Integrative Analysis of Metabolomics and Transcriptomics Data Identifies Prognostic Biomarkers Associated With Oral Squamous Cell Carcinoma

Biological biomarkers of oral cancer

The oral squamous cell carcinoma (OSCC) 5 year survival rate of 41% has marginally improved in the last few years, with less than a 1% improvement per year from 2005 to 2017, with higher survival rates when detected at early stages. Based on histopathological grading of oral dysplasia, it is estimated that severe dysplasia has a malignant transformation rate of 7%-50%. Despite these numbers, oral dysplasia grading does not reliably predict its clinical behavior. Thus, more accurate markers predicting oral dysplasia progression to cancer would enable better targeting of these lesions for closer follow-up, especially in the early stages of the disease. In this context, molecular biomarkers derived from genetics, proteins, and metabolites play key roles in clinical oncology. These molecular signatures can help predict the likelihood of OSCC development and/or progression and have the potential to detect the disease at an early stage and, support treatment decision-making and predict treatment responsiveness. Also, identifying reliable biomarkers for OSCC detection that can be obtained non-invasively would enhance management of OSCC. This review will discuss biomarkers for OSCC that have emerged from different biological areas, including genomics, transcriptomics, proteomics, metabolomics, immunomics, and microbiomics.

Application of metabolomics in oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is a prevalent malignancy affecting the head and neck region. The prognosis for OSCC patients remains unfavorable due to the absence of precise and efficient early diagnostic techniques. Metabolomics offers a promising approach for identifying distinct metabolites, thereby facilitating early detection and treatment of OSCC.

OBJECTIVE

This review aims to provide a comprehensive overview of recent advancements in metabolic marker identification for early OSCC diagnosis. Additionally, the clinical significance and potential applications of metabolic markers for the management of OSCC are discussed.

RESULTS

This review summarizes metabolic changes during the occurrence and development of oral squamous cell carcinoma and reviews prospects for the clinical application of characteristic, differential metabolites in saliva, serum, and OSCC tissue. In this review, the application of metabolomic technology in OSCC research was summarized, and future research directions were proposed.

CONCLUSION

Metabolomics, detection technology that is the closest to phenotype, can efficiently identify differential metabolites. Combined with statistical data analyses and artificial intelligence technology, it can rapidly screen characteristic biomarkers for early diagnosis, treatment, and prognosis evaluations.

A prognostic model built on amino acid metabolism patterns in HPV-associated head and neck squamous cell carcinoma

OBJECTIVES

To compare amino acid metabolism patterns between HPV-positive and HPV-negative head and neck squamous cell carcinoma (HNSCC) patients and identify key genes for a prognostic model.

DESIGN

Utilizing the Cancer Genome Atlas dataset, we analyzed amino acid metabolism genes, differentiated genes between HPV statuses, and selected key genes via LASSO regression for the prognostic model. The model's gene expression was verified through immunohistochemistry in clinical samples. Functional enrichment and CIBERSORTx analyses explored biological functions, molecular mechanisms, and immune cell correlations. The model's prognostic capability was assessed using nomograms, calibration, and decision curve analysis.

RESULTS

We identified 1157 key genes associated with amino acid metabolism in HNSCC and HPV status. The prognostic model, featuring genes like IQCN, SLC22A1, SYT12, and TLX3, highlighted functions in development, metabolism, and pathways related to receptors and enzymes. It significantly correlated with immune cell infiltration and outperformed traditional staging in prognosis prediction, despite immunohistochemistry results showing limited clinical identification of HPV-related HNSCC.

CONCLUSIONS

Distinct amino acid metabolism patterns differentiate HPV-positive from negative HNSCC patients, underscoring the prognostic model's utility in predicting outcomes and guiding therapeutic strategies.

