Evaluation of CDCP1 ( CD318 ) and endoglin ( CD105 ) expression as prognostic markers in acute myeloid leukemia

Abstract

BACKGROUND:

The most commonly used prognostic factors in acute myeloid leukemia (AML) are cytogenetic, molecular, and morphological markers. However, AML prognosis is still unfavorable particularly in adults. So, further reliable markers are urgently needed to improve the risk stratification and treatment decisions. CUB domain-containing protein 1 (CDCP1; CD318) and endoglin (CD105) are new markers correlated with poor prognosis in different solid tumors, but their role in AML prognosis is not fully evaluated.

OBJECTIVES:

This work aimed to evaluate the prognostic role of CD318 and CD105 in AML and their impact on the outcomes.

METHODS:

Sixty-five newly diagnosed AML patients were included in this study. CD318 and CD105 expression was assessed by quantitative real-time polymerase chain reaction. Patients were followed up for $\sim$ 2 years to evaluate the prognostic impact of gene expression on the outcomes.

RESULTS:

Patients with high CD318 and CD105 showed higher white blood cell (WBC) count, M2 subtype, poor cytogenetic risk, reduced complete remission, and a greater number of deaths compared to low CD318 and CD105. CD318 was correlated with CD105, and both were correlated with WBC count, bone marrow blasts, and peripheral blood blasts. After a follow-up period of up to 24 months, relapse-free survival for high CD318 and CD105 was significantly different (42.1% and 52.6% vs. 64.5% and 58.1% for low CD318 and CD105, respectively). Survival was worse in patients with high CD318 and CD105, as the mean survival time was 13.9 and 13.3 months compared to 24 and 22.7 months in low CD318 and CD105, respectively.

CONCLUSIONS:

CD318 and CD105 are upregulated in AML patients. Their overexpression was associated with poor response to treatment and poor outcomes. Therefore, CD318 and CD105 can be useful prognostic markers in AML.

Keywords

AML CDCP1 (CD318)endoglin (CD105)

Abbreviations

AML	acute myeloid leukemia
BM	bone marrow
CDCP1	CUB domain-containing protein 1
CR	complete remission
FAB	French-American-British

HIF	hypoxia-inducible factor
OS	overall survival
PB	peripheral blood
PR	poor response
RFS	relapse-free survival
RR	refractory response
TGF- $\beta$	transforming growth factor- $\beta$

1. Introduction

Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults [1]. Despite advancement in the treatment of AML have led to significant improvements in outcomes for younger patients, prognosis in the elderly who account for most new cases remains poor [2]. The elderly AML patients aged from 65 to 74 years die within one to two years following AML diagnosis because they are often unable to tolerate current treatment regimens [3].

In the United States, the age-adjusted annual incidence of AML is 4.3 per 100,000. Incidence increases with age, with a median age at diagnosis of 68 years. AML can arise in patients with an underlying hematological disorder, or as a result of previous therapy (topoisomerases II, alkylating agents or radiation) [4]. However, in majority of cases, it appears as a de novo malignancy in previously healthy individuals. The pathogenesis of AML involves the abnormal proliferation and differentiation of a clonal population of myeloid stem cells. In most AML cases, the etiology is unclear. However, AML is sometimes caused by previous exposure to therapeutic, occupational, or environmental DNA-damaging agents [1].

AML is a highly heterogeneous disease; although cases can be stratified into favorable, intermediate, and adverse-risk groups based on their cytogenetic profile, prognosis within these categories varies widely. Its prognosis usually depends on cytogenetic and morphological markers [5]. The commonly used cytogenetic and molecular features include inversion (16), translocation (8; 21), translocation (15; 17). Translocations (8; 21) in core-binding factor AML (CBF-AML) or (15; 17) in acute promyelocytic leukemia (APL) result in the formation of chimeric proteins (RUNX1-RUNX1T1 and PML-RARA, respectively), which alter the normal maturation process of myeloid precursor cells [6].

Besides, genetic mutations are identified in more than 97% of cases [6] often in the absence of any large chromosomal abnormality [7]. Animal models proposed two-hit model of leukemogenesis provided two classes of mutation [8]. Class I mutations activates the pro-proliferative pathways, such as FLT3-ITD, TKD, K/NRAS, TP53 and c-KIT [7]. Class II mutations which impair normal hematopoietic differentiation [9] such as NPM1 and CEBPA and have a better prognosis [7]. Epigenetic regulation has recently considered the third class of mutations, with downstream effects on both cellular differentiation and proliferation including mutations in the DNA-methylation related genes DNMT3A, TET2, and IDH-1 and IDH-2 [6]. Proteomic biomarkers include PTP, PTK and PIP [10].

The identification of current genetic mutations, such as FLT3-ITD, NMP1, MLL and CEBPA are used to refine individual prognosis and guide management [11]. However, new markers are required for the improvement of the risk stratification and treatment decision guiding in AML.

CUB domain-containing protein 1 (CDCP1), also known as CD318, is a transmembrane protein expressed on the surface of hematopoietic mesenchymal and neural stem cells and in fibroblasts [12]. CD318 promotes solid tumor metastasis via interaction with integrins and antiapoptotic signaling via Akt [13, 14]. CD318 expression has been observed on CD34- and CD133-positive AML blasts. CD318 overexpression has been correlated with poor overall survival (OS) in the colon, breast, renal, hepatocellular (HCC), and pancreatic carcinoma [13, 14, 15, 16, 17].

Endoglin (CD105) is a type I transmembrane glycoprotein located on the endothelial cell surface, which serves as a transforming growth factor- $\beta$ (TGF- $\beta$ ) coreceptor. CD105 is also detected on stromal cells, melanocytes, and different healthy cells of the hematopoietic system [18]. CD105 induces the activation and proliferation of endothelial cells [19]. TGF- $\beta$ receptors activate SMAD2/3 to translocate to the nucleus and control gene expression. This leads to uncontrolled proliferation, invasion, and metastasis in cancer [20]. TGF- $\beta$ , together with hypoxia, induces an increase in endoglin gene promoter expression. It is expressed in hematologic malignancies and on solid tumor vessels affecting different organs involved in solid tumor angiogenesis [21]. TGF- $\beta$ has also been expressed in myelodysplastic syndrome, AML, and acute lymphoblastic leukemia [22]. Changes in CD105 expression levels in tumor cells contribute to the deregulation of TGF- $\beta$ -dependent and TGF- $\beta$ -independent signaling pathways and malignant progression [23]. CD105 expression on tumor vessels is correlated with poor prognosis in colorectal, breast, and prostate carcinoma and HCC [24, 25, 26, 27].

The prognostic significance of CD318 and CD105 in AML has not been fully assessed. Heitmann et al. evaluated the role of CD318 as a new prognostic marker in AML by immunophenotyping of leukemic blasts. Flow cytometric analysis revealed a substantial expression in 57% of all cases. CD318 surface levels were significantly correlated with OS [28]. Kauer et al. generated a new CD105 antibody to analyze the expression and prognostic significance of CD105 protein expression in AML by flow cytometry. Flow cytometric analysis revealed that high CD105 expression correlated significantly with poor OS and progression-free survival [29]. Allogeneic stem cell transplantation for eligible candidates is a treatment modality in AML. Märklin et al. detected high CD105 expression in 65.8% of AML patients following allogeneic hematopoietic stem cell transplantation [30]. No previous studies studied both markers together or evaluated their gene expression to the authors’ knowledge.

