Mini Review

Comparative study of gene expression profiles in myeloid leukemia and lymphoblastic leukemia

Hanadi M Baeissa 1, Manal A Tashkandi 1, and Dalia M Alammari 1*

1 Department of Biochemistry, College of Science, University of Jeddah, Jeddah-21589 Saudi Arabia.

* Correspondence: hmbaeissa@uj.edu.sa (H.M.B.)

Citation: Baeissa, H.M.Tashkandi, M.A., and Alammari, D. Comparative study of gene expression profiles in myeloid leukemia and lymphoblastic leukemia. Glob. Jour. Bas. Sci. 2025, 1(8). 1-8.

Received: April 11, 2025

Revised: May 21, 2025

Accepted: May 28, 2025

Published: May 29, 2025

doi: 10.63454/jbs20000035

ISSN: 3049-3315

Volume 1; Issue 7

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Abstract: Leukemia represents a heterogeneous group of hematological malignancies characterized by abnormal proliferation and impaired differentiation of hematopoietic cells. Among these, myeloid leukemia and lymphoblastic leukemia constitute the two major lineages with distinct biological, molecular, and clinical characteristics. Advances in high-throughput transcriptomic technologies have enabled comprehensive profiling of gene expression patterns, providing critical insights into leukemia pathogenesis, heterogeneity, and therapeutic response. This review presents a comparative analysis of gene expression profiles in myeloid leukemia and lymphoblastic leukemia, highlighting lineage-specific transcriptional signatures, dysregulated signaling pathways, epigenetic influences, and their clinical implications. Understanding shared and distinct molecular features between these leukemia types offers opportunities for improved diagnostics, prognostic stratification, and the development of targeted and personalized therapies.

Keywords: Myeloid leukemia; Lymphoblastic leukemia; Gene expression profiling; Transcriptomics; Biomarkers; Hematologic malignancies

1. Introduction

Leukemia is a group of clonal hematopoietic malignancies characterized by the uncontrolled proliferation, impaired differentiation, and accumulation of abnormal blood cells in the bone marrow and peripheral circulation. The disease originates from malignant transformation of hematopoietic stem cells (HSCs) or lineage-committed progenitor cells, leading to disruption of normal hematopoiesis and progressive bone marrow failure. Clinically, leukemia manifests with anemia, thrombocytopenia, immunodeficiency, and infiltration of leukemic cells into extramedullary tissues, contributing to significant morbidity and mortality worldwide. Based on the lineage of the transformed cell, leukemia is broadly classified into myeloid and lymphoid types, reflecting differentiation toward either myeloid or lymphoid hematopoietic pathways [1-5]. Each lineage is further subdivided into acute and chronic forms according to the degree of cellular differentiation and disease progression. Among these, acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) represent the most intensively investigated entities due to their aggressive clinical course, high genetic heterogeneity, and substantial therapeutic challenges. AML predominantly affects adults and older individuals, whereas ALL is more common in children, though both diseases span all age groups and display marked biological diversity [4-10].

Despite arising from a shared hematopoietic hierarchy, myeloid and lymphoblastic leukemias exhibit profound differences in cellular identity, developmental arrest, and molecular architecture. These differences are reflected in distinct differentiation programs, lineage-specific transcription factors, signaling pathways, and epigenetic landscapes. AML is typically characterized by disruption of myeloid differentiation, aberrant activation of self-renewal programs, and deregulation of transcription factors such as CEBPA, RUNX1, and PU.1. In contrast, ALL is driven by altered lymphoid lineage commitment and maturation, involving dysregulation of key regulators such as PAX5, IKZF1, NOTCH1, and TAL1. These molecular distinctions underlie differences in disease biology, clinical behavior, and therapeutic response. Historically, leukemia classification relied heavily on morphological examination, cytochemistry, and immunophenotyping. While these approaches remain clinically valuable, they provide limited insight into the underlying molecular mechanisms driving leukemogenesis. The advent of high-throughput genomic and transcriptomic technologies has revolutionized leukemia research, enabling systematic interrogation of genetic alterations and global gene expression patterns. In particular, gene expression profiling has emerged as a powerful approach to capture the functional output of genetic and epigenetic alterations, offering a comprehensive view of cellular states and regulatory networks in leukemia [1-7].

