| Title |
The utility of biomarkers in hepatocellular carcinoma: review of urine-based 1H-NMR studies – what the clinician needs to know
|
|---|---|
| Published in |
International Journal of General Medicine, November 2017
|
| DOI | 10.2147/ijgm.s150312 |
| Pubmed ID | |
| Authors |
Caroline R Cartlidge, M R Abellona U, Alzhraa M A Alkhatib, Simon D Taylor-Robinson |
| Abstract |
Hepatocellular carcinoma (HCC) is the fifth most common malignancy, the third most common cause of cancer death, and the most common primary liver cancer. Overall, there is a need for more reliable biomarkers for HCC, as those currently available lack sensitivity and specificity. For example, the current gold-standard biomarker, serum alpha-fetoprotein, has a sensitivity of roughly only 70%. Cancer cells have different characteristic metabolic signatures in biofluids, compared to healthy cells; therefore, metabolite analysis in blood or urine should lead to the detection of suitable candidates for the detection of HCC. With the advent of metabonomics, this has increased the potential for new biomarker discovery. In this article, we look at approaches used to identify biomarkers of HCC using proton nuclear magnetic resonance (1H-NMR) spectroscopy of urine samples. The various multivariate statistical analysis techniques used are explained, and the process of biomarker identification is discussed, with a view to simplifying the knowledge base for the average clinician. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 41% |
| Lecturer | 1 | 6% |
| Student > Master | 1 | 6% |
| Student > Bachelor | 1 | 6% |
| Professor > Associate Professor | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 5 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 4 | 24% |
| Engineering | 2 | 12% |
| Medicine and Dentistry | 2 | 12% |
| Computer Science | 1 | 6% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 6 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 November 2017.
All research outputs
#15,483,707
of 23,008,860 outputs
Outputs from International Journal of General Medicine
#653
of 1,465 outputs
Outputs of similar age
#206,076
of 329,172 outputs
Outputs of similar age from International Journal of General Medicine
#8
of 14 outputs
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