Despite advances in the supportive care of AML, the backbone of therapy remains a combination of cytarabine- and anthracycline-based regimens with allogeneic stem cell transplantation for eligible candidates. New molecular targeted therapies provide effective anti-leukemic activity with reduced toxicity. Due to the molecular diversity of AML, targeted therapies are better to be combined. Discovery of new treatments depends on improved genetic profile and risk stratification which may benefit in remission and survival. The recognition of gene expression profile of specific cell surface markers can provide a therapeutic target for recombinant monoclonal antibodies. The key role is selectivity in targeting leukemic myeloid cells while sparing non-malignant myeloid precursors. Besides, the development of well-tolerated oral therapies, will help elderly patients at a higher risk of mortality due to intolerance to current treatment regimens.

So, this study investigated the prognostic role of both CD318 and CD105 overexpression in AML by quantitative real-time polymerase chain reaction (qRT-PCR) and tried to explore their impact on patient outcomes.

2. Materials and methods

2.1 Study design

This prospective study was carried out on 65 patients with newly diagnosed AML. This study was carried out in the Departments of Clinical Pathology, Medical Microbiology and Immunology and Medical Biochemistry and Molecular Biology, Faculty of Medicine, Zagazig University, from March 2018 to June 2021. The diagnosis and classification of AML samples relied on the morphology and cytochemistry of bone marrow (BM) and classified according to the French-American-British (FAB) classification [31]. Research ethical committee approval (ZU-IRB# 4906/810-2018) and informed written consent from all participants in this research were fulfilled.

Bone marrow (BM) cells were collected from all AML patients at the time of diagnosis. BM specimens were collected on potassium ethylene diamine tetra-acetic acid (K-EDTA) (1.5 mg/mL) for morphologic, immunophenotypic, and molecular analysis.

2.2 Flow cytometric analysis

Immunophenotyping for acute leukemia using the standard panel of FITC/PE/PerCP-labeled monoclonal antibodies (CD45, CD34, CD38, HLA-DR, MPO, terminal deoxynucleotidyl transferase, CD64, CD14, CD2, CD3, CD5, CD7, CD19, CD79a, CD20, and CD22) was performed (Becton Dickinson, San Diego, CA, USA). Antigens were scored positive using a cutoff value of $\geqslant$ 20% leukemic blasts staining brighter than an isotype-matched negative control (anti-mouse IgG1; Becton Dickinson). Isotopic quality control was used to exclude nonspecific binding and autofluorescence. Thirty thousand events (cells) were acquired to pass in front of the laser beam.

Table 1
Primer sequence of the studied genes

	Forward	Reverse
CD318	TTACCCCAAGGACTGTGGAC	CGAGGGCAGACAGCAGTAA
CD105	CCACTAGCCAGGTCTCGAAG	GATGCAGGAAGACACTGCTG
GAPDH	CTGAGTACGTCGTGGAGTC	ACTGAACCTGACCGTACACAGAGATGATGACCCTTTTG

2.3 RT-PCR

Total RNA was extracted using easy-Red ${}^{\text{TM}}$ Total RNA Extraction kit (iNtRON Biotechnology, Seongnam, Korea) according to the manufacturer’s protocol. Total RNA was extracted from 1 mL BM with EDTA. The extraction procedure was done within 24 hours of the sample collection avoiding RNA degradation. To assess the quality and the integrity of the harvested RNA, it was run on the gel electrophoresis and measured on Nanodrop spectrophotometry (ND 1000-NanoDrop ${}^{\@setsize{\scriptsize}{8pt}{\viipt}{\@viipt}\textregistered}$ ). The extracted RNA was treated with DNase before cDNA synthesis to ensure that the designed primer will amplify only the RNA part, not to contaminated DNA. Then, the cDNA was synthesized with the Maxime RT Premix Kit from 1 $\mu$ g RNA according to the manufacturer instruction (iNtRON Biotechnology, Seongnam, Korea) with a total reaction volume of 20 $\mu$ L in MicroAmp ${}^{\text{TM}}$ fast 96-well reaction plate (Applied Biosystem) thermal cycler with cycling condition of; 25 ${}^{\circ}$ C for 2 min, 45 ${}^{\circ}$ C for 60 min, and 95 ${}^{\circ}$ C for 5 min for enzyme deactivation. cDNA was diluted 1:5 and stored at $-$ 20 ${}^{\circ}$ C for further use.

qRT-PCR was carried out on an Mx3005P RT-PCR System (Stratagene, La Jolla, CA, USA). Aliquots of 1 $\mu$ l cDNA samples were mixed with 1 $\mu$ l each of forward/reverse primer (Biolegio, Nijmegen, The Netherlands), 7 $\mu$ l RNase-free water, and 10 $\mu$ l TOPreal ${}^{\text{TM}}$ qPCR 2 $\times$ PreMIX (SYBR Green with low ROX; Enzynomics). Primers for CDCP1 (CD318), endoglin (CD105), and glyceraldehyde 3-phosphate dehydrogenase (GAPDH; housekeeping gene) are listed in Table 1. The samples were run in triplicate. The reaction conditions were as follows: 95 ${}^{\circ}$ C for 10 min for initial denaturation, followed by 45 cycles; each cycle was performed at 95 ${}^{\circ}$ C for 10 s, 60 ${}^{\circ}$ C for 15 s, and 72 ${}^{\circ}$ C for 20 s. The relative CDCP1 (CD318) and endoglin (CD105) gene expression was calculated by the 2 ${}^{-\Delta\Delta\text{Ct}}$ method. GAPDH was used as an internal control.

2.4 Treatment plan

All 50 fit patients were given an anthracycline and cytarabine-based induction chemotherapy regimen; 3 $+$ 7 regimen consisting of continuous infusion cytarabine (100 mg/m ${}^{2}$ ) daily for 7 consecutive days combined with 3 days of doxorubicin (25 mg/m ${}^{2}$ ). Patients who achieved complete remission (CR) received consolidation therapy (postinduction therapy) comprised of three to four courses of high-dose cytosine arabinoside (1–2 g/m ${}^{2}$ every 12 h on days 1, 3, and 5; a total of 12 g/m ${}^{2}$ ).

Fifteen patients who were not fit for aggressive chemotherapy were treated by alternative low-intensity therapy, including low-dose cytarabine (10 mg/m ${}^{2}$ ) every 12 h for 14 days (hydroxyurea or hypomethylating agents).

Patients with acute promyelocytic leukemia are treated with all-trans retinoic acid or Vesanoid as a differentiating factor plus chemotherapy: anthracycline alone or anthracycline plus cytarabine.

