ArticleShock (Augusta, Ga.)2026
Identification and Validation of Key Genes Related to Arginine Methylation Modification in Sepsis Using Transcriptome Combined with Mendelian Randomization Analysis.
Article in Shock (Augusta, Ga.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundPatients with sepsis often exhibit a decrease in lymphatic numbers, which can be facilitated by protein arginine methyltransferase (PRMT). However, it is unclear how PRMT contributes to lymphopenia in sepsis.
methodsThis study employed the sepsis-related datasets (GSE65682 and GSE134347) and nine PRMT genes. First, we intersected the differentially expressed genes with the weighted gene co-expression network analysis module genes to identify differentially expressed PRMT-related genes. Thereafter, candidate key genes were obtained after Mendelian randomization analysis and machine learning screening. Eventually, we subjected key genes identified by expression analysis and receiver operating characteristic curves to gene set enrichment analysis, immune infiltration analysis, immune checkpoint analysis, molecular docking, regulatory networks construction, and nomogram development.
resultsWe first intersected 4,246 differentially expressed genes with 1,884 PRMT scoring module genes to obtain 969 differentially expressed PRMT-related genes. Further Mendelian randomization analysis and machine learning jointly identified five candidate genes ( CRLF3 , ELAC2 , PBX2 , MCTP2 , and EMB ). Among these, ELAC2 , PBX2 , MCTP2 , and EMB demonstrated consistent expression trends, with the area under the curve values of the receiver operating characteristic curve exceeding 0.7 in GSE65682 and GSE134347. Therefore, they were defined as key PRMT-related genes. The gene set enrichment analysis showed enrichment in cytoplasmic translation ( ELAC2 , MCTP2 ), noncoding RNA metabolism ( EMB ), and metabolic processes ( PBX2 ). The immune infiltration analysis revealed a significant correlation between PBX2 and neutrophils, as well as between ELAC2 / MCTP2 / EMB with activated natural killer cells, CD8+ T cells.
conclusionIn this study, ELAC2 , PBX2 , MCTP2 , and EMB were identified as key genes related to PRMT for sepsis, which provided a theoretical basis for the study of sepsis.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.