| Title |
Noncoding RNAs as potential biomarkers to predict the outcome in pancreatic cancer
|
|---|---|
| Published in |
Drug Design, Development and Therapy, February 2015
|
| DOI | 10.2147/dddt.s77597 |
| Pubmed ID | |
| Authors |
Kaizhou Jin, Guopei Luo, Zhiwen Xiao, Zuqiang Liu, Chen Liu, Shunrong Ji, Jin Xu, Liang Liu, Jiang Long, Quanxing Ni, Xianjun Yu |
| Abstract |
Pancreatic ductal adenocarcinoma (PDAC), a common digestive system cancer, is highly malignant and has a poor disease outcome. Currently, all available examination and detection methods cannot accurately predict the clinical outcome. Therefore, it is extremely important to identify novel molecular biomarkers for personalized medication and to significantly improve the overall outcome. The "noncoding RNAs" (ncRNAs) are a group of RNAs that do not code for proteins, and they are categorized as structural RNAs and regulatory RNAs. It has been shown that microRNAs and long ncRNAs function as regulatory RNAs to affect the progression of various diseases. Many studies have confirmed a role for ncRNAs in the progression of PDAC during the last few years. Because of the significant role of ncRNAs in PDAC, ncRNA profiling may be used to predict PDAC outcome with high accuracy. This review comprehensively analyzes the value of ncRNAs as potential biomarkers to predict the outcome in PDAC and the possible mechanisms thereof. |
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 % |
|---|---|---|
| France | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 25% |
| Student > Ph. D. Student | 6 | 17% |
| Student > Master | 4 | 11% |
| Student > Doctoral Student | 3 | 8% |
| Student > Bachelor | 3 | 8% |
| Other | 6 | 17% |
| Unknown | 5 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 14 | 39% |
| Biochemistry, Genetics and Molecular Biology | 7 | 19% |
| Agricultural and Biological Sciences | 5 | 14% |
| Computer Science | 1 | 3% |
| Business, Management and Accounting | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 6 | 17% |
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 11 March 2015.
All research outputs
#19,942,887
of 25,371,288 outputs
Outputs from Drug Design, Development and Therapy
#1,310
of 2,268 outputs
Outputs of similar age
#254,529
of 361,167 outputs
Outputs of similar age from Drug Design, Development and Therapy
#37
of 62 outputs
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