Hematologic CR is defined as meeting all the following response criteria: $<$ 5% blasts in BM, no blasts with Auer rods, no extramedullary disease (e.g., central nervous system and soft-tissue disease), neutrophils $\geqslant$ 1000/ $\mu$ L, platelets $\geqslant$ 100,000/ $\mu$ L, and transfusion independent [32]. Relapse is defined as the recurrence of disease after CR, meeting one or more of the following criteria: $\geqslant$ 5% blasts in BM or peripheral blood (PB), extramedullary disease, or disease determined by a physician upon clinical assessment.

2.5 Statistical analysis

Data were analyzed using IBM SPSS 23.0 for Windows (SPSS, Inc., Chicago, IL, USA) and NCSS 11 for Windows (NCSS LCC, Kaysville, UT, USA). Quantitative data were expressed as the mean $\pm$ standard deviation (SD). Qualitative data were expressed as the frequency and percentage. Independent-sample $t$ -test and Mann-Whitney (MW) test were performed for nonnormally distributed data. $\chi^{2}$ and Fisher’s exact tests were performed for qualitative data analysis. Kaplan-Meier test was performed for survival analysis, and a log-rank test was performed to compare survival between groups. Pearson’s correlation was performed. For predictive cutoff value estimation, CD318 and CD105 were subgrouped according to the corresponding OS and treatment. Receiver operating characteristic (ROC) curve analysis was performed, and the highest Youden index value was used as a cutoff. $P<$ 0.05 was considered statistically significant, and $P<$ 0.001 was considered highly significant.

3. Results

3.1 Basic clinicodemographic characteristics and outcomes among the participants

According to the FAB classification of AML patients, M4 was the most frequent (29.2%), followed by M2 (26.2%), and the least was M3 (6.2%). Based on cytogenetics, patients were categorized according to the National Comprehensive Cancer Network (NCCN) risk score. The intermediate cytogenetic risk was detected in 38.5% of the patients, followed by favorable risk (30.8%), and poor risk was detected in 24.6% of the patients. Response to chemotherapy was defined according to the European Leukemia Network definition. CR was defined as a presentation with normocellular BM containing $<$ 5% blasts and neutrophilic granulocytes in PB recovered to 1500/ $\mu$ l and platelets recovered to 100,000/ $\mu$ l. CR to treatment was detected in 64.6%, refractory response (RR) in 20%, and poor response (PR) in 15.4% of the patients. Patients showed relapse-free survival (RFS) in 43.1% and relapse in 33.8%, and the death percentage was 23.1% (Table 2).

Table 2
Demographic and basic characteristics among the participants

		AML cases $N=$ 65
		Mean $\pm$ SD
Age (years)		50.9 $\pm$ 11.5
Platelets (10 ${}^{9}$ /L)		55.9 $\pm$ 11.6
WBCs (10 ${}^{9}$ /L)		71.7 $\pm$ 23.5
Hemoglobin (g/dL)		7.61 $\pm$ 1.53
PB blast		23.4 $\pm$ 14.3
BM blast		63.9 $\pm$ 23.83
CD105		7.3 $\pm$ 1.7
CD318		3.6 $\pm$ 1.1
		$N$	%
Gender	Male	34	52.3
	Female	31	47.7
FAB	M0 M1 M2 M3 M4 M5	6 12 17 4 19 7	9.2 18.5 26.2 6.2 29.2 10.8
Cytogenetic risk	Favorable Intermediate Poor Missed	20 25 16 4	30.8 38.5 24.6 6.2
Induction	Anthracycline Alternative	50 15	76.9 23.1
Cause	Primary	50	76.9
	Secondary	15	23.1
Outcome	RFS	28	43.1
	Dead	15	23.1
	Relapse	22	33.8
Response	CR	42	64.6
	PR	10	15.4
	RR	13	20
High CD105	$\geqslant$ 5.3	31	47.7
High CD318	$\geqslant$ 1.9	32	49.2

FAB: French-American-British; CR: Complete response to treatment, PR: partial response, RR: refractory response, RFS: relapse-free survival.

3.2 Relation between CD318 expression and basic clinical characteristics of the participants

At a cutoff value of $\leqslant$ 5.3 CD318 expression, patients were divided into two groups: high expression group $\geqslant$ 5.3 (49.3%) and low expression group $<$ 5.3 (50.7%). Significant differences were found between the groups concerning platelet count ( $P=$ 0.01), white blood cells (WBCs; $P<$ 0.001), BM blasts ( $P=$ 0.01), CD105 expression ( $P<$ 0.001), M2 subtype ( $P=$ 0.04), and poor cytogenetic risk ( $P=$ 0.01; Table 3).

Table 3
Relation between CD318 level and basic clinical characteristics of the studied cases

		Low CD318 $N=$ 33 (50.7%)		High CD318 $N=$ 32 (49.3%)		$t$ -test/ MW ${}^{\ast}$	$P$ value
		Mean $\pm$ SD		Mean $\pm$ SD
Age (years)		49.5 $\pm$ 11.5		52.3 $\pm$ 11.4		0.95	0.35
Platelets (10 ${}^{9}$ /L)		59.5 $\pm$ 10.9		52.2 $\pm$ 11.3		2.66	0.01 ${}^{*}$
WBCs (10 ${}^{9}$ /L)		59.7 $\pm$ 18.7		84.1 $\pm$ 21.2		4.84	$<$ 0.001 ${}^{**}$
Hemoglobin (g/dL)		7.4 $\pm$ 1.63		7.8 $\pm$ 1.41		1.08	0.29
PB blast		21.7 $\pm$ 12.7		25.2 $\pm$ 15.9		0.99 ${}^{\ast}$	0.32
BM blast		55.9 $\pm$ 24.8		72.3 $\pm$ 19.9		2.56 ${}^{\ast}$	0.01 ${}^{*}$
CD105		4.59 $\pm$ 0.89		8.3 $\pm$ 1.8		6.94 ${}^{\ast}$	$<$ 0.001 ${}^{**}$
CD318		1.58 $\pm$ 0.17		6.22 $\pm$ 1.1		6.95 ${}^{\ast}$	$<$ 0.001 ${}^{**}$
		$N$	%	$N$	%	$X^{2}$	$P$
Gender	Male	18	54.5	16	50	0.13	0.71
	Female	15	45.5	16	50
FAB	M0 M1 M2 M3 M4 M5	2 7 5 4 11 4	6.1 21.2 15.2 12.1 33.3 12.1	4 5 12 0 8 3	12.5 15.6 37.5 0.0 25 9.4	Fisher 0.34 4.21 Fisher 0.55 Fisher	0.37 0.56 0.04* 0.09 0.46 0.72
Cytogenetic risk	Favorable Intermediate Poor NOS	16 11 4 2	48.5 33.3 12.1 6.1	4 14 12 2	12.5 43.8 37.5 6.2	9.87 0.74 5.63 Fisher	0.001 ${}^{}$ 0.38 0.01 ${}^{}$ 0.97
Induction	Anthracycline Alternative	25 8	75.8 24.2	25 7	78.1 21.9	0.05	0.82
Cause	Primary	29	87.9	21	65.6	5.54	0.03 ${}^{*}$
	Secondary	4	12.1	11	34.4
Outcome	RFS	20	60.6	8	25	8.4	0.003 ${}^{*}$
	Dead	2	6.1	13	40.6	10.9	$<$ 0.001 ${}^{**}$
	Relapse	11	33.3	11	34.4	0.007	0.92
Response	CR	27	81.8	15	46.9	8.76	0.003 ${}^{*}$
	PR	4	12.1	6	18.8	0.54	0.45
	RR	2	6.1	11	34.4
High CD105	$\geqslant$ 5.3	2	6.1	29	90.6	Fisher	$<$ 0.001 ${}^{**}$

FAB: French-American-British; CR: Complete response to treatment, PR: partial response, RR: refractory response, RFS: relapse-free survival. ${}^{*}P$ -value $<$ 0.05 is significant, ${}^{**}P$ -value $<$ 0.001 is highly significant.

