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Abstract

Objectives: VAV family genes (VAV1, VAV2, and VAV3) are associated with prognosis in various cancers; however, they have not been evaluated in acute myeloid leukemia (AML). In this study, the prognostic value of VAV expression in AML was evaluated by a single-center study in combination with bioinformatics analyses. Methods: The expression and prognostic value of VAVs in patients with AML were investigated using various databases, including GEPIA, CCLE, EMBL-EBI, UALCAN, cBioPortal, STRING, and DAVID. Blood samples from 35 patients with AML (non-M3 subtype) and 13 benigh individuals were collected at our center. VAV expression levels were detected by real-time quantitative PCR (RT-qPCR) and western blotting. Clinical data were derived from medical records. Results: Based on data from multiple databases, the expression levels of VAV1, VAV2, and VAV3 were significantly higher in AML than in control tissues (P < 0.05). RT-qPCR and western blotting results showed that VAV expression in mRNA and protein levels were higher in patients with AML that in the control group (P < 0.05). Complete remission rates were lower and risks were higher in patients with AML with high VAV1 expression than with low VAV1 expression (P < 0.05). High levels of VAV2, VAV3, and VAV1 were related to a poor overall survival, and this relationship was significant for VAV1 (P < 0.05). High expression levels of genes correlated with VAV1, such as SIPA1, SH2D3C, and HMHA1 were also related to a poor prognosis in AML. Functional and pathways enrichment analyses indicated that the contribution of the VAV family to AML may be mediated by the NF-κB, cAMP, and other pathways. Conclusion: VAVs were highly expressed in AML. In particular, VAV1 has prognostic value and is a promising therapeutic target for AML.

Keywords

acute myeloid leukemia VAV family VAV1 prognostic marker complete remission

Introduction

Acute myeloid leukemia (AML) is a malignant disease of hematopoietic stem cells characterized by the clonal expansion of abnormally differentiated myeloid cells. As the most common type of leukemia in adults, its incidence increases with age. The incidence of AML is approximately 5.06 per 100,000, and AML in children accounts for about 20% of childhood leukemia cases.¹ It is estimated that 20,240 new cases of AML will be diagnosed in the United States in 2021, and 11,400 people will die of AML.² Accordingly, the survival rate is still low. At present, the treatment of AML still mainly involves stratified chemotherapy, targeted therapy, supportive therapy, and hematopoietic stem cell transplantation, and targeted therapy is the focus of current clinical research. Therefore, research is urgently needed to identify potential therapeutic targets and prognostic biomarkers for AML.

VAV family genes (VAVs), located downstream of protein tyrosine kinases, include VAV1, VAV2, and VAV3. They are a group of signal transduction molecules regulated by tyrosine phosphorylation.³ This gene family is related to the occurrence, development, and prognosis of many cancers. In non-small cell lung cancer, VAV2 is involved in the inhibition of the epithelial-mesenchymal transition, migration, and metastasis⁴ and VAV3 can promote the metastasis.⁵ High VAV1 expression in esophageal squamous cell carcinoma predicts a poor prognosis.⁶ In human papillomavirus-negative head and neck squamous cell carcinoma⁷ and adrenocortical carcinoma,⁸ VAV2 expression is related to a poor prognosis. VAV3 is associated with a poor prognosis in pancreatic cancer,⁹ gastric cancer,¹⁰ and prostate cancer.¹¹ However, the prognostic value of VAVs in AML remains unclear. Therefore, the aim of this study was to explore the expression and prognostic value of VAVs in AML by in vitro and bioinformatics approaches. In particular, we analyzed (1) VAV expression in AML, (2) the relationship between VAV expression and prognosis, and (3) functional enrichment of genes correlated with VAVs.

Materials and Methods

GEPIA Dataset

Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/) was used to analyze the relationship between VAV family gene expression levels and AML prognosis. The database contains publicly available cancer and normal tissue microarray data.¹² Data were downloaded on January 2, 2021.

PrognoScan Dataset

PrognoScan (http://dna00.bio.kyutech.ac.jp/PrognoScan/) employs the minimum P-value approach for grouping patients for survival analyses.¹³ The GSE12417 dataset was selected to evaluate correlations between VAV expression levels and overall survival (OS) in AML. Data were obtained on August 28, 2021.

CCLE Database

CCLE (https://www.broadinstitute.org/ccle) is a compilation of gene expression, chromosome copy number, and massively parallel sequencing data for 947 human cancer cell lines.¹⁴ Expression levels of VAV gene family members were evaluated in different types of cancer tissues. Data were obtained on January 25, 2021.

EMBL-EBI Database

The European Bioinformatics Institute (EMBL-EBI) (https://www.ebi.ac.uk)¹⁵ was used to analyze the expression of VAV genes in AML cell lines. Data were accessed on March 23, 2021.

UALCAN Database

UALCAN (http://ualcan.path.uab.edu) uses TCGA level 3 RNA-seq and clinical data for 31 cancer types to analyze TCGA gene expression data.¹⁶ This website was used to identify the expression levels of VAV genes in AML subtypes based on the French-American-British (FAB) classification. Data were accessed on March 3, 2021.

LinkedOmics Database

The LinkedOmics database (http://www.linkedomics.org) contains multi-omics data and clinical data for 32 cancers and 11,158 patients from the Cancer Genome Atlas (TCGA) project.¹⁷ This database was used to analyze and verify the relationship between the expression levels of VAV genes and prognosis in AML. Data were accessed on January 2, 2021.

CBioPortal Database

The cBio Cancer Genomics Portal (http://cbioportal.org) currently includes data for more than 5000 tumor samples from 20 cancer studies.¹⁸ This database was used to calculate the frequency of gene alterations and mRNA expression z-scores (RNA Seq V2 RSEM). Data were accessed on March 4, 2021.

