Transcriptome profiling and WGCNA analysis correlated to B-ALL phenotypes
Considering the importance of early diagnosis of B-ALL which can lead to effective treatment of patients, we evaluated the expression profile of B-ALL compared to ITP samples. RNA-seq data related to Iranian samples were deposited in the SRA BioProject database with accession number PRJNA589314. A total of 2136 dysregulated genes were recognized (adjusted P -value < 0.05), including 920 downregulated genes and 1216 upregulated genes, which are demonstrated as a volcano plot in Fig. 1.
WGCNA was applied to identify the association between the gene expression patterns of . Our analysis identified 18 separate gene co-expression modules, each containing a group of genes that tended to be co-expressed across samples. Based on WGCNA results, blue and turquoise modules were positively correlated with B-ALL status, while yellow and magenta modules were negatively correlated (Fig. 2A). Key modules were identified by evaluating the association between these 18 modules and clinical traits, and the results are demonstrated as a heatmap in Fig. 2B. Accordingly, 12 models were identified, whose key genes included ADPRHL1, SPRING1, LINC01343, FCN1, UBASH3B, FHL1, LINC00692, BCL7A, MYSM1, NRIP1, GAB1, H2AC20 . Moreover, the expression level of key genes was significantly correlated to some biological processes (BP), such as the lipopolysaccharide-mediated signaling pathway via GO enrichment analysis. The in-silico data showed significant upregulation of SPRING1 in B-ALL samples. Accordingly, we selected SPRING1 as a novel marker to investigate its effects in B-ALL tumorigenesis.