Abstract
Background
The relationship between ferroptosis and the progression and treatment
of hematological tumors has been extensively studied, although its
precise association with chronic myeloid leukemia (CML) remains
uncertain.
Methods
Multi-transcriptome sequencing data were utilized to analyze the
ferroptosis level of CML samples and its correlation with the tumor
microenvironment, disease progression, and treatment response. Machine
learning algorithms were employed to identify diagnostic
ferroptosis-related genes (FRGs). The consensus clustering algorithm
was applied to identify ferroptosis-related molecular subtypes.
Clinical samples were collected for sequencing to validate the results
obtained from bioinformatics analysis. Cell experiments were conducted
to investigate the therapeutic efficacy of induced ferroptosis in
drug-resistant CML.
Results
Ferroptosis scores were significantly lower in samples from patients
with CML compared to normal samples, and these scores further decreased
with disease progression and non-response to treatment. Most FRGs were
downregulated in CML samples. A high ferroptosis score was also
associated with greater immunosuppression and increased activity of
metabolic pathways. Through support vector machine recursive feature
elimination (SVM-RFE), least absolute shrinkage selection operator
(LASSO), and random forest (RF) algorithms, we identified five FRGs
(ACSL6, SLC11A2, HMOX1, SLC38A1, AKR1C3) that have high diagnostic
value. The clinical diagnostic value of these five FRGs and their
effectiveness in differentiating CML from other hematological
malignancies were validated using additional validation cohorts and our
real-world cohort. There are significant differences in immune
landscape, chemosensitivity, and immunotherapy responsiveness between
the two ferroptosis-related molecular subtypes. By conducting cellular
experiments, we confirmed that CML-resistant cells are more sensitive
to induction of ferroptosis and can enhance the sensitivity of imatinib
treatment.
Conclusion
Our study unveils the molecular signature of ferroptosis in samples
from patients with CML. FRG identified by a variety of machine learning
algorithms has reliable clinical diagnostic value. Furthermore, the
characterization of different ferroptosis-related molecular subtypes
provides valuable insights into individual patient characteristics and
can guide clinical treatment strategies. Targeting and inducing
ferroptosis holds great promise as a therapeutic approach for
drug-resistant CML.
Keywords: chronic myeloid leukemia, ferroptosis, immune
microenvironment, treatment, machine learning, diagnosis
Introduction
Chronic myeloid leukemia (CML) is a hematological neoplasm initiated by
the fusion gene BCR-ABL ([35]1). The introduction of tyrosine kinase
inhibitors (TKIs), such as imatinib, has significantly enhanced
therapeutic efficacy for CML patients while substantially improving
their prognosis ([36]2). However, intricate escape mechanisms employed
by tumor cells inevitably hinder the effectiveness of these kinase
drugs and lead to the gradual development of drug resistance in
patients with CML ([37]3). These resistance mechanisms include both
primary and secondary factors; among them, mutations in BCR-ABL protein
play a crucial role ([38]4). Despite the advancements achieved in the
development of novel TKIs that target specific mutation sites
associated with enhanced treatment response in CML ([39]5); challenges
persist due to emerging new mutation sites over time as well as
non-mutation-based resistance mechanisms that arise during therapy
course ([40]6). Therefore, a more comprehensive analysis of the
molecular biology and metabolic characteristics of CML cells holds
significant clinical value for treatment decision-making and prognosis
evaluation in patients with CML.
Ferroptosis is a novel form of cell death, characterized by distinct
mechanisms and morphology compared to apoptosis, necrosis, and
autophagy ([41]7). The process is initiated by intracellular divalent
iron or ester oxygenase, resulting in the peroxidation of highly
expressed unsaturated fatty acids on the cell membrane and subsequent
induction of ferroptosis ([42]8–[43]10). Morphological changes observed
in cells undergoing ferroptosis include disruption of the cell
membrane, mitochondrial outer membrane, and loss of cristae ([44]11).
The occurrence of ferroptosis involves various regulatory pathways such
as the classical GPX4-regulated mechanism (Cyst(e)ine/GSH/GPX4 axis)
([45]12), as well as GPX4-independent mechanisms like
NAD(P)H/FSP1/CoQ10 axis ([46]13), GCH1/BH4/DHFR axis ([47]14), and
squalene accumulation. Additionally, signaling pathways including
E-cadherin-NF2-Hippo-YAP, AMPK, and HIF2α-HILPDA also modulate cellular
sensitivity to ferroptosis ([48]15–[49]17). Numerous studies have
demonstrated that targeted induction of ferroptosis holds promise as a
new therapeutic strategy for acute myeloid leukemia ([50]18–[51]20).
Liu et al.’s research revealed TXNRD1’s crucial role in cysteine
depletion-induced ferroptosis in CML cells in vitro ( [52]18, [53]21).
However, there remains a limited understanding regarding the
relationship between ferroptosis and CML, as well as its underlying
mechanism, necessitating further comprehensive investigation.
In this study, we conducted a comprehensive analysis of the ferroptosis
pathway and gene expression characteristics in CML, aiming to elucidate
the underlying mechanism of ferroptosis and its interaction with the
CML tumor microenvironment. Through multi-group cohort analysis, we
validated the diagnostic value of ferroptosis-related genes (FRGs) in
CML, and subsequent experiments further confirmed the potential
therapeutic significance of targeting ferroptosis in overcoming drug
resistance.
