The occurrence and progression of skin cutaneous melanoma (SKCM) is strongly associated with immune cells infiltrating the tumor microenvironment (TME). This study examined the expression, prognosis, and immune relevance of SIGLEC9 in SKCM using multiple online databases. Analysis of the GEPIA2 and Ualcan databases revealed that SIGLEC9 is highly expressed in SKCM, and patients with high SIGLEC9 expression had improved overall survival (OS). Furthermore, the mutation rate of SIGLEC9 in SKCM patients was found to be 5.41%, the highest observed. The expression of SIGLEC9 was positively correlated with macrophages, neutrophils and B cells, CD8 + T cells, CD4 + T cells, and dendritic cells, according to TIMER. Based on TCGA-SKCM data, we verified that high SIGLEC9 expression is closely associated with a good prognosis for SKCM patients, including overall survival, progression-free interval, and disease-specific survival. This positive prognosis could be due to the infiltration of immune cells into the TME. Additionally, our analysis of single-cell transcriptome data revealed that SIGLEC9 not only played a role in the normal skin immune microenvironment, but is also highly expressed in immune cell subpopulations of SKCM patients, regulating the immune response to tumors. Our findings suggest that the close association between SIGLEC9 and SKCM prognosis is primarily mediated by its effect on the tumor immune microenvironment.

BACKGROUND: Advanced skin cutaneous melanoma (SKCM) is responsible for the majority of skin cancer-related deaths. Apart from the rare BRAF V600F mutation, which can be targeted with specific drugs, there are currently no other novel effective therapeutic targets.
METHODS: We used SMR analysis with cis-expressed quantitative trait locus (cis-eQTL) as the exposure variable and SKCM as the outcome variable to identify potential therapeutic targets for SKCM. Colocalization assays and HEIDI tests are used to test whether SKCM risk and gene expression are driven by common SNPs. Replication analysis further validated the findings, and we also constructed protein-protein interaction networks to explore the relationship between the identified genes and known SKCM targets. Drug prediction and molecular docking further validated the medicinal value of drug targets. Transcriptome differential analysis further validated that there were differences between normal tissues and SKCM for the selected targets.
RESULTS: We identified 13 genes significantly associated with the risk of SKCM, including five protective genes and eight harmful genes. The HEIDI test and co-localization analysis further indicates a causal association between genes (SOX4, MAFF) and SKCM, categorized as Class 1 evidence targets. The remaining 11 genes, except for HELZ2 show a moderately causal association with SKCM, categorized as Class 2 evidence targets. Target druggability predictions from DGIdb suggest that SOX4, MAFF, ACSF3, CDK10, SPG7, and TCF25 are likely to be future drug targets.
CONCLUSIONS: The study provides genetic evidence for targeting available drug genes for the treatment of SKCM.

BACKGROUND: Skin cutaneous melanoma (SKCM) is an aggressive form of malignant melanoma with poor prognosis and high mortality rates. Disulfidptosis is a newly discovered cell death regulatory mechanism caused by the abnormal accumulation of disulfides. This unique pathway is guiding significant new research to understand cancer progression for targeted treatment. However, the correlation between disulfidptosis with long non-coding RNAs (lncRNAs) in SKCM remains unknown at present.
METHODS: The Cancer Genome Atlas database furnished lncRNA expression data and clinical information for SKCM patients. Pearson correlation and Cox regression analyses identified disulfidptosis-related lncRNAs associated with SKCM prognosis. ROC curves and a nomogram validated the model. TME, immune infiltration, GSEA analysis, immune checkpoint gene expression profiling, and drug sensitivity were assessed in high and low-risk groups. Consistent clustering categorized SKCM patients for personalized clinical treatment guidance.
RESULTS: A total of twelve disulfidptosis-related lncRNAs were identified for the development of prognosis prediction models. The area under the curve (AUC) values of the ROC curve and the nomogram provided reliable discrimination to evaluate the prognostic potential for SKCM patients. The TME played a crucial role in tumorigenesis, progression and prognosis, and the risk scores were closely related to immune cell infiltration. Meanwhile, the combination of chemotherapy, targeted therapy, and immunotherapy was recommended for low-risk patients based on drug sensitivity and immune efficacy analyses.
CONCLUSIONS: We identified a risk model of twelve disulfidptosis-related lncRNAs that could be used to predict the prognosis of SKCM patients and help guide immunotherapy and chemotherapy for personalized treatment plans.

