Briefing in family characteristics of microRNAs and their applications in cancer research

A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder

RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/.

Covid-19: a novel challenge to human immune genetic machinery

COVID-19 also called corona virus emerged in China in December 2019. This turned into a global pandemic in a short period of time. Covid-19 is a novel strain of corona virus that was not seen earlier in human beings. It is important to study the molecular structure of Covid-19 so as to aid in the development of therapeutic measures. Existing Covid-19 pandemic poses an extraordinary risk to health and healthcare systems worldwide. Corona viruses are made of single stranded RNA present within the coat proteins. The virus has a diameter of nearly 80–120 nm. Usually, Covid-19 presents with the signs and symptoms of respiratory illness. Cough commonly dry cough, fever, associated with myalgias and sometimes breathing difficulties due to decrease in oxygen saturation rates are also present in these patients. Some people show fever with body aches, while some are relatively asymptomatic. Corona virus is primarily transmitted in humans through respiratory route and is highly contagious. Mostly old people and those having comorbid illnesses suffer most. After invading into the human body, the virus may lead to a sequence of processes such as viral invasion, replication, and programmed cell death, that is, apoptosis. To control and prevent this viral infection, we need to study the molecular aspects of Covid-19 in detail so as to design therapeutic agents as well as for vaccine formation. The micro-RNA is defined as the single-stranded noncoding RNA molecule. They have a length of about 22 nucleotides approximately and help in the post transcriptional regulation of gene expression. Micro RNAs regulate many types of cancers in addition to Covid-19 and other infections. Viral micro RNA is a newer type of mi-RNA and controls the host cell expression and viral target genes. This was completed by inducing micro-RNA cleavage, breakdown, translation, inhibition, or other mechanisms. The micro-RNAs of Covid-19 are explained to give an authoritative means to study this novel coronavirus. These control the host cell expression and also viral target genes by inducing micro-RNA cleavage, breakdown, translation, inhibition, and also other mechanisms.

MiR-365a-3p-Mediated Regulation of HELLS/GLUT1 Axis Suppresses Aerobic Glycolysis and Gastric Cancer Growth

Gastric cancer (GC) is a common and invasive malignancy, which lacks effective treatment and is the third main reason of cancer death. Metabolic reprogramming is one of the main reasons that GC is difficult to treat in various environments. Particularly, abnormal glycolytic activity is the most common way of metabolism reprogramming in cancer cells. Numerous studies have shown that microRNAs play important roles in reprogramming glucose metabolism. Here, we found a microRNA-miR-365a-3p, was significantly downregulated in GC according to bioinformatics analysis. Low expression of miR-365a-3p correlated with poor prognosis of GC patients. Overexpression of miR-365a-3p in GC cells significantly inhibited cell proliferation by inducing cell cycle arrest at G1 phase. Notably, miR-365a-3p induced downregulation of HELLS through binding to its 3′ untranslated region (UTR). Additionally, we found that miR-365a-3p suppressed aerobic glycolysis by inhibiting HELLS/GLUT1 axis. Lastly, we shown that overexpression of miR-365a-3p significantly inhibited tumor growth in nude mice. Conversely, Reconstituted the expression of HELLS rescued the suppressive effects of miR-365a-3p. Our data collectively indicated that miR-365a-3p functioned as a tumor suppressor in GC through downregulating HELLS. Therefore, targeting of the novel miR-365a-3p/HELLS axis could be a potentially effective therapeutic approach for GC.

Annotation of miRNAs in COVID-19 coronavirus

The COVID-19 coronavirus is a new strain of coronavirus that had not been previously detected in humans. As its severe pathogenicity is concerned, it is important to study it thoroughly to aid in the discovery of a cure. In this study, the microRNAs (miRNAs) of COVID-19 were annotated to provide a powerful tool for the study of this novel coronavirus. We obtained 16 novel coronavirus genome sequences and the mature sequences of all viruses in the microRNA database (miRbase), and then used the miRNA mature sequences of the virus to perform the Basic Local Alignment Search Tool (BLAST) analysis in the coronavirus genome, extending the matched regions of approximately 20 bp to two segments by 200 bp. Six sequences were obtained after deleting redundant sequences. Then, the hairpin structures of the mature miRNAs were determined using RNAfold. The mature sequence on one hairpin arm was selected into a total of 4 sequences, and finally the relevant miRNA precursor prediction tools were used to verify whether the selected sequences are miRNA precursor sequences of the novel coronavirus. The miRNAs of the novel coronavirus were annotated by our newly developed method, which will lay the foundation for further study of this virus.

Identification and Functional Characterization of Plant MiRNA Under Salt Stress Shed Light on Salinity Resistance Improvement Through MiRNA Manipulation in Crops

Salinity, as a major environmental stressor, limits plant growth, development, and crop yield remarkably. However, plants evolve their own defense systems in response to salt stress. Recently, microRNA (miRNA) has been broadly studied and considered to be an important regulator of the plant salt-stress response at the post-transcription level. In this review, we have summarized the recent research progress on the identification, functional characterization, and regulatory mechanism of miRNA involved in salt stress, have discussed the emerging manipulation of miRNA to improve crop salt resistance, and have provided future direction for plant miRNA study under salt stress, suggesting that the salinity resistance of crops could be improved by the manipulation of microRNA.

Interactome of miRNAs and transcriptome of human umbilical cord endothelial cells exposed to short-term simulated microgravity

Adaptation of humans in low gravity conditions is a matter of utmost importance when efforts are on to a gigantic leap in human space expeditions for tourism and formation of space colonies. In this connection, cardiovascular adaptation in low gravity is a critical component of human space exploration. Deep high-throughput sequencing approach allowed us to analyze the miRNA and mRNA expression profiles in human umbilical cord vein endothelial cells (HUVEC), cultured under gravity (G), and stimulated microgravity (MG) achieved with a clinostat. The present study identified totally 1870 miRNAs differentially expressed in HUVEC under MG condition when compared to the cells subjected to unitary G conditions. The functional association of identified miRNAs targeting specific mRNAs revealed that miRNAs, hsa-mir-496, hsa-mir-151a, hsa-miR-296-3p, hsa-mir-148a, hsa-miR-365b-5p, hsa-miR-3687, hsa-mir-454, hsa-miR-155-5p, and hsa-miR-145-5p differentially regulated the genes involved in cell adhesion, angiogenesis, cell cycle, JAK-STAT signaling, MAPK signaling, nitric oxide signaling, VEGF signaling, and wound healing pathways. Further, the q-PCR based experimental studies of upregulated and downregulated miRNA and mRNAs demonstrate that the above reported miRNAs influence the cell proliferation and vascular functions of the HUVEC in MG conditions effectively. Consensus on the interactome results indicates restricted fluctuations in the transcriptome of the HUVEC exposed to short-term MG that could lead to higher levels of endothelial functions like angiogenesis and vascular patterning.