Methods
Data acquisition and preprocessing
The sequencing data of CML cohorts [54]GSE13159, [55]GSE144119,
[56]GSE4170, and [57]GSE44589 were obtained from the Gene Expression
Omnibus (GEO) database. The analysis cohort for this project was the
[58]GSE13159 cohort, which consisted of 76 CML samples and 74 normal
samples. Raw sequencing data were downloaded and normalized for
subsequent analysis. The validation cohort ([59]GSE144119) included 48
newly diagnosed CML samples, 32 remission CML samples, and 17 normal
samples that were converted to transcripts per kilobase million (TPM)
values. For clinical validation purposes, transcriptome sequencing was
performed on five chronic-phase CML samples, five blast crisis samples,
and five normal control samples with written consent from patients
approved by the Ethics Committee of the Second Affiliated Hospital of
Nanchang University; these data were also transformed into TPM values
for further validation. To differentiate between other types of
leukemia such as acute lymphoblastic leukemia (750 cases), acute
myeloid leukemia (542 cases), chronic lymphocytic leukemia (448 cases),
and myelodysplastic syndromes (206 cases), a subset of the [60]GSE13159
cohort was utilized. Furthermore, we used the imatinib-treated sample
dataset from [61]GSE44589 containing 198 sequenced samples to evaluate
treatment response in CML patients. Additionally, single-cell RNA-seq
data from the [62]GES76312 cohort were employed to visualize clusters
using the uniform manifold approximation and projection (UMAP)
algorithm. Finally, we retrieved ferroptosis pathway genes from the
MSigDB database
([63]https://www.gsea-msigdb.org/gsea/msigdb/index.jsp).
Differential expression analysis of FRG
The “limma” software package was employed for conducting differential
expression analysis of FRG. Adjusted p values below 0.05 were
considered significant, indicating the presence of differentially
expressed FRGs (DEFRGs) between CML and normal samples. Subsequently,
we performed Gene Ontology (GO) annotation and Kyoto Encyclopedia of
Genes and Genomes (KEGG) pathway enrichment analysis on these genes
using the “clusterProfiler” package ([64]22). To quantify the activity
of a biological pathway or gene set, we utilized the Gene Set Variation
Analysis (GSVA) algorithm to calculate an enrichment score ([65]23).
Correlation analysis and protein-protein interaction (PPI) network
construction
The Spearman method was employed for correlation analysis. The STRING
database ([66]https://string-db.org/) was used to analyze the PPI of
DEFRG. Subsequently, the PPI network was visualized using Cytoscape
software.
Analysis of immune cell infiltration
The estimation of immune cell infiltration was conducted by employing
the deconvolution algorithm “CIBERSORT” to accurately quantify the
proportions of 22 distinct immune cell types based on the gene
expression profiles of individual samples ([67]24).
Potential regulatory mechanisms associated with ferroptosis
Weighted correlation network analysis (WGCNA) was employed to identify
the genes associated with ferroptosis scores in the [68]GSE13159 cohort
([69]25). Pearson correlation analysis was utilized to construct the
adjacency matrix for all matched genes, and the scale-free topology of
this matrix was established based on an optimal soft threshold power.
Subsequently, the adjacency matrix was transformed into a topological
overlap matrix (TOM). By employing the TOM dissimilarity measure,
modules consisting of genes exhibiting similar expression patterns were
identified through average linkage hierarchical clustering, with a
minimum module size set at 30 and a cut height at 0.2. Finally, an
evaluation of the correlation between module signature genes (MEs) and
ferroptosis score was performed.
Analysis of the diagnostic value of FRGs
To identify diagnostic biomarkers for CML, three machine learning
algorithms, namely support vector machine recursive feature elimination
(SVM-RFE), least absolute shrinkage selection operator (LASSO), and
random forest (RF) were employed to screen the diagnostic FRGs.
Additionally, LASSO regression analysis was used to calculate
regression coefficients for the diagnostic FRGs, and a CML risk score
diagnostic model was constructed using the following formula:
[MATH:
Risk sc
ore=∑1i(Coefi * ExpGenei), :MATH]
where i represents the specific diagnostic FRG and “Coef” and “ExpGene”
denote the regression coefficient and expression value of that
particular FRG respectively. By constructing this risk score model, we
can further assess the combined diagnostic value of FRGs.
Revealing molecular subtypes via FRG expression profiling
To comprehensively assess inter-individual variations in CML patients,
we employed the “ConsensusClusterplus” package to conduct a cluster
analysis of CML samples based on the expression profiles of the
diagnostic FRGs, aiming to identify distinct molecular subtypes within
CML ([70]26). The robustness and stability of the clustering results
were confirmed through 1000 iterations. Additionally, principal
component analysis (PCA) was utilized for classification validation.
Prediction of the sensitivity of CML samples to TKI treatment and
immunotherapy
The expression matrix and drug response data of blood cell lines from
the Cancer Genome Project (CGP) database were utilized in this study to
predict the half-maximal inhibitory concentrations (IC50) of CML
samples to TKIs. This prediction was made using the “pRRophetic”
package, a computational tool commonly used for such analyses ([71]27).
To further investigate the response of different risk score groups
towards anti-PD-1 and anti-CTLA4 immune checkpoint inhibitors, we
employed the “SubMap” algorithm available at a publicly accessible
website called GenePattern. The SubMap algorithm is widely recognized
for its ability to forecast treatment responses based on gene
expression profiles. To assess the level of immune escape exhibited by
tumor cells in CML samples, we computed the TIDE score using an
established online resource known as Tumor Immune Dysfunction and
Exclusion (TIDE).
