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BACKGROUND: Disturbances in bone metabolism have a significant association with ferroptosis in steroid-induced osteonecrosis of the femoral head (SONFH). Furthermore, the pathologic process of SONFH is characterized by the presence of cartilage damage and degeneration. However, the specific regulatory targets and the relationship between ferroptosis and cartilage concerning SONFH remain unclear. OBJECTIVE: To employ bioinformatics and machine learning techniques to identify specific genes associated with ferroptosis that target cartilage and to investigate the correlation between ferroptosis and cartilage, thereby providing novel ideas and methodologies for the study and treatment of SONFH. METHODS: Disease datasets pertinent to the study and ferroptosis-related genes were retrieved from the GEO and FerrDb databases. Subsequently, the disease datasets were normalized and differential analysis using the R language to identify ferroptosis-related differential genes (Fe-DEGs). We conducted Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of Fe-DEGs. Furthermore, ferroptosis-related signature genes were filtered based on the protein-protein interaction network of Fe-DEGs and machine learning methods. Finally, the rabbits were divided into normal and model groups. The normal group was given the same dose of saline to simulate the modeling drug, and the animal model of SONFH in rabbits was constructed by injection of modified horse serum combined with methylprednisolone. After successful modeling, the expression of signature gene was verified between different groups, and the phenotype of ferroptosis in cartilage was analyzed. RESULTS AND CONCLUSION: Through the normalization and differential analysis of the dataset, a total of 1 315 differentially expressed genes were identified. Additionally, 379 ferroptosis-related genes were obtained from the FerrDb database. After intersecting both gene sets, 19 Fe-DEGs were obtained. The GO analysis revealed that Fe-DEGs were mainly involved in biological processes such as cell migration and cellular response to oxidative stress, cellular components such as kinase complexes, amino acid complexes, and cytoplasmic membranes, as well as molecular functions such as kinase activity, receptor activity, and protein binding. The KEGG analysis revealed that Fe-DEGs were mainly enriched in the FoxO signaling pathway, vascular endothelial growth factor signaling pathway, and FcγR-mediated phagocytosis. Constructing a protein-protein interaction network and using machine learning, we identified the ferroptosis-related signature gene, CA9. The gene set enrichment analysis of the signature gene CA9 revealed an upregulated expression in biological processes such as fatty acid metabolism and O-GlcNAc glycosylation modification, while being inhibited in terms of neural activity and ligand-receptor interactions. RT-PCR and western blot results showed that compared with the normal group, the expressions of ACSL4 and CA9 at mRNA and protein levels were significantly higher in the model group (P < 0.05), while the expressions of SLC7A11 and GPX4 at mRNA and protein levels were significantly lower in the model group (P < 0.05), coinciding with the expression levels of the signature genes in the dataset. These findings indicate that the cartilage of SONFH is closely related to ferroptosis, and targeting the signature gene may provide certain ideas and directions for the study and treatment of SONFH. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the potential role of ferroptosis/hypoxia-related genes in cervical cancer to improve early management and treatment of cervical cancer. Methods: AH data were downloaded from public databases. Ferroptosis/hypoxia-related genes associated with cervical cancer prognosis were selected to construct a risk score model. The relationship between risk score and clinical features, immune microenvironment and prognosis were analysed. Results: Risk score model was constructed based on eight signature genes. Drug prediction analysis showed that bevacizumab and cisplatin were related to vascular endothelial growth factor A. Risk score, as an independent prognostic factor of cervical cancer, had a good survival prediction effect. The two groups differed significantly in degree of immune cell infiltration, gene expression, tumour mutation burden and somatic variation. Conclusions: We developed a novel prognostic gene signature combining ferroptosis/hypoxia-related genes, which provides new ideas for individual treatment of cervical cancer. [ABSTRACT FROM AUTHOR]

