PLK1 as one novel target for the poor prognosis of bladder cancer: An observational study

Bladder cancer (BC) is one of the most common male malignant tumors and the most common urological tumor. However, the molecular mechanism and role of PLK1 on bladder cancer were unclear. Therefore, the study aims to explore the potential part of the overall survival of bladder cancer through bioinformatics analysis. {"type":"entrez-geo","attrs":{"text":"GSE121711","term_id":"121711"}}GSE121711 and {"type":"entrez-geo","attrs":{"text":"GSE130598","term_id":"130598"}}GSE130598, from the Gene Expression Omnibus database. The GEO2R screened differently expressed genes, and DAVID and Metascape were used for functional annotation. The cytoHubba made hub genes identification and expression. A total of 50 BC participants were recruited. After surgery, 50 BC tumor samples from BC patients and 50 adjacent standard bladder tissue samples were obtained. The RT-qPCR assay was performed to verify the expression of hub genes. The Kaplan–Meier Plotter analyzed the effect of hub gene expression for overall survival of BC. The compulsory module of Molecular Complex Detection tool analysis was shown, which included CDK1, TTK, AURKB, MELK, PLK1, and BUB1. And the six hub genes were up-regulated in the BC compared with the normal tissues. The relative expression levels of CDK1, TTK, AURKB, MELK, PLK1, and BUB1 were significantly higher in BC samples compared with the regular kidney tissue groups. The result demonstrated that CDK1, TTK, AURKB, MELK, PLK1, and BUB1 might be considered biomarkers for BC. Overall survival analysis showed that BC patients with high expression level of PLK1 had poorer overall survival times than those with low expression level (P < .05). The expression levels of CDK1, TTK, AURKB, MELK, and BUB1 was not related to the overall survival of BC patients (P > .05). The PLK1 gene might provide new ideas and evidence for bladder cancer research.     

Identification of potential biomarkers for colorectal cancer by clinical database analysis and Kaplan–Meier curves analysis

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 {"type":"entrez-geo","attrs":{"text":"GSE89076","term_id":"89076"}}GSE89076, {"type":"entrez-geo","attrs":{"text":"GSE73360","term_id":"73360"}}GSE73360, and {"type":"entrez-geo","attrs":{"text":"GSE32323","term_id":"32323"}}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.     

Identification of CKS2 and RRM2 as potential markers of vitiligo using bioinformatics analysis

Previous studies have attempted to elucidate the molecular mechanism of vitiligo; however, its pathogenesis remains unclear. This study aimed to explore biomarkers related to vitiligo through bioinformatic analysis. The microarray datasets {"type":"entrez-geo","attrs":{"text":"GSE53146","term_id":"53146"}}GSE53146 and {"type":"entrez-geo","attrs":{"text":"GSE65127","term_id":"65127"}}GSE65127 were downloaded from the Gene Expression Omnibus database. Firstly, differentially expressed genes (DEGs) in {"type":"entrez-geo","attrs":{"text":"GSE53146","term_id":"53146"}}GSE53146 were screened, and then an enrichment analysis was performed. Secondly, the protein-protein interaction (PPI) network of DEGs was constructed using the STRING database, and the key genes were screened using the MCODE plugin in Cytoscape and verified using {"type":"entrez-geo","attrs":{"text":"GSE65127","term_id":"65127"}}GSE65127. Finally, quantiseq was used to evaluate immune cell infiltration in vitiligo, then to observe the correlation between biomarkers and immune cells. In total, 544 DEGs were identified, including 342 upregulated and 202 downregulated genes. Gene Ontology (GO) enrichment showed that DEGs were related to inflammatory and immune responses, and Kyoto Encyclopedia of Genes and Genomes enrichment showed that DEGs were involved in many autoimmune diseases. In the PPI network, 7 key genes, CENPN, CKS2, PLK4, RRM2, TPX2, CCNA2, and CDC45 were identified by MCODE cluster and verified using the {"type":"entrez-geo","attrs":{"text":"GSE65127","term_id":"65127"}}GSE65127 dataset. With an area under the curve (AUC) > 0.8 as the standard, 2 genes were screened, namely CKS2 and RRM2. Further immune infiltration analysis showed that M2 macrophages were involved in the pathogenesis of vitiligo, whereas CKS2 and RRM2 were both related to M2 macrophages. This study shows that CKS2 and RRM2 have potential as biomarkers of vitiligo and provides a theoretical basis for a better understanding of the pathogenesis of vitiligo.     

