Bioinformatics-Based Identification of Genetic Characteristics in Colorectal Liver Metastasis
Bioinformatics-Based Identification of Genetic Characteristics in Colorectal Liver Metastasis
Masoumeh Nomani,1,*Ghassem Amoabediny,2Fardin Rahimi,3
1. Research Centre for New Technologies in Life Science Engineering, University of Tehran, Tehran, Iran- Research Center of Thoracic Oncology (RCTO), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Science, Tehran, Iran 2. Research Centre for New Technologies in Life Science Engineering, University of Tehran, Tehran, Iran- Department of Pharmaceutical Engineering and biotechnology, Faculty of Chemical Engineering, University of Tehran, Tehran, Iran 3. Department of medical biotechnology, Faculty of medicine, Shahed University, Tehran, Iran
Introduction: Colorectal cancer (CRC) as the second most common cancer diagnosed in women and third most common in men and ranks among the deadliest cancers globally. Microarray technology can be employed to identify crucial biomarkers and gain deeper insights into the molecular mechanisms underlying colorectal liver metastasis. For individuals diagnosed with colorectal cancer, the liver is the most prevalent location for the development of metastatic tumors. Throughout the progression of their disease, a minimum of 25% of colorectal cancer patients will experience the spread of cancer to the liver, a condition known as colorectal liver metastases (CRLM). Although there have been substantial improvements in diagnostic and treatment methods, the survival rate for patients with colorectal liver metastases (CRLM) remains significantly low. The development of CRLM is a complex cascade of events that involves multiple factors and processes, resulting in intricate and diverse molecular mechanisms. This study aimed to investigate the differences in gene expression patterns between colorectal cancer (CRC) and colorectal liver metastases. The main objective was to identify the key genes and pathways that play a crucial role in the initiation and progression of CRC
Methods: Data was obtained from the Gene Expression Omnibus (GEO) database, specifically from GSE40367 and GSE31595. The microarray data and expression profiles for colorectal adenocarcinoma and colorectal liver metastasis were analyzed and compared. The samples consist of 15 files from colon adenocarcinoma with liver metastasis and 37 files from colon adenocarcinoma. To investigate and identify genes with expression differences, the R programming environment was utilized. Probes were converted into gene symbols based on platform annotation information from the raw data using R packages. Probes lacking gene information were excluded. Preprocessing and normalization of the bioinformatics data, aimed at enhancing the study's quality, were performed using the mas5 method via the R package limma (http://www.R-project.org). Genes with differential expression were determined using two indices adjusted P-values < 0.05 and log2FoldChange |> 1.5 to be used in further analysis. The relationships between the differentially expressed genes (DEGs) were assessed using the STRING database. A gene network was constructed using this database. The genes identified from this database were then imported into Cytoscape, an open-source software based on the Java platform, which is designed to visualize protein interaction networks and biological pathways. Key genes were selected based on indices such as degree, betweenness centrality, closeness centrality, and eigenvector. Finally, the metabolic pathways of the top genes were analyzed using the Enrichr database. The Kaplan-Meier method was used to assess the relationship between the expression levels of the hub genes and patient survival outcomes. The validity of these hub genes in liver metastatic CRC patients was further confirmed using the GEPIA platform
Results: The quality control of the samples was assessed using box plot and heat map diagrams. The analysis revealed that approximately 776 genes exhibited up-regulated in the samples. We focused on over-expressed genes, as they could serve as early diagnostic biomarkers or therapeutic targets.Using Cytoscape, the top genes were identified by calculating centrality parameters. This network revealed that 21 genes are significantly involved in colorectal cancer progression. To assess the impact of these key genes on patient prognosis, we examined their relationship with survival rates. We considered a hazard ratio (HR) with 95% confidence and log-rank p-values <0.05 as the cutoff. Thirteen genes (BRD4, TP53BP1, RBM39, PKM, YWHAZ, USP7, FOXO3, FANCD2, RPL14, SMAD3, SETD2, APOE, and NUP153) were selected. This indicates that these genes could serve as potential prognostic biomarkers for this condition. Further examination of the biological processes associated with differentially expressed genes across tissues revealed that these genes were involved in several key pathways including: Ribosome, AMPK Signaling Pathway, Spliceosome, FoxO Signaling Pathway, cell cycle, and Adherens junction. These findings suggest that these biological processes may play a significant role in the observed gene expression differences
Conclusion: Understanding the mechanisms driving liver metastasis in CRC patients could lead to the discovery of biomarkers for early diagnosis and the development of targeted chemotherapy treatments. This highlights the urgent need for new treatment strategies. Techniques like microarray analysis allow researchers to identify genes implicated in cancer development. This knowledge can lead to improved prevention, diagnosis, and treatment approaches, ultimately paving the way for more effective cancer care