ObjectiveSerum microRNAs (miRNAs) may serve as biomarkers in various cancers. Our study aims to explore the roles of miR-497 and miR-1246 in hepatocellular carcinoma (HCC).MethodsThe expression levels of miR-497 and miR-1246 were measured by RT-PCR. A correlation analysis was conducted between the expression levels of miR-497 and miR-1246 and clinicopathological characteristics of patients. The receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic efficacy in HCC. In addition, bioinformatics tools were also utilized to predict the potential targets of miR-497 and miR-1246.ResultsThe expression level of miR-497 in HCC was significantly down-regulated compared with the control group while the miR-1246 revealed a significantly higher expression level in HCC. There was a significant correlation demonstrated between the expression levels of miR-497 and miR-1246 in preoperative serum of HCC and the differentiation degree, Tumor Node Metastasis (TNM) classification, and metastasis. The expression levels of serum miR-497 and miR-1246 were significantly associated with the diagnosis, prognosis, and overall survival rate of patients with HCC. Moreover, the potential target genes of miR-497 in HCC include ARL2, UBE2Q1, PHF19, APLN, CHEK1, CASK, SUCO, CCNE1, and KIF23. The low expression of these nine genes is associated with a better prognosis of HCC patients. AUTS2 is a novel target gene of miR-1246, and its low expression is significantly related to the low overall survival rate of HCC patients.ConclusionsmiR-497 and miR-1246 are possibly involved in the progression of HCC by regulating target genes, respectively, and could serve as biomarkers in HCC.

Background Hepatocellular carcinoma (HCC) is still the fourth leading cause of cancer-related death. Better prognosticators are warranted for HCC. Hsa-miR-18a has been considered implicated in the pathogenesis of several tumors including HCC. Methods Bioinformatic analyses were conducted to predict target genes and carry out enrichment analysis. Validated downstream genes of hsa-miR-18a were obtained from PubMed database. Differential expression analysis was conducted within the “edgeR” R package based on the TCGA datasets. Survival analysis was performed by Kaplan-Meier survival analysis. All the visualizations were implemented by R. Results Bioinformatic analysis obtained a total of 90 target genes of hsa-miR-18a and revealed that target genes were involved in pathways essential for cancer onset and development such as cell cycle and PI3K/AKT signaling pathway. A review of literatures found target genes of miR-18a indeed participating in the biological processes of HCC. CHRM2 was identified as a special gene after the intersection analysis of TCGA differentially expressed genes (DEGs) and predicted target genes. Survival analysis validated that hsa-miR-18a and CHRM2 significantly affected the prognosis of HCC patients. Conclusion There is a strong association between hsa-miR-18a with tumors including HCC via participating essential tumor-promoting pathways including cell cycle, PI3K/AKT signaling pathway, etc. Furthermore, high miR-18a expression and low CHRM2 expression could lead to a poor prognosis in HCC. In conclusion, miR-18a could serve as an expectational prognostic biomarker in HCC.

Background PTEN is a multifunctional tumor suppressor gene mutating at high frequency in a variety of cancers. However, its expression in pan-cancer, correlated genes, survival prognosis, and regulatory pathways are not completely described. Here, we aimed to conduct a comprehensive analysis from the above perspectives in order to provide reference for clinical application. Methods we studied the expression levels in cancers by using data from TCGA and GTEx database. Obtain expression box plot from UALCAN database. Perform mutation analysis on the cBioportal website. Obtain correlation genes on the GEPIA website. Construct protein network and perform KEGG and GO enrichment analysis on the STRING database. Perform prognostic analysis on the Kaplan-Meier Plotter website. We also performed transcription factor prediction on the PROMO database and performed RNA-RNA association and RNA-protein interaction on the RNAup Web server and RPISEq. The gene 3D structure, protein sequence and conserved domain were obtained in NCBI respectively. Results PTEN was underexpressed in all cancers we studied. It was closely related to the clinical stage of tumors, suggesting PTEN may involved in cancer development and progression. The mutations of PTEN were present in a variety of cancers, most of which were truncation mutations and missense mutations. Among cancers (KIRC, LUAD, THYM, UCEC, Gastric Cancer, Liver Cancer, Lung Cancer, Breast Cancer), patients with low expression of PTEN had a shorter OS time and poorer OS prognosis. The low expression of PTEN can cause the deterioration of RFS in certain cancers (TGCT, UCEC, LIHC, LUAD, THCA), suggesting that the expression of PTEN was related to the clinical prognosis. Our study identified genes correlated with PTEN and performed GO enrichment analysis on 100 PTEN-related genes obtained from the GEPIA website. Conclusions The understanding of PTEN gene and the in-depth exploration of its related regulatory pathways may provide insight for the discovery of tumor-specific biomarkers and clinical potential therapeutic targets.

Background: PTEN is a multifunctional tumor suppressor gene mutating at high frequency in a variety of cancers. However, its expression in pan-cancer, correlated genes, survival prognosis, and regulatory pathways are not completely described. Here, we aimed to conduct a comprehensive analysis from the above perspectives in order to provide reference for clinical application. Methods: we studied the expression levels in cancers by using data from TCGA and GTEx database. Obtain expression box plot from UALCAN database. Perform mutation analysis on the cBioportal website. Obtain correlation genes on the GEPIA website. Construct protein network and perform KEGG and GO enrichment analysis on the STRING database. Perform prognostic analysis on the Kaplan-Meier Plotter website. We also performed transcription factor prediction on the PROMO database and performed RNA-RNA association and RNA-protein interaction on the RNAup Web server and RPISeq. The gene 3D structure, protein sequence and conserved domain were obtained in NCBI respectively.Results: PTEN was underexpressed in all cancers we studied. It was closely related to the clinical stage of tumors, suggesting PTEN may involved in cancer development and progression. The mutations of PTEN were present in a variety of cancers, most of which were truncation mutations and missense mutations. Among cancers (KIRC, LUAD, THYM, UCEC, Gastric Cancer, Liver Cancer, Lung Cancer, Breast Cancer), patients with low expression of PTEN had a shorter OS time and poorer OS prognosis. The low expression of PTEN can cause the deterioration of RFS in certain cancers (TGCT, UCEC, LIHC, LUAD, THCA), suggesting that the expression of PTEN was related to the clinical prognosis. Our study identified genes correlated with PTEN and performed GO enrichment analysis on 100 PTEN-related genes obtained from the GEPIA website. Conclusions: The understanding of PTEN gene and the in-depth exploration of its related regulatory pathways may provide insight for the discovery of tumor-specific biomarkers and clinical potential therapeutic targets.