Key Prediction Genes of Nasopharyngeal Carcinoma:Screening Based on Systematic Bioinformatics and Validation by Cell Experiments / 中国医学科学院学报

Objective To screen out the potential prediction genes for nasopharyngeal carcinoma(NPC)from the gene microarray data of NPC samples and then verify the genes by cell experiments.Methods The NPC dataset was downloaded from Gene Expression Omnibus,and limma package was employed to screen out the differentially expressed genes.Weighted correlation network analysis package was used for weighted gene co-expression network analysis,and Venn diagram was drawn to find the common genes.The gene ontology annotation and Kyoto encyclopedia of genes and genomes pathway enrichment were then performed for the common genes.The biomarkers for NPC were further explored by protein-protein interaction network,LASSO regression,and non-parametric tests.Real-time quantitative PCR and Western blotting were employed to determine the mRNA and protein levels of key predictors of NPC,so as to verify the screening results.Results There were 622 up-regulated genes and 351 down-regulated genes in the GSE12452 dataset.A total of 116 common genes were obtained by limma analysis and weighted gene co-expression network analysis.The common genes were mainly involved in the biological processes of cell proliferation and regulation and regulation of intercellular adhesion.They were mainly enriched in Rap1,Ras,and tumor necrosis factor signaling pathways.Six key genes were screened out,encoding angiopoietin-2(ANGPT2),dual oxidase 2(DUOX2),coagulation factor Ⅲ(F3),interleukin-15(IL-15),lipocalin-2,and retinoic acid receptor-related orphan receptor B(RORB).Real-time quantitative PCR and Western blotting showed that the NPC cells had up-regulated mRNA and protein levels of ANGPT2 and IL-15 and down-regulated mRNA and protein levels of DUOX2,F3,and RORB,which was consistent with the results predicted by bioinformatics.Conclusion ANGPT2,DUOX2,F3,IL-15 and RORB are potential predictive molecular markers and therapeutic targets for NPC,which may be involved in Rap1,Ras,tumor necrosis factor and other signaling pathways.

Bioinformatic analysis identifies potential key genes in the pathogenesis of age-related macular degeneration

Purpose: Age?related macular degeneration (AMD) is the leading cause of irreversible blindness in older individuals. More studies focused on screening the genes, which may be correlated with the development of AMD. With advances in various technologies like multiple microarray datasets, researchers could identify differentially expressed genes (DEGs) more accurately. Exploring abnormal gene expression in disease status can help to understand pathophysiological changes in complex diseases. This study aims to identify the key genes and upstream regulators in AMD and reveal factors, especially genetic association, and the prognosis of the development of this disease. Methods: Data from expression profile GSE125564 and profile GSE29801 were obtained from the Gene Expression Omnibus (GEO) database. We analyzed DEGs using R software (version 3.6.3). Functional enrichment and PPI network analysis were performed using the R package and online database STRING (version 11.0). Results: We compared AMD with normal and found 68 up?regulated genes (URGs) and 25 down?regulated genes (DRGs). We also compared wet AMD with dry AMD and found 41 DRGs in dry AMD. Further work including PPI network analysis, GO classification, and KEGG analysis was done to find connections with AMD. The URGs were mainly enriched in the biological process such as DNA replication, nucleoplasm, extracellular exosome, and cadherin binding. Besides, DRGs were mainly enriched in these functions such as an integral component of membrane and formation of the blood?aqueous barrier (BAB). Conclusion: This study implied that core genes might involve in the process of AMD. Our findings may contribute to revealing the pathogenesis, developing new biomarkers, and raising strategies of treatment for AMD

Differential gene expression and bioinformatics analysis in sepsis secondary to pneumonia / 中华危重病急救医学

