bioinformatics

摘要： Proteomics is a powerful tool that can be used to elucidate the underlying mechanisms of diseases and identify new biomarkers.Therefore,it may also be helpful for understanding the detailed pathological mechanism of traumatic brain injury(TBI).In this study,we performed Tandem Mass Tag-based quantitative analysis of cortical proteome profiles in a mouse model of TBI.Our results showed that there were 302 differentially expressed proteins in TBI mice compared with normal mice 7 days after injury.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that these differentially expressed proteins were predominantly involved in inflammatory responses,including complement and coagulation cascades,as well as chemokine signaling pathways.Subsequent transcription factor analysis revealed that the inflammation-related transcription factors NF-κB1,RelA,IRF1,STAT1,and Spi1 play pivotal roles in the secondary injury that occurs after TBI,which further corroborates the functional enrichment for inflammatory factors.Our results suggest that inflammation-related proteins and inflammatory responses are promising targets for the treatment of TBI.

摘要： Ferroptosis plays a key role in aggravating the progression of spinal cord injury(SCI),but the specific mechanism remains unknown.In this study,we constructed a rat model of T10 SCI using a modified Allen method.We identified 48,44,and 27 ferroptosis genes that were differentially expressed at 1,3,and 7 days after SCI induction.Compared with the sham group and other SCI subgroups,the subgroup at 1 day after SCI showed increased expression of the ferroptosis marker acyl-CoA synthetase long-chain family member 4 and the oxidative stress marker malondialdehyde in the injured spinal cord while glutathione in the injured spinal cord was lower.These findings with our bioinformatics results suggested that 1 day after SCI was the important period of ferroptosis progression.Bioinformatics analysis identified the following top ten hub ferroptosis genes in the subgroup at 1 day after SCI:STAT3,JUN,TLR4,ATF3,HMOX1,MAPK1,MAPK9,PTGS2,VEGFA,and RELA.Real-time polymerase chain reaction on rat spinal cord tissue confirmed that STAT3,JUN,TLR4,ATF3,HMOX1,PTGS2,and RELA mRNA levels were up-regulated and VEGFA,MAPK1 and MAPK9 mRNA levels were down-regulated.Ten potential compounds were predicted using the DSigDB database as potential drugs or molecules targeting ferroptosis to repair SCI.We also constructed a ferroptosis-related mRNA-miRNA-lncRNA network in SCI that included 66 lncRNAs,10 miRNAs,and 12 genes.Our results help further the understanding of the mechanism underlying ferroptosis in SCI.

摘要： BACKGROUND The N-Myc downstream-regulated gene(NDRG)family is comprised of four members(NDRG1-4)involved in various important biological processes.However,there is no systematic evaluation of the prognostic of the NDRG family in hepatocellular carcinoma(HCC).AIM To analyze comprehensively the biological role of the NDRG family in HCC.METHODS The NDRG family expression was explored using The Cancer Genome Atlas.DNA methylation interactive visualization database was used for methylation analysis of the NDRG family.The NDRG family genomic alteration was assessed using the cBioPortal.Single-sample Gene Set Enrichment Analysis was used to determine the degree of immune cell infiltration in tumors.RESULTS NDRG1 and NDRG3 were up-regulated in HCC,while NDRG2 was down-regulated.Consistent with expression patterns,high expression of NDRG1 and NDRG3 was associated with poor survival outcomes(P<0.05).High expression of NDRG2 was associated with favorable survival(P<0.005).An NDRG-based signature that statistically stratified the prognosis of the patients was constructed.The percentage of genetic alterations in the NDRG family varied from 0.3%to 11.0%,and the NDRG1 mutation rate was the highest.NDRG 1-3 expression was associated with various types of infiltrated immune cells.Gene ontology analysis revealed that organic acid catabolism was the most important biological process related to the NDRG family.Gene Set Enrichment Analysis showed that metabolic,proliferation,and immune-related gene sets were enriched during NDRG1 and NDRG3 high expression and NDRG2 low expression.CONCLUSION Overexpression of NDRG1 and NDRG3 and down-expression of NDRG2 are correlated with poor overall HCC prognosis.Our results may provide new insights into the indispensable role of NDRG1,2,and 3 in the development of HCC and guide a promising new strategy for treating HCC.