Integrative single-cell and bulk transcriptomes analyses reveals heterogeneity of serine-glycine-one-carbon metabolism with distinct prognoses and therapeutic vulnerabilities in HNSCC

Metabolic heterogeneity plays a central role in sustaining uncontrolled cancer cell proliferation and shaping the tumor microenvironment (TME), which significantly compromises the clinical outcomes and responses to therapy in head and neck squamous cell carcinoma (HNSCC) patients. This highlights the urgent need to delineate the intrinsic heterogeneity and biological roles of metabolic vulnerabilities to advance precision oncology. The metabolic heterogeneity of malignant cells was identified using single-cell RNA sequencing (scRNA-seq) profiles and validated through bulk transcriptomes. Serine-glycine-one-carbon (SGOC) metabolism was screened out to be responsible for the aggressive malignant properties and poor prognosis in HNSCC patients. A 4-SGOC gene prognostic signature, constructed by LASSO-COX regression analysis, demonstrated good predictive performance for overall survival and therapeutic responses. Patients in the low-risk group exhibited greater infiltration of exhausted CD8+ T cells, and demonstrated better clinical outcomes after receiving immunotherapy and chemotherapy. Conversely, high-risk patients exhibited characteristics of cold tumors, with enhanced IMPDH1-mediated purine biosynthesis, resulting in poor responses to current therapies. IMPDH1 emerged as a potential therapeutic metabolic target. Treatment with IMPDH inhibitors effectively suppressed HNSCC cell proliferation and metastasis and induced apoptosis in vitro and in vivo by triggering GTP-exhaustion nucleolar stress. Our findings underscore the metabolic vulnerabilities of HNSCC in facilitating accurate patient stratification and individualized precise metabolic-targeted treatment.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Integration of transcriptomics and metabolomics reveals a novel gene signature guided by FN1 associated with immune response in oral squamous cell carcinoma tumorigenesis

PURPOSE

Oral squamous cell carcinomas (OSCCs) are primary head and neck malignant tumours with a high incidence and mortality. However, the molecular mechanisms involved in OSCC tumorigenesis are not fully understood.

METHODS

OSCC and paired para-carcinoma samples were collected and used to perform multi-omics study. Transcriptomic analysis was used to reveal significant alterations in inflammatory and immune processes in OSCC. Ingenuity Pathway Analysis (IPA) combined with the LASSO Cox algorithm was used to identify and optimize a crucial gene signature. Metabolomics analysis was performed to identify the important metabolites which linked to the crucial gene signature. The public data TCGA-HNSCC cohort was used to perform the multiple bioinformatic analysis.

RESULTS

These findings identified a FN1-mediated crucial network that was composed of immune-relevant genes (FN1, ACP5, CCL5, COL1A1, THBS1, BCAT1, PLAU, IGF2BP3, TNF, CSF2, CXCL1 and CXCL5) associated with immune infiltration and influences the tumour microenvironment, which may contribute to OSCC tumorigenesis and progression. Moreover, we integrated the relevant genes with altered metabolites identified by metabolic profiling and identified 7 crucial metabolites (Glu-Glu-Lys, Ser-Ala, Ser-Ala, N-(octadecanoyl) sphing-4-enine-1-phosphocholine, N-methylnicotinamide, pyrrhoxanthinol and xanthine) as potential downstream targets of the FN1-associated gene signature in OSCC. Importantly, FN1 expression is positively correlated with immune infiltration levels in HNSCC, which was confirmed at the single-cell level.

CONCLUSIONS

Overall, these results revealed the differential genetic and metabolic patterns associated with OSCC tumorigenesis and identified an essential molecular network that plays an oncogenic role in OSCC by affecting amino acid and purine metabolism. These genes and metabolites might, therefore, serve as predictive biomarkers of survival outcomes and potential targets for therapeutic intervention in OSCC.

Identification of Metabolism-Associated Biomarkers for Early and Precise Diagnosis of Oral Squamous Cell Carcinoma

The 5-year survival rate for oral squamous cell carcinoma (OSCC), one of the most common head and neck cancers, has not improved in the last 20 years. Poor prognosis of OSCC is the result of failure in early and precise diagnosis. Metabolic reprogramming, including the alteration of the uptake and utilisation of glucose, amino acids and lipids, is an important feature of OSCC and can be used to identify its biomarkers for early and precise diagnosis. In this review, we summarise how recent findings of rewired metabolic networks in OSCC have facilitated early and precise diagnosis of OSCC.