ArticleJournal of inflammation research2025
Integrated Analysis of Ferroptosis- and Cellular Senescence-Related Biomarkers in Atherosclerosis Based on Machine Learning and Single-Cell Sequencing Data.
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. Cited by 2 papers.
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
2 citing papers in PubMed.
- Transcriptomic Analysis and Multiple Machine Learning Approaches Identify ZDHHC20 and Its Highly Correlated Gene AK5 as Biomarkers in Multiple System Atrophy.Journal of molecular neuroscience : MN · 2026Article
- Identification and Functional Characterization of COPD Molecular Subtypes Based on Oxeiptosis-Related Genes via WGCNA and Machine Learning.International journal of chronic obstructive pulmonary disease · 2026Article
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
Background: Atherosclerosis is a chronic inflammatory disease characterized by lipid accumulation in the vascular wall. The roles of ferroptosis and cellular senescence in Atherosclerosis remain unclear. This study aimed to identify genes related to ferroptosis and cellular senescence in Atherosclerosis using bioinformatics approaches. Methods: Atherosclerosis gene expression datasets were obtained from the GEO database. Differentially expressed genes (DEGs) were identified and intersected with key genes from WGCNA modules, ferroptosis-related genes, and senescence-related genes to obtain common genes (CF-DEGs). Consensus clustering based on CF-DEGs was conducted to identify molecular subtypes, followed by differential expression analysis. Enrichment and immune infiltration analyses were used to investigate the biological functions and immune features of subtype-specific differentially expressed genes. Eight machine learning algorithms were applied to identify hub genes and construct a diagnostic model. Single-cell RNA-seq data were used to assess the roles of hub genes in cell communication and differentiation. Finally, animal experiments were performed to validate the expression of the hub genes. Results: A total of 23 CF-DEGs were identified, based on which two molecular subtypes were defined. A total of 421 DEGs were found between subtypes. Immune infiltration analysis revealed significant differences in eight immune cell types, including activated dendritic cells, macrophages, NK cells, and several T cell subsets. Enrichment analysis showed that these genes were involved in fatty acid metabolism, inflammation, and immune regulation. Conclusion:
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.