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.