Objective:To analyze and screen the key genes of sepsis secondary to pulmonary infection by bioinformatics, and to provide theoretical basis for the basic research of the disease and find an ideal animal model program.Methods:Experiment 1 (bioinformatics analysis): gene expression data sets of pulmonary infection secondary sepsis patients and multiple sepsis animal models were screened by Gene Expression Omnibus (GEO) Database, and gene differences were analyzed by R software. Differential genes were analyzed by gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Correlation analysis was conducted between differential genes and clinical symptoms in the data set of pulmonary infection secondary sepsis, and the correlation heat map between differential genes and clinical symptoms was drawn. Key genes were screened by weighted gene co-expression network analysis (WGCNA) and protein-protein interaction network analysis (PPIN) clustering. Experiment 2 (sepsis animal model preparation): male mice weighing 21-25 g were randomly divided into the key genes group and the control (Sham) group. And cecal ligation and puncture (CLP) was used to establish mouse sepsis model, while the mice in sham group were performed by exposure of cecum. And all the mice were scarified 24 hours after surgery to extract the total RNA from lung tissue, real time fluorescent quantitative polymerase chain reaction (RT-qPCR) was used to detect mRNA expression of key genes.Results:Experiment 1 (bioinformatics analysis): 319 differential genes were showed by GSE 134364 and GSE 65682 data set analysis of pulmonary infection secondary sepsis. And there was no genetic difference between community acquired pneumonia (CAP) and hospital acquired pneumonia (HAP) in patients with pulmonary infection secondary to sepsis. Obvious differences existed between differential genes in animal models, and there was no common differential gene. Differential genes in patients and animal models were similarly enriched in GO function, mainly in cell differentiation, regulation of cell process, and regulation of cellular response to stimuli, there were significant differences in pathway enrichment, among which, CLP animal models showed higher consistency with patients. The key genes obtained by WGCNA and PPIN analysis were MAPK14, NLRC4 and LCN2. Experiment 2 (sepsis animal model preparation): animal experiment results showed that the mRNA expressions of MAPK14, NLRC4 and LCN2 in lung tissue of CLP model mice were significantly up-regulated compared with the sham group.Conclusions:MAPK14, NLRC4 and LCN2 are key genes involved in the regulation of biological processes of pulmonary sepsis secondary to infection, and are potential research directions of this disease. What's more, CLP animal model can better reflect the biological characteristics of patients with pulmonary infection secondary sepsis, and is one of the ideal animal model schemes for pulmonary infection secondary sepsis.

Expression and relevant bioinformatics analysis of miR-135b-5p in oral squamous cell carcinoma / 口腔疾病防治

Objective @# To observe the clinical significance of miR-135b-5p in oral squamous cell carcinoma (OSCC) tissues and to conduct a bioinformatics analysis of its predicted target genes.@*Methods @#The expression levels of miR-135b-5p in OSCC tissues and adjacent normal tissues were compared using data from TCGA and GEO databases, and the correlations of miR-135b-5p expression level with clinicopathologic characteristics were analyzed. Fresh tissues were collected in the clinic, and the expression of miR-135b-5p was verified by quantitative real-time PCR. The target genes with enriched pathways were analyzed by using bioinformatics methods. A protein-protein interaction network was constructed to screen hub genes.@*Results @#The expression levels of miR-135b-5p were significantly upregulated in OSCC tissues compared to adjacent normal tissues (P < 0.001) and had a good diagnostic capability (AUC=0.960, P < 0.001). The expression level of miR-135b-5p was positively correlated with histopathological grading (P=0.011). Enrichment analyses revealed that the target genes of miR-135b-5p were significantly associated with tumor-related signaling pathways, such as the calcium signaling pathway, the cGMP-PKG signaling pathway and the cAMP signaling pathway. Ten core target genes were obtained by screening: DLG2, ANK3, ERBB4, SCN2B, NBEA, GABRB2, ATP2B2, SNTA1, CACNA1D, and SPTBN4.@*Conclusion@#miR-135b-5p may act as an oncogene miRNA in OSCC and has the potential value of acting as a diagnostic biomarker and therapeutic target for OSCC.

Pathogenesis and therapeutic targets of inflammatory cardiomyopathy based on bioinformatics analysis / 西安交通大学学报(医学版)

