Evaluation of Methylation and Changes in the Transcriptomics and Proteomics of the GRHL3, PHLDA3, and in Patients with Head and Neck Squamous Cell Carcinoma

Abstract

Introduction

Head and Neck Squamous Cell Carcinoma (HNSCC) is the sixth most common malignancy in the world. High mortality and severe complications are critical features of head and neck cancer. Changes in intracellular signaling pathways are a general tumor formation and progression mechanism. Due to the objectivity that the PI3K pathway plays a critical role in HNSCC. the negative regulators involved in this pathway such as GRHL3, PHLDA3, which have been reported to reduce their expression in malignancies, can achieve significant results in the detection, prognosis, and targeted treatment of HNSCC if changes in transcriptome, proteome, and methylation levels of these genes are observed.

Method

45 fresh head and neck cancer cells and 45 control samples were collected. Protein expression was also studied using Western blot. Additionally, promoter methylation was investigated using the qMSP method to observe changes in the regulatory regions.

Results

The results indicate a significant decrease in GRHL3 expression and a significant increase in PHLDA3 expression. Notably, these expression changes were not confirmed at the protein level. Additionally, methylation analysis revealed hypermethylation of the promoter region in GRHL3 and hypomethylation in PHLDA3.

Conclusion

This study is the first to examine the genes GRHL3 and PHLDA3 at the transcriptome, proteome, and promoter methylation levels. Based on the results, we hope that further studies will confirm the potential of GRHL3 and PHLDA3 as prognostic biomarkers.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12070-024-05057-0.

Introduction

Head and neck squamous cell carcinomas (HNSCCs) develop from the mucosal epithelium in the oral cavity, pharynx, and larynx. They are the most common malignancies that arise in the head and neck. The incidence of HNSCC is estimated to be approximately 500,000 new cases and 300,000 deaths reported each year [1]. In 2022, there were approximately 931,931 new cases of head and neck cancer worldwide, representing about 4.9% of all new cancer cases. Additionally, head and neck cancers accounted for around 467,125 deaths globally, making up about 4.9% of all cancer-related deaths [2]. Many factors play crucial roles in HNSCC development, such as genetic and epigenetic factors. It is important to note that accumulating genetic lesions and changes in the epigenetic landscape promote HNSCC carcinogenesis. Epigenetic modifications found in HNSCC include DNA methylation, histone modification, non-coding RNA activity, and RNA methylation [3, 4]. These changes regulate the expression of target genes, including tumor suppressor genes (TSG) and oncogenes[5]. Despite significant advancements in diagnostic, imaging, and treatment methods, the 5-year survival rate for oral cavity squamous cell carcinoma remains largely unchanged. However, recent progress in understanding the molecular mechanisms and genetic pathways involved in oncogenesis offers promising avenues for individualized cancer therapies. The development of novel biomarkers, including indicators of genomic instability, miRNA expression regulators, and various genetic and protein markers, has enhanced the ability to predict the malignancy potential of premalignant lesions. Integrating these molecular tests into clinical practice is crucial for improving patient outcomes, necessitating a deeper understanding of the genetic pathways that lead to malignancy. This comprehensive review highlights the latest advancements in oral carcinogenesis, malignant transformation, and the identification of molecular markers for oral cancers[6].

Changes in DNA methylation patterns are common among many types of tumors. Of all epigenetic modifications, hypermethylation, which causes transcriptional repression of tumor suppressor gene promoters leading to gene silencing, has been the most widely studied. However, global hypomethylation has also been recognized as a cause of cancer [7–9].

