Comprehensive analysis of SPAG1 expression as a prognostic and predictive biomarker in acute myeloid leukemia by integrative bioinformatics and clinical validation

Abstract

Background Recently, an increasing number of studies have reported that sperm-associated antigen (SPAG) proteins play crucial roles in solid tumorigenesis, and may serve as potentially helpful biomarkers for cancer diagnosis and prognosis. However, very few studies systematically investigated the expression of SPAG family members and their clinical significance in acute myeloid leukemia (AML). Methods The expression of SPAGs and their prognostic significance in AML were determined by a systematic analysis on data gathered from public databases, and the results were validated in clinical samples. Results Using public data, we identified only increased SPAG1 expression negatively associated with survival in AML by Cox regression (P < 0.001) and Kaplan–Meier analysis (P < 0.001). The prognostic value of SPAG1 expression was further confirmed in other independent cohorts. Clinically, higher SPAG1 expression was significantly correlated with white blood cell counts (P = 0.014) and French–American–British (FAB) subtypes (P = 0.024). Moreover, higher SPAG1 expression was more common in + 8 patients (P = 0.034), rarely found with t(8;21) (P = 0.014), and correlated with FLT3 (P < 0.001) and DNMT3A mutations (P = 0.001). Despite these associations, multivariate analysis confirmed the independent prognostic value of SPAG1 expression in AML (P < 0.001). Notably, AML patients with higher SPAG1 expression may benefit from hematopoietic stem cell transplantation (HSCT), whereas patients with lower SPAG1 expression appeared less likely to benefit. Finally, we further validated that SPAG1 expression was significantly increased in newly diagnosed AML patients compared with normal controls (P < 0.001) and with AML patients who achieved complete remission (P < 0.001). Additionally, SPAG1 expression could act as a potentially helpful biomarker for the diagnosis and prognosis of AML (P < 0.001 and = 0.034, respectively). Conclusions Our findings demonstrated that SPAG1 overexpression may serve as an independent prognostic biomarker and may guide the choice between HSCT and chemotherapy in patients with AML.

Transcriptome analysis of beta-catenin-related genes in CD34+ haematopoietic stem and progenitor cells from patients with AML

“… 42 According to Gu et al, patients with newly diagnosed AML had significantly higher levels of SPAG5 expression than both normal controls and patients with AML who observed complete remission. 43 Expression of SPAG5 , a beta-catenin-related gene, was found to be high in the AML patient group with high beta-catenin levels in our study, in parallel with previous studies. Several human malignancies exhibit aberrant overexpression of two other proteins, NIMA-related kinase 2 ( NEK2 ) and Human Baculoviral inhibitor of apoptosis repeat-con-taining 5 ( BIRC 5), which are important in the progression of the cell cycle and mitosis.…”

T cell-mediated tumor killing sensitivity gene signature-based prognostic score for acute myeloid leukemia

“…Our TTKPI prognostic scoring system incorporates 24 differentially expressed T-cell-mediated tumor-killing sensitivity genes, several of which have been shown to play critical roles in the pathogenesis of AML. For instance, Sperm-associated antigen 1 (SPAG1) is widely expressed in acute myeloid leukemia (AML) patients and is associated with a poor prognosis [ 59 ]. It promotes AML cell proliferation and survival by activating the ERK/MAPK signaling pathway and affects AML cell susceptibility to venetoclax, a chemotherapy drug [ 60 ].…”

Comprehensive analysis of ID genes reveals the clinical and prognostic value of ID3 expression in acute myeloid leukemia using bioinformatics identification and experimental validation

“…All procedures regarding the bioinformatics analysis were carried out as described in our previous studies [ 15 , 16 ]. To obtain the differentially expressed genes/miRNAs (DEGs/DEmiRs), analysis of the RNA sequencing data was conducted based on the raw read counts with the R/Bioconductor package “edgeR”.…”