摘要： We analysed four gene microarray datasets by GEO2R and obtained differential genes expressed in oesophageal cancer.To further elaborate the functions of DGEs,this study performed gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis of DEGs.We constructed protein interaction networks of DGEs through the String database and screened core genes.We used the GEPIA online database with the Kaplan-Meier plotter database to verify the expression of Hub genes in expressed normal versus tumour tissues and the effect of Hub genes on overall and disease-free survival in oesophageal cancer.To further understand the relationship between Hub gene and tumour metastasis,we analysed the difference in Hub gene expression in patients without metastatic oesophageal cancer versus those with metastatic oesophageal cancer with the help of the HCMDB database.The relationship between Hub genes and tumour immune infiltration was analysed by the TIMER database.We obtained a total of 149 DEGs,of which 49 were up-regulated genes and 100 were down-regulated genes.These DGEs were importantly enriched in IL-17 signalling pathway,ECM-receptor interactions,p53 signalling pathway,estrogen signalling pathway,complement and coagulation cascade response.We screened 10 Hub genes,MMP9,CXCL8,COL1A1,TIMP1,POSTN,MMP3,MMP1,COL3A1,SERPINE1,LUM,among 149 DGEs.hub genes were all up-regulated in expression in esophageal cancer tissues,in addition,MMP9,T1MP1,CXCL8,POSTN and The expression of COL3A1,LUM,MMP1,MMP3,MMP9,POSTN,SERPINE1 and TIMP1 was positively correlated with the infiltration of immune cells in the tumor microenvironment.In conclusion,our study identified 10 signature genes for oesophageal cancer.These genes are associated with the development,metastasis,prognosis and immune infiltration of oesophageal cancer and may be markers of development,metastasis and prognosis as well as targets for immunotherapy.

摘要： Hepatocellular carcinoma(HCC)is the second cause of cancer-related mortality.The diagnosis of HCC depends mainly on-fetoprotein,which is limited in its diagnostic and screening capabilities.There is an urgent need for a biomarker that detects early HCC to give the patients a chance for curative treatment.New targets of therapy could enhance survival and create future alternative curative methods.In silico analysis provides both;discovery of biomarkers,and understanding of the molecular pathways,to pave the way for treatment development.This review discusses the role of in silico analysis in the discovery of biomarkers,molecular pathways,and the role the author has contributed to this area of research.It also discusses future aspirations and current limitations.A literature review was conducted on the topic using various databases(PubMed,Science Direct,and Wiley Online Library),searching in various reviews,and editorials on the topic,with overviewing the author’s own published and unpublished work.This review discussed the steps of the validation process from in silico analysis to in vivo validation,to incorporation into clinical practice guidelines.In addition,reviewing the recent lines of research of bioinformatic studies related to HCC.In conclusion,the genetic,molecular and epigenetic markers discoveries are hot areas for HCC research.Bioinformatics will enhance our ability to accomplish this understanding in the near future.We face certain limitations that we need to overcome.