3.3 Relation between CD105 expression and basic clinical characteristics of the studied cases

Patients were divided according to the CD105 expression level at a cutoff value of $<$ 1.9 as low expression group (52.3%) and $\geqslant$ 1.9 as high expression group (47.7%). Comparing AML patients with high CD105 expression and those with low CD105 expression, a significant difference was found between the groups concerning platelets ( $P<$ 0.001), WBCs ( $P<$ 0.001), BM blasts ( $P=$ 0.02), CD318 expression ( $P<$ 0.001), M3 subtype ( $P=$ 0.04), and poor cytogenetic risk ( $P=$ 0.01; Table 4).

Table 4
Relation between CD105 level and basic clinical characteristics of studied cases

		Low CD105 $N=$ 34 (52.35%)		High CD105 $N=$ 31 (47.7%)		$t$ -test/ MW ${}^{\ast}$	$P$ value
		Mean $\pm$ SD		Mean $\pm$ SD
Age (years)		50.1 $\pm$ 11.6		51.7 $\pm$ 11.5		0.58	0.57
Platelets (10 ${}^{9}$ /L)		60.6 $\pm$ 9.7		50.6 $\pm$ 11.4		3.79	$<$ 0.001 ${}^{**}$
WBCs (10 ${}^{9}$ /L)		59.1 $\pm$ 18.8		85.5 $\pm$ 20.2		5.46	$<$ 0.001 ${}^{**}$
Hemoglobin (g/dL)		7.4 $\pm$ 1.61		7.8 $\pm$ 1.42		1.15	0.26
PB blast		21.3 $\pm$ 12.6		25.7 $\pm$ 15.9		1.32 ${}^{\ast}$	0.18
BM blast		56.3 $\pm$ 25.1		72.3 $\pm$ 19.5		2.41 ${}^{\ast}$	0.02 ${}^{*}$
CD105		2.8 $\pm$ 1.21		5.8 $\pm$ 1.8		6.92 ${}^{\ast}$	$<$ 0.001 ${}^{**}$
CD318		1.91 $\pm$ 1.88		4.87 $\pm$ 1.3		6.43 ${}^{\ast}$	$<$ 0.001 ${}^{**}$
		$N$	%	$N$	%	$X^{2}$	$P$
Gender	Male	20	58.8	14	45.2	1.21	0.27
	Female	14	41.2	17	54.8
FAB	M0 M1 M2 M3 M4 M5	2 6 6 4 12 4	5.9 17.6 17.6 11.8 35.3 11.8	4 6 11 0 7 3	12.9 19.4 35.5 0.0 22.6 9.7	0.95 0.31 2.67 3.88 1.26 0.07	0.32 0.85 0.10 0.04* 0.26 0.78
Cytogenetic risk	Favorable Intermediate Poor NOS	16 11 4 3	47.1 32.4 11.8 8.8	4 14 12 1	12.9 45.2 38.7 3.2	8.88 1.12 6.34 0.87	0.002 ${}^{}$ 0.29 0.01 ${}^{}$ 0.34
Induction	Anthracycline Alternative	25 9	73.5 26.5	25 6	80.6 19.4	0.46	0.497
Cause	Primary	30	88.2	20	64.5	5.14	0.02 ${}^{*}$
	Secondary	4	11.8	11	35.5
Outcome	RFS	18	52.9	10	32.3	2.89	0.09
	Dead	3	8.8	12	38.7	8.15	0.004 ${}^{*}$
	Relapse	13	38.2	9	29	0.61	0.43
Response	CR	26	76.5	16	51.6	4.38	0.03 ${}^{*}$
	PR	5	14.7	5	16.1	0.02	0.87
	RR	3	8.8	10	32.3
High CD318	$\geqslant$ 1.9	3	8.8	29	93.5	Fisher	$<$ 0.001 ${}^{**}$

3.4 Relation between high CD105 and high CD38 patients and basic clinical characteristics of studied cases

No significant difference was found between the two groups concerning any of the studied parameters ( $P>$ 0.05; Table 5).

Table 5
Relation between high CD105 and high CD38 patients and basic characteristics of studied cases

		High CD318 $N=$ 32		High CD105 $N=$ 31		$t$ -test/ MW ${}^{*}$	$P$ value
		Mean $\pm$ SD		Mean $\pm$ SD
Age (years)		52.3 $\pm$ 11.4		51.7 $\pm$ 11.5		0.21	0.84
Platelets (10 ${}^{9}$ /L)		52.2 $\pm$ 11.3		50.6 $\pm$ 11.4		0.39	0.91
WBCs (10 ${}^{9}$ /L)		84.1 $\pm$ 21.2		85.5 $\pm$ 20.2		0.27	0.79
Hemoglobin (gm/dl)		7.8 $\pm$ 1.41		7.8 $\pm$ 1.42		0.00	1.00
PB blast		25.2 $\pm$ 15.9		25.7 $\pm$ 15.9		0.42 ${}^{*}$	0.78
BM blast		72.3 $\pm$ 19.9		72.3 $\pm$ 19.5		0.00	1.00
		$N$	%	$N$	%	$X^{2}$	$P$
Gender	Male	16	50	14	45.2	1.21	0.27
	Female	16	50	17	54.8
FAB	M0 M1 M2 M3 M4 M5	4 5 12 0 8 3	12.5 15.6 37.5 0.0 25 9.4	4 6 11 0 7 3	12.9 19.4 35.5 0.0 22.6 9.7	Fisher 0.152 0.02 Fisher 0.05 Fisher	1.0 0.69 0.86 1.0 0.82 1.0
NCCN	Favorable Intermediate Poor NOS	4 14 12 2	12.5 43.8 37.5 6.2	4 14 12 1	12.9 45.2 38.7 3.2	Fisher Fisher Fisher 0.31	1.00 1.00 1.00 0.57
Induction	Anthra Alternative	25 7	78.1 21.9	25 6	80.6 19.4	0.114	0.57
Cause	Primary	21	65.6	20	64.5	0.104	0.72
	Secondary	11	34.4	11	35.5
Outcome	Alive	8	25	10	32.3	0.40	0.52
	Dead	13	40.6	12	38.7	0.02	0.57
	Relapse	11	34.4	9	29	0.20	0.64
Response	CR	15	46.9	16	51.6	0.14	0.70
	PR	6	18.8	5	16.1	0.07	0.78
	Ref	11	34.4	10	32.3

$P$ -value $>$ 0.05 is not significant.

