Gene‐Based Clustering Identifies QSOX1 and IL1RAP as Biomarkers of Metabolic Dysfunction‐Associated Steatotic Liver Disease

BACKGROUND AND AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a progressive liver disease that ranges from simple steatosis to inflammation, fibrosis and cirrhosis. To address the unmet need for new MASLD biomarkers, we aimed to identify candidate biomarkers using publicly available RNA sequencing (RNA-seq) and proteomics data. METHODS: An approach involving unsupervised gene clustering was performed using homogeneously processed and integrated RNA-seq data of 625 liver specimens to screen for MASLD biomarkers, in combination with public proteomics data from healthy controls and MASLD patients. Additionally, we validated the results in the MASLD and healthy cohorts using enzyme-linked immunosorbent assay (ELISA) of plasma and immunohistochemical staining (IHC) of liver samples. RESULTS: We generated a database (https://dreamapp.biomed.au.dk/NAFLD/) for exploring gene expression changes along MASLD progression to facilitate the identification of genes and pathways involved in the disease's progression. Through cross-analysis of the gene and protein clusters, we identified 38 genes as potential biomarkers for MASLD severity. Up-regulation of Quiescin sulfhydryl oxidase 1 (QSOX1) and down-regulation of Interleukin-1 receptor accessory protein (IL1RAP) were associated with increasing MASLD severity in RNA-seq and proteomics data. Particularly, the QSOX1/IL1RAP ratio in plasma demonstrated effectiveness in diagnosing MASLD, with an area under the receiver operating characteristic (AUROC) of up to 0.95 as quantified by proteomics profiling and an AUROC of 0.82 with ELISA. CONCLUSIONS: We discovered a significant association between the levels of QSOX1 and IL1RAP and MASLD severity. Furthermore, the QSOX1/IL1RAP ratio shows promise as a non-invasive biomarker for diagnosing MASLD and assessing its severity.