Identification of signature gene set as highly accurate determination of metabolic dysfunction-associated steatotic liver disease progression.

BACKGROUND/AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. METHODS: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. RESULTS: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. CONCLUSION: We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.

Long non-coding RNAs: key regulators of liver and kidney fibrogenesis.

Fibrosis is a pathological condition that is characterized by an abnormal buildup of extracellular matrix (ECM) components, such as collagen, in tissues. This condition affects various organs of the body, including the liver and kidney. Early diagnosis and treatment of fibrosis are crucial, as it is a progressive and irreversible process in both organs. While there are certain similarities in the fibrosis process between the liver and kidney, there are also significant differences that must be identified to determine molecular diagnostic markers and potential therapeutic targets. Long non-coding RNAs (lncRNAs), a class of RNA molecules that do not code for proteins, are increasingly recognized as playing significant roles in gene expression regulation. Emerging evidence suggests that specific lncRNAs are involved in fibrosis development and progression by modulating signaling pathways, such as the TGF-ß/Smad pathway and the ß-catenin pathway. Thus, identifying the precise lncRNAs involved in fibrosis could lead to novel therapeutic approaches for fibrotic diseases. In this review, we summarize lncRNAs related to fibrosis in the liver and kidney, and propose their potential as therapeutic targets based on their functions. [BMB Reports 2023; 56(7): 374-384].