A Cancer Survival Prediction Method Based on Graph Convolutional Network

BACKGROUND AND OBJECTIVE

Cancer, as the most challenging part in the human disease history, has always been one of the main threats to human life and health. The high mortality of cancer is largely due to the complexity of cancer and the significant differences in clinical outcomes. Therefore, it will be significant to improve accuracy of cancer survival prediction, which has become one of the main fields of cancer research. Many calculation models for cancer survival prediction have been proposed at present, but most of them generate prediction models only by using single genomic data or clinical data. Multiple genomic data and clinical data have not been integrated yet to take a comprehensive consideration of cancers and predict their survival.

METHOD

In order to effectively integrate multiple genomic data (including genetic expression, copy number alteration, DNA methylation and exon expression) and clinical data and apply them to predictive studies on cancer survival, similar network fusion algorithm (SNF) was proposed in this paper to integrate multiple genomic data and clinical data so as to generate sample similarity matrix, min-redundancy and max-relevance algorithm (mRMR) was used to conduct feature selection of multiple genomic data and clinical data of cancer samples and generate sample feature matrix, and finally two matrixes were used for semi-supervised training through graph convolutional network (GCN) so as to obtain a cancer survival prediction method integrating multiple genomic data and clinical data based on graph convolutional network (GCGCN).

RESULT

Performance indexes of GCGCN model indicate that both multiple genomic data and clinical data play significant roles in the accurate survival time prediction of cancer patients. It is compared with existing survival prediction methods, and results show that cancer survival prediction method GCGCN which integrates multiple genomic data and clinical data has obviously superior prediction effect than existing survival prediction methods.

CONCLUSION

All study results in this paper have verified effectiveness and superiority of GCGCN in the aspect of cancer survival prediction.

iCircDA-MF: identification of circRNA-disease associations based on matrix factorization

Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.

Pattern of cell-to-cell transfer of microRNA by gap junction and its effect on the proliferation of glioma cells

MicroRNA is expected to be a novel therapeutic tool for tumors. Gap junctions facilitate the transfer of microRNA, which exerts biological effects on tumor cells. However, the length of microRNA that can pass through certain gap junctions composed of specific connexin remains unknown. To address this question, the present study investigated the permeability of gap junctions composed of various connexins, including connexin 43, connexin 32 or connexin 37, to microRNAs consisting of 18-27 nucleotides in glioma cells and cervical cancer cells. Results indicated that all of the microRNAs were able to be transferred from donor glioma cells to neighboring cells through the connexin 43 composed gap junction, but not the gap junctions composed of connexin 32 or connexin 37, in cervical cancer cells. Downregulation of the function of gap junctions comprising connexin 43 by pharmacological inhibition and shRNA significantly decreased the transfer of these microRNAs. In contrast, gap junction enhancers and overexpression of connexin 43 effectively increased these transfers. In glioma cells, cell proliferation was inhibited by microRNA-34a. Additionally, these effects of microRNA-34a were significantly enhanced by overexpression of connexin 43 in U251 cells, indicating that gap junctions play an important role in the antitumor effect of microRNA by transfer of microRNA to neighboring cells. Our data are the first to clarify the pattern of microRNA transmission through gap junctions and provide novel insights to show that antitumor microRNAs should be combined with connexin 43 or a connexin 43 enhancer, not connexin 32 or connexin 37, in order to improve the therapeutic effect.

Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and Gene-Expression Data

A subpathway is defined as the local region of a biological pathway with specific biological functions. With the generation of large-scale sequencing data, there are more opportunities to study the molecular mechanisms of cancer development. It is necessary to investigate the potential impact of DNA methylation, copy number variation (CNV), and gene-expression changes in the molecular states of oncogenic dysfunctional subpathways. We propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), by integrating multi-omics data and pathway topological information to identify dysfunctional subpathways. We first calculated gene-risk scores by integrating the three following types of data: DNA methylation, CNV, and gene expression. Second, we performed a greedy search algorithm to identify the key dysfunctional subpathways within pathways for which the discriminative scores were locally maximal. Finally, a permutation test was used to calculate the statistical significance level for these key dysfunctional subpathways. We validated the effectiveness of ICDS in identifying dysregulated subpathways using datasets from liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), cervical squamous cell carcinoma, and endocervical adenocarcinoma. We further compared ICDS with methods that performed the same subpathway identification algorithm but only considered DNA methylation, CNV, or gene expression (defined as ICDS_M, ICDS_CNV, or ICDS_G, respectively). With these analyses, we confirmed that ICDS better identified cancer-associated subpathways than the three other methods, which only considered one type of data. Our ICDS method has been implemented as a freely available R-based tool (https://cran.r-project.org/web/packages/ICDS).

A Novel Cluster-Based Computational Method to Identify miRNA Regulatory Modules

The identification of miRNA regulatory modules can help decipher miRNAs combinatorial regulation effects on the pathogenesis underlying complex diseases, especially in cancer. By integrating miRNA/mRNA expression profiles and sequence-based predicted target site information, we develop a novel cluster-based computational method named CoModule for identifying miRNA regulatory modules (MRMs). The ultimate goal of CoModule is to detect the MRMs, in which the miRNAs in each module are expected to present cooperative mechanisms in regulating their targets mRNAs. Here, the co-expression of miRNAs are believed to present cooperative regulatory relationship, therefore, the critical step of CoModule is first to partition the miRNAs with similar expression into a cluster by employing rough set clustering. After gaining credible miRNA clusters, the targets of regulator are naturally added into corresponding clusters to produce the final miRNA regulatory modules. We apply this present method to ovarian cancer datasets and make a comparison with the other two existing prominent approaches. The results indicate that the modules identified by CoModule perform better than the other two methods ranging from the topological aspects to the biological function. Survival analysis detects a number of prognostic modules with statistical significance, which can help reveal the potential diagnostic for ovarian cancer.