The phosphatidylinositol 3-kinase (PI3K) pathway is one of the most commonly activated pathways in human cancer. PI3K is activated by receptor tyrosine kinases such as EGFR and catalyzes several enzymatic reactions, ultimately leading to AKT phosphorylation. Activated AKT phosphorylated proteins are involved in cell proliferation and survival. Dysregulation of the PI3K/AKT pathway is common in HNSCC [10, 11]. There is ample evidence indicating that negative feedback regulators of the PI3K pathway act as tumor suppressors, and their loss of function leads to cancer initiation, proliferation, metastasis, and chemotherapy resistance [12]. Because the PI3K/AKT pathway plays an essential role in head and neck cancer, assessment of the transcriptional, proteomic, and methylation status of negative regulators of this pathway is essential in the diagnosis, prognosis, and treatment of HNSCC. Negative regulators of this pathway include Grainyhead Like Transcription Factor 3 (GRHL3) and Pleckstrin Homology Like Domain Family A Member 3 (PHLDA3). Reduced expression of these negative regulators has been reported in various malignancies[13].

GRHL3 is one of the ancestral and highly conserved transcription factors essential for ectoderm development and homeostasis in many species [14]. GRHL3 also activates AKT and eNOS, two factors needed to prevent apoptosis [15]. Loss of GRHL3 results in the loss of anti-apoptotic effects and may lead to faster tumor progression[16].

PHLDA3 was initially recognized as a protein that mediates the DNA damage response of P53, and P53 directly regulates its expression [17]. PHLDA3 cooperates with P53 in triggering apoptosis, and the overexpression of PHLDA3 leads to an increase in apoptosis[18].

Based on previous studies, downregulation of GRHL3 and PHLDA3 has been reported in multiple malignancies [19]. Assessment of genomic and proteomic changes would be helpful in the diagnosis and targeted therapy of HNSCC. Therefore, the aim of this study was to assess the influence of DNA methylation on protein levels of GRHL3, PHLDA3, and genes in HNSCC and present these methylation changes as novel biomarkers for personalized treatment strategies in HNSCC.

Materials and Methods

In this case–control study, we employed the following methodology. The research was carried out over a span of 24 months at the Tehran University of Medical Sciences, a tertiary care facility located in Tehran, Iran.

Methylation Analysis

To predict the significance of methylation in the genes of interest, we utilized the UALCAN online tool to estimate methylation alterations in Head and Neck Squamous Cell tumors. Next, the NCBI Genome Data Viewer was employed to identify CpG islands within the promoter region of the genes. We collected a total of 90 samples, including 45 samples of HNSCC tumor tissue obtained from the oral cavity and 45 samples of normal tissue adjacent to the tumor, from hospital visitors during the years 2021 to 2023. It should be noted that the sample size was determined based on previous studies and statistical calculations to ensure sufficient power to detect clinically significant differences. The collection was carried out under the supervision of an ENT specialist and pathologist, adhering to Tehran University of Medical Sciences’ ethical guidelines (IR.TUMS.IKHC.REC.1396.4550). DNA extraction was performed using a commercially available DNA extraction kit (Qiagen DNeasy Mericon Kit) following the manufacturer’s protocol, and we assessed its quantity and quality using a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, US). Subsequently, we conducted bisulfite treatment using the EpiTect Fast DNA Bisulfite Kit (Qiagen, Cat. No. 59824), which converted unmethylated cytosine residues to uracil. As a result, the sense strand and antisense strand of the genomic DNA were no longer complementary to each other. Methylation-specific primers (MSP) and Bisulfite Specific Primers (BSP) were designed using the Methprimer online tool. Following primer design, we performed qRT-PCR using the MSP and BSP primers. Table 1 provides specific details for each primer.

Table 1.

