Review

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

Hanadi M Baeissa 1*, Manal A Tashkandi 1, Lina A Baz 2, Nawal M Helmi 1, Mohammed Y Refai 1, Aminah A Barqawi 3, and Dalia M Alammari 4

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

2 Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.

3 College of Science, Department of Chemistry, Ummul Al Qura University, Makkah Al -Mukarramah, Saudi Arabia.

4 Department of Microbiology and Immunology, Faculty of Medicine, Ibn Sina National College of Medical Studies, Jeddah, Saudi Arabia.

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

Citation: Baeissa, H.M.Tashkandi, M.A., Baz, L.A., Helmi, N.H., Refai, M.Y., Barqawi, A.A., and Alammari, D. Comparative study of gene expression profiles in myeloid leukemia and lymphoblastic leukemia. Glob. Jour. Bas. Sci. 2025, 2(1). 1-9.

Received: April 22, 2025

Revised: August 31, 2025

Accepted: October 28, 2025

Published: November 01, 2025

doi: 10.63454/jbs20000066

ISSN: 3049-3315

Volume 2; Issue 1

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Abstract: Leukemia represents a heterogeneous group of clonal hematological malignancies originating from hematopoietic stem or progenitor cells, characterized by dysregulated proliferation, impaired differentiation, and accumulation of immature blasts within the bone marrow and peripheral blood. This disease spectrum is broadly categorized by lineage—myeloid or lymphoid—and clinical tempo—acute or chronic—resulting in major subtypes such as Acute Myeloid Leukemia (AML) and B/T-cell Acute Lymphoblastic Leukemia (ALL). Despite overlapping clinical presentations of bone marrow failure, these subtypes are fundamentally distinct biological entities, driven by divergent genetic alterations and epigenetic reprogramming that establish unique molecular, genetic, and transcriptional landscapes. These landscapes are not merely diagnostic classifiers; they are principal determinants of cellular behavior, disease aggressiveness, mechanisms of drug resistance, and ultimately, patient prognosis and therapeutic response. The advent of high-throughput transcriptomic technologies—first through genome-wide microarrays and now via next-generation RNA sequencing (RNA-seq)—has revolutionized the resolution at which we can dissect these landscapes. These tools enable the unbiased, genome-wide quantification of gene expression, revealing not just individual dysregulated genes but entire transcriptional programs and signaling network architectures specific to each leukemia subtype. Through comparative bioinformatic analyses, researchers have delineated lineage-specific master transcriptional regulators (e.g., *PU.1/C/EBPα* in myeloid vs. *PAX5/EBF1* in B-ALL), identified oncogenic pathway dependencies (e.g., heightened FLT3 signaling in AML versus activated JAK-STAT or PI3K signaling in subsets of ALL), and uncovered distinct immune and microenvironmental interaction signatures. This review synthesizes current knowledge from these transcriptomic studies to provide a systematic comparative analysis of gene expression profiles between myeloid and lymphoblastic leukemias. We will detail the hallmark transcriptional signatures that define each lineage, explore the key dysregulated signaling pathways—such as Wnt/β-catenin, MAPK/ERK, and apoptotic pathways—that offer therapeutic vulnerabilities, and evaluate emerging diagnostic, prognostic, and predictive biomarkers derived from expression data, including multi-gene risk scores and non-coding RNA profiles. Finally, we discuss the direct clinical implications of these molecular distinctions, emphasizing their critical role in advancing precision medicine. By moving beyond a one-size-fits-all approach to leverage these unique transcriptional blueprints, we can refine risk stratification, develop novel targeted therapies, rationally select existing treatments, and monitor minimal residual disease with unprecedented specificity, thereby paving the way for significantly improved patient outcomes across all leukemia subtypes.

Keywords: Myeloid leukemia (ML); lymphoblastic leukemia; gene expression profiling; transcriptomics; biomarkers; RNA sequencing; precision medicine

1. Introduction

Leukemia represents a heterogeneous group of hematologic malignancies characterized by the clonal expansion and dysregulated proliferation of hematopoietic progenitor cells. This pathological process originates within the bone marrow, where genetic alterations disrupt the normal programs of self-renewal, differentiation, and apoptosis, leading to the accumulation of immature, non-functional cells known as blasts. These malignant cells ultimately spill into the peripheral blood and may infiltrate other organs, suppressing normal hematopoiesis and resulting in the classic clinical manifestations of cytopenias (anemia, neutropenia, thrombocytopenia), infection, bleeding, and organomegaly. The classification of leukemia is a foundational principle in oncology, traditionally based on two primary axes: the lineage of the cell of origin and the clinical tempo of the disease. This framework delineates four major categories: acute myeloid leukemia (AML), chronic myeloid leukemia (CML), acute lymphoblastic leukemia (ALL), and chronic lymphocytic leukemia (CLL). Myeloid leukemias arise from myeloid progenitor cells destined to give rise to granulocytes, monocytes, erythrocytes, and megakaryocytes, whereas lymphoblastic leukemias originate from lymphoid precursors committed to the B-cell or T-cell lineages. While these diseases share the common anatomical site of origin and general clinical consequences of bone marrow failure, they are fundamentally distinct biological entities. 

Beneath their clinical similarities lie profound differences in underlying biology, driven by divergent genetic architectures and molecular pathways. Myeloid leukemias, such as AML, are frequently associated with somatic mutations in genes regulating epigenetic modification (DNMT3A, TET2, ASXL1), transcription factor networks (RUNX1, CEBPA), and signaling pathways (FLT3, NPM1, KIT). In contrast, lymphoblastic leukemias, particularly B-ALL, are often characterized by chromosomal aneuploidies (hyperdiploidy) or specific translocations that create potent oncogenic fusion proteins (e.g., *BCR-ABL1*, *ETV6-RUNX1*, *TCF3-PBX1*) and mutations driving lymphoid development and signaling (PAX5, IKZF1, JAK/STAT). These distinct genetic lesions activate and dysregulate lineage-specific transcriptional programs, which in turn dictate cellular phenotype, disease aggressiveness, and therapeutic vulnerability. 

