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.     

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.     

Identification of G6PC as a potential prognostic biomarker in hepatocellular carcinoma based on bioinformatics analysis

Hepatocellular carcinoma (HCC) has high mortality and incidence rates around the world with limited therapeutic options. There is an urgent need for identification of novel therapeutic targets and biomarkers for early diagnosis and predicting patient survival with HCC.Several studies ({"type":"entrez-geo","attrs":{"text":"GSE102083","term_id":"102083"}}GSE102083, {"type":"entrez-geo","attrs":{"text":"GSE29722","term_id":"29722"}}GSE29722, {"type":"entrez-geo","attrs":{"text":"GSE101685","term_id":"101685"}}GSE101685, and {"type":"entrez-geo","attrs":{"text":"GSE112790","term_id":"112790"}}GSE112790) from the GEO database in HCC were screened and analyzed by GEO2R, gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were conducted with the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction network was plotted and the module analysis was performed using Search Tool for the Retrieval of Inter-acting Genes/Proteins database and Cytoscape. The expression and survival of key genes were identified using UALCAN, Kaplan–Meier Plotter and ONCOMINE online databases, and the immune infiltration level of key genes was analyzed via the Tumor Immune Estimation Resource (TIMER) database.Through database analysis, eight key genes were finally screened out, and the expressions of cyclin-dependent kinase regulatory subunit 2 and glucose-6-phosphatase catalytic (G6PC), which were closely related to the survival of HCC patients, was detected by using UALCAN. Further analysis on the differential expression of G6PC in multiple cancerous tumors and normal tissues revealed low expression in many solid tumors by Oncomine and TIMER. In addition, Kaplan–Meier plotter and UALCAN database analysis to access diseases prognosis suggested that low expression of G6PC was significantly associated with poor overall survival in HCC patients. Finally, TIMER database analysis showed a significant negative correlation between G6PC and infiltration levels of six kinds of immune cells. The somatic copy number alterations of G6PC were associated with B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, dentritic cells and neutrophils.These bioinformatic data identified G6PC as a potential key gene in the diagnosis and prognosis of HCC.     

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 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.     

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.     

The role of ferroptosis-related genes in airway epithelial cells of asthmatic patients based on bioinformatics

It has been reported that airway epithelial cells and ferroptosis have certain effect on asthma. However, the action mechanism of ferroptosis-related genes in airway epithelial cells of asthmatic patients is still unclear. Firstly, the study downloaded the {"type":"entrez-geo","attrs":{"text":"GSE43696","term_id":"43696"}}GSE43696 training set, {"type":"entrez-geo","attrs":{"text":"GSE63142","term_id":"63142"}}GSE63142 validation set and {"type":"entrez-geo","attrs":{"text":"GSE164119","term_id":"164119"}}GSE164119 (miRNA) dataset from the gene expression omnibus database. 342 ferroptosis-related genes were downloaded from the ferroptosis database. Moreover, differentially expressed genes (DEGs) between asthma and control samples in the {"type":"entrez-geo","attrs":{"text":"GSE43696","term_id":"43696"}}GSE43696 dataset were screened by differential analysis. Consensus clustering analysis was performed on asthma patients to classify clusters, and differential analysis was performed on clusters to obtain inter-cluster DEGs. Asthma-related module was screened by weighted gene co-expression network analysis. Then, DEGs between asthma and control samples, inter-cluster DEGs and asthma-related module were subjected to venn analysis for obtaining candidate genes. The last absolute shrinkage and selection operator and support vector machines were respectively applied to the candidate genes to screen for feature genes, and functional enrichment analysis was performed. Finally, a competition endogenetic RNA network was constructed and drug sensitivity analysis was conducted. There were 438 DEGs (183 up-regulated and 255 down-regulated) between asthma and control samples. 359 inter-cluster DEGs (158 up-regulated and 201 down-regulated) were obtained by screening. Then, the black module was significantly and strongly correlated with asthma. The venn analysis yielded 88 candidate genes. 9 feature genes (NAV3, ITGA10, SYT4, NOX1, SNTG2, RNF182, UPK1B, POSTN, SHISA2) were screened and they were involved in proteasome, dopaminergic synapse etc. Besides, 4 mRNAs, 5 miRNAs, and 2 lncRNAs collectively formed competition endogenetic RNA regulatory network, which included RNF182-hsa-miR-455-3p-LINC00319 and so on. The predicted therapeutic drug network map contained NAV3-bisphenol A and other relationship pairs. The study investigated the potential molecular mechanisms of NAV3, ITGA10, SYT4, NOX1, SNTG2, RNF182, UPK1B, POSTN, SHISA2 in airway epithelial cells of asthmatic patients through bioinformatics analysis, providing a reference for the research of asthma and ferroptosis.     