Sequence information of primers

Primer	Sequence	Size (bp)	Optimal Temperature
GRHL3 cap forward	5׳ GGGTTTAAGTGGTTAGGGTATTG 3׳	190	58 ˚C
GRHL3 cap reverse
	5׳ CCACACCCACCTAAAAAAATCC 3׳
GRHL3 BSP forward	5׳ GGGAGTAGATTTTAGGTTAGTTG 3׳	195	56 ˚C
GRHL3 BSP reverse	5׳ AACCTCCCTTTCTCAAATCC 3׳
GRHL3 MSP forward	5׳ GAGGAGGTAGGGAGTTTTG 3׳	200	56 ˚C
GRHL3 MSP reverse	5׳ ATTACCCTTTTAAATACCTCC 3׳
PHLDA3 Cap forward	5׳ CGGCGTCGTTAAGGTAATAG 3׳	550	62 ˚C
PHLDA3 Cap reverse	5׳ CGAACTCGACCCCAACAACT 3׳
PHLDA3 BSP forward	5׳ CGCGTAGCGTTTAGGTTTT 3׳	250	60 ˚C
PHLDA3 BSP reverse	5׳ CGACCTTACGAAAAACCTAAAAC 3
PHLDA3 BSP forward	5׳ GTTTCGTTTTTATAACGTTTAGTTTGT 3׳	239	60 ˚C
PHLDA3 BSP reverse	5׳ CCTTACGAAAAACCTAAAACCAAAT 3׳

The PCR amplification was carried out as follows: An initial denaturation step at 94 °C for 10 min, followed by 30 cycles of denaturation at 94 °C for 30 s, annealing for 40 s (56 °C for GRHL3 and 60 °C for PHLDA3), extension at 72 °C for 40 s, and a final extension step of 5 min at 72 °C.

SDS Page and Western Blotting

Tissues from both non-cancerous and cancerous samples were lysed in a buffer containing 1 mM EDTA, 50 mM β-glycerophosphate, 2 mM sodium orthovanadate, 1% Triton-100, 10% glycerol, 1 mM DTT, and protease inhibitors (10 mg/ml benzamidine, 2 mg/ml antipain, and 1 mg/ml leupeptin). The protein concentration was determined using the Bradford assay, which relies on the shift in the absorbance maximum of Coomassie Brilliant Blue G-250 dye from 465 to 595 nm after binding to denatured proteins in solution. A standard curve was generated based on the absorbance of the standard samples at 529 nm and their concentrations, allowing us to determine the protein concentration of the unknown samples. Subsequently, the proteins were separated on 5% polyacrylamide gels using SDS-gel electrophoresis. To ensure consistency, we added 1 volume of 5 × loading buffer to 5 volumes of protein supernatant (the loading buffer consists of 0.25 M Tris–HCl, pH 6.8, 10% SDS, 50% glycerol, and 0.5% bromophenol blue) and denatured them by heating at 95 °C for 5 min before loading. Additionally, prestained protein ladders (Sinaclon, Cat. No. PR901641) were used. After separation, the proteins were transferred onto an Immuno-Blot polyvinylidene difluoride membrane (Bio-Rad). The membrane was blocked in 8 ml of a 5% BSA blocking buffer for 2 h at room temperature. Subsequently, 2 ml of the primary antibody (dilution 1:3000) was added, and the membrane was incubated overnight at room temperature, followed by three washes with 0.05% Tween 20 in PBS. The membrane was then incubated in a Goat anti-mouse IgG secondary antibody HRP-conjugated antibody for 2 h at room temperature (dilution 1:5000). The membrane was washed three times with 0.05% Tween 20 in PBS. Membrane-bound secondary antibodies were detected using an enhanced chemiluminescence method following the manufacturer’s instructions.

Statistical Analyses

The methylation level was calculated based on the 2^-ΔΔct value, with the reference gene being commercially purchased methylated DNA. Furthermore, we conducted ROC curve analysis for each gene and assessed the correlation between them using GraphPad Prism. A significance level of p < 0.05 was considered as the threshold for statistical significance and an alpha value of 0.05 and a power of 80% were used to calculate the necessary sample size which is a conventional standard for clinical research to minimize the risk of Type II errors. Additionally, the color density of the gel bands in SDS-PAGE was semi-quantified using J Image software.

