Gene networks in basal cell carcinoma of the eyelid, analyzed using gene expression profiling

Pathological mutations reveal the key role of the cytosolic iRhom2 N-terminus for phosphorylation-independent 14-3-3 interaction and ADAM17 binding, stability, and activity

The protease ADAM17 plays an important role in inflammation and cancer and is regulated by iRhom2. Mutations in the cytosolic N-terminus of human iRhom2 cause tylosis with oesophageal cancer (TOC). In mice, partial deletion of the N-terminus results in a curly hair phenotype (cub). These pathological consequences are consistent with our findings that iRhom2 is highly expressed in keratinocytes and in oesophageal cancer. Cub and TOC are associated with hyperactivation of ADAM17-dependent EGFR signalling. However, the underlying molecular mechanisms are not understood. We have identified a non-canonical, phosphorylation-independent 14-3-3 interaction site that encompasses all known TOC mutations. Disruption of this site dysregulates ADAM17 activity. The larger cub deletion also includes the TOC site and thus also dysregulated ADAM17 activity. The cub deletion, but not the TOC mutation, also causes severe reductions in stimulated shedding, binding, and stability of ADAM17, demonstrating the presence of additional regulatory sites in the N-terminus of iRhom2. Overall, this study contrasts the TOC and cub mutations, illustrates their different molecular consequences, and reveals important key functions of the iRhom2 N-terminus in regulating ADAM17.

HLA-G, cytokines, and cytokine receptors in the non-aggressive basal cell carcinoma microenvironment

Non-aggressive basal cell carcinoma (BCC) growth is slow and might be mediated by the immune system. This study analysed the human leukocyte antigen (HLA)-G expression and cytokine profile in non-aggressive BCC subtypes from distinct locations. HLA-G was evaluated via immunohistochemistry and cytokine expression was analysed by a quantitative real-time polymerase chain reaction in 26 primary BCC samples, including nodular BCC (nBCC, n = 16) and superficial BCC (n = 10) from cephalic (ceBCC, n = 12) and non-cephalic (n = 14) locations, and by bioinformatics analysis of public GEO databases. Inflammatory infiltrate was concentrated around the tumour nests. HLA-G-positive inflammatory cells (53.85%) were more abundant than HLA-G-positive tumour cells (21.54%, p < 0.001). HLA-G immunoreactivity was predominantly cytoplasmic in BCC cells and was primarily associated with lymphocytes and macrophages surrounding the tumour. nBCC showed a higher percentage of HLA-G-positive tumour cells (p = 0.04), and ceBCC showed stronger intensity (p = 0.04). IFN-gamma and IL-10 expression were 1.95 and 1.22-fold higher, respectively, relative to that in normal skin, with a positive correlation between them (r = 0.61; p = 0.002). IL-23 expression was higher in nBCC (p = 0.04) and positively correlated (r = 0.47; p = 0.05) with slight intensity of HLA-G-positive tumour cells. The up-regulation of IL23A and IL10RB and down-regulation of IFNGR1 and IL4R gene expression in BCC compared to levels in adjacent tissues were demonstrated in the GSE125285 dataset. The exhibited cytokine profile was consistent with the induction of HLA-G expression in non-aggressive BCC subtypes. HLA-G expression in tumour cells and inflammatory cells surrounding BCCs supports the generation of inhibitory signals on various immune cells that exert anti-tumour responses.

Identification of Potential Biomarkers Associated with Basal Cell Carcinoma

Purpose

This work is aimed at identifying several molecular markers correlated with the diagnosis and development of basal cell carcinoma (BCC).

Methods

The available microarray datasets for BCC were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between BCC and healthy controls. Afterward, the functional enrichment analysis and protein-protein interaction (PPI) network analysis of these screened DEGs were performed. An external validation for the DEG expression level was also carried out, and receiver operating characteristic curve analysis was used to evaluate the diagnostic values of DEGs.

Result

In total, five microarray datasets for BCC were downloaded and 804 DEGs (414 upregulated and 390 downregulated genes) were identified. Functional enrichment analysis showed that these genes including CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1 were closely correlated with the cell process and PTCH1 played central roles in the BCC signaling pathway. Moreover, EGFR was a hub gene in the PPI network. The expression changes of six genes (CYFIP2, HOXB5, FOXN3, PTPN3, MARCKSL1, and FAS) were validated by an external GSE74858 dataset analysis. Finally, ROC analysis revealed that CYFIP2, HOXB5, PTPN3, MARCKSL1, PTCH1, and CDC20 could distinguish BCC and healthy individuals.

Conclusion

Nine gene signatures (CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1) may serve as promising targets for BCC detection and development.