Identification of Atrial Fibrillation-Related lncRNA Based on Bioinformatic Analysis

Identification and validation of m6A-associated ferroptosis genes in renal clear cell carcinoma

Urinary cancer is synonymous with clear cell renal cell carcinoma (ccRCC). Unfortunately, existing treatments for this illness are ineffective and unpromising. Finding novel ccRCC biomarkers is crucial to creating successful treatments. The Cancer Genome Atlas provided clear cell renal cell carcinoma transcriptome data. Functional enrichment analysis was performed on ccRCC and control samples' differentially expressed N6-methyladenosine RNA methylation and ferroptosis-related genes (DEMFRGs). Machine learning was used to find and model ccRCC patients' predicted genes. A nomogram was created for clear cell renal cell carcinoma patients. Prognostic genes were enriched. We examined patients' immune profiles by risk score. Our prognostic genes predicted ccRCC treatment drugs. We found 37 DEMFRGs by comparing 1913 differentially expressed ccRCC genes to 202 m6A RNA methylation FRGs. Functional enrichment analysis showed that hypoxia-induced cell death and metabolism pathways were the most differentially expressed methylation functional regulating genes. Five prognostic genes were found by machine learning: TRIB3, CHAC1, NNMT, EGFR, and SLC1A4. An advanced renal cell carcinoma nomogram with age and risk score accurately predicted the outcome. These five prognostic genes were linked to various cancers. Immunological cell number and checkpoint expression differed between high- and low-risk groups. The risk model successfully predicted immunotherapy outcome, showing high-risk individuals had poor results. NIACIN, TAE-684, ROCILETINIB, and others treat ccRCC. We found ccRCC prognostic genes that work. This discovery may lead to new ccRCC treatments.

Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis

Background: Globally, the most common form of arrhythmias is atrial fibrillation (AF), which causes severe morbidity, mortality, and socioeconomic burden. The application of machine learning algorithms in combination with weighted gene co-expression network analysis (WGCNA) can be used to screen genes, therefore, we aimed to screen for potential biomarkers associated with AF development using this integrated bioinformatics approach. Methods: On the basis of the AF endocardium gene expression profiles GSE79768 and GSE115574 from the Gene Expression Omnibus database, differentially expressed genes (DEGs) between AF and sinus rhythm samples were identified. DEGs enrichment analysis and transcription factor screening were then performed. Hub genes for AF were screened using WGCNA and machine learning algorithms, and the diagnostic accuracy was assessed by the receiver operating characteristic (ROC) curves. GSE41177 was used as the validation set for verification. Subsequently, we identified the specific signaling pathways in which the key biomarkers were involved, using gene set enrichment analysis and reverse prediction of mRNA-miRNA interaction pairs. Finally, we explored the associations between the hub genes and immune microenvironment and immune regulation. Results: Fifty-seven DEGs were identified, and the two hub genes, hypoxia inducible factor 1 subunit alpha inhibitor (HIF1AN) and mitochondrial inner membrane protein MPV17 (MPV17), were screened using WGCNA combined with machine learning algorithms. The areas under the receiver operating characteristic curves for MPV17 and HIF1AN validated that two genes predicted AF development, and the differential expression of the hub genes was verified in the external validation dataset. Enrichment analysis showed that MPV17 and HIF1AN affect mitochondrial dysfunction, oxidative stress, gap junctions, and other signaling pathway functions. Immune cell infiltration and immunomodulatory correlation analyses showed that MPV17 and HIF1AN are strongly correlated with the content of immune cells and significantly correlated with HLA expression. Conclusion: The identification of hub genes associated with AF using WGCNA combined with machine learning algorithms and their correlation with immune cells and immune gene expression can elucidate the molecular mechanisms underlying AF occurrence. This may further identify more accurate and effective biomarkers and therapeutic targets for the diagnosis and treatment of AF.

Identification of Immune-Related Genes as Biomarkers for Uremia

Purpose

Uremia, which is characterized by immunodeficiency, is associated with the deterioration of kidney function. Immune-related genes (IRGs) are crucial for uremia progression.

