Expression Levels Of Hub Genes Research Articles

Hippocampal transcriptomic analyses reveal the potential antiapoptotic mechanism of a novel anticonvulsant agent Q808 on pentylenetetrazol-induced epilepsy in rats

Brain apoptosis is one of the main causes of epileptogenesis. The antiapoptotic effect and potential mechanism of Q808, an innovative anticonvulsant chemical, have never been reported. In this study, the seizure stage and latency to reach stage 2 of pentylenetetrazol (PTZ) seizure rat model treated with Q808 were investigated. The morphological change and neuronal apoptosis in the hippocampus were detected by hematoxylin and eosin (HE) and terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) staining, respectively. The hippocampal transcriptomic changes were observed using RNA sequencing (RNA-seq). The expression levels of hub genes were verified by quantitative reverse-transcription PCR (qRT-PCR). Results revealed that Q808 could allay the seizure score and prolong the stage 2 latency in seizure rats. The morphological changes of neurons and the number of apoptotic cells in the DG area were diminished by Q808 treatment. RNA-seq analysis revealed eight hub genes, including Map2k3, Nfs1, Chchd4, Hdac6, Siglec5, Slc35d3, Entpd1, and LOC103690108, and nine hub pathways among the control, PTZ, and Q808 groups. Hub gene Nfs1 was involved in the hub pathway sulfur relay system, and Map2k3 was involved in the eight remaining hub pathways, including Amyotrophic lateral sclerosis, Cellular senescence, Fc epsilon RI signaling pathway, GnRH signaling pathway, Influenza A, Rap1 signaling pathway, TNF signaling pathway, and Toll-like receptor signaling pathway. qRT-PCR confirmed that the mRNA levels of these hub genes were consistent with the RNA-seq results. Our findings might contribute to further studies exploring the new apoptosis mechanism and actions of Q808.

Bioinformatics proved the existence of potential hub genes activating autophagy to participate in cartilage degeneration in osteonecrosis of the femoral head.

The obvious degeneration of articular cartilage occurs in the late stage of osteonecrosis of the femoral head (ONFH), which aggravates the condition of ONFH. This study aimed to demonstrate aberrant activation of autophagy processes in ONFH chondrocytes through bioinformatics and to predict and identify relevant hub genes and pathways. Differentially expressed genes (DEGs) were identified using R software in the GSE74089 dataset from the GEO database. DEGs were crossed with the Human Autophagy Database (HADb) autophagy genes to screen out autophagy-related differential genes (AT-DEGs). GSEA, GSVA, GO, and KEGG pathway enrichment analyses of AT-DEGs were performed. The STRING database was used to analyze the protein-protein interaction (PPI) of the AT-DEGs network, and the MCODE and CytoHubba plugin in the Cytoscape software was used to analyze the key gene cluster module and screen the hub genes. The PPI network of hub genes was constructed using the GeneMANIA database, and functional enrichment and gene connectivity categories were analyzed. The expression levels of hub genes of related genes in the ONFH patients were verified in the dataset GSE123568, and the protein expression was verified by immunohistochemistry in tissues. The analysis of DEGs revealed abnormal autophagy in ONFH cartilage. AT-DEGs in ONFH have special enrichment in macroautophagy, autophagosome membrane, and phosphatidylinositol-3-phosphate binding. In the GSE123568 dataset, it was also found that ATG2B, ATG4B, and UVRAG were all significantly upregulated in ONFH patients. By immunohistochemistry, it was verified that ATG2B, ATG4B, and UVRAG were significantly overexpressed. These three genes regulate the occurrence and extension of autophagosomes through the PI3KC3C pathway. Finally, we determined that chondrocytes in ONFH undergo positive regulation of autophagy through the corresponding pathways involved in three genes: ATG2B, ATG4B, and UVRAG.

Knockdown of CENPM activates cGAS-STING pathway to inhibit ovarian cancer by promoting pyroptosis.

We aimed to screen novel gene signatures for ovarian cancer (OC) and explore the role of biomarkers in OC via regulating pyroptosis using bioinformatics analysis. Differentially expressed genes (DEGs) of OC were screened from GSE12470 and GSE16709 datasets. Hub genes were determined from protein-protein interaction networks after bioinformatics analysis. The role of Centromeric protein M (CENPM) in OC was assessed by subcutaneous tumor experiment using hematoxylin-eosin and immunohistochemical staining. Tumor metastasis was evaluated by detecting epithelial-mesenchymal transition-related proteins. The proliferation, migration, and invasion were determined using cell counting kit and transwell assay. Enzyme-linked immunosorbent assay was applied to measure inflammatory factors. The mRNA and protein expression were detected using real-time quantitative PCR and western blot. We determined 9 hub genes (KIFC1, PCLAF, CDCA5, KNTC1, MCM3, OIP5, CENPM, KIF15, and ASF1B) with high prediction value for OC. In SKOV3 and A2780 cells, the expression levels of hub genes were significantly up-regulated, compared with normal ovarian cells. CENPM was selected as a key gene. Knockdown of CENPM suppressed proliferation, migration, and invasion of OC cells. Subcutaneous tumor experiment revealed that CENPM knockdown significantly suppressed tumor growth and metastasis. Additionally, pyroptosis was promoted in OC cells and xenograft tumors after CENPM knockdown. Furthermore, CENPM knockdown activated cGAS-STING pathway and the pathway inhibitor reversed the inhibitory effect of CENPM knockdown on viability, migration, and invasion of OC cells. CENPM was a novel biomarker of OC, and knockdown of CENPM inhibited OC progression by promoting pyroptosis and activating cGAS-STING pathway.

