Research ArticleIdentification of Molecular Characteristics and New PrognosticTargets for Thymoma by Multiomics Analysis

Dazhong Liu, Pengfei Zhang, Jiaying Zhao, Lei Yang, and Wei Wang

Department of Thoracic Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China

Correspondence should be addressed to Wei Wang; h05732@hrbmu.edu.cn

Received 16 January 2021; Revised 16 March 2021; Accepted 1 April 2021; Published 20 May 2021

Academic Editor: Tao Huang

Copyright © 2021 Dazhong Liu et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background. Thymoma is a heterogeneous tumor originated from thymic epithelial cells. The molecular mechanism of thymomaremains unclear. Methods. The expression profile, methylation, and mutation data of thymoma were obtained from TCGAdatabase. The coexpression network was constructed using the variance of gene expression through WGCNA. Enrichmentanalysis using clusterProfiler R package and overall survival (OS) analysis by Kaplan-Meier method were carried out for theintersection of differential expression genes (DEGs) screened by limma R package and important module genes. PPI networkwas constructed based on STRING database for genes with significant impact on survival. The impact of key genes on theprognosis of thymoma was evaluated by ROC curve and Cox regression model. Finally, the immune cell infiltration,methylation modification, and gene mutation were calculated. Results. We obtained eleven coexpression modules, and three ofthem were higher positively correlated with thymoma. DEGs in these three modules mainly involved in MAPK cascade andPPAR pathway. LIPE, MYH6, ACTG2, KLF4, SULT4A1, and TF were identified as key genes through the PPI network. AUCvalues of LIPE were the highest. Cox regression analysis showed that low expression of LIPE was a prognostic risk factor forthymoma. In addition, there was a high correlation between LIPE and T cells. Importantly, the expression of LIPE was modifiedby methylation. Among all the mutated genes, GTF2I had the highest mutation frequency. Conclusion. These results suggestedthat the molecular mechanism of thymoma may be related to immune inflammation. LIPE may be the key genes affectingprognosis of thymoma. Our findings will help to elucidate the pathogenesis and therapeutic targets of thymoma.

1. Introduction

Thymoma is the most common anterior mediastinal com-partment tumor, originating from the thymic epithelial cellpopulation [1]. The incidence of thymomas is approximately2.5 cases per million people per year, with an age distributionranging from 10 to 80 years [2]. In addition, thymoma isoften associated with autoimmune diseases, especially myas-thenia gravis (MG) [3, 4]. However, the potential molecularoncogenesis of thymoma remains unknown. Generally, whena thymoma is diagnosed, the patient will receive surgicaltreatment. For stages III and IV patients, the 5-year survivalrates were 74% and <25%, respectively [5]. At present, nei-ther surgeon nor physician can predict the prognosis andmetastasis status of thymoma patients through X-ray exami-nation, nor can detailed treatment plan be formulated before

operation [6]. Obviously, the establishment of additional pre-dictors is very beneficial for the identification and treatmentof thymoma.

The pathogenesis of thymoma is various, and the rapiddevelopment of “genome” technology, including whole-genome expression analysis and next-generation sequencing(NGS), provides new means to explore the complexity andmap of genomic alterations in thymoma [7–9]. Epigeneticmodifications, including epigenetic alterations, are a featureof cancer because they play an important role in the processof carcinogenesis [10, 11]. In addition, the thymus provides aspecial microenvironment for the development and selectionof mature T cells. Recent evidence suggests that immuneresponses such as T cells are involved in the developmentof thymoma [12, 13]. However, the understanding of thepathogenesis of thymoma is still limited.

HindawiBioMed Research InternationalVolume 2021, Article ID 5587441, 15 pageshttps://doi.org/10.1155/2021/5587441

In recent years, with the development of molecular biol-ogy, more and more research projects have begun to exploremethods to accurately predict the prognosis of thymoma. Inthis study, we used multiomics datasets from the tumorgenome map (TCGA). The results may be helpful to under-stand the pathogenesis of thymoma and identify LIPE as apotential new therapeutic target through bioinformaticsanalysis. The novelty of this work is that we combined thevariance and difference of gene expression to screen the genesrelated to the prognosis of thymoma through coexpressionnetwork and PPI network. Then, the key genes were furtherscreened by methylation modification.