A Computational Systems Biology Approach for Identifying Candidate Drugs for Repositioning for Cardiovascular Disease

We report an in silico method to screen for receptors or pathways that could be targeted to elicit beneficial transcriptional changes in a cellular model of a disease of interest. In our method, we integrate: (1) a dataset of transcriptome responses of a cell line to a panel of drugs; (2) two sets of genes for the disease; and (3) mappings between drugs and the receptors or pathways that they target. We carried out a gene set enrichment analysis (GSEA) test for each of the two gene sets against a list of genes ordered by fold-change in response to a drug in a relevant cell line (HL60), with the overall score for a drug being the difference of the two enrichment scores. Next, we applied GSEA for drug targets based on drugs that have been ranked by their differential enrichment scores. The method ranks drugs by the degree of anti-correlation of their gene-level transcriptional effects on the cell line with the genes in the disease gene sets. We applied the method to data from (1) CMap 2.0; (2) gene sets from two transcriptome profiling studies of atherosclerosis; and (3) a combined dataset of drug/target information. Our analysis recapitulated known targets related to CVD (e.g., PPARγ; HMG-CoA reductase, HDACs) and novel targets (e.g., amine oxidase A, δ-opioid receptor). We conclude that combining disease-associated gene sets, drug-transcriptome-responses datasets and drug-target annotations can potentially be useful as a screening tool for diseases that lack an accepted cellular model for in vitro screening.

ZBTB7/miR-137 Autoregulatory Circuit Promotes the Progression of Renal Carcinoma

Renal carcinoma greatly threatens human health, but the involved molecular mechanisms are far from complete understanding. As a master oncogene driving the initiation of many other cancers, ZBTB7 has not been established to be associated with renal cancer. Our data revealed that ZBTB7 is highly expressed in renal carcinoma specimens and cell lines, compared with normal cells. The silencing of ZBTB7 suppressed the proliferation and invasion of renal cancer cells. ZBTB7 overexpression rendered normal cells with higher proliferation rates and invasiveness. An animal study further confirmed the role of ZBTB7 in the growth of renal carcinoma. Moreover, miR-137 was identified to negatively regulate the expression of ZBTB7, and its abundance is inversely correlated with that of ZBTB7 in renal carcinoma specimens and cell lines. ZBTB7 overexpression may be induced by miR-137 downregulation. Interestingly, ZBTB7 can also suppress miR-137 expression by binding to its recognition site within the miR-137 promoter region. Taken together, we identified an autoregulatory loop consisting of ZBTB7 and miR-137 in gastric cancers, and targeting this pathway may be an effective strategy for renal carcinoma cancer therapy.

MiR-93-5p Promotes Cell Proliferation through Down-Regulating PPARGC1A in Hepatocellular Carcinoma Cells by Bioinformatics Analysis and Experimental Verification

Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PPARGC1A, formerly known as PGC-1a) is a transcriptional coactivator and metabolic regulator. Previous studies are mainly focused on the association between PPARGC1A and hepatoma. However, the regulatory mechanism remains unknown. A microRNA associated with cancer (oncomiR), miR-93-5p, has recently been found to play an essential role in tumorigenesis and progression of various carcinomas, including liver cancer. Therefore, this paper aims to explore the regulatory mechanism underlying these two proteins in hepatoma cells. Firstly, an integrative analysis was performed with miRNA–mRNA modules on microarray and The Cancer Genome Atlas (TCGA) data and obtained the core regulatory network and miR-93-5p/PPARGC1A pair. Then, a series of experiments were conducted in hepatoma cells with the results including miR-93-5p upregulated and promoted cell proliferation. Thirdly, the inverse correlation between miR-93-5p and PPARGC1A expression was validated. Finally, we inferred that miR-93-5p plays an essential role in inhibiting PPARGC1A expression by directly targeting the 3′-untranslated region (UTR) of its mRNA. In conclusion, these results suggested that miR-93-5p overexpression contributes to hepatoma development by inhibiting PPARGC1A. It is anticipated to be a promising therapeutic strategy for patients with liver cancer in the future.

MicroRNA-493-5p promotes apoptosis and suppresses proliferation and invasion in liver cancer cells by targeting VAMP2

The aim of the present study was to explore the role of miR‑493-5p in liver cancer tissues and cell lines, and its effect on cell behavioral characteristics. The expression of miR-493-5p was detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in liver cancer tissues and cell lines (hepatic cell line HL-7702 and the liver cancer cell lines HCCC-9810, HuH-7 and HepG2). In addition, the mechanism by which miR-493-5p mediates its effects was analyzed via the transfection of miR-493-5p mimic and negative control miRNA into HepG2 cells. The viability, proliferation, apoptosis and invasion of the cells were analyzed using MTT assay, flow cytometry and Transwell chamber experiments. Furthermore, the effect of miR-493-5p on the expression of vesicle associated membrane protein 2 (VAMP2) was assayed using a dual-luciferase reporter system, and VAMP2 protein levels were determined by western blot analysis. In addition, following the cotransfection of HepG2 cells with pcDNA3.1‑VAMP2 plasmid and miR‑493-5p mimic, the role of miR-493-5p as a regulator of VAMP2 was evaluated using MTT assay, flow cytometry and Transwell chamber experiments. RT-qPCR analysis indicated that the expression of miR-493-5p in liver cancer tissues and cell lines was decreased significantly compared with that in adjacent normal liver tissues and normal liver cell lines, respectively. Compared with the control group, the cells transfected with miR-493-5p mimic (the miR-493-5p overexpression group) exhibited reduced cell viability, a reduced percentage of cells in the S phase and an increased percentage of apoptotic cells. In addition, fewer cells passed through the Transwell membrane in the miR-493-5p overexpression group compared with the control group. In the dual-luciferase reporter assay, luciferase activity in the miR‑493-5p overexpression group was attenuated compared with that in the control group. In addition, western blot analysis indicated that the VAMP2 protein levels in the miR‑493-5p overexpression group were lower than those in the control group. Furthermore, in cells overexpressing miR-493-5p and VAMP2 simultaneously, the biological behavior of the cells, including cell viability, cell cycle and cell invasiveness, was significantly rescued compared with that of the control group transfected with miR‑493-5p alone. In conclusion, miR-493-5p is indicated to be a tumor suppressor gene, and is downregulated in human liver cancer. miR-493-5p overexpression promotes cell apoptosis and inhibits the proliferation and migration of liver cancer cells by negatively regulating the expression of VAMP. These observations suggest the potential of treating liver cancer by the overexpression of microRNA-493-5p.

Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources

Since the discovery of the regulatory function of microRNA (miRNA), increased attention has focused on identifying the relationship between miRNA and disease. It has been suggested that computational method are an efficient way to identify potential disease-related miRNAs for further confirmation using biological experiments. In this paper, we first highlighted three limitations commonly associated with previous computational methods. To resolve these limitations, we established disease similarity subnetwork and miRNA similarity subnetwork by integrating multiple data sources, where the disease similarity is composed of disease semantic similarity and disease functional similarity, and the miRNA similarity is calculated using the miRNA-target gene and miRNA-lncRNA (long non-coding RNA) associations. Then, a heterogeneous network was constructed by connecting the disease similarity subnetwork and the miRNA similarity subnetwork using the known miRNA-disease associations. We extended random walk with restart to predict miRNA-disease associations in the heterogeneous network. The leave-one-out cross-validation achieved an average area under the curve (AUC) of 0:8049 across 341 diseases and 476 miRNAs. For five-fold cross-validation, our method achieved an AUC from 0:7970 to 0:9249 for 15 human diseases. Case studies further demonstrated the feasibility of our method to discover potential miRNA-disease associations. An online service for prediction is freely available at http://ifmda.aliapp.com.

An ATP-Responsive Codelivery System of Doxorubicin and MiR-34a To Synergistically Inhibit Cell Proliferation and Migration

Establishing stimulus-responsive nanosystems for the codelivery of anticancer drug and oligonucleotide is a promising strategy in cancer treatment owing to the combination of chemotherapy and gene therapy in a synergistic manner. Herein, an ATP aptamer and its cDNA sequence were first hybridized to produce the duplex, into which chemotherapeutic agent doxorubicin (DOX) interacted through the GC-rich motif of duplex, and PEI25K was then employed as a carrier to condense the DOX-loading duplex and miR-34a to construct the ternary nanocomplex PEI/DOX-Duplex/miR-34a. The nanocomplex exhibited a favorable drug release profile through the response to high concentration of ATP in the cytosol. The ATP-responsive delivery system was demonstrated to possess higher antiproliferative effect (cell viability of <40%) than the single cargo delivery, which could be attributed to the synergistic induction of cell apoptosis and cell cycle arrest from DOX and miR-34a. Furthermore, wound healing and Transwell assay elucidated the higher antimigration effect of ternary nanocomplex than DOX-Duplex or miR-34a delivery. Overall, the combinatorial delivery of DOX and miR-34a through an ATP-responsive manner could trigger the rapid release of cargoes in the cytosol and enhance the inhibition of cell proliferation and migration through the synergistic manner of these two components.

MiR-613 suppresses retinoblastoma cell proliferation, invasion, and tumor formation by targeting E2F5

Retinoblastoma is a common intraocular malignancy that occurs during childhood. MicroRNAs play critical roles in the regulation of retinoblastoma initiation and progression, and aberrant expression of miR-613 had been reported in various types of cancer. However, the role and mechanism of its function in retinoblastoma are still unclear. In this study, we found that miR-613 was downregulated in retinoblastoma tissues and cell lines. Overexpression of miR-613 suppressed retinoblastoma cell proliferation, migration, and invasion and induced cell cycle arrest in vitro. Additionally, overexpressed miR-613 also inhibited tumor formation of retinoblastoma cells in vivo. We further identified E2F5 as a direct target of miR-613. Reintroduction of E2F5 without 3'-untranslated region reversed the inhibitory effects of miR-613 on cell proliferation and invasion. Our data collectively indicate that miR-613 functions as a tumor suppressor in retinoblastoma through downregulating E2F5, supporting the targeting of the novel miR-613/E2F5 axis as a potentially effective therapeutic approach for retinoblastoma.

Comprehensive Analysis of MILE Gene Expression Data Set Advances Discovery of Leukaemia Type and Subtype Biomarkers

Large collections of data in studies on cancer such as leukaemia provoke the necessity of applying tailored analysis algorithms to ensure supreme information extraction. In this work, a custom-fit pipeline is demonstrated for thorough investigation of the voluminous MILE gene expression data set. Three analyses are accomplished, each for gaining a deeper understanding of the processes underlying leukaemia types and subtypes. First, the main disease groups are tested for differential expression against the healthy control as in a standard case-control study. Here, the basic knowledge on molecular mechanisms is confirmed quantitatively and by literature references. Second, pairwise comparison testing is performed for juxtaposing the main leukaemia types among each other. In this case by means of the Dice coefficient similarity measure the general relations are pointed out. Moreover, lists of candidate main leukaemia group biomarkers are proposed. Finally, with this approach being successful, the third analysis provides insight into all of the studied subtypes, followed by the emergence of four leukaemia subtype biomarkers. In addition, the class enhanced DEG signature obtained on the basis of novel pipeline processing leads to significantly better classification power of multi-class data classifiers. The developed methodology consisting of batch effect adjustment, adaptive noise and feature filtration coupled with adequate statistical testing and biomarker definition proves to be an effective approach towards knowledge discovery in high-throughput molecular biology experiments.

miRNA-218-loaded carboxymethyl chitosan - Tocopherol nanoparticle to suppress the proliferation of gastrointestinal stromal tumor growth

Gastrointestinal stromal tumors (GIST) are one of the most common forms of mesenchymal cancers of the gastrointestinal tract. Although chemotherapeutic drugs inhibited the proliferation of GIST, however, sizable proportion of people developed resistance and therefore difficult to treat. In the present study, O-carboxymethyl chitosan (OCMC)-tocopherol polymer conjugate was synthesized and formulated into stable polymeric nanoparticles. The main aim of present study was to increase the therapeutic efficacy of miR-218 in GIST. The mean size of nanoparticles was ~110nm with a spherical shape. The miR-218 NP has been shown inhibit the cell proliferation and exhibited a superior cell apoptosis. The miR-218 NP inhibited the cell invasion and promoted the apoptosis of GIST cancer cells. In the present study, we have successfully showed that KIT1 is the target gene of miR-218 as shown by the luciferase reporter assay. These findings collectively suggest the miR-218 loaded nanoparticle by virtue of effective transfection could act as a tumor suppressor miRNA in the treatment of GIST.