Table 6

Correlation between CD105 and CD318 markers with other laboratory data

		CD105		CD318
CD105	$r$	1		0	.811
	$p$			$<$ 0	.001 ${}^{**}$
WBCs	$r$	0	.434	0	.331
	$p$	0	.003 ${}^{*}$	0	.01 ${}^{}$
Platelets	$r$	$-$ 0	.018	$-$ 0	.263
	$p$	0	.888	0	.04 ${}^{*}$
BM blast	$r$	0	.533	0	.425
	$p$	0	.009 ${}^{*}$	0	.03 ${}^{*}$
PB blast	$r$	0	.455	0	.607
	$p$	0	.02 ${}^{*}$	0	.004 ${}^{*}$
Hemoglobin	$r$	0	.138	0	.03 ${}^{*}$
	$p$	0	.289	0	.816

3.5 Correlation between CD105 and CD318 expression with other laboratory data

CD318 expression was positively correlated with CD105 expression ( $r=$ 0.811), WBCs ( $r=$ 0.331), BM blasts ( $r=$ 0.425), and PB blasts ( $r=$ 0.607). However, CD318 expression was negatively correlated with platelets ( $r=-$ 0.263). CD105 expression was positively correlated with WBCs ( $r=$ 0.434), BM blasts ( $r=$ 0.533), and PB blasts ( $r=$ 0.455; Table 6).

3.6 Response to treatment and patient outcomes among high and low CD318 and CD105 groups

CR, RR, and PR were detected in 46.9%, 34.4%, and 18.8% in the high CD318 expression group and 81.7%, 6.1%, and 12.1% in the low CD318 expression group, respectively, with a significant difference between the two groups regarding CR ( $P=$ 0.003). There was a significant difference between the groups concerning patient outcomes, such as RFS and death ( $P=$ 0.003 and $<$ 0.001, respectively). They were detected in 25% and 40.6% in the high expression group compared to 60.6% and 6.1% in the low expression group, respectively (Table 3).

CR was significantly different between patients with high CD105 expression (76.5%) and those with low CD105 expression (51.6%) ( $P=$ 0.03). There was a significant difference between groups concerning patient outcomes, such as death ( $P=$ 0.004). They were detected in 38.7% in the high expression group compared to 8.8% in the low expression group (Table 4).

Figure 1.

Kaplan-Meier overall survival analysis of studied cases according to CD105 expression. The mean time of survival for patients with high CD105 was 13.3 months and 22.7 months for patients with low CD105.

Figure 2.

Kaplan-Meier overall survival analysis of studied cases according to CD318 expression. The mean time of survival for patients with high CD318 was 13.9 months and 24 months for patients with low CD318.

3.7 CD318 and CD105 expression concerning survival

After a follow-up period of up to 24 months, the mean survival time was 13.9 and 13.3 months, and the number of deaths was 13 and 12 patients in the high CD318 and CD105 expression groups, respectively. The mean survival time was 24 and 22.7 months, and the number of deaths was 2 and 3 patients in the low CD318 and CD105 expression groups, respectively. There were significant differences between the groups ( $P=$ 0.02 and 0.004, respectively; Table 7; Figs 1 and 2).

Table 7
Survival study of CD318 and CD105 by log-rank test

		Estimated mean of survival/month	Total patients	N of deaths	Survival	$P$
CD318	High	13.9	32	13	19	0.02 ${}^{*}$
	Low	24	33	2	31
CD105	High	13.3	31	12	19	0.004 ${}^{*}$
	Low	22.7	34	3	31

${}^{*}P$ -value $<$ 0.05 is significant.

4. Discussion

CD318 and CD105 overexpression has been correlated with poor OS in different solid tumors [19, 20, 21, 22, 23, 24, 25, 26]. Their prognostic role in AML is not far fully evaluated. This study assessed the significance of CD318 and CD105 as novel prognostic markers in AML and their impact on patient outcomes. This study found that 49.3% and 47.7% of the patients showed high CD318 and CD105 expression, respectively. These results were similar that of Heitmann et al., who detected high CD318 protein expression in 42.8% of AML patients [28], and Kauer et al., who detected high CD105 protein expression in 51.6% of AML patients [29]. Our study is different from the previous two studies in the combined study of the two markers together from gene expression view for the first time.

Our study found that FAB M2 subtype, poor cytogenetic risk, secondary cause and WBC count were significantly increased, but favorable cytogenetic risk and platelet count were significantly decreased in high CD318 AML patients compared to low CD318 patients. CD318 was also significantly correlated with CD105, WBC count, platelet count, BM blasts, and PB blasts. These results are supported by Heitmann et al., who detected a significantly higher percentage of poor cytogenetic risk, M2 FAB subtype, and secondary AML patients in the CD318 high expression group. In contrast, they detected that the WBC count was significantly increased in the CD318 low expression group [28].

In this study, WBC count, BM blasts, CD318 expression, poor cytogenetic risk, and secondary AML were significantly higher, but platelet count and favorable cytogenetic risk were significantly lower in patients with high CD105 expression compared to those with low CD105 expression. The percentage of patients with the FAB M2 subtype was increased in the CD105 high expression group compared to the low CD105 expression group, but no significant difference was detected. CD105 was significantly correlated with CD318, WBC count, platelet count, BM blasts, and PB blasts. Following these results, Kauer et al. detected a significant difference between patients with high and low CD105 protein expression in AML concerning WBC count, poor cytogenetic risk, and FAB classification, but no significant difference was found in platelet count [29]. Similar results were detected by Abd El-Ghany et al., who found a significant correlation between CD105 with WBC count and BM blasts but no significant correlation with platelet count [33]. In contrast, Märklin et al. detected significant differences in CD105 expression regarding FAB classification and NCCN risk score but no significant difference between primary and secondary AML concerning CD105 expression, after allogenic stem cell transplantation [30].

Cytogenetic changes are the strongest prognostic factors for CR and OS in AML. AML is classified into favorable, intermediate, or adverse prognostic risk groups depending on their cytogenetic profile. The chromosomal rearrangements have a favorable prognosis. CN-AML with a mutated CEBPA or a mutated NPM1 in the absence of FLT3-ITD has a prognostic risk like that of AML with favorable cytogenetic changes. FLT3-ITD is associated with a poor prognosis. TP53 mutations, found in 2–8% of cases and frequently associated with unfavorable cytogenetic and a complex karyotype, are associated with a very poor prognosis and it is the single worst genetic prognostic factor. Mutated DNA methyl transferase gene (DNMT)3A is associated with a poor prognosis in CN-AML and adverse-risk AML. MLL, which encodes a histone methyltransferase, is associated with a poor prognosis in CN-AML. The prognostic impact of IDH-1/IDH-2 mutations is likely modified by co-occurring mutations. Among cases of FLT3-ITD-negative and NPM1-mutated CN-AML, IDH-1/IDH-2 mutations have been shown to improve OS [5].