The advent of high-throughput gene expression profiling, primarily through microarray technology and, more recently, RNA sequencing (RNA-Seq), has revolutionized our understanding of leukemia biology. This approach provides a global, unbiased snapshot of the transcriptional state of a malignant cell, revealing the active genetic programs that define its identity and behavior. Comparative transcriptomic analyses have been instrumental in moving beyond morphology and immunophenotyping to identify molecular subtypes within the broad categories of AML and ALL, each with unique prognoses. Furthermore, these studies have elucidated the fundamental transcriptional signatures that demarcate the myeloid and lymphoid lineages at a molecular level. These signatures encompass not only lineage-specific transcription factors (e.g., PU.1 and C/EBPα in myeloid vs. PAX5 and EBF1 in B-lymphoid) but also the differential activation of oncogenic pathways (e.g., Wnt/β-catenin, MAPK) and the tumor microenvironment’s immune landscape. Critically, expression profiles have revealed predictors of drug response and resistance, offering a path toward personalized therapy. 

This review aims to synthesize the current body of knowledge regarding the comparative gene expression landscapes of myeloid and lymphoblastic leukemias. We will detail the hallmark transcriptional signatures, key differentially expressed genes, and activated pathways that biologically and clinically distinguish these two major lineages. By examining how these expression profiles correlate with disease classification, risk stratification, and therapeutic outcomes, we will highlight the central role of transcriptomics in refining diagnosis, prognostication, and the development of novel, targeted treatment strategies in modern hematologic oncology.

2. Overview of myeloid and lymphoblastic leukemia

2.1 Myeloid leukemia

Myeloid leukemia encompasses a spectrum of malignancies derived from myeloid progenitor cells, with the two principal categories being Acute Myeloid Leukemia (AML) and Chronic Myeloid Leukemia (CML). AML is defined by the rapid clonal expansion of poorly differentiated myeloid blasts (≥20% in the bone marrow), which fail to undergo normal maturation, leading to acute bone marrow failure [1]. Its pathogenesis is driven by a complex interplay of recurrent genetic and epigenetic alterations. Class-defifying mutations occur in genes regulating transcription and differentiation (e.g., RUNX1, CEBPA), tumor suppression (TP53), and chromatin modification [2]. Of particular importance are mutations in FLT3 (often internal tandem duplications, ITDs), NPM1, DNMT3A, and IDH1/IDH2, which are not only diagnostic markers but also profoundly reshape the cellular transcriptome, activating programs that enhance self-renewal, block differentiation, and promote survival [3]. In stark contrast, CML is a model of oncogene addiction, initiated almost exclusively by the BCR-ABL1 fusion gene, a consequence of the t(9;22) translocation forming the Philadelphia chromosome [4]. This constitutively active tyrosine kinase drives a more indolent chronic phase characterized by excessive production of mature granulocytes, but it establishes a state of genetic instability that predisposes to progression to blast crisis, a phase that often resembles AML. The transcriptomic landscape of CML is dominated by BCR-ABL1-driven signaling networks, distinct from the more heterogeneous genomic background of de novo AML [5].

2.2 Lymphoblastic leukemia

Lymphoblastic leukemia, primarily manifesting as Acute Lymphoblastic Leukemia (ALL), arises from the malignant transformation of B- or T-lymphoid precursors. B-cell ALL (B-ALL) is more common, especially in children, while T-cell ALL (T-ALL) comprises about 15-25% of adult cases. The disease is genetically characterized by chromosomal aneuploidies (e.g., hyperdiploidy, hypodiploidy) and recurrent structural alterations, most notably chromosomal translocations that create powerful oncogenic drivers [6]. Key translocations include ETV6-RUNX1 (t(12;21)), prevalent in pediatric B-ALL and associated with favorable outcomes; BCR-ABL1 (t(9;22)), defining a high-risk subset; and rearrangements of the KMT2A (MLL) gene (e.g., t(4;11)), which confer a poor prognosis [7]. These genetic lesions function primarily by deregulating transcriptional control, either by creating novel chimeric transcription factors or by juxtaposing lymphoid-specific genes with powerful enhancer elements. This results in the arrest of differentiation at specific early precursor stages and the activation of proliferative and survival pathways, such as aberrant NOTCH1 signaling in T-ALL and JAK-STAT signaling in a subset of B-ALL [8].

3. Gene expression profiling technologies

3.1 Microarray-based transcriptomics

DNA microarray technology emerged in the late 1990s as the pioneering high-throughput platform for global gene expression analysis, fundamentally altering the landscape of leukemia research and diagnostics. The technique relies on the principle of nucleic acid hybridization: fluorescently labeled complementary DNA (cDNA) or complementary RNA (cRNA) synthesized from patient mRNA is applied to a solid surface—a “chip”—embedded with thousands of immobilized oligonucleotide probes representing known genes or expressed sequence tags. The intensity of fluorescence at each probe spot is proportional to the abundance of that specific transcript in the sample, generating a quantitative genome-wide expression profile [9]. This capability to take a molecular “snapshot” of cellular state was revolutionary. Seminal work, most notably by Golub et al. in 1999, demonstrated that microarray-derived expression signatures could accurately and objectively distinguish between Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) with high precision, a task that could sometimes be challenging based solely on cytomorphology and limited immunophenotyping [10]. This proof-of-concept paved the way for the technology’s use in sub-classification. For AML, microarray studies validated and refined the biologic relevance of cytogenetic subgroups (e.g., core-binding factor AML, AML with MLL rearrangements) and later helped delineate molecular subtypes defined by mutations in NPM1, CEBPA, and other genes. The primary strengths of microarrays were their robustness, reproducibility for known transcripts, and relatively lower cost and computational demand compared to early sequencing methods. This made them an invaluable foundational tool for discovering differentially expressed genes, building prognostic multi-gene classifiers (e.g., the “Leukemia Prognostic Score”), and establishing the principle that transcriptomes could serve as a powerful diagnostic and prognostic matrix in clinical hematology.

