Baseline patient characteristics
A total of 58 CN-AML patients were included in the research. Thirty patients received chemotherapy alone, and the remaining twenty-eight patients were proceeded with allogeneic hematopoietic stem cell transplantation. We divided the patients into high expression group and low expression group according to the CD52 gene median FPKM value. The clinical and molecular characteristics between the two groups were compared (Table 1). The median ages of CD52high and CD52low were 51 years (range, 21–75 years) and 63 years (range, 21–88 years), respectively. The patients in CD52high cohort were older than low expression cohort (P = 0.019) and the mutation ratio of DNMT3A was higher in the latter group (P = 0.014). There were no significant differences between the two groups in sex, race, FAB classification, chemotherapy, transplant, genetic mutations (NPM1, FLT3, IDH1, IDH2, RUNX1) (all P value > 0.05). The baseline characteristics patients in GSE12417 and GSE71014 had already described in previous studies(18, 19).
Table 1
Pretreatment characteristics of 58 CN-AML patients CD52lowand CD52high cohort
Characteristics | TCGA | P-value |
CD52-low | CD52-high |
N | | 29 | 29 | |
Sex (%) | | | | 0.066 |
| Female | 11(18.97%) | 18(31.03%) | |
| Male | 18(31.03%) | 11(18.91%) | |
Age(median/range) | 51(21–75) | 63(21–88) | 0.019 |
| ≥ 65 | 4(6.90%) | 12(20.69%) | |
| < 65 | 25(43.10%) | 17(29.31%) | |
Race (%) | | | | 0.769 |
| Black | 23(39.66%) | 20(34.48%) | |
| White | 2(3.45%) | 2(3.45) | |
| Unknown | 5(8.63%) | 7(12.07) | |
FAB (%) | | | | 0.136 |
| M1 | 1(1.72%) | 2(3.45%) | |
| M2 | 13(22.41%) | 5(8.62%) | |
| M4 | 8(13.79%) | 8(13.79%) | |
| M5 | 3(5.17%) | 9(15.52%) | |
| M6 | 4(6.9%) | 5(8.62%) | |
Treatment (%) | | | 0.115 |
| Chemotherapy | 12(20.69%) | 18(31.03%) | |
| HSCT | 17(29.31%) | 11(18.97%) | |
Relapse (%) | | | 0.785 |
| Yes | 18(31.03%) | 19(32.76%) | |
| NO | 11(18.97%) | 10(17.24%) | |
CEBPA (%) | | | 0.004 |
| Mutation | 8(13.79%) | 0(0%) | |
| WT | 21(36.21%) | 29(50%) | |
FLT3-ITD (%) | | | 0.588 |
| Mutation | 10(17.24%) | 12(20.31%) | |
| WT | 19(32.76%) | 17(29.31) | |
NPM1 (%) | | | | 0.293 |
| Mutation | 17(29.31%) | 13(22.41%) | |
| WT | 12(20.69%) | 16(27.59%) | |
IDH1 (%) | | | | 0.253 |
| Mutation | 6(10.34%) | 2(3.45%) | |
| WT | 23(39.66%) | 27(46.55%) | |
IDH2 (%) | | | | 0.145 |
| Mutation | 2(3.45%) | 7(12.07%) | |
| WT | 27(46.55%) | 23(39.66%) | |
RUNX1 (%) | | | 0.414 |
| Mutation | 2(3.45%) | 4(6.9%) | |
| WT | 27(46.55%) | 23(39.66%) | |
DNMT3A (%) | | | 0.014 |
| Mutation | 6(10.34%) | 15(25.86%) | |
| WT | 23(39.66%) | 14(24.14%) | |
High expression of CD52 is a poor prognostic marker for CN-AML patients
In order to evaluate the impact of CD52 expression on the survival CN-AML of patients, we employed the Kaplan–Meier method and log-rank test in 58 CN-AML patients from TCGA dataset. Results showed that CD52high group had shorter EFS (Fig. 1a; P = 0.056) and OS (Fig. 1b; P = 0.043). To validate the results, we analyzed CD52 gene expression profile of 242 CN-AML in GSE12417 and 103 CN-AML in GSE71014. The patients with high level of CD52 expression showed a poor OS in independent cohorts (Fig. 1c-d; P = 0.0197, P = 0.0197, respectively).
58 CN-AML were divided into the chemotherapy-only group (n = 30) and allo-HSCT group (n = 28). Kaplan-Meier survival curves suggested that CD52high was an adverse factor for chemotherapy group (EFS, p = 0.041; OS, p = 0.013; Fig. 1e-f), whereas the expression level of CD52 played a small role in the survival of HSCT group (EFS, p = 0.3647; OS, p = 0.4812; Additional file: Fig.S1 a-b). HSCT may prolong overall survival of CD52high (p = 0.0795) patients in some extent but CD52low(p = 0.4812) (Additional file: Fig.S1 c-d).
