Identification of hub genes and pathways in mouse with cold exposure

Background Tuberculosis (TB) is a serious chronic bacterial infection caused by Mycobacterium tuberculosis (MTB). It is one of the deadliest diseases in the world and a heavy burden for people all over the world. However, the hub genes involved in the host response remain largely unclear. Methods The data set GSE11199 was studied to clarify the potential gene network and signal transduction pathway in TB. The subjects were divided into latent tuberculosis and pulmonary tuberculosis, and the distribution of differentially expressed genes (DEGs) was analyzed between them using GEO2R. We verified the enriched process and pathway of DEGs by making use of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The construction of protein-protein interaction (PPI) network of DEGs was achieved through making use of the Search Tool for the Retrieval of Interacting Genes (STRING), aiming at identifying hub genes. Then, the hub gene expression level in latent and pulmonary tuberculosis was verified by a boxplot. Finally, through making use of Gene Set Enrichment Analysis (GSEA), we further analyzed the pathways related to DEGs in the data set GSE11199 to show the changing pattern between latent and pulmonary tuberculosis. Results We identified 98 DEGs in total in the data set GSE11199, 91 genes upregulated and 7 genes downregulated included. The enrichment of GO and KEGG pathways demonstrated that upregulated DEGs were mainly abundant in cytokine-mediated signaling pathway, response to interferon-gamma, endoplasmic reticulum lumen, beta-galactosidase activity, measles, JAK-STAT signaling pathway, cytokine-cytokine receptor interaction, etc. Based on the PPI network, we obtained 4 hub genes with a higher degree, namely, CTLA4, GZMB, GZMA, and PRF1. The box plot showed that these 4 hub gene expression levels in the pulmonary tuberculosis group were higher than those in the latent group. Finally, through Gene Set Enrichment Analysis (GSEA), it was concluded that DEGs were largely associated with proteasome and primary immunodeficiency. Conclusions This study reveals the coordination of pathogenic genes during TB infection and offers the diagnosis of TB a promising genome. These hub genes also provide new directions for the development of latent molecular targets for TB treatment.

Peripheral T-cell lymphoma (PTCL) is a very aggressive and heterogeneous hematological malignancy and has no effective targeted therapy. The molecular pathogenesis of PTCL remains unknown. In this study, we chose the gene expression profile of GSE6338 from the Gene Expression Omnibus (GEO) database to identify hub genes and key pathways and explore possible molecular pathogenesis of PTCL by bioinformatic analysis. Differentially expressed genes (DEGs) between PTCL and normal T cells were selected using GEO2R tool. Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, the Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) were utilized to construct protein-protein interaction (PPI) network and perform module analysis of these DEGs. A total of 518 DEGs were identified, including 413 down-regulated and 105 up-regulated genes. The down-regulated genes were enriched in osteoclast differentiation, Chagas disease and mitogen-activated protein kinase (MAPK) signaling pathway. The up-regulated genes were mainly associated with extracellular matrix (ECM)-receptor interaction, focal adhesion and pertussis. Four important modules were detected from the PPI network by using MCODE software. Fifteen hub genes with a high degree of connectivity were selected. Our study identified DEGs, hub genes and pathways associated with PTCL by bioinformatic analysis. Results provide a basis for further study on the pathogenesis of PTCL.

This study aimed to explore critical genes as potential biomarkers for the diagnosis and prognosis of colorectal cancer (CRC) for clinical utility. To identify and screen candidate genes involved in CRC carcinogenesis and disease progression, we downloaded microarray datasets GSE89076, GSE73360, and GSE32323 from the GEO database identified differentially expressed genes (DEGs), and performed a functional enrichment analysis. A protein-protein interaction network was constructed, and correlated module analysis was performed using STRING and Cytoscape. The Kaplan-Meier survival curve shows the survival of the hub genes. The expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), and PCNA in tissues and changes in tumor grade were analyzed. A total of 329 DEGs were identified, including 264 upregulated and 65 downregulated genes. The functions and pathways of DEGs include the mitotic cell cycle, poly(A) RNA binding replication, ATP binding, DNA replication, ribosome biogenesis in eukaryotes, and RNA transport. Forty-seven Hub genes were identified, and biological process analysis showed that these genes were mainly enriched in cell cycle and DNA replication. Patients with mutations in CDK1, PCNA, and CCNB1 had poorer survival rates. CDK1, PCNA, and CCNB1 were significantly overexpressed in the tumor tissues. The expression of CDK1 and CCNB1 gradually decreased with increasing tumor grade. CDK1, CCNB1, and PCNA can be used as potential markers for the diagnosis and prognosis of CRC. These genes are overexpressed in colon cancer tissues and are associated with low survival rates in CRC patients.

