Clinical characteristics and prognostic analysis of gene mutations in Myelodysplastic Syndrome

doi:10.21203/rs.3.rs-4177524/v1

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Clinical characteristics and prognostic analysis of gene mutations in Myelodysplastic Syndrome

https://doi.org/10.21203/rs.3.rs-4177524/v1

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Myelodysplastic syndromes (MDS) are heterogeneous clonal diseases characterized by cytopenia caused by ineffective hematopoiesis and high risk of transformation into acute myeloid leukemia (AML). Genetic mutations are an essential pathogenesis of MDS. In order to analyze gene mutations, clinical features, and their correlation with survival prognosis in patients with MDS, Clinical data of 248 MDS patients were selected for statistical analysis. The gene chip dataset was obtained from GEO and subjected to bioinformatics analysis. Among 248 MDS patients, TET2 was the most common mutated gene (31%), followed by SF3B1 (26%), ASXL1 (23%), SRSF2 (14%), DNMT3A (12%), and RUNX1 (11%). Age, gender, and ASXL1 mutations are independent prognostic factors for OS. Survival analysis showed that co-mutations in the EZH2 or ETV6 genes significantly reduced the survival time of patients with ASXL1 mutations. Based on the KEGG and IPP analysis results, the MYC gene may be related to the mechanism of action of ASXL1 mutation in MDS patients. Our research findings demonstrate a correlation between gene mutations and survival outcomes. Mutations in ETV6 or EZH2 co-mutations are associated with poorer clinical outcomes in MDS patients carrying ASXL1mt and suggest that MYC may become a potential therapeutic target for MDS patients carrying ASXL1 mutations.

MDS

genetic mutations

ASXL1

MYC

prognosis

Myelodysplastic syndrome (MDS) is a common malignant hematological disease characterized by malignant proliferation of bone marrow hematopoietic stem cells, leading to ineffective hematopoiesis, decreased blood cells, and a significant risk of evolving into acute myeloid leukemia (AML)^[1]. The diagnosis and treatment of MDS rely on bone marrow biopsy, blood routine, and chromosome karyotype detection, among other parameters. With the advancement of molecular biology, gene mutation detection has also become an essential diagnostic and therapeutic measure in diagnosing and treating MDS.

Genetic mutations are an essential pathogenesis of MDS. Studies have shown that 89.5% of MDS patients carry at least one gene mutation, with TET2, SF3B1, ASXL1, SRSF2, DNMT3A, and RUNX1 being common gene mutations^[2]. Multiple research analyses have shown that mutations in genes such as RUNX1, TP53, DNMT3A, and ASXL1 are independent poor prognostic factors for MDS patients^{[3, 4]}. Gene mutations may become one of the prognostic risk assessment indicators for MDS. Based on clinical research, Bernard proposed a prognostic model for IPSS-M combined with genetic mutations ^[5]. After verification, Wu et al. and Wan et al. proposed that compared to IPSS-R, IPSS-M may be more accurate in predicting prognostic risk, which is significant for guiding personalized clinical treatment ^{[6, 7]}. Genetic mutations have great potential in predicting the prognostic risk of MDS patients. However, there currently needs to be a unified standard, and more research is needed to refine the results. In this study, a correlation analysis between gene mutations and clinical prognosis was conducted on 248 MDS patients, proving that gender, age, and ASXL1 gene mutations are independent adverse prognostic factors for MDS patients, providing data for constructing prognostic risk prediction models in the future.

ASXL1 has a promoting effect on self-renewal and differentiation of hematopoietic stem cells. ASXL1 gene mutations are common in chronic myeloid leukemia, AML, and MDS and can lead to histone modifications and transcriptional dysregulation. In previous studies, ASXL1 gene mutations were considered independent poor prognostic factors for MDS patients. Knockout of ASXL1 in mice can induce MDS-like manifestations. The ASXL1 gene mutation is an essential pathogenesis of MDS, but its specific mechanism has yet to be elucidated, and further exploration is needed. In this study, KEGG and IPP analysis was performed on ASXL1 mutant patients using the GEO database, and it was found that the hub gene MYC is a potential new therapeutic target.

