ArticleJournal of inflammation research2025
Combined Analysis of Transcriptome and Mendelian Randomization Reveals
Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: This study aimed to identify diagnostic and therapeutic biomarkers related to glucose metabolism in sepsis, as hyperglycemia and blood glucose fluctuations influence sepsis progression. Methods: Datasets from public databases were analyzed using various methods, including differential expression analysis, PPI network screening, machine learning algorithms and Mendelian randomization. A nomogram model was developed, and biomarker functions were explored through enrichment analysis, immunoinfiltration analysis, transcription factors (TFs) and microRNA (miRNA) prediction, and drug prediction. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to validate the expression of biomarkers in sepsis and control group. Results: There were 3,899 differential expressed genes (DEGs) in sepsis, with 141 related to glucose metabolism. Eleven hub genes were identified from the PPI network, and six biomarkers were selected through machine learning and area under the curve (AUC) validation. Notably, Conclusion: In summary,
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Registered trials
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