• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.275-283
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• 2018

Cancer show distinct pattern of gene expression when it is compared to normal. This difference results malignant characteristic of cancer. Many cancer drugs are targeting this difference so that it can selectively kill cancer cells. One of the recent demand for personalized treating cancer is retrieving normal tissue from a patient so that the gene expression difference between cancer and normal be assessed. However, in most clinical situation it is hard to retrieve normal tissue from a patient. This is because biopsy of normal tissues may cause damage to the organ function or a risk of infection or side effect what a patient to take. Thus, there is a challenge to estimate normal cell's gene expression where cancers are originated from without taking additional biopsy. In this paper, we propose in-silico based prediction of normal cell's gene expression from gene expression data of a tumor sample. We call this challenge as reverting the cancer into normal. We divided this challenge into two parts. The first part is making a generator that is able to fool a pretrained discriminator. Pretrained discriminator is from the training of public data (9,601 cancers, 7,240 normals) which shows 0.997 of accuracy to discriminate if a given gene expression pattern is cancer or normal. Deceiving this pretrained discriminator means our method is capable of generating very normal-like gene expression data. The second part of the challenge is to address whether generated normal is similar to true reverse form of the input cancer data. We used, cycle-consistent adversarial networks to approach our challenges, since this network is capable of translating one domain to the other while maintaining original domain's feature and at the same time adding the new domain's feature. We evaluated that, if we put cancer data into a cycle-consistent adversarial network, it could retain most of the information from the input (cancer) and at the same time change the data into normal. We also evaluated if this generated gene expression of normal tissue would be the biological reverse form of the gene expression of cancer used as an input.

• Journal of Gastric Cancer
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• v.17 no.1
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• pp.43-51
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• 2017

Purpose: This study aimed to analyze G3BP1 and VEZT expression profiles in patients with gastric cancer, and examine the possible relationship between the expressions of each gene and clinicopathological factors. Materials and Methods: Expression of these genes in formalin-fixed paraffin embedded (FFPE) tissues, collected from 40 patients with gastric cancer and 40 healthy controls, was analyzed. Differences in gene expression among patient and normal samples were identified using the GraphPad Prism 5 software. For the analysis of real-time polymerase chain reaction products, GelQuantNET software was used. Results: Our findings demonstrated that both VEZT and G3BP1 mRNA expression levels were downregulated in gastric cancer samples compared with those in the normal controls. No significant relationship was found between the expression of these genes and gender (P-value, 0.4835 vs. 0.6350), but there were significant changes associated with age (P-value, 0.0004 vs. 0.0001) and stage of disease (P-value, 0.0019 vs. 0.0001). In addition, there was a direct relationship between VEZT gene expression and metastasis (P-value, 0.0462), in contrast to G3BP1 that did not demonstrate any significant correlation (P-value, 0.1833). Conclusions: The results suggest that expression profiling of VEZT and G3BP1 can be used for diagnosis of gastric cancer, and specifically, VEZT gene could be considered as a biomarker for the detection of gastric cancer progression.

• Molecules and Cells
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• v.42 no.8
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• pp.579-588
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• 2019

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.6067-6071
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• 2015

Background: Gastric cancer accounts for about 8% of the total cancer cases and 10% of total cancer deaths worldwide. It is the second lethal cancer after esophageal cancer and is considered the fourth most common cancer in north and northwest Iran. The bcl2 family has a key role in the regulation of apoptosis and change in its expression can contribute to cancer. This study initially scheduled to determine the expression of bcl2 gene in tissue samples of adenocarcinoma cancer patients. Materials and Methods: A total of 10 samples of gastric adenocarcinoma and 10 of normal tissues from Sari hospital were selected and after DNA extraction from tissues, bcl2 gene expression assayed by real-time PCR. Results: Our results demonstrated higher expression of the bcl2 gene in control compared with cancer and marginal cancer tissues. Conclusions: On one hand BCL2 plays an important role as an oncogene to inhibit apoptosis; on the other hand, it can initiate cell cycle arrest at G0 stage. Our observed association between its expression and patient survival is quite conflicting and may be tissue-specific. The data suggest expression both tumoural and non-tumoral(marginal) groups have lowered expression than controls (P>0.05). Due to the low number of samples we could not examine the relationship with clinicopathological features. However, bcl-2 expression may be important for prognostic outcome or a useful target for therapeutic intervention.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup3
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• pp.305-309
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• 2016

PTEN protein is an important tumour suppressor factor detectable by immunohistochemistry. The goal of the present study was to investigate the prognostic role of PTEN gene expression focusing on length of survival in breast cancer patients. This descriptive-analytical study was conducted on 100 breast cancer cases referred to Sabzevar hospitals in the north east of Iran between 2010 and 2011, followed up to 2015. The PTEN gene expression of tumour tissue samples was determined using specific monoclonal antibodies. The data were analyzed using Chi-square test and Fisher's exact test. Patient length of survival was analyzed after 4 years of follow-up using the Cox regression model. The PTEN gene was expressed in 70 of 100 samples, while being found at a high level in all noncancerous samples. There was an inverse significant relationship between expression of PTEN and tumour stage and grade (p<0.001). In addition, expression of PTEN in invasive ductal tumours was less than in non-invasive tumours. There was also an inverse significant relationship between the likelihood of death and PTEN gene expression (p<0.01). These findings indicate that lack of PTEN gene expression can be sign for a worse prognosis and poor survival in breast cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.457-461
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• 2013