Abstract The development of intracerebral hemorrhage (ICH) is a dynamic process and intervention during the acute phase of ICH is critical for subsequent recovery. Therefore, it is crucial to screen potential signature genes and therapeutic target genes in the acute phase of ICH. In this study, based on the results of mRNA sequencing in mouse ICH and mRNA sequencing of human ICH from online databases, top five potential signature genes after ICH, Tyrobp, Itgb2, Tlr2, Ptprc and Itgam, were screened. Quantitative PCR results showed higher mRNA expression of Tyrobp, Itgb2, Tlr2, Ptprc, and Itgam in the 1-, 3- and 5-day mouse ICH groups compared to the sham-operated group. Immune infiltration correlation analysis shows that the top-ranked signature gene, Tyrobp, is negatively correlated with M2 macrophages and plasma cells, and Western blot analysis shows higher expression of the Tyrobp protein in the 1-, 3-, and 5-day mouse ICH groups compared to the sham-operated group. Furthermore, immunohistochemistry revealed that TYROBP protein expression was significantly higher in human ICH tissues than in normal brain tissues. Our results suggest that Tyrobp is a signature gene in the acute phase of ICH and may be a potential target for the treatment of the acute phase of ICH.

BACKGROUND: Hypoxia is strongly associated with the development and progression of osteoarthritic chondrocyte injury, but the specific targets and regulatory mechanisms are unclear. OBJECTIVE: A machine learning approach was used to identify KDEL(Lys-Asp-Glu-Leu) receptor 3 (KDELR3) as a characteristic gene for osteoarthritis hypoxia and immune infiltration analysis, to provide new ideas and methods for the treatment of osteoarthritis. METHODS: The osteoarthritis-related datasets were downloaded from the GEO database and the GSEA website to obtain hypoxia-related genes. The osteoarthritis datasets were batch-corrected and immune infiltration analyzed using R language, and osteoarthritis hypoxia genes were extracted for differential analysis. Differentially expressed genes were analyzed for GO function and KEGG signaling pathway. Weighted correlation network analysis (WGCNA) and machine learning were also used to screen osteoarthritis hypoxia signature genes, and in vitro cellular experiments were performed to validate expression and correlate immune infiltration analysis using the datasets and qPCR. RESULTS AND CONCLUSION: (1) 8 492 osteoarthritis genes were obtained by batch correction and principal component analysis, mainly strongly associated with immune cells such as Macrophages M2 and Mast cells resting; 200 hypoxia genes were also obtained, resulting in 41 osteoarthritis hypoxia differentially expressed genes. (2) GO analysis involved mainly biological processes such as response to nutrient levels and glucocorticoids; cellular components such as lysosomal lumen and Golgi lumen; and molecular functions such as 14-3-3 protein binding and DNA-binding transcriptional activator activity. (3) KEGG analysis of osteoarthritis hypoxia differentially expressed genes was associated with signaling pathways such as PI3K-Akt, FoxO, and microRNAs in cancer. (4) The characteristic gene KDELR3 was obtained after using WGCNA analysis and machine learning screening. (5) The gene expression of KDELR3 was found to be higher in the test group than in the control group in the synovium (P=0.014) but lower in the meniscus (P=0.024) after validation by gene microarray. (6) In vitro chondrocyte assay showed that the expression of KDELR3 was higher in cartilage than in the control group (P=0.005), while KDELR3 was closely associated with Macrophages M0 (P=0.014) and T cells follicular helper (P=0.014). Using a machine learning approach, we confirmed that KDELR3 can be used as a hypoxic signature gene for osteoarthritis and may intervene in osteoarthritis pathogenesis by improving hypoxia, expecting to provide a new direction for better treatment of osteoarthritis. [ABSTRACT FROM AUTHOR]

After ischemic stroke (IS), secondary injury is intimately linked to endoplasmic reticulum (ER) stress and body-brain crosstalk. Nonetheless, the underlying mechanism systemic immune disorder mediated ER stress in human IS remains unknown. In this study, 32 candidate ER stress-related genes (ERSRGs) were identified by overlapping MSigDB ER stress pathway genes and DEGs. Three Key ERSRGs (ATF6, DDIT3 and ERP29) were identified using LASSO, random forest, and SVM-RFE. IS patients with different ERSRGs profile were clustered into two groups using consensus clustering and the difference between 2 group was further explored by GSVA. Through immune cell infiltration deconvolution analysis, and middle cerebral artery occlusion (MCAO) mouse scRNA analysis, we found that the expression of 3 key ERSRGs were closely related with peripheral macrophage cell ER stress in IS and this was further confirmed by RT-qPCR experiment. These ERS genes might be helpful to further accurately regulate the central nervous system and systemic immune response through ER stress and have potential application value in clinical practice in IS.

