Navigating the Landscape of Exosomal microRNAs: Charting Their Pivotal Role as Biomarkers in Hematological Malignancies
Abstract
1. Introduction
General Considerations on Exosomes
2. Biogenesis of Extracellular Vesicles and Their RNA Cargo
2.1. Exosomes
2.2. Tumor Derived Exosomes (TEXs)
2.3. MIRNAs
3. Bioengineered Exosomes for miRNA Targeted Delivery and Related Therapeutic Prospectives
4. Examining the Exosomal Mirnome in Hematological Malignancies
4.1. Hematological Malignancies of Lymphoid Lineage
4.1.1. Exosomal miRNAs and Multiple Myeloma (MM)
4.1.2. Exosomal miRNAs and Chronic Lymphoid Leukemia (CLL)
4.1.3. Exosomal miRNAs and Lymphomas
4.1.4. Exosomal miRNAs and Acute Lymphoblastic Leukemia (ALL)
4.2. Hematological Malignancies of Myeloid Lineage
4.2.1. Exosomal miRNAs and Chronic Myeloid Leukemia
4.2.2. Exosomal miRNAs and Myelodisplastic Syndromes
4.2.3. Exosomal miRNAs and Acute Myeloid Leukemia
4.2.4. Exosomes miRNAs and Systemic Mastocytosis (SM)
4.2.5. Exosomes miRNAs and BCR-ABL Negative Myeloproliferative Neoplasms (MPNs)
4.3. Exosomal miRNAs and Late-Onset Acute Graft Versus Host Disease (LA-GVHD)
5. An Overview on Mechanisms of Exosomal MiRNA Interference in the Hematologic Niche
6. Recommended Panels and Current Challenges
- TECHNICAL VARIABILITY IN ISOLATION METHODOLOGIES: Traditional ultracentrifugation has long been regarded as the gold standard for exosome isolation. Nevertheless, it suffers from poor reproducibility and contamination due to several intrinsic limitations. Specifically, this method relies on sequential high-speed spins to pellet EVs based on their density and size, yet these conditions often cause EV aggregation and co-isolation of non-vesicular contaminants such as protein polymers, viruses, and high-density lipoproteins. These issues are particularly pronounced in complex and viscous biological fluids like plasma. Furthermore, the intense centrifugal forces can damage vesicle integrity, potentially altering their morphology and biological activity. These limitations, along with time-consuming protocols and the requirement for expensive instrumentation, hinder the scalability of ultracentrifugation for routine clinical use or high-throughput applications [187]. Microfluidic platforms are miniaturized systems that manipulate fluids within chips containing microscopic channels and are used in exosome and miRNA research to rapidly isolate exosomes from small biological samples such as blood, plasma, or saliva [187,188]. In the context of exosome and miRNA research, these platforms provide a promising alternative to traditional isolation techniques. One of the most advanced modalities is acoustic-based microfluidic separation, which employs ultrasonic standing waves to exert differential acoustic radiation forces on particles based on their size, density, and compressibility. These devices typically consist of two modular zones: the first removes larger components (>1 µm), including cells and debris, while the second isolates EVs by filtering out larger microvesicles and apoptotic bodies, thereby enriching for exosomes (<200 nm). Moreover, the cutoff size for separation can be dynamically adjusted to achieve precise discrimination between EV subtypes. However, challenges remain, including limited standardization across platforms, which can affect reproducibility and downstream molecular profiling [187]. One major issue is the lack of standardization in chip design and materials, such as polydimethylsiloxane (PDMS), glass, or plastic, which affects how efficiently exosomes are captured from different bodily fluids. In addition, key settings like flow rate, electric or acoustic field strength, and channel size are often operator-dependent, making results difficult to reproduce across different laboratories. These design differences can also lead to the isolation of different EV types (e.g., more microvesicles vs. fewer exosomes), which alter downstream analyses like miRNA or protein profiling. Additionally, without agreed standards for measuring exosome purity or concentration, it is hard to apply these platforms consistently in clinical settings [189]. Furthermore, by analyzing the lipidomic and proteomic cargo of isolated exosomes, it results in a certain heterogeneity in exosomal cargo according to the isolation technique. For instance, techniques such as ultracentrifugation, size exclusion chromatography (SEC), and ultrafiltration differ significantly in their ability to retain or remove contaminating proteins, lipoproteins, and soluble factors. Ultracentrifugation often co-isolates protein aggregates, while SEC offers improved purity but may lose smaller vesicles or underrepresent certain subpopulations. This technical variability contributes to inconsistencies in downstream proteomic and lipidomic profiling, as certain proteins or lipids may be enriched or depleted depending on the method used [190].
