Background
Oral cancer is the sixth most common cancer type in the world [
1]. Oral squamous cell carcinoma (OSCC) is the most commonly occurring oral cancer [
2]. The OSCC represents a major public health issue, especially in the developing countries for example China [
3]. OSCC usually arises from and develops in the oral cavity and oropharynx [
4], which can induce damage in speech, swallowing and chewing function [
2]. The risk factors of OSCC include smoking, excessive alcohol consumption, areca nut chewing (especially in Asia and Pacific area), occupational exposure to carcinogens, autoimmune chronic disease, persistent viral infections (e.g. human papillomavirus, HPV) and so on [
5,
6]. Treatment options for the OSCC patients comprise of surgical resection, adjuvant radiotherapy and chemotherapy, as well as the rising immunotherapy [
7‐
9]. But due to the tendency to metastasize [
10], patients with advanced OSCC are likely to have a poor prognosis [
11]. Traditional prognostic indicators for example stages and grades of tumor are difficult to distinguish carcinomas with different biological characteristics within the same histological subgroup [
12,
13]. Novel indicators such as immune-related genes [
14], systemic inflammatory biomarkers [
15], ferroptosis-related genes [
16] are emerging as effective biomarkers to stratify patients with different prognosis. These identified biomarkers provide a relatively comprehensive understanding of prognosis in OSCC and provide an additional tool for selecting patients who need more aggressive treatment. In order to improve accuracy of the prediction, more biomarkers are urgently needed to be explored to provide an additional tool for prediction of prognosis for cancer patients [
17,
18].
Tumor mutational burden (TMB) is defined as the number of mutations existing within a tumor and is often reported as the number of mutations per DNA megabase of genomic territory [
19]. Because of the development of next generation sequencing techniques, a cost- and time-effective sequencing of genes makes significant improvement in detecting gene mutations [
20]. Growth and progression of cancers are reported to be related to the immune suppression, and in order to evade immunosurveillance and eradication of the host immune system, tumors often upregulate immune checkpoints [
21,
22]. Immunotherapies based on immune checkpoint inhibitors (ICIs) have emerged as a new treatment for many types of cancers [
23]. High TMB levels is often connected with better prognosis and higher rates of treatment response after ICIs therapy which may attribute to higher potential immunogenic neoantigens facilitating anti-tumor immune response [
24,
25]. And TMB levels are emerging as a novel prognostic biomarker for the response to immunotherapy in oncology clinic [
21,
26,
27]. Previous studies have reported that cancer patients with higher TMB levels have higher response rates following ICI therapy than those with lower TMB levels, for example non-small cell lung cancer (NSCLC) [
28], melanoma [
29] and breast cancer [
26]. TMB levels are used for the prediction of the prognosis for cancer patients following immunotherapy in solid tumors such as breast cancer, lung cancer and gastrointestinal cancers [
30]. And Kang et.al. have reported that TMB was also related to the prognosis of cutaneous melanoma and prognostic model constructed by TMB-related grenes might be used to predict prognosis of cancer patients [
31]. These researches support that TMB has the potential as a promising biomarker for predicting the cancer patients with different prognosis [
32]. Although previous studied have identified the prognostic signature constructed by TMB-related genes for patients with ovarian cancers [
33] and the prognostic value of TMB for patients with head and neck squamous cell carcinoma has also been studied [
34], there are few articles about the prognostic value of TMB-related genes for OSCC patients and the prognostic signature constructed by TMB-related genes for OSCC patients has not been throughly explored. We aimed to explore the prognostic value of TMB-related genes for and to build a prognostic signature for OSCC patients. Besides, Risk Score models constructed by molecular biomarkers utilizing LASSO Cox regression analysis have already been used to diagnose and to predict the prognosis of patents in solid tumors [
35].
In this study, we explored the connection between TMB-related genes and the prognosis of OSCC patients through bioinformatic analysis. We hoped to construct a prognostic Risk Score model to be helpful in separating patients with different prognosis.
Discussion
OSCC is one of the most common cancers in the world. It poses a great challenge to the medical industry because of the high death rate,. In this research, we constructed a prognostic model for OSCC patients based on the TMB-related genes to predict the prognosis.
In this study, functional enrichment analysis was performed on the TMB-related DEGs. The focal adhesion was listed on the top 20 KEGG pathways. Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase which is associated with poor prognosis and can promotes breast cancer cell migration and metastasis [
43,
44]. Over-expression and phosphorylation of FAK also correlate with invasion and metastasis therefore affect the prognosis [
45,
46]. FAK-mediated signaling and functions are involved in the development and progress of tumor [
47]. Applying of the FAK inhibitor can effectively reduce the invasion and metastasis of tumor tissue [
48]. These are in keeping with the our results, which indicating prognosis of OSCC is associated with the TMB-related DEGs we screened.