Construction of microRNA (miRNA) regulatory network for diagnostic FRGs
We employed miRTarBase, miRDB, and TargetScan databases to predict the
binding sites of miRNAs on CML diagnostic ARGs. Subsequently, we
filtered out the miRNA-target pairs that were predicted by all three
databases. The [72]GSE90773 cohort was utilized to identify
differentially expressed miRNAs between CML cells and normal cells,
which served as the basis for constructing the miRNA regulatory
network.
In vitro experiments
The CML cell line K562 was cultured in RPMI1640 medium supplemented
with 10% fetal bovine serum and 1% penicillin-streptomycin in a
humidified incubator saturated with 5% CO2 at 37°C. The K562 cells were
exposed to imatinib, and the concentration was gradually increased
until the development of K562/IR cells capable of sustained growth in a
medium containing 1μM of imatinib. This concentration is considered
physiologically relevant and may simulate the peak plasma/serum level
of imatinib (5μM). Transcriptome sequencing analysis was conducted on
K562, K562/IR, K562/IR control, and erastin-treated K562/IR cells. The
processing procedure employed in this study was based on our previous
research ([73]28). The concentration of imatinib was gradually
increased until the induction of resistant cells was completed. Cell
viability was assessed using the cell counting kit-8 (CCK-8) assay. For
this assay, 5-e3 cells were seeded in 96-well plates, and each group
was repeated three times. After the indicated culture time, 10 μL of
CCK8 solution was added, followed by incubation at 37°C for 2 hours.
The optical density (OD) value at 450 nm was measured using a
microplate reader. Apoptosis detection involved staining cells with the
Annexin V-PE/7-AAD apoptosis detection kit and subsequent examination
in a flow cytometer. Additionally, reactive oxygen species (ROS) were
detected using a fluorescent probe DCFH-DA in flow cytometry. The
levels of GSH and GSSH were determined using Solarbio’s BC1175 and
BC1185 kits, respectively. Bioss’ AK091 kit was used for GPX4 activity
measurement. All reagents were employed following the manufacturer’s
instructions. Cell homogenization was performed using lysate buffer to
facilitate the reaction between REDOX substances in the sample and
reagents, resulting in the formation of adducts that can be quantified
through colorimetry.
Statistical analysis
All analyses were conducted using the R software and corresponding
software packages. Differences between two or more groups were assessed
using the Wilcoxon rank sum test and the Kruskal-Wallis test,
respectively. The diagnostic value of biomarkers was determined through
receiver operating characteristic (ROC) curve analysis. A bilateral
P-value less than 0.05 indicates a statistically significant
difference.
Results
Molecular characteristics linked to ferroptosis in CML
We conducted a comprehensive evaluation of ferroptosis activity and
molecular characteristics in CML using transcriptomics analysis. The
GSVA algorithm was utilized to calculate ferroptosis scores, revealing
significantly lower ferroptosis scores in CML samples compared to
normal samples ([74] Figure 1A ), while the ferroptosis score increased
following treatment remission ([75] Figure 1B ). Patients in blast
crisis (BC) exhibited even lower ferroptosis scores than those in the
chronic phase (CP) ([76] Figures 1C, D ) (Due to the limited sample
size and vulnerability to individual outliers, although [77]Figure 1C
does not exhibit a statistically significant difference, the overall
trend persists that BC patients display lower ferroptosis scores
compared to CP patients.), and individuals with major molecular
responses displayed higher ferroptosis scores compared to
non-responders ([78] Figure 1E ). Single-cell analysis consistently
demonstrated a trend of decreased ferroptosis scores in CML patients,
particularly those in BC, which subsequently increased after treatment
with TKI ([79] Figures 1F-H ). Differential expression analysis
indicated the down-regulation of numerous genes associated with
ferroptosis in CML samples ([80] Figures 1I, J ), including those
involved in iron ion homeostasis, mitochondrial outer membrane
function, and ligase activity ([81] Figure 1K ). These differentially
expressed genes were primarily enriched in signaling pathways related
to ferroptosis, metabolic pathways, mineral absorption, and cysteine
and methionine metabolism ([82] Figure 1L ). PPI network analysis
identified STEAP3, TFRC NCQA4 TP53 IREB2 as hub genes within the
network formed by these DEFRG ([83] Figure 1M ). Volcano plot analysis
further revealed down-regulation of gene expression for various
suppressors of ferroptosis in CML samples ([84] Figures 1N, O ).
Therefore, we speculate that the observed lower ferroptosis score in
CML may be attributed to an overall decrease in inhibition of this
process within cancer cells indicating their heightened susceptibility
towards undergoing cell death through the mechanism of the ferroptosis
pathway. Ferroptosis is closely linked to lipid metabolism, and our
findings reveal a significant increase in the activity of unsaturated
fatty acids such as linoleic acid, arachidonic acid, and α-linolenic
acid in CML ([85] Figure 1P ). Considering that the peroxidation of
unsaturated fatty acids is a prerequisite for ferroptosis to occur,
this result further supports the hypothesis that CML exhibits
heightened susceptibility to ferroptosis. These results collectively
indicate an aberrant regulation of ferroptosis in CML samples, which
may have implications for the initiation and progression of the
disease.
Figure 1.
