Risk prediction model construction for post myocardial infarction heart failure by blood immune B cells

Abstract

Background: Myocardial infarction (MI) is a common cardiac condition with a high incidence of morbidity and mortality. Despite extensive medical treatment for MI, the development and outcomes of post-MI heart failure (HF) continue to be major factors contributing to poor post-MI prognosis. Currently, there are few predictors of post-MI heart failure.

Methods: In this study, we re-examined single-cell RNA sequencing and bulk RNA sequencing datasets derived from the peripheral blood samples of patients with myocardial infarction, including patients who developed heart failure and those who did not develop heart failure after myocardial infarction. Using marker genes of the relevant cell subtypes, a signature was generated and validated using relevant bulk datasets and human blood samples.

Results: We identified a subtype of immune-activated B cells that distinguished post-MI HF patients from non-HF patients. Polymerase chain reaction was used to confirm these findings in independent cohorts. By combining the specific marker genes of B cell subtypes, we developed a prediction model of 13 markers that can predict the risk of HF in patients after myocardial infarction, providing new ideas and tools for clinical diagnosis and treatment.

Conclusion: Sub-cluster B cells may play a significant role in post-MI HF. We found that the STING1, HSPB1, CCL5, ACTN1, and ITGB2 genes in patients with post-MI HF showed the same trend of increase as those without post-MI HF.

Keywords: B cells; heart failure (HF); myocardial infarction (MI); risk prediction model; single cell RNA-seq.

Figure 1

The framework of this research. Bulk RNA sequencing data can be found in GSE60993, GSE61144, and GSE59867, while single cell RNA sequencing data can be found in GSE145154. Myocardial infarction (MI), protein-protein interaction, differentially expressed genes, and heart failure all refer to gene sets that have been enriched in a specific way.

Figure 2

Genes from genomes enriched in nine significant differentially expressed genes using GSE60993, GSE61144 and their adjusted p-values for receiver operating characteristic curves. The Benjamini-Hochberg method was used to correct the p-values for the false discovery rate.

Figure 3

Examination of B cells in the MI dataset (A) Uniform Manifold Approximation and Projection plot of B cells in the dataset of GSE145154, with the sample divided into five subgroups. (B) Two subgroups were divided according to disease status. The bars show the proportion of cells grouped by cluster (left) and disease status (right). (C) Heatmap of scale-normalized expression of the top 10 specific marker genes for the B cell subclusters in GSE145154, identified by a two-sided Wilcoxon rank sum test with false discovery rate correction (p < 0.05). (D) MI prediction-related B subtype was identified by locating the effective predictive gene set expression via gene set variation analysis (GSVA). (E) Identification of MI prediction-associated B cell subclusters by GSVA to locate validly (receiver operating characteristic p.adjust < 0.05) predicted gene set expression. (F) violin plot of expression levels of marker genes specifically expressed in the B cell subclusters of GSE145154. A two-sided Wilcoxon test was used to determine the significance between the subclusters of interest and other subclusters. p < 0.0001.

Figure 4

GSVA analysis of B cells and examination of HF.Sig (A) Feature map of the HF.Sig GSVA score showing that it can specifically characterize B cell C4 subcluster. (B) Boxplot found and validated significantly higher heart failure (HF) scores than normal scores in HF.Sig GSVA scores. Cohorts of B cells and (C) Gene set enrichment analysis found that HF.Sig was enriched in B_C4 was enriched in HF and normal. The p-value was false discovery rate-adjusted by the Benjamini-Hochberg method. (D) GSE59867 were verified by GSVA analysis to have significantly higher GSVA scores for HF than non-HF, center line, median, box limits, upper and lower quartiles. whiskers, 1.5 interquartile range. points beyond whiskers, outliers. A two-sided Wilcoxon test was used to determine significance.

Figure 5

HF signatures are effective in predicting clinical outcomes in patients with MI. The bulk RNA-seq dataset GSE59867 was analyzed. (A) Univariate logistic regression model of HF.Sig in predicting MI outcomes. (B) Bar graph showing the area under the curve of gene combinations, maximum area under the curve per cycle (different gene-number combinations). Dashed line: 13-gene combination (C) The 13-gene combination had a significantly high predictive value for the outcome of whether HF occurred after MI in the GSE59867 cohort. (D) Evaluation of the above 13 genes using the GSVA score identified four genes that were found to be genes with increased expression for heart failure occurring after MI compared to HF not occurring after MI.

Figure 6

Heatmap made using the original HF.Sig of genes differentially expressed in cohort GSE59867, protein-protein interaction analysis and performance of the corresponding enriched Top10 pathway.

Figure 7

Validation of CCL5, STING1, HSPB1, ACTN1 and ITGB2 in human blood samples (non-HF: n = 7 biologically independent samples, HF: n = 7 biologically independent samples). Expression status of CCL5, ACTN1 and ITGB2 in the blood of post-MI patients. Expression status of STING1 and HSPB1 in the blood of post-MI patients. Wilcoxon rank-sum test, **p < 0.01, ***p < 0.001. ns, not statistically significant.