Comprehensive Bioinformatic Analysis for Identification of Crucial Genes and Signaling Transduction Pathways in Head and Neck Squamous Cell Carcinoma

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Comprehensive Bioinformatic Analysis for Identification of Crucial Genes and Signaling Transduction Pathways in Head and Neck Squamous Cell Carcinoma

https://doi.org/10.21203/rs.3.rs-3912796/v1

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Head and neck squamous cell carcinoma (HNSCC) is a prevalent global cancer, ranking sixth in incidence. Investigating the molecular basis of tumorigenesis and metastasis is crucial for early detection and effective treatment. Our study utilized three gene expression profile datasets (GSE6791, GSE29330, and GSE58911) to identify co-up or down-regulated differentially expressed genes (DEGs) between HNSCC tumor and normal tissue samples, associated primarily with processes like extracellular matrix (ECM) organization, proteolysis, ECM disassembly, and keratinization. A protein-protein interaction (PPI) network revealed eight hub genes, notably including the up-regulated SPP1 and down-regulated KRT78. Importantly, these hub genes demonstrated correlations with tumor grade, clinical individual cancer stage, and poor prognosis in patients with HNSCC. The comprehensive bioinformatics-driven investigation not only pinpointed co-DEGs but also illuminated associated pathways, providing valuable insights into the molecular mechanisms steering disease progression. These findings have substantial clinical potential, offering avenues for early diagnosis and the development of innovative therapeutic targets for individuals grappling with HNSCC. The identified genes and pathways contribute to a deeper understanding of the intricate molecular landscape of HNSCC, paving the way for more targeted and effective interventions in the clinical setting.

Biological sciences/Molecular biology

Biological sciences/Cancer

Biological sciences/Cancer/Head and neck cancer

Biological sciences/Cancer/Head and neck cancer/Oral cancer

Biological sciences/Computational biology and bioinformatics

Biological sciences/Computational biology and bioinformatics/Databases

Head and neck squamous cell carcinoma (HNSCC) accounts for over 95% of malignancies in the head and neck region, making it the sixth most common cancer globally^1–3. Each year, a staggering influx of approximately 890,000 new HNSCC cases arises, contributing to a toll of 450,000 deaths⁴. The advent of immune checkpoint inhibitors targeting programmed cell death protein-1 and programmed death-ligand 1 has revolutionized treatment outcomes for several malignancies. However, in the realm of HNSCC, despite substantial advancements and considerable investment in these therapies, their efficacy remains dishearteningly low^5,6. This may be linked to the challenges of diagnosing HNSCC, as most patients present at advanced stages, leading to poor prognoses⁷. Additionally, the five-year overall survival rate for HNSCC remains below 40%⁸. Therefore, understanding the molecular mechanisms underlying HNSCC is crucial for developing early diagnostic methods and identifying therapeutic targets.

Microarray-based bioinformatics analysis has gained increasing popularity in recent years for studying various diseases, including breast, lung, and pancreatic cancer. The aim is to unravel the complex molecular mechanisms underlying each disease and identify critical genes associated with them^9,10, ultimately leading to the development of effective therapeutic targets selection and accurate diagnostic procedures. Despite several bioinformatics studies conducted on HNSCC, a comprehensive and integrative analysis remains absent. Therefore, conducting such analyses is essential to fill this knowledge gap and improve cancer treatment outcomes.

In this study, we analyzed differentially expressed genes (DEGs) and co-DEGs in various datasets using Gene Ontology (GO) terminology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Additionally, we constructed a protein-protein interaction (PPI) network from the co-DEGs to identify hub genes. Finally, we validated the mRNA and protein expression levels of hub genes and their association with the survival rate of patients with HNSCC.

1. Microarray Data

The National Center for Biotechnology Information Gene Expression Omnibus is a publicly accessible database at https://www.ncbi.nlm.nih.gov/geo. We searched for relevant keywords such as ‘head and neck cancer,’ ‘head and neck squamous cell carcinoma,’ and ‘HNSCC’ and identified three gene expression datasets available for download and analysis through GEO2R (https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html)¹¹. These datasets include GSE6791 (42 HNSCC tissues, 14 normal tissues), GSE29330 (13 HNSCC tissues, 12 normal tissues), GSE58911 (15 HNSCC tissues, 15 normal tissues).

