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| ORIGINAL ARTICLE |
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| Year : 2020 | Volume
: 12
| Issue : 1 | Page : 13-23 |
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Study on TNFRSF mRNA Alterations and P53 Mutation in Head and Neck Squamous Cell Carcinoma
Thavarajah Rooban MDS, Professor 1, Immanuel Joseph2, Selvan Preetha2, Joshua Elizabeth2, Umadevi Krishna Mohan Rao2, Kannan Ranganathan2
1 Marundeeswara Oral Pathology Services and Analytics, Shollinganallur, Chennai, India
2 Department of Oral Pathology and Microbiology, Ragas Dental College and Hospital, Affiliated to the Tamil Nadu Dr MGR Medical University, Uthandi, Chennai, India
| Date of Submission | 14-Nov-2019 |
| Date of Decision | 18-Jan-2020 |
| Date of Acceptance | 17-Feb-2020 |
| Date of Web Publication | 12-Jun-2020 |
Correspondence Address:
Dr. Thavarajah Rooban
Marundeeswara Oral Pathology Services and Analytics, B-1, Mistral Apartments, Wipro Street, off Rajiv Gandhi IT Highway, Shollinganallur 600119, Chennai, India and Department of Oral Pathology and Microbiology, Ragas Dental College and Hospital, Affiliated to the Tamil Nadu Dr MGR Medical University, 2/102, East Coast Road, Uthandi, 600119, Chennai
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jofs.jofs_139_19
Introduction: Head and neck squamous cell cancer (HNSCC) is a common cancer worldwide. It has been associated with TP53 mutation and chronic inflammation. The control genes of inflammation, Tumor Necrosis Factor Receptor Superfamily (TNFRSF) in HNSCC has not been widely reported. The impact of the TNFRSF and survival and cell death regulation signalling (SCDRS) can be studied at protein, gene, mRNA and transcription level. In this manuscript, the association of mRNA of TNFRSF and SCDRS genes in treatment naïve HNSCC with TP53 mutation is studied. Materials and Methods: TP53 mutation, tobacco use and mRNA levels of TNFRSF and SCDRS genes of 520 HNSCC cases were collated and analysed. Statistical and differential expression (DE) analysis was performed. Results: A total of 12 genes of the 51 genes studied were DE between TP53 subgroups. They were SCDRS genes (BAD, CASP9, GSK3B, NFKB2, TGFBR1, TGFBR2) and TNFRSF genes (TNFRSF10A/11B/14/25/6B/9). The network analysis and subsequent KEGG pathway analysis identified several key pathways including vital cancer pathways and transcriptional pathways in cancer. The key genes in the network that modulate TNFRSF and SCDRS mRNA expression in wild and mutant TP53 situation are presented. Conclusion: The present work identified certain key TNFRSF and SCDRS mRNAs that could differ based on TP53 status and count with tobacco use. Also this study identified certain pathways where the gene network could potentially alter the HNSCC progression, treatment response and prognosis. This adds to our knowledge of TP53 and inflammation in HNSCC carcinogenesis.
