Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 

Ahead of print publication  

Identification of key gene signatures and their characterization by expression correlation with drug sensitivity in smoking-associated oral squamous cell carcinoma

1 Central Research Laboratory, K. S. Hegde Medical Academy, Nitte (Deemed to be University), Deralakatte, Mangalore, Karnataka, India
2 Department of Oncology, Justice K S Hegde Charitable Hospital, K. S. Hegde Medical Academy, Nitte (Deemed to be University), Deralakatte, Mangalore, Karnataka, India
3 Department of Biochemistry, K. S. Hegde Medical Academy, Nitte (Deemed to be University), Deralakatte, Mangalore, Karnataka, India

Date of Submission13-Jul-2021
Date of Decision12-Aug-2021
Date of Web Publication11-Nov-2022

Correspondence Address:
Prakash Patil,
Central Research Laboratory, K S Hegde Medical Academy, Nitte (Deemed to be University), Deralakatte, Mangalore - 575 018, Karnataka
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.jcrt_1120_21

 > Abstract 

Aims: Oral squamous cell carcinoma (OSCC), a most frequent type of head-and-neck cancer, is becoming more common and posing a substantial health risk. Using a network biology strategy, this study intended to find and investigate critical genes associated with OSCC.
Materials and Methods: The extended protein–protein interaction networks for differentially expressed genes related to smoking and nonsmoking conditions of OSCC were constructed and visualized using Cytoscape software. The hub genes/proteins were determined based on degree and betweenness centrality measures and then evaluated and validated for expression using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and their relationship to the sensitivity of small molecules was discovered utilizing the Gene Set Cancer Analysis (GSCA) web server.
Results: A total of 596 differentially expressed genes were screened, and four genes, interleukin (IL)-6, JUN, tumor necrosis factor (TNF), and vascular endothelial growth factor A (VEGFA), were identified as hub proteins, and their expression and overall survival in head-and-neck cancers were further investigated using GEPIA2. TNF and VEGFA gene expressions were considerably greater in cancers when compared to normal samples, while JUN and IL-6 gene expressions were not statistically significant. Further, these hub proteins are found to have a substantial favorable correlation with overall survival of head-and-neck cancer patients. Finally, GSCA was used to predict gene-specific potential drugs that act on these molecules by combining mRNA expression and drug sensitivity data from the Genomics of Drug Sensitivity in Cancer and the Cancer Therapeutics Response Portal.
Conclusions: The hub genes/proteins identified in this study could help researchers better understand the molecular processes involved in the progression and metastasis of oral cancer in smokers.

Keywords: Hub genes, oral squamous cell carcinoma, protein–protein interaction networks, smoking, topological analysis

How to cite this URL:
Gollapalli P, Alagundagi D, Ghate SD, Shetty VV, Shetty P, Patil P. Identification of key gene signatures and their characterization by expression correlation with drug sensitivity in smoking-associated oral squamous cell carcinoma. J Can Res Ther [Epub ahead of print] [cited 2022 Dec 9]. Available from: https://www.cancerjournal.net/preprintarticle.asp?id=361018

 > Introduction Top

Oral cancer is on the rise, with an estimated 657,000 new cases with >330,000 deaths reported globally.[1],[2] Although cancer incidence is distributed unevenly, oral squamous cell carcinoma (OSCC) makes up 40% of all the cancers reported from Southeast Asia, with India showing the highest.[3],[4] In India, the incidence and mortality rates of oral cancer are approximately 10.4% and 9.3%, respectively, which ranks number one among men and number three among women.[5] Despite the advances in the treatment or therapeutic strategies, the 5-year survival rate has been remained at 50% for the past few decades.[6] The major risk factors for the development and metastases of oral cancers in India are the consumption of tobacco (17.4%) and excessive consumption of alcohol (6.5%).[5] In addition, other risk factors such as poor oral hygiene, viral infection, occupational exposure to chemicals/toxins, malnutrition, and also genetic factors have been attributed to the development of oral cancer.[7],[8],[9] The chewing of betel quid containing areca nut, tobacco, and lime has long been strongly considered to be associated with an increased risk of oral cancer in the Indian subcontinent and some parts of Southeast Asia. Therefore, the use of tobacco and alcohol has been synergistically associated and is the most important risk factor for developing squamous cell carcinomas in the oral cavity.[10]