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.     

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.     

Network pharmacology-based identification of miRNA expression of Astragalus membranaceus in the treatment of diabetic nephropathy

Diabetic nephropathy (DN) is a common microvascular complication of diabetic patients, along with hypertension, hyperlipemia, proteinuria, edema, and other clinical manifestations. Astragalus membranaceus (AM) is a traditional Chinese medicine and has shown significant clinical efficacy against DN. However, the overall molecular mechanism of this therapeutic effect has not been entirely elucidated. Using network pharmacology, we aimed to identify the key active ingredients and potential pharmacological mechanisms of AM in treating DN and provide scientific evidence of its clinical efficacy.The active ingredients of AM were obtained from the traditional Chinese medicine systems pharmacology database, and the potential targets of AM were identified using the therapeutic target database. DN-related target genes were acquired from the Gene Expression Omnibus microarray dataset {"type":"entrez-geo","attrs":{"text":"GSE1009","term_id":"1009"}}GSE1009 and 3 widely used databases-DisGeNET, GeneCards, and Comparative Toxicogenomics Database. The DN–AM common target protein interaction network was established by using the STRING database. Active ingredients candidate targets proteins networks were constructed using Cytoscape software for visualization. Additionally, gene ontology (GO) and Kyoto encyclopedia of genes and genomes pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery database. Target-regulating microRNAs (miRNAs) of these hub genes were obtained from the therapeutic target database, which could then be used for further identification of AM-regulated key miRNAs.A total of 17 active ingredients and 214 target proteins were screened from AM. 61 candidate co-expressed genes with therapeutic effects against DN were obtained and considered as potential therapeutic targets. GO and Kyoto encyclopedia of genes and genomes enrichment analysis showed that these genes were mainly involved in inflammatory response, angiogenesis, oxidative stress reaction, HIF signaling pathway, tumor necrosis factor signaling pathway, and VEGF signaling pathway. In all, 636 differentially expressed genes were identified between the DN patients and control group by using microarray data, {"type":"entrez-geo","attrs":{"text":"GSE1009","term_id":"1009"}}GSE1009. Lastly, VEGFA, epidermal growth factor receptor, STAT1, and GJA1 were screened as hub genes. The relationships between miRNAs and hub genes were constructed, which showed that miR-302-3p, miR-372-3p, miR-373-3p, and miR-520-3p were regulated by VEGFA and epidermal growth factor receptor. Meanwhile, VEGFA also influenced miR-15-5p, miR-16-5p, miR-17-5p, miR-20-5p, miR-93-5p, miR-106-5p, miR-195-5p, miR-424-5p, miR-497-5p, and miR-519-3p. In addition, miR-1-3p and miR-206 were regulated by VEGFA and GJA1, and miR-23-3p was regulated by STAT1 and GJA1.To our knowledge, this study revealed for the first time the characteristic multiple components, multiple targets, and multiple pathways of AM that seem to be the underlying mechanisms of action of AM in the treatment of DN with respect to miRNAs.Private information from individuals will not be published. This systematic review also does not involve endangering participant rights. Ethical approval will not be required. The results may be published in a peer-reviewed journal or disseminated at relevant conferences.     

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.     

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.     

CircRNA expression profile and potential role of hsa_circ_0040039 in intervertebral disc degeneration

