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Identification of immune-related molecular markers in intracranial aneurysm (IA) based on machine learning and cytoscape-cytohubba plug-in

Overview of attention for article published in BMC Genomic Data, April 2023
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  • Among the highest-scoring outputs from this source (#30 of 114)
  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

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Title
Identification of immune-related molecular markers in intracranial aneurysm (IA) based on machine learning and cytoscape-cytohubba plug-in
Published in
BMC Genomic Data, April 2023
DOI 10.1186/s12863-023-01121-w
Pubmed ID
Authors

Zhengfei Ma, Ping Zhong, Peidong Yue, Zhongwu Sun

Abstract

Intracranial aneurysm (IA) is a common cerebrovascular disease. The immune mechanism of IA is more complicated, and it is unclear so far. Therefore, it is necessary to continue to explore the immune related molecular mechanism of IA. All data were downloaded from the public database. Limma package and ssGSEA algorithm was used to identify differentially expressed mRNAs (DEmRNAs) and analyze immune cell infiltration, respectively. Machine learning and cytoscape-cytohubba plug-in was used to identify key immune types and multicentric DEmRNAs of IA, respectively. Multicentric DEmRNAs related to key immune cells were screened out as key DEmRNAs by Spearman correlation analysis. Diagnostic models, competing endogenous RNA (ceRNA) regulatory network and transcription factor regulatory network were constructed based on key DEmRNAs. Meanwhile, drugs related to key DEmRNAs were screened out based on DGIdb database. The expression of key DEmRNAs was also verified by real time-PCR. In this study, 7 key DEmRNAs (NRXN1, GRIA2, SLC1A2, SLC17A7, IL6, VEGFA and SYP) associated with key differential immune cell infiltration (CD56bright natural killer cell, Immature B cell and Type 1 T helper cell) were identified. Functional enrichment analysis showed that VEGFA and IL6 may be involved in the regulation of the PI3K-Akt signaling pathway. Moreover, IL6 was also found to be enriched in cytokine-cytokine receptor interaction signaling pathway. In the ceRNA regulatory network, a large number of miRNAs and lncRNAs were found. In the transcription factor regulatory network, the transcription factor SP1 was correlated with VEGFA, SYP and IL6. It is also predicted that drugs related to key DEmRNAs such as CARBOPLATIN, FENTANYL and CILOSTAZOL may contribute to the treatment of IA. In addition, it was also found that SVM and RF models based on key DEmRNAs may be potential markers for diagnosing IA and unruptured intracranial aneurysm (UIA), respectively. The expression trend of key DEmRNAs verified by real-time PCR was consistent with the bioinformatics analysis results. The identification of molecules and pathways in this study provides a theoretical basis for understanding the immune related molecular mechanism of IA. Meanwhile, the drug prediction and diagnosis model construction may also be helpful for clinical diagnosis and management.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 8%
Other 1 8%
Student > Doctoral Student 1 8%
Student > Bachelor 1 8%
Student > Master 1 8%
Other 1 8%
Unknown 6 50%
Readers by discipline Count As %
Arts and Humanities 1 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Agricultural and Biological Sciences 1 8%
Immunology and Microbiology 1 8%
Other 1 8%
Unknown 6 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 13 May 2023.
All research outputs
#14,025,207
of 23,751,351 outputs
Outputs from BMC Genomic Data
#30
of 114 outputs
Outputs of similar age
#140,312
of 344,898 outputs
Outputs of similar age from BMC Genomic Data
#5
of 13 outputs
Altmetric has tracked 23,751,351 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 114 research outputs from this source. They receive a mean Attention Score of 2.1. This one has gotten more attention than average, scoring higher than 72% 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 344,898 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 57% of its contemporaries.
We're also able to compare this research output to 13 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 69% of its contemporaries.