Cancer metastases are a leading cause of death because cancer cells are known to rewire their metabolism and energy production networks in the tumor microenvironment to support and enable rapid proliferation, progression, and survival in harsh conditions, by dysregulating tumor suppressor and oncogenes.[11] In the present clinical practices, intra-tumor (within a tumor) and inter-tumor (tumor by tumor) molecular heterogeneity and complexity have sometimes led to low-response rates to therapy.[12] These heterogeneous responses could be due to the varied molecular interactions between various proteins involved in development and metastases of oral cancer. Hence, we hypothesize that these differences are varied due to the different etiological factors including smoking and nonsmoking conditions. In this respect, this study applies a comprehensive network biology approach to mine the differentially expressed genes (DEGs) associated with OSCC in both nonsmoking and smoking conditions from the public database, NCBI. An extended protein–protein interaction (PPI) network was constructed for both conditions of OSCC by seed proteins, which consist of direct PPI neighbors and their interactions. The significant hub genes in the networks were determined by topological analysis and their association with molecular pathways that regulate the oral cancer was investigated. Further, the backbone network was constructed to identify key nodes and the shortest paths were also investigated.

 > Materials and Methods Top

Mining of differentially expressed genes associated with oral squamous cell carcinoma

The DEGs associated with OSCC were identified using NCBI database (http://www.ncbi.nlm.nih.gov/) for both smoking and nonsmoking conditions. The DEGs associated with OSCC in nonsmoking were retrieved by using keywords, namely “Oral Squamous Cell Carcinoma” AND “Homo sapiens” AND “Differentially Expressed Genes” AND “Non-smoking.” Further, the search terms “Oral Squamous Cell Carcinoma” AND “Homo sapiens” AND “Differentially Expressed Genes” AND “Smoking” were used to retrieve the DEGs associated with OSCC in smoking individuals. A total of 437 DEGs associated with nonsmoking and 159 genes with smoking-related OSCC were obtained as seed proteins for the construction of PPI networks [Table S1].

Construction of protein–protein interaction networks associated with oral squamous cell carcinoma

The Search Tool for the Retrieval of Interacting Genes/Proteins database (STRING 11.0) was utilized to construct the PPI networks associated with OSCC in both nonsmoking and smoking conditions.[13] The STRING search for the nearest neighbors with direct interactions by giving a list of proteins/genes as input. The source of interaction in STRING is based on text mining, experiments, databases, co-expression, neighborhood, gene fusion, and co-occurrence. A high confidence (0.700) was used to construct the PPI network. Further, these PPI networks were visualized by using Cytoscape 3.3.0 tool[14] and default parameters are used to calculate the network node properties.

Topological profiling of PPI networks and hub genes identification

Several topological parameters such as degree (D), betweenness centrality (BC), clustering coefficient, and closeness centrality (CC) were considered to evaluate the nodes of the PPI networks. The hub or bottleneck protein in a network was determined based on degree and BC measures. The network analyzer tool, a plug-in for Cytoscape 3.3.0, was used to calculate the topological properties of two PPI networks.[15] The subnetwork from the two global/giant networks (nonsmoking and smoking OSCCs) was obtained using cytoHubba, a plug-in for Cytoscape. This tool provides 11 topological analysis methods, where we used bottleneck, degree, BC, CC, and radiality parameters to find the top 10 proteins in the entire network.[16]

Creation of the backbone network of protein–protein interaction

According to the graph theory, the proteins with high BC values are often regarded to constitute the bottlenecks that control the information flow in the transportation network.[17] We chose 5% of the total nodes in the network as the crucial node with the highest BC value. A backbone network will be formed by the proteins with higher BC values and the links connecting them. Thus, we used this parameter to create a backbone network for smoking- and nonsmoking-associated OSCC.

Differential expression and overall survival analysis of hub genes

We used the gene expression data available for head-and-neck squamous cell carcinoma (HNSCC) to analyze the differential expression of hub genes using the Gene Expression Profiling Interactive Analysis (GEPIA) online tool (http://gepia2.cancer-pku.cn/) because there are no databases that can give expression data for OSCC.[18] In this study, the expression DIY (box plot) was performed with a log2FC value of 2 and a P value of 0.05. The expression was matched to TCGA normal and GTEx data with a jitter size of 0.4. Furthermore, the influence of hub genes on the survival of patients with HNSCC was also investigated using Kaplan-Meier plotter website (http://kmplot.com/analysis/) with default parameters.[19]

Determination of relationship between expression of hub genes and drug sensitivity

