• Asian Pacific Journal of Cancer Prevention
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• 제15권14호
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• pp.5815-5818
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• 2014

For an exact comparison of mRNA transcription in different samples or tissues with real time quantitative reverse transcription-polymerase chain reaction (qRT-PCR), it is crucial to select a suitable internal reference gene. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and beta-actin (ACTB) have been frequently considered as house-keeping genes to normalize for changes in specific gene expression. However, it has been reported that these genes are unsuitable references in some cases, because their transcription is significantly variable under particular experimental conditions and among tissues. The present study was aimed to investigate which reference genes are most suitable for the study of gastric cancer tissues using qRT-PCR. 50 pairs of gastric cancer and corresponding peritumoral tissues were obtained from patients with gastric cancer. Absolute qRT-PCR was employed to detect the expression of GAPDH, ACTB, RPII and 18sRNA in the gastric cancer samples. Comparing gastric cancer with corresponding peritumoral tissues, GAPDH, ACTB and RPII were obviously upregulated 6.49, 5.0 and 3.68 fold, respectively. Yet 18sRNA had no obvious expression change in gastric cancer tissues and the corresponding peritumoral tissues. The expression of GAPDH, ${\beta}$-actin, RPII and 18sRNA showed no obvious changes in normal gastric epithelial cells compared with gastric cancer cell lines. The carcinoembryonic antigen (CEA), a widely used clinical tumor marker, was used as a validation gene. Only when 18sRNA was used as the normalizing gene was CEA obviously elevated in gastric cancer tissues compared with peritumoral tissues. Our data show that 18sRNA is stably expressed in gastric cancer samples and corresponding peritumoral tissues. These observations confirm that there is no universal reference gene and underline the importance of specific optimization of potential reference genes for any experimental condition.

• Asian Pacific Journal of Cancer Prevention
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• 제17권5호
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• pp.2519-2525
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• 2016

There are several existing reports of microarray chip use for assessment of altered gene expression in different diseases. In fact, there have been over 1.5 million assays of this kind performed over the last twenty years, which have influenced clinical and translational research studies. The most commonly used DNA microarray platforms are Affymetrix GeneChip and Quality Control Software along with their GeneChip Probe Arrays. These chips are created using several quality controls to confirm the success of each assay, but their actual impact on gene expression profiles had not been previously analyzed until the appearance of several bioinformatics tools for this purpose. We here performed a data mining analysis, in this case specifically focused on ovarian cancer, as well as healthy ovarian tissue and ovarian cell lines, in order to confirm quality control results and associated variation in gene expression profiles. The microarray data used in our research were downloaded from ArrayExpress and Gene Expression Omnibus (GEO) and analyzed with Expression Console Software using RMA, MAS5 and Plier algorithms. The gene expression profiles were obtained using Partek Genomics Suite v6.6 and data were visualized using principal component analysis, heat map, and Venn diagrams. Microarray quality control analysis showed that roughly 40% of the microarray files were false negative, demonstrating over- and under-estimation of expressed genes. Additionally, we confirmed the results performing second analysis using independent samples. About 70% of the significant expressed genes were correlated in both analyses. These results demonstrate the importance of appropriate microarray processing to obtain a reliable gene expression profile.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• 제34권5호
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• pp.518-524
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• 2008

Chromosome 18q alteration plays a key role in colorectal tumorigenesis, and loss of heterozygosity at 18q is associated with a poor prognosis in colon cancer. DCC(Deleted in Colorectal Cancer) is a putative tumor- suppressor gene at 18q21 that encodes a transmembrane protein with structural similarity to neural cell adhesion molecule that is involved in both epithelial and neuronal cell differentiation. DCC is implicated in regulation of cell growth, survival and proliferation. Thus, tumor progression in squamous cell carcinoma, stomach cancer, colorectal cancer correlates with downregulation of DCC expression. The mechanism for DCC suppression is associated with hypermethylation of the DCC gene promoter region. Hence, the goal of this study is to identify the promoter methylation responsible for the down-regulation of DCC expression in oral squamous cell carcinoma. 12 of tissue specimens for the study are excised and gathered from 12 patients who are diagnosed as SCC in department of OMS, dental hospital, dankook university. To find expression of DCC in each tissue samples, immunohistochemical staining, RT-PCR gene analysis and methylation specific PCR are processed. The results are as follows. 1. In the DCC gene RT-PCR analysis, 5(41.6%) of 12 specimens of oral squamous cell carcinoma did not expressed DCC gene. 2. In the promoter methylation specific PCR analysis, 5(41.6%) of 12 specimens showed promoter methylation of DCC gene. 3. In the immunohistochemical staining of poor differentiated and invasive oral squamous cell carcinoma, loss of DCC expression was observed. These findings suggest that methylation of the DCC gene may play a role in loss of gene expression in invasive oral squamous cell carcinoma.

