Identification of signature genes and subtypes for heart failure diagnosis based on machine learning.

Background: Heart failure (HF) is a multifaceted clinical condition, and our comprehension of its genetic pathogenesis continues to be significantly limited. Consequently, identifying specific genes for HF at the transcriptomic level may enhance early detection and allow for more targeted therapies for these individuals.

Methods: HF datasets were acquired from the Gene Expression Omnibus (GEO) database (GSE57338), and through the application of bioinformatics and machine-learning algorithms. We identified four candidate genes (FCN3, MNS1, SMOC2, and FREM1) that may serve as potential diagnostics for HF. Furthermore, we validated the diagnostic value of these genes on additional GEO datasets (GSE21610 and GSE76701). In addition, we assessed the different subtypes of heart failure through unsupervised clustering, and investigations were conducted on the differences in the immunological microenvironment, improved functions, and pathways among these subtypes. Finally, a comprehensive analysis of the expression profile, prognostic value, and genetic and epigenetic alterations of four potential diagnostic candidate genes was performed based on The Cancer Genome Atlas pan-cancer database.

Results: A total of 295 differential genes were identified in the HF dataset, and intersected with the blue module gene with the highest correlation to HF identified by weighted correlation network analysis (r = 0.72, p = 1.3 × 10^-43), resulting in a total of 114 key HF genes. Furthermore, based on random forest, least absolute shrinkage and selection operator, and support vector machine algorithms, we finally identified four hub genes (FCN3, FREM1, MNS1, and SMOC2) that had good potential for diagnosis in HF (area under the curve > 0.7). Meanwhile, three subgroups for patients with HF were identified (C1, C2, and C3). Compared with the C1 and C2 groups, we eventually identified C3 as an immune subtype. Moreover, the pan-cancer study revealed that these four genes are closely associated with tumor development.

Conclusions: Our research identified four unique genes (FCN3, FREM1, MNS1, and SMOC2), enhancing our comprehension of the causes of HF. This provides new diagnostic insights and potentially establishes a tailored approach for individualized HF treatment.