In this study, we used the Gene Set Cancer Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/GSCA/#/), an online tool for analysis of cancer genomics (expression, single-nucleotide variation, copy number variation, and methylation) and immunogenomics to identify the potentially sensitive drugs for the hub genes.[20] It combines the mRNA expression and drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/) and the Cancer Therapeutics Response Portal (CTRP, https://portals.broadinstitute.org/ctrp/). GDSC has the collection of IC50 for 265 small molecules in 860 cell lines and its corresponding mRNA gene expression data. However, the CTRP has a collection of IC50 for 481 small molecules in 1001 cell lines and its corresponding mRNA gene expression.[21] The correlation between the expression of hub genes and drug IC50 in pan-cancer was determined by Pearson correlation analysis using the P value adjusted to the false discovery rate <0.05.[22],[23]

 > Results Top

Although the large number of studies had been reported on OSCC s, there are no investigations that analyzed the network of OSCC under nonsmoking and smoking conditions. Therefore, we mined the literature using NCBI database to extract the DEGs related to nonsmoking and smoking OSCCs and construct the PPI network.[17] Besides, we performed network topological analysis to evaluate the DEGs in functional level, which are significant in these conditions of OSCC. Subsequently, topological parameters were analyzed in order to find the other key proteins in the network.

The giant component of the protein–protein interaction network

The giant component of nonsmoking and smoking OSCC-associated PPI networks generated by STRING consists of 326 nodes with 4649 edges and 263 nodes with 3023 edges, respectively [Figure 1]a and [Figure 1]b. The results of topological parameter analysis of each node from two giant networks are listed in [Table 1], which includes degree (D), BC, and CC. The largest degree in the giant network of nonsmoking and smoking OSCCs was found to be 132 and 68, respectively [Table 1]. It is observed that each of the giant networks was characterized by the small number of highly connected nodes, and other some nodes have very few conditions, which is a classical character of PPI network.[24] The hub genes were selected based on the five classification methods in cytoHubba. The top ten genes are shown in [Table 2], and finally, the central genes for both conditions were identified by overlapping the ten genes from each method. The overlapped genes TP53, EGFR, AKT1, MAPK1 and JUN, VEGFA, TP53, AKT1, interleukin-6 (IL)-6, and tumor necrosis factor (TNF) were found for nonsmoking and smoking-related OSCC, respectively [Figure 2]a and [Figure 2]b. These results reveal that JUN, VEGFA, IL-6, and TNF genes were found to be critical during the pathogenesis of smoking-associated OSCC, when compared to nonsmoking OSCC.
Figure 1: The extended giant network of differentially expressed genes developed from Cytoscape 3.0 for nonsmoking oral squamous cell carcinoma condition containing 326 nodes connected with 4649 edges (a) and the smoking oral squamous cell carcinoma condition consisting of 263 nodes with 3023 edges (b)

Click here to view
Figure 2: Venn diagram of the hub genes from nonsmoking (a) and smoking-associated oral squamous cell carcinoma (b) identified by overlapping the first 10 genes in the five classification methods of cytoHubba

Click here to view
Table 1: The general network measurements for giant and subnetworks of oral squamous cell carcinoma in nonsmoking and smoking conditions

Click here to view
Table 2: Top 10 hub genes rank in cytoHubba based on degree, betweenness centrality, closeness centrality, bottleneck, and radiality centrality measures of the nodes in the nonsmoking and smoking networks

Click here to view

Key nodes in the protein–protein interaction network

A highly connected small number nodes are crucial than any other less connected nodes in the network, which are considered as hub nodes and is one of the scale-free distribution network properties.[25],[26] Even when the node's degree is low, the nodes with large BC function as a bottleneck. Here, we selected the nodes with BC and/or degree values larger than the mean plus standard deviation. The network for nonsmoking conditions comprises 50 nodes with large degree values [Table 3] and 24 nodes with high BC values [Table 4] were determined. Furthermore, the 25 nodes were also defined with both large degrees and BC [Table 5]. Similarly, the network for smoking condition comprises 10 nodes with large degree [Table 3], 23 nodes with high BC [Table 4] value, and 10 nodes with both large degree and high BC [Table 5].
Table 3: List of nodes with large degree and their closeness centrality values from the giant networks of nonsmoking and smoking conditions

Click here to view
Table 4: List of nodes with high betweenness centrality and their closeness centrality values from the giant networks of nonsmoking and smoking conditions

Click here to view
Table 5: List of nodes with high betweenness centrality and large degree with their function from the giant networks of nonsmoking and smoking oral squamous cell carcinoma conditions

