Abstract
Multiple myeloma (MM) is a prevalent hematological malignancy that
poses significant challenges for treatment. Dysregulated cholesterol
metabolism has been linked to tumorigenesis, disease progression, and
therapy resistance. However, the correlation between cholesterol
metabolism-related genes (CMGs) and the prognosis of MM remains
unclear. Univariate Cox regression analysis and LASSO Cox regression
analysis were applied to construct an overall survival-related
signature based on the Gene Expression Omnibus database. The signature
was validated using three external datasets. Enrichment analysis and
immune analysis were performed between two risk groups. Furthermore, an
optimal nomogram was established for clinical application, and its
performance was assessed by the calibration curve and C-index. A total
of 6 CMGs were selected to establish the prognostic signature,
including ANXA2, CHKA, NSDHL, PMVK, SCAP and SQLE. The prognostic
signature demonstrated good prognostic performance and correlated with
several important clinical parameters, including number of transplants,
International Staging System, albumin, beta2-Microglobulin and lactate
dehydrogenase levels. The function analysis and immune analysis
revealed that the metabolic pathways and immunologic status were
associated with risk score. The nomogram incorporating the signature
along with other clinical characteristics was constructed and the
discrimination was verified by the calibration curve and C-index. Our
findings indicated the potential prognostic connotation of cholesterol
metabolism in MM. The development and validation of the prognostic
signature is expected to aid in predicting prognosis and guiding
precision treatment for MM.
Subject terms: Cancer, Computational biology and bioinformatics,
Metabolomics
Introduction
Multiple myeloma (MM) is a hematologic malignancy that arises from the
heterogeneous clonal proliferation of plasma cells and accounts for
over 17% of all cases in this category^[36]1. While the emergence of
novel therapies like immunotherapy, monoclonal antibodies, and
proteasome inhibitors has led to a significant improvement in MM
survival rates over the past 15 years, the disease remains incurable
due to high rates of recurrence and mortality^[37]2–[38]4. For example,
bortezomib, a reversible inhibitor of proteasome, has been applied in
MM patients for several years^[39]5. However, while bortezomib
effectively eliminates bortezomib-sensitive tumor subclones, it
unfortunately also stimulates the growth and expansion of
bortezomib-resistant subclones, which contributes to the relapse of
MM^[40]6,[41]7. Thus, it’s important to develop new biomarkers and
effective models to stratify the risk and prognosis of MM, as well as
guide the individual treatment.
Metabolic reprogramming has emerged as an important aspect of cancer,
and dysregulated cholesterol metabolism has been identified as an
integral part of tumor proliferation, invasion, and
metastasis^[42]8,[43]9. Cholesterol is an essential component of
mammalian membrane structure and plays a crucial role in maintaining
cell homeostasis and critical cellular structure^[44]10–[45]12. Several
genes related to cholesterol metabolism have been found to be
dysregulated in tumor samples, with LDLR overexpression, which mediates
cholesterol uptake, being associated with the development and
occurrence of most cancer cells^[46]13–[47]15. Additionally, various
transcription factors that regulate cholesterol metabolism, including
SREBP2 and RORγ, have been found to be upregulated in different tumors
such as colon cancer, breast cancer, and glioblastoma^[48]16–[49]18.
The proliferation and survival of MM cells are also found to be
correlated with cholesterol metabolism. Liver X receptors (LXR), a
transcriptional regulator that regulates lipid and cholesterol
homeostasis, could influence clonogenic tumor growth and self-renewal
potential in MM^[50]19. 5,6-epoxycholesterol isomers, a kind of
cholesterol, has been found to induce oxiapoptophagy and demonstrate
anti-tumor activity against MM cells^[51]20. Previous researches have
also demonstrated that high-dose statins could suppress MM cell
survival through inhibition of cholesterol biosynthesis^[52]21,[53]22.
Furthermore, Recent studies have shown that cholesterol can regulate
immune cells within the tumor microenvironment, playing a role in T
cell exhaustion and macrophage polarization and influencing the
efficacy of cancer immunotherapy^[54]23–[55]25. However, there is
currently no relevant research on the prognostic value of cholesterol
metabolism-related genes (CMGs) in MM.