Dissecting the regulation rules of cancer-related miRNAs based on network analysis

miRNAs (microRNAs) are a set of endogenous and small non-coding RNAs which specifically induce degradation of target mRNAs or inhibit protein translation to control gene expression. Obviously, aberrant miRNA expression in human cells will lead to a serious of changes in protein-protein interaction network (PPIN), thus to activate or inactivate some pathways related to various diseases, especially carcinogenesis. In this study, we systematically constructed the miRNA-regulated co-expressed protein-protein interaction network (CePPIN) for 17 cancers firstly. We investigated the topological parameters and functional annotation for the proteins in CePPIN, especially for those miRNA targets. We found that targets regulated by more miRNAs tend to play a more important role in the forming process of cancers. We further elucidated the miRNA regulation rules in PPIN from a more systematical perspective. By GO and KEGG pathway analysis, miRNA targets are involved in various cellular processes mostly related to cell cycle, such as cell proliferation, growth, differentiation, etc. Through the Pfam classification, we found that miRNAs belonging to the same family tend to have targets from the same family which displays the synergistic function of these miRNAs. Finally, the case study on miR-519d and miR-21-regulated sub-network was performed to support our findings.

BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species

MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs' essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.

A Dynamic 3D Graphical Representation for RNA Structure Analysis and Its Application in Non-Coding RNA Classification

With the development of new technologies in transcriptome and epigenetics, RNAs have been identified to play more and more important roles in life processes. Consequently, various methods have been proposed to assess the biological functions of RNAs and thus classify them functionally, among which comparative study of RNA structures is perhaps the most important one. To measure the structural similarity of RNAs and classify them, we propose a novel three dimensional (3D) graphical representation of RNA secondary structure, in which an RNA secondary structure is first transformed into a characteristic sequence based on chemical property of nucleic acids; a dynamic 3D graph is then constructed for the characteristic sequence; and lastly a numerical characterization of the 3D graph is used to represent the RNA secondary structure. We tested our algorithm on three datasets: (1) Dataset I consisting of nine RNA secondary structures of viruses, (2) Dataset II consisting of complex RNA secondary structures including pseudo-knots, and (3) Dataset III consisting of 18 non-coding RNA families. We also compare our method with other nine existing methods using Dataset II and III. The results demonstrate that our method is better than other methods in similarity measurement and classification of RNA secondary structures.

Construction and Identification of the RNAi Recombinant Lentiviral Vector Targeting Human DEPDC7 Gene

Human DEP domain containing 7 (DEPDC7) gene was originally found expressing high in liver tissue and low in most other tissues, but its function was largely unknown. In this study, we construct an RNA interference (RNAi) recombinant lentiviral vector particle targeting DEPDC7 in order to knockdown its gene expression in human hepatocellular carcinoma cell line HepG2. We screened three RNAi sequences targeting DEPDC7 and a scramble sequence by the aid of short hairpin RNAs (shRNA) design tools. Then, these sequences were separately cloned into the pLV-H1-EF1α-puro vector to construct four lentiviral vectors (pshRNA-DEPDC7-NC, pshRNA-DEPDC7-RNAi1, pshRNA-DEPDC7-RNAi2 and pshRNA-DEPDC7-RNAi3). All of the recombinant plasmids were identified and confirmed by double digestion and DNA sequencing. After infecting HepG2 cells, the DEPDC7 mRNA and protein expression levels were examined by real-time PCR and western blot, respectively, and the gene expression was significantly down-regulated at both levels (P < 0.01). Cell motility and invasiveness were detected by Matrigel migration and invasion assay, and the results revealed that migration and invasion of HepG2 cells were significantly increased (P < 0.05). Our study showed successful construction of three lentiviral RNAi vectors targeting DEPDC7 gene and shRNA-mediated knockdown of DEPDC7 enable promotion of cell migration and invasion.

WITHDRAWN: Identification of microRNA precursor based on gapped n-tuple structure status composition kernel

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iMiRNA-SSF: Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions

The identification of microRNA precursors (pre-miRNAs) helps in understanding regulator in biological processes. The performance of computational predictors depends on their training sets, in which the negative sets play an important role. In this regard, we investigated the influence of benchmark datasets on the predictive performance of computational predictors in the field of miRNA identification, and found that the negative samples have significant impact on the predictive results of various methods. We constructed a new benchmark set with different data distributions of negative samples. Trained with this high quality benchmark dataset, a new computational predictor called iMiRNA-SSF was proposed, which employed various features extracted from RNA sequences. Experimental results showed that iMiRNA-SSF outperforms three state-of-the-art computational methods. For practical applications, a web-server of iMiRNA-SSF was established at the website http://bioinformatics.hitsz.edu.cn/iMiRNA-SSF/.

Mining for novel tumor suppressor genes using a shortest path approach

Cancer, being among the most serious diseases, causes many deaths every year. Many investigators have devoted themselves to designing effective treatments for this disease. Cancer always involves abnormal cell growth with the potential to invade or spread to other parts of the body. In contrast, tumor suppressor genes (TSGs) act as guardians to prevent a disordered cell cycle and genomic instability in normal cells. Studies on TSGs can assist in the design of effective treatments against cancer. In this study, we propose a computational method to discover potential TSGs. Based on the known TSGs, a number of candidate genes were selected by applying the shortest path approach in a weighted graph that was constructed using protein-protein interaction network. The analysis of selected genes shows that some of them are new TSGs recently reported in the literature, while others may be novel TSGs.

miR-409-3p suppresses breast cancer cell growth and invasion by targeting Akt1

Altered levels and functions of microRNAs (miRNAs) are correlated with carcinogenesis. While miR-409-3p has been shown to play important roles in several cancer types, its function in the context of breast cancer (BC) remains unknown. In this study, miR-409-3p was significantly downregulated in BC tissues and cell lines, compared with the corresponding control counterparts. Overexpression of miR-409-3p inhibited BC cell proliferation, migration and invasion in vitro and suppressed tumor growth in vivo. Notably, miR-409-3p induced downregulation of Akt1 protein through binding to its 3' untranslated region (UTR). Conversely, restoring Akt1 expression rescued the suppressive effects of miR-409-3p. Our data collectively indicate that miR-409-3p functions as a tumor suppressor in BC through downregulating Akt1, supporting the targeting of the novel miR-409-3p/Akt1 axis as a potentially effective therapeutic approach for BC.

