Integrated Bioinformatic Analysis to Evaluate Target Genes and Pathways in Chronic Lymphocytic Leukemia

Abstract

Objective: The most common type of leukemia, chronic lymphocytic leukemia (CLL), is characterized by progressive accumulation of monoclonal B cells with a specific immunophenotype in the blood, bone marrow, and lymphoid organ. The goal of this research was to use bioinformatic analysis to comprehend the molecular mechanisms causing CLL and to investigate potential targets for the diagnosis and therapy of CLL.
 Material and Method: Expression data from CLL patients with accession numbers GSE22529 and GSE26725 were downloaded from the GEO database for bioinformatic analysis. GSE22529 data was studied with samples from 41 CLL patients and 11 healthy groups, while GSE26725 data was studied with blood samples from 12 CLL patients and 5 healthy groups. GEO2R was used to find differentially expressed genes (DEGs) in CLL patient samples and healthy control samples. The DAVID program was used to perform GO and KEGG enrichment analyses on DEGs. Using the Cytoscape software, a protein-protein interaction (PPI) network was created, and hub genes associated with CLL were identified.
 Result and Discussion: DEGs with p 0.05 and log2FC 0, log2FC>0 were chosen after analysis with GEO2R. In the GSE22529 dataset, 942 genes had higher expression levels in CLL patients compared with controls, while the expression of 1007 genes decreased. In the GSE26725 dataset, CLL patients had lower expression levels for 916 genes compared with controls, while 939 genes showed an increase in expression. 229 DEGs with higher expression levels and 308 DEGs with lower expression levels were found in both sets of data. It has been observed that these common genes, whose expression has changed, are enriched in protein processing in the ER, Chemokine, B-cell receptor, T-cell receptor, protein export pathways. Additionally, DDOST, RPL18, RPL18A, RPL19, RPL31, GNB3, GNB4, GNG11, GNGT1, NEDD8, UBE2M RBX1, FBXO21, SKP1, KLHL9 and CAND1 were identified as the most important genes. Our study's findings demonstrated that newly discovered genes and pathways may be candidates for CLL biomarkers that can be used for both the diagnosis and drug treatment of the disease.