Background Rheumatoid arthritis (RA) is an autoimmune, inflammatory joint disease. There is growing evidence that ferroptosis is involved in the pathogenesis of RA. This study aimed to search for diagnostic markers of ferroptosis in RA and to analyse the potential mechanisms and clinical value.Materials and methods RA-associated datasets were used from the publicly available GEO database. Three methods of machine learning were applied to screen biomarkers. The diagnostic efficacy of the results was also verified by receiver operating characteristic (ROC) curve, external dataset, qRT-PCR and Western blot. Enrichment analysis was performed in this process, while protein–protein interaction (PPI) analysis and immune infiltration correlation analysis were performed using biomarkers, and competing endogenous RNA (ceRNA) networks were constructed to search for prospective therapeutic targets.Results MMP13 and GABARAPL1 can be used as ferroptosis diagnostic genes in RA. The ROC curve and PPI result demonstrated that MMP13 and GABARAPL1 had an excellent diagnostic value. The results of signature genes in the external dataset, qRT-PCR and Western blot further confirm our findings. The enrichment analysis showed that p53, MAPK and NOD-like receptor signalling pathways may be involved in the process of ferroptosis in RA. In addition, two ferroptosis diagnostic genes in RA participate in the occurrence of ferroptosis in RA via oxidative stress, metabolism and immune response. Immune infiltration analysis showed that RA extensively infiltrated B cells, T cells, macrophages and other immune cells. Persistent immune activation may be an essential reason for the progression of ferroptosis in RA. We also obtained five potential therapeutic agents for RA and some long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) regulating ferroptosis diagnostic genes.Conclusions Our study suggests that MMP13 and GABARAPL1, which are closely linked with oxidative stress and immunological modulation, can be used as ferroptosis-related potential diagnostic markers in RA and provide new clues regarding the diagnostic and therapeutic targets of ferroptosis in RA.

Background: Osteoarthritis (OA) is a degenerative joint disease frequently diagnosed in the elderly and middle-aged population. However, its specific pathogenesis has not been clarified. This study aimed to identify biomarkers for OA diagnosis and elucidate their potential mechanisms for restoring OAdys-regulated autophagy and inhibiting chondrocyte apoptosis in vitro. Material and methods: Two publicly available transcriptomic mRNA OA-related datasets (GSE10575 and GSE51588) were explored for biomarker identification by least absolute shrinkage and selection operator (LASSO) regression, weighted gene co-expression network analysis (WGCNA), and support vector machine recursive feature elimination (SVM-RFE). We applied the GSE32317 and GSE55457 cohorts to validate the markers' efficacy for diagnosis. The connections of markers to chondrocyte autophagy and apoptosis in OA were also comprehensively explored in vitro using molecular biology approaches, including qRT-PCR and Western blot. Results: We identified 286 differentially expressed genes (DEGs). These DEGs were enriched in the ECM-receptor interaction and PI3K/AKT signaling pathway. After external cohort validation and protein-protein interaction (PPI) network construction, PDK1 was finally identified as a diagnostic marker for OA. The pharmacological properties of BX795-downregulated PDK1 expression inhibited LPS-induced chondrocyte inflammation and apoptosis and rescued OA-dysregulated autophagy. Additionally, the phosphorylation of the mediators associated with the MAPK and PI3K/AKT pathways was significantly downregulated, indicating the regulatory function of PDK1 in apoptosis and autophagy via MAPK and PI3K/AKT-associated signaling pathways in chondrocytes. A significantly positive association between the PDK1 expression and Neutrophils, Eosinophils, Plasma cells, and activated CD4 memory T cells, as well as an evident negative correlation between T cells follicular helper and CD4 naive T cells, were detected in the immune cell infiltration analysis. Conclusions: PDK1 can be used as a diagnostic marker for OA. Inhibition of its expression can rescue OA-dysregulated autophagy and inhibit apoptosis by reducing the phosphorylation of PI3K/AKT and MAPK signaling pathways. [ABSTRACT FROM AUTHOR]

