Lnc2Cancer (http://www.bio-bigdata.net/lnc2cancer) is a manually curated database of cancer-associated long non-coding RNAs (lncRNAs) with experimental support that aims to provide a high-quality and integrated resource for exploring lncRNA deregulation in various human cancers. LncRNAs represent a large category of functional RNA molecules that play a significant role in human cancers. A curated collection and summary of deregulated lncRNAs in cancer is essential to thoroughly understand the mechanisms and functions of lncRNAs. Here, we developed the Lnc2Cancer database, which contains 1057 manually curated associations between 531 lncRNAs and 86 human cancers. Each association includes lncRNA and cancer name, the lncRNA expression pattern, experimental techniques, a brief functional description, the original reference and additional annotation information. Lnc2Cancer provides a user-friendly interface to conveniently browse, retrieve and download data. Lnc2Cancer also offers a submission page for researchers to submit newly validated lncRNA-cancer associations. With the rapidly increasing interest in lncRNAs, Lnc2Cancer will significantly improve our understanding of lncRNA deregulation in cancer and has the potential to be a timely and valuable resource.

Triggering receptor expressed on myeloid cells 2 (TREM2) is a microglial surface receptor genetically linked to the risk for Alzheimer’s disease (AD). A proteolytic product, soluble TREM2 (sTREM2), is abundant in the cerebrospinal fluid and its levels positively correlate with neuronal injury markers. To gain insights into the pathological roles of sTREM2, we studied sTREM2 in the brain of 5xFAD mice, a model of AD, by direct stereotaxic injection of recombinant sTREM2 protein or by adeno-associated virus (AAV)-mediated expression. We found that sTREM2 reduces amyloid plaque load and rescues functional deficits of spatial memory and long-term potentiation. Importantly, sTREM2 enhances microglial proliferation, migration, clustering in the vicinity of amyloid plaques and the uptake and degradation of Aβ. Depletion of microglia abolishes the neuroprotective effects of sTREM2. Our study demonstrates a protective role of sTREM2 against amyloid pathology and related toxicity and suggests that increasing sTREM2 can be explored for AD therapy.

Recently, pancreatic ductal adenocarcinoma (PDAC) has emerged as one of the most aggressive malignant tumors with the worst prognosis. Previous studies have demonstrated that long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is increased in pancreatic cancer and is identified as a diagnostic biomarker. Nonetheless, the molecular mechanism of elevated MALAT1 levels and tumor aggressiveness remains unknown. In this study, MALAT1 was found to be highly expressed in PDAC tissues, and elevated expression was associated with poorer prognoses. In addition, MALAT1 was positively linearly correlated with the expression of LC3B mRNA. Furthermore, several molecules involved in cellular autophagic flux were modulated following the downregulation of MALAT1, including LC3, P62, and LAMP-2. Mechanistically, we found that MALAT1 interacted with RNA binding protein HuR, and silencing of MALAT1 greatly enhanced the posttranscriptional regulation of TIA-1 and had further effects on inhibiting autophagy. MALAT1 was speculated to regulate tumorigenesis via HuR-TIA-1-mediated autophagic activation. Hence, we investigated the biological properties of MALAT1 in terms of tumor proliferation and metastasis by promoting autophagy in vitro In brief, these data demonstrate that MALAT1 could facilitate the advanced progression of tumors in vivo Our study highlights the new roles of MALAT1 on protumorigenic functioning and anticancer therapy via activating autophagy in pancreatic cancer. Mol Cancer Ther; 15(9); 2232-43. ©2016 AACR.

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.

