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Potential application of the bulk RNA sequencing in routine MPN clinics

BMC Cancer volume 25, Article number: 746 (2025) Cite this article

Abstract

Background

Philadelphia chromosome-negative myeloproliferative neoplasms (MPNs) are chronic hematological malignancies characterized by driver and nondriver mutations, leading to a deregulated immune system with aberrant cytokines and immune cells. Understanding the gene mutation landscape and immune state at various disease stages is crucial for guiding treatment decisions. While advanced techniques like single-cell RNA sequencing and mass cytometry provide valuable insights, their high costs and complexity limit clinical application. In contrast, bulk RNA sequencing (RNA-Seq) offers a cost-effective complementary approach for evaluating genetic mutations and immune profiles.

Methods

Peripheral blood and bone marrow samples from treatment-naïve patients diagnosed with polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF) were analyzed using RNA sequencing. Additionally, data from the microarray datasets [GSE26049, GSE2191] were included in this study. Bioinformatics methods were employed to interpret gene mutations and immune landscapes in MPN patients.

Results

Our findings demonstrate the potential value of RNA-Seq in identifying gene mutations and characterizing the immune profile, including immune cell infiltration, cytokine profiles, and distinct immune-related pathways involved in the development of MPN.

Conclusion

Bulk RNA-Seq is a feasible tool for routine clinical practice, providing comprehensive insights into the immune and genetic landscape of MPNs. This approach could enhance personalized treatment strategies and improve prognostic accuracy, ultimately contributing to better management of MPN patients.

Peer Review reports

Introduction

Classic myeloproliferative neoplasms (MPNs) are clonal hematopoietic stem cell disorders primarily driven by mutations such as JAK2V617F, MPLW515L/K, and calreticulin, alongside nondriver mutations like EZH2, ASXL1, RAS, SRSF2, TP53, and U2AF1 [1, 2]. These mutations significantly influence disease progression and development. Clinically, MPNs manifest through constitutional symptoms, including fatigue, abdominal discomfort, itching, bone pain, night sweats, and weight loss [3]. Routine blood tests often reveal elevated blood cell counts, while bone marrow examinations confirm malignant hematopoietic stem cell proliferation [4]. Major complications include bleeding and thrombosis, contributing to high morbidity and mortality rates [5]. A hallmark of MPNs is the chronic inflammatory state characterized by immune dysregulation, abnormal immune cells, and aberrant cytokines [6, 7]. Understanding the interplay between gene mutations and the immune landscape is crucial for guiding treatment decisions and adapting therapies throughout the disease course [8, 9].

Dysregulated cytokines and immune cells play a pivotal role in the pathogenesis and progression of MPNs, particularly PMF. Key cytokines implicated include IL-1β, TNF-α, IL-6, IL-8, VEGF, PDGF, IFNs, and TGF-β [10,11,12,13,14,15], with studies highlighting the therapeutic potential of targeting IL-1β in JAK2-mutated models [16]. Immune cell subsets, such as T-cells (CD4, CD8, Treg, Th1, Th17), NK cells, and myeloid cells (MDSCs, DCs, monocytes, macrophages), are notably dysregulated in MPNs [17,18,19,20,21,22,23]. Monocytosis, especially intermediate and non-classical monocytes, is a critical prognostic factor in PMF [24, 25], with increased TNF-α secretion and angiogenic gene expression observed in Tie2 + monocytes [26, 27]. Macrophages, particularly CD68-positive subsets, are significantly elevated in PMF compared to CML, PV, and ET, suggesting their role in bone marrow fibrosis [28]. Transcriptomic analyses further underscore the importance of monocytes and macrophages in MPN pathogenesis, providing insights into potential therapeutic targets [29].

