A Comparative Bioinformatics Analysis of the Transcriptomic Profiles of Peri-Implantitis and Periodontitis and Their Common Signaling Pathways with Atherosclerosis.

The adenylate cyclase toxin of Bacillus anthracis is a potent promoter of TH17 cell development

In the present study, gene expression profiles were analyzed to identify the molecular mechanisms underlying gastric cardia adenocarcinoma (GCA) and gastric non-cardia adenocarcinoma (GNCA). A gene expression dataset (accession number GSE29272) was downloaded from Gene Expression Omnibus, and consisted of 62 GCA samples and 62 normal controls, as well as 72 GNCA samples and 72 normal controls. The two groups of differentially-expressed genes (DEGs) were compared to obtain common and unique DEGs. A differential analysis was performed using the Linear Models for Microarray Data package in R. Functional enrichment analysis was conducted for the DEGs using the Database for Annotation, Visualization and Integrated Discovery. Protein-protein interaction (PPI) networks were constructed for the DEGs with information from the Search Tool for the Retrieval of Interacting Genes. Subnetworks were extracted from the whole network with Cytoscape. Compared with the control, 284 and 268 genes were differentially-expressed in GCA and GNCA, respectively, of which 194 DEGs were common between GCA and GNCA. Common DEGs [e.g., claudin (CLDN)7, CLDN4 and CLDN3] were associated with cell adhesion and digestion. GCA-unique DEGs [e.g., MAD1 mitotic arrest deficient like 1, cyclin (CCN)B1, CCNB2 and CCNE1] were associated with the cell cycle and the regulation of cell proliferation, while GNCA-unique DEGs (e.g., GATA binding protein 6 and hyaluronoglucosaminidase 1) were implicated in cell death. A PPI network with 141 nodes and 446 edges were obtained, from which two subnetworks were extracted. Genes [e.g., fibronectin 1, collagen type I α2 chain (COL1A2) and COL1A1] from the two subnetworks were implicated in extracellular matrix organization. These common DEGs could advance our understanding of the etiology of gastric cancer, while the unique DEGs in GCA and GNCA could better define the properties of specific cancers and provide potential biomarkers for diagnosis, prognosis or therapy.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

The objective was to use bioinformatics to analyze the potential key genes involved in the mechanism of sulforaphane's protective effects in sepsis-associated acute kidney injury (SA-AKI) and to identify potential intervention targets. Gene Expression Omnibus (GEO) gene chip datasets containing gene expression profiles from kidney tissues of SA-AKI patients and normal controls were selected. Upregulated differentially expressed genes (DEGs) were identified using GEO2R. Protein-protein interaction (PPI), GO, and KEGG enrichment analyses were performed. Allyl isothiocyanate's target genes were analyzed in the ChEMBL database, and intersections with the above DEGs were presented in Venn diagrams. Rat tissues were examined for FKBP1A expression using qRT-PCR. A total of 17 DEGs related to SA-AKI were obtained (|log fold change| > 0 and P < .05). KEGG pathway analysis indicated that the primary pathways linked to the elevated DEGs were glycogen breakdown, leukocyte trans-endothelial migration, and T-cell receptor, TNF, and NF-κB signaling. The module and PPI network analysis of common DEGs revealed one cluster and four candidate genes, including OASL, TRRAP, FKBP1A, and BANF. ChEMBL database analysis identified 339 target genes for allyl isothiocyanate, and the intersection with upregulated DEGs related to SA-AKI injury yielded two co-expressed genes, FKBP1A and TRRAP. According to the findings of the qRT-PCR assay, the kidney tissues of the model cohort showed significantly higher expression levels of FKBP1A mRNA than the control cohort (P = .0142). Allyl isothiocyanate may alleviate SA-AKI injury by targeting FKBP1/NF-κB.

BackgroundThe severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body’s ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients.MethodsCOVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the “Deseq2” package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the “limma” package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification.ResultsIn this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients.ConclusionIn this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.