The aim of this study is to develop a prognostic signature based on
CMGs and explore its significance in MM patients. We validated the
predictive value of our signature using both training cohort and three
external validation cohorts. Additionally, we investigated the
connection between the signature and functional signaling pathways, as
well as immune status. Our findings are expected to help the diagnosis
and precision treatment of MM.
Methods
Study population and data acquisition
Our study included five cohorts of MM patients obtained from the Gene
Expression Omnibus (GEO) database
([56]https://www.ncbi.nlm.nih.gov/geo/) with accession numbers
[57]GSE136324, [58]GSE136337, [59]GSE118985, [60]GSE24080, and
[61]GSE4204. The microarray expression data and detailed clinical
information were downloaded. We excluded samples if corresponding
survival data were missing. The [62]GSE136324 dataset served as the
training cohort, while the [63]GSE136337, [64]GSE24080, and [65]GSE4204
datasets were used as validation cohorts. The [66]GSE118985 dataset was
used to compare the expression of CMGs between tumor and normal
samples.
Collection of cholesterol metabolism-associated genes
Five genesets related to cholesterol metabolism, including Hallmark
cholesterol homeostasis genes, GOBP regulation of cholesterol genes, WP
cholesterol metabolism genes, WP cholesterol biosynthesis genes and
Reactome cholesterol biosynthesis genes were obtained from the
Molecular Signature Database
([67]https://www.gsea-msigdb.org/gsea/msigdb/index.jsp)^[68]26,[69]27.
A total of 140 CMGs were collected after removing the overlapping genes
from the aforementioned five genesets. After intersecting the 140 CMGs
with all the genes included in the five MM datasets, we retrieved 123
reliable CMGs for further analysis (Supplementary Fig. [70]1 and
Supplementary Table [71]1).
Construction of prognostic signature
Univariate Cox hazards regression analysis was applied to identify CMGs
that had a significant correlation with overall survival (OS) in the
[72]GSE136324 and [73]GSE136337 datasets (P < 0.05). Then, we used the
least absolute shrinkage and selection operator (LASSO) regression
analysis to determine the crucial signatures and corresponding
coefficients for model construction with 1000-fold cross-validation
based on the eligible OS-related CMGs^[74]28. The cholesterol
metabolism index (CMI) could be calculated using the following formula:
[MATH: CMI=∑βi∗Ei :MATH]
βi represents the corresponding regression coefficient while Ei
represents the expression level of each gene. The CMI was then
normalized and transformed by subtracting the minimum value in each
dataset and dividing by the maximum value, allowing CMI to map to the
range of 0–1. Based on the median cutoff of CMI, patients were divided
into high and low-risk groups.
Assessment of drug responsiveness
The “OncoPredict” package was used to assess the drug
susceptibility^[75]29. The transcriptomic and cell line response data
from the Sanger’s Genomics of Drug Sensitivity in Cancer (GDSC) was
downloaded and applied as training cohort^[76]30.
Differential gene analysis and functional enrichment analysis
The differentially expressed genes (DEGs) were identified using Limma
with the significance criteria set to |log2FC|> 0.4 and false discovery
rate (FDR) < 0.05. Gene set enrichment analysis (GSEA) was then
performed to explore the enriched signaling pathways and intrinsic
functions. The GSEA software (version 4.3.2, available at
[77]http://software.broadinstitute.org/gsea) with the
“h.all.v7.5.1.entrez.gmt” and “msigdb.v7.5.1.entrez.gmt” molecular
signature databases was utilized, with FDR < 0.05 considered
statistically significant^[78]26. One thousand total permutations were
applied to ensure the validity of the results. Additionally, functional
protein–protein interaction network analysis was conducted using
STRING^[79]31.
Estimation of immune cell infiltration
Cell-type Identification By Estimating Relative Subsets Of RNA
Transcripts (CIBERSORT) was used to analyze immune cell infiltration in
the high and low-risk groups based on LM22 signatures with 1000
permutations^[80]32.
Development and validation of cholesterol metabolism-correlated
clinicopathologic nomogram
Univariate and multivariate Cox regression analyses were conducted to
determine whether CMI was an independent prognostic predictor of MM.
Based on the results of these analyses, a cholesterol
metabolism-correlated clinicopathologic nomogram was developed by
incorporating CMI with five other clinical characteristics, using the R
packages “cmprsk” and “rms”. To assess the predictive discrimination of
the nomogram, calibration curves and concordance indices (C-index) were
calculated^[81]33,[82]34. In a well-calibrated model, the predictions
of the calibration curve are expected to fall on a 45° diagonal line,
and the C-index ranges from 0.5 to 1.0, with 0.5 indicating random
chance and 1.0 indicating perfect discrimination.
