ASH2L mediates epidermal differentiation and hair follicle morphogenesis via H3K4me3 modification.

The processes of epidermal development in mammals are regulated by complex molecular mechanisms, such as histone modifications. Histone H3 lysine K4 (H3K4) methylation mediated by COMPASS methyltransferase is associated with gene activation, but its effect on epidermal lineage development remains unclear. Therefore, we constructed a mouse model of specific ASH2L (COMPASS methyltransferase core subunit) deletion in epidermal progenitor cells and investigated its effect on the development of mouse epidermal lineage. Furthermore, downstream target genes regulated by H3K4me3 were screened using RNA-sequencing combined with Cleavage Under Targets and Tagmentation (CUT&Tag) sequencing. Deletion of ASH2L in epidermal progenitor cells caused thinning of the suprabasal layer of the epidermis and delayed hair follicle morphogenesis in newborn mice. These phenotypes may be related to the reduced proliferative capacity of epidermal and hair follicle progenitor cells. ASH2L depletion may also lead to depletion of the epidermal stem cell pools in late mouse development. Finally, genes related to hair follicle development (Shh, Edar and Fzd6), Notch signaling pathway (Notch2, Notch3, Hes5 and Nrarp) and ΔNp63 were identified as downstream target genes regulated by H3K4me3. Collectively, ASH2L-dependent H3K4me3 modification served as an upstream epigenetic regulator in epidermal differentiation and hair follicle morphogenesis in mice.

KIF4A functions as a diagnostic and prognostic biomarker and regulates tumor immune microenvironment in skin cutaneous melanoma.

BACKGROUND: KIF4A is upregulated in various malignancies and serves as an independent risk factor. However, its function in skin cutaneous melanoma (SKCM) and the regulation of the immunological environment remains unknown. METHODS: We first explored the mRNA and protein levels of KIF4A in SKCM through public databases. Then, the co-expressed genes with KIF4A in SKCM and their functional enrichment analysis were performed. Moreover, the clinical value, relationship with immune infiltration and tumor microenvironment (TME), as well as the correlation between KIF4A and immunomodulators were evaluated. In addition, we validated the function of KIF4A by in vitro experiments such as CCK-8 assay, clone formation and wound healing assay. RESULTS: Our data reveal that the mRNA and protein levels of KIF4A are highly expressed in SKCM. Moreover, functional enrichment analysis of the top 50 co-expressed genes with KIF4A showed significant association with organelle fission, tubulin binding and immune processes. KIF4A can distinguish SKCM from normal tissue and predict a poorer prognosis. A negative association was observed between KIF4A and TME, and KIF4A exhibited a negative correlation with most immunomodulators. Additionally, the knockdown of KIF4A inhibited the proliferation and migration ability of A375 cells. CONCLUSIONS: Our findings suggest that KIF4A promotes the progression of SKCM and is negatively associated with immune infiltration and immunomodulators, which indicates a poor prognosis.

Weighted gene co-expression network analysis and machine learning identified the lipid metabolism-related gene LGMN as a novel biomarker for keloid.

The aetiology of keloid formation remains unclear, and existing treatment modalities have not definitively established a successful approach. Therefore, it is necessary to identify reliable and novel keloid biomarkers as potential targets for therapeutic interventions. In this study, we performed differential expression analysis and functional enrichment analysis on the keloid related datasets, and found that multiple metabolism-related pathways were associated with keloid formation. Subsequently, the differentially expressed genes (DEGs) were intersected with the results of weighted gene co-expression network analysis (WGCNA) and the lipid metabolism-related genes (LMGs). Then, three learning machine algorithms (SVM-RFE, LASSO and Random Forest) together identified legumain (LGMN) as the most critical LMGs. LGMN was overexpressed in keloid and had a high diagnostic performance. The protein-protein interaction (PPI) network related to LGMN was constructed by GeneMANIA database. Functional analysis of indicated PPI network was involved in multiple immune response-related biological processes. Furthermore, immune infiltration analysis was conducted using the CIBERSORT method. M2-type macrophages were highly infiltrated in keloid tissues and were found to be significantly and positively correlated with LGMN expression. Gene set variation analysis (GSVA) indicated that LGMN may be related to promoting fibroblast proliferation and inhibiting their apoptosis. Moreover, eight potential drug candidates for keloid treatment were predicted by the DSigDB database. Western blot, qRT-PCR and immunohistochemistry staining results confirmed that LGMN was highly expressed in keloid. Collectively, our findings may identify a new biomarker and therapeutic target for keloid and contribute to the understanding of the potential pathogenesis of keloid.

