Secreted protein markers in oral squamous cell carcinoma (OSCC)

The role of molecular biomarkers in the diagnosis, prognosis, and treatment stratification of oral squamous cell carcinoma: A comprehensive review

One of the most common cancers targeting the area of the head and neck is oral squamous cell carcinoma (OSCC), carrying a heavy global health cost. With a high incidence of metastasis and recurrence, the outlook for OSCC remains dismal despite advancements in treatment. This has sparked an investigation into molecular biomarkers, which have the potential to improve early diagnosis, forecast patient outcomes, and direct therapeutic approaches. An extensive summary of the function of molecular biomarkers in OSCC diagnosis, prognosis, and medical care stratification is given in this article. Complex genetic mutations, epigenetic changes, and dysregulated signalling pathways are all part of the aetiology of OSCC. Tumor protein p53 (Tp53), Epidermal growth factor receptor (EGFR-targeted), Cyclin D1 (CCND1), and Human papilloma virus (HPV) status are examples of molecular biomarkers that have demonstrated potential in recognising disease at an early stage and identifying malignant changes. The non-invasive detection capabilities of diagnostic biomarkers such as salivary proteins, circulating tumour DNA (ctDNA), and microRNAs are being explored more and more because they may provide early intervention and better patient outcomes. Prognostically, tumour aggressiveness, recurrence risk, and overall survival have all been linked to biomarkers such as matrix metalloproteinases (MMPs), E-cadherin, and different cytokines. Furthermore, immune checkpoints such as cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed death-ligand 1 (PD-L1) are becoming recognised as important markers of the tumour microenvironment's function in the course of the disease and its reaction to immunotherapy. The significance of biomarkers in personalised medicine has been further highlighted by the recognition of subgroups with elevated risk that might gain benefit from more aggressive treatment options thanks to the genetic profiling of OSCC. Predictive biomarkers are essential for therapy classification because they allow therapeutic regimens to be tailored. For example, (Kirsten rat sarcoma viral oncogene homologous) KRAS mutations and EGFR expression influence the effectiveness of targeted therapies, and the existence of specific epigenetic markers influences choices about radiation or chemotherapy. It is expected that the incorporation of multi-omics techniques, which integrate transcriptome, proteome, and genomic data, will improve these tactics and increase accuracy in OSCC treatment. Molecular indicators have the potential to significantly improve the medical treatment of ovarian cancer. Better patient outcomes will eventually result from earlier identification, more precise prognostication, and individualised therapy regimens made possible by advancements in biomarker research. For these biomarkers to be widely used, further research must be done on verifying them and incorporating them into standard clinical practice.

γδ17T Cells Aggravate Carcinogen-Induced Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy, with a low 5-y survival rate and frequent local recurrence or metastasis. This study explores the role of γδT cells in the development and progression of OSCC. γδT cells, which exhibit innate and adaptive immune characteristics, are known for their dual role in cancer, acting as anti- and protumor agents depending on the context. Using a murine model of OSCC induced by the carcinogen 4-nitroquinoline-1-oxide (4NQO), which adequately mimics the progression of human OSCC, we investigated the impact of γδT cells on tumor growth and the tumor microenvironment. We first characterized the γδT cells of the tongue epithelium, the primary site for cancer development in this model. The results indicate that γδT cells are predominantly of the Vγ6+ subset, expanding postnatally in a microbiota-dependent manner. Upon 4NQO administration, depletion of γδT cells did not significantly alter the kinetics of OSCC progression but did result in a reduction in tumor size and number, suggesting a role in promoting tumor growth. Interestingly, the absence of IL-17, a key cytokine produced by the Vγ6+ subset, also resulted in reduced tumor volume without affecting disease progression, corroborating the protumor role of these cells in OSCC. Further analysis revealed that IL-17-producing γδT cells facilitate angiogenesis within the tumor microenvironment by promoting the expression of angiogenic factors. Of note, while 4NQO treatment increased the oral microbial load and altered its composition, IL-17 deficiency did not affect the oral microbiota, indicating that the effects of IL-17-producing γδT cells on OSCC are independent of microbial changes. This study highlights the pathologic role of IL-17-producing γδT cells in OSCC, particularly in promoting tumor growth through angiogenesis. This underscores the importance of γδT cells in OSCC and the need for further research into therapeutic strategies targeting these cells.

Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers

Introduction

Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions for identifying tumor-derived biomarkers.

Methods

We analyzed a multicenter public dataset comprising 61 OSCC patients and 61 healthy controls. Plasma metabolomics data were processed to extract 29 numerical and 47 ratio features. The Extra Trees (ET) algorithm was applied for feature selection, and the TabPFN model was used for classification and prediction.

Results

The model achieved an area under the curve (AUC) of 93% and an overall accuracy of 76.6% when using top-ranked individual biomarkers. Key metabolic features significantly differentiated OSCC patients from healthy controls, providing a detailed metabolic fingerprint of the disease.

Discussion

Our findings demonstrate the utility of integrating omics data with advanced machine learning techniques to develop accurate, non-invasive diagnostic tools for OSCC. The study highlights actionable metabolic signatures that have potential applications in personalized therapeutics and early intervention strategies.

Expression and clinical significance of U2AF homology motif kinase 1 in oral squamous cell carcinoma

OBJECTIVE

U2AF homology motif kinase 1 (UHMK1) is a newly discovered molecule that may have multiple functions. Recent studies have revealed that UHMK1 had aberrant expression in many tumors and was associated with tumor progression. However, UHMK1 was rarely reported in oral squamous cell carcinoma (OSCC).

STUDY DESIGN

In this study, Western blot, quantitative real-time polymerase chain reaction (PCR), and immunohistochemistry were used to detect the expression of UHMK1 in OSCC and peritumoral non-neoplastic tissues. Then, its relationship with clinicopathologic parameters was analyzed. The Kaplan-Meier method and Cox regression model were used to analyze the effects of UHMK1 expression on the prognosis and survival of OSCC patients.

RESULTS

Our results showed that UHMK1 had higher expression in OSCC tissues compared with in peritumoral non-neoplastic tissues, and its high expression was associated with high TNM stage and lymph node metastasis. High UHMK1 expression was related to short overall and disease-free survival times. Moreover, UHMK1 expression was identified as an independent prognostic factor that influences overall and disease-free survival of OSCC patients.

CONCLUSIONS

High expression of UHMK1 is associated with the poor prognosis of patients, and it can be used as a potential prognostic molecule for OSCC.

GraphPI: Efficient Protein Inference with Graph Neural Networks

The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled data sets, a challenge compounded by the high costs and complexities of accurate protein annotation. In this study, we introduce GraphPI, a novel framework that treats protein inference as a node classification problem. We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a graph neural network-based architecture to elucidate their interrelations. To address label scarcity, we train the model on a set of unlabeled public protein data sets with pseudolabels derived from an existing protein inference algorithm, enhanced by self-training to iteratively refine labels based on confidence scores. Contrary to prevalent methodologies necessitating data set-specific training, our research illustrates that GraphPI, due to the well-normalized nature of Percolator features, exhibits universal applicability without data set-specific fine-tuning, a feature that not only mitigates the risk of overfitting but also enhances computational efficiency. Our empirical experiments reveal notable performance on various test data sets and deliver significantly reduced computation times compared to common protein inference algorithms.

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.

