A histrory of bioinformatics was reviewed.Application of bioinformatics was discussed from three aspects,such as bioinformatics and drug research,bioinformatics and human genome project,bioinformatics and the third technological revolution.At last,problem and prospection in the field of bioinformatics was put forward.
Purpose: Bioinformatics-based approach to screen and analyze differentially expressed genes associated with the biological characteristics of Ewing sarcoma. Means: The GSE17674 dataset was selected for analysis, obtained by data retrieval based on the GEO public database. The R language limma toolkit was used to screen DEmRNAs. After the data were normalized, the Metascape online analysis software and the R language clusterProfiler package were used to analyze the GO function and KEGG pathway enrichment of DEmRNAs lines, respectively. The string database was selected for PPI analysis, and the results were imported into Cytoscape software to derive the core modules and predicted core genes. The genes selected above were analyzed for tissue localization specificity. Results: Through the analysis of GSE17674, differentially expressed genes were screened out, and GO and KEGG analyses were performed on the differentially expressed genes. The GO functional enrichment analysis was mainly enriched in the process of muscle system, muscle contraction, myocyte development, contractile fibers, myogenic fibers, myofibers, myofibrillar segments, actin binding, structural composition of muscle, and actin filament binding. KEGG pathway analysis showed that the core pathways associated with the development of ES were the core genes for myocardial contraction, congestive cardiomyopathy, and hypertrophic cardiomyopathy. Five Hub genes were obtained based on Cytoscape prediction. Tissue localization specificity analysis of Hub genes was performed, and a total of 2 Hub genes with tissue specificity were screened;MYH6 was specifically expressed in cardiac cells and MYL1 was specifically expressed in skeletal muscle cells. Conclusions: The differential genes screened will help to understand the molecular mechanisms underlying the highly invasive and metastasis-prone biological characteristics of ES, as well as provide new ideas for clinical drug-targeted treatment of ES.
Objective To screen and analyze the differentially expressed genes of Ewing’s sarcoma (ES) and Tuberculosis (TB) by bioinformatics. Methods GEO gene chip public database in NCBI was used for data retrieval, and chip data GSE17674 and GSE57736 were selected as analysis objects. The R language limma toolkit was used to screen DEmRNAs, and the data were standardized, and the common differentially expressed genes were screened by Venn diagram. The GO function and KEGG pathway enrichment of common differentially expressed genes were analyzed by using the R cluster Profiler package. String database was selected for PPI analysis, and the results were imported into Cytoscape software to obtain PPI interaction map, core module and Hub gene. Import Hub gene into BioGPS database. Results: A total of 3 Hub genes were screened, namely CD3D, LCK, KLRB1;The genes were imported into BioGPS database to obtain the specific genes. Conclusion The selected differential genes and related signaling pathways are helpful to understand the molecular mechanism of ES and TB, and can provide the basis for early diagnosis of ES complicated with TB. It also provides new ideas for clinical treatment and diagnosis.
Jun HuangQi LuJunxiu ZhouWenzhao ZhangChongyao XuGuiyun Wei
Objective: To screen and analyze the differentially expressed genes between dilated cardiomyopathy (DCM) and chronic heart failure (CHF) based on bioinformatics methods. Methods: The Gene Expression Omnibus (GEO) database was used for data retrieval, and the chip data GSE3585 was downloaded, which was the original data of DCM and normal control group. At the same time, the chip data GSE76701 was downloaded, which was the original data of CHF and control group. Differentially expressed mRNAs (DEmRNAs) were screened by R language limma package, the data were standardized, and the common differentially expressed genes were screened. GO function and KEGG pathway enrichment analysis were performed on the common differentially expressed genes. String11.0 online tool was used for data analysis to obtain differentially expressed genes, and the results were imported into Cytoscape 3.9.1 software. The results were imported into Cytoscape 3.9.1 software, and the common expression gene module was obtained by MOCDE algorithm. Nine Hub genes were obtained by 10 algorithms such as MCC. Results: A total of 248 differentially expressed genes were screened. GO analysis showed that differentially expressed genes were mainly concentrated in 9 different physiological and pathological processes. KEGG analysis showed that the main signaling pathways involved in differentially expressed genes were 2, and 9 key differentially expressed genes were predicted: NPPB, NPPA, MYH6, FRZB, ASPN, SFRP4, RPS4Y1, DDX3Y. Conclusion: This study preliminarily explored the molecular mechanism of DCM and CHF, and obtained the common differentially expressed genes of the two diseases. Further experimental studies are needed to verify the correlation between gene expression and clinicopathological features. Provide new ideas for clinical drug treatment research.