Network Analysis

The Search Tool for the Retrieval of Intercept Genes/Proteins (STRING) database (https://string-db.org/) collects, scores, and integrates publicly available sources of protein–protein interaction (PPI) information and supplements these sources with computational predictions.¹⁹ DAVID (https://david.ncifcrf.gov/) can be used for gene classification, functional annotation, or cluster analyses.^20,21 Cytoscape (https://cytoscape.org/) is an important tool for network biology analysis and visualization, applicable to any molecular components and interaction systems.^22,23 Using these tools, we generated a PPI network and performed Gene Ontology (GO) and KEGG pathway functional enrichment analyses of VAV family genes. Original data were downloaded on March 5, 2021.

Cell Lines and Cell Culture

The human AML cell lines KG-1 were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The MV4 to 11 cell lines were obtained from the American Type Culture Collection (Manassas, VA, USA). The human AML cell lines KG-1 and MV4 to 11 were cultured at 37 °C and 5% CO₂ in RPMI-1640 medium containing 10% fetal bovine serum and 1% penicillin–streptomycin.

Clinical Samples

Peripheral blood was obtained from 35 patients with AML (non-M3 subtype) and 13 controls (including three cases of iron deficiency anemia and ten cases of thrombocytopenia) admitted to the *** from January 1, 2021 to June 31, 2021. Patients with AML were diagnosed according to the WHO 2016 standards and classified according to the FAB classification. Treatment was mainly based on the Chinese guidelines for diagnosis and treatment of adult AML (non-APL).²⁴ Clinical data, including age, sex, gene mutation/fusion, subtype classification, risk stratification, complete remission (CR), white blood cell (WBC) count, hemoglobin count, platelet count, and bone marrow blast cell count were collected. This study was conducted in accordance with the guidelines of the Ethics Committee of the *** and in accordance with the World Medical Association Declaration of Helsinki. This study was approved by the Medical Ethics Committee of the *** (NO.:KY2021166), and individual consent for this retrospective analysis was not required.

Antibodies and Reagents

The following antibodies were used: VAV1 antibody (Abcam, Cambridge, UK), VAV2 antibody (Abcam, Cambridge, UK), VAV3 antibody (Abcam, Cambridge, UK), β-actin antibody (Bioss, Massachusetts, USA), and horseradish peroxidase (HRP)-linked anti-rabbit immunoglobulin G (IgG) (CST, Massachusetts, USA). The reagent was RIPA Lysis Buffer (Biyuntian Biotechnology, Shanghai, China).

Quantitative Real-Time PCR (qRT-PCR)

Total RNA was extracted using the RNAsimple Total RNA Kit (TIANGEN, Beijing, China). The TransScript All-in-One First-Strand cDNA Synthesis SuperMix for qPCR Kit (TransGen Biotech, China, Beijing) was used to synthesize cDNA, and PerfectStart Green qPCR SuperMix (Transgen) was used to detect mRNA levels. The primer sequences were as follows: VAV1-forward, 5′-TCAGTGCGTGAACGAGGTCAAG-3′, VAV1-reverse, 5′-CCATAGTGAGCCAGAGACTGGT-3′, VAV2-forward, 5′-CTGCTGTTCCACAAGATGACCG-3′, VAV2-reverse, 5′-GGTC-AGTCAGTCAGTCAG-AGCCTGGTCAGAGCCTG5TCAG-AGCCTGVGCCT-AGG-3′, VAV3-reverse, 5′-CACGTTGCATAGGAACCACAAGC-3′, β-actin-forward, 5′-GGCGGCACCACCATGTACC-3′, β-actin-reverse, 5′-CCACACGGAGTACTTGCGC-3′. The 2^−ΔΔCT method was used to calculate the relative mRNA expression levels.

Protein Extraction and Western Blotting

Total protein was extracted using whole-cell lysis buffer (Beyotime Biotechnology, Beijing, China). Then, 20 µg of protein from each sample was loaded onto gels for separation by 10% sodium lauryl sulfate-polyacrylamide gel electrophoresis, followed by transfer to a polyvinylidene fluoride (PVDF) membrane (0.45 μm; Millipore, Burlington, MA, USA). After blocking with 5% skim milk, the PVDF membrane was incubated with the primary antibody overnight at 4 °C. The PVDF membrane was then incubated with the secondary antibody at 25 °C for 1 h. Chemiluminescence ECL reagent (Tanon, Woburn, MA, USA) was used to observe protein expression.

Statistical Analysis

Statistical analyses were performed using SPSS (Version 25.0, IBM Corp.. Armonk, NY, USA) and GraphPad Prism (Version 9, GraphPad Software). Student's t-tests were used to analyze differences between two groups. According to the mean VAV gene expression level, patients were divided into groups with high (VAV-high) and low expression (VAV-low), and Fisher's exact test was used to for comparisons between these groups. Statistical significance was set at P < 0.05. A Cox regression analysis was used to determine the prognostic value of VAV genes.

Results

VAVs are Highly Expressed in AML (GEPIA Database and Clinical Samples)

The GEPIA database included information for 173 patients with AML from TCGA and 70 normal blood samples from the Genotype-Tissue Expression (GTEx) portal. Based on these samples, the expression levels of VAV family genes in AML samples were significantly higher than those in normal blood samples (P < 0.05) (Figure 1A).

Figure 1.

Comparison of VAV expression levels in AML and normal samples. (A) Expression levels of VAVs in AML analyzed by GEPIA. (B) RT-qPCR validation of VAVs in AML and normal samples. (C, D) Western blot validation of VAVs in AML and normal samples. N1, 2, 3: Normal 1, 2, 3; P1, 2, 3: Patient 1, 2, 3. * P < 0.05; **** P＜0.0001.