Click here to view

Backbone network of the protein–protein interaction network

To establish a backbone network, we extracted the genes with the top 5% of BC values and the linkages between them from the PPI for DEGs of nonsmoking and smoking conditions. BC was first introduced to determine the centrality of nodes in a network. By definition, most of a network's shortest paths pass through nodes with high BC. These nodes serve as a bottleneck, limiting communication between other nodes in the network. The backbone network for nonsmoking condition comprised 19 nodes with high BC values, each having a size that corresponded to their BC value, as well as the 170 links that connected them [Figure 3]a. AKT1 was located at the center of the backbone network with the highest CC value. The AKT1 also had the second BC and second-largest degree values of 0.0611 and 140, respectively; thus, it controls the information flow in the backbone network [Table S2]. In comparison, the backbone network for smoking comprises the 6 nodes with high BC values connected with 15 edges [Figure 3]b, where IL-6 was located at the center of backbone network with the highest CC value, second high BC value, and fourth highest degree value [Table S3].
Figure 3: The topology of the backbone network for nonsmoking oral squamous cell carcinoma condition consists of 19 nodes with high betweenness centrality value (5% of the total nodes) (a) and smoking oral squamous cell carcinoma condition with 6 nodes with high betweenness centrality value (b)

Click here to view

Validation of hub genes

GEPIA2 online tool was used to analyze the differential expression of the hub genes IL-6, JUN, TNF, and VEGFA, which might be critical for smoking-associated OSCC. The GEPIA box plot analysis of the identified 519 HNSCC and 44 normal samples based on the TCGA data reveals that the expression levels of TNF and VEGFA genes were significantly higher (P = 0.01) in HNSCC, compared to normal. However, the expression of JUN gene was not much significant between HNSCC and normal samples, but IL-6 gene, which was located at the center of backbone network of smoking-associated OSCC, was found not differentially expressed in HNSCC and normal samples [Figure 4]. Furthermore, the association between hub gene expression and the overall survival in HNSCC patients was examined using Kaplan-Meier plotter tool. According to the median value of hub genes, JUN, TNF, and VEGFA expressions were found to be positively associated with the overall survival of HNSCC patients, whereas IL-6 expression was found to be negatively associated with the overall survival [Figure 5]. Overall, these results suggest that the two highly expressed genes TNF and VEGFA might predict a worse prognosis in OSCC patients (P < 0.05).
Figure 4: The expression levels of the four hub genes interleukin-6, JUN, tumor necrosis factor, and vascular endothelial growth factor A in smoking-associated oral squamous cell carcinoma. The results are based on TCGA data screening in head-and-neck squamous cell carcinoma (n = 519) and normal control (n = 44) from Gene Expression Profiling Interactive Analysis 2 database through the expression DIY (box plot) (P < 0.05), the black boxes are represented as the tumor sample and the white boxes are represented as the normal tissue

Click here to view
Figure 5: Kaplan–Meier survival curves of overall survival for the four hub genes interleukin-6, JUN, tumor necrosis factor, and vascular endothelial growth factor A in smoking-associated oral squamous cell carcinoma. The survival curves are plotted using the Kaplan-Meier plotter web server. Survival curves are represented as red and black lines for high and low expression, respectively. The number of tumor and normal tissues is 499 and 86, respectively. The P values are calculated using log-rank statistics

Click here to view

Correlation of smoking-associated oral squamous cell carcinoma hub gene expression with drug sensitivity

The bubble plot analysis using the GDSC and CTRP IC50 drug data from GSCA database was performed to find the correlation of four hub genes (IL-6, JUN, TNF, and VEGFA) expression in pan-cancer and their sensitivity to the small molecule drugs [Figure 6]. We found that the expression of JUN has a high positive correlation while the expression of TNF has a negative correlation with the sensitivity of the top 30 GDSC and CTRP drugs in pan-cancer. In addition, the IL-6 expression has a moderate positive correlation and no correlation to the top 30 CTRP and GDSC drugs' sensitivity, respectively. However, VEGFA expression was found no correlation to the sensitivity of top 30 CTRP and GDSC drugs. In total, the positive correlation means that the gene high expression is resistant to the drug, vice versa. For example, looking up from the GDSC IC50 data, the top five small molecule drugs that are found to be positively correlated with the high expression of hub genes IL-6 are WZ3105, MPS-1-IN-1, THZ-2-102-1, NPK76-II-72-1, and I-BET-762; JUN are I-BET-762, TPCA-1, vorinostat, TL-1-85, and NG-25; TNF are lapatinib, 17-AAG, BMS-754807, erlotinib, and docetaxel; and VEGFA are vorinostat, navitoclax, CP466722, TPCA-1, and GSK1070916 [Table 6].
Figure 6: The correlation between the expression of hub genes and the drug sensitivity in head-and-neck squamous cell carcinoma using Genomics of Drug Sensitivity in Cancer and Cancer Therapeutics Response Portal IC50 data in Gene Set Cancer Analysis web server