[86]Figure 1
[87]Open in a new tab
The characteristics of ferroptosis score and FRG expression in CML
samples. (A-E) Differences in ferroptosis scores between CML samples
and normal samples were observed in various datasets: (A) [88]GSE13159,
(B) [89]GSE144119, (C) our clinical cohort, (D) [90]GSE4170, (E)
[91]GSE44589. (F-H) UMAP analysis of the CML single-cell sequencing
dataset [92]GSE76312 revealed the distribution of ferroptosis scores
among different patients. (I-J) Volcano map (I) and heat map (J)
illustrated the expression characteristics of FRG. (K, L) Functional
annotation (K) and pathway enrichment analysis (L) were conducted on
DEFRG. (M) PPI network analysis was performed on DEFRG. (N, O)
Expression characteristics of ferroptosis suppressors and drivers were
examined. (P) Differences in lipid metabolic pathway scores between
normal and CML samples. BC refers to blast crisis, CP to chronic phase,
MMR to major molecular response, and NR to no response. *p < 0.05; **p
< 0.01; ***p < 0.001.
The correlation between the ferroptosis score and the immune microenvironment
as well as signaling pathways
The relationship between the ferroptosis score and the immune
microenvironment of CML as well as cancer pathways was further
analyzed. It was observed that there were significant associations
between the ferroptosis score and key tumor marker pathways, including
xenobiotic metabolism, reactive oxygen species pathway, heme
metabolism, and epithelial mesenchymal transition activities ([93]
Figure 2A ). Moreover, positive correlations were found with glycolysis
and hypoxia, while negative correlations were observed with Notch
signaling and WNT beta-catenin signaling. These findings suggest that
an increased activity in the ferroptosis pathway is accompanied by
enhanced cancer cell metabolism. Immune infiltration analysis revealed
a positive correlation between the ferroptosis score and eosinophil
infiltration, M0 macrophage infiltration, as well as regulatory T cell
(Treg) infiltration; meanwhile, a negative correlation was identified
with naive CD4+ T cells ([94] Figure 2B ). Furthermore, a significant
positive correlation was also found between the ferroptosis score and
gene expression of immune checkpoints LAG3 and TNFRSF9 ([95] Figure 2C
), indicating potential immunosuppression among patients with high
ferroptosis scores.
Figure 2.
[96]Figure 2
[97]Open in a new tab
The correlation between the ferroptosis score and the immune
microenvironment and signaling pathways. (A-C) Correlation analysis
revealed associations between the ferroptosis score and enrichment
scores of tumor marker gene sets (A), infiltration of immune cells (B),
and expression of immune checkpoints (C). (D) Cluster plot displaying
CML samples. (E, F) Scale-free fitting index and average connectivity
were used to analyze various soft threshold powers. (G) Clustering was
performed on different modules, with a cutting height set at 0.2
represented by the red line. (H) Cluster plots were generated based on
different measures using 1-TOM calculation. (I) Heatmap illustrating
the correlation between module genes and ferroptosis score. (J, K) KEGG
enrichment analysis was conducted for yellow module genes, as well as
sienna3 module genes.
To gain a deeper understanding of the underlying mechanisms associated
with ferroptosis in CML, we conducted WGCNA to explore the network of
co-expressed genes significantly correlated with ferroptosis scores.
The cluster dendrogram depicted the clustering characteristics of all
CML samples ([98] Figure 2D ). [99]Figures 2E, F illustrate the
scale-free fit exponential and average connectivity analysis for
various soft threshold powers. We set the cut height at 0.2 to include
modules exhibiting a correlation coefficient greater than 0.8 ([100]
Figure 2G ). Based on an optimal soft threshold power β=15 (unscaled
R^2 = 0.9), WGCNA classified the top 5000 genes with the highest
standard deviation into 23 independent co-expression modules ([101]
Figure 2H ). The correlograms depicting module-trait relationships
revealed that both yellow and sienna3 modules exhibited strong
correlations with ferroptosis scores ([102] Figure 2I ). KEGG
enrichment analysis demonstrated that these two modules were enriched
in porphyrin and chlorophyll metabolism as well as metabolic pathways
([103] Figures 2J, K ). Additionally, yellow module genes were found to
be associated with nitrogen metabolism, adipocytokine signaling
pathway, mTOR signaling pathway, and mitophagy; while sienna3 module
genes showed enrichment in hippo signaling pathway, glutathione
metabolism, glycolysis/gluconeogenesis, and carbon metabolism. The
findings suggest that metabolic reprogramming may contribute to the
malignant proliferation of CML cells, while also enhancing the
susceptibility of CML cells to ferroptosis by generating higher levels
of ROS and unsaturated fatty acids ([104]11, [105]29).
Analysis of the diagnostic value of FRG
We conducted further analysis on the diagnostic value of FRG in CML.
Three machine learning algorithms, namely LASSO, RF, and SVM-RFE, were
employed for dimensionality reduction to select the most informative
FRGs. From the DEFRGs, we identified 5, 6, and 6 variables that
accurately distinguished CML samples from normal samples, respectively
([106] Figures 3A–E ). Among these variables, there were five
overlapping diagnostic FRGs (ACSL6, SLC11A2, HMOX1, SLC38A1, and
AKR1C3) included among them ([107] Figure 3F ). The expression levels
of all five FRGs were significantly downregulated in CML samples
compared to normal samples ([108] Figure 3G ). Using LASSO regression
analysis, we developed a risk score model to assess the combined
diagnostic value of FRG ([109] Figure 3H , [110]Supplementary Table S1
). The risk score levels were significantly elevated in the CML samples
([111] Figure 3I ). ROC curve analysis revealed high diagnostic AUC
values for ACSL6 (0.818), SLC11A2 (0.864), HMOX1 (0.782),
SLC38A1(0.783), AKR1C3(0.791), as well as for the risk score (0.920)
([112] Figure 3J ). The combination of these five FRGs further improved
their diagnostic value.