Statistical analysis
The statistical analyses were conducted using R software (version
4.2.1, [83]http://www.R-project.org) and relevant packages. The
Kaplan–Meier method was employed to generate survival curves, and the
log-rank test was used to compare them. Univariate and multivariate Cox
regression analyses were conducted to identify OS-related CMGs and
independent prognostic indicators of OS. When appropriate, the Wilcoxon
test and Kruskal–Wallis test were used to compare continuous variables
across different groups. Spearman’s correlation test was applied for
correlation analysis. A two-sided P-value of less than 0.05 was
considered statistically significant. The false discovery rate
correction was used for multiple tests to reduce the false-positive
rate.
Results
Establishment of prognostic signature based on cholesterol
metabolism-associated genes
To determine the potential prognostic value of each available CMG, we
utilized the univariate Cox hazards regression analysis in the
[84]GSE136324 and [85]GSE136337 datasets to identify prognostic
relevance of CMGs, resulting in 47 and 21 significant OS-related CMGs,
respectively (Supplementary Table [86]2). By intersecting the results
of the two cohorts, we identified 11 overlapping OS-related CMGs that
were eligible for further analysis (Fig. [87]1A). LASSO Cox regression
model was further performed in the [88]GSE136324 cohort aiming to
unearth the optimal CMGs for establishing the prognostic signature.
Ultimately, we identified six genes to construct the signature,
including ANXA2, CHKA, NSDHL, PMVK, SCAP and SQLE (Fig. [89]1B,C). The
CMI of each patient was calculated using the signature formula:
CMI = 0.0949 * ANXA2 + 0.1037 * CHKA + 0.4595 * NSDHL + 0.0803 *
PMVK + 0.0783 * SCAP. The protein–protein interaction network of these
genes revealed that SQLE was the hub gene (Fig. [90]1D and
Supplementary Table [91]3). Moreover, the expression of most signature
genes was significantly correlated with each other (Fig. [92]1E and
Supplementary Fig. [93]2).
Figure 1.
[94]Figure 1
[95]Open in a new tab
Establishment of prognostic signature based on cholesterol
metabolism-associated genes. (A) Venn diagram to identify overlapping
prognostic CMGs between [96]GSE136324 and [97]GSE136337 (B) Coefficient
profiles of LASSO Cox regression analysis. (C) Partial likelihood
deviance for LASSO Cox regression analysis. (D) The protein–protein
interaction network of CMGs included in the signature. (E) The
correlation matrix plot displaying the correlation features among CMGs
included in the signature.
Verifying the expression and prognostic capability of signature-containing
genes
Figure [98]2A shows the validation of the expression levels of the 6
CMGs in normal and MM tissues using the [99]GSE118985 dataset. The
results indicated that ANXA2, PMVK and SQLE were significantly
upregulated in MM samples, while CHKA was downregulated. However, NSDHL
and SCAP did not show significant changes in expression. Besides, in
the separated survival analyses of OS, it was observed that MM patients
with high expression of all selected genes had worse prognoses compared
to those with low expression in all four datasets, except that SCAP in
the [100]GSE4204 did not reach the statistical significance
(Fig. [101]2B and Supplementary Fig. [102]3).
Figure 2.
[103]Figure 2
[104]Open in a new tab
Validation of the expression and prognostic capability of
signature-containing genes. (A) Boxplot showing the expression
difference of 6 CMGs between tumor samples and normal samples in
[105]GSE118985. (B) Kaplan–Meier analyses of overall survival based on
expression levels of 6 CMGs in [106]GSE136324. **, P < 0.01; ****,
P < 0.0001; ns, not significant.
Assessment and validation of prognostic signature
In the [107]GSE136324 dataset, we categorized MM patients into high-
and low-CMI groups based on the median threshold of CMI (Fig. [108]3A).
