A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics

Bioinformatic analysis of related immune cell infiltration and key genes in the progression of osteonecrosis of the femoral head

Objective

Osteonecrosis of the femoral head (ONFH) is a common orthopedic condition that will prompt joint dysfunction, significantly impacting patients' quality of life. However, the specific pathogenic mechanisms underlying this disease remain elusive. The objective of this study is to examine the differentially expressed messenger RNAs (DE mRNAs) and key genes linked to ONFH, concurrently investigating the immune cell infiltration features in ONFH patients through the application of the CIBERSORT algorithm.

Methods

Microarray was applied to scrutinize mRNA expression profiles in both ONFH patients and healthy controls, with data integration sourced from the GEO database. DE mRNAs were screened using the Limma method. The biological functions of DE mRNAs were explored through the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) functional analysis, and Gene Set Enrichment Analysis (GSEA). Additionally, support vector machine-recursive feature elimination (SVM-RFE) and the least absolute shrinkage and selection operator (LASSO) were employed to discern diagnostic biomarkers associated with the disease. Receiver operating characteristic (ROC) analysis was utilized to assess the statistical performance of the feature genes. The validation of key genes was performed using qRT-PCR in bone tissues obtained from ONFH patients and healthy controls. Osteogenic differentiation of BMSC was then performed and detected by alkaline phosphatase staining (ALP) and qRT-PCR to verify the correlation between key genes and osteogenic differentiation. Finally, immune cell infiltration analysis was executed to evaluate immune cell dysregulation in ONFH, concurrently exploring the correlation between the infiltration of immune cells and key genes.

Results

After consolidating the datasets, the Limma method revealed 107 DEGs, comprising 76 downregulated and 31 upregulated genes. Enrichment analysis revealed close associations of these DE mRNAs with functions such as cell migration, osteoblast differentiation, cartilage development and extracellular region. Machine learning algorithms further identified APOD, FBXO43 and LRP12 as key genes. ROC curves demonstrated the high diagnostic efficacy of these genes. The results of qRT-PCR showed that the expression levels of key genes were consistent with those of microarray analysis. In addition, the results of in vitro experiments showed that APOD was closely related to osteogenic differentiation of BMSC. Immune infiltration analysis suggested a close correlation between ONFH and imbalances in levels of Neutrophils, Monocytes, Macrophages M2, Dendritic cells activated and Dendritic cells resting.

Conclusion

APOD is closely related to osteogenic differentiation of BMSCs and can be used as a diagnostic marker of ONFH. Immune cell infiltration significantly differs between controls and ONFH patients.

A comprehensive and visualized analysis of relationship between ferroptosis and tumor using bibliometrics and bioinformatics

This study aimed to summarize the current developments and hub genes in the ferroptosis field using bibliometrics and bioinformatics and provide guidance for future developments. The publications on ferroptosis from 2012 to 2021 were extracted from the Web of Science database. VOSviewer software and CiteSpace software were used to visualize and predict the trend of ferroptosis research. The key genes related to ferroptosis were selected from the Web of Genecards, and Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Ontology (GO) analysis was performed. Cytoscape software and online survival curve analysis platform were also used to screen hub genes and analyze their roles. Chinese researchers published the highest number of publications in this field, while American publications exhibited higher quality. In terms of institutions, Central South University and Zhejiang University have the highest number of publications. Cell Death Disease published more studies than other journals. The application of ferroptosis is a major research area, and, importantly, "RCD", "FTH1", and "nomogram" are the keywords. We also found tumor-related pathways of interest in the field of ferroptosis. Sirtuin 3 (SIRT3), Glutathione Peroxidase 4 (GPX4), and transferrin receptor (TFRC) genes were of significance for the prognosis of tumors. The number of publications on ferroptosis may increase in the future. Cooperation among countries and disciplines is particularly important in this regard. Also, the applications of ferroptosis, especially in chemotherapy and immunotherapy for tumors, will be the focus of future research. Keywords "RCD", "FTH1", and "nomogram" is receiving high attention, and in-depth studies on tumor-related genes SIRT3, GPX4, and TFRC may provide new therapeutic targets.

LncRNA NEAT1: A novel regulator associated with the inflammatory response in acute respiratory distress syndrome

BACKGROUND

Acute respiratory distress syndrome (ARDS) is a life-threatening condition with an unfavorable prognosis. As the pathogenesis of ARDS remains unclear, we aimed to identify the core genes associated with ARDS and the mechanisms by which competing endogenous RNAs (ceRNAs) regulate the disease's progression.

METHODS

Three mRNA microarray datasets (GSE17355, GSE48787, and GSE130936), derived from the Gene Expression Omnibus (GEO) database, were selected. Common differentially expressed genes (DEGs) related to acute lung injury (ALI) were identified and subjected to enrichment analysis. Then, hub genes were figured out through the protein-protein interaction (PPI) network and functional analysis, and targeted miRNAs and lncRNAs were predicted. Finally, the ceRNA networks associated with ALI were constructed and validated experimentally.

