Gene Expression Profiling in CD34+ Cells of Patients with Chronic Myeloid Leukemia

Buket Altınok Güneş

doi:10.4274/atfm.galenos.2024.36025

Araştırma Makalesi

Kronik Miyeloid Lösemili Hastaların CD34+ Hücrelerinde Gen İfade Profili

Yıl 2024, Cilt: 77 Sayı: 1, 20 - 27, 28.03.2024

Buket Altınok Güneş

Öz

Amaç: Kronik miyeloid lösemi (KML), hematopoietik kök hücrelerden kaynaklanan malign, klonal ve proliferatif bir hastalıktır. Bu çalışmanın amacı, KML’nin moleküler mekanizmalarını araştırmak için kronik fazdaki KML hastalarında rol oynayan potansiyel anahtar genleri ve yolakları belirlemek için biyoinformatik analiz yapmaktır.

Gereç ve Yöntem: Biyoinformatik analiz için Gen Ekspresyonu Omnibus’u (GEO) veritabanından GSE5550 erişim numarasına sahip 9 KML hasta ve 8 sağlıklı bireyden alınan CD34+ hücrelerinin mRNA mikrodizin verileri indirildi. KML hastalarından alınan örneklerle sağlıklı bireylerden alınan örnekler farklı şekilde ifade edilen genleri (DEG) bulmak için GEO2R ile analiz edildi. DEG’ler için gen ontoloji ve Kyoto gen ve genom ansiklopedisi zenginleştirme analizleri ile protein-protein etkileşimi ağ analizi gerçekleştirildi ve KML ile ilişkili önemli genler belirlendi.

Bulgular: GEO2R ile analiz sonrası p<0,01 ve log2FC<0, log2FC>0 olan DEG’ler seçildi. GSE5550 veri setinde KML hastalarında sağlıklı kontrol grubuna göre 1894 genin ifadesi artmış, 796 genin ifadesi azalmıştır. Sağlıklı kontrol grubuna göre KML hasta grubunda, farklı ifade edilen genlerin metabolik yolaklarda, RNA transportu, ribozom, endoplazmik retikulumda protein işlenmesi ve Ubiquitin aracılı proteoliz gibi yolaklarda zenginleştiği görülmüştür. Buna ilaveten RPL35, RPL39, RPS12, eEF1A1, RPLP1, RPL12, ODC1, PSMD7, USP14, PSMA1, GLI2, PSMC6 en önemli aday genler olarak belirlenmiştir.

Sonuç: Çalışmamızın sonucu, ortaya çıkan genlerin ve yolakların lösemik kök hücreleri hedef alacak ve ilaç tedavisinde kullanılabilecek birer biyobelirteç adayı olabileceğini göstermiştir.

