Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure

Dynamics of SERPINA3 in response to anthracycline treatment and cardiovascular dysfunction

BACKGROUND

SERPINA3 recently emerged as potential prognostic biomarker in heart failure. In a population of cancer survivors with cancer therapy-related cardiac dysfunction (CTRCD) circulating SERPINA3 was elevated compared to age-matched controls. We aimed to assess the longitudinal dynamics of circulating SERPINA3 levels in patients with cancer treated with anthracycline chemotherapy (AnC) and its relation to CTRCD.

METHODS

In this single centre cohort study, 55 patients with cancer scheduled for AnC were prospectively enrolled. Cardiac evaluation (echocardiography, high-sensitive cardiac troponin I and NT-proBNP) was performed and SERPINA3 levels in plasma were assessed at 4 timepoints: before chemotherapy, directly after the end of chemotherapy, three months and twelve months after the end of chemotherapy.

RESULTS

Forty-two out of 55 patients (76.4%) developed CTRCD within 1 year after end of treatment. CTRCD was mild in 32 and moderate in 10 patients, defined as a change in cardiac biomarkers or GLS and LVEF decline < 50% respectively. Overall, median SERPINA3 levels decreased from baseline to three months after AnC (215.7 [62.0-984.0] to 176.9 [94.7-678.0] µg/ml, p = 0.031). This decrease was most prominent in patients without CTRCD (30.8% decrease, p = 0.007), followed by mild CTRCD (9.0% decrease, p = 0.022), while patients with moderate CTRCD did not show a reduction in SERPINA3 (5.1% increase, p = 0.987). SERPINA3 values at three months after AnC were positively correlated with NT-proBNP (r = 0.47, p = 0.002). Several malignancy, treatment and patient characteristics were associated with higher SERPINA3 values.

CONCLUSION

Circulating SERPINA3 levels show dynamic changes in a population of patients with cancer, with an overall decrease following AnC. However, in patients that developed moderate CTRCD, SERPINA3 levels remained elevated. The potential of SERPINA3 dynamics as a biomarker for CTRCD, deserves validation in larger cohorts.

Unveiling epigenetic regulatory elements associated with breast cancer development

Breast cancer is the most common cancer in women and the 2nd most common cancer worldwide, yearly impacting over 2 million females and causing 650 thousand deaths. It has been widely studied, but its epigenetic variation is not entirely unveiled. We aimed to identify epigenetic mechanisms impacting the expression of breast cancer related genes to detect new potential biomarkers and therapeutic targets. We considered The Cancer Genome Atlas database with over 800 samples and several omics datasets such as mRNA, miRNA, DNA methylation, which we used to select 2701 features that were statistically significant to differ between cancer and control samples using the Monte Carlo Feature Selection and Interdependency Discovery algorithm, from an initial total of 417,486. Their biological impact on cancerogenesis was confirmed using: statistical analysis, natural language processing, linear and machine learning models as well as: transcription factors identification, drugs and 3D chromatin structure analyses. Classification of cancer vs control samples on the selected features returned high classification weighted Accuracy from 0.91 to 0.98 depending on feature-type: mRNA, miRNA, DNA methylation, and classification algorithm. In general, cancer samples showed lower expression of differentially expressed genes and increased β-values of differentially methylated sites. We identified mRNAs whose expression is well explained by miRNA expression and differentially methylated sites β-values. We recognized differentially methylated sites possibly affecting NRF1 and MXI1 transcription factors binding, causing a disturbance in NKAPL and PITX1 expression, respectively. Our 3D models showed more loosely packed chromatin in cancer. This study successfully points out numerous possible regulatory dependencies.

m6A RNA methylation modification is involved in the disease course of heart failure

