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Abstract

BACKGROUND:

Bone morphogenetic protein and activin membrane-bound inhibitor (BAMBI) and mothers against decapentaplegic homolog 7 (SMAD7) are important transforming growth factor- $\beta$ (TGF- $\beta$ ) signaling antagonists, however their roles in acute myeloid leukemia (AML) remains unclear. Telomerase reverse transcriptase (TERT) may be involved in regulating BAMBI and SMAD7 expressions; a role beyond telomeres that is not clinically validated yet.

OBJECTIVE:

In this study, we examined the expression levels and prognostic values of BAMBI, SMAD7 and TERT and their association with AML patients’ outcomes.

METHODS:

Blood samples were collected from 74 de-novo AML patients and 16 controls. Real-time quantitative PCR (qRT-PCR) was performed to analyze BAMBI, SMAD7 and TERT expressions.

RESULTS:

BAMBI and SMAD7 expression in AML were significantly upregulated versus controls ( $p<$ 0.05). BAMBI, SMAD7 and TERT levels were significantly correlated together ( $p<$ 0.001). Kaplan-Meier analysis indicated that patients with high BAMBI, SMAD7 and TERT expression levels had markedly shorter event free survival (EFS) and overall survival (OS) time ( $p<$ 0.01). Furthermore, multivariate analysis revealed that only high BAMBI expression was an independent risk factor for OS ( $p=$ 0.001).

CONCLUSIONS:

BAMBI is a novel biomarker in predicting prognosis in AML patients. Moreover, a potential interplay is found between BAMBI, SMAD7 and TERT in AML pathogenies.

Keywords

Acute myeloid leukemia transforming growth factor-β BAMBI SMAD7 TERT

1. Introduction

Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults with the lowest survival rates of all the leukemias [1]. It is a highly heterogeneous hematological malignancy originating from hematopoietic stem cells and is characterized by the clonal expansion of myeloid blasts in the peripheral blood, bone marrow, and/or other tissues [2]. Moreover, AML is associated with a wide variety of molecular and cytogenetic abnormalities [3, 4] which are critically involved in the dismal outcome of many AML patients even those attaing initial complete remsion (CR) [5]. Recently, the role of several genes in AML pathogenesis has become increasingly studied, to discover new discriminative molecular markers for improving diagnosis and outcome prediction [6, 7]. In fact, the interesting role of the transforming growth factor- $\beta$ (TGF- $\beta$ ) in leukemogenesis is an important research pivot [8].

The TGF- $\beta$ signaling pathway is implicated in various biological events [9]. Mechanistically, it is well known that the TGF- $\beta$ normally signals through type I and type II serine-threonine kinase receptor complexes. During ligands binding, type II receptors recruit and phosphorylate type I receptors which subsequently phosphorylate a group of mothers against decapentaplegic (SMAD) proteins known as receptor-regulated SMADs (R-SMADs) allowing the formation of R-SMADs/SMAD4 complexes which in turn propagates the TGF- $\beta$ downstream signaling intracellularly [8, 10]. Interestingly, TGF- $\beta$ is suggested to play a dual role in solid cancers, having tumor suppressor effects in several solid tumors at early cancer stages, while being involved in tumor metastasis in advanced tumor stages [11]. However, as opposed to solid tumors, TGF- $\beta$ signaling pathway does not appear to have this dichotomous tumor suppressor/tumor promoter role in hematologic malignancies, as there is no evidence that high levels of TGF- $\beta$ are associated with more aggressive disease or poorer prognosis for patients with hematologic malignancies [12]. In this context, it was reported that TGF- $\beta$ inhibits proliferation during normal hematopoiesis, while stimulates differentiation and apoptosis when needed, thus customarily acting as a tumor suppressor. On the other hand, in hematologic malignancies, resistance to these homeostatic effects develops through decreased expression TGF- $\beta$ receptors on the cell surface or repression of TGF-signaling by oncoproteins [13]. Thus, targeting the TGF- $\beta$ pathway may be a straighter forward task in many hematologic malignancies [12], including AML. Surprisingly, despite the accumulating knowledge regarding TGF-ß resistance in several cancers, the mechanisms are still poorly understood.

An important TGF- $\beta$ antagonist is the bone morphogenetic protein and activin membranebound inhibitor (BAMBI) [14]. Interestingly, BAMBI is a pseudoreceptor that is related to TGF- $\beta$ receptor type I, but lacks an intracellular kinase domain. Hence, it acts as a decoy preventing the formation of receptor complexes between the type I and type II receptors after TGF- $\beta$ ligands binding, thus hampering the TGF- $\beta$ signaling pathway leading to cancer progression [14]. In this context, it was found that BAMBI is aberrantly elevated in a wide number of solid tumors including most colorectal cancers, gastric cancer cells, osteosarcoma and ovarian cancers [15, 16, 17, 18]. Moreover, it was found to be correlated with distal metastasis and disease recurrence in patients with gastric cancer [16]. However, the expression levels and the potential clinical and prognostic role of BAMBI in hematological malignancies, including AML, still remain unclear.

Another important TGF- $\beta$ signaling antagonist is the mothers against decapentaplegic homolog 7 (SMAD7); an inhibitory SMAD that works in a negative feedback loop to inhibit the TGF- $\beta$ signaling by preventing the development of R-SMADs/SMAD4 complexes and/or interacting with activated TGF- $\beta$ type I receptor thus blocking R-SMADs interaction, phosphorylation and activation [8, 19]. In this context, SMAD7 has been found to be overexpressed in many cancers, including some types of leukemia [20, 21]. However, till today its expression level in AML patients is not widely reported. Interestingly, it has been found that BAMBI forms a complex with SMAD7, so it is proposed that BAMBI and SMAD7 function synergistically to inhibit TGF- $\beta$ signaling [22]. However, their expression levels together and their correlation to cancer patients’ prognosis haven’t been widely reported clinically except in one recent study in gastric cancer patients only [23].

