Transcriptional reprogramming in oral squamous carcinoma

doi:10.21203/rs.3.rs-4943366/v1

Download PDF

Article

Transcriptional reprogramming in oral squamous carcinoma

https://doi.org/10.21203/rs.3.rs-4943366/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Oral squamous cell carcinoma (OSCC) is a prevalent form of cancer globally, originating from a diverse range of neoplastic transformations in the oral mucosa. This condition is characterized by its complex genetic underpinnings, involving the intricate regulation of multiple genes. Genetic factors influence cellular processes such as growth, differentiation, and apoptosis of oral mucosal cells, thereby promoting or inhibiting tumor formation and progression. Furthermore, environmental factors—including smoking, alcohol consumption, and human papillomavirus (HPV) infection—can significantly increase the risk of developing OSCC. These external influences can impact the disease in several ways. Delayed clinical detection and the absence of specific biomarkers, coupled with expensive treatment alternatives, contribute to poor prognoses among OSCC patients. Thus, identifying OSCC biomarkers has become imperative. This study investigates publicly accessible sequencing data of oral mucosal tissues from four distinct datasets—GSE23558, GSE30784, GSE36090, and GSE51010—archived in the Gene Expression Omnibus (GEO) database. By analyzing these datasets, which encompass a range of genetic profiles and experimental conditions, the study seeks to uncover critical biomarkers and molecular pathways involved in the early stages of OSCC development. The primary objective is to identify pivotal genes linked to the onset of OSCC. The findings provide preliminary evidence for therapeutic targets in OSCC and may serve as a robust foundation for subsequent biological research endeavors.

Biological sciences/Biotechnology

Biological sciences/Cell biology

Biological sciences/Computational biology and bioinformatics

Health sciences/Medical research

oral squamous carcinoma

RRA

WGCNA

key gene

biological function

Oral squamous cell carcinoma (OSCC) is a type of malignant tumor that arises from the squamous epithelial cells lining the oral mucosa. This carcinoma develops within the outermost layer of the oral mucosal tissue, which is primarily composed of squamous cells—flat, thin cells that form the surface layer of the mucous membranes. As OSCC progresses, it invades deeper into the surrounding tissues and structures, leading to aggressive growth and potential metastasis. OSCC ranks as one of the most frequently encountered malignant tumors within the oral cavity. This cancer often originates from several potential sites, such as the tongue, the floor of the mouth, the buccal mucosa (inner cheeks), and the lips. Its development has been closely linked to certain high-risk lifestyle behaviors. Specifically, the habitual use of tobacco, excessive alcohol consumption, and betel nut chewing have all been identified as significant contributors to the increased risk of OSCC. ^{[1, 2]}. Oral squamous carcinoma occurs globally, but the prevalence varies significantly in different regions. For example, the prevalence is higher in Asia and South America, while it is relatively lower in North America and Europe ^{[3, 4]}. Oral squamous cell carcinoma (OSCC) is notably more prevalent in men compared to women, with men exhibiting an incidence rate that is roughly double that of women. This disparity is primarily due to higher rates of smoking and alcohol consumption among men, both of which are significant risk factors for developing this type of cancer. ^[5–7]. Oral squamous carcinoma occurs mainly in the middle-aged and older population, especially those over 50 years of age. However, in recent years it has been found that the incidence of oral squamous carcinoma has also increased in younger people, in part related to an increase in HPV infection. Certain specific strains of the human papillomavirus (HPV), notably HPV-16 and HPV-18, play a significant role in the development of oral squamous cell carcinoma. These particular types of HPV are deemed crucial factors in the pathogenesis of this malignancy. HPV infections are transmitted through sexual contact, and can affect the cell growth and transformation process of the oral mucosa ^{[8, 9]}.