Advanced technologies such as single-cell transcriptomics and mass cytometry have significantly enhanced our understanding of the immune landscape in MPNs [30, 31]. However, these methods are often prohibitively expensive and require specialized expertise, making them difficult to implement in routine clinical practice. Currently, flow cytometry is widely used to assess the phenotypic and functional characteristics of a limited range of immune cell types, but it lacks comprehensiveness [32]. Sanger sequencing and Next-generation DNA sequencing are commonly employed to investigate gene mutations associated with MPNs, but they typically focus on a limited number of genes due to cost constraints. In contrast, bulk RNA sequencing (RNA-Seq) has emerged as a preferred method for comprehensive transcriptome analysis at a relatively low cost. RNA-Seq not only facilitates the discovery of gene expression differences but also offers valuable insights into the immune landscape through bioinformatics tools like CIBERSORTx, which estimates the relative abundance of immune cell types based on gene expression data [33,34,35]. Recent studies have demonstrated the accuracy of RNA-Seq in interpreting immune cell subsets compared to protein expression evaluated by mass cytometry [36]. This suggests that bulk RNA-Seq can effectively characterize the immune and gene mutation landscape throughout the disease course, including before treatment, after treatment, and during disease progression. While JAK2 inhibitors have been shown to improve the prognosis for many MPN patients, some individuals do not respond favorably due to severe side effects [37]. Therefore, it is crucial to tailor treatment strategies based on the specific immune and gene mutation profiles of each patient. For certain patients, alternative therapies such as IFN-α or other immune-modulatory drugs may be more appropriate.

This study enrolled treatment-naïve patients diagnosed with PV, ET, and PMF. By analyzing peripheral blood (PB) and bone marrow (BM) samples, the researchers aimed to identify gene mutations and characterize the immune landscape using advanced bioinformatics tools, such as CIBERSORTx for immune cell infiltration analysis. It emphasizes the feasibility of implementing bulk RNA-Seq in routine clinical practice for MPNs, providing valuable insights into the gene mutation landscape and immune characteristics of patients. This approach could enhance personalized treatment strategies and improve prognostic accuracy, ultimately contributing to better management of MPNs. The findings advocate for further validation with larger cohorts and integration of RNA-Seq data into clinical workflows to optimize patient care.

Materials and methods

Patient samples

Three PV patients, 23 ET patients, and 8 PMF patients with confirmed diagnoses without treatment were enrolled in this study. They were treated at the Zhongnan Hospital of Wuhan University between July 1, 2022, and June 30, 2023. Peripheral blood (PB) routine, bone marrow (BM) aspiration and bone biopsy were performed for routine laboratory tests; the residual samples after routine examination of gene mutation and flow cytometry analysis were used further for RNA extraction and RNA sequencing. This study was approved by the Ethics Committee at Zhongnan Hospital of Wuhan University. All methods and procedures associated with this study were conducted following the Good Clinical Practice guidelines and the ethical principles of the Declaration of Helsinki, as well as the local laws.

RNA‑sequencing library

The first strand of cDNA was synthesized via the M-MULV reverse transcriptase system. Endogenous RNAs are degraded by RNaseH, and the second strand is synthesized by DNA polymerase I with dNTPs. After purification, the double-stranded cDNA was repaired at the end, followed by the addition of a tail and the connection of sequencing adapters. AMPure XP beads were used to screen cDNAs. PCR amplification was performed, and the PCR products of AMPure XP beads were again used to purify each cDNA. The shot-gun libraries were sequenced on an Illumina NovaSeq 6000, and paired-end reads of 150 bp were generated.

Comprehensive analysis of immune characteristics

To examine immune cell infiltration in the bone marrow, we utilized CIBERSORTx (https://cibersortx.stanford.edu) [33], as described previously, to quantify the infiltration of 22 immune cells and provide an estimation of the abundances of various cell types in a mixed cell population via gene expression data.

Enrichment analysis and correlation analysis

Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were implemented via the clusterProfiler package in R v4.3.2. GO terms with adjusted P values less than 0.05 were considered significantly enriched in the DEGs. Correlation analysis was implemented with the R packages psych and corrplot.

Weighted gene co‐expression network analysis (WGCNA)

WGCNA is a systematic biological approach that enables the characterization of gene association patterns between different samples, with the capacity to identify highly covarying gene sets. In our study, we employed the R package WGCNA to perform WGCNA on MPN bulk RNA-seq data and a microarray dataset. Initially, a suitable soft threshold β was calculated to meet the criteria for constructing a scale-free network. We then transformed the weighted adjacency matrix into a topological overlap matrix (TOM) and calculated the dissimilarity (dissTOM). To perform gene clustering and module identification, we applied the dynamic tree-cut approach. Finally, the modules highly correlated with ET and PMF were identified for subsequent analysis. The R package “linkET” was used to perform Mantel test analysis between the MPN subtypes (PV, ET, PMF) and gene sets in WGCNA.

