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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Transcriptomic and Metabolomic Analysis of Liver Cirrhosis

Author(s): Xiao Kuang, Jinyu Li, Yiheng Xu, Lihong Yang, Xiaoxiao Liu, Jinhui Yang and Wenlin Tai*

Volume 27, Issue 6, 2024

Published on: 31 July, 2023

Page: [922 - 932] Pages: 11

DOI: 10.2174/1386207326666230717094936

Price: $65

Abstract

Background: Liver cirrhosis is one of the leading causes of decreased life expectancy worldwide. However, the molecular mechanisms underlying liver cirrhosis remain unclear. In this study, we performed a comprehensive analysis using transcriptome and metabolome sequencing to explore the genes, pathways, and interactions associated with liver cirrhosis.

Methods: We performed transcriptome and metabolome sequencing of blood samples from patients with cirrhosis and healthy controls (1:1 matched for sex and age). We validated the differentially expressed microRNA (miRNA) and mRNAs using real-time quantitative polymerase chain reaction.

Results: For transcriptome analysis, we screened for differentially expressed miRNAs and mRNAs, analyzed mRNAs to identify possible core genes and pathways, and performed coanalysis of miRNA and mRNA sequencing results. In terms of the metabolome, we screened five pathways that were substantially enriched in the differential metabolites. Next, we identified the metabolites with the most pronounced differences among these five metabolic pathways. We performed receiver operating characteristic (ROC) curve analysis of these five metabolites to determine their diagnostic efficacy for cirrhosis. Finally, we explored possible links between the transcriptome and metabolome.

Conclusion: Based on sequencing and bioinformatics, we identified miRNAs and genes that were differentially expressed in the blood of patients with liver cirrhosis. By exploring pathways and disease-specific networks, we identified unique biological mechanisms. In terms of metabolomes, we identified novel biomarkers and explored their diagnostic efficacy. We identified possible common pathways in the transcriptome and metabolome that could serve as candidates for further studies.

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