We explored N6-methyladenosine (m6A) RNA methylation as one of the gene regulatory mechanisms in heart failure (HF) biology. Understanding the different physiological mechanisms will facilitate the prevention and individualized treatment of HF. The Gene Expression Omnibus (GEO) database served as the source of the data. In GSE116250, differential analysis between ischemic cardiomyopathy (ICM), dilated cardiomyopathy (DCM) and controls yielded differentially expressed m6A regulators. Differential analysis between HF and controls in GSE131296 identifies m6A-modified genes and then performs enrichment analysis. Protein-protein interaction (PPI) network analysis was performed for the differentially expressed ICM- or DCM-associated genes in GSE116250 and GSE55296, respectively. Finally, the diagnostic genes for ICM and DCM were predicted using receiver operating characteristic (ROC) curve. YTHDC1, HNRNPC and HNRNPA2B1 were significantly downregulated in GSE116250 in DCM and ICM compared with controls. A total of 195 genes were identified in GSE131296 as subject to m6A alteration. These genes may play a role in HF through the MAPK signaling pathway and p53 signaling pathway. PPI network analysis identified CCL5, CXCR4 and CCL2 as key genes for ICM and IL-6 as a key gene for DCM. Through ROC curves, we identified m6A-modified APLP1, KLF2 as potential diagnostic genes for ICM, and m6A-modified FGF7, FREM1 and C14orf132 as potential diagnostic genes for DCM. Our findings support m6A modifying mechanisms in HF etiology that contribute to the treatment of HF. Thus, our data suggest that m6A methylation may be an interesting target for therapeutic intervention.

Identifying plasma proteomic signatures from health to heart failure, across the ejection fraction spectrum

Circulating proteins may provide insights into the varying biological mechanisms involved in heart failure (HF) with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF). We aimed to identify specific proteomic patterns for HF, by comparing proteomic profiles across the ejection fraction spectrum. We investigated 4210 circulating proteins in 739 patients with normal (Stage A/Healthy) or elevated (Stage B) filling pressures, HFpEF, or ischemic HFrEF (iHFrEF). We found 2122 differentially expressed proteins between iHFrEF-Stage A/Healthy, 1462 between iHFrEF-HFpEF and 52 between HFpEF-Stage A/Healthy. Of these 52 proteins, 50 were also found in iHFrEF vs. Stage A/Healthy, leaving SLITRK6 and NELL2 expressed in lower levels only in HFpEF. Moreover, 108 proteins, linked to regulation of cell fate commitment, differed only between iHFrEF-HFpEF. Proteomics across the HF spectrum reveals overlap in differentially expressed proteins compared to stage A/Healthy. Multiple proteins are unique for distinguishing iHFrEF from HFpEF, supporting the capacity of proteomics to discern between these conditions.

Plasma SMOC2 Predicts Prognosis in Patients with Heart Failure: A Prospective Cohort

Background

Heart failure (HF) is a chronic disease with a poor prognosis, making it extremely important to assess the prognosis of patients with HF for accurate treatment. Secreted modular calcium-binding protein 2 (SMOC2) is a cysteine-rich acidic secreted protein that plays a pathophysiological role in many diseases, including regulation of vascular growth factor activity. It has previously been found that SMOC2 plays an essential role in cardiac fibrosis in our previous preclinical study, but whether it can be used as a clinical marker in heart failure patients remains unclear. The purpose of this research was to evaluate the correlation between plasma levels of SMOC2 and the prognosis for individuals with HF.

Methods

HF patients diagnosed with ischemic cardiomyopathy were enrolled from January to December 2021. Baseline plasma levels of SMOC2 were measured after demographic and clinical features were collected. Linear and nonlinear multivariate Cox regression models were used to determine the association between plasma SMOC2 and patient outcomes during follow-up. All analysis was performed using SPSS, EmpowerStats, and R software.

