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Identification of hub genes for the diagnosis associated with heart failure using multiple cell death patterns
Hua-Jing Yuan1, Hui Yu1, Yi-Ding Yu1
1First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Insights
This study identified DHRS11 and LRKK2 as key genes in heart failure (HF) related to programmed cell death (PCD). These findings offer new diagnostic biomarkers and insights into HF pathogenesis.
Area of Science:
- Genomics
- Molecular Biology
- Cardiovascular Research
Background:
- Heart failure (HF) is a global health concern with complex pathological mechanisms.
- Programmed cell death (PCD) significantly contributes to the development and progression of HF.
Purpose of the Study:
- To identify key genes associated with HF, specifically those linked to PCD.
- To develop a diagnostic model for HF based on identified hub genes.
- To elucidate the molecular mechanisms and immune landscape of HF.
Main Methods:
- Utilized bioinformatics and machine learning on HF gene expression datasets from the GEO database.
- Performed functional enrichment analysis to understand gene ontology and pathways.
- Conducted immune infiltration analysis to assess immune cell expression in relation to hub genes.
Main Results:
- Identified 95 key HF genes primarily involved in inflammation and immunomodulation.
- DHRS11 and LRKK2 were identified as PCD-associated HF hub genes.
- Developed a diagnostic model with effective value, confirming hub genes as HF biomarkers.
- Observed significant immune imbalance in T-cell populations, monocytes, and M2 macrophages in HF.
Conclusions:
- DHRS11 and LRKK2 are identified as crucial hub genes in HF associated with PCD.
- The developed HF diagnostic model and immune infiltration insights provide novel perspectives on HF molecular mechanisms.
- This research offers potential advancements in HF diagnosis and treatment strategies.
Aims:
Heart failure (HF) is an important public health problem worldwide, and programmed cell death (PCD) plays a crucial role in its pathologic process. This study aims to identify the hub genes associated with HF through PCD in order to better understand the pathogenesis of HF and improve its diagnosis and treatment.
Methods And Results:
The gene expression dataset of HF was obtained from the GEO database. Bioinformatics and machine learning algorithms were utilized to screen the HF key genes and PCD-related HF hub genes, and an HF diagnostic model was constructed on this. Functional enrichment analysis clarified the gene ontology and signalling pathways of HF. The immune infiltration analysis of HF was performed to explore the expression levels of immune cells in each hub gene. Through bioinformatics analysis, 95 HF key genes were obtained. Functional enrichment analysis showed that they were mainly involved in inflammation, immunomodulation and other mechanisms. DHRS11 and LRKK2 were identified as PCD-associated HF hub genes by machine learning algorithms. The hub genes were confirmed as significant biomarkers of HF in the training and validation datasets, and their constructed nomogram had effective diagnostic value. Immune infiltration analysis showed significant immune imbalance of T-cell populations, monocytes and macrophages M2 in HF.
Conclusions:
In this study, DHRS11 and LRKK2 were identified as hub genes. HF diagnostic model construction and immune infiltration analyses were performed, which provided new ideas for the molecular mechanisms of HF development and treatment.
