Dataset Information

Molecular Subtypes Based on Cell Differentiation Trajectories in Head and Neck Squamous Cell Carcinoma: Differential Prognosis and Immunotherapeutic Responses.

ABSTRACT:

Objective

Head and neck squamous cell carcinoma (HNSCC) is one of the most common and lethal malignant tumors. We aimed to investigate the HNSCC cell differentiation trajectories and the corresponding clinical relevance.

Methods

Based on HNSCC cell differentiation-related genes (HDRGs) identified by single-cell sequencing analysis, the molecular subtypes and corresponding immunity, metabolism, and stemness characteristics of 866 HNSCC cases were comprehensively analyzed. Machine-learning strategies were used to develop a HNSCC cell differentiation score (HCDscore) in order to quantify the unique heterogeneity of individual samples. We also assessed the prognostic value and biological characteristics of HCDscore using the multi-omics data.

Results

HNSCCs were stratified into three distinct molecular subtypes based on HDRGs: active stroma (Cluster-A), active metabolism (Cluster-B), and active immune (Cluster-C) types. The three molecular subtypes had different characteristics in terms of biological phenotype, genome and epigenetics, prognosis, immunotherapy and chemotherapy responses. We then demonstrated the correlations between HCDscore and the immune microenvironment, subtypes, carcinogenic biological processes, genetic variation, and prognosis. The low-HCDscore group was characterized by activation of immunity, enhanced response to anti-PD-1/PD-L1 immunotherapy, and better survival compared to the high-HCDscore group. Finally, by integrating the HCDscore with prognostic clinicopathological characteristics, a nomogram with strong predictive performance and high accuracy was constructed.

Conclusions

This study revealed that the cell differentiation trajectories in HNSCC played a nonnegligible role in patient prognosis, biological characteristics, and immune responses. Evaluating cancer cell differentiation will help to develop more effective immunotherapy, metabolic therapy, and chemotherapy strategies.

SUBMITTER: Huang ZD

PROVIDER: S-EPMC8739483 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundChromatin regulators (CRs) serve as indispensable factors in tumor biological processes by influencing tumorigenesis and the immune microenvironment and have been identified in head and neck squamous cell carcinoma (HNSCC). Hence, CR-related genes (CRRGs) are considered potential biomarkers for predicting prognosis and immune infiltration in HNSCC. In this study, we established a novel signature for predicting the prognosis and immunotherapeutic response of HSNCC.MethodsA total of 870 CRRGs were obtained according to previous studies. Subsequently, patients in the TCGA-HNSC cohort were divided into different clusters based on the expression of prognostic CRRGs. Kaplan‒Meier (K‒M) survival analysis was conducted to compare the prognosis in clusters, and the CIBERSORT and ssGSEA methods assessed the immune infiltration status. In addition, the differences in immunotherapeutic responses were determined based on the TICA database. Furthermore, the differentially expressed CRRGs between clusters were identified, and the predictive signature was established according to the results of univariate Cox, least absolute shrinkage and selection operator regression analysis, and multivariate Cox. The predictive effects of the risk model were evaluated according to the area under the receiver operating characteristic (ROC) curve (AUC) in both the training and external test cohorts. A nomogram was established, and survival comparisons, functional enrichment analyses, and immune infiltration status and clinical treatment assessments were performed. In addition, the hub gene network and related analysis were conducted with the Cytohubba application.ResultsBased on the expression of prognostic CRRGs, patients were divided into two clusters, in which Cluster 1 exhibited a better prognosis, more enriched immune infiltration, and a better immunotherapeutic response but exhibited chemotherapy sensitivity. The AUC values of the 1-, 3- and 5- year ROC curves for the risk model were 0.673, 0.732, and 0.692, respectively, as well as 0.645, 0.608, and 0.623 for the test set. In addition, patients in the low-risk group exhibited more immune cell enrichment and immune function activation, as well as a better immunotherapy response. The hub gene network indicated ACTN2 as the core gene differentially expressed between the two risk groups.ConclusionWe identified molecular subtypes and established a novel predictive signature based on CRRGs. This effective CRRS system can possibly provide a novel research direction for exploring the correlation between CRs and HNSCC and requires further experimental validation.