Prostate cancer is a leading cause of death in male populations across the globe. With the advent of gene expression arrays, many microarray studies have been conducted in prostate cancer, but the results have varied across different studies. To better understand the genetic and biologic mechanisms of prostate cancer, we conducted a meta-analysis of two studies on prostate cancer. Eight key genes were identified to be differentially expressed with progression. After gene co-expression analysis based on data from the GEO database, we obtained a co-expressed gene list which included 725 genes. Gene Ontology analysis revealed that these genes are involved in actin filament-based processes, locomotion and cell morphogenesis. Further analysis of the gene list should provide important clues for developing new prognostic markers and therapeutic targets.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.29-34
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• 2005

DNA microarray becomes a major tool for the investigation of global gene expression in all aspects of cancer and biomedical research. DNA microarray experiment generates enormous amounts of data and they are meaningful only in the context of a detailed description of microarrays, biomaterials, and conditions under which they were generated. MicroArray Gene Expression Data (MGED) society has established microarray standard for structured management of these diverse and large amount data. MGED MAGE-OM (MicroArray Gene Expression Object Model) is an object oriented data model, which attempts to define standard objects for gene expression. To assess the relevance of DNA microarray analysis of cancer research it is required to combine clinical and genomics data. MAGE-OM, however, does not have an appropriate structure to describe clinical information of cancer. For systematic integration of gene expression and clinical data, we create a new model, Cancer Genomics Object Model.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.7
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• pp.4209-4214
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• 2013

Background: Breast cancer is among the five most common cancers and ranks first among cancers diagnosed in Iranian women. Screening and treatment of this disease with molecular methods, especially regarding high incidences at early age and advanced stage, is essential. Several genes with altered expression have been identified by cDNA microarray studies in breast cancer, with the Bcl-2 gene indicated as a likely candidate. In this study, we studied Bcl-2 gene expression levels in parallel tumor and non-tumor breast tissues. Materials and Methods: Forty samples including 21 tumor, 16 non tumor (marginal) and 3 benign breast tissues which were all pathologically diagnosed, were subjected to RNA extraction and polyA RT-PCR with the expression level of Bcl-2 quantified using real-time PCR. Results: There is higher expression levels of the Bcl-2 gene in tumor samples compared with marginal samples, but not attaining significance(p>0.05). Bcl-2 expression in 14 (66.7%) of the cases of tumor samples and 9 (56.3%) cases of the marginal samples were positive. Comparison of the expression of the Bcl-2 gene in histological grade showed that a high expression of Bcl-2 was associated with a high histological grade (p<0.41). Conclusions: Our data suggests that dysregulated Bcl-2 gene expression is potentially involved in the pathogenesis of breast cancer. Using gene expression analysis may significantly improve our ability for screening cancer patients and will prove a powerful tool in the diagnosis and prognostic evaluation of the disease whilst aiding the cooperative group trials in the Bcl-2 based therapy project.

• BMB Reports
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• v.42 no.4
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• pp.217-222
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• 2009

Controlled gene expression in specific cells is a valuable tool for gene therapy. We attempted to determine whether the lentivirus-mediated Tet-On inducible system could be applied to cancer gene therapy. In order to select the genes that induce cancer cell death, we compared the ability of the known pro-apoptotreic genes, Bax and tBid, and a cell cycle inhibitor, p21cip1/waf1, and determined that Bax was the most effective. For the cancer cell-specific expression of $rtTA2^S$-M2, we tested the matrix metalloproteinase-2 (MMP-2) promoter and determined that it is highly expressed in cancer cell lines, including SNU475 cells. The co-transduction of two lentiviruses that contain sequences for TRE-Bax and $rtTA2^S$-M2, the expression of which is controlled by the MMP-2 promoter, resulted in the specific cell death of SNU475, whereas other cells with low MMP-2 expression did not evidence significant cell death. Our data indicate that the lentivirus-mediated Tet-On system using the cancer-specific promoter is applicable for cancer gene therapy.

• Tuberculosis and Respiratory Diseases
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• v.70 no.4
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• pp.315-322
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• 2011

Background: A variety of diagnostic modalities for lung cancer have been developed. To achieve efficient and early detection of lung cancer, we tried to measure the expression rates of the melanoma associated gene (MAGE) and synovial sarcoma on X chromosome (SSX) genes. Methods: We designed primers for the SSX gene. In addition to the pre-developed MAGE A primer, using an SSX gene primer was attempted to increase the detection rate. We obtained cancer tissues and cancer-free lung tissues from resected lung, sputum from lung cancer patients who had not undergone surgery, and sputum from healthy people and patients with benign intrathoracic diseases. Results: The sensitivity of the MAGE or SSX gene RT-PCR to identifying cancer tissue of the 69 lung cancer patients was 95.2% for squamous cell carcinoma (scc), 87.0% for adenocarcinoma, and 100% for small cell carcinoma. The mean sensitivity value was 94.2% (p=0.001). For adenocarcinoma, the additional use of the SSX gene resulted in a higher expression rate than MAGE alone (87% vs. 69.6%). The expression rate for the cancer-free lung tissue was 14.3% in scc, 17.4% in adenocarcinoma, and 25.0% in small cell carcinoma. In the induced sputum of 49 lung cancer patients who had not undergone surgery, the expression rate for one of the two genes was 65.5%. The expression rate for the sputum of healthy people and benign intrathoracic diseases by MAGE or SSX gene reverse transcription polymerase chain reaction (RT-PCR) was 3.8% and 17.7%. Conclusion: Detecting lung cancer using the expression of MAGE and SSX genes in lung cancer tissue has high sensitivity.