Biomarkers Identification in the Microenvironment of Oral Squamous Cell Carcinoma: A Systematic Review of Proteomic Studies

An important determinant for oral squamous cell carcinoma (OSCC) onset and outcome is the composition of the tumor microenvironment (TME). Thus, the study of the interactions occurring among cancer cells, immune cells, and cancer-associated fibroblasts within the TME could facilitate the understanding of the mechanisms underlying OSCC development and progression, as well as of its sensitivity or resistance to the therapy. In this context, it must be highlighted that the characterization of TME proteins is enabled by proteomic methodologies, particularly mass spectrometry (MS). Aiming to identify TME protein markers employable for diagnosing and prognosticating OSCC, we have retrieved a total of 119 articles spanning 2001 to 2023, of which 17 have passed the selection process, satisfying all its criteria. We have found a total of 570 proteins detected by MS-based proteomics in the TME of OSCC; among them, 542 are identified by a single study, while 28 are cited by two or more studies. These 28 proteins participate in extracellular matrix remodeling and/or energy metabolism. Here, we propose them as markers that could be used to characterize the TME of OSCC for diagnostic/prognostic purposes. Noteworthy, most of the 28 individuated proteins share one feature: being modulated by the hypoxia that is present in the proliferating OSCC mass.

Secretome processing for proteomics: A methods comparison

The cancer cell secretome comprises a treasure-trove for biomarkers since it reflects cross-talk between tumor cells and their surrounding environment with high detectability in biofluids. In this study, we evaluated six secretome sample processing workflows coupled to single-shot mass spectrometry: (1) Protein concentration by ultrafiltration with a molecular weight cut-off (MWCO) filter and sample preparation through in-gel digestion (IGD); (2) Acetone protein precipitation coupled to IGD; (3) MWCO filter-based protein concentration followed by to in-solution digestion (ISD); (4) Acetone protein precipitation coupled to ISD; (5) Direct ISD; (6) Secretome lyophilization and ISD. To this end, we assessed workflow triplicates in terms of total number of protein identifications, unique identifications, reproducibility of protein identification and quantification and detectability of small proteins with important functions in cancer biology such as cytokines, chemokines, and growth factors. Our findings revealed that acetone protein precipitation coupled to ISD outperformed the other methods in terms of the number of identified proteins (2246) and method reproducibility (correlation coefficient between replicates (r = 0.94, CV = 19%). Overall, especially small proteins such as those from the classes mentioned above were better identified using ISD workflows. Concluding, herein we report that secretome protein precipitation coupled to ISD is the method of choice for high-throughput secretome proteomics via single shot nanoLC-MS/MS.

Identification and validation of genes associated with copper death in oral squamous cell carcinoma based on machine learning and weighted gene co-expression network analysis

OBJECTIVE

To identify copper-induced death-associated hub genes in oral squamous cell carcinoma (OSCC) and understand their functional and biological significance using machine learning and Weighted Gene Co-expression Network Analysis (WGCNA).

METHODS

OSCC transcriptomic data from GEO and TCGA databases were subjected to data integration, batch effect removal, background correction, and quantile normalization to select cuproptosis-associated genes using Spearman's correlation analysis. The 'limma' R package was used to filter differentially expressed genes (DEGs). Core module genes selected using gene co-expression network analysis with R package 'WGCNA' were screened using Support Vector Machine (SVM), LASSO regression, and Random Forest (RF) machine learning algorithms and validated using TCGA database samples. Core gene expression variations between OSCC and adjacent normal tissues were validated using immunohistochemistry. Immune infiltration analysis using package 'CIBERSORT' correlated hub genes with immune cells.

RESULTS

From 19 cuproptosis-related genes (identified from literature), 2382 cuproptosis-related mRNA were obtained through Spearman's correlation analysis; 112 DEGs using 'limma' R package and 32 hub genes using WGCNA were obtained. Hub genes TMPRSS11B, SERPINH1, and CDH3 were identified using machine learning algorithms. TCGA validation showed that TMPRSS11B significantly underexpressed (P < 0.001) but SERPINH1 and CDH3 significantly overexpressed (P < 0.001) in tumor samples. The AUC for TMPRSS11B, SERPINH1, and CDH3 in ROC curve analysis were 78.1%, 95.6%, and 87.5%, respectively.

CONCLUSION

TMPRSS11B, SERPINH1, and CDH3 may be pivotal for OSCC development and progression and potential targets for new therapeutic and predictive strategies. However, their specific functions and mechanisms underlying OSCC remain to be elucidated.

