Identification of immune-related molecular clusters and diagnostic markers in chronic kidney disease based on cluster analysis

Integrating bioinformatics and machine learning to discover sumoylation associated signatures in sepsis

Small Ubiquitin-like MOdifier-mediated modification (SUMOylation) is associated with sepsis; however, its molecular mechanism remains unclear. Herein, hub genes and regulatory mechanisms in sepsis was investigated. The GSE65682 and GSE95233 datasets were extracted from public databases. Differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA) were conducted in GSE65682 to identify differentially expressed genes (DEGs) and key module genes. Candidate genes were derived by intersecting with SUMOylation-related genes (SUMO-RGs). The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were utilized to identify significant feature genes. The convergence of those genes was utilized for diagnostic assessment and expression validation. Hub genes were defined as those exhibiting an area under the curve (AUC) greater than 0.7, significant gene expression, and a consistent trend. Localization and functional analyses of hub genes were conducted to enhance the understanding of these genes. Immune analysis, regulatory network construction, and drug prediction were performed. Six hub genes were identified: RORA, L3MBTL2, PHC1, RPA1, CHD3, and RANGAP1. These genes possessed considerable diagnostic significance for sepsis and were also markedly downregulated in the condition. Hub genes were predominantly enriched in the ribosome pathway and exhibited a strong correlation with differential immune cells. Activated CD8 + T cells exhibited a positive correlation with RORA. Based on the predicted and established regulatory network, AC004687.1 was observed to modulate PHC1 expression via hsa-miR- 142 - 5p. A total of six hub genes (RORA, L3MBTL2, PHC1, RPA1, CHD3, and RANGAP1) associated with SUMOylation was identified in sepsis in the current study. The findings are likely to aid in the differentiation between control and disease states, offering substantiation for the diagnosis of sepsis.

Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary Fibrosis

Background

Studies suggest that immune and inflammation processes may be involved in the development of idiopathic pulmonary fibrosis (IPF); however, their roles remain unclear. This study aims to identify key genes associated with immune response and inflammation in IPF using bioinformatics.

Methods

We identified differentially expressed genes (DEGs) in the GSE93606 dataset and GSE28042 dataset, then obtained differentially expressed immune- and inflammation-related genes (DE-IFRGs) by overlapping DEGs. Two machine learning algorithms were used to further screen key genes. Genes with an area under curve (AUC) of > 0.7 in receiver operating characteristic (ROC) curves, significant expression and consistent trends across datasets were considered key genes. Based on these key genes, we carried out nomogram construction, enrichment and immune analyses, regulatory network mapping, drug prediction, and expression verification.

Results

27 DE-IFRGs were identified by intersecting 256 DEGs, 1793 immune-related genes, and 1019 inflammation-related genes. Three genes (RNASE3, S100A12, S100A8) were obtained by crossing two machine algorithms (Boruta and LASSO),which had good diagnostic performance with AUC values. These key genes were all enriched in the same pathways, such as GOCC_azurophil_granule, IL-12 signalling and production in macrophages is the pathway with the strongest role for key genes. Six distinct immune cells, including naive CD4 T cells, T cells CD4 memory resting, T cells regulatory (Tregs), Monocytes, Macrophages M2, Neutrophils were identified. Real-time quantitative polymerase chain reaction (RT-qPCR) results were consistent with the training and validation sets, and the expression of these key genes was significantly upregulated in the IPF samples.

Conclusion

This study identified three key genes (RNASE3, S100A12 and S100A8) associated with immune response and inflammation in IPF, providing valuable insights into the diagnosis and treatment of IPF.

Association of periodontal disease with the prognosis of chronic kidney disease: A meta-analysis

BACKGROUND

To assess the association between periodontal disease (PD) and the prognosis of chronic kidney disease (CKD).

METHODS

A systematic literature search was conducted using PubMed, Embase, and Cochrane Library to identify eligible cohort studies until April 2023. Relative risk (RR) with a 95% CI was used to evaluate the strength of the relationship between PD and CKD prognosis using the random-effects model.

