Systematic Characterization Of lncRNAs Research Articles

RP11-616M22.7 recapitulates imatinib resistance in gastrointestinal stromal tumor

Emerging evidence has shown that long non-coding RNAs (lncRNAs) play crucial roles in human cancers. However, systematic characterization of lncRNAs and their roles in gastrointestinal stromal tumor (GIST) therapy have been lacking. We performed high-throughput RNA sequencing (RNA-seq) of 20 GIST and paired adjacent normal samples. We characterized the transcriptional landscape and dysregulation of lncRNAs in GIST. We identified 866 upregulated and 1,268 downregulated lncRNAs in GIST samples, the majority of which were GIST-specific over other cancer types. Most hallmarks were found to be dysregulated in GIST samples, and lncRNAs were highly associated with cancer-related hallmarks. RP11-616M22.7 was identified to increase in imatinib-resistant samples compared to those in non-resistant samples. Further analysis revealed that RP11-616M22.7 was closely associated with the Hippo signaling pathway. By treating GIST cells with different doses of imatinib, we verified that RP11-616M22.7 knockdown promotes the sensitivity of tumor cells, whereas RP11-616M22.7 overexpression induces resistance to imatinib. We further confirmed reducing of resistance to imatinib by knocking down RP11-616M22.7 in vivo. Additionally, RP11-616M22.7 was observed to interact with RASSF1 protein. Our study revealed that deficiency of RP11-616M22.7 was able to reduce resistance of the GIST cell response to imatinib treatment both in vitro and in vivo.

Genome-Wide Identification of Long Non-coding RNAs in the Gravid Ectoparasite Varroa destructor.

Long non-coding RNAs (lncRNAs) emerge as critical regulators across a wide variety of biological functions in living organisms. However, to date, no systematic characterization of lncRNAs has been investigated in the ectoparasitic mite Varroa destructor, the most severe biotic threat to honey bees worldwide. Here, we performed an initial genome-wide identification of lncRNAs in V. destructor via high-throughput sequencing technology and reported, for the first time, the transcriptomic landscape of lncRNAs in the devastating parasite. By means of a lncRNA identification pipeline, 6,645 novel lncRNA transcripts, encoded by 3,897 gene loci, were identified, including 2,066 sense lncRNAs, 2,772 lincRNAs, and 1,807 lncNATs. Compared with protein-coding mRNAs, V. destructor lncRNAs are shorter in terms of full length, as well as of the ORF length, contain less exons, and express at lower level. GO term and KEGG pathway enrichment analyses of the lncRNA target genes demonstrated that these predicted lncRNAs may be potentially responsible for the regulatory functions of cellular and biological progresses in the reproductive phase of V. destructor. To our knowledge, this is the first catalog of lncRNA profile in the parasitiformes species, providing a valuable resource for genetic and genomic studies. Understanding the characteristics and features of lncRNAs in V. destructor would promote sustainable parasite control.

Competing Endogenous RNA and Coexpression Network Analysis for Identification of Potential Biomarkers and Therapeutics in association with Metastasis Risk and Progression of Prostate Cancer.

Prostate cancer (PCa) is the most frequently diagnosed malignant neoplasm in men. Despite the high incidence, the underlying pathogenic mechanisms of PCa are still largely unknown, which limits the therapeutic options and leads to poor prognosis. Herein, based on the expression profiles from The Cancer Genome Atlas (TCGA) database, we investigated the interactions between long noncoding RNA (lncRNA) and mRNA by constructing a competing endogenous RNA network. Several competing endogenous RNAs could participate in the tumorigenesis of PCa. Six lncRNA signatures were identified as potential candidates associated with stage progression by the Kolmogorov-Smirnov test. In addition, 32 signatures from the coexpression network had potential diagnostic value for PCa lymphatic metastasis using machine learning algorithms. By targeting the coexpression network, the antifungal compound econazole was screened out for PCa treatment. Econazole could induce growth restraint, arrest the cell cycle, lead to apoptosis, inhibit migration, invasion, and adhesion in PC3 and DU145 cell lines, and inhibit the growth of prostate xenografts in nude mice. This systematic characterization of lncRNAs, microRNAs, and mRNAs in the risk of metastasis and progression of PCa will aid in the identification of candidate prognostic biomarkers and potential therapeutic drugs.

