in silico study on the micro-array data of GSE۳۲۸۶۳ for identifying key overexpressed genes in lung adenocarcinoma

Lung adenocarcinoma with a high incidence rate and unfavorable prognosis is the most common primary lung cancer, accounting for about ۳۰ percent of all cases. Many risk factors are involved in developing lung adenocarcinoma including smoking tobacco, prolonged exposure to air pollution, genetic susceptibility, and exposure to materials like uranium. In most cases, the diagnosis of adenocarcinoma lung cancer is delayed because it does not have clear symptoms in the early stages. Based on studies, the genomic abnormality is a crucial factor in the development of lung adenocarcinoma, therefore this research aims to identify the overexpressed genes of lung adenocarcinoma to detect key genes as biomarkers that are involved in this malignancy and propose effective screening programs. Methods: In this research, the bioinformatics method was used to analyze the data. Gene expression profile GSE۳۲۸۶۳ with ۵۸ healthy and ۵۸ patient samples and platform: GPL۶۸۸۴ were used for data analysis. Genes with increased expression were extracted using GEO۲R. Cytoscape software was used in the next step to construct the protein network. finally, overexpressed hub genes were extracted by CytoHoba software, one of the important plugins in Cytoscape software. Results: Based on the results of the Cytoscape software, ۱۸ nodes and ۲۱ edges were obtained. After the screening that was done using the CytoHoba plugin on the network, ۶ genes were identified as overexpressed hub genes in lung adenocarcinoma. These genes were identified based on the rank order including matrix metalloproteinase ۹ (MMP۹) (rank:۱, Score:۸), collagen type I alpha ۱ (COL۱A۱) (rank:۱, Score:۸) secreted phosphoprotein ۱ (SPP۱) (rank:۳, Score:۷), cartilage oligomeric matrix protein (COMP) (rank:۳, Score:۷), DNA topoisomerase II alpha (TOP۲A) (rank:۵, Score:۵),ubiquitin-conjugating enzyme E۲ C (UBE۲C) (rank:۵, Score:۵). Conclusion: This research provides a new perspective for designing new biomarkers and therapeutic targets for lung adenocarcinoma by in silico approach. For further validation, laboratory confirmation of the obtained data is necessary.

کلیدواژه ها:

Adenocarcinoma of Lung ، Lung Neoplasms ، Computational Biology ، Genes ، Biomarker