Another, interesting cancer specific target, is the human telomerase reverse transcriptase (hTERT), which is the catalytic subunit of the well-known telomerase enzyme. The hTERT allows cells to limitlessly divide and evade apoptosis by extending chromosomes telomeres, hence it is up-regulated in 80–90% of cancers including AML [24]. This is widely known as the telomerase canonical function [25]. However, accumulated evidence suggests that hTERT may have a non-canonical telomerase-independent role in cancer through transcriptionally regulating some genes [26]. In this context, an in vitro study reported that activated hTERT upregulated TGF- $\beta$ and Wnt pathway related genes such as BAMBI and SMAD7 [27]. Interestingly, hTERT was reported to be down regulated by TGF- $\beta$ signaling [28], thus it can be postulated that there is a feedback loop between hTERT and TGF- $\beta$ signaling, whereas on one hand TGF- $\beta$ signaling can inhibit hTERT, but on the other hand hTERT activation can inhibit TGF- $\beta$ signaling probably by telomerase independent functions through activating TGF- $\beta$ signaling antagonists as BAMBI and SMAD7. However, this interesting telomerase independent activity of TERT has not been yet validated clinically in AML patients nor in any other cancer. Thus, this study aimed to explore BAMBI and SMAD7 expression levels in AML patients, as well as to explore the possible associations of BAMBI, SMAD7 and TERT expression levels with AML prognosis.

2. Subjects and methods

2.1 Subjects

The study enrolled a total of 74 patients newly diagnosed with de-novo AML as well as 16 healthy age and sex matched volunteers. The AML patients were recruited from the Clinical Hematology and Stem Cell Transplantation Unit, Ain-shams University Hospitals, Cairo, Egypt. The clinical characteristics of patients is described in Table 1. Patients with therapy-related or myelodysplastic syndrome (MDS)-related AML were excluded from the study. The selection of these patients was based on the following criteria: full history taking, thorough clinical examination, and standard diagnostic methods, including cytomorphological, cytogenetic, and immunophenotypic (IPT) evaluation, which was established using Beckman Coulter Navios EX flow cytometer (Frederick, Maryland, USA). The study was carried out in accordance with the regulations and recommendations of the Declaration of Helsinki and informed consent was obtained from every patient for laboratory studies according to the guidelines of Committee of Medical Ethics of Ain Shams University Hospitals (ASUH) (Ethical committee approval number: 68 on May 10, 2018). The study was conducted in the period from May 2018 to June 2020.

2.2 Sampling

Both peripheral blood (PB) and bone marrow (BM) aspiration samples were collected at diagnosis from the 74 AML patients, while PB samples only were obtained from the control group. The diagnosis of AML was made based on the morphologic findings from Giemsa-stained smears of BM aspirates and IPT analyses of leukemic cells. The PB samples were withdrawn from the patients before they received any treatment and divided into two aliquots. The first aliquot of blood was collected on vacutainer tubes containing Na2 EDTA for determination of white blood cells (WBC) count, platelets count, hemoglobin, IPT as well as total RNA purification from human whole blood. The second aliquot was collected on plain vacutainer tube for serum preparation for the assay of serum lactate dehydrogenase (LDH). Patients were classified according to the French-American-British (FAB) classification: AML-M0; AML-M1; AML-M2; AML-M3; AML-M4 and AML-M5.

2.3 Methods

2.3.1 RNA isolation, reverse transcription and real-time quantitative PCR

Total RNA was extracted and purified from human whole blood using a QIAamp RNA blood mini kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol and stored at $-$ 80 $\circ$ C until use. Complementary DNA (cDNA) was synthesized using the high capacity cDNA reverse transcription kit (Applied Biosystems, CA, USA) according to the manufacturer’s protocol and stored at $-$ 20 ${}^{\circ}$ C till use. The mRNA expression levels of BAMBI, SMAD7, TERT and glyceraldehydes-3-phosphate dehydrogenase (GAPDH), as an internal control, were analyzed in patients and controls using real-time quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), which was performed according to the manufacturer’s instructions using maxima SYBR green qPCR master mix kit (Thermo Fisher Scientific Inc, Rockford, USA) and using Corbett Rotor-Gene ${}^{\text{TM}}$ Q qRT-PCR device system and software (Qiagen, Hilden, Germany). BAMBI was amplified with the BAMBI forward primer (5’-ATTCGATGCTACTGTGATGCTGCC-3’) and reverse primer (5’-ATTCCAATGTGGGTATGGTGGTGC-3’). SMAD7 was amplified with the SMAD 7 forward primer (5’-GCCCTCTCTGGATATCTTCT-3’) and reverse primer (5’-GCTGCATAAACTCGTGGTCA-3’). TERT was amplified with TERT forward primer (5’-CGGAAGAGTGTCTGGAGCAA-3’) and reverse primer (5’-GGATGAAGCGGAGTCTGGA-3’), while GAPDH was amplified using the GAPDH forward primer (5’-AGGGCTGCTTTTAACTCTGGT-3’) and reverse primer (5’-CCCCACTTGATTTTGGAGGGA-3’). The qRT cycler was programmed according to the manufacturer’s instructions. The expression levels of the genes in tested samples were expressed in the form of Ct (cycle threshold) level, then the normalized copy number (relative quantitation) was calculated by the (2 ${}^{-\Delta\Delta\text{Ct}}$ ) method; where $\Delta\Delta$ Ct $=$ [(Ct target gene $-$ Ct GAPDH) ${}^{\text{Patient}}$ $-$ (Ct target gene $-$ Ct GAPDH) ${}^{\text{Control}}$ ]. The analyzed results were finally expressed as relative units.

