Upregulation of lnc-FOXD2-AS1, CDC45, and CDK1 in patients with primary non-M3 AML is associated with a worse prognosis

Saba Manoochehrabadi; Morteza Talebi; Hossein Pashaiefar; Soudeh Ghafouri‑Fard; Mohammad Vaezi; Mir Davood Omrani; Mohammad Ahmadvand

doi:10.1007/s44313-024-00002-0

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Blood Res (2024) 59:4

Published online February 19, 2024

https://doi.org/10.1007/s44313-024-00002-0

Upregulation of lnc-FOXD2-AS1, CDC45, and CDK1 in patients with primary non-M3 AML is associated with a worse prognosis

Saba Manoochehrabadi¹, Morteza Talebi², Hossein Pashaiefar³, Soudeh Ghafouri‑Fard¹, Mohammad Vaezi³, Mir Davood Omrani^1* and Mohammad Ahmadvand^3*

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¹Department of Medical Genetics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. ²Department of Medical Genetics and Molecular Biology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran. ³Cell Therapy and Hematopoietic Stem Cell Transplantation Research Center, Hematology and Cell Therapy, Research Institute for Oncology, Tehran University of Medical Sciences, Tehran, Iran.

Correspondence to : *Mir Davood Omrani
davood_omrani@yahoo.co.uk
Mohammad Ahmadvand
mahmadvand@sina.tums.ac.ir

Received: September 12, 2023; Accepted: January 3, 2024

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Abstract

Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy with an unfavorable outcome. The present research aimed to identify novel biological targets for AML diagnosis and treatment. In this study, we performed an in-silico method to identify antisense RNAs (AS-RNAs) and their related co-expression genes. GSE68172 was selected from the AML database of the Gene Expression Omnibus and compared using the GEO2R tool to find DEGs. Antisense RNAs were selected from all the genes that had significant expression and a survival plot was drawn for them in the GEPIA database, FOXD2-AS1 was chosen for further investigation based on predetermined criteria (logFC ≥|1| and P < 0.05) and its noteworthy association between elevated expression level and a marked reduction in the overall survival (OS) in patients diagnosed with AML. The GEPIA database was utilized to investigate FOXD2-AS1-related co-expression and similar genes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and gene ontology (GO) function analysis of the mentioned gene lists were performed using the DAVID database. The protein–protein interaction (PPI) network was then constructed using the STRING database. Hub genes were screened using Cytoscape software. Pearson correlation analysis was conducted using the GEPIA database to explore the relationship between FOXD2-AS1 and the hub genes. The transcription of the selected coding and non-coding genes, including FOXD2-AS1, CDC45, CDC20, CDK1, and CCNB1, was validated in 150 samples, including 100 primary AML non-M3 blood samples and 50 granulocyte colony stimulating factor (G-CSF)-mobilized healthy donors, using quantitative Real-Time PCR (qRT-PCR). qRT-PCR results displayed significant upregulation of lnc-FOXD2-AS1, CDC45, and CDK1 in primary AML non-M3 blood samples compared to healthy blood samples (P = 0.0032, P = 0.0078, and P = 0.0117, respectively). The expression levels of CDC20 and CCNB1 were not statistically different between the two sets of samples (P = 0.8315 and P = 0.2788, respectively). We identified that AML patients with upregulation of FOXD2-AS1, CDK1, and CDC45 had shorter overall survival (OS) and Relapse-free survival (RFS) compared those with low expression of FOXD2-AS1, CDK1, and CDC45. Furthermore, the receiver operating characteristic (ROC) curve showed the potential biomarkers of lnc -FOXD2-AS1, CDC45, and CDK1 in primary AML non-M3 blood samples. This research proposed that the dysregulation of lnc-FOXD2-AS1, CDC45, and CDK1 can contribute to both disease state and diagnosis as well as treatment. The present study proposes the future evolution of the functional role of lnc-FOXD2-AS1, CDC45, and CDK1 in AML development.

Keywords Acute myeloid leukemia, Bioinformatics, Lnc -FOXD2-AS1, CDC45, CDK1, CDC20 and CCNB1

Article

RESEARCH

Blood Res 2024; 59():

Published online February 19, 2024 https://doi.org/10.1007/s44313-024-00002-0

Upregulation of lnc-FOXD2-AS1, CDC45, and CDK1 in patients with primary non-M3 AML is associated with a worse prognosis

Saba Manoochehrabadi¹, Morteza Talebi², Hossein Pashaiefar³, Soudeh Ghafouri‑Fard¹, Mohammad Vaezi³, Mir Davood Omrani^1* and Mohammad Ahmadvand^3*

Correspondence to:*Mir Davood Omrani
davood_omrani@yahoo.co.uk
Mohammad Ahmadvand
mahmadvand@sina.tums.ac.ir

Received: September 12, 2023; Accepted: January 3, 2024

Abstract

Keywords: Acute myeloid leukemia, Bioinformatics, Lnc -FOXD2-AS1, CDC45, CDK1, CDC20 and CCNB1

Abstract

Fig 1.