- LACK OF A CONSENSUS ON EXOSOME CHARACTERIZATION STRATEGIES: Techniques such as Transmission Electron Microscopy (TEM) and Cryogenic Electron Microscopy (Cryo-EM) provide high-resolution imaging of exosomal morphology, yet they are limited by artifacts from sample preparation and low throughput; nanoparticle Tracking Analysis (NTA) and Tunable Resistive Pulse Sensing (TRPS) allow quantification of particle size and concentration but struggle to distinguish exosomes from contaminants. Surface plasmon resonance (SPR) and Surface-Enhanced Raman Spectroscopy (SERS) enable label-free, real-time cargo profiling but require sophisticated nanotechnology expertise. Flow Cytometry (FCM), especially when combined with imaging (Imaging Flow Cytometry, IFCM), facilitates multiparametric surface marker analysis but is hampered by sensitivity limitations for submicron particles. Asymmetric Flow Field-Flow Fractionation (AF4) has shown promise in distinguishing exosomal subpopulations but remains a niche technology [191]. At present, no single method fulfills all those critical criteria (high purity, yield, reproducibility, scalability, and affordability) needed for robust clinical employment.
- INTER-PATIENT VARIABILITY IN EXOSOMAL MIRNA EXPRESSION. Intrinsic interindividual variability and disease-independent factors can both impair the accuracy in the interpretation of circulating miRNAs and challenges efforts to establish universal reference ranges [192]. As widely explained in previous sections, the first cause of this variability is the tumor itself (and associated inflammation and immune deregulation) since miRNAs represent a specific neoplastic signature. In other words, they vary according to molecular subtypes and tumor features (e.g., oncogene overexpression, etc.). Secondly, specific signatures of circulating miRNA have also been associated with a variety of pathological conditions which can coexist with tumor as comorbidities, such as cardiovascular diseases, diabetes, liver pathologies, and sepsis [193]. In addition, other factors can influence the diversity of miRNAs levels in circulation: race, gender, lifestyle, drug assumption, smoking habits, diet, and physical activity. However, there are other variables which are more difficult to verify such as polymorphisms in miRNAs chromosome loci. An example is represented by copy number variations (CNVs) occurring in coding regions of the genome. As a result, they can deregulate certain miRNAs, alter their expression, and thus, contribute to the development of the disease [192]. Interestingly, even diet represents a variable. Several dietary constituents (resveratrol, curcumin, isoflavones, catechins, indoles, vitamins A and D) play a certain role in affecting miRNAs expression profile [194]. The rationale of the effect exerted by these substances may depend on homeostatic changes in circulating miRNA-containing vehicles (including exosomes) [195]. In addition to these considerations, it should be said that the amount of circulating miRNA may vary in the same patient over time. For example, it can be influenced by common medications (aspirin has shown to reduce miR-126 levels). Therefore, miRNAs appear as potentially useful parameters in pharmacodynamic studies [192].
