Analysis of differentially expressed genes related to cerebral ischaemia in young rats based on the Gene Expression Omnibus database

BACKGROUND The incidence rate of cerebral infarction in young people is increasing day by day,the age of onset tends to be younger,and its internal pathogenesis and mechanism are very complicated,which leads to greater difficulties in treatment.Therefore,it is essential to analyze the key pathway that affects the onset of cerebral infarction in young people from the perspective of genetics.AIM To compare the differentially expressed genes in the brain tissue of young and aged rats with middle cerebral artery occlusion and to analyse their effect on the key signalling pathway involved in the development of cerebral ischaemia in young rats.METHODS The Gene Expression Omnibus 2R online analysis tool was used to analyse the differentially expressed genes in the GSE166162 dataset regarding the development of cerebral ischaemia in young and aged groups of rats.DAVID 6.8 software was further used to filter the differentially expressed genes.These genes were subjected to Gene Ontology(GO)function analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis to determine the key gene pathway that affects the occurrence of cerebral ischaemia in young rats.RESULTS Thirty-five differentially expressed genes(such as Igf2,Col1a2,and Sfrp1)were obtained;73 GO enrichment analysis pathways are mainly involved in biological processes such as drug response,amino acid stimulation response,blood vessel development,various signalling pathways,and enzyme regulation.They are involved in molecular functions such as drug binding,protein binding,dopamine binding,metal ion binding,and dopamine neurotransmitter receptor activity.KEGG pathway enrichment analysis showed a significantly enriched pathway:The cyclic adenosine monophosphate(c-AMP)signalling pathway.CONCLUSION The c-AMP signalling pathway might be the key pathway in the intervention of cerebral infarction in young people.

Identification of ITGA3 as an Oncogene in Human Tongue Cancer via Integrated Bioinformatics Analysis

Human tongue cancer （TC） is an aggressive malignancy with a very poor prognosis. There is an urgent need to elucidate the underlying molecular mechanisms involved in TC progression, mRNA expression profiles play a vital role in the exploration of cancer-related genes. Therefore, the purpose of our study was to identify the progression associated candidate genes of TC by bioinformatics analysis. Five microarray datasets of TC samples were downloaded from the Gene Expression Onmibus （GEO） database and the data of 133 TC patients were screened from The Cancer Genome Atlas （TCGA） head and neck squamous cell carcinoma （HNSC） database. The integrated analysis of five microarray datasets and the RNA sequencing data of TC samples in TCGA-HNSC was performed to obtain 1023 overlapping differentially expressed genes （DEGs） in TC and adjacent normal tissue （ANT） samples. Next, Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway analysis was conducted to enrich the significant pathways of the 1023 DEGs and PI3K- Akt signaling pathway （P=0.011） was selected to be the candidate pathway. A total of 23 DEGs with ｜log2 fold change （FC）｜ ≥1.0 in phosphatidylinositol 3-kinase-serine/threonine kinase （PI3K-Akt） signaling pathway were subjected to survival analysis of 125 eligible TC samples in TCGA database, indicating increased integrin-α3 gene （ITGA3） expression was significantly associated with poorer prognosis. Taken together, our study suggested ITGA3 may facilitate the development of TC via activating PI3K-Akt signaling pathway.

Analysis of key pathogenic target genes of ovarian cancer and experimental verification of cells in vitro