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Figure 1.KEGG A and biological pathway analysis B

Blood Research 2024; 59: https://doi.org/10.1007/s44313-024-00002-0

Fig 2.

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Figure 2.Relative expression in non-M3 AML cases and healthy groups detected by qRT-PCR analysis

Blood Research 2024; 59: https://doi.org/10.1007/s44313-024-00002-0

Fig 3.

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Figure 3.ROC curve analysis of lnc -FOXD2-AS1 and related genes as biomarkers to discriminate non-M3 AML patients from healthy controls. A, B, C The sensitivity and specificity of lnc -FOXD2-AS1, CDK1 and CDC45 transcription were 82% and 70% (AUC = 0.643, P = 0. 004). 72% and 50% (AUC = 0.62, P = 0.012). 45% and 78% (AUC = 0.64, P = 0.003) respectively

Blood Research 2024; 59: https://doi.org/10.1007/s44313-024-00002-0

Fig 4.

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Figure 4.Kaplan–Meier curves of OS and RFS and of non-M3 AML cases stratified by the FOXD2-AS1, CDK1 and CDC45 expression levels. Patients with high FOXD2-AS1, CDK1 and CDC45 expression levels had shorter RFS and OS than those with low FOXD2-AS1 CDK1 and CDC45 transcription levels (p = 0.01 and 0.002, p = 0.05 and 0.02, p = 0.03 and 0.02, respectively)

Blood Research 2024; 59: https://doi.org/10.1007/s44313-024-00002-0

Table 1 . Clinicopathological characteristics of Non-M3 AML cases and transcription status of FOXD2AS1, CDK1 and CDC45.

Patients’ parameters	High FOXD2AS1 expression (n = 50)	Low FOXD2AS1 expression (n = 50)	P value	High CDK1 expression (n = 50)	Low CDK1 expression (n = 50)	P value	High CDC45 expression (n = 50)	Low CDC45 expression (n = 50)	P value
Sex, male/female	30/20	25/25	0.314879	25/25	30/20	0.314879	24/26	31/19	0.159412
Median age, years (range)	57 (16–75)	55 (20–68)	0.215444	55 (16–74)	56 (21–75)	0.602357	55 (16–75)	56 (20–74)	0.371669
Median WBC, × 10⁹/L (range)	64 (3–163)	48 (10–140)	0.025866^*	59 (3–163)	48 (4–135)	0.005017^*	65 (3–163)	44 (4–135)	0.002552^*
Median hemoglobin, g/L (range)	8 (4–15)	8.3 (3.9–12)	0.436450	8 (3.9–15)	8.25 (4.6–14)	0.058183^*	8 (4–12)	9 (5–15)	0.005790^*
Median platelets, × 10⁹/L (range)	54.5 (4–302)	58.5 (4–190)	0.881138	55 (4–302)	56 (4–170)	0.746145	53 (4–302)	59 (4–190)	0.375813
BM blasts, % (range)	45.5 (12–90)	36 (20–92)	0.00054^*	38 (12–90)	36 (18–92)	0.194970	38 (12–92)	36 (18–90)	0.587286
FAB, n (%)
M0	9	9		8	10		10	8
M1	13	15		18	10	0.214784	16	12
M2	16	14	0.975619	13	17		13	17	0.735896
M4	8	9		6	11		7	10
M5	4	3		5	2		4	3
M6	-	-		-	-		-	-
Karyotype classification, n (%)
Favorable	29	36	0.142213	27	38	0.021098^*	30	35	0.294507
AND Intermediate
Unfavorable	21	14		23	12		20	15
Gene mutation
NPM1 (+ /–)	9/41	6/44	0.400814	6/44	9/41	0.400814	7/43	8/42	0.779435
FLT3-ITD (+ /–)	15/35	10/40	0.248213	8/42	17/33	0.37667	8/42	17/33	0.037667^*
Response to treatment, n (%)
CR	33	38	0.270506	36	35	0.825575	36	35	0.825575
NR	17	12		14	15		14	15
Genetic risk (ELN)
Favorable	13	14	0.1023	13	16	0.04120^*	14	15	0.1785
Intermediate	19	20		17	20		18	19
Poor	18	16		20	14		18	16