Volume 11, Issue 3 (Aug 2023)
Res Mol Med (RMM) 2023, 11(3): 203-212 |
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Sedaghat Anbouhi T, Sazegar H, Rahimi E. In Silico Analysis of Dysregulated Genes and Drug Resistance in Epstein-Barr Virus Associated Gastric Cancer. Res Mol Med (RMM) 2023; 11 (3) :203-212
URL: http://rmm.mazums.ac.ir/article-1-549-en.html
URL: http://rmm.mazums.ac.ir/article-1-549-en.html
1- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran.
2- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran. ,hosseinsazegar731@gmail.com
3- Department of Food Hygiene, Faculty of Veterinary Medicine, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran.
2- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran. ,
3- Department of Food Hygiene, Faculty of Veterinary Medicine, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran.
Abstract: (568 Views)
Background: Gastric cancer (GC) poses a significant health challenge worldwide. Recognizing its complex and diverse nature, the cancer genome atlas (TCGA) research network has identified four distinct subtypes of GC. Among these subtypes, Epstein-Barr virus (EBV) associated GC accounts for around 9% of all GC cases. The primary aim of this study was to identify dysregulated genes in EBV-positive samples in contrast to EBV-negative samples, with the secondary goal of assessing their potential utility as diagnostic biomarkers. In addition, the study also aimed to evaluate the correlation between the expression levels of these candidate genes and drug resistance and sensitivity.
Materials and Methods: Differential gene expression analysis was employed to compare gene expression patterns between the EBV-positive and EBV-negative groups within the TCGA-stomach adenocarcinoma (TCGA-STAD) cohort. Gene ontology (GO) analyses were performed to elucidate the biological roles of the candidate genes. GSE13861 was used to confirm the gene expression levels in GC samples compared to the normal samples.
Results: Our findings revealed that 128 genes exhibited up-regulation in EBV-positive samples compared to EBV-negative samples. CCL1, CCL5, CXCL1, CXCL10, CXCL11, KLRK1, and TBX21 genes were notably enriched in the process of leukocyte migration, which emerged as the hub pathway with the highest degree of interactions among the identified terms. Our analysis indicated that most of these genes could be deemed potential diagnostic biomarkers, as their area under the curve values exceeded 0.9. Additionally, our results demonstrated a correlation between some of these genes and resistance to specific drugs, including panobinostat, L-685458, L-BW242, and sorafenib.
Conclusion: Our study identified several key genes closely linked to EBV status and demonstrated a strong association with drug resistance. These genes hold promise as molecular markers for predicting EBV-positive samples.
Materials and Methods: Differential gene expression analysis was employed to compare gene expression patterns between the EBV-positive and EBV-negative groups within the TCGA-stomach adenocarcinoma (TCGA-STAD) cohort. Gene ontology (GO) analyses were performed to elucidate the biological roles of the candidate genes. GSE13861 was used to confirm the gene expression levels in GC samples compared to the normal samples.
Results: Our findings revealed that 128 genes exhibited up-regulation in EBV-positive samples compared to EBV-negative samples. CCL1, CCL5, CXCL1, CXCL10, CXCL11, KLRK1, and TBX21 genes were notably enriched in the process of leukocyte migration, which emerged as the hub pathway with the highest degree of interactions among the identified terms. Our analysis indicated that most of these genes could be deemed potential diagnostic biomarkers, as their area under the curve values exceeded 0.9. Additionally, our results demonstrated a correlation between some of these genes and resistance to specific drugs, including panobinostat, L-685458, L-BW242, and sorafenib.
Conclusion: Our study identified several key genes closely linked to EBV status and demonstrated a strong association with drug resistance. These genes hold promise as molecular markers for predicting EBV-positive samples.
Keywords: Gastric cancer (GC), Epstein-Barr virus (EBV) associated gastric cancer (EBVaGC), Biomarker, Drug resistance
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