Identification of diagnosis and prognosis gene markers in B-ALL with ETV6-RUNX1 fusion by integrated bioinformatics analysis

Abstract

B-acute lymphoblastic leukemia (B-ALL) is characterized by clonal expansion of immature B-lymphocytes in the bone marrow, blood, or other tissues. Chromosomal translocations have often been reported in B-ALL, which are important for its prognosis. B-ALL patients with ETV6-RUNX1 fusion have favorable outcomes, but the mechanisms remain to be clarified. In the present study, we crossed the selected WGCNA module genes and differential expression genes to obtain core genes, and random forest algorithm, a type of supervised learning analysis, was conducted to evaluate the importance of those core genes in distinguishing B-ALL samples with ETV6-RUNX2 fusion with extracting 5 genes as gene markers for ETV6-RUNX2 fusion. Moreover, we calculated the immune infiltration profiles and screened out the ETV6-RUNX2 association immune cells using the CIBERSORT algorithm. In conclusion, combined with various solid informatics methods, we depicted the underlying molecular and immune mechanism of ETV6-RUNX2 fusion and providing potential biological targets for diagnosing and treating B-ALL in the future.