• Genomics & Informatics
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• 제4권3호
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• pp.110-117
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• 2006

In microarray technology, many diverse experimental features can cause biases including RNA sources, microarray production or different platforms, diverse sample processing and various experiment protocols. These systematic effects cause a substantial obstacle in the analysis of microarray data. When such data sets derived from different experimental processes were used, the analysis result was almost inconsistent and it is not reliable. Therefore, one of the most pressing challenges in the microarray field is how to combine data that comes from two different groups. As the novel trial to integrate two data sets with batch effect, we simply applied standardization to microarray data before the significant gene selection. In the gene selection step, we used new defined measure that considers the distance between a gene and an ideal gene as well as the between-slide and within-slide variations. Also we discussed the association of biological functions and different expression patterns in selected discriminative gene set. As a result, we could confirm that batch effect was minimized by standardization and the selected genes from the standardized data included various expression pattems and the significant biological functions.

• Journal of Microbiology and Biotechnology
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• 제32권3호
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• pp.365-377
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• 2022

Mammalian target of rapamycin (mTOR) is a serine-threonine kinase member of the cellular phosphatidylinositol 3-kinase (PI3K) pathway, which is involved in multiple biological functions by transcriptional and translational control. mTOR is a downstream mediator in the PI3K/Akt signaling pathway and plays a critical role in cell survival. In cancer, this pathway can be activated by membrane receptors, including the HER (or ErbB) family of growth factor receptors, the insulin-like growth factor receptor, and the estrogen receptor. In the present work, we congregated an electronic network of mTORC1 built on an assembly of data using natural language processing, consisting of 470 edges (activations/interactions and/or inhibitions) and 206 nodes representing genes/proteins, using the Cytoscape 3.6.0 editor and its plugins for analysis. The experimental design included the extraction of gene expression data related to five distinct types of cancers, namely, pancreatic ductal adenocarcinoma, hepatic cirrhosis, cervical cancer, glioblastoma, and anaplastic thyroid cancer from Gene Expression Omnibus (NCBI GEO) followed by pre-processing and normalization of the data using R & Bioconductor. ExprEssence plugin was used for network condensation to identify differentially expressed genes across the gene expression samples. Gene Ontology (GO) analysis was performed to find out the over-represented GO terms in the network. In addition, pathway enrichment and functional module analysis of the protein-protein interaction (PPI) network were also conducted. Our results indicated NOTCH1, NOTCH3, FLCN, SOD1, SOD2, NF1, and TLR4 as upregulated proteins in different cancer types highlighting their role in cancer progression. The MCODE analysis identified gene clusters for each cancer type with MYC, PCNA, PARP1, IDH1, FGF10, PTEN, and CCND1 as hub genes with high connectivity. MYC for cervical cancer, IDH1 for hepatic cirrhosis, MGMT for glioblastoma and CCND1 for anaplastic thyroid cancer were identified as genes with prognostic importance using survival analysis.

• Asian Pacific Journal of Cancer Prevention
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• 제16권13호
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• pp.5557-5563
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• 2015

Background: Cholelithiasis is associated in 54%-98% of patients with carcinoma of the gallbladder, and a high incidence among females suggests a role of female hormones in the etiology of the disease. Cytochrome $P450C17{\alpha}$ (CYP-17) is a key enzyme involved in estrogen metabolism and polymorphisms in CYP-17 are associated with altered serum levels of estrogens. Thus, we investigated whether the CYP-17 MspA1 gene polymorphism might impact on risk of gall bladder cancers or gallstones, as well as to determine if this gene polymorphism might be linked with estrogen serum levels and lipid profile among the North Indian gall bladder cancer or gallstone patients. Materials and Methods: CYP-17 gene polymorphisms (MspA1) were genotyped with PCR-RFLP in cancer patients (n=96), stone patients (n=102), cancer + stone patients (n=52) and age/sex matched control subjects (n= 256). Lipid profile was estimated using a commercial kit and serum estrogen was measured using ELISA. Results: The majority of the patients in all groups were females. The lipid profile and estrogen level were significantly higher among the study as compared to control groups. The frequency of mutant allele A2 of CYP17 MspA1 gene polymorphism was higher among cancer (OR=5.13, 95% CI+3.10-8.51, p=0.0001), stone (OR=5.69, 95%CI=3.46-9.37, p=0.0001) and cancer + stone (OR=3.54, 95%CI=1.90-6.60, p=0.0001) when compared with the control group. However there was no significant association between genotypes of CYP17 MspA1 gene polymorphism and circulating serum level of estrogen and lipid profile. Conclusions: A higher frequency of mutant genotype A1A2 as well as mutant allele A2 of CYP-17 gene polymorphism is significantly associated with risk of gallbladder cancer and stones. Elevated levels of estrogen and an altered lipid profile can be used as predictors ofgall bladder stones and cancer in post menopausal females in India.

