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This study aimed to identify novel long noncoding RNA (lncRNA) biomarkers for hepatocellular carcinoma (HCC) using publicly available tissue genomic datasets and validate their diagnostic utility for early-stage HCC. Differentially expressed lncRNAs between 371 HCC and 50 nontumor tissues were obtained from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA_LIHC) project. Subsequently, the expression of the serum- and extracellular vesicle (EV)-derived lncRNA was assessed in 10 patients with HCC and 10 healthy controls using RT-qPCR. The candidate lncRNAs were validated in 90 HCC and 92 non-HCC (29 healthy control, 28 chronic hepatitis, 35 liver cirrhosis) patients. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated for the candidate lncRNAs and the current HCC biomarker, alpha-fetoprotein (AFP). SFTA1P, HOTTIP, HAGLROS, LINC01419, HAGLR, CRNDE, and LINC00853 were markedly upregulated in HCC in TCGA_LIHC dataset. Among them, LINC00853 has not been reported in relation to HCC before. In patients with HCC, only expression of small EV-derived LINC00853 (EV-LINC00853) was increased. EV-LINC00853 showed excellent discriminatory ability in the diagnosis of all-stage HCC (AUC = 0.934, 95% confidence interval = 0.887-0.966). Moreover, using a 14-fold increase and 20 ng·mL-1 as cutoffs for EV-LINC00853 expression and AFP level, respectively, EV-LINC00853 was found to have a sensitivity of 93.75% and specificity of 89.77%, while AFP showed only 9.38% sensitivity and 72.73% specificity for the diagnosis of early-stage HCC (mUICC stage I). EV-LINC00853 had a positivity of 97% and 67% in AFP-negative and AFP-positive early HCC, respectively. Serum EV-derived LINC00853 may be a novel potential diagnostic biomarker for early HCC, especially for AFP-negative HCC.
Pubmed ID: 32525601 RIS Download
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Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsSoftware package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.
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View all literature mentionsCell line Huh-7 is a Cancer cell line with a species of origin Homo sapiens (Human)
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