Application and Perspectives of Immunotherapy in Head and Neck Squamous Cell Carcinoma

Immunology, Journal Year: 2025, Volume and Issue: unknown

Published: May 21, 2025

ABSTRACT Head and neck squamous cell carcinoma poses grave challenges to clinicians patients due its tumour invasiveness treatment uncertainties. Despite the substantial improvements in conventional modalities such as surgery, chemotherapy radiotherapy, recurrence rate mortality of HNSCC remain stubbornly high. Traditionally, front‐line therapy for recurrent/metastatic has been amalgamation platinum‐based drugs/paclitaxel, 5‐fluorouracil cetuximab, yet it only improves prognosis some patients, overall situation remains severe. Immunotherapy, an emerging luminary domain cancer treatment, is committed improving microenvironment stimulating immune system perform anti‐tumour functions. The continuous updates checkpoint inhibitors have also obtained favourable clinical feedback are expected overcome constraints traditional therapies. This article elaborates on trends immunotherapy within TME progress immunotherapy, aiming provide new ideas regimens therapeutic landscape offer hope patients.

Language: Английский

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Integrating immune multi-omics and machine learning to improve prognosis, immune landscape, and sensitivity to first- and second-line treatments for head and neck squamous cell carcinoma

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 28, 2024

In recent years, immune checkpoint inhibitors (ICIs) has emerged as a fundamental component of the standard treatment regimen for patients with head and neck squamous cell carcinoma (HNSCC). However, accurately predicting effectiveness ICIs at same TNM stage remains challenge. this study, we first combined multi-omics data (mRNA, lncRNA, miRNA, DNA methylation, somatic mutations) 10 clustering algorithms, successfully identifying two distinct cancer subtypes (CSs) (CS1 CS2). Subsequently, immune-regulated genes (IRGs) machine learning algorithms were utilized to construct consensus learning-driven prediction immunotherapy signature (CMPIS). Further, prognostic model was validated compared across multiple datasets, including clinical characteristics, external previously published models. Ultimately, response different CMPIS immunotherapy, targeted therapy, radiotherapy chemotherapy also explored. First, Two molecular identified by integrating immunomics techniques, it discovered that CS1 subtype tended be classified "cold tumors" or "immunosuppressive tumors", whereas CS2 more likely represent "hot "immune-activated tumors". Second, 303 employed models average C-index value each calculated various cohorts. StepCox [forward] + Ridge algorithm, which had highest 0.666, selected algorithm used predictive comprising 16 key genes. Third, patients' features, such age, gender, stage, grade stage. The findings indicated exhibited best performance in terms AUC values. Additionally, found ranked top 5 among 94 TCGA, GSE27020, GSE41613, GSE42743, GSE65858, META datasets. Lastly, study revealed lower sensitive chemotherapy, while those higher responsive radiation therapy EGFR-targeted treatments. summary, our CSs CS2) HNSCC using predicted patient prognosis constructing IRGs underscores importance biomarkers providing targeted, precise, personalized plans patients, significantly contributing optimization outcomes.

Language: Английский

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Integrating Immune Multi-Omics and Machine Learning to Improve Prognosis, Immune Landscape, and Sensitivity to First- and Second-Line Treatments for Head and Neck Squamous Cell Carcinoma

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 26, 2024

Background: In recent years, immune checkpoint inhibitors (ICIs) has emerged as a fundamental component of the standard treatment regimen for patients with head and neck squamous cell carcinoma (HNSCC). However, accurately predicting effectiveness ICIs at same TNM stage remains challenge. Methods: this study, we first combined over ten types omics data clustering algorithms, successfully identifying two distinct cancer subtypes (CSs) (CS1 CS2). Subsequently, immune-regulated genes (IRGs) machine learning algorithms were utilized to construct consensus learning-driven prediction immunotherapy signature (CMPIS). Further, prognostic model was validated compared across multiple datasets, including clinical characteristics, external previously published models. Ultimately, response different CMPIS immunotherapy, targeted therapy, radiotherapy chemotherapy also explored. Results: First, Two molecular identified by integrating immunomics techniques, it discovered that CS1 subtype tended be classified "cold tumors" or "immunosuppressive tumors", whereas CS2 more likely represent "hot "immune-activated tumors". Second, 303 employed models average C-index value each calculated various cohorts. StepCox [forward] + Ridge algorithm, which had highest 0.666, selected algorithm used predictive comprising 16 key genes. Third, patients' features, such age, gender, stage, grade stage. The findings indicated exhibited best performance in terms AUC values. Additionally, found ranked top 5 among 94 TCGA, GSE27020, GSE41613, GSE42743, GSE65858, META datasets. Lastly, study revealed lower sensitive chemotherapy, while those higher responsive radiation therapy EGFR-targeted treatments. Conclusion: In summary, our CSs CS2) HNSCC using multi-omics predicted patient prognosis constructing IRGs 300 underscores importance biomarkers providing targeted, precise, personalized plans patients, significantly contributing optimization outcomes.

Language: Английский

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Roles of ZEB1 and ZEB2 in E‐cadherin expression and cell aggressiveness in head and neck cancer

Genes to Cells, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 3, 2024

Zinc finger E-box binding homeobox 1 (ZEB1) has been identified as a key factor in cancer cell differentiation and metastasis, well studied the field of biology. ZEB2 highly similar conformation to ZEB1, but its role head neck squamous carcinoma (HNSCC) cells is not fully understood. Here, we separately overexpressed ZEB1 C57BL/6 mouse oral (MOC) investigated their cellular characteristics, including E-cadherin levels, motile properties, chemoresistance, metastatic ability immunocompetent mice. Both overexpression reduced epithelial traits converted an aggressive phenotype. Surprisingly, increased endogenous level MOC cells, vice versa. The molecular mechanisms underlying these findings remain unclear. However, vitro anchorage-independent growth overexpressing was considerably greater than that ZEB1. These suggest ZEB2, like induce into those with traits.

Language: Английский

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Carboxymethyl chitosan-modified folate-targeted carbon nanotube-baicalin complexes for cancer progression and metastasis

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: 282, P. 136896 - 136896

Published: Oct. 30, 2024

Language: Английский

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Cottontail Rabbit Papillomavirus (CRPV) Related Animal Models for Head and Neck Cancer Research: A Comprehensive Review of the Literature

Viruses, Journal Year: 2024, Volume and Issue: 16(11), P. 1722 - 1722

Published: Oct. 31, 2024

Having suitable animal models is crucial to mimic human disease states and for the successful transfer of experimental data into clinical practice. In field papillomavirus research, domestic rabbit (

Language: Английский

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Radioterapia e erosão - avaliação da estrutura dentária simulando diferentes cenários clínicos

Published: July 25, 2024

O objetivo deste estudo foi avaliar o efeito da radioterapia/erosão nas propriedades físicas

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