Genome-wide identification and characterization of microRNA genes and their targets in large yellow croaker (Larimichthys crocea)

MicroRNAs (miRNAs or miRs) are a class of non-coding RNAs of 20-25 nucleotides (nt) in length, which regulates the expression of gene in eukaryotic organism. Studies has been confirmed that miRNA plays an important role in various biological and metabolic processes in both animals and plants. Predicting new miRNAs by computer based homology search analysis is an effective way to discover novel miRNAs. Though a large number of miRNAs have been reported in many fish species, reports of miRNAs in large yellow croaker (L. crocea) are limited especially via the computational-based approaches. In this paper, a method of comparative genomic approach by computational genomic homology based on the conservation of miRNA sequences and the stem-loop hairpin secondary structures of miRNAs was adopted. A total of 199 potential miRNAs were predicted representing 81 families. 12 of them were chose to be validated by real time RT-PCR, apart from miR-7132b-5p which was not detected. Results indicated that the prediction method that we used to identify the miRNAs was effective. Furthermore, 948 potential target genes were predicted. Gene ontology (GO) analysis revealed that 175, 287, and 486 target genes were involved in cellular components, biological processes and molecular functions, respectively. Overall, our findings provide a first computational identification and characterization of L. crocea miRNAs and their potential targets in functional analysis, and will be useful in laying the foundation for further characterization of their role in the regulation of diversity of physiological processes.

A functional module-based exploration between inflammation and cancer in esophagus

Inflammation contributing to the underlying progression of diverse human cancers has been generally appreciated, however, explorations into the molecular links between inflammation and cancer in esophagus are still at its early stage. In our study, we presented a functional module-based approach, in combination with multiple data resource (gene expression, protein-protein interactions (PPI), transcriptional and post-transcriptional regulations) to decipher the underlying links. Via mapping differentially expressed disease genes, functional disease modules were identified. As indicated, those common genes and interactions tended to play important roles in linking inflammation and cancer. Based on crosstalk analysis, we demonstrated that, although most disease genes were not shared by both kinds of modules, they might act through participating in the same or similar functions to complete the molecular links. Additionally, we applied pivot analysis to extract significant regulators for per significant crosstalk module pair. As shown, pivot regulators might manipulate vital parts of the module subnetworks, and then work together to bridge inflammation and cancer in esophagus. Collectively, based on our functional module analysis, we demonstrated that shared genes or interactions, significant crosstalk modules, and those significant pivot regulators were served as different functional parts underlying the molecular links between inflammation and cancer in esophagus.

DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation

DNA-binding proteins play an important role in most cellular processes. Therefore, it is necessary to develop an efficient predictor for identifying DNA-binding proteins only based on the sequence information of proteins. The bottleneck for constructing a useful predictor is to find suitable features capturing the characteristics of DNA binding proteins. We applied PseAAC to DNA binding protein identification, and PseAAC was further improved by incorporating the evolutionary information by using profile-based protein representation. Finally, Combined with Support Vector Machines (SVMs), a predictor called iDNAPro-PseAAC was proposed. Experimental results on an updated benchmark dataset showed that iDNAPro-PseAAC outperformed some state-of-the-art approaches, and it can achieve stable performance on an independent dataset. By using an ensemble learning approach to incorporate more negative samples (non-DNA binding proteins) in the training process, the performance of iDNAPro-PseAAC was further improved. The web server of iDNAPro-PseAAC is available at http://bioinformatics.hitsz.edu.cn/iDNAPro-PseAAC/.

Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR

The high-throughput analysis of microRNAs (miRNAs) circulating within the blood of healthy and diseased individuals is an active area of biomarker research. Whereas quantitative real-time reverse transcription polymerase chain reaction (qPCR)-based methods are widely used, it is yet unresolved how the data should be normalized. Here, we show that a combination of different algorithms results in the identification of candidate reference miRNAs that can be exploited as normalizers, in both discovery and validation phases. Using the methodology considered here, we identify normalizers that are able to reduce nonbiological variation in the data and we present several case studies, to illustrate the relevance in the context of physiological or pathological scenarios. In conclusion, the discovery of stable reference miRNAs from high-throughput studies allows appropriate normalization of focused qPCR assays.

Cooperative target mRNA destabilization and translation inhibition by miR-58 microRNA family in C. elegans

In animals, microRNAs frequently form families with related sequences. The functional relevance of miRNA families and the relative contribution of family members to target repression have remained, however, largely unexplored. Here, we used the Caenorhabditis elegans miR-58 miRNA family, composed primarily of the four highly abundant members miR-58.1, miR-80, miR-81, and miR-82, as a model to investigate the redundancy of miRNA family members and their impact on target expression in an in vivo setting. We found that miR-58 family members repress largely overlapping sets of targets in a predominantly additive fashion. Progressive deletions of miR-58 family members lead to cumulative up-regulation of target protein and RNA levels. Phenotypic defects could only be observed in the family quadruple mutant, which also showed the strongest change in target protein levels. Interestingly, although the seed sequences of miR-80 and miR-58.1 differ in a single nucleotide, predicted canonical miR-80 targets were efficiently up-regulated in the mir-58.1 single mutant, indicating functional redundancy of distinct members of this miRNA family. At the aggregate level, target binding leads mainly to mRNA degradation, although we also observed some degree of translational inhibition, particularly in the single miR-58 family mutants. These results provide a framework for understanding how miRNA family members interact to regulate target mRNAs.

Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

microRNA-218 promotes gemcitabine sensitivity in human pancreatic cancer cells by regulating HMGB1 expression

OBJECTIVE

The purpose of this study was to examine the effect of gemcitabine (GEM) on microRNA-218 (miR-218) expression in human pancreatic cancer cells.

METHODS

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to examine the differences in miR-218 expression between the GEM-sensitive BxPC-3 pancreatic cancer cells and GEM-resistant PANC-1 cells. The effect of GEM on the expression of miR-218 in PANC-1 cells was also investigated. PANC-1 cells were transfected either with HMGB1 siRNA to knock down the expression of HMGB1 or with the recombinant HMGB1 expression vector (pcDNA3.1-HMGB1) to overexpress HMGB1. The effect of ectopic expression of HMGB1 on the apoptosis of miR-218-transfected and GEM-treated PANC-1 cells was examined by flow cytometric analysis.