Towards system genetics analysis of head and neck squamous cell carcinoma using the mouse model, cellular platform, and clinical human data

Head and neck squamous cell cancer (HNSCC) is a leading global malignancy. Every year, More than 830 000 people are diagnosed with HNSCC globally, with more than 430 000 fatalities. HNSCC is a deadly diverse malignancy with many tumor locations and biological characteristics. It originates from the squamous epithelium of the oral cavity, oropharynx, nasopharynx, larynx, and hypopharynx. The most frequently impacted regions are the tongue and larynx. Previous investigations have demonstrated the critical role of host genetic susceptibility in the progression of HNSCC. Despite the advances in our knowledge, the improved survival rate of HNSCC patients over the last 40 years has been limited. Failure to identify the molecular origins of development of HNSCC and the genetic basis of the disease and its biological heterogeneity impedes the development of new therapeutic methods. These results indicate a need to identify more genetic factors underlying this complex disease, which can be better used in early detection and prevention strategies. The lack of reliable animal models to investigate the underlying molecular processes is one of the most significant barriers to understanding HNSCC tumors. In this report, we explore and discuss potential research prospects utilizing the Collaborative Cross mouse model and crossing it to mice carrying single or double knockout genes (e.g. Smad4 and P53 genes) to identify genetic factors affecting the development of this complex disease using genome-wide association studies, epigenetics, microRNA, long noncoding RNA, lncRNA, histone modifications, methylation, phosphorylation, and proteomics.

Nuciferine Inhibits Oral Squamous Cell Carcinoma Partially through Suppressing the STAT3 Signaling Pathway

Oral squamous cell carcinoma (OSCC) poses a significant obstacle to the worldwide healthcare system. Discovering efficient and non-toxic medications is crucial for managing OSCC. Nuciferine, an alkaloid with an aromatic ring, is present in the leaves of Nelumbo nucifera. It has been proven to play a role in multiple biological processes, including the inhibition of inflammation, regulation of the immune system, formation of osteoclasts, and suppression of tumors. Despite the demonstrated inhibitory effects of nuciferine on different types of cancer, there is still a need for further investigation into the therapeutic effects and potential mechanisms of nuciferine in OSCC. Through a series of in vitro experiments, it was confirmed that nuciferine hindered the growth, movement, and infiltration, while enhancing the programmed cell death of OSCC cells. Furthermore, the administration of nuciferine significantly suppressed the signal transducer and activator of transcription 3 (STAT3) signaling pathway in comparison to other signaling pathways. Moreover, the activation of the STAT3 signaling pathway by colivelin resulted in the reversal of nuciferine-suppressed OSCC behaviors. In vivo, we also showed the anti-OSCC impact of nuciferine using the cell-based xenograft (CDX) model in nude mice. Nonetheless, colivelin diminished the tumor-inhibiting impact of nuciferine, suggesting that nuciferine might partially impede the advancement of OSCC by suppressing the STAT3 signaling pathway. Overall, this research could offer a fresh alternative for the pharmaceutical management of OSCC.

Serum autoantibody profiling of oral squamous cell carcinoma patients reveals NUBP2 as a potential diagnostic marker

Introduction

Oral Squamous Cell Carcinoma (OSCC), a common malignancy of the head and neck region, is frequently diagnosed at advanced stages, necessitating the development of efficient diagnostic methods. Profiling autoantibodies generated against tumor-associated antigens have lately demonstrated a promising role in diagnosis, predicting disease course, and response to therapeutics and relapse.

Methods

In the current study, we, for the first time, aimed to identify and evaluate the diagnostic value of autoantibodies in serum samples of patients with OSCC using autoantibody profiling by an immunome protein array. The utility of anti-NUBP2 antibody and tissue positivity in OSCC was further evaluated.