Comparative gene expression analyses have revealed that leukemic phenotypes are not dictated solely by individual driver mutations but rather by coordinated transcriptional programs shaped by complex interactions between genetic lesions, epigenetic modifications, and microenvironmental cues. These transcriptional programs determine lineage identity, differentiation arrest, proliferative capacity, and treatment sensitivity. Importantly, gene expression profiling has demonstrated its utility in refining leukemia classification, identifying biologically distinct subgroups, predicting prognosis, and uncovering novel therapeutic targets. Understanding similarities and differences in gene expression profiles between myeloid leukemia and lymphoblastic leukemia is therefore critical for elucidating shared oncogenic mechanisms as well as lineage-specific vulnerabilities. Such comparative analyses provide insight into fundamental principles of hematopoietic transformation and may reveal conserved regulatory networks that transcend lineage boundaries, alongside distinct transcriptional signatures that define myeloid versus lymphoid leukemogenesis [11-15].

This review aims to synthesize and critically evaluate current knowledge on gene expression landscapes in myeloid leukemia and lymphoblastic leukemia. By integrating findings from bulk and single-cell transcriptomic studies, we highlight key transcriptional differences and commonalities, explore their biological and clinical implications, and discuss how comparative gene expression profiling can inform diagnosis, risk stratification, and the development of precision therapeutic strategies.

2. Overview of Myeloid and Lymphoblastic Leukemias

Myeloid and lymphoblastic leukemias represent the two principal lineage-based categories of hematologic malignancies, each arising from distinct developmental pathways within the hematopoietic system. Although both originate from hematopoietic stem or progenitor cells, differences in lineage commitment, differentiation arrest, and molecular regulation give rise to unique biological and clinical characteristics.

2.1 Myeloid Leukemia

Myeloid leukemia originates from progenitor cells committed to the myeloid lineage, which under normal physiological conditions differentiate into granulocytes, monocytes, erythrocytes, and megakaryocytes. Acute myeloid leukemia (AML) is characterized by the clonal expansion and accumulation of immature myeloid blasts in the bone marrow and peripheral blood, leading to suppression of normal hematopoiesis. Gene expression profiling studies in AML consistently reveal activation of self-renewal and stemness-associated transcriptional programs, coupled with repression of differentiation-related genes. These transcriptional abnormalities are frequently driven by dysregulated signaling pathways, including FLT3, MAPK, PI3K/AKT, and JAK/STAT, which promote leukemic cell proliferation, survival, and resistance to apoptosis [1,5-10]. Together, these alterations contribute to the aggressive nature and marked heterogeneity of AML.

2.2 Lymphoblastic Leukemia

Lymphoblastic leukemia arises from immature lymphoid progenitors of either B-cell or T-cell origin, with acute lymphoblastic leukemia (ALL) representing the most common pediatric cancer. Transcriptional profiling of ALL reveals profound disruption of normal lymphoid differentiation programs, resulting in developmental arrest at early precursor stages. Aberrant activation of signaling pathways such as NOTCH (particularly in T-ALL), JAK/STAT, and cytokine receptor–mediated pathways is a defining feature of this disease. In addition, altered expression of key lineage-determining transcription factors, including PAX5, IKZF1, and TAL1, plays a central role in maintaining leukemic identity and driving disease progression.

