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Dove Medical Press

ANLN functions as a key candidate gene in cervical cancer as determined by integrated bioinformatic analysis

Overview of attention for article published in Cancer Management and Research, April 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

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1 news outlet
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3 X users

Citations

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64 Dimensions

Readers on

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40 Mendeley
Title
ANLN functions as a key candidate gene in cervical cancer as determined by integrated bioinformatic analysis
Published in
Cancer Management and Research, April 2018
DOI 10.2147/cmar.s162813
Pubmed ID
Authors

Leilei Xia, Xiaoling Su, Jizi Shen, Qi Meng, Jiuqiong Yan, Caihong Zhang, Yu Chen, Han Wang, Mingjuan Xu

Abstract

Cervical cancer, one of the leading causes of female deaths, remains a top cause of mortality in gynecologic oncology and tends to affect younger individuals. However, the pathogenesis of cervical cancer is still far from clear. Given the high incidence and mortality of cervical cancer, uncovering the causes and pathogenesis as well as identifying novel biomarkers are of great significance and are desperately needed. First, raw data were downloaded from the Gene Expression Omnibus database. The Robuse Multi-Array Average algorithm and combat function of the sva package were subsequently applied to preprocess and remove batch effects. Differentially expressed genes (DEGs) analyzed with the limma package were followed by gene ontology and pathway analysis, and a protein-protein interaction (PPI) network based on the STRING website and the Cytoscape software was constructed. Weighted Correlation Network Analysis (WGCNA) was utilized to build the coexpression network. Subsequently, UALCAN websites were employed to conduct survival analysis. Finally, the oncomine database was used to validate the expression of ANLN in other datasets. GSE29570 and GSE89657, including 49 cervical cancer tissues and 20 normal cervical tissues, were screened as the datasets. Three-hundred-twenty-four DEGs were identified and, among them, 123 were upregulated, while 201 were downregulated. The DEGs PPI network complex, contained 305 nodes and 4,962 edges, and 8 clusters were calculated according to k-core =2. Among them, cluster 1, which had 65 nodes and 1,780 edges, had the highest score in these clusters. In coexpression analysis, there were 86 hubgenes from the Brown modules that were chosen for further analysis. Sixty-one key genes were identified as the intersecting genes of the Brown module of WGCNA and DEGs. In survival analysis, only ANLN was a prognostic factor, and the survival was significantly better in the low-expression ANLN group. Our study suggested that ANLN may be a potential tumor oncogene and could serve as a biomarker for predicting the prognosis of cervical cancer patients.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 10%
Student > Bachelor 3 8%
Student > Doctoral Student 3 8%
Other 3 8%
Student > Ph. D. Student 3 8%
Other 6 15%
Unknown 18 45%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 23%
Agricultural and Biological Sciences 4 10%
Medicine and Dentistry 2 5%
Social Sciences 2 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 0 0%
Unknown 22 55%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 16 May 2022.
All research outputs
#2,494,803
of 23,041,514 outputs
Outputs from Cancer Management and Research
#73
of 2,017 outputs
Outputs of similar age
#55,721
of 330,195 outputs
Outputs of similar age from Cancer Management and Research
#3
of 57 outputs
Altmetric has tracked 23,041,514 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,017 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done particularly well, scoring higher than 96% 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 330,195 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.