2.1. Patient collection

Retrospective collection of 14 MDS patients admitted to Fujian Provincial Hospital from January 2013 to January 2024. The diagnostic and subdivision criteria refer to the 2016 World Health Organization (WHO) standards. All patients were classified for risk according to the revised International Prognostic Score System (IPSS). Follow-up will be conducted from the date of MDS diagnosis until January 2024 or death through telephone interviews or clinic visits. This study has been approved by the Ethics Committee of Fujian Provincial Hospital and follows the principles of the Helsinki Declaration.

Download information on 141 MDS patients from the GSE129828 Gene Expression Omnibus (GEO) dataset and obtain information on 176 MDS patients from the attachment of Gerstung M et al.'s article^[8]. After removing patients without age or survival time from two datasets, we obtained complete clinical and follow-up data from 234 patients. Since the incidence rate of MDS is low, this study combines the cases of our institution with the screened data set to increase our statistical analysis ability. This study analyzed a total of 248 patients.

2.2. Gene sequencing

14 MDS patients underwent heparinization of bone marrow (BM, 2–3 mL). Gene amplification was performed on BM cells using polymerase chain reaction (PCR). Then, the Illumina platform was used to sequence the PCR products. MDS related gene mutations were detected, including ASXL1, TET2, SF3B1, RUNX1, SRSF2, DNMT3A, TP53, EZH2, NRAS, IDH1, IDH2, CBL, JAK2, U2AF1, STAG2, BCOR, KRAS, GATA2, PTPN11, KIT, MPL, NF1, SETBP1, GATA1, CSF3R, KMT2D, ARID2, D5S721, SH2B3, JAK3, EGR1, CALR.

2.3. Bioinformatics analysis

The GSE58831 dataset of GEO is the gene chip data of the corresponding patients in the article by Gerstung M et al. ^[8]. After removing samples without age and survival time, 118 samples were obtained. Using R software (R 4.3.1) and related R packages, ASXL1 mutation patients were screened from 118 MDS patients and divided into ASXL1^mt and ASXL1^wt. Use GEOquery and limma packages for differential analysis. Screen differentially expressed genes with logFC absolute values greater than 0.5 and P values less than 0.05 and perform KEGG enrichment through the DAVID website.

Subsequently, the Search Tool for Retrieval of Interaction Gene (STRING) database and Cytoscape software (V.3.9.1) were used to analyze the PPI network and hub genes. Cytoscape was used to visualize the PPI network.

2.4. Statistical analysis

Statistical analysis was conducted using SPSS (version 27.0). Overall survival (OS) is the time from diagnosis to death or the last follow-up. Kaplan Meier method and log-rank test were used to estimate OS differences, and a forward LR Cox proportional hazards regression model was used for multivariate analysis. Bilateral p < 0.05 is a significant difference. Use R software (R 4.3.1) and related R packages to plot survival curves with P < 0.05 significant differences.

3.1. Description of MDS patient characteristics

A total of 248 MDS patients were included in this study, with a median age of 70 years at diagnosis and 67.74% of patients being male. In the IPSS-R risk stratification, the low-risk group of patients was the most common (26.72%), and in the IPSS risk stratification, the medium risk − 1 patient was the most common (43.18%). A more detailed description of the features can be found in Table 1.

Table 1

Baseline characteristics of the 248 MDS cases.
Baseline characteristic	Number	%
Age	70 years median; 19–90 years range
Gender
Male	168	67.74
Female	80	32.26
Blood and bone marrow counts at diagnosis
ANC	1.71 ×10⁹/L median; 0.08–28.04 ×10⁹/L range; 14 missing
HB	95 g L^− 1 median; 41–152 g L^− 1 range; 6 missing
PLT	117 ×10⁹/Lmedian; 6-1042 ×10⁹/Lrange; 6 missing
Bone marrow blasts	4% median; 0–63% range; 13 missing
IPSS-R risk group 116
Very low	21	18.10
Low	31	26.72
Intermediate	18	15.52
High	24	20.69
Very high	14	12.07
Missing	8	6.90
IPSS risk group 132
Low	35	26.52
Intermediate-1	57	43.18
Intermediate-2	24	18.18
High	6	4.55
Missing	10	7.58

Among 248 patients, 217 (87.5%) carried at least one gene mutation, with a median of 2 mutations detected in each patient. TET2 is the most common mutated gene (31%), followed by SF3B1 (26%), ASXL1 (23%), SRSF2 (14%), DNMT3A (12%), and RUNX1 (11%) (mutations occur in > 10% of cases). Based on clinical information and mutated genes, a landscape map of gene abnormalities was generated (Fig. 1).

