Introduction about NAFLD RNAseq Database
Background and Aims: Non-alcoholic fatty liver disease (NAFLD) is a progressive liver disease that ranges from simple steatosis to inflammation, fibrosis and cirrhosis. To address the unmet need for new NAFLD biomarkers, we aimed to identify candidate biomarkers using publicly available RNA sequencing (RNA-seq) and proteomics data.
Methods: We retrieved and homogeneously processed public RNA-seq data from 7 studies, comprising 625 specimens of NAFLD patients with available NAFLD activity scores (NAS) and fibrosis scores. A novel approach involving unsupervised gene clustering was performed using integrated RNA-seq data to screen for NAFLD biomarkers, in combination with public proteomics data from healthy controls and NAFLD patients. Additionly, we validated the results by investigating plasma biomarker levels using enzyme-linked immunosorbent assay (ELISA) and immunohistochemical staining (IHC) of liver samples from NAFLD patients.
Results: Through cross-analysis of the gene and protein clusters, we identified Quiescin sulfhydryl oxidase 1 (QSOX1) and Interleukin-1 receptor accessory protein (IL-1RAP) were as biomarkers. QSOX1 and IL-1RAP exhibited up-regulation or down-regulation associated with increasing NAFLD severity in both RNA-seq and proteomics data. Particularly the QSOX1/IL1RAP ratio in plasma demostrated effectiveness in diagnosing NAFLD, with an area under the receiver operating characteristic (AUROC) of up to 0.95 in public proteomics data and 0.82 using ELISA.
Conclusions: We discovered a significant association between the levels of QSOX1 and IL1RAP and the severity of NAFLD. Furthermore, the QSOX1/IL1RAP ratio shows promise as a non-invasive biomarker for diagnosing NAFLD and assessing its severity.
Lay Summary
To identify non-invasive biomarkers for NAFLD, we collected publicly available RNA-seq data and analyzed them using a novel gene clustering approach. In addition, we performed ananalysis of public proteomics data. Furthermore, we validated the findings by conducting plasma enzyme-linked immunosorbent assay (ELISA) and liver immunohistochemical staining (IHC). Our study revealedthe up-regulation of QSOX1 and the down-regulation of IL1RAP, which wereassociated with increasing NAFLD severity. Importantly the ratio of their expression in plasma may serve as an alternative non-invasive diagnostic strategy for assessing NAFLD severity, thus avoiding the need for liver biopsy.
Please cite the NAFLD RNAseq Database as:
Wenfeng Ma, Jinrong Huang, Benqiang Cai, Mumin Shao, Xuewen Yu, Mikkel Breinholt Kjae, Minling Lv, Xin Zhong, Shaomin Xu, Bolin Zhan, Qun Li, Qi Huang, Mengqing Ma, Lei Cheng, View ORCID ProfileYonglun Luo, Henning Gronbaek, Xiaozhou Zhou, Lin Lin. medRxiv doi: https://doi.org/10.1101/2023.07.26.23293038
Contact:
Wenfeng Ma, Ph.D., Associate Chief Physician, Shenzhen Traditional Chinese Medicine Hospital, China. Email: wenfeng@clin.au.dk
Lin Lin, Ph.D., Associate Professor
Group leader, Department of Biomedicine, Aarhus University
Email: lin.lin@biomed.au.dk
Address: Building 1116-152, Hoegh-Guldbergs Gade 10, 8000 Aarhus
Sample information vs gene expression on reduced dimensions
In this tab, users can visualise both sample information and gene expression side-by-side on low-dimensional representions.Dimension Reduction
Coexpression of two genes on reduced dimensions
In this tab, users can visualise the coexpression of two genes on low-dimensional representions.Dimension Reduction
Sample information / gene expression violin plot / box plot
In this tab, users can visualise the gene expression or continuous sample information (e.g. Number of UMIs / module score) across groups of cells (e.g. libary / clusters).Proportion / cell numbers across different sample information
In this tab, users can visualise the composition of single cells based on one discrete sample information across another discrete sample information. Usage examples include the library or cellcycle composition across clusters.Gene expression bubbleplot / heatmap
In this tab, users can visualise the gene expression patterns of multiple genes grouped by categorical sample information (e.g. library / cluster).The normalised expression are averaged, log-transformed and then plotted.
2023.10.31: add project details in introduction tab, add user guide tab
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