HMOX2-driven crosstalk between vascular aging and heart failure: A multimodal bioinformatics and explainable machine learning approach with experimental validation
5 affiliations
- 1Guangzhou University of Chinese Medicine, Guangzhou, China
- 2The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- 3Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
- 4The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- 5State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangdong, China
Abstract
Purpose: The molecular mechanisms linking vascular aging (VA) and heart failure (HF) remain elusive, hindering therapeutic strategies for their comorbidity. This study aimed to identify key biomarkers and pathways potentially involved in VA-HF synergy using integrative computational approaches. Methods: We analyzed the GSE57338 dataset (136 controls, 177 HF samples) to identify HF-associated differentially expressed genes (DEGs) and constructed a weighted gene co-expression network (WGCNA). Vascular aging-related targets were retrieved from GeneCards (n = 16,243). Consensus genes (CGs) were derived by intersecting DEGs, WGCNA hub genes, and VA-related targets. Functional enrichment, machine learning prioritization (LASSO regression, Random Forest, and SHAP-XGBoost), and network analysis (GeneMANIA) were applied to identify potential regulators. Top candidates were experimentally validated using qPCR in doxorubicin-induced rat primary vascular smooth muscle cells and human VSMC cell line for the VA model, and H9C2 cardiomyoblast injury model for HF. Further validation was performed in a mouse model of doxorubicin-induced HF, assessing cardiac function by echocardiography, myocardial fibrosis by Masson’s trichrome staining, and vascular aging markers (P16, P21) by qPCR. Results: We identified 272 CGs enriched in cGMP-PKG signaling, cytoskeletal regulation, and PPAR pathways. Machine learning prioritized 12 core genes, with HMOX2 as the top predictor (AUC = 0.978). qPCR analysis confirmed upregulation of HMOX2, S1PR3, and SERPINA3 in doxorubicin-treated rat primary vascular smooth muscle cells, human VSMC cell line, and H9C2 cardiomyoblasts compared to controls (P < 0.05). In the mouse model, doxorubicin administration induced significant cardiac dysfunction and myocardial fibrosis, accompanied by elevated expression of senescence markers P16 and P21 in vascular tissues. These findings collectively suggest that these genes may play important roles in VA-HF comorbidity (P < 0.05).
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BibTeX
@article{li2026hmox2driven,
title = {{HMOX2-driven crosstalk between vascular aging and heart failure: A multimodal bioinformatics and explainable machine learning approach with experimental validation}},
author = {Jinze Li and Guiting Zhou and Qiaochu Wang and Zhixuan Song and Jiantao Liu and Changzao Shen and Chuanjin Luo and Dawei Wang},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357886},
url = {https://doi.org/10.1371/journal.pone.0357886},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - HMOX2-driven crosstalk between vascular aging and heart failure: A multimodal bioinformatics and explainable machine learning approach with experimental validation
AU - Jinze Li
AU - Guiting Zhou
AU - Qiaochu Wang
AU - Zhixuan Song
AU - Jiantao Liu
AU - Changzao Shen
AU - Chuanjin Luo
AU - Dawei Wang
PY - 2026
DA - 2026/09/15
JO - PLOS One
DO - 10.1371/journal.pone.0357886
UR - https://doi.org/10.1371/journal.pone.0357886
AB - Purpose: The molecular mechanisms linking vascular aging (VA) and heart failure (HF) remain elusive, hindering therapeutic strategies for their comorbidity. This study aimed to identify key biomarkers and pathways potentially involved in VA-HF synergy using integrative computational approaches. Methods: We analyzed the GSE57338 dataset (136 controls, 177 HF samples) to identify HF-associated differentially expressed genes (DEGs) and constructed a weighted gene co-expression network (WGCNA). Vascular aging-related targets were retrieved from GeneCards (n = 16,243). Consensus genes (CGs) were derived by intersecting DEGs, WGCNA hub genes, and VA-related targets. Functional enrichment, machine learning prioritization (LASSO regression, Random Forest, and SHAP-XGBoost), and network analysis (GeneMANIA) were applied to identify potential regulators. Top candidates were experimentally validated using qPCR in doxorubicin-induced rat primary vascular smooth muscle cells and human VSMC cell line for the VA model, and H9C2 cardiomyoblast injury model for HF. Further validation was performed in a mouse model of doxorubicin-induced HF, assessing cardiac function by echocardiography, myocardial fibrosis by Masson’s trichrome staining, and vascular aging markers (P16, P21) by qPCR. Results: We identified 272 CGs enriched in cGMP-PKG signaling, cytoskeletal regulation, and PPAR pathways. Machine learning prioritized 12 core genes, with HMOX2 as the top predictor (AUC = 0.978). qPCR analysis confirmed upregulation of HMOX2, S1PR3, and SERPINA3 in doxorubicin-treated rat primary vascular smooth muscle cells, human VSMC cell line, and H9C2 cardiomyoblasts compared to controls (P < 0.05). In the mouse model, doxorubicin administration induced significant cardiac dysfunction and myocardial fibrosis, accompanied by elevated expression of senescence markers P16 and P21 in vascular tissues. These findings collectively suggest that these genes may play important roles in VA-HF comorbidity (P < 0.05).
SN - 1932-6203
ER -