3.2. Prognostic analysis of MDS patients

Prognostic analysis was conducted on clinical data of 248 patients after screening, mainly including mutated genes. In univariate analysis, age (P = 0.005), gender (P = 0.002), ASXL1 mutation (P = 0.002), RUNX1 mutation (P < 0.001), and EZH2 mutation (P = 0.003) were associated with OS in MDS patients (P < 0.1). Further multivariate Cox regression analysis showed that age (HR 1.502, CI 1.060–2.129, P = 0.022), gender (HR 0.588, CI 0.393–0.882, P = 0.010), and ASXL1 mutation (HR 1.654, CI 1.097–2.493, P = 0.016) were independent prognostic factors for OS. (Table 2)

Table 2

**Univariate and multivariate analysis of overall survival rate in 248 MDS patients**
Variable	NO. of patients	Univariant analysis P-value	Multivariate analysis
Table 2	NO. of patients	Univariant analysis P-value	HR	95% CI	P-value
Age		zero point zero zero five	one point five zero two	1.060–2.129	zero point zero two two
≤ 70 years	one hundred and thirty-six
> 70 years	one hundred and twelve
Gender		zero point zero zero two	zero point five eight eight	0.393–0.882	zero point zero one zero
Male	one hundred and sixty-eight
Female	eighty
SF3B1		zero point one two eight
MUT	sixty-five
WT	one hundred and eighty-three
TET2		zero point seven eight one
MUT	seventy-six
WT	one hundred and seventy-two
SRSF2		zero point two one nine
MUT	thirty-five
WT	two hundred and thirteen
ASXL1		zero point zero zero two	one point six five four	1.097–2.493	zero point zero one six
MUT	fifty-seven
WT	one hundred and ninety-one
DNMT3A		zero point six six five
MUT	twenty-nine
WT	two hundred and nineteen
RUNX1		< 0.001	one point six one one	0.964–2.690	zero point zero six nine
MUT	twenty-eight
WT	two hundred and twenty
U2AF1		zero point two zero three
MUT	twenty-three
WT	two hundred and twenty-five
EZH2		zero point zero zero three	one point six two five	0.826–3.197	zero point one six zero
MUT	seventeen
WT	two hundred and thirty-one
IDH2		zero point seven four nine
MUT	five
WT	two hundred and forty-three
CBL		zero point seven zero four
MUT	seven
WT	two hundred and forty-one
NRAS		zero point three three zero
MUT	eight
WT	two hundred and forty
JAK2		zero point zero eight seven
MUT	nine
WT	two hundred and thirty-nine
IDH1		zero point seven three nine
MUT	three
WT	two hundred and forty-five
KRAS		zero point nine zero five
MUT	three
WT	two hundred and forty-five
PTPN11		zero point three six six
MUT	three
WT	two hundred and forty-five

3.3. Survival analysis of ASXL1 mutation patients

There are a total of 57 patients with ASXL1 mutations, and except for 3 patients, all of them have at least one other gene mutation. TET2 mutation is the most common among them, accounting for 40.4% (23/57). Other genes with mutation frequencies greater than 10% include SRSF2 (24.6%, 14/57), RUNX1 (24.6%, 14/57), SF3B1 (15.8%, 9/57), U2AF1 (17.5%, 10/57), EZH2 (17.5%, 10/57), and NRAS (12.3%, 7/57).

