Oral squamous cell carcinoma (OSCC) is often diagnosed at a late stage and may be malignantly transformed from oral leukoplakia (OL). This study aimed to identify potential plasma microRNAs (miRNAs) for the early detection of oral cancer. Plasma from normal, OL, and OSCC patients were evaluated. Small RNA sequencing was used to screen the differently expressed miRNAs among the groups. Next, these miRNAs were validated with individual samples by quantitative real-time polymerase chain reaction (qRT-PCR) assays in the training phase (n = 72) and validation phase (n = 178). The possible physiological roles of the identified miRNAs were further investigated using bioinformatics analysis. Three miRNAs (miR-222-3p, miR-150-5p, and miR-423-5p) were identified as differentially expressed among groups; miR-222-3p and miR-423-5p negatively correlated with T stage, lymph node metastasis status, and clinical stage. A high diagnostic accuracy (Area under curve = 0.88) was demonstrated for discriminating OL from OSCC. Bioinformatics analysis reveals that miR-423-5p and miR-222-3p are significantly over-expressed in oral cancer tissues and involved in various cancer pathways. The three-plasma miRNA panel may be useful to monitor malignant progression from OL to OSCC and as potential biomarkers for early detection of oral cancer.

Lower respiratory tract sampling from endotracheal aspirate (EA) and bronchoalveolar lavage (BAL) are both common methods to identify pathogens in severe pneumonia. However, the difference between these two methods in microbiota profiles remains unclear. We compared the microbiota profiles of pairwise EA and BAL samples in ICU patients with respiratory failure due to severe pneumonia. We prospectively enrolled 50 ICU patients with new onset of pneumonia requiring mechanical ventilation. EA and BAL were performed on the first ICU day, and samples were analyzed for microbial community composition via 16S rRNA metagenomic sequencing. Pathogens were identified in culture medium from BAL samples in 21 (42%) out of 50 patients. No difference was observed in the antibiotic prescription pattern, ICU mortality, or hospital mortality between BAL-positive and BAL-negative patients. The microbiota profiles in the EA and BAL samples are similar with respect to diversity, microbial composition, and microbial community correlations. The antibiotic treatment regimen was rarely changed based on the BAL findings. The samples from BAL did not provide more information than EA in the microbiota profiles. We suggest that EA is more useful than BAL for microbiome identification in mechanically ventilated patients.

Aquatic animals have great economic and ecological importance. Among them, non-model organisms have been studied regarding eco-toxicity, stress biology, and environmental adaptation. Due to recent advances in next-generation sequencing techniques, large amounts of RNA-seq data for aquatic animals are publicly available. However, currently there is no comprehensive resource exist for the analysis, unification, and integration of these datasets. This study utilizes computational approaches to build a new resource of transcriptomic maps for aquatic animals. This aquatic animal transcriptome map database dbATM provides de novo assembly of transcriptome, gene annotation and comparative analysis of more than twenty aquatic organisms without draft genome. To improve the assembly quality, three computational tools (Trinity, Oases and SOAPdenovo-Trans) were employed to enhance individual transcriptome assembly, and CAP3 and CD-HIT-EST software were then used to merge these three assembled transcriptomes. In addition, functional annotation analysis provides valuable clues to gene characteristics, including full-length transcript coding regions, conserved domains, gene ontology and KEGG pathways. Furthermore, all aquatic animal genes are essential for comparative genomics tasks such as constructing homologous gene groups and blast databases and phylogenetic analysis. In conclusion, we establish a resource for non model organism aquatic animals, which is great economic and ecological importance and provide transcriptomic information including functional annotation and comparative transcriptome analysis. The database is now publically accessible through the URL http://dbATM.mbc.nctu.edu.tw/ .

Currently, the accompanying side effects of anti-cancer drugs owing to incorrect delivery to normal tissues should be reduced. We present a database (MRTDD) with identified cancer highly-expressed membrane receptors (CHMRs) which can be used in targeted drug delivery. To evaluate the probability of occurrence of incorrect delivery, we calculate tissue index for each CHMR and expect to identify good candidates. The information provided includes: (1) genomic annotations; (2) gene expression profiles of membrane receptors in cancer tissue vs. corresponding normal tissue, normal tissues of body and cancer cell-lines; (3) available antibody services of manufacturers. MRTDD is available at http://mrtdd.mbc.nctu.edu.tw/.

Gene therapy is a potent method to increase the therapeutic efficacy against cancer. However, a gene that is specifically expressed in the tumor area has not been identified. In addition, nonspecific expression of therapeutic genes in normal tissues may cause side effects that can harm the patients’ health. Certain promoters have been reported to drive therapeutic gene expression specifically in cancer cells; however, low expression levels of the target gene are a problem for providing good therapeutic efficacy. Therefore, a specific and highly expressive promoter is needed for cancer gene therapy. Bioinformatics approaches were utilized to analyze transcription factors (TFs) from high-throughput data. Reverse transcription polymerase chain reaction, western blotting and cell transfection were applied for the measurement of mRNA, protein expression and activity. C57BL/6JNarl mice were injected with pD5-hrGFP to evaluate the expression of TFs. We analyzed bioinformatics data and identified three TFs, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), cyclic AMP response element binding protein (CREB), and hypoxia-inducible factor-1α (HIF-1α), that are highly active in tumor cells. Here, we constructed a novel mini-promoter, D5, that is composed of the binding sites of the three TFs. The results show that the D5 promoter specifically drives therapeutic gene expression in tumor tissues and that the strength of the D5 promoter is directly proportional to tumor size. Our results show that bioinformatics may be a good tool for the selection of appropriate TFs and for the design of specific mini-promoters to improve cancer gene therapy.