基于急性髓系白血病患者骨髓单个核细胞中诊断候选基因筛选鉴定及其对预后影响的生物信息学分析和实验验证

doi:10.13481/j.1671-587X.20260321

Abstract

Abstract:

Objective To discuss the diagnostic value of differentially expressed genes （DEGs） in bone marrow mononuclear cells of the patients with acute myeloid leukemia （AML）， and to clarify their clinical significance and potential functions. Methods The GSE9476 dataset （bone marrow samples from 26 AML patients and 10 healthy donors） was obtained from the Gene Expression Omnibus （GEO） database； GEO2R was used to screen the DEGs the fold change （FC） was calculated， and |log₂FC|≥2， P≤0.05， and adj.P≤0.05 were the criteria； the DAVID database was used to perform Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） signaling pathway enrichment analysis； STRING was used to construct a protein-protein interaction（PPI） network； Cytoscape was used to screen core， key， and bottleneck genes； the Venn diagram was used to obtain diagnostic candidate genes by intersection； the GEPIA and Kaplan-Meier Plotter online databases were used to test the expression differences of the candidate genes and their significance with patient survival； real-time fluorescence quantitative PCR （RT-qPCR） was used to validate the related mRNA expression levels in bone marrow mononuclear cells from clinical specimens of newly diagnosed non-acute promyelocytic leukemia （APL） type AML patients and healthy donors； through recombinant plasmid construction， lentiviral packaging and infection， AML cell models overexpressing platelet factor 4 （PF4） and pro-platelet basic protein （PPBP） were established； CCK-8 method was used to detect the proliferation activities of the cells in various groups； Western blotting method was used to detect the expression levels of apoptosis-related proteins in the cells in various groups. Results A total of 496 DEGs were screened， including 69 up-regulated genes and 427 down-regulated genes. The enrichment analysis results showed that the DEGs were mainly involved in immune response， cell cycle， and chemokine signaling pathways. The PPI network analysis identified 10 candidate genes， which were cyclin-dependent kinase 1 （CDK1）， CD36 molecule （CD36）， Runt-related transcription factor 1 （RUNX1）， CD3 delta subunit of T-cell receptor complex （CD3D）， vascular endothelial growth factor A （VEGFA）， cyclin B1 （CCNB1）， amyloid beta precursor protein （APP）， Src family tyrosine kinase （FYN）， PPBP， and PF4. The results from GEPIA and Kaplan-Meier Plotter online databases showed that compared with control group， the expression levels of APP， CD36， FYN， RUNX1， PF4， and PPBP mRNA in bone marrow aspirate fluid of the patients in AML group were increased （P<0.05）， while the expression levels of CCNB1， CD3D， CDK1， and VEGFA mRNA were decreased （P<0.05）； high expression of APP， CD36， PF4， PPBP， and RUNX1， as well as low expression of CD3D and VEGFA， were significantly associated with poor prognosis （P<0.05）. The real-time fluorescence quantitative PCR results showed that compared with control group， the expression levels of PF4 and PPBP mRNA in bone marrow aspirate fluid of the patients in AML group were significantly increased （P<0.01）. The CCK-8 assay results showed that compared with control group， after overexpression of PF4 or PPBP， the proliferation activities of THP-1 cells in OE-PF4 group and OE-PPBP group significantly increased （P<0.05）. The Western blotting method results showed that compared with control group， after overexpression of PF4 or PPBP， the expression levels of B-cell lymphoma 2 （Bcl-2） protein in THP-1 cells in OE-PF4 group and OE-PPBP group were significantly increased （P<0.05）， while the expression levels of Bcl-2-associated X protein （Bax） protein were significantly decreased （P<0.05）. Conclusion The screened PPBP and PF4 are abnormally expressed in AML and are correlated with the patient prognosis； they may participate in AML progression by promoting cell proliferation and inhibiting apoptosis， and have the potential to serve as the diagnostic and prognostic biomarkers.

