Signatures and Prognostic Values of Related Immune Targets in Tongue Cancer

doi:10.21203/rs.3.rs-997544/v1

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Signatures and Prognostic Values of Related Immune Targets in Tongue Cancer

https://doi.org/10.21203/rs.3.rs-997544/v1

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Tongue cancer, as one of the most malignant oral cancers, is highly invasive and has a high risk of recurrence. At present, tongue cancer in the advanced stage is not obvious, easy to miss the opportunity of early diagnosis. It is important to find markers that can predict the occurrence and progression of tongue cancer. Bioinformatics analysis plays an important role in the acquisition of marker genes. GEO and TCGA data are very important public databases. In addition to expression data, TCGA database also contains corresponding clinical data. In this study, we screened three GEO datasets included GSE13601, GSE34105 and GSE34106 that met the standard. These data sets were combined using the SVA package to prepare the data for differential expression analysis, and then the LIMMA package was used to set the standard to p<0.05 and |log2 (FC)| ≥1.5. We got 170 DEGs (104, raised 66 downgrade). Besides, the DEseq package was used for differential expression analysis using the same criteria for samples in TCGA database. It ended up with 1589 DEGs (644 up-regulated, 945 down-regulated). By merging these two sets of DEGs, 5 common up-regulated DEGs (CCL20, SCG5, SPP1, KRT75 and FOLR3) and 15 common down-regulated DEGs were obtained. Further functional analysis of the DEGs showed that CCL20, SCG5 and SPP1 is closely related to prognosis and may be a therapeutic target of TSCC.

Epigenetics & Genomics

tongue cancer

DEGs

CCL20

SCG5

SPP1

Head and neck tumors are the fifth most common malignancy in the world, and smoking and alcohol consumption are two of the most common risk factors associated with these lesions [1]. Oral cancer accounts for 2.1% of all new cancer cases worldwide, and squamous cell carcinoma is a known malignancy, accounting for more than 90% of all oral cancers [2, 3]. Tongue squamous cell carcinoma (TSCC) is one of the most common malignancies diagnosed in the oral cavity and is associated with poor prognosis due to its high regional recurrence rate and lymphatic metastasis [4]. It is reported that there are about 500,000 new cases of tongue cancer every year, and the global incidence of tongue cancer is increasing year by year, and the onset of tongue cancer is getting younger and younger [5]. Numerous lymphatic vessels of the tongue are abundant in blood circulation, and the tongue is active frequently. These factors promote the early spread of cancer cells to adjacent tissues and organs, such as lymph nodes, the base of the mouth, throat and neck. And although tongue cancer treatments are improving as technology advances, the five-year survival rate is still poor [3, 6]. Oral squamous cell carcinoma (OTSCC) has a lower survival rate than squamous cell carcinoma elsewhere in the mouth. There is currently a lack of OTSCC markers, so it is of great interest to find reliable predictive markers [7].

Data preparation

The expression matrix was download from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). In order to improve the accuracy of the study, we screened three eligible datasets. These datasets included tongue cancer and normal tissues. GSE13601, GSE34105 and GSE34106 contains 57, 78 and 43 samples respectively. The specific information is shown in Supplementary Table 1. The SVA software package in R language is used to combine the samples of these three data sets to form a combined data set with 178 samples. Then we downloaded the expression data of head and neck tumors from the TCGA database, and screened the tongue squamous cell carcinoma samples, a total of 147 samples, including 140 tumor samples and 7 normal samples.

Screening and analysis of differentially expressed genes (DEGs)

The limma package and DESeq package in R language was used for differential expression analysis, and the standard of cut-off was set to False Discovery Rate (FDR) <0.05 and |log2 (FC)| ≥1.5 for both GEO and for TCGA respectively [8, 9]. The volcano package and heatmap package in R language were used to visualize DEGs. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were performed for DEGs. Metascape website (https://metascape.org/) was used for pathway enrichment analysis of DEGs in TCGA. T test was used to compare the expression in the two groups, and ANOVA was used to compare that in multiple groups. We further verified the up-regulated DEGs expression using the GEPIA (http://gepia.cancer-pku.cn/).

Protein interaction network

The interaction between proteins corresponding to each gene was analyzed using the website String ( https://string-db.org/ ), and then the results were visualized using CytoScope. Genemania website ( http://genemania.org/ ) was used for functional enrichment analysis of DEGs.

Identify the Hub gene

The DEGs obtained from combined dataset were crossed with those obtained from TCGA, including 5 up-regulated genes and 15 down-regulated genes.

Survival analysis

Kaplan-Meier Plotter website ( https://kmplot.com/ ) (citation) was used for survival analysis of the screened HUB genes.

