Comprehensive Analysis of Mrna Expression Profiles in Head and Neck Cancer by Using Robust Rank Aggregation and Weighted Gene Coexpression Network Analysis

Hindawi BioMed Research International Volume 2020, Article ID 4908427, 21 pages https://doi.org/10.1155/2020/4908427

Research Article Comprehensive Analysis of mRNA Expression Profiles in Head and Neck Cancer by Using Robust Rank Aggregation and Weighted Gene Coexpression Network Analysis

Zaizai Cao , Yinjie Ao , Yu Guo , and Shuihong Zhou

Department of Otolaryngology, The First Aﬃliated Hospital, College of Medicine, Zhejiang University, Zhejiang Province 310003, China

Correspondence should be addressed to Shuihong Zhou; [email protected]

Received 3 June 2020; Revised 2 November 2020; Accepted 23 November 2020; Published 10 December 2020

Academic Editor: Hassan Dariushnejad

Copyright © 2020 Zaizai Cao 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. Head and neck squamous cell cancer (HNSCC) is the sixth most common cancer in the world; its pathogenic mechanism remains to be further clarified. Methods. Robust rank aggregation (RRA) analysis was utilized to identify the metasignature dysregulated genes, which were then used for potential drug prediction. Weighted gene coexpression network analysis (WGCNA) was performed on all metasignature genes to find hub genes. DNA methylation analysis, GSEA, functional annotation, and immunocyte infiltration analysis were then performed on hub genes to investigate their potential role in HNSCC. Result. A total of 862 metasignature genes were identified, and 6 potential drugs were selected based on these genes. Based on the result of WGCNA, six hub genes (ITM2A, GALNTL1, FAM107A, MFAP4, PGM5, and OGN) were selected (GS > 0:1, MM > 0:75,GSp value < 0.05, and MM p value < 0.05). All six genes were downregulated in tumor tissue (FDR < 0:01) and were related to the clinical stage and prognosis of HNSCC in different degrees. Methylation analysis showed that the dysregulation of ITM2A, GALNTL1, FAM107A, and MFAP4 may be caused by hypermethylation. Moreover, the expression level of all 6 hub genes was positively associated with immune cell infiltration, and the result of GSEA showed that all hub genes may be involved in the process of immunoregulation. Conclusion. All identified hub genes could be potential biomarkers for HNSCC and provide a new insight into the diagnosis and treatment of head and neck tumors.

1. Introduction different screening criteria have a great impact on the research results. To solve this problem and obtain stable bio- Head and neck squamous cell carcinoma (HNSC) is the sixth markers, researchers proposed a novel rank aggregation most common cancer in the world [1]. Worldwide, more method: robust rank aggregation (RRA) [5], which has been than 300000 patients die of HNSC every year [2]. Although implemented as an R package (RobustRankAggreg) [5], to many treatments for HNSC such as surgery, chemotherapy, identify the overlapping genes among ranked gene lists [6], and radiotherapy have obtained some success, the 5-year sur- thus making the result more reliable. vival rate is still only 40-50% [3]. The chances of survival for WGCNA [7] is an effective method to find the clusters of patients with HNSCC depend largely on the initial stage of highly correlated genes and identify the hub genes of each cancer. Therefore, early detection and accurate diagnosis cluster [7]. This method has been widely applied in various are crucial for patients with HNSCC to receive treatment. biological contexts. In our study, a total of 24 independent In the past twenty years, with the application of microar- gene datasets were included in RRA analysis to identify ray and next-generation sequencing technologies, a great robust DEGs. We used these DEGs to predict the potential number of novel diagnostic or therapeutic biomarkers have small molecular drugs. The coexpression network was then been identified in HNSCC [4]. However, small samples in established by WGCNA to identify hub genes in these robust independent research, different platform technologies, and DEGs. The role of all hub genes in HNSCC was then 2 BioMed Research International

24 HNSCC GEO datasets

Diﬀerential analysis (p value < 0.05) Diﬀerentially expressed genes in each GEO datasets

RRA (adj p value < 0.05)

Screen related small GO and KEGG Metasignature DEGs molecule drugs (CMap) enrichment analysis

WGCNA in GSE65858

Key module : blue

GS and MM ﬁltering

Explore the correlation Function annotation by 6 hub genes between hub genes and GSEA immunocyte inﬁltration

Explore the correlation (i) Validation of the diagnostic role of hub genes. between methylation and (TCGA and GSE30784) expression of hub genes (ii) Validation of the correlation between hub genes and clinical characteristic (TCGA) (iii) III Explore the expression of hub genes in dysplasia tissue (GSE30784) 4 Explore the prognostic role of hub genes in HNSCC (TCGA)

Figure 1: Simple flow chart of the entire study. validated by other independent databases. Furthermore, we MAL (FDR = 8:50e − 35), CRISP3 (FDR = 1:60e − 34), also utilized multiple online tools such as DiseaseMeth [8], CRNN (FDR = 4:32e − 33), and KRT4 (FDR = 1:24e − 30) MEXPRESS [9], and MethSurv [10] to evaluate the methyla- were the most significant downregulated genes. The chromo- tion level of hub genes. TIMER was used to assess the associ- somal locations and expression patterns of the top 100 DEGs ation between immune infiltration and hub genes. GSEA [11] are visualized in Figure 2. Chromosome 1 contains most analysis was applied to explore the biological functions of metasignature DEGs while X and Y chromosome contains these hub genes. To the best of our knowledge, this is the first no DEGs. It is clear that almost all displayed genes have the time to utilize RRA and WGCNA simultaneously for screen- same expression pattern in most of the independent studies, ing biomarkers of HNSCC. which indicates the reliability of the RRA analysis result.