Next, we conducted a survival analysis on patients carrying ASXL1 mutations. The results showed that in the context of ASXL1 mutation, EZH2^MT patients had a significantly poorer prognosis than EZH2^WT patients (median OS: 266.5 vs. 539 days, P = 0.028). The median OS of ETV6^MT patients was significantly lower than that of ETV6^WT patients (median OS: 279.5 vs. 512 days, P = 0.028). The Kaplan-Meier analysis results for genes with frequencies > 10% in other combinations are shown in the figure. Compared with the TET2^WT group, patients in the TET2^MT group had shorter OS (median OS: 293 vs. 545.5 days, P = 0.51). (Fig. 2)

3.4. Key pathways and PPI network analysis of differentially expressed genes

To further understand the mechanism of ASXL1 gene mutation in MDS, GSE58831 data was divided into ASXL1 mut group and non-ASXL1 mut group for inter-group differential gene analysis. A total of 169 differential genes with logFC absolute values greater than 0.5 and p-values less than 0.05 were selected for KEGG analysis. The results showed that the KEGG pathway is mainly enriched in mineral absorption, cancer transcriptional dysregulation, acute myeloid leukemia, EB virus infection, and the JAK-STAT signaling pathway. (Fig. 3), among which the MYC gene can be enriched in multiple pathways (Table 3).

We used STRING and Cytoscape software to analyze the interactions between 169 screened differentially expressed genes and obtained the top 5 hub genes based on the degree value: MYC, MT2A, MT1E, TPR, NFKBIE, TNFAIP3. The MYC gene showed the highest degree score (Fig. 4). The KEGG clustering and degree scoring results indicate that MYC may be the core gene responsible for ASXL1 gene mutation (Fig. 4).

Table 3

KEGG pathways with p-values less than 0.05 identified through DAVID analysis associated with 169 genes
Term	Count	Pvalue	Genes
Mineral absorption	4	0.007845958	MT2A,MT1G,MT1HL1,MT1E
Transcriptional misregulation in cancer	6	0.009948083	SMAD1,WT1,MYC,ZBTB16,ITGB7,DUSP6
Acute myeloid leukemia	4	0.010620748	MYC,ZBTB16,PIK3R3,DUSP6
Epstein-Barr virus infection	6	0.011956422	ENTPD1,PSMD11,MYC,PIK3R3,TEFAIP3,NFKBIE
JAK-STAT signaling pathway	5	0.026357171	TSLP,MYC,STAT4,PIK3R3,MCL1

Genetic mutations are an essential component of the pathogenesis of MDS. Different gene mutations lead to differences in survival outcomes and show a steady decrease in leukemia-free survival as the number of driving mutations increases. Therefore, gene mutations are of great significance for predicting the survival outcomes of MDS^{[2, 9]}. However, there currently needs to be a unified standard for applying gene mutations in predicting the risk of MDS, and more clinical data analysis is needed to support the establishment of subsequent prognostic risk assessment models. In this study, we included 248 MDS patients to explore the relationship between gene mutations and survival outcomes in MDS patients. The research results indicate that ASXL1 gene mutation is an independent prognostic risk factor for MDS. ASXL1 is associated with normal hematopoiesis of hematopoietic stem cells, and this study also explores the mechanism of action of ASXL1 in MDS.

In our study, 69.7% of patients carried at least one gene mutation, with TET2 being the most common mutated gene (31%), followed by SF3B1 (26%), ASXL1 (23%), SRSF2 (14%), DNMT3A (12%), and RUNX1 (11%). Although the gene mutation rate differs from other studies^[3], there is still a high proportion of TET2, SF3B1, and ASXL1 mutation rates. In addition, our further multivariate Cox regression analysis showed that except for the ASXL1 gene mutation, there was no significant correlation between other gene mutations and the prognosis of MDS. ASXL1 gene mutation is an independent prognostic factor for shorter survival outcomes in MDS. However, in previous studies, multiple studies have shown that SF3B1 mutations are independent factors associated with longer survival outcomes in MDS ^{[10, 11]}, and TP53 and RUNX1 mutations are also associated with poor prognosis in MDS^{[4, 10, 12]}. The differences in these research results may be due to insufficient sample size, and further large sample and multicenter studies are needed for validation.