BackgroundBecause of the complex mechanisms of injury, conventional surgical treatment and early blood pressure control does not significantly reduce mortality or improve patient prognosis in cases of intracerebral hemorrhage (ICH). We aimed to identify the hub genes associated with intracerebral hemorrhage, to act as therapeutic targets, and to identify potential small-molecule compounds for treating ICH.MethodsThe GSE24265 dataset, consisting of data from four perihematomal brain tissues and seven contralateral brain tissues, was downloaded from the Gene Expression Omnibus (GEO) database and screened for differentially expressed genes (DEGs) in ICH, with a fold change (FC) value of (|log2FC|) > 2 and a P-value of <0.05 set as cut-offs. The functional annotation of DEGs was performed using Gene Ontology (GO) resources, and the cell signaling pathway analysis of DEGs was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG), with a P-value of <0.05 set as the cut-off. We constructed a protein-protein interaction (PPI) network to clarify the interrelationships between the different DEGs and to select the hub genes with significant interactions. Next, the DEGs were analyzed using the CMap tool to identify small-molecule compounds with potential therapeutic effects. Finally, we verified the expression levels of the hub genes by RT-qPCR on the rat ICH model.ResultA total of 59 up-regulated genes and eight down-regulated genes associated with ICH were identified. The biological functions of DEGs associated with ICH are mainly involved in the inflammatory response, chemokine activity, and immune response. The KEGG analysis identified several pathways significantly associated with ICH, including but not limited to HIF-1, TNF, toll-like receptor, cytokine-cytokine receptor interaction, and chemokine molecules. A PPI network consisting of 57 nodes and 373 edges was constructed using STRING, and 10 hub genes were identified with Cytoscape software. These hub genes are closely related to secondary brain injury induced by ICH. RT-qPCR results showed that the expression of ten hub genes was significantly increased in the rat model of ICH. In addition, a CMap analysis of three small-molecule compounds revealed their therapeutic potential.ConclusionIn this study we obtained ten hub genes, such as IL6, TLR2, CXCL1, TIMP1, PLAUR, SERPINE1, SELE, CCL4, CCL20, and CD163, which play an important role in the pathology of ICH. At the same time, the ten hub genes obtained through PPI network analysis were verified in the rat model of ICH. In addition, we obtained three small molecule compounds that will have therapeutic effects on ICH, including Hecogenin, Lidocaine, and NU-1025.

Aims: Exploring key genes and potential molecular pathways of ferroptosis in immunoglobulin A nephropathy (IgAN). Methods: The IgAN datasets and ferroptosis-related genes (FRGs) were obtained in the Gene Expression Omnibus (GEO) and FerrDb database. Differentially expressed genes (DEGs) were identified using R software and intersected with FRGs to obtain differentially expressed FRGs (DE-FRGs). After that, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (PEA) and Gene Ontology (GO) functional annotation were performed on DE-FRGs. In the Search Tool for the Retrieval of Interacting Genes (STRING) website, we construct a protein-protein interaction (PPI) network. The PPI network was further investigated with screening hub genes with Cytoscape software. The core genes were then subjected to gene set enrichment analysis (GSEA). Finally, the samples were analyzed for immune infiltration in R, and the correlation between hub genes and immune cells was analyzed. Results: A total of 347 DEGs were identified. CD44, CDO1, CYBB, IL1B, RRM2, AKR1C1, activated transcription factor-3 (ATF3), CDKN1A, GDF15, JUN, MGST1, MIOX, MT1G, NR4A1, PDK4, TNFAIP3, and ZFP36 were determined as DE-FRGs. JUN, IL1B, and ATF3 were then screened as hub genes. GSEA and immune infiltration analysis revealed that the hub genes were closely associated with immune inflammatory responses such as NOD-like receptor signaling, IL-17 signaling, and TNF signaling. Conclusions: Our results show that JUN and ATF3 are possibly critical genes in the process of IgAN ferroptosis and may be related with immune cell infiltration.