The proportion of deceased patients was higher in the high-CMI group
than in the low-CMI group (Fig. [109]3B). Furthermore, as CMI
increased, expression levels of all signature genes also increased
(Fig. [110]3C). Patients with MM in the high-CMI group had
significantly worse overall survival compared to those in the low-CMI
group (P = 0.00056) (Fig. [111]3D). To confirm these findings'
prognostic significance for CMI, we conducted similar analyses using
three external validation datasets: [112]GSE136337, [113]GSE24080 and
[114]GSE4204. Using an identical calculation formula for CMI
categorization into high- and low-groups among MM patients revealed
that consistent with our previous results from [115]GSE136324 dataset;
patients belonging to a high-CMI group experienced worse OS than those
belonging to a low-CMI group across all validation cohorts
(Fig. [116]3D). Furthermore, we found that MM patients in the high-CMI
group had worse progression-free survival (PFS) in the [117]GSE136324
and [118]GSE136337 datasets, while there was only trend for significant
difference of event-free survival (EFS) in the [119]GSE24080 dataset
(Supplementary Fig. [120]4).
Figure 3.
[121]Figure 3
[122]Open in a new tab
Assessment and validation of prognostic signature in the training and
validation cohorts. (A) Distribution of the patients’ normalized CMI.
(B) Scatter plots of patients’ overall survival time and their CMI. (C)
Heatmaps showing the expression levels of 6 CMGs in MM samples. (D)
Kaplan–Meier curves showing overall survival of MM patients in the
high- and low-CMI groups.
Integrated analysis of prognostic signature and clinicopathologic factors
We examined the relationship between CMI and clinicopathologic
characteristics. Significant differences in CMI were observed across
various clinical parameters, such as number of transplants,
International Staging System (ISS), revised-ISS (R-ISS), albumin
levels, beta-2 microglobulin (β2M) levels, and lactate dehydrogenase
(LDH) levels (Fig. [123]4A–F). Our findings suggested that higher CMI
was associated with higher β2M and LDH levels, ISS and R-ISS staging,
as well as lower albumin levels. However, we did not observe any
correlation between CMI and age, gender or race (Supplementary
Fig. [124]5). The result was further confirmed using the [125]GSE24080
dataset (Supplementary Fig. [126]6). Moreover, we observed that higher
CMI was correlated with GEP-70 high risk status, deletion (del)1p32,
del1p, del1q, del13q and del16q (Supplementary Fig. [127]7). We also
estimated the drug response to the chemotherapeutic agents and found
that the high-CMI cohort showed more sensitive to cytarabine,
epirubicin, vorinostat and bortezomib compared to the low-CMI group
(Fig. [128]4G and Supplementary Fig. [129]8).
Figure 4.
[130]Figure 4
[131]Open in a new tab
Integrated analysis of prognostic signature and clinicopathologic
factors. Comparison of normalized CMI according to (A) number of
transplants, (B) International Staging System (ISS), (C) revised-ISS
(R-ISS), (D) albumin levels, (E) beta-2 microglobulin (β2M) levels and
(F) lactate dehydrogenase (LDH) levels. (G) Evaluations of the drug
susceptibility between high- and low-CMI groups in the [132]GSE136324
dataset. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.
Functional analysis of prognostic signature
We screened for differentially expressed genes between high- and
low-CMI groups in the [133]GSE136324 database. We identified a total of
98 DEGs, with 45 significantly upregulated genes and 47 essentially
downregulated genes in the high-CMI group (Fig. [134]5A). Kyoto
Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed
that the high-CMI group was mainly enriched in metabolic pathways
including biosynthesis of amino acids, steroid biosynthesis and
biosynthesis of nucleotide sugars while the low-CMI group was mainly
enriched in motor proteins (Fig. [135]5B)^[136]35. The results of GSEA
analysis using the KEGG database demonstrated that aminoacyle-tRNA
biosynthesis, proteasome, ribosome biogenesis in eukaryotes, steroid
biosynthesis and terpenoid backbone biosynthesis were significantly
enriched in the high-CMI group, while cholesterol homeostasis, DNA
repair, MYC targets and unfolded protein response were fundamentally
abundant in the high-CMI group through the GSEA results using the
hallmark database (Fig. [137]5C,D and Supplementary Fig. [138]9).
Figure 5.
[139]Figure 5
[140]Open in a new tab
Functional analysis of prognostic signature using [141]GSE136324
dataset. (A) Volcano plot showing differentially expressed genes
between high- and low-CMI groups. (B) Upregulated and downregulated
pathways in the high-CMI group based on KEGG pathway enrichment
analysis of differentially expressed genes. (C, D) Summary of GSEA
results according to KEGG pathway and hallmark pathway.