Seven TMB-related genes (CTSG, COL6A5, GRIA3, CCL21, ZNF662, TDRD5 and GSDMB) were selected via differential analysis, univariate Cox analysis and LASSO Cox analysis. Among the 7 genes, CTSG is confirmed as a potential biomarker in OSCC and NSCLC, and expression of CTSG is highest in adenocarcinoma [
49,
50]. The expression of CCL21 is related to the poor clinical outcomes in OSCC patients via CCL21/CCR7 axis by activating the JAK2/STAT3 signaling pathway [
51,
52]. ZNF662 gene caused by epigenetic changes through DNA methylation is also related to the progression of OSCC [
53]. Moreover, a risk signature constructed by using COL6A5 performed well in stratifying OSCC patients with different prognosis and could distinguish survival status of OSCC patients [
54]. GRIA3, as glutamate receptor, is involved in the process of tumor progression in pancreatic cancer [
55]. The TDRD5 is involved in the DNA methylation and has prognostic value for patients with hepatocellular carcinoma [
56]. The GSDMB is highly expressed in cancer tissues and is connected with poor prognosis by relapse-free survival, and therefore has been used as a potential novel prognostic marker [
57]. These indicate that the TMB-related genes we screened may relate to the prognosis of cancer patients. The results agree with the researches that TMB-related genes have been identified in many types of cancers to help us understand progression of cancers and may assist clinical doctors to predict the prognosis of many types of cancers [
58‐
60], which is in accord with our results.
Patients were then assigned to high- and low-risk groups according to the median of Risk Score. The results showed that patients in the high-risk group had lower OS than those in the low-risk group. The Risk Score model might be a reliable prognostic indicator for the OSCC patients. Even when taking into account other clinical variables, the Risk Score model had independent prognostic value. Head and neck squamous cell carcinoma patients with high TMB level have worse prognosis than those with low TMB [
34]. And TMB-related genes have been described as a powerful prognostic biomarker for patients with bladder cancer [
61]. TMB-related genes may also serve as a potential biomarker with clinical benefit in patients with NSCLC [
62]. There are also many prognostic model characterizing TMB-related genes expression levels in other cancer like hepatocellular carcinoma, osteosarcoma and colon cancer [
63‐
65]. Our results are consistent with these previous researches. It indicates that the Risk Score model constructed by 7 TMB-related genes may be helpful for the prediction of OSCC with different prognosis. Previous study has identified a 13-gene signature to predict survival of patients with OSCC [
66], and the model in our study was simplified to 7 TMB-related genes. Nomogram model, built by using degree of differentiation and Risk Score, was also able to be reliable in predicting the OS of OSCC patients at 1 year, 3 years and 5 years, which makes the prognostic value of Risk Score model more reliable.
The immune cell infiltration results showed that the infiltration proportions of the native B cells, M2 macrophage, resting mast cells and CD4 memory resting T cells were higher in low-risk group compared to the high-risk group, while the infiltration proportions of eosinophils, activated NK cells and follicular helper T cells were lower in low-risk group compared to the high-risk group. The M2 macrophage is reported to promote cancer progress and to be connected with poor outcome in certain cancer types [
67]. The activated NK cells may serve as anti-tumor therapy by secreting IFN-γ and TNF-α to suppress tumor cell cycle [
68]. These articles are in line with our results, suggesting the Risk Score model was reliable to stratify patients by survival time.
However, there are some limitations of our study. Firstly, the main sources of our data were from public database and it was driven by statistics of retrospective data, so the best cutoff value is needed to be determined before clinical application. Secondly, the establishment and verification of the signature were based on the TCGA and GEO datasets. And the HPV status of the TCGA cohort was unknown, which makes the prognosis of the OSCC patients less reliable because the HPV status is an important risk factor affecting the prognosis of patients with head and neck cancer [
69]. Thirdly, the inhomogeneity of data in the public databases also makes the Risk Score model less reliable regarding to the prognosis of cancer patients. Therefore, robustness of the signature will be necessary to be verified using larger external datasets in the future.
In
conclusion, the prognostic signature reliably predicted the survival of patients with OSCC. Potential clinical use of the signature are driven by its strong prognostic performance, but the performance of the signature is still required to be verified in larger clinical samples.
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