BackgroundMyocardial infarction (MI) ranks among the most prevalent cardiovascular diseases. Insufficient blood flow to the coronary arteries always leads to ischemic necrosis of the cardiac muscle. However, the mechanism of myocardial injury after MI remains unclear. This article aims to explore the potential common genes between mitophagy and MI and to construct a suitable prediction model.MethodsTwo Gene Expression Omnibus (GEO) datasets (GSE62646 and GSE59867) were used to screen the differential expression genes in peripheral blood. SVM, RF, and LASSO algorithm were employed to find MI and mitophagy-related genes. Moreover, DT, KNN, RF, SVM and LR were conducted to build the binary models, and screened the best model to further external validation (GSE61144) and internal validation (10-fold cross validation and Bootstrap), respectively. The performance of various machine learning models was compared. In addition, immune cell infiltration correlation analysis was conducted with MCP-Counter and CIBERSORT.ResultsWe finally identified ATG5, TOMM20, MFN2 transcriptionally differed between MI and stable coronary artery diseases. Both internal and external validation supported that these three genes could accurately predict MI withAUC = 0.914 and 0.930 by logistic regression, respectively. Additionally, functional analysis suggested that monocytes and neutrophils might be involved in mitochondrial autophagy after myocardial infarction.ConclusionThe data showed that the transcritional levels of ATG5, TOMM20 and MFN2 in patients with MI were significantly different from the control group, which might be helpful to further accurately diagnose diseases and have potential application value in clinical practice.

After ischemic stroke (IS), secondary injury is intimately linked to endoplasmic reticulum (ER) stress and body-brain crosstalk. Nonetheless, the underlying mechanism systemic immune disorder mediated ER stress in human IS remains unknown. In this study, 32 candidate ER stress-related genes (ERSRGs) were identified by overlapping MSigDB ER stress pathway genes and DEGs. Three Key ERSRGs (ATF6, DDIT3 and ERP29) were identified using LASSO, random forest, and SVM-RFE. IS patients with different ERSRGs profile were clustered into two groups using consensus clustering and the difference between 2 group was further explored by GSVA. Through immune cell infiltration deconvolution analysis, and middle cerebral artery occlusion (MCAO) mouse scRNA analysis, we found that the expression of 3 key ERSRGs were closely related with peripheral macrophage cell ER stress in IS and this was further confirmed by RT-qPCR experiment. These ERS genes might be helpful to further accurately regulate the central nervous system and systemic immune response through ER stress and have potential application value in clinical practice in IS. • ERS related genes for IS were identified by advanced machine learning algorithms and logistic regression. • ERSRGs was confirmed through MCAO single-cell data and human brain organoid data. • Three genes were found highly related to macrophage-related secondary injury events during IS. [ABSTRACT FROM AUTHOR]

Background: Early diagnosis of septic shock in children is critical for prognosis. This study committed to investigate the signature genes and their connection with immune cells in pediatric septic shock. Methods: We screened a dataset of children with septic shock from the GEO database and analyzed differentially expressed genes (DEGs). Functional enrichment analysis was performed for these DEGs. Weighted gene co-expression network analysis (WCGNA) was used to screen the key modules. Least absolute shrinkage and selection operator (LASSO) and random forest analysis were finally applied to identify the signature genes. Then gene set enrichment analysis (GSEA) was exerted to explore the signaling pathways related to the hub genes. And the immune cells infiltration was subsequently classified via using CIBERSORT. Results: A total of 534 DEGs were screened from GSE26440. The data then was clustered into 17 modules via WGCNA, which MEgrey module was significantly related to pediatric septic shock (cor=-0.62, p<0.0001). LASSO and random forest algorithms were applied to select the signature genes, containing UPP1, S100A9, KIF1B, S100A12, SLC26A8. The receiver operating characteristic curve (ROC) of these signature genes was 0.965, 0.977, 0.984, 0.991 and 0.989, respectively, which were verified in the external dataset from GSE13904. GSEA analysis showed these signature genes involve in positively correlated fructose and mannose metabolism and starch and sucrose metabolism signaling pathway. CIBERSORT suggested these signature genes may participate in immune cells infiltration. Conclusion: UPP1, S100A9, KIF1B, S100A12, SLC26A8 emerge remarkable diagnostic performance in pediatric septic shock and involved in immune cells infiltration. [ABSTRACT FROM AUTHOR]

Bacterial load in clinical samples is relatively low and difficult to detect. Improvements in assay sensitivity will greatly reduce false negative results and contribute to more accurate diagnoses. In the present study, we present a new strategy to improve the sensitivity of a nucleic acid assay by detecting the presence of a multi-copy gene. By using Brucella as a test model, we screened the genome and identified IS711 as a multiple copy gene. Distribution analysis of insertion sequence IS711 among different species and strains showed that each of the strains have 5 to 13 copies of IS711. Compared with the BMEI1001, BMEI0775 and BMEI0027, the assays of high copy genes IS711 showed higher sensitivity and is an ideal high copy signature gene for Brucella. Detection of clinical samples with assays targeting the signature genes showed that IS711 exist in higher concentrations than BMEI1001, BMEI0775 and BMEI0027. In addition, IS711 assay is more sensitive than other signature genes assay. Analysis of several other pathogenic bacteria successfully identified high copy number genes that could be used as signature genes. Therefore, this strategy of targeting high copy signature genes represents a universal strategy for the ultrasensitive detection of bacteria.

