(2024) 22:352
Zhuang et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03573-y

BMC Medicine

RESEARCH

Open AccessRadiogenomic profiling of global DNA
methylation associated with molecular
phenotypes and immune features in glioma
Zhuokai Zhuang1,2,3,4,5†, Jinxin Lin1,2,3,4,5†, Zixiao Wan2,3,4,5†, Jingrong Weng1,2,3,4, Ze Yuan2,3,4,5, Yumo Xie1,2,3,4,
Zongchao Liu6, Peiyi Xie7, Siyue Mao8, Zongming Wang9, Xiaolin Wang2,3,4,5, Meijin Huang1,2,3,4,5,
Yanxin Luo1,2,3,4,5 and Huichuan Yu2,3,4,5*Abstract
Background The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist
the molecular diagnosis and tumor treatment.
Methods We apply the machine learning approaches to identify the magnetic resonance imaging (MRI) featuresthat are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecularfeature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise
two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA).
Results The generated MoRad models perform well for profiling each molecular feature with radiomic features,
including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models havea remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we findthat global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced
regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes.
Conclusions Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global
methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.
Keywords Radiogenomics, Molecular characterization, DNA methylation, Magnetic resonance imaging†

Zhuokai Zhuang, Jinxin Lin, and Zixiao Wan contribute equally to this work.

*Correspondence:
Huichuan Yu
yuhch5@mail.sysu.edu.cn
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International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
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Zhuang et al. BMC Medicine(2024) 22:352

Background
The imaging markers identified from the radiomic profiles with comprehensive traits of cancers have beenshown to predict clinical outcomes with high accuracy[1–5]. A number of studies have made efforts to develop
genome-wide hallmarks of cancer, which could systematically assess molecular alterations and improve the early
management and therapeutic intervention for cancers [6,7]. Recent radiogenomic studies have explored the linkage between tumor imaging phenotypes and molecularalterations that are valuable in clinical decision-making[8–12]. These efforts have promoted personalized cancer care by recognizing the radiomic signatures that wereassociated with specific molecular phenotypes, including mutations of isocitrate dehydrogenase (IDH) [13],epidermal growth factor receptor (EGFR) [14], and telomerase reverse transcriptase (TERT) [15], disturbing
pathways [16, 17], and microsatellite status [18, 19].
Among these molecular hallmarks, DNA methylationbased markers have reshaped the clinical decisions inglioma [20]. IDH mutation-related DNA methylation
patterns, such as glioma-CpG island methylator phenotype (G-CIMP), are highly associated with the prognosisof glioma and are commonly used to stratify patients inthe WHO classification [21–23]. Besides the well-elucidated function of the hypermethylated promoter in genesilencing, such as O-6-methylguanine-DNA methyltransferase (MGMT) [24, 25], several studies have reported
the role of global DNA methylation loss in genetic alterations and oncogenesis in glioma [26–28]. Recent studies
also reported the association of global DNA methylation
with immune response [28] and intratumoral heterogeneity [29] in glioma. Furthermore, global DNA methylation has been utilized as a marker for phenotyping ingliomas [30, 31]. However, the association of global DNA
methylation with current molecular phenotypes and clinical outcomes remains to be explored.
Despite the significance of global DNA methylation
and related molecular phenotypes, there is still a lack ofa fast and non-invasive approach to evaluate global DNA
methylation with cost-effective and less labor-intensive
procedures. Considering that the radiomic feature is an
emerging non-invasive marker for tumor phenotyping,
we performed this radiogenomic analysis to characterize the connection between radiological images andmulti-omic profiles in gliomas. In this study, we generated a comprehensive radiogenomic map that could alignradiological images to quantitative molecular profiles. To
profile the global DNA methylation level, we analyzed
the methylation array probes targeting genomic repetitive elements. In addition, we devised two novel enrichment approaches to enable the radiological interpretationof molecular profiles. The novel radiological insights into

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the specific molecular alterations in tumors may provide
the understanding of molecular alteration in shaping thetumor features that decide the radiological images andfacilitate the imaging marker-driven clinical trials.