CD52 expression is an independent risk factor for prognosis in patients with CN-AML
Univariate and Multivariate cox regression analysis were implemented to evaluate the prognostic value of clinical and biological variables. We analyzed sex, age, CD52 gene expression and gene mutations, such as FLT3, NPM1, IDH1, IDH2, RUNX1, DNMT3A in univariate cox regression analysis. Univariate analysis showed that CD52 gene expression level was associated with shorter OS (Fig. 2a; HR = 1.465; P = 0.001) and EFS (Fig. 2b; HR = 1.29; P = 0.036). Besides, FLT3 mutation also contributed to worse OS (HR = 1.903; P = 0.049) and EFS (HR = 2.084; P = 0.030). Age only affected OS (HR = 2.466; P = 0.007) rather than EFS (P = 0.215). Other variables had no significance for the prognosis of CN-AML patients (all P value > 0.05). Therefore, age, FLT3 genes mutation and CD52 gene expression levels were chosen for multivariate cox regression analysis. Age (HR = 3.045; 95% confidence interval [CI]:1.524 − 6.086; P = 0.002), FLT3 mutation (HR = 2.219; 95%CI:1.123 − 4.382; P = 0.022), CD52 gene expression level (HR = 1.503; 95%CI: 1.158 − 1.949; P = 0.002) were independent risk factors of OS for CN-AML patients (Fig. 2c). FLT3 was the only factor that affect EFS (Fig. 2d; HR = 2.318, 95%CI: 1.138 − 4.722, P = 0.021) in CN-AML patients.
Area under the receiver operating characteristic curve (AUC-ROC) analysis was performed to assess the prediction accuracy of EFS and OS with CD52 gene expression. CD52 gene expression show a predictive effect on EFS (Fig. 3a; 1 year survival-AUC:0.685, 2 year survival-AUC:0.752) and OS (Fig. 3b;1 year survival-AUC: 0.717, 2 year survival-AUC:0.770).
Correlation of CD52 mRNA expression with other biomarkers in CN-AML patients
To further explore the possible mechanism of CD52 molecules, we analyzed the relationship of mRNA expression between CD52 and the genes mutations that were reported to affect the prognosis of CN-AML patients. The mutation of CEBPA was associated with lower levels of CD52 mRNA (Fig. 4a, p = 0.00047), while the mutation of DNMT3A tended to show higher levels of CD52 (Fig. 4b, p = 0.00388). The gene mutation status, such as FLT3 (p = 0.188), NPM1 (p = 0.839), IDH1 (p = 0.125), IDH2 (p = 0.179), RUNX1 (p = 0.818), showed no relationships with the expression level of CD52. We also analyzed the relationship between the expression of CD52 and other aberrantly expressed genes that affecting prognosis, such as WT1(20), EVI1(21), FLT3(22), MN1(5, 23), ERG(5, 24), ID1(25), CDKN1B(5) and BAALC(5). There was no correlation between CD52 and gene above in the level of mRNA (Fig. 4c; all p value > 0.05).
DNA methylation was the common factors associated with abnormal gene expression. We evaluate the correlations among CD52 gene mRNA expression and DNA methylation (average β-values). There was a moderate, significant, inverse correlation between DNA methylation and average gene expression (Fig. 4d; r=-0.683, P = 4.234 × 10− 6). CD52 gene DNA CpG sites, such as cg16068833 (r=-0.665; p = 1.178 × 10− 6), cg19677267(r=-0.607; p = 1.607 × 10− 5), cg19743891(r=-0.648; p = 2.642 × 10− 6) also show a correlation with gene expression, which was not show here.
Functional annotation and pathway enrichment of differentially expressed genes (DEGs)
To gain insights into the biological function of CD52, we analyze different gene expression in CD52high group and CD52low group. A total of 933 differentially expressed genes had been found (214 downregulated genes; 719 downregulated genes). The significantly expressed genes between two groups were shown in volcano plot (Fig. 4e). Go and KEGG functional annotation analysis was showed below (Fig. 4f-g). GO analysis found that T cell activation was important GO category (32 gene; P = 0.0005) in the biological process (BP) ontology. In cellular component (CC) ontology, the most significant GO category was T cell receptor complex (7 gene; P = 0.000061). In molecular function (MF) ontology, the major histocompatibility complex (MHC) protein binding was the most important GO category (9 gene; P = 0.0003). KEGG analysis also show that differentially expressed proteins are mainly enriched pathway was T cell receptor signaling pathway. In addition to T cell activation-related pathways, some DEGs are enriched in leukocyte cell-cell adhesion and regulation of leukocyte cell-cell adhesion (Table 2).
Table 2
Enrichment analysis of Gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway
ID | Description | p.adjust | geneID | Count |
GO:0007159 | leukocyte cell-cell adhesion | 0.0033037 | DPP4/ADTRP/ICOS/CD6/ETS1/CD3E/IL7R/SKAP1/CD40LG/LCK/GATA3/TIGIT/ LAG3/TBX21/IFNG/BMP7/EFNB3/CTLA4/CCR7/SIRPG/CD28/SELP/HES1/IDO1 | 24 |
GO:0022409 | positive regulation of cell-cell adhesion | 0.0043429 | DPP4/ICOS/CD6/ETS1/CD3E/IL7R/SKAP1/CD40LG/LCK/GATA3/IFNG/ BMP7/EFNB3/CTLA4/CCR7/SIRPG/CD28/ANK3/WNT5A/HES1 | 20 |
GO:1903037 | regulation of leukocyte cell-cell adhesion | 0.0043429 | DPP4/ADTRP/ICOS/CD6/ETS1/CD3E/IL7R/SKAP1/CD40LG/LCK/GATA3/ TIGIT/LAG3/TBX21/IFNG/EFNB3/CTLA4/CCR7/SIRPG/CD28/HES1/IDO1 | 22 |
hsa04514 | Cell adhesion molecules (CAMs) | 0.0249930 | CD226/ICOS/CD2/CD6/CD40LG/TIGIT/CADM1/CD8A/CTLA4/ CD28/SELP/CD8B | 12 |