Renal cell carcinoma (RCC) is the most common form of kidney cancer, caused by renal epithelial cells. RCC remains to be a challenging public health problem worldwide. Metastases that are resistant to radiotherapy and chemotherapy are the major cause of death from cancer. However, the underlying molecular mechanism regulating the metastasis of RCC is poorly known. Publicly available databases of RCC were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using GEO2R analysis, whereas the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by Gene Set Enrichment Analysis (GSEA) and Metascape. Protein-protein interaction (PPI) network of DEGs was analyzed by STRING online database, and Cytoscape software was used for visualizing PPI network. Survival analysis of hub genes was conducted using GEPIA online database. The expression levels of hub genes were investigated from The Human Protein Atlas online database and GEPIA online database. Finally, the comparative toxicogenomics database (CTD; http://ctdbase.org) was used to identify hub genes associated with tumor or metastasis. We identified 229 DEGs comprising 135 downregulated genes and 94 upregulated genes. Functional analysis revealed that these DEGs were associates with cell recognition, regulation of immune, negative regulation of adaptive immune response, and other functions. And these DEGs mainly related to P53 signaling pathway, cytokine-cytokine receptor interaction, Natural killer cell mediated cytotoxicity, and other pathways are involved. Ten genes were identified as hub genes through module analyses in the PPI network. Finally, survival analysis of 10 hub genes was conducted, which showed that the MMP2 (matrix metallo peptidase 2), DCN, COL4A1, CASR (calcium sensing receptor), GPR4 (G protein-coupled receptor 4), UTS2 (urotensin 2), and LDLR (low density lipoprotein receptor) genes were significant for survival. In this study, the DEGs between RCC and metastatic RCC were analyzed, which assist us in systematically understanding the pathogeny underlying metastasis of RCC. The MMP2, DCN, COL4A1, CASR, GPR4, UTS2, and LDLR genes might be used as potential targets to improve diagnosis and immunotherapy biomarkers for RCC.

The aim of this study was to explore the key genes, microRNA (miRNA), and the pathogenesis of oral squamous cell carcinoma (OSCC) at the molecular level through the analysis of bioinformatics, which could provide a theoretical basis for the screening of drug targets. Data of OSCC were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified via GEO2R analysis. Next, protein-protein interaction (PPI) network of DEGs was constructed through Search Tool for the Retrieval of Interacting Gene and visualized via Cytoscape, whereas the hub genes were screened out with Cytoscape. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by Database for Annotation, Visualization and Integrated Discovery. The miRNA, which might regulate hub genes, were screened out with TargetScan and GO and KEGG analysis of miRNA was performed by DNA Intelligent Analysis-miRPath. Survival analyses of DEGs were conducted via the Kaplan-Meier plotter. Finally, the relationships between gene products and tumors were analyzed by Comparative Toxicogenomics Database. A total of 121 differential genes were identified. One hundred thirty-five GO terms and 56 pathways were obtained, which were mainly related to PI3K-Akt signals pathway, FoxO signaling pathway, Wnt signaling pathway, cell cycle, p53 signaling pathway, cellular senescence, and other pathways; 10 genes were identified as hub genes through modules analyses in the PPI network. Finally, a survival analysis of 10 hub genes was conducted, which showed that the low expression of matrix metalloproteinase (MMP)1, MMP3, and C-X-C motif chemokine ligand (CXCL)1 and the high expression of CXCL9 and CXCL10 resulted in a significantly poor 5-year overall survival rate in patients with OSCC. In this study, the DEGs of OSCC was analyzed, which assists us in a systematic understanding of the pathogenicity underlying occurrence and development of OSCC. The MMP1, MMP3, CXCL1, CXCL9, and CXCL10 genes might be used as potential targets to improve diagnosis and as immunotherapy biomarkers for OSCC.

