The Bioinformatical Analysis And Clinical Evaluation On Single Nucleotide Polymorphisms Of Interleukin-18 Gene

Background:Bioinformaties has become a cross subject with the mark of the biologicalinformation era after the Human Genome Project set out in 1990. Bioinformatics istypically associated with massive databases of gene and protein sequences andstructure/function information into which new sequences are deposited. The databasesare searched by remote computer access in order to compare and contrast to knownequences. The emerging field of functional bioinformatics focuses on thedevelopement of ontologies or concept classifications fed into algorithms used toperform computations of the functions of biomolecules. Other emerging applicationsare automated preprocessing of sequences obtained in large-scale sequencing labs,integrating statistical genetic methods, sequence information, gene variablity inpopulations, and epidemiology data, all in an integrated environment modelinggenetic and metabolic networks, and various data mining methods.Single Nucleotide Polymorphisms (SNPs) represent a natural genetic variabilityat the highest density in the human genome. Most diseases are complex genetic traitscaused by multiple genetic and environmental components. It has been proposed that common genetic variations, mainly single nucleotide polymorphisms (SNPs),influence the susceptibility to complex diseases. Now, it is the most important workto seek the candidate genes of complex diseases. There are about 3 million SNPs (onaverage, one in every thousand bases) in our DNA. How to find out the SNPs whichmay affect gene expression in disease situations or be present in the gene itself andaffect protein function? This is a problem. In the article, we will look for the genepolymorphisms involved in susceptibility for a disease with a bioinformatical method.Objective:1. To establish a bioinformatical method which can find the gene polymorphismsinvolved in susceptibility for a disease.2. To screen single nucleotide polymorphisms of IL-18 gene involved insusceptibility of cervical cancer and analyze their correlations to abnormal genefunctions by sequencing.3. To explore the role of interleukin-18 concentration in serum and its genepolymorphisms in the development of cervical cancer (CC).Methods:1. Bioinformatic analysis for SNPs of IL-18 geneWe mainly studied the SNPs in the coding sequence and regulatory regionswhich are candidates for functional variation. SNPs of IL-18 were screened from apublic database dbSNP by SNPper software, and relevant FASTA subsequenceswere also obtained from dbSNP. PARSESNP software was used to analyzecoding-region SNPs. We screened the SNPs from regulatory regions by searchingtranscription factor binding sites.2. Association of interleukin-18 gene polymorphisms with cervical cancerBy literature retrieval, we selected IL-18 gene as the candidate gene of cervical cancer. A manual population was built by ruling in healthy individuals and cervicalcancer patients. The candidate SNPs were identified by sequencing. The genotypefrequency and the allele frequency were compared between the CC subjects andcontrol subjects that matched the cervical carcinoma cases in age and residence.3. The relationship between concentration, gene polymorphisms ofinterleukin-18 and the risk of cervical cancerA manual population was built by ruling in healthy individuals and cervicalcancer patients. The interleukin-18 concentration in serum was measured bysandwich ELISA. The relationship between concentration, gene polymorphisms ofinterleukin-18 and the risk of cervical cancer was analyzed by Factorial ANOVA(analysis of variance).4. Statistical methods or StatisticsAll data in the experiments were analyzed by SPSS10.0 software. The Crosstabswith Chi-square Test was used to compare the genotype frequency between thepatients and controls. Factorial ANOVA was used to analyze the relationshipbetween concentration, gene polymorphisms of interleukin-18 and the risk of cervicalcancer.Results:1. By a bioinformatics approach, we screened five SNPs in the regulatory regions ofIL-18 gene at position rs5744224,rs1946519,rs1946518,rs5744225 and rs5744226,and two SNPs in the coding regions of IL-18 gene at position rs549908,rs5744290.The two SNPs in the coding regions are all synonymous cSNPs that do not alter theamino-acid sequence of the encoded protein. So they were excluded fromparticipation. Among the five SNPs in the regulatory regions, only the SNP atposition rs1946518 was validated to be located in the transcription factor bindingsites, and is mostly likely to influence disease. 2. The five SNPs in the regulatory regions of IL-18 gene were all identified bysequencing both in CC subjects and control subjects. There are three types ofgenotype as TF-AA,GG-CC,TG-AC in the two linkage SNP sites (rs1946519,rs1946518) located in the up-stream of IL-18 gene. The frequency of rs1946518alleles and genotype was analyzed. The frequency of T alleles was 42% (42/100) innormal controls and 73% (73/100) in patients, showing significant difference(x~2=19.662, P=0.000). The frequency of TT genotype was 18% (9/50) in normalcontrols and 56% (28/50) in patients. The frequency of GG genotype was 34%(17/50) in normal.controls and 10% (5/50) in patients. The frequency of TGgenotype was 48% (24/50) in normal controls and 34% (17/50) in patients. Asignificant difference of the three genotype frequency was observed between patientsand controls (x~2=17.497, P=0.000).3. The mean value of interleukin-18 concentrations in serum of cc group was(95.470±18.827)pg/ml, and 116.756±16.262 pg/ml in controls, showing significantdifference (F=14.445, P=0.000). The mean value of interleukin-18 concentrations inserum of the TT group was (90.668±20.363)pg/ml, (119.641±15.338)pg/ml in the GGgroup, and (112.793±13.326)pg/ml in the TG group, showing significant difference(F=11.307, P=0.000). A significant difference of the interaction effect was observedbetween the two fixed factors (F=4.223, P=0.018).In the controls group, the difference in the interleukin-18 concentrations betweendifferent genetype groups was not significant. In the cc group, the difference in theinterleukin-18 concentrations between different genetype groups was significant. Theinterleukin-18 concentrations of the TT group were lower than the other two genetypegroups.Conclusion:1. Bioinformatics approach is an inexpensive and effective method, by which we screened some important SNPs involved in susceptibility for cancers.2. IL-18 gene is proved to be one of the candidate genes of cervical cancer.3. By Factorial ANOVA, the interleukin-18 concentration of cc patient is proved tobe affected by many factors.4. Association of interleukin-18 with cervical cancer is illustrated with protein andgene theories. IL-18 is to be an important cytokine for preventing and controllingcervical cancers.