2.3.2 Clinical outcome assessment

The risk stratification for each patient was done by a specialized hematologist who follows these patients and case by case judgment was done based mainly on karyotype as well as other clinical risk factors that are widely known to affect patient’s overall prognosis like patient’s age, CR achievement, relapse and/or patient’s

Table 1
Clinical and molecular characteristics of the patients

Characteristics	AML patients	Status of BAMBI expression				Status of SMAD7 expression				Status of TERT expression
	$n=$ 74	BAMBI ${}^{\text{low}}$ ( $n=$ 37)	BAMBI ${}^{\text{high}}$ ( $n=$ 37)	Test value	$p$ -value	SMAD7 ${}^{\text{low}}$ ( $n=$ 37)	SMAD7 ${}^{\text{high}}$ ( $n=$ 37)	Test value	$p$ -value	TERT ${}^{\text{low}}$ ( $n=$ 37)	TERT ${}^{\text{high}}$ ( $n=$ 37)	Test value	$p$ -value
Age (years), mean $\pm$ SE	47.57 $\pm$ 1.41	45.84 $\pm$ 1.92	49.30 $\pm$ 2.04	$-$ 1.234 ${}^{**}$	0.221	44.38 $\pm$ 1.88	50.76 $\pm$ 1.98	$-$ 2.334 ${}^{**}$	0.022	46.76 $\pm$ 2.06	48.38 $\pm$ 1.93	$-$ 0.574 ${}^{**}$	0.568
Age groups, $n$ (%)				2.467 ${}^{\#}$	0.116			6.850 ${}^{\#}$	0.009			0.274 ${}^{\#}$	0.601
$<$ 60 years	54 (73.0%)	30 (81.1%)	24 (64.9%)			32 (86.5%)	22 (59.5%)			28 (75.7%)	26 (70.3%)
$\geqslant$ 60 years	20 (27.0%)	7 (18.9%)	13 (35.1%)			5 (13.5%)	15 (40.5%)			9 (24.3%)	11 (29.7%)
Sex, $n$ (%)				2.720 ${}^{\#}$	0.099			0.056 ${}^{\#}$	0.814			1.388 ${}^{\#}$	0.239
Females	31 (41.9%)	19 (51.4%)	12 (32.4%)			16 (43.2%)	15 (40.5%)			18 (48.6%)	13 (35.1%)
Males	43 (58.1%)	18 (48.6%)	25 (67.6%)			21 (56.8%)	22 (59.5%)			19 (51.4%)	24 (64.9%)
WBC Count ( $\times$ 10 ${}^{9}$ /L), median (range)	24.25 (1.10–352.00)	26.2 (2.6–352.0)	20.5 (1.1–302.0)	$-$ 0.903 ${}^{*}$	0.367	26.4 (2.7–352.0)	16.6 (1.1–302.0)	$-$ 1.487 ${}^{*}$	0.137	27.1 (2.7–352.0)	10.8 (1.1–302.0)	$-$ 1.914 ${}^{*}$	0.056
WBC groups, $n$ (%)				0.492 ${}^{\#}$	0.483			1.367 ${}^{\#}$	0.242			2.680 ${}^{\#}$	0.102
$<$ 20 ( $\times$ 10 ${}^{9}$ /L)	33 (44.6%)	15 (40.5%)	18 (48.6%)			14 (37.8%)	19 (51.4%)			13 (35.1%)	20 (54.1%)
$\geqslant$ 20 ( $\times$ 10 ${}^{9}$ /L)	41 (55.4%)	22 (59.5%)	19 (51.4%)			23 (62.2%)	18 (48.6%)			24 (64.9%)	17 (45.9%)
BM blast (%), median (range)	69.5 (9–98)	50 (9–98)	78 (17–97)	$-$ 2.349 ${}^{*}$	0.019	50 (10–98)	85 (9–96)	$-$ 2.398 ${}^{*}$	0.017	60 (9–98)	76 (17–96)	$-$ 1.331 ${}^{*}$	0.183
Hemoglobin (g/dl), mean $\pm$ SE	7.82 $\pm$ 0.25	7.96 $\pm$ 0.38	7.68 $\pm$ 0.32	0.564 ${}^{**}$	0.575	8.03 $\pm$ 0.35	7.61 $\pm$ 0.34	0.850 ${}^{**}$	0.398	8.12 $\pm$ 0.36	7.52 $\pm$ 0.33	1.218 ${}^{**}$	0.227
Platelets Count ( $\times$ 10 ${}^{9}$ /L), median (range)	43.0 (5–430)	48 (9–422)	37 (5–430)	$-$ 2.147 ${}^{*}$	0.032	47 (9–422)	38 (5–430)	$-$ 1.395 ${}^{*}$	0.163	45 (9–422)	40 (5–430)	$-$ 0.876 ${}^{*}$	0.381
LDH (IU/L), median (range)	585.5 (208–5121)	488 (230–3920)	780 (208–5121)	$-$ 2.465 ${}^{*}$	0.014	500 (230–392)	799 (208–5121)	$-$ 2.568 ${}^{*}$	0.010	525 (230–3920)	650 (208–5121)	$-$ 1.622 ${}^{*}$	0.105
CD 13, $n$ (%)				1.930 ${}^{\#}$	0.165			1.930 ${}^{\#}$	0.165			1.930 ${}^{\#}$	0.165
Negative ( $-$ )	5 (6.8%)	4 (10.8%)	1 (2.7%)			4 (10.8%)	1 (2.7%)			4 (10.8%)	1 (2.7%)
Positive ( $+$ )	69 (93.2%)	33 (89.2%)	36 (97.3%)			33 (89.2%)	3 (97.3%)			33 (89.2%)	36 (97.3%)
CD 33, $n$ (%)				0.000 ${}^{\#}$	1.0			1.057 ${}^{\#}$	0.304			0.000 ${}^{\#}$	1.000
Negative ( $-$ )	4 (5.4%)	2 (5.4%)	2 (5.4%)			3 (8.1%)	1 (2.7%)			2 (5.4%)	2 (5.4%)
Positive ( $+$ )	70 (94.6%)	35 (94.6%)	35 (94.6%)			34 (91.9%)	36 (97.3%)			35 (94.6%)	35 (94.6%)
CD 34, $n$ (%)				0.987 ${}^{\#}$	0.321			2.220 ${}^{\#}$	0.136			0.247 ${}^{\#}$	0.619
Negative ( $-$ )	24 (32.4%)	14 (37.8%)	10 (27.0%)			15 (40.5%)	9 (24.3%)			13 (35.1%)	11 (29.7%)
Positive ( $+$ )	50 (67.6%)	23 (62.2%)	27 (73.0%)			22 (59.5%)	28 (75.7%)			24 (64.9%)	26 (70.3%)
HLA-DR, $n$ (%)				0.294 ${}^{\#}$	0.588			0.294 ${}^{\#}$	0.588			0.294 ${}^{\#}$	0.588
Negative ( $-$ )	18 (24.3%)	8 (21.6%)	10 (27.0%)			8 (21.6%)	10 (27.0%)			8 (21.6%)	10 (27.0%)
Positive ( $+$ )	56 (75.7%)	29 (78.4%)	27 (73.0%)			29 (78.4%)	27 (73.0%)			29 (78.4%)	27 (73.0%)
FAB subtypes, $n$ (%)				8.052 ${}^{\#}$	0.153			8.241 ${}^{\#}$	0.143			10.196 ${}^{\#}$	0.070
M0	4 (5.4%)	1 (2.7%)	3 (8.1%)			0 (0.0%)	4 (10.8%)			0 (0.0%)	4 (10.8%)
M1	15 (20.3%)	6 (16.2%)	9 (24.3%)			6 (16.2%)	9 (24.3%)			5 (13.5%)	10 (27.0%)
M2	26 (35.1%)	11 (29.7%)	15 (40.5%)			13 (35.1%)	13 (35.1%)			13 (35.1%)	13 (35.1%)
M3	3 (4.1%)	3 (8.1%)	0 (0.0%%)			3 (8.1%)	0 (0.0%)			3 (8.1%)	0 (0.0%)
M4	17 (23.0%)	9 (24.3%)	8 (21.6%)			10 (27.0%)	7 (18.9%)			10 (27.0%)	7 (18.9%)
M5	9 (12.2%)	7 (18.9%)	2 (5.4%)			5 (13.5%)	4 (10.8%)			6 (16.2%)	3 (8.1%)
Karyotype, $n$ (%)				17.786 ${}^{\#}$	0.013			19.143 ${}^{\#}$	0.008			16.643 ${}^{\#}$	0.020
Normal	56 (75.7%)	23 (62.2%)	33 (94.6%)			24 (64.9%)	32 (86.5%)			25 (67.6%)	31 (83.8%)
t(8;21)/RUNX1-RUNX1T1	6 (8.1%)	6 (16.2%)	0 (0.0%)			6 (16.2%)	0 (0.0%)			6 (16.2%)	0 (0.0%)
t(15;17)/PML-RARA	2 (2.7%)	2 (5.4%)	0 (0.0%)			2 (5.4%)	0 (0.0%)			2 (5.4%)	0 (0.0%)
t(16;16)/CBF $\beta$ -MYH11	3 (4.1%)	3 (8.1%)	0 (0.0%)			3 (8.1%)	0 (0.0%)			3 (8.1%)	0 (0.0%)
t(9;22)/BCR-ABL1	2 (2.7%)	2 (5.4%)	0 (0.0%)			2 (5.4%)	0 (0.0%)			0 (0.0%)	2 (5.4%)
$+$ 8	2 (2.7%)	1 (2.7%)	1 (2.7%)			0 (0.0%)	2 (5.4%)			1 (2.7%)	1 (2.7%)
-5/5q-	1 (1.4%)	0 (0.0%)	1 (2.7%)			0 (0.0%)	1 (2.7%)			0 (0.0%)	1 (2.7%)
Complex	2 (2.7%)	0 (0.0%)	2 (5.4%)			0 (0.0%)	2 (5.4%)			0 (0.0%)	2 (5.4%)