On comparing between patients with high and low CD318 and CD105 concerning the response to treatment and patient outcomes, patients with RR were significantly increased in the high CD318 and CD105 groups. Dead patients were significantly increased in patients with high CD318 and CD105, as well. CR was significantly higher in patients with low CD318 and CD105. In agreement with these results, Kauer et al. found that CR after induction therapy occurred in a significantly higher number of cases in the CD105 low expression group than in the CD105 high expression group, in line with the observation that the CD105 high expression group comprised more poor cytogenetic risk [29]. Märklin et al. detected CR in 62% of the low CD105 expression group and 57% of the high CD105 expression group [30]. Number of patients with RFS were significantly higher in low CD318 expression group compared to high CD318 expression group

After a follow-up period of up to 24 months, the survival study revealed that the mean survival time in CD318 and CD105 was shorter in the high expression groups than the low expression groups, and the numbers of dead patients were higher in the high expression groups than the low expression groups. The results followed that of Märklin et al., who reported that low CD105 expression was significantly correlated with a better response to induction therapy, and high CD105 levels were associated with poor OS [30].

Kauer et al. stated that the association of CD105 with inferior OS was unclear and speculated that CD105 might have influenced the occurrence of treatment resistance and reduced the response [29]. Hand by hand with this study, Abd El-Ghany et al. detected that patients with positive CD105 expression had a significantly lower CR rate and a significantly higher relapse and death rate. They also found a significant decrease in both OS and DFS in the positive CD105 expression group [33]. Dourado et al. reported that CD105 was associated with the increased leukemogenic activity of blast cells, and this may decrease both OS and DFS [34].

Poor prognosis with high CD105 expression can be attributed to that CD105 induces the activation and proliferation of endothelial cells through the promotion of TGF- $\beta$ /ALK-1 signaling [35]. This activates bone morphogenetic protein-2 [36]. CD105 expression is upregulated by hypoxic endothelium via hypoxia-inducible factor (HIF)-1 $\alpha$ , which inhibits the apoptosis of endothelial cells and drives angiogenesis [37, 38]. HIF-1 $\alpha$ upregulation on AML blasts is correlated with multidrug resistance, leading to poor CD105 prognosis [39].

In contrast, Heitmann et al. failed to detect a significant difference between high and low CD318 protein levels in AML concerning OS. However, when they grouped patients according to the applied treatment, they found that patients with high CD318 who received the best available alternative therapy showed increased survival, but those who received intense AML treatment and expressed low CD318 had longer OS [28]. They attributed this difference with treatment to the dependency of AML blasts on the promoter methylation of tumor suppressor genes [40], which is inhibited by hypomethylating agents [41]. Therefore, CD318 could be used as a marker for the degree of DNA methylation in AML [28].

In lung carcinoma, CD318 expression has been associated with the presence of genetic aberrations, such as Ras mutations [42]. CD318 expression is induced via HIF-2 $\alpha$ [43]. HIF-2 $\alpha$ protects hematopoietic progenitors and AML cells from apoptosis due to endoplasmic reticulum stress [44]. CD318 has been attributed to the enhanced metastasis formation by its interaction with activated integrin- $\beta$ 1 during stromal invasion and trans endothelial migration [45]. Metastasis promotion by CD318 includes cleaving the membrane-bound protein by serine proteases and subsequent phosphorylation by Src kinases [46]. Finally, Akt activation inhibits poly (ADP-ribose) polymerase 1-mediated apoptosis and promotes tumor cell survival during extravasation and tissue invasion [47].

5. Conclusion

CD318 and CD105 are upregulated in AML. They are significantly correlated with poor patient outcomes. Therefore, they can be useful novel prognostic markers in AML. Besides, a phase II clinical trial of TRC105 (anti-endoglin antibody) in adults with advanced/metastatic urothelial carcinoma was performed. Monoclonal antibodies are recommended in AML as they exert anti-tumor activity through direct antibody-dependent cytotoxicity or through the conjugation of cytotoxic agents to ensure targeted delivery of potent chemotherapy to neoplastic cells avoiding long-term myelosuppression with selectively target leukemic myeloid cells. This is considered a targeted delivery of potent chemotherapy to neoplastic cells.

Funding

None.

Author’s contributions

Conception: Huda F. Ebian and Samia Hussein.

Interpretation or analysis of data: Rasha L. Etewa, Dina R Issa, Amira S. Al-Karamany, Hanaa M. El Maghraby and Samia Hussein.

Preparation of the manuscript: Huda F. Ebian and Samia Hussein.

Revision for important intellectual content: All authors.

Supervision: Huda F. Ebian and Samia Hussein.

Footnotes

Acknowledgments

None.

Conflict of interest

None.

References

Shallis

R.M.

Wang

Davidoff

and Zeidan

A.M.

, Epidemiology of acute myeloid leukemia: Recent progress and enduring challenges, Blood Reviews 36 (2019), 70–87.

Shah

Andersson

T.M.

Rachet

Björkholm

and Lambert

P.C.

, Survival and cure of acute myeloid leukaemia in England, 1971–2006: A population-based study, British journal of haematology 162(4) (2013), 509–516.

Sekeres

M.A.

Guyatt

Abel

Alibhai

et al., American Society of Hematology 2020 guidelines for treating newly diagnosed acute myeloid leukemia in older adults, Blood Adv, Published online August 11, 2020. doi: 10.1182/bloodadvances.2020001920.

Sill

Olipitz

Zebisch

Schulz

and Wölfler

, Therapy-related myeloid neoplasms: Pathobiology and clinical characteristics, British Journal of Pharmacology 162(4) (2011), 792–805.

De Kouchkovsky

and Abdul-Hay

, Acute myeloid leukemia: A comprehensive review and 2016 update, Blood Cancer Journal 6 (2016), e441.

Patel

J.P.

Gönen

Figueroa

M.E.

Fernandez

Sun

Racevskis

Van Vlierberghe

Dolgalev

Thomas

Aminova

Huberman

Cheng

Viale

Socci

N.D.

Heguy

Cherry

Vance

Higgins

R.R.

Ketterling

R.P.

Gallagher

R.E.

Litzow

van den Brink

M.R.

Lazarus

H.M.

Rowe

J.M.

Luger

Ferrando

Paietta

Tallman

M.S.

Melnick

Abdel-Wahab

and Levine

R.L.

, Prognostic relevance of integrated genetic profiling in acute myeloid leukemia, New England Journal of Medicine 366 (2012), 1079–1089.

Cancer Genome Atlas Research Network Ley

T.J.

Miller

Ding

Raphael

B.J.

Mungall

A.J.

Robertson

Hoadley

Triche

T.J.

Laird

P.W.

Baty

J.D.

Fulton

L.L.

Fulton

Heath

S.E.

Kalicki-Veizer

Kandoth

Klco

J.M.

Koboldt

D.C.

Kanchi

K.L.

Kulkarni

Lamprecht

T.L.

Larson

D.E.

Lin

McLellan

M.D.

McMichael

J.F.

Payton

Schmidt

Spencer

D.H.

Tomasson

M.H.

Wallis

J.W.

Wartman

L.D.

Watson

M.A.

Welch

Wendl

M.C.

Ally

Balasundaram

Birol

Butterfield

Chiu

Chu

Chuah

Chun

H.J.

