Identification of differential expressed lnc<scp>RNA</scp>s in human thyroid cancer by a genome‐wide analyses

Lu, Wei; Xu, Yongcan; Xu, Jiewei; Wang, Zhong; Ye, Guochao

doi:10.1002/cam4.1627

Cited by 49 publications

(49 citation statements)

References 34 publications

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“…For example, a bioinformatics analysis study showed that FAM95B1 and UCA1 were correlated with cervical lymph node metastasis, tumor staging, and TC prognosis. Lu et al reported that the dysregulation of RUNDC3A-AS1, FOXD-AS1, RUNDC3A-AS1 and FOXD-AS1 was correlated to a shorter overall survival time in patients with TC [25]. However, only a small part of lncRNAs were reported in TC.…”

Section: Discussionmentioning

confidence: 99%

In Silico Analysis Identifies Differently Expressed lncRNAs as Novel Biomarkers for the Prognosis of Thyroid Cancer

Rao

Liu

Yan

et al. 2020

Computational and Mathematical Methods in Medicine

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Background. Thyroid cancer (TC) is one of the most common type of endocrine tumors. Long noncoding RNAs had been demonstrated to play key roles in TC. Material and Methods. The lncRNA expression data were downloaded from Co-lncRNA database. The raw data was normalized using the limma package in R software version 3.3.0. The differentially expressed mRNA and lncRNAs were identified by the linear models for the microarray analysis (Limma) method. The DEGs were obtained with thresholds of ∣logFC∣>1.5 and P<0.001. The hierarchical cluster analysis of differentially expressed mRNAs and lncRNAs was performed using CLUSTER 3.0, and the hierarchical clustering heat map was visualized by Tree View. Results. In the present study, we identified 6 upregulated and 85 downregulated lncRNAs in TC samples. Moreover, we for the first time identified 16 downregulated lncRNAs was correlated to longer disease-free survival time in patients with TC, including ATP1A1-AS1, CATIP-AS1, FAM13A-AS1, LINC00641, LINC00924, MIR22HG, NDUFA6-AS1, RP11-175K6.1, RP11-727A23.5, RP11-774O3.3, RP13-895J2.2, SDCBP2-AS1, SLC26A4-AS1, SNHG15, SRP14-AS1, and ZNF674-AS1. Conclusions. Bioinformatics analysis revealed these lncRNAs were involved in regulating the RNA metabolic process, cell migration, organelle assembly, tRNA modification, and hormone levels. This study will provide useful information to explore the potential candidate biomarkers for diagnosis, prognosis, and drug targets for TC.

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Section: Discussionmentioning

confidence: 99%

In Silico Analysis Identifies Differently Expressed lncRNAs as Novel Biomarkers for the Prognosis of Thyroid Cancer

Rao

Liu

Yan

et al. 2020

Computational and Mathematical Methods in Medicine

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“…Previous observations showed that LINC01354 participates in TP53 signaling pathways via hsa‐mir‐107 and hsa‐mir‐497‐5p in colorectal cancer, regulating CDK6 and CCNE1 , respectively . Notably, it was recently reported that LINC01354 dysregulation is related to genomic alterations in thyroid cancer . The results of this study show that LINC01354 is also a noncoding gene involved in the genesis of HNSCC, and its role in the regulation of tumorigenesis and development deserves further exploration.…”

Section: Discussionmentioning

confidence: 52%

Analysis of methylation‐driven genes for predicting the prognosis of patients with head and neck squamous cell carcinoma

Pan

Song

Cheng

et al. 2019

J of Cellular Biochemistry

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To help provide evidence for prognosis prediction and personalized targeted therapy for patients with head and neck squamous cell carcinoma (HNSCC), we investigated prognosis-specific methylation-driven genes in HNSCC. Survival time data, RNA sequencing data, and methylation data for HNSCC patients were downloaded from The Cancer Genome Atlas. The MethylMix R package based on the β mixture model was utilized to screen genes with different methylation statuses in tumor tissues and adjacent normal tissues, and a total of 182 HNSCC-related methylation-driven genes were then identified. A survival prediction scoring model based on multivariate Cox analysis was developed to screen the genes related to the prognosis of HNSCC, and a linear risk model of the methylation status of six genes (INA, LINC01354, TSPYL4, MAGEB2, EPHX3, and ZNF134) was constructed. The prognostic values of the six genes were further independently explored by survival analysis combined with methylation and gene expression analyses. The 5-year survival rate in the highrisk group of patients in the test set was 30.4% (95% CI: 22.7%-40.8%) and that in the low-risk group of patients was 65.5% (95% CI: 56.1%-76.5%). The area under the receiver operating characteristic curve for the model was 0.723, which further verified the specificity and sensitivity of the model. In addition, subsequent combined survival analysis revealed that all six genes could be used as independent prognostic markers and thus might be potential drug targets. The innovative method provides new insight into the molecular mechanism and prognosis of HNSCC. K E Y W O R D SDNA methylation-driven genes, epigenetics, HNSCC, prognostic risk model, survival analysis

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“…transcribed from UNC5B is a newly confirmed lncRNA in thyroid cancer [26]. UNC5B-AS1 is 652 nucleotides in length, containing 2 exons and located in the genome 10q22.1 [27].…”

Section: Discussionmentioning

confidence: 99%

A long non-coding RNA signature predicts survival for glioblastoma as prognostic biomarkers

Z¹,

Z²,

Lu³

et al. 2020

Preprint

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Background: Glioblastoma (GBM) is one of the most fatal tumors in the central nervous system. Its prognosis is very poor. There is increasing evidence that long noncoding RNA (lncRNA) participates in the biological process of glioblastoma. Nevertheless, the role of lncRNA in predicting the prognosis of GBM is still uncertain. Methods: In this study, using RNA-Seq and clinical follow-up data of GBM patients from The Cancer Genome Atlas (TCGA), we performed differential analysis of lncRNA, univariable and multivariable Cox regression analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Ontology (GO) analysis.Results: We identified four lncRNAs closely interrelated with survival and prognosis of GBM patients. This lncRNA signature was effective in both the training set and the testing set, and it was independent to clinical factors.Conclusions: Our data suggested that the four lncRNAs could be used as promising biomarkers for predicting prognosis in GBM patients.

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Identification of differential expressed lncRNAs in human thyroid cancer by a genome‐wide analyses

Cited by 49 publications

References 34 publications

In Silico Analysis Identifies Differently Expressed lncRNAs as Novel Biomarkers for the Prognosis of Thyroid Cancer

In Silico Analysis Identifies Differently Expressed lncRNAs as Novel Biomarkers for the Prognosis of Thyroid Cancer

Analysis of methylation‐driven genes for predicting the prognosis of patients with head and neck squamous cell carcinoma

A long non-coding RNA signature predicts survival for glioblastoma as prognostic biomarkers

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