Methods
Study patients

To analyze the association of imaging features withmolecular profiles in tumors, we analyzed glioma
patients with molecular and magnetic resonance imaging (MRI) profiles according to the research protocol
approved by the Institutional Review Board of The Sixth
Affiliated Hospital of Sun Yat-sen University. The molecular and imaging profiles along with clinicopathologicalinformation and treatment outcomes were acquired fromthe data release of The Cancer Imaging Archive [32] andThe Cancer Genome Atlas [33, 34]. The patients withglioblastoma (GBM) or low-grade glioma (LGG) andavailable pre-operative multimodal MRI (mMRI) were
included. The patients with inconsistent MRI scanners or
without complete sequences of MRI data were excluded.
Finally, a total of 146 patients were included for subsequent radiogenomic analysis (Additional file 1: Fig. S1).
The baseline characteristics of study patients were shown
in Additional file 2: Table S1.
Molecular and immunological characterization

We calculated the tumor mutation burden (TMB) of eachcase by dividing the total number of mutations by thesize of the coding region [35]. The intra-tumor heterogeneity (ITH) score evaluated at the genomic level basedon somatic mutations and copy number variation (CNV)
profiles of bulk tumors was acquired from previous studies [36]. The well-characterized molecular subtypes forgliomas, including pan-glioma DNA methylation subtype[37], pan-glioma RNA expression subtype [37], reversephase protein array (RPPA) subtype [38], transcriptional
subtype [39, 40], and glioma subtype [37], were obtained
from previous studies. In addition, we analyzed the threesubtypes classified by Brat et al. with the IDH mutationand 1p/19q codeletion status: IDH-mutant glioma with
1p/19q codeletion, IDH-mutant glioma without 1p/19q
codeletion, and IDH-wild glioma [33]. Furthermore, wecharacterized the MGMT promoter methylated status[41], ATRX mutation [37] and TERT expression [42],which were shown to have biological and clinical significance. The fractions of 22 tumor-infiltrating immune
cells in the tumor microenvironment of each sample were
estimated by CIBERSORT [43]. The detailed informationfor each molecular phenotype and marker was shown in
Additional file 2: Table S2.

Zhuang et al. BMC Medicine(2024) 22:352

Global DNA methylation profiling

The DNA methylation data profiled by Illumina Infinium HumanMethylation450 (450 K) BeadChip array
was obtained and analyzed as we previously described[44, 45]. To determine the global DNA methylation levelof each sample, we analyzed the methylation probes forwhich at least 90% of sequences (≥ 45 bp) mapped to theyoung subfamilies of long interspersed nuclear element-1(LINE-1) and Alu [28, 46]. The documented probes
targeted to Alu and LINE-1 repeated sequences wereobtained as previously described [46], and we averaged
the beta values of these probes to estimate the global
DNA methylation of each tumor.
Radiomic feature extraction

The MRI images were used to extract radiomic featuresfor GBM and LGG as previously described [32, 47].
Four MRI sequences were available, including T1weighted (T1WI), gadolinium-enhanced T1-weighted(T1WI + Gd), T2-weighted (T2WI), and T2-fluidattenuated inversion recovery (T2-FLAIR) images (Additional
file 3: Supplementary Method 1 [47–52]). The extracted
radiomic features of regions of interest (ROI), including
enhancing region of the tumor core (ET), non-enhancing
region of the tumor core (NET), and peritumoral edema(ED), were obtained from the previous studies using thesoftware “Cancer Imaging Phenomics Toolkit” (CaPTK)[53, 54]. As a result, a total of 726 radiomic features comprising of 5 subgroups were obtained from each individual
case: (1) intensity features; (2) histogram-based features;(3) volumetric features, including volume, spatial, morphologic, and solidity; (4) textural parameters, including gray-level co-occurrence matrix (GLCM), gray-level
run-length matrix (GLRLM), gray-level size zone matrix(GLSZM), and neighborhood gray-tone difference matrix(NGTDM); (5) tumor growth model (TGM) parameter[55] (Additional file 2: Table S3).
Framework for mapping radiogenomic signature
to molecular profile

The patients were randomly divided into the training andvalidation sets with a size ratio of 7:3. We applied themulti-step pipeline that integrates correlation or ANOVA
test followed by machine-learning-based feature selection to construct models for each molecular feature. For
continuous variables, including global DNA methylation
loss, TMB, and ITH, the Pearson correlation test was
performed to select the radiomic features linearly related
to molecular features for 9 molecular feature-specificradiomic sets (MRS9) matrix; then, a liner regression
optimized by the elastic-net method was applied to further select the features and construct MoRad models