Keywords: Cell survival, head and neck cancers, inflammation, TNF, P53, TNFRSF, squamous cell carcinoma
How to cite this article:
Rooban T, Joseph I, Preetha S, Elizabeth J, Rao UM, Ranganathan K. Study on TNFRSF mRNA Alterations and P53 Mutation in Head and Neck Squamous Cell Carcinoma. J Orofac Sci 2020;12:13-23 |
How to cite this URL:
Rooban T, Joseph I, Preetha S, Elizabeth J, Rao UM, Ranganathan K. Study on TNFRSF mRNA Alterations and P53 Mutation in Head and Neck Squamous Cell Carcinoma. J Orofac Sci [serial online] 2020 [cited 2023 Jun 9];12:13-23. Available from: https://www.jofs.in/text.asp?2020/12/1/13/286479 |
| Introduction | |  |
Head and Neck Squamous Cell Carcinoma (HNSCC) is a major health burden. Globally, HNSCC is the sixth most common cancer that carries significant mortality, morbidity and financial implications, mostly in developing nations. HNSCC has strong association with tobacco use.[1]Several genes including the TP53 are involved in Oral Carcinogenesis (OC). Mutation of the wild type TP53 is associated with more than 50% of all tumors and 50 to 75% of all HNSCC.[2] The gene TP53 controls the genomic stability and mutations in this gene, confers the cell an array of pro-oncologic capabilities, including modulations of inflammatory factor such as NFκB, Tumor Necrosis Factors (TNF) and growth factors.[3] The significant role of TP53 mutants correlates in chronic tissue inflammation and has been the subject of research in recent years.[4],[5],[6],[7]
The association of chronic inflammation and carcinogenesis is well documented.[4],[5],[6],[7] There is an emerging body of literature on the association of TP53 and the inflammatory process. Mutant p53 promotes inflammation associated with cancer/carcinogenesis through the TNF signalling pathway, notably interacting with tumor suppressor RasGAP Disabled2 interacting Protein (DAB2IP), which in turn modulates the NF-κB.[3]However, certain animal studies report that TNF induced chronic inflammation does not affect tumorigenesis driven by p53 mutation.[6]
The downstream effect of TP53 pathway is cell survival or death. This is regulated by a group of genes, referred to as survival and cell death regulation signalling (SCDRS) genes. These genes have been implicated in HNSCC.[2] These genes are known to be involved in imparting the transformed cells the ability to evade immune surveillance, by-pass apoptosis check mechanisms and acquire immortality.
TNF signalling pathways and TNF receptors are being increasingly associated with several disease process including cancer and autoimmune disorders.[8],[9],[10],[11] The role of the TNF receptor super family(TNRSF) in HNSCC largely remains unexplored, though their association has been previously documented in several other pathologies such as rheumatoid arthritis.[9],[10]
The aim of the present work was to study the mRNA expression of TNRSF genes as well as their relation to the expression of SCDRS genes in a cohort of HNSCC. The present work also aims to study the influence of the p53 mutations and influence of tobacco use on mRNA expressions of TNRSF and SCDRS genes.
| Materials and Methods | | |
This study was a secondary data analysis of HNSCC data obtained from the Cancer genome Atlas More Details.[12],[13] Only patients that had data pertaining to tobacco habits were included for the study. The data used in the study have been previously published.[14]
Using the web-portal www.cBioportal.com the clinical details of age (in years), gender (male/female), tobacco use (never user/ever user) were downloaded along with TP53 mutation status (wild-wTP53)/mutated-mTP53). HNSCC comprises of heterogenous entities with respect to molecular aspect, prognosis and management. However, in this study cohorts were obtained from dataset, in which the region/site was mostly unspecified (Not otherwise specified, 417 of 540 cases, 77.22%).
The currently described 29 TNFRSF genes were listed.[15] The HNSCC mRNA expression of the TNFRSF genes were downloaded via the portal. Similarly the common survival and cell death regulation signalling genes (SCDRS) viz., NFKB1, NFKB2, CHUK, DIRAS3, FAS, HLA-G, BAD, BCL2, BCL2L1, APAF1, CASP9, CASP8, CASP10, CASP3, CASP6, CASP7, GSK3B, ARL11, WWOX, PEG3, TGFB1, TGFBR1 and TGFBR2 genes mRNA expression were downloaded.
Data thus collected were entered and analysed using the SPSS ver. 24.0. Basic descriptive statistics were presented. Using the www.NetworkAnalyst.ca, (a visual analytics platform for comprehensive gene expression profiling and meta-analysis), the raw data were then analysed for differential expression(DE) using a statistical, visual and network based approach for meta-analysis of expression data tool −www.networkanalyst.ca (version 3; 30.07.2019) platform.[16] Using the single gene expression mode, the date of the TNFRSF along with SCDRS data were subjected to DE analysis. We aimed to identify the genes that are DE between the TP53 statuses, and later defined by the secondary factor of tobacco use.
For the single gene expression table mode, the default variance filter (to remove the data that appears to be less informative or erroneous) and low abundance (to filter off the data with count lower than a threshold) were used.[16] For reliable detection of mRNA transcriptional differences and ensure uniform count distribution, continuous mRNAdata were normalized using log2counts per million. We used commonly used Limma type of statistical approach with p53 mutation status as a primary factor alone and then habit (ever smoking/non-smoking) as secondary, blocking factor.[16] P-value was set as ≤0.05 and data presented.