Key words: Acute myeloid leukemia, Differentially expressed genes, Diagnostic candidate genes, Biomarkers, Bioinformatics

CLC Number:

R733.71

Zhipeng PAN,Le WANG,Weiwen HUANG,Zhen YI,Yuxuan GAO. Bioinformatics analysis and experimental validation based on screening and identification of diagnostic candidate genes of bone marrow mononuclear cells of acute myeloid leukemia patients and their impact on prognosis[J].Journal of Jilin University(Medicine Edition), 2026, 52(3): 791-805.

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Number	Gender	Age	WBC（10⁹ L^-1）	FAB	Gene mutation
AML-1	Female	44	108.50	M5	-
AML-2	Male	24	6.27	M5	NRAS mutation; WT1 over-expression
AML-3	Female	24	58.04	M5b	-
AML-4	Male	48	20.74	M5	IDH1, PTPN11 mutation, TPM4, P2PY8, LATS2 positive for fusion gene
AML-5	Male	40	221.28	M5b	DNMT3A, NPM1, FLT3 mutation
AML-6	Male	36	114.66	M5	KIT, SRP72, PMS2, USB1 mutation, CEFB-MYH11, TPM4-KLF3, P2RY8-CD99 positive for fusion gene
AML-7	Female	28	1.93	M5b	RUNX1/TPM4 positive for fusion gene, KRAS/WT1 mutation
AML-8	Female	40	188.27	M2	DEK/CAN positive for fusion gene, FLT3-ITD mutation
AML-9	Male	48	1.89	M2a	DNMT3A, IDH2 mutation
AML-10	Female	35	3.35	M5b	HOX11 positive for gene; With IDH2, NPM1, ETV6 mutation
AML-11	Male	29	81.00	M1	CEBPA, NRAS, IDH1 mutation
AML-12	Male	35	6.05	M5b	CEBPA, NF1 mutation
AML-13	Male	51	21.44	M5	DNMT3A, CEBPA, NRAS, NPM1 mutation
AML-14	Male	39	2.80	M5	PTPN11 mutation
AML-15	Male	34	79.50	M1	FLT3, IDH1 mutation
AML-16	Female	50	2.82	M5	PTPN11, KMT2A-MLLT10 mutation
AML-17	Female	44	2.93	M0	WT1, PTPN11, RUNX1, SF3B1 mutation
AML-18	Female	47	6.59	M5b	CEBPA mutation
AML-19	Female	34	79.50	M1	FLT3/IDH1 mutation
AML-20	Female	39	4.39	M5	CEBPA/SRSF2 mutation
AML-21	Female	39	4.39	M5	CEBPA, SRSF2 mutation
HD-1	Male	50	-	HD	-
HD-2	Female	37	-	HD	-
HD-3	Female	21	-	HD	-
HD-4	Male	33	-	HD	-
HD-5	Female	26	-	HD	-
HD-6	Male	40	-	HD	-
HD-7	Male	30	-	HD	-
HD-8	Male	23	-	HD	-
HD-9	Male	33	-	HD	-
HD-10	Male	30	-	HD	-
HD-11	Female	31	-	HD	-
HD-12	Female	29	-	HD	-
HD-13	Male	33	-	HD	-

Primer	Sequence（5'-3'）
PPBP-F	GAGACCACTTCATGCCTTGC
PPBP-R	ATGCAGCGGAGTTCAGCAT
PF4-F	CCTGTGTGTGAAGACCACCT
PF4-R	TCTTCAGCGTGGCTATCAGT
APP-F	CCTGCTGCTGCTGCTGCTGCT
APP-R	GCAGCAGCAGCAGCAGCAGCA
CD36-F	GTGCTGCTGCTGCTGCTGCT
CD36-R	CAGCAGCAGCAGCAGCAGCA
RUNX1-F	ACCTC GAAACACAAGGCAGA
RUNX1-R	GCACTTGAGAGTCGACTGGA
FYN-F	CTGCTGCTGCTGCTGCTGCT
FYN-R	CAGCAGCAGCAGCAGCAGCA
CCNB1-F	GACCTGTGTCA GGCTTTCTCTG
CCNB1-R	GGTATTTTGG TCTGACTGCTTGC
CD3D-F	GTCATTGCCACTCTGCTCCTTG
CD3D-R	CCTGGTCATTCCTCAACAGAGC
CDK1-F	GATGTGCTTATGCAGGATTTC
CDK1-R	ATGTACTGACCAGGAGGGATA
VEGFA-F	CTGCTGCTGCTGCTGCTGCT
VEGFA-R	CAGCAGCAGCAGCAGCAGCA
BAX-F	TCGCCCTTTTCTACTTTGCCA
BAX-R	CGGAGGAAGTCCAATGTCCAG
Bcl-2-F	GACTTCGCCGAGATGTCCAG
Bcl-2-R	GAACTCAAAGAAGGCCACAATC
β-actin-F	CATCCGCAAAGACCTGTACGC
β-actin-R	AGTACTTGCGCTCAGGAGGAG