Background This study was performed to identify genes related to acquired trastuzumab resistance in gastric cancer (GC) and to analyze their prognostic value. Methods The gene expression profile GSE77346 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by using GEO2R. Functional and pathway enrichment was analyzed by using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, and MCODE were then used to construct the protein-protein interaction (PPI) network and identify hub genes. Finally, the relationship between hub genes and overall survival (OS) was analyzed by using the online Kaplan-Meier plotter tool. Results A total of 327 DEGs were screened and were mainly enriched in terms related to pathways in cancer, signaling pathways regulating stem cell pluripotency, HTLV-I infection, and ECM-receptor interactions. A PPI network was constructed, and 18 hub genes (including one upregulated gene and seventeen downregulated genes) were identified based on the degrees and MCODE scores of the PPI network. Finally, the expression of four hub genes (ERBB2, VIM, EGR1, and PSMB8) was found to be related to the prognosis of HER2-positive (HER2+) gastric cancer. However, the prognostic value of the other hub genes was controversial; interestingly, most of these genes were interferon- (IFN-) stimulated genes (ISGs). Conclusions Overall, we propose that the four hub genes may be potential targets in trastuzumab-resistant gastric cancer and that ISGs may play a key role in promoting trastuzumab resistance in GC.

Background: Hepatocellular carcinoma (HCC) frequently recurs and has poor prognosis, and thus it is essential to investigate the molecular mechanisms associated with HCC development using integrated bioinformatics approaches to identify potential therapeutic targets. Methods: Gene expression data from three microarray datasets, namely, GSE36376, GSE45267, and GSE51401 and 318 HCC tissues and 266 adjacent non-tumorous tissues from HCC patients were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were selected with the limma package in R language, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional enrichment analysis. A protein-protein interaction (PPI) network and a sub-network were established using Search Tool for the Retrieval of Interacting Genes (STRING) and visualized with Cytoscape. Results: A total of 2,249 DEGs were identified in the three datasets, which included 1,735 upregulated and 514 downregulated DEGs. Functional annotation of the DEGs using GO analysis identified categories that were mainly associated with mitotic nuclear division, chromosome segregation, and sister chromatid segregation. KEGG pathway analysis showed that the categories of cell cycle and the p53 signaling pathway, which contributes to the development of HCC, were mainly enriched with DEGs. PPI network and sub-network analyses identified cyclin dependent kinase 2 (CDK2), cyclin B1 (CCNB1), and cell division cycle 20 (CDC20) as hub genes. Furthermore, the categories of cell cycle and p53 signaling pathway were enriched with the hub genes CCNB1 and CDK2. Conclusions: DEGs such as CCNB1, CDC20, and CDK2 as well as classified under the categories of the p53 signaling pathway and the cell cycle were associated with HCC and thus may be potentially utilized as therapeutic targets for the treatment of HCC.

This present study was designed to explore key biological characteristics and biomarkers associated with dermal vascular endothelial cells of keloids. GSE121618 dataset was downloaded in the Gene Expression Omnibus (GEO) Database, including the KECs group and NVECs group. Through GEO2R, we have screened the differentially expressed genes (DEGs) and performed gene ontology (GO), Gene Set Enrichment Analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Then, we constructed a protein–protein interaction (PPI) network and analyzed hub genes via the Search Tool for the Retrieval of Interacting Genes (STRING) Online Database and Cytoscape software. Furthermore, experiments were performed to validate the expression of selected genes, including H&E staining, immunohistochemical staining, Western blot, and RT-qPCR. A total of 1040 DEGs were selected with GEO2R online tools. Most of the enriched pathways and processes focus on cell migration, tube development, chemotaxis, cell motility, and regulation of apoptosis. With the assistance of STRING and Cytoscape, hub genes were selected. In our validation experiments of RT-qPCR, the mRNA expression of selected genes has significant differences between different groups in tissue and cell experiments. As was shown in immunohistochemical staining, the proteins of CXCR4, CXCL9, and Caspase-9 had higher expression levels in tissue samples of the Keloid group than the Normal skin group. Western blot and RT-qPCR in dermal vascular endothelial cell experiments were consistent with the aforementioned results. This study has provided a deeper analysis of the pathogenesis of dermal vascular endothelial cells in keloids. Genes of CXCR4, CXCL9, and Caspase-9 may influence the processes of inflammatory responses and vascular endothelial cell apoptosis to exert crucial effects in the development of keloids. Abbreviations: GEO: gene expression omnibus; DEGs: differentially expressed genes; KVECs: keloid vascular endothelial cells; NVECs: normal skin vascular endothelial cells; GO: gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; PPI: protein protein interaction; BP: biological process; CC: cellular component; MF: molecular function; GSEA: gene set enrichment analysis; STRING: search tool for the retrieval of interacting genes; MCODE: molecular complex detection