Anahtar Kelimeler

KML, biyoinformatik analiz, mikrodizin, gen ifadesi

Proje Numarası

Kaynakça

1. Cokic VP, Mojsilovic S, Jaukovic A, et al. Gene expression profile of circulating CD34(+) cells and granulocytes in chronic myeloid leukemia. Blood Cells Mol Dis. 2015;55:373-381.
2. Diaz-Blanco E, Bruns I, Neumann F, et al. Molecular signature of CD34(+) hematopoietic stem and progenitor cells of patients with CML in chronic phase. Leukemia. 2007;21:494-504.
3. Soverini S, De Santis S, Monaldi C, et al. Targeting Leukemic Stem Cells in Chronic Myeloid Leukemia: Is It Worth the Effort? Int J Mol Sci.2021;22:7093.
4. FApperley J. Part II: Management of resistance to imatinib in chronic myeloid leukaemia. Lancet Oncology. 2007;8:1116-1128.
5. Keramatinia A, Ahadi A, Akbari ME, et al. Genomic Profiling of Chronic Myelogenous Leukemia: Basic and Clinical Approach. Journal of Cancer Prevention. 2017;22:74-81.
6. O’Brien SG, Guilhot F, Larson RA, et al. Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia. New England Journal of Medicine. 2003;348:994-1004.
7. Chomel JC, Bonnet ML, Sorel N, et al. Leukemic stem cell persistence in chronic myeloid leukemia patients with sustained undetectable molecular residual disease. Blood. 2011;118:3657-3660.
8. Chu S, McDonald T, Lin A, et al. Persistence of leukemia stem cells in chronic myelogenous leukemia patients in prolonged remission with imatinib treatment. Blood. 2011;118:5565-5572.
9. Holyoake T, Jiang XY, Eaves C, et al. Isolation of a highly quiescent subpopulation of primitive leukemic cells in chronic myeloid leukemia.Blood. 1999;94:2056-2064.
10. Gao CD, Zhou C, Zhuang J, et al. Identification of key candidate genes and miRNA-mRNA target pairs in chronic lymphocytic leukemia by integrated bioinformatics analysis. Molecular Medicine Reports. 2019;19:362-374.
11. Zhang H, Wang PR, Song T, et al. Screening and identification of key genes in imatinib-resistant chronic myelogenous leukemia cells: a bioinformatics study. Hematology. 2021;26:408-414.
12. Yan HM, Zheng GF, Qu JW, et al. Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis. Journal of Cellular Physiology. 2019;234:23785-23797.
13. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Research. 2013;41:D991-D995.
14. Huang DW, Sherman BT, Tan Q, et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biology. 2007;8:R183.
15. Eden E, Navon R, Steinfeld I, et al. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics.2009;10:48.
16. Klukas C, Schreiber F. Dynamic exploration and editing of KEGG pathway diagrams. Bioinformatics. 2007;23:344-350.Ankara Üniversitesi Tıp Fakültesi Mecmuası 2024;77(1):20-27 Altınok Güneş ve Özkan. KML Hastalarında Gen İfade Profili 27
17. Franceschini A, Szklarczyk D, Frankild S, et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Research. 2013;41:D808-D815.
18. Shannon P, Markiel A, Ozier O, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research.2003;13:2498-2504.
19. Bandettini WP, Kellman P, Mancini C, et al. MultiContrast Delayed Enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study. J Cardiovasc Magn Reson. 2012 Nov;14:83.
20. Cheloni G, Tanturli M, Tusa I, et al. Targeting chronic myeloid leukemia stem cells with the hypoxia-inducible factor inhibitor acriflavine. Blood. 2017;130:655-665.
21. Delvecchio VS, Fierro C, Giovannini S, et al. Emerging roles of the HECTtype E3 ubiquitin ligases in hematological malignancies. Discov Oncol. 2021;12:39.
22. Bhingarkar A, Vangapandu HV, Rathod S, et al. Amino Acid Metabolic Vulnerabilities in Acute and Chronic Myeloid Leukemias. Front Oncol. 2021;11:694526.
23. Karlikova R, Siroka J, Friedecky D, et al. Metabolite Profiling of the Plasma and Leukocytes of Chronic Myeloid Leukemia Patients. J Proteome Res. 2016;15:3158-3166.
24. Caocci G, Deidda M, Noto A, et al. Metabolomic Analysis of Patients with Chronic Myeloid Leukemia and Cardiovascular Adverse Events after Treatment with Tyrosine Kinase Inhibitors. J Clin Med. 2020;9:1180.