Next, we analyzed the peripheral blood of 35 patients with AML (non-M3 subtype) at our hospital. As determined by RT-qPCR, the expression levels of VAV1 (P = 0.0249), VAV2 (P = 0.0223), and VAV3 (P = 0.0488) in AML samples were significantly higher than those in the control group (Figure 1B), verifying the difference in VAV gene expression between AML and normal tissues from a clinical perspective. We further verified the high expression of VAVs in patients with AML by western blotting (Figure 1C, D).

Clinical Characteristics of Patients with AML

Further analysis of clinical information for 35 patients with AML from our center (Table 1) revealed that there were no significant differences in age, sex, WBC count, hemoglobin count, platelet count, or bone marrow blast cell count between the VAV1 high expression group and the VAV1 low expression group (P > 0.05). The CR rate was significantly lower in the VAV1 high expression group than in the VAV1 low expression group (P < 0.05), while the CR rates did not differ significantly between the VAV2 and VAV3 high expression and low expression groups (P > 0.5).

Table 1.

Relationship between baseline characteristics and VAVs mRNA expression. (Fisher exact test)

			VAV1-low	VAV1-high	P value	VAV2-low	VAV2-high	P value	VAV3-low	VAV3-high	P value
Age	＜60	22	11	11	1.000	9	13	0.305	9	13	0.305
Age	≥60	13	6	7	1.000	8	5	0.305	8	5	0.305
Gender	Male	17	8	9	1.000	8	9	1.000	9	8	0.740
Gender	Female	18	9	9	1.000	9	9	1.000	8	10	0.740
Immunophenoyping	M0	1	0	1	0.806	1	0	0.162	1	0	0.943
	M1	14	6	8		7	7		6	8
	M2	9	4	5		3	6		4	5
	M4	5	3	2		1	4		3	2
	M5	6	4	2		5	1		3	3
Gene mutation/fusion	ASXL1	4	4	0		1	3		2	2
	BCOR	1	0	1		1	0		0	1
	BCR-ABL	1	1	0		0	1		0	1
	CBFβ-MYH11	1	0	1		0	1		1	0
	DNMT3A	1	0	1		1	0		0	1
	dulMLL	1	1	0		0	1		0	1
	ETO	6	5	1		3	3		3	3
	FLT3-ITD	3	3	0		2	2		2	2
	IDH2	6	2	4		6	0		3	3
	IDN1	1	0	1		0	1		0	1
	MPL	1	0	1		0	1		0	1
	NF1	1	1	0		0	1		0	1
	NPM1	5	2	3		2	3		3	2
	NRAS	10	5	5		6	4		5	5
	PHF6	2	2	0		1	1		1	1
	WT1	10	4	6		6	4		5	5
Risk	Good	2	1	1	0.840	1	1	1.000	1	1	1.000
	Intermediate	8	3	5		4	4		4	4
	Poor	25	13	12		12	13		12	13
Hemocyte parameter (means ± SD)	WBC		39.51 ± 67.76	19.59 ± 35.08	0.505	25.42 ± 46.09	34.51 ± 63.43	0.505	35.52 ± 72.46	23.82 ± 25.94	1.000
	Hb		70.00 ± 18.63	80.18 ± 27.153	0.181	70.11 ± 19.83	80.06 ± 26.32	1.000	66.83 ± 16.81	83.53 ± 26.69	0.092
	Plt		73.06 ± 51.95	59.61 ± 66.71	0.181	50.72 ± 58.40	83.26 ± 56.70	0.740	77.47 ± 62.48	54.94 ± 54.70	0.505
	Blast		54.31 ± 23.41	63.59 ± 25.98	0.181	60.833 ± 27.90	56.68 ± 21.63	0.202	60.42 ± 23.78	57.118 ± 26.41	1.000
Complete remission		18	12(66.67%)	6(33.33%)	0.044	9(50.00%)	9(50.00%)	1	10(55.56%)	8(44.44%)	0.505

A univariate Cox analysis was performed based on data for patients with AML from TCGA, including VAV expression, FLT3 mutation, risk stratification, WBC count, hemoglobin count, platelet count, and bone marrow blast cell count. High VAV1 expression and high-risk risk stratification were related to a shorter OS in AML. Subsequently, significant parameters in the univariate analysis (P < 0.2) were included in a Cox multivariate analysis. VAV1 (P = 0.019), FLT3 mutation (P = 0.043), and risk stratification (P = 0.011) were independent risk factors for survival in AML (Table 2).

Table 2.

Univariate and multivariate COX analysis for survival analysis of TCGA patients

Parameter	Univariate analysis			Multivariate analysis
	HR	95%CI	P value	HR	95%CI	P value
VAV1(high/low)	1.705	1.111 to 2.617	0.015*	1.709	1.013 to 2.827	0.019*
VAV2(high/low)	1.235	0.810 to 1.882	0.328	1.170	0.750 to 1.823
VAV3(high/low)	1.345	0.880 to 2.055	0.171	1.177	0.724 to 1.911	0.596
FLT3(mut/wt）	1.380	0.876 to 2.176	0.165	1.723	1.013 to 2.933	0.043*
Risk stratification [Good/ Intermediate/Poor]	3.233	1.664 to 6.281	0.001*	2.586	1.273 to 5.252	0.011*
WBC counting	1.334	0.873 to 2.039	0.183	1.138	0.694 to 1.866	0.682
Hemoglobin	1.323	0.864 to 2.027	0.198	1.156	0.728 to 1.838	0.593
Platelet counting	1.088	0.713 to 1.659	0.696	0.913	0.585 to 1.426
Bone marrow blasts	1.089	0.714 to 1.660	0.692	0.865	0.525 to 1.425

A further analysis of TCGA data showed that there was no significant difference in VAV1 expression between the mutant and wild-type FLT3 groups (P > 0.05) (Figure 2A), while the expression of VAV1 in the low-risk group was significantly lower than that in the high-risk group (P＜0.05) (Figure 2B). Finally, we verified the relationship between VAV1 expression and ELN risk stratification by analyzing 471 patients with AML from the VIZOME database, and the results were consistent with those of the TCGA analysis (Figure 2C). Clinical characteristics of AML patients from TCGA and VIZOME databases can been seen in Table 3.