Methods

The co-expression network was constructed to identify key modular genes associated with uremia. IRGs were intersected with differentially expressed genes (DEGs) between uremia and control groups and key modular genes to obtain differentially expressed IRGs (DEIRGs). DEIRGs were subjected to functional enrichment analysis. The protein-protein interaction (PPI) network was constructed. The candidate genes were identified using the cytoHubba tool. The biomarkers were identified using various machine learning algorithms. The diagnostic value of the biomarkers was evaluated using receiver operating characteristic (ROC) analysis. The immune infiltration analysis was implemented. The biological pathways of biomarkers were identified using gene set enrichment analysis and ingenuity pathway analysis. The mRNA expression of biomarkers was validated using blood samples of patients with uremia and healthy subjects with quantitative real-time polymerase chain reaction (qRT-PCR).

Results

In total, four biomarkers (PDCD1, NGF, PDGFRB, and ZAP70) were identified by machine learning methods. ROC analysis demonstrated that the area under the curve values of individual biomarkers were > 0.9, indicating good diagnostic power. The nomogram model of biomarkers exhibited good predictive power. The proportions of six immune cells significantly varied between the uremia and control groups. ZAP70 expression was positively correlated with the proportions of resting natural killer (NK) cells, naïve B cells, and regulatory T cells. Functional enrichment analysis revealed that the biomarkers were mainly associated with translational function and neuroactive ligand-receptor interaction. ZAP70 regulated NK cell signaling. The PDCD1 and NGF expression levels determined using qRT-PCR were consistent with those determined using bioinformatics analysis.

Conclusion

PDCD1, NGF, PDGFRB, and ZAP70 were identified as biomarkers for uremia, providing a theoretical foundation for uremia diagnosis.

Selection of M7G-related lncRNAs in kidney renal clear cell carcinoma and their putative diagnostic and prognostic role

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients.

METHODS

Transcriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples.

RESULTS

By intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints.

CONCLUSION

In summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC.

Identification of functional lncRNAs in atrial fibrillation based on RNA sequencing

BACKGROUND

Atrial fibrillation (AF) is one of the most common arrhythmia contributing to serious conditions such as stroke and heart failure. Recent studies demonstrated that long noncoding RNAs (lncRNAs) were related to cardiovascular disease. However, the molecular mechanisms of AF are not fully clear. This study intended to discover lncRNAs that are differentially expressed in AF compared with controls and evaluate the potential functions of these lncRNAs.

METHODS

Ninety-seven patients (49 patients with AF and 48 patients without AF) were included in this study. Among these patients, leucocyte suspensions of 3 AF patients and 3 controls were sent for RNA-seq analysis to select differentially expressed lncRNA and mRNA. Different lncRNA expressions were validated in another samples (46 AF patients and 45 controls). Gene ontology (GO) enrichment analysis was conducted to annotate the function of selected mRNAs. Alternative splicing (AS) analysis was performed and a lncRNA-mRNA network was also constructed. The receiver operating characteristics (ROC) curve was used to evaluate diagnostic values. Logistic regression analysis was utilized to assess the risk or protective factor of AF.

RESULTS

A total of 223 mRNAs and 105 lncRNAs were detected in AF patients compared with controls. Total 4 lncRNAs (LINC01781, AC009509.2, AL662844.3, AL662844.4) associated with AF were picked out for validation in another samples by quantitative real-time PCR (qRT-PCR), detecting that upregulated AC009509.2 and downregulated LINC01781 in AF patients. Multivariate logistic regression analysis illustrated that left atrial diameter (OR 1.201; 95% CI 1.093-1.320; P=0.000) and AC009509.2 (OR 1.732; 95% CI 1.092-2.747; P=0.020) were related to AF respectively. ROC curve showed that AC009509.2, LINC01781 and left atrial diameter (LAD) were predictors of AF. For LINC01781, the area under the curve (AUC) was 0.654 (95% CI 0.541-0.767, P=0.0113). For AC009509.2, the AUC was 0.710 (95% CI 0.599-0.822, P=0.0005). Bioinformatic methods (GO enrichment, AS analysis and lncRNA-mRNA network construction) were performed to reveal the role of lncRNAs.

CONCLUSIONS

This study discussed differentially expressed lncRNA and their potential interaction with mRNA in AF. LncRNA AC009509.2 could be a new potential biomarker for AF prediction.

