Identification of tumor antigens and immune subtypes in head and neck squamous cell carcinoma for mRNA vaccine development

Identification of cuproptosis-related gene clusters and immune cell infiltration in major burns based on machine learning models and experimental validation

Introduction

Burns are a global public health problem. Major burns can stimulate the body to enter a stress state, thereby increasing the risk of infection and adversely affecting the patient's prognosis. Recently, it has been discovered that cuproptosis, a form of cell death, is associated with various diseases. Our research aims to explore the molecular clusters associated with cuproptosis in major burns and construct predictive models.

Methods

We analyzed the expression and immune infiltration characteristics of cuproptosis-related factors in major burn based on the GSE37069 dataset. Using 553 samples from major burn patients, we explored the molecular clusters based on cuproptosis-related genes and their associated immune cell infiltrates. The WGCNA was utilized to identify cluster-specific genes. Subsequently, the performance of different machine learning models was compared to select the optimal model. The effectiveness of the predictive model was validated using Nomogram, calibration curves, decision curves, and an external dataset. Finally, five core genes related to cuproptosis and major burn have been was validated using RT-qPCR.

Results

In both major burn and normal samples, we determined the cuproptosis-related genes associated with major burns through WGCNA analysis. Through immune infiltrate profiling analysis, we found significant immune differences between different clusters. When K=2, the clustering number is the most stable. GSVA analysis shows that specific genes in cluster 2 are closely associated with various functions. After identifying the cross-core genes, machine learning models indicate that generalized linear models have better accuracy. Ultimately, a generalized linear model for five highly correlated genes was constructed, and validation with an external dataset showed an AUC of 0.982. The accuracy of the model was further verified through calibration curves, decision curves, and modal graphs. Further analysis of clinical relevance revealed that these correlated genes were closely related to time of injury.

Conclusion

This study has revealed the intricate relationship between cuproptosis and major burns. Research has identified 15 cuproptosis-related genes that are associated with major burn. Through a machine learning model, five core genes related to cuproptosis and major burn have been selected and validated.

S100 proteins in head and neck squamous cell carcinoma (Review)

The most common tumor affecting the head and neck is head and neck squamous cell carcinoma (HNSCC). The characteristics of HNSCC include a rapid onset, a lack of early diagnosis, drug resistance, relapse and systemic adverse effects, leading to inadequate prevention, diagnosis and treatment. Notably, previous research suggests that there is an association between S100 proteins and HNSCC. S100A8, S100A9 and S100A14 interfere with tumor cell proliferation by blocking the cell cycle. The present review discusses this association. S100A4 enhances cancer stem cell properties, and interacts with actin and tropomyosin to promote tumor cell migration. S100A1, S100A8, S100A9, S100A10, S100A14 and S100P are involved in the initiation and progression of HNSCC via Hippo, nuclear factor κB, phosphatidylinositol kinase/protein kinase B/mammalian target of rapamycin and other signaling pathways. In addition, certain long non-coding RNAs and microRNAs are involved in regulating the expression of S100 proteins in HNSCC. Reducing the expression of certain members of the S100 protein family may enhance the chemosensitivity of HNSCC. Collectively, it is suggested that S100 proteins may function as markers and targets for the prevention, diagnosis and treatment of HNSCC.