Role of Single Nucleotide Polymorphism-Related Genes in Tumour Immune Cell Infiltration and Prognosis of Cutaneous Melanoma.

Background: Advances in cancer research have allowed for early diagnosis and improved treatment of cutaneous melanoma (CM). However, its invasiveness and recurrent metastasis, along with rising resistance to newer therapies, have lent urgency to the search for novel biomarkers and the underlying molecular mechanisms of CM. Methods: Single nucleotide polymorphism- (SNP-) related genes were obtained from the sequencing data of 428 CM samples in The Cancer Genome Atlas. Functional enrichment of these genes was analysed in clusterProfiler. Additionally, a protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Gene (STRING) database. Gene Expression Profiling Interactive Analysis (GEPIA) was used to identify the expression and prognostic value of mutated genes. Finally, the Tumour Immune Estimation Resource (TIMER) analysed the relationship between gene expression and immune cell infiltration. Results: We constructed a PPI network from the top 60 SNP-related genes. Mutated genes were mainly involved in calcium and oxytocin signalling pathways, as well as circadian entrainment. In addition, three SNP-related genes, BRAF, FLG, and SORL1, were significantly associated with patient prognosis. BRAF and SORL1 were positively associated with infiltration abundance of B cells, CD8+ T cells, CD4+ T cells, neutrophils, and dendritic cells, whereas FLG expression was negatively associated. Furthermore, higher immune cell infiltration was positively correlated with good prognosis. Conclusions: Our study provides vital bioinformatic data and a relevant theoretical basis to further explore the molecular pathogenesis of CM and improve patient prognosis.

Mining Database for the Clinical Significance and Prognostic Value of ESRP1 in Cutaneous Malignant Melanoma.

BACKGROUND: Epithelial splicing regulatory protein 1 (ESRP1) has been described as an RNA-binding protein involved in cancer development. However, the expression and regulatory network of ESRP1 in cutaneous malignant melanoma (CMM) remain unclear. METHODS: From the sequencing data of 103 CMM samples in The Cancer Genome Atlas database, the expression level of ESRP1 and its correlation with the clinicopathological characteristics were analyzed using the Oncomine 4.5, Gene Expression Profiling Interactive Analysis (GEPIA), and UALCAN tools, while LinkedOmics was used to identify differential gene expression with ESRP1 and to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Gene enrichment analysis examined target networks of kinases, miRNAs, and transcription factors. Finally, TIMER was used to analyze the relationship between ESRP1 and tumor immune cell infiltration. RESULTS: We found that ESRP1 was lowly expressed in CMM tissues, and a low level of ESRP1 expression correlated with better overall survival. Expression of this gene was linked to functional networks involving the condensed chromosomes, epidermal development, and translation initiation. Functional network analysis suggested that ESRP1 regulated ribosome metabolism, drug metabolism, and chemical carcinogenesis via pathways involving several cancer-related kinases, miRNAs, and transcription factors. Furthermore, our results suggested that ESRP1 played an important role in regulating tumor-associated macrophage polarization, dendritic cell infiltration, Treg cells, and T cell exhaustion. CONCLUSION: Our study demonstrates ESRP1 expression, prognostic value, and potential regulatory networks in CMM, thereby shedding light on the clinical significance of ESRP1, and provides a novel biomarker for determining prognosis and immune infiltration in CMM.

Integrative Analysis of Long Noncoding RNA (lncRNA), microRNA (miRNA) and mRNA Expression and Construction of a Competing Endogenous RNA (ceRNA) Network in Metastatic Melanoma.

Worldwide, metastatic melanoma of the skin has an aggressive course with high morbidity and mortality. Therefore, an increased understanding of the pathogenesis of metastatic melanoma has gained increasing attention, including the role of epigenetic modification and competing endogenous RNA (ceRNA). This study aimed to used bioinformatics data to undertake an integrative analysis of long noncoding RNA (lncRNA), microRNA (miRNA) and mRNA expression to construct a ceRNA network in metastatic melanoma. Data from the Cancer Genome Atlas (TCGA), the Gene Ontology (GO) database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed. There were 471 cases that included 103 primary solid tumors and 368 cases of metastatic melanoma that included transcriptome sequencing data (including lncRNA and mRNA); 452 cases had miRNA sequencing data. Analysis of chip data identified 85 6 mRNAs, 67 miRNAs, and 250 lncRNAs that were differentially expressed in cases of metastatic melanoma, of which 25 miRNAs, 18 lncRNAs, and 18 mRNAs participated in the formation of ceRNAs. Survival analysis identified seven differentially expressed mRNAs, five differentially expressed miRNAs (miRNA-29c, miRNA-100, miR-142-3p, miR-150, miR-516a-2), and six differentially expressed lncRNAs (AC068594.1, C7orf71, FAM41C, GPC5-AS1, MUC19, LINC00402) that were correlated with survival time in patients with metastatic melanoma. Bioinformatics data and integrative analysis identified lncRNA, miRNA, and mRNA expression to construct a ceRNA and patient survival network in metastatic melanoma. These findings support the need for further studies on the mechanisms involved in the regulation of metastatic melanoma by ceRNAs.