Early symptoms of squamous oral cancer are not obvious and may manifest as chronic ulcers, plaques or nodules of abnormal color. These symptoms are often mistaken for oral inflammation or ulcers, thus often leading to late diagnosis. Advanced oral squamous cell carcinoma (OSCC) poses significant treatment challenges, characterized by its resistance to therapy and high likelihood of recurrence. As the disease progresses, it becomes increasingly difficult to manage effectively, leading to a more complex and intensive treatment regimen. For early-stage oral squamous cell carcinoma (OSCC), surgical resection is a widely used treatment approach. This method involves the removal of the tumor and surrounding tissue to achieve a cure, as the cancer is localized and has not yet spread significantly. In contrast, the management of advanced OSCC often requires a more comprehensive strategy. In such cases, radiotherapy and chemotherapy are frequently employed in conjunction with surgical treatment to enhance patient outcomes. Radiotherapy uses high-energy radiation to target and destroy cancer cells, while chemotherapy involves the use of cytotoxic drugs to eliminate cancer cells throughout the body. Both treatments are aimed at reducing tumor size, targeting any remaining cancerous cells, and addressing potential metastases. This multi-modal approach not only helps in controlling the progression of the disease but also in improving overall prognosis and survival rates for patients with more advanced stages of OSCC ^{[10, 11]}. In recent years, targeted therapy and immunotherapy have emerged as promising treatment options for advanced or refractory oral squamous cell carcinoma (OSCC), offering new hope for patients who may not be suitable candidates for traditional chemotherapy or radiotherapy ^{[12, 13]}. The prognosis for patients with OSCC is often poor, primarily due to the limited availability of early diagnostic tools and reliable biological markers in clinical settings ^{[14, 15]}. In this study, we employed machine learning techniques to integrate and analyze sequencing data with the goal of identifying key genes associated with oral squamous cell carcinoma (OSCC). By applying advanced computational methods to large-scale genomic datasets, we aimed to uncover critical genetic markers that are pivotal in the development and progression of OSCC. This approach seeks to enhance our understanding of the molecular underpinnings of the disease and identify potential biomarkers for early diagnosis. Additionally, the insights gained from this analysis are intended to inform the development of targeted therapeutic strategies, offering a solid foundation for both early detection and the creation of novel treatment options for OSCC.

1.Data processing method and software sources

The original data for this study were obtained from the Gene Expression Omnibus (GEO) database ^[16], and consist of transcriptome gene expression matrices. These datasets include four distinct gene expression profiles: GSE23558, GSE30784, GSE36090, and GSE51010. Each dataset represents a cohort of oral squamous cell carcinoma (OSCC) cases alongside healthy control samples.

Data processing and analysis were performed using R statistical software (version 4.4.0, available at https://www.r-project.org/). To correct for batch effects across the various datasets, we utilized the "sva" package ^[17]. Differential gene expression analysis was carried out with the "limma" package ^[18], allowing us to identify genes with significant expression changes. For the enrichment analysis of differentially expressed genes (DEGs), we employed the "clusterProfiler" package ^[19], which facilitated the exploration of biological pathways and functions associated with the DEGs. Visualization of the selected DEGs was achieved through the "pheatmap" package, which provided clear heatmaps of gene expression patterns. Additionally, gene co-expression analysis was conducted using the "WGCNA" package ^[20], enabling us to identify clusters of genes with correlated expression profiles and explore their potential functional relationships.

2.RRA Analysis

Robust rank aggregation (RRA) is a widely utilized method in bioinformatics and computational biology, designed to synthesize rankings derived from various sources into a cohesive consensus list ^[21]. This approach is especially useful when working with disparate datasets or varying methodologies, which may each introduce their own biases or sources of noise. By systematically combining these rankings, RRA method improves the overall reliability of the final integrated ranking. RRA achieves this by effectively mitigating individual dataset inconsistencies and noise, thereby offering a more precise and reliable summary of shared insights across multiple datasets. This enhanced accuracy in the integrated ranking allows for a more robust interpretation of the data, reflecting a consensus that is less affected by the inherent variability and potential biases of the individual datasets.

In this study, we utilized the RRA package to integrate and analyze data from four separate datasets. To identify significant genes, we established criteria that included a p-value threshold of less than 0.05 and an absolute log2 fold change (FC) greater than 0.585. This stringent selection process enabled us to accurately identify key genes associated with OSCC. By applying these criteria, we were able to highlight genes with substantial and statistically significant expression changes, thereby deepening our understanding of their potential roles in OSCC. This robust approach not only clarified the relevance of these genes in the context of the disease but also laid the groundwork for future biological research aimed at exploring their functional implications and therapeutic potential.