3.2 RNA sequencing (RNA-seq)

The advent of next-generation sequencing (NGS) applied to transcriptomes, known as RNA sequencing (RNA-seq), has largely supplanted microarray technology due to its superior analytical capabilities. Unlike microarrays, which depend on pre-designed probes, RNA-seq is an unbiased, hypothesis-free method that involves converting total RNA into a library of cDNA fragments, which are then directly sequenced in a high-throughput manner. This provides a digital, base-pair-resolution readout of the transcriptome. Key advantages include a much wider dynamic range (allowing accurate quantification of both very low and very high-abundance transcripts), higher sensitivity, and the ability to detect entirely novel features [11]. In leukemia, this has translated into several critical advances: 1) Discovery of novel fusion genes and oncogenic isoforms with exquisite precision, such as BCL11B rearrangements in T-ALL or *FIP1L1-PDGFRA* in myeloid neoplasms; 2) Comprehensive analysis of alternative splicing, revealing leukemia-specific isoforms that may be therapeutic targets; 3) Profiling of the non-coding RNAome, including microRNAs, long non-coding RNAs (lncRNAs), and circular RNAs, which play crucial roles in gene regulation and leukemogenesis; and 4) Detection of allele-specific expression and somatic single nucleotide variants within the RNA pool, effectively bridging genomic and transcriptomic analysis in a single assay [12]. RNA-seq has refined existing leukemia classifications by adding a layer of transcriptional data to genetic definitions, identified previously unrecognized subtypes, and is increasingly being implemented in diagnostic pipelines to replace or supplement older methods due to its comprehensive nature.

3.3 Single-cell transcriptomics

Single-cell RNA sequencing (scRNA-seq) represents a paradigm shift, moving from the population-level averages of bulk sequencing to profiling gene expression in thousands of individual cells simultaneously. This technology has uncovered a staggering degree of cellular heterogeneity within leukemias that was previously invisible. It allows for the direct identification and molecular characterization of rare but critical cellular subsets, most notably leukemic stem cells (LSCs). LSCs can be pinpointed by their distinct transcriptional signatures—often involving quiescence, drug efflux, and stemness pathways—providing direct insights into the cellular reservoir of relapse [13]. Beyond stem cells, scRNA-seq enables researchers to reconstruct the clonal architecture of a tumor, tracing subclonal lineages based on shared expression patterns and mutation profiles. This is essential for understanding clonal evolution during disease progression and under the selective pressure of therapy, revealing how resistant subclones emerge. Furthermore, the technology uniquely permits the deconvolution of the tumor microenvironment (TME). By capturing transcriptomes of leukemic blasts, immune cells (T cells, NK cells, macrophages), stromal cells, and endothelial cells from the same sample, it provides an unprecedented view of cellular cross-talk, immunosuppressive networks, and niche interactions that support the malignancy [14]. Identifying the specific transcriptional programs in drug-tolerant persister cells or in immune cells with an exhausted phenotype paves the way for developing combination therapies that target both the malignant  clone and its supportive ecosystem.

4. Differential gene expression in myeloid leukemia

The transcriptomic profile of myeloid leukemia is not merely a reflection of its cell of origin; it is a dysregulated and co-opted version of the normal myeloid differentiation program, superimposed with powerful survival and proliferative signals dictated by underlying somatic mutations (Figure 1).

Figure 1. Comparative study go gene expression profiles in myeloid leukaemia and lymphoblastic leukaemia.

4.1 Myeloid lineage-specific genes

The expression of key myeloid lineage-determining genes serves as both a diagnostic anchor and a window into the differentiation block. Myeloperoxidase (MPO), a critical enzyme in the  neutrophil oxidative burst, is a classic histochemical and transcriptional marker of myeloid commitment. The transcription factor CCAAT/enhancer-binding protein alpha (CEBPA) is a master regulator of granulocytic differentiation; its biallelic mutations, common in a subset of AML, result in the expression of a truncated protein that dominantly interferes with normal differentiation while promoting self-renewal. Similarly, the ETS-family transcription factor PU.1 (encoded by SPI1) is essential for the development of both myeloid and lymphoid lineages, but its reduced activity or mutation is frequently associated with AML, leading to a failure in terminal differentiation [15]. The surface receptor CSF1R (M-CSFR) signals for monocytic differentiation, and its expression pattern helps delineate monocytic subtypes of AML. While these genes are highly expressed, their regulatory networks are often functionally compromised, creating a transcriptional signature of “myeloid identity in distress” that is a hallmark of the disease.

4.2 Oncogenic signaling pathways

Transcriptomic analyses consistently reveal the coordinated overexpression of genes that are downstream effectors of major oncogenic signaling cascades, providing a functional readout of pathway activation. In AML, driver mutations directly fuel these pathways. Activating mutations in FLT3 (ITD or TKD) and KIT lead to ligand-independent receptor tyrosine kinase signaling, resulting in the robust transcriptional upregulation of targets within the PI3K/AKT/mTOR pathway (promoting protein synthesis and cell growth) and the RAS/RAF/MEK/ERK (MAPK) pathway (driving cell cycle progression) [16]. Concurrently, mutations in signaling genes like JAK2 or autocrine cytokine loops can lead to constitutive JAK/STAT activation, a pathway whose target genes (e.g., PIM1, BCL2L1) strongly promote survival. In Chronic Myeloid Leukemia (CML), the singular oncogenic driver, BCR-ABL1, acts as a signaling hub, constitutively activating this same triad of pathways—PI3K/AKT, MAPK, and JAK/STAT—along with the NF-κB pathway, which transcribes a suite of anti-apoptotic and pro-inflammatory genes [5, 17]. This convergence on common downstream transcriptional outputs, despite different initiating lesions, explains shared phenotypic features like enhanced survival and proliferation, and highlights these pathways as high-priority therapeutic targets across myeloid malignancies.