2. Identification of DEGs and Visualizing the Data

Using GEO2R, we analyzed DEGs between HNSCC and normal tissues across all three datasets. We identified DEGs within each dataset (GSE6791, GSE29330, and GSE58911) based on an adjusted p value <0.05, and a log fold change (log FC) threshold of |log FC| >2. Volcano and heatmap visualizations for each DEG dataset were conducted using Hiplot (https://hiplot-academic.com).¹²

3. GO and KEGG Pathway Analysis of Up and Down-regulated DEGs

We utilized the Database for Annotation, Visualization, and Integrated Discovery (DAVID) web server (https://david.ncifcrf.gov) for gene list annotation and functional enrichment analysis¹³. Specifically, we performed GO and KEGG pathway analyses on both up- and down-regulated DEGs using version 7.0 of the DAVID database.

4. PPI Network Build Up on Up and Down-regulated DEGs for Hub Genes Identification

STRING (https://string-db.org/) is a web-based database offering pre-computed protein association networks¹⁴. We used version 12.0 for our PPI network analysis to identify hub genes in both up- and down-regulated DEGs.

5. Analyzing the Hub Genes Expression and Their Impact on Survival Rate in Patients with Cancer

The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) (http://ualcan.path.uab.edu/), is a valuable resource for interactive network analysis¹⁵. We used UALCAN to analyze hub genes in control and HNSCC tumor tissues, along with clinical data from the Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Additionally, immunohistochemistry data on protein expression was obtained from the Human Protein Atlas (HPA) (https://www.proteinatlas.org/)¹⁶. Finally, we employed TIMER 2.0 (http://timer.cistrome.org) to analyze the expression of hub genes and their impact on survival rate¹⁷.

1. Identification of DEGs in Three HNSCC Data Sets

Using GEO2R, we analyzed three datasets (GSE6791, GSE29330, and GSE58911) and identified a total of 63,565 up-regulated and 79,091 down-regulated DEGs. Reciprocal volcano maps for each dataset illustrate the distribution of significantly altered genes (Figures 1A). Representative heatmaps showcase 20 DEGs in each dataset (Figures 1B). Notably, cross-analysis revealed 28 co-DEGs (eight up-regulated and 20 down-regulated), visualized in a Venn diagram (Figures 1 C and D).

2. GO and KEGG Pathway Analysis on Up-regulated DEGs

To understand the functional implications of DEGs, we performed GO and KEGG pathway analyses for each dataset. As depicted in Figure 2A, in GSE6791, up-regulated DEGs were significantly enriched in pathways related to extracellular matrix (ECM) organization, innate immune response, defense response to viruses, and other related processes. GSE29330 also showed enrichment in pathways involving cell adhesion, collagen fibril organization, ECM organization, and other relevant processes. Notably, GSE58911 revealed associations with ECM organization, proteolysis, collagen catabolic process, and other relevant processes.

KEGG pathway analysis further highlighted specific pathways enriched with up-regulated DEGs. GSE6791 exhibited significant enrichment in diabetic complications, amoebiasis, and ECM-receptor interaction pathways. Similarly, GSE29330 DEGs were enriched in protein digestion and absorption, ECM-receptor interaction, and advanced glycation endproducts-receptor for advanced glycation endproducts signaling pathways in diabetic complication. Interestingly, GSE58911 DEGs were primarily involved in interleukin (IL)-17 signaling pathway, rheumatoid arthritis, and lipid and atherosclerosis pathways (Figure 2B).

3. GO and KEGG Pathway Analysis on Down-regulated DEGs

In GSE6791, prominent enriched GO terms were associated with proteolysis, keratinization, and epithelial cell differentiation. Similarly, GSE29330 down-regulated DEGs exhibited enrichment in GO terms related to immune response, adaptive immune response, and cell surface receptor signaling pathway. Notably, muscle contraction, keratinization, and sarcomere organization emerged as the most frequent GO terms for down-regulated DEGs in GSE58911 (Figure 3A).

