Identification of Long Non-Coding RNA Expression Profiles and Co-Expression Genes in Thyroid Carcinoma Based on The Cancer Genome Atlas (TCGA) Database

Zhang, Yun; Jin, Taobo; Shen, Haipeng; Yan, Junfeng; Guan, Ming; Jin, Xin

doi:10.12659/msm.917845

Cited by 31 publications

(34 citation statements)

References 35 publications

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“…LINC01833 (also called SIX3os ), the long noncoding opposite strand transcript of homeodomain factor Six3, known to be involved in neuronal differentiation and retinal cell specification, was also downregulated ( Figure 6 B,E) [ 22 ]. Captured downregulated lncNRAs also included annotated transcripts not yet associated with neurodevelopment, such as LINC01918 and AL139393.2 , implicated in thyroid and medulloblastoma carcinoma, respectively [ 23 , 24 ], as well as SMCR2 , also known as lncSREBF1 , which plays a role in lipid metabolism ( Figure 6 B,E and Supplementary Figure S3D ) [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

Single-Cell Profiling of Coding and Noncoding Genes in Human Dopamine Neuron Differentiation

Nilsson

Storm

Sozzi

et al. 2021

Cells

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Dopaminergic (DA) neurons derived from human pluripotent stem cells (hPSCs) represent a renewable and available source of cells useful for understanding development, developing disease models, and stem-cell therapies for Parkinson’s disease (PD). To assess the utility of stem cell cultures as an in vitro model system of human DA neurogenesis, we performed high-throughput transcriptional profiling of ~20,000 ventral midbrain (VM)-patterned stem cells at different stages of maturation using droplet-based single-cell RNA sequencing (scRNAseq). Using this dataset, we defined the cellular composition of human VM cultures at different timepoints and found high purity DA progenitor formation at an early stage of differentiation. DA neurons sharing similar molecular identities to those found in authentic DA neurons derived from human fetal VM were the major cell type after two months in culture. We also developed a bioinformatic pipeline that provided a comprehensive long noncoding RNA landscape based on temporal and cell-type specificity, which may contribute to unraveling the intricate regulatory network of coding and noncoding genes in DA neuron differentiation. Our findings serve as a valuable resource to elucidate the molecular steps of development, maturation, and function of human DA neurons, and to identify novel candidate coding and noncoding genes driving specification of progenitors into functionally mature DA neurons.

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Section: Resultsmentioning

confidence: 99%

Single-Cell Profiling of Coding and Noncoding Genes in Human Dopamine Neuron Differentiation

Nilsson

Storm

Sozzi

et al. 2021

Cells

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“…Identification of Prognostic EMT-Related lncRNA colorectal cancer patients (18); LINC02532 was demonstrated to act as an oncogene in gastric cancer (GC) and promoted the GC cell proliferation, migration, and invasion (19); DOCK9-DT was revealed to play a protective role in the prognosis of thyroid carcinoma (20); THUMPD3-AS1 regulated non-small cell lung cancer cell self-renewal via the expression of miR-543 and ONECUT2, and THUMPD3-AS1 can serve as a potential biomarker or therapeutic target in non-small cell lung cancer (21). Additionally, APCDD1L-DT exhibits a significant prognostic value in lung squamous cell carcinoma (LUSC) and can independently predict the overall survival in LUSC patients (22).…”

Section: Discussionmentioning

confidence: 99%

Identification of Epithelial–Mesenchymal Transition-Related lncRNA With Prognosis and Molecular Subtypes in Clear Cell Renal Cell Carcinoma

Zhong

Zhang²,

Huang

et al. 2020

Front. Oncol.

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Epithelial–mesenchymal transition (EMT), a reversible cellular program, is critically important in tumor progression and is regulated by a family of transcription factors, induction factors, and an array of signaling pathway genes. The prognostic role and biological functions of EMT-related lncRNAs in ccRCC are largely unknown. In the present study, we analyzed the gene expression data and clinical information retrieved from The Cancer Genome Atlas (TCGA) database (N=512) and International Cancer Genome Consortium (ICGC) database (N=90) which served as training and external validation dataset, respectively. Then, we constructed an EMT-related lncRNA risk signature based on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information. The Kaplan-Meier curve analysis revealed that patients in the low-risk and high-risk groups exhibited significant divergence in the overall survival (OS) and disease-free survival (DFS) of ccRCC, as was confirmed in the validation dataset. The Cox regression analysis of the clinical factors and risk signature in the OS and DFS demonstrated that the risk signature can be utilized as an independent prognostic predictor. Moreover, we developed an individualized prognosis prediction model relying on the nomogram and receive operator curve (ROC) analysis based on the independent factors. The Gene Set Enrichment Analysis (GSEA) indicated that patients in the low-risk group were associated with adherens junction, focal adhesion, MAPK signaling pathway, pathways in cancer, and renal cell carcinoma pathway. In addition, we identified three robust subtypes (named C1, C2 and C3) of ccRCC with distinct clinical characteristics and prognostic role in the TCGA dataset and ICGC dataset. Among them, C1 was associated with a better survival outcome, whereas C2 and C3 was associated with a worse survival outcome and have more advanced-stage patients. Moreover, C2 was more likely to respond to immunotherapy and was sensitive to chemo drugs, this may provide insights to clinicians to develop an individualized treatment. Collectively, this work developed a reliable EMT-related lncRNA risk signature that can independently predict the OS and DFS of ccRCC. Besides, we identified three stable molecular subtypes based on the EMT-related lncRNA expression, which may comprehensively be vital in elucidating the underlying molecular mechanism of ccRCC.

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“…It may be a diagnostic and therapeutic target for low-grade gliomas, but it hasn’t been tested yet ( Jianzhi Deng et al, 2019 ). LINC01878 may play a vital role in thyroid cancer, which also had no experimental confirmation ( Zhang et al, 2019 ). In this model, the other lncRNAs have not been reported in the literature.…”

Section: Discussionmentioning

confidence: 99%

Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

Shen

Wang

et al. 2021

Front. Cell Dev. Biol.

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BackgroundLung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.MethodsAll data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P < 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan–Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.ResultsThrough univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.ConclusionThe immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

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Identification of Long Non-Coding RNA Expression Profiles and Co-Expression Genes in Thyroid Carcinoma Based on The Cancer Genome Atlas (TCGA) Database

Cited by 31 publications

References 35 publications

Single-Cell Profiling of Coding and Noncoding Genes in Human Dopamine Neuron Differentiation

Single-Cell Profiling of Coding and Noncoding Genes in Human Dopamine Neuron Differentiation

Identification of Epithelial–Mesenchymal Transition-Related lncRNA With Prognosis and Molecular Subtypes in Clear Cell Renal Cell Carcinoma

Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

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