2. Result 2.2. Functional Enrichment Analysis. We select the top 300 DEGs to perform GO and KEGG enrichment analyses. 2.1. Metasignature DEGs Identified by RRA Analysis. The Among the KEGG pathway database, we can find that these workflow of our study is shown in Figure 1, 24 independent DEGs were enriched in multiple cancer-related pathways like studies were used in RRA analysis, and a total of 466 upreg- focal adhesion, PI3K-Akt signal pathway, pathway in cancer, ulated genes and 396 downregulated genes were identified. small-cell lung cancer, transcriptional misregulation in can- The top 5 upregulated genes in tumor tissue were MMP1 cer, and chemical carcinogenesis (Figure 3(a)). Furthermore, (FDR = 4:77e − 53), MMP10 (FDR = 8:25e − 40), PTHLH in all terms of KEGG and GO, we found that these meta- (FDR = 1:48e − 38), MMP3 (FDR = 5:38e − 38), and SPP1 signature genes mostly involved in pathways associated with (FDR = 2:79e − 37) while TMPRSS11B (FDR = 1:53e − 36), the construction of ECM such as ECM receptor interaction, BioMed Research International 3

ISG15 PDPN

MFAP2

IFI6 CLCA4 TGFBR3 SLC16A1

CRNN S100A7A DPT FMO2 LAMC2 KIF14 IL24 Heatmap APOL1 LAMB3 MMP11 8.49 RSAD2 FAM3B MAL FAP HOXD10 COL5A2 TPX2 MYO1B TGM3 C2orf54 CEACAM1 CEACAM5 CEACAM7

BST2 FUT3

KAT2B GPD1L LAMA3 FAM4107A FAM3D MGLL ABCA8 RBP1 HLF COL1A1 KRT16 KRT13 MFAP4

TMPRSS11B CDH3 IL8 GDPD3 CXCL10 PPL CXCL11 TNFRSF12A SPP1 ADH7 TDO2 0 HPGD

RHCG

TRIP13 TGFBI SPINK5 PAX9 SPINK7

COL4A2 PTK7 COL4A1 SCEL PLA2G7 CRISP3 POSTIN SH3BGRL2 DCBLD1

WDR66 KRT78 KRT4 FSCN1 ENDOU DFNA5 PTHLH INHBA FOXM1 CYP3A5 SERPINE1 ATP6V0A4 MMP13 –8.49 MMP12 MMP3

MMP1 LOXL2

EPHX2 SNAI2 SLURP1

SERPINH1 MAMDC2 TNC

IFT3

PLAU

PPP1R3C

Figure 2: Circular visualization of expression patterns and chromosomal positions of top 100 DEGs. Red indicates gene upregulation, blue indicates downregulation, and white indicates genes that do not exist in a given dataset. extracellular matrix organization, collagen catabolic process, expression data of metasignature DEGs. The correlation collagen binding, collagen trimer, extracellular region, and coefficients between each module and each clinical trait were extracellular exosome (Figure 3). calculated, and it is clear that only the blue module and gray module were significantly associated with T grade of HNSCC 2.3. Screen the Candidate Small Molecule Drugs for HNSCC. (Figure 4(e)). Because genes in the gray module are not sig- According to our screen criteria, 6 small molecule drugs (repa- nificantly coexpressed with each other, we only chose the glinide ES = −0:848, thiostrepton ES = −0:863,levamisoleES blue module as a key module. A total of 102 genes were = −0:75, cortisone ES = −0:866, zimeldine ES = −0:784,and included in blue modules, and the result of enrichment anal- cyproterone ES = −0:742) were identified (Table 1). Their 2D ysis for these genes showed that the most significant GO and structures are visualized in Supplementary Fig 1. These poten- KEGG terms were related to cell metabolism, chemokine tial drugs can to some extent reverse the robust dysregulated activity, and transmembrane transport (Supplementary Fig genes in HNSCC, thus providing suggestions for us to develop 2). According to the value of GS and MM (GS > 0:1, MM > targeted drugs. 0:75,GSp value < 0.05, and MM p value < 0.05), 6 genes (ITM2A, GALNTL1, OGN, FAM107A, MFAP4, and PGM5), 2.4. Identification of Hub Genes in HNSCC Patients. To iden- which were also significantly correlated with each other tify the hub genes, we performed WGCNA on the GSE65858, (Figure 4(f)), were selected from the blue module. which included 270 samples from HNSCC patients with complete clinical data. Six different gene modules were iden- 2.5. Validate the Role of Hub Genes in HNSCC. To further tified (Figure 4) according to the result of cluster analysis on validate the diagnostic role of hub genes, we compare the 4 BioMed Research International

⁎ –1 log10 (p value) 10.0 Tyrosine metabolism Transcriptional misregulation in cancer

Toll-like receptor signaling pathway Small-cell lung cancer

Rheumatoid arthritis Regulation of actin cytoskeleton

Protein digestion and absorption 7.5 P13K-Akt signaling pathway

Pathways in cancer

Metabolism of xenobiotics by cytochrome P450

KEGG Focal adhesion

ECM-receptor interaction 5.0 Drug metabolism-cytochrome P450

Cytokine-cytokine receptor interaction

Complement and coagulation cascades Chemokine signaling pathway

Chemical carcinogenesis

Arrhythmogenic right ventricular cardiomyopathy (ARVC) Arachidonic acid metabolism Amoebiasis 2.5 246810 Fold enrichment

Count 12 4 8 16 (a)

Figure 3: Continued. BioMed Research International 5

⁎ –1 log10 (p value)

Type I interferon signaling pathway Response to virus

Response to drug Proteolysis 15 Positive regulation of cell proliferation Positive regulation of cell migration