Results

The study included 188 patients, and the average follow-up time was 489.5±88.3 days. The plasma SMOC2 concentrations were positively correlated with N-terminal pro-B-type Natriuretic Peptide (NT-proBNP), left ventricular end-diastolic diameter (LVEDd), and length of hospital stay and were negatively correlated with left ventricular ejection fraction (LVEF) at baseline. A total of 53 patients (28.2%) were rehospitalized due to cardiac deterioration, 14 (7.4%) died, and 37 (19.7%) developed malignant arrhythmias. A fully adjusted multivariate COX regression model showed that SMOC2 is associated with readmission (HR = 1.02, 95% CI:1.012-1.655). A significant increase in rehospitalization risk was observed in group Q2 (HR =1.064, 95% CI: 1.037, 3.662, p=0.005) and group Q3 (HR =1.085, 95% CI:1.086, 3.792, p=0.009) in comparison with group Q1. The p for trend also shows a linear correlation across the three models (P < 0.001). SMOC2 was associated with the severity of HF in patients, but not with all-cause deaths and arrhythmias during follow-up.

Conclusion

Plasma SMOC2 is associated with the severity of HF and readmission rate, and is a good predictor of the risk of readmission in patients.

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning

OBJECTIVES

Hypertrophic cardiomyopathy (HCM), a leading cause of heart failure and sudden death, requires early diagnosis and treatment. This study investigated the underlying pathogenesis and explored potential diagnostic gene biomarkers for HCM.

METHODS

Transcriptional profiles of myocardial tissues from patients with HCM (dataset GSE36961) were downloaded from the Gene Expression Omnibus database and subjected to bioinformatics analyses, including differentially expressed gene (DEG) identification, enrichment analyses, and protein-protein interaction (PPI) network analysis. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination were performed to identify candidate diagnostic gene biomarkers. mRNA expression levels of candidate biomarkers were tested in an external dataset (GSE141910); area under the receiver operating characteristic curve (AUC) values were obtained to validate diagnostic efficacy.

RESULTS

Overall, 156 DEGs (109 downregulated, 47 upregulated) were identified. Enrichment and PPI network analyses indicated that the DEGs were involved in biological functions and molecular pathways including inflammatory response, platelet activity, complement and coagulation cascades, extracellular matrix organization, phagosome, apoptosis, and VEGFA-VEGFR2 signaling. RASD1, CDC42EP4, MYH6, and FCN3 were identified as diagnostic biomarkers for HCM.

CONCLUSIONS

RASD1, CDC42EP4, MYH6, and FCN3 might be diagnostic gene biomarkers for HCM and can provide insights concerning HCM pathogenesis.

SMOC2 plays a role in heart failure via regulating TGF-β1/Smad3 pathway-mediated autophagy

Heart failure (HF) is a major global cause of morbidity and mortality. This study aimed to elucidate the role of secreted protein acidic and rich in cysteine-related modular calcium-binding protein 2 (SMOC2) in HF development and its underlying mechanism. Using a rat HF model, SMOC2 expression was examined and then knocked down via transfection to assess its impact on cardiac function and damage. The study also evaluated the effects of SMOC2 knockdown on autophagy-related molecules and the transforming growth factor beta 1 (TGF-β1)/SMAD family member 3 (Smad3) signaling pathway. Intraperitoneal injection of the TGF-β agonist (SRI-011381) into the HF rat model was performed to explore the SMOC2-TGF-β1/Smad3 pathway relationship. SMOC2 expression was elevated in HF rats, while its downregulation improved cardiac function and damage. SMOC2 knockdown reversed alterations in the LC3-II/I ratio, Beclin-1, and p62 levels in HF rats. Through transmission electron microscope, we observed that SMOC2 knockdown restored autophagosome levels. Furthermore, SMOC2 downregulation inhibited the TGF-β1/Smad3 signaling pathway, which was counteracted by SRI-011381. In conclusion, SMOC2 knockdown inhibits HF development by modulating TGF-β1/Smad3 signaling-mediated autophagy, suggesting its potential as a therapeutic target for HF.

