Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients

Shang, Zhenglu; Sun, Jiashun; Hui, Jingjiao; Yu, Yanhua; Bian, Xiaoyun; Yang, Bowen; Deng, Kewu; Li, Lin

doi:10.3389/fgene.2021.781011

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…Previous studies have also reported the involvement of IFIT2 and IFITM2 in cardiac diseases. IFIT2 has been identified as a potential biomarker of ischemic cardiomyopathy in human and rat heart tissue samples [ 58 ], and it has been found to be highly expressed in HF patients with pulmonary arterial hypertension [ 59 ]. Additionally, studies on rat cardiomyocyte development have suggested that IFITM2 may play a role in differentiation and cell proliferation during cardiac development [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of diagnostic model in heart failure with myocardial fibrosis and conduction block by integrated gene co-expression network analysis

Yuan,

Niu,

et al. 2024

BMC Med Genomics

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Background Despite the advancements in heart failure(HF) research, the early diagnosis of HF continues to be a challenging issue in clinical practice. This study aims to investigate the genes related to myocardial fibrosis and conduction block, with the goal of developing a diagnostic model for early treatment of HF in patients. Method The gene expression profiles of GSE57345, GSE16499, and GSE9128 were obtained from the Gene Expression Omnibus (GEO) database. After merging the expression profile data and adjusting for batch effects, differentially expressed genes (DEGs) associated with conduction block and myocardial fibrosis were identified. Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, and gene set enrichment analysis (GSEA) were utilized for functional enrichment analysis. A protein-protein interaction network (PPI) was constructed using a string database. Potential key genes were selected based on the bioinformatics information mentioned above. SVM and LASSO were employed to identify hub genes and construct the module associated with HF. The mRNA levels of TAC mice and external datasets (GSE141910 and GSE59867) are utilized for validating the diagnostic model. Additionally, the study explores the relationship between the diagnostic model and immune cell infiltration. Results A total of 395 genes exhibiting differential expression were identified. Functional enrichment analysis revealed that these specific genes primarily participate in biological processes and pathways associated with the constituents of the extracellular matrix (ECM), immune system processes, and inflammatory responses. We identified a diagnostic model consisting of 16 hub genes, and its predictive performance was validated using external data sets and a transverse aortic coarctation (TAC) mouse model. In addition, we observed significant differences in mRNA expression of 7 genes in the TAC mouse model. Interestingly, our study also unveiled a correlation between these model genes and immune cell infiltration. Conclusions We identified sixteen key genes associated with myocardial fibrosis and conduction block, as well as diagnostic models for heart failure. Our findings have significant implications for the intensive management of individuals with potential genetic variants associated with heart failure, especially in the context of advancing cell-targeted therapy for myocardial fibrosis.

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Section: Discussionmentioning

confidence: 99%

Identification of diagnostic model in heart failure with myocardial fibrosis and conduction block by integrated gene co-expression network analysis

Yuan,

Niu,

et al. 2024

BMC Med Genomics

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“…By applying the support vector machine-recursive feature elimination algorithm, our group found that EPB42 and IFIT2 were highly expressed in PAH patients, while FOSB and SNF1LK showed opposite trends. These four potential genes may distinguish PH patients from healthy individuals and can be used for early diagnosis of PH (56). Up to 60% of patients with severe LV systolic insufficiency and 70% of patients with pure LV diastolic insufficiency may develop PH (57,58).…”

Section: Characterization Of Ph In Hfref and Hfpefmentioning

confidence: 99%

The emerging role of sacubitril/valsartan in pulmonary hypertension with heart failure

Yang

Hui

et al. 2023

Front. Cardiovasc. Med.

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Pulmonary hypertension due to left heart disease (PH-LHD) represents approximately 65%–80% of all patients with PH. The progression, prognosis, and mortality of individuals with left heart failure (LHF) are significantly influenced by PH and right ventricular (RV) dysfunction. Consequently, cardiologists should devote ample attention to the interplay between HF and PH. Patients with PH and HF may not receive optimal benefits from the therapeutic effects of prostaglandins, endothelin receptor antagonists, or phosphodiesterase inhibitors, which are specific drugs for pulmonary arterial hypertension (PAH). Sacubitril/valsartan, the angiotensin receptor II blocker-neprilysin inhibitor (ARNI), was recommended as the first-line therapy for patients with heart failure with reduced ejection fraction (HFrEF) by the 2021 European Society of Cardiology Guidelines. Although ARNI is effective in treating left ventricular (LV) enlargement and lower ejection fraction, its efficacy in treating individuals with PH and HF remains underexplored. Considering its vasodilatory effect at the pre-capillary level and a natriuretic drainage role at the post-capillary level, ARNI is believed to have a broad range of potential applications in treating PH-LHD. This review discusses the fundamental pathophysiological connections between PH and HF, emphasizing the latest research and potential benefits of ARNI in PH with various types of LHF and RV dysfunction.

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“…Machine learning techniques have already found widespread application in the clinical diagnosis of cardiovascular diseases, such as coronary artery calcification scoring. The integration of key mRNAs and traditional diagnostic methods shows promise in enhancing the latter's accuracy [5]. In this study, we obtained gene expression data sets from stroke patients and post-stroke cognitive impairment patients in the Gene Expression Omnibus (GEO) database.…”

Section: Introductionmentioning

confidence: 99%

Screening Targets for Diagnosis and Treatment of Cognitive Dysfunction in Stroke through Transcriptome Combined with Machine Learning

Chen,

Li,

Hou

et al. 2023

jbacteriolmycol

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Post-Stroke Cognitive Impairment (PSCI) is one of the major complications after stroke. The evaluation of PSCI usually depends on neuropsychology tests, but the results of these tests are subjective and inaccurate. Need to find more objective indicators as identification markers of PSCI. In this study, we use machine learning to find biomakers of PSCI, and established regulatory networks at transcriptional level. Several gene such as ORC1, TOMM40L and SHISAL2A are identified biomakers, and several miRNA such as hsa-mir-130b-3p and hsa-mir-484 are interacted most tight with this biomakers genes. The results of this study help to better distinguish patients with PS and PSCI in clinical practice, and identifying relevant biomarker genes and miRNAs that can serve as potential therapeutic sites.

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Construction of a Support Vector Machine–Based Classifier for Pulmonary Arterial Hypertension Patients

Cited by 4 publications

References 29 publications

Identification of diagnostic model in heart failure with myocardial fibrosis and conduction block by integrated gene co-expression network analysis

Identification of diagnostic model in heart failure with myocardial fibrosis and conduction block by integrated gene co-expression network analysis

The emerging role of sacubitril/valsartan in pulmonary hypertension with heart failure

Screening Targets for Diagnosis and Treatment of Cognitive Dysfunction in Stroke through Transcriptome Combined with Machine Learning

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