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with liner combination of these features by their coefficients. For categorical variables, the ANOVA or t-test
was performed for each molecular feature to select themolecular phenotype-specific radiomic features for the
MRS9 matrix; then, a multinominal regression optimizedby the least absolute shrinkage and selection operator(LASSO) method was applied to further select radiomic
features and generate MoRad models that could classify
each patient into the specific molecular phenotype. We
have shown that this pipeline could generate well-performed predicting models in the previous studies [2, 56]
by eliminating the potential redundance, overfitting, andbias induced by confounding features in model construction. Finally, radiomic models for profiling each molecular feature were constructed based on selected radiomic
features and their coefficients with elastic net regression
or LASSO approaches (Additional file 3: Supplementary
Method 2).
Radiomic ontology (RO) and radiomic set enrichment
analysis (RSEA)

To provide radiological insights into the molecularprofiles of tumors, we devised two tools called “RSEA”
and “RO” analysis. Both RO and RSEA evaluate radiomic and molecular data at the level of radiomic sets.
We developed the RSEA tool on the basis of Subramanian et al.’s Gene Set Enrichment Analysis methodology [57]. We first hierarchically classified extracted
radiomic features into four predefined radiomic setsbased on prior clinical and radiological knowledge
and CaPTK instruction [53, 54]. Briefly, they werecategorized into three subtypes: (1) image sequences,including T1, T1Gd, T2, and FLAIR; (2) lesion location, including ET, ED, and NET; (3) algorithm features, including intensity, histogram, volumetric,
textural, and TGM parameter. Detailed informationand description of the radiomic sets were provided insupplementary files (Additional file 2: Table S4). To
perform RSEA, the 726 radiomic features were initiallyranked based on the coefficients of the correlation
with each molecular feature. The goal of RSEA was todetermine whether the members of a specific radiomic
set were randomly distributed throughout the featurelist or primarily found at the top or bottom, in whichcase the radiomic set was believed to correlate with themolecular variable. The normalized enrichment scores(NES) and corresponding statistical significance were
calculated by increasing a running-sum statistic when
we encountered a radiomic feature in the specific set
and decreasing it when we encountered radiomic features not in the set as we traversed the entire list. It
reflects the degree to which a specific radiomic set wasoverrepresented at the extremes (top or bottom) of theZhuang et al. BMC Medicine(2024) 22:352

entire list. In the RO analysis, the selected radiomic
features were tested for their associations with specificradiomic sets using the hypergeometric test. We calculated the statistical significance of which radiomic
sets appeared more frequently than would be expectedby chance when examining the sets annotated to the
input features. As the detailed feature matrix is not
required, RO is applicable when the feature values are
not accessible and convenient for quick enrichment asa primary screening. A full description and discussionof the methodology and collected radiomic sets forrunning RSEA and RO were provided in the supplementary materials.
Statistical analysis

The group comparisons were performed using thet-test or Mann–Whitney U test according to the variable distribution. Pearson’s test was performed for correlation analysis. The Kaplan-Meier analysis was used
to estimate and compare the long-term survival. The
principal component analysis (PCA) was conductedusing the “ggord” package. All the statistical analyses were conducted with the R software version 4.0.0(http://​w ww.R-​proje​ct.​org). The statistical significancelevels were all set to be 0.05 with two sides.

Results
Analysis of radiomic signatures associated with molecularprofilesThe concept and design of this study were shown in
Fig. 1a. A total of 136,154, 131, and 16 radiomic features
were identified to be linearly related to Alu methylation,
LINE-1 methylation, TMB, and ITH, respectively. For themolecular subtypes, we identified 264, 178, 156, 296, and
137 most varied radiomic features among subtypes forthe pan-glioma DNA methylation subtype, pan-glioma
RNA expression subtype, transcriptional subtype, glioma
subtype, and RPPA subtype, respectively. As a result,
MRS9 was generated (Additional file 2: Table S5). Overall, the radiomic features in MRS9 were well correlatedwith molecular profiles (Fig. 1b).

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Strong association of radiological features with DNA
methylation