KEGG analysis further unveiled specific pathways enriched with down-regulated DEGs. As depicted in Figure 3B, GSE6791 showed significant enrichment in pathways related to drug metabolism involving cytochrome P450, taurine, and hypotaurine metabolism. Down-regulated DEGs in GSE29330 were concentrated in B cell receptor signaling, cell adhesion molecules, and hematopoietic cell lineage. Interestingly, GSE58911 down-regulated DEGs were primarily associated with hypertrophic and dilated cardiomyopathy, and motor proteins.

4. Identification of co-DEGs and GO and KEGG Pathway Analysis on those

We next turned our attention to the overlapping co-DEGs across all three datasets and explored their shared functional roles. GO analysis revealed enrichment in terms related to ECM organization, proteolysis, and collagen catabolic processes (Figure 4A), suggesting a crucial role in ECM dynamics. Interestingly, KEGG pathway analysis highlighted significant enrichment in ECM-receptor interaction, rheumatoid arthritis, and IL-17 signaling pathway (Figure 4B).

5. PPI Network Construction of co-DEGs and Identification of Hub Genes

To identify hub genes potentially driving HNSCC progression, we analyzed all 28 co-DEGs (eight up-regulated and 20 down-regulated) from GSE6791, GSE29330, and GSE58911 using the STRING database for PPI networks. We considered genes with connectivity exceeding six as hub genes, revealing the right promising candidates. Among these, cornulin (CRNN), matrix metalloproteinase (MMP) 1, and MMP3 exhibited the highest connectivity (seven), followed by keratin (KRT) 4, KRT78, MMP12, sciellin (SCEL), and secreted phosphoprotein (SPP) 1 (connectivity of six) (Figure 4C).

6. Expression Level of Selected Hub Genes in Tumor of Patients with HNSCC

Using the UALCAN database, we analyzed HNSCC data from TCGA and CPTAC. As expected, MMP1, MMP3, MMP12, and SPP1 displayed significantly higher expression in tumor tissues compared to normal samples (Figure 5A). Conversely, CRNN, KRT4, KRT78, and SCEL expression were down-regulated in HNSCC tumors (Figure 5B). Further analysis revealed a striking correlation between tumor grade or individual cancer stages and hub gene expression. Notably, SPP1 expression increased markedly with higher tumor grades (Figure 6A), while KRT78 expression decreased progressively with advancing cancer stages (Figure 6B).

Consistent with the mRNA data, protein expression analysis using the HPA confirmed significantly elevated levels of MMP1, MMP3, MMP12, and SPP1 in HNSCC tumors compared to normal tissues (Figure 7A). Conversely, protein levels of CRNN, KRT4, KRT78, and SCEL remained down-regulated (Figures 7B and 8A).

7. Survival Rate Related to Hub Genes Expression in Patients with Cancer

Finally, we investigated the potential link between hub gene expression and patient survival rates. Interestingly, high SPP1 and low KRT78 expression levels were significantly associated with poorer overall survival outcomes (Figure 8B).

HNSCC is the dominant subtype of head and neck cancer, representing over 90% of all cases¹. Despite its high incidence and metastatic potential, early diagnosis remains challenging, and treatment options like immunotherapy lack consistent efficacy⁵. In light of these limitations, comprehensive research endeavors are crucial. In recent years, bioinformatics has offered a faster and more efficient approach to analyzing biological datasets, paving the way for novel insights¹⁸. In this study, we employed bioinformatics to analyze gene expression profiles from three datasets GSE6791 (42 HNSCC, 14 healthy controls), GSE29330 (13 HNSCC, 12 healthy controls), and GSE58911 (15 HNSCC, 15 healthy controls). Our analysis identified 28 co-DEGS in HNSCC, comprising eight up-regulated and 20 down-regulated genes. Furthermore, eight hub genes were identified, four up-regulated and four down-regulated. Notably, among the hub genes, up-regulated SPP1 and downregulated KRT78 expression significantly correlated with tumor grade, individual cancer stages, and poor survival in HNSCC patients.

