ArticleBrazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas2026
Identification of anoikis-related biomarkers linking immune infiltration to acute myocardial infarction.
Article in Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas, 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
No grant is acknowledged in the PubMed record.
Abstract
This study was designed to identify new biomarkers for early diagnosis of acute myocardial infarction (AMI). GSE66360 and GSE48060 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The "limma" tool was used to screen differentially expressed genes (DEGs). A total of 557 anoikis-related genes (ARGs) were obtained from the GeneCard database. Differentially expressed ARGs (DEARGs) were obtained by intersecting DEGs with ARGs. The least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and Random Forest (RF) were used to screen the hub DEARGs. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to determine the expression of hub DEARGs. A total of 21 DEARGs were obtained, all of which were up-regulated in AMI samples. Functional enrichment analysis showed that the DEARGs were mainly enriched in peptidase activity and extracellular matrix. Immune cell infiltration analysis revealed a significant difference in 14 immune cells between the AMI and normal groups. Nine feature risk genes, including ITPRIP, MMP9, NAMPT, CDKN1A, PLAUR, PLAU, SERPINA1, THBS1, and FN1 were screened by LASSO, SVM, and RF. The RT-qPCR analyses verified that the feature genes were up-regulated in AMI patients, which were basically consistent with the main bioinformatics analysis results. We also validated 9 hub DEARGs in the GSE48060 dataset and constructed a nomogram by integrating these DEARGs. This study analyzed the differential expression of ARGs and immune profiles in AMI and normal samples, screened 9 risk feature genes for predicting AMI, and provided a theoretical basis for the immunotherapy regimen of AMI.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.