Abstract
Liver cancer (LC) is one of the most common cancers and represents the third highest cause of cancer-related deaths worldwide. Extracellular vesicle (EVs) cargoes, which are selectively enriched in RNA, offer great promise for the diagnosis, prognosis and treatment of LC. Our study analyzed the RNA cargoes of EVs derived from 4 liver-cancer cell lines: HuH7, Hep3B, HepG2 (hepato-cellular carcinoma) and HuH6 (hepatoblastoma), generating two different sets of sequencing libraries for each. One library was size-selected for small RNAs and the other targeted the whole transcriptome. Here are reported genome wide data of the expression level of coding and non-coding transcripts, microRNAs, isomiRs and snoRNAs providing the first comprehensive overview of the extracellular-vesicle RNA cargo released from LC cell lines. The EV-RNA expression profiles of the four liver cancer cell lines share a similar background, but cell-specific features clearly emerge showing the marked heterogeneity of the EV-cargo among the individual cell lines, evident both for the coding and non-coding RNA species.
Highlights
Human liver cancer (LC) is among the most common forms of cancer and has a dismal clinical outcome, accounting for the third highest cause of cancerrelated deaths worldwide [1]
Previous studies have demonstrated that the hepatoblastoma-like (HuH6) and the hepatocellularcarcinoma-like (HuH7, Hep3B, HepG2) cancer cell-lines maintain the hepatocarcinogenic phenotype at gene, miRNA, and protein expression levels, and are useful to gain new insights into the pathogenesis of hepatoblastoma and hepatocellular carcinoma, providing novel biomarkers [20, 21]
The present study represents a detailed analysis of all the coding and non-coding transcripts carried by Extracellular vesicle (EVs) derived from 4 liver-cancer cell (LCC)-lines, profiling gene expression through RNA-seq, and identifying large RNAs, microRNAs, isomiRs, and snoRNAs
Summary
Human liver cancer (LC) is among the most common forms of cancer and has a dismal clinical outcome, accounting for the third highest cause of cancerrelated deaths worldwide [1]. It should be underlined that detection at an early stage of development of the disease does significantly increase the 5-year survival rate. It is of great interest to develop molecular and cellular diagnostic assays with the potential to aid early diagnosis, clinical decisionmaking, and patient management [4]. The ideal human liver cancer biomarker is one that enables clinicians to diagnose asymptomatic LC patients and which can be widely used in screening processes. Advances in translating cancer genomics into clinical oncology strongly indicate that it is essential to move to predictive models that are personalized and based on molecular classification and targeted therapy. The personalized approach to clinical care promises to increase the efficacy of treatment while reducing its toxicity and cost
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