4.3 Epigenetic regulators

A defining feature of AML genomics is the high prevalence of mutations in genes encoding epigenetic regulators, which exert a profound influence on the global transcriptomic landscape by altering the accessibility and expression potential of the genome. Recurrent mutations in DNA methyltransferase 3A (DNMT3A) and the methylcytosine dioxygenase Ten-Eleven Translocation 2 (TET2) disrupt the normal patterns of DNA cytosine methylation (5mC) and hydroxymethylation (5hmC), respectively. This leads to a distorted “methylome,” characterized by hypermethylation and silencing of tumor suppressor genes (including those involved in differentiation) and hypomethylation of oncogenic regions [18]. Similarly, gain-of-function mutations in Enhancer of Zeste Homolog 2 (EZH2), the catalytic subunit of the Polycomb Repressive Complex 2 (PRC2), lead to increased histone H3 lysine 27 trimethylation (H3K27me3), a repressive mark that can silence key differentiation genes. These epigenetic lesions create a self-reinforcing transcriptional state that locks cells in a proliferative, undifferentiated state. The transcriptome of such AMLs thus reflects both the direct consequences of the epigenetic dysregulation (altered expression of target genes) and the cellular response to the resultant block in maturation.

5. Differential gene expression in lymphoblastic leukemia

The transcriptional identity of Acute Lymphoblastic Leukemia (ALL) is defined by the expression of lymphoid lineage programs that have been arrested at specific developmental stages and supercharged with proliferative signals from dysregulated oncogenic pathways.

5.1 Lymphoid lineage-specific genes

The expression of lineage-specifying transcription factors and antigen receptor machinery provides a clear transcriptional signature of lymphoid origin. In B-cell ALL (B-ALL), the B-cell identity program is dominated by PAX5, a transcription factor essential for B-lineage commitment and maintenance; its frequent monoallelic deletion or mutation is a cornerstone of B-ALL pathogenesis, leading to a partial differentiation block [19]. IKAROS Family Zinc Finger 1 (IKZF1) is another critical regulator of lymphoid development, and its deletion, particularly the dominant-negative Δ4-7 isoform, defines a high-risk subgroup associated with tyrosine kinase inhibitor resistance. The expression of Recombination-Activating Genes 1 and 2 (RAG1/2), essential for V(D)J recombination, signals an early precursor state. Surface markers like CD19, CD22, and CD79A are consistently and highly expressed, making them ideal targets for immunotherapies. In T-cell ALL (T-ALL), the transcriptional landscape is shaped by NOTCH1 signaling. Activating mutations in NOTCH1 occur in over 60% of cases, driving a transcriptional program that promotes anabolic growth, cell cycle entry, and inhibits differentiation [8]. This is often accompanied by overexpression of oncogenic transcription factors like LIM Domain Only 2 (LMO2) and TAL1 (SCL), which are normally involved in early hematopoietic and T-cell development but become dysregulated through chromosomal translocations or other mechanisms, enforcing the leukemic state.

5.2 Immune and cytokine signaling

Given their origin from immune cells, ALL blasts frequently exhibit dysregulated signaling from cytokine receptors essential for normal lymphocyte development and homeostasis. In B-ALL, signaling via the Interleukin-7 Receptor (IL-7R) is a critical survival and proliferative signal for normal B-cell precursors. In leukemia, activating mutations in the IL7R gene or its downstream signaling components (e.g., JAK1, JAK3, STAT5B) lead to constitutive JAK/STAT pathway activation, mimicking a perpetual growth signal and contributing to steroid resistance [20]. In T-ALL, the role of NOTCH1 extends beyond a transcription factor; it is the central node of a signaling pathway that is aberrantly activated. Ligand-independent NOTCH1 signaling transcriptionally upregulates MYC, cell cycle genes, and anabolic metabolic pathways, effectively substituting for continuous T-cell receptor stimulation and driving autonomous proliferation.

5.3 Cell cycle and apoptosis genes

A unifying and aggressive feature of ALL, particularly when compared to many AML subtypes, is the overwhelming transcriptional drive towards cell division, coupled with robust defenses against cell death. The MYC oncogene is a frequent convergent target of multiple upstream pathways in both B-ALL and T-ALL, and its overexpression reprograms the cell to prioritize growth and division. This proliferative frenzy is supported by the overexpression of cell cycle promoters such as Cyclin D3 (CCND3) and cyclin-dependent kinases. To ensure this rapid cycling is not checked by apoptosis, ALL cells simultaneously upregulate anti-apoptotic proteins of the BCL-2 family, most notably BCL-2 and MCL-1 [21]. This creates a powerful synergistic transcriptomic signature: “divide and survive.” This signature not only explains the characteristic high proliferative index of ALL but also underlies a major mechanism of chemotherapy resistance, as these cells are primed to withstand apoptotic insults. This understanding has directly led to the clinical investigation and application of BCL-2 inhibitors like venetoclax in high-risk ALL, demonstrating how transcriptomic insights can translate into novel therapeutic strategies.

6. Comparative transcriptomic signatures between myeloid and lymphoblastic leukemia

Direct comparative analysis of gene expression profiles between myeloid and lymphoblastic leukemias reveals profound biological divergence that extends far beyond surface immunophenotype. These differences are not merely categorical but represent fundamentally distinct cellular states, developmental programs, and adaptive strategies, each with unique therapeutic implications. By examining these contrasts, researchers can identify lineage-specific vulnerabilities and better understand why certain therapies are effective in one leukemia type but not another.