Hub gene target of glioblastoma: LOX,SERPINH1 and TGFBI

Glioblastoma (GBM) is a malignant tumor. The long-term prognosis of the patients is poor. Therefore, it is of important clinical value to further explore the pathogenesis and look for molecular markers for early diagnosis and targeted treatment. Two expression profiling datasets [{"type":"entrez-geo","attrs":{"text":"GSE50161","term_id":"50161"}}GSE50161 ({"type":"entrez-geo","attrs":{"text":"GPL570","term_id":"570"}}GPL570 platform), {"type":"entrez-geo","attrs":{"text":"GSE116520","term_id":"116520"}}GSE116520 ({"type":"entrez-geo","attrs":{"text":"GPL10558","term_id":"10558"}}GPL10558 platform)] were respectively downloaded from the gene expression omnibus database. Volcano diagrams show the Differently expressed genes (DEGs) of {"type":"entrez-geo","attrs":{"text":"GSE50161","term_id":"50161"}}GSE50161 and {"type":"entrez-geo","attrs":{"text":"GSE116520","term_id":"116520"}}GSE116520. A Venn diagram revealed 467 common DEGs between the 2 datasets. Lysyl oxidase (LOX), serpin family H member 1 (SERPINH1) and transforming growth factor beta induced (TGFBI) were negatively correlated with the overall survival rate in patients with GBM. The hub genes are high in GBM tumor tissues. The relative expression levels of LOX, SERPINH1 and TGFBI were significantly higher in GBM samples, compared with the normal brain tissues groups. Bioinformatics technology could be a useful tool to predict progression of GBM and to explore the mechanism of GBM.LOX, SERPINH1 and TGFBI may be involved in the mechanism of the occurrence and development of GBM, and may be used as molecular targets for early diagnosis and specific treatment.DEGs identified using GEO2R. Functional annotation of DEGs using Kyoto Encyclopedia of Genes and Genomes and gene body pathway enrichment analysis. Construction of a protein-protein interaction network. The pathway and process enrichment analysis of the hub genes were performed by Metascape. Survival analysis was performed in gene expression profiling interactive analysis. Real-time fluorescent quantitative polymerase chain reaction assay was performed to verify. The animal model was established for western blot test analysis.     

Five-hub genes identify potential mechanisms for the progression of asthma to lung cancer

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 {"type":"entrez-geo","attrs":{"text":"GSE143303","term_id":"143303"}}GSE143303 and {"type":"entrez-geo","attrs":{"text":"GSE18842","term_id":"18842"}}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.     

Identification of endometriosis-associated genes and pathways based on bioinformatic analysis

Endometriosis is associated with dysmenorrhea, chronic pelvic pain, and infertility. The specific mechanism of endometriosis remains unclear. The aim of this study was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of endometriosis.The gene expression profiles of {"type":"entrez-geo","attrs":{"text":"GSE25628","term_id":"25628"}}GSE25628, {"type":"entrez-geo","attrs":{"text":"GSE5108","term_id":"5108"}}GSE5108, and {"type":"entrez-geo","attrs":{"text":"GSE7305","term_id":"7305"}}GSE7305 were downloaded from the gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis was performed using GEO2R. The database for annotation, visualization, and integrated discovery (DAVID) was utilized to analyze the functional enrichment, gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway of the differentially expressed genes. A protein-protein interaction (PPI) network was constructed and module analysis was performed using search tool for the retrieval of interacting genes and cytoscape.A total of 119 common differentially expressed genes were extracted, consisting of 51 downregulated genes and 68 upregulated genes. The enriched functions and pathways of the DEGs and hub genes include DNA strand separation, cellular proliferation, degradation of the extracellular matrix, encoding of smooth muscle myosin as a major contractile protein, exiting the proliferative cycle and entering quiescence, growth regulation, and implication in a wide variety of biological processes.A bioinformatics approach combined with cell experiments in this study revealed that identifying DEGs and hub genes leads to better understanding of the molecular mechanisms underlying the progression of endometriosis, and efficient biomarkers underlying this pathway need to be further investigated.     