Figure 3.
[113]Figure 3
[114]Open in a new tab
Identification of diagnostic FRG. (A, B) Diagnostic FRGs were
identified by the LASSO regression algorithm. (C, D) Diagnostic FRGs
were identified by the RF algorithm. (E) Diagnostic FRGs were
identified by the SVM-RFE algorithm. (F) Venn diagram of variables
identified by LASSO, RF, and SVM-RFE algorithms. (G) Differences in
expression of the three diagnostic FRGs between CML samples and normal
samples in the [115]GSE13159 cohort. (H) Coefficients of risk score
model. (I) Differences in risk score between CML samples and normal
samples in the [116]GSE13159 cohort. (J) ROC curve analysis was used to
evaluate the diagnostic value of the five FRGs and risk score in the
[117]GSE13159 cohort.
Validation of the diagnostic value of FRG and analysis of their role in the
evaluation of therapeutic effect
We confirmed the diagnostic value of the five FRGS. In the
[118]GSE144119 cohort, we observed a significant decrease in expression
levels of all five FRGS in CML samples, which showed partial
restoration after treatment response ([119] Figure 4A ). Furthermore,
the risk score levels were significantly increased in CML samples and
exhibited a significant decrease after treatment remission ([120]
Figure 4B ), thereby demonstrating the therapeutic evaluation value of
FRG. ROC curve analysis revealed that ACSL6, SLC11A2, HMOX1, SLC38A1,
AKR1C3, and the risk score model had AUC values of 0.949, 0.934, 0.868,
0.842, and 0.975 respectively ([121] Figures 4C-H ); thus confirming
their diagnostic value in CML cases. In our clinically independent
cohort study, we also observed a significant decrease in ACSL6,
SLC11A2, HMOX1, and SLC38A1 expression in CML samples while AKR1C3 did
not show a significant difference due to small sample size issues
([122] Figure 4I ). The risk score levels were also significantly
increased in CML samples ([123] Figure 4J ). ROC curve analysis
demonstrated an AUC value of 1 for the risk score model ([124]
Figure 4K ). Clinical sample-based sequencing data further verified the
high diagnostic value associated with these five FRGs in CML. In
conclusion, we have identified highly reliable FRGs which could
potentially serve as a novel adjunctive tool for clinical diagnosis and
treatment decision-making in patients with CML.
Figure 4.
[125]Figure 4
[126]Open in a new tab
Validation of the diagnostic value of the diagnostic FRG. (A, B)
Differences in expression of the five diagnostic FRGs and risk score
between CML samples and normal samples in the [127]GSE144119 cohort
(The Kruskal-Wallis test was employed for the comparison among the
three groups). (C-H) ROC curve analysis was used to evaluate the
diagnostic value of the five FRGs and risk score in the [128]GSE144119
cohort. (I, J) Differences in expression of the five diagnostic FRGs
and risk score between CML samples and normal samples in our clinical
cohort. (K) ROC curve analysis was used to evaluate the diagnostic
value of risk score in our clinical cohort. **p < 0.01; ***p < 0.001.
Analysis of the differential diagnostic value of FRG
We conducted a comprehensive analysis to evaluate the differential
diagnostic value of the five FRGs. The [129]GSE13159 cohort included
sequencing data from 750 ALL samples, 542 AML samples, 448 CLL samples,
and 206 MDS samples. Interestingly, the expression levels of most FRGs,
including SLC38A1, SLC11A2, and HMOX1, were found to be lower in CML
samples compared to other types of hematologic tumors. Conversely,
ACSL6 exhibited higher expression levels ([130] Figure 5A ).
Furthermore, subsequent calculations revealed that CML samples
displayed the highest risk score ([131] Figure 5B ). ROC curve analysis
demonstrated that the risk score effectively distinguished CML from
other hematological malignancies with high accuracy (AUC=0.844) ([132]
Figure 5C ). The diagnostic value of FRG has been systematically
evaluated, and we have also endeavored to investigate the regulatory
mechanisms governing FRG expression. In this study, our focus lies on
miRNA, as we aim to construct a miRNA regulatory network to identify
potential miRNAs that could inhibit FRG expression by binding to FRG in
CML cells ([133] Figure 5D ).
Figure 5.
[134]Figure 5
[135]Open in a new tab
Differential diagnostic value of the five FRGs in CML and other
hematological malignancies. (A) Expression differences of the five
diagnostic FRGs among CML, AML, CLL, ALL, MDS, and normal samples. (B)
differences in risk scores among CML, AML, CLL, ALL, MDS, and normal
samples. (C) ROC curve analysis of risk scores in CML and other
hematological malignancies. (D) Regulatory network of miRNAs and the
five diagnostic FRGs; red indicates miRNA expression is up-regulated in
CML samples, and green indicates expression is down-regulated. ***p <
0.001.
