Nouran Yonis , Ahmed Mousa , Mohamed H. Yousef , Ahmed M. Ghouneimy , Areeg M. Dabbish , Hana Abdelzaher , Mohamed Ali Hussein , Shahd Ezzeldin , Abdelmoneim A. Adel , Yosra H. Mahmoud , Nashwa El-Khazragy , Anwar Abdelnaser
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引用次数: 0
Abstract
Background
Non-alcoholic fatty liver disease (NAFLD) involves abnormal fat accumulation in the liver, mainly as triglycerides. It ranges from steatosis to non-alcoholic steatohepatitis (NASH), which can lead to inflammation, cellular damage, liver fibrosis, cirrhosis, or hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) are crucial for regulating gene expression across various conditions. LncRNAs are emerging as potential putative diagnostic markers for NAFLD-associated HCC.
Methods
We used two human and two mouse datasets from the Gene Expression Omnibus to analyze the expression profiles of mRNAs and lncRNAs. We created a network linking lncRNAs, miRNAs, and mRNAs to investigate the relationships among these RNA types. Additionally, we identified NAFLD-related lncRNAs from existing literature. We then quantified the expression levels of four specific lncRNAs, including PVT1, DUBR, SNHG17, and SNHG14, in the serum of 92 Egyptian participants using qPCR. Finally, we performed a Receiver Operating Characteristic analysis to evaluate the diagnostic potential of the candidate lncRNAs.
Results
Our data suggests that maternally expressed gene 3 (MEG3), H19, and DPPA2 Upstream Binding RNA (DUBR) were significantly upregulated, and plasmacytoma variant translocation 1 (PVT1) was markedly downregulated. PVT1 showed the highest diagnostic accuracy for both NAFLD and NASH. The combined panels of PVT1 +H19 for NAFLD and PVT1 +H19 +DUBR for NASH demonstrated high diagnostic potential. Uniquely, PVT1 can distinguish between NAFLD and NASH. PVT1 exhibited strong diagnostic potential for NAFLD and NASH, individually and in combination with other lncRNAs.
Conclusion
Our study identifies four lncRNAs as putative biomarkers with high specificity and accuracy, individually or combined, for differentiating between NAFLD and NASH. Healthy volunteers with PVT1 possess the highest diagnostic accuracy and significantly discriminate between NAFLD and NASH.
期刊介绍:
Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered.
Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered.
Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.