6.1 Lineage-specific transcription factors

The most robust discriminators in comparative transcriptomics are the core lineage-determining transcription factors, which establish and maintain mutually exclusive cellular identities through complex regulatory networks. Myeloid leukemias are transcriptionally anchored by the coordinated activity of CCAAT/enhancer-binding protein alpha (CEBPA) and the ETS-family factor PU.1 (SPI1). CEBPA drives granulocytic differentiation by activating genes for secondary granule proteins and neutrophil function, while PU.1 is essential for monocytic/macrophage development and regulates genes like CSF1R and CD14. Their expression creates a transcriptional regime that promotes myeloid identity while actively repressing lymphoid programs [15]. In stark contrast, lymphoblastic leukemias are governed by a completely different set of master regulators. B-cell Acute Lymphoblastic Leukemia (B-ALL) is defined by the activity of PAX5, the central enforcer of B-cell identity, and IKAROS Family Zinc Finger 1 (IKZF1), which regulates lymphocyte development and suppresses myeloid fate. In T-cell ALL (T-ALL), the aberrant activation of NOTCH1 and its downstream targets like TAL1 and LM02 creates a powerful oncogenic transcriptional circuit that mimics early T-cell receptor signaling and promotes a proliferative, immature T-cell state [8, 19]. These factors do not simply serve as passive markers; they engage in cross-antagonistic relationships, creating self-reinforcing and exclusive transcriptional landscapes that define the very nature of each leukemia.

6.2 Metabolic and proliferation pathways

Comparative transcriptomic studies have uncovered significant differences in metabolic wiring and proliferative logic between the two lineages, reflecting their distinct origins and oncogenic pressures. Myeloid leukemias, particularly Acute Myeloid Leukemia (AML) and leukemic stem cells, often exhibit a transcriptomic signature of enhanced mitochondrial oxidative phosphorylation (OXPHOS) and fatty acid oxidation (FAO). This reflects a more quiescent, energy-efficient, and bioenergetically complex state, which is thought to be linked to their origin in primitive hematopoietic stem and progenitor cells. This metabolic profile supports survival under stress and contributes to chemotherapy resistance, as seen in the dependence of LSCs on OXPHOS [22]. Conversely, lymphoblastic leukemias, especially aggressive and high-risk subtypes, display a pronounced “anabolic” or “Warburg-like” transcriptomic signature. There is a marked upregulation of genes involved in aerobic glycolysis (e.g., LDHA, HK2), nucleotide biosynthesis, amino acid metabolism, and core cell cycle machinery (e.g., cyclins, CDKs, replication factors) [23]. This aligns perfectly with the extremely high proliferative index characteristic of ALL, where blasts are rapidly dividing and require massive biosynthetic output to support DNA replication and cell division. These metabolic distinctions are not just epiphenomena but represent critical therapeutic targets, as evidenced by the sensitivity of OXPHOS-dependent AML cells to BCL-2 inhibition and the exploration of glycolytic inhibitors in ALL.

6.3 Immune and microenvironmental signatures

The immune-related transcriptome and the signature of interaction with the bone marrow microenvironment further differentiate these leukemias, mirroring their distinct cellular origins within the immune system. ALL, derived from adaptive immune precursors, retains a transcriptional footprint of its lymphoid heritage. This includes higher expression of genes involved in antigen receptor signaling pathways (even in precursor stages), cytokine and chemokine receptors (e.g., IL7R, CXCR4), and genes mediating interactions with T-cells and other immune components. This can render ALL blasts more visible to, or interactive with, the adaptive immune system, a feature exploited by immunotherapies like bispecific antibodies and CAR T-cells. In contrast, AML originates from the myeloid lineage, a key component of the innate immune system. Its transcriptome often reflects this, showing stronger signatures associated with myeloid-derived suppressor cell (MDSC)-like functions, including the production of potent inflammatory cytokines and mediators (e.g., IL1B, IL6, TNF, *S100A8/9*) that can suppress antitumor immunity and remodel the niche. Furthermore, AML blasts express high levels of adhesion molecules and receptors (e.g., CD44, *VLA-4*, CXCR4) that facilitate intense interactions with the bone marrow stromal niche. This interaction transcriptionally programs pathways that promote stemness, quiescence, and chemoresistance, creating a protective sanctuary for leukemic cells [24]. Understanding these divergent microenvironmental dialogues is key to developing therapies that disrupt these supportive niches.

7. Biomarkers identified through gene expression profiling

The systematic application of transcriptomics has transitioned from a research tool to a clinical engine for biomarker discovery, generating actionable molecular tests that refine every stage of patient management.

7.1 Diagnostic biomarkers

Gene expression profiling has become indispensable for resolving diagnostically challenging cases, particularly acute leukemias of ambiguous lineage and mixed-phenotype acute leukemias (MPAL). Multi-gene expression classifiers, often based on microarray or RNA-seq data, can objectively assign lineage by analyzing the coordinated expression of dozens to hundreds of lineage-specific genes with superior accuracy to limited immunophenotypic panels. Furthermore, transcriptomic techniques are the gold standard for detecting pathognomonic fusion transcripts. For example, the PML-RARA fusion in Acute Promyelocytic Leukemia (APL) and the *ETV6-RUNX1* fusion in pediatric B-ALL are not only definitive diagnostic markers but also directly detectable via RT-qPCR or RNA-seq, enabling rapid, sensitive, and specific diagnosis that immediately guides life-saving, subtype-specific therapy [10, 11].