Gene mutation analysis

In this study, we used spliced transcript alignment to a reference (STAR) and the Genome Analysis Toolkit (GATK) to perform gene mutation analysis. The detailed analysis process is described on GATK’s official website (https://gatk.broadinstitute.org/hc/enus/categories/360002302312). To investigate the mutations associated with MPNs, we utilized the R package “maftools” to generate waterfall plots and a Lollipop chart, which displays mutation landscapes in MPN patients.

Statistical analysis

Trimmomatic and fastp were used for the preprocessing of the raw RNA-Seq reads. Clean reads were mapped to human reference genome sequences (GRCh38) via HISAT2 with the GRCh38/V34 annotation file in Gencode (https://www.gencodegenes.org/human/). The read count matrix was obtained by using the feature count. The rank-in strategy described previously [38] (http://www.badd-cao.net/rank-in/index.html) was used to remove the batch effects between the bulk RNA-Seq data and the microarray dataset. PCA plots were subsequently generated with the Factoextra package and FactoMineR package [39, 40]. The limma package in R v4.3.2 was used to process the expression matrix and identify the DEGs among BMs (PV, ET, and PMF), following the standard analysis process [|log (FC)|> 2 and adjusted P value < 0.05, |log (FC)|> 4 for significant DEGs]. Heatmaps were generated with the pheatmap package, and volcano plots were generated with the ggplot2 and ggVolcano packages. Protein‒protein interaction (PPI) analysis was performed via the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://cn.string-db.org) and Cytoscape software.

Data access

The data generated in this paper have been deposited in the OMIX, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences under the accession no. OMIX008313 (https://ngdc.cncb.ac.cn/omix/view/OMIX008313). The microarray datasets [GSE26049, GSE2191] of patients with MPN and HCs [PV_PB (n = 41), ET_PB (n = 19), PMF_PB (n = 9), HC_PB (n = 21), HC_BM (n = 4)] from the GEO database were retrieved.

Results

Patient characteristics

Five peripheral blood (PB) and 30 bone marrow (BM) samples from 34 patients without treatment, including 3 PV patients, 23 ET patients, and 8 PMF patients, were used for bulk RNA-Seq. The baseline features of these patients are detailed in Table 1. Three patients were diagnosed with triple negative; one patient presented with CALR mutation; and the remaining patients carried the JAK2V617F mutation. Among these treatment-naïve patients, blood test results were missing for 6 outpatients. Leucocytosis (leukocytes ≥10×109/L) was observed in 15/28 patients, and elevated lactate dehydrogenase (LDH) levels were observed in 14/21 patients. Additionally, 4 ET patients whose Next-generation DNA Sequencing results revealed gene mutations, such as JAK2V617F, ASXL1, TET2, EPPK1, and CSMD1, are shown in Supplementary Table S1.

Table 1 Clinical features of MPN patients
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Gene mutations in MPNs evaluated with RNA-Seq data

Gene mutations were identified in PV, ET, and PMF according to mutation type, mutation variation classification, and mutation burden (Supplementary Fig. S1). Frequent driver gene mutations and nondriver mutations, such as those in TP53, JAK2, ASXL1, EZH2, EPPK1, and MPL, are shown in Fig. 1. JAK2 mutations were detected in all 3 PV patients. Moreover, two patients presented with multihit JAK2 mutations, as indicated in black (Fig. 1A). For 19 ET patients, JAK2 mutations were detected in 13/19 patients, ASXL1 mutations were detected in 4/19 patients, and EPPK mutations were detected in 3/19 patients (Fig. 1C). Moreover, NGS results from 4 ET patients confirmed EPPK mutations or ASXL1, which was also detected via the transcriptome (Supplementary Table S1). Among the 8 PMF patients, 7 presented with JAK2 mutations, 6 presented with ASXL1 mutations, and 1 patient presented with an EPPK1 mutation (Fig. 1B). TP53 gene mutations, specifically p.P33R (25/30), p.R205P (1/30), and p.R141H (1/30), were identified in MPNs. Analysis of their clinical significance using the IARC TP53 Database (http://p53.iarc.fr) revealed that p.R205P and p.R141H are pathogenic, while p.P33R is classified as benign. Missense mutation and frame-shift-ins mutation were the major types of these genes in the MPNs. Mutation loci were also identified, as demonstrated with the Lollipop chart in Fig. 1D, for TP53 and JAK2, and most mutations tended to converge at several loci. More examples of gene mutations, such as those in TP53, JAK2, ASXL1, and EPPK1, are shown in Supplementary Fig. S2A, S2B, and S2C for PV, ET, and PMF, respectively.