3. Technologies for Gene Expression Profiling

Advances in high-throughput technologies have fundamentally transformed gene expression profiling in leukemia research, enabling comprehensive analysis of transcriptional programs at unprecedented resolution. Early large-scale studies relied heavily on microarray-based expression analysis, which allowed simultaneous measurement of thousands of transcripts across leukemic samples. Microarrays played a pivotal role in identifying lineage-specific expression signatures, refining leukemia classification, and uncovering gene expression patterns associated with prognosis and treatment response. The introduction of RNA sequencing (RNA-seq) further expanded the scope and accuracy of transcriptomic analyses. Bulk RNA-seq provides a quantitative and unbiased assessment of gene expression, enabling detection of low-abundance transcripts, alternative splicing events, and gene fusions relevant to leukemogenesis. More recently, single-cell RNA sequencing has emerged as a powerful approach to resolve cellular heterogeneity within leukemic populations, revealing distinct transcriptional states that are often masked in bulk analyses [2-5,16-23].

Integration of single-cell transcriptomics with other omics layers, including genomics, epigenomics, and proteomics, has enabled multi-dimensional characterization of leukemic cells. These integrative platforms facilitate reconstruction of regulatory networks and signaling pathways that drive disease progression. Collectively, these technologies have enhanced the identification of diagnostic markers, prognostic indicators, and therapeutic targets, providing a foundation for precision medicine approaches in leukemia.

4. Comparative Gene Expression Signatures

4.1 Lineage-Specific Transcriptional Programs

Comparative studies consistently demonstrate distinct transcriptional landscapes between myeloid and lymphoblastic leukemias. Myeloid leukemia exhibits elevated expression of genes involved in myelopoiesis, innate immune signaling, and inflammatory responses, including CEBPA, SPI1 (PU.1), MPO, and CSF1R. In contrast, lymphoblastic leukemia shows enrichment of genes associated with lymphoid development, antigen receptor signaling, and adaptive immunity, such as CD3, CD19, RAG1/2, and IL7R.

4.2 Shared Oncogenic Expression Patterns

Despite lineage differences, some oncogenic pathways are commonly dysregulated in both leukemia types. These include cell cycle regulators, apoptosis-related genes, metabolic pathways, and stress-response networks. Overexpression of anti-apoptotic genes (BCL2, MCL1) and proliferation-associated genes (MYC, CCND2) has been observed across both myeloid and lymphoblastic leukemias, reflecting shared hallmarks of malignant transformation.

 

5. Signaling Pathway Dysregulation

Gene expression profiling has revealed profound differences in signaling pathway activation between myeloid and lymphoblastic leukemias, reflecting their distinct cellular origins and biological behaviors. In acute myeloid leukemia (AML), transcriptional analyses frequently demonstrate upregulation of tyrosine kinase–driven signaling pathways, including FLT3, KIT, and downstream MAPK and PI3K/AKT cascades. These pathways promote uncontrolled proliferation, survival, and impaired differentiation of myeloid progenitors. In addition, AML often exhibits altered expression of epigenetic regulators and transcription factors that cooperate with signaling pathways to sustain leukemic stem cell programs and disease persistence. In contrast, acute lymphoblastic leukemia (ALL) displays a transcriptional landscape dominated by cytokine-mediated signaling and lineage-specific pathways. Elevated expression of cytokine receptors and components of the JAK/STAT pathway is commonly observed, particularly in high-risk ALL subtypes. NOTCH signaling is a defining feature of T-cell ALL, where aberrant activation drives proliferation and blocks normal T-cell differentiation. In B-cell ALL, gene expression profiles reveal dysregulation of B-cell receptor–associated signaling and transcription factors essential for lymphoid development [19-32].

These distinct signaling signatures underpin lineage-specific therapeutic vulnerabilities and have direct clinical relevance. Targeted inhibition of tyrosine kinases has shown efficacy in AML, whereas therapies aimed at cytokine signaling and immune-related pathways are particularly effective in ALL.  

6. Role of Epigenetic Regulation

Epigenetic regulation plays a fundamental role in shaping gene expression programs during normal hematopoiesis, and its disruption is a hallmark of both myeloid and lymphoblastic leukemias. Epigenetic modifiers control chromatin accessibility and transcriptional output through mechanisms such as DNA methylation, histone modification, and chromatin remodeling, thereby regulating lineage commitment, differentiation, and self-renewal. In leukemia, mutations or aberrant expression of key epigenetic regulators—including DNMT3A, TET2, EZH2, and KMT2A—lead to profound alterations in chromatin structure and global transcriptional reprogramming [6,22].