Oxidation-reduction process Ossiﬁcation

Negative regulation of cell proliferation

Inﬂammatory response 10 Immune response

Biology process Extracellular matrix organization Extracellular matrix disassembly

Epidermis development

Defense response to virus

Collagen catabolic process 5 Cell proliferation Cell adhesion

Cell-cell signaling Angiogenesis

4 8 12 Fold enrichment

Count 10 25 15 30 20 (b)

Figure 3: Continued. 6 BioMed Research International

–1 ⁎ log10 (p value)

Structural molecule activity Structural constituent of cytoskeleton

Serine-type endopeptidase inhibitor activity 5 Serine-type endopeptidase activity

Receptor binding Protein homodimerization activity

Oxidoreductase activity Metalloendopeptidase activity 4

Iron ion binding

Integrin binding Identical protein binding

Heparin binding Molecular function Heme binding 3

Growth factor activity

Extracellular matrix structural constituent Enzyme binding

Cytokine activity 2 Collagen binding

Chemokine activity Calcium ion binding

2.5 5.0 7.5 Fold enrichment

Count 10 20 15 25 (c)

Figure 3: Continued. BioMed Research International 7

–1 ⁎ log10 (p value)

Proteinaceous extracellular matrix

Perinuclear region of cytoplasm Organelle membrane 20 Midbody

Integral component of plasma membrane Golgi apparatus

Focal adhesion Extracellular space 15

Extracellular region Extracellular matrix Extracellular exosome

Cell component External side of plasma membrane Endoplasmic reticulum membrane 10

Endoplasmic reticulum lumen Endoplasmic reticulum Collagen trimer Cell surface 5 Cell-cell junction

Basement membrane Apical plasma membrane

24 6 Fold enrichment

Count 25 50 75 (d)

Figure 3: GO and KEGG analyses of the top 300 DEGs. (a) The correlation between genes and KEGG pathway. (b) The correlation between genes and GO terms of biological process. (c) The correlation between genes and GO terms of molecular function. (d) The correlation between genes and GO terms of cellular component.

Table 1: Six candidate small molecule drugs. hub genes and tumor T grade, we also used the TCGA database to validate the role of hub genes in TN grade of HNSCC. Mean value Number of Enrichment In Figure 5(a), it is clear that all 6 hub genes were remarkably Drug name of correlation p value ff experiments score di erent between normal and tumor tissues, and the ROC coefficient curve indicates a high diagnostic value of all hub genes (Sup- Repaglinide -0.685 4 -0.848 0.00097 plementary Fig 4A). In Figure 5(b), we can see that five hub Thiostrepton -0.659 4 -0.863 0.00064 genes (ITM2A, GALNTL1, FAM107A, MFAP4, and PGM5) Levamisole -0.619 4 -0.75 0.00784 were upregulated in the T1-T2 stage and downregulated in Cortisone -0.606 3 -0.866 0.00479 the T3-T4 stage, which is consistent with the result in WGCNA. However, there is no correlation between hub Zimeldine -0.575 5 -0.784 0.00086 genes and tumor N stage (Supplementary Fig 3). The result Cyproterone -0.552 4 -0.742 0.00875 above indicated that these hub genes may affect the growth rather than metastasis of the tumor. expression level of these genes between normal tissue and 2.6. Explore the Role of Hub Genes in Malignant tumor tissue in the TCGA database. Considering the result Transformation and Prognosis. GSE30748 provides the gene of WGCNA which revealed a negative association between expression data of oral dysplasia tissue. Compared with 8 BioMed Research International

Sample dendrogram and trait heatmap 35

25 20

Height 15

Gender

t-category

n-category

Distant-metastasis

(a) Scale independence Mean connectivity

10 11 1 9 12 7 8 6 150

2 0.8

4 0.6 100

0.4

3 Mean connectivity 50 2 0.2 Scale free topology model ﬁt, signed R 2 3 4 0.0 5 1 0 6 789101112 12108642 12108642 So threshold (power) So threshold (power) (b)

Figure 4: Continued. BioMed Research International 9

Cluster dendrogram 1.0

0.9

0.8 Height

0.7

0.6

Dynamic tree cut

Merged dynamic

1.2

0.8 Height

0.4 MEblack MEblue MEbrown

0.0 MEturquoise MEyellow MEgrey MEpink MEred MEgreen (d)

Figure 4: Continued. 10 BioMed Research International

Module-trait relationships

0.042 –0.14 0.048 –0.013 MEblue (0.5) (0.2) (0.4) (0.8)

–0.0073 –0.095 –0.021 –0.019 MEturquoise 0.5 (0.9) (0.1) (0.7) (0.8)

–0.077 0.094 –0.085 –0.067 MEblack (0.2) (0.4) (0.2) (0.3)

–0.049 0.03 0.067 –0.0095 MEbrown (0.4) (0.6) (0.3) (0.9)

0.027 0.069 –0.11 0.0097 MEyellow (0.7) (0.3) (0.7) (0.9) –0.5

MEgrey 0.035 0.14 –0.066 –0.0094 (0.6) (0.03) (0.3) (0.9)

–1 Gender t_category n_category distant_metastasis (e)