Digging out the biology properties of tRNA-derived small RNA from black hole

An unique subclass of functional non-coding RNAs generated by transfer RNA (tRNA) under stress circumstances is known as tRNA-derived small RNA (tsRNA). tsRNAs can be divided into tRNA halves and tRNA-derived fragments (tRFs) based on the different cleavage sites. Like microRNAs, tsRNAs can attach to Argonaute (AGO) proteins to target downstream mRNA in a base pairing manner, which plays a role in rRNA processing, gene silencing, protein expression and viral infection. Notably, tsRNAs can also directly bind to protein and exhibit functions in transcription, protein modification, gene expression, protein stabilization, and signaling pathways. tsRNAs can control the expression of tumor suppressor genes and participate in the initiation of cancer. It can also mediate the progression of diseases by regulating cell viability, migration ability, inflammatory factor content and autophagy ability. Precision medicine targeting tsRNAs and drug therapy of plant-derived tsRNAs are expected to be used in clinical practice. In addition, liquid biopsy technology based on tsRNAs indicates a new direction for the non-invasive diagnosis of diseases.

Long Non-coding RNA Involved in the Pathophysiology of Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is a prevalent and chronic cardiovascular disorder associated with various pathophysiological alterations, including atrial electrical and structural remodeling, disrupted calcium handling, autonomic nervous system dysfunction, aberrant energy metabolism, and immune dysregulation. Emerging evidence suggests that long non-coding RNAs (lncRNAs) play a significant role in the pathogenesis of AF.

OBJECTIVE

This discussion aims to elucidate the involvement of AF-related lncRNAs, with a specific focus on their role as miRNA sponges that modulate crucial signaling pathways, contributing to the progression of AF. We also address current limitations in AF-related lncRNA research and explore potential future directions in this field. Additionally, we summarize feasible strategies and promising delivery systems for targeting lncRNAs in AF therapy.

CONCLUSION

In conclusion, targeting AF-related lncRNAs holds substantial promise for future investigations and represents a potential therapeutic avenue for managing AF.

Retracted: Identification of Atrial Fibrillation-Related lncRNA Based on Bioinformatic Analysis

[This retracts the article DOI: 10.1155/2022/8307975.].

A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis

Introduction

Osteoarthritis (OA) is a chronic disease with high morbidity and disability rates whose molecular mechanism remains unclear. This study sought to identify OA markers associated with synovitis and cartilage apoptosis by bioinformatics analysis.

Methods

A total of five gene-expression profiles were selected from the Gene Expression Omnibus database. We combined the GEO with the GeneCards database and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genome analyses; then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to identify the characteristic genes, and a predictive risk score was established. We used the uniform manifold approximation and projection (UMAP) method to identify subtypes of OA patients, while the CytoHubba algorithm and GOSemSim R package were used to screen out hub genes. Next, an immunological assessment was performed using single-sample gene set enrichment analysis and CIBERSORTx.

Results

A total of 56OA-related differential genes were selected, and 10 characteristic genes were identified by the LASSO algorithm. OA samples were classified into cluster 1 and cluster 2 subtypes byUMAP, and the clustering results showed that the characteristic genes were significantly different between these groups. MYOC, CYP4B1, P2RY14, ADIPOQ, PLIN1, MFAP5, and LYVE1 were highly expressed in cluster 2, and ANKHLRC15, CEMIP, GPR88, CSN1S1, TAC1, and SPP1 were highly expressed in cluster 1. Protein-protein interaction network analysis showed that MMP9, COL1A, and IGF1 were high nodes, and the differential genes affected the IL-17 pathway and tumor necrosis factor pathway. The GOSemSim R package showed that ADIPOQ, COL1A, and SPP1 are closely related to the function of 31 hub genes. In addition, it was determined that mmp9 and Fos interact with multiple transcription factors, and the ssGSEA and CIBERSORTx algorithms revealed significant differences in immune infiltration between the two OA subtypes. Finally, a qPCR experiment was performed to explore the important genes in rat cartilage and synovium tissues; the qPCR results showed that COL1A and IL-17A were both highly expressed in synovitis tissues and cartilage tissues of OA rats, which is consistent with the predicted results.

Discussion

In the future, common therapeutic targets might be found forsimultaneous remissions of both phenotypes of OA.

Analyses of m6A regulatory genes and subtype classification in atrial fibrillation

Objective

To explore the role of m6A regulatory genes in atrial fibrillation (AF), we classified atrial fibrillation patients into subtypes by two genotyping methods associated with m6A regulatory genes and explored their clinical significance.