Corbett

Dhalla

Guin

Hirst

Holt

R.A.

Jones

Karsan

Lee

H.I.

Marra

M.A.

Mayo

Moore

R.A.

Mungall

Parker

Pleasance

Plettner

Schein

Stoll

Swanson

Tam

Thiessen

Varhol

Wye

Zhao

Gabriel

Getz

Sougnez

Zou

Leiserson

M.D.

Vandin

H.T.

Applebaum

Baylin

S.B.

Akbani

Broom

B.M.

Chen

Motter

T.C.

Nguyen

Weinstein

J.N.

Zhang

Ferguson

M.L.

Adams

Black

Bowen

Gastier-Foster

Grossman

Lichtenberg

Wise

Davidsen

Demchok

J.A.

Shaw

K.R.

Sheth

Sofia

H.J.

Yang

Downing

J.R.

and Eley

, Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia, New England Journal of Medicine 368 (2013), 2059–2074.

Gilliland

D.G.

and Griffin

J.D.

, The roles of FLT3 in hematopoiesis and leukemia, Blood 100 (2002), 1532–1542.

Takahashi

, Current findings for recurring mutations in acute myeloid leukemia, Journal of Hematology & Oncology 4 (2011), 36.

10.

Suguna

Farhana

Kanimozhi

Kumar

P.S.

Kumaramanickavel

and Kumar

C.S.

, Acute myeloid leukemia: Diagnosis and management based on current molecular genetics approach, Cardiovascular & Hematological Disorders Drug Targets 18(3) (2018), 199–207.

11.

Mrózek

Heerema

N.A.

and Bloomfield

C.D.

, Cytogenetics in acute leukemia, Blood Reviews 18 (2004), 115–136.

12.

Enyindah-Asonye

Ruth

J.H.

Spassov

D.S.

Hebron

K.E.

Zijlstra

Moasser

M.M.

Wang

Singer

N.G.

Cui

Ohara

R.A.

Rasmussen

S.M.

Fox

D.A.

and Lin

, CD318 is a ligand for CD6, Proceedings of the National Academy of Sciences of the United States of America 114 (2017), E6912–E6921.

13.

Perry

S.E.

Robinson

Melcher

Quirke

Bühring

H.J.

Cook

G.P.

and Blair

G.E.

, Expression of the CUB domain containing protein 1 (CDCP1) gene in colorectal tumour cells, FEBS Letters 581 (2007), 1137–1142.

14.

Alajati

Guccini

Pinton

Garcia-Escudero

Bernasocchi

Sarti

Montani

Rinaldi

Montemurro

Catapano

Bertoni

and Alimonti

, Interaction of CDCP1 with HER2 enhances HER2-driven tumorigenesis and promotes trastuzumab resistance in breast cancer, Cell Reports 11 (2015), 564–576.

15.

Awakura

Nakamura

Takahashi

Kotani

Mikami

Kadowaki

Myoumoto

Akiyama

Ito

Kamoto

Manabe

Nobumasa

Tsujimoto

and Ogawa

, Microarray-based identification of CUB-domain containing protein 1 as a potential prognostic marker in conventional renal cell carcinoma, Journal of Cancer Research & Clinical Oncology 134 (2008), 1363–1369.

16.

Cao

Gao

Zhou

Huang

You

Guo

Fang

Zhang

Song

and Zhang

, HIF-2α regulates CDCP1 to promote PKδ-mediated igration in hepatocellular carcinoma, Tumour Biology 37 (2016), 1651–1662.

17.

Miyazawa

Uekita

Hiraoka

Fujii

Kosuge

Kanai

Nojima

and Sakai

, CUB domain-containing protein 1, a prognostic factor for human pancreatic cancers, promotes cell migration and extracellular matrix degradation, Cancer Research 70 (2010), 5136–5146.

18.

Pomeraniec

Hector-Greene

Ehrlich

Blobe

G.C.

and Henis

Y.I.

, Regulation of TGF-beta receptor hetero-oligomerization and signaling by endoglin, Molecular Biology of the Cell 26 (2015), 3117–3127.

19.

Lebrin

Goumans

M.J.

Jonker

Carvalho

R.L.

Valdimarsdottir

Thorikay

Mummery

Arthur

H.M.

and ten Dijke

, Endoglin promotes endothelial cell proliferation and TGF-beta/ALK1 signal transduction, EMBO Journal 23 (2004), 4018–4028.

20.

Smith

A.L.

Robin

T.P.

and Ford

H.L.

, Molecular pathways: Targeting the TGF-beta pathway for cancer therapy, Clinical Cancer Research 18 (2012), 4514–4521.

21.

Minhajat

Mori

Yamasaki

Sugita

Satoh

and Tokunaga

, Organ-specific endoglin (CD105) expression in the angiogenesis of human cancers, Pathology International 56 (2006), 717–723.

22.

Cosimato

Scalia

Raia

Gentile

Cerbone

Visconte

Statuto

Valvano

D’Auria

Calice

Graziano

Musto

and Del Vecchio

, Surface endoglin (CD105) expression on acute leukemia blast cells: An extensive flow cytometry study of 1002 patients, Leukemia & Lymphoma 59 (2018), 2242–2245.

23.

Pérez-Gómez

Del Castillo

Juan Francisco

López-Novoa

J.M.

Bernabéu

and Quintanilla

, The role of the TGF-β coreceptor endoglin in cancer, The Scientific World Journal 10 (2010), 2367–2384.

24.

El-Gohary

Y.M.

Silverman

J.F.

Olson

P.R.

Liu

Y.L.

Cohen

J.K.

Miller

and Saad

R.S.

, Endoglin (CD105) and vascular endothelial growth factor as prognostic markers in prostatic adenocarcinoma, American Journal of Clinical Pathology 127 (2007), 572–579.

25.

Saad

R.S.

Liu

Y.L.

Nathan

Celebrezze

Medich

and Silverman

J.F.

, Endoglin (CD105) and vascular endothelial growth factor as prognostic markers in colorectal cancer, Modern Pathology 17 (2004), 197–203.

26.

Dales

J.P.

Garcia

Bonnier

Duffaud

Andrac-Meyer

Ramuz

Lavaut

M.N.

Allasia

and Charpin

, CD105 expression is a marker of high metastatic risk and poor outcome in breast carcinomas, Correlations between immunohistochemical analysis and long-term follow-up in a series of 929 patients, American Journal of Clinical Pathology 119 (2003), 374–380.

27.

Kasprzak

and Adamek

, Role of endoglin (CD105) in the progression of hepatocellular carcinoma and anti-angiogenic therapy, International Journal of Molecular Sciences 19 (2018), 3887.

28.

Heitmann

J.S.

Hagelstein

Hinterleitner

Roerden

Jung

Salih

H.R.

Märklin

and Kauer

, Identification of CD318 (CDCP1) as novel prognostic marker in AML, Annals of Hematology 99 (2020), 477–486.

29.

Kauer

Schwartz

Tandler

Hinterleitner

Roerden

Jung

Salih

H.R.

Heitmann

J.S.

and Märklin

, CD105 (endoglin) as negative prognostic factor in AML, Scientific Reports 9 (2019), 18337.

