FASLG as a Key Member of Necroptosis Participats in Acute Myocardial Infarction by Regulating Immune Infiltration
Bibliographic record
Abstract
Background: Acute myocardial infarction (AMI) is a major cause of human health risk. Necroptosis is a newly and recently reported mode of cell death, whose role in AMI has not been fully elucidated. This study aimed to search for necroptosis biomarkers associated with the occurrence of AMI and to explore their possible molecular mechanisms through bioinformatics analysis. Methods: The dataset GSE48060 was used to perform weighted gene co-expression network analysis (WGCNA) and differential analysis. Key modules, differential genes, and necroptosis-related genes (NRGs) were intersected to obtain candidate biomarkers. Groups were classified and differentially analyzed according to the expression of the key biomarker. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA), and construction of protein-protein interaction (PPI) networks are performed on differentially expressed genes (DEGs). Finally, CIBERSORT was used to assess immune cell infiltration in AMI and the correlation of key biomarkers with immune cells. Immune cell infiltration analysis revealed the correlation between FASLG and multiple screened immune cells. Results: WGCNA determined that the MEsaddlebrown module was the most significantly associated with AMI. Intersecting it with DEGs as well as NRGs, we obtained two key genes, FASLG and IFNG. But only FASLG showed statistically significant differences between the AMI group and the normal control group. Further analysis suggested that the down-regulation of FASLG may exert its function through the regulation of the central genes CD247 and YES1. Furthermore, FASLG was positively correlated with T-cell CD4 memory activation and T-cell gamma delta, and negatively correlated with macrophage M0. Conclusion: In conclusion, FASLG and its regulatory genes CD247 and YES1 might be involved in the development of AMI by regulating immune cell infiltration. FASLG might be a potential biomarker for AMI and provides a new direction for the diagnosis of AMI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".