GO analysis revealed that co-DEGs were primarily enriched in terms related to ECM organization, proteolysis, collagen catabolic process, and ECM disassembly. KEGG pathway analysis further highlighted significant enrichment in ECM-receptor interaction, rheumatoid arthritis, and the IL-17 signaling pathway. This finding aligns with existing evidence associating ECM with tumor development and progression^19–21. Li et al. reported that proteolytic ECM degradation, driven by elevated plasminogen activator urokinase levels, contributes significantly to HNSCC metastasis²². Similarly, Tanis et al. observed increased expression of ECM-related genes like MMPs, laminin, and collagen in oral squamous cell carcinoma (OSCC), another major HNSCC subtype, linking these genes to OSCC metastasis²³. Notably, MMPs, known for their role in ECM degradation and protein up-regulation across various cancers²⁴, have been shown to accelerate tumor metastasis and invasion in nasopharyngeal carcinoma, another HNSCC type, through activity regulation²⁵. Regarding ECM formation, overexpression of ECM-composing proteins like collagen and hyaluronic acid has been linked to increased matrix stiffness in the tumor microenvironment²⁶. This aligns with our GO analysis suggesting abnormal ECM organization and catabolic reactions might contribute to cancer progression.

Our KEGG analysis further strengthens this notion by highlighting enrichment in ECM-receptor interaction consistent with Huang et al.’s findings emphasizing the crucial role of membrane receptors in recognizing ECM components in cancer²⁶. Additionally, the enrichment in the rheumatoid arthritis pathway aligns with studies demonstrating enhanced immune response to Epstein-Barr virus (EBV) infection in rheumatoid arthritis patients compared to healthy controls²⁷. Given the association of EBV with both rheumatoid arthritis and HNSCC, potential similarities in immune responses between these diseases cannot be ruled out²⁸. Further, clinical studies in OSCC have reported a higher frequency of T_H17 cells and a positive correlation between IL-17 expression and tumor budding^29,30.

Through PPI network analysis, we identified four up-regulated hub genes (MMP1, MMP3, MMP12, and SSP1) and four down-regulated genes (CRNN, KRT4, KRT78, and SCEL). To determine their role in HNSCC, we conducted several analyses focusing on the relationship between hub gene expression and the disease. Our analysis of the TCGA HNSCC database revealed significantly elevated expression of MMP1, MMP3, MMP12, and SPP1 in tumor tissues compared to normal tissues. This finding aligns with established knowledge in several other cancers, where MMPs have been implicated in poor prognosis. For example, Zhang et al. observed increased MMP1 genes and protein expression in HNSCC, with over-expression positively correlating with advanced tumor size and metastasis. Similar findings have been reported for MMP1 in colorectal cancer, where knockdown experiments confirmed its pro-tumorigenic role^31,32. Additionally, a bioinformatic study mirrored our results by demonstrating MMP1, MMP3, and MMP12 up-regulation in colorectal cancer, highlighting the potential universal relevance of these MMPs in tumor progression³³. This up-regulation might be related to the general tendencies of MMPs in cancer, where MMP1 is significantly and almost universally upregulated, and MMP3 and MMP12 show significant upregulation in at least 10 types of cancer³⁴.

Conversely, our analysis revealed significantly reduced expression of CRNN, KRT4, KRT78, and SCEL in HNSCC. Notably, this pattern was consistent across protein expression pattern data from both the CPTAC and HPA databases, further substantiating their potential involvement in the disease. CRNN, known for its tumor-suppressive functions in cell cycle regulation, is also down-regulated in other squamous cell carcinoma types, suggesting its broader role in epithelial malignancies^35–37. Interestingly, CRNN down-regulation might contribute to KRT4 down-regulation, as CRNN acts as a keratinocyte proliferation marker³⁸. Therefore, the co-downregulation of CRNN and KRT4 might synergistically promote the progression of squamous cell carcinomas like HNSCC. Regarding SCEL, identified as a precursor of the cornified envelope in keratinizing tissues, our finding of its down-regulation in HSCC aligns with observations in melanoma, where lower expression correlated with poor overall survival^39,40. However, it’s worth noting that SCEL expression seems to vary depending on cancer type, as it has been found in gallbladder and pancreatic cancers^41,42. Therefore, further investigations are needed to elucidate the context-specific roles of SCEL in different malignancies.