METTL14-mediated N6-methyladenosine modification of Col17a1/Itgα6/Itgß4 governs epidermal homeostasis.

BACKGROUND: N6-methyladenosine (m6A) is the most abundant and reversible modification occurring in eukaryotic mRNAs, however, its functions in mammalian epidermal development are still not fully elucidated. OBJECTIVE: To explore the role of METTL14 (Methyltransferase like 14), one of the m6A methyltransferases, in maintaining epidermal homeostasis. METHODS: We constructed mice with Mettl14-inactivation in the epidermal basal cells. The phenotype was explored by H&E staining and immunofluorescence staining. To explore the underlying mechanisms, we performed RNA-seq, Ribosome profiling and MeRIP-seq on wild-type and Mettl14-inactivation epidermal keratinocytes. Moreover, HaCaT cells were used for in vitro validation. RESULTS: Inactivation of Mettl14 in murine epidermis led to transient thicker epidermis and exhaustion of the epidermal stem cell pool. Interestingly, we found that the mRNA of type XVII collagen (Col17a1), integrin ß4 (Itgß4) and α6 (Itgα6) had m6A modifications, and the proteins expression were decreased in Mettl14-inactivated epidermis. Furthermore, in epidermis-specific Mettl4-inactivated mice, the epidermis was detached from the dermis and presented a phenotype similar to junctional epidermolysis bullosa (JEB), which may result from hemidesmosomes damage (decrease of COL17A1, ITGB4 and ITGA6). Knockdown of Mettl14 in HaCaT cells impaired the self-renewal and decreased the protein level of COL17A1, ITGB4 and ITGA6 and Itgß4 knockdown inhibited colony formation. CONCLUSION: Our study highlighted the role of METTL14 in the maintenance of epidermal homeostasis and identified its critical role through m6A-mediated translational inhibition of Col17a1, Itgß4 and Itgα6. Our study suggested that METTL14 may be a potential therapeutic target for the treatment of hemidesmosomes-deficient diseases, such as JEB.

Identification and validation of a TTN-associated immune prognostic model for skin cutaneous melanoma.

TTN is the most commonly mutated gene in skin cutaneous melanoma (SKCM). Tumor mutational burden (TMB) can generate new antigens that regulate the recognition of T cells, which will significantly affect the prognosis of patients. The TTN gene has a long coding sequence and a high number of mutant sites, which allows SKCM patients to produce higher TMB and may influence the immune response. It has been found that the overall survival (OS) of SKCM patients with TTN mutation was significantly higher than that of wild-type patients. However, the effect of TTN mutation on the immune microenvironment of SKCM has not been fully investigated. Here, we systematically explored the relationship and potential mechanisms between TTN mutation status and the immune response. We first revealed that TTN mutated SKCM were significantly associated with four immune-related biological processes. Next, 115 immune genes differentially expressed between TTN mutation and wild-type SKCM patients were found to significantly affect the OS of SKCM patients. Then, we screened four immune-related genes (CXCL9, PSMB9, CD274, and FCGR2A) using LASSO regression analysis and constructed a TTN mutation-associated immune prognostic model (TM-IPM) to distinguish the SKCM patients with a high or low risk of poor prognosis, independent of multiple clinical characteristics. SKCM in the low-risk group highly expressed a large number of immune-related genes, and functional enrichment analysis of these genes showed that this group was involved in multiple immune processes and pathways. Furthermore, the nomogram constructed by TM-IPM with other clinicopathological parameters can provide a predictive tool for clinicians. Moreover, we found that CD8+ T cells were significantly enriched in the low-risk group. The expression level of immune checkpoints was higher in the low-risk group than in the high-risk group. Additionally, the response to chemotherapeutic agents was higher in the low-risk group than in the high-risk group, which may be related to the long survival in the low-risk group. Collectively, we constructed and validated a TM-IPM using four immune-related genes and analyzed the potential mechanisms of TM-IPM to predict patient prognosis and response to immunotherapy from an immunological perspective.