CCNB2 as a potential biomarker of bladder cancer via the high throughput technology

Bladder cancer and oral squamous cell carcinoma (OSCC) seriously affect people’s health. However, the relationship between bladder cancer and OSCC remains unclear. Got {"type":"entrez-geo","attrs":{"text":"GSE138206","term_id":"138206"}}GSE138206, {"type":"entrez-geo","attrs":{"text":"GSE146483","term_id":"146483"}}GSE146483, {"type":"entrez-geo","attrs":{"text":"GSE184616","term_id":"184616"}}GSE184616, and bladder cancer datasets {"type":"entrez-geo","attrs":{"text":"GSE65635","term_id":"65635"}}GSE65635, {"type":"entrez-geo","attrs":{"text":"GSE100926","term_id":"100926"}}GSE100926 from Gene Expression Omnibus database. Weighted gene co-expression network analysis was used to identify the significant module. Functional enrichment analysis was performed via the Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes. Furthermore, the Gene Set Enrichment Analysis was also used to complete the enrichment analysis. Comparative Toxicogenomics Database found most relevant diseases to core genes. TargetScan is used to forecast analysis of microRNA and target genes. In Gene Ontology analysis, differentially expressed genes were mostly concentrated in cell differentiation, extrallular region, structural molecule activity, and actin binding. In Kyoto Encyclopedia of Genes and Genomes analysis, the differentially expressed genes were mainly enriched in PI3K-Akt signaling pathway, pathway in cancer, and extracellular matrix-receptor interaction. Seven hub genes (cyclin B2 [CCNB2], TK1, CDC20, PCNA, CKS1B, CDCA5, MCM4) were obtained. Hub genes (CCNB2, CDC20) are highly expressed in OSCC and bladder cancer samples. CCNB2 was one common oncogene of bladder cancer and OSCC.     

Identification of potential core genes in esophageal carcinoma using bioinformatics analysis

Background:Esophageal squamous cell carcinoma (ESCC) is a common human malignancy worldwide. The tumorigenesis mechanism in ESCC is unclear.Materials and methods:To explore potential therapeutic targets for ESCC, we analyzed 3 microarray datasets ({"type":"entrez-geo","attrs":{"text":"GSE20347","term_id":"20347"}}GSE20347, {"type":"entrez-geo","attrs":{"text":"GSE38129","term_id":"38129"}}GSE38129, and {"type":"entrez-geo","attrs":{"text":"GSE67269","term_id":"67269"}}GSE67269) derived from the gene expression omnibus (GEO) database. Then, the GEO2R tool was used to screen out differently expressed genes (DEGs) between ESCC and normal tissue. Gene ontology function and kyoto encyclopedia of genes and genomes pathway enrichment analysis were performed using the database for annotation, visualization and integrated discovery to identify the pathways and functional annotation of DEGs. Protein–protein interaction of these DEGs was analyzed based on the search tool for the retrieval of interacting genes database and visualized by Cytoscape software. In addition, we used encyclopedia of RNA interactomes (ENCORI), gene expression profiling interactive analysis (GEPIA), and the human protein atlas to confirm the expression of hub genes in ESCC. Finally, GEPIA was used to evaluate the prognostic value of hub genes expression in ESCC patients and we estimated the associations between hub genes expression and immune cell populations (B Cell, CD8⁺ T Cell, CD4⁺ T Cell, Macrophage, Neutrophil, and Dendritic Cell) in esophageal carcinoma (ESCA) using tumor immune estimation resource (TIMER).Results:In this study, 707 DEGs (including 385 upregulated genes and 322 downregulated genes) and 6 hub genes (cyclin B1 [CCNB1], cyclin dependent kinase 1 [CDK1], aurora kinase A [AURKA], ubiquitin conjugating enzyme E2C [UBE2C], cyclin A2 [CCNA2], and cell division cycle 20 [CDC20]) were identified. All of the 6 hub genes were highly expressed in ESCC tissues. Among of them, only CCNB1 and CDC20 were associated with stage of ESCC and all of them were not associated with survival time of patients.Conclusion:DEGs and hub genes were confirmed in our study, providing a thorough, scientific and comprehensive research goals for the pathogenesis of ESCC.     

