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pmid:33352935
Circulating Exosomal MicroRNA-1307-5p as a Predictor for Metastasis in Patients with Hepatocellular Carcinoma.
Jung Woo Eun · Chul Won Seo · Geum Ok Baek · Moon Gyeong Yoon · Hye Ri Ahn · Ju A Son · Suna Sung · Do Wan Kim · Soon Sun Kim · Hyo Jung Cho · Jae Youn Cheong
Cancers · 2020 doi:10.3390/cancers12123819
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Experiment
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Data
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Analysis
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Complete file
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To identify pro-metastatic circulating exo-miRs in hepatocellular carcinoma (HCC), next-generation sequencing-based plasma exo-miR profiles of 14 patients with HCC (eight non-metastatic and six with metastasis within 1 year of follow-up) were analyzed. Sixty-one miRs were significantly overexpressed among patients with metastatic HCC. Candidate miRs were selected through integrative analyses of two different public expression datasets, GSE67140 and The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA_LIHC). Integrative analyses revealed 3 of 61 miRs (miR-106b-5p, miR-1307-5p, and miR-340-5p) commonly overexpressed both in metastasis and vascular invasion groups, with prognostic implications. Validation was performed using stored blood samples of 150 patients with HCC. Validation analysis showed that circulating exo-miR-1307-5p was significantly overexpressed in the metastasis group (p = 0.04), as well as in the vascular invasion and tumor recurrence groups. Circulating exo-miR-1307-5p expression was significantly correlated with tumor stage progression (p < 0.0001). Downstream signaling pathways of miR-1307 were predicted using TargetScan and Ingenuity Pathway Analysis. On comprehensive bioinformatics analysis, the downstream pathway of miR-1307-5p, promoting epithelial-mesenchymal transition (EMT), showed SEC14L2 and ENG downregulation. Our results show that circulating exo-miR-1307-5p promotes metastasis and helps predict metastasis in HCC, and SEC14L2 and ENG are target tumor suppressor genes of miR-1307 that promote EMT.", "claims": [{"id": "claim-data-access", "statement": "To estimate miR expression levels in HCC, public genomic data were obtained from TCGA_LIHC (https://cancergenome.nih.gov) and the GEO database (GSE67140) [16] of the National Center for Biotechnology Information.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33352935", "locator": "sections[10] \"4.2. 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