RESULTS

The miR-218 expression level was lower in GEM-resistant PANC-1 cells compared to GEM-sensitive BxPC-3 cells (P<0.05). The percentage of apoptotic PANC-1 cells was significantly increased in the miR-218 mimic + GEM group compared to the mimic ctrl + GEM group and the normal control group (P<0.01). The HMGB1 expression level was markedly decreased in PANC-1 cells transfected with HMGB1 siRNA but was significantly increased in PANC-1 cells transfected with the recombinant HMGB1 expression vector, pcDNA3.1-HMGB1 (P<0.01). The proportion of apoptotic PANC-1 cells was significantly lower in the miR-218 mimic + GEM + pcDNA3.1-HMGB1 group compared to the miR-218 mimic + GEM + HMGB1 siRNA group (P<0.01).

CONCLUSIONS

The expression level of miR-218 was downregulated in the GEM-resistant cell line. miR-218 promoted the sensitivity of PANC-1 cells to GEM, which was achieved mainly through regulating the expression of HMGB1 in PANC-1 cells.

microRNA-218 suppresses the proliferation, invasion and promotes apoptosis of pancreatic cancer cells by targeting HMGB1

OBJECTIVE

To detect the expression profiles of microRNA-218 (miR-218) in human pancreatic cancer tissue (PCT) and cells and their effects on the biological features of human pancreatic cancer cell line PANC-1 and observe the effect of miR-218 on the expression of the target gene high mobility group box 1 (HMGB1), with an attempt to provide new treatment methods and strategies for pancreatic cancer.

METHODS

The expressions of miR-218 in PCT and normal pancreas tissue as well as in various pancreatic cancer cell lines including AsPC-1, BxPC-3, and PANC-1 were determined with quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR). The change of miR-218 expression in PANC-1 cells was detected using qRT-PCT after the transfection of miR-218 mimic for 48 h. Cell Counting Kit-8 (CCK-8) was applied for detecting the effect of miR-218 on the activity of PANC-1 cells. The effects of miR-218 on the proliferation and apoptosis of PANC-1 cells were analyzed using the flow cytometry. The effect of miR-218 on the migration of PANC-1 cells was detected using the Trans-well migration assay. The HMGB1 was found to be a target gene of miR-218 by luciferase reporter assay, and the effect of miR-218 on the expression of HMGB1 protein in cells were determined using Western blotting.

RESULTS

As shown by qRT-PCR, the expressions of miR-218 in PCT and in pancreatic cancer cell line significantly decreased when compared with the normal pancreatic tissue (NPT) (P<0.01). Compared with the control group, the miR-218 expression significantly increased in the PANC-1 group after the transfection of miR-218 mimic for 48 h (P<0.01). Growth curve showed that the cell viability significantly dropped after the overexpression of miR-218 in the PANC-1 cells for two days (P<0.05). Flow cytometry showed that the S-phase fraction significantly dropped after the overexpression of miR-218 (P<0.01) and the percentage of apoptotic cells significantly increased (P<0.01). As shown by the Trans-well migration assay, the enhanced miR-218 expression was associated with a significantly lower number of cells that passed through a Transwell chamber (P<0.01). Luciferase reporter assay showed that, compared with the control group, the relative luciferase activity significantly decreased in the miR-218 mimic group (P<0.01). As shown by the Western blotting, compared with the control group, the HMGB1 protein expression significantly decreased in the PANC-1 group after the transfection of miR-218 mimic for 48 h (P<0.01).

CONCLUSIONS

The miR-218 expression decreases in human PCT and cell lines. miR-218 can negatively regulate the HMGB1 protein expression and inhibit the proliferation and invasion of pancreatic cancer cells. A treatment strategy by enhancing the miR-218 expression may benefit the patients with pancreatic cancer.

Similarity computation strategies in the microRNA-disease network: a survey

Various microRNAs have been demonstrated to play roles in a number of human diseases. Several microRNA-disease network reconstruction methods have been used to describe the association from a systems biology perspective. The key problem for the network is the similarity computation model. In this article, we reviewed the main similarity computation methods and discussed these methods and future works. This survey may prompt and guide systems biology and bioinformatics researchers to build more perfect microRNA-disease associations and may make the network relationship clear for medical researchers.

Emerging role of microRNAs in major depressive disorder: diagnosis and therapeutic implications

Major depressive disorder (MDD) is a major public health concern. Despite tremendous advances, the pathogenic mechanisms associated with MDD are still unclear. Moreover, a significant number of MDD subjects do not respond to the currently available medication. MicroRNAs (miRNAs) are a class of small noncoding RNAs that control gene expression by modulating translation, messenger RNA (mRNA) degradation, or stability of mRNA targets. The role of miRNAs in disease pathophysiology is emerging rapidly. Recent studies demonstrating the involvement of miRNAs in several aspects of neural plasticity, neurogenesis, and stress response, and more direct studies in human postmortem brain provide strong evidence that miRNAs can not only play a critical role in MDD pathogenesis, but can also open up new avenues for the development of therapeutic targets. Circulating miRNAs are now being considered as possible biomarkers in disease pathogenesis and in monitoring therapeutic responses because of the presence and/or release of miRNAs in blood cells as well as in other peripheral tissues. In this review, these aspects are discussed in a comprehensive and critical manner.

microRNA-218 increase the sensitivity of gastrointestinal stromal tumor to imatinib through PI3K/AKT pathway