Microarray analysis of hub genes and pathways in damaged cartilage tissues of knee

The aim of this study was to identify genes and functional pathways associated with damaged cartilage tissues of knee using microarray analysis.The gene expression profile {"type":"entrez-geo","attrs":{"text":"GSE129147","term_id":"129147"}}GSE129147 including including 10 knee cartilage tissues from damaged side and 10 knee nonweight-bearing healthy cartilage was downloaded and bioinformatics analysis was made.A total of 182 differentially-expressed genes including 123 up-regulated and 59 down-regulated genes were identified from the {"type":"entrez-geo","attrs":{"text":"GSE129147","term_id":"129147"}}GSE129147 dataset. Gene ontology and pathway enrichment analysis confirmed that extracellular matrix organization, collagen catabolic process, antigen processing and presentation of peptide or polysaccharide antigen, and endocytic vesicle membrane were strongly associated with cartilage injury. Furthermore, 10 hub differentially-expressed genes with a higher connectivity degree in protein–protein interactions network were found such as POSTN, FBN1, LOX, insulin-like growth factor binding proteins3, C3AR1, MMP2, ITGAM, CDKN2A, COL1A1, COL5A1.These hub genes and pathways provide a new perspective for revealing the potential pathological mechanisms and therapy strategy of cartilage injury.     

Identifying key genes and small molecule compounds for nasopharyngeal carcinoma by various bioinformatic analysis

Nasopharyngeal carcinoma (NPC) is one of the most prevalent head and neck cancer in southeast Asia. It is necessary to proceed further studies on the mechanism of occurrence and development of NPC.In this study, we employed the microarray dataset {"type":"entrez-geo","attrs":{"text":"GSE12452","term_id":"12452"}}GSE12452 and {"type":"entrez-geo","attrs":{"text":"GSE53819","term_id":"53819"}}GSE53819 including 28 normal samples and 49 nasopharyngeal carcinoma samples downloaded from the Gene Expression Omnibus(GEO) to analysis. R software, STRING, CMap, and various databases were used to screen differentially expressed genes (DEGs), construct the protein–protein interaction (PPI) network, and proceed small molecule compounds analysis, among others.Totally, 424 DEGs were selected from the dataset. DEGs were mainly enriched in extracellular matrix organization, cilium organization, PI3K-Akt signaling pathway, collagen-containing extracellular matrix, and extracellular matrix-receptor interaction, among others. Top 10 upregulated and top 10 downregulated hub genes were identified as hub DEGs. Piperlongumine, apigenin, menadione, 1,4-chrysenequinone, and chrysin were identified as potential drugs to prevent and treat NPC. Besides, the effect of genes CDK1, CDC45, RSPH4A, and ZMYND10 on survival of NPC was validated in GEPIA database.The data revealed novel aberrantly expressed genes and pathways in NPC by bioinformatics analysis, potentially providing novel insights for the molecular mechanisms governing NPC progression. Although further studies needed, the results demonstrated that the expression levels of CDK1, CDC45, RSPH4A, and ZMYND10 probably affected survival of NPC patients.     

Machine learning-based screening of the diagnostic genes and their relationship with immune-cell infiltration in patients with lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the most common type of lung cancer, and has a dismal mortality rate of 80%, mainly due to diagnosis at an advanced stage. Biomarkers with high specificity and sensitivity for the early diagnosis of LUAD are sparse. This study aimed to identify markers for the early diagnosis of LUAD.MethodsThe {"type":"entrez-geo","attrs":{"text":"GSE32863","term_id":"32863"}}GSE32863 and {"type":"entrez-geo","attrs":{"text":"GSE75037","term_id":"75037"}}GSE75037 data sets were standardized and merged to screen for differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. The intersected DEGs from the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) regression analyses were considered the hub genes. Then the diagnostic ability and expression of hub genes was tested in {"type":"entrez-geo","attrs":{"text":"GSE63459","term_id":"63459"}}GSE63459 data set, Finally, CIBERSORT was used to analyze the correlation between the immune-infiltrating cells and hub genes.ResultsThe following 7 DEGs were intersected by the LASSO and SVM regression analyses: Locus 401286 (LOC401286), flavin-containing monooxygenase 2 (FMO2), XLKD1, Ras homolog family member J (RHOJ), scavenger receptor Class A member 5 (SCARA5), heat shock protein beta-2 (HSPB2), and serine incorporator 2 (SERINC2). The area under the receiver operating characteristic curve (AUC) of LOC401286, FMO2, XLKD1, RHOJ, SCARA5, HSPB2, and SERINC2 was 0.99, 1.00, 0.99, 1.00, 0.99, 0.99, and 0.98, respectively in the training groups. The AUC of LOC401286, FMO2, XLKD1, RHOJ, SCARA5, HSPB2, and SERINC2 was 0.97, 0.96, 0.94, 0.88, 0.85, 0.94 and 0.89, respectively in the validation group. The immune-cell infiltrations of naive B cells, memory B cells, plasma cells, naive cluster of differentiation (CD) 4 T cells, T follicular helper cells, regulatory T cells, gamma delta T cells, monocytes, M0 macrophages, M1 macrophages, resting mast cells, activated mast cells, and neutrophils were different between the normal and tumor tissues. Notably, these immune cells were correlated with the above-mentioned 7 diagnostic genes.ConclusionsWe identified 7 DEGs in LUAD tissue that can be considered diagnostic genes based on 2 machine-learning regression methods, which could be very helpful for the early diagnosis of LUAD in clinical practice.     