Linking Disulfide Levels and NAD+ Metabolism with Alzheimer's Disease for Diagnostic Modeling and Target Drug Analysis.

Alzheimer's disease (AD) is a condition that affects the nervous system and that requires considerably more in-depth study. Abnormal Nicotinamide Adenine Dinucleotide (NAD+) metabolism and disulfide levels have been demonstrated in AD. This study investigated novel hub genes for disulfide levels and NAD+ metabolism in relation to the diagnosis and therapy of AD. Data from the gene expression omnibus (GEO) database were analyzed. Hub genes related to disulfide levels, NAD+ metabolism, and AD were identified from overlapping genes for differentially expressed genes (DEGs), genes in the NAD+ metabolism or disulfide gene sets, and module genes obtained by weighted gene co-expression network analysis (WGCNA). Pathway analysis of these hub genes was performed by Gene Set Enrichment Analysis (GSEA). A diagnostic model for AD was constructed based on the expression level of hub genes in brain samples. CIBERSORT was used to evaluate immune cell infiltration and immune factors correlating with hub gene expression. The DrugBank database was also used to identify drugs that target the hub genes. We identified 3 hub genes related to disulfide levels in AD and 9 related to NAD+ metabolism in AD. Pathway analysis indicated these 12 genes were correlated with AD. Stepwise regression analysis revealed the area under the curve (AUC) for the predictive model based on the expression of these 12 hub genes in brain tissue was 0.935, indicating good diagnostic performance. Additionally, analysis of immune cell infiltration showed the hub genes played an important role in AD immunity. Finally, 33 drugs targeting 10 hub genes were identified using the DrugBank database. Some of these have been clinically approved and may be useful for AD therapy. Hub genes related to disulfide levels and NAD+ metabolism are promising biomarkers for the diagnosis of AD. These genes may contribute to a better understanding of the pathogenesis of AD, as well as to improved drug therapy.

IRF4 Knockdown Inhibits the Chronic Rhinosinusitis Without Nasal Polyps Development by Regulating NLRP3/Caspase-1/GSDMD-Mediated Pyroptosis.

Chronic rhinosinusitis without nasal polyps (CRSsNP) is a CRS phenotype. However, the mechanisms of CRSsNP remains unclear. Differentially expressed genes (DEGs) were obtained from the GSE36830 and GSE198950 datasets through the GEO2R tool. The six hub genes were screened by the protein-protein interaction (PPI) network analysis and Cytoscape software. Then we constructed the mouse models of CRS and verified the expression levels of hub genes by reverse transcription quantitative PCR (RT-qPCR). Hematoxylin-eosin (HE) staining was employed to observe pathological alterations in mouse tissues. Casepase-3 expression was detected by immunohistochemistry (IHC). The levels of TNF-α, IL-12, IL-6, IL-1β, LDH, and IL-18 were evaluated using enzyme-linked immunosorbent assay (ELISA). Pyroptosis-related protein expressions were measured by western blotting. Cell counting kit-8 (CCK-8) and flow cytometry were performed to assess the proliferation and apoptosis of lipopolysaccharide (LPS)-induced NP69 cells. Six hub DEGs were identified. The expression levels of IRF4, IKZF1, and CD79A were obviously increased in CRSsNP, while those of ADH6, ADH1A, and LDHC were significantly decreased. IRF4 knockdown attenuated the pathologic features of CRSsNP. IRF4 knockdown reduced levels of the TNF-α, IL-12, IL-6 IL-1β, LDH, and IL-18 as well as the proteins expression of Casepase-1, GSDMD, and NLRP3 both in vivo and in vitro, implying that inflammation and pyroptosis were inhibited. IRF4 knockdown hinders the development of CRSsNP by inhibiting the inflammatory response and NLRP3/Caspase-1/GSDMD-mediated pyroptosis, which offers novel promising treatment strategies for clinical intervention.