Identification of ferroptosis-related molecular subtypes and analysis of
differences in biological characteristics between subtypes
To comprehensively analyze the biological significance of FRGs in CML,
we utilized the expression profiles of the five diagnostic FRGs in CML
samples to identify two distinct molecular subtypes, namely Cluster C1
and Cluster C2, employing a consensus clustering algorithm ([136]
Figure 6A , [137]Supplementary Table S2 ). The distribution
characteristics of these two molecular subtypes were further confirmed
by PCA, revealing significant and discernible differences ([138]
Figure 6B ). Subsequently, through heatmap visualization, it was
observed that ACSL6, SLC11A2, HMOX1, and AKR1C3 exhibited up-regulation
in subtype C1 while SLC38A1 displayed higher expression levels in
subtype C2 ([139] Figure 6C ). To explore additional distinctions
between these subtypes at a biological level, immune infiltration
analysis demonstrated that subtype C1 had an increased proportion of
CD8+ T cells, follicular helper T cells, activated dendritic T cells,
and eosinophils compared to subtype C2 ([140] Figure 6D ). Furthermore,
there were notable variations in the expression levels of immune
checkpoint genes; specifically within subtype C1 where PD-L1, CTLA-4,
HAVCR2, PD-1, and CD80 showed elevated expressions ([141] Figure 6E ).
This suggests that subtype C1 may exhibit certain immunosuppressive
tendencies leading to potential exhaustion of CD8+ T cells. These
findings were corroborated by higher TIDE scores for subtype C1 ([142]
Figure 6F ). Conversely, C2subtype appeared more likely to benefit from
immunotherapy ([143] Figure 6G ).
Figure 6.
[144]Figure 6
[145]Open in a new tab
Identification of ferroptosis-related molecular subtypes and analysis
of their differences in biological characteristics and treatment
sensitivity. (A) Based on the expression of DEFRG, CML patients were
divided into two ferroptosis-related molecular subtypes by consensus
clustering algorithm. (B) PCA algorithm was used to analyze the
distribution differences of patients between subtypes. (C-F)
Differences in expression of DEFRG (C), infiltration of 22 immune cells
(D), expression of immune checkpoints (E), TIDE score (F),
immunotherapy response (G), activity of tumor hallmark gene sets (H),
ferroptosis scores (I), risk score (J), and therapeutic sensitivity to
four TKIs (K-N) between the two molecular subtypes. *p < 0.05; **p <
0.01.
Additionally, our GSVA analysis revealed that the C1 subtype
demonstrates heightened activation of signal transduction pathways such
as hedgehog signaling and TNFA signaling via NFKB ([146] Figure 6H ).
Moreover, we observed increased activity in cancer-promoting pathways
including hypoxia and reactive oxygen species pathway. In contrast, the
C2 subtype exhibited elevated activity in proliferation-related
pathways such as G2M checkpoint, E2F targets, and MYC targets V1.
Notably, C1 displayed a higher ferroptosis score while C2 had a higher
risk score ([147] Figures 6I, J ). Drug prediction analysis indicated
that imatinib, nilotinib, dasatinib, and bosutinib demonstrated greater
efficacy against subtype C1 compared to subtype C2 ([148] Figures 6K-N
). These findings will significantly contribute to the development of
personalized treatment strategies for patients with CML.
In vitro experiments confirmed that CML-resistant cells were more sensitive
to ferroptosis treatment
The expression of five FRGs was detected in CML cell lines K562 and
imatinib-resistant cell lines K562/IR. In comparison to K562, SLC38A1
expression showed a slight up-regulation in K562/IR, whereas ACSL6,
SLC11A2, and AKR1C3 expressions were down-regulated (HMOX1 gene
expression was not detected and therefore not shown) ([149] Figure 7A
). In our study above, our preliminary analysis indicated that CML
cells may exhibit sensitivity to ferroptosis, while CML cells in blast
crisis demonstrate resistance towards TKI treatment and potentially
higher sensitivity. To validate these findings, we conducted in vitro
experiments. However, it was observed that the CML cell line K562 did
not display sensitivity to erastin-induced ferroptosis ([150] Figure 7B
); nevertheless, erastin exhibited a certain cytotoxic effect on
imatinib-resistant K562 cells (K562/IR) with an IC50 of 5.099 μM ([151]
Figure 7C ). Furthermore, treatment of K562/IR cells with the
ferroptosis inhibitor Fer-1 significantly restored cellular viability
([152] Figure 7D ). Compared to K562 cells, there was a significant
increase in ROS levels within K562/IR cells which further escalated
after erastin treatment-indicating ROS as a crucial factor for inducing
ferroptosis ([153] Figure 7E ). Additionally, it was discovered that
low-dose erastin enhanced the therapeutic sensitivity of imatinib
towards K562/IR cells by reducing the IC50 from 3.184 μM to 1.886 μM
([154] Figure 7F ). Moreover, low-dose erastin promoted apoptosis
levels in K562/IR cells treated with imatinib ([155] Figures 7G, H ).
GSH and GPX4 are important indicators of ferroptosis. We found that
after erastin treatment, GSH content, GSH/GSSH ratio, and GPX4 enzyme
activity of K562/IR cells were significantly decreased, and GPX4 mRNA
expression level was slightly increased ([156] Figures 7I-L ),
indicating that erastin inhibited GSH production. In turn, the GPX4
enzyme activity is reduced, which can not inhibit the production of
excess ROS, resulting in ferroptosis of K562/IR cells. Finally, we also
detected GPX4 expression in K562 and K562/IR cells and CML samples, and
the results showed that GPX4 expression in K562/IR cells was lower than
that in K562 cells, and there was no significant difference in GPX4
expression between BC-CML and CP-CML samples and normal samples ([157]
Figures 7M, N ). These results suggest that important mechanisms of
ferroptosis resistance in CML-resistant cells may not be regulated by
GPX4.