摘要： Objective Docetaxel-based combination chemotherapy has traditionally been the standard treatment for metastatic castration-resistant prostate cancer(PCa).However,most patients eventually develop resistance to this treatment,which further reduces their survival.This study aimed to determine key molecular genes in docetaxel-resistant PCa cell lines using bioinformatic approaches.Methods The analysis of microarray data GSE33455(including DU-145/DU-145R and PC-3/PC-3R cell lines)obtained from the Gene Expression Omnibus(GEO)database was performed using GEO2R.Differentially expressed genes(DEGs)of DU-145/DU-145R and PC-3/PC-3R cell lines were selected,and the intersection of DEGs between the two groups was obtained.DEGs were annotated with the Gene Ontology(GO)function and enriched with the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway using an online platform(https://cloud.oebiotech.cn/task/detail/array_enrichment/).The online tool Search Tool for the Retrieval of Interacting Genes(https://string-db.org/)was used to obtain the DEG network graph and matrix list,which was imported into Cytoscape 3.6.1 and analyzed using the Molecular Complex Detection plug-in to detect potential functional modules in the network.Results A total of 131 intersection DEGs were identified between non-treated and docetaxel-resistant PCa cell lines.GO functional annotation showed that the main genes involved were present in the plasma membrane and were involved in positive regulation of ubiquitin-protein transferase activity,positive regulation of pseudopodium assembly,centriolar subdistal appendage,and heterophilic cell-cell adhesion via plasma membrane cell adhesion molecules.KEGG pathway enrichment analysis revealed that DEGs were mainly involved in IL-17 signaling pathway,cytokine-cytokine receptor interaction,rheumatoid arthritis,legionellosis,and folate biosynthesis.We identified two distinct hubs of DEGs:(1)CD274,C-X-C motif chemokine ligand(CXCL)1,DExD/H-box helicase 58,CXCL2,CXCL8,colony-stimulating factor 2,C-X-C motif chemokine receptor 4(CXCR4),CXCL5,and CXCL6 and(2)argininosuccinate lyase,argininosuccinate synthase 1,and asparagine synthetase.Except for the CXCR4 gene that was downregulated,the other 11 genes showed upregulated expression.Conclusion Certain differential genes may be potential targets for predicting and treating metastatic docetaxel-resistant PCa.

摘要： BACKGROUND Heart failure is a health burden responsible for high morbidity and mortality worldwide, and dilated cardiomyopathy(DCM) is one of the most common causes of heart failure. DCM is a disease of the heart muscle and is characterized by enlargement and dilation of at least one ventricle alongside impaired contractility with left ventricular ejection fraction < 40%. It is also associated with abnormalities in cytoskeletal proteins, mitochondrial ATP transporter, microvasculature, and fibrosis. However, the pathogenesis and potential biomarkers of DCM remain to be investigated.AIM To investigate the candidate genes and pathways involved in DCM patients.METHODS Two expression datasets(GSE3585 and GSE5406) were downloaded from the Gene Expression Omnibus database. The differentially expressed genes(DEGs) between the DCM patients and healthy individuals were identified using the R package “linear models for microarray data.” The pathways with common DEGs were analyzed via Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes(KEGG), and gene set enrichment analyses. Moreover, a protein-protein interaction network(PPI) was constructed to identify the hub genes and modules. The MicroRNA Database was applied to predict the microRNAs(miRNAs) targeting the hub genes. Additionally, immune cell infiltration in DCM was analyzed using CIBERSORT.RESULTS In total, 97 DEGs(47 upregulated and 50 downregulated) were identified. GO analysis showed that the DEGs were mainly enriched in “response to growth factor,” “extracellular matrix,” and “extracellular matrix structural constituent.” KEGG pathway analysis indicated that the DEGs were mainly enriched in “protein digestion and absorption” and “interleukin 17(IL-17) signaling pathway.” The PPI network suggested that collagen type Ⅲ alpha 1 chain(COL3A1) and COL1A2 contribute to the pathogenesis of DCM. Additionally, visualization of the interactions between miRNAs and the hub genes revealed that hsa-miR-5682 and hsa-miR-4500 interacted with both COL3A1 and COL1A2, and thus these miRNAs might play roles in DCM. Immune cell infiltration analysis revealed that DCM patients had more infiltrated plasma cells and fewer infiltrated B memory cells, T follicular helper cells, and resting dendritic cells.CONCLUSION COL1A2 and COL3A1 and their targeting miRNAs, hsa-miR-5682 and hsa-miR-4500, may play critical roles in the pathogenesis of DCM, which are closely related to the IL-17 signaling pathway and acute inflammatory response. These results may provide useful clues for the diagnosis and treatment of DCM.