3.WGCNA Analysis

In our gene co-expression analysis, we utilized the Weighted Gene Co-expression Network Analysis (WGCNA) package to construct gene regulatory networks that provide insights into underlying disease mechanisms. To ensure the quality and reliability of our analysis, we calculated Pearson correlation coefficients to detect and address any outliers present in the data, thereby safeguarding its integrity. By applying a soft threshold of 0.9, we carefully selected nodes from the gene expression profiles, optimizing the network structure to maintain a scale-free topology. Subsequently, we delved into the intricate relationships between specific gene expression profiles and characteristic genes, leveraging this network to cluster genes exhibiting similar expression patterns into distinct modules. This systematic approach allowed us to unveil potential regulatory networks and uncover meaningful associations between genes, shedding light on the intricate molecular mechanisms underlying the pathogenesis of the disease.

4.GO and KEGG analysis

The Gene Ontology (GO) functional enrichment analysis encompasses three primary components: Molecular Function (MF), Biological Process (BP), and Cellular Component (CC). These components evaluate distinct facets of gene function and their localization within biological systems. To perform the GO term enrichment analysis, we used the "enrichGO" R package ^[19], which identifies statistically significant GO terms linked to the provided gene set. This analysis highlights relevant biological processes, molecular functions, and cellular components associated with the genes under investigation. Additionally, for a more comprehensive understanding of functional roles, we employed the "enrichKEGG" R package ^[19] to conduct functional enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. This approach provided insights into the pathways and biological networks that are pertinent to the genes in our study, enhancing our grasp of their functional significance and involvement in various metabolic and signaling pathways.This approach helps elucidate the biological pathways and processes in which the identified genes are involved, providing insights into their potential roles and interactions within cellular systems. Overall, these analyses enable a comprehensive understanding of gene functions, biological processes, cellular components, and pathway associations, crucial for interpreting the molecular mechanisms underlying complex biological phenomena.

Ethical Declaration

All data utilized in this research were sourced from publicly available databases. This study does not involve experiments or investigations conducted on animals or humans. The use of existing datasets ensures compliance with ethical standards and regulations governing research involving human and animal subjects, emphasizing the reliance on secondary data analysis for scientific inquiry.

1.Data details

This study incorporates a total of four datasets, each described in detail in Table 1. The characteristics and specifics of these datasets are comprehensively outlined, providing essential information for understanding their relevance and contribution to the research conducted.

Table 1

Details of the included datasets.
GSE ID	partipants	species	Analysis type	Year
GSE23558	27cases and 5control	human	Array	2011
GSE36090	10cases and 3control	human	Array	2012
GSE30784	167cases and 45control	human	Array	2011
GSE51010	48cases and 8control	human	Array	2014

2.Overall workflow

In this study, we obtained raw gene expression matrices of two datasets from the GEO database. Each dataset underwent RRA and WGCNA analyses separately. WGCNA analysis identified 225 differentially expressed genes, which were intersected with 101 differentially expressed genes identified by RRA, resulting in a final set of 3 genes. These 5 genes, namely SH3BP4, RRAGC, and SQRDL, were identified as key genes. Details of the specific workflow are depicted in Fig. 1.

3.Data pre-processing

The microarray datasets were first normalized using the quantile normalization method to ensure consistency across samples. Differential analysis was then performed individually on each of the four datasets, leading to the identification and labeling of the top 10 genes with the most significant p-values, as illustrated in Fig. 2. To address potential issues such as noise, redundancy, and batch effects, we applied the Combat function from the sva R package. This step harmonized the four datasets, aligning them to minimize discrepancies and improve overall data quality. The outcomes of this harmonization process are depicted in Fig. 3, demonstrating the effectiveness of our approach in achieving more reliable and integrated results.