In myeloid leukemia, mutations in DNMT3A and TET2 disrupt normal DNA methylation dynamics, resulting in inappropriate silencing of differentiation-associated genes and sustained expression of stemness-related programs. Similarly, alterations in histone-modifying enzymes such as EZH2 affect the balance between repressive and activating chromatin marks, reinforcing leukemic cell identity and blocking maturation. In lymphoblastic leukemia, rearrangements and dysregulation of KMT2A (MLL) drive aberrant histone methylation and activation of oncogenic transcriptional programs that promote uncontrolled proliferation and impaired lymphoid differentiation. Comparative epigenomic and transcriptomic analyses indicate that epigenetic dysregulation contributes significantly to lineage plasticity, enabling leukemic cells to adopt hybrid or intermediate transcriptional states. This plasticity may partially explain the presence of overlapping gene expression features between myeloid and lymphoblastic leukemias, despite their distinct developmental origins. Moreover, epigenetic alterations often cooperate with genetic mutations and signaling pathway dysregulation, amplifying transcriptional heterogeneity and influencing disease progression.

Importantly, the reversible nature of epigenetic modifications presents therapeutic opportunities. Epigenetic therapies, including DNA methyltransferase inhibitors and histone deacetylase inhibitors, have demonstrated clinical activity, highlighting the translational relevance of understanding epigenetic control of gene expression in leukemia.

7. Clinical Implications of Gene Expression Differences

7.1 Diagnostic and Prognostic Value

Gene expression profiling has substantially enhanced the diagnostic and prognostic framework of leukemia, moving beyond traditional classification systems based solely on morphology, cytochemistry, and immunophenotyping. Distinct transcriptional signatures provide a molecular “fingerprint” that allows accurate discrimination between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), even in diagnostically ambiguous cases. Moreover, gene expression–based stratification has enabled the identification of biologically and clinically distinct subtypes within each leukemia lineage, many of which are associated with differential survival outcomes and relapse risk.

In AML, expression signatures reflecting stemness, inflammatory signaling, or metabolic reprogramming have been linked to poor prognosis and treatment resistance, while differentiation-associated profiles often correlate with favorable outcomes. Similarly, in ALL, transcriptional programs driven by specific genetic alterations—such as BCR–ABL1-like expression patterns or IKZF1-associated signatures—are strongly predictive of high-risk disease and inferior response to conventional therapy. Expression-based classifiers and risk scores are increasingly incorporated into clinical decision-making, informing treatment intensity, transplant eligibility, and post-remission strategies. Importantly, these molecular tools complement genomic and cytogenetic data, offering a more comprehensive assessment of disease biology [29-36].

7.2 Therapeutic Target Identification

Comparative transcriptomic analyses have also played a pivotal role in identifying therapeutic targets and guiding precision treatment strategies. By revealing lineage-specific and shared dysregulated pathways, gene expression profiling supports rational drug development and optimized therapeutic selection. In AML, transcriptional activation of tyrosine kinase signaling pathways has provided a strong rationale for the use of kinase inhibitors, particularly in patients with FLT3-driven expression profiles. Similarly, overexpression of anti-apoptotic genes has informed the clinical application of BCL-2 inhibitors.

In contrast, ALL displays high expression of lineage-restricted surface antigens such as CD19 and CD22, which form the basis for immunotherapeutic approaches including monoclonal antibodies and chimeric antigen receptor (CAR) T-cell therapy. Comparative transcriptomic insights further suggest opportunities for combination therapies that target both lineage-specific vulnerabilities and shared oncogenic pathways, thereby overcoming resistance mechanisms. Overall, integrating gene expression data into therapeutic decision-making enhances the precision and durability of leukemia treatment.