Multiple myeloma (MM), second most common hematological malignancy, still remains beyond cure because of acquirement of drug resistance. Proteasome inhibitor such as carfilzomib (Cfz) therapy which has been used as one of the key therapies against MM recently, is obstructed by the incidence of Cfz resistance. The underlying mechanism of this acquired Cfz resistance in MM is very little understood. Therefore, the current study was aimed to investigate the differentially expressed genes (DEGs), associated micro RNAs (miRNAs), and transcription factors (TFs) from the microarray datasets of Cfz resistant and Cfz sensitive MM cell lines, obtained from Gene Expression Omnibus (GEO) database. DEGs were detected using GEO2R tool from two datasets and common DEGs were identified through Venn diagram. Gene ontology (GO) and pathway enrichment analysis were performed on DAVID database. Through STRING database and Cytoscape tool, protein–protein interaction (PPI) network of DEGs was constructed. Genetic alterations in DEGs were investigated using COSMIC database. Interaction network between DEGs and miRNAs as well as TFs were obtained and constructed by using mirDIP, TRRUST, and miRNet tools. Drug gene interaction analysis was performed to identify potential drug molecules on DGIdb tool. Several common DEGs were identified in Cfz resistant MM. PDGF, VEGF, and Wnt signaling pathways were significantly enriched and might be involved in MM progression. miRNA analysis identified hsa-mir-124-3p, hsa-mir-26a-5p that can target DEGs. Various drug molecules such as dabrafenib, vemurafenib, and venetoclax that could potentially attenuate the MM pathophysiology, were detected. The entire study might provide a new understanding about the Cfz resistance in MM.

The complexity of interstitial cystitis/bladder pain syndrome (IC/BPS) has led to considerable uncertainty in terms of diagnosis and prevalence of the condition. Here, we try to identify the IC/BPS-associated genes through an integrated analysis of Gene Expression Omnibus (GEO) datasets and confirm experimentally to predict the pathologic diagnosis of IC/BPS. Data mining analysis of GEO datasets (GSE621, GSE11783, GSE28242, and GSE57560) revealed a total of 53 (51 upregulated and two downregulated) common differentially expressed genes (DEGs) in IC/BPS. A protein–protein interaction (PPI) network was then constructed with the 53 common DEGs using Cytoscape v3.7.2, and subsequently, six hub genes (CD5, CD38, ITGAL, IL7R, KLRB1, and IL7R) were identified using cytoHubba v0.1 that were upregulated in IC/BPS. Enrichment analysis of common DEGs revealed that hematopoietic cell lineage, immune system, and T-cell receptor (TCR) signaling in naïve CD4+ T cell signaling pathways were prominently involved with the common 51 upregulated DEGs. The two common downregulated DEGs may enrich linoleic acid metabolism and synthesis of epoxy (EET) and dihydroxyeicosatrienoic acid (DHET) signaling pathways in IC/BPS. Moreover, our RT-PCR data confirmed that the expression of the five hub genes (CD38, ITGAL, IL7R, KLRB1, and IL7R) was significantly augmented in IC/BPS patients’ samples when compared with their normal counterparts. In this study, we systematically predict the significant biomarkers and possible signaling pathways involved in IC/BPS, confirming the differential expression of the hub genes in tissue samples from patients with IC/BPS. Thus, the hub genes might be used as potential diagnostic biomarkers of IC/BPS.

This study aimed at investigating the crucial mechanisms underlying non-small cell lung cancer (NSCLC). NSCLC-related microarray data GSE27262 were downloaded from Gene Expression Omnibus, including 7 NSCLC 1a samples, 18 NSCLC 1b samples, and their matched normal samples. The common differentially expressed genes (DEGs) between NSCLC 1a and NSCLC 1b samples were identified, followed by protein-protein interaction (PPI) network construction, functional enrichment analysis, and weighted gene co-expression network analysis (WGCNA). Further, the key DEGs were confirmed based on the lung adenocarcinoma (LUAD) data from the Cancer Genome Atlas (TCGA) database, followed by clinical prognostic analysis. There were 802 (NSCLC 1a) and 734 (NSCLC 1b) DEGs identified. By intersection analysis, we obtained 255 upregulated and 97 downregulated common DEGs. Upregulated DEGs were significantly enriched in the plasma membrane and extracellular region, whereas the downregulated DEGs were significantly enriched in the cytoskeleton and cell cycle process. Topoisomerase (DNA) II alpha (TOP2A) and cyclin B1 (CCNB1) were hub nodes in the PPI network. Based on WGCNA, 5 modules were obtained. In the module MEgreen, DEGs were significantly enriched in cytokine-cytokine receptor interaction and focal adhesion. Notably, 1797 DEGs were identified based on the LUAD data from the TCGA database; among them, 285 DEGs were common DEGs identified from GSE27262 data. Upregulation of TOP2A and CCNB1 was correlated with poor survival of patients. The hub genes and key pathways identified in this study are helpful for a comprehensive knowledge of the molecular mechanisms of NSCLC.

