ArticleJournal of inflammation research2025
Integrative Machine Learning Analysis of Programmed Cell Death Pathways Identifies Novel Diagnostic Biomarkers for Atrial Fibrillation.
Article in Journal of inflammation research, 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Atrial fibrillation (AF) is a leading cause of stroke, heart failure, and mortality, yet the molecular mechanisms remain incompletely defined. Patients and Methods: We integrated bulk transcriptomes from GEO with weighted gene co-expression network analysis, consensus clustering, and a 12-algorithm machine-learning pipeline (66 model combinations) to map programmed cell death (PCD) pathways and pinpoint diagnostic genes. Immune infiltration was profiled by CIBERSORT, xCell, and ssGSEA. Hub-gene expression was validated in an HL-1 atrial pacing model and in peripheral blood mononuclear cells (PBMCs) from patients with persistent AF. Results: Four hub genes-SGPL1, NPC2, PTGDS, and RCAN1-were identified and incorporated into a nomogram and a PCD-based risk score (PCDscore). The nomogram showed robust discrimination in the training cohort and two independent validation datasets. Patients with a high PCDscore exhibited markedly increased immune-cell infiltration and dysregulated immune modulators, with macrophages consistently enriched across algorithms. qRT-PCR confirmed up-regulation of SGPL1, NPC2, and RCAN1 and down-regulation of PTGDS in AF cell models; NPC2 and SGPL1 were further elevated in PBMCs from AF patients. Conclusion: Our integrative framework reveals PCD-linked remodeling in AF and nominates SGPL1, NPC2, PTGDS, and RCAN1 as candidate diagnostic biomarkers, providing a PCD-based nomogram and risk score that may inform patient stratification and hypothesis-generating targeted interventions.
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.