ABSTRACT
@#【Objective】 The two databases，GEO（gene expression omnibus，GEO）and TCGA（the cancer genome alas ，TCGA），were analyzed using bioinformatics methods to screen differentially expressed genes associated and their related regulatory networks in prostate carcinoma. 【Methods】 The prostate carcinoma gene expression chip data （GSE46602 ，GSE55945） downloaded from the GEO database were integrated into the RNA- seq data of the TCGA database. And the differentially expressed genes analysis was performed using GEO2R and the edgeR package of R software to extract common significant differentially expressed genes. The clusterProfiler package of R software was used to enrich the GO（gene ontology ，GO）function enrichment analysis and KEGG（kyoto encyclopedia of genes and genomes， KEGG）pathway analysis. Differentially expressed genes were further constructed into a protein-protein interaction（PPI） network to screen out key genes for regulatory protein expression in prostate carcinoma. Gene analysis results were combined with TCGA clinical follow-up data to analyze the clinical prognostic value of key node genes. 【Results】A total of 278 significant differentially expressed genes were extracted，of which 178 genes were down- regulated and 100 genes were up-regulated. These genes were closely associated with the function and pathway enrichment such as the regulation of proliferation of epithelial cells，metabolism of benzene- containing compounds，the glutathione metabolism，and focal adhesion. The protein-protein interaction network analysis revealed three key protein expression modules and 12 key node genes. Among these key genes，EDN3（endothelin-3），EDNRB（endothelin receptor B）and AMACR（alpha-methylacyl- coa racemase）were closely related to the survival rate of prostate cancer patients. 【Conclusion】Through bioinformatics analysis of gene chip and RNA-seq data in prostate carcinoma，we found that EDN3，EDNRB and AMACR may play an important role in the occurrence and development of prostate carcinoma.
ABSTRACT
<p><b>OBJECTIVE</b>To investigate the expressions of Oct4 and CD133 and their correlation in colonic cancer.</p><p><b>METHODS</b>The expression of Oct4 and CD133 were detected by immunohistochemistry in 30 colon cancer specimens and the paired adjacent tissues.</p><p><b>RESULTS</b>The positivity rates of Oct4 and CD133 expression were 83.3% (25/30) and 73.3% (22/30) in colonic cancer tissue, respectively, and their expressions were positively correlated (r=0.586, P<0.05). The matched adjacent tissues showed significantly lower levels of Oct4 and CD133 expressions (P<0.01).</p><p><b>CONCLUSION</b>The expressions of Oct4 and CD133 are upregulated in colonic cancer compared with those in the adjacent tissues and show a positive correlation. Oct4 and CD133 may play an important role in the development of colon cancer.</p>
Subject(s)
Adult , Aged , Female , Humans , Male , Middle Aged , Young Adult , AC133 Antigen , Adenocarcinoma , Metabolism , General Surgery , Antigens, CD , Metabolism , Colonic Neoplasms , Metabolism , General Surgery , Glycoproteins , Metabolism , Immunochemistry , Octamer Transcription Factor-3 , Metabolism , Peptides , Metabolism , Up-RegulationABSTRACT
<p><b>OBJECTIVE</b>To study the correlation of polymorphisms of CYP1A1 MSPI and glutathiones S-transferase (GST-M1) independently and in combination with the risk of lung cancer.</p><p><b>METHODS</b>A case control study which included 91 cases of lung cancer and 138 controls collected from the First Affiliated Hospital of Sun Yat-sen University of Medical Sciences, Guangzhou Tumor Hospital and The Red Cross Hospital of Guangzhou or conmunity area. All subjects were investigated with a uniform questionnaire. Blood samples were collected from all cases and controls for detecting CYP1A1 MSPI and GST-M1 polymorphisms which were analyzed by PCR and RFLP.</p><p><b>RESULTS</b>It showed that there was no significant difference in frequencies of this genotypes of CYP1A1 MSPI between the two groups. The frequency of GST-M1 null (0/0) genotype was higher in the case group than in the control group, with an OR of 1.38 (95% CI 0.81 - 2.38), but there was no statistical significance. However, combination of several genotypes was strongly associated with lung cancer. There was a synergistic interaction between the m2m2 genotype of CYP1A1 MSPI and GST-M1 (0/0) genotype, with an OR of 2.47 (95% CI 1.03 - 5.90).</p><p><b>CONCLUSION</b>The combination of two genetic polymorphisms significantly increases the risk of lung cancer.</p>