Objective: The mortality and morbidity rates associated with pancreatic cancer (PaCa) are extremely high. Various studies have demonstrated that pancreatic cancer will be the fourth cancer-related death by 2030, raising more concern for scholars to find effective methods to prevent and treat in order to improve the pancreatic cancer outcome. Using bioinformatic analysis, this study aims to pinpoint key genes that could impact PaCa patients’ prognosis and could be used as therapeutic targets. Methods: The TCGA and GEO datasets were integratively analyzed to identify prognosis-related differentially expressed genes. Next, the STRING database was used to develop PPI networks, and the MCODE and CytoNCA Cytoscape in Cytoscape were used to screen for critical genes. Through CytoNCA, three kinds of topology analysis were considered (degree, betweenness, and eigenvector). Essential genes were confirmed as potential target treatment through Go function and pathways enrichment analysis, a developed predictive risk model based on multivariate analysis, and the establishment of nomograms using the clinical information. Results: Overall, the GSE183795 and TCGA datasets associated 1311 and 2244 genes with pancreatic cancer prognosis, respectively. We identified 132 genes that were present in both datasets. The PPI network analysis using, the centrality analysis approach with the CytoNCA plug-in, showed that CDK2, PLK1, CCNB1, and TOP2A ranked in the top 5% across all three metrics. The independent analysis of a risk model revealed that the four key genes had a Hazard Ratio (HR) > 1. The monogram showed the predictive risk model and individual patient survival predictions were accurate. The results indicate that the effect of the selected vital genes was significant and that they could be used as biomarkers to predict a patient’s outcome and as possible target therapy in patients with pancreatic cancer. GO function and pathway analysis demonstrated that crucial genes might affect the P53 signaling pathway and FoxO signaling
Background: Hypertension, also known as increased blood pressure, is a phenomenon in which blood flows in blood vessels and causes persistently higher-than-normal pressure on the vessel wall. The identification of novel prognostic and pathogenesis biomarkers plays a key role in the management of hypertension. Methods: The GSE7483 and GSE75815 datasets from the gene expression omnibus (GEO) database were used to identify the genes associated with hypertension that were differentially expressed genes (DEGs). The functional role of the DEGs was elucidated by gene body (GO) enrichment analysis. In addition, we performed an immune infiltration assay and GSEA on the DEGs of hypertensive patients and verified the expression of novel DEGs in the blood of hypertensive patients by RT-qPCR. Results: A total of 267 DEGs were identified from the GEO database. GO analysis revealed that these genes were associated mainly with biological processes such as fibroblast proliferation, cell structural organization, extracellular matrix organization, vasculature development regulation, and angiogenesis. We identified five possible biomarkers, Ecm1, Sparc, Sphk1, Thbsl, and Mecp2, which correlate with vascular development and angiogenesis characteristic of hypertension by bioinformatics, and explored the clinical expression levels of these genes by RT-qPCR, and found that Sparc, Sphk1, and Thbs1 showed significant up-regulation, in agreement with the results of the bioinformatics analysis. Conclusion: Our study suggested that Sparc, Sphk1 and Thbs1 may be potential novel biomarkers for the diagnosis, treatment and prognosis of hypertension and that they are involved in the regulation of vascular development and angiogenesis in hypertension.
Background: Hypertension is a universal risk factor for cardiovascular diseases and is thus the leading cause of death worldwide. The identification of novel prognostic and pathogenesis biomarkers plays a key role in disease management. Methods: The GSE145854 and GSE164494 datasets were downloaded from the Gene Expression Omnibus (GEO) database and used for screening and validating hypertension signature genes, respectively. Gene Ontology (GO) enrichment analysis was performed on the differentially expressed genes (DEGs) related to calcium ion metabolism in patients with hypertension. The core genes related to immune infiltration were analyzed and screened, and the activity of the signature genes and related pathways was quantified using gene set enrichment analysis (GSEA). The infiltration of immune cells in the blood samples was analyzed, and the DEGs that were abnormally expressed in the clinical blood samples of patients with hypertension were verified via RT-qPCR. Results: A total of 176 DEGs were screened. GO showed that DEGs was involved in the regulation of calcium ion metabolism in biological processes (BP), actin mediated cell contraction, negative regulation of cell movement, and calcium ion transmembrane transport, and in the regulation of protease activity in molecular functions (MF). KEGG analysis revealed that the DEGs were involved mainly in the cGMP-PKG signaling pathway, ubiquitin-protein transferase, tight junction-associated proteins, and the regulation of myocardial cells. MF analysis revealed the immune infiltration function of the cells. RT-qPCR revealed that the expression of Cacna1d, Serpine1, Slc8a3, and Trpc4 was up regulated in hypertension, the expression of Myoz2 and Slc25a23 was down regulated. Conclusion: Cacna1d, Serpine1, Slc8a3, Trpc4, Myoz2 and Slc25a23 may be involved in the regulation of calcium metabolism pathways and play key roles in hypertension. These differentially expressed calcium metabolism-related genes may serve as prognostic markers of hypertension.
Large language model(LLM)-based chatbots like Chat Generative Pre-trained Transformer(ChatGPT),equipped with broad biological knowledge[1],have demonstrated an impressive capability for bioinformatics coding[2].When given well-crafted instructions,these chatbots hold the potential to significantly augment bioinformatics education and research[3,4].However,opportunities entail both rewards and risks.This commentary explores the challenges of using chatbots in bioinformatics and proposes strategies to manage the associated risks while maximizing the benefits.