Figure 2.

Relationship between VAV1 Expression and clinical characteristics of AML patients. (A) Relationship between VAV1 Expression and FLT3 mutant in AML. (B) Relationship between VAV1 expression and risk of AML patients from TCGA. (C) Relationship between VAV1 expression and risk of AML patients from VIZOME.

Table 3.

Clinical characteristics and VAVs mRNA expression of AML patients from TCGA and VIZOME databases

		Cases from TCGA	Cases from VIZOME
VAVs mRNA expression	VAV1-low	70	233
	VAV1-high	70	238
	VAV2-low	70	-
	VAV2-high	70	-
	VAV3-low	70	-
	VAV3-high	70	-
Gene mutation/fusion	FLT3(mut)	40	-
Gene mutation/fusion	FLT3(wt)	100	-
Risk	Good	31	131
	Intermediate	77	171
	Poor	32	169
Hemocyte parameter (means ± SD)	WBC	33.72 ± 41.12	-
	Hb	9.54 ± 1.426	-
	Plt	63.85 ± 53.89	-
	Blast	37.09 ±30.98	-

Transcript levels of VAVs in French-American and British (FAB) subtypes of AML

Transcript levels of each VAV in the M0 to M7 FAB subtypes of 171 patients with AML from the TCGA database were analyzed using UALCAN. There were significant differences in VAV expression among AML subtypes. The expression levels of VAV1 and VAV2 were highest in AML-M5 and lowest in AML-M3. Compared with levels in the other subtypes, VAV3 expression was higher in patients with AML-M0 and lowest in patient in AML-M3. VAV3 expression differed significantly between M3 and M5 (P = 9.55729999585486E-08) (Figure 3A).

Figure 3.

Expression levels of VAVs (A) in AML based on FAB classification (UALCAN), (B) in different cancer cell lines (CCLE). FAB: French-American-British.

Expression of VAV family genes in cancer cell lines

We then used the CCLE database to analyze VAV gene expression in different cancer cell lines. Although VAVs showed differential expression in different tumor cell lines, they were all highly expressed in AML cells (Figure 3B). These results are consistent with the results obtained by GEPIA. Among them, VAV1 was most highly expressed in AML, followed by VAV3 and VAV2.

Consistent with these findings, using EMBL-EBI (Figure 4A), we found that VAVs are highly expressed in various AML cell lines and the expression levels of VAV1 are significantly higher than those of VAV2 and VAV3 . In western blotting analyses of peripheral blood mononuclear cells (PBMCs) from patients with AML at our center and KG-1 and MV4 to 11 cells, VAV levels were high in the KG-1 and MV4 to 11 cell lines (Figure 4B, C).

Figure 4.

VAVs expression in different AML cell lines. (A) The expression levels of VAVs in AML cell lines analyzed by EMBL-EBI. (B, C) Western blot validation of VAVs in AML cell lines and PBMCs. PBMCs: peripheral blood mononuclear cells. ** P＜0.01; *** P＜0.001; **** P＜0.0001.

Alterations in VAVs in AML

By analyzing the ‘TCGA Provisional’ data set in the cBioPortal database, 24 of 163 AML samples (14.72%) harbored genetic mutations, of which 22 mutations were associated with an increase in VAV mRNA expression (Figure 5A, B). These results indicate that high mRNA levels were the major modification of VAVs.

Figure 5.

Analysis of the alterations of VAVs in AML (cBioPortal). (A) Summary of VAVs alterations in AML. (B) Details of VAVs alterations in AML.

High VAV1 expression is associated with a poor prognosis in AML

Using the GEPIA database, the relationship between the expression levels of VAV family genes and the prognosis of AML was investigated. As shown in Figure 6A, high VAV expression was associated with a poor OS in AML. However, this association was only significant for VAV1 expression (P = 0.0055). We obtained similar results using PrognoScan (Figure 6B), further confirming that VAV1 is closely related to prognosis in AML.

Figure 6.

The prognostic value of VAVs expression level in AML (GEPIA and PrognoScan). (A) The prognostic value of VAVs expression level in AML, which was analyzed by GEPIA. (B) The prognostic value of VAVs expression level in AML, which was analyzed by PrognoScan. (C) Prognostic analysis of SIPA1, SH2D3C， and HMHA1 in AML (LinkedOmics).

Next, we used LinkedOmics to analyze the relationship between the expression of VAV1-related genes and the prognosis of AML in TCGA (Appendix 1, 2). The results showed that SIPA1, SH2D3C, and HMHA1 expression levels, which were significantly positively correlated with VAV1 expression, were significantly related to the prognosis of AML (P < 0.05) (Figure 6C). However, the expression of genes that were significantly positively correlated with VAV2 and VAV3 expression were not significantly correlated with the prognosis of AML (Appendix 3). Taken together, our analyses indicate that VAV1 is a potential prognostic marker for patients with AML.