UNASSIGNED: High levels of UV exposure are a significant factor that can trigger the onset and progression of SKCM. Moreover, this exposure is closely linked to the malignancy of the tumor and the prognosis of patients. Our objective is to identify a tumor biomarker database associated with UV exposure, which can be utilized for prognostic analysis and diagnosis and treatment of SKCM.
UNASSIGNED: This study used the weighted gene co-expression network analyses (WGCNA) and gene mutation frequency analyses to screen for UV-related target genes using the GSE59455 and the cancer genome atlas databases (TCGA). The prognostic model was created using Cox regression and least absolute shrinkage and selection operator analyses (LASSCO). Furthermore, in vitro experiments further validated that the overexpression or knockdown of COL4A3 could regulate the proliferation and migration abilities of SKMEL28 and A357 melanoma cells.
UNASSIGNED: A prognostic model was created that included six genes with a high UV-related mutation in SKCM: COL4A3, CHRM2, DSC3, GIMAP5, LAMC2, and PSG7. The model had a strong patient survival correlation (P˂0.001, hazard ratio (HR) = 1.57) and significant predictor (P˂0.001, HR = 3.050). Furthermore, the model negatively correlated with immune cells, including CD8+ T cells (Cor=-0.408, P˂0.001), and M1-type macrophages (Cor=-0.385, P˂0.001), and immune checkpoints, including programmed cell death ligand-1. Moreover, we identified COL4A3 as a molecule with significant predictive functionality. Overexpression of COL4A3 significantly inhibited the proliferation, migration, and invasion abilities of SKMEL28 and A357 melanoma cells, while knockdown of COL4A3 yielded the opposite results. And overexpression of COL4A3 enhanced the inhibitory effects of imatinib on the proliferation, migration, and invasion abilities of SKMEL28 and A357 cells.
UNASSIGNED: The efficacy of the prognostic model was validated by analyzing the prognosis, immune infiltration, and immune checkpoint profiles. COL4A3 stands out as a novel diagnostic and therapeutic target for SKCM, offering new strategies for small-molecule targeted drug therapies.

Skin cutaneous melanoma (SKCM) is a highly malignant form of skin cancer, known for its unfavorable prognosis and elevated mortality rate. RARRES1, a gene responsive to retinoic acid receptors, displays varied functions in various cancer types. However, the specific role and underlying mechanisms of RARRES1 in SKCM are still unclear. GSE15605 was utilized to analyze the expression of RARRES1 in SKCM. Subsequently, the TCGA and GEO databases were employed to investigate the relationships between RARRES1 and clinicopathological parameters, as well as the prognostic implications and diagnostic efficacy of RARRES1 in SKCM. GO, KEGG, and GSEA analyses were conducted to explore the potential functions of RARRES1. Furthermore, the associations between RARRES1 and immune infiltration were examined. Genomic alterations and promoter methylation levels of RARRES1 in SKCM were assessed using cBioPortal, UALCAN, and the GEO database. Finally, RARRES1 expression in SKCM was validated through immunohistochemistry, and its functional role in SKCM progression was elucidated via in vivo and in vitro experiments. We found that RARRES1 was downregulated in SKCM compared with normal tissues, and this low expression was associated with worse clinicopathological features and poor prognosis of SKCM. The diagnostic efficacy of RARRES1, as determined by ROC analysis, was 0.732. Through GO, KEGG, and GSEA enrichment analysis, we identified 30 correlated genes and pathways that were mainly enriched in the tumor immune microenvironment, proliferation, apoptosis, and autophagy. Additionally, RARRES1 expression was found to be positively related to the infiltration of various immune cells in SKCM, particularly macrophages and T helper cells, among others. Analysis of genomic alterations and promoter methylation revealed that shallow deletion and hypermethylation of the RARRES1 promoter could lead to reduced RARRES1 expression. IHC validation confirmed the downregulation of RARRES1 in SKCM. Moreover, overexpression of RARRES1 inhibited the proliferation and migration of A375 cells, promoted apoptosis, and inhibited autophagic flux. In the mouse xenograft model, RARRES1 overexpression also suppressed SKCM tumor growth. Collectively, these findings suggest that RARRES1 may function as a suppressor and could potentially serve as a prognostic biomarker and therapeutic target for SKCM.

