مقالات پذیرفته شده در نهمین کنگره بین المللی زیست پزشکی
Identification of genes associated with metastasis in hepatocellular carcinoma through microarray data analysis
Identification of genes associated with metastasis in hepatocellular carcinoma through microarray data analysis
Seyedeh Negar Marashi,1,*Razieh Heidari,2Seyed Abbas Mirzaei,3
1. Department of Medical Biotechnology , School of medicine , Mashhad University of Medical Sciences , Mashhad , Iran 2. Department of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran 3. Department of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran
Introduction: Liver cancer is the fifth most common cancer and the fourth leading cause of cancer-related deaths worldwide, with hepatocellular carcinoma (HCC) being the most prevalent type and associated with a poor prognosis. The five-year survival rate for HCC patients in the United States is a mere 19.6% (National Cancer Institute's SEER database). Key risk factors include hepatitis B, hepatitis C, dietary exposure to aflatoxins, and alcohol consumption. Current treatment options primarily involve chemical therapies, which often lead to significant side effects and drug resistance. Metastasis further complicates the prognosis, resulting in a median survival of 7.8 to 8.6 months (Harding and Abou-Alfa, 2014) and a five-year survival rate of less than 16%. Traditional diagnostic methods lack the sensitivity needed to accurately predict metastasis due to the heterogeneity of HCC. This study aims to develop a metastasis-related mRNA prognostic model utilizing bioinformatics to improve the prediction of HCC outcomes and enhance patient management.
Methods: 1. Data Collection
We selected the dataset GSE158408 from the GEO repository (www.ncbi.nlm.nih.gov/geo) to investigate key regulatory genes associated with hepatocellular carcinoma (HCC).
2. Identification of Differentially Expressed Genes (DEGs)
The raw expression data from GSE158408 was preprocessed using R v3.4.1 (https://www.r-project.org/). Normalization was assessed through a boxplot diagram. The LIMMA package in Bioconductor was employed to identify DEGs by comparing expression values between groups. A volcano plot was generated using the Enhanced Volcano package, with the following cut-off criteria: P-Value < 0.05 and |log2FC| > ± 1.
3. Protein-Protein Interaction (PPI) Networks of Overlapping DEGs
We utilized the STRING database (http://string.embl.de/) to construct PPI networks, aimed at analyzing functional interactions among proteins. The PPI network of overlapping DEGs was extracted and visualized using Cytoscape (version 3.7.2). Hub genes within the network were identified using the CytoHubba plugin, applying the DEGREE method to calculate their significance.
4. Validation and Survival Analysis of Hub Genes
The GEPIA database (http://gepia2.cancer-pku.cn/) was utilized to examine hub gene expression between HCC and normal tissues. We performed a comparative analysis with a statistical threshold of P-Value < 0.01. Additionally, the Kaplan–Meier plotter (http://kmplot.com/analysis/) was employed to explore the prognostic significance of these genes on patient overall survival, with a P-Value threshold of < 0.05 deemed statistically significant.
5. Enrichment Analysis of Overlapping DEGs
To assess functional enrichment, we conducted Gene Ontology (GO) analysis focusing on three main categories: biological process (BP), molecular function (MF), and cellular component (CC). Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and GO annotation were performed using FunRich software, with a significance threshold set at P-Value < 0.05.
Results: In this study, 10 main genes associated with metastasis were found, which included MRPL13, RAD51, ATM, SOD2, RELA, RPS3, MCM4, SKP2, NDUFB9, and UMPS, respectively. These genes were obtained based on Degree's calculations, and the survival rate was directly correlated with the expression of these genes (less than 0.5 was significant).
Conclusion: In this study, we utilized bioinformatics methods to explore crucial genes and pathways associated with hepatocellular carcinoma (HCC), providing a reliable predictive model for HCC metastasis. Given the grim prognosis of HCC, especially as many patients are diagnosed at advanced stages when treatment options are limited, it is essential to seek innovative strategies to address this disease. Recent advancements in high-throughput techniques and publicly available gene databases have facilitated the identification of disease-associated genes through extensive microarray data analysis, revealing new potential treatment targets.