Fig. 1
figure 1

Gene mutations identified by RNA-seq in MPNs. A, B, C Examples of mutation types for several genes, such as TP53, JAK2, ASXL1, MPL, EZH2, and EPPK1, in PV_BM, PMF_BM, and ET_BM, respectively. D Lollipop chart of the mutation loci for TP53 and JAK2 in ET

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Differential immune gene expression and prominent immune pathways implicated in MPNs

Thirty-five samples from 34 patients were sequenced, including 5 PB samples and 30 BM samples (3 PV, 19 ET, and 8 PMF samples). Since very few PB samples were included in our cohort, 90 PB samples from datasets (GSE26049), including 21 healthy controls, 41 PV, 19 ET, and 9 PMF samples, were used for gene differential expression analysis, and another 4 microarray datasets of healthy BM controls from GSE2191 were also included in the analysis. Therefore, a total of 129 samples were included in this study. The whole sample was well differentiated by cluster analysis (Supplementary Fig. S3A) after removing the batch effect via the rank-in method. The PCA results revealed that the gene expression features of the three MPN subtypes from PB and BM could be distinguished from those from PB and BM in the healthy control group (Supplementary Fig. S3B), and the features from PB were also very different from those from BM. As indicated in Fig. 2A, ET and PMF presented some common gene expression features because there are overlapping features between the later stage of ET and the early stage of PMF; even in the clinic, it is difficult to make a differential diagnosis between ET and overt PMF. Compared with those of HCs, each MPN subtype presented approximately 700–800 significantly differentially expressed genes (DEGs); among these genes, 620 DEGs were common to PV, ET, and PMF (Fig. 2B), which is likely related to MPNs. The immune-related genes among the DEGs are indicated in the heatmap (Fig. 2C). These immune-related genes were subsequently subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and the results suggested that the common pathways of PV, ET and PMF, such as the NF-Kappa B signaling pathway, Th 17 cell differentiation, Th1 and Th2 cell differentiation, NK cell-mediated cytotoxicity, MAPK, cytokine‒cytokine receptor interaction and antigen processing and presentation, are potentially involved in MPN development. There were also several differential pathways among these three entities, such as the IL17 and B-cell receptor signaling pathways, which were enriched only in the PV, the Toll-like receptor signaling pathway was enriched more in the PV and ET, and the Ras and Rap1 signaling pathways were enriched only in the PMF (Fig. 2D). There were unique immune-related features for each MPN subtype, which was confirmed by KEGG and GO pathway analysis (Supplementary Fig. S4).

Fig. 2
figure 2

Immune genes and pathways in MPNs. A PCA diagram after removing the batch effect for all samples from PV_BM, ET_BM, and PMF_BM. B Venn diagram of DEGs between each MPN subtype and HCs. C Heatmap of significant DEGs between each MPN subtype and HCs. D Immune-related pathways identified via KEGG analysis in each MPN subtype compared with HCs. The size of the dots represents the -log10 (adjusted p value)

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MPN-related immune gene traits evaluated by transcription

To explore the associations between immune traits and disease subtypes, WGCNA was used to identify modules of highly correlated genes and to investigate the relationships between these modules and MPN subtypes. All these genes were separated into 43 different gene modules by hierarchical clustering (Supplementary Fig. S5), and each module is presented in an individual color, as indicated in Fig. 3A. On the basis of correlation analysis (Supplementary Fig. S6), several disease-related trait modules associated with PV, ET, and PMF with different grades of correlations were identified. The modules therein, which presented a high correlation with both PMF and ET, were labeled module complex 1 (MC1: red box) and module complex 2 (MC2: blue box) in Fig. 3A. MC1 was positively correlated with PMF and negatively correlated with ET. MC2, in contrast, was negatively correlated with PMF and positively correlated with ET. Furthermore, the Mantel test results (Fig. 3B) revealed that all the modules in MC1 and MC2 were strongly correlated with the PMF and ET subtypes, and most of the modules presented a certain degree of correlation with PV.