4.Results of the RRA Analysis

In the RRA analysis, we applied a criterion of p-values less than 0.05 to identify differentially expressed genes, leading to the selection of 101 significant genes for further examination. To aid in the interpretation and subsequent research, we visualized the top 10 upregulated and top 10 downregulated genes separately. These visualizations, presented in Fig. 4, provide a clear and focused overview of the most prominent gene expression changes, facilitating a more detailed exploration of their roles and potential implications in the context of our study.

5.Results of the WGCNA Analysis

Weighted Gene Co-expression Network Analysis (WGCNA) is a robust method widely employed in disease research to uncover candidate biomarkers, elucidate disease mechanisms, and identify potential therapeutic targets. This technique organizes genes into co-expression modules, where it groups genes that exhibit similar expression patterns, thus highlighting clusters of highly correlated genes. This grouping is instrumental in discovering potential biomarkers associated with the disease. Moreover, by constructing and analyzing gene expression networks, WGCNA identifies key hub genes that play crucial roles in disease progression. These hub genes are often central to the network and can serve as critical targets for further investigation. Consequently, WGCNA provides valuable insights into the molecular underpinnings of diseases and offers promising strategies for the development of biomarkers and therapeutic interventions aimed at mitigating disease progression.

To explore the broad impact of oral squamous cell carcinoma on physiological functions, we applied WGCNA to gene expression matrices. Despite previous studies on gene function, many aspects remain unknown. Initially, we used the functions "goodSamplesGenes()" and "hclust()" to reduce outliers in the sample data. Subsequently, we employed the "pickSoftThreshold()" function to select an appropriate threshold matrix. Using clustering results and distance matrices, we applied the dynamic tree cut algorithm to identify modules and set minimum gene thresholds for each module. Modules were assigned colors using "labels2colors()" and module eigengenes were computed using "moduleEigengenes()" to assess inter-module correlations. Finally, genes with 90% similarity were merged into modules, and hub genes within each module were identified for further exploration, as depicted in Fig. 5.

6.Results of the GO and KEGG Analysis

Subsequently, we conducted Gene Ontology (GO) functional annotation and KEGG pathway enrichment analyses on the combined set of genes. This set includes 101 Differentially Expressed Genes (DGEs) identified through the Robust Rank Aggregation (RRA) analysis and 225 Differentially Expressed Genes (DEGs) detected by the Weighted Gene Co-expression Network Analysis (WGCNA). By integrating these two sets of differentially expressed genes, we aimed to gain a comprehensive understanding of their biological functions and the pathways they influence. The findings from these analyses are illustrated in Fig. 6, providing detailed insights into the functional roles and pathway associations of the identified genes.

In our study, we investigated potential biological targets for oral squamous cell carcinoma by analyzing four independent datasets. We employed integrated methodologies, including Robust Rank Aggregation (RRA) and Weighted Gene Co-expression Network Analysis (WGCNA), to identify critical genes and significant pathways related to the disease. Through RRA, we consolidated differential expression data to highlight key genes consistently associated with oral squamous cell carcinoma. Complementarily, WGCNA was utilized to map gene co-expression networks and uncover pivotal genes involved in disease progression. The combined insights from these approaches revealed new potential therapeutic targets and offered a deeper understanding of the molecular mechanisms underlying oral squamous cell carcinoma. These findings pave the way for future research and the development of targeted treatments aimed at improving patient outcomes.