BackgroundPatients with severe burns continue to display a high mortality rate during the initial shock period. The precise molecular mechanism underlying the change in host response during severe burn shock remains unknown. This study aimed to identify key genes leading to the change in host response during burn shock.MethodsThe GSE77791 dataset, which was utilized in a previous study that compared hydrocortisone administration to placebo (NaCl 0.9%) in the inflammatory reaction of severe burn shock, was downloaded from the Gene Expression Omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs). Functional enrichment analyses of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed. The protein–protein interaction (PPI) network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and then visualized in Cytoscape. In addition, important modules in this network were selected using the Molecular Complex Detection (MCODE) algorithm, and hub genes were identified in cytoHubba, a Cytoscape plugin.ResultsA total of 1059 DEGs (508 downregulated genes and 551 upregulated genes) were identified from the dataset. The DEGs enriched in GO terms and KEGG pathways were related to immune response. The PPI network contained 439 nodes and 2430 protein pairs. Finally, important modules and hub genes were identified using the different Cytoscape plugins. The key genes in burn shock were identified as arginase 1 (ARG1), cytoskeleton-associated protein (CKAP4), complement C3a receptor (C3AR1), neutrophil elastase (ELANE), gamma-glutamyl hydrolase (GGH), orosomucoid (ORM1), and quiescin sulfhydryl (QSOX1).ConclusionThe DEGs, functional terms and pathways, and hub genes identified in the present study can help shed light on the molecular mechanism underlying the changes in host response during burn shock and provide potential targets for early detection and treatment of burn shock.

Arsenic is a toxic metalloid that can cause acute and chronic adverse health problems. Unfortunately, rice, the primary staple food for more than half of the world's population, is generally regarded as a typical arsenic-accumulating crop plant. Evidence indicates that arsenic stress can influence the growth and development of the rice plant, and lead to high concentrations of arsenic in rice grain. But the underlying mechanisms remain unclear. In the present research, the possible molecules and pathways involved in rice roots in response to arsenic stress were explored using bioinformatics methods. Datasets that involving arsenic-treated rice root and the "study type" that was restricted to "Expression profiling by array" were selected and downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between the arsenic-treated group and the control group were obtained using the online web tool GEO2R. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to investigate the functions of DEGs. The protein-protein interactions (PPI) network and the molecular complex detection algorithm (MCODE) of DEGs were analyzed using STRING and Cystoscope, respectively. Important nodes and hub genes in the PPI network were predicted and explored using the Cytoscape-cytoHubba plug-in. Two datasets, GSE25206 and GSE71492, were downloaded from Gene Expression Omnibus (GEO) database. Eighty common DEGs from the two datasets, including sixty-three up-regulated and seventeen down-regulated genes, were then selected. After functional enrichment analysis, these common DEGs were enriched mainly in 10 GO items, including glutathione transferase activity, glutathione metabolic process, toxin catabolic process, and 7 KEGG pathways related to metabolism. After PPI network and MCODE analysis, 49 nodes from the DEGs PPI network were identified, filtering two significant modules. Next, the Cytoscape-cytoHubba plug-in was used to predict important nodes and hub genes. Finally, five genes [Os01g0644000, PRDX6 (Os07g0638400), PRX112 (Os07g0677300), ENO1(Os06g0136600), LOGL9 (Os09g0547500)] were verified and could serve as the best candidates associated with rice root in response to arsenic stress. In summary, we elucidated the potential pathways and genes in rice root in response to arsenic stress through a comprehensive bioinformatics analysis.

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.