Purpose:Circular RNAs (circRNAs) play an critical role in the pathological processes associated with IDD. However, the potential roles of circRNAs in IDD remain largely unclear. Here, we identify the circRNAs expression profiles and elucidate the potential role of candidate circRNAs in the pathogenesis of intervertebral disc degeneration (IDD) through microarray data and bioinformatics analyses.Methods:We obtained the datasets of microarrays ({"type":"entrez-geo","attrs":{"text":"GSE67566","term_id":"67566"}}GSE67566 and {"type":"entrez-geo","attrs":{"text":"GSE116726","term_id":"116726"}}GSE116726) from the Gene Expression Omnibus database. The differentially expressed circRNAs and miRNAs were identified using the Limma R package. The target miRNAs and target genes of the candidate circRNAs were predicted using an online tool. Functional enrichment analyses of the target genes were performed using the clusterProfiler R package. A protein-protein interaction (PPI) network was constructed using STRING.Results:A total of 104 differentially expressed circRNAs were identified between the IDD and the control groups, including 41 upregulated circRNAs and 63 downregulated circRNAs (cutoff criteria (|log₂ fold change| > 2, P < .05)). Hsa_circ_0040039, which was the most upregulated circRNA (log₂ fold change = 2.95), was selected for further analysis. The regulatory circRNA-miRNA-mRNA network comprised hsa_circ_0040039, 2 target miRNAs (hsa-miR-424-5p and hsa-miR-15b-5p), and 77 target genes. Functional enrichment analysis showed that the 77 promising target genes are mainly enriched in the ubiquitin proteasome system and Wnt signaling pathway. Further, the PPI network showed that the top 3 hub genes are BRTC, SIAH1, and UBE2V1.Conclusions:A total of 104 differentially expressed circRNAs were identified between the IDD and control groups. Hsa_circ_0040039 may serve as a sponge of hsa-miR-424-5p and hsa-miR-15b-5p, to regulate the expression of downstream genes (such as BRTC, SIAH1, and UBE2V1); thus, it may be involved in IDD-associated pathological processes via the Wnt/β-catenin signaling pathway. Further studies are required to confirm the potential roles of hsa_circ_0040039 in IDD.     

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.     

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 immunocell infiltrates and effective diagnostic biomarkers in laryngeal carcinoma

Laryngeal cancer (LC) is a malignant tumor that occurs in the head and neck. Laryngeal cancer is one of the most common cancers of the neck and head, and its prognosis has always been poor. The incidence of LC increased gradually and showed an early rising trend. Laryngeal cancer is rarely studied in relation to immunity, Malignant tumors will change the state of the human body in various ways to adapt to their own survival and avoid the immune system. This study aims to explore the immune molecular mechanism of laryngeal cancer through bioinformatics analysis. The gene expression data was downloaded for 3 microarray datasets: {"type":"entrez-geo","attrs":{"text":"GSE27020","term_id":"27020"}}GSE27020, {"type":"entrez-geo","attrs":{"text":"GSE59102","term_id":"59102"}}GSE59102, and {"type":"entrez-geo","attrs":{"text":"GSE51985","term_id":"51985"}}GSE51985. CIBERSORT algorithm was performed to evaluate immune cell infiltration in tissues between LC and healthy control (HC). Differentially expressed genes (DEGs) were screened. Functional correlation of DEGs were analyzed by Gene Ontology, Gene Set Enrichment Analysis and Kyoto encyclopedia of genes and genomes. Candidate biomarkers were identified by cytoHubba of Cytoscape. Spearman correlations between the above biomarkers and infiltrating immune cells were explored using R software analysis. The immune cell types of LC and HC were significantly different. Twenty-one DEGs were obtained by cross-screening. The function of DEGs is closely related to the number of immune cells. Five central genes (TNNT3, TNNI2, Desmin, matrix metallopeptidase 9 and cytotoxic T lymphocyte antigen 4) were screened. The HUB gene was demonstrated to have the ability to diagnose LC and HC with good specificity and sensitivity. The correlation between immune cells and biomarkers showed that hub gene was positively correlated with macrophages and dendritic cells, and negatively correlated with CD4 + T cell. TNNT3, TNNI2, Desmin, matrix metallopeptidase 9 and cytotoxic T lymphocyte antigen 4 can be used as diagnostic biomarker for LC. Macrophages, dendritic cells and CD4 + T cell may participate in the occurrence and development of LC.     

MYL2 as a potential predictive biomarker for rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is a common malignant soft tissue sarcoma, which is the third most common soft tissue sarcoma after malignant fibrohistoma and liposarcoma. The discovery of potential postbiomarkers could lead to early and more effective treatment measures to reduce the mortality of RMS. The discovery of biomarker is expected to be the direction of targeted therapy, providing a new direction for the precise treatment of RMS.Gene Expression Omnibus database was used to download the tow gene profiles, {"type":"entrez-geo","attrs":{"text":"GSE28511","term_id":"28511"}}GSE28511 and {"type":"entrez-geo","attrs":{"text":"GSE135517","term_id":"135517"}}GSE135517. GEO2R was applied to identify differently expressed genes (DEGs) between RMS and normal group. Database for Annotation, Visualization and Integrated Discovery and Metascape can perform the enrichment analysis for the DEGs. Protein-protein interaction network was constructed, and the hub genes was identified by the Cytoscape. Expression and overall survival analysis of hub genes were performed.A total of 15 common DEGs were screened between RMS and normal tissues. The enrichment analysis here showed that the DEGs mainly enriched in the muscle filament sliding, myofibril, protein complex, sarcomere, myosin complex, nuclear chromosome, and tight junction. The 6 hub genes (DNA Topoisomerase II Alpha, Insulin Like Growth Factor 2, HIST1H4C, Cardiomyopathy Associated 5, Myosin Light Chain 2 [MYL2], Myosin Heavy Chain 2) were identified. Compared with the normal tissues, MYL2 were down-regulated in the RMS tissues. RMS patients with low expression level of MYL2 had poorer overall survival times than those with high expression levels (P < .05).In summary, lower expression of MYL2 was 1 prediction for poor prognosis of RMS. MYL2 is hope to be the target of therapy, which leads to more effective treatment and reduces the mortality rate of RMS.     