Gene	Log₂(FC)	P value
HIST2H2BE	4.599 806 6	1.54×10^－13
MIR196B	3.903 822 0	5.69×10^－5
HIST2H2AA4	3.846 160 7	2.28×10^－9
RUNX1	3.683 064 5	1.19×10^－9
HOXA9	3.477 107 3	2.39×10^－5
HIST2H2AA4	3.425 436 6	1.43×10^－9
FLT3	3.377 645 2	2.65×10^－12
HIST1H2BC	3.360 457 5	1.72×10^－6
HOXA10	3.341 138 3	4.37×10^－6
HOXA5	3.284 080 3	1.85×10^－4
AHSP	－7.507 207 2	7.25×10^－22
HBA2	－7.350 381 2	2.61×10^－8
GYPA	－7.283 757 0	7.30×10^－22
CA1	－6.982 895 9	7.64×10^－21
ALAS2	－6.676 403 7	7.04×10^－23
HBD	－6.556 821 0	4.78×10^－10
HBB	－6.485 769 5	2.08×10^－8
IL7R	－6.282 300 3	3.07×10^－16
EPB42	－6.099 078 9	2.25×10^－29
IGHA2	－6.098 573 1	5.20×10^－9

Genotype category	Gene symbol
Core gene	APP，CCR7，CCL5，PF4，GPR18，CCR2，CX3CR1，CCL4，CXCR4，FPR2，NMU，PPBP，FPR1，C5AR1， CCNB1，CCNA2，CDK1，CCNA1，HIST1H2BK，HIST1H2BJ，HIST1H2BH，HIST1H2BD，HIST1H2AC， HIST2H2BE，H2BFS，RUNX1，SYNE1，GATA3，SYNE2，KAT2B，CD3E，CD8B，CD86，LCK，CD247， CD8A，CD3D，CD28，FYN，CD36，SLC2A3，ATP8B4，BLAUR，CYBB，SLC2A5，TNFRSF1B，FCAR， CKAP4，ENPF，KIF2C，BIRC5，BUB1B，KIF11，AURKA，CDC20，CCNB2，SERPING1，F5，VEGFA， SERPINA1，PRTN3，RETN，ELANE，ARG1，MNDA，MPO，CECR1
Hub gene	APP，FPR2，FYN，CDK1，HIST2H2BE，CCNB1，PPBP，HIST1H2BJ，HIST1H2BK，HIST1H2AC，LCK， H2BFS，VEGFA，CCR7，PF4，CXCR4，HIST1H2BD，HIST1H2BH，RUNX1，NMU，CD28，CCR2，CD3D， FPR1，CX3CR1，GATA3，PLAUR，CD247，CKAP4，CCL5，CD36，CDC20，GPR18，C5AR1，CCL4
Bottleneck gene	FYN，RUNX1，CDH1，CCNB1，VEGFA，PF4，CD36，PPBP，CD3D，APP，NCOR2，MPO，HMBS，CDK1， PAX5，CD34，HBB，CEBPA
Diagnostic candidate gene	CDK1，CD36，RUNX1，CD3D，VEGFA，CCNB1，APP，FYN，PPBP，PF4

Bioinformatics analysis and experimental validation based on screening and identification of diagnostic candidate genes of bone marrow mononuclear cells of acute myeloid leukemia patients and their impact on prognosis

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