- NORMALIZATION OF EXOSOMAL MIRNA QUANTIFICATION: A major translational barrier in exosome-based diagnostics is the lack of standardized normalization strategies for exosomal miRNA quantification. Although qRT-PCR remains the gold standard for miRNA detection due to its sensitivity and specificity, its reliability is highly dependent on reference miRNAs whose expression is stable across different biological conditions, disease states, and technical protocols. Current reference small RNAs such as U6 or RNU44, traditionally used in cellular RNA studies, have shown inconsistent expression in serum- or plasma-derived exosomes, particularly in pathological contexts like cancer or inflammation [196,197]. In an important effort to address this gap, Damanti et al. performed a systematic assessment of RNA-seq datasets and identified miR-26a-5p and miR-486-5p as promising endogenous reference candidates in pediatric hematological malignancies. Their validation across diverse disease subtypes of lymphomas and B-cell ALL demonstrated superior stability of miR-26a-5p, independent of disease status or exosome isolation method (ultracentrifugation vs. kit-based protocols). This interesting data positions miR-26a-5p as a bona fide universal calibrator for plasma exosomal miRNA studies. Conversely, miR-486-5p, while abundant and stable across disease groups, was highly susceptible to the choice of isolation technique, showing significant variation between protocols [16]. This observation is particularly relevant in multi-center or retrospective studies, where differences in isolation methods (e.g., ultracentrifugation, SEC, precipitation kits) are common and often unavoidable (as previously discussed). Therefore, using normalization controls that are method-sensitive could bias miRNA levels, leading to false-positive/negative biomarker signals. Critically, several issues remain unresolved as follows: (1) this study is limited to pediatric lymphoid malignancies and extrapolation to adult cohorts or myeloid neoplasms remains speculative; (2) the identified normalizers may not generalize across biofluid types (e.g., urine, CSF) or technical platforms (e.g., digital PCR, NGS). On a final analysis, the study provides valuable empirical evidence supporting miR-26a-5p as a technical normalizer, but broader standardization efforts (including reference selection) are still needed.
7. Recent Advances
- Ubiquitous tetraspanins such as CD9, CD63, and CD81 remain gold-standard markers used in affinity capture workflows, given their abundant expression on exosomal membranes across cell types. These membrane proteins are known to possess multiple functional roles in several biological processes, such as cell adhesion, fusion, signaling, and trafficking [198]. Apart from these classical markers, surface antigens such as epithelial cell adhesion molecule (EpCAM), EGFR, integrins (e.g., α6β4, αvβ5), and even PD-L1 have been used for immunoaffinity capture of tumor-derived exosomes, significantly improving specificity and reducing contamination from non-vesicular particles or lipoproteins [199]. A recent study introduced the “EVs on Demand” (EVOD) chip, designed to selectively capture cancer-related exosome subpopulations. The chip uses a chemical reaction between tetrazine-tagged antibodies (targeting EpCAM and EGFR) and a specially coated microfluidic surface to isolate exosomes. It successfully captured 76% more EGFR-positive exosomes from cancer patients compared to healthy individuals. However, this approach is still limited by high costs [200].
- Magnetic bead-based immunoaffinity enrichment is another separation method that has recently gained attention. This approach employs antibody-modified magnetic beads to capture exosomes. In a later passage, exosomes are separated by magnetic force. Furthermore, novel immuno-affinitive superparamagnetic nanoparticles (IS-NPs) have shown higher yield and increased purity than conventional separation methods. For example, in a study by Fang et al., superparamagnetic nanoparticles were combined with anti-CD63 antibodies through a molecular interaction between β-cyclodextrin (β-CD, a heptasaccharide derived from glucose) and 4-aminoazobenzene (AAB, an aromatic amine). This system achieved impressive results, with exosome capture and release efficiencies reaching 80% and 86.5%, respectively, in artificial model sample [201].
- Lipid-based separation techniques exploit the natural structure of exosome membranes, which are made of lipid bilayers. These lipids can interact with specially designed molecules to help isolate exosomes efficiently. In a study, Wan et al. created a special probe called a lipid nanoprobe to quickly extract exosomes from plasma. This probe includes a molecule called DSPE-PEG-biotin. More specifically, DSPE is a lipid that can insert itself into the exosome’s membrane through hydrophobic interactions; polyethylene glycol (PEG) is attached to DSPE to make the molecule soluble in water, preventing aggregation. Once the probe embeds into the exosome membrane, the biotin on its surface binds strongly to NeutrAvidin, which is coated on magnetic beads [202]. This interaction allows the exosomes to be pulled out quickly (just 15 min) and efficiently using a magnet, a much faster process than traditional isolation techniques.