Background: Rheumatoid arthritis (RA) is an autoimmune, inflammatory joint disease. There is growing evidence that ferroptosis is involved in the pathogenesis of RA. This study aimed to search for diagnostic markers of ferroptosis in RA and to analyse the potential mechanisms and clinical value., Materials and Methods: RA-associated datasets were used from the publicly available GEO database. Three methods of machine learning were applied to screen biomarkers. The diagnostic efficacy of the results was also verified by receiver operating characteristic (ROC) curve, external dataset, qRT-PCR and Western blot. Enrichment analysis was performed in this process, while protein-protein interaction (PPI) analysis and immune infiltration correlation analysis were performed using biomarkers, and competing endogenous RNA (ceRNA) networks were constructed to search for prospective therapeutic targets., Results: MMP13 and GABARAPL1 can be used as ferroptosis diagnostic genes in RA. The ROC curve and PPI result demonstrated that MMP13 and GABARAPL1 had an excellent diagnostic value. The results of signature genes in the external dataset, qRT-PCR and Western blot further confirm our findings. The enrichment analysis showed that p53, MAPK and NOD-like receptor signalling pathways may be involved in the process of ferroptosis in RA. In addition, two ferroptosis diagnostic genes in RA participate in the occurrence of ferroptosis in RA via oxidative stress, metabolism and immune response. Immune infiltration analysis showed that RA extensively infiltrated B cells, T cells, macrophages and other immune cells. Persistent immune activation may be an essential reason for the progression of ferroptosis in RA. We also obtained five potential therapeutic agents for RA and some long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) regulating ferroptosis diagnostic genes., Conclusions: Our study suggests that MMP13 and GABARAPL1, which are closely linked with oxidative stress and immunological modulation, can be used as ferroptosis-related potential diagnostic markers in RA and provide new clues regarding the diagnostic and therapeutic targets of ferroptosis in RA.

Background: This study aimed to investigate the potential role of ferroptosis/hypoxia-related genes in cervical cancer to improve early management and treatment of cervical cancer., Methods: All data were downloaded from public databases. Ferroptosis/hypoxia-related genes associated with cervical cancer prognosis were selected to construct a risk score model. The relationship between risk score and clinical features, immune microenvironment and prognosis were analysed., Results: Risk score model was constructed based on eight signature genes. Drug prediction analysis showed that bevacizumab and cisplatin were related to vascular endothelial growth factor A. Risk score, as an independent prognostic factor of cervical cancer, had a good survival prediction effect. The two groups differed significantly in degree of immune cell infiltration, gene expression, tumour mutation burden and somatic variation., Conclusions: We developed a novel prognostic gene signature combining ferroptosis/hypoxia-related genes, which provides new ideas for individual treatment of cervical cancer.

Abstract: Endometrial carcinoma (EC) is the most common malignant tumor of the female genital tract in developed countries. The prognosis of early stage EC is favorable, but a subset faces high risk of cancer progression or recurrence. EC has a poor prognosis upon progression to advanced or metastatic stages. Therefore, our goal is to build a robust prognostic model for predicting overall survival (OS) in EC patients. In this study, 1571 genes were identified as being associated with OS based on genomewide expression profiles using a training dataset. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that these genes were involved in various cancer‐related signaling pathways. Nine signature genes were further selected using stepwise selection, and their potential role in the development of EC was demonstrated by performing differential expression analysis between EC and normal uterine tissues. A prognostic model that aggregated these nine signature genes was ultimately established and effectively divided EC patients into two risk groups. OS for patients in the high‐risk group was significantly poorer compared with that of the low‐risk group. This nine‐gene model was subsequently validated and evaluated using the TCGA dataset and shown to have a high discriminating power to distinguish EC patients with an elevated risk of mortality based on the FIGO staging system and other prognostic factors. This study provides a novel prognostic model for the identification of EC patients with elevated risk of mortality and will help to improve our understanding of the underlying mechanisms involved in prognostic EC factors. [ABSTRACT FROM AUTHOR]

