High Throughput Genomics Study for the Identification of Novel Genes Functional in B-Cell Non-Hodgkin Lymphoma

Non-Hodgkin's lymphoma (NHL) represents approximately 90% of all lymphoma cases in humans. This study aimed to discover novel genes associated with B-cell NHL by employing a machine learning approach using linear regression. Microarray data analysis was conducted using CEL files (12 samples across 6 types of B-cell NHL) obtained from the GEO database (accession ID: GSE132929). Differentially expressed genes (DEGs) were identified through R and Bioconductor packages, with DEG identification carried out using the Limma package (Linear Models for Microarray Data). RNA-seq data were analysed using paired-end sequencing (accession no. SRX4624931), and variant analysis was performed via the Galaxy server. Data enrichment for DEGs was conducted using the DAVID and GO databases. Pathway enrichment analysis of the microarray data revealed 14 pathways, with the most notable results observed in the group5-4 set. Among 10 genes, only KRAS proto-oncogene and cyclin D1 (CCND1) were implicated in these 14 pathways. RNA-Seq analysis identified 15 out of 189 genes involved in cancer-related pathways. Combined analysis of microarray and RNA-Seq data indicated significant differential expression of KRAS, CCND1, and RAF genes in B-cell NHL. Further experimental validation is required to explore these genes' potential in developing targeted therapies for NHL.