Click here to view
Table 6: List of top five drugs from the Genomics of Drug Sensitivity in Cancer half-maximal inhibitory concentration data that have a positive correlation to the expression of hub genes

Click here to view

 > Discussion Top

In this study, 596 DEGs have been searched in association with nonsmoking- (n = 437) and smoking-associated OSCC (n = 159). The network derived from seed proteins converted from these genes consists of giant network and subnetworks. In the giant network of nonsmoking OSCC, there are 17 proteins and there are 10 proteins in smoking OSCC network with large and BC. The investigation was carried out using network analyzer, a Cytoscape software plugin. Although various topology parameters exist in a network theory, the two fundamental measures degree and betweenness had been widely used to evaluate the proteins in the different PPIs associated with diseases. The node degree distribution is the most important topological characteristic of any network since it determines whether it is scale free or not. On a logarithmic scale, the degree distribution of a scale-free network may be shown to follow a power-law line, with fewer high-degree biomolecules/nodes (hubs) and a large number of low-degree biomolecules/nodes.[27],[28] The effect of degree and BC is disentangled by dividing all proteins in a network into two categories: nonhub-bottleneck (small degree and large BC); hub-nonbottleneck (large degree but low BC); nonhub-bottleneck (small degree but high BC); and hub-bottleneck (large degree and large BC).[25]

Four hub genes TNF, VEGFA, JUN, and IL-6 specific to smoking-associated OSCC were identified via constructing the protein–protein interaction network and topological profiling analysis using the DEGs in OSCC by thorough screening, analysis, and verification. Among the four hub genes, TNF and VEGFA gene expressions were significantly higher in HNSCC patients when compared to normal individuals, while the expression of JUN and IL-6 genes was not significantly different between OSCC and normal. Further, we found a significant positive correlation of TNF, VEGFA, and JUN genes with the overall survival of the HNSCC patients indicating their role in poor prognosis and might serve as potential biomarkers for OSCC as well. Earlier reports found that the higher expression of TNF-α in the saliva of OSCCs is mediated by production and activity of cytokines in the tumor microenvironment and correlates with the HNSCC clinical stages of poor differentiation.[29],[30],[31] In oral cavity cells, higher TNF expression with other inflammatory molecules affects the proliferation, migration, and the growth factor levels that are necessary for the homeostasis and healing of oral mucosa.[32] Furthermore, the TNF-α enhances the expression of different angiogenic factors, influences different biological processes through ERK signaling, and promotes angiogenesis of tumor lymph in HNSC.[33] Along with TNF, VEGFA is also involved in angiogenesis, a process necessary for tumor development, invasion, and metastasis, which aids in the development and spread of tumor through supplying nutrition and oxygen to tumor cells.[34],[35] Earlier, it has been reported that the higher expression of VEGFA was observed in OSCC patients and also its expression pattern was statistically significant across the tumor grades but failed to find a significant association with the OSCC clinicopathology.[36],[37],[38] However, a recent study found an association of VEGFA expression with the histological differentiation and size of tumor, and the expression was significantly decreased after radiotherapy in HNSCC patients.[39],[40] In addition, HNSCC patients with lower serum VEGFA levels had a greater treatment response rate to radiotherapy or radio-chemotherapy, compared to those with higher VEGFA levels in the serum.[41]