An attempt was made to identify the gene network of generic association. The networks had a node (visual representation of an involved gene/protein), edge (visual representation of a relation, line that connects two nodes) and a seed (gene/protein). Besides the genes, the default settings of degree (number of connections that a node has with other nodes) and the betweenness (number of shortest path, through a particular node) were used.[14] If the network was large, minimum network option was used.[16]Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis was done to identify significant pathways along with the total number of genes in pathway, number of genes in the depicted pathway, P-value and false detection rate (FDR).[16]
The significant genes/mRNA identified in the parts of the study, were further analysed using network for: 1. Generic Protein-Protein Interaction (PPI); 2. Gene-miRNA interactions; 3. Transcription factor-Gene interactions; 4. Transcription factor-miRNA co-regulatory networks.
For the generic PPI, we employed the InnateDB.[17] For the Gene-MiRNA interaction, we employed arbase and miTarBase databases.[18] For the TF- gene interaction, we used the ENCODE chip sequential data.[19] For TF-miRNA co-regulatory interactions, we employed the human RegNetwork repository data.[20]
| Results | | |
The data retrieved from the cBioportal cancer genomic database consisted of 520 patients of HNSCC, of which males (n = 384) were found to be predominantly affected with a mean age at diagnosis 59.65±11 years and the mean age for females (n = 136) was 64.26±13.51 years. This difference was statistically significant with P = 0.0001.
The mean age at diagnosis was 60.85±11.87 years. The mean age of patients who had mTP53 mutation was 61.23±12.44 (n = 359) and wTP53 mutation was 60.01±10.46 (n = 161). There was no statistical significance between TP53 mutation and the mean age of patients (P = 0.276). Of the 520 patients of HNSCC studied, males were most commonly affected (n = 384, 74%), (P = 0.426). Majority of the male population (n = 260) had mTP53 gene mutated with a mean age of 59.96±11.6 years and the other (n = 124) male patients had wTP53 gene (59±9.63 years). Similarly, majority of females (n = 99) had mTP53 gene with the mean age at diagnosis 64.59±13.94 years and the other 37 female patients had wTP53 (63.38±12.43) and had no significant difference (P = 0.644).
When the TP53 mutation was studied in relation to the habit of the patient, the majority of cases (n = 285; 71%) were ever-smokers, whereas 118 cases (29%) who were also ever-smokers had no mutation (P = 0.078). Seventy four cases (63%) who were non-smokers had mTP53 mutated. The most common type of mutation [Figure 1] found was the missense mutation (n = 180;50%). | Figure 1 A: Minimized network showing Protein-Protein Interaction network B: Minimized network showing Transcription factor-Gene interaction; C: Network of gene-miRNA interaction D: Network of Transcription factor- miRNA coregulatory network Red circle - Seeds; Purple circle - Nodes; Blue and Magenta circles - Edges; Blue Square - miRNAs and transcription factors; Green rhomboid - Transcription factor
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As a first step, the difference in mRNA expression of considered genes between the wild and mutant TP53 were studied. None of the SCDRS and TNFRSF genes DE at a fold change of 1. The SCDRS genes (APAF1, BCL2L1, CASP3, 6, 7, CHUK, NFKB1) and TNFRSF1A were removed due to low abundance.