Previous study revealed that higher expression of transforming growth factor beta induced (TGFBI) is correlated to poorer cancer-specific survival and higher proportion of tumor necrosis and Fuhrman grades III and IV in clear cell renal cell carcinomas. However, the relationships between TGFBI expression and malignant phenotypes of gliomas remain unclear. We downloaded and analyzed data from seven GEO datasets (GSE68848, GSE4290, GSE13041, GSE4271, GSE83300, GSE34824 and GSE84010), the TCGA database and the REMBRANDT database to investigate whether TGFBI could be a biomarker of glioma. From microarray data (GSE68848, GSE4290) and RNA-seq data (TCGA), TGFBI expression levels were observed to correlate positively with pathological grade, and TGFBI expression levels were significantly higher in gliomas than in normal brain tissues. Furthermore, in GSE13041, GSE4271 and the TCGA cohort, TGFBI expression in the mesenchymal (Mes) subtype high-grade glioma (HGG) was significantly higher than that in the proneural subtype. Kaplan–Meier survival analysis of GBM patients in the GSE83300 dataset, REMBRANDT and TCGA cohort revealed that patients in the top 50% TGFBI expression group survived for markedly shorter periods than those in the bottom 50%. Analysis of grade III gliomas showed that the median survival time was significantly shorter in the TGFBI high expression group than in the TGFBI low expression group. In addition, we found that TGFBI expression levels might relate to several classical molecular characterizations of glioma, such as, IDH mutation, TP53 mutation, EGFR amplification, etc. These results suggest that TGFBI expression positively correlates with glioma pathological grades and that TGFBI is a potential signature gene for Mes subtype HGG and a potential prognostic molecule. [ABSTRACT FROM AUTHOR]

Background: Myocardial infarction (MI) ranks among the most prevalent cardiovascular diseases. Insufficient blood flow to the coronary arteries always leads to ischemic necrosis of the cardiac muscle. However, the mechanism of myocardial injury after MI remains unclear. This article aims to explore the potential common genes between mitophagy and MI and to construct a suitable prediction model., Methods: Two Gene Expression Omnibus (GEO) datasets (GSE62646 and GSE59867) were used to screen the differential expression genes in peripheral blood. SVM, RF, and LASSO algorithm were employed to find MI and mitophagy-related genes. Moreover, DT, KNN, RF, SVM and LR were conducted to build the binary models, and screened the best model to further external validation (GSE61144) and internal validation (10-fold cross validation and Bootstrap), respectively. The performance of various machine learning models was compared. In addition, immune cell infiltration correlation analysis was conducted with MCP-Counter and CIBERSORT., Results: We finally identified ATG5, TOMM20, MFN2 transcriptionally differed between MI and stable coronary artery diseases. Both internal and external validation supported that these three genes could accurately predict MI withAUC = 0.914 and 0.930 by logistic regression, respectively. Additionally, functional analysis suggested that monocytes and neutrophils might be involved in mitochondrial autophagy after myocardial infarction., Conclusion: The data showed that the transcritional levels of ATG5, TOMM20 and MFN2 in patients with MI were significantly different from the control group, which might be helpful to further accurately diagnose diseases and have potential application value in clinical practice., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Yang, Sun and Wang.)