30.

Märklin

Hagelstein

Hinterleitner

Salih

H.R.

Kauer

and Heitmann

J.S.

, CD105 (endoglin) as risk marker in AML patients undergoing stem cell transplantation, International Journal of Hematology 112 (2020), 57–64.

31.

Bennett

J.M.

Catovsky

Daniel

M.T.

Flandrin

Galton

D.A.

Gralnick

H.R.

and Sultan

, Proposals for the classification of the acute leukaemias, French-American-British (FAB) co-operative group, French, British Journal of Haematology 33 (1976), 451–458.

32.

De Greef

G.E.

van Putten

W.L.J.

Boogaerts

Huijgens

P.C.

Verdonck

L.F.

Vellenga

Theobald

Jacky

and Löwenberg

, Dutch-belgian hemato-oncology co-operative group HOVON and swiss group for clinical cancer research SAKK, criteria for defining a complete remission in acute myeloid leukaemia revisited, An analysis of patients treated in HOVON-SAKK co-operative group studies, British Journal of Haematology 128 (2005), 184–191.

33.

Abd El-Ghany

S.S.

Ahmed

A.Y.

Gawaly

A.M.

and Hazzaa

S.M.

, Role of endoglin expression in acute myeloblastic leukemia, The Medical Journal of Cairo University 87(June) (2019), 1395–1402.

34.

Dourado

K.M.C.

Baik

Oliveira

V.K.P.

Beltrame

Yamamoto

Theuer

C.P.

Figueiredo

C.A.V.

Verneris

M.R.

and Perlingeiro

R.C.R.

, Endoglin: A novel target for therapeutic intervention in acute leukemias revealed in xenograft mouse models, Blood 129 (2017), 2526–2536.

35.

Goumans

M.J.

Liu

and ten Dijke

, TGF-beta signaling in vascular biology and dysfunction, Cell Research 19 (2009), 116–127.

36.

Barbara

N.P.

Wrana

J.L.

and Letarte

, Endoglin is an accessory protein that interacts with the signaling receptor complex of multiple members of the transforming growth factor-beta superfamily, Journal of Biological Chemistry 274 (1999), 584–594.

37.

Dallas

N.A.

Samuel

Xia

Fan

Gray

M.J.

Lim

S.J.

and Ellis

L.M.

, Endoglin (CD105): A marker of tumor vasculature and potential target for therapy, Clinical Cancer Research 14 (2008), 1931–1937.

38.

Issa

Kumar

Hampson

I.N.

Lopez-Novoa

J.M.

Bernabeu

and Kumar

, CD105 prevents apoptosis in hypoxic endothelial cells, Journal of Cell Science 116 (2003), 2677–2685.

39.

Wang

Jiang

Ouyang

and Chen

, The association between expression of hypoxia inducible factor-α and multi-drug resistance of acute myeloid leukemia, Translational Cancer Research 6 (2017), 198–205.

40.

Herman

J.G.

and Baylin

S.B.

, Gene silencing in cancer in association with promoter hypermethylation, New England Journal of Medicine 349 (2003), 2042–2054.

41.

Covey

J.M.

D’Incalci

Tilchen

E.J.

Zaharko

D.S.

and Kohn

K.W.

, Differences in DNA damage produced by incorporation of 5-aza-2′-deoxycytidine or 5,6-dihydro-5-azacytidine into DNA of mammalian cells, Cancer Research 46 (1986), 5511–5517.

42.

Uekita

Fujii

Miyazawa

Iwakawa

Narisawa-Saito

Nakashima

Tsuta

Tsuda

Kiyono

Yokota

and Sakai

, Oncogenic Ras/ERK signaling activates CDCP1 to promote tumor invasion and metastasis, Molecular Cancer Research 12 (2014), 1449–1459.

43.

Emerling

B.M.

Benes

C.H.

Poulogiannis

Bell

E.L.

Courtney

Liu

Choo-Wing

Bellinger

Tsukazawa

K.S.

Brown

Signoretti

Soltoff

S.P.

and Cantley

L.C.

, Identification of CDCP1 as a hypoxia-inducible factor 2α (HIF-α) target gene that is associated with survival in clear cell renal cell carcinoma patients, Proceedings of the National Academy of Sciences of the United States of America 110 (2013), 3483–3488.

44.

Rouault-Pierre

Lopez-Onieva

Foster

Anjos-Afonso

Lamrissi-Garcia

Serrano-Sanchez

Mitter

Ivanovic

de Verneuil

Gribben

Taussig

Rezvani

H.R.

Mazurier

and Bonnet

, HIF-α protects human hematopoietic stem/progenitors and acute myeloid leukemic cells from apoptosis induced by endoplasmic reticulum stress, Cell Stem Cell 13 (2013), 549–563.

45.

Casar

Rimann

Kato

Shattil

S.J.

Quigley

J.P.

and Deryugina

E.I.

, In vivo cleaved CDCP1 promotes early tumor dissemination via complexing with activated β1 integrin and induction of FAK/PI3K/Akt motility signaling, Oncogene 33 (2014), 255–268.

46.

Casar

Iconomou

Hooper

J.D.

Quigley

J.P.

and Deryugina

E.I.

, Blocking of CDCP1 cleavage in vivo prevents Akt-dependent survival and inhibits metastatic colonization through PARP1-mediated apoptosis of cancer cells, Oncogene 31 (2012), 3924–3938.

47.

Deryugina

E.I.

Conn

E.M.

Wortmann

Partridge

J.J.

Kupriyanova

T.A.

Ardi

V.C.

Hooper

J.D.

and Quigley

J.P.

, Functional role of cell surface CUB domain-containing protein 1 in tumor cell dissemination, Molecular Cancer Research 7 (2009), 1197–1211.

Evaluation of CDCP1 ( CD318 ) and endoglin ( CD105 ) expression as prognostic markers in acute myeloid leukemia

Abstract

BACKGROUND:

OBJECTIVES:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

Abbreviations

1. Introduction

2. Materials and methods

2.1 Study design

2.2 Flow cytometric analysis

Table 1 Primer sequence of the studied genes

2.4 Treatment plan

2.5 Statistical analysis

3. Results

3.1 Basic clinicodemographic characteristics and outcomes among the participants

Table 2 Demographic and basic characteristics among the participants

Table 3 Relation between CD318 level and basic clinical characteristics of the studied cases

Table 4 Relation between CD105 level and basic clinical characteristics of studied cases

Table 5 Relation between high CD105 and high CD38 patients and basic characteristics of studied cases

3.6 Response to treatment and patient outcomes among high and low CD318 and CD105 groups

Table 7 Survival study of CD318 and CD105 by log-rank test

5. Conclusion

Funding

Author’s contributions

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Primer sequence of the studied genes

Table 2
Demographic and basic characteristics among the participants

Table 3
Relation between CD318 level and basic clinical characteristics of the studied cases

Table 4
Relation between CD105 level and basic clinical characteristics of studied cases

Table 5
Relation between high CD105 and high CD38 patients and basic characteristics of studied cases

Table 7
Survival study of CD318 and CD105 by log-rank test