The mRNA DE expression, revealed that SCDRS genes (BAD, CASP9, GSK3B, TGFBR1, TGFBR2, NFKB2) and TNFRSF genes (TNFRSF25, TNFRSF9, TNFRSF14, TNFRSF11B, TNFRSF10A, TNFRSF6B) were significantly differentially expressed [Table 1]. The significant genes were subjected to building networks for PPI, Gene-miRNA, TF-Gene and TF-miRNA. The resultant networks were very large and subsequently, minimal network option engaged. The same is given as [Figure 1]. | Table 1 Comparison of mRNA differential expression of genes based on TP53 mutation status
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As the second stage, tobacco use was used as a blocking factor. The results of first stage were replicated with different significance levels) [Table 2]. The KEGG pathway analysis of non-minimal PPI network revealed the involvement of several significant pathways. The most important pathway involved in this network is listed in [Table 3]. In the TF-gene network, the most significant KEGG pathway to be involved (in descending order) were Transcriptional misregulation in cancer (22 of 186; P=3.95E-16; FDR=1.25E-13), TGF-beta signalling pathway (10of92;P=1.68E-07;FDR=1.78E-05) and Pathways in cancer (22of530;P=3.5E-07;FDR=2.34E-05). In the gene-miRNA network, the most common KEGG pathway are the Cytokine-cytokine receptor interaction (7of294;P=1.16E-08;FDR=3.69e-06) and Pathways in cancer (5of530;P=0.000279;FDR=0.0178). Similarly, the pathways associated with the TF-miRNA co-regulatory network are given in [Table 3]. | Table 2 Comparison of mRNA differential expression of genes based on TP53 mutation status and smoking
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These significant DE genes in PPI analysis initially had 597 nodes, 710 edges from 12 seeds that was minimized, by using minimal network option to 35 nodes and 68 edges.[16]The minimized network revealed the role of proteins such as GEMIN4 and STRAP along with other genes of the at the TNFRSF and SCDRS families [Figure 1] . For the Gene-miRNA interaction analysis minimized network had 23 nodes, 28 edges from 10 seeds. It further revealed the role of certain significant miRNA, many of which are related to P arm of third and fifth chromosome [Figure 1]. When the TF- gene interaction was studied, the minimized network was with 32 nodes, 65 edges from 11 seeds. This network revealed that role of DPF2, BCL6, KLF1, GSK3B and Jun-D [Figure 1]. The generic human TF-miRNA co-regulatory interactions study revealed a single island network with 954 nodes, 1069 edges from 12 seeds. This was minimized to 32 nodes, 58 edges and 12 seeds. From this network the role of SP1 and SMAD family were revealed [Figure 1].
| Discussion | | |
The role of inflammation, as a causative factor, in HNSCC is not widely studied. The influence of such phenomenon on p53 mutation and its downstream pathways has been a subject matter of several researches. The relationship between inflammation and TP53 mediated carcinogenesis is still obscure.[3],[4],[5],[6],[7] The prime mediators of inflammation are the TNF family of proteins and its receptors—TNFRSF proteins. The end point of inflammation and TP53 pathways, relates to the survival or cell death, which is regulated by SCDRS group of genes. The relationship between TP53, TNFRSF and SCDRS among HNSCC was studied at the mRNA level.
When considering TP53 mutation as a sole difference, the DE of mRNA of TNFRSF’s was significantly different for TNFRSF-6B/9/10A/11B/14/25. The association of these genes with oral carcinogenesis has been very recently reported in few large-scale studies.[9],[11],[21],[22],[23],[24],[25],[26],[27]HNSCC patients with wTP53 had higher mRNA expression of TNFRSF14 while the mTP53 has lower expression (P = 0.000198). This has to be interpreted in light of the fact that TNFRSF14 is a novel non-canonical p53 target that contributes to tumor suppression, as reported earlier.[28] TNFRSF25 is also known tumor suppressor by virtue of its capability to modulate CD4 and 8 cells. It was observed that the mRNA expression was high in normal than wTP53.[23],[29] TNFRSF6B, also known as Decoy receptor 3, is reported to be an endogenous immunomodulator in cancer growth and inflammatory reactions. It is known to neutralize cytotoxic ligands of TNFS14, TNFSF15 and TNFSF6/FASL to protect against apoptosis. They can induce cell adhesion and anti-inflammation property, needs to be further studied in the future. It is increased in wTP53 than mTP53 (P = 0.04).[26],[30] TNFRSF10A or DR4 is regulated by p53, and can trigger apoptosis or enhance apoptosis induced by TRAIL and chemotherapeutic agents. In mTP53, its level is increased and consistent with previous reports.[31] In melanomas, TNFRSF11B is known to be influenced by TP53 mutation. In present study, the levels of mRNA of TNFRSF11B are higher in mTP53 than wTP53 mirroring the previous reports.[32] These indicate that these entities could play a major role in oral carcinogenesis and are influenced by the TP53 mutation. The exact pathway through which the TP53 influences the TNFRSF genes is still to be unravelled.