Results and discussion

We identified a total of 53 autoantibodies with significant differential levels between OSCC and control groups, including 25 that were increased in OSCC and 28 that were decreased. These included autoantibodies against Thymidine kinase 1 (TK1), nucleotide-binding protein 2 (NUBP2), and protein pyrroline-5-carboxylate reductase 1 (PYCR1), among others. Immunohistochemical validation indicated positive staining of NUBP2 in a large majority of cases (72%). Further, analysis of OSCC data available in TCGA revealed higher NUBP2 expression correlated with better disease-free patient survival. In conclusion, the differential serum autoantibodies identified in the current study, including those for NUBP2, could be used as potential biomarkers for early diagnosis or as screening biomarkers for OSCC pending investigation in a larger cohort.

The role of microRNA-185 in the pathogenesis of human diseases: A focus on cancer

MicroRNAs (miRNAs) are a widely-studied class of non-coding RNAs characterized by their short length (18-25 nucleotides). The precise functions of miRNAs are not well-elucidated; however, an increasing number of studies suggest their involvement in various physiologic processes and deregulation in pathologic conditions. miRNA-185 (miR-185) is among the mostly-studied miRNAs in human diseases, which is found to play putative roles in conditions like metabolic disorders, asthma, frailty, schizophrenia, and hepatitis. Notably, many cancer studies report the downregulation of miR-185 in cell lines, tumor tissues, and plasma specimens of patients, while it demonstrates a suppressing role on the malignant properties of cancer cells in vitro and in vivo. Accordingly, miR-185 can be considered a tumor suppressor miRNA in human malignancies, while a few studies also report inconsistent findings. Being suggested as a prognostic/diagnostic biomarker, mi-185 is also found to offer clinical potentials, particularly for early diagnosis and prediction of the prognosis of cancer patients. In this review, we have outlined the studies that have evaluated the functions and clinical significance of miR-185 in different human diseases with a particular focus on cancer.

IL17RB and IL17REL Expression Are Associated with Improved Prognosis in HPV-Infected Head and Neck Squamous Cell Carcinomas

Changes in the cellular secretome are implicated in virus infection, malignancy, and anti-tumor immunity. We analyzed the association between transcriptional signatures (TS) from 24 different immune and stromal cell types on the prognosis of HPV-infected and HPV-free head and neck squamous carcinoma (HNSCC) patients from The Cancer Genome Atlas (TCGA) cohort. We found that HPV-positive HNSCC patients have tumors with elevated immune cell TS and improved prognosis, which was specifically associated with an increased tumor abundance of memory B and activated natural killer (NK) cell TS, compared to HPV-free HNSCC patients. HPV-infected patients upregulated many transcripts encoding secreted factors, such as growth factors, hormones, chemokines and cytokines, and their cognate receptors. Analysis of secretome transcripts and cognate receptors revealed that tumor expression of IL17RB and IL17REL are associated with a higher viral load and memory B and activated NK cell TS, as well as improved prognosis in HPV-infected HNSCC patients. The transcriptional parameters that we describe may be optimized to improve prognosis and risk stratification in the clinic and provide insights into gene and cellular targets that may potentially enhance anti-tumor immunity mediated by NK cells and memory B cells in HPV-infected HNSCC patients.

Emerging roles of alternative RNA splicing in oral squamous cell carcinoma

Alternative RNA splicing (ARS) is an essential and tightly regulated cellular process of post-transcriptional regulation of pre-mRNA. It produces multiple isoforms and may encode proteins with different or even opposite functions. The dysregulated ARS of pre-mRNA contributes to the development of many cancer types, including oral squamous cell carcinoma (OSCC), and may serve as a biomarker for the diagnosis and prognosis of OSCC and an attractive therapeutic target. ARS is mainly regulated by splicing factors, whose expression is also often dysregulated in OSCC and involved in tumorigenesis. This review focuses on the expression and roles of splicing factors in OSCC, the alternative RNA splicing events associated with OSCC, and recent advances in therapeutic approaches that target ARS.