PPI network analysis of VAVs and co-expressed genes

Using VAV-related genes reported in the cBioportal database, the top 26 significantly co-expressed genes were screened for further analyses. We then analyzed the co-expression PPI network of VAVs using the STRING database and used Cytoscape to construct and visualize the PPI network (Figure 7A). The core modules related to the VAV family were obtained using the MCODE plug-in (Figure 7B).

Figure 7.

PPI network analysis of VAVs and their co-expressed genes (STRING and Cytoscape). (A) PPI network. (B) Core modules related to VAVs.

GO and KEGG pathway enrichment analyses of VAVs and co-expressed genes

Genes in the core module related to the VAV family were imported into DAVID to obtain enriched GO functions and KEGG pathways (Figure 8A–D).

Figure 8.

GO function, and KEGG pathway analysis of VAV family and their co-expressed genes (DAVID). (A) BP:Biological Process. (B) MF:Molecular Function. (C)CC: Celluar Component. (D) KEGG.

VAV-related genes were significantly enriched for various biological processes, including the inflammatory response (GO:0006954) and immune response (GO:0006955) (Figure 8A). A molecular function analysis revealed enrichment for SH3/SH2 adaptor activity (GO:0005070), Rho guanyl-nucleotide exchange factor activity (GO:0005089), guanyl-nucleotide exchange factor activity (GO:0005085), protein binding (GO:0005515), Rac guanyl-nucleotide exchange factor activity (GO:0030676), and epidermal growth factor receptor binding (GO:0005154) (Figure 8B). A cell component analysis revealed that the cytosol (GO:0005829), cell–cell junction (GO:0005911), plasma membrane (GO:0005886), and intracellular structures (GO:0005622) were significantly regulated by VAV family members (Figure 8C). A KEGG analysis showed that the genes were mainly involved in the T cell receptor signaling pathway, natural killer cell-mediated cytotoxicity, B cell receptor signaling pathway, NF-kappa B signaling pathway, leukocyte transendothelial migration, and cAMP signaling pathway (Figure 8D). These results provide potential functions by which differentially expressed VAVs may participate in the occurrence and development of AML.

Discussion

VAVs are highly expressed in a variety of cancers and are associated with prognosis. However, the prognostic value of VAVs in AML remains unclear. Here, we clearly establish the importance of VAV1 expression for prognosis in AML by a bioinformatics approach and analyses of clinical data from our center.

We found that VAV expression levels are significantly elevated in AML based on GEPIA data (Figure 1A) and VAV1, VAV2, and VAV3 levels were higher in clinical AML samples than in the control group (Figure 1B). In addition, we confirmed that VAVs are highly expressed in patients with AML by western blotting (Figure 1C) and PCR (Figure 1D). To explore the value of VAVs in greater detail, we analyzed relationships between VAV expression and the clinical characteristics of patients with AML (Table1 2). In 35 cases of AML at our center and TCGA cases, there were no significant differences in WBC, hemoglobin count, platelet count, and bone marrow blast cell count between the VAV high expression group and VAV low expression group. Data from our center showed that the CR rate was significantly lower in the VAV1 high expression group than in the VAV1 low expression group, with no significant differences in CR rates between the high and low expression groups for VAV2 and VAV3. A TCGA univariate analysis showed that middle- to high-risk stratification and high VAV1 expression predict a shorter OS in AML. A Cox multivariate analysis showed that high expression of VAV1, FLT3 mutation, and high-risk stratification in ELN are independent risk factors affecting patient survival. ELN-2017 is a revised version of the European Leukemia Network (ELN) for the diagnosis and management of adult AML. Previous studies have shown that the ELN-2017 risk classification system is related to the prognosis of patients with AML, with a shorter OS for the middle- to high-risk AML group than the low-risk group. Our results confirmed the significance of FLT3 mutation and ELN risk stratification for the prognosis of AML. In correlation analyses, there was no significant difference in VAV1 expression with respect to the FLT3 mutation status, while VAV1 expression was significantly lower in the low-risk group than in the medium-risk and high-risk groups (Figure 2A, B). By an analysis of 471 patients with AML from the VIZOME database, we again verified that the intermediate and high-risk patients had higher VAV1 expression levels (Figure 2C).

CCLE results showed that VAV expression levels were altered in various tumor cell lines. As shown in Figure 3B, VAV1 was mainly highly expressed in hematological tumors, while VAV2 and VAV3 were also highly expressed in breast cancer, digestive system tumors, and other non-hematological tumors. It has been reported that VAV1 is largely confined to the circulatory system,^25,26 while VAV2 and VAV3 are more broadly distributed,^27,28 consistent with our analysis using CCLE. An EMBL-EBI analysis also showed that VAVs are highly expressed in different AML cell lines. In particular, the expression levels of VAV1 in various AML cell lines were higher than those of VAV2 and VAV3 (Figure 4A). At the protein level, VAVs were highly expressed in the AML cell lines KG-1 and MV4 to 11(Figure 4B).

The link between VAVs and prognosis has been established in various cancers. For example, in esophageal squamous cell carcinoma tissues, the OS of patients with high VAV1 expression is significantly lower than that of patients with low VAV1 expression (P = 0.014).⁶ VAV2 expression is closely related to a poor prognosis in head and neck squamous cell carcinoma⁷ and adrenocortical carcinoma.⁸ The overexpression of VAV3 may be an independent risk factor for the prognosis of gastric cancer.¹⁰ Therefore, we studied the relationship between VAV expression and prognosis based on 106 AML samples from TCGA and 79 AML samples from GSE12417 dataset. A bioinformatics analysis showed that high expression levels of VAV2 and VAV3 indicated a poor OS of AML, but these relationships were not significant, while high VAV1 expression was significantly correlated with a poor OS in AML (Figure 6A, B). Next, using LinkedOmics, we found that SIPA1, SH2D3C, and HMHA1 levels were significantly positively correlated with VAV1 levels (Appendix 1, 2). Previous studies have shown that HMHA1 significantly promotes the proliferation, invasion, and migration of melanoma cells.²⁹ SIPA1 deficiency-induced bone marrow niche alterations lead to the initiation of myeloproliferative neoplasm.³⁰ In hematologic tumors, Singh et al. demonstrated that the significant down-regulation of SH2D3C promotes the premature failure of hematopoietic stem cells and the development of myeloproliferative diseases.³¹ Consistent with these previous results, we found that the expression levels of VAV genes were significantly related to the prognosis of AML (Figure 6C). Taken together, our analyses indicate that VAV1 is a potential prognostic marker for patients with AML.