Comparison of critical biomarkers in 2 erectile dysfunction models based on GEO and NOS-cGMP-PDE5 pathway

Background:Erectile dysfunction is a disease commonly caused by diabetes mellitus (DMED) and cavernous nerve injury (CNIED). Bioinformatics analyses including differentially expressed genes (DEGs), enriched functions and pathways (EFPs), and protein-protein interaction (PPI) networks were carried out in DMED and CNIED rats in this study. The critical biomarkers that may intervene in nitric oxide synthase (NOS, predominantly nNOS, ancillary eNOS, and iNOS)-cyclic guanosine monophosphate (cGMP)-phosphodiesterase 5 enzyme (PDE5) pathway, an important mechanism in erectile dysfunction treatment, were then explored for potential clinical applications.Methods:{"type":"entrez-geo","attrs":{"text":"GSE2457","term_id":"2457"}}GSE2457 and {"type":"entrez-geo","attrs":{"text":"GSE31247","term_id":"31247"}}GSE31247 were downloaded. Their DEGs with a |logFC (fold change)| > 0 were screened out. Database for Annotation, Visualization and Integrated Discovery (DAVID) online database was used to analyze the EFPs in Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes networks based on down-regulated and up-regulated DEGs respectively. PPI analysis of 2 datasets was performed in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. Interactions with an average score greater than 0.9 were chosen as the cutoff for statistical significance.Results:From a total of 1710 DEGs in {"type":"entrez-geo","attrs":{"text":"GSE2457","term_id":"2457"}}GSE2457, 772 were down-regulated and 938 were up-regulated, in contrast to the 836 DEGs in {"type":"entrez-geo","attrs":{"text":"GSE31247","term_id":"31247"}}GSE31247, from which 508 were down-regulated and 328 were up-regulated. The 25 common EFPs such as aging and response to hormone were identified in both models. PPI results showed that the first 10 hub genes in DMED were all different from those in CNIED.Conclusions:The intervention of iNOS with the hub gene complement component 3 in DMED and the aging process in both DMED and CNIED deserves attention.     

Bioinformatics analysis identified immune infiltration,risk and drug prediction models of copper-induced death genes involved in salivary glands damage of primary Sjögren’s syndrome

This study aimed to identify copper-induced death genes in primary Sjögren’s syndrome (pSS) and explore immune infiltration, risk and drug prediction models for salivary glands (SGs) damage. The 3 datasets, including {"type":"entrez-geo","attrs":{"text":"GSE40611","term_id":"40611"}}GSE40611, {"type":"entrez-geo","attrs":{"text":"GSE23117","term_id":"23117"}}GSE23117, and {"type":"entrez-geo","attrs":{"text":"GSE7451","term_id":"7451"}}GSE7451 from the Gene Expression Omnibus database were downloaded. The datasets were processed using the affy in R (version 4.0.3). In immune cells, copper-induced death genes were strongly expressed in “activated” dendritic cells (aDCs), macrophages and regulatory T cells (Treg). In immune functions, copper-induced death genes were strongly expressed in major histocompatibility complex (MHC) class I, human leukocyte antigen (HLA) and type I interferon (IFN) response. Correlation analysis showed that 5 genes including SLC31A1, PDHA1, DLD, ATP7B, and ATP7A were significantly correlated with immune infiltration. The nomogram suggested that the low expression of PDHA1 was significant for predicting the risk of pSS and the area under curve was 0.678. Drug model suggested that “Bathocuproine disulfonate CTD 00001350,” “Vitinoin CTD 00007069,” and “Resveratrol CTD 00002483” were the drugs most strongly associated with copper-induced death genes. In summary, copper-induced death genes are associated with SGs injury in pSS, which is worthy of clinicians’ attention.     

