ArticleHereditas2025
Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism.
Article in Hereditas, 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
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
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
4 authors.
Funding
Abstract
backgroundSphingolipid metabolism (SM) is linked to acute myocardial infarction (AMI), but its role remains unclear. This study explored SM-related genes (SMRGs) in AMI to support clinical diagnosis.
methodsWe analyzed datasets GSE48060 and GSE123342 to identify differentially expressed genes (DEGs) and key module genes. Protein-protein interaction (PPI) network analysis and machine learning were used to screen potential biomarkers, which were validated via receiver operating characteristic (ROC) curves and expression assessment. Further analyses included artificial neural networks (ANN), enrichment analysis, immune infiltration, drug prediction, and molecular docking. Single-cell RNA sequencing (scRNA-seq) identified key cell types and their functions. Biomarkers were validated via reverse transcription quantitative polymerase chain reaction (RT-qPCR).
resultsIntersection of 95 DEGs and 2,196 module genes yielded 20 genes, with ANXA3 and SOCS3 identified as biomarkers. The ANN model showed superior diagnostic performance compared to individual markers. Biomarkers were enriched in the toll-like receptor (TLR) signaling pathway. Immune infiltration analysis revealed differences in five immune cell types between AMI and control groups. ANXA3 correlated positively with neutrophils and negatively with resting memory CD4 T cells. Drugs targeting ANXA3 included ethanolamine, difluocortolone, and fluocinolone acetonide, with strong binding affinity. scRNA-seq identified B cells and monocytes as key cells; ANXA3 and SOCS3 expression increased during monocyte differentiation before decreasing, while B cells showed no significant changes.
conclusionANXA3 and SOCS3 were identified as SM-related biomarkers in AMI, providing insights for clinical diagnosis.
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.