By a PPI network analysis of VAVs and their co-expressed genes (Figure 7A), we obtained core modules related to VAVs (Figure 7B). The genes contained in this module included the VAV family, PLCG1, ZAP70, BLNK, SYK, LCP2, and GLAP2, most of which have been proven by experimental studies to be of great significance in the occurrence, development, and prognosis of hematologic tumors. Spleen tyrosine kinase (SYK) induces the proliferation of AML cells, and the overexpression of SYK promotes the resistance to targeted therapy. The hyperexpression of phosphorylated SYK is associated with a poor prognosis in AML.^32–34 The expression of ζ-related protein (ZAP-70) is observed in the vast majority of patients with CLL/SLL and Richter syndrome.³⁵ B cell linker protein is a selective target of PAX5-PML to inhibit differentiation, which can lead to the development of acute lymphoblastic leukemia.³⁶ A systematic study of diffuse large B-cell lymphoma has shown that LCP2 is a prognostic marker and its high expression is associated with good survival.^37–39 These results confirmed that the functional module identified in our study has important significance in hematological tumors.

To explore the role and function of the core module related to VAVs in hematological tumors, we conducted GO and KEGG pathway enrichment analyses and found that VAVs are mainly involved in the immune response and inflammatory response and in the pathway NF-kappa B and cAMP signaling pathways. Previous studies have shown that the immune response and inflammatory response via NF-κB and cAMP signal transduction play important roles in the development and prognosis of AML. During bone marrow transplantation, the Rac-specific activator VAV1 is critical for the functions of hematopoietic stem and progenitor cells in response to inflammation.⁴⁰ NF-κB is a key regulator of inflammatory activation in hematopoietic stem cells.⁴¹ The down-regulation of the NF-κB signaling pathway can inhibit the proliferation of HL60 and K562 leukemia cells and induce apoptosis.⁴² An increase in cAMP can reduce inflammation and the immune response.⁴³ The inhibition of the cAMP signaling pathway leads to resistance to dexamethasone. The synergistic effect of cAMP signaling and dexamethasone can increase the death of human T-ALL cells resistant to GC, resulting in GC re-sensitization in T-ALL.⁴⁴ These results show that the immune response and inflammatory response can affect the development and prognosis of leukemia via the NF-κB and cAMP signaling pathways. It is known that the interaction between AML cells and various components of the environment is very complex. The development of combination therapies is an important research topic. Our bioinformatics analysis revealed that VAVs play an important role in immune and inflammatory responses. This suggests that VAVs play a role in the disease via the NF-κB and cAMP pathways. Based on the significance of VAV1 in the prognosis of AML, we speculate that VAV1-targeted therapy, NF-κB signaling pathway inhibitors and cAMP signaling pathway inhibitors may be new directions for improving the prognosis of AML.

This study combined database tools, clinical specimens, and laboratory technology to evaluate AML from multiple perspectives. However, the study had some limitations, including the small number of clinical specimens and the lack of verification of the relationship between FAB subtypes and VAVs. Cell transfection and in vivo experiments were not performed. Further studies are needed to verify the relationship between the VAV family and AML prognosis, and this will be the focus of our future research.

Conclusions

In summary, our results indicated that VAV expression levels are significantly higher in AML than in normal tissues. In particular, high VAV1 expression is significantly related to a poor prognosis, making it a promising prognostic marker and potential therapeutic target for AML.

Ethics Approval and Consent to Participate

This study was carried out in accordance with the guidelines of the Ethics Committee of the *** and was conducted according to the World Medical Association Declaration of Helsinki. It was approved by the Medical Ethics Committee of the *** (NO.:KY2021166) and individual consent for this retrospective analysis was waived.

This study was carried out in accordance with the guidelines of the Ethics Committee of the Affiliated Hospital of Southwest Medical University and was conducted according to the World Medical Association Declaration of Helsinki. It was approved by the Medical Ethics Committee of the Affiliated Hospital of Southwest Medical University (NO.:KY2021166) and individual consent for this retrospective analysis was waived.

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Basic Research Project of Sichuan Province (grant no. 2019YJ0690); and the Major Science and Technology Projects in Sichuan Province (grant no. 2019YFS0531).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics Approval and Consent to Participate

ORCID iD

Dan Mu

Appendix 1.

Genes correlated to VAVs in AML (LinkedOmics). (A) Volcano map of genes correlated to VAV1 expression in AML, and heat maps of the top 50 genes positively and negatively correlated with VAV1. (B) Volcano map of genes correlated to VAV2 expression in AML, and heat maps of the top 50 genes positively and negatively correlated with VAV2. (C) Volcano map of genes correlated to VAV3 expression in AML, and heat maps of the top 50 genes positively and negatively correlated with VAV3.

Appendix 2.

Gene correlation expression analysis of VAVs (LinkedOmics). (A) The scatter plot shows the Pearson correlation between VAV1 expression and SIPA1, SH2D3C and HMHA1 expression. (B) The scatter plot shows the Pearson correlation between the expression of VAV2 and SRGAP2, MAPK7 and RELL2. (C) Scatter plot showing the Pearson correlation between VAV3 expression and CNST, GUCY1A3 and SLC9A7 expression.