SIRT1 and ZNF350 as novel biomarkers for osteoporosis: a bioinformatics analysis and experimental validation.

Osteoporosis (OP) is characterized by bone mass decrease and bone tissue microarchitectural deterioration in bone tissue. This study identified potential biomarkers for early diagnosis of OP and elucidated the mechanism of OP. Gene expression profiles were downloaded from Gene Expression Omnibus (GEO) for the GSE56814 dataset. A gene co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) to identify key modules associated with healthy and OP samples. Functional enrichment analysis was conducted using the R clusterProfiler package for modules to construct the transcriptional regulatory factor networks. We used the "ggpubr" package in R to screen for differentially expressed genes between the two samples. Gene set variation analysis (GSVA) was employed to further validate hub gene expression levels between normal and OP samples using RT-PCR and immunofluorescence to evaluate the potential biological changes in various samples. There was a distinction between the normal and OP conditions based on the preserved significant module. A total of 100 genes with the highest MM scores were considered key genes. Functional enrichment analysis suggested that the top 10 biological processes, cellular component and molecular functions were enriched. The Toll-like receptor signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, osteoclast differentiation, JAK-STAT signaling pathway, and chemokine signaling pathway were identified by Kyoto Encyclopedia of Genes and Genomes pathway analysis. SIRT1 and ZNF350 were identified by Wilcoxon algorithm as hub differentially expressed transcriptional regulatory factors that promote OP progression by affecting oxidative phosphorylation, apoptosis, PI3K-Akt-mTOR signaling, and p53 pathway. According to RT-PCR and immunostaining results, SIRT1 and ZNF350 levels were significantly higher in OP samples than in normal samples. SIRT1 and ZNF350 are important transcriptional regulatory factors for the pathogenesis of OP and may be novel biomarkers for OP treatment.

Pyroptosis is involved in the immune microenvironment regulation of unexplained recurrent miscarriage.

Unexplained recurrent miscarriage (URM) is a common pregnancy complication with few effective therapies. Moreover, little is known regarding the role of pyroptosis in the regulation of the URM immune microenvironment. To address this issue, gene expression profiles of publicly available placental datasets GSE22490 and GSE76862 were downloaded from the Gene Expression Omnibus database. Pyroptosis-related differentially expressed genes were identified and a total of 16 differentially expressed genes associated with pyroptosis were detected, among which 1 was upregulated and 15 were downregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that the functionally enriched modules and pathways of these genes are closely related to immune and inflammatory responses. Four hub genes were identified: BTK, TLR8, NLRC4, and TNFSF13B. BTK, TLR8, and TNFSF13B were highly connected with immune cells, according to the correlation analysis of four hub genes and 20 different types of immune cells (p < 0.05). The four hub genes were used as research objects to construct the interaction networks. Chorionic villus tissue was used for quantitative real-time polymerase chain reaction and western blot to confirm the expression levels of hub genes, and the results showed that the expression of the four hub genes was significantly decreased in the chorionic villus tissue in the URM group. Collectively, the present study indicates that perhaps pyroptosis is essential to the diversity and complexity of the URM immune microenvironment, and provides a theoretical basis and research ideas for subsequent target gene verification and mechanism research.

Identification and validation of mutual hub genes in idiopathic pulmonary fibrosis and rheumatoid arthritis-associated usual interstitial pneumonia

ObjectivesThe study aims at exploring common hub genes and pathways in idiopathic pulmonary fibrosis (IPF) and rheumatoid arthritis-associated usual interstitial pneumonia (RA-UIP) through integrated bioinformatics analyses. MethodsThe GSE199152 dataset containing lung tissue samples from IPF and RA-UIP patients was acquired from the Gene Expression Omnibus (GEO) database. The identification of overlapping differentially expressed genes (DEGs) in IPF and RA-UIP was carried out through R language. Protein–protein interaction (PPI) network analysis and module analysis were applied to filter mutual hub genes in the two diseases. Enrichment analyses were also conducted to analyze the possible biological functions and pathways of the overlapped DEGs and hub genes. The diagnostic value of key genes was assessed with R language, and the expressions of these genes in pulmonary cells of IPF and rheumatoid arthritis-associated interstitial lung disease (RA-ILD) patients were analyzed with single cell RNA-sequencing (scRNA-seq) datasets. The expression levels of hub genes were validated in blood samples from patients, specimens of human lung fibroblasts, lung tissue samples from mice, as well as external GEO datasets. ResultsFour common hub genes (THBS2, TIMP1, POSTN, and CD19) were screened. Enrichment analyses showed that the abnormal expressions of DEGs and hub genes may be connected with the onset of IPF and RA-UIP by regulating the progression of fibrosis. ScRNA-seq analyses illustrated that for both IPF and RA-ILD patients, THBS2, TIMP1, and POSTN were mainly expressed in lung fibroblasts, while CD19 was uniquely high-expressed in B cells. The qRT-PCR and immunohistochemistry (IHC) results verified that the expression levels of hub genes were mostly in accordance with the findings obtained from the bioinformatics analyses. ConclusionThough IPF and RA-UIP are distinct diseases, they may to some extent have mutual pathogenesis in the development of fibrosis. THBS2, TIMP1, POSTN, and CD19 may be the potential biomarkers of IPF and RA-UIP, and intervention on related pathways of these genes could offer new strategies for the precision treatment of IPF and RA-UIP.