Figure 4: Continued. BioMed Research International 11

FAM107A MFAP4 ITM2A OGN PGM5 GALNTL1 1

FAM107A 0.8

0.6 MFAP4 0.6 0.4

0.2 ITM2A 0.75 0.6 0

OGN 0.67 0.74 0.74 0.2

0.4 PGM5 0.55 0.57 0.66 0.7 0.6

0.8 GALNTL1 0.51 0.55 0.63 0.66 0.65 –1 (f)

Figure 4: Identification of hub genes in HNSCC by using WGCNA. (a) Clustering dendrograms of genes. (b) Analysis of the scale-free fit index and the mean connectivity for various soft-thresholding powers. (c) Dendrogram of all DEGs clustered based on a dissimilarity measure. (d) Clustering of modules. (e) Heatmap of the correlation between modules and clinical traits. (f) Correlation matrix of each hub genes. tumor tissue, we found that all hub genes were significantly 2.8. Immune Infiltration and Hub Genes. The tumor micro- higher expressed in dysplasia tissue (Figure 6(b)); except for environment comprises multiple kinds of cells such as epi- FAM107A with AUC = 0:66, the other 5 hub genes have thelial cells, fibroblasts, and immune cells. A great number AUC > 0:7 (Supplementary Fig 4B). We also explore the of studies have revealed the significant role of immune cells prognostic role of all these hub genes by using the GEPIA in various cancers. Therefore, we used TIMER to investigate website [12]. The KM curve showed that the lower expression the association between hub genes and different kinds of of four hub genes (ITM2A HR = 0:72, GALNTL1 HR = 0:74, cells. It is interesting that we found that all hub genes were FAM107A HR = 0:72, and MFAP4 HR = 0:76) was signifi- negatively correlated with tumor purity. On the contrary, 6 cantly associated with poor overall survival (Figure 6(a)). hub genes were all positively related to the infiltration of immune cells (Figure 8). 2.7. DNA Methylation and Expression of Hub Genes. As we all know, methylation can significantly affect the expression of 2.9. GSEA Revealed Pathway Dysregulated by Hub Genes. To multiple genes; therefore, we at first used DiseaseMeth 2.0 further explore the expression pathway of all 6 hub genes, to explore the mean methylation level of hub genes. Because GSEA analysis was performed for each gene. Supplementary OGN was not included in DiseaseMeth, we only explore the Fig 7 represents the top 10 enriched pathways in each hub other 5 genes. We found that the mean methylation level of gene (ranked by enrichment score). According to the result ITM2A, GALNTL1, FAM107A, and MFAP4 was significantly of GSEA, we found that multiple immune-related pathways higher in tumor tissue while the methylation level of PGM5 were significantly enriched in the higher expression group was higher in normal tissue (Supplementary Fig 5). This indi- of hub genes like allograft rejection, primary immunodefi- cates that the low expression PGM5 in HNSCC may not be ciency, intestinal immune network for IgA production, T cell caused by methylation. We next explore the relationship receptor signaling pathway, B cell receptor signaling path- between four hub genes and their methylation site. From way, autoimmune thyroid disease, graft-versus-host disease, Supplementary Fig 6, we can see that various methylation human T cell leukemia virus 1 infection, leukocyte transen- sites on each gene were negatively correlated with the dothelial migration, Th1 and Th2 cell differentiation, Th17 expression level of the corresponding gene, indicating that cell differentiation, and asthma. downregulation of four hub genes (ITM2A, GALNTL1, FAM107A, and MFAP4) may be caused by hypermethyla- 3. Discussion tion. To find the key methylation site of hub genes, we also used MethSurv to explore the prognostic role of these meth- To identify the robust dysregulated genes in HNSCC, we ylation sites (r <0and adjusted p value < 0.05). A total of 15 included a total of 24 independent datasets for RRA analysis. methylation sites were found to be important prognostic fac- A total of 466 upregulated genes and 396 downregulated tors for HNSCC (Figure 7). genes were identified. The top 5 upregulated genes mostly 12 BioMed Research International

p < 0.001 p < 0.001 p < 0.001 6 4 5

3 4 4 3 2 OGN GALNTL1 ITM2A 2 2 1 1

0 0 0 Normal Tumor Normal Tumor Normal Tumor Classification Classification Classification

p < 0.001 p < 0.001 p < 0.001

5 8 3

4 6 2 3

4 PGM5 MFAP4

FAM107A 2 1 2 1

0 0 0

Normal Tumor Normal Tumor Normal Tumor Classification Classification Classification (a)

Figure 5: Continued. BioMed Research International 13

p < 0.001 p < 0.001 p = 0.852 5 3 5

4 4

2 3 3 OGN ITM2A 2

2 GALNTL1 1 1 1

0 0 0 1234 1234 1234 T_stage T_stage T_stage

p = 0.002 p = 0.048 p < 0.001

3 8 3

6 2 2 4 PGM5 MFAP4 FAM107A 1 1 2

0 0 0 1234 1234 1234 T_stage T_stage T_stage (b)