Screening of sphingolipid metabolism-related genes associated with immune cells in myocardial infarction: a bioinformatics analysis

BackgroundInflammation and immune cell infiltration in infarcted myocardial tissue are critical to myocardial infarction (MI) prognosis, and alterations in sphingolipid metabolism (SM) have been shown to potentially influence the inflammatory response and induce cardioprotection, but the underlying mechanisms are unclear. We therefore performed bioinformatics analysis to screen for key genes of SM in MI immune cells.MethodsThree matrix files including {"type":"entrez-geo","attrs":{"text":"GSE61145","term_id":"61145"}}GSE61145, {"type":"entrez-geo","attrs":{"text":"GSE23294","term_id":"23294"}}GSE23294, and {"type":"entrez-geo","attrs":{"text":"GSE71906","term_id":"71906"}}GSE71906 were downloaded from the Gene Expression Omnibus (GEO) database. {"type":"entrez-geo","attrs":{"text":"GSE61145","term_id":"61145"}}GSE61145 was a human peripheral blood database, and {"type":"entrez-geo","attrs":{"text":"GSE23294","term_id":"23294"}}GSE23294 and {"type":"entrez-geo","attrs":{"text":"GSE71906","term_id":"71906"}}GSE71906 were 2 mouse myocardial tissue databases. R and annotation packages were used to screen for differentially-expressed genes (DEGs). Datasets of human and mouse cardiac tissues were downloaded from the GEO database for subsequent validation. The downloaded platform and matrix files were processed using R language and annotation packages. Key targets and enrichment pathways were identified using Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The Wilcoxon test was performed on the genes involved in SM pathways in neutrophils.ResultsA total of 261 DEGs were obtained from human peripheral blood datasets, among which 101 were immune-related. GO analysis revealed that neutrophil activation, T cell activation, and T cell differentiation were significantly enriched in the immune-related DEGs. Three types of immune cells were identified in infarcted myocardial tissues. In addition, 194 DEGs were obtained from mouse myocardial tissue data, among which 6 SM-related genes (Asah1, Degs1, Neu1, Sptlc2, Sphk1, and Gba2) were significantly associated with MI. Evaluation of the relationships between these DEGs and neutrophils showed that the expression of the Sptlc2 gene was significantly upregulated in neutrophils of the MI group, while the expression levels of the Asah1 and Degs1 genes were downregulated.ConclusionsWe identified 3 SM-related genes that were highly associated with neutrophils in MI, which may advance our understanding of SM in immune cells after MI.     

High expression of KIF20A in bladder cancer as a potential prognostic target for poor survival of renal cell carcinoma

Urinary system tumors are malignant tumors, including renal cancer and bladder cancer. however, molecular target of them remains unclear. {"type":"entrez-geo","attrs":{"text":"GSE14762","term_id":"14762"}}GSE14762 and {"type":"entrez-geo","attrs":{"text":"GSE53757","term_id":"53757"}}GSE53757 were downloaded from GEO database to screen differentially expressed genes (DEGs). Weighted gene co-expression network analysis was performed. Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes were used for enrichment analysis. Gene ontology and Kyoto encyclopedia of genes and genomes analyses were performed on whole genome, as formulated by gene set enrichment analysis. Survival analysis was also performed. Comparative toxicogenomics database was used to identify diseases most associated with hub genes. A total of 1517 DEGs were identified. DEGs were mainly enriched in cancer pathway, HIF-1 signaling pathway, organic acid metabolism, glyoxylate and dicarboxylate metabolism, and protein homodimerization activity. Ten hub genes (TPX2, ASPM, NUSAP1, RAD51AP1, CCNA2, TTK, PBK, MELK, DTL, kinesin family member 20A [KIF20A]) were obtained, which were up-regulated in tumor tissue. The expression of KIF20A was related with the overall survival of renal and bladder cancer. KIF20A was up-regulated in the tumor tissue, and might worsen the overall survival of bladder and kidney cancer. KIF20A could be a novel biomarker of bladder and kidney cancer.     