Identification of key genes and immune infiltration in osteoarthritis through analysis of zinc metabolism-related genes

BackgroundOsteoarthritis (OA) represents a prominent etiology of considerable pain and disability, and conventional imaging methods lack sensitivity in diagnosing certain types of OA. Therefore, there is a need to identify highly sensitive and efficient biomarkers for OA diagnosis. Zinc ions feature in the pathogenesis of OA. This work aimed to investugate the role of zinc metabolism-related genes (ZMRGs) in OA and the diagnostic characteristics of key genes.MethodsWe obtained datasets GSE169077 and GSE55235 from the Gene Expression Omnibus (GEO) and obtained ZMRGs from MSigDB. Differential expression analysis was conducted on the GSE169077 dataset using the limma R package to identify differentially expressed genes (DEGs), and the intersection of DEGs and ZMRGs yielded zinc metabolism differential expression-related genes (ZMRGs-DEGs). The clusterProfiler R package was employed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of ZMRGs-DEGs. Potential small molecule drugs were predicted using the CMap database, and immune cell infiltration and function in OA individuals were analyzed using the ssGSEA method. Protein-protein interaction (PPI) networks were constructed to detect Hub genes among ZMRGs-DEGs. Hub gene expression levels were analyzed in the GSE169077 and GSE55235 datasets, and their diagnostic characteristics were assessed using receiver operating characteristic (ROC) curves. The gene-miRNA interaction network of Hub genes was explored using the gene-miRNA interaction network website.ResultsWe identified 842 DEGs in the GSE169077 dataset, and their intersection with ZMRGs resulted in 46 ZMRGs-DEGs. ZMRGs-DEGs were primarily enriched in functions such as collagen catabolic processes, extracellular matrix organization, metallopeptidase activity, and pathways like the IL-17 signaling pathway, Nitrogen metabolism, and Relaxin signaling pathway. Ten potential small-molecule drugs were predicted using the CMap database. OA patients exhibited distinct immune cell abundance and function compared to healthy individuals. We identified 4 Hub genes (MMP2, MMP3, MMP9, MMP13) through the PPI network, which were highly expressed in OA and demonstrated good diagnostic performance. Furthermore, two closely related miRNAs for each of the 4 Hub genes were identified.Conclusion4 Hub genes were identified as potential diagnostic biomarkers and therapeutic targets for OA.

Analysis and validation of potential ICD-related biomarkers in development of myopia using machine learning.

We aimed to identify and verify potential biomarkers in the development of myopia associated with immunogenic cell death (ICD). We download high myopia (HM) dataset GSE136701 from Gene Expression Omnibus. Differentially expressed genes in HM were identified to overlapped with ICD-related genes. Least absolute shrinkage and selection operator were used to select the Hub genes. Furthermore, the correlation between the hub genes and immune infiltration, immune response activities, and hub genes Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis was investigated using Spearman's rank correlation. Prediction of the miRNAs upstream of the Hub genes was based on the TargetScan database. We used guinea pig lens-induced myopia model's scleral tissues performed quantitative real-time polymerase chain reaction. We identified overlapped with ICD-related genes (LY96, IL1A, IL33, and AGER) and two genes (LY96 and AGER) as hub genes. Single sample gene set enrichment analysis and Spearman's rank correlation revealed that hub gene expression levels in HM were significantly correlated with the infiltration percentages of CD56dim natural killer cells, macrophages, immature B cells, and the immune response activities of APC co-stimulation and Kyoto Encyclopedia of Genes and Genomes pathways, such as terpenoid backbone biosynthesis, aminoacyl-trna biosynthesis, Huntington's disease, oxidative phosphorylation; there were a few additional signaling pathways compared to normal samples. Additionally, several miRNA were predicted as upstream regulators of LY96 and AGER. LY96 was identified as a significantly differentially expressed biomarker in myopia guinea pig's scleral tissues, as verified by qPCR. LY96 was identified and verified as a ICD-related potential myopia biomarker. Molecular mechanisms or pathways involved in myopia development by LY96 requires further research.

Identification of key genes of diabetic cardiomyopathy in hiPSCs-CMs based on bioinformatics analysis.

