Back to results
Bibliographic record · Consultation and access
Artículo

Identification of hub targets involved in carotid atherosclerosis through bioinformatics and machine learning approaches

Luyao Jia et al · BMC · 2026

Open-access full text
Quick overview. Review the resource’s basic details, then access the content using the main button. This page shows only the information needed to identify, cite, and open the work.

Resource access

Open the content from the main option or choose another available source.

DOAJ DOAJ Articles
Entrar por DOAJ
Main access

Open-access full text

Texto completo identificado como acceso abierto.
Open text

Summary

Descripción general del contenido del recurso.

Abstract Background Carotid atherosclerosis is a kind of systemic atherosclerosis in the carotid arteries. It remains one of the leading causes of ischemic stroke. However, the efficiency of treatment is insufficient. Thus, it is urgent to deepen the understanding of the underlying mechanisms in carotid atherosclerosis, which may facilitate the development of effective therapeutic interventions. Phenotypic switching of vascular smooth muscle cells (VSMCs) is recognized as a central process in atherosclerosis progression. However, the key regulatory genes involved in this process during carotid atherosclerosis are not fully understood. Methods Three gene expression datasets, GSE43292, GSE100927, and GSE28829 were downloaded from Gene Expression Omnibus (GEO) database, covering carotid atherosclerosis and control groups. we integrated bioinformatics analysis with three machine learning algorithms to identify the hub genes associated with carotid atherosclerosis. Subsequent validation using clinical specimens and murine atherosclerosis confirmed the expression of the hub genes at both the mRNA and protein levels. Furthermore, in vitro phenotypic switching model using human aortic smooth muscle cells (HASMCs) treated with pro‑atherogenic stimuli was established to identify the expression of hub genes and to investigate how knockdown of these hub genes in HASMCs influences VSMCs phenotypic switching. Results Through the integration of bioinformatics analysis and three machine learning algorithms, we identified PALS2 and CASQ2 as consistently downregulated genes in carotid atherosclerotic plaques compared to normal tissues. Gene interaction network analysis suggested that PALS2 and CASQ2 may cooperatively regulate calcium homeostasis and calcification in VSMCs. This finding was further supported by consistent downregulation of both genes in clinical atherosclerotic samples, murine atherosclerosis, and in HASMCs exposed to pro‑atherogenic stimuli. Functionally, knockdown of either gene enhanced VSMCs phenotypic switching, calcium deposition and amplifies CREB1 phosphorylation, collectively demonstrating their protective role in mitigating atherosclerosis. Conclusions This study identifies PALS2 and CASQ2 as novel regulators involved in the VSMCs phenotypic switching and calcification in carotid atherosclerosis. Both genes are consistently downregulated in atherosclerotic plaques and function as upstream suppressors of a calcium-CREB1 calcification axis. These findings provide novel insights into the molecular mechanisms of atherosclerosis and highlight PALS2 and CASQ2 as potential therapeutic targets for intervention.

How to cite

Elegí el formato que necesitás y copiá la referencia al portapapeles.

APA 7

al, L. J. E. (2026). Identification of hub targets involved in carotid atherosclerosis through bioinformatics and machine learning approaches. https://doi.org/10.1186/s12872-026-05748-2

MLA

al, Luyao Jia et. "Identification of hub targets involved in carotid atherosclerosis through bioinformatics and machine learning approaches." 2026. https://doi.org/10.1186/s12872-026-05748-2.

Chicago

al, Luyao Jia et. 2026. "Identification of hub targets involved in carotid atherosclerosis through bioinformatics and machine learning approaches.". https://doi.org/10.1186/s12872-026-05748-2.

Harvard

al, L. J. E. 2026, Identification of hub targets involved in carotid atherosclerosis through bioinformatics and machine learning approaches, BMC, available at: https://doi.org/10.1186/s12872-026-05748-2 [Accessed 6 Aug. 2026].

Share and print

Save the record, copy its permanent link, or print it as a PDF.

Export reference

You can export the record in common formats for use in a reference manager.

Resource details

Bibliographic information to help confirm that this is the correct material.

Title
Identification of hub targets involved in carotid atherosclerosis through bioinformatics and machine learning approaches
Author / contributors
Luyao Jia et al
Publisher
BMC
Publication year
2026
ISSN
1471-2261
ISSN
1471-2261
Language
English

Subjects

Explore related resources through these subjects.

Copied