Differential expression analysis of GEO merged dataset

We downloaded three datasets GSE13601, GSE34105 and GSE34106 containing tongue squamous cell carcinoma (TSCC) in GEO database. Each dataset contains tumor samples and normal tissue samples. We used SVA package to merge these datasets and obtain a combined dataset. To identify differences between TSCC samples and normal tongue samples, we performed principal component analysis (PCA) and found that the two groups could be separated in the combined dataset (Figure 1A). We used limma package (|log2 (FC)| ≥ 1.5, p < 0.05) in R to analyze the 178 samples and screened for DEGs. We identified 170 DEGs, including 104 up-regulated genes and 66 down-regulated gene which were shown in the volcano diagram, where the red on the right represents the up-regulated differentially expressed genes and the blue one represents the down-regulated differentially expressed genes (Figure 1B). In the heatmap, groups were made according to datasets and sample characteristics to explore the expression of different genes. The top annotation represents the different datasets. DEGs are marked on the right side of the figure (Figure 1C). In order to further explore the functions of the screened DEGs, we use R language for further functional enrichment analysis of these genes in KEGG database, and enriched some up-regulated pathways such as Cytokine-Cytokine receptor interaction, Viral protein interaction with cytokine and cytokine receptor and primary immunodeficiency (Figure 1D). Then GO analysis was performed on these genes, and the pathways enriched in up-regulated genes were mainly cartilage development, connective tissue development, skeletal system development and chondrocyte differentiation in the biological process (BP) module (Figure 1E). In cellular component (CC) module, the enrichment pathways were mainly collagen-containing extracellular matrix, basement membrane, apical part of cell and laminin complex. In Molecular Function (MF) module, mainly enriched in receptor ligand activity, signaling receptor activator activity and metalloendopeptidase activity. GSEA analysis was also performed on the first six up-regulated pathways, and the results were consistent with the previous analysis (Figure 2). The KEGG correspondence is in Supplementary Table2.

Differential expression analysis of TSCC in TCGA database

DEGs of TSCC vs normal samples in TCGA database was performed using DESeq package in R language (|log2 (FC)| ≥ 1.5, p < 0.05). 644 DEGs up-regulated and 945 down-regulated DEGs were found as shown in the volcano diagram (Figure 3A). We used Metascape website to analyze these up-regulated DEGs and found that the mainly enriched GO pathway was aminoglycoside antibiotic metabolic process,proximal/distal pattern formation and negative regulation of blood coagulation (Figure 3B). KEGG enrichment analysis of up-regulated DEGs showed that the up-regulated pathways were mainly Folate biosynthesis, Steroid hormone biosynthesis, Complement and coagulation cascades, Staphylococcus aureus infection and Cytokine-cytokine receptor interaction. The main down-regulated pathways were Salivary secretion, Calcium signaling pathway and Pancreatic secretion (Figure 3C).

Acquisition and analysis of hub genes

DEGs acquired in GEO were crossed with those in TCGA, and screened 5 common up-regulated genes (CCL20, SCG5, SPP1, FOLR3 and KRT75) and 15 common down-regulated genes (MAOB, HLFSELENBP1, CRISP3, TMPRSS2, TLX1, TGFBR3, APOD, PPP1R3C, SLITRK5, MFAP4, STATH, GSTM5, RNASE4, COX7A1, FHL1, SLC25A4, EFHD1, MYH11, MGP, SCGB2A1, LPIN1, ELF5, ADH1B, CKMT2) (Figure 4A). The protein-protein interaction (PPI) network analysis of these five up-regulated genes found that proteins expressed by other genes were all functionally related except FOLR3 (Figure 4B). Then, we conducted interaction network analysis of these genes individually (Figure 4C).

Further expression verification of these hub genes

We further verified the expression of up-regulated DEGs using GSE13601 dataset. This dataset includes tumor samples and their paired normal samples. After remove the individual samples in the database, we obtained 20 pairs of paired samples and then we compared the expressions of CCL20, SCG5, SPP1, FOLR3 and KRT75 genes in tumor tissues vs normal tissues. It was shown that the expression of these 5 genes was higher in TSCC than in normal tissue both in the whole sample and the most paired samples (Figure 5A). The same results can be obtained using TCGA dataset (Figure 5B).

To explore the relationship between the expression of these Hub genes and clinical stage,we used TCGA database to explore and found that the expression level of CCL20 was significantly increased in patients with stage III and IV compared with stage I (P < 0.05), the expression of SPP1 and SCG5 in stage IV was significantly higher than that in stage I patients (P < 0.05) (Figure 5C).

