In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. 2361-2361
Kurzfassung:
Hepatocellular carcinoma (HCC) is a disease associated with high morbidity and mortality for which there is currently no curative treatment and therapeutic targeting remains ineffective. To search for new therapeutic targets, we have evaluated the coding and non-coding transcriptome of all RNA-Seq data from TCGA, comprising over 10000 patients and 34 different tumors with HCC among them. To this, we have added the transcriptomes of additional 10000 samples from 53 human tissues from the GTEx project. Using neural networks and bioinformatics analyses we have identified coding and non-coding genes with preferential and differential expression and obtained a more precise location of HCC in the human transcriptional landscape. The analyses of tumor-deregulated gene expression indicated that coding genes have a high tumor promiscuity. Non-coding genes, on the contrary, showed higher tissue specificity and their expression was altered in a more tumor-specific manner, predictive of a therapeutic target with fewer secondary effects. We have identified 7321 long non-coding RNAs (lncRNAs) deregulated in at least one tumor type. In HCC, we found 1128 deregulated lncRNAs of which 138 were specific of HCC. Bioinformatic functional predictions indicated a positive relationship of deregulated lncRNAs with cell proliferation and motility while immune response was negatively associated. Ten lncRNAs with oncogenic potential were selected and their expression was validated in two independent cohorts of HCC patients. Most tumor samples showed high expression levels of these lncRNAs when compared to their paired peritumoral sample. Furthermore, several candidates were found to be expressed at very low levels in healthy liver. When expression of 2 of them was inhibited with antisense drugs in HCC cells, a drastic stall in proliferation was observed. Proliferation was recovered after restoring lncRNA expression. We think these candidates may play a key role in the proliferation and motility of cancer cells and show great potential as therapeutic targets. Our studies suggest that big data analyses can give a much-needed boost to HCC target search and to personalized medicine against cancer and other diseases. Citation Format: Juan P. Unfried, Guillermo Serrano, Beatriz Suárez, Valeria Ferretti, Paloma Sangro, Celia Prior, Loreto Boix, Jordi Bruix, Bruno Sangro, Victor Segura, Puri Fortes. Big data analysis allows the identification of long non-coding RNAs with therapeutic potential against hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2361.
Materialart:
Online-Ressource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2018-2361
Sprache:
Englisch
Verlag:
American Association for Cancer Research (AACR)
Publikationsdatum:
2018
ZDB Id:
2036785-5
ZDB Id:
1432-1
ZDB Id:
410466-3
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