Diabetic cardiomyopathy (DbCM) is one of the most common vascular complications of diabetes, and can cause heart failure and threaten the life of patients. The pathogenesis is complex, and key genes have not fully identified. In this study, bioinformatics analysis was used to predict DbCM-related gene targets. Published datasets from the NCBI Gene Expression Omnibus with accession numbers GSE62203 and GSE197850 were selected for analysis. Differentially expressed genes (DEGs) were identified by the online tool GEO2R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID online database. Protein-protein interaction network construction and hub gene identification were performed using STRING and Cytoscape. We used 30mM and 1μM hydrocortisone-stimulated AC16 cells as an in vitro model of diabetic cardiomyopathy. Quantitative real-time PCR (qRT-PCR) was performed to validate the expression levels of hub genes. A total of 73 common DEGs were identified in both datasets, including 47 upregulated and 26 downregulated genes. GO and KEGG pathway enrichment analyses revealed that the DEGs were significantly enriched in metabolism, hypoxia response, apoptosis, cell proliferation regulation, and cytoplasmic and HIF signalling pathways. The top 10 hub genes were LDHA, PGK1, SLC2A1, ENO1, PFKFB3, EGLN1, MYC, PDK1, EGLN3 and BNIP3. In our in vitro study, we found that PGK1, SLC2A1, PFKFB3, EGLN1, MYC, EGLN3 and BNIP3 were upregulated, ENO1 was downregulated, and LDHA was unchanged. Except for PGK1 and ENO1, these hub genes have been previously reported to be involved in DbCM. In summary, we identified DEGs and hub genes and first reported PGK1 and ENO1 in DbCM, which may serve as potential candidate genes for DbCM targeted therapy.

Analysis of the lncRNA-miRNA-mRNA network to explore the regulation mechanism in human traumatic brain injury.

Traumatic brain injury (TBI) refers to brain dysfunction with or without traumatic structural injury induced by an external force. Nevertheless, the molecular mechanism of TBI remains undefined. Differentially expressed (DE) lncRNAs, DEmRNAs and DEmiRNAs were selected between human TBI tissues and the adjacent histologically normal tissue by high-throughput sequencing. Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis of overlapping DEmRNAs between predicted mRNAs of DEmiRNAs and DEmRNAs. The competitive endogenous RNA (ceRNA) network of lncRNA-miRNA-mRNA was established in light of the ceRNA theory. In the ceRNA network, the key lncRNAs were screened out. Then key lncRNAs related ceRNA subnetwork was constructed. After that, qRT-PCR was applied to validate the expression levels of hub genes. 114 DElncRNAs, 1807 DEmRNAs and 6 DEmiRNAs were DE in TBI. The TBI-related ceRNA network was built with 73 lncRNA nodes, 81 mRNA nodes and 6 miRNAs. According to topological analysis, two hub lncRNAs (ENST00000562897 and ENST00000640877) were selected to construct the ceRNA subnetwork. Subsequently, key lncRNA-miRNA-mRNA regulatory axes constructed by two lncRNAs including ENST00000562897 and ENST00000640877, two miRNAs including miR-6721-5p and miR-129-1-3p, two mRNAs including ketohexokinase (KHK) and cyclic nucleotide-gated channel beta1 (CNGB1), were identified. Furthermore, qRT-PCR results displayed that the expression of ENST00000562897, KHK and CNGB1 were significantly decreased in TBI, while the miR-6721-5p expression levels were markedly increased in TBI. The results of our study reveal a new insight into understanding the ceRNA regulation mechanism in TBI and select key lncRNA-miRNA-mRNA axes for prevention and treatment of TBI.

Identification of key genes and signalling pathways in clear cell renal cell carcinoma: An integrated bioinformatics approach.

Clear cell Renal Cell Carcinoma (ccRCC) is one of the most prevalent types of kidney cancer. Unravelling the genes responsible for driving cellular changes and the transformation of cells in ccRCC pathogenesis is a complex process. In this study, twelve microarray ccRCC datasets were chosen from the gene expression omnibus (GEO) database and subjected to integrated analysis. Through GEO2R analysis, 179 common differentially expressed genes (DEGs) were identified among the datasets. The common DEGs were subjected to functional enrichment analysis using ToppFun followed by construction of protein-protein interaction network (PPIN) using Cytoscape. Clusters within the DEGs PPIN were identified using the Molecular Complex Detection (MCODE) Cytoscape plugin. To identify the hub genes, the centrality parameters degree, betweenness, and closeness scores were calculated for each DEGs in the PPIN. Additionally, Gene Expression Profiling Interactive Analysis (GEPIA) was utilized to validate the relative expression levels of hub genes in the normal and ccRCC tissues. The common DEGs were highly enriched in Hypoxia-inducible factor (HIF) signalling and metabolic reprogramming pathways. VEGFA, CAV1, LOX, CCND1, PLG, EGF, SLC2A1, and ENO2 were identified as hub genes. Among 8 hub genes, only the expression levels of VEGFA, LOX, CCND1, and EGF showed a unique expression pattern exclusively in ccRCC on compared to other type of cancers.

