Fig. 2

A comprehensive data analysis pipeline for identifying 12 HCC EV-specific gene candidates. The pipeline begins with the preparation of our proprietary LiTA database, which was established by aggregating publicly available liver transcriptome data from 59 cohorts (n = 9,160) obtained from the GEO and Array Express repositories, resulting in 16,296 gene expression profiles for 8,685 liver tissue specimens. In the gene selection process, the top 16 HCC EV-specific gene candidates were selected through the following four steps: i) selecting upregulated genes across four etiology-based comparisons; ii) selecting highly expressed genes in HCC cell lines using CCLE dataset; iii) excluding highly expressed genes in immune cells using DMAP dataset; iv) selecting highly expressed genes in HCC EVs using exoRBase 2.0. qPCR was employed to measure the differential expression of the top 16 HCC EV-specific gene candidates in HepG2 cells and WBCs (Scale: 40 – cycle threshold value). Six genes (SORT1, ATAD2, H2AX, PUF60, TUBG1, and UBL4 A) with the top-ranking differential expressions (high in HepG2 cells and low in WBCs) were selected. In parallel, another six genes (ALB, FABP1, FGB, APOH, FGG, and TF) with performance in distinguishing early-stage HCC from liver cirrhosis were also identified. Combining these findings, we identified the 12 HCC EV-specific gene candidates. ALD, alcoholic liver disease; CCLE, Cancer Cell Line Encyclopedia dataset; DMAP, Differentiation MAP dataset; EV, extracellular vesicle; GEO, Gene Expression Omnibus; HBV, hepatitis B virus; HCC, hepatocellular carcinoma; HCV, hepatitis C virus; HDV, hepatitis D virus; LiTA, Liver Transcriptome Atlas; MASLD, metabolic dysfunction-associated steatotic liver disease; MGH, Massachusetts General Hospital; qPCR, quantitative PCR; WBC, white blood cell