Integrated bioinformatics analysis for identifying key genes and pathways in female and male patients with dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is a common cause of heart failure, and males are more likely to suffer from DCM than females. This research aimed at exploring possible DCM-associated genes and their latent regulatory effects in female and male patients. WGCNA analysis found that in the yellow module, 341 and 367 key DEGs were identified in females and males, respectively. A total of 22 hub genes in females and 17 hub genes in males were identified from the PPI networks of the key DEGs based on Metascape database. And twelve and eight potential TFs of the key DEGs were also identified in females and males, respectively. Eight miRNAs of 15 key DEGs were screened in both females and males, which may be differentially expressed in females and males. Dual-luciferase reporter assay demonstrated that miR-21-5P could directly target the key gene MATN2. Furthermore, Sex differences in KEGG pathways were identified. Both KOBAS and GSEA analysis identified 19 significantly enriched pathways related to immune response in both females and males, and the TGF-β signaling pathway was exclusively identified in males. Network pharmacology analysis revealed that seven key DEGs were potential targets for the treatment of DCM, of which the OLR1 gene was only identified in males, the expression levels of the seven genes were verified by RT-PCR. The above results could offer a novel understanding of sex differences in key genes and pathways in DCM progression.

Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy

Introduction

Machine learning (ML) has gained intensive popularity in various fields, such as disease diagnosis in healthcare. However, it has limitation for single algorithm to explore the diagnosing value of dilated cardiomyopathy (DCM). We aim to develop a novel overall normalized sum weight of multiple-model MLs to assess the diagnosing value in DCM.

Methods

Gene expression data were selected from previously published databases (six sets of eligible microarrays, 386 samples) with eligible criteria. Two sets of microarrays were used as training; the others were studied in the testing sets (ratio 5:1). Totally, we identified 20 differently expressed genes (DEGs) between DCM and control individuals (7 upregulated and 13 down-regulated).

Results

We developed six classification ML methods to identify potential candidate genes based on their overall weights. Three genes, serine proteinase inhibitor A3 (SERPINA3), frizzled-related proteins (FRPs) 3 (FRZB), and ficolin 3 (FCN3) were finally identified as the receiver operating characteristic (ROC). Interestingly, we found all three genes correlated considerably with plasma cells. Importantly, not only in training sets but also testing sets, the areas under the curve (AUCs) for SERPINA3, FRZB, and FCN3 were greater than 0.88. The ROC of SERPINA3 was significantly high (0.940 in training and 0.918 in testing sets), indicating it is a potentially functional gene in DCM. Especially, the plasma levels in DCM patients of SERPINA3, FCN, and FRZB were significant compared with healthy control.

Discussion

SERPINA3, FRZB, and FCN3 might be potential diagnosis targets for DCM, Further verification work could be implemented.

Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Objective

Inflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM.

Materials and methods

The microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC).

Results

64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility.

Conclusion

SERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective.

Integrated Strategies of Diverse Feature Selection Methods Identify Aging-Based Reliable Gene Signatures for Ischemic Cardiomyopathy

Objective: Ischemic cardiomyopathy (ICM) is a major cardiovascular state associated with prominently increased morbidity and mortality. Our purpose was to detect reliable gene signatures for ICM through integrated feature selection strategies.

Methods: Transcriptome profiles of ICM were curated from the GEO project. Classification models, including least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest, were adopted for identifying candidate ICM-specific genes for ICM. Immune cell infiltrates were estimated using the CIBERSORT method. Expressions of candidate genes were verified in ICM and healthy myocardial tissues via Western blotting. JC-1 staining, flow cytometry, and TUNEL staining were presented in hypoxia/reoxygenation (H/R)-stimulated H9C2 cells with TRMT5 deficiency.

Results: Following the integration of three feature selection methods, we identified seven candidate ICM-specific genes including ASPN, TRMT5, LUM, FCN3, CNN1, PCNT, and HOPX. ROC curves confirmed the excellent diagnostic efficacy of this combination of previous candidate genes in ICM. Most of them presented prominent interactions with immune cell infiltrates. Their deregulations were confirmed in ICM than healthy myocardial tissues. TRMT5 expressions were remarkedly upregulated in H/R-stimulated H9C2 cells. TRMT5 deficiency enhanced mitochondrial membrane potential and reduced apoptosis in H/R-exposed H9C2 cells.