Figure 5: Validation of hub genes in the TCGA database. (a) The correlation between hub genes and sample type. (b) The correlation between hub genes and tumor T stage. came from matrix metalloproteinase (MMP) families. Its tivity in head and neck cancer [15, 16]. Levamisole also has family members have been proved to play a vital role in the been used in HNSCC before, but its effect is still controversial progression, invasion, and metastasis of HNSCC [13]. The [17]. Cyproterone and cortisone are both hormone medi- most downregulated gene is TMPRSS11B, a member of the cines. However, there is no strong evidence that hormone type II transmembrane serine protease family. It has been therapy is effective for head and neck tumors. Repaglinide reported to be downregulated in multiple epithelial cancers is a hypoglycemic agent while zimeldine is a kind of antide- [14]. To further understand the biological function of these pressant drug, both of which have not been studied as a drug metasignature genes, we performed GO and KEGG analyses for HNSCC. Considering that the mortality rate of head and on the top 300 metasignature DEGs. Multiple cancer-related neck tumors has not improved significantly in the past ten pathways such as transcriptional misregulation, PI3K-Akt years, traditional treatment methods like surgery and radio- signaling pathway, pathways in cancer, and ECM receptor therapy may not be enough for HNSCC; it is meaningful to interaction were significantly enriched, confirming the further reveal the potential of chemical molecules in targeted important role of these DEGs in HNSCC. Furthermore, therapy of HNSCC. many enriched terms were associated with the construction To identify the hub genes among all 862 metasignature of ECM, indicating the importance of the microenvironment DEGs, WGCNA was utilized to construct a coexpression net- in the development of HNSCC. According to the results of work. Finally, we identified 6 hub genes (ITM2A, GALNTL1, enrichment analyses, we confirmed that these metasignature OGN, FAM107A, MFAP4, and PGM5) according to our DEGs are significantly related to the occurrence and develop- selection criteria. We used other independent databases to ment of HNSCC. validate the expression pattern and clinical relationship of After identifying the robust DEGs in HNSCC, we try to these hub genes. The result showed that all hub genes were use the expression pattern of these genes to predict the poten- downregulated in tumor tissue and were negatively corre- tial small molecule drugs. The CMap database was used, and lated with tumor T stage. Furthermore, compared with six small molecule drugs were selected because they can tumor tissue, these 6 hub genes were also downregulated in reverse the expression pattern of metasignature DEGs. dysplasia tissue. The ROC curve indicated that these genes Among all these drugs, four of which have been studied in may help us better identify the HNSCC. Besides, four genes HNSCC previously. For instance, thiostrepton has been (ITM2A, GALNTL1, FAM107A, and MFAP4) also performed reported to affect the proliferation, apoptosis, and radiosensi- well in the prognosis prediction of HNSCC. Interestingly, all 14 BioMed Research International

Overall survival Overall survival Overall survival 1.0 1.0 1.0 Logrank p = 0.017 Logrank p = 0.027 Logrank p = 0.017 HR (high) = 0.72 HR(high) = 0.74 HR(high) = 0.72 0.8 p (HR) = 0.017 0.8 p (HR) = 0.027 0.8 p (HR) = 0.017 n (high) = 255 n (high) = 253 n (high) = 258 n (low) = 257 n (low) = 255 n (low) = 259 0.6 0.6 0.6

0.4 0.4 0.4 Percent survival Percent survival Percent survival

0.2 0.2 0.2

0.0 0.0 0.0

050100 150 200 050100 150 200 050100 150 200 Months Months Months

Low FAM107A TPM Low GALNT16 TPM Low ITM2A TPM High FAM107A TPM High GALNT16 TPM High ITM2A TPM

Overall survival Overall survival Overall survival 1.0 1.0 1.0 Logrank p = 0.04 Logrank p = 0.66 Logrank p = 0.59 HR(high) = 0.76 HR(high) = 1.1 HR(high) = 0.93 0.8 p (HR) = 0.04 0.8 p (HR) = 0.67 0.8 p (HR) = 0.58 n (high) = 259 n (high) = 259 n (high) = 259 n (low) = 259 n (low) = 255 n (low) = 257 0.6 0.6 0.6

0.4 0.4 0.4 Percent survival Percent survival Percent survival

0.2 0.2 0.2

0.0 0.0 0.0

050100 150 200 050100 150 200 050100 150 200 Months Months Months

Low MFAP4 TPM Low OGN TPM Low PGM5 TPM High MFAP4 TPM High OGN TPM High PGM5 TPM (a)

Figure 6: Continued. BioMed Research International 15

p<0.001 p<0.001 p<0.001 12

5.0 10 10 4.5 8 8 4.0 OGN ITM2A 6 GALNTL1 3.5 6 4 3.0 4 2 Dysplasia Normal Tumor Dysplasia Normal Tumor Dysplasia Normal Tumor Type Type Type

p<0.001 p<0.001 p<0.001

10.0 9 8

7.5 7 6 PGM5 MFAP4 FAM107A

5 5.0 4

3 Dysplasia Normal Tumor Dysplasia Normal Tumor Dysplasia Normal Tumor Type Type Type (b)

Figure 6: The role of hub genes in malignant transformation and prognosis: (a) prognostic role of all hub genes; (b) the expression level of hub genes in normal, dysplasia, and tumor tissues.

6 hub genes were seldom explored in HNSCC previously. research potential, we also performed survival analysis and ITM2A, a family member of BRICHOS, has been reported found that hypermethylation of 15 methylation sites in to be downregulated in both breast and ovarian cancers FAM107A, GALNTL1, and MFAP4 was significantly associ- which may affect the proliferation and autophagy process of ated with poor overall survival. All selected hub genes and tumors [18, 19]. However, its role in HNSCC has not been their methylation conditions may help us better judge the fully studied. Similarly, the role of another 5 hub genes in state of HNSCC (inert or invasive), so as to develop a more cancer also has been reported previously to varying degrees. appropriate treatment strategy. For example, PGM5 was identified as a diagnostic and prog- A great number of previous researches have revealed that nostic biomarker in liver and colorectal cancers [20, 21]. The the infiltration of immune cells in the tumor microenviron- higher expression of antisense chain of PGM5 was showed to ment could largely affect the development of cancer cells inhibit the proliferation and metastasis of tumors [22]. The [25, 26]. Therefore, we used TIMER to explore the relation- higher expression of OGN was also reported to inhibit the ship between hub genes and immune cell infiltration. Inter- process of EMT through the EGFR/Akt pathway [23]. How- estingly, all six hub genes were positively correlated with ever, the role of these genes in the development of HNSCC infiltration of B cell, CD8+ T cell, CD4+ T cell, macrophage, remains unclear. neutrophil, and dendritic cells, indicating that our hub genes As we all know, hypermethylation is an important cause may to some extent play a role in immunological regulation. of the downregulation of gene expression. A recent study The results of GSEA further support this hypothesis; a great showed that hypermethylation may lead to the low expres- number of immune-related pathways were significantly sion of FAM107A in laryngeal cancer [24], which is consis- enriched in higher expression groups of hub genes. A recent tent with our results. Through methylation analysis, we also study confirmed our result; Hu et al. point out that higher find that the low expression of another three hub genes expression of OGN can promote the infiltration of CD8+ T (ITM2A, GALNTL1, and MFAP4) may be significantly asso- cells thus inhibiting the formation of new blood vessels in ciated with hypermethylation in multiple methylation sites. colorectal cancer [27]. Some studies also have described the Because DNA methylation is a reversible process, targeted role of some hub genes (ITM2A, MFAP4) in immunoregula- therapies for the unique methylation site of the tumor are tion [28, 29]. However, the role of these genes in tumor promising. To further screen out methylation sites with immune regulation is still not fully illustrated; we need more 16 BioMed Research International