Fig. 3
figure 3

Immune gene traits associated with MPNs. A WGCNA of gene expression; the gene modules in the red box are labeled MC1, and the gene modules in the blue box are labeled MC2. B Mantel test analysis between the MPN subtypes (PV, ET, PMF) and gene modules in MC1 and MC2. The color of the line reflects the P value, and the thickness of the line reflects the correlation r value. The color of the square reflects whether there is a positive or negative correlation between two modules; red indicates a positive correlation, and blue indicates a negative correlation

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Further analysis was performed with genes included in MC1 and MC2 to explore PMF and ET particular immune traits. A heatmap of immune genes, including those in MC1, is presented in Fig. 4A. GO enrichment analysis identified five groups of genes that were potentially related to disease: positive regulation of cytokine production, chemotaxis, taxis, response to molecules of bacterial origin, and cell killing (Fig. 4B). Important signaling pathways, including the cytokine‒cytokine receptor interaction, acute myeloid leukemia, Toll‒like receptor, and TNF signaling pathways, were enriched via KEGG analysis (Fig. 4C). Gene correlation analysis revealed correlations among the 46 top genes, such as IL4R, IL17RB, IFNAR1, CXCL10, and CXCL16 (Fig. 4D). To uncover the interaction between proteins encoded by these 46 genes, the STRING database was utilized to perform the analysis. Ten hub genes of MC1 genes were obtained via the cytoHubba plugin from the PPI network (Fig. 4E): MPO, ICAM1, RENT, CTSG, RNASE3, RNASE2, LTF, LCN2, ELANE, and CAMP.

Fig. 4
figure 4

Features of immune genes included in MC1. A Heatmaps of immune genes in MC1. B GO enrichment analysis of immune genes in MC1. C Immune-related pathways from KEGG analysis of immune genes in MC1. D Correlation analysis of immune genes in MC1; red indicates positive, and blue indicates negative. A dark color represents a strong correlation; a light color represents a weak correlation. E Top 10 hub genes from the PPI network analysis for the genes included in MC1. Nodes denote encoded proteins, and edges denote interactions between encoded proteins. The color represents the scores ranked by the MCC methods. A deeper color denotes more important genes with higher scores

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Similar analysis procedures were performed with genes in MC2. The heatmap of immune gene expression in the three MPN subtypes presented different colors, which reflected the relative expression of each gene (Fig. 5A). GO enrichment analysis revealed that the above genes are involved mainly in immune functions, such as leukocyte proliferation, including lymphocyte and mononuclear cell proliferation; the regulation of leukocyte proliferation; and the extrinsic apoptotic signaling pathway (Fig. 5B). Furthermore, pathways related to cytokine‒cytokine interactions, the PI3K‒Akt signaling pathway, acute myeloid leukemia, and Th 17 cell differentiation were enriched via KEGG analysis (Fig. 5C). Correlation analysis also revealed good correlations among these genes. PPI network analysis revealed the top 10 hub genes: CD48, KRAS, IL2RA, CD27, FAS, IL6ST, MAVS, CYBB, NFKB1, and PAK1.

Fig. 5
figure 5

Features of immune genes included in MC2. A Heatmaps of immune genes in MC2. B GO enrichment analysis of immune genes in MC2. C Immune-related pathways from KEGG analysis of immune genes in MC2. D Correlation analysis of immune genes in MC2; red indicates positive, and blue indicates negative. A dark color represents a strong correlation; a light color represents a weak correlation. E Top 10 hub genes from the PPI network analysis for the genes included in MC2. Nodes denote encoded proteins, and edges denote interactions between encoded proteins. The color represents the scores ranked by the MCC methods. A deeper color denotes more important genes with higher scores

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Immune cell compositions and cytokine gene expression levels in MPNs evaluated by transcriptional analysis

CIBRSORTx was used to delineate the transcriptome matrix and translate it into the abundance of immune cell subsets in the bone marrow and peripheral blood from different groups. As shown in Fig. 6, a comprehensive analysis of immune cell subsets, including T cells, B cells, monocytes, NK cells, Treg cells, neutrophils, and other cells, could be performed with gene expression data. Compared with those in healthy BM, the frequencies of naïve B cells, naïve CD4 + T cells, CD4 + Treg cells, CD8 + T cells, and monocytes were lower. Moreover, a significantly increased frequency of memory resting CD4 T cells, resting NK cells, eosinophils, and neutrophils was observed in the three MPN subtypes (Fig. 6). In PB, a decreased frequency of naïve B cells and an increased frequency of eosinophils were significantly different from those in healthy PB. However, in contrast to that observed in the BM, a significantly decreased frequency of resting memory CD4 T cells was identified. A slight increase in the frequency of monocytes was observed in PMF compared with PV and ET, but this difference was not significant (Supplementary Fig. S7).

