Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease

Abstract

Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease and cause significant morbidity and mortality. Abnormal coagulation cascade is one of the high-risk factors in CAD patients, but the molecular mechanism of coagulation in CAD is still limited. Methods: We clustered and categorized 352 CAD paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed. Results: CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset. Conclusion: Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.

Keywords: LASSO; artificial neural networks; coagulation; coronary artery disease; random forest.

FIGURE 1

Workflow diagram.

FIGURE 2

Principal component analysis (PCA) of the training group datasets. Visualization samples of the first two principal components before (A) and after (B) batch-effect removal.

FIGURE 3

Identification of coagulation-related subgroups. (A, B) Consensus clustering matrix for k = 2 (optimal cluster number) of the training group. (C) PCA analysis of the training group. Cluster analysis (D, E) and PCA analysis (F) of the validation group.

FIGURE 4

Immune landscape of coagulation subgroups. Gene set variation analysis (GSVA) (A) and differences in immune cell abundance (B) and immune indicators (C) between CRG-subgroups.

FIGURE 5

Functional analysis of DE-CRGs between two subgroups. Significance of difference analysis (A), Gene Ontology (GO) enrichment analysis (B), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis (C) between C1 and C2 CRG-subgroups.

FIGURE 6

Identification feature DE-CRGs between two coagulation-related subgroups. (A) Cross-validation for selecting optimal parameter (λ) in LASSO regression. (B) Model error during building (C) and importance of top 30 genes in random forest model. (D) Intersection genes of 19 genes obtained by LASSO regression and 22 genes with “MeanDecreaseGini” index >2.0 in the random forest model.

FIGURE 7

Evaluation and validation of the prediction performance of feature DE-CRGs. Unsupervised clustering (A) of 10 feature genes for C1 and C2 subgroups. ROC curves of OR10A5 (B), FOXL1 (C), FBN1 (D), PROKR1 (E), SFTPA1 (F), LHFPL5 (G), KLKB1 (H), HUMBINDC (I), CYP2B6 (J) and MAPK11 (K) for predicting the coagulation-related subgroups.

FIGURE 8

Construction and validation of the artificial neural network. Artificial neural network pattern plot (A) for predicting coagulation-related C1/C2 subgroups. ROC curves of training (B) and validation groups (D) for the model. The accuracy, F1-score, precision and recall of training (C) and validation groups (E) for the model.

FIGURE 9

Immunological characterization of 10 feature DE-CRGs in artificial neural networks. Correlation of the 10 genes with immune cell abundance (A) and immune function (B).