Methods

We downloaded datasets from the Gene Expression Omnibus (GEO) database. The m6A regulatory gene expression levels were extracted. We constructed and compared random forest (RF) and support vector machine (SVM) models. Feature genes were selected to develop a nomogram model with the superior model. We identified m6A subtypes based on significantly differentially expressed m6A regulatory genes and identified m6A gene subtypes based on m6A-related differentially expressed genes (DEGs). Comprehensive evaluation of the two m6A modification patterns was performed.

Results

The data of 107 samples from three datasets, GSE115574, GSE14975 and GSE41177, were acquired from the GEO database for training models, comprising 65 AF samples and 42 sinus rhythm (SR) samples. The data of 26 samples from dataset GSE79768 comprising 14 AF samples and 12 SR samples were acquired from the GEO database for external validation. The expression levels of 23 regulatory genes of m6A were extracted. There were correlations among the m6A readers, erasers, and writers. Five feature m6A regulatory genes, ZC3H13, YTHDF1, HNRNPA2B1, IGFBP2, and IGFBP3, were determined (p < 0.05) to establish a nomogram model that can predict the incidence of atrial fibrillation with the RF model. We identified two m6A subtypes based on the five significant m6A regulatory genes (p < 0.05). Cluster B had a lower immune infiltration of immature dendritic cells than cluster A (p < 0.05). On the basis of six m6A-related DEGs between m6A subtypes (p < 0.05), two m6A gene subtypes were identified. Both cluster A and gene cluster A scored higher than the other clusters in terms of m6A score computed by principal component analysis (PCA) algorithms (p < 0.05). The m6A subtypes and m6A gene subtypes were highly consistent.

Conclusion

The m6A regulatory genes play non-negligible roles in atrial fibrillation. A nomogram model developed by five feature m6A regulatory genes could be used to predict the incidence of atrial fibrillation. Two m6A modification patterns were identified and evaluated comprehensively, which may provide insights into the classification of atrial fibrillation patients and guide treatment.

Bioinformatic Identification of Potential RNA Alterations on the Atrial Fibrillation Remodeling from Human Pulmonary Veins

Atrial fibrillation (AF) is the most frequent persistent arrhythmia. Many genes have been reported as a genetic background for AF. However, most transcriptome analyses of AF are limited to the atrial samples and have not been evaluated by multiple cardiac regions. In this study, we analyzed the expression levels of protein-coding and long noncoding RNAs (lncRNAs) in six cardiac regions by RNA-seq. Samples were donated from six subjects with or without persistent AF for left atria, left atrial appendages, right atria, sinoatrial nodes, left ventricles, right ventricles, and pulmonary veins (PVs), and additional four right atrial appendages samples were collected from patients undergoing mitral valve replacement. In total, 23 AF samples were compared to 23 non-AF samples. Surprisingly, the most influenced heart region in gene expression by AF was the PV, not the atria. The ion channel-related gene set was significantly enriched upon analysis of these significant genes. In addition, some significant genes are cancer-related lncRNAs in PV in AF. A co-expression network analysis could detect the functional gene clusters. In particular, the cancer-related lncRNA, such as SAMMSON and FOXCUT, belong to the gene network with the cancer-related transcription factor FOXC1. Thus, they may also play an aggravating role in the pathogenesis of AF, similar to carcinogenesis. In the least, this study suggests that (1) RNA alteration is most intense in PVs and (2) post-transcriptional gene regulation by lncRNA may contribute to the progression of AF. Through the screening analysis across the six cardiac regions, the possibility that the PV region can play a role other than paroxysmal triggering in the pathogenesis of AF was demonstrated for the first time. Future research with an increase in the number of PV samples will lead to a novel understanding of the pathophysiology of AF.

Predictive model, miRNA-TF network, related subgroup identification and drug prediction of ischemic stroke complicated with mental disorders based on genes related to gut microbiome

Background

Patients with comorbid schizophrenia, depression, drug use, and multiple psychiatric diagnoses have a greater risk of carotid revascularization following stroke. The gut microbiome (GM) plays a crucial role in the attack of mental illness and IS, which may become an index for the diagnosis of IS. A genomic study of the genetic commonalities between SC and IS, as well as its mediated pathways and immune infiltration, will be conducted to determine how schizophrenia contributes to the high prevalence of IS. According to our study, this could be an indicator of ischemic stroke development.