【Objective】 To make bioinformatics analysis of inflammatory cardiomyopathy so as to screen out hub genes related to etiology and therapeutic targets. 【Methods】 Differential expression analysis of inflammatory cardiomyopathy gene chip data from Gene Expression Omnibus （GEO） Database was carried out via GEO2R tool. Protein-protein interaction（PPI）network and hub genes identification were realized by String database and CytoHubba. GO and KEGG enrichment analysis for functional annotation and pathway analysis of hub genes were conducted by R language. Web-based enrichment analysis platform Enrichr and Drug Signatures database were applied to screen out candidate drugs targeting hub genes for inflammatory cardiomyopathy. 【Results】 The 149 DEGs were statistically significant， among which 44 were upregulated and 105 were downregulated. To identify hub genes， PPI network consisting of 37 nodes and 116 edges was constructed， and 16 hub genes were NDUFB7， POLR2L， NDUFS7， UQCR11， NDUFA13， NDUFA2， PHPT1， NDUFB10， UBA52， ATP5D， NDUFA3， COX6B1， POLR2J， COX4I2， AURKAIP1 and MRPL41. Hub genes were enriched to 113 different GO terms， and the most significant terms were mitochondrial ATP synthesis coupled electron transport， respiratory electron transport chain， oxidative phosphorylation， respiratory chain， mitochondrial inner membrane， NADH dehydrogenase activity and oxidoreductase activity. DEGs were enriched to 13 different signal pathways， including oxidative phosphorylation， non-alcoholic fatty liver disease， diabetic cardiomyopathy， and cardiac muscle contraction. We screened out candidate drugs targeting hub genes， namely， metformin hydrochloride， clindamycin， and hydralazine. 【Conclusion】 Hub genes screened out by decoding the expression profiles are convolved in the etiology and mechanism of inflammatory cardiomyopathy， which might serve as latent therapeutic targets and benefit patients with inflammatory cardiomyopathy.

Identification of potential therapeutic agents for ER-negative breast cancer using bioinformatics analysis / 西安交通大学学报(医学版)

【Objective】 To explore the key genes and potential therapeutic drugs for ER-negative breast cancer by bioinformatics. 【Methods】 The gene expression profile of breast cancer (GSE22219) was downloaded from the Gene Expression Omnibus (GEO). Principal components analysis (PCA) of GSE22219, and analyses of differentially expressed genes (DEGs) between the ER-negative and ER-positive subjects and Gene Ontology (GO) analysis were performed by R software. We analyzed The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Protein-Protein Interaction (PPI) network using STRING. The hub genes were identified using Cytoscape and analyzed using online programs. Drugbank analysis was used to find small molecular compounds as potential therapeutic agents to target the DEGs. 【Results】 We detect 69 DEGs and 8 hub genes between the ER-negative and ER-positive subjects. We found the most significant KEGG pathway of DEGs was aldosterone-regulated sodium reabsorption. The Gene Ontology (GO) analysis indicated that the most significantly enriched in prostate gland morphogenesis. Totally 21 small molecular compounds were identified as potential therapeutic agents for ER-negative breast cancer. 【Conclusion】 The bioinformatical analysis combined with drug database can help us find potential therapeutic agents to treat diseases. This method is a new paradigm which can guide future research on drugs.

Long non-coding RNA SAP30-2:1 is downregulated in congenital heart disease and regulates cell proliferation by targeting HAND2 / 医学前沿

Congenital heart disease (CHD) is the most common birth defect worldwide. Long non-coding RNAs (lncRNAs) have been implicated in many diseases. However, their involvement in CHD is not well understood. This study aimed to investigate the role of dysregulated lncRNAs in CHD. We used Gene Expression Omnibus data mining, bioinformatics analysis, and analysis of clinical tissue samples and observed that the novel lncRNA SAP30-2:1 with unknown function was significantly downregulated in damaged cardiac tissues from patients with CHD. Knockdown of lncRNA SAP30-2:1 inhibited the proliferation of human embryonic kidney and AC16 cells and decreased the expression of heart and neural crest derivatives expressed 2 (HAND2). Moreover, lncRNA SAP30-2:1 was associated with HAND2 by RNA immunoprecipitation. Overall, these results suggest that lncRNA SAP30-2:1 may be involved in heart development through affecting cell proliferation via targeting HAND2 and may thus represent a novel therapeutic target for CHD.