Aims: This study was designed to investigate differentially expressed genes (DEGs) in the annulus fibrosus (AF), nucleus pulposus (NP), and whole blood (WB) of intervertebral disk degeneration (IDD) patients. Materials and Methods: We retrieved microarray data set GSE70362, which contains the gene expression profiles of 24 AF and 24 NP samples from the Gene Expression Omnibus and identified DEGs in degenerative AF (AF-DEGs) and NP (NP-DEGs) samples compared with nondegenerative samples. We also examined gene expression profiles in WB from patients with IDD and healthy volunteers to identify DEGs in WB (WB-DEGs). We performed functional analyses on the DEGs common to AF-DEGs, NP-DEGs, and WB-DEGs. Expression of the common DEGs was partially validated by quantitative real-time-polymerase chain reaction (QRT-PCR). Results: In total, 846 AF-DEGs, 902 NP-DEGs, and 862 WB-DEGs were identified, and 22 DEGs were common among the three groups. Functional analyses showed that the common DEGs were enriched in 33 biological processes, 16 cellular components, 4 molecular functions, and 9 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways; 13 of the common DEGs were included in the protein-protein interaction (PPI) network and superoxide dismutase 2 (SOD2) was identified as a hub gene in the PPI network. The QRT-PCR results for the expression of the genes protein disulfide isomerase family A member 4, FKBP prolyl isomerase 11, ectonucleotide pyrophosphatase/phosphodiesterase 4, SOD2, and actin binding LIM protein 1, were consistent with the gene chip hybridization results. Conclusions: This study identified key genes for future investigations of the underlying molecular mechanisms of IDD. These genes may provide future targets for the clinical treatment and diagnosis of IDD.

BackgroundParkinson’s disease (PD) is a degenerative neurologic disease. This study aimed to undertake bioinformatics analysis using the publicly available Gene Expression Omnibus (GEO) database to integrate mRNA expression data from patients with PD and to compare differentially expressed genes (DEGs) in tissue from the substantia nigra and whole blood from patients with PD and normal controls.Material/MethodsIntegrated network analysis included GEO datasets to identify DEGs in the substantia nigra and whole blood of patients with PD. Bioinformatics analysis was used to identify the roles of the DEGs and included the development of protein–protein interaction (PPI) networks and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Expression levels of DEGs were validated using GSE100054.ResultsIn patients with PD, there were 1,076 upregulated DEGs and 1,075 down-regulated DEGs in the substantia nigra tissue, and 699 upregulated and 930 down-regulated DEGs in whole blood samples. The apoptotic process, the mitogen-activated protein kinase (MAPK) signaling pathway, the Wnt signaling pathway, and the Notch signaling pathway were significantly enriched in DEGs in the substantia nigra in PD. In both the substantia nigra and whole blood, the most common DEGs were significantly enriched in lysosomes, PD, Alzheimer’s disease, Huntington’s disease. SORT1 and CRYAB were the hub proteins in the network of the substantia nigra; PSMA1 and SDHA were the hub proteins in the network of whole blood in PD.ConclusionsDEGs, including SORT1, CRYAB, PSMA1, and SDHA may have roles in the pathogenesis of PD through the MAPK, Wnt, and Notch signaling pathways.

Breast cancer is one of the most common cancers. Although the present molecular classification improves the treatment effect and prognosis of breast cancer, the heterogeneity of the molecular subtype remains very complex, and the applicability and effectiveness of treatment methods are still limited leading to poorer patient prognosis than expected. Further identification of more refined molecular typing based on gene expression profile will yield better understanding of the heterogeneity, improving treatment effects and prolonging prognosis of patients. Here, we downloaded the mRNA expression profiles and corresponding clinical data of patients with breast cancer from public databases and performed typical molecular typing using PAM50 (Prediction Analysis of Microarray 50) method. Comparative analyses were performed to screen the common and specific differentially expressed genes (DEGs) between cancer and corresponding para-cancerous tissues in each breast cancer subtype. The GO and KEGG analyses of the DEGs were performed to enrich the common and specific functional progress and signaling pathway involved in breast cancer subtypes. A total of 38 key common and specific DEGs were identified and selected based on the validated results, GO/KEGG enrichments, and the priority of expression, including four common DEGs and 34 specific DEGs in different subtypes. The prognostic value of these key common and specific DEGs was further analyzed to obtain useful potential markers in clinic. Finally, the potential roles and the specific prognostic values of the common and specific DEGs were speculated and summarized in total breast cancer and different subtype breast cancer based on the results of these analyses. The findings of our study provide the basis of more refined molecular typing of breast cancer, potential new therapeutic targets and prognostic markers for different breast cancer subtypes