Result

Methylation Analysis

The qRT-PCR analysis of methylation revealed significant differences in the methylation levels of the regulatory regions of GRHL3 and PHLDA3 between tumor and adjacent normal tissues. The qRT-PCR analysis of methylation revealed a significant increase in the methylation of the regulatory region of GRHL3 in tumor tissue compared to adjacent tissue (P Value = 0.006) (Fig. 1a). Conversely, a significant decrease in methylation of the PHLDA3 regulatory region was observed (P Value = 0.001) (Fig. 1d). The relationship between the demographic and pathological information of patients and the methylation levels of these genes is presented in Table 2. The demographic and pathological characteristics of the patients included in the study are summarized below, along with their association with the methylation levels of GRHL3 and PHLDA3.

Fig.1.

The methylation of the regulatory region of GRHL3 and PHLDA3 in tumor tissue compared to adjacent tissue, correlation between promoter methylation and gene expression, ROC analysis to classify HNSCC cases versus healthy controls using logistic regression models

Table 2.

Demographic and pathologic information of patients

Parameters	Number (%)	Methylation of GRHL3 (P value)	Methylation of PHLDA (P value)
Age	< 60	30 (67.7)	0.9053	0.9540
	≥ 60	15 (33.3)
Gender	Male	33 (73.3)	0.4244	0.1471
	Female	12 (26.7)
Stage	II	18 (40)	0.72	0.9693
	III	27 (60)
Grade	I	9 (20)	0.4213	0.0053**
	II	15 (33.3)
	III	21 (46.7)
Smoking	YES	30 (66.7)	0.2488	0.2351
	NO	15 (33.6)

The patient cohort included 30 patients (67.7%) under 60 years of age and 15 patients (33.3%) aged 60 or older. Analysis revealed no significant difference in the methylation levels of GRHL3 (P = 0.9053) or PHLDA3 (P = 0.9540) between these two age groups. Out of the 45 patients, 33 (73.3%) were male and 12 (26.7%) were female. There was no significant difference in the methylation levels of GRHL3 (P = 0.4244) or PHLDA3 (P = 0.1471) between male and female patients. The cancer stages of the patients were categorized into stage II and stage III. There were 18 patients (40%) with stage II and 27 patients (60%) with stage III. No significant differences were found in the methylation levels of GRHL3 (P = 0.72) or PHLDA3 (P = 0.9693) between these stages. Patients were classified into three cancer grades: grade I, grade II, and grade III. The distribution included 9 patients (20%) with grade I, 15 patients (33.3%) with grade II, and 21 patients (46.7%) with grade III. While no significant difference in the methylation of GRHL3 was observed among the different grades (P = 0.4213), a significant difference in the methylation of PHLDA3 was noted, with grade I showing significantly different methylation levels compared to grades II and III (P = 0.0053). Regarding smoking status, 30 patients (66.7%) were smokers and 15 patients (33.6%) were non-smokers. Analysis showed no significant difference in the methylation levels of GRHL3 (P = 0.2488) or PHLDA3 (P = 0.2351) between smokers and non-smokers. The analysis of demographic and pathological parameters revealed that age, gender, stage of cancer, and smoking status did not significantly influence the methylation levels of GRHL3 or PHLDA3. However, cancer grade showed a significant association with the methylation levels of PHLDA3, with grade I presenting significantly different methylation compared to grades II and III. This highlights the potential role of PHLDA3 methylation in the progression and severity of HNSCC.

In our previous study, we analyzed the expression levels of each gene[13]. In this study, we calculated the correlation between promoter methylation and gene expression. However, neither of the two mentioned genes showed any significant correlation between promoter methylation and gene expression (Fig. 1c,f).

To further estimate the extent of methylation levels of GRHL3 and PHLDA3 in HNSCC patients, we conducted ROC analysis to classify HNSCC cases versus healthy controls using logistic regression models for these two genes. We calculated sensitivity, specificity, and the area under the curve (AUC). As depicted in (Fig. 1b,e), the methylation levels of GRHL3 and PHLDA3 significantly differ between HNSCC tumor tissue and adjacent tissue (AUC = 0.6681 and AUC = 0.7822, respectively).