To further test the association of radiomic signatureswith molecular profiles, we sought to map the radiomic
features to molecular profiles. We developed the fitting model for each molecular feature using the MRS9.
Overall, the generated MoRad models performed well for
profiling each molecular feature, in which the molecularfeatures profiled by MoRad were significantly correlatedwith their actual status profiled by array or sequencing,including mutational, methylation, transcriptional, andprotein profiles (Fig. 2 and Additional file 1: S2).
Among the molecular profiles, MoRad models have aremarkable performance in detecting DNA methylation
profiles. The array-profiled global DNA methylation was
linearly correlated with those profiled by radiogenomic
signatures in the training (Alu, r = 0.86, P < 0.001; LINE1, r = 0.79, P < 0.001; Fig. 2) and validation (Alu, r = 0.67,
P < 0.001; LINE-1, r = 0.7, P < 0.001) sets (Fig. 2). In
addition, this linear correlation was confirmed in both
IDH-mutant and IDH-wild gliomas (Fig. 2). Of interest,
the linear association of radiogenomic signatures with
TMB was inferior to that with global DNA methylation(Additional file 1: Fig. S2). Expectedly, the MoRad models could well discriminate the methylation subtypes ofLGm2 and G-CIMP-low with area under curves (AUCs)
of 0.970 and 0.973, respectively (Additional file 1: Fig.
S2). Taken together, we uncovered a strong association
between radiological features and DNA methylation andconstructed the radiogenomic signature that could be
used to quantitatively map global DNA methylation.
Radiological annotation of the molecular featureswith devised ontology analysis framework

To provide radiological insights into the molecular profiles, we devised two algorithms and applied them in
the integrating analysis of radiomic and molecular data
(Fig. 3a). The RO enrichment analysis for DNA methylation-specific radiomic features showed that global
DNA methylation could be mapped to the volumetricand textural features in the enhanced region of gliomas(Fig. 3b and Additional file 2: Table S6). In addition,
TMB could be mapped to the volumetric and textural(See figure on next page.)
Fig. 1 Landscape of radiomic and molecular features in glioma. We developed the MoRad tool with machine learning algorithm to bridge the gap
between visible images and invisible molecules, including the genomic landscape of methylation, mutation, and transcriptomic and proteomic
profiles (a). By using MoRad, we demonstrated the radio-molecular trajectories mapping the molecular features to radiological images. The unionof primary screening features of ten MoRad models gained 436 radiomic features and the heatmap was created for an overview of the radiomic
atlas (b). Overall, the unsupervised hierarchical clustering based on the union features showed the connection between the molecular profiles
and radiomic features. LINE-1, long interspersed nuclear element-1; TMB, tumor mutation burden; ITH, intra-tumor heterogeneity; CL, classical; ME,mesenchymal; NE, neural; PN, proneural; RPPA, reverse-phase protein array

Zhuang et al. BMC Medicine(2024) 22:352

Fig. 1 (See legend on previous page.)

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Fig. 2 Development and validation of MoRad tool for molecular profiling by using radiomic features. The correlation analysis showedthat the MoRad-profiled methylation levels were highly correlated with array-profiled methylation levels for Alu and LINE-1 in the training, validation
and whole sets. This correlation was further confirmed in the IDH-mutation and IDH-wild subgroups for the MoRad-Alu and MoRad-L1 models

features of peritumoral edema in the T1WI + Gd images(Fig. 3b and Additional file 2: Table S6). However, noradiomic sets were significantly enriched in the RO
analysis for other molecular features.
Next, we investigated the MRS9 with the RSEA
tool. We found that the global DNA methylation level
could be represented in the enhanced region of T2WI
with textural variation (Fig. 3c and Additional file 2:
Table S7). Moreover, TMB and ITH could be represented by the textural features. However, TMB wasfeatured in the enhanced region and peritumoral
edema from both T1WI and T2WI images, while ITH

was featured in both the enhanced and non-enhanced
regions. (Fig. 3c and Additional file 2: Table S7).
We further explored the unique and common radiological features among MRS9. The global DNA methylation-specific radiogenomic signatures shared several
common features with other molecular profile-specificradiogenomic signatures (Additional file 1: Fig. S3). Next,
we performed RO analysis for the most common features
across all the molecular profiles. Interestingly, we foundthat most molecular profiles could be represented by theenhanced region with volumetric and textural alterations (Fig. 3d and Additional file 2: Table S8). We also