RESULTS

A total of 155 upregulated and 93 downregulated DEGs were identified in the three datasets. The TNF signaling pathway and IL-17 signaling pathway were the most enriched pathways. Then, eleven DEGs enriched in the IL-17 signaling pathway were selected as the hub genes. Three miRNAs (mmu-mir-155-5p, mmu-mir-21a-5p, and mmu-mir-122-5p), which were located in the lung tissue and predicted to bind the hub genes at the same time, and two lncRNAs (Neat1 and Tug1), which have binding sites for the aforementioned miRNAs, were filtered. With qPCR verification, we identified a ceRNA network composed of NEAT1, miR-21-5p, MMP9, and CXCL5. NEAT1 knockdown promoted the migration and reduced the expression of pro-inflammatory factor and reactive oxygen species (ROS) in lung epithelial cells. We eventually confirmed that NEAT1/miR-21-5p/CXCL5/MMP9 played a pivotal role in regulating the inflammatory response in ALI.

CONCLUSION

The IL-17 signaling pathway is of great importance in the pathogenesis of ARDS. NEAT1/miR-21-5p is involved in the inflammation of ALI by regulating CXCL5 and MMP9.

Constructing a prognostic risk model for Alzheimer's disease based on ferroptosis

Introduction

The aim of this study is to establish a prognostic risk model based on ferroptosis to prognosticate the severity of Alzheimer's disease (AD) through gene expression changes.

Methods

The GSE138260 dataset was initially downloaded from the Gene expression Omnibus database. The ssGSEA algorithm was used to evaluate the immune infiltration of 28 kinds of immune cells in 36 samples. The up-regulated immune cells were divided into Cluster 1 group and Cluster 2 group, and the differences were analyzed. The LASSO regression analysis was used to establish the optimal scoring model. Cell Counting Kit-8 and Real Time Quantitative PCR were used to verify the effect of different concentrations of Aβ_1-42 on the expression profile of representative genes in vitro.

Results

Based on the differential expression analysis, there were 14 up-regulated genes and 18 down-regulated genes between the control group and Cluster 1 group. Cluster 1 and Cluster 2 groups were differentially analyzed, and 50 up-regulated genes and 101 down-regulated genes were obtained. Finally, nine common differential genes were selected to establish the optimal scoring model. In vitro, CCK-8 experiments showed that the survival rate of cells decreased significantly with the increase of Aβ_1-42 concentration compared with the control group. Moreover, RT-qPCR showed that with the increase of Aβ_1-42 concentration, the expression of POR decreased first and then increased; RUFY3 was firstly increased and then decreased.

Discussion

The establishment of this research model can help clinicians make decisions on the severity of AD, thus providing better guidance for the clinical treatment of Alzheimer's disease.

The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis

Background

Systemic lupus erythematosus (SLE) is one of the most prevalent systemic autoimmune diseases, and metabolic syndrome (MetS) is the most common metabolic disorder that contains hypertension, dyslipidemia, and obesity. Despite clinical evidence suggested potential associations between SLE and MetS, the underlying pathogenesis is yet unclear.

Methods

The microarray data sets of SLE and MetS were obtained from the Gene Expression Omnibus (GEO) database. To identify the shared genes between SLE and MetS, the Differentially Expressed Genes (DEGs) analysis and the weighted gene co-expression network analysis (WGCNA) were conducted. Then, the GO and KEGG analyses were performed, and the protein-protein interaction (PPI) network was constructed. Next, Random Forest and LASSO algorithms were used to screen shared hub genes, and a diagnostic model was built using the machine learning technique XG-Boost. Subsequently, CIBERSORT and GSVA were used to estimate the correlation between shared hub genes and immune infiltration as well as metabolic pathways. Finally, the significant hub genes were verified using single-cell RNA sequencing (scRNA-seq) data.

Results

Using limma and WGCNA, we identified 153 shared feature genes, which were enriched in immune- and metabolic-related pathways. Further, 20 shared hub genes were screened and successfully used to build a prognostic model. Those shared hub genes were associated with immunological and metabolic processes in peripheral blood. The scRNA-seq results verified that TNFSF13B and OAS1, possessing the highest diagnostic efficacy, were mainly expressed by monocytes. Additionally, they showed positive correlations with the pathways for the metabolism of xenobiotics and cholesterol, both of which were proven to be active in this comorbidity, and shown to be concentrated in monocytes.

Conclusion

This study identified shared hub genes and constructed an effective diagnostic model in SLE and MetS. TNFSF13B and OAS1 had a positive correlation with cholesterol and xenobiotic metabolism. Both of these two biomarkers and metabolic pathways were potentially linked to monocytes, which provides novel insights into the pathogenesis and combined therapy of SLE comorbidity with MetS.

Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro

Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.

“Big Data” Approaches for Prevention of the Metabolic Syndrome

Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these “big data”, the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of “big data” applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.