Identification of the Nerve-Cancer Cross-Talk-Related Prognostic Gene Model in Head and Neck Squamous Cell Carcinoma

Interplay between MRI radiomics and immune gene expression signatures in oral squamous cell carcinoma

With the advances in immunotherapy and the challenge of poor responsiveness in oral squamous cell carcinoma (OSCC) patients, understanding the tumor microenvironment is crucial. Radiogenomics offers the potential to provide pre-operative, non-invasive image-derived immune biomarkers. To this aim, the present study explores the capability of MRI-based radiomics to describe patients' immune state in OSCC. Seven MRI-based radiomic, 29 immune-related gene expression signatures were computed and deconvolution analysis was performed for a subset of OSCC from the BD2Decide database. A correlation-driven analysis identified key associations between radiomic and immune-related signatures and cell populations. Radiomic classifiers of the gene expression signatures were then developed to evaluate their capability to stratify patients based on immune status. MRI-based radiomic models showed promising results in predicting a gene expression signature associated with significant prognostic value for HNSCC patients who underwent radiotherapy (AUC = 0.92), suggesting these models' potential in distinguishing radioresistant from radiosensitive patients, aiding treatment decisions. Additionally, radiomic signatures reflected immune infiltrating cells in our cohort (M1, CD8 + T, B cells). MRI-radiomic signatures and associated models could become non-invasive methods to evaluate the prognosis and treatment choice in OSCC patients. Based on our promising results, and upon external validation, MRI-radiomics could enhance personalized medicine approaches.

A neural-related gene risk score for head and neck squamous cell carcinoma

OBJECTIVES

This study aimed to establish a neural-related gene risk score (NRGRS) for the prediction of head and neck squamous cell carcinoma prognosis and explore its predictive value on the benefit of immune checkpoint inhibitor therapy.

METHODS

Based on the transcriptome data of HNSCC patients (n = 546) from The Cancer Genome Atlas database, 37 neural-related hub genes were identified by weighted gene co-expression network analysis. Four genes (ITGA5, PYGM, GNG7 and ATP2A3) were identified to construct NRGRS using Lasso-Cox regression method based on the derivation cohort and validated in the Gene Expression Omnibus cohort (n = 109). The survival analysis was performed to validate the prognostic value of NRGRS and immune characteristics in NRGRS-defined subgroups were analyzed.

RESULTS

NRGRS-high patients had a worse overall survival than NRGRS-low patients. Tumors with high NRGRS were more likely to have high infiltration of naive CD4+ T cells, M0, M2 macrophages and resting mast cells, which illustrated suppressive immunity and less benefit from immunotherapy therapy.

CONCLUSION

NRGRS strongly correlates with survival and is a promising biomarker to predict immunotherapy benefits for head and neck cancer patients. This study provides evidence for the potential correlation between neural-related transcriptome alteration and immune activity.

Natural killer cell-related prognosis signature characterizes immune landscape and predicts prognosis of HNSCC

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC), the most common head and neck cancer, is highly aggressive and heterogeneous, resulting in variable prognoses and immunotherapeutic outcomes. Natural killer (NK) cells play essential roles in malignancies' development, diagnosis, and prognosis. The purpose of this study was to establish a reliable signature based on genes related to NK cells (NRGs), thus providing a new perspective for assessing immunotherapy response and prognosis of HNSCC patients.

METHODS

In this study, NRGs were used to classify HNSCC from the TCGA-HNSCC and GEO cohorts. The genes were evaluated using univariate cox regression analysis based on the differential analysis of normal and tumor samples in TCGA-HNSCC conducted using the "limma" R package. Thereafter, we built prognostic gene signatures using LASSO-COX analysis. External validation was carried out in the GSE41613 cohort. Immunity analysis based on NRGs was performed via several methods, such as CIBERSORT, and immunotherapy response was evaluated by TIP portal website.

RESULTS

With the TCGA-HNSCC data, we established a nomogram based on the 17-NRGs signature and a variety of clinicopathological characteristics. The low-risk group exhibited a better effect when it came to immunotherapy.

CONCLUSIONS

17-NRGs signature and nomograms demonstrate excellent predictive performance and offer new perspectives for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology research.