Identification of novel biomarkers and candidate small molecule drugs in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis of high‐throughput data

Background: Rheumatoid arthritis (RA) and osteoarthritis (OA) are two major types of joint diseases. The present study aimed to identify hub genes involved in the pathogenesis and further explore the potential treatment targets of RA and OA.Methods: The gene expression profile of {"type":"entrez-geo","attrs":{"text":"GSE12021","term_id":"12021"}}GSE12021 was downloaded from Gene Expression Omnibus (GEO). Total 31 samples (12 RA, 10 OA and 9 NC samples) were used. The differentially expressed genes (DEGs) in RA versus NC, OA versus NC and RA versus OA groups were screened using limma package. We also verified the DEGs in {"type":"entrez-geo","attrs":{"text":"GSE55235","term_id":"55235"}}GSE55235 and {"type":"entrez-geo","attrs":{"text":"GSE100786","term_id":"100786"}}GSE100786. Functional annotation and protein–protein interaction (PPI) network construction of OA‐ and RA‐specific DEGs were performed. Finally, the candidate small molecules as potential drugs to treat RA and OA were predicted in CMap database.Results: 165 up-regulated and 163 down-regulated DEGs between RA and NC samples, 73 up-regulated and 293 down-regulated DEGs between OA and NC samples, 92 up-regulated and 98 down-regulated DEGs between RA and OA samples were identified. Immune response and TNF signaling pathway were significantly enriched pathways for RA‐ and OA‐specific DEGs, respectively. The hub genes were mainly associated with ‘Primary immunodeficiency’ (RA vs. NC group), ‘Ribosome’ (OA vs. NC group), and ‘Chemokine signaling pathway’ (RA vs. OA group). Arecoline and Cefamandole were the most promising small molecule to reverse the RA and OA gene expression.Conclusion: Our findings suggest new insights into the underlying pathogenesis of RA and OA, which may improve the diagnosis and treatment of these intractable chronic diseases.     

The promising novel biomarkers and candidate small molecule drugs in lower-grade glioma: Evidence from bioinformatics analysis of high-throughput data

Overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past 30 years, with survival averaging approximately 7 years. This study aimed to identify novel promising biomarkers of LGG and reveal its potential molecular mechanisms by integrated bioinformatics analysis. The microarray datasets of GSE68848 and GSE4290 were selected from GEO database for integrated analysis. In total, 293 overlapping differentially expressed genes (DEGs) were detected using the limma package. One hundred and eighty-eight nodes with 603 interactions were obtained from the establishment of protein-protein interaction (PPI) network. Functional and signaling pathway enriched were significantly correlated with the synapse and calcium signaling pathway, respectively. Module analysis revealed eight hub genes with high connectivity, which included CHRM1, DLG2, GABRD, GRIN1, HTR2A, KCNJ3, KCNJ9, and NUSAP1, and they were markedly correlated with patients’ prognosis. The mining of the Gene Expression Profiling Interactive Analysis database and qPCR further confirmed the abnormal expression of these key genes with their prognostic value in LGG. We eventually predicted the 20 most vital small molecule drugs, which potentially reverse the carcinogenic state of LGG, as per the CMap (connectivity map) database and these DEGs, and MS-275 (enrichment score = −0.939) was considered as the most promising small molecule to treat LGG. In conclusion, our study provided eight reliable novel molecular biomarkers for diagnosis, prognosis prediction, and treatment targets for LGG. These conclusions will contribute to a better comprehension of molecular mechanisms fundamental to LGG occurrence and progression, and providing new insights for future development of genomic individualized treatment in LGG.     

Identification of potential key genes in anaplastic thyroid cancer using bioinformatics analysis

Anaplastic thyroid cancer (ATC) has a high degree of malignancy and poor prognosis. The purpose of this study was to determine differentially expressed genes (DEGs) in ATC through biometric analysis technology, clarify potential interactions between them, and screen genes related to the prognosis of ATC. Using obtained DEGs, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein-protein interaction (PPI), and survival analysis were performed. After R integration analysis of the four datasets, 764 DEGs were obtained, i.e., 314 upregulated genes and 450 downregulated genes. Among the hub DEGs obtained from the PPI network, the expression levels of TYMS, FN1, CHRDL1, SDC2, ITGA2, COL1A1, COL9A3, and COL23A1 were associated with ATC prognosis. These results showed that the recurrence-free survival (RFS) of ATC was associated with TYMS, FN1, ITGA2, COL23A1, SDC2, and CHRDL1 statistically significantly in the KM plotter (P<0.05). Thus, the study suggests that TYMS, FN1, ITGA2, COL23A1, SDC2, and CHRDL1 may be used as potential biomarkers of ATC. These findings provide new insights for the detection of novel diagnostic and therapeutic biomarkers for ATC.     

Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy.     

Identification of key candidate biomarkers for severe influenza infection by integrated bioinformatical analysis and initial clinical validation

One of the key barriers for early identification and intervention of severe influenza cases is a lack of reliable immunologic indicators. In this study, we utilized differentially expressed genes screening incorporating weighted gene co-expression network analysis in one eligible influenza GEO data set ( GSE111368 ) to identify hub genes associated with clinical severity. A total of 10 genes (PBI, MMP8, TCN1, RETN, OLFM4, ELANE, LTF, LCN2, DEFA4 and HP) were identified. Gene set enrichment analysis (GSEA) for single hub gene revealed that these genes had a close association with antimicrobial response and neutrophils activity. To further evaluate these genes' ability for diagnosis/prognosis of disease developments, we adopted double validation with (a) another new independent data set ( GSE101702 ); and (b) plasma samples collected from hospitalized influenza patients. We found that 10 hub genes presented highly correlation with disease severity. In particular, BPI and MMP8 encoding proteins in plasma achieved higher expression in severe and dead cases, which indicated an adverse disease development and suggested a frustrating prognosis. These findings provide new insight into severe influenza pathogenesis and identify two significant candidate genes that were superior to the conventional clinical indicators. These candidate genes or encoding proteins could be biomarker for clinical diagnosis and therapeutic targets for severe influenza infection.     

Identification of key microRNAs and hub genes in non-small-cell lung cancer using integrative bioinformatics and functional analyses

Non-small-cell lung cancer (NSCLC) is an extremely debilitating respiratory malignancy. However, the pathogenesis of NSCLC has not been fully clarified. The main objective of our study was to identify potential microRNAs (miRNAs) and their regulatory mechanism in NSCLC. Using a systematic review, two NSCLC-associated miRNA data sets (GSE102286 and GSE56036) were obtained from Gene Expression Omnibus, and the differentially expressed miRNAs (DE-miRNAs) were accessed by GEO2R. Survival analysis of candidate DE-miRNAs was conducted using the Kaplan-Meier plotter database. To further illustrate the roles of DE-miRNAs in NSCLC, their potential target genes were predicted by miRNet and were annotated by the Database for Annotation, Visualization and Integrated Discovery (DAVID) program. Moreover, the protein-protein interaction (PPI) and miRNA-hub gene regulatory network were established using the STRING database and Cytoscape software. The function of DE-miRNAs in NSCLC cells was evaluated by transwell assay. Compared with normal tissues, a total of eight DE-miRNAs was commonly changed in two data sets. The survival analysis showed that six miRNAs (miR-21-5p, miR-31-5p, miR-708-5p, miR-30a-5p, miR-451a, and miR-126-3p) were significantly correlated with overall survival. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that target genes of upregulated miRNAs were enriched in pathways in cancer, microRNAs in cancer and proteoglycans in cancer, while the target genes of downregulated miRNAs were mainly associated with pathways in cancer, the PI3K-Akt signaling pathway and HTLV-I infection. Based on the miRNA-hub gene network and expression analysis, PTEN, EGFR, STAT3, RHOA, VEGFA, TP53, CTNNB1, and KRAS were identified as potential target genes. Furthermore, all six miRNAs exhibited significant effects on NSCLC cell invasion. These findings indicate that six DE-miRNAs and their target genes may play important roles in the pathogenesis of NSCLC, which will provide novel information for NSCLC treatments.     

Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis

Multiple myeloma (MM) is a common hematologic malignancy for which the underlying molecular mechanisms remain largely unclear. This study aimed to elucidate key candidate genes and pathways in MM by integrated bioinformatics analysis. Expression profiles GSE6477 and GSE47552 were obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) with p < .05 and [logFC] > 1 were identified. Functional enrichment, protein–protein interaction network construction and survival analyses were then performed. First, 51 upregulated and 78 downregulated DEGs shared between the two GSE datasets were identified. Second, functional enrichment analysis showed that these DEGs are mainly involved in the B cell receptor signaling pathway, hematopoietic cell lineage, and NF-kappa B pathway. Moreover, interrelation analysis of immune system processes showed enrichment of the downregulated DEGs mainly in B cell differentiation, positive regulation of monocyte chemotaxis and positive regulation of T cell proliferation. Finally, the correlation between DEG expression and survival in MM was evaluated using the PrognoScan database. In conclusion, we identified key candidate genes that affect the outcomes of patients with MM, and these genes might serve as potential therapeutic targets.     