Western Blot

SDS-PAGE results indicate total protein extract from tissue (Fig. 2). The PHLDA3 protein content (molecular weight ~ 13.9 kDa) exhibited a change in tumor tissue compared to adjacent tissue (Fig. 3a), whereas no significant difference was observed in the GRHL3 protein content (molecular weight ~ 67 kDa) in tumor tissue compared to adjacent tissue (Fig. 3b).

Fig. 2.

The protein expression SDS PAGE samples

Fig. 3.

protein expression in tumor tissue being three times higher compared to adjacent tissue

To confirm the altered expression levels of the genes of interest in tumor samples as compared to controls at the protein level, we conducted Western blot analysis. The results indicated that there was no significant difference in the protein expression of GRHL3 in tumor samples compared to controls. However, the protein expression of PHLDA3 in tumor samples, when compared to controls, exhibited a significant change within a range of 3 × to 4 × when compared to margin samples (Fig. 3).

Discussion

In a previous study, we examined the expression changes of the PHLDA3 and GRHL3 genes, which inhibit the PI3K/AKT pathway, in the cancer tissue of 45 patients with HNSCC in comparison to 45 adjacent tissues. This study also explored changes in gene expression at the protein level. Subsequently, we investigated the methylation of the promoter regions of the genes that exhibited changes in expression. The goal was to determine whether these changes in expression were influenced by variations in methylation levels in the promoter region or if other factors, such as histone methylation, regulatory elements like microRNAs, long non-coding RNAs, etc., played a role in altering the gene expression.

In our previous study, the GRHL3 gene in the tumor group displayed a significant decrease in mRNA expression compared to healthy tissue. Additionally, the PHLDA3 gene exhibited a substantial increase in expression in the tumor group compared to the adjacent normal tissue. However, in this study, after assessing the protein-level expression within the same statistical population, we observed no significant changes in the protein pattern of GRHL3 in the tumor group compared to the control. Conversely, in the case of the PHLDA3 gene, according to semi-quantitative analysis, a significant alteration in protein expression was noted, with the amount of protein expression in tumor tissue being three times higher compared to adjacent tissue (Fig. 3).

It’s worth noting that while there were significant changes in the expression pattern of GRHL3 among the studied groups, these changes were not pronounced enough to be detected at the protein level. Protein expression is regulated separately from mRNA levels, which could explain the disparity between gene expression and protein levels.

Concerning the investigation of the methylation status of the GRHL3 and PHLDA3 gene promoters, in the GRHL3 gene promoter region, we identified a CpG Island and examined the 195 bp region. The results indicated an increase in methylation in the promoter of this gene in cancer tissue compared to normal tissue, which aligns with the reduction in mRNA expression. In the promoter region of the PHLDA3 gene, we examined a 200-base-pair region and found a decrease in methylation or hypomethylation in tumor tissue compared to normal tissue. The SDS-PAGE gel results were consistent with the western blot results, both indicating increased expression of PHLDA3 protein in tumor tissues. However, it’s important to note that the increase in mRNA may not have a linear relationship with the increase in protein in tumor tissues.

For GRHL3, the changes in gene expression between tumor and control samples were not detectable in the SDS-PAGE results, and no significant differences were observed in the western blot results. However, the results of Real-time PCR indicated a 2.4-fold decrease in GRHL3 gene expression. This raises questions about the sensitivity of SDS-PAGE and western blot tests in detecting changes in the expression level of tumor tissue cells compared to control tissue (The sensitivity of SDS-PAGE is approximately 0.1–0.5 μg of protein).

Although in previous studies, the analysis of RNA expression levels based on gender, age, and smoking was significant for the GRHL3 and PHLDA3 genes, these demographic factors were not correlated with changes in methylation levels of GRHL3 and PHLDA3 genes.