OBJECTIVE: Disulfidptosis is a new metabolic-related regulated cell death associated with cancer growth. This study aimed to investigate the molecular mechanisms associated with disulfidptosis in skin cutaneous melanoma (SKCM) and establish a disulfidptosis-related gene signature for prognostic prediction in SKCM.
METHODS: Disulfidptosis-associated genes were identified from RNA-seq data of SKCM. A risk score signature was developed and validated through univariate Cox and LASSO analyses. Additionally, the immune microenvironment related to the risk score signature was investigated. Finally, a disulfidptosis-related genes-transcription factor -miRNA network was developed, and the expression levels of five disulfidptosis-related genes were initially verified in SKCM cell lines.
RESULTS: A total of 107 disulfidptosis-related differentially expressed genes in SKCM samples were identified. A ten-disulfidptosis-gene signature was established, including BIN2, CCL3L3, CCL8, CD79A, CIITA, CXCR3, DEFB1, GPR171, IL2RB, and SOCS1. The SKCM samples were divided into high- and low-risk groups, of which samples in the low-risk group showed better survival performance. The receiver operating characteristic curve analysis confirmed the good potency of the disulfidptosis-related gene prognostic model. Except for DEFB1, the other nine genes were positively related with T cell CD8+, T cell CD4+ memory activated, T cell gamma delta, NK cell activated, and macrophage M1, and they were all negatively related with NK cell resting, macrophage M0, macrophage M2, and mast cell activated. Finally, we verified downregulated levels of SOCS1 and DEFB1 and upregulated CXCR3, BIN2, and CCL3L3 in A875 and A375.
CONCLUSIONS: We successfully established ten disulfidptosis-related genes\' prediction prognostic signatures for SKCM patients.

LncRNAs have been demonstrated to regulate biological processes in malignant tumors. In our previous study, we identified the immune-related LncRNA RNF144A-AS1 as a potential regulator in SKCM. However, its precise function and regulatory mechanism remain unclear. In this study, we observed upregulation of RNF144A-AS1 in SKCM and found that knockdown of RNF144A-AS1 suppressed proliferation, migration, invasion, and epithelial-mesenchymal transition abilities of melanoma cells. Mechanistically, as a high-risk prognostic factor, RNF144A-AS1 regulated biological processes of SKCM by interacting with TAF15 through an RNA-binding protein-dependent (RBP-dependent) manner. Furthermore, we confirmed that TAF15 activated downstream transcriptional regulation of YAP1 to modulate malignant behaviors in melanoma cells. In vivo experiments revealed that knockdown of RNF144A-AS1 inhibited tumorigenic capacity of melanoma cells and exhibited promising therapeutic effects. Collectively, these findings highlight the significance of the RNF144A-AS1/TAF15/YAP1 axis in promoting malignant behaviors in SKCM and provide novel insights into potential prognostic biomarkers and therapeutic targets for this disease.

