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Attention Score in Context
| Title |
A transcriptome profile in hepatocellular carcinomas based on integrated analysis of microarray studies
|
|---|---|
| Published in |
Diagnostic Pathology, January 2017
|
| DOI | 10.1186/s13000-016-0596-x |
| Pubmed ID | |
| Authors |
Feifei Wang, Ruliang Wang, Qiuwen Li, Xueling Qu, Yixin Hao, Jingwen Yang, Huixia Zhao, Qian Wang, Guanghui Li, Fengyun Zhang, He Zhang, Xuan Zhou, Xioumei Peng, Yang Bian, Wenhua Xiao |
| Abstract |
Despite new treatment options for hepatocellular carcinomas (HCC) recently, 5-year survival remains poor, ranging from 50 to 70%, which may attribute to the lack of early diagnostic biomarkers. Thus, developing new biomarkers for early diagnosis of HCC, is extremely urgent, aiming to decrease HCC-related deaths. In the study, we conducted a comprehensive characterization of gene expression data of HCC based on a bioinformatics method. The results were confirmed by real time polymerase chain reaction (RT-PCR) and TCGA database to prove the credibility of this integrated analysis. After integrating analysis of seven HCC gene expression datasets, 1167 differential expressed genes (DEGs) were identified. These genes mainly participated in the process of cell cycle, oocyte meiosis, and oocyte maturation mediated by progesterone. The results of experiments and TCGA database validation in 10 genes was in full accordance with findings in integrated analysis, indicating the high credibility of our integrated analysis of different gene expression datasets. ASPM, CCT3, and NEK2 was showed to be significantly associated with overall survival of HCC patients in TCGA database. This method of integrated analysis may be a useful tool to minish the heterogeneity of individual microarray, hopefully outputs more accurate HCC transcriptome profiles based on large sample size, and explores some potential biomarkers and therapy targets for HCC. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 20% |
| Researcher | 3 | 15% |
| Student > Master | 3 | 15% |
| Student > Postgraduate | 2 | 10% |
| Lecturer | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 5 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 20% |
| Agricultural and Biological Sciences | 4 | 20% |
| Medicine and Dentistry | 3 | 15% |
| Immunology and Microbiology | 2 | 10% |
| Computer Science | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 6 | 30% |
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 15 January 2017.
All research outputs
#20,390,619
of 22,940,083 outputs
Outputs from Diagnostic Pathology
#950
of 1,135 outputs
Outputs of similar age
#356,820
of 421,590 outputs
Outputs of similar age from Diagnostic Pathology
#7
of 10 outputs
Altmetric has tracked 22,940,083 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,135 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 421,590 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.