Immune associated analysis of prognostic signature
Immune checkpoint inhibitors have been developed and evaluated for
various types of cancer. In this study, we compared the expression
levels of several immune checkpoints between low- and high-CMI groups.
Our findings indicate that CD274 (PD-L1), LAG3, BTLA, CD47, TIGIT and
CD28 were significantly upregulated in the high-CMI group
(Fig. [142]6A). Additionally, we used the CIBERSORT algorithm to
evaluate 22 distinct types of tumor-infiltrating immune cells in low-
and high-CMI patients. The results showed that abundance of macrophages
M1 subtype were significantly lower in the high-CMI group in all four
datasets (Fig. [143]6B,C and Supplementary Fig. [144]10). High-CMI
group also had significantly less resting dendritic cells in three
datasets. Furthermore, there were less CD8^+ T cells and neutrophils in
the High-CMI patients in the [145]GSE136324 and [146]GSE136337
datasets, while more monocytes and activated NK cells were found in the
High-CMI patients in the [147]GSE24080 and [148]GSE4204 datasets.
Figure 6.
[149]Figure 6
[150]Open in a new tab
Immune analysis of prognostic signature. (A) Boxplot showing the
comparison of several immune checkpoints between high- and low-CMI
groups. (B, C) Boxplot showing the comparison of 22 different immune
cell types estimated by CIBERSORT between high- and low-CMI groups in
(B) [151]GSE136324 and (C) [152]GSE24080 datasets. *, P < 0.05; **,
P < 0.01; ***, P < 0.001; ****, P < 0.0001.
Development and evaluation of cholesterol metabolism-correlated
clinicopathologic nomogram
To determine whether CMI was an independent prognostic indicator of MM,
we conducted univariate Cox regression analysis on the [153]GSE136324
dataset. Factors with P < 0.05 were included in multivariate Cox
regression analysis (Fig. [154]7A,B). Our findings revealed that age,
ISS, albumin, β2M, and CMI were independent risk factors for OS among
MM patients. We then developed a clinicopathologic nomogram by
combining these variables to predict the survival probability of MM
patients (Fig. [155]7C). By summing up all points and locating them on
the bottom scales, we could easily calculate estimated 3-, 5- and
7-year OS probabilities. The calibration plots for the nomogram showed
acceptable agreement between predicted estimates and observed outcomes
(Fig. [156]7D). The Harrell's C-index of the nomogram for predicting OS
was significantly higher than that of the ISS staging system for OS
(0.678 [95% confidence interval (CI) 0.648–0.708] vs. 0.634; [95% CI
0.605–0.664]; P < 0.001), indicating that combining CMI with clinical
characteristics provided a better prognostic indicator than using only
ISS staging system. We further validated the predictive value of
nomogram in the external [157]GSE136337 and [158]GSE24080 datasets
using the calibration plots (Supplementary Fig. [159]11). The C-index
was also acceptable for 0.677 (95% CI 0.638–0.717) and 0.682 (95% CI
0.637–0.727) in the [160]GSE136337 and [161]GSE24080 respectively.
Figure 7.
[162]Figure 7
[163]Open in a new tab
Development and evaluation of cholesterol metabolism-correlated
clinicopathologic nomogram. (A, B) Forest plots showing (A) univariate
Cox regression analysis and (B) multivariate Cox regression analysis of
the prognostic signature and clinicopathologic factors. (C) Nomogram
for predicting 3-, 5-, and 7-year overall survival in MM patients. (D)
Calibration curve to evaluate the consistency of predicted and actual
overall survival.
Discussion
Reprogramming of metabolism, which includes dysregulated cholesterol
metabolism, has been associated with tumor progression and response to
treatment in various types of cancer^[164]36. Cholesterol homeostasis
is crucial for the growth and survival of mammalian cells. However,
malignant cells require more cholesterol than normal cells for their
rapid growth. Abnormal expression of genes involved in cholesterol
biosynthesis and uptake has been observed in breast cancer, ovarian
cancer, renal cancer, and other types of
cancers^[165]24,[166]37,[167]38. For instance, HMGCR, a rate-limiting
enzyme in cholesterol biosynthesis, was upregulated in many tumors;
knockdown of HMGCR could impede tumor proliferation and
metastasis^[168]39,[169]40. Several studies have suggested that
targeting cholesterol metabolism could be an effective anti-tumor
strategy, with targeted inhibition of HMGCR being preliminarily applied
to treat cancer patients^[170]36,[171]41,[172]42. Nevertheless, the
specific role played by cholesterol metabolism in MM remains unclear.