MKI67 an potential oncogene of oral squamous cell carcinoma via the high throughput technology

Oral squamous cell carcinoma is a malignant tumor that occurs in the oral cavity, with poor prognosis and easy recurrence. However, the relationship between MKI67 and oral squamous cell carcinoma remains unclear. The oral squamous cell carcinoma datasets {"type":"entrez-geo","attrs":{"text":"GSE138206","term_id":"138206"}}GSE138206, {"type":"entrez-geo","attrs":{"text":"GSE146483","term_id":"146483"}}GSE146483 and {"type":"entrez-geo","attrs":{"text":"GSE184616","term_id":"184616"}}GSE184616 were downloaded from the gene expression omnibus database, and the differentially expressed genes (DEGs) were screened. The protein-protein interaction network was constructed and analyzed by search tool for the retrieval of interacting genes database and Cytoscape software. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) were used for functional enrichment analysis. GO and KEGG analyses were performed on the whole genome, as formulated by gene set enrichment analysis. comparative toxicogenomics database was used to identify the diseases most associated with the core genes. TargetScan was used to screen miRNA regulating central DEGs. A total of 1472 DEGs were identified. GO analysis showed that the differentially expressed genes were mainly enriched in the tissues of extracellular matrix, type i interferon signaling pathway, human papillomavirus infection, adhesion spot, hepatitis C and ECM-receptor interaction. Enrichment items were similar to GO and KEGG enrichment items of differentially expressed genes. 10 core genes were obtained, and their expression was different between oral squamous cell carcinoma and normal tissue samples. MKI67 is highly expressed in oral squamous cell carcinoma and may be an oncogene in oral squamous cell carcinoma.     

Identification and validation of miR-509-5p as a prognosticator for favorable survival in osteosarcoma

Osteosarcoma (OS) is the most common primary bone cancer diagnosed in children. This study aims to explore the aberrantly expressed miRNAs that are prognostically related and to provide potential biomarkers for the prognosis prediction of OS. The miRNA profiles of OS and adjacent normal controls were obtained from 2 gene expression omnibus cohorts (i.e., {"type":"entrez-geo","attrs":{"text":"GSE28423","term_id":"28423"}}GSE28423 and {"type":"entrez-geo","attrs":{"text":"GSE65071","term_id":"65071"}}GSE65071). {"type":"entrez-geo","attrs":{"text":"GSE39058","term_id":"39058"}}GSE39058 and Therapeutically Applicable Research to Generate Effective Treatments cohorts, which respectively contained 91 and 85 OS samples with both miRNA expression and clinical characteristics, were employed to perform survival and multivariate Cox regression analyses. Lymphocyte infiltration abundance between distinct subgroups was evaluated with the CIBERSORT algorithm and a previously proposed method. Gene set enrichment analysis was used to infer the dysregulated signaling pathways within each subgroup. Of the 31 differentially expressed miRNAs, miR-509-5p (miR-509) was the most significantly prognostic miRNA in the {"type":"entrez-geo","attrs":{"text":"GSE39058","term_id":"39058"}}GSE39058 cohort and its high expression was associated with the better OS prognosis (Log-rank P = .008). In the Therapeutically Applicable Research to Generate Effective Treatments validation cohort, the association of high miR-509 expression with favorable survival was also observed (Log-rank P = .014). The results remained still significant even adjusted for clinical confounding factors in multivariate Cox regression models. Further immunology analyses demonstrated that elevated infiltration of lymphocytes, decreased infiltration of immune-suppressive cells, and immune response-related pathways were significantly enriched in patients with miR-509 high expression. Our study suggests that miR-509 may serve as a potential biomarker for evaluating OS prognosis and provides clues for tailoring OS immunotherapy strategies.