SH3BP4 (SH3 Domain Binding Protein 4) is a protein-coding gene that interacts with SH3 domains. The SH3 domain is a small and commonly occurring domain crucial in signaling and intracellular communication. SH3BP4 interacts with SH3 domains of other proteins, participating in the regulation of various cellular processes ^[22–24]. SH3BP4 consists of two regions: the SH3 Binding Region and other functional domains. In organisms, SH3BP4 plays the following roles: 1. Signal regulation: It modulates cellular signaling pathways, potentially influencing pathway activity through interactions with other proteins ^[25]. 2. Cytoskeletal regulation: It plays a crucial role in the remodeling and dynamic regulation of the cytoskeleton, orchestrating changes in the structure and organization of the cytoskeletal framework. 3. Cell adhesion and migration: It may affect cell adhesion and migration capabilities, crucial processes in development and disease progression ^[26–28]. Studies suggest SH3BP4 may be implicated in tumorigenesis and progression. For instance, its dysregulation could impact cell proliferation and migration, thereby promoting tumorigenesis. In a separate research investigation, the discovery of Single Nucleotide Polymorphisms (SNPs) within the SH3BP4 gene revealed an intriguing overlap with predicted microRNA binding regions. This phenomenon suggests a plausible scenario where these SNPs could interfere with multiple miRNA-mRNA interactions, influencing the prognosis of patients with laryngeal cancer. This intricate interplay between genetic variations within SH3BP4 and miRNA-mediated regulatory mechanisms may have significant implications for understanding the molecular basis of laryngeal cancer progression and overall patient survival outcomes. Further exploration of these interactions could potentially unveil novel therapeutic targets and personalized treatment strategies in the context of laryngeal cancer management ^[29]. Consequently, SH3BP4 emerges as a promising candidate for both early detection and therapeutic intervention in oral squamous cell carcinoma.Its potential as a biomarker for early diagnosis is underscored by its involvement in key molecular pathways related to the disease, offering a valuable tool for identifying cancer at an earlier, more treatable stage. Additionally, SH3BP4's role in disease progression and its interaction with critical regulatory networks position it as a viable therapeutic target. Targeting SH3BP4 could lead to the development of novel treatments aimed at disrupting cancer-specific pathways, potentially improving treatment efficacy and patient outcomes in oral squamous cell carcinoma. Further research into SH3BP4's functions and interactions will be crucial for validating its utility in clinical settings and optimizing therapeutic strategies.

RRAGC (Ras Related GTP Binding C) encodes a small GTP-binding protein that is a member of the Rag GTPase family. This gene is integral to cellular processes involving nutrient sensing and signal transduction. The RRAGC protein comprises multiple functional domains, including a GTP-binding domain and specific effector-binding regions. Its structure resembles other Rag GTPases, featuring an N-terminal GTPase domain and a C-terminal effector-binding domain ^{[30, 31]}. In conjunction with RRAGA, RRAGC is instrumental in regulating the localization and activity of the mTORC1 complex. This regulation is crucial for overseeing essential physiological processes including cellular metabolism, proliferation, and growth. RRAGC significantly influences how cells respond to amino acid availability by modulating the signaling pathways of mTORC1. Through its role in these signaling cascades, RRAGC helps ensure that cellular functions are appropriately adjusted according to nutrient levels, thereby maintaining cellular balance and promoting optimal growth and metabolic efficiency. This dynamic regulation underscores RRAGC's importance in adapting to fluctuating nutrient conditions and its impact on broader physiological processes. RRAGC forms a complex with RRAGA as part of the Rag GTPase complex, regulating mTORC1 activation state through GTP hydrolysis ^[31–36]. Studies indicate that mutations activating mTORC1 via RRAGC are implicated in follicular lymphoma, suggesting a role of RRAGC in cancer development ^[37–39]. Hence, RRAGC regulates cancer occurrence and may be a key gene in the modulation of oral squamous cell carcinoma.