Exploring the Molecular Mechanisms and Shared Gene Signatures Between Systemic Lupus Erythematosus and Bladder Urothelial Carcinoma.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease associated with increased susceptibility to cancer, including bladder urothelial carcinoma (BLCA). This study investigates the shared molecular mechanisms and gene signatures between SLE and BLCA, shedding light on potential biomarkers and therapeutic targets. We compiled gene datasets related to SLE and BLCA from various databases and identified common genes. Differential gene expression analysis, protein-protein interaction networks, and hub gene identification were performed. We studied functional enrichment, immune infiltration, and transcription factor/miRNA regulation networks. We also explored gene-disease interactions and protein-chemical/drug networks. Hub gene expression levels and diagnostic values were validated in TCGA and GEO databases. Prognostic analysis was performed on the core gene MMP9 in the TCGA-BLCA database to study its prognostic value. Finally, the mRNA expression of MMP9 was verified in bladder cancer cell lines and BLCA patient blood. The diagnostic value of MMP9 for BLCA was verified by receiver operating characteristic(ROC) curve analysis of the expression of MMP9 in patients' blood. We identified 524 common genes between SLE and BLCA, enriched in pathways related to apoptosis and cytokine regulation. Immune infiltration analysis for two diseases. Transcription factors and microRNAs were implicated in regulating these common genes. The gene-disease network linked hub genes with various diseases, emphasizing their roles in autoimmune disease and cancer. Protein-chemical/drug networks highlighted potential treatment options. Finally, our study found that MMP9 is a potential therapeutic target with diagnostic and prognostic value and Immune-related biomarkers in patients with BLCA and SLE. Our study reveals shared molecular mechanisms, genetic signatures, and immune infiltrates between SLE and BLCA. MMP9 emerges as a potential diagnostic and prognostic biomarker in BLCA, warranting further investigation. These findings provide insights into the pathogenesis of SLE-associated BLCA and may guide future research and therapeutic strategies.

Integrating single-cell and bulk RNA sequencing data unveils antigen presentation and process-related CAFS and establishes a predictive signature in prostate cancer

BackgroundCancer-associated fibroblasts (CAFs) are heterogeneous and can influence the progression of prostate cancer in multiple ways; however, their capacity to present and process antigens in PRAD has not been investigated. In this study, antigen presentation and process-related CAFs (APPCAFs) were identified using bioinformatics, and the clinical implications of APPCAF-related signatures in PRAD were investigated.MethodsSMART technology was used to sequence the transcriptome of primary CAFs isolated from patients undergoing different treatments. Differential expression gene (DEG) screening was conducted. A CD4 + T-cell early activation assay was used to assess the activation degree of CD4 + T cells. The datasets of PRAD were obtained from The Cancer Genome Atlas (TCGA) database and NCBI Gene Expression Omnibus (GEO), and the list of 431 antigen presentation and process-related genes was obtained from the InnateDB database. Subsequently, APP-related CAFs were identified by nonnegative matrix factorization (NMF) based on a single-cell seq (scRNA) matrix. GSVA functional enrichment analyses were performed to depict the biological functions. A risk signature based on APPCAF-related genes (APPCAFRS) was developed by least absolute shrinkage and selection operator (LASSO) regression analysis, and the independence of the risk score as a prognostic factor was evaluated by univariate and multivariate Cox regression analyses. Furthermore, a biochemical recurrence-free survival (BCRFS)-related nomogram was established, and immune-related characteristics were assessed using the ssGSEA function. The immune treatment response in PRAD was further analyzed by the Tumor Immune Dysfunction and Exclusion (TIDE) tool. The expression levels of hub genes in APPCAFRS were verified in cell models.ResultsThere were 134 upregulated and 147 downregulated genes, totaling 281 differentially expressed genes among the primary CAFs. The functions and pathways of 147 downregulated DEGs were significantly enriched in antigen processing and presentation processes, MHC class II protein complex and transport vesicle, MHC class II protein complex binding, and intestinal immune network for IgA production. Androgen withdrawal diminished the activation effect of CAFs on T cells. NMF clustering of CAFs was performed by APPRGs, and pseudotime analysis yielded the antigen presentation and process-related CAF subtype CTSK + MRC2 + CAF-C1. CTSK + MRC2 + CAF-C1 cells exhibited ligand‒receptor connections with epithelial cells and T cells. Additionally, we found a strong association between CTSK + MRC2 + CAF-C1 cells and inflammatory CAFs. Through differential gene expression analysis of the CTSK + MRC2 + CAF-C1 and NoneAPP-CAF-C2 subgroups, 55 significant DEGs were identified, namely, APPCAFRGs. Based on the expression profiles of APPCAFRGs, we divided the TCGA-PRAD cohort into two clusters using NMF consistent cluster analysis, with the genetic coefficient serving as the evaluation index. Four APPCAFRGs, THBS2, DPT, COL5A1, and MARCKS, were used to develop a prognostic signature capable of predicting BCR occurrence in PRAD patients. Subsequently, a nomogram with stability and accuracy in predicting BCR was constructed based on Gleason grade (p = n.s.), PSA (p < 0.001), T stage (p < 0.05), and risk score (p < 0.01). The analysis of immune infiltration showed a positive correlation between the abundance of resting memory CD4 + T cells, M1 macrophages, resting dendritic cells, and the risk score. In addition, the mRNA expression levels of THBS2, DPT, COL5A1, and MARCKS in the cell models were consistent with the results of the bioinformatics analysis.ConclusionsAPPCAFRS based on four potential APPCAFRGs was developed, and their interaction with the immune microenvironment may play a crucial role in the progression to castration resistance of PRAD. This novel approach provides valuable insights into the pathogenesis of PRAD and offers unexplored targets for future research.

