ArticleBrain sciences2022
Identification and Validation of Novel Potential Pathogenesis and Biomarkers to Predict the Neurological Outcome after Cardiac Arrest.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- PSMD14 as a translational target in cancer and beyond: from deubiquitination mechanisms to drug resistance and precision therapy.Cancer drug resistance (Alhambra, Calif.) · 2026Review
- Wolf Creek XVII Part 8: Neuroprotection.Resuscitation plus · 2024Review
- Ischemia-Reperfusion Programming of Alzheimer's Disease-Related Genes-A New Perspective on Brain Neurodegeneration after Cardiac Arrest.International journal of molecular sciences · 2024Article
- Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning.Functional & integrative genomics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 1 institution in 1 country.
Funding
Abstract
Predicting neurological outcomes after cardiac arrest remains a major issue. This study aimed to identify novel biomarkers capable of predicting neurological prognosis after cardiac arrest. Expression profiles of GSE29540 and GSE92696 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between high and low brain performance category (CPC) scoring subgroups. Weighted gene co-expression network analysis (WGCNA) was used to screen key gene modules and crossover genes in these datasets. The protein-protein interaction (PPI) network of crossover genes was constructed from the STRING database. Based on the PPI network, the most important hub genes were identified by the cytoHubba plugin of Cytoscape software. Eight hub genes (RPL27, EEF1B2, PFDN5, RBX1, PSMD14, HINT1, SNRPD2, and RPL26) were finally screened and validated, which were downregulated in the group with poor neurological prognosis. In addition, GSEA identified critical pathways associated with these genes. Finally, a Pearson correlation analysis showed that the mRNA expression of hub genes EEF1B2, PSMD14, RPFDN5, RBX1, and SNRPD2 were significantly and positively correlated with NDS scores in rats. Our work could provide comprehensive insights into understanding pathogenesis and potential new biomarkers for predicting neurological outcomes after cardiac arrest.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.