Identification of hub genes and transcription factors involved in periodontitis on the basis of multiple microarray analysis / 华西口腔医学杂志

OBJECTIVES@#To identify the differentially expressed genes (DEGs) during the pathogenesis of periodontitis by bioinformatics analysis.@*METHODS@#GEO2R was used to screen DEGs in GSE10334 and GSE16134. Then, the overlapped DEGs were used for further analysis. g:Profiler was used to perform Gene Ontology analysis and pathway analysis for upregulated and downregulated DEGs. The STRING database was used to construct the protein-protein interaction (PPI) network, which was further visua-lized and analyzed by Cytoscape software. Hub genes and key modules were identified by cytoHubba and MCODE plug-ins, respectively. Finally, transcription factors were predicted via iRegulon plug-in.@*RESULTS@#A total of 196 DEGs were identified, including 139 upregulated and 57 downregulated DEGs. Functional enrichment analysis showed that the upregulated DEGs were mainly enriched in immune-related pathways including immune system, viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, leukocyte transendothelial migration, and chemokine receptors bind chemokines. On the contrary, the downregulated DEGs were mainly related to the formation of the cornified envelope and keratinization. The identified hub genes in the PPI network were CXCL8, CXCL1, CXCR4, SEL, CD19, and IKZF1. The top three modules were involved in chemokine response, B cell receptor signaling pathway, and interleukin response, respectively. iRegulon analysis revealed that IRF4 scored the highest.@*CONCLUSIONS@#The pathogenesis of periodontitis was closely associated with the expression levels of the identified hub genes including CXCL8, CXCL1, CXCR4, SELL, CD19, and IKZF1. IRF4, the predicted transcription factor, might serve as a dominant upstream regulator.

Key genes in malignant transformation of lung epithelial cells induced by hexavalent chromium based on GEO database / 环境与职业医学

Background Hexavalent chromium [Cr(VI)] can induce malignant transformation of lung epithelial cells, but its molecular mechanism is still unclear. Objective This study aims to explore the key genes of Cr(VI)-induced malignant transformation of lung epithelial cells and the mechanism of the transformation by bioinformatics analysis. Methods High-throughput gene expression profile data related to Cr(VI)-induced toxic effect was downloaded from the Gene Expression Omnibus(GEO) database, and the co-expressed genes were obtained by the intersection of differentially expressed genes in each dataset. DAVID 6.8 was used to analyze the function enrichment of gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathways of the selected differential expression genes. STRING, and Cytoscape 3.8.2 were applied to construct and visualize the protein-protein interaction network. The expressions of Hub genes in lung tumor were obtained by GEPIA2. Results A total of 234 differentially expressed genes were screened out from the GSE24025 and GSE36684 datasets, among which 99 genes were up-regulated while 135 genes were down-regulated. The results of GO and KEGG analyse were mainly concentrated in cell adhesion, negative regulation of cell proliferation, and transcription disorders. A rotein-protein interaction network was generated by STRING database and Cytoscape software. Four functional modules with high scores and 6 Hub genes were finally retrieved. The expression trend of FBLN1 in lung cancer subtypes was consistent with the results of transcriptome screening. Conclusion Cr(VI) exposure causes the differential expression of multiple genes in lung epithelial cells, involving cell morphology, movement, survival fate, phenotype function and signal pathway related to cancer development. FBLN1 may be the critical gene related to malignant cytopathy.

Screening and bioinformatics of rheumatoid arthritis markers based on GEO chip / 西安交通大学学报(医学版)

Objective To perform bioinformatics analysis of the genetic chip data of rheumatoid arthritis (RA) in order to search for the characteristic gene expression profiles. Methods Differential expression analysis of RA Gene chip data in GEO database was performed using GEO2R, and GO and KEGG enrichment analysis of functional annotation and pathway analysis of differentially expressed genes (DEGs) were conducted by DAVID6.8 and R language. Protein-protein interaction (PPI) and target genes acquisition were realized by String-database and software Cytoscape3.7.1. Results The 1 184 DEGs in synovial tissues isolated from the knee joints of RA patients were statistically significant. Among them 664 were up-regulated and 520 were down-regulated. DEGs were enriched to 70 different GOterms, and the most significant terms were signal transduction, plasma membrane and protein binding. DEGs were enriched to 62 different signal pathways, including cytokine-cytokine receptor interaction, osteoclast differentiation, rheumatoid arthritis, Th17 cell differentiation, and IL17 signal pathway. PPI analysis screened out 19 pivotal target genes, namely, NKG7, BCL6, SEMA4D, NFIL3, RAC2, MLIP, SEL1L3, GUSBP11, IGLV1-44, IGLJ3, IGLC1, IGKV1OR2-118, IGKV1OR2-108, IGKC, IGHV4-31, IGHV3-23, IGHM, IGHD and CYAT1. Conclusion Partial DEGs screened out by analyzing the expression profiles are involved in the key links affecting the development of synovial inflammation in RA, which may provide an important theoretical basis for early diagnosis and treatment of this disease and development of targeted drugs.