2. Materials and Methods

2.1. TCGA Dataset Processing and Coexpression Analysis.Thymoma mRNA-seq expression data, methylation data,mutation data, and clinical materials were obtained fromTCGA website (https://portal.gdc.cancer.gov/). The varianceof gene expression was calculated, and the top 1/4 genes wereintercepted for coexpression analysis through weighted genecoexpression network analysis (WGCNA).

2.2. Screening of Differentially Expressed Genes. The differen-tially expressed genes (DEGs) between thymoma and controlwere identified by limma R package. Set the filtering thresh-old P < 0:05.

2.3. Construction of PPI Network. The gene was mapped intothe STRING database (https://string-db.org) to obtain theprotein-protein interaction (PPI) network. A significant PPInetwork was obtained by comprehensive score ≥ 0:7, whichwas demonstrated by the Cytoscape software. The selectionof key genes was based on their association with other pro-teins: genes with higher connectivity were considered to playan important role in the PPI network [14, 15].

2.4. Enrichment Analysis. In order to analyze the biologicalfunctions and signaling pathways of differentially expressedgenes in thymoma-related modules, we performed enrich-ment analysis. Gene Ontology (GO) and the Kyoto Encyclo-paedia of Genes and Genomes (KEGG) were enriched byclusterProfiler R package. P < 0:05 was the threshold usedfor the significant terms. Gene set enrichment analysis (GSEA)was performed with the GSEA software for genes [16, 17].

2.5. Differential Methylation and Mutation Analysis. Thequality of the original probe data obtained from the methyl-ated microarray was checked, including background correc-tion, probe type difference adjustment, and probe exclusion.According to these in sample standardized procedures,DNA methylation was scored as a β value. We used samr Rpackage for differential methylation analysis. For a CpG siteto be considered differentially methylated, the difference inthe median β value in thymoma and normal samples shouldbe at least 0.1 and the P value <0.05. The nonsilent mutation(gene-level) data were analyzed using Maftools R-package.

2.6. Statistical Analysis. Statistical analysis was performedusing the SPSS software, version 23.0 (SPSS Inc., Chicago,

USA). Kaplan-Meier method was used to estimate the overallsurvival (OS). Cox regression model and Cox proportionalhazards regression method were used to identify predictorsof OS [18]. P value <0.05 was considered statistically signifi-cant [19].

3. Results

3.1. Coexpression of Genes in Thymoma. According to thevariance results of thymoma gene expression, the top 1/4genes with larger variance were selected for coexpressionanalysis. A coexpression network consisting of 5758 geneswas obtained. Taken 0.9 as the threshold of correlation coef-ficient, select the soft threshold as 7 (Figure 1(a)). A total of11 coexpression modules were identified through WGCNAanalysis (Figure 1(b)). In addition, we calculated the correla-tion between module genes and thymoma. We found thatMEgreen, MEblue, and MEturquoise had the highest correla-tion with tumor samples (Figure 1(c)). Furthermore, 2559differentially expressed genes (DEGs) were screened betweenthymoma and control group (P < 0:05) (Figure 1(d)).

3.2. Enrichment of Differentially Expressed Genes in Modules.Further, 913 intersection genes between DEGs and the threemodules with the highest correlation were selected as theimportant genes for subsequent study and enrichment analy-sis. The results of GO enrichment showed that these geneswere involved in 1234 biological processes (BP), 151 cell com-ponents (CC), and 214 molecular functions (MF). It mainlyincluded cell growth, positive regulation of MAPK cascade,ERK1 and ERK2 cascade, response to transforming growthfactor beta, and Wnt signaling pathway (Figure 2(a)). KEGGenrichment results showed a total of 40 terms, mainly involv-ing cell adhesion molecules, ECM-receptor interaction, focaladhesion, and PPAR signaling pathway (Figure 2(b)). In addi-tion, the GSEA results showed some of the same results asKEGG, mainly including cGMP-PKG signaling pathway, cho-lesterol metadata, and PPAR signaling pathway (Figure 2(c)).These same signaling pathways cover a large number of differ-entially expressed genes (Figure 2(d)).