Figure 7.
[158]Figure 7
[159]Open in a new tab
Therapeutic effects of erastin on CML cells. (A) Analysis of FRG
expression between K562 and K562/IR cells. (B, C) Effect of different
concentrations of erastin on cell viability of K562 and K562/IR cells
after 48h treatment. (D) The activity of K562/IR cells after treatment
with 5 μM erastin and the addition of 1μM ferroptosis inhibitor Fer-1
for 48h. (E) ROS levels in K562, K562/IR, and K562/IR were treated with
5 μM erastin after 24h. (F) Changes in cell viability with or without
1.25 μM erastin and treated with different concentrations of imatinib
for K562/IR after 48h treatment. (G, H) Changes in apoptosis levels
after K562/IR treatment with or without 1.25 μM erastin and 1 μM
imatinib of 24h. (I-L) Changes of GSH level, GSH/GSSH ratio, GPX4
activity, and GPX4 mRNA expression in K562/IR cells after 5 μM erastin
treatment for 48h. (M, N) The difference in GPX4 mRNA expression
between K562 and K562/IR cells, as well as among normal samples, CP-CML
samples, and BC-CML samples. The IC50 value of the drug was calculated
by GraphPad software. **p < 0.01; ***p < 0.001; ns, no significance.
Discussion
Ferroptosis, a newly discovered mode of cell death in recent years,
plays a crucial role in regulating various physiological and
pathological processes ([160]10). In the context of tumors, ferroptosis
is closely associated with the biological characteristics of tumor
cells. The hypoxic microenvironment easily triggers the generation of
ROS, while the lipid metabolism required for rapid proliferation
creates favorable conditions for lipid peroxidation ([161]7). These
features collectively indicate that tumor cells are inclined to undergo
ferroptosis. The induction of ferroptosis in tumor cells and the
attenuation of their protective capacity have significant clinical
value for cancer therapy, aiming to enhance tumor cell death or develop
novel targeted therapies against apoptosis resistance ([162]30).
In this study, we conducted a systematic analysis of ferroptosis levels
in samples from patients with CML using transcriptome sequencing data.
Our findings confirm the clinical significance of FRG in diagnosing and
evaluating treatment outcomes for CML. Analysis of data from multiple
cohorts reveals a significant reduction in ferroptosis scores in CML
samples, which further decreases with disease progression.
Non-responders also exhibit lower ferroptosis scores compared to CML
patients who respond to TKI therapy. Subsequent analyses indicate that
lower ferroptosis scores may be associated with decreased expression of
genes involved in suppressing ferroptosis, suggesting that CML cells
with weaker inhibition against ferroptosis may be more susceptible to
induction therapy targeting this process. Through additional cell
experiments, we validate that CML-resistant cells are more sensitive to
the induction of ferroptosis and can enhance the sensitivity of
imatinib treatment, providing a novel target and strategy for
overcoming drug resistance in CML. Furthermore, our results demonstrate
that the ferroptosis score serves as an informative indicator
reflecting the characteristics of the tumor microenvironment in CML.
Patients with high ferroptosis scores exhibit increased infiltration by
Tregs and higher expression levels of immune checkpoint genes LAG3 and
TNFRSF9, which are associated with immunosuppression. Additionally,
there is a positive correlation between ferroptosis scores and activity
levels within most tumor signature pathways. By conducting WGCNA
analysis, we have further identified metabolic pathways as crucial
determinants influencing the activity of the ferroptosis pathway
itself. Therefore, metabolic reprogramming plays a crucial role not
only in promoting malignant proliferation but also contributes to
triggering ferroptosis ([163]8, [164]29).
The expression profile and clinical significance of FRG were further
analyzed in this study. The majority of differentially expressed FRGs
were found to be down-regulated in CML samples, suggesting their
potential involvement in the pathogenesis of CML. Additionally, these
FRGs were found to participate in various metabolic pathways,
highlighting their multifaceted functions beyond regulating
ferroptosis. To comprehensively validate the diagnostic value of FRG,
three machine learning algorithms were employed to identify five
CML-specific diagnostic FRGs: ACSL6, SLC11A2, HMOX1, SLC38A1, and
AKR1C3. These genes showed significantly reduced expression levels in
CML samples compared to normal samples.
The diagnostic value of these five FRGs was confirmed not only within
the analysis cohort and validation cohort but also in a real-world
clinical cohort. This comprehensive validation enhanced the performance
of the risk score model based on their expression levels for diagnosing
CML patients accurately. Furthermore, it was observed that as treatment
remission occurred in CML patients, the expression levels of FRGs
increased while the risk scores decreased accordingly. Importantly,
these five FRGs can also be utilized for distinguishing CML from other
hematological malignancies with clinical relevance. These
bioinformatics findings provide strong evidence supporting the
diagnostic and therapeutic evaluation potential of FRG specifically in
CML patients. Additionally, based on distinct patterns of FRG
expressions identified through our analysis approach, we classified two
molecular subtypes within the population of CML patients: subtype C1,
characterized by a higher proportion of CD8+ T cell infiltration and
elevated immune checkpoint gene expressions suggesting
immunosuppression; these patients are predicted to exhibit greater
sensitivity towards TKI treatments compared to subtype C2. In
conclusion, the proposed molecular subtypes will significantly enhance
our understanding of the distinct disease characteristics exhibited by
patients with CML, thereby providing valuable insights for tailored
clinical guidance in personalized treatment strategies.