Therefore, this study aims to develop a novel prognostic signature
comprising multiple genes related to cholesterol metabolism for MM
patients.
In this study, we screened 6 CMGs (ANXA2, CHKA, NSDHL, PMVK, SCAP and
SQLE) based on a systematical analysis using GEO datasets and
established and validated a model which could predict the prognosis of
MM. There have been many researches focusing on the genes identified in
our study, which could verify the reliability of model to some extent.
For instance, annexin A2 (ANXA2) is a calcium-regulated
membrane-binding protein which could inhibit PCSK9-enhanced LDLR
degradation, reduce PCSK9 protein levels via a translational mechanism
and compete with LDLR for binding with PCSK9^[173]43,[174]44. ANXA2 is
found to promote MM cell growth, reduce apoptosis in MM cell lines,
increase osteoclast formation and have a significant impact on survival
of myeloma patients^[175]45.Choline kinase alpha (CHKA) is the initial
enzyme involved in the biosynthesis of phosphatidylcholine and
correlated with cholesterol homeostasis^[176]46. Previous studies have
demonstrated that expression levels of CHKA could affect proliferation,
metastasis, and survival of ovarian cancer and glioma^[177]47,[178]48.
Inhibition of CHKA expression could also overcome resistance to
TRAIL-mediated apoptosis in ovarian cancer cells^[179]47. NAD(P)
dependent steroid dehydrogenase-like (NSDHL) is an enzyme involved in
cholesterol biosynthesis and is found to promote breast cancer growth
and metastasis, as well as serve as a biomarker for early detection of
gastric cancer^[180]49–[181]51. Phosphomevalonate kinase (PMVK)
catalyzes the conversion of mevalonate 5-phosphate to mevalonate
diphosphate, a key step in the mevalonate pathway of isoprenoid
biosynthesis. Previous researches reveal that PMVK could stabilize
β-catenin signaling via mevalonate diphosphate and also associate with
responses to chemotherapeutic agents^[182]52,[183]53. Sterol regulatory
element binding protein (SREBP) cleavage-activating protein (SCAP)
could bind to SREBPs and mediate their transport from the endoplasmic
reticulum to the Golgi in the presence of cholesterol, leading to the
regulation of sterol biosynthesis^[184]54. EGFR signaling in cancer
cells could promote N-glycosylation of SCAP by increasing glucose
uptake and enhance tumor progression^[185]55. Squalene epoxidase
(SQLE), which is the rate-limiting enzyme catalyzes the stereospecific
oxidation of squalene, is one of the most significantly upregulated
CMGs in numerous tumors^[186]56. SQLE promotes the growth and
metastasis of various tumors and it’s a potential metabolic target for
cancer therapy^[187]57,[188]58. In together, our study further
elucidates the important role of these 6 CMGs in MM, and the prognostic
signature constructed by these genes has good performance in predicting
the overall survival of MM patients.
Functional analysis revealed that aminoacyle-tRNA biosynthesis, steroid
biosynthesis, terpenoid backbone biosynthesis, cholesterol homeostasis,
DNA repair, MYC targets and other related pathways were associated with
cholesterol metabolism in MM. As an essential component of lipid rafts
in mammalian cells, cholesterol is involved in many oncogenic signaling
pathways in tumor cells, including MYC, MAPK and Wnt pathway^[189]23.
MYC could enhance cholesterol biosynthesis and dysregulate cholesterol
transport and storage, leading to tumor cell
progression^[190]59,[191]60. Previous studies have also reported that
there was interaction between cholesterol biosynthesis and DNA repair
genes^[192]61,[193]62. Inhibition of the cholesterol biosynthesis could
lead to the accumulation of toxic metabolic intermediates, which causes
replicative stress and replication checkpoint-activated cell-cycle
arrest. Taken together, our results have reconfirmed the relationship
between cholesterol metabolism and signaling pathways.