(See figure on next page.)
Fig. 3 Radiomic set enrichment analysis (RSEA) and radiomic ontology (RO) analysis. The workflow of RSEA and RO analysis (a). The RO enrichment
for the top correlated radiomic features with molecular-profiled features showed that global DNA methylation variation was associated with texturaland volumetric abnormal of enhanced gliomas, which could be observed more clearly in T1- weighted images (b). TMB could be mapped
to the volumetric and textural features of ED in the T1WI + Gd images (b). The RSEA indicated that global DNA methylation variation was mostly
associated with textural features in enhanced gliomas of T2 series images with the highest normalized enrichment scores (NES) (c). The TMB
and ITH were found to be reflected by the textural features. TMB was featured in the enhanced region and ED from both T1WI and T2WI images
and ITH was featured in both the enhanced and non-enhanced regions (c). The RO analysis of the most common feature sets included in at least
7 MoRad models showed that all molecular characterization could be reflected by Busyness NGTDM and volumetric abnormalities that weremost apparent in the enhanced region of gliomas (d). The RO analysis for the unique feature sets of specific MoRad models showed the exclusive
feature set enriched for transcriptional subtype, glioma subtype, pan-glioma DNA methylation subtype, pan-glioma RNA expression subtype,
and the intratumor heterogeneity (d). The dotted line represented the cut-off P value of 0.05. ITH, intra-tumor heterogeneity; T1WI, T1-weighted;
T1WI + Gd, gadolinium-enhanced T1-weighted; T2WI, T2-weighted; ET, enhancing region of the tumor core; ED, peritumoral edema; NET,
non-enhancing region of the tumor core; TGM parameter, tumor growth model parameter; NGTDM, neighborhood gray-tone difference matrix;
GLSZM, gray-level size zone matrix; GLN, gray-level nonuniformity

Zhuang et al. BMC Medicine(2024) 22:352

Fig. 3 (See legend on previous page.)

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Zhuang et al. BMC Medicine(2024) 22:352

performed RO analysis for the unique radiogenomic features that were exclusive for each molecular profile. We
found that the DNA methylation subtype had unique features in the non-enhancing region with the volumetricand histogram-based alterations, which is distinct fromthe unique features for mutational, transcriptional, andprotein subtypes (Fig. 3d and Additional file 2: Table S8).
Taken together, we provided the radiological annotation
for global DNA methylation and showed its exclusive
radiological features in the non-enhancing region compared with those of other molecular profiles.
Radiogenomic signatures for global DNA methylation areassociated with clinicopathological and immunologicalfeaturesWe next investigated the association of MoRad-profiled
molecular features with clinicopathological featuresand clinical outcomes. The radiogenomic global methylation was defined as the MoRad-profiled methylationsof Alu and LINE-1, which was found to be successively
decreased as the histological grade increased (all P < 0.05;
Fig. 4a). The radiogenomic global methylation loss wasmore prevalent in GBM compared with astrocytoma,
oligodendroglioma, and oligoastrocytoma (all P < 0.05;
Fig. 4b). Moreover, the radiogenomic global methylation
was significantly lower in unmethylated-MGMT, ATRXmutation, TERT-expressed, and IDH-wild tumors (allP < 0.05; Fig. 4c, e). In the comparison among DNA methylation-based molecular phenotypes, including glioma
subtype and pan-glioma DNA methylation subtype, theradiogenomic global methylation was significantly lower
in the well-documented phenotypes with lower genomewide methylation such as classic-like, mesenchymal-like,
G-CIMP-low, and LGm4-6 subtypes (all P < 0.05; Fig. 4c,e). Interestingly, in the gene expression-based phenotypes, we found radiogenomic methylation loss was moreprevalent in the RPPA-K1, classic-like, and LGr4 subtypes (all P < 0.05; Fig. 4c, e).
Furthermore, we analyzed the prognostic value of
global DNA methylation in gliomas. We found thatradiogenomic global methylation loss was significantly
associated with unfavorable survival outcomes (allP < 0.001; Fig. 4). Finally, we investigated the association

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of immune response with global DNA methylation. As aresult, we found that the radiogenomic global methylation loss was correlated with a specific glioma microenvironment featured by abundant infiltration of lymphocyte,
M2 macrophage, follicular T helper cell, resting memory
CD4 + T cell, and CD8 + T cell (Fig. 4g). Taken together,
we demonstrated the comprehensive associations ofglobal DNA methylation with clinicopathological andmolecular features.

