Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Hu, Hai; Somvanshi, Sonal; Patel, Ektaben; Chen, Ting-Wen; Singh, Vivek P.; Zorman, Barry; Patil, Sagar L.; Pan, Yinghong; Chatterjee, Satabdi; Sood, Anil K.; Gunaratne, Preethi H.; Sumazin, Pavel

doi:10.1016/j.celrep.2018.03.064

Cited by 215 publications

(202 citation statements)

References 94 publications

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“…Several lines of evidence indicate that ncRNA regulate multiple stages of life cycle, including development, differentiation and aging, through the regulation of target gene expression . Broadly, ncRNA can be classified into two groups: small ncRNA 18‐200 nt in length and lncRNA …”

Section: Introductionmentioning

confidence: 99%

Development of miRNA‐based therapeutic approaches for cancer patients

Takahashi

Prieto‐Vila

Kohama³

et al. 2019

Cancer Science

109

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Over the past few decades, si RNA and mi RNA have attracted a great deal of attention from researchers and clinicians. These molecules have been extensively studied from the standpoint of developing biopharmaceuticals against various diseases, including heart disease, diabetes and cancers. si RNA suppresses only a single target, whereas each mi RNA regulates the expression of multiple target genes. More importantly, because mi RNA are also secreted from cancer cells, and their aberrant expression is associated with tumor development and progression, they represent not only therapeutic targets but also promising biomarkers for diagnosis and prognosis. Therefore, mi RNA may be more effective tools against cancers, in which multiple signal pathways are dysregulated. In this review, we summarize recent progress in the development of mi RNA therapeutics for the treatment of cancer patients, and describe delivery systems for oligonucleotide therapeutics.

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Section: Introductionmentioning

confidence: 99%

Development of miRNA‐based therapeutic approaches for cancer patients

Takahashi

Prieto‐Vila

Kohama³

et al. 2019

Cancer Science

109

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“…A β-uniform mixture model 20 was used to adjust for multiple comparisons and estimate significant features of AA race at different false-discovery rates (FDRs). LncRNA expression profiles were normalized using The Atlas of Noncoding RNA in Cancer (https ://bioin forma tics.mdand erson.org/public-softw are/ tanri c/) 21,22 based on TCGA RNA sequencing data in 11 tumor data sets, with estimates of expression levels represented in reads per kilobase million. The significance of the differential expression of each lncRNA, comparing its expression estimates between AA and EA patients with cancer, was computed using U tests while controlling for the FDR for each tumor type.…”

Section: Study Population Covariates and Statistical Analysesmentioning

confidence: 99%

Pan‐cancer clinical and molecular analysis of racial disparities

Lara

Wang

Asare

et al. 2019

Cancer

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Background Racial disparities in cancer outcomes are increasingly recognized, but comprehensive analyses, including molecular studies, are limited. The objective of the current study was to perform a pan‐cancer clinical and epigenetic molecular analysis of outcomes in African American (AA) and European American (EA) patients. Methods Cross‐platform analyses using cancer databases (the Surveillance, Epidemiology, and End Results program database and the National Cancer Data Base) and a molecular database (The Cancer Genome Ancestry Atlas) were performed to evaluate clinical and epigenetic molecular differences between AA and EA patients based on genetic ancestry. Results In the primary pan‐cancer survival analysis using the Surveillance, Epidemiology, and End Results database (2,045,839 patients; 87.5% EA and 12.5% AA), AA patients had higher mortality rates for 28 of 42 cancer types analyzed (hazard ratio, >1.0). AAs continued to have higher mortality in 13 cancer types after adjustment for socioeconomic variables using the National Cancer Database (5,150,023 patients; 11.6% AA and 88.4% EA). Then, molecular features of 5,283 tumors were analyzed in patients who had genetic ancestry data available (87.2% EA and 12.8% AA). Genes were identified with altered DNA methylation along with increased microRNA expression levels unique to AA patients that are associated with cancer drug resistance. Increased miRNAs (miR‐15a, miR‐17, miR‐130‐3p, miR‐181a) were noted in common among AAs with breast, kidney, thyroid, or prostate carcinomas. Conclusions The current results identified epigenetic features in AA patients who have cancer that may contribute to higher mortality rates compared with EA patients who have cancer. Therefore, a focus on molecular signatures unique to AAs may identify actionable molecular abnormalities.