Fig. 6
figure 6

Immune cell subsets evaluated with CIBERSORTx for PV_BM, ET_BM, PMF_BM, and HC_BM. (* indicates P ≤ 0.05; ** indicates P ≤ 0.01; *** indicates P ≤ 0.001; ns, no significance)

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Dysregulated cytokines are a major feature of MPN. Major immune cytokine genes, such as IL-4, IL-6, IL-2, IFNG, CSF1R, EPO, and CCR1, were included among the immune genes, as demonstrated with heatmaps (Fig. 2C). Cytokines can be divided into several groups, such as the interleukin family, colony-stimulating factors, growth factors, and tumor necrosis factors. Each group of cytokine-related gene expression levels could also be explored as indicated in Supplementary Fig. S8.

Discussion

In summary, this study aimed to explore the potential of bulk RNA-Seq in identifying gene mutations and the immune landscape in MPNs. Our results highlight the feasibility of incorporating bulk RNA-Seq into routine clinical practice, providing comprehensive insights into gene mutations and immune pathways implicated in MPNs. However, further validation with larger sample sizes and in-depth analyses of the correlations between gene mutations and immune traits is needed.

Previous research utilizing RNA microarrays and chips on PB and BM samples has demonstrated differential gene expression in MPNs, underlining the importance of RNA-Seq for identifying MPN-related genes and pathways [41,42,43]. Transcriptomic analyses of specific cell types such as monocytes, macrophages, granulocytes, platelets, and CD34 + cells have revealed mechanisms involved in MPN development [29, 44,45,46,47]. Additionally, detailed studies using single-cell RNA-Seq of CD34 + hematopoietic stem cells have shed light on their role in MPN initiation [48,49,50,51]. Collectively, these findings suggest that the transcriptome of MPNs may clarify underlying mechanisms of disease progression.

Bulk RNA-Seq can effectively detect various gene mutations including type, classification, burden, and loci offering valuable insights into the mutation landscape of MPN patients [34]. Its high performance and low cost make bulk RNA-Seq an attractive complementary approach to current Sanger DNA sequencing for investigating gene mutations, as Sanger sequencing typically focuses on hotspot regions of genes rather than analyzing the entire gene. For instance, in our cohort, 7 out of 8 PMF patients had ASXL1 mutations, which are often overlooked in routine clinical practice but are critical for prognosis. ASXL1 mutations promote fibrosis and lead to poor outcomes in PMF patients [52, 53]. Furthermore, identifying multiple hit genes through bulk RNA-Seq may elucidate the limited efficacy of JAK2 inhibitors in certain patients, making it possible to construct a detailed gene mutation landscape for MPNs in clinical settings. However, the gene mutations identified through RNA-Seq require further confirmation by DNA sequencing, as these mutations are predominantly single nucleotide polymorphisms (SNPs) that may not necessarily be pathogenic. For example, the frequency of TP53 mutations was found to be significantly higher in transcriptional analysis compared to the 16.1% (less than 25%) reported in studies using DNA sequencing [54, 55]. After cross-referencing these mutations with TP53 databases, only two patients were found to carry TP53 mutations of pathogenic significance, representing a frequency of 6.7% (2/30) [56]. These mutations identified through RNA-seq should be carefully interpreted by assessing their clinical significance using databases such as COSMIC or ClinVar to confirm their specificity. Given that current clinical DNA sequencing primarily targets hotspot regions of genes, which may miss certain mutations, the value of RNA-seq in identifying gene mutations should be emphasized. RNA-seq is particularly valuable for detecting splicing variants and fusion genes, which are often overlooked in traditional DNA sequencing.

