79 (0.65-0.93) for the training set and 0.79 (0.55-1.00) for the validation set. Conclusion: This study demonstrated that potential markers for the early prediction of ATB-DILI can be found through plasma metabolomics and lipidomics. The random forest model showed good clinical predictive value for ATB-DILI.

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TANC1 methylation as a novel biomarker for the diagnosis of patients with anti-tuberculosis drug-induced liver injury.

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