Hepatocellular carcinoma (HCC)is the fifth most common malignancy associated with high mortality. One of the risk factors for HCC is chronic hepatitis B virus (HBV) infection. The treatment strategy for the disease is dependent on the stage of HCC, and the Barcelona clinic liver cancer (BCLC) staging system is used in most HCC cases. However, the molecular characteristics of HBV-related HCC in different BCLC stages are still unknown. Using GSE14520 microarray data from HBV-related HCC cases with BCLC stages from 0 (very early stage) to C (advanced stage) in the gene expression omnibus (GEO) database, differentially expressed genes (DEGs), including common DEGs and unique DEGs in different BCLC stages, were identified. These DEGs were located on different chromosomes. The molecular functions and biology pathways of DEGs were identified by gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and the interactome networks of DEGs were constructed using the NetVenn online tool. The results revealed that both common DEGs and stage-specific DEGs were associated with various molecular functions and were involved in special biological pathways. In addition, several hub genes were found in the interactome networks of DEGs. The identified DEGs and hub genes promote our understanding of the molecular mechanisms underlying the development of HBV-related HCC through the different BCLC stages, and might be used as staging biomarkers or molecular targets for the treatment of HCC with HBV infection.

Atherosclerotic cerebral infarction (ACI) seriously threatens the health of the senile patients, and the strategies are urgent for the diagnosis and treatment of ACI. This study investigated the mRNA profiling of the patients with ischemic stroke and atherosclerosis via excavating the datasets in the GEO database and attempted to reveal the biomarkers and molecular mechanism of ACI. In this study, GES16561 and GES100927 were obtained from Gene Expression Omnibus (GEO) database, and the related differentially expressed genes (DEGs) were analyzed with R language. Furthermore, the DEGs were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Besides, the protein-protein interaction (PPI) network of DEGs was analyzed by STRING database and Cytoscape. The results showed that 133 downregulated DEGs and 234 upregulated DEGs were found in GES16561, 25 downregulated DEGs and 104 upregulated DEGs were found in GSE100927, and 6 common genes were found in GES16561 and GES100927. GO enrichment analysis showed that the functional models of the common genes were involved in neutrophil activation, neutrophil degranulation, neutrophil activation, and immune response. KEGG enrichment analysis showed that the DEGs in both GSE100927 and GSE16561 were connected with the pathways including Cell adhesion molecules (CAMs), Cytokine-cytokine receptor interaction, Phagosome, Antigen processing and presentation, and Staphylococcus aureus infection. The PPI network analysis showed that 9 common DEGs were found in GSE100927 and GSE16561, and a cluster with 6 nodes and 12 edges was also identified by PPI network analysis. In conclusion, this study suggested that FCGR3A and MAPK pathways were connected with ACI.

Lupus nephritis (LN) is one of the serious complications of systemic lupus erythematosus. The aim of this study was to identify core genes and pathways involved in the pathogenesis of LN. We screened differentially expressed genes (DEGs) in LN patients using mRNA expression profile data from the Gene Expression Omnibus. The functional and pathway enrichment analysis of DEGs was performed utilizing the Database for annotation, Visualization and Integrated Discovery. Target genes with differentially expressed miRNAs (DEMIs) were predicted using the miRTarBase database, and the intersection between these target genes and DEGs was selected to be studied further. In total, 107 common DEGs (CDEGs) were identified from the Tub_LN group and Glom_LN group, and 66 DEMIs were identified. Fifty-three hub genes and two significant modules were identified from the protein-protein interaction (PPI) network, and a miRNA-mRNA network was constructed. The CDEGs, module genes in the PPI network and genes intersecting with the CDEGs and target genes of DEMIs were all associated with the PI3K-Akt signalling pathway. In summary, this study reveals some crucial genes and pathways potentially involving in the pathogenesis of LN. These findings provide a new insight for the research and treatment of LN.

COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein–protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.