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“…These interactions are evidenced from experiments validation, neighborhood, gene fusion, and co-occurrence information of lncRNAs connecting with miRNA-, TF-, ASF-, and switched genes. Specifically, HCC LncRNA-target networks were compiled from the following resources: Chiu et al, 25 , miWalker2.0 26 , STARBASE v2 27 , and lncRNA-disease 28 29 , which were analyzed from several high-throughput assays, including ENCODE enhanced version of the crosslinking and immunoprecipitation assay (eCLIP) and chromatin immunoprecipitation sequencing (ChIP-seq) data 30 . HCC specific miRNA-target networks has been described in our previous published results 16 ; TF-target predicted interaction network were manually curation from the following databases and publications Chiu et al 25, Table S5 , HTRIdb 31 , Whitfield 32 , and TRANSFAC 33 based on combined evidence from ENCODE ChIP-Seq assays and position weighted matrix (PWM) for TF motif analysis.…”

Section: Construction Of As-associated Lncrna Epigenetic Regulatory Imentioning

confidence: 99%

Comprehensive network analysis reveals alternative splicing-related lncRNAs in hepatocellular carcinoma

Wang

Chen

et al. 2019

Preprint

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A number of recent studies have highlighted the findings that certain lncRNAs are associated with alternative splicing (AS) in tumorigenesis and progression. Although existing work showed the importance of linking certain misregulations of RNA splicing with lncRNAs, a primary concern is the lack of genome-wide comprehensive analysis for their associations.We analyzed an extensive collection of RNA-seq data, quantified 198,619 isoform expressions, and found systematic isoform usage changes between hepatocellular carcinoma (HCC) and normal liver tissue. We identified a total of 1375 splicing switched isoforms and further analyzed their biological functions.To predict which lncRNAs are associated with these AS genes, we integrated the coexpression networks and epigenetic interaction networks collected from text mining and database searching, linking lncRNA modulators such as splicing factors, transcript factors, and miRNAs with their targeted AS genes in HCC. To model the heterogeneous networks in a single framework, we developed a multi-graphic random walk (RWMG) network method to prioritize the lncRNAs associated with AS in HCC. RWMG showed a good performace evaluated by ROC curve based on cross-validation and bootstrapping strategy.As a summary, we identified 31 AS-related lncRNAs including MALAT1 and HOXA11-AS, which have been reported before, as well as some novel lncRNAs such as DNM1P35, HAND2-AS1, and DLX6-AS1. Survival analysis further confirmed the clinical significance of identified lncRNAs.Keywords: lncRNA, alternative splicing, random walk, network analysis in tumorigeneses. Therefore, a key open question is: how many novel lncRNAs are associated with AS modulations in tumorigenesis, genome-wisely.Here we utilized a novel network propagation technology, random walk-based multigraphic model (RWMG), to simultaneously integrate complicated biological connections among lncRNA -effectors (TF, ASF, and miRNA) -AS interaction networks and co-expression networks in a single analysis framework. This method is an extended application inspired by Random walk with restart algorithm to prioritize important lncRNAs that are involved in AS based on the hypothesis that more important genes are likely to receive more links from other networks. In comparison with traditional random walk algorithms, which treat all genes equally, our flexible, scalable method can be formulated to rank a subset of vertices (e.g., PCGs, lncRNAs), based on pre-knowledge as the starting walking vertices. This method is more accurate than other traditional "shortest path" network-based integrative methods, as it can overcome the "noisy" and "incomplete" highly dimensional heterogeneous data.In addition, previous tumor and normal comparison studies are limited to normal adjacent to tumor (NAT) tissues. However, these tissues are not truly 'normal' as they usually surrounded by tumor contaminations. Therefore, many potential cancer biomarkers involved in AS may be missed. By combining the 'pure' healthy liver organs from GTEx with TCGA expression...

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Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Cited by 215 publications

References 94 publications

Development of miRNA‐based therapeutic approaches for cancer patients

Development of miRNA‐based therapeutic approaches for cancer patients

Pan‐cancer clinical and molecular analysis of racial disparities

Comprehensive network analysis reveals alternative splicing-related lncRNAs in hepatocellular carcinoma

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