Identification of a functional circRNA-miRNA-mRNA regulatory network in infantile hemangioma by bioinformatics analysis

Several circRNA have been reported to serve critical roles in various biological processes of human body. The present study aimed to build a circRNA-based competing endogenous RNA (ceRNA) network and explore the regulatory mechanisms of circRNA in infantile hemangiomas (IH). Differentially expressed circRNA, miRNA, and mRNA were downloaded from the gene expression synthesis (GEO) microarray database ({"type":"entrez-geo","attrs":{"text":"GSE98795","term_id":"98795"}}GSE98795, {"type":"entrez-geo","attrs":{"text":"GSE69136","term_id":"69136"}}GSE69136, and {"type":"entrez-geo","attrs":{"text":"GSE127487","term_id":"127487"}}GSE127487). Cancer-specific circRNA database (CSCD), miRDB and Targetscan were employed to predict the targets of RNA. A total of 855 DEcircRNAs, 69 differentially expressed miRNAs (DEmiRNAs), and 3233 differentially expressed mRNAs (DEmRNAs) appeared as genes that were aberrantly expressed in IH. The circRNA-miRNA-mRNA network was constructed based on 108 circRNAs, 7 miRNAs, 274 mRNAs in IH. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis indicated hypoxia-inducible factors (HIF)-1 signaling pathway and Notch signaling pathway were significantly enriched in IH with being constructed a ceRNA regulatory network. Furthermore, protein-protein interaction (PPI) network and Cytoscape showed the top 10 hub genes that regulate angiogenesis, namely FBXW7, CBLB, HECW2, FBXO32, FBXL7, KLHL5, EP300, MAPK1, MEF2C, and PLCG1. Our findings provide a deeper understanding the circRNA-related ceRNA regulatory mechanism in IH. This study further perfected the circRNA-miRNA-mRNA regulatory network related to IH and explored the potential function of mRNA in this network. It provides more understanding for the circRNA-related ceRNA regulation mechanism in the pathogenesis of IH.     

Exploring new targets for the treatment of hepatitis-B virus and hepatitis-B virus-associated hepatocellular carcinoma: A new perspective in bioinformatics

Background:Hepatitis B Virus (HBV) infection is a global public health problem. After infection, patients experience a natural course from chronic hepatitis to cirrhosis and even Hepatitis B associated Hepatocellular Carcinoma (HBV-HCC). With the multi-omics research, many differentially expressed genes from chronic hepatitis to HCC stages have been discovered. All these provide important clues for new biomarkers and therapeutic targets. The purpose of this study is to explore the differential gene expression of HBV and HBV-related liver cancer, and analyze their enrichments and significance of related pathways.Methods:In this study, we downloaded four microarray datasets {"type":"entrez-geo","attrs":{"text":"GSE121248","term_id":"121248"}}GSE121248, {"type":"entrez-geo","attrs":{"text":"GSE67764","term_id":"67764"}}GSE67764, {"type":"entrez-geo","attrs":{"text":"GSE55092","term_id":"55092"}}GSE55092, {"type":"entrez-geo","attrs":{"text":"GSE55092","term_id":"55092"}}GSE55092 and {"type":"entrez-geo","attrs":{"text":"GSE83148","term_id":"83148"}}GSE83148 from the Gene Expression Omnibus (GEO) database. Using these four datasets, patients with chronic hepatitis B (CHB) differentially expressed genes (CHB DEGs) and patients with HBV-related HCC differentially expressed genes (HBV-HCC DEGs) were identified. Then Protein–protein Interaction (PPI) network analysis, Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to excavate the functional interaction of these two groups of DEGs and the common DEGs. Finally, the Kaplan website was used to analyze the role of these genes in HCC prognostic.Results:A total of 241 CHB DEGs, 276 HBV-HCC DEGs, and 4 common DEGs (cytochrome P450 family 26 subfamily A member 1 (CYP26A1), family with sequence similarity 110 member C(FAM110C), SET and MYND domain containing 3(SMYD3) and zymogen granule protein 16(ZG16)) were identified. CYP26A1, FAM110C, SMYD3 and ZG16 exist in 4 models and interact with 33 genes in the PPI network of CHB and HBV-HCC DEGs,. GO function analysis showed that: CYP26A1, FAM110C, SMYD3, ZG16, and the 33 genes in their models mainly affect the regulation of synaptic vesicle transport, tangential migration from the subventricular zone to the olfactory bulb, cellular response to manganese ion, protein localization to mitochondrion, cellular response to dopamine, negative regulation of neuron death in the biological process of CHB. In the biological process of HBV-HCC, they mainly affect tryptophan catabolic process, ethanol oxidation, drug metabolic process, tryptophan catabolic process to kynurenine, xenobiotic metabolic process, retinoic acid metabolic process, steroid metabolic process, retinoid metabolic process, steroid catabolic process, retinal metabolic process, and rogen metabolic process. The analysis of the 4 common DEGs related to the prognosis of liver cancer showed that: CYP26A1, FAM110C, SMYD3 and ZG16 are closely related to the development of liver cancer and patient survival. Besides, further investigation of the research status of the four genes showed that CYP26A1 and SMYD3 could also affect HBV replication and the prognosis of liver cancer.Conclusion:CYP26A1, FAM110C, SMYD3 and ZG16 are unique genes to differentiate HBV infection and HBV-related HCC, and expected to be novel targets for HBV-related HCC occurrence and prognostic judgement.     