Multivariate COX regression of TSCC in TCGA database for each clinical feature and prognosis

To further explore the clinical data of TCGA, Multivariate Cox regression was performed using clinical data from TCGA database (Figure 6). We found that ajcc_pathologic_T > 2 vs ajcc_pathologic_T <= 2 the risk increased. Similarly, years of smoking also has an impact on the survival of patients, when the smoking time is more than 29 years, the risk of patients is significantly increased. Besides, we found that when TSCC stage is higher than grade III, age > 59, N stage > 1 and the number of cigarettes smoked per day > 2.5 increased the risk, but P value of multivariate Cox did not meet the statistical standards. Kaplan-meier analysis was performed on clinical data using SPSS software, and we found that patients with stage higher than III had a significantly worse prognosis than patients with stage lower than III. AJCC_pathologic_T had a poor prognosis when it was higher than T2. Similarly, AJCC_pathologic_N stage higher than N1 and smoking years higher than 29 years remained risk factors (Figure 7).

Hub genes analysis in HNCS

These up-regulated hub genes were further verified by GEPIA, and the results showed that the expression of these 5 up-regulated genes in Head and Neck squamous cell carcinoma (HCNC) was also higher than that in normal tissues (Figure 8). Kaplan – Meier Plotter was used to analyze the relationship between the expression of these genes and prognosis, and the results showed that high expression of these genes was strongly associated with poor prognosis in HCNC (P<0.05) (Figure 9).

Key molecular alterations need to be identified to identify effective therapeutic targets [10–12]. In order to explore the expression genes related to the occurrence and progression of tongue cancer, we used GEO database and TCGA database for exploration. To increase the sample size, we used the SVA package in R language for GSE13601, GSE34105 and GSE34106.Three datasets were combined to obtain a dataset containing 178 samples (121 TSCC and 57 normal samples). Use Limma package for DEGs analysis and set |Log₂FC| > 1.5, p < 0.05. Finally, we get 104 up-regulated genes, 66 down-regulation genes. Then, DEGs of TCGA were obtained by using DEseq package in R language, and the cut-off value was set as same before. DEGs obtained from GEO database and TCGA dataset were crossed, obtaining 5 up-regulated DEGs and 15 down-regulated DEGs. The 5 up-regulated DEGs are CCL20, SCG5, SPP1, FOLR3 and KET75.

Chemokine (C-C motif) Ligand 20 (CCL20) is a substance involved in tissue validation and homeostasis with a specific receptor C-C Chemokine receptor 6 (CCR6). It is expressed in various tissues and immune cells in human body, and the CCL20-CCR6 axis is closely associated with inflammation and infectious diseases. It has been reported that the CCL20-CCR6 axis is closely related to a variety of cancers which can directly promote the progress of cancer by enhancing the migration and proliferation of cancer cells, and can also regulate the tumor microenvironment by immune cells.

CCL20 has been reported to indirectly promote tumor progression by recruiting Treg, Th17, and Th22 cells to maintain the development and immunosuppressive microenvironment [13]. Studies have shown that TSCC cells produce CCL20 after interacting with macrophages, and the cells can express CCR6 in the TSCC microenvironment. The CCL20-CCR6 axis may be associated with OSCC progression by inducing CD163 expression in macrophages [14]. SPP1 is secreted glycophosphoprotein, which is involved in a variety of functions, including cell adhesion, migration and invasion [15–18]. FOLR3 is highly overexpressed on several tumor cells, including ovarian, non-small cell lung cancer, kidney, brain, endometrial, colorectal, breast, pancreatic, gastric, prostate, testicular, and bladder,thus it was known as therapeutic target for cancer treatment [19]. It has been reported that these genes are associated with the progression of a variety of tumors. In addition, according to the results of survival analysis, we found that CCL20, SPP1 and SCG5 are closely related to the prognosis of TSCC and HCSN. We have reason to believe that these genes can be developed into prognostic indicator and are expected to become a therapeutic target of TSCC.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the [NAME] repository(https://github.com/Wangyouzhi88/-/commit/b99e2fb3f4340a78ae80ff3704dd56cc00f569a2).

Competing interests

The authors declare that they have no competing interests.
Funding

Not applicable.

Authors' contributions

Conceptualization, Xi Yu, Xiaofei Lv; material preparation, Xi Yu; writing-original draft preparation, Xi Yu, Xiaofei Lv; data analysis and interpretation, Xi Yu, Xiaofei Lv; writing editing and review, All authors; All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

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No competing interests reported.

supplementaryTable.docx
Supplementary table 1 Total samples/Platform information of the three datasets(GSE13601(57), GSE34105(78), GSE34106(43)).
Supplementary table 2 The KEGG correspondence of the first six up-regulated pathways.

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Signatures and Prognostic Values of Related Immune Targets in Tongue Cancer

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Data preparation

Screening and analysis of differentially expressed genes (DEGs)

Protein interaction network

Identify the Hub gene

Survival analysis

Results

Differential expression analysis of GEO merged dataset

Differential expression analysis of TSCC in TCGA database

Acquisition and analysis of hub genes

Further expression verification of these hub genes

Multivariate COX regression of TSCC in TCGA database for each clinical feature and prognosis

Hub genes analysis in HNCS

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1