Revealing potential long non-coding RNA biomarkers in lung adenocarcinoma using long non-coding RNA-mediated competitive endogenous RNA network

Zhu, TG; Xiao, Xue; Wei, Qiushi; Ming, Yue; Lx, Zhang

doi:10.1590/1414-431x20176297

Cited by 30 publications

(26 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We identified some cancer markers by building and analyzing specific or common ceRNA networks [ 9 , 33 , 34 ]. The specific treatment of 2 subtypes of non-small cell lung cancer is a problem that both researchers and clinicians want to solve, but so far there is no good explanation or evidence [ 35 – 37 ]. We constructed 2 specific networks with mRNA and lncRNA using the specificity of lncRNA to find markers and survival-related genes of 2 different cancers.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Specific Subtypes and Common Markers of Non-Small Cell Lung Cancer Based on Competing Endogenous RNA Network

Liu

Wang²,

Yang

et al. 2020

Med Sci Monit

View full text Add to dashboard Cite

Background There are various pathological types of lung cancer, including squamous cell carcinoma and adenocarcinoma. Although both of them are lung cancers, there are significant differences in diagnosis, pathogenesis, location, imaging, metastasis, and treatment. According to the competing endogenous RNA (ceRNA) theory, long non-coding RNAs (lncRNAs) compete with encoding protein genes (mRNAs) to connect with miRNAs, thus affecting the level of mRNA. Material/Methods First, using the t test, we identified mRNAs and lncRNAs that have different expressions (fold change >2, P<0.01) in normal samples and in tumor samples. We calculated the significance of the shared miRNAs for mRNAs and lncRNAs by hypergeometric test (P<0.01). Further, mRNA and lncRNA pairs with co-expression relationships in cancer samples were used to establish ceRNA networks. Then, the random walk algorithm was used to optimize the specific ceRNA networks and identify potential prognostic markers of survival. Finally, we built a common ceRNA network to find markers of non-small-cell lung cancer. Results We identified some potential key markers, such as PVT1, LINC00472, CDKN2A, and FAM83B, in LUSC and HOXA11-AS, HNF1A-AS1, LINC00511, and HOTAIR in LUAD by analyzing the ceRNA networks. Moreover, a number of common ceRNA pairs, such as CDC25C/CDK1/RRM2-LINC00355, have been found, and they are also significant markers for tumor survival and prognosis. Conclusions In summary, the present study provides a comparative analysis in 2 kinds of lung cancer ceRNA networks. Some specific and common markers we predicted that may be of great importance for clinical diagnosis and treatment.

show abstract

Section: Discussionmentioning

confidence: 99%

Prediction of Specific Subtypes and Common Markers of Non-Small Cell Lung Cancer Based on Competing Endogenous RNA Network

Liu

Wang²,

Yang

et al. 2020

Med Sci Monit

View full text Add to dashboard Cite

show abstract

“…The functional roles of LINC00472 in lung cancer have been revealed by bioinformatics analyses. For instance, Sui et al ( 27 ) and Zhu et al ( 28 ) reported that LINC00472 was downregulated in lung cancer by constructing an lncRNA-mediated competitive endogenous RNA network. To the best of our knowledge, the present study was the first to identify that LINC00312 and LINC00472 are associated with smoking-induced lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Network analysis of differentially expressed smoking‑associated mRNAs, lncRNAs and miRNAs reveals key regulators in smoking‑associated lung cancer