Severe neurotoxicity after chimeric antigen receptor T cell (CAR-T) therapy can be a crucial lifethreatening event in diffuse large B-cell lymphoma (DLBCL), and management of those toxicities is still a serious clinical challenge. The underlying mechanisms of CAR-T cell-mediated neurotoxicity remain poorly elucidated because very few studies examine the intact tumor microenvironment before CAR-T cell infusion. Herein, we pur-posed to identify differentially expressed genes (DEGs) related to CAR-T cell-mediated neurotoxicity in the DLBCL microenvironment before CAR-T cell infusion and reveal their potential mechanisms. The mRNA expression profile data of GSE153438 were obtained from the GEO database. The GSE153438 dataset includes 26 samples with non-severe neurotoxicity (grade 0 - 2) and 10 samples with severe neurotoxicity (grade 3 or higher). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) patway enrichment assessment was carried out. We screened the hub gene by protein-protein interaction (PPI) network analysis and Cytoscape software. Gene set enrichment analysis (GSEA) was also analyzed with the GSEA software. Moreover, the predictive value of the hub gene for severe neurotoxicity was evaluated via receiver operating characteristic (ROC) curve analysis. We identified a total of 25 up-regulated DEGs and 26 downregulated DEGs associated with CAR-T cell-mediated neurotoxicity in the DLBCL microenvironment before CAR-T cell infusion. Results of GO analysis showed that DEGs were mainly enriched in T cell activation, leukocyte cell-cell adhesion, and positive regulation of cell adhesion. The KEGG analysis revealed that DEGs were significantly enriched in T cell receptor signaling pathway, cell adhesion molecules, and Epstein-Barr virus infection. GSEA revealed that the glycolysis pathway was significantly associated with severe neurotoxicity. The top centrality hub gene GZMB was identified from the PPI network. ROC curve analysis showed that GZMB had a potential predictive value for severe neurotoxicity. In DLBCL microenvironment before CAR-T cell infusion, we identified T cell activation and glycolysis pathways significantly associated with CAR-T cell-mediated severe neurotoxicity. GZMB might be used as a predictive and therapeutic molecular marker for neurotoxicity. The study suggested that the tumor microenviron-ment before CAR-T cell infusion plays an essential role in the early prediction of neurotoxicity.

The aim of the present study was to identify potential key candidate genes and mechanisms associated with rheumatoid arthritis (RA). Gene expression data from GSE55235, GSE55457 and GSE1919 datasets were downloaded from the Gene Expression Omnibus database. These datasets comprised 78 tissue samples collectively, including 25 healthy synovial membrane samples and 28 RA synovial membrane samples, whilst the 25 osteoarthritis (OA) samples were not included in the analysis. The differentially expressed genes (DEGs) between the two types of samples were identified with the Linear Models for Microarray Analysis package in R. Gene Ontology (GO) functional term and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analyses were also performed. In addition, Protein-Protein Interaction (PPI) network and module analyses were visualized using Cytoscape, and subsequent hub gene identification as well as GO and KEGG enrichment analyses of the modules was performed. Finally, reverse transcription-quantitative PCR (RT-qPCR) was used to validate the expression of the DEGs identified by GO and KEGG analysis in vitro. The analysis identified 491 DEGs, including 289 upregulated and 202 downregulated genes, which were mainly enriched in the following pathways: ‘Cytokine-cytokine receptor interaction’, ‘Rheumatoid arthritis’, ‘Chemokine signaling pathway’, ‘Intestinal immune network for IgA production’ and ‘Primary immunodeficiency’. The top 10 hub genes identified from the PPI network were IL-6, protein tyrosine phosphatase receptor type C, VEGFA, CD86, EGFR, C-X-C chemokine receptor type 4, matrix metalloproteinase 9, CC-chemokine receptor type (CCR)7, CCR5 and selectin L. KEGG signaling pathway enrichment analysis of the top two modules identified from the PPI network revealed that the genes in Module 1 were mainly enriched in the ‘Cytokine-cytokine receptor interaction’ and ‘Chemokine signaling pathway’, whereas analysis of Module 2 revealed that the genes were mainly enriched in ‘Primary immunodeficiency’ and ‘Cytokine-cytokine receptor interaction’. Finally, the results of the RT-qPCR and western blot analysis demonstrated that the expression levels of inflammation and NF-κB signaling pathway-related mRNAs were significantly upregulated following lipopolysaccharide stimulation. In conclusion, the findings of the present study identified key genes and signaling pathways associated with RA, which may improve the current understanding of the molecular mechanisms underlying its development and progression. The identified hub genes may also be used as potential targets for RA diagnosis and treatment.