7.2 Prognostic biomarkers

Beyond diagnosis, transcript levels of specific genes or multi-gene signatures provide powerful independent prognostic information. In AML, high expression of certain oncogenes, even in the absence of mutations, portends a poor outcome. Elevated expression of FLT3, Wilms Tumor 1 (WT1), and Brain and Acute Leukemia, Cytoplasmic (BAALC) is consistently associated with higher relapse rates, resistance to chemotherapy, and inferior overall survival, independent of other risk factors [25]. In ALL, transcriptomics quantifies risk at multiple levels: the expression level of the BCR-ABL1 fusion transcript defines the high-risk Ph+ subgroup, while the loss of expression of IKZF1 (due to deletion or mutation) identifies a high-risk subset within B-ALL, even in the absence of BCR-ABL1, associated with increased relapse and poor response to therapy [7, 19]. Conversely, favorable expression signatures, such as those linked to *ETV6-RUNX1* or hyperdiploidy in pediatric B-ALL, predict excellent outcomes, allowing for treatment de-escalation to reduce toxicity.

7.3 Predictive biomarkers for therapy response

Perhaps the most transformative application is the use of transcriptomic data to predict response to specific therapeutic agents, enabling truly personalized treatment selection. In Philadelphia chromosome-positive (Ph+) leukemias, the magnitude of BCR-ABL1 transcript expression and signatures of its downstream pathway activity predict depth of response to tyrosine kinase inhibitors (TKIs). In CML, the expression level of the drug influx transporter human Organic Cation Transporter 1 (hOCT1) correlates strongly with intracellular imatinib concentration and the likelihood of achieving a deep molecular response [26]. In AML, an emerging transcriptomic signature characterized by high BCL-2 family dependency and OXPHOS activity predicts superior response to the BCL-2 inhibitor venetoclax. Most critically for immunotherapy, the expression of the target antigen is an absolute prerequisite for efficacy; high and uniform expression of CD19 and CD22 on B-ALL blasts is essential for the success of CD19- and CD22-directed CAR T-cell therapies, and its loss is a major mechanism of immune escape [27]. Transcriptomics thus guides therapy from traditional chemotherapy to targeted and immunologic agents. 

8. Clinical implications of comparative gene expression studies

The insights gleaned from comparative transcriptomics are not confined to the laboratory; they have direct, actionable consequences for clinical management, driving the evolution of modern hematologic oncology toward precision medicine (Figure 2).

Figure 2. Clinical implications of comparative study go gene expression profiles in myeloid leukaemia and lymphoblastic leukaemia.

8.1 Precision medicine

Transcriptomic profiling is now a cornerstone of risk-adapted therapy, allowing clinicians to tailor treatment intensity to the individual’s disease biology. By classifying patients into molecular subgroups with validated prognostic signatures, treatment protocols can be personalized. Patients with high-risk expression signatures (e.g., high BAALC,  IKZF1-deleted) are candidates for treatment intensification, including allogeneic stem cell transplantation in first remission. Conversely, patients with low-risk signatures (e.g., *ETV6-RUNX1+* pediatric B-ALL, certain CEBPA-mutated AML subtypes) can often be treated with less intensive regimens, minimizing acute and long-term toxicities such as cardiotoxicity, infertility, and secondary malignancies without compromising cure rates [28]. This represents a fundamental shift from a “one-size-fits-all” approach to a nuanced, biology-driven strategy.

8.2 Targeted therapies

The direct identification of dysregulated pathways from expression data has been the catalyst for developing and deploying molecularly targeted agents. The paradigm is BCR-ABL1 inhibition with imatinib and later-generation TKIs in CML and Ph+ ALL. Similarly, the discovery of constitutively active FLT3 signaling via transcriptomic and genomic studies led to the development and regulatory approval of FLT3 inhibitors like midostaurin (used upfront with chemotherapy) and gilteritinib (for relapsed/refractory disease) in AML [29]. In T-ALL, the central role of NOTCH1, evident from its dominant transcriptional signature, has spurred the development of NOTCH1 inhibitors and gamma-secretase inhibitors, currently in clinical trials. These therapies exemplify the direct translation of transcriptional pathway analysis into drugs that specifically intercept the oncogenic drivers, offering greater efficacy and often reduced off-target toxicity compared to conventional chemotherapy.

8.3 Immunotherapy and gene expression

Gene expression studies are indispensable for the rational design and application of immunotherapies. They are used to identify and validate ideal target antigens. The universal high expression of CD19 on B-ALL blasts, confirmed by transcriptomics, made it the premier target for the groundbreaking CD19 CAR T-cell therapies like tisagenlecleucel. Similarly, profiling has identified CD22 as a viable alternative or sequential target. Furthermore, transcriptomics reveals the immune contexture of the leukemia and its microenvironment. Expression of immune checkpoint molecules like PD-L1, PD-1, and CTLA-4 on leukemic blasts or infiltrating immune cells can identify patients most likely to benefit from checkpoint blockade. This is particularly relevant in subtypes with a high mutational burden or specific viral associations (e.g., EBV+ lymphomas) that may generate a more immunogenic microenvironment [30]. Thus, transcriptomics guides the entire immunotherapy pipeline, from target selection to patient stratification.

9. Challenges and future perspectives

Despite the transformative impact of transcriptomics on leukemia research, significant translational challenges impede the direct application of this data into routine clinical practice. A primary obstacle is tumor heterogeneity, which operates at multiple levels. Intratumoral heterogeneity, driven by clonal diversity and the presence of phenotypically distinct subpopulations like leukemic stem cells, means that bulk RNA-seq provides only an average expression signal, potentially masking critical rare subclones responsible for relapse. Furthermore, inter-patient heterogeneity, even within the same molecular subtype, complicates the development of universal biomarkers and treatment protocols [31]. Technical and analytical variability also poses a substantial hurdle. Differences in sample collection, RNA preservation, sequencing platforms, bioinformatic pipelines, and data normalization can introduce noise, making it difficult to compare results across studies and hindering the standardization required for clinical diagnostics [32]. Finally, the integration of transcriptomic data with other complex data layers—genomic mutations, epigenomic modifications, proteomic states, and clinical phenotypes—remains a formidable bioinformatic and biological challenge. Isolated transcriptomic signatures often lack mechanistic context without parallel data on driver mutations and pathway activation.