Appendix 3.

Prognostic analysis of VAV expression-correlated genes in AML patients (LinkedOmics). (A) OS curve of SRGAP2, MAPK7, and RELL2 in AML. (B) OS curve of CNST, GUCY1A3, and SLC9A7 in AML.

References

Zhao

Chang

. [Diagnosis and risk stratification in patients with acute myeloid leukemia]. Zhonghua Nei Ke Za Zhi. 2021;60(3):259‐263. doi:10.3760/cma.j.cn112138-20201210-01003

Siegel

Miller

Fuchs

Jemal

. Cancer statistics, 2021. CA Cancer J Clin. 2021;71(1):7‐33. doi:10.3322/caac.21654

Rodríguez-Fdez

Lorenzo-Martín

Fabbiano

, et al. New functions of Vav family proteins in cardiovascular biology, skeletal muscle, and the nervous system. Biology (Basel. 2021;10[9]:857. doi:10.3390/biology10090857

Zhu

Liu

Duan

Chang

Zhang

. MicroRNA-331-3p inhibits epithelial-mesenchymal transition by targeting ErbB2 and VAV2 through the Rac1/PAK1/beta-catenin axis in non-small-cell lung cancer. Cancer Sci. 2019;110(6):1883‐1896. doi:10.1111/cas.14014

Chen

, et al. Up-regulated LINC01234 promotes non-small-cell lung cancer cell metastasis by activating VAV3 and repressing BTG2 expression. J Hematol Oncol. 2020;13(1):7. doi:10.1186/s13045-019-0842-2

Zhu

Jin

Xia

, et al. Vav1 expression is increased in esophageal squamous cell carcinoma and indicates poor prognosis. Biochem Biophys Res Commun. 2017;486(2):571‐576. doi:10.1016/j.bbrc.2017.03.091

Lorenzo-Martin

Fernandez-Parejo

Menacho-Marquez

, et al. VAV2 Signaling promotes regenerative proliferation in both cutaneous and head and neck squamous cell carcinoma. Nat Commun. 2020;11(1):4788. doi:10.1038/s41467-020-18524-3

Ruggiero

Doghman-Bouguerra

Sbiera

, et al. Dosage-dependent regulation of VAV2 expression by steroidogenic factor-1 drives adrenocortical carcinoma cell invasion. Sci Signal. 2017;10(469):eaal2464. doi:10.1126/scisignal.aal2464

Tsuboi

Taniuchi

Furihata

, et al. Vav3 is linked to poor prognosis of pancreatic cancers and promotes the motility and invasiveness of pancreatic cancer cells. Pancreatology. 2016;16(5):905‐916. doi:10.1016/j.pan.2016.07.002

10.

Tan

Shi

, et al. Expression of Vav3 protein and its prognostic value in patients with gastric cancer. Pathol Res Pract. 2017;213(5):435‐440. doi:10.1016/j.prp.2017.01.028

11.

Xiao

Wang

Liu

. LINC00265 Targets miR-382-5p to regulate SAT1, VAV3 and angiogenesis in osteosarcoma. Aging (Albany NY). 2020;12(20):20212‐20225. doi:10.18632/aging.103762

12.

Tang

Kang

Gao

Zhang

. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98‐W102. doi:10.1093/nar/gkx247

13.

Mizuno

Kitada

Nakai

Sarai

. Prognoscan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genomics. 2009;2:18. doi:10.1186/1755-8794-2-18

14.

Barretina

Caponigro

Stransky

, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603‐607. doi:10.1038/nature11003

15.

Squizzato

Park

Buso

, et al. The EBI search engine: providing search and retrieval functionality for biological data from EMBL-EBI. Nucleic Acids Res. 2015;43(W1):W585‐W588. doi:10.1093/nar/gkv316

16.

Chandrashekar

Bashel

Balasubramanya

SAH

, et al. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 2017;19(8):649‐658. doi:10.1016/j.neo.2017.05.002

17.

Vasaikar

Straub

Wang

Zhang

. Linkedomics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res. 2018;46(D1):D956‐D963. doi:10.1093/nar/gkx1090

18.

Cerami

Gao

Dogrusoz

, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401‐404. doi:10.1158/2159-8290.CD-12-0095

19.

Szklarczyk

Gable

Lyon

, et al. STRING V11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607‐D613. doi:10.1093/nar/gky1131

20.

Huang da

Sherman

Lempicki

. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1‐13. doi:10.1093/nar/gkn923

21.

Huang da

Sherman

Lempicki

. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44‐57. doi:10.1038/nprot.2008.211

22.

Otasek

Morris

Boucas

Pico

Demchak

. Cytoscape automation: empowering workflow-based network analysis. Genome Biol. 2019;20(1):185. doi:10.1186/s13059-019-1758-4

23.

Shannon

Markiel

Ozier

, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498‐2504. doi:10.1101/gr.1239303

24.

Dohner

Estey

Grimwade

, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129(4):424‐447. doi:10.1182/blood-2016-08-733196

25.

Bustelo

Rubin

Suen

Carrasco

Barbacid

. Developmental expression of the vav protooncogene. Cell Growth Differ. 1993;4(4):297‐308.

26.

Katzav

Martin-Zanca

. Barbacid M. vav, a novel human oncogene derived from a locus ubiquitously expressed in hematopoietic cells. EMBO J. 1989;8(8):2283‐2290.

27.

Schuebel

Bustelo

Nielsen

, et al. Isolation and characterization of murine vav2, a member of the vav family of proto-oncogenes. Oncogene. 1996;13(2):363‐371.

