Upregulated YTHDF1 associates with tumor immune microenvironment in head and neck squamous cell carcinomas

High expression of ARPC1B correlates with immune infiltration and poor outcomes in glioblastoma

Objective

To investigate the role of ARPC1B in GBM and its prognostic value.

Methods

mRNA and protein expression of ARPC1B in GBM was analyzed using the TCGA; TIMER2 and the HPA databases, and protein expression differences were detected using immunohistochemistry. K-M analysis and Cox regression analysis were performed on high and low ARPC1B expression groups in the TCGA database. The relationship between immune cells and ARPC1B expression was explored using the TIMER2 database. GO and KEGG analyses were conducted to investigate the functions of ARPC1B-related genes in GBM.

Results

ARPC1B was highly expressed in both GBM tissues and cell lines, and it was demonstrated as a prognostic biomarker for GBM. ARPC1B expression levels showed associations with immune cell populations within the GBM microenvironment.

Conclusion

ARPC1B can regulating immune infiltration in the GBM microenvironment, indicating its potential as a novel therapeutic target for GBM.

Exploration of prognostic biomarkers in head and neck squamous cell carcinoma microenvironment from TCGA database

Background

Immune checkpoint blockade (ICB) therapies have redefined human cancer treatment, including for head and neck squamous cell carcinoma (HNSCC). However, clinical responses to various immune checkpoint inhibitors are often accompanied by immune-related adverse events (irAEs). Therefore, it is crucial to obtain a comprehensive understanding of the association between different immune tumor microenvironments (TMEs) and the immunotherapeutic response.

Methods

The research data were obtained from The Cancer Genome Atlas (TCGA) database. We applied RNA-seq genomic data from tumor biopsies to assess the immune TME in HNSCC. As the TME is a heterogeneous system that is highly associated with HNSCC progression and clinical outcome, we relied on the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores that were evaluated based on the immune or stromal components in the TME. Then, the Tumor Immune Dysfunction and Exclusion algorithm (TIDE) was used to predict the benefits of ICB to each patient. Finally, we identified specific prognostic tumor-infiltrating immune cells (TIICs) by quantifying the cellular composition of the immune response in HNSCC and its association to survival outcome, using the CIBERSORT algorithm.

Results

Utilizing the HNSCC cohort of the TCGA database and TIDE and ESTIMATE algorithm-derived immune scores, we obtained a list of microenvironment-associated lncRNAs that predicted different clinical outcomes in HNSCC patients. We validated these correlations in a different HNSCC cohort available from the TCGA database and provided insight into the prediction of response to ICB therapies in HNSCC.

Conclusions

This study confirmed that CD8⁺ T cells were significantly associated with better survival in HNSCC and verified that the top five significantly mutated genes (SMGs) in the TCGA HNSCC cohort were TP53, TTN, FAT1, CDKN2A, and MUC16. A high level of CD8⁺ T cells and high immune and stroma scores corresponded to a better survival probability in HNSCC.