RESULTS

Ten cohort studies involving 10 144 patients with CKD were selected for the meta-analysis. The summary results indicated that PD was associated with an increased risk of all-cause mortality in patients with CKD (RR: 1.32; 95% CI, 1.10-1.59; p = 0.003). Although no association was observed between PD and the risk of cardiac death in patients with CKD ( p = 0.180), while sensitivity analysis revealed PD may be associated with the risk of cardiac death (RR: 1.31; 95% CI, 1.05-1.64; p = 0.017). In addition, subgroup analyses revealed that the strength of the association of PD with the risks of all-cause mortality and cardiac death varies when stratified by region, sex, and CKD stage.

CONCLUSION

PD might exert a harmful effect on the risk of all-cause mortality, with a potential but unconfirmed association with cardiac death in patients with CKD.

Identification and preliminary validation of biomarkers associated with mitochondrial and programmed cell death in pre-eclampsia

Background

The involvement of mitochondrial and programmed cell death (mtPCD)-related genes in the pathogenesis of pre-eclampsia (PE) remains inadequately characterized.

Methods

This study explores the role of mtPCD genes in PE through bioinformatics and experimental approaches. Differentially expressed mtPCD genes were identified as potential biomarkers from the GSE10588 and GSE98224 datasets and subsequently validated. Hub genes were determined using support vector machine, least absolute shrinkage and selection operator, and Boruta based on consistent expression profiles. Their performance was assessed through nomogram and artificial neural network models. Biomarkers were subjected to localization, functional annotation, regulatory network analysis, and drug prediction. Clinical validation was conducted via real-time quantitative polymerase chain reaction (RT-qPCR), immunofluorescence, and Western blot.

Results

Four genes [solute carrier family 25 member 5 (SLC25A5), acyl-CoA synthetase family member 2 (ACSF2), mitochondrial fission factor (MFF), and phorbol-12-myristate-13-acetate-induced protein 1 (PMAIP1)] were identified as biomarkers distinguishing PE from normal controls. Functional analysis indicated their involvement in various biological pathways. Immune analysis revealed associations between biomarkers and immune cell activity. A regulatory network was informed by biomarker expression and database predictions, in which KCNQ1OT1 modulates ACSF2 expression via hsa-miR-200b-3p. Drug predictions, including clodronic acid, were also proposed. Immunofluorescence, RT-qPCR, and Western blot confirmed reduced expression of SLC25A5, MFF, and PMAIP1 in PE, whereas ACSF2 was significantly upregulated.

Conclusion

These four mtPCD-related biomarkers may play a pivotal role in PE pathogenesis, offering new perspectives on the disease's diagnostic and mechanistic pathways.

Construction of Clinical Predictive Models for Heart Failure Detection Using Six Different Machine Learning Algorithms: Identification of Key Clinical Prognostic Features

Purpose

Heart failure (HF) is a clinical syndrome in which structural or functional abnormalities of the heart result in impaired ventricular filling or ejection capacity. In order to improve the adaptability of models to different patient populations and data situations. This study aims to develop predictive models for HF risk using six machine learning algorithms, providing valuable insights into the early assessment and recognition of HF by clinical features.

Patients and Methods

The present study focused on clinical characteristics that significantly differed between groups with left ventricular ejection fractions (LVEF) [≤40% and >40%]. Following the elimination of features with significant missing values, the remaining features were utilized to construct predictive models employing six machine learning algorithms. The optimal model was selected based on various performance metrics, including the area under the curve (AUC), accuracy, precision, recall, and F1 score. Utilizing the optimal model, the significance of clinical features was assessed, and those with importance values exceeding 0.8 were identified as crucial to the study. Finally, a correlation analysis was conducted to examine the relationships between these features and other significant clinical features.

