Purpose: MicroRNAs (miRNAs) play important roles in the development and progression of cancer. The aim of this study is to identify miRNA expression signatures in hepatocellular carcinoma and delineate their clinical significance for hepatocellular carcinoma.Experimental Design: Patients with hepatocellular carcinoma, undergoing hepatectomy were randomly divided into training set (60 patients) and test set (50 patients). Other 56 patients were used as an independent cohort. The miRNA expression levels were detected by microarray and verified by quantitative real-time reverse transcription-PCR (qRT-PCR).Results: A 30-miRNA signature consisting of 10 downregulated and 20 upregulated miRNAs was established for distinguishing hepatocellular carcinoma from noncancerous liver tissues in the training set with 99.2% accuracy. The classification accuracies of this signature were 97% and 90% in the test set and independent cohort, respectively. The expression level of four miRNAs in the 30-miRNA signature was verified by qRT-PCR in the training set. Twenty miRNAs were then selected to construct prognostic signature in the training set. Of the 20 miRNAs, six were risk factors and 14 were protective factors. A formula based on the 20 miRNAs was built to compute prognostic index. Kaplan-Meier analysis showed that patients with a higher prognostic index had a significantly lower survival than those with a low index. This was verified in the test and independent sets. Multivariate analysis indicated that the 20-miRNA signature was an independent prognostic predictor.Conclusions: The 30-and 20-miRNA signatures identified in this study should provide new molecular approaches for diagnosis and prognosis of patients with hepatocellular carcinoma and clues for elucidating molecular mechanism of hepatocarcinogenesis.

Occurring in over 60% of human genes, alternative polyadenylation (APA) results in numerous transcripts with differing 3’ends, thus greatly expanding the diversity of mRNAs and of proteins derived from a single gene. As a key molecular mechanism, APA is involved in various gene regulation steps including mRNA maturation, mRNA stability, cellular RNA decay, and protein diversification. APA is frequently dysregulated in cancers leading to changes in oncogenes and tumor suppressor gene expressions. Recent studies have revealed various APA regulatory mechanisms that promote the development and progression of a number of human diseases, including cancer. Here, we provide an overview of four types of APA and their impacts on gene regulation. We focus particularly on the interaction of APA with microRNAs, RNA binding proteins and other related factors, the core pre-mRNA 3’end processing complex, and 3’UTR length change. We also describe next-generation sequencing methods and computational tools for use in poly(A) signal detection and APA repositories and databases. Finally, we summarize the current understanding of APA in cancer and provide our vision for future APA related research.

Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer-related functions and pathways. Then, the STRING database was used to construct the protein-protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy. K E Y W O R D Sbioinformatics, differentially expressed genes, GEO, robust rank aggregation, thyroid cancer

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There is no gene signature for predicting relapse and survival of cervical cancer with early stage currently. In this study, we investigate whether gene expression profiling of cervical cancer could be used to predict the prognosis of patient. A series of 100 primary cervical cancer patients who underwent radical hysterectomy between January 2001 and October 2006 were analyzed for gene expression profiles by using a custom oligonucleotide microarray containing probes for 1440 human tumor-related gene transcripts. Supervised analysis of gene expression data identified 19 genes that exhibited differential expression between cervical cancer and normal cervix. Then, all 100 patients were divided into the training (n=50) and testing sets (n=50). Using Cox regression and risk-score analysis, we identified a 7-gene (UBL3, FGF3, BMI1, PDGFRA, PTPRF, RFC4, and NOL7) signature for predicting relapse of patient in the training set. The 7-gene signature was validated by the testing set (sensitivity, 84.6%; specificity, 91.9%; positive predictive value, 78.6%; negative predictive value, 94.4%). Patients with high-risk 7-gene signature had poor relapse-free survivals (RFS) than patients with low-risk 7-gene signature in both training set (P=0.026) and testing set (P=0.042). Multivariate analysis showed that the FIGO stage and 7-gene signature are independent prognostic factors associated with RFS of cervical cancer patients. The 7-gene signature can predict cancer recurrence and survival of cervical cancer patients. This may have prognostic or therapeutic implications for the future management of cervical cancer patients.