ArticleHeliyon2025
Identification of lethality-related m7G methylation modification patterns and the regulatory features of immune microenvironment in sepsis.
Article in Heliyon, 2025. 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: N7-methylguanosine (m7G) modification is closely related to the occurrence of human diseases, but its roles in sepsis remain unclear. This study aimed to explore the patterns of lethality-related m7G regulatory factor-mediated RNA methylation modification and immune microenvironment regulatory features in sepsis. Methods: Three sepsis-related datasets (E-MTAB-4421 and E-MTAB-4451 as training sets and GSE185263 as a validation set) were collected, and differentially expressed m7G-related genes were analyzed between survivors and non-survivors. Lethality-related m7G signature genes were then screened using machine learning methods, followed by the construction of a survival recognition model. Additionally, differences in immune cell distribution were determined and differentially expressed genes (DEGs) between different subtypes were analyzed. Weighted gene co-expression network analysis (WGCNA) was used to select important modules and related hub genes. Results: In total, 10 differentially expressed m7G-related genes were identified between the survivors and non-survivors, and after further analysis, Conclusion: The constructed survival status diagnostic model based on the five lethality-related m7G signature genes may help predict the survival status of patients, and the 10 hub genes obtained may be potential therapeutic targets for 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.