Discussion
In the current study, we mapped the features in radiological images to molecular profiles in glioma. We uncovered
the innate associations between visible images and invisible molecules using matched MRI images and molecular profiles of diffuse gliomas. We found that radiologicalimages could represent molecular features, including
DNA methylation and gene expression. Among them,
the MoRad signature could accurately reflect the global
DNA methylation. Two approaches called RO and RSEA
with tractable pipelines were devised and demonstrated
to yield readable radiological annotation of global DNA
methylation. We found that global DNA methylation
could be reflected by the volumetric and textural features in the enhanced region of gliomas. We also demonstrated the associations of global DNA methylation withclinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and
ATRX, MGMT methylation, multiple methylation-highsubtypes, tumor-infiltrating lymphocytes, and long-term
survival outcomes.
Previous studies have reported that IDH-1 mutation could induce the distinct increase of genome-wide
methylation of gliomas probably by inhibiting DNA
demethylases, which is associated with the G-CIMP andfavorable survival outcome [37, 42]. LGGs are prevalent
to carry IDH mutations (65–90%), while the vast majority of primary GBMs retain wild-type IDH [13, 39]. In
our study, patients with IDH mutations had significantly
higher radiogenomic global methylation, which is consistent with previous studies. Moreover, we performed
the analysis in the IDH-mutation (IDH-MUT) and IDHwild type (IDH-WT) groups separately to clarify that the(See figure on next page.)
Fig. 4 Association of MoRad models with current cancer omics and clinical information. The MoRad-profiled methylation levels with MoRad-Alu
and MoRad-L1 models were both significantly different among the groups defined by histological grades (a) and classification (b). The
MoRad-profiled methylation levels were significantly different among consensus molecular subtypes (c, e). The Kaplan–Meier analysis showedthat both low MoRad-Alu and MoRad-L1 groups had longer survival than high groups (d, f). Both the MoRad-Alu and MoRad-L1 were negatively
correlated with the ratio of M2 macrophage in tumor microenvironment. The MoRad-L1 was also negatively correlated with the ratio of follicular T
helper cell, lymphocyte, resting memory CD4 + T cell, and CD8 + T cell (g). The range between the lower whisker and lower edge of the box covers
the minimum to the lowest 25% of the data values, and the range between the upper whisker and upper edge of the box covers the highest 25%
to the maximum of the data values. *, P < 0.05

Zhuang et al. BMC Medicine(2024) 22:352

Fig. 4 (See legend on previous page.)

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Zhuang et al. BMC Medicine(2024) 22:352

association of DNA methylation with radiological features was stable independent of IDH mutation.
The MoRad achieved an inferior performance in profiling TMB compared with that in profiling global DNA
methylation. We speculated that DNA methylation may
play a superior role in deciding the heterogeneity in radiological images. The RSEA analysis indicated that TMB
was associated with the imaging changes in peritumoral
edema which was in concordance with previous studies
that the peripheral tumor area was important in determining TMB and immunotherapy response [58, 59]. The
NET and ET features were both enriched for the ITH
feature sets in the RSEA analysis, suggesting that the
involvement of tumor heterogeneity may not be limitedin the enhanced region, which can be supported by thefindings in the previous study [60].
Radiogenomic profiling of global DNA methylation is
widely associated with multiple molecular subtypes. In
the pan-glioma DNA methylation subtype, the patientswith higher MoRad-profiled global DNA methylationlevels were mostly enriched in the LGm1-3 groups thatmostly exhibit IDH mutation and increased genomewide methylation [37]. In the methylation-based glioma
subtype, higher MoRad-profiled global DNA methylation
was recognized in the Codel and G-CIMP-high groups,
which were mostly hypermethylated with prolonged survival [37]. In addition, the G-CIMP-low patients had thelowest MoRad-profiled global DNA methylation level
among the three groups. In the pan-glioma RNA expression cluster, the MoRad-profiled global methylationlevel was significantly lower in the LGr4 group that has
been recognized to exclusively harbor IDH-WT tumors.
Taken together, these association analyses further validate the MoRad models and demonstrate its biomedicalsignificance.
DNA methylation loss has been well recognized as asurrogate marker for advanced tumor phenotype andunfavorable outcomes in various types of tumors [61, 62].
The strong association of global DNA methylation withradiomic signatures in gliomas may reveal the critical roleof genomic-wide methylation alteration in shaping radiological image by regulating tumor growth and differentiation. Previous studies have discussed the epigenetic
alteration in glioma and regarded it as an important eventin the development of glioma [63, 64]. By using MoRad in
profiling the epigenetic landscape, we found that patientswith global DNA methylation loss tended to have higher
malignancy grades and unfavorable outcomes, which
was concordant with current knowledge [65]. Furthermore, several radiological and pathological studies haveuncovered the heterogeneity in the enhanced region
with microvascular proliferation and/or pseudo-palisading necrosis in GBMs [66]. Our study validated these