摘要： Background:Tumor heterogeneity is closely related to the occurrence,progression and recurrence of renal clear cell carcinoma(ccRCC),making early diagnosis and effective treatment difficult.DNA methylation is an important regulator of gene expression and can affect tumor heterogeneity.Methods:In this study,we investigated the prognostic value of subtypes based on DNA methylation status in 506 ccRCC samples with paired clinical data from the TCGA database.Differences in DNA methylation levels were associated with differences in T,N and M categories,age,stage and prognosis.Finally,the samples were divided into the training group and the testing group according to 450K and 27K.Univariate and multivariate Cox regression analysis was used to construct the prediction model in the training group,and the model was verified and evaluated in the testing group.Results:By univariate Cox regression analysis,21,122 methylation sites and 6,775 CpG sites were identified as potential DNA methylation biomarkers for overall survival of ccRCC patients(P<0.05).3,050 CpG sites independently associated with prognosis were identified with T,N,M,stage and age as covariables.Consensus cluster of 3,050 potential prognostic methylation sites was used to identify different DNA methylation subsets of ccRCC for prognostic purposes.We performed functional enrichment analysis on these 3,640 genes and identified 75 significantly enriched pathways(P<0.05).We then researched the expression of methylated genes in subgroups.Verifing with the training set,suggesting that DNA methylation levels generally reflect the expression of these genes.Conclusion:Based on TCGA database and a series of bioinformatics methods,We identified prognostic specific methylation sites and established prognostic prediction models for ccRCC patients.This model helps to identify novel biomarkers,precision drug targets and disease molecular subtypes in patients with ccRCC.Therefore,this model may be useful in predicting the prognosis,clinical diagnosis and management of patients with different epigenetic subtypes of ccRCC.

摘要： Objective:To investigate the core target genes of miR-29b-3p,and analyze the clinical significance of the core target genes in glioma.Methods:Bioinformatics analysis was used to predict and screen the target genes of miR-29b-3p.STRING and Cytoscape software were used to analyze the protein-protein interaction(PPI)of target genes.the differences expression and survival prognosis in glioma were analyzed by GEPIA and CGGA.Independent prognostic factors analyzed by univariate and multivariate Cox proportional hazards regression model.Results:22 target genes of miR-29b-3p were predicted using LinkedOmics,miRDB,miRTarBase,TargetScan,and starbase databases.Through the construction of the PPI network,genes out of the network were removed,and a total of 16 genes were screened for further study of their clinical significance.Based on analysis of GEPIA and CGGA databases,COL2A1,DNMT3A,and DNMT3B were excluded.Through further analysis of the univariate and multivariate Cox proportional hazard regression model,finally identified three core target genes:SERPINH1,LOXL2,CDK6.Conclusion:Bioinformatics analysis showed that miR-29b-3p targeted three core genes such as SERPINH1,LOXL2,and CDK6 in glioma.The expression of these genes was different between brain normal tissues and gliomas,between different grades of tumor,IDH mutation status and 1p/19q codeletion status.Its high expression had adverse effects on overall survival and recurrence-free survival.These core target genes can be used as an independent prognostic factor.

摘要： BACKGROUND Melanomas are malignant tumors that can occur in different body parts or tissues such as the skin,mucous membrane,uvea,and pia mater.Long non-coding RNAs(lncRNAs)are key factors in the occurrence and development of many malignant tumors,and are involved in the prognosis of some patients.AIM To identify autophagy-related lncRNAs in melanoma that are crucial for the diagnosis,treatment,and prognosis of melanoma patients.METHODS We retrieved transcriptome expression profiles and clinical information of 470 melanoma patients from The Cancer Genome Atlas(TCGA)database.Then,we identified autophagy-related genes in the Human Autophagy Database.Using R,coexpression analysis of lncRNAs and autophagy-related genes was conducted to obtain autophagy-related lncRNAs and their expression levels.We also performed univariate and multivariate Cox proportional risk analyses on the obtained datasets,to systematically evaluate the prognostic value of autophagyrelated lncRNAs in melanoma.Fifteen autophagy-related lncRNAs were identified and an autophagy-related prognostic signature for melanoma was established.The Kaplan-Meier and univariate and multivariate Cox regression analyses were used to calculate risk scores.Based on the risk scores,melanoma patients were randomly divided into high-and low-risk groups.Receiver operating characteristic curve analysis,dependent on time,was performed to assess the accuracy of the prognostic model.At the same time,we also downloaded the melanoma data sets GSE65904,GSE19234,and GSE78220 from the GENE EXPRESSION OMNIBUS database for model verification.Finally,we performed Gene Set Enrichment Analysis functional annotation,which showed that the low and the high-risk groups had different enriched pathways.RESULTS The co-expression network for autophagy-related genes was constructed using R,and 936 lncRNAs related to autophagy were identified.Then,52 autophagy-related lncRNAs were significantly associated with TCGA melanoma patients’survival by univariate Cox proportional risk analysis(P<0.01).Further,the 52 autophagy-related lncRNAs mentioned above were analyzed by multivariate Cox analysis with R.Fifteen lncRNAs were selected:LINC01943,AC090948.3,USP30-AS1,AC068282.1,AC004687.1,AL133371.2,AC242842.1,PCED1B-AS1,HLADQB1-AS1,AC011374.2,LINC00324,AC018553.1,LINC00520,DBH-AS1,and ITGB2-AS1.The P values in all survival analyses using these 15 lncRNAs were<0.05.These lncRNAs were used to build a risk model based on the risk score.Negative correlations were observed between risk scores and overall survival rate in melanoma patients over time.Additionally,the melanoma risk curve and scatter plot analyses showed that the death number increased along with the increase in the risk score.Overall,we identified and established a new prognostic risk model for melanoma using 15 autophagy-related lncRNAs.The risk model constructed with these lncRNAs can help and guide melanoma patient prognosis predictions and individualized treatments in the future.CONCLUSION Overall,the risk model developed based on the 15 autophagy-related lncRNAs can have important prognostic value and may provide autophagy-related clinical targets for melanoma treatment.