IL-6, a pro-inflammatory cytokine, levels were found varied concentrations in saliva and blood of OSCC patients, and the recent study revealed that the salivary IL-6 levels are considerably higher than blood of OSCC IL-6 levels.[42] Moreover, the histopathological studies have shown that IL-6 may be used as a biomarker to predict the severity of OSCC in affected individuals as it shows a connection between IL-6 genotype and cancer risk.[43] In addition, it has been observed that HNSC patients with high IL-6 expression in tumors had low overall survival, showing its association with poor prognosis in HNSCC sufferers.[44] A recent preclinical study reveals the role of IL-6 as a modulator of dasatinib-cetuximab response while the clinical studies suggested IL-6 as a predictive biomarker for dasatinib-cetuximab combination treatment outcome in HNSCC patients.[45] c-JUN, a subunit of transcription factor AP-1, has been recognized as a cancer promoter and its role in the onset and progression of OSCC has already been reported elsewhere.[46],[47] However, the overexpression of JUN has a minor impact on OSCC metastasis or invasion, but it is found significant for the development of several cancers.[48] Along with c-FOS, c-JUN controls the overexpression of AXL, which is involved in developing resistance to anticancer treatments in HNSC patients and cell lines.[49] Furthermore, the siRNA-mediated knockdown and CRISPR/Cas9-mediated knockout of JunB have shown significantly reduced invasion and migration of HNSC cells without the loss of morphological mesenchymal features and markers, thereby suggesting the potential role of JunB in the invasion, migration, and metastasis of HNSC.[50]

 > Conclusions Top

In this study, the four hub genes JUN, VEGFA, IL-6, and TNF were identified by comparative topological analysis of PPI networks of DEGs and they might be regarded as promising biomarkers in smoking-associated OSCC. Also, identified the potential sensitive drugs in correlation to the overexpression of these hub genes. These findings will need to be further studied in order to explore their application in the clinical setting.


The authors are thankful to the Staff and Faculty of Central Research Laboratory, K S Hegde Medical Academy, and the Registrar, Nitte (Deemed to be University), Mangalore, India, for providing all the support and facilities to complete this work.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