Screening of molecular markers in gastric cancer based on GEO database / 中国肿瘤生物治疗杂志

@#[Abstract] Objective: Bioinformatics combined with Gene Expression Omnibus (GEO) was used to screen key genes involved in the development of gastric cancer in order to obtain molecular markers for diagnosis, target selection and prognosis prediction of gastric cancer. Methods: The chip data sets related to gastric cancer (GC) from the GEO database were downloaded, and differentially expressed genes (DEG) were screened. Functional enrichment analysis on DEG was performed, and protein-protein interaction network (PPI) was constructed to screen key genes. Then, co-expression networks were further constructed, and survival curves were drawn and hierarchical clustering analysis was performed. Results: A total of 261 GC-related DEGs were selected, and 14 key genes were obtained through analysis, which were PLOD1, PLOD3, COL1A1, COL1A2, COL2A1, COL3A1, COL4A1, COL4A2, COL8A1, COL12A1, COL15A1, ITGA2, LUM and SERPINH1. Key genes are mainly involved in biological processes such as generation of collagen fiber tissues, extracellular matrix tissues, extracellular structure tissues, skin morphogenesis, collagen biosynthesis and vascular development. Survival curve analysis showed that the change in the expression of COL3A1 (P=0.0241) significantly reduced the overall survival rate of patients with gastric cancer; the change in the expression of ITGA2 (P=0.0679) also showed a correlation with the reduction of disease-free survival in gastric cancer patients. Compared with normal gastric tissues, hierarchical cluster analysis showed that the expressions of genes PLOD1, PLOD3, COL3A1, ITGA2, COL1A2, COL1A1, COL4A1, LUM, COL12A1, SERPINH1 and COL8A1 in GC tissues were up-regulated. Conclusion: The key genes obtained after screening can be used as potential molecular markers for early diagnosis, treatment target selection and prognosis judgment of gastric cancer, which provide reference for subsequent research.

Screening of molecular markers in breast cancer based on GEO database / 中国肿瘤生物治疗杂志

@# Objective: To investigate the differentially expressed genes (DEGs) associated with the occurrence and development of breast cancer and to screen the molecular markers for breast cancer by bioinformatic analysis. Methods: Three breast cancer microarray datasets were downloaded from Gene Expression Omnibus (GEO) database. GEO2R was used to identify DEGs. The differentially co-expressed genes in the three datasets were screened by Venn diagram. GO function enrichment analysis and KEGG signal pathway analysis were performed using DAVID. The protein-protein interaction (PPI) network of DEGs was constructed using STRING. The most important modules in the PPI network were analyzed using Molecular Complex Detection (MCODE), and the genes with degree≥10 were identified as Hub genes. Hierarchical clustering analysis of hub genes was conducted using UCSC Cancer Genomics Brower. The survival curve and the co-expression network of hub genes were constructed using cBioPortal. Results: A total of 65 DEGs were screened from the three data sets. Eight hub genes, CTNNB1, CDKN1A, CXCR4, RUNX3, CASP8, TNFRSF10B, CFLAR and NRG1, were finally obtained, which exerted important roles in cell adhesion, proliferation and apoptosis regulation etc. Clustering analysis showed that the differential expression levels of CTNNB1, CFLAR, NRG1 and CXCR4 were associated with the occurrence of breast cancer. The overall survival analysis indicated that the patients with elevated CDKN1Aexpression had significantly shorter overall survival time (P<0.01). Conclusion: The hub genes identified in the present study can be used as molecular markers for breast cancer, providing candidate targets for diagnosis, treatment and prognostic prediction of breast cancer.

