Search for a novel diagnostic marker for liver cancer.
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA668068 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12794412_1.fastq.gz | Fastqsanger.gz | |||
| SRR12794412_2.fastq.gz | Fastqsanger.gz | |||
| SRR12794413_1.fastq.gz | Fastqsanger.gz | |||
| SRR12794413_2.fastq.gz | Fastqsanger.gz | |||
| SRR12794414_1.fastq.gz | Fastqsanger.gz |
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Project description:Search for a novel diagnostic marker for liver cancer.
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Project description:Dr. Kate Olson's Lab is interested to expand these analysis to comparisons between human normal lung tissue and human lung tumor tissue. In hopes that this will help in finding a diagnostic marker for lung cancer. Since there will be more variability between these samples, we would like to get preliminary data on ten normal lung tissue and ten lung tumor tissues from the same patient.
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Project description:Dr. Kate Olson's Lab is interested to expand these analysis to comparisons between human normal lung tissue and human lung tumor tissue. In hopes that this will help in finding a diagnostic marker for lung cancer. Since there will be more variability between these samples, we would like to get preliminary data on ten normal lung tissue and ten lung tumor tissues from the same patient. We have already analyzed 4 cell lines and we saw gene expression variability related to how metastatic the cell lines were. This has been written and submitted for publication. We would like to expand these analysis to comparisons between human normal lung tissue and human lung tumor tissue. We hope that this will help in finding a diagnostic marker for lung cancer. Since there will be more variability between these samples, we would like to get preliminary data on ten normal lung tissue and ten lung tumor tissues from the same patient. The samples were dissected by a pathologist prior to extraction to maximize the amount of normal tissue included in the tumor samples. RNA samples from non tumor and tumor lung cancer patients were received by Core E. The RNA was amplified, labeled, and hybridized to the GLYCOv3 microarrays.
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Project description:MiR-146a-5p and miR-191-5p as novel diagnostic marker candidates for ovarian clear cell carcinoma
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Project description:In cancer management, early and accurate diagnosis of hepatocellular carcinoma (HCC) is important for enhancing survival rate of patients. Currently, serum alpha-fetoprotein (AFP) is the only one biomarker for detection of HCC. However, serum AFP is not satisfactory for diagnosis of HCC due to its low accuracy (about 60-70%). In this study, we collected 109 serum samples (discovery set) from healthy control (HC) and patients with chronic hepatitis B (CHB), liver cirrhosis (LC) and HCC, and analyzed them with custom lncRNA microarray. Profiling analysis shows 181 differentially expressed lncRNAs between HCs and patients with CHB, LC and HCC. Then a 48-lncRNA diagnostic signature was identified with 100% predictive accuracy for all subjects in the discovery set. This diagnostic signature was verified with a cross-validation analysis in the discovery set. To further corroborate the signature, we gathered another 66 serum samples (validation set) and also analyzed them with microarray. The result indicates that the same signature has similar diagnostic accuracy for HC (100%), CHB (73%), LC (88%) and HCC (95%), implying a reproducible diagnostic biomarker for HCC. Receiver operating characteristic (ROC) analysis exhibits that this signature has significantly higher diagnostic accuracy for HCC and non-cancerous subjects (area under curve [AUC]: 0.994) than AFP (AUC: 0.773) in the discovery set and this was also verified in the validation set (0.964 vs 0.792). More importantly, the signature detected small HCC (<3cm) with 100% (13/13) accuracy while AFP with only 61.5% (8/13). Altogether, this study demonstrates that the serum 48-lncRNA signature is not only a powerful and sensitive biomarker for diagnosis of HCC but also a potential biomarker for LC. ***************************************************************** Submitter declares these data are subject to patent number ZL 2016 1 0397094. *****************************************************************
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