SQRDL (Sulfide quinone reductase-like) is a protein gene involved in cellular metabolic regulation and redox reactions. It typically consists of functional domains, potentially including domains interacting with sulfides or quinoline compounds, as well as domains facilitating electron transfer. The protein encoded by this gene is likely localized within the mitochondria, where it plays a critical role in mitigating sulfide toxicity. It accomplishes this by catalyzing the biochemical conversion of sulfides into thiosulfates. This enzymatic process helps to neutralize potentially harmful sulfide compounds, thereby protecting mitochondrial function and cellular health. By efficiently managing sulfide levels, this protein contributes to the maintenance of cellular homeostasis and prevents the accumulation of toxic substances that could impair mitochondrial performance and overall cellular well-being ^[40–44]. Alternative splicing produces several transcript variants that all encode the same protein. SQRDL is essential for proper skeletal development and has been closely linked to osteoporosis, particularly in postmenopausal women. This gene's alternative splicing may lead to different isoforms of the protein, potentially affecting its function and contribution to bone health. SQRDL's involvement in skeletal development underscores its importance in bone formation and maintenance, while its association with osteoporosis highlights its potential role in the pathogenesis of this condition ^{[45, 46]}. Studies have shown that SQRDL also modulates the biological functions of colorectal cancer ^[47]. Despite its known role in skeletal development and association with osteoporosis, there is currently a significant gap in research regarding the connection between SQRDL and oral squamous cell carcinoma (OSCC). To fully understand whether and how SQRDL might influence the development or progression of OSCC, further biological experiments are required. These investigations should focus on elucidating the potential roles of SQRDL in the context of oral cancer, examining its expression levels, functional impacts, and possible mechanisms of action. Such studies could provide valuable insights into whether SQRDL is involved in the pathogenesis of OSCC and could uncover novel aspects of its biological functions, potentially leading to new diagnostic or therapeutic approaches for managing oral squamous cell carcinoma. Understanding this connection could potentially shed light on novel therapeutic strategies targeting SQRDL in cancer treatment contexts.

In summary, by combining data from four datasets through the RRA and WGCNA algorithms, we successfully identified differentially expressed genes (DEGs) linked to the development of OSCC. This integrated approach provides valuable insights that could support the diagnosis and treatment of OSCC. Additionally, our GO functional annotation and KEGG pathway enrichment analyses highlighted key signaling pathways enriched among these DEGs. However, the precise mechanisms by which these DEGs contribute to the carcinogenic process in oral squamous cell carcinoma remain uncertain. Further research is needed to clarify the roles of these DEGs and validate their potential as biomarkers or therapeutic targets in OSCC.

Author Contribution

Xianyang Cheng wrote the main manuscript text andShan Shen analyzed the data. All authors reviewed the manuscript

Data Availability

The raw data for all four datasets involved in the manuscript are available in the GEO database, https://www.ncbi.nlm.nih.gov/.