No abstract

Lnc2Cancer 2.0 (http://www.bio-bigdata.net/lnc2cancer) is an updated database that provides comprehensive experimentally supported associations between lncRNAs and human cancers. In Lnc2Cancer 2.0, we have updated the database with more data and several new features, including (i) exceeding a 4-fold increase over the previous version, recruiting 4989 lncRNA-cancer associations between 1614 lncRNAs and 165 cancer subtypes. (ii) newly adding about 800 experimentally supported circulating, drug-resistant and prognostic-related lncRNAs in various cancers. (iii) appending the regulatory mechanism of lncRNA in cancer, including microRNA (miRNA), transcription factor (TF), variant and methylation regulation. (iv) increasing more than 70 high-throughput experiments (microarray and next-generation sequencing) of lncRNAs in cancers. (v) Scoring the associations between lncRNA and cancer to evaluate the correlations. (vi) updating the annotation information of lncRNAs (version 28) and containing more detailed descriptions for lncRNAs and cancers. Moreover, a newly designed, user-friendly interface was also developed to provide a convenient platform for users. In particular, the functions of browsing data by cancer primary organ, biomarker type and regulatory mechanism, advanced search following several features and filtering the data by LncRNA-Cancer score were enhanced. Lnc2Cancer 2.0 will be a useful resource platform for further understanding the associations between lncRNA and human cancer.

Abstract. The rapidly evolving nanotechnology field highlights the need of better understanding the relationship between nanoparticle (NP) properties and NP transport in solid tumors. The present study tested the hypothesis that the diffusive transport and spatial distribution of NP can be predicted based on the following parameters: interstitial NP diffusivity, NP-cell interaction parameters (cell surface binding capacity, rate constants of association, dissociation, and internalization). We (a) established the models and equations; (b) experimentally measured, in monolayer pharynx FaDu cells, the model parameters for three NP formulations (negatively charged polystyrene beads, near-neutral liposomes, and positively charged liposomes, with respective diameter of 20, 110, and 130 nm); and (c) used the models and parameters to simulate NP diffusion in 3-dimensional (3D) systems. We next measured the NP concentration-depth profiles in tumor cell spheroids, an avascular 3D system, and found good agreement between model-simulated and experimental data in spheroids for the negative and neutral NP (>90% predicted data points at three NP concentrations and three treatment times were within the 95% confidence intervals of experimental data). Model performance was inferior for positive liposomes containing a fusogenic lipid. The present study demonstrated the possibility of using in vitro NP-cell biointerface data in monolayer cultures with in silico studies to predict the NP diffusive transport and concentration-time-depth profiles in 3D systems, as functions of NP concentrations and treatment times. Extending this approach to include convective transport may yield a cost-effective means to predict the NP delivery and residence in solid tumors.

An updated Lnc2Cancer 3.0 (http://www.bio-bigdata.net/lnc2cancer or http://bio-bigdata.hrbmu.edu.cn/lnc2cancer) database, which includes comprehensive data on experimentally supported long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) associated with human cancers. In addition, web tools for analyzing lncRNA expression by high-throughput RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) are described. Lnc2Cancer 3.0 was updated with several new features, including (i) Increased cancer-associated lncRNA entries over the previous version. The current release includes 9254 lncRNA-cancer associations, with 2659 lncRNAs and 216 cancer subtypes. (ii) Newly adding 1049 experimentally supported circRNA-cancer associations, with 743 circRNAs and 70 cancer subtypes. (iii) Experimentally supported regulatory mechanisms of cancer-related lncRNAs and circRNAs, involving microRNAs, transcription factors (TF), genetic variants, methylation and enhancers were included. (iv) Appending experimentally supported biological functions of cancer-related lncRNAs and circRNAs including cell growth, apoptosis, autophagy, epithelial mesenchymal transformation (EMT), immunity and coding ability. (v) Experimentally supported clinical relevance of cancer-related lncRNAs and circRNAs in metastasis, recurrence, circulation, drug resistance, and prognosis was included. Additionally, two flexible online tools, including RNA-seq and scRNA-seq web tools, were developed to enable fast and customizable analysis and visualization of lncRNAs in cancers. Lnc2Cancer 3.0 is a valuable resource for elucidating the associations between lncRNA, circRNA and cancer.