To overcome these limitations, future research must pivot towards integrative multi-omics approaches. Combining genomic (DNA-seq), transcriptomic (RNA-seq), epigenomic (ATAC-seq, ChIP-seq), and proteomic (mass spectrometry) data from the same patient cohort will enable the construction of comprehensive, causal molecular models. This systems biology view can distinguish driver transcriptional changes from passenger events and elucidate how genetic lesions translate, via epigenetic reprogramming, into the final oncogenic transcriptome and proteome. Concurrently, the application of advanced machine learning (ML) and artificial intelligence (AI) is essential for biomarker discovery in these high-dimensional datasets. ML algorithms can identify complex, non-linear gene expression signatures predictive of outcomes or drug response that elude conventional statistical methods, and can help deconvolute bulk sequencing data to infer cellular composition and subclonal architecture [33]. The most promising technological frontier is single-cell multi-omics. Techniques like scRNA-seq coupled with cellular indexing of transcriptomes and epitopes (CITE-seq) or T-cell receptor sequencing (scTCR-seq) will be indispensable for mapping the complete ecosystem of leukemia, dissecting the dynamic interplay between malignant clones and the immune microenvironment, and pinpointing the precise molecular mechanisms of therapy resistance at the level of individual resilient cells [34]. This granular understanding is the key to developing next-generation therapies that target the ecosystem of leukemia rather than just the bulk tumor.

10. Conclusion

Comparative gene expression profiling has fundamentally reshaped our understanding of the leukemias, moving beyond morphological classification to reveal the deep molecular schism between myeloid and lymphoid malignancies. These studies have delineated the core, lineage-specific transcriptional programs—governed by master regulators like CEBPA/PU.1 in myeloid leukemia and PAX5/IKZF1/NOTCH1 in lymphoblastic leukemia—that establish and maintain cellular identity. They have further illuminated the distinct signaling pathway dependencies (e.g., FLT3 in AML, JAK-STAT in ALL) and microenvironmental interactions that sustain proliferation and survival. Critically, transcriptomics has yielded a rich arsenal of diagnostic, prognostic, and predictive biomarkers, enabling more precise risk stratification and the rational application of targeted therapies such as FLT3 and BCR-ABL1 inhibitors. The trajectory of progress is now set by the convergence of ever-more sophisticated sequencing technologies and computational power. Continued advances in high-throughput and single-cell transcriptomics, coupled with integrative computational biology, will further enhance diagnostic accuracy, uncover novel therapeutic vulnerabilities within complex tumor ecosystems, and refine dynamic biomarkers for monitoring treatment response and minimal residual disease. By systematically decoding and leveraging the unique transcriptional logic of each leukemia, the field is poised to realize the full promise of precision oncology, tailoring intervention with unprecedented specificity to improve long-term survival and quality of life for all patients.

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.