Bortezomib is now widely used in the treatment of MM, yielding
excellent responses. Bortezomib could inhibit the proliferation and
induce the apoptosis of MM cells by blocking cytokine circuits, cell
adhesion, and angiogenesis^[194]63. However, not all patients treated
with bortezomib experience favorable outcomes. Previous researches have
reported that bortezomib resistance might be associated with
cholesterol metabolism^[195]64,[196]65. Our study also revealed that
low-CMI patients showed resistant to bortezomib and several other
chemotherapeutic agents, demonstrating that cholesterol metabolism in
MM might contribute to the response of treatment. Moreover, several
researches have suggested that activation of autophagy is associated
with bortezomib resistance, while there was minor connection between
CMI and autophagy in our research^[197]63,[198]66.
In addition, immunotherapy has become a promising treatment of cancer
in recent years, but how to identify suitable patients for
immunotherapy remains unclear^[199]67. Immune checkpoint inhibitor has
been employed in preclinical or clinical trials as a major strategy of
immunotherapy. In this study, we chose 6 immune checkpoints and
compared the expression levels between the low- and high-CMI groups of
MM. The results revealed that they were significantly upregulated in
high-CMI group, indicating that MM patients with higher CMI might
obtain a better response to immune checkpoint inhibitors. Furthermore,
the effect of immunotherapy is closely related to infiltration of
immune cells, while cholesterol is found to be associated with number
and function of immune cells within tumor microenvironment. Cholesterol
could decrease the number of CD8^+ T cells and cause exhaustion of T
cells^[200]68. Besides, Cancer cells could promote cholesterol efflux
from the plasma membrane and induce pro-tumor phenotype of
macrophages^[201]24,[202]69. Consistent with these findings, our study
revealed that the high-CMI group has lower CD8^+ T cells and higher
number of M1 macrophages than the low-CMI group. Collectively, the
result implicated that MM patients in the high-CMI group may benefit
from immunotherapy.
Several limitations still remain in our study. First, we performed the
analyses based on public datasets. The performance of the prognostic
signature should be further validated by prospective clinical trials.
Second, the value and mechanism of 6 signature-contained CMGs in MM
remains unclear and needs further in vitro and in vivo experiments to
assess. Third, the relationship between the prognostic signature and
application of immunotherapy needs to be further explored.
Conclusions
In conclusion, we constructed and validated a gene signature based on 6
CMGs to predict the prognosis of MM. This gene signature could provide
a novel option for the prognosis prediction of MM and help the
diagnosis and precision therapy of MM.
Supplementary Information
[203]Supplementary Information.^ (2.2MB, pdf)
Abbreviations
β2M
Beta-2 microglobulin
CIBERSORT
Cell-type identification by estimating relative subsets of RNA
transcripts
CI
Confidence interval
C-index
Concordance index
CMG
Cholesterol metabolism-related gene
CMI
Cholesterol metabolism index
DEGs
Differentially expressed gene
FDR
False discovery rate
GEO
Gene expression omnibus
GSEA
Gene set enrichment analysis
ISS
International staging system
KEGG
Kyoto encyclopedia of genes and genomes
LASSO
Least absolute shrinkage and selection operator
LDH
Lactate dehydrogenase
MM
Multiple myeloma
OS
Overall survival
R-ISS
Revised international staging system
Author contributions
Z.L. and H.Z. designed the research study. N.Z., C.Q., Y.Y., H.L., Y.L.
performed the data acquisition and analysis. N.Z. and C.Q. interpreted
the data and wrote the main manuscript text. Z.L. and H.Z. critically
revised the manuscript. All authors read and approved the final
manuscript.
Funding
This study was supported by the Project of Key Medical Specialty and
Treatment Center of Pudong Hospital of Fudan University (Grant No:
zdzk2020-06), the Research Grant for Health Science and Technology of
Pudong Municipal Commission of Health committee of Shanghai (Grant No:
PW2020A-70) and the Scientific Research Foundation provided by Pudong
Hospital affiliated to Fudan University (Grant No: YJ2019-13).
Data availability
The raw data of our study were downloaded from the GEO database
([204]https://www.ncbi.nlm.nih.gov/geo/).
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
These authors contributed equally: Na Zhao and Chunxia Qu.
Contributor Information
Hongbo Zhu, Email: bonniezhu2009@163.com.
Zhiguo Long, Email: zg_long@fudan.edu.cn.
Supplementary Information
The online version contains supplementary material available at
10.1038/s41598-023-46426-z.
References