Chai, A., Lim, K. P. & Cheong, S. C. Translational genomics and recent advances in oral squamous cell carcinoma[J]. Semin Cancer Biol. 61, 71–83 (2020).
Thomson, P. J. Perspectives on oral squamous cell carcinoma prevention-proliferation, position, progression and prediction[J]. J. Oral Pathol. Med. 47 (9), 803–807 (2018).
Pekarek, L. et al. Emerging histological and serological biomarkers in oral squamous cell carcinoma: Applications in diagnosis, prognosis evaluation and personalized therapeutics (Review)[J]. Oncol. Rep., 50(6). (2023).
Ling, Z., Cheng, B. & Tao, X. Epithelial-to-mesenchymal transition in oral squamous cell carcinoma: Challenges and opportunities[J]. Int. J. Cancer. 148 (7), 1548–1561 (2021).
Badwelan, M. et al. Oral Squamous Cell Carcinoma and Concomitant Primary Tumors, What Do We Know? A Review of the Literature[J]. Curr. Oncol. 30 (4), 3721–3734 (2023).
Sarode, G. et al. Epidemiologic aspects of oral cancer[J]. Dis. Mon. 66 (12), 100988 (2020).
Meng, X. et al. The role of non-coding RNAs in drug resistance of oral squamous cell carcinoma and therapeutic potential[J]. Cancer Commun. (Lond). 41 (10), 981–1006 (2021).
Hubbers, C. U. & Akgul, B. HPV and cancer of the oral cavity[J]. Virulence. 6 (3), 244–248 (2015).
Chieng, C. Y., Dalal, A. & Ilankovan, V. Occupational exposure and risk of oral and oropharyngeal squamous cell carcinoma: systematic review and 25-year retrospective cohort study of patients[J]. Br. J. Oral Maxillofac. Surg. 61 (1), 39–48 (2023).
Machiels, J. P. et al. Squamous cell carcinoma of the oral cavity, larynx, oropharynx and hypopharynx: EHNS-ESMO-ESTRO Clinical Practice Guidelines for diagnosis, treatment and follow-up[J]. Ann. Oncol. 31 (11), 1462–1475 (2020).
Oliva, M. et al. Transitions in oral and gut microbiome of HPV + oropharyngeal squamous cell carcinoma following definitive chemoradiotherapy (ROMA LA-OPSCC study) [J]. Br. J. Cancer. 124 (9), 1543–1551 (2021).
Cao, M. et al. Personalized Targeted Therapeutic Strategies against Oral Squamous Cell Carcinoma. An Evidence-Based Review of Literature[J]. Int. J. Nanomed. 17, 4293–4306 (2022).
Choi, Y. S. et al. Human Papillomavirus and Epidermal Growth Factor Receptor in Oral Cavity and Oropharyngeal Squamous Cell Carcinoma: Correlation With Dynamic Contrast-Enhanced MRI Parameters[J]. AJR Am. J. Roentgenol. 206 (2), 408–413 (2016).
Rivera, C. Essentials of oral cancer[J]. Int. J. Clin. Exp. Pathol. 8 (9), 11884–11894 (2015).
Fu, J. et al. Improving oral squamous cell carcinoma diagnosis and treatment with fluorescence molecular imaging[J]. Photodiagnosis Photodyn Ther. 44, 103760 (2023).
Clough, E. et al. NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update[J]. Nucleic Acids Res. 52 (D1), D138–D144 (2024).
Leek, J. T. et al. The sva package for removing batch effects and other unwanted variation in high-throughput experiments[J]. Bioinformatics. 28 (6), 882–883 (2012).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res. 43 (7), e47 (2015).
Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data[J]. Innov. (Camb). 2 (3), 100141 (2021).
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis[J]. BMC Bioinform. 9, 559 (2008).
Kolde, R. et al. Robust rank aggregation for gene list integration and meta-analysis[J]. Bioinformatics. 28 (4), 573–580 (2012).
Kim, Y. M. & Kim, D. H. dRAGging amino acid-mTORC1 signaling by SH3BP4[J]. Mol. Cells. 35 (1), 1–6 (2013).
Kim, K. H., Lee, T. R. & Cho, E. G. SH3BP4, a novel pigmentation gene, is inversely regulated by miR-125b and MITF[J]. Exp. Mol. Med. 49 (8), e367 (2017).
Kim, Y. M. et al. SH3BP4 is a negative regulator of amino acid-Rag GTPase-mTORC1 signaling[J]. Mol. Cell. 46 (6), 833–846 (2012).
Burckhardt, C. J., Minna, J. D. & Danuser, G. SH3BP4 promotes neuropilin-1 and alpha5-integrin endocytosis and is inhibited by Akt[J]. Dev. Cell. 56 (8), 1164–1181 (2021).
Antas, P. et al. SH3BP4 Regulates Intestinal Stem Cells and Tumorigenesis by Modulating beta-Catenin Nuclear Localization[J]. Cell. Rep. 26 (9), 2266–2273 (2019).
Chat, V. et al. A genome-wide association study of germline variation and melanoma prognosis[J]. Front. Oncol. 12, 1050741 (2022).