Methods

We selected two datasets of IS from the Gene Expression Omnibus (GEO), one for training and the other for the verification group. Five genes related to mental disorders and GM were extracted from Gene cards and other databases. Linear models for microarray data (Limma) analysis was utilized to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. It was also used to conduct machine learning exercises such as random forest and regression to identify the best candidate for immune-related central genes. Protein-protein interaction (PPI) network and artificial neural network (ANN) were established for verification. The receiver operating characteristic (ROC) curve was drawn for the diagnosis of IS, and the diagnostic model was verified by qRT-PCR. Further immune cell infiltration analysis was performed to study the IS immune cell imbalance. We also performed consensus clustering (CC) to analyze the expression of candidate models under different subtypes. Finally, miRNA, transcription factors (TFs), and drugs related to candidate genes were collected through the Network analyst online platform.

Results

Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93-0.71) and the verification group (AUC 0.81, CI 0.90-0.72) had a good phenotype in the qRT-PCR test. And in verification group 2 we validated between the two groups with and without carotid-related ischemic cerebrovascular events (AUC 0.87, CI 1-0.64). Furthermore, we investigated cytokines in both GSEA and immune infiltration and verified cytokine-related responses by flow cytometry, particularly IL-6, which played an important role in IS occurrence and progression. Therefore, we speculate that mental illness may affect the development of IS in B cells and IL-6 in T cells. MiRNA (hsa-mir-129-2-3p, has-mir-335-5p, and has-mir-16-5p) and TFs (CREB1, FOXL1), which may be related to IS, were obtained.

Conclusion

Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93-0.71) and the verification group (AUC 0.81, CI 0.90-0.72) had a good phenotype in the qRT-PCR test. And in verification group 2 we validated between the two groups with and without carotid-related ischemic cerebrovascular events (AUC 0.87, CI 1-0.64). MiRNA (hsa-mir-129-2-3p, has-mir-335-5p, and has-mir-16-5p) and TFs (CREB1, FOXL1), which may be related to IS, were obtained.

Noncoding RNAs as Key Regulators for Cardiac Development and Cardiovascular Diseases

Noncoding RNAs (ncRNAs) play fundamental roles in cardiac development and cardiovascular diseases (CVDs), which are a major cause of morbidity and mortality. With advances in RNA sequencing technology, the focus of recent research has transitioned from studies of specific candidates to whole transcriptome analyses. Thanks to these types of studies, new ncRNAs have been identified for their implication in cardiac development and CVDs. In this review, we briefly describe the classification of ncRNAs into microRNAs, long ncRNAs, and circular RNAs. We then discuss their critical roles in cardiac development and CVDs by citing the most up-to-date research articles. More specifically, we summarize the roles of ncRNAs in the formation of the heart tube and cardiac morphogenesis, cardiac mesoderm specification, and embryonic cardiomyocytes and cardiac progenitor cells. We also highlight ncRNAs that have recently emerged as key regulators in CVDs by focusing on six of them. We believe that this review concisely addresses perhaps not all but certainly the major aspects of current progress in ncRNA research in cardiac development and CVDs. Thus, this review would be beneficial for readers to obtain a recent picture of key ncRNAs and their mechanisms of action in cardiac development and CVDs.

Non-Coding RNAs in Tuberculosis Epidemiology: Platforms and Approaches for Investigating the Genome's Dark Matter

A growing amount of information about the different types, functions, and roles played by non-coding RNAs (ncRNAs) is becoming available, as more and more research is done. ncRNAs have been identified as potential therapeutic targets in the treatment of tuberculosis (TB), because they may be essential regulators of the gene network. ncRNA profiling and sequencing has recently revealed significant dysregulation in tuberculosis, primarily due to aberrant processes of ncRNA synthesis, including amplification, deletion, improper epigenetic regulation, or abnormal transcription. Despite the fact that ncRNAs may have a role in TB characteristics, the detailed mechanisms behind these occurrences are still unknown. The dark matter of the genome can only be explored through the development of cutting-edge bioinformatics and molecular technologies. In this review, ncRNAs' synthesis and functions are discussed in detail, with an emphasis on the potential role of ncRNAs in tuberculosis. We also focus on current platforms, experimental strategies, and computational analyses to explore ncRNAs in TB. Finally, a viewpoint is presented on the key challenges and novel techniques for the future and for a wide-ranging therapeutic application of ncRNAs.