3.3. Identification of Key Prognostic Genes. The overall survival(OS) analysis of selected important genes identified 88 geneswith significant impact on prognosis (P < 0:05). Mapping thesegenes into the STRING database yielded a PPI network of 45genes, which was displayed by Cytoscape (Figure 3(a)). Thetop six genes with the highest connectivity were analyzed indepth as key genes. Among them, the expression of MYH6and SULT4A1 in osteosarcoma was higher than that in controlgroup, while the expression of LIPE, ACTG2, KLF4, and TFwas decreased (Figure 3(b)). In addition, high expression ofLIPE and MYH6 could improve the OS of patients, andACTG2, KLF4, SULT4A1, and TF decreased the OS of patients(Figure 3(c)). ROC curves showed that the AUC values of thesesix genes were all greater than 0.6, especially those of LIPE, andKLF4 and TF were greater than 0.9 (Figure 3(d)).

3.4. The Effect of Key Genes on Prognosis. Multivariate sur-vival analysis was performed by Cox regression model, andnomogram was generated by Cox regression coefficients.

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The nomogram showed that low expression of LIPE was a riskfactor for predicting the overall survival time of thymoma at 5and 8 years (Figure 4(a)). Calibration plots showed that thenomograms performed well compared with an ideal model(Figure 4(b)). In addition, Cox risk ratio model suggested thatthe survival rate of the high-risk population for thymoma waspoor (Figure 4(c)). Among them, low expression of LIPE andMYH6 and high expression of ACTG2, KLF4, SULT4A1, andTF were important risk factors.

3.5. Changes of Immune Microenvironment in Thymoma. Bycomparing the immune cell infiltration between thymoma

and control, we found that dendritic cells (DC) decreasedmost significantly in thymoma (Figure 5(a)). These differen-tially infiltrated immune cells were clustered into four groups(Figure 5(b)). The strongest correlation was found between Tcells and CD8 T cells or Th17 cells in thymoma tissues(Figure 5(c)). In addition, we analyzed the correlation betweenkey genes and immune cells (Figure 5(d)). LIPE had the stron-gest positive correlation with T cells and Th2 cells, MYH6 hadthe strongest positive correlation with NK cells, TF, KLF4, andaDC had the strongest positive correlation, SULT4A1 andpDC had the strongest positive correlation, and ACTG2 andneutrophils had the strongest positive correlation.

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Figure 1: Coexpression analysis of gene expression in thymoma. (a) Determination of soft threshold power in coexpression analysis. The leftimage shows the scale-free fit index (y-axis) as a function of the soft-thresholding power (x-axis). The right image shows the averageconnectivity (degree, y-axis) as a function of the soft-thresholding power (x-axis). (b) Module cluster tree of thymoma genes with largevariance. Branches with different colors correspond to different modules. (c) The correlation between module and clinical trait. Each rowcorresponds to a module, and each column corresponds to a feature. Each cell contains the corresponding correlation and P value. (d)The differentially expressed genes between thymoma and control. Red nodes were significantly upregulated genes, and green nodes weresignificantly downregulated genes.

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GO:0016049

GO

:0043410

GO:0070371

GO:00

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GO:0016055

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IDGO:0016049

Description

GO:0043410GO:0070371GO:0071559GO:0016055

Cell growthPositive regulation of MAPK cascade

ERK1 and ERK2 cascadeResponse to transforming growth factor beta

Wnt signaling pathway

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ECM−receptor interactionPPAR signaling pathway

Focal adhesionCell adhesion molecules

CD36COL2A1

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LAMA2LAMA4

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CategoryCell adhesion moleculesECM−receptor interaction

Focal adhesionPPAR signaling pathway

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3.6. Regulatory Factors Associated with Thymoma. By com-paring gene methylation modifications between thymomaand control, we obtained 943 differential methylation sites(Figure 6(a)). Among them, the methylation sites of chr1

accounted for the most, accounting for 13% (Figure 6(b)).Fourteen genes were identified as methylation factorsbecause they had opposite levels of methylation and expres-sion (Figure 6(c)). Among them, LIPE was significantly

p value0.0044

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KEGG_CGMP−PKG SIGNALING PATHWAY

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Figure 2: Enrichment analysis of thymoma-related module genes. (a) Important genes were involved in biological processes. Red nodes areupregulated genes, and blue nodes are downregulated genes. (b) Important genes were involved in KEGG pathway. Different line colorsrepresent different signaling pathways which genes involved in. (c) KEGG pathway in GSEA for important genes. These pathways weresignificantly upregulated in thymoma. (d) The DEGs involved in the same KEGG pathway in the results of enrichment and GSEA.Different colors represent genes involved in different signaling pathways.