Objective:To mine genes highly related to the pathogenesis of ovarian cancer by using multichip integrated bioinformatics methods and verify them in cells,which provided key genes and important theoretical basis for targeted research of ovarian cancer.Methods:Three datasets,GSE38666,GSE40595 and GSE54388,were downloaded from the Gene Expression Integrated Database database for differential gene(DEGs)screening,including 26 normal samples and 65 ovarian cancer samples.Gene ontology functional annotation of selected DEGs was performed through DAVID online database to clarify the biological characteristics of DEGs.The main pathways of DEGs were obtained by enrichment analysis using Kyoto gene and genomic encyclopedia method.Based on the STRING database,the DEGs protein-protein interaction network was constructed by using CytoScape software,and the key genes were screened by GEPIA2 database to verify the expression at the cell level.Results:A total of 238 DEGs were screened from GSE38666,GSE40595 and GSE54388 datasets,of which 168 DEGs were upregulated and 70 DEGs were down-regulated.The co-expressed DEGs were mainly enriched in biological functions such as mitotic nuclear division,spindle,chromosomal region and DNA helicase activity in ovarian cancer.They were mainly involved in biological processes such as cell cycle,DNA replication,oxidative phosphorylation and biosynthesis of amino acids,thereby affecting the occurrence and development of ovarian cancer.Six genes were highly expressed and associated with the development of ovarian cancer,including IFI27,EPCAM,CXCR4,PEA15,CLDN3 and CAPG.Cell verification showed that the mRNA expression of the six genes in ovarian cancer cells was higher than that in normal ovarian cells(P<0.05),which was consistent with the previous screening results.Conclusion:Multi-chip integrated bioinformatics is an effective method to find ovarian cancer target genes.IFI27,EPCAM,CXCR4,PEA15,CLDN3 and CAPG are highly correlated with the occurrence and development of ovarian cancer,which can be used as target genes for ovarian cancer research.

Rice Expression Database(RED)：An integrated RNA-Seq-derived gene expression database for rice

Rice is one of the most important stable food as well as a monocotyledonous model organism for the plant research community.Here,we present RED（Rice Expression Database;http：//expression.ic4r.org）,an integrated database of rice gene expression profiles derived entirely from RNA-Seq data.RED features a comprehensive collection of 284 high-quality RNA-Seq experiments,integrates a large number of gene expression profiles and covers a wide range of rice growth stages as well as various treatments.Based on massive expression profiles,RED provides a list of housekeeping and tissue-specific genes and dynamically constructs co-expression networks for gene（s） of interest.Besides,it provides user-friendly web interfaces for querying,browsing and visualizing expression profiles of concerned genes.Together,as a core resource in BIG Data Center,RED bears great utility for characterizing the function of rice genes and better understanding important biological processes and mechanisms underlying complex agronomic traits in rice.

Prognostic biomarkers and immune cell infiltration characteristics in small cell lung cancer

Background Small cell lung cancer(SCLC)is a highly malignant and aggressive neuroendocrine tumor.With the rise of immunotherapy,it has provided a new direction for SCLC.However,due to the lack of prognostic biomarkers,the median overall survival of SCLC is still to be improved.This study aimed to explore novel biomarkers and tumor-infiltrating immune cell characteristics that may serve as potential diagnostic and prognostic markers in SCLC.Methods Gene expression profiles from patients with SCLC were downloaded from the Gene Expression Omnibus(GEO)database,and tumor microenvironment(TME)infiltration profile data were obtained using CIBERSORT.The robust rank aggregation(RRA)method was utilized to integrate three SCLC microarray datasets downloaded from the GEO database and identify robust differentially expressed genes(DEGs)between normal and tumor tissue samples.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses were performed to explore the functions of the robust DEGs.Subsequently,protein-protein interaction networks and key modules were constructed by Cytoscape,and hub genes were selected from the whole network using the plugin cytoHubba.Survival analysis of hub genes was performed by Kaplan-Meier plotter in 18 patients with extensive-stage SCLC.Results A total of 312 robust DEGs,including 55 upregulated and 257 downregulated genes,were screened from 129 SCLC tissue samples and 44 normal tissue samples.GO and KEGG enrichment analyses revealed that the robust DEGs were predominantly involved in human T-cell leukemia virus 1 infection,focal adhesion,complement and coagulation cascades,tumor necrosis factor(TNF)signaling pathway,and ECM-receptor interaction,which are closely associated with the development and progression of SCLC.Subsequently,three DEGs modules and six hub genes(ITGA10,DUSP12,PTGS2,FOS,TGFBR2,and ICAM1)were identified through screening with the Cytoscape plugins MCODE and cytoHubba,respectively.Immune cell infiltration analysis by the CIBERSORT algorithm revealed that resting memory CD4+T cells were the predominant infiltrating immune cells in SCLC.In addition,Kaplan-Meier plotter revealed that the gene prostaglandin-endoperoxide synthase 2(PTGS2)was a potential prognostic biomarker of SCLC.Conclusions Hub genes and tumor-infiltrating immune cells may be the molecular mechanisms underlying the development of SCLC,and this finding could contribute to the formulation of individualized immunotherapy strategies for SCLC.