Phospholipase C beta 1 (PLCβ1) expresses in gliomas and cultured glial cells, but its expression is barely detectable in normal glial cells. We analyzed data from Gene Expression Omnibus (GEO-GDSxxx), The Cancer Genome Atlas (TCGA), and the Repository for Molecular Brain Neoplasia Data (REMBRANDT) to explore the potential role of PLCβ1 as a biomarker in high-grade glioma (HGG). PLCβ1 expression is significantly higher in grade III gliomas than that in grade IV gliomas from GDS1815 ( n = 24 vs. 76), GDS1962 ( n = 19 vs. 81), and GDS1975 ( n = 26 vs. 59). In GDS1815, PLCβ1 expression correlates with several known proneural (PN) signature genes; its expression from PN subtype ( n = 15) is significantly higher than that from mesenchymal (Mes) subtype ( n = 33) HGG. In GDS1962, PLCβ1 expression is the highest in nontumor brain tissue ( n = 23) and is significantly higher than its expression in grade II gliomas [astrocytomas ( n = 7) and oligodendrogliomas ( n = 37)]. A Kaplan-Meier survival curve from a REMBRANDT cohort demonstrates that glioma patients with intermediate PLCβ1 expression ( n = 103) survived significantly longer than PLCβ1 downregulated (2X) groups ( n = 226). From TCGA data, PLCβ1 RNA-Seq signal inversely correlates with the pathological grades, and PLCβ1 expression in PN ( n = 8) is of significantly higher levels than that in Mes ( n = 8) subtypes of glioblastoma. The top 50 % of PLCβ1 expression subgroup ( n = 294) of gliomas (grades II to IV merged) survived significantly longer than the low 50 percentile of the PLCβ1 expression subgroup ( n = 293). p values are less than 0.05 for all these analyses. We conclude that PLCβ1 is a candidate signature gene for PN subtype HGG, and its expression inversely correlates with glioma pathological grade and is a potential prognostic factor. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a degenerative joint disease frequently diagnosed in the elderly and middle-aged population. However, its specific pathogenesis has not been clarified. This study aimed to identify biomarkers for OA diagnosis and elucidate their potential mechanisms for restoring OA-dysregulated autophagy and inhibiting chondrocyte apoptosis in vitro ., Material and Methods: Two publicly available transcriptomic mRNA OA-related datasets (GSE10575 and GSE51588) were explored for biomarker identification by least absolute shrinkage and selection operator (LASSO) regression, weighted gene co-expression network analysis (WGCNA), and support vector machine recursive feature elimination (SVM-RFE). We applied the GSE32317 and GSE55457 cohorts to validate the markers' efficacy for diagnosis. The connections of markers to chondrocyte autophagy and apoptosis in OA were also comprehensively explored in vitro using molecular biology approaches, including qRT-PCR and Western blot., Results: We identified 286 differentially expressed genes (DEGs). These DEGs were enriched in the ECM-receptor interaction and PI3K/AKT signaling pathway. After external cohort validation and protein-protein interaction (PPI) network construction, PDK1 was finally identified as a diagnostic marker for OA. The pharmacological properties of BX795-downregulated PDK1 expression inhibited LPS-induced chondrocyte inflammation and apoptosis and rescued OA-dysregulated autophagy. Additionally, the phosphorylation of the mediators associated with the MAPK and PI3K/AKT pathways was significantly downregulated, indicating the regulatory function of PDK1 in apoptosis and autophagy via MAPK and PI3K/AKT-associated signaling pathways in chondrocytes. A significantly positive association between the PDK1 expression and Neutrophils, Eosinophils, Plasma cells, and activated CD4 memory T cells, as well as an evident negative correlation between T cells follicular helper and CD4 naive T cells, were detected in the immune cell infiltration analysis., Conclusions: PDK1 can be used as a diagnostic marker for OA. Inhibition of its expression can rescue OA-dysregulated autophagy and inhibit apoptosis by reducing the phosphorylation of PI3K/AKT and MAPK signaling pathways., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Meng, Du, Lv, Lu, Li and Yao.)

Atrial fibrillation (AF) is a relatively common arrhythmia in clinical practice. Although significant progress has been achieved in the treatment of AF and its associated complications, research on AF prevention lags behind, mainly due to the lack of a deep understanding of AF pathogenesis. In recent years, as our knowledge has grown, the role of the inflammatory/immune response in the occurrence and progression of AF has gradually gained attention. In this paper, based on existing gene expression data in the Gene Expression Omnibus database, a detailed description of immune infiltration status in AF is presented using a series of analytical methods, including differential analysis, Gene Ontology categorization, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and weighted gene coexpression network analysis, and analysis tools such as CIBERSORTx and Cytoscape. Several new AF/immune infiltrations-related signature genes were identified, and the AF/immune infiltration pathology was classified based on these immune signature genes, thus providing novel insights into the pathogenesis of AF based on the inflammatory response.

