Subtypes identification on heart failure with preserved ejection fraction via network enhancement fusion using multi-omics data

Abstract

Heart failure with preserved ejection fraction (HFpEF) is associated with multiple etiologic and pathophysiologic factors. HFpEF leads to significant cardiovascular morbidity and mortality. There are various reasons that fail to identify effective therapeutic interventions for HFpEF, primarily due to its clinical heterogeneity causing significant difficulties in determining physiologic and prognostic implications for this syndrome. Thus, identifying clinical subtypes using multi-omics data has great implications for efficient treatment and prognosis of HFpEF patients. Here we proposed to integrate mRNA, DNA methylation and microRNA (miRNA) expression data of HFpEF with a similarity network fusion (SNF) method following a network enhancement (ne-SNF) denoising technique to form a fused network. A spectral clustering method was then used to obtain clusters of patient subtypes. Experiments on HFpEF datasets demonstrated that ne-SNF significantly outperforms single data subtype analysis and other integrated methods. The identified subgroups were shown to have statistically significant differences in survival. Two HFpEF subtypes were defined: a high-risk group (16.8%) and a low-risk group (83.2%). The 5-year mortality rates were 63.3% and 33.0% for the high- and low-risk group, respectively. After adjusting for the effects of clinical covariates, HFpEF patients in the high-risk group were 2.43 times more likely to die than the low-risk group. A total of 157 differentially expressed (DE) mRNAs, 2199 abnormal methylations and 121 DE miRNAs were identified between two subtypes. They were also enriched in many HFpEF-related biological processes or pathways. The ne-SNF method provides a novel pipeline for subtype identification in integrated analysis of multi-omics data.

Keywords: Biomarkers; HFpEF; Multi-omics data integration; Subtypes identification; ne-SNF.

Graphical abstract

Fig. 1

Schematic representation of the pipeline for the proposed ne-SNF method.

Fig. 2

Heatmaps of similarity matrix derived from single data type (mRNA, DNA methylation and miRNA) and using different methods (UMKL, SNF and ne-SNF). In each similarity matrix, the color shade represents the degree of patient-patient similarity: the darker the color, the higher the similarity between two individuals. Patients with high similarities define a subtype group, called a subgroup.

Fig. 3

Comparison of −log₁₀(p-value) based on single data type, UMKL, SNF and ne-SNF under different number of clusters (2,3,4 and 5).

Fig. 4

Kaplan-Meier survival curves for the low- and high-risk groups (left), and 3D scatter plots of the first three PCs (right).

Fig. 5

The heatmap of DEmRNAs, abnormal methylations and DEmiRNAs between the low- and high-risk group. Each row represents an individual feature and each column represents a patient. Red and blue color represents relatively high and low expression respectively, with the intensity of the color representing the magnitude of high/low expression. The heatmap indicates that the high- and low-risk groups of HFpEF patients are highly heterogeneous among the three data types. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 6

The heatmap of the selected top15 mRNAs, methylation genes and miRNAs that are associated with the two risk subgroups.

Fig. 7

Plots of survival curves of the top 3 most significant features in mRNAs, methylations and miRNAs: the top 3 most significant mRNAs, namely, EIF4A1, SH3BGRL2 and MTERF1 (A-C), the top 3 most significant methylations, namely, IMPG2, PYDC2 and ATP6V1G2 (D-F), and the top 3 most significant miRNAs, namely, hsa-miR-19a-3p, hsa-miR-186-5p-a1, hsa-miR-186-5p-a2 (G-I).

Fig. 8

GO biological process enrichment analysis of 241 core genes.

Fig. 9

KEGG enrichment analysis of 241 core genes.