25. Elhamamsy AR, Metge BJ, Alsheikh HA, et al. Ribosome Biogenesis: A Central Player in Cancer Metastasis and Therapeutic Resistance. Cancer Res.2022;82:2344-2353.
26. Pecoraro A, Pagano M, Russo G, et al. Ribosome Biogenesis and Cancer: Overview on Ribosomal Proteins. Int J Mol Sci. 2021;22:5496.
27. Alsamman K, Alamri AM, Vatte C, et al. Potential Candidate Genes for Therapeutic Targeting in Chronic Myeloid Leukemia: A Pilot Study. Asian Pac J Cancer Prev. 2023;24:3077-3085.
28. Dapas B, Pozzato G, Zorzet S, et al. Effects of eEF1A1 targeting by aptamer/ siRNA in chronic lymphocytic leukaemia cells. Int J Pharm. 2020;574:118895.
29. Lin CY, Beattie A, Baradaran B, et al. Contradictory mRNA and protein misexpression of EEF1A1 in ductal breast carcinoma due to cell cycle regulation and cellular stress. Sci Rep. 2018;8:13904.
30. Jiang F, Gao Y, Dong C, et al. ODC1 inhibits the inflammatory response and ROS-induced apoptosis in macrophages. Biochem Biophys Res Commun. 2018;504:734-741.
31. Ye Z, Zeng Z, Shen Y, et al. ODC1 promotes proliferation and mobility via the AKT/GSK3beta/beta-catenin pathway and modulation of acidotic microenvironment in human hepatocellular carcinoma. Onco Targets Ther.2019;12:4081-4092.
32. Pathare GR, Nagy I, Sledz P, et al. Crystal structure of the proteasomal deubiquitylation module Rpn8-Rpn11. Proc Natl Acad Sci U S A.2014;111:2984-2989.
33. Ryan K, Bauer DL. Finishing touches: post-translational modification of protein factors involved in mammalian pre-mRNA 3’ end formation. Int J Biochem Cell Biol. 2008;40:2384-2396.
34. Shah SA, Potter MW, McDade TP, et al. 26S proteasome inhibition induces apoptosis and limits growth of human pancreatic cancer. J Cell Biochem. 2001;82:110-122.
35. Ito S. Proteasome Inhibitors for the Treatment of Multiple Myeloma.Cancers (Basel). 2020;12:265.
36. Hungria VTM, Crusoe EQ, Bittencourt RI, et al. New proteasome inhibitors in the treatment of multiple myeloma. Hematol Transfus Cell Ther. 2019;41:76-83.
37. Mahmoudian M, Valizadeh H, Lobenberg R, et al. Bortezomib-loaded lipidic-nano drug delivery systems; formulation, therapeutic efficacy, and pharmacokinetics. J Microencapsul. 2021;38:192-202.
38. Teng X, Yang T, Yuan B, et al. Prognostic analysis of patients with breast cancer based on tumor mutational burden and DNA damage repair genes.Front Oncol. 2023;13:1177133.
39. Shi K, Zhang JZ, Zhao RL, et al. PSMD7 downregulation induces apoptosis and suppresses tumorigenesis of esophageal squamous cell carcinoma via the mTOR/p70S6K pathway. FEBS Open Bio. 2018;8:533-543.
40. Yang Q, Lu Y, Shangguan J, et al. PSMA1 mediates tumor progression and poor prognosis of gastric carcinoma by deubiquitinating and stabilizing TAZ. Cell Death Dis. 2022;13:989.
41. He YJ, Li WL, Liu BH, et al. Identification of differential proteins in colorectal cancer cells treated with caffeic acid phenethyl ester. World J Gastroenterol.2014;20:11840-11849.
42. Ding XQ, Wang ZY, Xia D, et al. Proteomic Profiling of Serum Exosomes From Patients With Metastatic Gastric Cancer. Front Oncol. 2020;10:1113.
43. Zhang JY, Shi KZ, Liao XY, et al. The Silence of PSMC6 Inhibits Cell Growth and Metastasis in Lung Adenocarcinoma. Biomed Res Int. 2021;2021:9922185.
44. Jiang L, He Q, Chen X, et al. Inhibition of proteasomal deubiquitinases USP14 and UCHL5 overcomes tyrosine kinase inhibitor resistance in chronic myeloid leukaemia. Clin Transl Med. 2022;12:e1038.
45. Sadarangani A, Pineda G, Lennon KM, et al. GLI2 inhibition abrogates human leukemia stem cell dormancy. J Transl Med. 2015;13:98.
46. Radich JP, Dai H, Mao M, et al. Gene expression changes associated with progression and response in chronic myeloid leukemia. Proc Natl Acad Sci U S A. 2006;103:2794-2799.
47. Crawford LJ, Chan ET, Aujay M, et al. Synergistic effects of proteasome inhibitor carfilzomib in combination with tyrosine kinase inhibitors in imatinib-sensitive and -resistant chronic myeloid leukemia models.Oncogenesis. 2014;3:e90.