Integrated computational approaches to screen gene expression data to determine key genes and therapeutic targets for type-2 diabetes mellitus

There is a rapid rise in cases of Type-2-diabetes mellitus (T2DM) globally, irrespective of the geography, ethnicity or any other variable factors. The molecular mechanisms that could cause the condition of T2DM need to be more thoroughly analysed to understand the clinical manifestations and to derive better therapeutic regimes. Tools in bioinformatics are used to trace out key gene elements and to identify the key causative gene elements and their possible therapeutic agents. Microarray datasets were retrieved from the Gene expression omnibus database and studied using R to derive different expressed gene (DEG) elements. With the comparison of the expressed genes with disease specific genes in DisGeNET, the final annotated genes were taken for analysis. Gene Ontology studies, Protein–protein interaction (PPI), Co-expression analysis, Gene-drug interactions were performed to scale down the hub genes and to identify the novelty across the genes analysed so far. In vivo and invitro analysis of key genes and the trace of interaction pathway is crucial to better understand the unique outcomes from the novel genes, forming the basis to understand the pathway that ends up causing T2DM. Afterwards, docking was executed enabling recognition of interacting residues involved in inhibition. The complex CCL5-265 and CD8A-40585 thus docked showed best results as is evident from its PCA analysis and MMGBSA calculation. There is now scope for deriving candidate drugs that could possibly detect personalized therapies for T2DM.     

Screening and identification of hub genes in pancreatic cancer by integrated bioinformatics analysis

Pancreatic cancer (Pa) is a malignant tumor of the digestive tract with high degree of malignancy, this study aimed to obtain the hub genes in the tumorigenesis of Pa. Microarray datasets GSE15471, GSE16515, and GSE62452 were downloaded from Gene Expression Omnibus (GEO) database, GEO2R was conducted to screen the differentially expressed genes (DEGs), and functional enrichment analyses were carried out by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the hub genes were identified by Cytoscape. Totally 205 DEGs were identified, consisting of 51 downregulated genes and 154 upregulated genes enriched in Gene Ontology terms including extracellular matrix (ECM) organization, collagen binding, cell adhesion, and pathways associated with ECM-receptor interaction, focal adhesion, and protein digestion. Two modules in the PPI were chosen and biological process analyses showed that the module genes were mainly enriched in ECM and cell adhesion. Twenty-four hub genes were confirmed, the survival analyses from the cBioPortal online platform revealed that topoisomerase (DNA) II α (TOP2A), periostin (POSTN), plasminogen activator, urokinase (PLAU), and versican (VCAN) may be involved in the carcinogenesis and progression of Pa, and the receiver-operating characteristic curves indicated their diagnostic value for Pa. Among them, TOP2A, POSTN, and PLAU have been previously reported as biomarkers for Pa, and far too little attention has been paid to VCAN. Analysis from R2 online platform showed that Pa patients with high VCAN expression were more sensitive to gemcitabine than those with low level, suggesting that VCAN may be an indicator to guide the use of the chemotherapeutic drug. In vitro experiments also showed that the sensitivity of the VCAN siRNA group to gemcitabine was lower than that of the control group. In conclusion, this study discerned hub genes and pathways related to the development of Pa, and VCAN was identified as a novel biomarker for the diagnose and therapy of Pa.     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

生信分析筛选心衰氧化应激相关关键[]基因并预测候选药物

为探究氧化应激相关基因在心力衰竭发生发展中的作用,并发现核心基因进行靶基因药物预测。从GEO数据库下载GSE120895基因表达图谱,通过GEO2R筛选差异表达基因,将差异表达基因与GeneCard数据库中筛选的氧化应激相关基因取交集,得到心力衰竭氧化应激相关差异表达基因,利用R软件对差异表达基因进行GO及KEGG分析,利用Cytoscape进行PPI网络的模块以及关键基因的筛选。之后在GSE17800基因表达图谱中验证关键基因的表达,并针对关键基因进行相互作用药物预测。差异表达基因与氧化应激相关基因取交集后,共筛选出52个上调的氧化应激相关差异表达基因,在此基础上,筛选出ACTB,STAT3,FN1,EDN1,CAT共5个关键基因,在GSE17800基因表达图谱中验证后,针对4个关键基因预测了19个靶基因潜在药物。总之,本研究通过生物信息学方法鉴定关键基因,并预测潜在治疗药物,从而为了解心力衰竭的分子机制及其诊治方法提供新的见解。     

Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC-related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC-associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC-associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC-associated genes for their potentiality in diagnosis, prognosis, and targeted therapy.     

妊娠期糖尿病产妇妊娠晚期阴道微生态改变与围产结局的相关性分析

目的 探讨妊娠期糖尿病(GDM)产妇妊娠晚期阴道微生态的改变与围产结局的关系.方法 选择2017年1月至2019年6月期间在我院门诊进行产检并于我院产科分娩的150例GDM孕妇组成GDM组,并选择同期于我院产检及分娩的150例健康孕妇作为对照组(NC组),孕检时所有研究对象进行阴道分泌物标本采集,并行白带常规检查.检查...