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DACT1

MST1WNT4

ABCC9 KLF4 ESPN

APOC1POU5F1BPNCK

TF

SULT4A1 SDSL

CFDLIPE

GPD1 DGAT2

AP3B2

OLFM1

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ADCYAP1R1

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MEGF11

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ACTG2SPAG6 NUDT14FGF17

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ACTG2 KLF4 LIPE MYH6 SULT4A1 TF

Tumor Normal Tumor Normal Tumor Normal Tumor Normal Tumor Normal Tumor Normal

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Figure 3: Continued.

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associated with OS in thymoma. In addition, GTF2I, the genewith the highest frequency of mutations in thymoma, wasmissense mutation in all samples (Figure 6(d)).

4. Discussion

Like other malignant tumors, the growth and proliferation ofthymoma have many biological factors. However, the exact

molecular basis of thymoma occurrence remains unclear. Inthis study, the possible molecular mechanism and regulatoryfactors of thymoma were explored through multiomics.

Early studies have shown that changes in certain genesseem to be associated with the development of thymic tumors[20, 21]. Our data suggest that there is a large difference ingene expression between thymoma and control. By identify-ing coexpression network constructed by genes with larger

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LIPE=highLIPE=low

Survival analysis of MYH6 Survival analysis of SULT4A1 Survival analysis of KLF4

Survival analysis of LIPE Survival analysis of TF

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MYH6, AUC = 0.64SULT4A1, AUC = 0.77TF, AUC = 0.9

(d)

Figure 3: Identification of key genes affecting overall survival of thymoma. (a) Cytoscape software shows the PPI network of important genesbased on the STRING database. (b) The expression of six genes with the highest connectivity in the PPI network. ∗∗∗P < 0:001. (c) The effectof six genes with the highest connectivity in the PPI network on the overall survival of thymoma (Kaplan-Meier plot). Red and green curvesare for high expression and low expression, respectively. (d) ROC curve of key genes. Different color curves represent different genes.

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Points0 10 20 30 40 50 60 70 80 90 100

ACTG2 0 800

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MYH6 7000 6000 5000 4000 3000 2000 1000 0

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Figure 4: Continued.

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variance, module genes with high correlation with thymomawere obtained. Intersection with differentially expressedgenes yielded 913 genes possibly associated with thymomadevelopment.

GO functional enrichment analysis is very powerful andwidely used to identify biological functions of gene expres-sion data [22]. In the GO functional enrichment results,MAPK cascade, ERK1 and ERK2 cascade, response to trans-forming growth factor beta (TGF-β), and Wnt signalingpathway were mainly involved. Mitogen-activated proteinkinase (MAPK) is a complex and interrelated signal cascade,which is closely related to the occurrence and progress oftumor, and plays an important regulatory role in cell prolifer-ation, differentiation, migration, and survival [23, 24]. ERK1/2 is also an effective target for anticancer [25]. Studies haveshown that MAPK signal and ERK 1/2 were significantlyactivated in thymoma [26]. TGF-β inhibited apoptosis andhad reduced expression of IFN-γ in effector cell, a key medi-ator of antitumor immunity [27]. Recently, it had been

proved that Wnt pathway was activated in human thymoma,which may be involved in the tumorigenesis [28]. These find-ings further confirmed that a variety of inflammatory pro-cesses and cytokines were involved in the pathogenesis ofthymoma.

