The Identification and Analysis of MicroRNAs Combined Biomarkers for Hepatocellular Carcinoma Diagnosis.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common malignant tumor with high morbidity and mortality globally. Compared with traditional diagnostic methods, microRNAs (miRNAs) are novel biomarkers with higher accuracy. OBJECTIVE: We aimed to identify combinatorial biomarkers of miRNAs to construct a classification model for the diagnosis of HCC. METHODS: The mature miRNA expression profile data of six cancers (liver, lung, gastric, breast, prostate, and colon) were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database with accession number GSE36915, GSE29250, GSE99417, GSE41970, GSE64333 and GSE35982. The messenger RNA (mRNA) expression profile data of these six cancers were obtained from TCGA. Three R software packages, student's t-test, and a normalized foldchange method were utilized to identify HCC-specific differentially expressed miRNAs (DEMs). Using all combinations of obtained HCC-specific DEMs as input features, we constructed a classification model by support vector machine searching for the optimal combination. Furthermore, target genes prediction was conducted on the miRWalk 2.0 website to obtain differentially expressed mRNAs (DEmRNAs), and KEGG pathway enrichment was analyzed on the DAVID website. RESULTS: The optimal combination consisted of four miRNAs (hsa-miR-130a-3p, hsa-miR-450b-5p, hsa-miR-136-5p, and hsa-miR-24-1-5p), of which the last one has not been currently reported to be relevant to HCC. The target genes of hsa-miR-24-1-5p (CDC7, ACACA, CTNNA1, and NF2) were involved in the cell cycle, AMPK signaling pathway, Hippo signaling pathway, and insulin signaling pathway, which affect the proliferation, metastasis, and apoptosis of cancer cells. Moreover, the area under the receiver operating characteristic curves of the four miRNAs were all higher than 0.85. CONCLUSION: These results suggest that the miRNAs combined biomarkers were reliable for the diagnosis of HCC. Hsa-miR-24-1-5p was a novel biomarker for HCC diagnosis identified in this study.

Quantitative Structure Activity/Pharmacokinetics Relationship Studies of HIV-1 Protease Inhibitors Using Three Modelling Methods.

BACKGROUND: HIV-1 protease inhibitor (PIs) is a good choice for AIDS patients. Nevertheless, for PIs, there are several bugs in clinical application, like drug resistance, the large dose, the high costs and so on, among which, the poor pharmacokinetics property is one of the important reasons that leads to the failure of its clinical application. OBJECTIVE: We aimed to build computational models for studying the relationship between PIs structure and its pharmacological activities. METHODS: We collected experimental values of koff/Ki and structures of 50 PIs through a careful literature and database search. Quantitative structure activity/pharmacokinetics relationship (QSAR/QSPR) models were constructed by support vector machine (SVM), partial-least squares regression (PLSR) and back-propagation neural network (BPNN). RESULTS: For QSAR models, SVM, PLSR and BPNN all generated reliable prediction models with the r2 of 0.688, 0.768 and 0.787, respectively, and r2pred of 0.748, 0.696 and 0.640, respectively. For QSPR models, the optimum models of SVM, PLSR and BPNN obtained the r2 of 0.952, 0.869 and 0.960, respectively, and the r2pred of 0.852, 0.628 and 0.814, respectively. CONCLUSION: Among these three modelling methods, SVM showed superior ability than PLSR and BPNN both in QSAR/QSPR modelling of PIs, thus, we suspected that SVM was more suitable for predicting activities of PIs. In addition, 3D-MoRSE descriptors may have a tight relationship with the Ki values of PIs, and the GETAWAY descriptors have significant influence on both koff and Ki in PLSR equations.

FOXM1 facilitates breast cancer cell stemness and migration in YAP1-dependent manner.

Breast cancer has the highest incidence and mortality in the female population. Forkhead box M1 (FOXM1) known as a transcription factor is upregulated and associated with poor prognosis in a variety of cancers. However, the molecular mechanisms of FOXM1 on breast cancer progression are poorly understood. In this study, we found that FOXM1 was up-regulated in breast cancer. FOXM1 promoted cell proliferation, clonal formation, and migration capacity in triple negative breast cancer by increasing transcriptional activity of YAP1. FOXM1 also maintained cell stemness via the Hippo pathway. The YAP1-TEAD binding inhibitor Verteporfin reduced the transcription level of OCT4 and NANOG but the Hippo pathway activator XMU-MP-1 could increase the transcription level of OCT4 and NANOG. In summary, our findings indicated that FOXM1 promoted breast cancer progression through the Hippo pathway, and it was suggested a new strategy to treat breast cancer.