Table 1, continued
Characteristics	AML patients	Status of BAMBI expression				Status of SMAD7 expression				Status of TERT expression
	$n=$ 74	BAMBI ${}^{\text{low}}$ ( $n=$ 37)	BAMBI ${}^{\text{high}}$ ( $n=$ 37)	Test value	$p$ -value	SMAD7 ${}^{\text{low}}$ ( $n=$ 37)	SMAD7 ${}^{\text{high}}$ ( $n=$ 37)	Test value	$p$ -value	TERT ${}^{\text{low}}$ ( $n=$ 37)	TERT ${}^{\text{high}}$ ( $n=$ 37)	Test value	$p$ -value
Risk, $n$ (%)				13.672#	0.001			18.737#	0.000			16.518#	0.000
Poor	38 (51.4%)	14 (37.8%)	24 (64.9%)			11 (29.7%)	27 (73.0%)			12 (32.4%)	26 (70.3%)
Intermediate	25 (33.8%)	12 (32.4%)	13 (35.1%)			15 (40.5%)	10 (27.0%)			14 (37.8%)	11 (29.7%)
Favorable	11 (14.9%)	11 (29.7%)	0 (0.0%)			11 (29.7%)	0 (0.0%)			11 (29.7%)	0 (0.0%)
Achieving CR, $n$ (%)				4.378 ${}^{\#}$	0.036			12.162 ${}^{\#}$	0.000			9.135 ${}^{\#}$	0.003
Negative ( $-$ )	37 (50.0%)	14 (37.8%)	23 (62.2%)			11 (29.7%)	26 (70.3%)			12 (32.4%)	25 (67.6%)
Positive ( $+$ )	37 (50.0%)	23 (62.2%)	14 (37.8%)			26 (70.3%)	11 (29.7%)			25 (67.6%)	12 (32.4%)
Relapse, $n$ (%)				5.745 ${}^{\#}$	0.017			3.677 ${}^{\#}$	0.055			0.919 ${}^{\#}$	0.338
Negative ( $-$ )	46 (62.2%)	28 (75.7%)	18 (48.6%)			27 (73.0%)	19 (51.4%)			25 (67.6%)	21 (56.8%)
Positive ( $+$ )	28 (37.8%)	9 (24.3%)	19 (51.4%)			10 (27.0%)	18 (48.6%)			12 (32.4%)	16 (43.2%)
Mortality, $n$ (%)				9.243 ${}^{\#}$	0.002			15.806 ${}^{\#}$	0.000			12.306 ${}^{\#}$	0.000
Alive	33 (44.6%)	23 (62.2%)	10 (27.0%)			25 (67.6%)	8 (21.6%)			24 (64.9%)	9 (24.3%)
Died	41 (55.4%)	14 (37.8%)	27 (73.0%)			12 (32.4%)	29 (78.4%)			13 (35.1%)	28 (75.7%)