Figure 7: 15 methylation sites of hub genes with prognostic ability in HNSCC. experiments to validate the association between these hub logical regulation in the microenvironment of HNSCC genes and immune inﬁltration. which need more experiment to verify.

5. Materials and Method 4. Conclusion 5.1. Selection of Included Datasets. The mRNA expression In conclusion, by utilizing the RRA method, we identified a profile-related datasets were searched in the GEO database series of robust DEGs in HNSCC. Based on WGCNA, 6 by using the keywords head and neck cancer, larynx, laryn- hub genes (ITM2A, GALNTL1, OGN, FAM107A, MFAP4, geal, tongue, mouth, oral, oropharynx, tonsil, hypopharynx, and PGM5) in the blue module were selected. All hub genes and hard palate. Two people independently screened the were significantly downregulated in tumor tissue of HNSCC. datasets based on the inclusion criteria as follows: (1) The expression pattern of four hub genes (ITM2A, Included datasets must provide the gene expression profile GALNTL1, FAM107A, and MFAP4) may be caused by hyper- of HNSCC and corresponding normal tissue control. (2) methylation. All six hub genes may play a role in immuno- Each group of one dataset should contain at least 5 samples. BioMed Research International 17

Purity B cell CD8+ T cell CD4+ T cell Macrophage Neutrophil Dendritic cell 6 cor = –0.306 Partial.cor = 0.519 Partial.cor = 0.587 Partial.cor = 0.614 Partial.cor = 0.594 Partial.cor = 0.54 Partial.cor = 0.708 p = 3.8e–12 p = 3.21e–34 p = 2.82e–45 p = 4.35e–51 p = 2.03e–47 p = 1.24e–37 p = 1.46e–74

4 HNSC 2

ITM2A expression level (log2 TPM) level expression ITM2A 0.25 0.50 0.75 1.00 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.2 0.4 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.25 0.50 0.75 1.00 1.25 Inﬁltration level

Purity B cell CD8+ T cell CD4+ T cell Macrophage Neutrophil Dendritic cell cor = –0.136 Partial.cor = 0.347 partial.cor = 0.281 partial.cor = 0.458 Partial.cor = 0.406 Partial.cor = 0.283 Partial.cor = 0.402 p = 2.43e–03 p = 6.73e–15 p = 4.84e–10 p = 2.97e–26 p = 1.26e–20 p = 2.72e–10 p = 4.12e–20 4

2 HNSC

0.25 0.50 0.75 1.00 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.2 0.4 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.25 0.50 0.75 1.00 1.25 GALNTL1 expression level (log2 TPM) level GALNTL1 expression Inﬁltration level

Purity B cell CD8+ T cell CD4+ T cell Macrophage Neutrophil Dendritic cell cor = –0.147 Partial.cor = 0.288 Partial.cor = 0.251 Partial.cor = 0.401 Partial.cor = 0.513 Partial.cor = 0.161 Partial.cor = 0.343 6 p = 1.07e–03 p = 1.42e–10 p = 2.89e–08 p = 5.68e–20 p = 8.72e–34 p = 4.19e–04 p = 9.24e–15

2 HNSC

OGN expression level (log2 TPM) level OGN expression 0.25 0.50 0.75 1.00 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.2 0.4 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.25 0.50 0.75 1.00 1.25 Inﬁltration level

Purity B cell CD8+ T cell CD4+ T cell Macrophage Neutrophil Dendritic cell cor = –0.263 Partial.cor = 0.341 Partial.cor = 0.23 Partial.cor = 0.488 Partial.cor = 0.426 Partial.cor = 0.248 Partial.cor = 0.396 4 p = 2.84e–09 p = 1.83e–14 p = 4.15e–07 p = 4.47e–30 p = 9.78e–23 p = 3.67e–08 p = 1.51e–19

2 HNSC 1

PGM5 expression level (log2 TPM) level PGM5 expression 0.25 0.50 0.75 1.00 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.2 0.4 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.25 0.50 0.75 1.00 1.25 Inﬁltration level

Purity B cell CD8+ T cell CD4+ T cell Macrophage Neutrophil Dendritic cell 5 cor = –0.217 Partial.cor = 0.395 Partial.cor = 0.417 Partial.cor = 0.379 Partial.cor = 0.371 Partial.cor = 0.231 Partial.cor = 0.373 p = 1.19e–06 p = 3.15e–19 p = 2.49e–21 p = 7.19e–18 p = 3.53e–17 p = 3.35e–07 p = 2.36e–17 4

2 HNSC

0 0.25 0.50 0.75 1.00 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.2 0.4 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.25 0.50 0.75 1.00 1.25 FAM107A expression level (log2 TPM) level expression FAM107A Inﬁltration level

Purity B cell CD8+ T cell CD4+ T cell Macrophage Neutrophil Dendritic cell cor = –0.182 Partial.cor = 0.443 Partial.cor = 0.299 Partial.cor = 0.469 Partial.cor = 0.484 Partial.cor = 0.182 Partial.cor = 0.389 p = 5.03e–05 p = 2.90e–24 p = 2.84e–11 p = 1.40e–27 p = 1.11e–29 p = 6.27e–05 p = 7.33e–19 7.5

5.0 HNSC

2.5

0.0

MFAP4 expression level (log2 TPM) level expression MFAP4 0.25 0.50 0.75 1.00 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.2 0.4 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.25 0.50 0.75 1.00 1.25 Inﬁltration level

Figure 8: The correlation between hub genes and inﬁltration of immune cells.