Background: Early diagnosis of septic shock in children is critical for prognosis. This study committed to investigate the signature genes and their connection with immune cells in pediatric septic shock., Methods: We screened a dataset of children with septic shock from the GEO database and analyzed differentially expressed genes (DEGs). Functional enrichment analysis was performed for these DEGs. Weighted gene co-expression network analysis (WCGNA) was used to screen the key modules. Least absolute shrinkage and selection operator (LASSO) and random forest analysis were finally applied to identify the signature genes. Then gene set enrichment analysis (GSEA) was exerted to explore the signaling pathways related to the hub genes. And the immune cells infiltration was subsequently classified via using CIBERSORT., Results: A total of 534 DEGs were screened from GSE26440. The data then was clustered into 17 modules via WGCNA, which MEgrey module was significantly related to pediatric septic shock (cor=-0.62, p <0.0001). LASSO and random forest algorithms were applied to select the signature genes, containing UPP1, S100A9, KIF1B, S100A12, SLC26A8. The receiver operating characteristic curve (ROC) of these signature genes was 0.965, 0.977, 0.984, 0.991 and 0.989, respectively, which were verified in the external dataset from GSE13904. GSEA analysis showed these signature genes involve in positively correlated fructose and mannose metabolism and starch and sucrose metabolism signaling pathway. CIBERSORT suggested these signature genes may participate in immune cells infiltration., Conclusion: UPP1, S100A9, KIF1B, S100A12, SLC26A8 emerge remarkable diagnostic performance in pediatric septic shock and involved in immune cells infiltration., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Fan, Shi, Qiu, Liu and Shu.)

Bacterial load in clinical samples is relatively low and difficult to detect. Improvements in assay sensitivity will greatly reduce false negative results and contribute to more accurate diagnoses. In the present study, we present a new strategy to improve the sensitivity of a nucleic acid assay by detecting the presence of a multi-copy gene. By using Brucella as a test model, we screened the genome and identified IS711 as a multiple copy gene. Distribution analysis of insertion sequence IS711 among different species and strains showed that each of the strains have 5 to 13 copies of IS711. Compared with the BMEI1001, BMEI0775 and BMEI0027, the assays of high copy genes IS711 showed higher sensitivity and is an ideal high copy signature gene for Brucella . Detection of clinical samples with assays targeting the signature genes showed that IS711 exist in higher concentrations than BMEI1001, BMEI0775 and BMEI0027. In addition, IS711 assay is more sensitive than other signature genes assay. Analysis of several other pathogenic bacteria successfully identified high copy number genes that could be used as signature genes. Therefore, this strategy of targeting high copy signature genes represents a universal strategy for the ultrasensitive detection of bacteria., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dong, Chen, Wei, Liu, Shen, Liu, Zhang, Wang and Chen.)

Genome-scale single-cell biology has recently emerged as a powerful technology with important implications for both basic and medical research. There are urgent needs for the development of computational methods or analytic pipelines to facilitate large amounts of single-cell RNA-Seq data analysis. Here, we present a detailed protocol for SINCERA (SINgle CEll RNA-Seq profiling Analysis), a generally applicable analytic pipeline for processing single-cell data from a whole organ or sorted cells. The pipeline supports the analysis for the identification of major cell types, cell type-specific gene signatures, and driving forces of given cell types. In this chapter, we provide step-by-step instructions for the functions and features of SINCERA together with application examples to provide a practical guide for the research community. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html .

Objective: We validated the accuracy of host selected signature gene set using unstimulated whole blood (WB), and peripheral blood mononuclear cells (PBMC) in the diagnosis of tuberculosis (TB)., Methods: The unstimulated WB and PBMC from 1417 individuals with active pulmonary TB patients, other lung diseases and healthy participants were analyzed using real time polymerase chain reaction (RT-PCR)., Results: The WB cohort test demonstrates that the combination of GBP5 and KLF2 can differentiate active TB versus HC with sensitivity and specificity of 77.8% and 87.1%, respectively; but most importantly active TB versus OD with sensitivity and specificity of 96.1% and 85.2%, respectively. Again during treatment course, the TB score of GBP5 and KLF2, analytes secretion and clinical parameters were found to be associated in disease progression. In the PBMC cohort test, we found that the only and best discriminatory combination was GBP5, DUSP3 and KLF2 inthe active TB versus HC with a sensitivity and specificity of 76.4% and 85.9%, respectively., Conclusions: Our study reveals that GBP5 and KLF2 may be useful as a diagnostic tool for active TB, also the two-gene set may serve as surrogate biomarkers for monitoring TB therapy., (Copyright © 2017. Published by Elsevier Ltd.)