In addition, in KEGG enrichment results, ECM also regu-lated intercellular communication, cell connectivity plasticity,and cell adhesion molecules interacting with various cytoki-nes/chemokines or growth factors [29, 30]. There were 34%of the genes in the ECM-receptor interaction pathway mutatedrepeatedly in cancer [31]. Focal adhesion kinase (FAK) ishighly expressed in thymic epithelial tumors and can be usedas an independent prognostic biomarker [32]. PPARγ overex-pression more than doubled insulin-stimulated thymoma viralprotooncogene phosphorylation during low lipid availability[33]. GSEA results had the same terms as KEGG enrichmentresults, in which cholesterol accumulation was a common fea-ture of cancer tissues. Recent evidence showed that cholesterolplayed a crucial role in the progress of cancer including breast,

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Figure 4: The expression of key genes affects the prognosis of patients with thymoma. (a) Nomogram for predicting overall survival inpatients with thymoma. (b) Plots depict the calibration of each model in terms of agreement between predicted and observed 5-year and8-year outcomes. (c) Risk factor correlation diagram. The green dot was the survival thymoma patient, and the red dot was the deadthymoma patient. The dotted line was the median risk score, the left side was the low-risk group, and the right side was the high-risk group.

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Figure 5: Continued.

10 BioMed Research International

prostate, and colorectal cancer [34]. Activation of cGMP PKGsignal may promote the growth of cervical cancer cells [35].

By screening the DEGs that had a significant impact on theprognosis of thymoma, we identified LIPE, MYH6, ACTG2,KLF4, SULT4A1, and TF as key genes. LIPEwas also predictedas a new prognostic marker of thymoma in other studies [36].Consistent with our analysis, MYH6 was differentiallyexpressed in thymoma [37]. We found that MYH6 may be apotential target for thymoma. ACTG2, KLF4, SULT4A1, andTF were all involved in the occurrence or development of can-cer, but their biological significance in thymoma was not clear[38–41]. This needs further study and discussion of the follow-up experiments.

From the perspective of immunemicroenvironment, innateimmune cells such as DC and adaptive immune cells such as Tcells played an important role in thymoma [13, 42, 43]. Therewas a strong correlation between LIPE and immune cells, sug-gesting that LIPE may participate in the prognosis of thymomaby regulating the immunity system. Interestingly, we found thatLIPE was also a gene regulated by methylation. DNA and RNAmethylation genes are commonly studied as biomarkers [44,45], which also seems to be a way for LIPE to participate inthe development of thymoma [46]. On the other hand, geneticdifference in thymoma was also an effective way to screenpotential therapeutic targets [9]. GTF2I mutation occurs at highfrequency in thymoma and is a marker of good prognosis [47].

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aDCB cellsCD8 T cellsCytotoxic cellsDCEosinophilsiDCMacrophagesMast cellsNeutrophilsNK CD56bright cellsNK CD56dim cellsNK cellspDCT cellsT helper cellsTcmTemTFHTgdTh1 cellsTh17 cellsTh2 cellsTReg −1

−0.5

0

0.5

1

(c)

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ACTG2

aDC

B ce

llsCD

8 T

cells

Cyto

toxi

c cel

lsD

CEo

sinop

hils

iDC

Mac

roph

ages

Mas

t cel

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ophi

lsN

K CD

56br

ight

cells

NK

CD56

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cells

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cells

pDC

T ce

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help

er ce

llsTc

mTe

mTF

HTg

dTh

1 ce

llsTh

17 ce

llsTh

2 ce

llsTR

egKLF4

LIPE

MYH6

SULT4A1

TF

Immune_cells

−0.50

−0.25

0.00

0.25

0.50

⁎ P < 0.05⁎⁎ P < 0.01

CorrelationHub genes cor with immune cells in THYM

(d)

Figure 5: Immune cell infiltration in thymoma. (a) The difference of immune cell infiltration between thymoma and control. The blue linerepresents a significant difference. (b) Clustering of immunocytes with differential infiltration. The red line represents the positive correlationbetween immune cells, and the blue line represents the negative correlation. (c) Correlation between immune cells in thymoma. Redrepresents positive correlation between immune cells, and blue line represents negative correlation. The size of the node represents thesize of the correlation coefficient. (d) Correlation between key genes and immune cells. Red represents positive correlation betweenimmune cells, and blue line represents negative correlation. ∗P < 0:05 and ∗∗P < 0:01.