Considering MPNs are chronic inflammatory diseases with dysregulated immune systems, monitoring the immune state is crucial throughout disease progression. Currently, flow cytometry and ELISA kits evaluate immune cell subsets and cytokine levels, respectively, but these methods provide limited insights. While advanced techniques like mass cytometry and single-cell RNA-Seq are useful, their high costs and accessibility issues hinder widespread use. Thus, Bulk RNA-seq offers several key advantages in studying the immune landscape of MPNs. First, it provides comprehensive transcriptome coverage, capturing a global view of gene expression that includes both immune and non-immune components. This allows for the quantification of cytokines, chemokines, and other immune-related genes, revealing the immune and inflammatory status of the MPNs [57]. Second, computational deconvolution tools like CIBERSORT enable the estimation of immune and stromal cell proportions, facilitating the characterization of immune cell infiltration, such as T cells, macrophages, and dendritic cells [58]. Third, it is cost-effective and scalable, making it suitable for tracking systematically changes in immune signatures, including analyses at different time points before and after treatment, thereby better detecting changes in the body's immune status and treatment responses.

Baseline immune features can guide personalized therapies, such as IFN therapy for active IFN signaling or JAK2/NF-κB inhibitors for hyperactive JAK-STAT/NF-κB pathways. Dynamic RNA-Seq monitoring at various time points enables the evaluation of therapeutic efficacy and the tailoring of interventions based on immune variations. Importantly, for patients on long-term JAK2 inhibitor therapy, RNA-Seq can detect early immune suppression, facilitating timely switches to alternative therapies to restore immune function and reduce infection risks. This precision approach enhances patient safety and optimizes therapeutic outcomes by aligning treatments with individual immune dynamics.

In conclusion, the systematic evaluation of immune landscape changes and gene mutation variations throughout the entire disease cycle is crucial in managing MPNs. Bulk RNA-Seq offers significant advantages for monitoring the evolution of MPNs by simultaneously providing data on gene mutations and immune landscape variations at a low cost. It could support personalized and precision medicine, ultimately enhancing the management of MPNs and improving patient outcomes. Its implementation in clinical settings is highly feasible, requiring minimal sample input and offering an affordable cost. However, RNA-Seq has notable limitations. First, it measures gene expression and functional insights, which may not always align with protein expression and function. Second, accurate interpretation of RNA-Seq data requires expertise in genetic, immunology and bioinformatics to avoid overinterpretation or misleading conclusions. Therefore, validation with large clinical samples and high-dimensional technologies such as mass cytometry and single-cell sequencing is essential to ensure the accuracy of transcriptome interpretations. Integrating matched data into an artificial intelligence learning system could further enhance the accuracy of interpreting gene mutations and immune landscapes derived from bulk RNA-Seq. Additionally, developing an online tool for interpreting raw transcriptome data would be beneficial. With advancements in translational medicine and bioinformatics, bulk RNA-Seq has the potential to deliver more precise genetic and immune insights for MPN patients, positioning it as a valuable complementary tool alongside current approaches in routine clinical practice.

Data availability

The data generated in this paper have been deposited in the OMIX, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences under the accession no. OMIX008313 (https://ngdc.cncb.ac.cn/omix/view/OMIX008313). The microarray datasets [GSE26049, GSE2191] included in this study were retrieved from the GEO database.

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Acknowledgements

We thank all the patients for their approval in providing their residual samples for this research.

Funding

This work was supported by the National Natural Science Foundation of China (82000135) and the National Science Foundation of Chongqing City (CSTB2023NSCQ-MSX0241).

Author information

Author notes

  1. Shenglong Li and Sanyun Wu contributed equally to this work.

Authors and Affiliations

  1. Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China

    Shenglong Li

  2. Department of Hematology, Zhongnan Hospital of Wuhan University, Donghu Road, No. 169, Wuhan, 430062, China

    Sanyun Wu & Xuelan Zuo

  3. Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, 400010, China

    Mingli Xu, Xuedong Li & Yingying Wang

  4. Chongqing Key Laboratory of Basic and Translational Research of Tumor Immunology, Chongqing Medical University, Chongqing, 400010, China

    Mingli Xu, Xuedong Li & Yingying Wang

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  1. Shenglong Li
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  3. Mingli Xu
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  4. Xuedong Li
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  5. Xuelan Zuo
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Contributions

Y-Y.W: Main researcher and primary writer of the manuscript. S-L.L: Conducted the analysis of the RNA transcriptome, contributed to the writing of the Materials and Methods section and also prepared the figures. S-Y.W: Collected clinical samples and data, and performed RNA extraction and sequencing and wrote part of the Materials and Methods section. M-L.X and X-D.L: Responsible for creating tables and arranging references. X-L.Z: Planned and supervised the study.