(3) The platform of each study should contain more than type data, and corresponding platform information from the 8000 genes. Finally, a total of 25 studies were included in GEO database. We used “limma” R package to normalize the our research, and among which, 24 independent studies were data and obtain DEGs of each study (p value < 0.05). The up- used for RRA analysis; one dataset (GSE30784) with gene or downregulated genes were arranged from large to small expression data of dysplasia tissue was used for further vali- according to the absolute value of logFC. The “RobustRan- dation and exploration. Detailed information of included kAggreg” package in R was created for comparison of ranked GEO datasets is shown in Table 2. gene lists and identification of metasignature genes. The result of RRA can help us identify more robust genes from 5.2. Identification of Robust DEGs. R package “GEOquery” different studies, and the detailed method of the RRA method was used to directly obtain series matrix files, sample pheno- has been described by previous articles [30]. In the end, the p 18 BioMed Research International

Table 2: Detailed information of 24 datasets which were used in RRA analysis.

Time Gene set Platform Number of probes Country Tumor type Tumor sample number Control sample number 2008 GSE10121 GPL6353 33484 Germany OSCC 35 6 2017 GSE103412 GPL23978 39321 Denmark OSCC 23 9 2008 GSE13399 GPL7540 36197 USA HNSCC 8 8 2008 GSE13601 GPL8300 12625 USA OSCC 31 26 2019 GSE138206 GPL570 9442 China OSCC 6 6 2020 GSE143224 GPL5175 19076 Brazil LSCC 14 11 2010 GSE23036 GPL571 22277 Spain HNSCC 63 5 2010 GSE23558 GPL6480 41000 India OSCC 27 5 2010 GSE25099 GPL5175 17881 China OSCC 57 22 2011 GSE29330 GPL570 54675 USA HNSCC 13 5 2011 GSE31056 GPL10526 17788 USA OSCC 23 73 2011 GSE33205 GPL5175 22011 USA HNSCC 44 25 2011 GSE34105 GPL14951 29377 Sweden OSCC 62 16 2012 GSE37991 GPL6883 24526 China OSCC 40 40 2012 GSE42743 GPL570 54645 USA OSCC 74 29 2013 GSE51985 GPL10558 47220 China LSCC 10 10 2014 GSE55550 GPL17077 50739 USA OSCC 139 16 2014 GSE58911 GPL6244 33297 USA HNSCC 15 15 2014 GSE59102 GPL6480 34664 Brazil LSCC 29 13 2007 GSE6631 GPL8300 12625 USA HNSCC 22 22 2007 GSE6791 GPL570 54675 USA HNSCC 42 14 2016 GSE83519 GPL4133 43376 Netherlands HNSCC 22 22 2016 GSE84957 GPL17843 77162 China LSCC 9 9 2007 GSE9844 GPL570 54645 USA OSCC 26 12 LSCC: laryngeal squamous cell cancer; OSCC: oral squamous cell cancer; HNSCC: head and neck cancer. value of the output result was subjected to Bonferroni correc- .pubchem.ncbi.nlm.gov) to visualize the 2D structure of tion, and mRNA with adjusted p value < 0.05 was considered selected small molecules. significantly dysregulated. Furthermore, “OmicCircos” R package was utilized to visualize the expression patterns of 5.5. Key Module and Hub Genes Identified by WGCNA. A the top 100 metasignature DEGs in each included study (dys- total of 862 metasignature genes were included for regulated genes according to adjusted p value). WGCNA with expression data from GSE65858. We construct a gene coexpression network for all metasignature 5.3. Enrichment Analysis. We used DAVID Bioinformatics DEGs; “WGCNA” R package was applied to explore the rela- Resources 6.8 (DAVID; http://david.abcc.ncifcrf.gov/) to tionship between each coexpression module and clinical phe- annotate the top 300 metasignature genes. GO and KEGG notype. A correlation matrix was constructed which was enrichment analyses were performed by using the prediction subsequently transformed to a TOM matrix based on the soft tool on the website. Bubble charts were used to visualize the threshold (β =7, R2 =0:9). All metasignature genes were dis- top 20 terms of enrichment results. tributed in different gene modules according to the value of the TOM matrix. Here, we set the minimal module size as 5.4. Identification of Potential Drug for HNSCC. The Connec- 15 and cut height as 0.5. The module with a significant corre- tivity Map (CMap) [31] database (http://www.broadinstitute lation with clinical characteristics was selected. GO and .org) can help us to predict the potential drugs which can KEGG enrichment analyses were performed on the clinical- reverse the expression of specific genes. In this study, we related modules. We selected the hub gene according to the input the top 300 metasignature genes (165 upregulated value of GS and MM (GS > 0:1, MM > 0:75,GSp value < and 135 downregulated genes) into the online tool of CMap 0.05, and MM p value < 0.05). for gene set enrichment analysis. Each small molecule will be assigned an enrichment score between -1 and 1. The lower 5.6. Verify the Clinical Relevance of Hub Genes. We used the the enrichment score, the better the drug effect to reverse the TCGA database at first to validate the diagnostic role of hub state of HNSCC cells. In our study, drugs with p value < 0.01 genes and the relationship between hub genes and clinical and the enrichment score < −0:7 were considered potential characteristics. We also used an independent dataset small molecules. We also used PubChem (http://www (GSE30748) to explore the hub genes’ expression levels BioMed Research International 19 between dysplasia tissue and tumor tissue. The Student t-test GEPIA: Gene expression profiling interactive analysis or one-way analysis of variance (ANOVA) was used appro- KM curve: Kaplan-Meier curve priately to test the result of the comparison. Furthermore, EMT: Epithelial to mesenchymal transition we also plot the ROC curves to assess