Identification and validation of iron metabolism genes in osteoporosis

BackgroundOsteoporosis is the most common metabolic bone disease in humans. Exploring the expression difference of iron metabolism-related genes in osteoporosis can provide a new target for diagnosis and treatment.MethodsFirst, we used online databases to identify differentially expressed genes (DEGs) related to iron metabolism in patients with osteoporosis. The differential genes were comprehensively analyzed by bioinformatics method (GO, KEGG, GSEA, immune infiltration analysis, PPI). The expression levels of hub genes and important signaling pathways were verified by qRT-PCR and Western blotting.ResultsA total of 23 iron metabolism-related genes with significant differences were identified, which were enriched in “regulation of protein dephosphorylation” and “negative regulation of protein dephosphorylation”. The GSEA results, heme metabolism and Myc targets v1 were among the top two pathways, both upregulated. The immune infiltration analysis revealed that the expressions of genes such as ABCA5, D2HGDH, GNAI2, and CTSW were correlated with the infiltration degree of significantly different cells. The PPI network contained 12 differentially expressed iron metabolism-related genes. Additionally, YWHAE, TGFB1, PPP1R15A, TOP2A, and CALR were mined as hub genes using the Cytoscape software. qRT PCR showed that the expression of TGF-β1, YWHAE, TOP2A and CALR increased. We also verified the expression of related proteins and genes in the oxidative stress signaling pathway by qRT PCR and Western blotting. The results showed that Mob1, YAP and TAZ molecules were highly expressed at the gene and protein levels.ConclusionsThese differentially expressed iron metabolism-related genes could provide new potential targets for the diagnosis and treatment of osteoporosis.

Identification of the Role of Apoptosis-Related Genes in Myocardial Ischemia-Reperfusion Injury

Myocardial ischemia-reperfusion injury (MIRI) is a complex phenomenon that often occurs in patients with ischemic heart disease. The potential molecular mechanism of MIRI needs to be more precise. This paper aims to identify the potential biomarkers of MIRI through a series of bioinformatics methods. As a kind of programmed cell death, focal death is closely related to inflammation. It plays a crucial role in tumor diseases. Therefore, this paper corrected several sets of transcriptome data of MIRI in the GEO cohort in batches, and differentially expressed genes (DEGs) were obtained. In addition, the genes related to scorch death were collected and intersected with DEGs to get the intersection gene. Then, we screen hub genes from the intersection genes based on three algorithms of cytoHubba. The hub genes were analyzed by the mRNA-miRNA interaction network, mRNA-TF interaction network, and mRNA-drug network. The hub genes obtained in this paper have interaction with many drugs, which may be the potential therapeutic target of MIRI. In addition, we performed RT-qPCR to validate the mRNA expression levels of hub genes.

Use of Machine Learning for the Identification and Validation of Immunogenic Cell Death Biomarkers and Immunophenotypes in Coronary Artery Disease.