Based on the analyses of the PHLDA3 gene’s promoter region, a reduction in methylation or hypomethylation was observed, along with an increase in the Stage level. Considering the increase in mRNA expression and the decrease in promoter methylation in the PHLDA3 gene, indicating the presence of regulatory elements in the promoter region, this gene could be considered a prognostic biomarker. Similarly, in this study, when examining the methylation of the GRHL3 gene promoter, we found an increase in methylation, suggesting hypermethylation. Given the decrease in the expression of this gene along with increased promoter methylation, it can be inferred that the regulatory elements in the promoter region also play a role in controlling the gene’s expression. Therefore, this gene could also be considered a prognostic biomarker.

The decrease in GRHL3 expression and the increase in PHLDA3 expression in HNSCC suggest that these genes may contribute to the dysregulation of the PI3K/AKT signaling pathway, ultimately leading to uncontrolled cancer progression. Given the established role of the PI3K/AKT pathway in prognosis, prediction, diagnosis, and drug therapy, as well as the potential for recurrence, it is advisable to investigate the expression profiles of these two genes along with other upstream and downstream genes involved in controlling the signaling pathways, such as EGFR and PTEN. This should be done in a larger sample size to develop prognostic biomarkers and guide appropriate treatment methods and drug administration in HNSCC.

Our study has revealed that the decrease in GRHL3 expression in tumor tissue is attributed to hypermethylation of its promoter region, while the increase in PHLDA3 expression in this tissue is associated with hypomethylation of its promoter region. Epigenetic changes constitute one of the early events in the development of cancer and persist throughout the progression of the disease. They are more prevalent than genetic alterations. The results of a study by Aryee et al. have shown that, despite the heterogeneity in the methylation patterns of the cancer cell genome, these patterns remain stable throughout the progression of cancer. This intra-individual homogeneity in methylation can be harnessed to develop specific treatments. [20]. Furthermore, numerous studies have suggested the pivotal role of DNA methylation in the development of biomarkers for predicting treatment response. In the case of head and neck cancer (HNSCC), the most common treatment modalities include surgery, radiotherapy, chemotherapy, or combinations thereof. Notably, six agents have received FDA approval for the noninvasive treatment of HNSCC, which are cisplatin, 5-fluorouracil, docetaxel, methotrexate, bleomycin, and cetuximab.

Platinum-based chemotherapeutic agents, specifically carboplatin and cisplatin, are widely used in the treatment of HNSCC and have demonstrated efficacy rates of up to 40%. These agents are often employed in combination with ionizing radiation. Their mechanism of action involves forming covalent bonds with nicotinic acid[21]. Cetuximab has emerged as a potential treatment for HNSCC, particularly after the discovery of significant EGFR overexpression in HNSCC cases, which is associated with a poorer prognosis and increased resistance to radiation therapy. In 2017, Alterio et al. introduced a novel approach to address HNSCC by focusing on EGFR overexpression. According to this study, EGFR expression, characterized by its membrane characteristics, is closely correlated with both overall survival and disease-free survival. Patients were classified based on the expression of EGFR in tumor tissue as either "heterogeneous" or "homogeneous." It was noted that patients with heterogeneous distribution of EGFR expression had worse outcomes in terms of disease-free survival (DFS) and overall survival (OS). This illustrates an example of personalized or individualized medicine in the context of HNSCC, tailoring treatment approaches based on the unique characteristics of each patient [22].

In this study, we also faced some limitations, such as the lack of access to more samples. Examining the expression of the studied genes and proteins in a larger statistical population can provide us with a more reliable results. Also, in this study, we only measured epigenetic and methylation factors on the change of gene expression, but there are other factors such as post-translational modifications and regulatory RNAs that play a role in the level of protein expression. examined non-coding RNAs involved involved in the pathway and their interaction with GRLH3. Although the protein coding genes ultimately perform their function through the function of the created protein, we measured the level of expression of these proteins in the pathogenesis of head and neck cancer in this study.

Conclusion

The decrease in GRHL3 expression and the increase in PHLDA3 expression in HNSCC suggest that these genes may play a role in the dysregulation of the PI3K/AKT signaling pathway. In terms of methylation in the promoter regions of PHLDA3 and GRHL3 genes, the GRHL3 gene promoter displayed an increase in methylation, becoming hypermethylated.

