Long non-coding RNAs (lncRNAs) play crucial roles in regulating gene expression, and a growing number of researchers have focused on the identification of target genes of lncRNAs. However, no online repository is available to collect the information on target genes regulated by lncRNAs. To make it convenient for researchers to know what genes are regulated by a lncRNA of interest, we developed a database named lncRNA2Target to provide a comprehensive resource of lncRNA target genes in 2015. To update the database this year, we retrieved all new lncRNA–target relationships from papers published from 1 August 2014 to 30 April 2018 and RNA-seq datasets before and after knockdown or overexpression of a specific lncRNA. LncRNA2Target database v2.0 provides a web interface through which its users can search for the targets of a particular lncRNA or for the lncRNAs that target a particular gene, and is freely accessible at http://123.59.132.21/lncrna2target.

DincRNA aims to provide a comprehensive web-based bioinformatics toolkit to elucidate the entangled relationships among diseases and non-coding RNAs (ncRNAs) from the perspective of disease similarity. The quantitative way to illustrate relationships of pair-wise diseases always depends on their molecular mechanisms, and structures of the directed acyclic graph of Disease Ontology (DO). Corresponding methods for calculating similarity of pair-wise diseases involve Resnik’s, Lin’s, Wang’s, PSB and SemFunSim methods. Recently, disease similarity was validated suitable for calculating functional similarities of ncRNAs and prioritizing ncRNA–disease pairs, and it has been widely applied for predicting the ncRNA function due to the limited biological knowledge from wet lab experiments of these RNAs. For this purpose, a large number of algorithms and priori knowledge need to be integrated. e.g. ‘pair-wise best, pairs-average’ (PBPA) and ‘pair-wise all, pairs-maximum’ (PAPM) methods for calculating functional similarities of ncRNAs, and random walk with restart (RWR) method for prioritizing ncRNA–disease pairs. To facilitate the exploration of disease associations and ncRNA function, DincRNA implemented all of the above eight algorithms based on DO and disease-related genes. Currently, it provides the function to query disease similarity scores, miRNA and lncRNA functional similarity scores, and the prioritization scores of lncRNA–disease and miRNA–disease pairs. http://bio-annotation.cn:18080/DincRNAClient/ Supplementary data are available at Bioinformatics online.

Lung cancer (LC) is one of the most serious malignant tumors, which has the fastest growing morbidity and mortality worldwide. A role of the lung microbiota in LC pathogenesis has been analyzed, but a comparable role of the gut microbiota has not yet been investigated. In this study, the gut microbiota of 30 LC patients and 30 healthy controls were examined via next-generation sequencing of 16S rRNA and analyzed for diversity and biomarkers. We found that there was no decrease in significant microbial diversity (alpha diversity) in LC patients compared to controls (P observed = 0.1422), while the composition (beta diversity) differed significantly between patients and controls (phylum [stress = 0.153], class [stress = 0.16], order [stress = 0.146], family [stress = 0.153]). Controls had a higher abundance of the bacterial phylum Actinobacteria and genus Bifidobacterium, while patients with LC showed elevated levels of Enterococcus. These bacteria were found as possible biomarkers for LC. A decline of normal function of the gut microbiome in LC patients was also observed. These results provide the basic guidance for a systematic, multilayered assessment of the role of the gut microbiome in LC, which has a promising potential for early prevention and targeted intervention.

Characterization of long non-coding RNA-associated ceRNA network to reveal potential prognostic lncRNA biomarkers in human ovarian cancer

Diffuse large B-cell lymphoma (DLBCL) is an aggressive and complex disease characterized by wide clinical, phenotypic and molecular heterogeneities. The expression pattern and clinical implication of long non-coding RNAs (lncRNAs) between germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subtypes in DLBCL remain unclear. This study aims to determine whether lncRNA can serve as predictive biomarkers for subtype classification and prognosis in DLBCL. Genome-wide comparative analysis of lncRNA expression profiles were performed in a large number of DLBCL patients from Gene Expression Omnibus (GEO), including GSE31312 cohort (N = 426), GSE10846 (N = 350) cohort and GSE4475 cohort (N = 129). Novel lncRNA biomarkers associated with clinically molecular subtype and prognosis were identified in the discovery cohort using differential expression analyses and weighted voting algorithm. The predictive value of the lncRNA signature was then assessed in two independent cohorts. The functional implication of lncRNA signature was also analyzed by integrative analysis of lncRNA and mRNA. Seventeen of the 156 differentially expressed lncRNAs between GCB and ABC subtypes were identified as candidate biomarkers and integrated into form a lncRNA-based signature (termed SubSigLnc-17) which was able to discriminate between GCB and ABC subtypes with AUC of 0.974, specificity of 89.6% and sensitivity of 92.5%. Furthermore, subgroups of patients characterized by the SubSigLnc-17 demonstrated significantly different clinical outcome. The reproducible predictive power of SubSigLnc-17 in subtype classification and prognosis was successfully validated in the internal validation cohort and another two independent patient cohorts. Integrative analysis of lncRNA-mRNA suggested that these candidate lncRNA biomarkers were mainly related to immune-associated processes, such as T cell activation, leukocyte activation, lymphocyte activation and Chemokine signaling pathway. Our study uncovered differentiated lncRNA expression pattern between GCB and ABC DLBCL and identified a 17-lncRNA signature for subtype classification and prognosis prediction. With further prospective validation, our study will improve the understanding of underlying molecular heterogeneities in DLBCL and provide candidate lncRNA biomarkers in DLBCL classification and prognosis.