The common SCDRS genes that are influenced by the TP53 mutation were BAD, CASP9, GSK3B, NFKB2, TGFBR1 and TGFBR2. BCL2 Associated Agonist of Cell Death gene, (BAD) is one of the gene regulators of apoptosis. This gene positively regulates cell apoptosis by acting with BCL gene products and reverses their death repressor activity. Proapoptotic activity of this protein is regulated through its phosphorylation, notably by protein kinases AKT. It has been shown that in response to a DNA damage, p53 binds to BAD promotor region and upregulating it. We report that, in wTP53 group, BAD mRNA levels are increased (1327±890) as compared to mTP53 (1022±600) with P = 1.2 × 10−7, indicating a possible, partial loss of this function, resonating the previous findings.[33]
The role of caspases family in apoptosis is well documented. Sequential activation of members of caspases family is vital for the execution-phase of cell apoptosis and induced by Fas and various apoptotic stimuli. CASP9 is associated with activation of caspase cascade towards apoptosis. It binds with APAF-1 to cleave CASP3. In ovary cancer cell lines it has been shown that p53 status, caspase 9 activation, apoptosis induction and chemosensitivity are inter-related. Loss of P53 impairs caspase, including CASP9 expression. Herein, CASP9 mRNA was increased in wTP53 as compared to mTP53 group (P = 0.04). This is in concurrence with previous reports.[34],[35]
GSK3B is a known negative regulator in the hormonal control of glucose homeostasis, Wnt signaling and regulation of transcription factors and microtubules. It mediates energy metabolism, inflammation, ER-stress, mitochondrial dysfunction, and apoptotic pathways. In the present study, in mTP53 causes increased DE of GSK3B with P = 0.03, indicating that this is a key pathway and a potential link between inflammation and p53 pathways. Our findings are consistent with reports of Mishra R that GSK3B increases with TP53 stimulation. The mechanism of GSK3β mediated p53 expression is obscure. In a 2015 study, in oral tumor samples nuclear accumulation of pS9GSK3β was observed and this may impede P53 activation, restricting uncontrolled cell division.[36],[37] A reciprocal relationship exists between P53 and NFKB. In our present study, the mRNA expression of NFKB2 were higher in wildTP53 group as against mTP53, with P = 0.037. This is consistent with previous reports.[38] In the present study, the mRNA expression TGF beta receptor genes − TGFBR1 and TGFBR2 in wild-TP53 were lower than the mutated-TP53 (P < 0.001), though the difference between TGFB1 mRNA expressions were not statistically significant (P = 0.7). This is in agreement with previous reported studies.[39],[40],[41]
The influence of tobacco use, irrespective of TP53 mutation status, shows significant differences between mRNA DE [Table 2]. Comparison of TP53 and smoking status revealed that the mRNA of BAD, CASP9, NFKB2, and TNFRSF10A was increased in non-smokers as compared to ever-smokers in wTP53 group. Among mTP53 group, the non-smokers had relatively less levels of mRNAs as compared to ever-smokers. On the contrary, GSK3B was decreased in non-smokers in wTP53 while it was increased in non-smokers in mTP53. It was also observed that the mRNA levels of TGFBR1, TGFBR2, TNFRSF14, TNFRSF6B and TNFRSF9 were increased in non-smokers as compared to ever-smokers, both in wild and mutant TP53 groups. The mRNA levels of TNFRSF11B and TNFRSF25 were decreased among non-smokers as compared to ever-smokers in both groups. The reason for this phenomenon could stem from difference in gene signatures associated with smoking and its impact on TP53 mutation.[42] This warrants further studies accounting for other confounding factors.
The significant genes of the single gene expression table approach (with and without consideration of tobacco use, [Table 1] and [Table 2]), when used to identify the network of association, revealed an extensive network of genes. Hence only important gene network was identified using the minimal network option [Figure 1]. The genes that were involved in this minimal network have been previously reported in HNSCC literature, though their impact on inflammation-TP53 pathway interaction has not been adequately reported. The role of these intermediary genes in network through which the BAD, CASP6, GSK3B, TGFBR1, TGFBR2, NFKB2, TNFRSF-6B/9/10A/11B/14/25 influence inflammation-p53 pathway the needs to be examined further.
