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      Genome-Wide Analysis Identified a Number of Dysregulated Long Noncoding RNA (lncRNA) in Human Pancreatic Ductal Adenocarcinoma

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          Abstract

          Background:

          Long noncoding RNAs have been shown to play crucial roles in cancer biology, while the long noncoding RNA landscapes of pancreatic ductal adenocarcinoma have not been completely characterized. We aimed to determine whether long noncoding RNA could serve as early diagnostic biomarkers for pancreatic ductal adenocarcinoma.

          Method:

          We conducted a genome-wide microarray analysis on pancreatic ductal adenocarcinoma and their adjacent noncancerous tissues from 8 Chinese patients.

          Results:

          A total of 3352 significantly differentially expressed long noncoding RNAs were detected. Of total, 1249 long noncoding RNAs were upregulated and 2103 were downregulated (fold change ≥2, P < 0.05, FDR <0.05). These differentially expressed long noncoding RNAs were not evenly distributed among chromosomes in human genome. Hierarchical clustering of these differentially expressed long noncoding RNAs revealed large variabilities in long noncoding RNA expression among individual patient, indicating that certain long noncoding RNAs could play a unique role or be used as a biomarker for specific subtype of pancreatic ductal adenocarcinoma. Gene Ontology enrichment and pathway analysis identified several remarkably dysregulated pathways in pancreatic ductal adenocarcinoma tissue, such as interferon-γ-mediated signaling pathway, mitotic cell cycle and proliferation, extracellular matrix receptor interaction, focal adhesion, and regulation of actin cytoskeleton. The co-expression network analysis detected 393 potential interactions between 80 differentially expressed long noncoding RNAs and 105 messenger RNAs. We experimentally verified 7 most markedly dysregulated long noncoding RNAs from the network.

          Conclusion:

          Our study provided a genome-wide survey of dysregulated long noncoding RNAs and long noncoding RNA/messenger RNA co-regulation networks in pancreatic ductal adenocarcinoma tissue. These dysregulated long noncoding RNA/messenger RNA networks could be used as biomarkers to provide early diagnosis of pancreatic ductal adenocarcinoma or its subtype, predict prognosis, and evaluate treatment efficacy.

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          Most cited references31

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          Stromal gene expression predicts clinical outcome in breast cancer.

          Although it is increasingly evident that cancer is influenced by signals emanating from tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. We used laser capture microdissection to compare gene expression profiles of tumor stroma from 53 primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumor-derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node-negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumor progression.
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            Is Open Access

            Discovery and validation of immune-associated long non-coding RNA biomarkers associated with clinically molecular subtype and prognosis in diffuse large B cell lymphoma

            Background 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. Methods 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. Results 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. Conclusions 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. Electronic supplementary material The online version of this article (doi:10.1186/s12943-017-0580-4) contains supplementary material, which is available to authorized users.
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              An Immune-Related Six-lncRNA Signature to Improve Prognosis Prediction of Glioblastoma Multiforme.

              Recent studies have demonstrated the utility and superiority of long non-coding RNAs (lncRNAs) as novel biomarkers for cancer diagnosis, prognosis, and therapy. In the present study, the prognostic value of lncRNAs in glioblastoma multiforme was systematically investigated by performing a genome-wide analysis of lncRNA expression profiles in 419 glioblastoma patients from The Cancer Genome Atlas (TCGA) project. Using survival analysis and Cox regression model, we identified a set of six lncRNAs (AC005013.5, UBE2R2-AS1, ENTPD1-AS1, RP11-89C21.2, AC073115.6, and XLOC_004803) demonstrating an ability to stratify patients into high- and low-risk groups with significantly different survival (median 0.899 vs. 1.611 years, p = 3.87e-09, log-rank test) in the training cohort. The six-lncRNA signature was successfully validated on independent test cohort of 219 patients with glioblastoma, and it revealed superior performance for risk stratification with respect to existing lncRNA-related signatures. Multivariate Cox and stratification analysis indicated that the six-lncRNA signature was an independent prognostic factor after adjusting for other clinical covariates. Further in silico functional analysis suggested that the six-lncRNA signature may be involved in the immune-related biological processes and pathways which are very well known in the context of glioblastoma tumorigenesis. The identified lncRNA signature had important clinical implication for improving outcome prediction and guiding the tailored therapy for glioblastoma patients with further prospective validation.
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                Author and article information

                Journal
                Technol Cancer Res Treat
                Technol. Cancer Res. Treat
                TCT
                sptct
                Technology in Cancer Research & Treatment
                SAGE Publications (Sage CA: Los Angeles, CA )
                1533-0346
                1533-0338
                17 January 2018
                2018
                : 17
                : 1533034617748429
                Affiliations
                [1 ]Department of Pancreatic Surgery, Huashan Hospital, Fudan University, Shanghai, China
                Author notes
                [*]Chen Jin, MB, PhD, Huashan Hospital, Fudan University, No.12, Urumqi Rd.(Middle), Shanghai 200040, China. Email: chenjin2018@ 123456outlook.com
                Article
                10.1177_1533034617748429
                10.1177/1533034617748429
                5784569
                29343207
                af26c033-bc85-4471-9c25-047e5d158e22
                © The Author(s) 2018

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 02 August 2017
                : 25 September 2017
                : 14 November 2017
                Funding
                Funded by: Shanghai Municipal Commission of Health and Family Planning;
                Award ID: 20134Y081
                Categories
                Original Article
                Custom metadata
                corrected-proof

                long noncoding rna (lncrna),pancreatic ductal adenocarcinoma (pdac),genome-wide analysis,biomarkers,lncrna/mrna co-expression

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