Skin cutaneous melanoma (SKCM), a form of skin cancer, ranks among the most formidable and lethal malignancies. Exploring tumor microenvironment (TME)-based prognostic indicators would help improve the efficacy of immunotherapy for SKCM patients. This study analyzed SKCM scRNA-seq data to cluster non-malignant cells that could be used to explore the TME into nine immune/stromal cell types, including B cells, CD4 T cells, CD8 T cells, dendritic cells, endothelial cells, Fibroblasts, macrophages, neurons, and natural killer (NK) cells. Using data from The Cancer Genome Atlas (TCGA), we employed SKCM expression profiling to identify differentially expressed immune-associated genes (DEIAGs), which were then incorporated into weighted gene co-expression network analysis (WGCNA) to investigate TME-associated hub genes. Discover candidate small molecule drugs based on pivotal genes. Tumor immune microenvironment-associated genes (TIMAGs) for constructing TIMAS were identified and validated. Finally, the characteristics of TIAMS subgroups and the ability of TIMAS to predict immunotherapy outcomes were analyzed. We identified five TIMAGs (CD86, CD80, SEMA4D, C1QA, and IRF1) and used them to construct TIMAS. In addition, five potential SKCM drugs were identified. The results showed that TIMAS-low patients were associated with immune-related signaling pathways, high MUC16 mutation frequency, high T cell infiltration, and M1 macrophages, and were more favorable for immunotherapy. Collectively, TIMAS constructed by comprehensive analysis of scRNA-seq and bulk RNA-seq data is a promising marker for predicting ICI treatment outcomes and improving individualized therapy for SKCM patients.

OBJECTIVE: The present investigation focuses on Skin Cutaneous Melanoma (SKCM), a melanocytic carcinoma characterized by marked aggression, significant heterogeneity, and a complex etiological background, factors which collectively contribute to the challenge in prognostic determinations. We defined a novel classifier system specifically tailored for SKCM based on multiomics.
METHODS: We collected 423 SKCM samples with multi omics datasets to perform a consensus cluster analysis using 10 machine learning algorithms and verified in 2 independent cohorts. Clinical features, biological characteristics, immune infiltration pattern, therapeutic response and mutation landscape were compared between subtypes.
RESULTS: Based on consensus clustering algorithms, we identified two Multi-Omics-Based-Cancer-Subtypes (MOCS) in SKCM in TCGA project and validated in GSE19234 and GSE65904 cohorts. MOCS2 emerged as a subtype with poor prognosis, characterized by a complex immune microenvironment, dysfunctional anti-tumor immune state, high cancer stemness index, and genomic instability. MOCS2 exhibited resistance to chemotherapy agents like erlotinib and sunitinib while sensitive to rapamycin, NSC87877, MG132, and FH355. Additionally, ELSPBP1 was identified as the target involving in glycolysis and M2 macrophage infiltration in SKCM.
CONCLUSIONS: MOCS classification could stably predict prognosis of SKCM; patients with a high cancer stemness index combined with genomic instability may be predisposed to an immune exhaustion state.Communicated by Ramaswamy H. Sarma.

Background: N6-methyladenosine (m6A) is the most common and abundant mRNA modification, playing an essential role in biological processes and tumor development. However, the role of m6A methylation in skin cutaneous melanoma (SKCM) is not yet clear. This study analyzed the expression of m6A-related functional genes in SKCM and aimed to explore the key demethylase ALKBH5 mediated m6A modification and its potential mechanism in human SKCM. Methods: Based on public databases, the m6A-related gene expression landscape in SKCM was portrayed. MeRIP-Seq and RNA-Seq were used to recognize the downstream target of ALKBH5. In vivo and in vitro functional phenotype and rescue functional experiments were performed to explore the mechanism of the ALKBH5-m6A-ABCA1 axis in SKCM. Results: We found ALKBH5 upregulated in SKCM, associated with poor prognosis. ALKBH5 can promote melanoma cell proliferation, colony formation, migration, and invasion and inhibit autophagy in vitro, facilitating tumor growth and metastasis in vivo. We identified ABCA1, a membrane protein that assists cholesterol efflux, as a downstream target of ALKBH5-mediated m6A demethylation. Finally, our data demonstrated that ALKBH5 promoted SKCM via mediating ABCA1 downregulation by reducing ABCA1 mRNA stability in an m6A-dependent manner. Conclusion: Our findings exhibited the functional value of the key demethylase ALKBH5 mediated m6A modification in the progression of SKCM, suggesting the ALKBH5-m6A-ABCA1 axis as a potential therapeutic target in SKCM.