Expression of FN1 in gastric cancer and its clinical significance / 医学研究生学报

Objective Fibronectin 1 (FN1) is a glycoprotein involved in cellular adhesion and migration processes. The aim of this study was to investigate the expression and clinicopathological significance of FN1 in gastric cancer and to predict the possible mechanism of FN1. Methods GEO data and TCGA data were downloaded. FN1 expression in gastric cancer and adjacent tissues was analyzed by GSE54129 data, and then verified by GSE29272 and TCGA data. According to the expression profile data and FN1 expression, mRNA expression value was divided into low expression(<-0.475), and medium expression(-0.475~1.036), high expression (>1.036). FN1 expression in gastric cancer and clinicopathological relationship were analyzed. TCGA data and Kaplan Meier were used to analyze the relationship between the expression level of FN1 and the prognosis of gastric cancer patients; while gene concentration analysis (GSEA) was used to predict the related path of FN1. Results TCGA data showed the medium survival time of low, medium, high FN1 expression was respectively 63.5, 55.7, 39.4 months, and the difference between low expression and high expression in survival time was of statistical significance. Kaplan Meier Plotter online data analysis showed the medium survival time of FN1 high expression was shorter than that of low expression(P<0.01), which meant the higher the FN1 expression was, the worse the prognosis was. FN1 expression is an independent prognostic factor (HR=0.480,95% CI:0.336~0.686). High expression of FN1 samples enriched gene sets such as KRAS (FDR=0.052), P53(FDR=0.052), TGF-β(FDR=0.052), cell adhesion(FDR=0.0), extracellular matrix (FDR=0.043)and cytoskeletal protein regulation (FDR=0.052). Conclusion The high expression of FN1 is a poor prognostic factor for gastric cancer and can be used as an effective biomarker for predicting the metastasis and prognosis of gastric cancer. High expression of FN1 leads to abnormalities in KRAS, P53, TGF-β, ECM, cell adhesion and cytoskeletal protein regulatory pathways.

Identify the molecular target of nasopharyngeal carcinoma by bioinformatics analysis / 天津医药

Objective To study the pathogenesis of nasopharyngeal carcinoma and identify potential biomarkers or therapeutic targets. Methods Microarray data (GSE12452 and GSE13597) were downloaded from Gene Expression Omnibus. Processing of original microarray data and screening of differentially expressed genes were performed through bioinformatics analysis. Then, GO and KEGG pathway enrichment analysis was performed for these genes using DAVID database. Real time-PCR and Western blot assay were used to detect the expression levels of the identified genes. Results A total of 260 overlap DEGs were obtained including 16 GO entries and 4 signal pathways. Eighteen potential therapeutic targets that relative to cell cycle were identified by gene enrichment analysis. Expression levels of 12 selected genes were confirmed by real-time PCR. Finally, 4 selected genes were confirmed by Western blot assay. Conclusion By bioinformatics analysis of two sets of microarray data and molecular biology research, four genes were found including CDC6, CDK1,MCM2 and CCNB1, which might be potential key genes that can be developed for therapy targets of NPC in the future.

Time-series analysis of expression profile of adrenocortical carcinoma in children for screening related targets / 天津医药

@#Objective To explore the potential pathogenic mechanism of adrenocortical carcinoma (ACT), and screen out genes that may be related to biological targets. Methods In this study, the gene expression datasets of ACT were obtained from the Gene Expression Omnibus (GEO) with the accession number of GSE75415. Through R programming software, the microarray preprocessing and differential expression analysis of 18 ACT tissue samples (experimental group) and 7 normal adrenocortical tissue samples (control group) were conducted to identify potential biomarkers for ACT in different stages. Besides, through functional enrichment and Kaplan-Meier analysis, several more reliable biomarkers for ACT were identified. At the same time, the two generation sequencing data of the TCGA database, including 79 ACT samples were analyzed, and the genes that can affect the survival of ACT patients were screened. Results There were 248, 334, 315 and 561 differentially expressed genes in stage1-4 respectively. There were 73 overlapping genes (OLDEGs) among the different grading samples. Central genes HSPA13, GARS, STXBP1, AKIRIN1 and TUBB3, were up-regulated in all of stages of ACT samples compared with those of normal samples, while, central genes ADH1B, DCN, RASSF2, PDGFRA, PLAT, C3 and FOS were down-regulated in ACT samples. They were found to be significantly associated with pathways of immune response, cell cycle, phosphorylation and cruor, which were all closely related with ACT progression. Besides, Kaplan-Meier analysis of 73 OLDEGs in 79 ACT samples from TCGA database identified several genes, including XPO1, RACGAP1, PDGFD, NR4A2, MXRA5, VPS51, TMED3, NDFIP1 and CDKN1C, which were significantly associated with ACT overall survival. Conclusion Differentially expressed genes and survival related genes in all of stages can serve as new targets for ACT therapy, and which should be helpful for the understanding of its pathogenesis and prognosis.