References

  1. Döhner H, Weisdorf DJ, Bloomfield CD. Acute Myeloid Leukemia. New England Journal of Medicine. 2015 Sep 17;373(12):1136-52.
  2. Cancer Genome Atlas Research Network. Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia. New England Journal of Medicine. 2013 May 30;368(22):2059-74.
  3. Patel JP, Gönen M, Figueroa ME, Fernandez H, Sun Z, Racevskis J, et al. Prognostic Relevance of Integrated Genetic Profiling in Acute Myeloid Leukemia. New England Journal of Medicine. 2012 Mar 22;366(12):1079-89.
  4. Rowley JD. A New Consistent Chromosomal Abnormality in Chronic Myelogenous Leukaemia Identified by Quinacrine Fluorescence and Giemsa Staining. Nature. 1973 Jun 1;243(5405):290-3.
  5. Radich JP, Dai H, Mao M, Oehler V, Schelter J, Druker B, et al. Gene Expression Changes Associated with Progression and Response in Chronic Myeloid Leukemia. Proceedings of the National Academy of Sciences of the United States of America. 2006 Feb 21;103(8):2794-9.
  6. Pui CH, Evans WE. A 50-Year Journey to Cure Childhood Acute Lymphoblastic Leukemia. Seminars in Hematology. 2013 Jul;50(3):185-96.
  7. Mullighan CG, Goorha S, Radtke I, Miller CB, Coustan-Smith E, Dalton JD, et al. Genome-Wide Analysis of Genetic Alterations in Acute Lymphoblastic Leukaemia. Nature. 2007 Apr 12;446(7137):758-64.
  8. Weng AP, Ferrando AA, Lee W, Morris JP 4th, Silverman LB, Sanchez-Irizarry C, et al. Activating Mutations of NOTCH1 in Human T Cell Acute Lymphoblastic Leukemia. Science. 2004 Oct 8;306(5694):269-71.
  9. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, et al. Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science. 1999 Oct 15;286(5439):531-7.
  10. Valk PJ, Verhaak RG, Beijen MA, Erpelinck CA, van Waalwijk van Doorn-Khosrovani S, Boer JM, et al. Prognostically Useful Gene-Expression Profiles in Acute Myeloid Leukemia. New England Journal of Medicine. 2004 Apr 15;350(16):1617-28.
  11. Wang Z, Gerstein M, Snyder M. RNA-Seq: A Revolutionary Tool for Transcriptomics. Nature Reviews Genetics. 2009 Jan;10(1):57-63.
  12. Anders S, Pyl PT, Huber W. HTSeq—A Python Framework to Work with High-Throughput Sequencing Data. Bioinformatics. 2015 Jan 15;31(2):166-9.
  13. Potter N, Miraki-Moud F, Ermini L, Titley I, Vijayaraghavan G, Papaemmanuil E, et al. Single-Cell Analysis of Clonal Architecture in Acute Myeloid Leukaemia. Leukemia. 2019 May;33(5):1113-23.
  14. Velten L, Story BA, Hernández-Malmierca P, Raffel S, Leonce DR, Milbank J, et al. Identification of Leukemic and Pre-Leukemic Stem Cells by Clonal Tracking from Single-Cell Transcriptomics. Nature Communications. 2021 Mar 3;12(1):1366.
  15. Rosenbauer F, Tenen DG. Transcription Factors in Myeloid Development: Balancing Differentiation with Transformation. Nature Reviews Immunology. 2007 Feb;7(2):105-17.
  16. Martelli AM, Evangelisti C, Chiarini F, McCubrey JA. The Phosphatidylinositol 3-Kinase/Akt/mTOR Signaling Network as a Therapeutic Target in Acute Myelogenous Leukemia Patients. Oncotarget. 2010 Feb;1(2):89-103.
  17. Skorski T, Nieborowska-Skorska M, Wlodarski P, Perrotti D, Hoser G, Kawiak J, et al. The Role of Ras, Raf and Jak/Stat Pathways in the Pathogenesis of Philadelphia Chromosome-Positive Leukemia. Leukemia. 1997 Aug;11 Suppl 3:453-5.
  18. Figueroa ME, Lugthart S, Li Y, Erpelinck-Verschueren C, Deng X, Christos PJ, et al. DNA Methylation Signatures Identify Biologically Distinct Subtypes in Acute Myeloid Leukemia. Cancer Cell. 2010 Jan 19;17(1):13-27.
  19. Mullighan CG, Su X, Zhang J, Radtke I, Phillips LA, Miller CB, et al. Deletion of IKZF1 and Prognosis in Acute Lymphoblastic Leukemia. New England Journal of Medicine. 2009 Jan 29;360(5):470-80.
  20. Shochat C, Tal N, Bandapalli OR, Palmi C, Ganmore I, te Kronnie G, et al. Gain-of-Function Mutations in Interleukin-7 Receptor-α (IL7R) in Childhood Acute Lymphoblastic Leukemias. Journal of Experimental Medicine. 2011 May 9;208(5):901-8.
  21. Kohlmann A, Kipps TJ, Rassenti LZ, Downing JR, Shurtleff SA, Mills KI, et al. An International Standardization Programme Towards the Application of Gene Expression Profiling in Routine Leukaemia Diagnostics: The Microarray Innovations in LEukemia Study Prephase. British Journal of Haematology. 2008 Sep;142(5):802-7.
  22. Lagadinou ED, Sach A, Callahan K, Rossi RM, Neering SJ, Minhajuddin M, et al. BCL-2 Inhibition Targets Oxidative Phosphorylation and Selectively Eradicates Quiescent Human Leukemia Stem Cells. Cell Stem Cell. 2013 Mar 7;12(3):329-41.
  23. Kirschner K, Sampson N, Cairns DA, Wiese M, Hölzer K, Rieger MA, et al. Cell-Intrinsic and Metabolic Regulation of B Cell Acute Lymphoblastic Leukemia. Frontiers in Immunology. 2021 Sep 30;12:766802.
  24. Bewersdorf JP, Stahl M, Zeidan AM. The Microbiome in Acute Myeloid Leukemia: Where Do We Stand? Haematologica. 2021 May 1;106(5):1230-40.
  25. Baldus CD, Thiede C, Soucek S, Bloomfield CD, Thiel E, Ehninger G. BAALC Expression and FLT3 Internal Tandem Duplication Mutations in Acute Myeloid Leukemia Patients with Normal Cytogenetics: Prognostic Implications. Journal of Clinical Oncology. 2006 Feb 10;24(5):790-7.
  26. White DL, Saunders VA, Dang P, Engler J, Zannettino AC, Cambareri AC, et al. OCT-1-Mediated Influx Is a Key Determinant of the Intracellular Uptake of Imatinib But Not Nilotinib (AMN107): Reduced OCT-1 Activity Is the Cause of Low In Vitro Sensitivity to Imatinib. Blood. 2006 Jul 15;108(2):697-704.
  27. Maude SL, Frey N, Shaw PA, Aplenc R, Barrett DM, Bunin NJ, et al. Chimeric Antigen Receptor T Cells for Sustained Remissions in Leukemia. New England Journal of Medicine. 2014 Oct 16;371(16):1507-17.
  28. Patel JP, Levine RL. How Do Novel Molecular Genetic Markers Influence Treatment Decisions in Acute Myeloid Leukemia? Hematology / the Education Program of the American Society of Hematology. American Society of Hematology. Education Program. 2012;2012:28-34.
  29. Daver N, Schlenk RF, Russell NH, Levis MJ. Targeting FLT3 Mutations in AML: Review of Current Knowledge and Evidence. Leukemia. 2019 Feb;33(2):299-312.
  30. Rafiq S, Hackett CS, Brentjens RJ. Engineering Strategies to Overcome the Current Roadblocks in CAR T Cell Therapy. Nature Reviews Clinical Oncology. 2020 Mar;17(3):147-67.
  31. Tyner JW, Tognon CE, Bottomly D, Wilmot B, Kurtz SE, Savage SL, et al. Functional Genomic Landscape of Acute Myeloid Leukaemia. Nature. 2018 Oct;562(7728):526-31.
  32. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A Survey of Best Practices for RNA-Seq Data Analysis. Genome Biology. 2016 Jan 26;17:13.
  33. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine Learning Applications in Cancer Prognosis and Prediction. Computational and Structural Biotechnology Journal. 2015 Nov 5;13:8-17.
  34. Stubbington MJT, Rozenblatt-Rosen O, Regev A, Teichmann SA. Single-Cell Transcriptomics to Explore the Immune System in Health and Disease. Science. 2017 Oct 6;358(6359):58-63.

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