همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

PPI network construction and Cytoscape analysis for metastatic gastric cancer to ovary

نویسندگان :

Ameneh Hassani¹ Ghasem Bagherpour² Ali Salari³ Behrooz Johari⁴ Abdolmajid Ghaempanah⁵

1- Zanjan University of Medical Sciences, Zanjan, Iran 2- Zanjan University of Medical Sciences, Zanjan, Iran 3- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran 4- Department of Medical Biotechnology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran 5- Pathology department, Musavi Hospital, Zanjan. Iran

کلمات کلیدی :

gastric cancer،metastases،biomarkers،protein-protein interaction (PPI) network،expression profiles

چکیده :

Introduction: Gastric cancer (GC) is a global health problem, and more than 1 million people worldwide are diagnosed with gastric cancer every year(1). Despite a global decrease in incidence and mortality over the past 5 decades, gastric cancer remains the third leading cause of cancer-related death. Many studies on molecular biomarkers of GC have been reviewed to find a wide range of diagnostic patterns in this field. However, during the last two decades, the proportion of gastric cancer patients who present with metastases has increased to over 40%(2). Influential factors contribute to the development and progression of GC disease, and finding these practical factors is essential for metastasis prognosis in GC(3). Methods and material: The expression profiles were extracted from the Gene Expression Omnibus database. The app R identified the differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was constructed using Cytoscape software. Cytoscape is an open source bioinformatics software platform for visualizing molecular interaction networks and integrating with gene expression profiles and other state data. Based on the DEGs obtained, a protein network was drawn to examine how the genes in the network are related and influenced. Also, hub genes were selected based on the degree scale (Degree is a measure that shows the number of interactions of each gene with other genes in the network) by Centiscape software, which is a popular software for finding important nodes in the network. Clusters whose genes have the most mutual interactions were drawn by MCODE software. Clusters were analyzed in Enrichr. Result : Cytoscape found only 788 genes out of 1169 genes, which have 1567 interactions with each other. And this may be because the remaining genes do not produce proteins like LncRNAs. Centiscape and the degree criterion also determined the hub genes. Nine genes from the network with a degree of more than 22 were selected as hub genes. These genes include RPS27A, RAC1, MYH11, COL1A1, FLNA, MYL9. The primary components of the network created using MCODE software were chosen based on a score exceeding 5, resulting in three distinct clusters. Conclusion: The analysis of these genes and the pathways in which they operate provided helpful information about the biology of malignant tumors and metastatic cells of ovarian cancer. These findings can be further investigated as biomarkers for cancer diagnosis and treatment.

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