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Hypermethylated DMPs Hypomethylated DMPs

cg25270788

cg27544288

cg03634592 cg02235894

cg06405765

cg20384325cg23138608cg18262591cg07846737

cg04510420

0

10

15

20

30

−0.25 0.00 0.25 0.50Methylation difference (beta−value)

Feature1st Exon3’UTR5’UTRBodyIsland

OpenseaShelfShoreTSS1500TSS200

51.22 % 48.78 %

6.2%

3.2%

9.3%

39.2%

3.4%

7.3%

1.4%

1.2%

12.1%

16.8%

1stExon

3’UTR

5’UTR

Body

Island

Opensea

Shelf

Shore

TSS1500

TSS200

−lo

g10

(P v

alue

)

(a)

chr19: 7%

chr4: 5%

chr7: 6%

chr11: 5%chr16: 3%chr15: 3%

chr8: 5%

chr12: 7%

chr14: 3%chr18: 2%

chr10: 5%

chr1: 13%

chr22: 1%

chr6: 7%chr2: 7% chr3: 5%

chr20: 2%chr21: 1%

chr17: 6%

chr5: 5%

chr9: 2%

chr13: 3%

(b)

Figure 6: Continued.

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TGFBR2

STX11

LIPE

LAMA4

KRT18

KLC3

HRAS

GPIHBP1

FZD4

FERMT2

ERBB4

EPHA1

CD36

ART4

Exp Methy

Sym

bol

TypeExpMethy

logFC123

45

StatusUPDOWN

Heatmap with Hub_Genes Exp in TCGAGroup

GroupTumorNormal

−2

0

2

(c)

0

688

NA

NA

SMARCA4

RYR2

NF1

DNAH3

DNAH17

CYLD

CUBN

CSMD2

CLIP2

BDP1

BCOR

TP53

OSGIN1

HDAC4

DMD

AL022578.1

MUC16

TTN

HRAS

GTF2I

2%

2%

2%

2%

2%

2%

2%

2%

2%

2%

2%

3%

3%

3%

3%

3%

7%

7%

8%

50%

0 62

Masaoka_stageVital_statusRaceGender

Missense_MutationSplice_SiteNonsense_Mutation

In_Frame_InsFrame_Shift_DelMulti_Hit

GenderMaleFemaleNA

RaceAsianWhiteNA

Not reportedBlack or African American

Vital_statusAliveDeadNA

Masaoka_stageStage IStage IIIStage IIb

Stage IIaNAStage IVb

Altered in 82 (66.67%) of 123 samples.

(d)

Figure 6: Methylation and mutation in thymoma. (a) Differential methylation sites between thymoma and control. (b) The proportion ofmethylation sites in different chromosomes. (c) The expression and methylation of methylation factors. Red node represents upregulation,and blue node represents downregulation. Yellow represents positive gene expression, while blue represents negative gene expression. (d)The top 20 genes with the highest mutation frequency in thymoma. Each cell represents a sample.

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However, this study also had some limitations. Firstly,conclusions may be limited by small samples, especially con-trol samples. Secondly, the results of this study had not beenverified by molecular experiments, so the interpretation ofthe results may be cautious. In this study, the possible molec-ular changes and pathogenesis of thymoma were investigatedusing the multiomics data from TCGA database. This studyidentified key genes related to the prognosis of thymoma,including LIPE, MYH6, ACTG2, KLF4, SULT4A1, and TF.The expression of these genes in thymoma may be a promis-ing biomarker, which needs further study.

5. Conclusion

In this study, potential targets associated with thymoma wereidentified by combining thymoma-related gene expression,methylation, and mutation data. Using a variety of bioinfor-matics analysis methods, we found that important genesrelated to thymoma were associated with immune inflamma-tory response. LIPE, MYH6, ACTG2, KLF4, SULT4A1, andTF were the key genes affecting the prognosis of thymoma.Among them, LIPE was also modified by methylation.

Data Availability

Thymoma mRNA-seq expression data, methylation data,mutation data, and clinical materials were obtained fromTCGA website (https://portal.gdc.cancer.gov/).

Conflicts of Interest

The authors declare that the research was conducted in theabsence of any commercial or financial relationships thatcould be construed as a potential conflict of interest.

Authors’ Contributions

Dazhong Liu and Pengfei Zhang contributed equally to thisarticle as co-first authors.

Acknowledgments

This study was supported by the Project of HeilongjiangHealth and Family Planning Commission (2016-066) andScience and Technology Innovation Talent Project of HarbinScience and Technology Bureau (2016RAQXJ149).

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