SE, standard error of mean; WBC, white blood cell; BM, bone marrow; LDH, lactate dehydrogenase; FAB, French American British; CR, complete remission. ${}^{*}$ , Mann-Whitney U Test; ${}^{**}$ , Independent $T$ -Test, ${}^{\#}$ , Pearson’s Chi-Square ( $\chi^{2}$ ) analysis.

full disease history. Event-free survival (EFS) and overall survival (OS) were set as clinical endpoints in this study. EFS was defined as the time from diagnosis until death, relapse, or the absence of complete remission (CR). OS was determined as the time from diagnosis to death or the end of the follow-up from any cause.

2.3.3 Statistical analysis

The clinical and molecular characteristics of subjects were summarized using descriptive statistics. The distribution of quantitative data was tested by Kolmogorov-Smirnov test of normality. Quantitative data were presented in the form of mean with standard error (SE) when parametric and median with range when non parametric. Qualitative data were presented in the form of numbers and percentages. Quantitative data with parametric distribution and non-parametric distribution were compared between two groups using Independent $T$ -test and Mann-Whitney U test, respectively. While, the Pearson’s Chi-Square ( $\chi^{2}$ ) analysis was utilized to compare the differences in categorical variables. Expression levels of the genes were related with the clinical and laboratory features of the studied patients at diagnosis including: age, WBC count, platelets count, hemoglobin, BM blast cell percentage and LDH using Spearman’s correlation coefficient. Kaplan-Meier analysis was used for survival analysis and the statistical significance of differences among curves was determined by log-rank (MantelCox) test. Moreover, univariate and multivariate analyses using the Cox proportional hazards model were performed to investigate the associations of the genes’ expression with OS and EFS in the absence/presence of other known prognostic factors. Patients were classified into BAMBI ${}^{\text{low}}$ and BAMBI ${}^{\text{high}}$ subgroups based on the median BAMBI expression values in patients’ group and similarly into SMAD7 ${}^{\text{low}}$ and SMAD7 ${}^{\text{high}}$ subgroups based on the median SMAD7 expression values in patients’ group. The results in all analyses were considered as statistically significant when the two-tailed $P$ -value was $<$ 0.05. Statistical analyses were performed with SPSS (version 20.0; IBM Corp, Armonk, NY, USA).

3. Results

3.1 Clinical and molecular characteristics of the subjects

The control group was selected from 16 age and sex matched healthy volunteers. It is noteworthy to mention that the ages of both controls (42.87 $\pm$ 2.14 years) and AML patients (47.57 $\pm$ 1.41 years) weren’t significantly different ( $p=$ 0.145). The control group included 7 females and 9 males constituting 43.8% and 56.2% of controls respectively, whereas AML patients included 31 females and 43 males comprising 41.9% and 58.1% patients respectively, with no statistical significance detected in respect to controls ( $p=$ 0.891). The clinical and molecular characteristics of patients are fully depicted in Table 1. The main French American British (FAB) subtypes were M1, M2, and M4 (78.4%). Eighteen patients had abnormal karyotypes. The proportion of poor, intermediate, and good risk patients were 51.4%, 33.8%, and 14.9%, respectively. There were 28 patients relapsed and 41 patients died. As for the expressions of BAMBI, SMAD7 and TERT, they were detected by qRT-PCR respectively. The transcript level of BAMBI in healthy controls ranged from 0.71 to 1.65 with a median level of 0.93. However, BAMBI transcript in AML patients (median 8.56, range 0.00–592.63) was significantly upregulated compared to controls ( $p=$ 0.027). As for the expression levels of SMAD7 in controls, it ranged from 0.47 to 1.77 with a median of 0.94. However, SMAD7 levels were significantly increased in AML patients (median 22.63, range 0.09–894.54), ( $p=$ 0.000). Moreover, significant up-regulation of TERT expression was shown in AML patients (median 4.86, range 0.06–362.04) versus controls (median 1.07, range 0.63–1.40), ( $p=$ 0.006).

Table 2
Correlation of the studied genes mRNA expression levels with each other

	BAMBI fold expression		SMAD7 fold expression		TERT fold expression
Parameter	$r$	$P$	$r$	$P$	$r$	$P$
BAMBI mRNA fold expression	–	–	0.874	0.000	0.816	0.000
SMAD7 mRNA fold expression	0.874	0.000	–	–	0.810	0.000
TERT mRNA fold expression	0.816	0.000	0.810	0.000	–	–

$r$ , spearman correlation coefficient; WBC, white blood cell; BM, bone marrow; LDH, lactate dehydrogenase.

