Integrated Bioinformatics Identifies FREM1 as a Diagnostic Gene Signature for Heart Failure

Heart failure-related genes associated with oxidative stress and the immune landscape in lung cancer

Background

Lung cancer is a common comorbidity of heart failure (HF). The early identification of the risk factors for lung cancer in patients with HF is crucial to early diagnosis and prognosis. Furthermore, oxidative stress and immune responses are the two critical biological processes shared by HF and lung cancer. Therefore, our study aimed to select the core genes in HF and then investigate the potential mechanisms underlying HF and lung cancer, including oxidative stress and immune responses through the selected genes.

Methods

Differentially expressed genes (DEGs) were analyzed for HF using datasets extracted from the Gene Expression Omnibus database. Functional enrichment analysis was subsequently performed. Next, weighted gene co-expression network analysis was performed to select the core gene modules. Support vector machine models, the random forest method, and the least absolute shrinkage and selection operator (LASSO) algorithm were applied to construct a multigene signature. The diagnostic values of the signature genes were measured using receiver operating characteristic curves. Functional analysis of the signature genes and immune landscape was performed using single-sample gene set enrichment analysis. Finally, the oxidative stress-related genes in these signature genes were identified and validated in vitro in lung cancer cell lines.

Results

The DEGs in the GSE57338 dataset were screened, and this dataset was then clustered into six modules using weighted gene co-expression network analysis; MEblue was significantly associated with HF (cor = -0.72, p < 0.001). Signature genes including extracellular matrix protein 2 (ECM2), methyltransferase-like 7B (METTL7B), meiosis-specific nuclear structural 1 (MNS1), and secreted frizzled-related protein 4 (SFRP4) were selected using support vector machine models, the LASSO algorithm, and the random forest method. The respective areas under the curve of the receiver operating characteristic curves of ECM2, METTL7B, MNS1, and SFRP4 were 0.939, 0.854, 0.941, and 0.926, respectively. Single-sample gene set enrichment analysis revealed significant differences in the immune landscape of the patients with HF and healthy subjects. Functional analysis also suggested that these signature genes may be involved in oxidative stress. In particular, METTL7B was highly expressed in lung cancer cell lines. Meanwhile, the correlation between METTL7B and oxidative stress was further verified using flow cytometry.

Conclusion

We identified that ECM2, METTL7B, MNS1, and SFRP4 exhibit remarkable diagnostic performance in patients with HF. Of note, METTL7B may be involved in the co-occurrence of HF and lung cancer by affecting the oxidative stress immune responses.

Identification MNS1, FRZB, OGN, LUM, SERP1NA3 and FCN3 as the potential immune-related key genes involved in ischaemic cardiomyopathy by random forest and nomogram

The immune molecular mechanisms involved in ischaemic cardiomyopathy (ICM) have not been fully elucidated. The current study aimed to elucidate the immune cell infiltration pattern of the ICM and identify key immune-related genes that participate in the pathologic process of the ICM. The differentially expressed genes (DEGs) were identified from two datasets (GSE42955 combined with GSE57338) and the top 8 key DEGs related to ICM were screened using random forest and used to construct the nomogram model. Moreover, the "CIBERSORT" software package was used to determine the proportion of infiltrating immune cells in the ICM. A total of 39 DEGs (18 upregulated and 21 downregulated) were identified in the current study. Four upregulated DEGs, including MNS1, FRZB, OGN, and LUM, and four downregulated DEGs, SERP1NA3, RNASE2, FCN3 and SLCO4A1, were identified by the random forest model. The nomogram constructed based on the above 8 key genes suggested a diagnostic value of up to 99% to distinguish the ICM from healthy participants. Meanwhile, most of the key DEGs presented prominent interactions with immune cell infiltrates. The RT-qPCR results suggested that the expression levels of MNS1, FRZB, OGN, LUM, SERP1NA3 and FCN3 between the ICM and control groups were consistent with the bioinformatic analysis results. These results suggested that immune cell infiltration plays a critical role in the occurrence and progression of ICM. Several key immune-related genes, including the MNS1, FRZB, OGN, LUM, SERP1NA3 and FCN3 genes, are expected to be reliable serum markers for the diagnosis of ICM and potential molecular targets for ICM immunotherapy.

Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm

Objective

Energy metabolism plays a crucial role in the improvement of heart dysfunction as well as the development of heart failure (HF). The current study is designed to identify energy metabolism-related diagnostic biomarkers for predicting the risk of HF due to myocardial infarction.

Methods

Transcriptome sequencing data of HF patients and non-heart failure (NF) people (GSE66360 and GSE59867) were obtained from gene expression omnibus (GEO) database. Energy metabolism-related differentially expressed genes (DEGs) were screened between HF and NF samples. The subtyping consistency analysis was performed to enable the samples to be grouped. The immune infiltration level among subtypes was assessed by single sample gene set enrichment analysis (ssGSEA). Random forest algorithm (RF) and support vector machine (SVM) were applied to identify diagnostic biomarkers, and the receiver operating characteristic curves (ROC) was plotted to validate the accuracy. Predictive nomogram was constructed and validated based on the result of the RF. Drug screening and gene-miRNA network were analyzed to predict the energy metabolism-related drugs and potential molecular mechanism.

Results

A total of 22 energy metabolism-related DEGs were identified between HF and NF patients. The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. More importantly, 10 drugs and 15 miRNA were predicted as drug target and hub miRNA that associated with energy metabolism-related genes, providing further information on clinical HF treatment.

Conclusion

This study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.