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Background. Hepcidin antimicrobial peptide (HAMP) is a key factor in maintaining iron metabolism, which may induce ferroptosis when upregulated. However, its prognostic value and relation to immune infiltrating cells remains unclear. Methods. This study analyzed the expression levels of HAMP in the Oncomine, Timer and Ualcan databases, and examined its prognostic potential in KIRC with R programming. The Timer and GEPIA databases were used to estimate the correlations between HAMP and immune infiltration and the markers of immune cells. The intersection genes and the co-expression PPI network were constructed via STRING, R programming and GeneMANIA, and the hub genes were selected with Cytoscape. In addition, we analyzed the gene set enrichment and GO/KEGG pathways by GSEA. Results. Our study revealed higher HAMP expression levels in tumor tissues including KIRC, which were related to poor prognosis in terms of OS, DSS and PFI. The expression of HAMP was positively related to the immune infiltration level of macrophages, Tregs, etc., corresponding with the immune biomarkers. Based on the intersection genes, we constructed the PPI network and used the 10 top hub genes. Further, we performed a pathway enrichment analysis of the gene sets, including Huntington’s disease, the JAK-STAT signaling pathway, ammonium ion metabolic process, and so on. Conclusion. In summary, our study gave an insight into the potential prognosis of HAMP, which may act as a diagnostic biomarker and therapeutic target related to immune infiltration in KIRC.

Background Increasing evidence indicates that psoriasis (PSO) and periodontitis (PD) are likely to occur together, however, the underlying mechanism remains unclear. Materials and methods The expression profiles of PSO (lesion vs non-lesion, GSE30999, GSE14905) and PD (affected vs unaffected gingival tissue, GSE16134, GSE10334) were downloaded from the GEO database. First, we investigated the common differentially expressed genes (DEGs) of PSO and PD. Then, GO and KEGG enrichment analysis, protein interaction network (PPI) construction, and hub gene identification analysis were carried out. Finally, GO and KEGG enrichment analysis, miRNA interaction analysis, and transcription factors (TFs) interaction analysis for hub genes were performed. Results Eighteen DEGs were identified for further analysis, including 15 up-regulated genes and 3 down-regulated genes. 9 hub genes were then identified via Cytohubba, including IL1B, CXCL1, CXCL8, MMP12, CCL18, SELL, CXCL13, FCGR3B, and SELE. Their functions are mainly enriched in two aspects: neutrophil chemotaxis and migration, chemokine activation and interaction. The enriched signaling pathways includes three categories: host defense, inflammation-related signaling pathways, and disease-related pathways. 9 common miRNAs based on experimental evidence and 10 common TFs were further identified in both PSO and PD. Conclusion Our study revealed possible comorbidity mechanisms in PSO and PD from the perspective of bioinformatics tentatively. The data can present new insight for joint prevention and treatment of in PSO and PD, as well as provide data support for further prospective studies.

Background. Skin cutaneous melanoma is one of most aggressive type of cancers worldwide. Therefore, the identification of SKCM biomarkers is of great importance. FLG gene is one of the genes that encode proteins involved in epidermal formation. This was the first time to study the role of FLG in the prognosis and immune infiltrates of skin cutaneous melanoma. Methods. We downloaded the somatic mutation data of 471 SKCM patients from the Cancer Genome Atlas (TCGA) database and analyzed the mutation profiles with “MafTools” package. The expression of FLG and the overall survival in SKCM were analyzed by GEPIA. Additionally, univariate and multivariate Cox analyses were used to compare several clinical features with survival rates. We used TIMER to investigate FLG expression and collection of immune infiltration levels in SKCM, as well as cumulative survival in SKCM. Meanwhile, we also used CIBERSORT to investigate the association between FLG and cancer immune infiltration. In addition, gene set enrichment analysis (GSEA) was performed using the TCGA dataset. Furthermore, data from GEO and HPA was used to validate the results. Results. Single nucleotide polymorphism (SNP) happened more frequently than insertion or deletion, and C > T was the most common of SNV in SKCM. We selected the first 15 mutated genes by analyzing 471 melanoma samples, and the prognosis analysis showed that only the high expression of mutated FLG gene was significantly correlated with the poor prognosis of SKCM. Multivariate Cox analysis showed that age, the worse tumor status, less lymph node metastasis, and FLG expression were independent factors for prognosis. Specifically, lower infiltration levels of B cell, CD8+ T cells, neutrophils, and dendritic cells correlated with poor survival outcomes in SKCM. GSEA revealed that FLG is closely related to cancer pathways and epidermal cell proliferation. In addition, the previous conclusions can be verified from external data from GEO and HPA. Conclusion. The discovery of mutant gene FLG as a biomarker of SKCM helps elucidate how changes in the immune environment promote the occurrence of cutaneous melanoma. Further analysis suggested that FLG might be a new predictor of SKCM prognosis.

Background: Skin cutaneous melanoma (SKCM) is considered one of the most aggressive and lethal cancers of the skin. G-protein coupled receptor 143 (GPR143), which has been reported to cause congenital nystagmus, belongs to the superfamily of G protein-coupled receptors. Methods and Results: We analyzed the expression of GPR143 and survival of SKCM patients in SKCM via Gene Expression Profiling Interactive Analysis (GEPIA). Then, logistic regression and multivariate cox analysis was used to analyze the influence of GPR143 expression on clinicopathological elements and survival. We explored the immune response of 22 TIICs in SKCM via CIBERSORT and used TIMER to assess the correlation of GPR143 expression and immune infiltration level. Finally, we used gene set enrichment analysis (GSEA) to assess the TCGA dataset. The outcomes suggest that GPR143 expression in tumor samples is remarkedly higher than in normal samples and high GPR143 expression is associated with poorer prognosis. The result of multivariate analysis indicated that increased GPR143 expression is an independent prognostic factor for prognosis. We found GPR143 expression level has prominent negative correlations with infiltrating levels of B cell, CD8+ T cells, etc. GSEA indicated that pigment metabolic process, pigment biosynthetic process and other pathways were identified as differentially enriched pathways in Gene Ontology (GO). Oxidative phosphorylation, Parkinson’s disease and other pathways were showed significantly differential enrichment in GPR143 high expression phenotype in Kyoto Encyclopedia of Genes and Genomes (KEGG).Conclusions: In conclusion, GPR143 may be a novel prognostic biomarker and associated with immune infiltrates in SKCM.