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. 2023 Oct 31;160(1):36.
doi: 10.1186/s41065-023-00298-5.

Insulin-like growth factor binding protein 2: a core biomarker of left ventricular dysfunction in dilated cardiomyopathy

Wei Yu #  1   2 , Hongli Gao #  1 , Tianyang Hu  3 , Xingling Tan  2 , Yiheng Liu  2 , Hongli Liu  2 , Siming He  2 , Zijun Chen  1 , Sheng Guo  4 , Jing Huang  5
Affiliations

Insulin-like growth factor binding protein 2: a core biomarker of left ventricular dysfunction in dilated cardiomyopathy

Wei Yu et al. Hereditas. .

Abstract

Background: RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined.

Methods: In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis.

Results: A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10-5). Single-cell analysis revealed that this gene was mainly expressed in endothelial cells.

Conclusion: In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.

Keywords: Dilated cardiomyopathy; IGFBP2; Left ventricular ejection fraction; Machine learning; RNA modifications.

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Conflict of interest statement

The authors declare that there is no conflict of interest in this study.

Figures

Fig. 1
Fig. 1
Differential expression analysis of RNA modification regulators (RMRs) in DCM. A Expression of RMRs in normal group versus DCM group. B The heatmap of RMRs between normal samples and DCM samples. Blue represents DCM group and red represents normal group. DCM, dilated cardiomyopathy. RMRs, RNA modification regulators; * represents p < 0.05; ** represents: p < 0.01; *** represents: p < 0.001
Fig. 2
Fig. 2
Screening for key RNA modification regulators (RMRs) in DCM. A Circle diagram of RMRs at different chromosomal locations. B Tuning feature screening in the LASSO model. C Curve of error versus number of decision trees. Red represents the DCM group, green represents the control group, and black represents all of them. D Results of the Gini coefficient method in random forest classifier. The x-axis indicates the genetic variable, and the y-axis represents the importance index. E Screening plot of key RMRs based on SVM-RFE algorithm. F The Venn diagram showing the 4 RMRs shared by LASSO, SVM-RFE and RF
Fig. 3
Fig. 3
Gene enrichment analysis of differentially expressed RNA modification regulators (DE-RMRs). A GO enrichment analysis of DE-RMRs. B KEGG pathway analysis of DE-RMRs. C DO enrichment analysis of DE-RMRs. Abbreviations: GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DO, Disease Ontology; DE-RMRs, differentially expressed RNA modification regulators
Fig. 4
Fig. 4
Construction and validation of nomogram. A Development of a prediction nomogram based on four key RMRs. B Calibration curves for predicting risk of dilated cardiomyopathy nomogram. C ROC curve of risk prediction model in the train group (GSE17800). D Assessment of the diagnostic efficacy of key RMRs in the train group
Fig. 5
Fig. 5
Assessment of gene expression and diagnostic efficacy in test group. Gene expression levels of (A) HNRNPA2B1, (B) HNRNPC, (C) IGFBP2 and (D) TRMT6 between normal and DCM samples in the test group. (E) ROC curve of risk prediction model in the test group (GSE116250). F Assessment of the diagnostic efficacy of key RMRs in the test group
Fig. 6
Fig. 6
Correlation analysis of 4 key RMRs with clinical characteristics. Correlation analysis of IGFBP2 (A), HNRNPC (B) and HNRNPA2B1 (C) with LVEF. Correlation analysis of IGFBP2 (D), HNRNPC (E) and HNRNPA2B1 F with LVIDD. LVEF, left ventricular ejection fraction; LVIDD, left ventricular internal diastolic dimension
Fig. 7
Fig. 7
Validation of IGFBP2 expression. Echocardiography of rats in DCM (A) and control (B) groups. C LVEDV, D LVESV, E LVIDD, F LVEF of rats treated with saline or doxorubicin. G Relative expression of IGFBP2 in the hearts of rats treated with doxorubicin, normalized to the expression of GAPDH. H Measurement of IGFBP2 expression in serum of DCM patients and normal individuals by ELISA. I Correlation of IGFBP2 with LVEF. J Correlation of IGFBP2 with LVIDD. * represents: p < 0.05; *** represents: p < 0.001
Fig. 8
Fig. 8
Single cell analysis of DCM patients. A Umap of eight cell types in GSE145154. B Expression of IGFBP2 in the eight cell populations. The legend shows the normalized expression of the color gradient. C Violin plot showing IGFBP2 expression in all cell populations. D Heatmap for single-cell rank-based gene set enrichment analysis. E Density scatterplot of TGF beta signaling. F Ridge plot of TGF beta signaling
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