Integration analysis of single-cell transcriptome reveals specific monocyte subsets associated with melanoma brain and leptomeningeal metastasis

Immune Evasion in Cancer Metastasis: An Unappreciated Role of Monocytes

Metastasis is the leading cause of cancer-related deaths. During the metastatic cascade, cancer cells tightly interact with immune cells influencing each other in the tumor microenvironment and systemically. Monocytes are important components of immune evasion and critical regulators of cancer progression. They circulate through the bloodstream and contribute to the formation of a pro-tumor microenvironment both in the tumor and pre-metastatic niche. Whereas monocyte participation in cancer development and response to therapy has been described extensively, its impact on metastasis remains a completely uncovered area. This review first summarizes data concerning the influence of monocytes on metastasis formation during their presence in the circulation, primary tumor, and pre-metastatic niche. We also highlight the latest examinations into the clinical relevance of targeting monocytes to prevent metastasis.

Identification of shared immune infiltration characteristic molecules in dermatomyositis and nasopharyngeal carcinoma using bioinformatics: Traits in dermatomyositis and nasopharyngeal cancer

BACKGROUND

Dermatomyositis (DM) is a kind of dermatologically associated autoimmune disease that is notably associated with an increased risk of concurrent malignancies, although the underlying mechanisms remain to be fully elucidated. The purpose of this investigation was to examine the immunological parallels between DM and nasopharyngeal carcinoma (NPC), with the aim of identifying pivotal biomarkers that could facilitate a deeper understanding and enhance the predictive capabilities of NPC in DM patients.

METHOD

Data for DM and NPC were sourced from the Gene Expression Omnibus (GEO) database. Immune infiltration was analyzed using the "cibersort" R package, differentially expressed genes (DEGs) were identified with the "limma" package, and functional pathways were investigated through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Characteristic genes were determined by Utilizing Protein-Protein Interaction (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO), and their features were validated using the GSE53819 dataset.

RESULTS

In comparison to normal samples, significant infiltration of macrophage M1 was observed in both DM and NPC. The analysis revealed 77 DEGs in DM and 1051 DEGs in NPC, with 22 genes found to be co-DEGs. Following PPI and LASSO analysis, six distinctive genes were retained. Notably, CCL8, IFIH1, CXCL10, and CXCL11 exhibited optimal diagnostic efficacy for NPC and displayed significant correlation with macrophage M1 infiltration within the carcinoma.

CONCLUSION

Four characteristic genes, CCL8, IFIH1, CXCL10, and CXCL11 are risk factors for both DM and NPC. They exhibit a robust correlation with the incidence of NPC and offer a commendable diagnostic efficacy. Furthermore, they may serve as prospective predictive biomarkers for the emergence of NPC in DM.

Neutrophil extracellular trap related risk score exhibits crucial prognostic value in skin cutaneous melanoma, associating with distinct immune characteristics

BACKGROUND

Neutrophil extracellular traps (NETs) are related to the prognosis of cancer patients. Nevertheless, the potential prognostic values of NETs in skin cutaneous melanoma (SKCM) remains largely unknown.

MATERIALS AND METHODS

The NET-related gene signature was constructed by LASSO Cox regression analysis using the TCGA-SKCM cohort. The overall survival (OS) and immune status in SKCM patients between the high- and low-NET score (high-score, low-score) groups were explored. The scRNA-seq dataset GSE115978 was used to understand the role of NET score in SKCM at single cell resolution.

RESULTS

A five NET genes-based signature (TLR2, CLEC6A, PDE4B, SLC22A4 and CYP4F3) was constructed as the NET-related prognostic model for SKCM. The OS of SKCM patients with low-score was better than that in patients with high-score. Additionally, NET score was negatively associated with infiltration of some immune cells (e.g. type I Macrophages, CD8-T cells, CD4-T cells). Moreover, patients with high-score had low stromal, immune and ESTIMATE scores. Furthermore, drug sensitivity analysis results showed that Lapatinib, Trametinib and Erlotinib may have better therapeutic advantages in patients with high-score.

CONCLUSION

We established a NET-related five gene signature in SKCM and found that the NET-related signature may exhibit a good predictive ability for SKCM prognosis. The NET score may not only predict the survival outcome and drug sensitivity in SKCM, but also reflect the immune conditions of SKCM patients.

Establishment of a CD8+ T cells-related prognostic risk model for acral melanoma based on single-cell and bulk RNA sequencing

BACKGROUND

CD8+ T cells have been recognized as crucial factors in the prognosis of melanoma. However, there is currently a lack of gene markers that accurately describe their characteristics and functions in acral melanoma (AM), which hinders the development of personalized medicine.

METHODS

Firstly, we explored the composition differences of immune cells in AM using single-cell RNA sequencing (scRNA-seq) data and comprehensively characterized the immune microenvironment of AM in terms of composition, developmental differentiation, function, and cell communication. Subsequently, we constructed and validated a prognostic risk scoring model based on differentially expressed genes (DEGs) of CD8+ T cells using the TCGA-SKCM cohort through Lasso-Cox method. Lastly, immunofluorescence staining was performed to validate the expression of four genes (ISG20, CCL4, LPAR6, DDIT3) in AM and healthy skin tissues as included in the prognostic model.

RESULTS

The scRNA-seq data revealed that memory CD8+ T cells accounted for the highest proportion in the immune microenvironment of AM, reaching 70.5%. Cell-cell communication analysis showed extensive communication relationships among effector CD8+ T cells. Subsequently, we constructed a prognostic scoring model based on DEGs derived from CD8+ T cell sources. Four CD8+ T cell-related genes were included in the construction and validation of the prognostic model. Additionally, immunofluorescence results demonstrated that ISG20 and CCL4 were downregulated, while LPAR6 and DDIT3 were upregulated in AM tissues compared to normal skin tissues.

CONCLUSION

Identifying biomarkers based on the expression levels of CD8+ T cell-related genes may be an effective approach for establishing prognostic models in AM patients. The independently prognostic risk evaluation model we constructed provides new insights and theoretical support for immunotherapy in AM.