Identification of Biomarkers for Lung Adenocarcinoma with Molecular Docking Analysis Targeting Topoisomerase II Alpha as a Key Protein in Hub Gene Network

Introduction Lung cancer is the leading cause of cancer-related deaths worldwide, with non-small cell lung cancer (NSCLC) being the most common type. Amongst the sub-types of NSCLC, lung adenocarcinoma (LUAD) accounts for nearly ۴۰% of all lung cancers, with its incidence notably rising, particularly among non-smokers. This in silico study seeks to identify potential biomarkers for LUAD and apply docking stimulation to help gain a deeper understanding of the molecular mechanisms underlying this disease. Methods The genetic alterations of gene expression between tumor and normal cells of two combined microarray datasets (GSE۴۳۵۸۰, GSE۴۰۷۹۱) obtained from the Gene Expression Omnibus database (GEO) were assessed using R software. Several normalization techniques were applied. The GCRMA normalization method demonstrated better performance, as evidenced by the comparison of the relative log expressions (RLE). This approach was used since it offered better results overall, even though the logFC values were somewhat compressed. Deferentially expressed genes (DEGs) were identified using the limma package. Next, the String database tool was utilized to construct a protein-protein interaction (PPI) network, which was refined using nine algorithms from Cytoscape plugins, ClusterMaker۲ and Centiscape۲.۲. Five algorithms—degree, betweenness, closeness, eigen vector, and eccentricity—were used to pinpoint hub genes within the network. Gephi software delineated two modularity classes of the refined network. TOP۲A, as a potential biomarker, emerged for molecular docking. Epirubicin, a recognized inhibitor of TOP۲A, was selected as the reference compound. This agent served as a template for the generation of a molecular library via the Swiss Similarity database. The UCSF ChimeraX program was used for preprocessing procedures on the receptor protein. Subsequently, using PyRx software, docking stimulations were performed on the receptor and the high-similarity ligands. Finally, interaction residues were identified using Discovery Studio software. Results ۳۸۳ differentially expressed genes (DEGs) from an initial selection of ۵,۰۰۰ by R were identified. Subsequent filtering reduced this number to ۱۰۰ DEGs, employing nine algorithms available through Cytoscape and its associated plugins. The intersection of the top ۳۰ genes identified by five of these algorithms led to the discovery of three hub genes: TOP۲A, HMMR, and BIRC۵. Following this, the network was analyzed using Gephi, which partitioned it into two distinct modules—one containing ۴۲ genes and the other comprising ۵۸. Notably, three genes within one of these modules—OGN, SYNPO۲, and PLN—had not previously been covered in relation to lung cancer. One of the hub genes, TOP۲A, was listed among the other module. TOP۲A encodes Topoisomerase II alpha, crucial for chromosome segregation and DNA supercoiling management, making it a significant target for various small-molecule therapies. From a library of ۳۹۵ compounds generated via the Swiss Similarity database, ۱۵ exhibited a similarity score exceeding ۰.۵. Using PyRx software for docking simulations, ten of these molecules demonstrated a binding affinity greater than -۸ kcal/mol. Conclusion The current study identified ten potential inhibitors of TOP۲A and three promising biomarkers for lung adenocarcinoma (LUAD). However, further investigation is required to validate the effectiveness of these biomarkers and to evaluate both the efficacy and toxicity of the proposed inhibitors in LUAD treatment.

کلیدواژه ها:

TOP۲A ، lung adenocarcinoma ، cancer biomarkers ، molecular docking