8. Tumor Heterogeneity and Single-Cell Insights

Tumor heterogeneity is a defining feature of leukemia and represents a major challenge for effective and durable therapeutic intervention. While bulk gene expression profiling has provided invaluable insights into the dominant transcriptional programs of myeloid and lymphoblastic leukemias, it inherently averages signals across millions of cells, thereby obscuring rare but clinically significant subpopulations. These hidden cellular subsets often drive disease progression, therapy resistance, and relapse, underscoring the importance of resolving leukemia at single-cell resolution.

The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized leukemia research by enabling the dissection of cellular diversity within individual tumors. Single-cell studies in acute myeloid leukemia (AML) have uncovered hierarchically organized populations that include leukemic stem cells (LSCs), progenitor-like cells, and more differentiated blasts, each characterized by distinct transcriptional states. These LSC-enriched populations frequently exhibit stemness-associated gene expression programs, enhanced survival signaling, and metabolic adaptations that confer resistance to chemotherapy and targeted therapies. In contrast, bulk transcriptomic profiles often fail to capture the contribution of these rare but highly tumorigenic cells. In lymphoblastic leukemia, particularly acute lymphoblastic leukemia (ALL), single-cell transcriptomic analyses have revealed complex clonal architectures shaped by aberrant lineage commitment and developmental arrest. Distinct subpopulations reflecting early lymphoid progenitors, pre-B or pre-T cell stages, and aberrantly differentiated cells coexist within the same tumor [22]. These transcriptional states are dynamically regulated and can shift in response to therapeutic pressure, facilitating clonal selection and disease recurrence. Comparative single-cell studies indicate that lymphoblastic leukemias may exhibit greater plasticity in lineage identity than previously appreciated, including evidence of lineage infidelity and mixed-lineage transcriptional programs.

Comparative analyses of single-cell transcriptomes between myeloid and lymphoblastic leukemias have further highlighted fundamental differences in clonal evolution and differentiation trajectories. Myeloid leukemias often display a relatively stable hierarchical structure rooted in stem-like compartments, whereas lymphoblastic leukemias tend to show more pronounced transcriptional branching and developmental flexibility. These distinctions have important implications for therapeutic targeting, as strategies aimed at eradicating stem-like populations may be particularly effective in myeloid leukemia, while interventions that disrupt lineage-specific signaling and plasticity may be more relevant for lymphoblastic leukemia. Beyond transcriptomic diversity, integrative single-cell approaches that combine gene expression with genomic, epigenomic, and proteomic data have provided deeper insights into the regulatory mechanisms driving heterogeneity. Single-cell multi-omics studies have demonstrated that transcriptional heterogeneity is tightly linked to epigenetic variability, chromatin accessibility, and mutation-specific regulatory programs. Such findings reinforce the notion that leukemic heterogeneity arises from coordinated genetic and epigenetic interactions rather than stochastic transcriptional noise alone [2,4,6,9,11-19].

Clinically, single-cell insights are reshaping our understanding of treatment failure and relapse. Residual disease after therapy is often composed of transcriptionally distinct subclones that preexist at diagnosis or emerge under selective pressure. Identifying gene expression signatures associated with these resistant populations may enable earlier detection of minimal residual disease and inform adaptive treatment strategies. As single-cell technologies become more accessible and standardized, their integration into clinical research and diagnostic pipelines holds promise for improving risk stratification and tailoring therapies to the cellular complexity of individual leukemias.

In summary, single-cell transcriptomic profiling has fundamentally advanced our understanding of tumor heterogeneity in myeloid and lymphoblastic leukemias. By revealing diverse cellular states, clonal architectures, and lineage commitment dynamics, these approaches provide a more nuanced and clinically relevant view of leukemia biology. Continued comparative single-cell analyses will be essential for identifying vulnerabilities within heterogeneous leukemic ecosystems and for designing therapies capable of achieving sustained disease remission.

9. Challenges and Limitations

Despite significant progress, several challenges remain. Variability in sample preparation, platform differences, and data analysis pipelines complicate cross-study comparisons. Additionally, gene expression profiles capture static snapshots and may not fully reflect dynamic transcriptional changes during disease progression or therapy.