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reports by finding that patients with GBMs have the lowest MoRad-profiled global DNA methylation level amongfour histologic classifications.
Interestingly, we found that the loss of DNA methylation determined by MoRad was associated with abundant
infiltration of follicular T helper cell, lymphocyte, resting
memory CD4 + T cell, and CD8 + T cell. The global DNA
methylation loss might serve as a source of genetic alteration and thus be correlated with an elevated burden of
neoantigen [26], which is a potential explain for the association between T cell infiltration and global DNA methylation loss. In addition, we found that the infiltration ofmacrophages was negatively correlated with DNA methylation. Kong et al.’s study found that global DNA methylation loss in various kinds of cancers, including LGG and
GBM, could reactivate the transposable elements such asLINE and stimulate immune cell infiltration via MHC or
antiviral responses. The infiltrated immune cells, including macrophages, may exert a complicated impact on
glioma. As described in the previous studies, intense
infiltration of M2 macrophages could facilitate glioblastoma growth by secreting abundant pleiotrophin to stimulate glioma stem cells [67, 68], which may account forunfavorable survival outcomes of patients with radiogenomic global methylation loss.
To bridge the gap between invisible molecules and visible images, it is crucial to interpret the high-dimension
radiomic information. Therefore, we developed the “RO”
and “RSEA” tools to perform radiomic feature enrichment analysis, which could translate the algorithm-based
radiomic features into human-readable imaging terms
that closely relate to biomedical settings to help understand the radiogenomic trajectories and better guideclinical practice. The RO and RSEA analysis for MRS9-L1
and MRS9-Alu feature sets suggested that the variation of
global DNA methylation was mainly related to volumetric alteration along with some textural changes, whichwere prone to be observed in enhanced tumor regionof T1WI and T2WI images. Biologically, the enhanced
tumor region implied leakage of contrast agents causedby severely disrupted blood–brain barrier and disordered
vascularization, which were commonly seen in advanced
gliomas [69].
Recent landmark studies have completed comprehensive molecular characterization for glioma to discriminate patients with different clinical outcomes after
specific targeted therapy by stratifying them based onmolecular profiles [33]. In our study, we constructed
MoRad models to profile five molecular phenotypes
based on epigenomics, genomics and proteomics. As
a result, MoRad was able to discriminate the subtypes
with good performance. This may provide a non-invasive imaging marker panel for stratifying patients toZhuang et al. BMC Medicine(2024) 22:352

facilitate cancer treatment and biomarker-based clinical trials. However, there are some limitations in ourstudy. The sample size of our study was limited, andthe generalization performance of the models would
be improved by training our pre-trained MoRad models in a larger dataset with diverse and balanced histological subtypes of gliomas. Although we have validated
the models in the validation cohort, their performance
should be further tested in the external cohorts.

Conclusions
In this study, we performed a comprehensive analysis
of molecular and radiomic profiles and uncovered theinnate associations between radiological images andmolecular features in gliomas. MoRad could particularly profile the genomic methylation level with highaccuracy, indicating the vital role of genomic methylation in shaping tumor features that decide the radiological images. We demonstrated how radiomic analysis
could be applied as a valuable tool to profile the molecular features and put them into the current perspectives of cancer omics and clinical information.
Abbreviations
AUC​	Area under curves
CaPTK	Cancer Imaging Phenomics Toolkit
CNV	Copy number variation
ED	Peritumoral edema
EGFR	Epidermal growth factor receptor
ET	Enhancing region of the tumor core
GBM	Glioblastoma
G-CIMP	Glioma-CpG island methylator phenotype
GLCM	Gray-level co-occurrence matrix
GLRLM	Gray-level run-length matrix
GLSZM	Gray-level size zone matrix
IDH	Isocitrate dehydrogenase
IDH-MUT	IDH-mutation
IDH-WT	IDH-wild type
ITH	Intra-tumor heterogeneity
LASSO	Least absolute shrinkage and selection operator
LGG	Low-grade glioma
LINE-1	Long interspersed nuclear element-1
MGMT	O-6-methylguanine-DNA methyltransferase
mMRI	Multimodal MRI
MRI	Magnetic resonance imaging
MRS9	9 Molecular feature-specific radiomic setsNET	Non-enhancing region of the tumor core
NES	Normalized enrichment scores
NGTDM	Neighborhood gray-tone difference matrix
PCA	Principal component analysis
RO	Radiomic ontology
ROI	Regions of interest
RPPA	Reverse-phase protein array
RSEA	Radiomic set enrichment analysis
T1WI	T1-weighted
T1WI + Gd	Gadolinium-enhanced T1-weighted
T2-FLAIR	T2-fluidattenuated inversion recovery
T2WI	T2-weighted
TERT	Telomerase reverse transcriptase
TGM	Tumor growth model
TMB	Tumor mutation burden