Dong, Z. & Zhou, H. Pan-cancer landscape of aberrant DNA Methylation across childhood Cancers: Molecular Characteristics and Clinical relevance[J]. Exp. Hematol. Oncol. 11 (1), 89 (2022).
Stoilkova-Hartmann, A. et al. COPD patient education and support - Achieving patient-centredness[J]. Patient Educ. Couns. 101 (11), 2031–2036 (2018).
Yonehara, R. et al. Structural basis for the assembly of the Ragulator-Rag GTPase complex[J]. Nat. Commun. 8 (1), 1625 (2017).
Anandapadamanaban, M. et al. Architecture of human Rag GTPase heterodimers and their complex with mTORC1[J]. Science. 366 (6462), 203–210 (2019).
Sancak, Y. et al. Ragulator-Rag complex targets mTORC1 to the lysosomal surface and is necessary for its activation by amino acids[J]. Cell. 141 (2), 290–303 (2010).
Tsun, Z. Y. et al. The folliculin tumor suppressor is a GAP for the RagC/D GTPases that signal amino acid levels to mTORC1[J]. Mol. Cell. 52 (4), 495–505 (2013).
Long, P. A. et al. De novo RRAGC mutation activates mTORC1 signaling in syndromic fetal dilated cardiomyopathy[J]. Hum. Genet. 135 (8), 909–917 (2016).
Napolitano, G. et al. A substrate-specific mTORC1 pathway underlies Birt-Hogg-Dube syndrome[J]. Nature. 585 (7826), 597–602 (2020).
Cui, Z. et al. Structure of the lysosomal mTORC1-TFEB-Rag-Ragulator megacomplex[J]. Nature. 614 (7948), 572–579 (2023).
Okosun, J. et al. Recurrent mTORC1-activating RRAGC mutations in follicular lymphoma[J]. Nat. Genet. 48 (2), 183–188 (2016).
Ying, Z. X. et al. Recurrent Mutations in the MTOR Regulator RRAGC in Follicular Lymphoma[J]. Clin. Cancer Res. 22 (21), 5383–5393 (2016).
Ortega-Molina, A. et al. Oncogenic Rag GTPase signaling enhances B cell activation and drives follicular lymphoma sensitive to pharmacological inhibition of mTOR[J]. Nat. Metab. 1 (8), 775–789 (2019).
Jackson, M. R., Melideo, S. L. & Jorns, M. S. Human sulfide:quinone oxidoreductase catalyzes the first step in hydrogen sulfide metabolism and produces a sulfane sulfur metabolite[J]. Biochemistry. 51 (34), 6804–6815 (2012).
Libiad, M. et al. Organization of the human mitochondrial hydrogen sulfide oxidation pathway[J]. J. Biol. Chem. 289 (45), 30901–30910 (2014).
Landry, A. P., Ballou, D. P. & Banerjee, R. Modulation of Catalytic Promiscuity during Hydrogen Sulfide Oxidation[J]. ACS Chem. Biol. 13 (6), 1651–1658 (2018).
Ackermann, M. et al. The vertebrate homologue of sulfide-quinone reductase in mammalian mitochondria[J]. Cell. Tissue Res. 358 (3), 779–792 (2014).
Ackermann, M. et al. The vertebrate homolog of sulfide-quinone reductase is expressed in mitochondria of neuronal tissues[J]. Neuroscience. 199, 1–12 (2011).
Cai, X. et al. Genetic susceptibility of postmenopausal osteoporosis on sulfide quinone reductase-like gene[J]. Osteoporos. Int. 29 (9), 2041–2047 (2018).
Jin, H. S. et al. Association of the I264T variant in the sulfide quinone reductase-like (SQRDL) gene with osteoporosis in Korean postmenopausal women[J]. PLoS One. 10 (8), e135285 (2015).
Zhang, S. et al. hsa-miR-29c-3p regulates biological function of colorectal cancer by targeting SPARC[J]. Oncotarget. 8 (61), 104508–104524 (2017).

No competing interests reported.

Download PDF

Reviews received at journal
05 Nov, 2024
Reviews received at journal
19 Oct, 2024
Reviewers agreed at journal
09 Oct, 2024
Reviewers agreed at journal
08 Oct, 2024
Reviewers invited by journal
11 Sep, 2024
Editor assigned by journal
11 Sep, 2024
Editor invited by journal
05 Sep, 2024
Submission checks completed at journal
05 Sep, 2024
First submitted to journal
20 Aug, 2024

You are reading this latest preprint version

Transcriptional reprogramming in oral squamous carcinoma

Status:

Version 1

Abstract

Figures

Introduction

Material and methods

1.Data processing method and software sources

2.RRA Analysis

3.WGCNA Analysis

4.GO and KEGG analysis

Ethical Declaration

Results

1.Data details

2.Overall workflow

3.Data pre-processing

4.Results of the RRA Analysis

5.Results of the WGCNA Analysis

6.Results of the GO and KEGG Analysis

Discussion

Declarations

Author Contribution

Data Availability

References

Additional Declarations

Status:

Version 1