To detect the expressions of microRNA-218 (miR-218) in an imatinib mesylate-sensitive human gastrointestinal stromal tumor (GIST) cells (GIST882) and an imatinib mesylate-resistant cell line (GIST430) and explore the roles of miR-218 and GIST cells in the sensitivity of gastrointestinal stromal tumor to imatinib mesylate and its potential signaling pathways, with an attempt to provide new insights for the treatment of GIST. The GIST cell lines (GIST882 and GIST430) were cultured in vitro. Quantitative real-time PCR (qRT-PCR) was utilized to determine the expression profiles of miR-218 in both GIST cell lines. Forty-eight hours after the transfection of the miR-218 mimic or miR-218 inhibitor in the GIST cells, the changes in the expression of miR-218 in the GIST cells were detected with qRT-PCR. The effects of the ectopic expression of miR-218 in GIST882 or GIST430 cells on the imatinib mesylate-induced GIST cell viability were determined by MTT. The effects of miR-218 ectopic expression on the apoptosis of imatinib mesylate-induce GIST cells were determined by Annexin V/PI double staining method and flow cytometry. The effects of miR-218 ectopic expression on the AKT and phospho-AKT (p-AKT) expressions of imatinib mesylate-induce GIST cells were determined by Western blot and flow cytometry with the PI3K pathway inhibitor Wortmannin. As shown by qRT-PCR, compared with that in the imatinib mesylate-sensitive GIST882, the expression of miR-218 in imatinib mesylate-resistant GIST430 was significantly decreased (P < 0.01). Compared with the control group, the expression of miR-218 significantly increased in the GIST882 48 h after the transfection of miR-218 mimic (P < 0.01) and significantly declined after the transfection of miR-218 inhibitor (P < 0.01). As shown by MTT and flow cytometry, after the expression of miR-218 was inhibited in GIST882 under the effect of imatinib mesylate, the cell viability significantly increased (P < 0.01) and the number of apoptotic cells significantly decreased (P < 0.05); on the contrary, the over-expression of miR-218 in GIST430 under the effect of imatinib mesylate resulted in the significantly decreased cell viability (P < 0.01) and the significantly increased number of apoptotic cells (P < 0.05). Western blot and flow cytometry showed that, in comparison to the control group, Wortmannin could significantly inhibit the expression of p-AKT in GIST430 cells (P < 0.01) and stimulated apoptosis (P < 0.01). The expression of miR-218 is down-regulated in an imatinib mesylate-resistant GIST cell line (GIST430), whereas miR-218 over-expression can improve the sensitivity of GIST cells to imatinib mesylate, with PI3K/AKT signaling pathway possibly involved in the mechanism.

The 'omics' of adrenocortical tumours for personalized medicine

Pan-genomic analyses of genetic and epigenetic alterations and gene expression profiles are providing important new insights into the pathogenesis and molecular classification of cancers. The technologies and methods used for these studies are rapidly diversifying and improving. The use of such methodologies for the analysis of adrenocortical tumours has revealed clear transcriptomic (mRNA and microRNA expression profiles), epigenomic (DNA methylation profiles) and genomic (DNA mutations and chromosomal alterations) differences between benign and malignant tumours. Interestingly, genomic studies of adrenal cancers have also identified subtypes of malignant tumours, which demonstrate distinct patterns of molecular alterations and are associated with different clinical outcomes. These discoveries have created the opportunity for classifying adrenocortical tumours on the basis of molecular analyses. Following these genomic studies, efforts to develop new molecular tools that improve diagnosis and prognostication of patients with adrenocortical tumours have also been made. This Review describes the progress that has been made towards classification of adrenocortical tumours to date based on key genomic approaches. In addition, the potential for the development and use of various molecular tools to personalize the management of patients with adrenocortical tumours is discussed.

Improved and Promising Identification of Human MicroRNAs by Incorporating a High-Quality Negative Set

MicroRNA (miRNA) plays an important role as a regulator in biological processes. Identification of (pre-) miRNAs helps in understanding regulatory processes. Machine learning methods have been designed for pre-miRNA identification. However, most of them cannot provide reliable predictive performances on independent testing data sets. We assumed this is because the training sets, especially the negative training sets, are not sufficiently representative. To generate a representative negative set, we proposed a novel negative sample selection technique, and successfully collected negative samples with improved quality. Two recent classifiers rebuilt with the proposed negative set achieved an improvement of ~6 percent in their predictive performance, which confirmed this assumption. Based on the proposed negative set, we constructed a training set, and developed an online system called miRNApre specifically for human pre-miRNA identification. We showed that miRNApre achieved accuracies on updated human and non-human data sets that were 34.3 and 7.6 percent higher than those achieved by current methods. The results suggest that miRNApre is an effective tool for pre-miRNA identification. Additionally, by integrating miRNApre, we developed a miRNA mining tool, mirnaDetect, which can be applied to find potential miRNAs in genome-scale data. MirnaDetect achieved a comparable mining performance on human chromosome 19 data as other existing methods.

MicroRNA-218 inhibits gastrointestinal stromal tumor cell and invasion by targeting KIT

The objectives of this study were to detect the expressions of microRNA-218 (miR-218) in human gastrointestinal stromal tumor (GIST) tissues and cells and explore its effects on the biological features of GIST-T1 cells and the expression of its target gene KIT, so as to provide new insights for GIST treatment. Using quantitative real-time polymerase chain reaction (qRT-PCR), we detected the expressions of miR-218 in the tissues and adjacent tissues of GIST and in the GIST cell lines including GIST882, GIST430, GIST48, and GIST-T1. Forty-eight hours after the miR-218 mimic was transfected into the GIST-T1 cells, the expression of miR-218 in the GIST-T1 cells was detected by qRT-PCR. The effect of miR-218 on the GIST-T1 cell viability was detected using MTT. The effect of miR-218 on the proliferation and apoptosis of GIST-T1 cell was analyzed using flow cytometry. Transwell invasion chamber was applied to detect the effect of miR-218 on the invasion of GIST-T1 cells. KIT was identified to be a target gene of miR-218 by the luciferase reporter enzyme system, and the effect of miR-218 on the expression of KIT protein in cells was determined using Western blotting. As shown by qRT-PCR, compared with that in the GIST adjacent tissue, the expressions of miR-218 in the tumor tissue and GIST cell lines were significantly decreased (P < 0.0001). Compared with the control group, the expression of miR-218 increased significantly in GIST-T1 cells transfected with miR-218 mimic for 48 h (P < 0.01). MTT showed that the cell viability decreased significantly after the overexpression of miR-218 in the GIST-T1 cells (P < 0.01). Flow cytometry showed that the cell proliferation index significantly declined after the overexpression of miR-218 (P < 0.01); meanwhile, the apoptosis of cells also significantly increased (P < 0.01). Detection using the Transwell invasion chamber showed that the number of cells passing through the Transwell chamber significantly dropped after the enhanced expression of miR-218 (P < 0.01). Luciferase reporter gene assay showed that, compared with the control group, the relative luciferase activity significantly declined in the miR-218 mimic transfection group (P < 0.01). Compared with the control group, the expression of KIT protein in the GIST-T1 cells transfected with miR-218 mimic for 48 h significantly decreased (P < 0.01). In conclusion, the expression of miR-218 decreases in human GIST tissue and cell lines. miR-218 can negatively regulate the expression of KIT protein and inhibit the proliferation and invasion of GIST cells. Treatment based on the enhanced expression of miR-218 may be a promising strategy for GIST.