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Published on: September 20, 2024
Identification of circadian rhythm-related gene classification patterns and immune infiltration analysis in heart
Xuefu Wang1, Jin Rao2, Li Zhang3
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Insights
This study identifies five key circadian rhythm genes (CRGs) linked to heart failure (HF) and reveals distinct HF patient subgroups based on CRG expression and immune infiltration. These findings offer potential therapeutic targets for HF treatment.
Area of Science:
- Cardiovascular Biology
- Chronobiology
- Genomics
Background:
- Circadian rhythms significantly influence cardiac function, but the molecular links to heart failure (HF) are not fully understood.
- Investigating the role of circadian rhythm-related genes (CRGs) in HF pathogenesis is crucial for developing novel therapeutic strategies.
Purpose of the Study:
- To identify differentially expressed circadian rhythm-related genes (DE-CRGs) in heart failure (HF).
- To develop diagnostic models for HF using machine learning algorithms based on feature genes.
- To explore molecular subtypes of HF based on CRG expression and their association with immune infiltration and biological functions.
Main Methods:
- Differential gene expression analysis of CRGs in HF samples using the Gene Expression Omnibus (GEO) database.
- Machine learning algorithms (LASSO regression) for feature gene selection and diagnostic model construction.
- Consensus clustering and non-negative matrix factorization (NMF) for HF sample subtyping, followed by immune infiltration and Gene Set Variation Analysis (GSVA).
Main Results:
- Thirteen CRGs were differentially expressed in HF patients, with five key diagnostic genes identified: NAMPT, SERPINA3, MAPK10, NPPA, and SLC2A1.
- HF patients were classified into two distinct clusters with varying biological functions and immune characteristics.
- Immune infiltration analysis revealed significant differences between subgroups, with one subgroup exhibiting higher immune scores and infiltration. Hub genes like GRIN2A, DLG1, ERBB4, LRRC7, and NRG1 were associated with HF.
Conclusions:
- The identified diagnostic genes offer potential therapeutic targets for heart failure.
- Understanding the interplay between circadian rhythm, immune response, and energy metabolism in HF can guide future treatment strategies.
- This research provides a foundation for further investigation into the molecular mechanisms of HF.
Background:
Circadian rhythms play a key role in the failing heart, but the exact molecular mechanisms linking changes in the expression of circadian rhythm-related genes to heart failure (HF) remain unclear.
Methods:
By intersecting differentially expressed genes (DEGs) between normal and HF samples in the Gene Expression Omnibus (GEO) database with circadian rhythm-related genes (CRGs), differentially expressed circadian rhythm-related genes (DE-CRGs) were obtained. Machine learning algorithms were used to screen for feature genes, and diagnostic models were constructed based on these feature genes. Subsequently, consensus clustering algorithms and non-negative matrix factorization (NMF) algorithms were used for clustering analysis of HF samples. On this basis, immune infiltration analysis was used to score the immune infiltration status between HF and normal samples as well as among different subclusters. Gene Set Variation Analysis (GSVA) evaluated the biological functional differences among subclusters.
Results:
13 CRGs showed differential expression between HF patients and normal samples. Nine feature genes were obtained through cross-referencing results from four distinct machine learning algorithms. Multivariate LASSO regression and external dataset validation were performed to select five key genes with diagnostic value, including NAMPT, SERPINA3, MAPK10, NPPA, and SLC2A1. Moreover, consensus clustering analysis could divide HF patients into two distinct clusters, which exhibited different biological functions and immune characteristics. Additionally, two subgroups were distinguished using the NMF algorithm based on circadian rhythm associated differentially expressed genes. Studies on immune infiltration showed marked variances in levels of immune infiltration between these subgroups. Subgroup A had higher immune scores and more widespread immune infiltration. Finally, the Weighted Gene Co-expression Network Analysis (WGCNA) method was utilized to discern the modules that had the closest association with the two observed subgroups, and hub genes were pinpointed via protein-protein interaction (PPI) networks. GRIN2A, DLG1, ERBB4, LRRC7, and NRG1 were circadian rhythm-related hub genes closely associated with HF.
Conclusion:
This study provides valuable references for further elucidating the pathogenesis of HF and offers beneficial insights for targeting circadian rhythm mechanisms to regulate immune responses and energy metabolism in HF treatment. Five genes identified by us as diagnostic features could be potential targets for therapy for HF.
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01:17Pathophysiology of Heart Failure