Future research will benefit from integrative approaches combining transcriptomics with genomics, epigenomics, proteomics, and metabolomics. Advances in single-cell and spatial transcriptomics will further refine our understanding of leukemia biology. Comparative systems-level analyses are expected to uncover conserved regulatory networks and novel therapeutic strategies applicable across leukemia lineages.

10. Conclusions
Comparative gene expression profiling has emerged as a transformative approach for elucidating the complex molecular landscapes of myeloid leukemia and lymphoblastic leukemia. By enabling systematic, genome-wide interrogation of transcriptional programs, transcriptomic analyses have revealed both profound lineage-specific differences and striking shared oncogenic features that underlie leukemic transformation. These insights reinforce the concept that leukemia is not driven by isolated genetic events but by coordinated dysregulation of gene networks governing cell fate determination, proliferation, survival, and differentiation.

Distinct transcriptional signatures in myeloid and lymphoblastic leukemias reflect their divergent developmental origins and lineage commitment pathways. Myeloid leukemias are characterized by altered expression of genes involved in myeloid differentiation, innate immune signaling, metabolic reprogramming, and epigenetic regulation, whereas lymphoblastic leukemias display dysregulation of lymphoid-specific transcription factors, antigen receptor signaling pathways, and adaptive immune processes. These lineage-restricted expression patterns provide a molecular basis for differences in disease phenotype, clinical behavior, and therapeutic responsiveness. At the same time, comparative analyses have consistently identified shared gene expression programs across leukemia subtypes, including aberrant activation of cell cycle regulators, anti-apoptotic pathways, DNA damage response mechanisms, and stemness-associated transcriptional networks. The convergence of these oncogenic programs across distinct lineages highlights common principles of leukemogenesis and suggests that targeting core transcriptional dependencies may offer therapeutic benefit beyond traditional lineage-based treatment strategies.

Importantly, advances in high-throughput and single-cell transcriptomic technologies have refined our understanding of intra-tumoral heterogeneity and clonal evolution in both myeloid and lymphoblastic leukemias. These approaches have uncovered dynamic transcriptional states that influence disease progression, therapeutic resistance, and relapse, underscoring the need to move beyond static classification models toward more adaptive, systems-level frameworks. Integrating bulk and single-cell gene expression data with genomic, epigenomic, and clinical information will be critical for constructing comprehensive models of leukemia biology. From a translational perspective, comparative gene expression profiling holds substantial promise for improving leukemia diagnosis, refining risk stratification, and guiding personalized treatment decisions. Expression-based classifiers and gene signatures are increasingly being incorporated into clinical workflows to identify high-risk patients, predict treatment response, and uncover actionable molecular targets. Furthermore, understanding transcriptional similarities between myeloid and lymphoblastic leukemias may facilitate the repurposing of targeted therapies and the development of combination strategies that address shared oncogenic pathways.

In conclusion, comparative transcriptomic analyses provide a powerful framework for bridging fundamental leukemia biology with clinical application. Continued integration of gene expression profiling with emerging technologies, large-scale patient cohorts, and functional validation studies will be essential for translating molecular insights into durable therapeutic advances. Ultimately, a deeper understanding of both lineage-specific and shared transcriptional networks will be pivotal in advancing precision medicine approaches and improving outcomes for patients with leukemia.

Author Contributions: Conceptualisation, S.F. and P.K.S.; software, S.F.; investigation, S.F. and P.K.S.;  writing—original draft preparation, S.F. and P.K.S.; writing—review and editing, S.F. and P.K.S.; visualisation, S.F. and P.K.S.; supervision, S.F. and P.K.S.; project administration, P.K.S. The author has read and agreed to the published version of the manuscript.

Funding: Not applicable.

Acknowledgments: We are grateful to the Department of Research and Development, Academy of Bioelectric Meridian Massage Australia (ABMMA), PO Box 463, Noosaville, QLD 4566, Australia for providing us all the facilities to carry out the entire work.

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: All the related data are supplied in this work or have been referenced properly.

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