Systematic characterization of lncRNAs' cell-to-cell expression heterogeneity in glioblastoma cells.

Glioblastoma (GBM) is the most common malignant adult brain tumor generally associated with high level of cellular heterogeneity and a dismal prognosis. Long noncoding RNAs (lncRNAs) are emerging as novel mediators of tumorigenesis. Recently developed single-cell RNA-seq provides an unprecedented way for analysis of the cell-to-cell variability in lncRNA expression profiles. Here we comprehensively examined the expression patterns of 2,003 lncRNAs in 380 cells from five primary GBMs and two glioblastoma stem-like cell (GSC) lines. Employing the self-organizing maps, we displayed the landscape of the lncRNA expression dynamics for individual cells. Further analyses revealed heterogeneous nature of lncRNA in abundance and splicing patterns. Moreover, lncRNA expression variation is also ubiquitously present in the established GSC lines composed of seemingly identical cells. Through comparative analysis of GSC and corresponding differentiated cell cultures, we defined a stemness signature by the set of 31 differentially expressed lncRNAs, which can disclose stemness gradients in five tumors. Additionally, based on known classifier lncRNAs for molecular subtypes, each tumor was found to comprise individual cells representing four subtypes. Our systematic characterization of lncRNA expression heterogeneity lays the foundation for future efforts to further understand the function of lncRNA, develop valuable biomarkers, and enhance knowledge of GBM biology.

Systematic Identification and Characterization of Long Non-Coding RNAs in the Silkworm, Bombyx mori.

Long noncoding RNAs (lncRNAs) are emerging as important regulators in various biological processes. However, to date, no systematic characterization of lncRNAs has been reported in the silkworm Bombyx mori. In the present study, we generated eighteen RNA-seq datasets with relatively high depth. Using an in-house designed lncRNA identification pipeline, 11,810 lncRNAs were identified for 5,556 loci. Among these lncRNAs, 474 transcripts were intronic lncRNAs (ilncRNAs), 6,250 transcripts were intergenic lncRNAs (lincRNAs), and 5,086 were natural antisense lncRNAs (lncNATs). Compared with protein-coding mRNAs, silkworm lncRNAs are shorter in terms of full length but longer in terms of exon and intron length. In addition, lncRNAs exhibit a lower level of sequence conservation, more repeat sequences overlapped and higher tissue specificity than protein-coding mRNAs in the silkworm. We found that 69 lncRNA transcripts from 33 gene loci may function as miRNA precursors, and 104 lncRNA transcripts from 72 gene loci may act as competing endogenous RNAs (ceRNAs). In total, 49.47% of all gene loci (2,749/5,556) for which lncRNAs were identified showed sex-biased expression. Co-expression network analysis resulted in 19 modules, 12 of which revealed relatively high tissue specificity. The highlighted darkgoldenrod module was specifically associated with middle and posterior silk glands, and the hub lncRNAs within this module were co-expressed with proteins involved in translation, translocation, and secretory processes, suggesting that these hub lncRNAs may function as regulators of the biosynthesis, translocation, and secretion of silk proteins. This study presents the first comprehensive genome-wide analysis of silkworm lncRNAs and provides an invaluable resource for genetic, evolutionary, and genomic studies of B. mori.

Predicting Long Non-coding RNAs Based on Genomic Sequence Information

The binary classification of coding and non-coding genes is simplified near to 50 years. Genome-wide transcriptome studies have revealed that there exist tens of thousands of long non-coding RNAs (lncRNAs), while the functions are being uncovered slowly. Accurate identification of lncRNAs is the initial step to the systematic characterization of lncRNAs. The diversity of transcription patterns for lncRNAs challenges the available non-coding RNA prediction algorithms. Until now, prediction of lncRNAs mostly relies on genomic sequence and cross-species alignment information. Here, we introduce the main strategies that can discriminate lncRNA from protein-coding transcripts. Especially, recently available machine learning algorithms are shown efficient to the rapid and accurate identification of lncRNAs from a large number of putative lncRNAs based on transcriptome assembled transcripts, which would provide the basis of understanding of lncRNA biology.