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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Bioinformatics analysis to screen the key prognostic genes in ovarian cancer
|
|---|---|
| Published in |
Journal of Ovarian Research, April 2017
|
| DOI | 10.1186/s13048-017-0323-6 |
| Pubmed ID | |
| Authors |
Li Li, Shengyun Cai, Shengnan Liu, Hao Feng, Junjie Zhang |
| Abstract |
Ovarian cancer (OC) is a gynecological oncology that has a poor prognosis and high mortality. This study is conducted to identify the key genes implicated in the prognosis of OC by bioinformatic analysis. Gene expression data (including 568 primary OC tissues, 17 recurrent OC tissues, and 8 adjacent normal tissues) and the relevant clinical information of OC patients were downloaded from The Cancer Genome Atlas database. After data preprocessing, cluster analysis was conducted using the ConsensusClusterPlus package in R. Using the limma package in R, differential analysis was performed to identify feature genes. Based on Kaplan-Meier (KM) survival analysis, prognostic seed genes were selected from the feature genes. After key prognostic genes were further screened by cluster analysis and KM survival analysis, they were performed functional enrichment analysis and multivariate survival analysis. Using the survival package in R, cox regression analysis was conducted for the microarray data of GSE17260 to validate the key prognostic genes. A total of 3668 feature genes were obtained, among which 75 genes were identified as prognostic seed genes. Then, 25 key prognostic genes were screened, including AXL, FOS, KLF6, WDR77, DUSP1, GADD45B, and SLIT3. Especially, AXL and SLIT3 were enriched in ovulation cycle. Multivariate survival analysis showed that the key prognostic genes could effectively differentiate the samples and were significantly associated with prognosis. Additionally, GSE17260 confirmed that the key prognostic genes were associated with the prognosis of OC. AXL, FOS, KLF6, WDR77, DUSP1, GADD45B, and SLIT3 might affect the prognosis of OC. |
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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 27% |
| Researcher | 6 | 16% |
| Student > Master | 4 | 11% |
| Student > Bachelor | 2 | 5% |
| Other | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 10 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 10 | 27% |
| Agricultural and Biological Sciences | 4 | 11% |
| Medicine and Dentistry | 3 | 8% |
| Immunology and Microbiology | 2 | 5% |
| Engineering | 2 | 5% |
| Other | 6 | 16% |
| Unknown | 10 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 02 June 2017.
All research outputs
#13,033,732
of 22,963,381 outputs
Outputs from Journal of Ovarian Research
#138
of 597 outputs
Outputs of similar age
#149,560
of 310,038 outputs
Outputs of similar age from Journal of Ovarian Research
#5
of 12 outputs
Altmetric has tracked 22,963,381 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 597 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done well, scoring higher than 76% of its peers.
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 310,038 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.