hub genes’ diagnostic MSigDB: Molecular signature database value; the area under the ROC curve (AUC) was calculated TIMER: Tumor immune estimation resource by the “pROC” R package. Survival analysis was also per- GSEA: Gene set variation analysis. formed on all hub genes by using GEPIA (a visualization website based on the TCGA database: http://gepia.cancer- Data Availability pku.cn/). The median is considered to be the cutoff for high and low expression of hub genes. Our manuscript report is the secondary analysis of data from a public database; all data used in the manuscript were 5.7. Methylation Analysis. In order to further explore the rea- mainly from GEO and TCGA databases. son for the dysregulation of hub gens, we performed methylation analysis on all hub genes based on DiseaseMeth 2.0 [8] Ethical Approval (http://bioinfo.hrbmu.edu.cn/diseasemeth/), which is a website focusing on collecting methylation data from various The authors state that they have obtained appropriate insti- tumor tissue. We compare the mean value of methylation tutional review board approval or have followed the princi- between HNSCC and corresponding normal tissue. Further- ples outlined in the Declaration of Helsinki for all human more, we also used MEXPRESS [9] (http://mexpress.be) to or animal experimental investigations. In addition, for inves- explore the association between the expression level of hub tigations involving human subjects, informed consent has genes and the methylation level of the corresponding methyl- been obtained from the participants involved. ation site. Those methylation sites that are negatively correlated with gene expression are defined as candidate sites. To Conflicts of Interest further screen potential key methylation sites, we also con- The authors have no relevant affiliations or financial involve- ducted survival analyses on these candidate sites by using fi MethSurv [10] (https://biit.cs.ut.ee/methsurv/). ment with any organization or entity with a nancial interest in or financial conflict with the subject matter or materials 5.8. Immune Cell Infiltration and Hub Genes. To explore the discussed in the manuscript. This includes employment, con- association between immune cell infiltration and expression sultancies, honoraria, stock ownership or options, expert tes- level of hub genes, we used TIMER [32] (https://cistrome timony, grants or patents received or pending, or royalties. .shinyapps.io/timer/), an online tool based on the TCGA No writing assistance was utilized in the production of this database, to evaluate the infiltration score for six kinds of manuscript. important immune cells (B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells). The Pearson Authors’ Contributions correlation coefficient between hub genes and the infiltration score were then calculated. Zaizai Cao and Shuihong Zhou designed and wrote the paper; Zaizai Cao and Yu Guo collected the related studies 5.9. Gene Set Enrichment Analysis. According to the mean and data; and Zaizai Cao and Yinjie Ao analyzed the data expression value of 6 hub genes, all HNSCC samples in the and made the figures and tables. TCGA database were divided into high expression groups and low expression groups. GSEA analysis was performed Acknowledgments and visualized by using the “clusterprofiler” R package. The KEGG gene set was directly downloaded from MSigDB We thank all the members of the Otolaryngology Depart- (http://software.broadinstitute.org/gsea/msigdb/index.jsp). ment for kind support. This work was supported by the Sci- ence and Technology Department of Zhejiang Province, Abbreviations China (No. 2016C33144). RRA: Robust rank aggregation Supplementary Materials WGCNA: Weighted gene coexpression network analysis HNSCC: Head and neck squamous cancer Supplementary Fig 1: 2D molecular structure of potential DEGs: Differentially expressed genes drugs: A: thiostrepton, B: cortisone, C: cyproterone, D: GO: Gene Ontology levamisole, E: zimeldine, and F: repaglinide. Supplementary KEGG: Kyoto Encyclopedia of Genes and Genomes Fig 2: GO and KEGG analyses of blue module: A: the corre- BP: Biology process lation between the blue module and KEGG pathway. B: the MF: Molecular function correlation between blue module and GO terms of biological CC: Cell components process. C: the correlation between blue module and GO ECM: Extracellular matrix terms of molecular function. D: the correlation between blue TOM: Topological overlap matrix module and GO terms of cellular component. Supplementary GS: Gene significance Fig 3: the correlation between hub genes and tumor N stage. MM: Module membership Supplementary Fig 4: ROC curves validate the diagnostic role 20 BioMed Research International of hub genes. A: diagnostic role of hub genes between normal ferentially expressed in epithelial cancers,” PLoS One, vol. 9, tissue and tumor tissue. B: diagnostic role of hub genes no. 2, article e87675, 2014. between tumor tissue and dysplasia tissue. Supplementary [15] J. C. Eckers, A. L. Kalen, E. H. Sarsour et al., “Forkhead box M1 Fig 5: mean methylation level of hub genes between normal regulates quiescence-associated radioresistance of human head and tumor tissues. Supplementary Fig 6: the relationship and neck squamous carcinoma cells,” Radiation Research, between four hub genes and their methylation site. Supple- vol. 182, pp. 420–429, 2014. mentary Fig 7: GSEA result of hub genes. (Supplementary [16] L. Jiang, X. Wu, P. 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