همایش ملی بیوانفورماتیک ایران

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

Identification of Essential Genes and Suitable Drug Combinations for Colorectal Cancer Treatment Based on Systems Biology Approaches

نویسندگان :

Yasna Kazemghamsari¹

1- Alzahra University

کلمات کلیدی :

colorectal cancer،essential genes،drug combinations،systems biology،PPI network

چکیده :

Colorectal cancer is the third most prevalent cancer globally, predominantly affecting the elderly population, with an estimated 2 million new cases and 1 million deaths annually. This incidence is projected to rise in the forthcoming years. Early diagnosis of the disease not only mitigates mortality rates but also reduces treatment costs. Given that colorectal cancer, like other malignancies, is a complex disease influenced by various factors, systems biology approaches that provide a holistic perspective on these factors and their interrelations are more effective and exhibit fewer side effects compared to traditional diagnostic and therapeutic approaches. In this article, we present a comprehensive analysis of diverse omics data to identify essential genes and optimal drug combinations for colorectal cancer treatment. Initially, we selected the expression dataset GSE21510, which encompasses gene expression levels from 148 samples, including 104 cancer patients and 44 healthy individuals (Tsukamoto and Ishikawa, 2011).To identify genes with significantly altered expression, we applied two criteria: p-value < 0.05 and |logFC| > 2, resulting in the identification of 451 significant genes. Additionally, to account for all genes associated with colorectal cancer, we utilized the COREMINE database, which is literature-based. From a total of 11,442 genes related to colorectal cancer, we selected 3,729 genes based on the p-value threshold. Subsequently, we identified 178 common genes between these two sets. These genes are statistically associated with colorectal cancer based on literature and are significant according to our expression data analysis. Using the STRING database, we constructed a protein-protein interaction (PPI) network among the 178 selected genes, incorporating physical and functional relationships with a confidence score of 0.4. In this network, centrality analysis revealed five hub proteins: GAPDH, CDK1, CCNB1, CD44, and HMMR. Furthermore, employing the DGIdb database, we identified drugs that target these hub genes. Three drugs—HISTAMINE, SELICICLIB, and HYALURONIC ACID—were identified as effective drug combinations affecting the five hub proteins. Among these, HISTAMINE plays a role in regulating intestinal physiological functions (Middleton and Sarno, 2002); SELICICLIB is utilized in treating lung cancer and leukemia (Iurisci and Filipski, 2006); while HYALURONIC ACID, known for its applications in pain relief and wound healing (Gupta and Lall, 2019), is proposed as a novel therapeutic agent for colorectal cancer due to its influence on two of the five essential genes associated with colorectal cancer development and progression.