Gene Expression Profiling in CD34+ Cells of Patients with Chronic Myeloid Leukemia

Yıl 2024, Cilt: 77 Sayı: 1, 20 - 27, 28.03.2024

Buket Altınok Güneş

Öz

Objectives: Chronic myeloid leukaemia (CML) is a malignant, clonal and proliferative disease originating from haematopoietic stem cells. The aim of this study is to use bioinformatic analysis to identify potential key genes and pathways involved in CML patients in the chronic phase to investigate the molecular mechanisms of CML.

Materials and Methods: For bioinformatic analysis, mRNA microarray data of CD34+ cells from 9 CML patients and 8 healthy individuals with accession number GSE5550 were downloaded from the Gene Expression Omnibus (GEO) database. Samples from CML patients and healthy individuals were analysed with GEO2R to find differentially expressed genes (DEGs). Gene ontology and Kyoto gene and genome encyclopedia enrichment analyses and protein-protein interaction network analysis were performed for DEGs and important CML related genes were identified.

Results: After analysis with GEO2R, DEGs with p<0.01 and log2FC<0, log2FC>0 were selected. In the GSE5550 data set, the expression of 1894 genes increased and 796 genes decreased in CML patients compared to the healthy control group. It was observed that DEGs were enriched in pathways such as metabolic pathways, RNA transport, ribosome, protein processing in endoplasmic reticulum and Ubikitin-mediated proteolysis in the CML patient group in comparison to the healthy controls. In addition, RPL35, RPL39, RPS12, eEF1A1, RPLP1, RPL12, ODC1, PSMD7, USP14, PSMA1, GLI2, PSMC6 were identified as the most important candidate genes.

Conclusion: The results of our study showed that the genes and pathways identified in our study may be biomarker candidates that can be used in
drug treatment to target leukaemic stem cells.

Anahtar Kelimeler

CML, bioinformatic analysis, microarray, gene expression

Etik Beyan

Etik kurul onayı tüm veriler anonim olduğundan bu çalışma için geçerli ve gerekli değildir.

