ArticleFrontiers in cardiovascular medicine2026
Screening and molecular functional analysis of telomere-related genes in abdominal aortic aneurysms based on bioinformatics.
Article in Frontiers in cardiovascular medicine, 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Abdominal aortic aneurysm (AAA) is a life-threatening vascular condition characterized by progressive aortic dilation. However, no effective pharmacotherapies exist to halt its progression. This study aimed to investigate telomere-related molecular signatures in AAA to identify potential targets for therapeutic development. Method: We obtained the dataset GSE57691, including the RNA-expression data of AAA and normal control samples, from the GEO database. Weighted Gene Co-expression Network Analysis (WGCNA) was performed using the "WGCNA" package to identify AAA phenotype-related gene modules, and differential expression analysis of differentially expressed genes (DEGs) was conducted using the "limma" package. Biomarker identification was achieved through LASSO and Support Vector Machine (SVM) algorithms using the "glmnet" and "e1071" packages. GSEA_4.2.2 was utilized for gene enrichment analysis, and "CIBERSORT" and "estimate" were employed for immune infiltration. An independent single-cell RNA-seq dataset (GSE237230) was analyzed using the Seurat pipeline to characterize the cellular landscape and validate biomarker expression at single-cell resolution. Finally, the hTFtarget and Encori databases were utilized to construct the transcriptional regulatory network, and the "oncoPredict" package was employed to identify potential drugs. We verified the reliability of the model genes Results: Following WGCNA, AAA-related modules (MEmagenta/MEpink) were identified. Intersecting gene modules, DEGs, and telomere genes yielded eight candidates, among which KLF15 and ZBTB16 were prioritized via Lasso and SVM. Low expression of these biomarkers correlated with immune-inflammatory activation, whereas high expression was linked to a suppressive microenvironment that alleviated AAA progression. Furthermore, a transcriptional regulatory network was constructed, identifying key target mRNAs and potential drugs. Single-cell analysis confirmed distinct clusters and specific enrichment of ZBTB16 in B cells. Angiotensin II (Ang II) treatment downregulated KLF15 and ZBTB16 in T/G HA-VSMCs. Overexpression of ZBTB16 attenuated Ang II-induced inflammation, apoptosis, and migration and suppressed MMP-2/9 levels, indicating inhibited migratory activity. Conclusion: We analyzed telomere-related signatures in AAA and identified two crucial biomarkers linked to the immune infiltration of patients. Our findings furthered the understanding of telomere signatures in AAA progression.
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.