• Asian Pacific Journal of Cancer Prevention
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• 제13권11호
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• pp.5375-5379
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• 2012

Introduction: Published studies on the association between Nijmegen breakage syndrome 1(NBS1) gene polymorphisms and breast cancer risk have been inconclusive, and a meta-analysis was therefore performed for clarification. Methods: Eligible articles were identified by a search of MEDLINE and EMBASE bibliographic databases for the period up to March 2012. The presence of between-study heterogeneity was investigated using the chi-square-based Cochran's Q statistic test. When there was statistical heterogeneity, the random effects model was chosen; otherwise, fixed effects estimates were reported as an alternative approach. Results: A total of 11 eligible articles (14 case-control studies) were identified, nine case-control studies were for the 657del5 mutation (7,534 breast cancer cases, 14,034 controls) and five case-control studies were for the I171V mutation (3,273 breast cancer cases, 4,004 controls). Our analysis results indicated that the 657del5 mutation was associated with breast cancer risk (carriers vs. non-carriers: pooled OR =2.63, 95% CI: 1.76-3.93), whereas the I171V mutation was not (carriers vs. non-carriers: pooled OR =1.52, 95% CI: 0.70-3.28). Conclusion: The present meta-analysis suggests that the 657del5 gene mutation in the NBS1 gene plays a role in breast cancer risk, while the I171V mutation does not exert a significant influence.

• Asian Pacific Journal of Cancer Prevention
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• 제15권22호
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• pp.9693-9698
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• 2014

Background: Associations between the 8473T>C polymorphism (rs5275) in the cyclooxygenase-2 (COX-2) gene and breast cancer (BC) risk are still inconclusive and ambiguous. The aim of this meta-analysis was to comprehensively estimate the genetic risk of 8473T>C polymorphism in the COX-2 gene for BC. Materials and Methods: We searched PubMed, Web of Science, Medline, Chinese biomedical (CBM), Weipu, China national knowledge infrastructure (CNKI), and Wanfang databases, covering all publications (last search was updated on Aug 17, 2014). Statistical analyses were performed using Revman 5.3 and STATA 10.0 software. Results: A total of 6,720 cases and 9,794 controls in 12 studies were included in this study. The results indicated no significant associations between the 8473T>C polymorphism of the COX-2 gene and BC risk for the CC+TC vs TT model (pooled odds ratio (OR)=0.97, 95% confidence interval (CI)=0.90-1.03, and p=0.29). On subgroup analysis, we also found that subdivision on ethnicity among Caucasians, Asians and others also revealed no relationship with BC susceptibility. With the study design (CC+TC vs TT), no significant associations were found in either population-based case-control studies (PCC), or hospital-based case-control studies (HCC). Conclusions: This present meta-analysis suggests that the 8473T>C polymorphism in the COX-2 gene is not a conspicuous low-penetrant risk factor for developing BC.

• Asian Pacific Journal of Cancer Prevention
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• 제14권5호
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• pp.3309-3314
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• 2013

Spleen tyrosine kinase (SYK) is a non-receptor type cytoplasmic protein and a known tumor suppressor gene in breast cancer. Polymorphisms in SYK have been reported to be associated with cell invasion/cell morality and an increased risk of cancer development. In this case control study, all exons of the SYK gene and its exon/ intron boundaries were amplified in 200 breast cancer cases and 100 matched controls and then analyzed by single stranded conformational polymorphism. Amplified products showing altered mobility patterns were sequenced and analyzed. Twelve variations were identified in exonic and intronic regions of DNA encoding SH2 domain and kinase domain of the SYK gene. All of these mutations are novel. Among them, 5 missense mutations were observed in exon 15 while one missense mutation was found in exon 8. In addition to these mutations, six mutations were also identified in intronic regions. We found a significant association between SYK mutations and breast cancer and observed that Glu241Arg, a missense mutation is associated with an increase risk of ~7 fold (OR=6.7, 95% CI=1.54-28.8), Thr581Pro (missense mutation) is associated with increased risk of ~16 fold (OR=15.5, 95%CI=2.07-115.45) and 63367 T>G (missense mutation) is associated with increased risk of ~13 fold (OR=12.8, 95%CI=1.71-96.71) for breast cancer. Significant associations were observed for each of these variations with both late menopause (p<0.01) and early menarche (p<0.005) cases when compared to controls. Our findings suggest that the polymorphic gene SYK may contribute to the development of breast cancer in at least the Pakistani population. This study provides an insight view of SYK which may provide a significant finding for the pharmaceutical and biotechnology industry.

• Genomics & Informatics
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• 제16권4호
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• pp.32.1-32.7
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• 2018

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.