The purpose of the present study was to assess if guanylate-binding protein (GBP) mRNAs could be prognostic biomarkers for patients with skin cutaneous melanoma (SKCM). The prognostic value of GBP mRNA expression in patients with SKCM was investigated by analyzing gene expression data in 459 SKCM patients. The data were extracted from the OncoLnc database of The Cancer Genome Atlas. A high expression of GBP1, GBP2, GBP3, GBP4 and GBP5 were correlated with favorable overall survival (OS) in the SKCM patients followed for over 30 years. In addition, a high expression of GBP6 mRNA was not correlated with OS in the SKCM patients. A joint effects analysis showed that the co-incidence of the high expression of GBP1-5 was correlated with favorable overall survival in SKCM patients. Our findings suggest that GBP1-5 mRNAs in SKCM are associated with favorable prognosis and may be potential prognostic biomarkers. The combination of GBP1-5 could improve the sensitivity for predicting OS in SKCM patients.

Objective : MicroRNAs (miRNAs) have been explored in malignancies. We investigated the functions of clustered miRNAs hsa-miR-221/222-3p in hepatocellular carcinoma (HCC). Methods : Human miRNA tissue atlas website was determined expression levels in liver tissue. Four databases, TarBase, miRTarBase, miRecords and miRPathDB, were found experimentally validated target genes of clustered miRNAs. TargetScanHuman was predicted target genes. The STRING website was depicted protein-protein interaction (PPI) networks. The OncoLnc website analyzed prognostic values for hsa-miR-221/222-3p and their target genes. The MCODE plugin calculated modules of PPI networks. Receiver operating characteristic (ROC) curves were predicted 1, 3, and 5 years prognostic values. Results : Expression of clustered miRNAs was high in liver tissues. A total of 1577 target genes were identified. Enrichment analysis showed that target genes were enriched mainly in cancer, Wnt signaling and ErbB signaling pathways. Two modules were calculated using PPI networks. Has-miR-221-3p was not associated with prognosis ( P = 0.401). Has-miR-222-3p and target genes ESR1 , TMED7 , CBFB , ETS2 , UBE2J1 and UBE2N of the clustered miRNAs were associated with HCC survival (all P < 0.05). Has-miR-222-3p, CBFB , and UBE2N showed good performance of ROC in prognosis prediction at 1, 3, and 5 years (all area under curves > 0.600). Conclusion : Has-miR-222-3p and target genes , especially CBFB , UBE2N , may serve as prognostic predictors for HCC.

Background : The functional significance of the proteasome activator subunit ( PSME ) gene family in the pathogenesis of skin cutaneous melanoma (SKCM) remains to be elucidated. Materials and methods : Clinical data for patients with SKCM, including expression levels of PSME genes, were extracted from TCGA. GO term and KEGG pathway enrichment analyses were performed. Correlations between the expression levels of PSME genes in SKCM were evaluated with the Pearson correlation coefficient. Functional and enrichment analyses were conducted using DAVID. Univariate and multivariate survival analyses adjusted by Cox regression were used to construct a prognostic signature. The mechanisms underlying the association between PSME gene expression and overall survival (OS) were explored with gene set enrichment analysis. Joint-effects survival analysis was performed to evaluate the clinical value of the prognostic signature. Results : The median expression levels of PSME1 , PSME2 and PSME3 were significantly higher in SKCM than in normal skin. PSME1 , PSME2, and PSME3 were significantly enriched in several biological processes and pathways including cell adhesion, adherens junction organization, regulation of autophagy, cellular protein localization, the cell cycle, apoptosis, and the Wnt and NF-κB pathways. High expression levels of PSME1 and PSME2 combined with a low expression level of PSME3 was associated with favorable OS. Conclusion : Knowledge of the expression levels of the PSME gene family could provide a sensitive strategy for predicting prognosis in SKCM.

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