Among the up-regulated hub genes, SPP1, also known as OSTEOPONTIN, stood out for its strong association with tumor grade progression, particularly grade 2 (G2). This aligns with its established role in promoting tumor development and metastasis⁴³. Feng et al. demonstrated that high SPP1 expression in HNSCC patients correlated with lymph node metastasis and macrophage infiltration, both of which contribute to cell proliferation and invasion of tumor⁴⁴. Similarly, Cho et al., showed that silencing SPP1 in non-small cell lung cancer decreased protein levels and inhibited tumor growth⁴⁵. Notably, elevated SPP1 is also observed in colon, gastric, and lung cancers^43,46,47. It's worth noting that tumor G2, characterized by intermediate growth and limited spread, might represent a tipping point for metastasis^48,49. While both grade 1 and G2 are typically curable with treatment, targeting SPP1, specifically at G2, could offer an effective therapeutic or diagnostic strategy.

Among the down-regulated hub genes, KRT78 expression displayed a significant association with individual cancer stages. These findings resonate with the established function of keratins, the intermediate filament proteins forming the cytoskeleton. Keratins are categorized into types 1 and 2, based on their characteristics, with KRT78 classified as type 2, alongside KRT4⁵⁰. Several studies have linked abnormal keratin expression to cancer progression⁵¹, in line with our GO analysis showing enriched keratinization in HNSCC co-DEGs. For instance, clinical studies report decreased keratin levels during the transition from normal to invasive HNSCC⁵², and down-regulation of both KRT4 and KRT13 in OSCC⁵³. Additionally, co-expression of KRT4, KRT13, and KRT78 in the epithelium basal layer has been established⁵⁴, further supporting our findings of co-downregulation of KRT4 and KRT78 in HNSCC. Interestingly Fortier et al. showed that loss of KRT8 and KRT18 in epithelial cells correlated with increased MMP2 and MMP9 activity, promoting collective cancer cell migration⁵⁵. This parallels our observation of decreased KRT4 and KRT78 alongside increased MMP1, MMP3, and MMP12 expression in HNSCC.

Our study delved into the molecular mechanisms of HNSCC progression by integrating three microarray datasets from GEO2R using R software and bioinformatics tools. We narrowed down to ten up-regulated and ten down-regulated genes most likely relevant to the disease process. Further, cross-linking analysis yielded 28 candidate co-DEGs potentially implicated in HNSCC development. GO and KEGG pathways analyses revealed these co-DEGs to be primarily involved in ECM organization, disassembly, and proteolysis. These findings provide valuable insights into the biological underpinnings of HNSCC and establish a theoretical foundation for further research. Furthermore, we successfully constructed a PPI network of co-DEGs in HNSCC, identifying hub genes playing crucial roles in disease progression. By utilizing various databases, we validated the impact of hub gene up-regulation and down-regulation on HNSCC development. These results significantly enhance our understanding of HNSCC pathogenesis and the molecular mechanisms driving its occurrence and progression. Our research possesses significant clinical implications, paving the way for improved detection, treatment, and prevention of HNSCC. Notably, identified hub genes like SPP1 and KRT78 emerge as potential targets for effective therapeutic inventions. However, thorough clinical, pharmacological, and biological studies, including in vivo and in vitro experiments, are essential to definitively confirm the functions of these hub genes and their direct involvement in HNSCC progression.

Acknowledgments

This study was supported by a grant from Korea University, Seoul, Republic of Ko-rea (Grant No. K2314111) and the Basic Science Research Program of the National Research Foundation of Korea, funded by the Ministry of Science and Technology and the Ministry of Science, ICT and Future Planning (2017R1A2B2003575 and NRF-2020R1A2C1006398); the Ministry of Science and ICT, Korea, under the ICT Creative Consilience program (IITP-2023-2020-0-01819), supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP); and the Korea Health Technology R&D Project (HI17C0387, HR22C1302) through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare.

Author contributions

J.C. and T.H.K played a guiding role in carrying out the studies, collecting data and drafting the manuscript. B.K., J.B., and S.J. helped to draft the manuscript. J.K., S.K., and J.S. were responsible for the finalization of the paper.

Competing interests

The authors declare no competing interests.

Data availability statement

All information produced or examined throughout this investigation is incorporated in this published article.

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All authors agree with the publication of this original article.

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