摘要： Sequence analysis has been an important researching problem in Bioinformatics. We investigate the relation between genes in the entire gene group level. The DNA sequence pattern of biologic nature population can provide the useful information to species evolution, heredity breeding and so on. Biology sequence pattern mining has become one of the hot points of biology sequence analysis. Researching this problem will give us applied value and academic value. In the schema of mining sequence pattern based on Multi-support, this paper presents a new algorithm for mining biology sequence pattern. The goal of the algorithm is to mine common patterns of multi-sequence and find common patterns in each one sequence. Comparing with Apriori Algorithm and the other algorithm based comparing each word, we also offer some experiments to show this Algorithm is efficient.

摘要： Sequence analysis has been an important researching problem in Bioinformatics. We investigate the relation between genes in the entire gene group level. The DNA sequence pattern of biologic nature population can provide the useful information to species evolution, heredity breeding and so on. Biology sequence pattern mining has become one of the hot points of biology sequence analysis. Researching this problem will give us applied value and academic value. In the schema of mining sequence pattern based on Multi-support, this paper presents a new algorithm for mining biology sequence pattern. The goal of the algorithm is to mine common patterns of multi-sequence and find common patterns in each one sequence. Comparing with Apriori Algorithm and the other algorithm based comparing each word, we also offer some experiments to show this Algorithm is efficient.

摘要： Sequence analysis has been an important researching problem in Bioinformatics. We investigate the relation between genes in the entire gene group level. The DNA sequence pattern of biologic nature population can provide the useful information to species evolution, heredity breeding and so on. Biology sequence pattern mining has become one of the hot points of biology sequence analysis. Researching this problem will give us applied value and academic value. In the schema of mining sequence pattern based on Multi-support, this paper presents a new algorithm for mining biology sequence pattern. The goal of the algorithm is to mine common patterns of multi-sequence and find common patterns in each one sequence. Comparing with Apriori Algorithm and the other algorithm based comparing each word, we also offer some experiments to show this Algorithm is efficient.

摘要： Sequence analysis has been an important researching problem in Bioinformatics. We investigate the relation between genes in the entire gene group level. The DNA sequence pattern of biologic nature population can provide the useful information to species evolution, heredity breeding and so on. Biology sequence pattern mining has become one of the hot points of biology sequence analysis. Researching this problem will give us applied value and academic value. In the schema of mining sequence pattern based on Multi-support, this paper presents a new algorithm for mining biology sequence pattern. The goal of the algorithm is to mine common patterns of multi-sequence and find common patterns in each one sequence. Comparing with Apriori Algorithm and the other algorithm based comparing each word, we also offer some experiments to show this Algorithm is efficient.