1- Bu-Ali Sina University
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Extended Abstract
Introduction: Although the causes of ovarian cancer, its genetics, and drugs that affect it in humans have been frequently studied, it has been less studied in farm animals, especially dairy cows, and little information is available about it, and only some cases have been reported in Holstein cows. Given that ovarian cancer and the body's immune responses to this disease have a genetic background, it is expected that the expression of some genes will change in the conditions of ovarian cancer. In addition, the interaction of genes involved in ovarian cancer in the form of networks and gene clusters is also expected. Despite the fact that the occurrence of ovarian cancer can affect the performance of the production system by causing mortality and infertility, few genetic studies have been conducted on ovarian cancer in dairy cows. Therefore, this study was conducted to investigate the gene network, gene clusters, and hub genes involved in ovarian cancer using microarray data.
Materials and Methods: Gene expression data related to healthy and cancerous ovarian stromal cells with accession number GSE225981 were extracted from the NCBI website and from the GEO Expression Omnibus database. The relevant data were classified into two groups. The first group was animals with ovarian cancer and the second group was healthy animals (control treatment). The list of significant genes was prepared based on P-value and LogFC statistics and introduced into the online DAVID software, and gene enrichment analysis, gene ontology, and analysis of the pathways in which the genes were involved performed. The gene list was also introduced to Cytoscape software to be used for network analysis with its algorithms, and the protein interaction network and gene clusters were drawn using the STRING resource. Next, gene clusters (highly dense areas of correlated genes in the main network) were identified using the ClusterONE plugin. Three methods were used to identify key genes in the network: degree of centrality, betweenness centrality, and closeness centrality. These network topology measures were calculated using the CytoNCA plugin. In addition, using the Cytohubba plugin, ten central genes in the network (hub genes) were identified and drawn as a network.
Findings: A total of 512 genes with significant differential expression between healthy and cancer cells were identified and formed the initial network. In the structure of this network, five significant gene clusters, which were dense areas within the gene network, were identified. For most of the clusters, pathways related to cancer occurrence were observed. For example, genes in cluster 1 were associated with cell division and cell cycle processes, cluster 2 with cell movement and cancer metastasis, cluster 3 with drug resistance and cancer induction, cluster 4 with intracellular oxidation processes, and cluster 5 with cortisol synthesis and secretion and purine metabolism. In terms of importance criteria including degree of centrality, betweenness centrality, and closeness centrality, ten genes EGFR, CD44, FGF2, ESR1, PTGS2, CCNA2, CDH2, BDNF, FGF10, and KLF4 were identified as the hub genes. These genes played a role in processes such as cell proliferation, cell death, inflammation, tumor growth, cancer metastasis, chemotherapy resistance, tumor growth suppression, and the occurrence of various types of cancer.
Conclusion: The results of this study showed that the genes involved in ovarian cancer are not only individually but also interact with each other in the form of networks and gene clusters. The expression of some genes in cancer cells was increased compared to healthy cells, while the expression of others was decreased. Overall, this study, by identifying genes expressed in ovarian cancer, can help to better understanding the genetic background of ovarian cancer. The EGFR, CD44, FGF2, ESR1, PTGS2, CCNA2, CDH2, BDNF, FGF10, and KLF4 genes identified in the present study can be used to identify drugs effective against the disease using the relevant online tool.
Introduction: Although the causes of ovarian cancer, its genetics, and drugs that affect it in humans have been frequently studied, it has been less studied in farm animals, especially dairy cows, and little information is available about it, and only some cases have been reported in Holstein cows. Given that ovarian cancer and the body's immune responses to this disease have a genetic background, it is expected that the expression of some genes will change in the conditions of ovarian cancer. In addition, the interaction of genes involved in ovarian cancer in the form of networks and gene clusters is also expected. Despite the fact that the occurrence of ovarian cancer can affect the performance of the production system by causing mortality and infertility, few genetic studies have been conducted on ovarian cancer in dairy cows. Therefore, this study was conducted to investigate the gene network, gene clusters, and hub genes involved in ovarian cancer using microarray data.
Materials and Methods: Gene expression data related to healthy and cancerous ovarian stromal cells with accession number GSE225981 were extracted from the NCBI website and from the GEO Expression Omnibus database. The relevant data were classified into two groups. The first group was animals with ovarian cancer and the second group was healthy animals (control treatment). The list of significant genes was prepared based on P-value and LogFC statistics and introduced into the online DAVID software, and gene enrichment analysis, gene ontology, and analysis of the pathways in which the genes were involved performed. The gene list was also introduced to Cytoscape software to be used for network analysis with its algorithms, and the protein interaction network and gene clusters were drawn using the STRING resource. Next, gene clusters (highly dense areas of correlated genes in the main network) were identified using the ClusterONE plugin. Three methods were used to identify key genes in the network: degree of centrality, betweenness centrality, and closeness centrality. These network topology measures were calculated using the CytoNCA plugin. In addition, using the Cytohubba plugin, ten central genes in the network (hub genes) were identified and drawn as a network.
Findings: A total of 512 genes with significant differential expression between healthy and cancer cells were identified and formed the initial network. In the structure of this network, five significant gene clusters, which were dense areas within the gene network, were identified. For most of the clusters, pathways related to cancer occurrence were observed. For example, genes in cluster 1 were associated with cell division and cell cycle processes, cluster 2 with cell movement and cancer metastasis, cluster 3 with drug resistance and cancer induction, cluster 4 with intracellular oxidation processes, and cluster 5 with cortisol synthesis and secretion and purine metabolism. In terms of importance criteria including degree of centrality, betweenness centrality, and closeness centrality, ten genes EGFR, CD44, FGF2, ESR1, PTGS2, CCNA2, CDH2, BDNF, FGF10, and KLF4 were identified as the hub genes. These genes played a role in processes such as cell proliferation, cell death, inflammation, tumor growth, cancer metastasis, chemotherapy resistance, tumor growth suppression, and the occurrence of various types of cancer.
Conclusion: The results of this study showed that the genes involved in ovarian cancer are not only individually but also interact with each other in the form of networks and gene clusters. The expression of some genes in cancer cells was increased compared to healthy cells, while the expression of others was decreased. Overall, this study, by identifying genes expressed in ovarian cancer, can help to better understanding the genetic background of ovarian cancer. The EGFR, CD44, FGF2, ESR1, PTGS2, CCNA2, CDH2, BDNF, FGF10, and KLF4 genes identified in the present study can be used to identify drugs effective against the disease using the relevant online tool.
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