28.

Trenkle

McClelland

Adlkofer

Welsh

. Major transcript variants of VAV3, a new member of the VAV family of guanine nucleotide exchange factors. Gene. 2000;245(1):139‐149. doi:10.1016/s0378-1119(00)00026-3

29.

, et al. Multiple pro-tumorigenic functions of the human minor histocompatibility antigen-1 (HA-1) in melanoma progression. J Dermatol Sci. 2017;88(2):216‐224. doi:10.1016/j.jdermsci.2017.07.004

30.

Xiao

Dolinska

Sandhow

, et al. Sipa1 deficiency-induced bone marrow niche alterations lead to the initiation of myeloproliferative neoplasm. Blood Adv. 2018;2(5):534‐548. doi:10.1182/bloodadvances.2017013599

31.

Singh

Bennett

Casado

Walrath

Welle

Gasiewicz

. Loss of aryl hydrocarbon receptor promotes gene changes associated with premature hematopoietic stem cell exhaustion and development of a myeloproliferative disorder in aging mice. Stem Cells Dev. 2014;23(2):95‐106. doi:10.1089/scd.2013.0346

32.

Polak

Bialopiotrowicz

Krzymieniewska

, et al. SYK Inhibition targets acute myeloid leukemia stem cells by blocking their oxidative metabolism. Cell Death Dis. 2020;11(11):956. doi:10.1038/s41419-020-03156-8

33.

Cremer

Ellegast

Alexe

, et al. Resistance mechanisms to SYK inhibition in acute myeloid leukemia. Cancer Discov. 2020;10(2):214‐231. doi:10.1158/2159-8290.CD-19-0209

34.

Elgamal

Mehmood

Jeon

, et al. Preclinical efficacy for a novel tyrosine kinase inhibitor, ArQule 531 against acute myeloid leukemia. J Hematol Oncol. 2020;13(1):8. doi:10.1186/s13045-019-0821-7

35.

Wasik-Szczepanek

Szymczyk

Szczepanek

, et al. Richter syndrome: a rare complication of chronic lymphocytic leukemia or small lymphocytic lymphoma. Adv Clin Exp Med. 2018;27(12):1683‐1689. doi:10.17219/acem/75903

36.

Imoto

Hayakawa

Kurahashi

, et al. B cell linker protein (BLNK) Is a selective target of repression by PAX5-PML protein in the differentiation block that leads to the development of acute lymphoblastic leukemia. J Biol Chem. 2016;291(9):4723‐4731. doi:10.1074/jbc.M115.637835

37.

Gao

Sun

, et al. Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy. Pharmacogenomics J. 2020;20(5):705‐716. doi:10.1038/s41397-020-0161-6

38.

Zhu

Chen

Huang

. Novel prognostic genes of diffuse large B-cell lymphoma revealed by survival analysis of gene expression data. Onco Targets Ther. 2015;8:3407‐3413. doi:10.2147/OTT.S90057

39.

Pan

Chen

, et al. Identification and validation of a prognostic gene signature for diffuse large B-cell lymphoma based on tumor microenvironment-related genes. Front Oncol. 2021;11:614211. doi:10.3389/fonc.2021.614211

40.

Vita

Harris

, et al. The gp130 cytokine IL-11 regulates engraftment of Vav1 − /− hematopoietic stem and progenitor cells in lethally irradiated recipients. Stem Cells. 2018;36(3):446‐457. doi:10.1002/stem.2760

41.

Yamashita

Passegué

. TNF-alpha Coordinates hematopoietic stem cell survival and myeloid regeneration. Cell Stem Cell. 2019;25(3):357‐372.e7. doi:10.1016/j.stem.2019.05.019

42.

Han

Kim

Jung

. Ampelopsin inhibits cell proliferation and induces apoptosis in HL60 and K562 leukemia cells by downregulating AKT and NF-κB signaling pathways. Int J Mol Sci. 2021;22(8):4265. doi:10.3390/ijms22084265

43.

Yougbare

Keravis

Lugnier

. NCS 613, A PDE4 inhibitor, by increasing cAMP level suppresses systemic inflammation and immune complexes deposition in kidney of MRL/lpr lupus- prone mice. Biochim Biophys Acta Mol Basis Dis. 2021;1867(3):166019. doi:10.1016/j.bbadis.2020.166019

44.

Roderick

Gallagher

Murphy

, et al. Prostaglandin E2 stimulates cAMP signaling and resensitizes human leukemia cells to glucocorticoid-induced cell death. Blood. 2021;137(4):500‐512. doi:10.1182/blood.2020005712

High Expression of VAV Gene Family Predicts Poor Prognosis of Acute Myeloid Leukemia

Abstract

Keywords

Introduction

Materials and Methods

GEPIA Dataset

PrognoScan Dataset

CCLE Database

EMBL-EBI Database

UALCAN Database

LinkedOmics Database

CBioPortal Database

Network Analysis

Cell Lines and Cell Culture

Clinical Samples

Antibodies and Reagents

Quantitative Real-Time PCR (qRT-PCR)

Protein Extraction and Western Blotting

Statistical Analysis

Results

VAVs are Highly Expressed in AML (GEPIA Database and Clinical Samples)

Clinical Characteristics of Patients with AML

Expression of VAV family genes in cancer cell lines

Alterations in VAVs in AML

High VAV1 expression is associated with a poor prognosis in AML

PPI network analysis of VAVs and co-expressed genes

GO and KEGG pathway enrichment analyses of VAVs and co-expressed genes

Discussion

Conclusions

Ethics Approval and Consent to Participate

Footnotes

Funding

Declaration of Conflicting Interests

Ethics Approval and Consent to Participate

ORCID iD

References