- Advanced microchip technologies such as nanoplasmonic exosome assay (nPLEX) have significantly improved the sensitivity and speed of exosome detection. This technology employs nanohole arrays embedded in a thin gold film, which are functionalized with antibodies targeting exosome surface markers (e.g., CD63 and EpCAM). The metal nanohole array supports surface plasmon resonance (SPR), a phenomenon where the light energy provided by laser or LED causes electrons on the metal surface to oscillate in resonance. These oscillations are sensitive to changes on the surface of the array, such as when exosomes bind to the antibodies anchored within the holes. The binding causes a shift in the resonance signal (e.g., change in transmitted light intensity or wavelength), which is measured by a sensor. By monitoring these optical changes, scientists can detect and quantify very small amounts of target molecules (thousands/µL) without the need to attach any additional markers (like fluorescent dyes, radioactive isotopes, or enzymes) to the particles they are trying to detect [203].
- A novel digital microfluidic (DMF) platform was developed for automated, rapid, and low-volume EVs pretreatment. The system combines a reusable DMF chip with a magnetic particle-based protocol, enabling complete exosome isolation and miRNA extraction within 20–30 min from as little as 20–40 μL of plasma. This DMF system operates by manipulating droplets on an electrode array chip, allowing for precise, programmable control of EVs isolation, washing, and lysis steps. The method was validated using clinical plasma samples from patients with non-small cell lung cancer (NSCLC). RT-qPCR analysis revealed that EV-derived miR-486-5p and miR-21-5p were effective biomarkers for NSCLC, and the results were consistent with those obtained using a commercial exosome RNA extraction kit. Importantly, the platform achieves over 77% isolation efficiency and is cost-effective due to chip reusability. This advance demonstrates strong potential for standardized, scalable EV-miRNA–based liquid biopsy applications in cancer diagnostics [204].
- Droplet digital PCR (ddPCR) is a highly sensitive technique that improves nucleic acid detection by dividing the PCR reaction mixture into thousands of tiny droplets. Typically, each droplet contains either zero or one copy of the target gene. After PCR, droplets are classified as positive or negative based on their fluorescence signal, and the concentration of the target molecule is calculated using the Poisson distribution and the ratio of positive droplets. Compared to traditional qPCR, ddPCR offers greater sensitivity and accuracy, particularly for detecting low-abundance targets such as urinary exosomal miRNAs. Recent studies highlight its utility for liquid biopsy in cancer diagnostics. For example, exosomal miR-15a-5p, measured by ddPCR, was found to effectively distinguish endometrial cancer patients from healthy individuals [205]. Therefore, ddPCR has shown to improve sensitivity and reproducibility in miRNAs detection from limited blood volumes. For this reason, it appears particularly suited to monitoring minimal residual disease (MRD) or detecting low-expression miRNAs in blood cancers, where early relapse detection is critical [206].
- The CRISPR/Cas system, widely known for its role in gene editing, has recently been adapted for the highly sensitive and specific detection of exosomal miRNAs. A notable example is the RACE (rolling circle amplification–Assisted CRISPR/Cas9 Cleavage) method developed by Wang et al., which combines nucleic acid amplification with CRISPR/Cas-based cleavage. In this approach, a specially designed DNA padlock probe (a short single-stranded DNA molecule) binds to the target miRNA, accurately identifying even single-nucleotide differences. This probe is then circularized. This circular DNA is amplified through rolling circle amplification (RCA), producing long single-stranded DNA (ssDNA) that contains multiple repeats of the target sequence along with PAM motifs (short, specific DNA sequences) necessary for Cas9 recognition. The CRISPR/Cas9 complex then cleaves these amplified sequences. Simultaneously, a TaqMan probe is added to the reaction and binds the target DNA. When Cas9 cuts the target DNA, the probe is cleaved, producing a measurable fluorescence signal that indicates the presence of the miRNA [207]. This method is highly sensitive even for small differences in miRNA sequence and is able to detect multiple miRNAs in a single test.