Finally, we discovered through further experimentation that
CML-resistant cells exhibited heightened sensitivity to ferroptosis,
potentially due to elevated levels of ROS in these cells. In tumor
cells, ROS acts as a signaling molecule and promotes various phenotypes
such as growth, metastasis, resistance to apoptosis, and
differentiation disorders by activating survival signaling pathways,
accelerating energy metabolism, and generating carcinogenic mutations
([165]31). Numerous studies have also confirmed that ROS serves as a
major source of genomic instability in different types of cancer. The
continuous mutation of cancer cell genomes is a significant cause of
drug resistance and relapse in cancer therapy ([166]32, [167]33).
Multiple studies have also substantiated the reasons behind the
substantial increase in ROS levels observed in CML-resistant cells.
This primarily stems from the activation of various downstream
signaling pathways by BCR-ABL1, including the PI3K/AKT/mTOR pathway
which enhances glucose metabolism and mitochondrial electron transport
chain activity excessively ([168]34, [169]35); augmentation of NADPH
oxidase activity ([170]36); and regulation of target gene transcription
for ROS generation via STAT5 ([171]37). Accumulation of ROS drives a
cycle of genomic instability leading to BCR-ABL1 mutations or other
chromosomal aberrations along with TKI resistance resulting in drug
resistance. Additionally, high levels of ROS can induce oxidative
damage to mitochondrial DNA within CML-resistant cells causing
mitochondrial dysfunction that disrupts the oxidative respiratory chain
leading to excessive electron leakage thereby further increasing ROS
production within resistant cells ([172]38). Elevated levels of ROS
facilitate the formation of more heteromutations while stimulating the
signaling capacity within cancer pathways thus generating additional
alternative mechanisms promoting CML resistance. Therefore, elevated
levels of ROS play a pivotal role in rendering CML-resistant cells more
susceptible to ferroptosis, thereby offering a novel therapeutic avenue
for overcoming CML resistance. Currently, numerous regulatory
mechanisms associated with ferroptosis have been elucidated, including
the involvement of HDAC3 via the Hippo signaling pathway ([173]39).
Further exploration into the mechanism underlying ferroptosis in CML is
warranted.
In summary, we have elucidated the molecular characteristics of
ferroptosis in CML from a bioinformatics perspective. The findings from
these analyses will contribute to a deeper understanding of the
biological significance of ferroptosis in CML. FRG, identified through
various machine learning algorithms and validated across multiple
cohorts, demonstrates reliable clinical diagnostic value. Moreover, the
introduction of ferroptosis-associated molecular subtypes has
significantly enhanced our comprehension of individualized traits among
CML patients and facilitated personalized treatment strategies. The
induction of ferroptosis may also serve as a promising therapeutic
approach for overcoming resistance in CML. However, our study does have
certain limitations, including the need for a larger sample size to
validate the bioinformatics findings, more cell lines and more
comprehensive experiments to elucidate the regulatory mechanisms
underlying ferroptosis in CML-resistant cells. In subsequent studies,
we will expand our sample collection and enhance our exploration of
relevant mechanisms through both in vivo and in vitro experiments.
Conclusion
The transcriptomic analysis conducted in this study has revealed the
molecular characteristics of ferroptosis in samples from patients with
CML. By employing machine learning algorithms, reliable clinical
diagnostic value was successfully identified for FRG expression
patterns. This understanding of individual molecular subtypes
associated with ferroptosis can effectively guide clinical treatment
strategies for CML patients. Furthermore, targeting and inducing
ferroptosis shows great promise as a potential therapeutic approach to
address drug-resistant CML.
Data availability statement
The original contributions presented in the study are included in the
article/[174] Supplementary Material . Further inquiries can be
directed to the corresponding authors.
Ethics statement
The studies involving humans were approved by Ethics Committee of the
Second Affiliated Hospital of Nanchang University. The studies were
conducted in accordance with the local legislation and institutional
requirements. The participants provided their written informed consent
to participate in this study.
Author contributions
FZ: Data curation, Formal analysis, Funding acquisition, Methodology,
Resources, Software, Validation, Visualization, Writing – original
draft. XZ: Validation, Visualization, Writing – original draft. ZW:
Validation, Visualization, Writing – original draft. XL: Funding
acquisition, Validation, Visualization, Writing – original draft. BH:
Funding acquisition, Validation, Visualization, Writing – original
draft. XW: Conceptualization, Funding acquisition, Project
administration, Resources, Supervision, Writing – review & editing. GK:
Conceptualization, Funding acquisition, Project administration,
Resources, Supervision, Writing – review & editing.
Funding Statement
The author(s) declare financial support was received for the research,
authorship, and/or publication of this article. The study was funded by
the National Natural Science Foundation of China (82160405, 82160038,
82301578, 82170140 and 82370146), the Natural Science Foundation of
Jiangxi Province (20232BAB216037, 20232BAB216050), the Key Research and
Development Program of Shaanxi Province (2024SF-YBXM-151), and the
Shaanxi Fundamental Science Research Project for Chemistry and Biology
(Grant No. 23JHZ007).
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary material
The Supplementary Material for this article can be found online at:
[175]https://www.frontiersin.org/articles/10.3389/fimmu.2024.1402669/fu
ll#supplementary-material
[176]Table_1.xlsx^ (12.3KB, xlsx)
References