Table 3

Kaplan-Meier analysis and the logrank (MantelCox) test

Expression level	EFS		Log rank test		OS		Log rank test
	Median	SE	X ${}^{2}$	$P$	Median	SE	X ${}^{2}$	$P$
BAMBI ${}^{\text{low}}$	72.092	6.775	16.548	0.000	63.270	6.964	9.889	0.002
BAMBI ${}^{\text{high}}$	32.061	6.158			34.050	6.715
SMAD7 ${}^{\text{low}}$	70.310	6.735	15.557	0.000	67.649	6.772	16.577	0.000
SMAD7 ${}^{\text{high}}$	32.553	6.525			29.493	6.294
TERT ${}^{\text{low}}$	65.583	6.960	7.708	0.005	65.919	6.788	13.697	0.000
TERT ${}^{\text{high}}$	38.896	7.350			31.372	6.551

EFS, event free survival; OS, overall survival.

Figure 1.

Kaplan-Meier curves of EFS and OS based on expression levels of BAMBI, SMAD7 and TERT in AML patients. A and B: Patients with high expression of BAMBI had shorter EFS and OS. C and D: Patients with high expression of SMAD7 had shorter EFS and OS. E and F: Patients with high expression of TERT had shorter EFS and OS.

3.2 Clinical and molecular characteristics of AML patients regarding different BAMBI or SMAD7 expression levels

The median BAMBI or SMAD7 expression levels at diagnosis, respectively, were used to divide the patients into low and high BAMBI or SMAD7 expression groups. Patients with BAMBI expression levels $\geqslant$ median were defined as high BAMBI expressers (BAMBI ${}^{\text{high}}$ ); others were defined as low BAMBI expressers (BAMBI ${}^{\text{low}}$ ). It is the same with SMAD7 expression. We compared the clinical and molecular characteristics within the divided subgroups. The results are summarized in Table 1. Patients with high expression of BAMBI had higher levels of bone marrow (BM) blasts ( $p=$ 0.019), serum LDH ( $p=$ 0.014). Moreover, interestingly, they showed poorer risk status ( $p=$ 0.001), lower percentages of achieving CR ( $p=$ 0.036) as well as higher rates of both relapse ( $p=$ 0.017) and mortality ( $p=$ 0.002), however they showed lower levels of platelets count ( $p=$ 0.032) and lower percentage of the favorable karyotype (t(8;21)/RUNX1-RUNX1T1) ( $p=$ 0.013) compared to those having low BAMBI expression. Whereas, there were no strikingly differences in age, gender, WBC count, hemoglobin and FAB classification between the BAMBI ${}^{\text{high}}$ and BAMBI ${}^{\text{low}}$ subgroups. As for the SMAD7 expression subgroups, patients with high expression of SMAD7 had higher levels of older patients ( $p=$ 0.009), BM blasts ( $p=$ 0.017), serum LDH ( $p=$ 0.010). Moreover, they showed poorer risk status ( $p=$ 0.000), lower percentages of achieving CR ( $p=$ 0.000) as well as higher rate of mortality ( $p=$ 0.000), however they showed lower percentage of the favorable karyotype (t(8;21)/RUNX1-RUNX1T1) ( $p=$ 0.008) compared to those having low SMAD7 expression. Whereas, there were no strikingly differences in gender, WBC count, hemoglobin, platelets count, FAB classification and percentage of relapsed patients between the SMAD ${}^{\text{high}}$ and SMAD ${}^{\text{low}}$ subgroups.

3.3 Association of the studied genes mRNA expression levels with each other

Spearman correlation coefficient was used to determine the potential correlation of the expression levels of the 3 studied genes together. Interestingly, there was a highly significant positive correlation between the expression levels of BAMBI, SMAD7 and TERT together at $p<$ 0.001 as depicted in Table 2.

3.4 Prognostic impact of the studied genes expression on outcome (EFS/OS) of AML patients

To investigate the prognostic impact of BAMBI, SMAD7 and TERT expression in AML, survival data were obtained for the AML patients with followup time of 96 weeks. We performed the Kaplan-Meier analysis and the logrank (MantelCox) test to analyze the effect of the studied genes expression on EFS/OS (Table 3). The survival curves showed that patients with high BAMBI, SMAD7 and TERT expression had significantly shorter EFS ( $p<$ 0.001) and OS ( $p<$ 0.001) than those having low expression levels of those genes (Fig. 1).

Table 4
Univariate analysis of prognostic factors for EFS/OS in AML patients

Variable	EFS			OS
	HR (95% CI)		$P$ -value	HR (95% CI)		$P$ -value
Age ( $\geqslant$ 60 vs $<$ 60 years)	2.388	(1.270–4.490)	0.007	3.818	(1.668–8.740)	0.002
WBC ( $\geqslant$ 20 vs $<$ 20 $\times$ 10 ${}^{9}$ /L)	0.797	(0.432–1.470)	0.467	0.990	(0.468–2.095)	0.979
Risk (poor vs intermediate vs favorable)	84.623	(11.356–630.601)	0.000	11.878	(4.222–33.418)	0.000
Achieving CR (positive vs negative)	0.008	(0.001–0.059)	0.000	0.07	(0.023–0.238)	0.000
BAMBI expression (high vs low)	2.653	(1.384–5.085)	0.003	4.528	(2.020–10.149)	0.000
SMAD7 expression (high vs low)	3.621	(1.834–7.148)	0.000	4.189	(1.912–9.175)	0.000
TERT expression (high vs low)	3.184	(1.637–6.195)	0.001	2.745	(1.289–5.847)	0.009

EFS, event-free survival; OS, overall survival; HR, hazard ratio; 95% CI, 95% confidence interval; WBC, white blood cell; CR, complete remission.