Chen

Pan

et al. 2018

Exp Ther Med

View full text Add to dashboard Cite

Lung cancer is the leading cause of cancer-associated mortality worldwide. Smoking is one of the most significant etiological contributors to lung cancer development. However, the molecular mechanisms underlying smoking-induced induction and progression of lung cancer have remained to be fully elucidated. Furthermore, long non-coding RNAs (lncRNAs) are increasingly recognized to have important roles in diverse biological processes. The present study focused on identifying differentially expressed mRNAs, lncRNAs and micro (mi)RNAs in smoking-associated lung cancer. Smoking-associated co-expression networks and protein-protein interaction (PPI) networks were constructed to identify hub lncRNAs and genes in smoking-associated lung cancer. Furthermore, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of differentially expressed lncRNAs were performed. A total of 314 mRNAs, 24 lncRNAs and 4 miRNAs were identified to be deregulated in smoking-associated lung cancer. PPI network analysis identified 20 hub genes in smoking-associated lung cancer, including dynein axonemal heavy chain 7, dynein cytoplasmic 2 heavy chain 1, WD repeat domain 78, collagen type III α 1 chain (COL3A1), COL1A1 and COL1A2. Furthermore, co-expression network analysis indicated that relaxin family peptide receptor 1, receptor activity modifying protein 2-antisense RNA 1, long intergenic non-protein coding RNA 312 (LINC00312) and LINC00472 were key lncRNAs in smoking-associated lung cancer. A bioinformatics analysis indicated these smoking-associated lncRNAs have a role in various processes and pathways, including cell proliferation and the cyclic guanosine monophosphate cGMP)/protein kinase cGMP-dependent 1 signaling pathway. Of note, these hub genes and lncRNAs were identified to be associated with the prognosis of lung cancer patients. In conclusion, the present study provides useful information for further exploring the diagnostic and prognostic value of the potential candidate biomarkers, as well as their utility as drug targets for smoking-associated lung cancer.

show abstract

“…Moreover, researches have demonstrated the significance of ceRNA network during the oncogenesis and progression of various cancers [16][17][18][19]. Some researchers have further identified potential prognostic mRNAs for LADC based on ceRNA network [20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analyses to identify LINC02310 as an enhancer in lung adenocarcinoma

Zhao

Wang

Luo

et al. 2020

Preprint

View full text Add to dashboard Cite

Lung adenocarcinoma (LADC) is a major subtype of non-small cell lung cancer (NSCLC) and has one of the highest mortality rates. An increasing number of long non-coding RNAs (lncRNAs) were reported to be associated with the occurrence and progression of LADC. In order to find potential biomarkers for LADC therapies among lncRNAs, 2431 differentially expressed lncRNAs (DElncRNAs) over LADC and adjacent normal samples were identified from The Cancer Genome Atlas (TCGA) expression data. Lnc-YARS2-5, lnc-NPR3-2 and LINC02310, which were the top-3 significant DElncRNAs related to overall survival, were selected for further analysis. Their overexpression indicated poor prognostic. PCR experimental results also showed that they were mainly up-regulated in the LADC tissues from Xiangya Hospital. Clinical analysis of these cases suggested that LINC02310 was significantly correlated with TNM-stage and T-stage, which was further validated by the clinical analysis based on TCGA. Reasonably, we assumed that LINC02310 acted as an enhancer in LADC, which was demonstrated by CCK-8 assay and colony formation assay. Moreover, 3 targeted miRNAs of LINC02310 and 414 downstream differentially expressed mRNAs (DEmRNAs) were predicted. The competing endogenous RNA (ceRNA) regulatory network was constructed with LINC02310, 3 miRNAs and 113 DEmRNAs involved. The downstream mRNAs were then enriched in 405 GO terms and 11 KEGG pathways, which revealed their potential functions and mechanisms. The protein-protein interaction (PPI) network showed the relationships between the downstream mRNAs. In conclusion, this study verified LINC02310 as an enhancer in LADC and conducted comprehensive analyses on its target miRNAs and downstream mRNAs.

show abstract

Revealing potential long non-coding RNA biomarkers in lung adenocarcinoma using long non-coding RNA-mediated competitive endogenous RNA network

Cited by 30 publications

References 38 publications

Prediction of Specific Subtypes and Common Markers of Non-Small Cell Lung Cancer Based on Competing Endogenous RNA Network

Prediction of Specific Subtypes and Common Markers of Non-Small Cell Lung Cancer Based on Competing Endogenous RNA Network

Network analysis of differentially expressed smoking‑associated mRNAs, lncRNAs and miRNAs reveals key regulators in smoking‑associated lung cancer

Comprehensive analyses to identify LINC02310 as an enhancer in lung adenocarcinoma

Contact Info

Product

Resources

About