Identification of key genes and microRNAs for multiple sclerosis using bioinformatics analysis

To better understand the molecular mechanism underlying the pathogenesis of multiple sclerosis (MS), we aimed to identify the key genes and microRNAs (miRNA) associated with MS and analyze their interactions. Differentially expressed genes (DEGs) and miRNAs (DEMs) based on the gene miRNA dataset {"type":"entrez-geo","attrs":{"text":"GSE17846","term_id":"17846"}}GSE17846 and mRNA dataset {"type":"entrez-geo","attrs":{"text":"GSE21942","term_id":"21942"}}GSE21942 were determined using R software. Next, we performed functional enrichment analysis and constructed a protein–protein interaction network. Data validation was performed to ensure the reliability of hub genes. The miRNA-mRNA regulatory network was constructed. In total, 47 DEMs and 843 DEGs were identified. Protein–protein interaction network analysis identified several hub genes, including JUN, FPR2, AKT1, POLR2L, LYZ, CXCL8, HBB, CST3, CTSZ, and MMP9, especially LYZ and CXCL8. We constructed an miRNA-mRNA regulatory network and found that hsa-miR-142-3p, hsa-miR-107, hsa-miR-140-5p, and hsa-miR-613 were the most important miRNAs. This study reveals some key genes and miRNAs that may be involved in the pathogenesis of MS, providing potential targets for the diagnosis and treatment of MS.     

Construction and validation of a robust ferroptosis-associated gene signature predictive of prognosis in lung adenocarcinoma

To construct and validate a ferroptosis-associated signature predictive of prognosis in lung adenocarcinoma (LUAD), and systematically evaluate the underlying molecular connections in cancer biology.We retrieved mRNAs sequencing profiles of LUAD from the cancer genome atlas (TCGA) data portal and clinical information from the cBio Cancer Genomics Portal. The differentially expressed ferroptosis-associated genes (DEFAGs) were screened between normal samples and LUAD by packages “limma” in R. Then the total TCGA cohort was randomly divided into training set and testing set. Based on the training set, a DEFAG signature was built and further validated in the test set, the total TCGA cohort and other independent cohorts from the gene expression omnibus data portal. A nomogram was constructed and validated, and the correlation between high-risk group and cancer biology was further evaluated.We initially identified 68 DEFAGs from TCGA cohort. A 6 DEFAG signature was built and further validated in the test set, the total TCGA cohort and other 2 independent cohorts including {"type":"entrez-geo","attrs":{"text":"GSE31210","term_id":"31210"}}GSE31210 and {"type":"entrez-geo","attrs":{"text":"GSE72094","term_id":"72094"}}GSE72094 from gene expression omnibus data portal. Further exploration indicated that high-risk group combined with TP53 mutation harbored the most unfavorable prognosis while low-risk group with TP53 wild-type status had the most favorable survival advantage over other groups. Moreover, high-risk group was associated with higher cancer stemness, tumor mutation burden, and CD274 (programmed cell death 1 ligand 1) expression.We constructed a robust ferroptosis-associated gene signature and a nomogram predictive of prognosis in LUAD, and provided a new perspective on associations between ferroptosis and cancer.