Immunogenic cell death (ICD) is part of the immune system's response to coronary artery disease (CAD). In this study, we bioinformatically evaluated the diagnostic and therapeutic utility of immunogenic cell death-related genes (IRGs) and their relationship with immune infiltration features in CAD. We acquired the CAD-related datasets GSE12288, GSE71226, and GSE120521 from the Gene Expression Omnibus (GEO) database and the IRGs from the GeneCards database. After identifying the immune cell death-related differentially expressed genes (IRDEGs), we developed a risk model and detected immune subtypes in CAD. IRDEGs were identified using least absolute shrinkage and selection operator (LASSO) analysis. Using a nomogram, we confirmed that both the LASSO model and ICD signature genes had good diagnostic performance. There was a high degree of coincidence and immune representativeness between two CAD groups based on characteristic genes and hub genes. Hub genes were associated with the interaction of neuroactive ligands with receptors and cell adhesion receptors. The two groups differed in terms of adipogenesis, allograft rejection, and apoptosis, as well as the ICD signature and hub gene expression levels. The two CAD-ICD subtypes differed in terms of immune infiltration. Quantitative real-time PCR (qRT-PCR) correlated CAD with the expression of OAS3, ITGAV, and PIBF1. The ICD signature genes are candidate biomarkers and reference standards for immune grouping in CAD and can be beneficial in precise immune-targeted therapy.

Identification of novel biomarkers in obstructive sleep apnea via integrated bioinformatics analysis and experimental validation

Background Obstructive sleep apnea (OSA) is a complex and multi-gene inherited disease caused by both genetic and environmental factors. However, due to the high cost of diagnosis and complex operation, its clinical application is limited. This study aims to explore potential target genes associated with OSA and establish a corresponding diagnostic model. Methods This study used microarray datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) related to OSA and perform functional annotation and pathway analysis. The study employed multi-scale embedded gene co-expression network analysis (MEGENA) combined with least absolute shrinkage and selection operator (LASSO) regression analysis to select hub genes and construct a diagnostic model for OSA. In addition, the study conducted correlation analysis between hub genes and OSA-related genes, immunoinfiltration, gene set enrichment analysis (GSEA), miRNA network analysis, and identified potential transcription factors (TFs) and targeted drugs for hub genes. Finally, the study used chronic intermittent hypoxia (CIH) mouse model to simulate OSA hypoxic conditions and verify the expression of hub genes in CIH mice. Results In this study, a total of 401 upregulated genes and 275 downregulated genes were identified, and enrichment analysis revealed that these differentially expressed genes may be associated with pathways such as vasculature development, cellular response to cytokine stimulus, and negative regulation of cell population proliferation. Through MEGENA combined with LASSO regression, seven OSA hub genes were identified, including C12orf54, FOS, GPR1, OR9A4, MYO5B, RAB39B, and KLHL4. The diagnostic model constructed based on these genes showed strong stability. The expression levels of hub genes were significantly correlated with the expression levels of OSA-related genes and mainly acted on pathways such as the JAK/STAT signaling pathway and the cytosolic DNA-sensing pathway. Drug-target predictions for hub genes were made using the Connectivity Map (CMap) database and the Drug-Gene Interaction database (Dgidb), which identified targeted therapeutic drugs for the hub genes. In vivo experiments showed that the hub genes were all decreasing in the OSA mouse model. Conclusions This study identified novel biomarkers for OSA and established a reliable diagnostic model. The transcriptional changes identified may help to reveal the pathogenesis, mechanisms, and sequelae of OSA.

Knockdown of SLC39A14 inhibits glioma progression by promoting erastin-induced ferroptosis SLC39A14 knockdown inhibits glioma progression

BackgroundFerroptosis is a newly classified form of regulated cell death with implications in various tumor progression pathways. However, the roles and mechanisms of ferroptosis-related genes in glioma remain unclear.MethodsBioinformatics analysis was employed to identify differentially expressed ferroptosis-related genes in glioma. The expression levels of hub genes were assessed using real-time reverse transcriptase-polymerase chain reaction (RT-qPCR). To explore the role of SLC39A14 in glioma, a series of in vitro assays were conducted, including cell counting kit-8 (CCK-8), 5-ethynyl-2’-deoxyuridine (EdU), flow cytometry, wound healing, and Transwell assays. Enzyme-linked immunosorbent assay (ELISA) was utilized to measure the levels of indicators associated with ferroptosis. Hematoxylin-eosin (HE) and immunohistochemistry (IHC) staining were performed to illustrate the clinicopathological features of the mouse transplantation tumor model. Additionally, Western blot analysis was used to assess the expression of the cGMP-PKG pathway-related proteins.ResultsSeven ferroptosis-related hub genes, namely SLC39A14, WWTR1, STEAP3, NOTCH2, IREB2, HIF1A, and FANCD2, were identified, all of which were highly expressed in glioma. Knockdown of SLC39A14 inhibited glioma cell proliferation, migration, and invasion, while promoting apoptosis. Moreover, SLC39A14 knockdown also facilitated erastin-induced ferroptosis, leading to the suppression of mouse transplantation tumor growth. Mechanistically, SLC39A14 knockdown inhibited the cGMP-PKG signaling pathway activation.ConclusionSilencing SLC39A14 inhibits ferroptosis and tumor progression, potentially involving the regulation of the cGMP-PKG signaling pathway.