The changes in GRLH3 gene expression in tumor cells and control samples were not discernible in the SDS-PAGE results, and there were no significant alterations in the Western blot results. However, Real-time PCR results indicated a 2.4-fold decrease in the expression of the GRLH3 gene. This raises questions about the sensitivity of SDS-PAGE and Western blot tests in detecting this decrease in the expression level of tumor tissue cells compared to control tissue.

On the other hand, Western blotting of PHLDA3 indicated an increased expression of PHLDA3 protein in tumor tissues. These findings align with the Real-Time results and demonstrate that at both the mRNA and protein expression levels, PHLDA3 protein was more highly expressed in tumor tissues. Nonetheless, it’s important to note that the increase in mRNA expression may not exhibit a linear relationship with the increase in protein expression in tumor tissues. Therefore, PHLDA3 could be considered as a potential prognostic biomarker in HNSCC.

The findings from this study have several important clinical implications for the management and understanding of head and neck squamous cell carcinoma (HNSCC). The significant differences in the methylation levels of PHLDA3 between cancer grades suggest that PHLDA3 methylation could serve as a potential biomarker for distinguishing between different stages or grades of HNSCC. Specifically, grade I tumors showed distinct methylation patterns compared to grades II and III, which may be useful for assessing tumor progression and guiding treatment decisions. Although age, gender, stage, and smoking status did not show a significant impact on the methylation levels of GRHL3 and PHLDA3, the study highlights the importance of these genetic markers in understanding tumor biology. Identifying tumors with specific methylation profiles could provide insights into tumor characteristics and help tailor individualized treatment plans. The observed changes in PHLDA3 methylation in different cancer grades suggest that this gene might be involved in tumor development or progression. Therapeutic strategies aimed at reversing abnormal methylation of PHLDA3 could potentially be explored as a novel approach to treating HNSCC or improving patient outcomes. The lack of significant correlation between promoter methylation and gene expression for both GRHL3 and PHLDA3 indicates that methylation changes alone may not fully explain gene expression variations. This underscores the complexity of gene regulation and the need for further research to understand the mechanisms underlying gene expression and its relationship with methylation in HNSCC. Integrating these methylation markers into routine clinical practice could enhance diagnostic accuracy and help in stratifying patients based on their tumor characteristics. This could potentially lead to more personalized treatment approaches and better management strategies for patients with HNSCC. The findings suggest areas for future research, including the exploration of additional epigenetic modifications and their interactions with genetic factors. Further studies are needed to validate these markers in larger and more diverse patient populations and to assess their utility in clinical settings.

In summary, the study’s findings emphasize the potential utility of methylation markers like PHLDA3 in understanding tumor grade and progression, highlighting their role in personalized medicine and guiding future research into targeted therapies for HNSCC.

Supplementary Information

Below is the link to the electronic supplementary material.

Author Contributions

Authors contributed equally to this work, and N.S is the corresponding author. All authors reviewed the manuscript.

Funding

This study was not supported by any sponsor or funder.

Data Availability

Raw analyzed data is available and can be provided if required.

Code Availability

https://www.graphpad.com/features, https://www.ncbi.nlm.nih.gov/, https://www.qiagen.com/us/products/discovery-and-translational-research/epigenetics/dna-methylation/methylation-specific-pcr/rotor-gene-q

Declarations

Conflict of interest

The Authors involved in the study do not have any financial, professional, or personal conflicts of interest that could potentially bias or influence the research findings or its interpretation.

Consent for Publications

This ethical practice ensures that individuals voluntarily agreed to participate in the research after being provided with comprehensive information about the study’s purpose, procedures, potential risks, and benefits.

Ethical Approval

This study protocol was reviewed and approved by Tehran University of Medical Sciences’ ethical guidelines, approval number [IR.TUMS.IKHC.REC.1396.4550].