 > References Top

World Health Organization (WHO). WHO | Cancer Country Profiles 2020 – Greece. WHO. World Health Organization; 2020. Available from: http://www.who.int/cancer/country-profiles/en/. [Last accessed on 2020 Nov 04].  Back to cited text no. 1
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394-424.  Back to cited text no. 2
Singh MP, Kumar V, Agarwal A, Kumar R, Bhatt ML, Misra S. Clinico-epidemiological study of oral squamous cell carcinoma: A tertiary care centre study in North India. Oral Biol Craniofacial Res 2016;6:32-5.  Back to cited text no. 3
Dhanuthai K, Rojanawatsirivej S, Thosaporn W, Kintarak S, Subarnbhesaj A, Darling M, et al. Oral cancer: A multicenter study. Med Oral Patol Oral Cir Bucal 2018;23:e23-9.  Back to cited text no. 4
Singh S, Singh J, Chandra S, Samadi FM. Prevalence of oral cancer and oral epithelial dysplasia among North Indian population: A retrospective institutional study. J Oral Maxillofac Pathol 2020;24:87-92.  Back to cited text no. 5
  [Full text]  
National Institute of Dental and Craniofacial Research. Oral Cancer 5-Year Survival Rates | Data and amp; Statistics | National Institute of Dental and Craniofacial Research. Available from: https://www.nidcr.nih.gov/research/data-statistics/oral-cancer/survival-rates. [Last accessed on 2020 Nov 04].  Back to cited text no. 6
Goldstein BY, Chang SC, Hashibe M, La Vecchia C, Zhang ZF. Alcohol consumption and cancers of the oral cavity and pharynx from 1988 to 2009: An update. Eur J Cancer Prev 2010;19:431-65.  Back to cited text no. 7
Perry BJ, Zammit AP, Lewowski AW, Bashford JJ, Dragovic AS, Perry EJ, et al. Sites of origin of oral cavity cancer in nonsmokers vs smokers: Possible evidence of dental trauma carcinogenesis and its importance compared with human papillomavirus. JAMA Otolaryngol Head Neck Surg 2015;141:5-11.  Back to cited text no. 8
Jiang X, Wu J, Wang J, Huang R. Tobacco and oral squamous cell carcinoma: A review of carcinogenic pathways. Tob Induc Dis 2019;17:29.  Back to cited text no. 9
Scully C. Oral cancer aetiopathogenesis; past, present and future aspects. Med Oral Patol Oral Cir Bucal 2011;16:306-11.  Back to cited text no. 10
Lyssiotis CA, Kimmelman AC. Metabolic interactions in the tumor microenvironment. Trends Cell Biol 2017;27:863-75.  Back to cited text no. 11
Lawrence MS, Sougnez C, Lichtenstein L, Cibulskis K, Lander E, Gabriel SB, et al. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 2015;517:576-82.  Back to cited text no. 12
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019;47:D607-13.  Back to cited text no. 13
Su G, Morris JH, Demchak B, Bader GD. Biological network exploration with Cytoscape 3. Curr Protoc Bioinformatics 2014;47: 8.13.1-8.13.24.  Back to cited text no. 14
Doncheva NT, Assenov Y, Domingues FS, Albrecht M. Topological analysis and interactive visualization of biological networks and protein structures. Nat Protoc 2012;7:670-85.  Back to cited text no. 15
Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 2014;8:S11.  Back to cited text no. 16
Ran J, Li H, Fu J, Liu L, Xing Y, Li X, et al. Construction and analysis of the protein-protein interaction network related to essential hypertension. BMC Syst Biol 2013;7:32.  Back to cited text no. 17
Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: An enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 2019;47:W556-60.  Back to cited text no. 18
Nagy Á, Munkácsy G, Győrffy B. Pancancer survival analysis of cancer hallmark genes. Sci Rep 2021;11:6047.  Back to cited text no. 19
Liu CJ, Hu FF, Xia MX, Han L, Zhang Q, Guo AY. GSCALite: A web server for gene set cancer analysis. Bioinformatics 2018;34:3771-2.  Back to cited text no. 20
Rees MG, Seashore-Ludlow B, Cheah JH, Adams DJ, Price EV, Gill S, et al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat Chem Biol 2016;12:109-16.  Back to cited text no. 21
Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 2012;483:570-5.  Back to cited text no. 22
Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 2013;41:D955-61.  Back to cited text no. 23
Lima-Mendez G, Van Helden J. The powerful law of the power law and other myths in network biology. Mol Biosyst 2009;5:1482-93.  Back to cited text no. 24
Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M. The importance of bottlenecks in protein networks: Correlation with gene essentiality and expression dynamics. PLoS Comput Biol 2007;3:713-20.  Back to cited text no. 25
Chang HC, Chu CP, Lin SJ, Hsiao CK. Network hub-node prioritization of gene regulation with intra-network association. BMC Bioinformatics 2020;21:1-11.  Back to cited text no. 26
Barabási AL, Oltvai ZN. Network biology: Understanding the cell's functional organization. Nat Rev Genet 2004;5:101-13.  Back to cited text no. 27
Ba Q, Li J, Huang C, Li J, Chu R, Wu Y, et al. Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene. Toxicol Appl Pharmacol 2015;283:83-91.  Back to cited text no. 28
Rhodus NL, Ho V, Miller CS, Myers S, Ondrey F. NF-κB dependent cytokine levels in saliva of patients with oral preneoplastic lesions and oral squamous cell carcinoma. Cancer Detect Prev 2005;29:42-5.  Back to cited text no. 29