Corresponding authors

Correspondence to Xuelan Zuo or Yingying Wang.

Ethics declarations

Ethics approval and consent to participate

This study was reviewed and approved by Zhongnan Hospital of Wuhan University in Wuhan, Hubei Province, China (2021–066). Informed consent to participate was obtained from all the participants in the study when they did the bone marrow aspiration.

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Not applicable.

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The authors declare no competing interests.

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Supplementary Information

12885_2025_13947_MOESM1_ESM.pdf

Supplementary Material 1: Supplementary Fig. S1. Gene mutations identified by RNA-seq in MPNs. (A) Gene variation type, variation classification, and top 10 mutated genes in ET. (B) Gene variation type, variation classification, and top 10 mutated genes in PV. (C) Gene variation type, variation classification, and top 10 mutated genes in PMF.

12885_2025_13947_MOESM2_ESM.pdf

Supplementary Material 2: Supplementary Fig. S2. Mutation landscape of four genes in MPN subtypes. (A) Lollipop chart of the mutation loci of TP53, JAK2, and ASXL1 in the PV. (B) Lollipop chart of the mutation loci of TP53, JAK2, ASXL1, and EPPK1 in ET. (C) Lollipop chart of the mutation loci of TP53, JAK2, ASXL1, and EPPK1 in PMF.

12885_2025_13947_MOESM3_ESM.tif

Supplementary Material 3: Supplementary Fig. S3. Clustering analysis between MPN and healthy control samples. (A) The gene cluster for different MPNs and healthy controls. Platform 1 included 30 BM samples and 5 PB samples, Platform 2 included 91 samples, and Platform 3 included 4 healthy BM samples from another dataset. Each color represents one group of samples. (B) PCA diagram after removing the batch effect for all the samples described previously, including MPN_BM, HC_BM, MPN_PB, and HC_PB.

12885_2025_13947_MOESM4_ESM.pdf

Supplementary Material 4: Supplementary Fig. S4. GO and KEGG analyses of immune genes with differential expression between each subtype of MPN. (A) Differential immune pathways identified by KEGG analysis between ET_BM and PMF_BM; ET_BM and PV_BM; PMF_BM and PV_BM. (B) Comparison of major immune functions identified by GO analysis between ET_BM and PMF_BM; ET_BM and PV_BM; PMF_BM and PV_BM. The size of the dots represents the number of genes. The color of the dots denotes the adjusted p value.

12885_2025_13947_MOESM5_ESM.tif

Supplementary Material 5: Supplementary Fig. S5. WGCNA showing the co-expression module in MPNs. (A) Dendrogram of the gene clusters. (B) Heatmaps of selected immune genes.

Supplementary Material 6: Supplementary Fig. S6. Correlation analysis between immune gene modules and MPN subtypes.

12885_2025_13947_MOESM7_ESM.pdf

Supplementary Material 7: Supplementary Fig. S7. Immune cell subsets evaluated with CIBERSORTx for PV_ PB, ET_ PB, PMF_ PB, and HC_ PB. (* indicates P ≤ 0.05; ** indicates P ≤ 0.01; *** indicates P ≤ 0.001; ns, no significance).

12885_2025_13947_MOESM8_ESM.pdf

Supplementary Material 8: Supplementary Fig. S8. Heatmaps of cytokine gene expression in MPN subtypes. (A) Heatmaps of major IL family gene expression. (B) Heatmaps of major chemokine family gene expression. (C) Heatmaps of major colony-stimulating factor gene expression. (D) Heatmaps of major growth factor gene expression. (E) Heatmaps of the gene expression of TNFα and its receptors TNFRSF1A and TNFRSF1B.

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Li, S., Wu, S., Xu, M. et al. Potential application of the bulk RNA sequencing in routine MPN clinics. BMC Cancer 25, 746 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-13947-x

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Keywords

BMC Cancer

ISSN: 1471-2407

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