Results

The logistic regression (LR) model was determined to be the optimal machine learning algorithm in this study, achieving an accuracy of 0.64, a precision of 0.45, a recall of 0.72, an F1 score of 0.51, and an AUC of 0.81 in the training set and 0.91 in the testing set. In addition, the analysis of feature importance indicated that blood calcium, angiotensin-converting enzyme inhibitors (ACEI) dosage, mean hemoglobin concentration, and survival duration were critical to the study, each possessing importance values exceeding 0.8. Furthermore, correlation analysis revealed a strong relationship between blood calcium and ionized calcium (|cor|=0.99), as well as a significant association between ACEI dosage (|cor|=0.68) and left ventricular metrics (|cor|=0.58); on the other hand, no correlations were observed between mean hemoglobin levels and other clinical characteristics.

Conclusion

The present study identified LR as the most effective risk prediction model for patients with HF, highlighting blood calcium, ACEI dosage, and mean hemoglobin level as significant predictors. These findings provide significant insights for the clinical prevention and early intervention of HF.

Lymphatic vessels in patients with crescentic glomerulonephritis: association with renal pathology and prognosis

BACKGROUND

Various immune cells, including T cells, B cells, macrophages, and neutrophils contribute to the development of crescentic glomerulonephritis. Previous animal studies have suggested that lymphangiogenesis is involved in the migration of inflammatory cells and the activation of adaptive immunity. However, the extent of the association between lymphatic vessels and crescentic glomerulonephritis severity and prognosis remains unknown.

METHODS AND RESULTS

In this study, we assessed lymphatic vessel density in 71 patients with crescentic glomerulonephritis who underwent renal biopsies between June 2017 and June 2022. By immunohistochemistry and immunofluorescence, we identified increased lymphatic vessel density in the kidneys of patients with crescentic glomerulonephritis compared to controls. Lymphatic vessels were categorized as total, periglomerular, and interstitial. Spearman's rank correlation analysis showed a positive correlation between total and periglomerular lymphatic vessel density and glomerular crescent proportion. High lymphatic vessel density (total and periglomerular) correlated with declining kidney function, increased proteinuria, and severe glomerular and interstitial pathology. Interstitial lymphatic vessel density had minimal relationship with renal lesions. After a median duration of 13 months of follow-up, higher total and periglomerular lymphatic vessel density was associated with poorer prognosis. Transcriptomic analysis revealed increased immune cell activation and migration in crescentic glomerulonephritis patients compared to healthy controls. Periglomerular lymphatic vessels might play a significant role in immune cell infiltration and renal injury.

CONCLUSION

Elevated lymphatic vessel density in patients with crescentic glomerulonephritis is associated with poor prognosis and may serve as a predictive factor for adverse outcomes in these patients.

Establish a novel tumor budding-related signature to predict prognosis and guide clinical therapy in colorectal cancer

Tumor budding is a long-established independent adverse prognostic marker for colorectal cancer (CRC), yet assessment of tumor budding was not reproducible. Therefore, development of precise diagnostic approaches to tumor budding is in demand. In this study, we first performed bioinformatic analysis in our single-center CRC patients' cohort (n = 84) and identified tumor budding-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify hub genes and construct a prognostic signature. Nomogram model was used to identified hub genes score for tumor budding, and the receiver operating characteristic (ROC) curve and calibration plot indicated high accuracy and stability of hub gene score for predicted the prognosis of CRC. The association between budding-associated hub genes and score and prognosis of CRC were further verified in TCGA CRC cohort (n = 342). Then gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to explore the signaling pathways related to the tumor budding and validated by immunohistochemistry (IHC) of our clinical samples. Subsequently, immune infiltration analysis demonstrated that there was a high correlation between hub genes score and M2-like macrophages infiltrated in tumor tissue. In addition, somatic mutation and chemotherapeutic response prediction were analyzed based on the risk signature. In summary, we established a tumor budding diagnostic molecular model, which can improve tumor budding assessment and provides a promising novel molecular marker for immunotherapy and prognosis of CRC.