Page 11 of 13

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03573-y.
Additional file 1: Fig. S1. The workflow of this study. Fig. S2. Comprehensive
analysis of MoRad performance. Fig. S3. Upset plot for the overview ofradiomic features in MRS9. Fig. S4. Association of MoRad-profiled TMB and
ITH with molecular profiles and clinical information.
Additional file 2: Table S1. Baseline and molecular characteristics of study
patients. Table S2. Description of molecular data used for analysis. Table S3.
Summary of extracted radiomics features. Table S4. Collected radiomic
sets: terms for RO and RSEA. Table S5. Molecular feature-specific radiomic
sets. Table S6. RO for the MRS9. Table S7. RSEA for the MRS9. Table S8. RO
for the union and unique features of MRS9.
Additional file 3: Supplementary Method 1. Data Collection and Image
Preprocessing. Supplementary Method 2. Feature Selection and Model
Construction. Supplementary Method 3 RO and RSEA.
Acknowledgements
Not applicable.
Authors’ contributions
HY, ZZ, ZW and JL conceived of and established the machine learning
framework for MoRad; HY, ZZ, SM and PX conceived of and established thepipeline for Ontology analysis; HY and YL provided the framework for arraybased global methylation analysis; ZZ and ZL collected the data of study
cohorts and performed the data analyses under the supervision of HY; XW,
YX, ZY, JW, JL, ZW, PX and SM assisted with the data sorting and analyses;
ZZ, ZW, PX, SM, ZW, MH, YL and HY jointly interpreted the results; MH and YL
supervised the whole project; ZZ, JL, XW, SM, ZW and HY wrote the original
manuscript; all co-authors critically revised the manuscript and approved thefinal manuscript.
Authors’ Twitter handles
Twitter handles: @huichuan_yu (Huichuan Yu).
Funding
This work was supported by the Project 5010 of Clinical Medical Research of
Sun Yat-sen University-5010 Cultivation Foundation (No. 2018026, YL), theSixth Affiliated Hospital of Sun Yat-sen University Clinical Research- “1010”
Program (MH; YL), the National Natural Science Foundation of China (No.
81972245, YL; No. 82173067, YL; No. 81902877, HY; No. 82272965, HY), the Natural Science Foundation of Guangdong Province (No. 2022A1515012656, HY;
No.2021A1515010134, MH), the Science and Technology Program of Guangzhou (202201011004, HY), the Scientific Research Project of the Sixth AffiliatedHospital Of Sun Yat-Sen University (2022JBGS07), the Talent Project of the Sixth
Affiliated Hospital of Sun Yat-sen University (No. P20150227202010251, YL), theExcellent Talent Training Project of the Sixth Affiliated Hospital of Sun Yat-sen
University (No. R2021217202512965, YL), the Fundamental Research Funds
for the Central Universities, Sun Yat-sen University (No. 23ykbj007, HY), theProgram of Introducing Talents of Discipline to Universities (YL), the Programof Guangdong Provincial Clinical Research Center for Digestive Diseases(2020B1111170004), and National Key Clinical Discipline (2012).
Availability of data and materials
The “MoRad” package with the codes for the algorithms of each radiomic
model for molecular profiling and RO and RSEA analysis is available at https://​github.​com/​hyu020/​MoRad. The sample codes for molecular characterization
using MoRad models could be found in the supplementary materials.

Declarations
Ethics approval and consent to participate
This is an observational study. The Institutional Review Board of the Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) has confirmed thatno ethical approval is required.

Zhuang et al. BMC Medicine(2024) 22:352

Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Department of General Surgery (Colorectal Surgery), The Sixth AffiliatedHospital, Sun Yat-Sen University, Guangzhou, Guangdong, China. 2 Guangdong
Institute of Gastroenterology, Guangzhou, Guangdong, China. 3 Guangdong
Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth
Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
4
Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen
University, Guangzhou, Guangdong 510655, China. 5 Ministry of Education,
Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-Sen
University), Guangzhou, Guangdong, China. 6 Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Cancer
Epidemiology, Peking University Cancer Institute, Beijing 100142, China.
7
Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University,
Guangzhou, Guangdong 510655, China. 8 Image and Minimally Invasive Intervention Center, Sun Yat-Sen University Cancer Center, State Key Laboratoryof Oncology in South China, and Collaborative Innovation Center for Cancer
Medicine, Guangzhou, China. 9 Department of Neurosurgery and Pituitary
Tumor Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou,
China.
Received: 10 April 2024 Accepted: 20 August 2024

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