8. Conclusions and Future Perspectives
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Disease | Principal miRNA | Principal Biological Function | Key Targets/Pathways | Clinical Role |
---|---|---|---|---|
MM | miR-20a-5p | Promotes chemoresistance | RUNX3, Rab27B, ATG7 | Early disease progression marker |
miR-103a-3p | Promotes progression | PTEN/PI3k-Akt | Biomarker of MGUS → MM shift | |
miR-4505 miR-10a | Enhances tumor aggressiveness Pro-oncogenic | Cytokine network EPAH8/SEMA5A | Prognostic marker Favors proliferation and metastasis | |
miR-16 | Regulates IGF1R signaling | IGF1R/CCND1/ELAVL1 | Low levels linked to poor prognosis | |
PRINS (lncRNA) | Reflects chromosomal instability | del(13)(q14), t(4;14) | Distinguishes MM from healthy donors | |
miR-135b miR-21 | Pro-angiogenic in hypoxic BM niche Counteract apoptosis | FIH-1 Rhob | Target for anti-angiogenic therapy Induces steroid-resistance | |
miR-15a-5p | Tumor suppressor | CCND1, BCL2 | Reduced in bortezomib-resistant MM | |
miR-17-5p | Both: tumor suppressor/onco-miR | HBP1, AIB1, E2F1 | Reduced in bortezomib-resistant MM | |
miR-18a | Drives extramedullary disease | Dicer, HIF-1α | Marker of aggressive disease | |
CLL | miR-15a, miR-16 | Induce apoptosis | BCL2 | Loss correlates with poor prognosis |
miR-155 | Promotes MDSCs | PI3K/Akt, PD-L1 | Marker of immune escape/resistance | |
miR-150 | Regulates T-cell differentiation | MYB, NOTCH | Prognostic marker/identifies CLL | |
miR-223 | Immune suppression | STAT3 | Downregulated in case of progression | |
miR-202-3p | Activates Hedgehog pathway | SMO | Marker for immune escape | |
miR-195 | Correlates with time-to-treatment | CCND1 | Early detection biomarker | |
HL | miR-24-3p | Regulates cell cycle | CDK6, SMAD4 | Diagnostic/reduced after treatment |
miR-127-3p | Regulates cell cycle | CDK6, SMAD4 | Diagnostic/reduced after treatment | |
miR-21-5p | Tumor growth/immune evasion | PTEN, SOCS1 | Marker for disease activity and relapse | |
miR-155-5p | Tumor growth/immune evasion | PTEN, SOCS1 | Marker for disease activity and relapse | |
Let-7a-5p | Tumor suppressor | RAS, HMGA2 | Monitoring disease response | |
DLBCL | miR-379-5p | Promotes survival/chemoresistance | BCL2, LIN28B | Early diagnostic marker |
miR-135a-3p | Promotes survival/chemoresistance | BCL2, LIN28B | Early diagnostic marker | |
miR-99a-5p | Negatively targets apoptosis | mTOR, IGF-1R | Marker of chemoresistance | |
miR-125b-5p | Negatively targets apoptosis | mTOR, IGF-1R | Predicts shorter PFS | |
miR-451a | Suppresses invasion | MIF | Low levels linked to poor prognosis | |
miR-155 | Regulates immune cell function | SHIP1, PU.1 | Marker of refractory-relapsed disease | |
miR-20a | Regulates immune cell function | SHIP1, PU.1 | Associated with higher mortality rate | |
miR-106a/b | Enhances cell survival and invasion | E2F1, TGFβ | Associated with higher mortality rate | |
ALL | miR-181a | Induces CNS involvement | MCL-1, BCL2 | CNS relapse biomarker |
miR-181b-5p | Suppresses apoptosis | PCNA, Ki-67 | Potential therapeutic target |
Disease | Principal miRNA | Principal Biological Function | Key Targets/Pathways | Clinical Role |
