BackgroundSerous epithelial ovarian cancer (SEOC) is a highly metastatic disease and its progression has been implicated with microRNAs. This study aimed to identify the differentially expressed microRNAs in Malaysian patients with SEOC and examine the microRNAs functional roles in SEOC cells.MethodsTwenty-two SEOC and twenty-two normal samples were subjected to miRNA expression profiling using the locked nucleic acid (LNA) quantitative real-time PCR (qPCR). The localization of miR-200c was determined via LNA in situ hybridization (ISH). Functional analysis of miR-200c and miR-31 on cell proliferation, migration and invasion and clonogenic cell survival were assessed in vitro. The putative target genes of the two miRNAs were predicted by miRWalk program and expression of the target genes in SEOC cell lines was validated.ResultsThe miRNA expression profiling revealed thirty-eight significantly dysregulated miRNAs in SEOC compared to normal ovarian tissues. Of these, eighteen were up-regulated whilst twenty miRNAs were down-regulated. We observed chromogenic miR-200c-ISH signal predominantly in the cytoplasmic compartment of both epithelial and inflammatory cancer cells. Re-expression of miR-200c significantly increased the cell proliferation and colony formation but reduced the migration and invasion of SEOC cells. In addition, miR-200c expression was inversely proportionate with the expression of deleted in liver cancer-1 (DLC-1) gene. Over-expression of miR-31 in SEOC cells resulted in decreased cell proliferation, clonogenic potential, cell migration and invasion. Meanwhile, miR-31 gain-of-function led to the down-regulation of AF4/FMR2 family member 1 (AFF1) gene.ConclusionsThese data suggested that miR-200c and miR-31 may play roles in the SEOC metastasis biology and could be considered as promising targets for therapeutic purposes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13048-015-0186-7) contains supplementary material, which is available to authorized users.

About 40% of lung cancer cases globally are diagnosed at the advanced stage. Lung cancer has a high mortality and overall survival in stage I disease is only 70%. This study was aimed at finding a candidate of transcription regulator that initiates the mechanism for metastasis by integrating computational and functional studies. The genes involved in lung cancer were retrieved using in silico software. 10 kb promoter sequences upstream were scanned for the master regulator. Transient transfection of shRNA NFIXs were conducted against A549 and NCI-H1299 cell lines. qRT-PCR and functional assays for cell proliferation, migration and invasion were carried out to validate the involvement of NFIX in metastasis. Genome-wide gene expression microarray using a HumanHT-12v4.0 Expression BeadChip Kit was performed to identify differentially expressed genes and construct a new regulatory network. The in silico analysis identified NFIX as a master regulator and is strongly associated with 17 genes involved in the migration and invasion pathways including IL6ST, TIMP1 and ITGB1. Silencing of NFIX showed reduced expression of IL6ST, TIMP1 and ITGB1 as well as the cellular proliferation, migration and invasion processes. The data was integrated with the in silico analyses to find the differentially expressed genes. Microarray analysis showed that 18 genes were expressed differentially in both cell lines after statistical analyses integration between t-test, LIMMA and ANOVA with Benjamini-Hochberg adjustment at p-value < 0.05. A transcriptional regulatory network was created using all 18 genes, the existing regulated genes including the new genes PTCH1, NFAT5 and GGCX that were found highly associated with NFIX, the master regulator of metastasis. This study suggests that NFIX is a promising target for therapeutic intervention that is expected to inhibit metastatic recurrence and improve survival rate.

BackgroundIdentifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression.ResultsThe proposed method relies on a β-weight function, which produces values between 0 and 1. The β-weight function with β = 0.2 is used as a measure of outlier detection. It assigns smaller weights (≥ 0) to outlying expressions and larger weights (≤ 1) to typical expressions. The distribution of the β-weights is used to calculate the cut-off point, which is compared to the observed β-weight of an expression to determine whether that gene expression is an outlier. This weight function plays a key role in unifying the robustness and efficiency of estimation in one-way ANOVA.ConclusionAnalyses of simulated gene expression profiles revealed that all eight methods (ANOVA, SAM, LIMMA, EBarrays, eLNN, KW, robust BetaEB and proposed) perform almost identically for m = 2 conditions in the absence of outliers. However, the robust BetaEB method and the proposed method exhibited considerably better performance than the other six methods in the presence of outliers. In this case, the BetaEB method exhibited slightly better performance than the proposed method for the small-sample cases, but the the proposed method exhibited much better performance than the BetaEB method for both the small- and large-sample cases in the presence of more than 50% outlying genes. The proposed method also exhibited better performance than the other methods for m > 2 conditions with multiple patterns of expression, where the BetaEB was not extended for this condition. Therefore, the proposed approach would be more suitable and reliable on average for the identification of DE genes between two or more conditions with multiple patterns of expression.

BackgroundMicroarray data enables the high-throughput survey of mRNA expression profiles at the genomic level; however, the data presents a challenging statistical problem because of the large number of transcripts with small sample sizes that are obtained. To reduce the dimensionality, various Bayesian or empirical Bayes hierarchical models have been developed. However, because of the complexity of the microarray data, no model can explain the data fully. It is generally difficult to scrutinize the irregular patterns of expression that are not expected by the usual statistical gene by gene models.ResultsAs an extension of empirical Bayes (EB) procedures, we have developed the β-empirical Bayes (β-EB) approach based on a β-likelihood measure which can be regarded as an ’evidence-based’ weighted (quasi-) likelihood inference. The weight of a transcript t is described as a power function of its likelihood, fβ(yt|θ). Genes with low likelihoods have unexpected expression patterns and low weights. By assigning low weights to outliers, the inference becomes robust. The value of β, which controls the balance between the robustness and efficiency, is selected by maximizing the predictive β0-likelihood by cross-validation. The proposed β-EB approach identified six significant (p<10−5) contaminated transcripts as differentially expressed (DE) in normal/tumor tissues from the head and neck of cancer patients. These six genes were all confirmed to be related to cancer; they were not identified as DE genes by the classical EB approach. When applied to the eQTL analysis of Arabidopsis thaliana, the proposed β-EB approach identified some potential master regulators that were missed by the EB approach.ConclusionsThe simulation data and real gene expression data showed that the proposed β-EB method was robust against outliers. The distribution of the weights was used to scrutinize the irregular patterns of expression and diagnose the model statistically. When β-weights outside the range of the predicted distribution were observed, a detailed inspection of the data was carried out. The β-weights described here can be applied to other likelihood-based statistical models for diagnosis, and may serve as a useful tool for transcriptome and proteome studies.

Bangladesh reported the first three laboratory-confirmed COVID-19 cases on March 8, 2020 in Dhaka and Narayanganj cities. As of April 8, 2020, 218 confirmed cases across the country, they have mostly detected from Dhaka (56.4%) and Narayanganj (21%) cities where the hotspots of an outbreak of COVID-19 disease. There were 6 cases in Dhaka district excluding metropolitan areas and rest of 43 (20%) cases in the 19 other regions. Local government-enforced completely shut down the hotspots areas on April 8 2020. However, peoples from hotspots travelled openly to the other districts. We aimed to understand the risk of open movement from hotspots. We studied 40 individuals who were infected with SARS-CoV-2 virus later at their destination. We developed a route map and density maps using Geographic Information System (GIS). Among the studied people, the average distance was 140.1 (75.1) kilometers (Km), and the range of distance was from 20.3 to 321.7 kilometers. Among them, 42.5% traveled <100 Km, 40.0% traveled between 100 and 200 Km and 17.5% traveled above 200 Km. Case numbers were increased 13.5 times more on April 20 than the cases as of April 8, 2020. Our analysis suggests that relaxed travel restriction could play an important role to spread COVID-19 transmission domestically. To reduce further spread of COVID-19, the government should closely monitor the public health intervention to stop the casual movement.