WGCNA-based identification of anoikis-related subtypes, prognostic significance, and characterisation of the immune microenvironment in Philadelphia-negative acute lymphoblastic leukaemia

Abstract

The clinical outcomes and incidence of Philadelphia chromosome-negative B cell acute lymphoblastic leukaemia (ph-neg B-ALL) vary significantly across different age groups, influencing the prognosis. Despite recent advancements in diagnostic and therapeutic techniques, the detailed prognosis for ph-negative B-ALL across age demographics remains to be elucidated. In this study, clinical data were obtained from 80 patients with ph-neg B-ALL who were diagnosed at our centre. Ribonucleic acid sequencing was performed using their initial bone marrow aspirate samples. By employing weighted gene co-expression network analysis (WGCNA) on 408 anoikis-related genes (ARGs), four different modules were identified and subsequently analysed through bioinformatics. The WGCNA revealed distinct co-expression modules among ARGs. Specifically, the ARGs in the turquoise module might assess the risk associated with newly diagnosed ph-neg B-ALL. Additionally, the study revealed significant heterogeneity in the immune microenvironment and genome variance, highlighting the notable heterogeneity within the disease. 408 ARGs were screened out and four different co-expression modules were constructed by WGCNA algorithms from the RNA-sequencing data of 80 ph-neg B-ALL patients; The ARGs in the turquoise module were the most, and it can be used to divide the de novo ph-neg B-ALL patients to different risk groups(high-risk and low-risk); The ph-neg B-ALL patients can be divided into PS-1 and PS-2, there is heterogeneity of genomes between PS-1 and PS-2; Immune infiltration difference exists in between PS-1 and PS-2. In conclusion, our study holds significant value in exploring the molecular pathways and mechanisms associated with anoikis implicated in ph-neg B-ALL, and in facilitating the development of treatments and prognostic tools for this disease