---|---|---|---|---|
CML | miR-92a | Modulates angiogenesis | Integrin α5 | Biomarker for vascular remodeling |
miR-210 | Promotes VEGF-mediated angiogenesis | EPHRIN-A3 | Potential therapeutic target | |
miR-126 | Affects adhesion/migration of CML cells | CXCL12, VCAM1 | Modulator of leukemic niche | |
MDS | miR-196a-5p | Regulates hematopoiesis/therapy resistance | HOXA, DNMT1, PTEN | Associated with progression to AML |
miR-126-5p | Regulates hematopoiesis/therapy resistance | PTTG3P | Diagnostic/predictive of AZA response | |
miR-192-5p let-7a mir-196-5b | Targets BCL2 and suppresses proliferation Associated with higher-risk karyotype Drives aberrant proliferation | BCL2 RAS/MYC CDKN1B | Predictor of AZA/LENA therapy success Favors proliferation Associated with progression to AML | |
AML | miR-150 | Modulate apoptosis and proliferation | PKCα, FOXO4 | Early detection marker |
miR-155 | Promotes leukemogenesis | SHIP1/SOCS1 | Early detection marker | |
miR-10b | Enhances tumor invasiveness | HOXD10, RhoC | Poor prognosis biomarker (shorter OS) | |
miR-125b | Promotes chemoresistance Inhibits differentiation | p53, BAK1, CBFβ TET2 | Associated with relapse and mortality Associated with relapse and mortality | |
miR-532 | Improves overall survival | Unknown | Positive prognostic marker | |
miR-1246 miR-425-5p | Induces survival and colony formation Tumor suppressor | LRIG1; JAK/STAT 23 genes (e.g., APOBEC3A) | Detection/aggressive disease marker Poor prognosis when deregulated | |
SM | miR-23a | Inhibits osteogenesis | RUNX2, SMAD1, SMAD5 | Potential targets for reversing bone loss |
miR-30a | Same as above | Same as above | Same as above | |
PV | miR-451, 144 | Promote erythroid maturation | MYC/COX10; RAB14/CAP1 | Enhance erythropoiesis |
miR-150 | Suppresses erythroid differentiation | c-MYB inhibition | Contributes to clonal expansion | |
miR-16-2 | Promotes erythropoiesis | (Independent of JAK/STAT) | Supports erythroid colony formation | |
let-7a, miR-182 | Regulate hematopoietic proliferation | PI3K/AKT | Their dysregulation alters hematopoiesis | |
miR-143, 145, 223 | Modulate platelet differentiation | PI3K/AKT | Contribute to aberrant hematopoiesis | |
ET | miR-34a | Induces apoptosis via p53/SIRT1 axis | p53/SIRT1 | Loss favors megakaryocytic hyperplasia |
miR-342, 326 | Regulate inflammatory responses | NOTCH, PI3K/AKT | Loss favors meyeloproliferation | |
miR-105, 149, 147 | Modulate inflammatory pathways | PI3K/AKT, NF-κB | Dysregulation promotes proliferation | |
miR-10a | Regulate megakaryocyte development | MPL/JAK2 | Favors megakaryocytic expansion | |
miR-150 miR-133a | Favors megakaryocytic lineage Tumor suppressor/regulate differentiation | c-MYB inhibition Cyclin D1; ERK1/2 | Thrombocytosis and risk of thrombosis Downregulation leads to proliferation | |
PMF | miR-31, 150, 95 | Downregulated: impairs differentiation | Myb; PI3K/AKT-TGFβ | Associated with marrow fibrosis |
miR-190 | Upregulated: pro-survival function | Unknown | Cellular persistence in fibrotic BM | |
miR-223, 146b | Regulate megakaryocyte proliferation | NF-κB, JAK/STAT | Contribute to fibrosis and splenomegaly | |