Conclusion: Collectively, our findings identified reliable gene signatures through combination strategies of diverse feature selection methods, which facilitated the early detection of ICM and revealed the underlying mechanisms.

Suppression of SMOC2 alleviates myocardial fibrosis via the ILK/p38 pathway

Background

Fibrosis of the myocardium is one of the main pathological changes of adverse cardiac remodeling, which is associated with unsatisfactory outcomes in patients with heart disease. Further investigations into the precise molecular mechanisms of cardiac fibrosis are urgently required to seek alternative therapeutic strategies for individuals suffering from heart failure. SMOC2 has been shown to be essential to exert key pathophysiological roles in various physiological processes in vivo, possibly contributing to the pathogenesis of fibrosis. A study investigating the relationship between SMOC2 and myocardial fibrosis has yet to be conducted.

Methods

Mice received a continuous ISO injection subcutaneously to induce cardiac fibrosis, and down-regulation of SMOC2 was achieved by adeno-associated virus-9 (AAV9)-mediated shRNA knockdown. Neonatal fibroblasts were separated and cultured in vitro with TGFβ to trigger fibrosis and infected with either sh-SMOC2 or sh-RNA as a control. The role and mechanisms of SMOC2 in myocardial fibrosis were further examined and analyzed.

Results

SMOC2 knockdown partially reversed cardiac functional impairment and cardiac fibrosis in vivo after 21 consecutive days of ISO injection. We further demonstrated that targeting SMOC2 expression effectively slowed down the trans-differentiation and collagen deposition of cardiac fibroblasts stimulated by TGFβ. Mechanistically, targeting SMOC2 expression inhibited the induction of ILK and p38 in vivo and in vitro, and ILK overexpression increased p38 phosphorylation activity and compromised the protective effects of sh-SMOC2-mediated cardiac fibrosis.

Conclusion

Therapeutic SMOC2 silencing alleviated cardiac fibrosis through inhibition of the ILK/p38 signaling, providing a preventative and control strategy for cardiac remodeling management in clinical practice.

VCAM1 expression in the myocardium is associated with the risk of heart failure and immune cell infiltration in myocardium

Ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) are the two most common etiologies of heart failure (HF). Both forms share common characteristics including ventricle dilation in the final stage. Immune mechanisms in HF are increasingly highlighted and have been implicated in the pathogeneses of IHD and DCM. A better understanding of adhesion molecule expression and correlated immune cell infiltration could enhance disease detection and improve therapeutic targets. This study was performed to explore the common mechanisms underlying IHD and DCM. After searching the Gene Expression Omnibus database, we selected the GSE42955, GSE76701, GSE5406, GSE133054 and GSE57338 datasets for different expressed gene (DEGs) selection and new cohort establishment. We use xcell to calculate immune infiltration degree, ssGSEA and GSEA to calculate the pathway and biological enrichment score, consensus cluster to identify the m6A modification pattern, and LASSO regression to make risk predicting model and use new combined cohort to validate the results. The screening stage revealed that vascular cell adhesion molecule 1 (VCAM1) play pivotal roles in regulating DEGs. Subsequent analyses revealed that VCAM1 was differentially expressed in the myocardium and involved in regulating immune cell infiltration. We also found that dysregulated VCAM1 expression was associated with a higher risk of HF by constructing a clinical risk-predicting model. Besides, we also find a connection among the m6A RNA modification ,expression of VCAM1 and immune regulation. Those connection can be linked by the Wnt pathway enrichment alternation. Collectively, our results suggest that VCAM-1 have the potential to be used as a biomarker or therapy target for HF and the m6A modification pattern is associated with the VCAM1 expression and immune regulation.