Destekleyen Kurum

Proje Numarası

Teşekkür

Kaynakça

1. Cokic VP, Mojsilovic S, Jaukovic A, et al. Gene expression profile of circulating CD34(+) cells and granulocytes in chronic myeloid leukemia. Blood Cells Mol Dis. 2015;55:373-381.
2. Diaz-Blanco E, Bruns I, Neumann F, et al. Molecular signature of CD34(+) hematopoietic stem and progenitor cells of patients with CML in chronic phase. Leukemia. 2007;21:494-504.
3. Soverini S, De Santis S, Monaldi C, et al. Targeting Leukemic Stem Cells in Chronic Myeloid Leukemia: Is It Worth the Effort? Int J Mol Sci.2021;22:7093.
4. FApperley J. Part II: Management of resistance to imatinib in chronic myeloid leukaemia. Lancet Oncology. 2007;8:1116-1128.
5. Keramatinia A, Ahadi A, Akbari ME, et al. Genomic Profiling of Chronic Myelogenous Leukemia: Basic and Clinical Approach. Journal of Cancer Prevention. 2017;22:74-81.
6. O’Brien SG, Guilhot F, Larson RA, et al. Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia. New England Journal of Medicine. 2003;348:994-1004.
7. Chomel JC, Bonnet ML, Sorel N, et al. Leukemic stem cell persistence in chronic myeloid leukemia patients with sustained undetectable molecular residual disease. Blood. 2011;118:3657-3660.
8. Chu S, McDonald T, Lin A, et al. Persistence of leukemia stem cells in chronic myelogenous leukemia patients in prolonged remission with imatinib treatment. Blood. 2011;118:5565-5572.
9. Holyoake T, Jiang XY, Eaves C, et al. Isolation of a highly quiescent subpopulation of primitive leukemic cells in chronic myeloid leukemia.Blood. 1999;94:2056-2064.
10. Gao CD, Zhou C, Zhuang J, et al. Identification of key candidate genes and miRNA-mRNA target pairs in chronic lymphocytic leukemia by integrated bioinformatics analysis. Molecular Medicine Reports. 2019;19:362-374.
11. Zhang H, Wang PR, Song T, et al. Screening and identification of key genes in imatinib-resistant chronic myelogenous leukemia cells: a bioinformatics study. Hematology. 2021;26:408-414.
12. Yan HM, Zheng GF, Qu JW, et al. Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis. Journal of Cellular Physiology. 2019;234:23785-23797.
13. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Research. 2013;41:D991-D995.
14. Huang DW, Sherman BT, Tan Q, et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biology. 2007;8:R183.
15. Eden E, Navon R, Steinfeld I, et al. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics.2009;10:48.
16. Klukas C, Schreiber F. Dynamic exploration and editing of KEGG pathway diagrams. Bioinformatics. 2007;23:344-350.Ankara Üniversitesi Tıp Fakültesi Mecmuası 2024;77(1):20-27 Altınok Güneş ve Özkan. KML Hastalarında Gen İfade Profili 27
17. Franceschini A, Szklarczyk D, Frankild S, et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Research. 2013;41:D808-D815.
18. Shannon P, Markiel A, Ozier O, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research.2003;13:2498-2504.
19. Bandettini WP, Kellman P, Mancini C, et al. MultiContrast Delayed Enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study. J Cardiovasc Magn Reson. 2012 Nov;14:83.
20. Cheloni G, Tanturli M, Tusa I, et al. Targeting chronic myeloid leukemia stem cells with the hypoxia-inducible factor inhibitor acriflavine. Blood. 2017;130:655-665.
21. Delvecchio VS, Fierro C, Giovannini S, et al. Emerging roles of the HECTtype E3 ubiquitin ligases in hematological malignancies. Discov Oncol. 2021;12:39.
22. Bhingarkar A, Vangapandu HV, Rathod S, et al. Amino Acid Metabolic Vulnerabilities in Acute and Chronic Myeloid Leukemias. Front Oncol. 2021;11:694526.
23. Karlikova R, Siroka J, Friedecky D, et al. Metabolite Profiling of the Plasma and Leukocytes of Chronic Myeloid Leukemia Patients. J Proteome Res. 2016;15:3158-3166.
24. Caocci G, Deidda M, Noto A, et al. Metabolomic Analysis of Patients with Chronic Myeloid Leukemia and Cardiovascular Adverse Events after Treatment with Tyrosine Kinase Inhibitors. J Clin Med. 2020;9:1180.
25. Elhamamsy AR, Metge BJ, Alsheikh HA, et al. Ribosome Biogenesis: A Central Player in Cancer Metastasis and Therapeutic Resistance. Cancer Res.2022;82:2344-2353.
26. Pecoraro A, Pagano M, Russo G, et al. Ribosome Biogenesis and Cancer: Overview on Ribosomal Proteins. Int J Mol Sci. 2021;22:5496.
27. Alsamman K, Alamri AM, Vatte C, et al. Potential Candidate Genes for Therapeutic Targeting in Chronic Myeloid Leukemia: A Pilot Study. Asian Pac J Cancer Prev. 2023;24:3077-3085.
28. Dapas B, Pozzato G, Zorzet S, et al. Effects of eEF1A1 targeting by aptamer/ siRNA in chronic lymphocytic leukaemia cells. Int J Pharm. 2020;574:118895.
29. Lin CY, Beattie A, Baradaran B, et al. Contradictory mRNA and protein misexpression of EEF1A1 in ductal breast carcinoma due to cell cycle regulation and cellular stress. Sci Rep. 2018;8:13904.
30. Jiang F, Gao Y, Dong C, et al. ODC1 inhibits the inflammatory response and ROS-induced apoptosis in macrophages. Biochem Biophys Res Commun. 2018;504:734-741.
31. Ye Z, Zeng Z, Shen Y, et al. ODC1 promotes proliferation and mobility via the AKT/GSK3beta/beta-catenin pathway and modulation of acidotic microenvironment in human hepatocellular carcinoma. Onco Targets Ther.2019;12:4081-4092.
32. Pathare GR, Nagy I, Sledz P, et al. Crystal structure of the proteasomal deubiquitylation module Rpn8-Rpn11. Proc Natl Acad Sci U S A.2014;111:2984-2989.
33. Ryan K, Bauer DL. Finishing touches: post-translational modification of protein factors involved in mammalian pre-mRNA 3’ end formation. Int J Biochem Cell Biol. 2008;40:2384-2396.
34. Shah SA, Potter MW, McDade TP, et al. 26S proteasome inhibition induces apoptosis and limits growth of human pancreatic cancer. J Cell Biochem. 2001;82:110-122.
35. Ito S. Proteasome Inhibitors for the Treatment of Multiple Myeloma.Cancers (Basel). 2020;12:265.
36. Hungria VTM, Crusoe EQ, Bittencourt RI, et al. New proteasome inhibitors in the treatment of multiple myeloma. Hematol Transfus Cell Ther. 2019;41:76-83.
37. Mahmoudian M, Valizadeh H, Lobenberg R, et al. Bortezomib-loaded lipidic-nano drug delivery systems; formulation, therapeutic efficacy, and pharmacokinetics. J Microencapsul. 2021;38:192-202.
38. Teng X, Yang T, Yuan B, et al. Prognostic analysis of patients with breast cancer based on tumor mutational burden and DNA damage repair genes.Front Oncol. 2023;13:1177133.
39. Shi K, Zhang JZ, Zhao RL, et al. PSMD7 downregulation induces apoptosis and suppresses tumorigenesis of esophageal squamous cell carcinoma via the mTOR/p70S6K pathway. FEBS Open Bio. 2018;8:533-543.
40. Yang Q, Lu Y, Shangguan J, et al. PSMA1 mediates tumor progression and poor prognosis of gastric carcinoma by deubiquitinating and stabilizing TAZ. Cell Death Dis. 2022;13:989.
41. He YJ, Li WL, Liu BH, et al. Identification of differential proteins in colorectal cancer cells treated with caffeic acid phenethyl ester. World J Gastroenterol.2014;20:11840-11849.
42. Ding XQ, Wang ZY, Xia D, et al. Proteomic Profiling of Serum Exosomes From Patients With Metastatic Gastric Cancer. Front Oncol. 2020;10:1113.
43. Zhang JY, Shi KZ, Liao XY, et al. The Silence of PSMC6 Inhibits Cell Growth and Metastasis in Lung Adenocarcinoma. Biomed Res Int. 2021;2021:9922185.
44. Jiang L, He Q, Chen X, et al. Inhibition of proteasomal deubiquitinases USP14 and UCHL5 overcomes tyrosine kinase inhibitor resistance in chronic myeloid leukaemia. Clin Transl Med. 2022;12:e1038.
45. Sadarangani A, Pineda G, Lennon KM, et al. GLI2 inhibition abrogates human leukemia stem cell dormancy. J Transl Med. 2015;13:98.
46. Radich JP, Dai H, Mao M, et al. Gene expression changes associated with progression and response in chronic myeloid leukemia. Proc Natl Acad Sci U S A. 2006;103:2794-2799.
47. Crawford LJ, Chan ET, Aujay M, et al. Synergistic effects of proteasome inhibitor carfilzomib in combination with tyrosine kinase inhibitors in imatinib-sensitive and -resistant chronic myeloid leukemia models.Oncogenesis. 2014;3:e90.

Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Kanser Hücre Biyolojisi
Bölüm	Makaleler
Yazarlar	Buket Altınok Güneş 0000-0002-8852-6626
Proje Numarası	-
Yayımlanma Tarihi	28 Mart 2024
Gönderilme Tarihi	17 Kasım 2023
Kabul Tarihi	9 Mart 2024
Yayımlandığı Sayı	Yıl 2024 Cilt: 77 Sayı: 1

Kaynak Göster

APA	Altınok Güneş, B. (2024). Gene Expression Profiling in CD34+ Cells of Patients with Chronic Myeloid Leukemia. Ankara Üniversitesi Tıp Fakültesi Mecmuası, 77(1), 20-27. https://doi.org/10.4274/atfm.galenos.2024.36025
AMA	Altınok Güneş B. Gene Expression Profiling in CD34+ Cells of Patients with Chronic Myeloid Leukemia. Ankara Üniversitesi Tıp Fakültesi Mecmuası. Mart 2024;77(1):20-27. doi:10.4274/atfm.galenos.2024.36025
Chicago	Altınok Güneş, Buket. “Gene Expression Profiling in CD34+ Cells of Patients With Chronic Myeloid Leukemia”. Ankara Üniversitesi Tıp Fakültesi Mecmuası 77, sy. 1 (Mart 2024): 20-27. https://doi.org/10.4274/atfm.galenos.2024.36025.
EndNote	Altınok Güneş B (01 Mart 2024) Gene Expression Profiling in CD34+ Cells of Patients with Chronic Myeloid Leukemia. Ankara Üniversitesi Tıp Fakültesi Mecmuası 77 1 20–27.
IEEE	B. Altınok Güneş, “Gene Expression Profiling in CD34+ Cells of Patients with Chronic Myeloid Leukemia”, Ankara Üniversitesi Tıp Fakültesi Mecmuası, c. 77, sy. 1, ss. 20–27, 2024, doi: 10.4274/atfm.galenos.2024.36025.
ISNAD	Altınok Güneş, Buket. “Gene Expression Profiling in CD34+ Cells of Patients With Chronic Myeloid Leukemia”. Ankara Üniversitesi Tıp Fakültesi Mecmuası 77/1 (Mart 2024), 20-27. https://doi.org/10.4274/atfm.galenos.2024.36025.
JAMA	Altınok Güneş B. Gene Expression Profiling in CD34+ Cells of Patients with Chronic Myeloid Leukemia. Ankara Üniversitesi Tıp Fakültesi Mecmuası. 2024;77:20–27.
MLA	Altınok Güneş, Buket. “Gene Expression Profiling in CD34+ Cells of Patients With Chronic Myeloid Leukemia”. Ankara Üniversitesi Tıp Fakültesi Mecmuası, c. 77, sy. 1, 2024, ss. 20-27, doi:10.4274/atfm.galenos.2024.36025.
Vancouver	Altınok Güneş B. Gene Expression Profiling in CD34+ Cells of Patients with Chronic Myeloid Leukemia. Ankara Üniversitesi Tıp Fakültesi Mecmuası. 2024;77(1):20-7.

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