Table 5

Multivariate analysis of prognostic factors for EFS/OS in AML patients

Variable	EFS			OS
	HR (95% CI)		$P$ -value	HR (95% CI)		$P$ -value
Age ( $\geqslant$ 60 vs $<$ 60 years)	1.181	(0.555–2.511)	0.666	2.345	(0.881–6.238)	0.088
WBC ( $\geqslant$ 20 vs $<$ 20 $\times$ 10 ${}^{9}$ /L)	1.292	(0.663–2.519)	0.452	1.378	(0.598–3.174)	0.451
Risk (poor vs intermediate vs favorable)	6.524	(0.157–270.501)	0.324	8.085	(1.173–55.712)	0.034
Achieving CR (positive vs negative)	0.041	(0.001–1.976)	0.106	0.320	(0.034–2.994)	0.318
BAMBI expression (high vs low)	2.112	(0.795–5.610)	0.134	8.853	(2.326–33.701)	0.001
SMAD7 expression (high vs low)	0.564	(0.194–1.636)	0.292	0.555	(0.132–2.330)	0.421
TERT expression (high vs low)	2.115	(0.742–6.034)	0.161	0.635	(0.140–2.885)	0.557

EFS, event-free survival; OS, overall survival; HR, hazard ratio; 95% CI, 95% confidence interval; WBC, white blood cell; CR, complete remission.

3.5 Univariate and multivariate analysis to determine independently predictive factors for OS and EFS in patients’ subgroups

To determine independent risk factors affecting OS and EFS, univariate and multivariate cox regression analyses were performed. Prognostic factors including; age ( $\geqslant$ 60/ $<$ 60 years), WBC ( $\geqslant$ 20/ $<$ 20 $\times$ 10 ${}^{9}$ /L), risk (poor/intermediate/favorable), CR (positive/negative), BAMBI expression (high/low), SMAD7 expression (high/low) and TERT expression (high/low) were selected. Univariate analysis should that age, risk status, CR achievement, BAMBI, SMAD7 and TERT expression levels were independent adverse prognostic factors for both OS and EFS (Tables 4 and 5). Interestingly, multivariate analysis revealed that only BAMBI expression remained an independent prognostic factor for OS in AML patients, where high BAMBI expression was associated with poor OS (HR $=$ 8.853, $p=$ 0.001) compared to low BAMBI expression.

4. Discussion

To the best of our knowledge, this is the first study to explore the interplay between BAMBI, SMAD7 and TERT expression levels in AML pathogenesis. In this study, we demonstrated that BAMBI and SMAD7 levels were strongly positively correlated to each other and were found to be highly upregulated in AML patients with poorer risk status and in those failing CR after induction therapy as well as those who died. Moreover, Kaplan Meier analysis revealed that patients with higher BAMBI, SMAD7 and hTERT expression had shorter OS and EFS than those having low expression levels of the 3 genes. Finally, BAMBI was identified as an independent prognostic factor for OS in AML patients. These data collectively indicate that BAMBI may serve as a novel promising prognostic marker in AML and that there is some sort of interplay between BAMBI and SMAD7 in AML pathogenesis and prognosis most probably involving a TERT non-canonical impact.

It is well known that AML is the most common form of acute leukemia among adults, accounting for the largest number of annual deaths from leukemias in the United States [29]. Surprisingly, despite the understanding of AML pathogenesis and the developments done in clinical therapy in the past decades, the outcomes of AML patients are still very poor in general [30]. Thus, more biomarkers need to be discovered to enhance diagnosis, risk stratification, and prognosis in AML that may accordingly help physicians in making appropriate therapeutic decisions [31]. In our study, we focused on the deregulation of the TGF- $\beta$ due to the important role it plays in AML pathogenesis [32, 33, 34].

Owing to the importance of the TGF- $\beta$ signaling pathway in hematological malignancies and the straightforward role it plays in them versus solid cancers [12], we first sought to explore the significance of one of its antagonists, BAMBI, in AML pathogenesis. BAMBI, a trans-membrane glycoprotein located at chromosomal 10p11.2-p12.3, is an interesting antagonist to the TGF- $\beta$ since it was found to act as a pseudo-receptor due to its similar structure with extracellular domain of type I receptors of the TGF- $\beta$ family [35]. Interestingly, BAMBI was identified to induce cell growth and invasion by negatively regulating TGF- $\beta$ signaling In human gastric carcinoma cell lines [36]. Moreover, Nozomi et al., showed that BAMBI levels were not only aberrantly elevated level in colorectal cancer patients, but also were linked to a potentially aggressive tumor phenotype and predicted tumor recurrence and cancer-related death [37]. Another study, showed that inhibition of BAMBI reduces the viability and motility of colon cancer and may involve activation of the TGF- $\beta$ /SMAD pathway in vitro and in vivo [38]. Furthermore, a study by Zhou et al., reported that BAMBI played a key role in the pathogenesis and progression of osteosarcoma [17]. Additionally, Dietmar et al., showed that BAMBI levels were upregulated in patients with ovarian cancer compared to healthy controls [18]. Recently, it was reported that the long non-coding RNA Plasmacytoma Variant Translocation 1 (PVT1) modulated BAMBI to promote tumor progression in non-small cell lung cancer [39]. Although all these studies and others extensively evaluated BAMBI expression levels in a number of solid cancers, yet to date no data is found about the levels and significance of BAMBI in any hematological malignancy especially AML. Interestingly, this study is the first to show that BAMBI levels were upregulated in AML patients compared to healthy volunteers confirming its role as a tumor oncogene in AML. Therefore, our study is in line with the previous studies suggesting that BAMBI, as a TGF- $\beta$ antagonist, is involved in cancer pathogenesis and progression [17, 18, 39].