In our study, there were 57 patients with ASXL1 mutations, of which 94.7% carried other gene mutations, with TET2 being the most common. Multiple gene mutations may affect the survival time under the background of ASXL1 mutations. We further analyze the survival time of gene co-mutations to obtain more accurate prognosis-related gene mutations. The survival analysis results showed that compared to ASXL1^mt/ETV6^wt and ASXL1^mt/EZH2^wt, patients with ASXL1^mt carrying ETV6 or EZH2 gene mutations had a reduced survival time and a poorer prognosis. In the context of ASXL1 mutation, mutations in RUNX1 and SRSF2 do not affect patient survival outcomes, which differs from previous research results ^[13] These differences in research may be partly due to limitations in sample size and partly due to the presence of three or more gene mutations in some samples, which affect survival outcomes and require further clinical screening and validation.

ASXL1 gene mutations have been found in various myeloid tumors and are associated with poor clinical prognosis. Previous studies have shown that ASXL1 knockout mice can exhibit MDS-like phenotypes, such as decreased multi-lineage blood cells and decreased levels of H3K27me3 and H3K4me3 methylation^[14]. Regarding the mechanism of ASXL1 mutation damaging hematopoietic function, Omar et al. Proposed that it is related to the inhibition of PCR2 recruitment^[15]. However, its specific mechanism of action in MDS is still unclear, and further research is needed. We conducted bioinformatics analysis on samples from MDS patients, comparing the differences between ASXL1^mt samples and samples without ASXL1^mt. Combined with KEGG enrichment and IPP analysis, we found that the MYC gene exhibited a high presence in multiple KEGG enrichment pathways. The MYC oncogene is one of the pathogenesis mechanisms of lymphoma^[16]. MYC is also commonly overexpressed in AML, which is a poor prognostic factor, and it is believed that the inactivation of PP2A is involved in MYC activation ^[17]. Reduced blood cells are a common clinical manifestation of MDS. Studies have shown that the absence of ASXL1 in mice can lead to excessive activation of MYC in granulocyte progenitor cells and red blood cell progenitor cells, hindering the maturation of neutrophils and red blood cells^[18]MYC is associated with the occurrence of various cancers, and it is also involved in the process of blood cell maturation disorders in ASXL1 deficiency. Based on our research results, MYC may become a new therapeutic target for MDS patients with ASXL1 mutations. There is still a lack of molecular biological evidence on the mechanism by which ASXL1 gene mutations in MDS affect MYC function. Future research can focus on verifying this hypothesis.

Our study suggests that age, gender, and ASXL1 mutations are independent prognostic factors for overall survival in MDS patients. We also found that in the context of ASXL1 mutation, co-mutations of EZH2 or ETV6 significantly reduce the survival time of MDS patients and have a poor prognosis. In addition, KEGG enrichment and IPP analysis indicate that MYC, as a hub gene, may play an important role in MDS patients carrying ASXL1 mutations and is a potential therapeutic target that can be further validated through walking molecular biology experiments.

Acknowledgments

The authors sincerely thank the National Library Medicine for providing the researchers with open resources and the participants and investigators.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

Patients were given written and oral information on the study and provided consent to participate in the study and for the use of their medical data for research purposes. The study was reviewed and approved by Ethics Committee of Fujian Provincial Hospital (K2023-11-026).

Author Contributions

Wang ZH and Chen YH conceived and designed the study. Chen YH and Andoh collected the data. Lai YT analyzed the data. Wang ZH and Ding LL supervised the study. Lai YT and Ding LL wrote the manuscript. Lai YT and Zhang WH prepared Figures 1-4. Zou MQ and Long ZF prepared Tables 1-3. All authors read and approved the final manuscript prior to submission.

Conflict of Interest

Not applicable.

Funding

This study was supported by the Joint Funds for the Innovation of Science and Technology, Fujian Province (grant number 2020Y9027), and Fujian Provincial Health technology project (grant number 2021CXA004).

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No competing interests reported.

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Clinical characteristics and prognostic analysis of gene mutations in Myelodysplastic Syndrome

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Version 1

Abstract

Figures

1. BACKGROUND

2. METHOD

2.1. Patient collection

2.2. Gene sequencing

2.3. Bioinformatics analysis

2.4. Statistical analysis

3. Results

3.1. Description of MDS patient characteristics

3.2. Prognostic analysis of MDS patients

3.3. Survival analysis of ASXL1 mutation patients

3.4. Key pathways and PPI network analysis of differentially expressed genes

4. DISCUSSION

Declarations

References

Additional Declarations

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