Identification of ITGA3 as an Oncogene in Human Tongue Cancer via Integrated Bioinformatics Analysis / 华中科技大学学报（医学）（英德文版）

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 Omnibus (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.

Identification of ITGA3 as an Oncogene in Human Tongue Cancer via Integrated Bioinformatics Analysis / 华中科技大学学报（医学）（英德文版）

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 Omnibus (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.

Bioinformatics analysis of radioresistance key gene and pathway in esophageal carcinoma / 中华放射医学与防护杂志

Objective To explore key gene and pathway of radioresistance in esophageal carcinoma and reveal its molecular mechanism of radioresistance. Methods The gene expression profiles of GSE61772 and GSE61620 were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes ( DEGs ) were screened by GEO2R. Gene Ontology ( GO ) enrichment, Kyoto Encyclopedia of Genes and Genomes ( Kegg ) enrichment and protein-protein interaction ( PPI ) network construction were performed by DAVID and String softwares. RT-PCR was used to detect the differences in the expression of different genes in different radiosensitivity cells. Results A total of 49 differentially expression genes were screened. These genes were mainly involved in the regulation of multicellular biosynthesis, ion transport, DNA synthesis, metabolism, cell proliferation and so on. The major biological pathways included a Wnt signal pathway. 12 DEGs interacted with each other, and CHN2 may be a key node. The expression of CHN2 gene had no obvious difference between TE13R and TE13. Conclusions 49 differentially expressed genes, including CHN2, may be involved in radioresistance of esophageal carcinoma, and the Wnt signaling pathway may be an important pathway in this radioresistance.

Bioinformatics analysis of radioresistance key gene and pathway in esophageal carcinoma / 中华放射医学与防护杂志

Objective To explore key gene and pathway of radioresistance in esophageal carcinoma and reveal its molecular mechanism of radioresistance. Methods The gene expression profiles of GSE61772 and GSE61620 were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes ( DEGs ) were screened by GEO2R. Gene Ontology ( GO ) enrichment, Kyoto Encyclopedia of Genes and Genomes ( Kegg ) enrichment and protein-protein interaction ( PPI ) network construction were performed by DAVID and String softwares. RT-PCR was used to detect the differences in the expression of different genes in different radiosensitivity cells. Results A total of 49 differentially expression genes were screened. These genes were mainly involved in the regulation of multicellular biosynthesis, ion transport, DNA synthesis, metabolism, cell proliferation and so on. The major biological pathways included a Wnt signal pathway. 12 DEGs interacted with each other, and CHN2 may be a key node. The expression of CHN2 gene had no obvious difference between TE13R and TE13. Conclusions 49 differentially expressed genes, including CHN2, may be involved in radioresistance of esophageal carcinoma, and the Wnt signaling pathway may be an important pathway in this radioresistance.

Analysis of the Clinical Significance of Bone Morphogenetic Protein 2 in Glioblastoma Multiforme / 中国医科大学学报

Objective To investigate the prognostic significance of bone morphogenetic protein 2 (BMP2) expression in glioblastoma multiforme (GBM) and related pathways.Methods RNASeqV2 data and GSE7696 series matrix data were downloaded from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database,respectively.The correlation between BMP2 expression and prognosis was evaluated using Kaplan-Meier and multivariate COX analysis.Gene set enrichment analysis (GSEA) was used to predict the functional gene sets and/or pathways modulated by BMP2.Results Decreased expression of BMP2 is an independent predictor of poor prognosis.Furthermore,genes that are upregulated in response to interferon-α proteins were enriched in samples with low BMP2 expression.Conclusion Low expression of BMP2 indicates poor prognosis in GBM patients.Genes that are upregulated in response to interferon-α proteins may also be implicated in prognosis.