Another interesting TGF- $\beta$ antagonist in this study is SMAD7. SMAD7 belongs to the inhibitory class of SMADs that can act as an oncogene through inhibiting TGF- $\beta$ signaling by several ways [8]. In this context, SMAD7 expression levels were found to be deregulated in some types of leukemias [20, 21]. However, its expression levels in AML patients specifically are not widely reported. In our study, SMAD7 showed a significantly higher expression levels compared to healthy controls, confirming the expected vital role it plays in AML as an oncogene. Moreover, its levels were associated with poorer prognosis. These results are in accordance with a recent study which reported that high expression levels of SMAD7 at diagnosis could predict poor prognosis in AML patients undergoing chemotherapy [40]. Interestingly, an in vitro study reported that BAMBI cooperates with SMAD7, to inhibit the TGF- $\beta$ signaling [22]. However, this finding was not clinically validated in any hematological malignancy including leukemias, hence we used the spearman correlation analysis to investigate the potential interplay between BAMBI and SMAD7 particularly in AML. Interestingly, our study showed that they not only had a highly significant positive correlation together, but also patients with high BAMBI and high SMAD7 expression levels had in common several abnormal variables including higher BM blasts, greater serum LDH, higher frequencies of poor risk status as well as higher rates of mortality. On the other hand, those patients showed lower chances of achieving CR and lower percentages of the favorable AML Karyotype t(8;21)/RUNX1-RUNX1T1 compared to those having low expression levels for both genes. Hence, these results highlight the possible cooperation and association of both genes with poor prognosis in AML patients. These results are also in line with Zhang and coworkers who showed that both genes were not only correlated together, but also linked to poor prognosis of gastric cancer patients [23].

Since it is well known that cancers develop due to a dysregulated set of events, it’s not surprising to assume that BAMBI and SMAD7, as TGF- $\beta$ antagonists may be transcriptionally upregulated in AML by other proteins playing an upper hand in TGF- $\beta$ signaling suppression. Interestingly, it was reported that TERT, beyond its well-known canonical role in telomeres, may be involved in epigenetic modifications or the modulation of chromatin structure, which indirectly affect gene expression [13]. In this context, hTERT was reported to upregulate some TGF- $\beta$ and Wnt pathway related genes such as BAMBI and SMAD7 [27], however, to the best of our knowledge, this non-canonical role of TERT, beyond telomeres, haven’t been investigated before in any clinical study. Hence, we sought to study the potential interplay between BAMBI, SMAD7 and TERT expression levels in AML patients’ prognosis. Intriguingly, our study showed that TERT levels had a highly significant positive correlation to each of BAMBI and SMAD7 levels which reflects a potential association between the 3 genes in AML pathogenesis and might refer to a possible non-canonical role TERT in regulating the expression of BAMBI and SMAD7, however, further clinical and mechanistic studies are required to confirm this finding. Moreover, we performed Kaplan-Meir analysis to determine the effect of these genes on AML prognosis and it showed that those having high expression levels of BAMBI, SMAD7 and TERT had significantly lower EFS and OS compared to low expressers of those gene, confirming the assumed interplay between these genes in AML pathogenesis and prognosis. Additionally, univariate and multivariate cox regression analysis was performed to determine the predictors for EFS and OS in this study. Interestingly, univariate analysis should that all the 3 genes are strong predictors for EFS and OS in AML patients. However, in multivariate COX regression analysis, high BAMBI expression was found to be the only prognostic factor for adverse outcomes, where patients with high BAMBI expression showed greater hazard for mortality than low BAMBI expressers, suggesting a vital role played by BAMBI in AML prognosis.

In conclusion, our study suggested that high expression of BAMBI can predict adverse prognosis in AML and may guide treatment decisions for AML. Moreover, this study demonstrated a potential interplay between BAMBI, SMAD7 and TERT in AML pathogenies. Finally, although the association between BAMBI, SMAD7 and TERT expression levels and clinical outcomes was illustrated in this pilot study, further detailed studies will be required to validate whether BAMBI and SMAD7 functions as a tumor suppressor potentially through a TERT non-canonical impact in larger AML cohorts, elucidate the underlying mechanisms, study the impact of different treatment strategies either chemotherapy or BM transplantation on the expression of these genes and also determine the effect of developing BAMBI receptor blockers as a potential novel treatment for AML to enhance patients’ outcomes and survival rates.

Authors’ contributions

Miral Magdy Shehata: conception, researched data, designed experiments, interpreted data and prepared the manuscript.

Al-Aliaa Mohamed Sallam: conception, designed experiments, revised the manuscript and provided supervision.

Mary Gamal Naguib: provided patients’ samples, interpreted data, and revised the manuscript.

Hala Osman El-Mesallamy: revised the intellectual content and provided supervision and interpretation of data.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Footnotes

Acknowledgments

The authors thank all the donors whose names were not included in the author list, but who participated in this study.

Conflict of interest

The authors declare that there is no conflict of interest.

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Overexpression of BAMBI and SMAD7 impacts prognosis of acute myeloid leukemia patients: A potential TERT non-canonical role

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Subjects and methods

2.1 Subjects

2.2 Sampling

2.3 Methods

2.3.1 RNA isolation, reverse transcription and real-time quantitative PCR

2.3.2 Clinical outcome assessment

Table 1 Clinical and molecular characteristics of the patients

3. Results

3.1 Clinical and molecular characteristics of the subjects

Table 2 Correlation of the studied genes mRNA expression levels with each other

3.3 Association of the studied genes mRNA expression levels with each other

3.4 Prognostic impact of the studied genes expression on outcome (EFS/OS) of AML patients

Table 4 Univariate analysis of prognostic factors for EFS/OS in AML patients

4. Discussion

Authors’ contributions

Funding

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Clinical and molecular characteristics of the patients

Table 2
Correlation of the studied genes mRNA expression levels with each other

Table 4
Univariate analysis of prognostic factors for EFS/OS in AML patients