Certain members of TNFRSF, TNF super family, TGF β and its receptors are identified in the PPI network. The role of these genes have been previously described in association with HNSCC.[5],[6],[8],[9],[11],[21],[22],[23],[24],[25],[26],[27] The other important nodes are also reported independently in HNSCC. TNFRSF10A is a receptor for cytotoxic ligand TNFSF10-TRAIL. Through the FADD, it mediates caspase-8 to form death-inducing signalling complex to mediate apoptosis. In this process, it also promotes activation of NF-kappa-B. TNFRSF14 is a receptor for BTLA and involved in lymphocyte activation. Similarly, TNFRSF9 has been associated with T-cell activation. Both the entities are associated with HNSCC. TNFRSF6B is a decoy receptor that is known to neutralize cytotoxic ligands of TNFS14, TNFSF15 and TNFSF6/FASL to protect against apoptosis. [5],[6],[8],[9],[11],[21],[22],[23],[24],[25],[26],[27]
The role of proteins like BCL6, CHD3, heat shock proteins (DNAJA1, HSP90AA1) GEMIN4, GRB2, IQGAP1, SQSTM1 and STRAP, in the role of HNSCC is known. The influence of TP53 expression needs to be studied in detail. Similarly the role of transcriptional factors or their gene intermediaries, like ARID1B, ARID4B, DPF2, HBP1, HMGN3, IRF4, JunD, KDM5B, KLF1, NR2F6, NR4A1, SIN3A, SMAD5, SP2, SUPT5H, DSIF, TFE3, ZNF382 and ZNF580 are known to be associated or influenced by TP53 expression and has been associated with HNSCC. The gene TRAF1 is known to mediate the TNFRSF genes and important link in TNF pathways[3],[4],[5],[6],[7],[8] [Figure 1]A.The role of miRNAs, particularly from 3P and 5P arms of chromosome have been implicated in HNSCC.[43],[44],[45] The multiple involvement of miRNAs in [Figure 1]C and 1D is an indication, that such miRNA need to be studied for possible association of inflammation, TP53 mutation and HNSCC [Figure 1].
The involvement of well described HNSCC related pathways in KEGG pathway analysis of non-minimal network [Table 3] reaffirms our present findings, that TP53 mutation has a direct bearing on inflammation, as reflected by the TNFRSF genes and SCDRS genes. The significant pathways identified in this study has been implicated in HNSCC previously, substantiating our findings.[46] Though with present study results, the cause-effect cannot be directly established, the association of TNFRSF, SCDRS and TP53 mutation has been established in HNSCC.
The limitations of the study are inherent feature of secondary data analysis. Influence of site on HNSCC associated Inflammation and response is a critical factor, and has been reported previously.[47] However, this being a secondary data analysis, the exact site of the lesions could not be accurately collected, as most of cases were marked as not otherwise specified (77.22%). Hence, the purpose of the study was to report the significance of differential mRNA expression of TNRSF genes and its relation to the expression of SCDRS genes, with HNSCC in toto. As there appears to be a valid DE of TNRSF and SCDRS genes, the role of site in influencing such expression would be the next logical step and future studies should be directed in this aspect.
| Conclusion | | |
The association of certain TGFRSF and SCDRS genes, in background of TP53 mutation has been described. Further studies using large samples, considering sub-sites, tobacco use intensity and human papilloma infection status may add to our understanding of the role of inflammation, its response and cell survival in background of TP53. Given the fact that the genetic landscape of HNSCC is extremely complex and involves scores of genes, identifying the strongest and weakest genes can be a challenge.
The present study underlines the importance of understanding TP53 status in background of inflammation, inflammatory response, immediate tumour environment, biologically distinct aggressive nature, epigenetics, immune response, hypoxia and the possible influence of the oral microbiome. Understanding the dynamicity of these factors and the mRNA and miRNA response to them would help expanding knowledge of HNSCC for optimal management.
Acknowledgement
The authors would like to acknowledge the Tamil Nadu Dr. MGR Medical University, Chennai for their constant encouragement and support.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1]
[Table 1], [Table 2], [Table 3]
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