Krishnan R, Thayalan DK, Padmanaban R, Ramadas R, Annasamy RK, Anandan N. Association of serum and salivary tumor necrosis factor-α with histological grading in oral cancer and its role in differentiating premalignant and malignant oral disease. Asian Pacific J Cancer Prev 2014;15:7141-8.  Back to cited text no. 30
Zielińska K, Karczmarek-Borowska B, Kwaśniak K, Czarnik-Kwaśniak J, Ludwin A, Lewandowski B, et al. Salivary IL-17A, IL-17F, and TNF-α are associated with disease advancement in patients with oral and oropharyngeal cancer. J Immunol Res 2020;2020:3928504.  Back to cited text no. 31
Basso FG, Soares DG, Pansani TN, Cardoso LM, Scheffel DL, de Souza Costa CA, et al. Proliferation, migration, and expression of oral-mucosal-healing-related genes by oral fibroblasts receiving low-level laser therapy after inflammatory cytokines challenge. Lasers Surg Med 2016;48:1006-14.  Back to cited text no. 32
Zhang C, Zhu M, Wang W, Chen D, Chen S, Zheng H. TNF-α promotes tumor lymph angiogenesis in head and neck squamous cell carcinoma through regulation of ERK3. Transl Cancer Res 2019;8:2439-48.  Back to cited text no. 33
Mãrgãritescu C, Pirici D, Stîngã A, Simionescu C, Raica M, Mogoantã L, et al. VEGF expression and angiogenesis in oral squamous cell carcinoma: An immunohistochemical and morphometric study. Clin Exp Med 2010;10:209-14.  Back to cited text no. 34
Mãrgãritescu C, Pirici D, Simionescu C, Mogoantã L, Raica M, Stîngã A, et al. VEGF and VEGFRs expression in oral squamous cell carcinoma. Rom J Morphol Embryol 2009;50:527-48.  Back to cited text no. 35
Lim J, Kim JH, Paeng JY, Kim MJ, Hong SD, Lee JI, et al. Prognostic value of activated Akt expression in oral squamous cell carcinoma. J Clin Pathol 2005;58:1199-205.  Back to cited text no. 36
Babiker AY, Almatroodi SA, Almatroudi A, Alrumaihi F, Abdalaziz MS, Alsahli MA, et al. Clinicopathological significance of VEGF and pAkt expressions in oral squamous cell carcinoma. All Life 2020;13:507-15.  Back to cited text no. 37
Subarnbhesaj A, Miyauchi M, Chanbora C, Mikuriya A, Nguyen PT, Furusho H, et al. Roles of VEGF-Flt-1 signaling in malignant behaviors of oral squamous cell carcinoma. PLoS One 2017;12:e0187092.  Back to cited text no. 38
Sridharan V, Margalit DN, Lynch SA, Severgnini M, Zhou J, Chau NG, et al. Definitive chemoradiation alters the immunologic landscape and immune checkpoints in head and neck cancer. Br J Cancer 2016;115:252-60.  Back to cited text no. 39
Kim SK, Park SG, Kim KW. Expression of vascular endothelial growth factor in oral squamous cell carcinoma. J Korean Assoc Oral Maxillofac Surg 2015;41:11-8.  Back to cited text no. 40
Srivastava VK, Gara RK, Rastogi N, Mishra DP, Ahmed MK, Gupta S, et al. Serum vascular endothelial growth factor – A (VEGF-A) as a biomarker in squamous cell carcinoma of head and neck patients undergoing chemoradiotherapy. Asian Pacific J Cancer Prev 2014;15:3261-5.  Back to cited text no. 41
Dineshkumar T, Ashwini BK, Rameshkumar A, Rajashree P, Ramya R, Rajkumar K. Salivary and serum interleukin-6 levels in oral premalignant disorders and squamous cell Carcinoma: Diagnostic value and clinicopathologic correlations. Asian Pacific J Cancer Prev 2016;17:4899-906.  Back to cited text no. 42
Shinagawa K, Yanamoto S, Naruse T, Kawakita A, Morishita K, Sakamoto Y, et al. Clinical roles of interleukin-6 and STAT3 in oral squamous cell carcinoma. Pathol Oncol Res 2017;23:425-31.  Back to cited text no. 43
Gao J, Zhao S, Halstensen TS. Increased interleukin-6 expression is associated with poor prognosis and acquired cisplatin resistance in head and neck squamous cell carcinoma. Oncol Rep 2016;35:3265-74.  Back to cited text no. 44
Stabile LP, Egloff AM, Gibson MK, Gooding WE, Ohr J, Zhou P, et al. IL6 is associated with response to dasatinib and cetuximab: Phase II clinical trial with mechanistic correlatives in cetuximab-resistant head and neck cancer. Oral Oncol 2017;69:38-45.  Back to cited text no. 45
Xu H, Jin X, Yuan Y, Deng P, Jiang L, Zeng X, et al. Prognostic value from integrative analysis of transcription factors c-Jun and Fra-1 in oral squamous cell carcinoma: A multicenter cohort study. Sci Rep 2017;7:1-9.  Back to cited text no. 46
Brennan A, Leech JT, Kad NM, Mason JM. Selective antagonism of cJun for cancer therapy. J Exp Clin Cancer Res 2020;39:1-16.  Back to cited text no. 47
Zhang X, Zhang L, Tan X, Lin Y, Han X, Wang H, et al. Systematic analysis of genes involved in oral cancer metastasis to lymph nodes. Cell Mol Biol Lett 2018;23:1-14.  Back to cited text no. 48
Badarni M, Prasad M, Balaban N, Zorea J, Yegodayev KM, Joshua BZ, et al. Repression of AXL expression by AP-1/JNK blockage overcomes resistance to PI3Ka therapy. JCI Insight 2019;5:125341.  Back to cited text no. 49
Hyakusoku H, Sano D, Takahashi H, Hatano T, Isono Y, Shimada S, et al. JunB promotes cell invasion, migration and distant metastasis of head and neck squamous cell carcinoma. Exp Clin Cancer Res 2016;35:6.  Back to cited text no. 50


  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]


     Search Pubmed for
    -  Gollapalli P
    -  Alagundagi D
    -  Ghate SD
    -  Shetty VV
    -  Shetty P
    -  Patil P
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

  >Abstract>Introduction>Materials and Me...>Results>Discussion>Conclusions>Article Figures>Article Tables
  In this article

 Article Access Statistics
    PDF Downloaded5    

Recommend this journal