miR-4319 | Downregulated: SETBP1 upregulation | SETBP1 pathway | Associated with progression to AML |
Disease | Micro-RNA | Main Function | Key Targets | Role as Biomarker |
---|---|---|---|---|
AML | miR-150 | Modulates apoptosis, differentiation, tumor growth | PKCα, FOXO4, iASSP, | Distinguishes AML from CML, ALL, MDS |
EIF4B, TET3 | ||||
miR-155 | Regulates proliferation and apoptosis via PI3K/Akt signaling | PU.1, SHIP1 | Indicates progression/refractoriness | |
miR-1246 | Promotes angiogenesis and tumor aggressiveness | PML, ALDH1, SOX2 | Indicates resistance/invasiveness | |
MM | miR-17-5p | Tumor suppressor and oncogene (context-dependent) | HBP1, AIB1, E2F1 | Reduced in bortezomib-resistant MM |
miR-20a-5p | Promotes proliferation, chemoresistance | RUNX3, Rab27B, | Reduced in bortezomib-resistant MM | |
Smad4, ATG7 | ||||
miR-15a-5p | Tumor suppressor, regulates apoptosis | CCND1, BCL2, | Associated with CHT resistance | |
BDNF, CXCL10 | ||||
miR-16-5p | Tumor suppressor, modulates TGF-β | Smad3 | Associated with CHT resistance | |
let-7b | Tumor suppressor, regulates cell cycle and resistance | CCND1, MTDH, CALU, | Associated with poor prognosis | |
ERK, AURKB | in NDMM | |||
miR-18a | Promotes growth and EMD, regulates miRNA biogenesis | Dicer, HIF-1α, | Indicates progression | |
ATXN1, STK4 | ||||
WM | miR-192-5p | Promotes proliferation, survival, metastasis | SEMA3A, XIAP, YY1, | Indicates progression |
USP1, PI3K/Akt | ||||
miR-320b | Tumor suppressor, reduces migration and invasion | c-Myc, CD71, TRIAP1, | Downregulated in aggressive forms | |
Wnt/β-catenin, MMP2/9 | ||||
let-7d | Dual role: regulates RAS, stem cell aging, stromal signaling | RAS, HMGA2, P16, | Useful to stratify high-risk patients | |
COL3A1, CCL7 | ||||
DLBCL | miR-99a-5p | Regulates tumor growth and survival | SMARCA5, SMARC1, | Indicates progression and useful to |
mTOR, IGF-1R, FGFR3 | distinguish disease subtypes | |||
miR-125b-5p | Tumor suppressor but also downregulates p53 | BCL-2, LIN28B, p53 | Useful to define subtype and risk level |
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Fazio, M.; Stagno, F.; Penna, G.; Mirabile, G.; Allegra, A. Navigating the Landscape of Exosomal microRNAs: Charting Their Pivotal Role as Biomarkers in Hematological Malignancies. Non-Coding RNA 2025, 11, 64. https://doi.org/10.3390/ncrna11050064
Fazio M, Stagno F, Penna G, Mirabile G, Allegra A. Navigating the Landscape of Exosomal microRNAs: Charting Their Pivotal Role as Biomarkers in Hematological Malignancies. Non-Coding RNA. 2025; 11(5):64. https://doi.org/10.3390/ncrna11050064
Chicago/Turabian StyleFazio, Manlio, Fabio Stagno, Giuseppa Penna, Giuseppe Mirabile, and Alessandro Allegra. 2025. "Navigating the Landscape of Exosomal microRNAs: Charting Their Pivotal Role as Biomarkers in Hematological Malignancies" Non-Coding RNA 11, no. 5: 64. https://doi.org/10.3390/ncrna11050064
APA StyleFazio, M., Stagno, F., Penna, G., Mirabile, G., & Allegra, A. (2025). Navigating the Landscape of Exosomal microRNAs: Charting Their Pivotal Role as Biomarkers in Hematological Malignancies. Non-Coding RNA, 11(5), 64. https://doi.org/10.3390/ncrna11050064