Identification of Exosome-Related Genes Associated with Prognosis and Immune Infiltration Features in Head-Neck Squamous Cell Carcinoma

Identification of exosome-related gene signature as a promising diagnostic and therapeutic tool for breast cancer

Background

Exosomes are promising tools for the development of new diagnostic and therapeutic approaches. Exosomes possess the ability to activate signaling pathways that contribute to the remodeling of the tumor microenvironment, angiogenesis, and the regulation of immune responses. We aimed to develop a prognostic score based on exosomes derived from breast cancer.

Materials and methods

Training was conducted on the TCGA-BRCA dataset, while validation was conducted on GSE20685, GSE5764, GSE7904, and GSE29431. A total of 121 genes related to exosomes were retrieved from the ExoBCD database. The Cox proportional hazards model is used to develop risk score model. The GSVA package was utilized to analyze single-sample gene sets and identify exosome signatures, while the WGCNA package was utilized to identify gene modules associated with clinical outcomes. The clusterProfiler and GSVA R packages facilitated gene set enrichment and variation analyses. Furthermore, CIBERSORT quantified immune infiltration, and a correlation between gene expression and drug sensitivity was assessed using the TIDE algorithm.

Results

An exosome-related prognostic score was established using the following selected genes: ABCC9, PIGR, CXCL13, DOK7, CD24, and IVL. Various immune cells that promote cancer immune evasion were associated with a high-risk prognostic score, which was an independent predictor of outcome. High-risk and low-risk groups exhibited significantly different infiltration abundances (p < 0.05). By conducting a sensitivity comparison, we found that patients with high-risk scores exhibited more favorable responses to immunotherapy than those with low-risk scores.

Conclusion

The exosome-related gene signature exhibits outstanding performance in predicting the prognosis and cancer status of patients with breast cancer and guiding immunotherapy.

EXT2: a novel prognostic and predictive biomarker for head and neck squamous cell carcinoma

OBJECTIVE

This study focused on EXT2, a member of the EXT family involved in heparan sulfate synthesis, to evaluate its potential as a prognostic and predictive biomarker in head-neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

The present study used the cancer genome atlas head-neck squamous cell carcinoma (TCGA-HNSC) dataset-based UALCAN database to analyze the EXT2 expression and its clinicopathological features. In addition, we recruited 51 oral squamous cell carcinoma patients (OSCC), the most common HNSCC subtype, to validate the EXT2 mRNA expression analysis. In addition, we identified the role of EXT2 in prognosis using a Kaplan-Meier plot and immune signature using the tumor infiltration level. Furthermore, functional roles were analyzed using the EXT2 gene and protein networks.

RESULTS

The expression of EXT2 mRNA was significantly upregulated in OSCC tumors, which is consistent with the UALCAN-based results. EXT2 protein was also significantly overexpressed in HNSCC samples and was correlated with clinicopathological features. High EXT2 expression is associated with poor survival outcomes in HNSCC patients. Functional analysis of EXT2 using in silico tools revealed its involvement in critical pathways, including Wnt signaling, proteoglycans in cancer, and cellular responses to fibroblast growth and inflammation.

CONCLUSION

These findings highlight the potential of EXT2 as a prognostic and predictive biomarker of HNSCC.

An exosome-based specific transcriptomic signature for profiling regulation patterns and modifying tumor immune microenvironment infiltration in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a highly heterogeneous tumor that lacks effective treatment and has a poor prognosis. Exosomes carry abundant genomic information and have a significant role in tumorigenesis, metastasis, and drug resistance. However, further exploration is needed to investigate the relationship between exosome-related genes and the heterogeneity and tumor immune microenvironment of TNBC. Based on the exosome-related gene sets, multiple machine learning algorithms, such as Cox boost, were used to screen the risk score model with the highest C-index. A 9-gene risk score model was constructed, and the TNBC population was divided into high- and low-risk groups. The effectiveness of this model was verified in multiple datasets. Compared with the low-risk group, the high-risk group exhibited a poorer prognosis, which may be related to lower levels of immune infiltration and immune response rates. The gene mutation profiles and drug sensitivity of the two groups were also compared. By screening for genes with the most prognostic value, the hub gene, CLDN7, was identified, and thus, its potential role in predicting prognosis, as well as providing ideas for the clinical diagnosis, treatment, and risk assessment of TNBC, was also discussed. This study demonstrates that exosome-related genes can be used for risk stratification in TNBC, identifying patients with a worse prognosis. The high-risk group exhibited a poorer prognosis and required more aggressive treatment strategies. Analysis of the genomic information in patient exosomes may help to develop personalized treatment decisions and improve their prognosis. CLDN7 has potential value in prognostic prediction in the TNBC population.